\\w+)\\)\\s*\\:(\\s*[\\d\\.]+)\\s', repeats=False) ) self._quantities.append( Quantity( 'optical_band_gap', r'\\-\\s*G0W0\\s*\\-\\s*\\-+\\s*[\\s\\S]*?Optical BandGap\\s*\\((?P<__unit>\\w+)\\)\\s*\\:(\\s*[\\d\\.]+)\\s', repeats=False) )","for v in val.split('\\n')], dtype=float) val = np.transpose(val) occs = val[-1] eigs =","sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.energies = band_energies[nb] + energy_fermi def _parse_band_out(self,","val is None: continue if name == 'time': msection.time_calculation = val else: setattr(msection,","= nkpts_segment[nb] sec_k_band_segment.value = band_energies[nb] + energy_fermi def parse_file(self, name, section): # TODO","self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1) return self._energy_unit @property def number_of_spin_channels(self): if self._nspin is None: if not","= np.array([v.split() for v in val[3:6]], dtype=float) return [nbands, mesh, origin, vector] self._quantities.append(","bs_files = ['dos.xml', 'TDOS.OUT'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break #","= sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi nkpts_segment = self.band_out_parser.number_of_k_points_per_segment for nb in range(len(band_energies)):","vi = vi[0] if len(vi) == 1 else vi properties[v[0].strip()] = vi *","sec_dos.n_energies = self.dos_parser.number_of_dos sec_dos.energies = self.dos_parser.energies + energy_fermi volume = self.info_parser.get_unit_cell_volume() totaldos =","xc_functional = self.get('initialization', {}).get('xc_functional', None) if xc_functional is None: return [] name =","None or atom_labels is None: return sec_system = sec_run.m_create(System) sec_atoms = sec_system.m_create(Atoms) sec_atoms.positions","d in data]) elif key == 'Znk': return np.array([d[1].get(key, None) for d in","self._diagram_key)) return self._total_dos @property def partial_dos(self): if self._partial_dos is None: self._partial_dos = self.root.findall('./%s/%s'","range(len(self.partial_dos)): spin = self.partial_dos[i].attrib.get(self._nspin_key, None) if spin is None: spin = (i %","sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters = self.fermisurf_parser.get('band_parameters', None) if band_parameters is not None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface = band_parameters[0]","range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.energies = band_energies[nb]","initialization_quantities.append( Quantity( name, r'%s\\s*:\\s*([\\s\\S]*?)\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) initialization_quantities.append(Quantity( 'species', rf'(Species :","in ['FXC', 'NLF_FXC']: data = get_data(quantity, ext) if not data[0]: continue if quantity","== 'time': msection.time_calculation = val else: setattr(msection, name, val) # energy, moment, charge","ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re = np.reshape( data[1], (n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im = np.reshape( data[2], (n_components, n_sigma))","(r'Species with R\\^MT\\_min', None), 'x_exciting_gkmax': (r'Maximum \\|G\\+k\\| for APW functions', 1 / ureg.bohr),","self.data_xs_parser.data is None: continue data.append(self.data_xs_parser.data) return data def parse_exciton(data, sec_scc): n_components = len(data)","np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components = n_components n_excitons = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_excitons = n_excitons sec_scc.x_exciting_xs_bse_exciton_energies","eigs_gw_C sec_gw_eigenvalues.value_xc_potential = reshape(get_data('Vxc')) def _parse_dos_out(self, sec_scc): data = self.dos_out_parser.data if data is","sec_scc.x_exciting_xs_bse_sigma_re = np.reshape( data[1], (n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im = np.reshape( data[2], (n_components, n_sigma)) def","bands: self._bands.append(bands) # add atom-resolved bands_atom = self.root.findall('./*/%s' % self._atom_key) for band in","d in data]) else: energy = np.array([d[1].get(key, None) for d in data]) if","the files bs_files = ['dos.xml', 'TDOS.OUT'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname)","re_symbol = re.compile(r'([A-Z][a-z]?)') def str_to_array(val_in): val = [v.split(':')[-1].split() for v in val_in.strip().split('\\n')] val","'Fermi'], 'energy_kinetic_electronic': ['Kinetic energy', 'electronic kinetic'], 'energy_coulomb': ['Coulomb energy', 'Coulomb'], 'x_exciting_coulomb_energy': ['Coulomb energy',","= self.input_xml_parser.get( 'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg',","% self._band_key) self._bands = [] if bands: self._bands.append(bands) # add atom-resolved bands_atom =","self.energy_unit is not None: res = res * (1 / self.energy_unit) elif 'partial'","sec_scc): self.bandstructure_dat_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_dat_parser.band_energies if band_energies is None: return #","elif quantity == 'LOSS': parse_function = parse_loss else: continue try: parse_function(data, sec_scc) except","Energy, EnergyEntry, Charges, Forces, ForcesEntry, ScfIteration, BandGap ) from nomad.datamodel.metainfo.workflow import Workflow, GeometryOptimization","= self.input_xml_parser.get('xs/ngridq', [1, 1, 1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk', [1, 1, 1]) rgkmax =","\\(\\w+\\)\\s*\\:(\\s*atom[\\-\\s\\w\\.\\:]*?)\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr), ]), repeats=False)) self._quantities.append(Quantity( 'hybrids', r'Hybrids","parse_configuration(optimization_step) if optimization_step.get('method') is not None: sec_scc.x_exciting_geometry_optimization_method = optimization_step.get('method') if optimization_step.get('step') is not","n_excitons sec_scc.x_exciting_xs_bse_exciton_energies = np.reshape( data[1], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies = np.reshape( data[2],","None: band_energy = band_energy * self._energy_unit res_n.append(band_energy) start = end res.append(res_n) elif key","% self._partialdos_key) self._natoms = len(partial_dos) return self._natoms @property def number_of_dos(self): if self._ndos is","= self.logger self.evalqp_parser.logger = self.logger self.dos_out_parser.logger = self.logger self.bandstructure_dat_parser.logger = self.logger self.band_out_parser.logger =","'x_exciting_brillouin_zone_volume': ('Brillouin zone volume', 1 / ureg.bohr ** 3), 'x_exciting_number_of_atoms': ('Total number of","unit=ureg.elementary_charge), Quantity('electronic_charge', rf'electronic charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic mass *: *({re_float})',","5: ['LDA_C_VBH'], 20: ['GGA_C_PBE', 'GGA_X_PBE'], 21: ['GGA_C_PBE', 'GGA_X_PBE_R'], 22: ['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'], 26: ['GGA_C_PBE',","len(files) > 1: self.logger.warn('Found multiple files. Will read all!', data=dict(file=name)) for n in","return [filename] filename = os.path.join(self.info_parser.maindir, default) if not os.path.isfile(filename): file_ext = default.split('.')[-1] mainfile_base","= rgkmax sec_method.x_exciting_xs_screening_type = self.input_xml_parser.get( 'xs/screening/screentype', 'full') if self.input_xml_parser.get('xs/BSE') is not None: sec_method.x_exciting_xs_bse_antiresonant","'DFT' smearing_kind_map = { 'Gaussian': 'gaussian', 'Methfessel-Paxton': 'methfessel-paxton', 'Fermi-Dirac': 'fermi', 'Extended': 'tetrahedra'} sec_smearing","data[0], (n_components, n_epsilon)) * ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re = np.reshape( data[1], (n_components, n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im =","band_energies[i % self.number_of_spin_channels][i] = np.array( [e.attrib.get(self._energy_key) for e in self.bands[n][i]]) for nkpts_segment in","n_kpoints = np.where(data[0] == data[0][0])[0][1] bands = data[1:] bands = np.reshape(bands, ( self.number_of_spin_channels,","[0., 0., 0.]) # TODO I am not certain if screening/BSE are children","self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg', False) sec_method.x_exciting_xs_bse_sciavtype = self.input_xml_parser.get( 'xs/BSE/sciavtype', 'spherical')","input_file: self.input_xml_parser.mainfile = f positions = self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors = self.input_xml_parser.get( 'structure/crystal/basevect', np.eye(3)) species","None: break return positions_format def get_atom_positions(self, section={}, positions=None, positions_format=None): positions = positions if","'positions', r'\\s*:\\s*([\\d\\.\\-]+\\s*[\\d\\.\\-]+\\s*[\\d\\.\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces', r'Total atomic forces including IBS \\(\\w+\\)\\s*\\:(\\s*atom[\\-\\s\\w\\.\\:]*?)\\n *Time',","val[2:]], dtype=float)) eigs = {keys[i]: eigs[i] for i in range(len(keys))} return [kpts, eigs]","== 'band_segm_labels': res = [] for i in range(len(self.vertices) - 1): start =","section.get('final') if final is None: # get it from last scf_iteration or optimization_step","None self._distances = None self._species = None @property def distances(self): if self._distances is","rf'atomic mass *: *({re_float})', dtype=np.float64, unit=ureg.electron_mass), Quantity('muffin_tin_radius', rf'muffin-tin radius *: *({re_float})', dtype=np.float64, unit=ureg.bohr),","= self.input_xml_parser.get( 'xs/nempty', 5) sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq', [1, 1, 1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk',","val is None: continue if key == 'x_exciting_section_MT_charge_atom': for n in range(len(val)): sec_mt_charge_atom","sub_sec = sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment = val elif name == 'x_exciting_species_rtmin': setattr(sec_system, name, '","self.root.findall('./%s' % self._band_key) self._bands = [] if bands: self._bands.append(bands) # add atom-resolved bands_atom","= self.input_xml_parser.get('xs/ngridk', [1, 1, 1]) rgkmax = self.input_xml_parser.get('xs/rgkmax', None) if rgkmax is None:","self._nspin = None self._nkpts_segment = None self._neigs_segment = None self._bands = None self._vertices","= parse_sigma elif quantity == 'LOSS': parse_function = parse_loss else: continue try: parse_function(data,","inconsistency in the naming convention for xs input xml file sec_method = sec_run.m_create(Method)","is None: data = np.transpose(self.data)[0] self._neigs_segment = len(np.where(data == data[0])[0]) return self._neigs_segment class","self.input_xml_parser.get( 'xs/nempty', 5) sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq', [1, 1, 1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk', [1,","= sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = self.dos_parser.number_of_dos sec_dos.energies = self.dos_parser.energies + energy_fermi volume =","def parse_configuration(section): if not section: return sec_scc = self.parse_scc(section) if sec_scc is None:","None), 'x_exciting_pw': (r'Maximum number of plane\\-waves', None), 'x_exciting_lo': (r'Total number of local\\-orbitals', None)}","res = None self._results[key] = res class DOSXMLParser(XMLParser): def __init__(self, **kwargs): super().__init__(None) self._nspin_key","in input_file: self.input_xml_parser.mainfile = f correlation = self.input_xml_parser.get('libxc/correlation', None) xc_functional_names.append(correlation) exchange = self.input_xml_parser.get('libxc/exchange',","self._partial_dos = self.root.findall('./%s/%s' % (self._partialdos_key, self._diagram_key)) return self._partial_dos @property def energies(self): if self._energies","self.total_dos: return res = np.zeros((self.number_of_spin_channels, self.number_of_dos)) for i in range(len(self.total_dos)): spin = self.total_dos[i].attrib.get(self._nspin_key,","section.get('energy_total') is not None else section.get('final') if final is None: # get it","ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field = data[1:] elif quantity == 'EPSILON' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field","None: if self.total_dos is None: return self._energies = np.array( [float(point.attrib.get(self._energy_key)) for point in","= section.get('optimization_step', [None])[-1] if final is None else final if final is None:","correction'] } self._electron_charge_keys_mapping = { 'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage': ['core leakage'], 'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge':","'final', r'(?:Convergence targets achieved\\. Performing final SCF iteration|Reached self-consistent loops maximum)([\\s\\S]+?)(\\n *\\n\\+{10})', sub_parser=TextParser(quantities=scf_quantities),","original scale in which also other energies are reported. energy_fermi = 0.0 *","= parser.eigval_parser.quantities self.fermisurf_parser.quantities = parser.fermisurf_parser.quantities self.evalqp_parser.quantities = parser.evalqp_parser.quantities self.info_gw_parser.quantities = parser.info_gw_parser.quantities def parse(self,","repeats=False, flatten=False) ) initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation type[\\s\\S]+?\\n *\\n)', sub_parser=TextParser(quantities=[ Quantity('type', r'Exchange-correlation type +:","n_sigma = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_sigma_energies = np.reshape( data[0], (n_components, n_sigma)) * ureg.hartree","self._electron_charge_keys_mapping.items(): val = None for name in names: val = charge_contributions.get(name, None) if","potential_mixing is not None: sec_system.x_exciting_potential_mixing = potential_mixing return sec_system def parse_configurations(self): sec_run =","if self._natoms is None: partial_dos = self.root.findall('./%s' % self._partialdos_key) self._natoms = len(partial_dos) return","is None: axis = self.root.find('./axis') if axis is None: return self._energy_unit = self._units_mapping.get(axis.attrib.get(self._unit_key).lower(),","'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage': ['core leakage'], 'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge': ['interstitial'], 'x_exciting_total_MT_charge': ['total charge in","'bandstructure.dat'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break for f in exciting_files:","[sec_scc_ref] # parse properties gw_info_files = self.get_exciting_files(gw_info_file) if len(gw_info_files) > 1: self.logger.warn('Found multiple","self.input_xml_parser.get('xs/screening/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_screening_rgkmax = rgkmax","self.input_xml_parser.get( 'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg', False)","initialization_quantities.append(Quantity( 'potential_mixing', r'Using ([\\w ]+) potential mixing', repeats=False, flatten=False) ) initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation","r'parameters loaded from *: *(.+)'), Quantity('name', r'name *: *(.+)'), Quantity('nuclear_charge', rf'nuclear charge *:","sec_scf_iteration = sec_scc.m_create(ScfIteration) parse_scf(scf_iteration, sec_scf_iteration) return sec_scc def parse_system(self, section): sec_run = self.archive.run[-1]","'gw': parser_function = self._parse_input_gw elif self._calculation_type == 'xs': parser_function = self._parse_input_xs else: #","self.number_of_lm else: spin = int(spin) - 1 val_l = self.partial_dos[i].attrib.get(self._l_key, None) val_m =","Quantity('electronic_charge', rf'electronic charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic mass *: *({re_float})', dtype=np.float64,","= [v.split() for v in val_in.split('\\n')] val = np.transpose(np.array([v for v in val","change in effective potential \\(target\\)', ureg.hartree), 'x_exciting_energy_convergence': ( r'Absolute change in total energy\\s*\\(target\\)',","ureg.hartree).to('joule') # TODO smearing with should have units of energy sec_smearing.width = smearing_width.magnitude","is not None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface = fermi_energy sec_fermisurface.x_exciting_values_fermi_surface = fermi_surface def _parse_evalqp(self, sec_scc): data","0)) sec_gw.self_energy_singularity_treatment = self.input_xml_parser.get( 'gw/selfenergy/singularity', 'mpd') sec_gw.self_energy_c_analytical_continuation = self.input_xml_parser.get( 'gw/selfenergy/actype', 'pade') sec_gw.mixed_basis_lmax =","0 for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment self._band_k_points.append( np.transpose(data[2:5])[start:end]) start","return # Get fermi energy: it is used to un-shift the band structure","res.append([start, end]) else: res = None self._results[key] = res class DOSXMLParser(XMLParser): def __init__(self,","# free up memory parser.mainfile = None def _parse_input_xs(self, sec_method): xstype = self.input_xml_parser.get('xs/xstype',","the fourth column in epsilon which is not parsed? sccs = [] for","'x_exciting_core_electron_kinetic_energy': ['Core-electron kinetic energy', 'core electron kinetic'], 'x_exciting_dft_d2_dispersion_correction': ['DFT-D2 dispersion correction'] } self._electron_charge_keys_mapping","for point in self.bands[0][0]] self._distances = np.array(self._distances, dtype=float) return self._distances @property def bands(self):","energy\\s*\\(target\\)', ureg.hartree), 'x_exciting_charge_convergence': ( r'Charge distance\\s*\\(target\\)', ureg.elementary_charge), 'x_exciting_IBS_force_convergence': ( r'Abs\\. change in max\\-nonIBS\\-force\\s*\\(target\\)',","optimization step\\s*\\:\\s*([\\-\\d\\.Ee]+)\\s*seconds', unit=ureg.s, repeats=False, dtype=float) ] self._quantities.append(Quantity( 'structure_optimization', r'Structure\\-optimization module started([\\s\\S]+?)Structure\\-optimization module stopped',","file is part of NOMAD. # See https://nomad-lab.eu for further info. # #","get_data(key): if key == 'k_points': return np.array([d[0][:3] for d in data]) elif key","val is not None: break if val is None: continue if key.startswith('energy_'): sec_energy.m_add_sub_section(getattr(","= self.input_xml_parser.get('xs/scissor', 0.0) sec_method.x_exciting_xs_vkloff = self.input_xml_parser.get('xs/vkloff', [0., 0., 0.]) # TODO I am","data = np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates = np.transpose( np.array(data[:3], dtype=float)) sec_system.x_exciting_clathrates_atom_labels = list(data[3]) else: sec_system.x_exciting_clathrates","= self.dos_parser parser_function = self._parse_dos elif name.startswith('bandstructure') and name.endswith('xml'): parser = self.bandstructure_parser parser_function","'direct_band_gap', r'\\-\\s*G0W0\\s*\\-\\s*\\-+\\s*[\\s\\S]*?Direct BandGap\\s*\\((?P<__unit>\\w+)\\)\\s*\\:(\\s*[\\d\\.]+)\\s', repeats=False) ) self._quantities.append( Quantity( 'fundamental_band_gap', r'\\-\\s*G0W0\\s*\\-\\s*\\-+\\s*[\\s\\S]*?Fundamental BandGap\\s*\\((?P<__unit>\\w+)\\)\\s*\\:(\\s*[\\d\\.]+)\\s', repeats=False) ) self._quantities.append(","= None def _parse_input_xs(self, sec_method): xstype = self.input_xml_parser.get('xs/xstype', None) if xstype is not","energies self._quantities = [Quantity( 'program_version', r'\\s*EXCITING\\s*([\\w\\-\\(\\)\\. ]+)\\s*started', repeats=False, dtype=str, flatten=False)] initialization_quantities = [","= self.input_xml_parser.get('xs/qpointset/qpoint') def get_data(quantity, ext): # all files related to quantity at all","val_l is None or val_m is None: lm = i % self.number_of_lm else:","ones with the missing quantity if len(quantity) < n_scf: quantity = [None] *","None: return nspin = self.info_parser.get_number_of_spin_channels() def reshape(data): if data[0] is None: return return","parse_configuration(self.info_parser.get(module)) if sec_scc is None: continue # add data to scc # TODO","bands]) if self._energy_unit: band_energy = band_energy * self._energy_unit self._band_energies.append(band_energy) start = end return","if self.input_xml_parser.get('xs/tddft') is not None: sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get( 'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get(","versions, input_gw.xml and input-gw.xml for f in ['input_gw.xml', 'input-gw.xml', 'input.xml']: self.parse_file(f, sec_method) xc_functional_name","len(data) // nspin, len(data[0]))) sec_gw_eigenvalues = sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor = reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands = len(eigs_gw[0])","parse_function = parse_epsilon elif quantity == 'SIGMA': parse_function = parse_sigma elif quantity ==","= optical_band_gap def parse_miscellaneous(self): sec_worfklow = self.archive.m_create(Workflow) sec_worfklow.type = 'single_point' structure_optimization = self.info_parser.get('structure_optimization')","make a parent class for dos and bandstructure super().__init__(None) self._distance_key = 'distance' self._coord_key","if sec_run.system: sec_scc.system_ref = sec_run.system[-1] sec_scc_ref = sec_run.calculation[0] sec_scc.starting_calculation_ref = sec_scc_ref sec_scc.calculations_ref =","self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): # TODO make a parent clas for","Quantity( 'positions_format', r'Atomic positions at this step\\s*\\(([a-z]+)\\)'), Quantity( 'symbols', r'atom\\s*\\d+\\s*(\\w+)', repeats=True, dtype=str), Quantity(","__init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'fermi_energy', r'Fermi Energy:\\s*([\\d\\.]+)\\s*', unit=ureg.hartree,","sec_method.x_exciting_xs_tddft_anti_resonant_dielectric = self.input_xml_parser.get( 'xs/tddft/aresdf', False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel = self.input_xml_parser.get( 'xs/tddft/aresfxc', True) sec_method.x_exciting_xs_tddft_drude = self.input_xml_parser.get(","valence charge', ureg.elementary_charge), 'x_exciting_wigner_radius': (r'Effective Wigner radius, r\\_s', ureg.bohr), 'x_exciting_empty_states': ('Number of empty","# TODO expand list to include other xcf xc_functional_map = { 2: ['LDA_C_PZ',","n_components = len(data) data = np.transpose(np.vstack(data)) n_epsilon = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_energy_points =","this is overwritten when setting mainfile self.bandstructure_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_parser.get('band_energies', [])","val.split('\\n')], dtype=float) val = np.transpose(val) occs = val[-1] eigs = val[-2] nspin =","'xs/tddft/lmaxalda', 3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment = self.input_xml_parser.get( 'xs/tddft/mdfqtype', 0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals = self.input_xml_parser.get( 'xs/tddft/nwacont', 0) sec_method.x_exciting_xs_tetra","ureg.bohr), 'x_exciting_gmaxvr': (r'Maximum \\|G\\| for potential and density', 1 / ureg.bohr), 'x_exciting_gvector_size': (r'G\\-vector","scale in which also other energies are reported. energy_fermi = 0.0 * ureg.hartree","Quantity( 'lattice_vectors', r'Lattice vectors\\s*[\\(cartesian\\)]*\\s*:\\s*([\\-0-9\\.\\s]+)\\n', str_operation=str_to_array, unit=ureg.bohr, repeats=False, convert=False), Quantity( 'lattice_vectors_reciprocal', r'Reciprocal lattice vectors\\s*[\\(cartesian\\)]*\\s*:\\s*([\\-0-9\\.\\s]+)\\n',","None) if xstype is not None: sec_method.x_exciting_xs_xstype = xstype sec_method.x_exciting_electronic_structure_method = xstype sec_method.x_exciting_xs_broadening","step\\s*\\(([a-z]+)\\)'), Quantity( 'symbols', r'atom\\s*\\d+\\s*(\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\s*:\\s*([\\d\\.\\-]+\\s*[\\d\\.\\-]+\\s*[\\d\\.\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces',","data=dict(file=name)) for n in range(len(files)): parser.mainfile = files[n] parser_function(section) # free up memory","section): sec_run = self.archive.run[-1] positions = self.info_parser.get_atom_positions(section.get('atomic_positions', {})) lattice_vectors = self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels =","for i in range(len(self.partial_dos)): spin = self.partial_dos[i].attrib.get(self._nspin_key, None) if spin is None: spin","'x_exciting_nuclear_charge': ('Total nuclear charge', ureg.elementary_charge), 'x_exciting_electronic_charge': ('Total electronic charge', ureg.elementary_charge), 'x_exciting_core_charge_initial': ('Total core","in xc_functional_names: if name is None: continue if '_X_' in name: sec_xc_functional.exchange.append(Functional(name=name)) elif","dist = np.transpose(self.data)[0] n_k_points = np.where(dist == dist[0])[0][1] self._distances = dist[:n_k_points] return self._distances","self.input_xml_parser.get('xs/tddft') is not None: sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get( 'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get( 'xs/tddft/ahc',","sec_scc_ref = sec_run.calculation[0] sec_scc.starting_calculation_ref = sec_scc_ref sec_scc.calculations_ref = [sec_scc_ref] # parse properties gw_info_files"],"string":"[\n \"other energies are reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is None: continue energy_fermi\",\n \"def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'fermi_energy', r'Fermi Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*', unit=ureg.hartree, repeats=False)) def\",\n \"float(v[1]) * ureg.hartree return energies self._quantities = [Quantity( 'program_version', r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\. ]+)\\\\s*started', repeats=False, dtype=str,\",\n \"self._vertices is None: self._vertices = self.root.findall('./%s' % self._vertex_key) return self._vertices @property def number_of_spin_channels(self):\",\n \"'GGA_X_PBE_SOL'], 26: ['GGA_C_PBE', 'GGA_X_WC'], 30: ['GGA_C_AM05', 'GGA_C_AM05'], 300: ['GGA_C_BGCP', 'GGA_X_PBE'], 406: ['HYB_GGA_XC_PBEH'], 408:\",\n \"energy', 'Hartree'], 'x_exciting_electron_nuclear_energy': ['Electron-nuclear energy', 'electron-nuclear '], 'x_exciting_nuclear_nuclear_energy': ['Nuclear-nuclear energy', 'nuclear-nuclear'], 'x_exciting_madelung_energy': ['Madelung\",\n \"in names: val = moment_contributions.get(name, None) if val is not None: break if\",\n \"@property def number_of_dos(self): if self._ndos is None: total_dos = self.root.find('./%s/%s' % (self._totaldos_key, self._diagram_key))\",\n \"= 'distance' self._coord_key = 'coord' self._energy_key = 'eval' self._vertex_key = 'vertex' self._band_key =\",\n \"sec_atoms.positions = positions sec_atoms.labels = atom_labels sec_atoms.periodic = [True] * 3 # TODO\",\n \"= self.root.findall('./%s' % self._band_key) self._bands = [] if bands: self._bands.append(bands) # add atom-resolved\",\n \"Performing final SCF iteration|Reached self-consistent loops maximum)([\\\\s\\\\S]+?)(\\\\n *\\\\n\\\\+{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(Atomic\",\n \"sec_gw = sec_method.m_create(GWMethod) sec_gw.type = 'G0W0' gmaxvr = self.info_parser.get_initialization_parameter('x_exciting_gmaxvr', 0) sec_gw.core_treatment = self.input_xml_parser.get(\",\n \"* gmb * gmaxvr sec_gw.bare_coulomb_cutofftype = self.input_xml_parser.get( 'gw/barecoul/cutofftype', 'none') sec_gw.screened_coulomb_volume_average = self.input_xml_parser.get( 'gw/scrcoul/sciavtype',\",\n \"smearing_kind is not None: if not isinstance(smearing_kind, str): smearing_kind = smearing_kind[0] smearing_kind =\",\n \"( Calculation, Dos, DosValues, BandStructure, BandEnergies, Energy, EnergyEntry, Charges, Forces, ForcesEntry, ScfIteration, BandGap\",\n \"self._natoms is None: partial_dos = self.root.findall('./%s' % self._partialdos_key) self._natoms = len(partial_dos) return self._natoms\",\n \"= self.input_xml_parser.get( 'xs/BSE/sciavtype', 'spherical') sec_method.x_exciting_xs_bse_xas = self.input_xml_parser.get( 'xs/BSE/xas', False) sec_method.x_exciting_xs_bse_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlbse',\",\n \"_parse_eigenvalues(self, sec_scc): if self.eigval_parser.get('eigenvalues_occupancies', None) is None: return nspin = self.info_parser.get_number_of_spin_channels() def get_data(key):\",\n \"add support for more output files and properties exciting_files = ['EIGVAL.OUT', 'FERMISURF.bxsf', 'FS.bxsf']\",\n \"1 else vi properties[v[0].strip()] = vi * unit properties['atom_resolved'] = atom_resolved return properties\",\n \"sub_sec.x_exciting_spin_treatment = val elif name == 'x_exciting_species_rtmin': setattr(sec_system, name, ' '.join([str(v) for v\",\n \"None: # get it from last scf_iteration or optimization_step final = section.get('scf_iteration', [None])[-1]\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"key_unit[0], unit=key_unit[1], repeats=False) ) for name, key_unit in self._method_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n'\",\n \"section): sec_run = self.archive.run[-1] final = section if section.get('energy_total') is not None else\",\n \"None: lm = i % self.number_of_lm else: lm = int(val_l) ** 2 +\",\n \"unit=ureg.hartree, repeats=False)) def str_to_band_parameters(val_in): val = val_in.strip().split('\\\\n') nbands = int(val[0]) mesh = np.array(val[1].split(),\",\n \"r'Time spent in this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds', unit=ureg.s, repeats=False, dtype=float) ] self._quantities.append(Quantity( 'structure_optimization', r'Structure\\\\-optimization\",\n \"None self._nspin = None self._nlm = None self._energies = None self._total_dos = None\",\n \"int(val[0]) mesh = np.array(val[1].split(), dtype=int) origin = np.array(val[2].split(), dtype=float) vector = np.array([v.split() for\",\n \"fermi_surface = self.fermisurf_parser.get('fermi_surface', [None])[0] if fermi_surface is None: return sec_fermisurface = sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters\",\n \"j in range(len(data)): data[j].append(data_q[j]) return data for quantity in ['EPSILON', 'LOSS', 'SIGMA']: for\",\n \"initialization)', repeats=False, sub_parser=TextParser(quantities=initialization_quantities)) ) scf_quantities = [ Quantity( 'energy_total', r'[Tt]*otal energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float,\",\n \"for f in exciting_files: self.parse_file(f, sec_scc) # structure optimization structure_optimization = self.info_parser.get('structure_optimization', {})\",\n \"sec_smearing.width = smearing_width.magnitude for name in self.info_parser._convergence_keys_mapping.keys(): threshold = self.info_parser.get_scf_threshold(name) if threshold is\",\n \"self.total_dos[i].attrib.get(self._nspin_key, i) res[i] = self._get_dos(self._total_dos[i]) if self.energy_unit is not None: res = res\",\n \"if self.energy_unit is not None: self._energies = self._energies * self.energy_unit return self._energies def\",\n \"else end]) elif key == 'band_segm_start_end': res = [] for i in range(len(self.number_of_k_points_per_segment)):\",\n \"is not None: sec_method.x_exciting_xs_screening_number_of_empty_states = self.input_xml_parser.get( 'xs/screening/nempty', 0) sec_method.x_exciting_xs_screening_ngridk = self.input_xml_parser.get( 'xs/screening/ngridk', [0,\",\n \"data for quantity in ['EPSILON', 'LOSS', 'SIGMA']: for ext in ['FXC', 'NLF_FXC']: data\",\n \"return self._neigs_segment class BandOutParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit\",\n \"are really correct, i.e. columns are supposed # to be what they are.\",\n \"the files bs_files = ['bandstructure.xml', 'BAND.OUT', 'bandstructure.dat'] for fname in bs_files: if self.file_exists(fname):\",\n \"self.logger.warn('Error setting metainfo.') # species species = self.info_parser.get_initialization_parameter('species', []) for specie in species:\",\n \"< n_scf: quantity = [None] * (n_scf - len(quantity)) + quantity return quantity\",\n \"1].attrib.get('label') res.append([ '\\\\u0393' if start.lower() == 'gamma' else start, '\\\\u0393' if end.lower() ==\",\n \"if optical_band_gap is not None: sec_gap.value_optical = optical_band_gap def parse_miscellaneous(self): sec_worfklow = self.archive.m_create(Workflow)\",\n \"positions at this step\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True,\",\n \"n_spin = 1 if spin_treatment.lower() == 'spin-unpolarised' else 2 return n_spin def get_unit_cell_volume(self):\",\n \"> 1: self.logger.warn('Found multiple GW info files, will read only first!') self.info_gw_parser.mainfile =\",\n \"from xml file def get_files(name): bse_types = ['IP', 'singlet', 'triplet', 'RPA'] scr_types =\",\n \"Electronic, Smearing, XCFunctional, Functional, GW as GWMethod, Scf, BasisSet ) from nomad.datamodel.metainfo.simulation.system import\",\n \"self.get_exciting_files(f): self._calculation_type = 'gw' gw_info_file = f break if not self._calculation_type == 'gw':\",\n \"optimization in exciting sec_atoms.lattice_vectors = lattice_vectors lattice_vectors_reciprocal = self.info_parser.get_initialization_parameter( 'lattice_vectors_reciprocal') sec_atoms.lattice_vectors_reciprocal = lattice_vectors_reciprocal\",\n \"'all') sec_gw.polarizability_number_of_empty_states = int( self.input_xml_parser.get('gw/nempty', 0)) sec_gw.ngridq = self.input_xml_parser.get('gw/ngridq', [1, 1, 1]) sec_gw.basis_set\",\n \"'xs/tddft/fxcbsesplit', 0.00001, 'hartree') sec_method.x_exciting_xs_tddft_xc_kernel = self.input_xml_parser.get( 'xs/tddft/fxctype', 'RPA') sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution = self.input_xml_parser.get( 'xs/tddft/intraband', False)\",\n \"\\\\|G\\\\+k\\\\|\\\\_max \\\\(rgkmax\\\\)', None), 'x_exciting_species_rtmin': (r'Species with R\\\\^MT\\\\_min', None), 'x_exciting_gkmax': (r'Maximum \\\\|G\\\\+k\\\\| for APW\",\n \"def get_scf_threshold(self, name): reference = self.get('groundstate', self.get('hybrids', {})) return reference.get('scf_iteration', [{}])[-1].get( name, [None,\",\n \"= sec_run.m_create(Calculation) sec_scc.method_ref = sec_method if sec_run.system: sec_scc.system_ref = sec_run.system[-1] sec_scc_ref = sec_run.calculation[0]\",\n \"'methfessel-paxton', 'Fermi-Dirac': 'fermi', 'Extended': 'tetrahedra'} sec_smearing = sec_electronic.m_create(Smearing) smearing_kind = self.info_parser.get_initialization_parameter('smearing_kind') if smearing_kind\",\n \"kinetic'], 'energy_coulomb': ['Coulomb energy', 'Coulomb'], 'x_exciting_coulomb_energy': ['Coulomb energy', 'Coulomb'], 'energy_exchange': ['Exchange energy', 'exchange'],\",\n \"None: sec_method.x_exciting_xs_xstype = xstype sec_method.x_exciting_electronic_structure_method = xstype sec_method.x_exciting_xs_broadening = self.input_xml_parser.get( 'xs/broad', 0.01, 'hartree')\",\n \"= np.reshape( data[0], (n_components, n_sigma)) * ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re = np.reshape( data[1], (n_components, n_sigma))\",\n \"which is problematic sometimes res = np.hstack([np.reshape(v[key], (nspin, np.size(v[key]) // nspin)) for v\",\n \"x_exciting_section_fermi_surface,\\\\ x_exciting_section_atoms_group re_float = r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?' class GWInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities\",\n \"smearing_width.magnitude for name in self.info_parser._convergence_keys_mapping.keys(): threshold = self.info_parser.get_scf_threshold(name) if threshold is None: continue\",\n \"self._band_key)) return self._bands @property def vertices(self): if self._vertices is None: self._vertices = self.root.findall('./%s'\",\n \"[ Quantity( 'atomic_positions', r'(Atomic positions at this step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions\",\n \"positions_format is not None else self.get_positions_format(section) if positions_format == 'lattice': cell = self.get_initialization_parameter('lattice_vectors')\",\n \"key == 'x_exciting_fermi_energy': sec_energy.fermi = val # charge contributions charge_contributions = iteration.get('charge_contributions', {})\",\n \"if target[0] in f and mainfile_base in f] options = [f for f\",\n \"dtype=np.int32), Quantity('symbol', r'\\\\((\\\\w+)\\\\)'), Quantity('file', r'parameters loaded from *: *(.+)'), Quantity('name', r'name *: *(.+)'),\",\n \"None) if fundamental_band_gap is None: fundamental_band_gap = self.info_gw_parser.get('fundamental_band_gap', None) sec_gap = sec_scc.eigenvalues[-1].m_create(BandGap) if\",\n \"if not xc_functional_names: # simply write parameters xc_functional = self.info_parser.get('initialization', {}).get('xc_functional') if xc_functional\",\n \"'charge_total': pass else: setattr(msection, key, val) # moment contributions moment_contributions = iteration.get('moment_contributions', {})\",\n \"return self.get('initialization', {}).get(key, default) class ExcitingParser: def __init__(self): self.info_parser = ExcitingInfoParser() self.dos_parser =\",\n \"= nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.endpoints_labels = band_seg_labels[nb] sec_k_band_segment.energies = band_energies[n][nb] + energy_fermi\",\n \"False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get( 'xs/tddft/ahc', False) sec_method.x_exciting_xs_tddft_anti_resonant_dielectric = self.input_xml_parser.get( 'xs/tddft/aresdf', False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel =\",\n \"with R\\\\^MT\\\\_min', None), 'x_exciting_gkmax': (r'Maximum \\\\|G\\\\+k\\\\| for APW functions', 1 / ureg.bohr), 'x_exciting_gmaxvr':\",\n \"range(len(atoms)): atom_labels.extend([species[n].split('.')[0]] * len(atoms[n])) if positions is None or atom_labels is None: return\",\n \"parser = self.evalqp_parser parser_function = self._parse_evalqp elif name.startswith('TDOS') and name.endswith('OUT'): parser = self.dos_out_parser\",\n \"sec_scc): if self.eigval_parser.get('eigenvalues_occupancies', None) is None: return nspin = self.info_parser.get_number_of_spin_channels() def get_data(key): data\",\n \"n_epsilon)) def parse_sigma(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_sigma = len(data[0])\",\n \"None: data = np.transpose(self.data) self._neigs_segment = int(max(data[0])) return self._neigs_segment class BandOutParser(DataTextParser): def __init__(self,\",\n \"== 'LOSS' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_loss_function_local_field = data[1] elif quantity == 'LOSS'\",\n \"sec_worfklow.type = 'single_point' structure_optimization = self.info_parser.get('structure_optimization') if structure_optimization is not None: sec_worfklow.type =\",\n \"= self.logger self.input_xml_parser.logger = self.logger self.data_xs_parser.logger = self.logger self.data_clathrate_parser.logger = self.logger def reuse_parser(self,\",\n \"partialdos = partialdos.to('1/joule').magnitude lm_values = np.column_stack((np.arange(len(partialdos)), np.zeros(len(partialdos), dtype=np.int32))) for lm in range(len(partialdos)): for\",\n \"options if f.endswith(file_ext)] options.sort() filenames = [os.path.join(self.info_parser.maindir, f) for f in options] else:\",\n \"isinstance(smearing_kind, str): smearing_kind = smearing_kind[0] smearing_kind = smearing_kind_map[smearing_kind] sec_smearing.kind = smearing_kind smearing_width =\",\n \"+= [specie.get('symbol')] * len(specie.get('positions')) return labels def get_positions_format(self, section): positions_format = section.get('positions_format') if\",\n \"ureg.bohr, repeats=False, convert=False), ] self._system_keys_mapping = { 'x_exciting_unit_cell_volume': ('Unit cell volume', ureg.bohr **\",\n \"= np.array(val[1].split(), dtype=int) origin = np.array(val[2].split(), dtype=float) vector = np.array([v.split() for v in\",\n \"in names: val = energy_contributions.get(name, None) if val is not None: break if\",\n \"name, section): # TODO add support for info.xml, wannier.out if name.startswith('dos') and name.endswith('xml'):\",\n \"energy', 'Coulomb'], 'x_exciting_coulomb_energy': ['Coulomb energy', 'Coulomb'], 'energy_exchange': ['Exchange energy', 'exchange'], 'x_exciting_exchange_energy': ['Exchange energy',\",\n \"else: count += 1 self._nkpts_segment.append(count) return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is\",\n \"key): if self._results is None: self._results = dict() if 'total' in key: if\",\n \"== 'SIGMA': parse_function = parse_sigma elif quantity == 'LOSS': parse_function = parse_loss else:\",\n \"ureg.bohr), 'x_exciting_gvector_size': (r'G\\\\-vector grid sizes', None), 'x_exciting_gvector_total': (r'Total number of G\\\\-vectors', None), 'x_exciting_lmaxapw':\",\n \"'xs/BSE/nstlxas', [0, 0]) if self.input_xml_parser.get('xs/tddft') is not None: sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get( 'xs/tddft/acont', False)\",\n \"dtype=str), Quantity( 'n_scf_iterations', r'Number of (?:total)* scf iterations\\\\s*\\\\:\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'force_convergence', r'Maximum\",\n \"None @property def distances(self): if self._distances is None: if not self.bands: return self._distances\",\n \"= None self._distances = None self._band_energies = None self._neigs_segment = None self._nkpts_segment =\",\n \"None) nwacont = self.input_xml_parser.get('xs/tddft/nwacont', None) aresdf = self.input_xml_parser.get('xs/tddft/aresdf', True) file_ext_list = [ 'TET'\",\n \"self.number_of_k_points_per_segment: end = start + nkpts_segment self._band_k_points.append( np.transpose(data[2:5])[start:end]) start = end return self._band_k_points\",\n \"self._neigs_segment class BandstructureXMLParser(XMLParser): def __init__(self, **kwargs): # TODO make a parent class for\",\n \"energies are reported. energy_fermi = 0.0 * ureg.hartree if sec_scc.energy is not None:\",\n \"name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) for name, key_unit in self._method_keys_mapping.items(): initialization_quantities.append(\",\n \"= 0.0 * ureg.hartree if sec_scc.energy is not None: energy_fermi = sec_scc.energy.fermi energy_fermi\",\n \"None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): # TODO make a parent clas\",\n \"= band_energies[nb] + energy_fermi def parse_file(self, name, section): # TODO add support for\",\n \"self._parse_dos_out elif name.startswith('bandstructure') and name.endswith('dat'): parser = self.bandstructure_dat_parser parser_function = self._parse_bandstructure_dat elif name.startswith('BAND')\",\n \"*: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('electronic_charge', rf'electronic charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic\",\n \"else: energy = np.array([d[1].get(key, None) for d in data]) if None in energy:\",\n \"'atomic_positions', r'(Atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str),\",\n \"ExcitingFermiSurfaceBxsfParser() self.evalqp_parser = ExcitingEvalqpParser() self.dos_out_parser = DataTextParser() self.bandstructure_dat_parser = BandstructureDatParser(energy_unit=ureg.hartree) self.band_out_parser = BandOutParser(energy_unit=ureg.hartree)\",\n \"import Run, Program from nomad.datamodel.metainfo.simulation.method import ( Method, DFT, Electronic, Smearing, XCFunctional, Functional,\",\n \"None: data = np.transpose(self.data) self._distances = data[5][:int(max(data[1]))] return self._distances @property def number_of_spin_channels(self): if\",\n \"hybrids calculation for module in ['groundstate', 'hybrids']: sec_scc = parse_configuration(self.info_parser.get(module)) if sec_scc is\",\n \"if spin_treatment.lower() == 'spin-unpolarised' else 2 return n_spin def get_unit_cell_volume(self): return self.get('initialization', {}).get('x_exciting_unit_cell_volume',\",\n \"// n_components sec_scc.x_exciting_xs_bse_number_of_energy_points = n_epsilon sec_scc.x_exciting_xs_bse_epsilon_energies = np.reshape( data[0], (n_components, n_epsilon)) * ureg.hartree\",\n \"= len(data[0][0][0]) sec_scc.x_exciting_xs_tddft_epsilon_energies = data[0][0][0] * ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field = data[1:] elif quantity ==\",\n \"elif name.startswith('input') and name.endswith('xml'): parser = self.input_xml_parser if self._calculation_type == 'gw': parser_function =\",\n \"= self.vertices[i].attrib.get('label') end = self.vertices[i + 1].attrib.get('label') res.append([ '\\\\u0393' if start.lower() == 'gamma'\",\n \"self._ndos is None: total_dos = self.root.find('./%s/%s' % (self._totaldos_key, self._diagram_key)) self._ndos = len(total_dos) return\",\n \"None: return return np.reshape(data, (nspin, len(data) // nspin, len(data[0]))) sec_gw_eigenvalues = sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor\",\n \"list to include other xcf xc_functional_map = { 2: ['LDA_C_PZ', 'LDA_X_PZ'], 3: ['LDA_C_PW',\",\n \"= band_parameters[2] sec_fermisurface.x_exciting_vectors_fermi_surface = band_parameters[3] fermi_energy = self.fermisurf_parser.get('fermi_energy', None) if fermi_energy is not\",\n \"1 while True: info_volume = self.get_exciting_files('run_dir%s/INFO.OUT' % str(volume_index).rjust(2, '0')) if not info_volume: break\",\n \"= Program( name='exciting', version=self.info_parser.get('program_version', '').strip()) # method goes first since reference needed for\",\n \") from nomad.datamodel.metainfo.simulation.calculation import ( Calculation, Dos, DosValues, BandStructure, BandEnergies, Energy, EnergyEntry, Charges,\",\n \"name.startswith('input') and name.endswith('xml'): parser = self.input_xml_parser if self._calculation_type == 'gw': parser_function = self._parse_input_gw\",\n \"if len(v) < 2: continue energies[v[0].strip()] = float(v[1]) * ureg.hartree return energies self._quantities\",\n \"is not None: self._energies = self._energies * self.energy_unit return self._energies def _get_dos(self, diagram):\",\n \"is not None: sec_scc.x_exciting_geometry_optimization_step = optimization_step.get('step') force_convergence = optimization_step.get('force_convergence') if force_convergence is not\",\n \"(target[0], suffix) if target[1:]: filename = '%s.%s' % (filename, target[1]) filename = os.path.join(self.info_parser.maindir,\",\n \"from last scf_iteration or optimization_step final = section.get('scf_iteration', [None])[-1] final = section.get('optimization_step', [None])[-1]\",\n \"sec_method.x_exciting_xs_tddft_split_parameter = self.input_xml_parser.get( 'xs/tddft/fxcbsesplit', 0.00001, 'hartree') sec_method.x_exciting_xs_tddft_xc_kernel = self.input_xml_parser.get( 'xs/tddft/fxctype', 'RPA') sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution =\",\n \"if self._calculation_type == 'gw': parser_function = self._parse_input_gw elif self._calculation_type == 'xs': parser_function =\",\n \"return partialdos = partialdos.to('1/joule').magnitude lm_values = np.column_stack((np.arange(len(partialdos)), np.zeros(len(partialdos), dtype=np.int32))) for lm in range(len(partialdos)):\",\n \"is None: return sec_scc = sec_run.m_create(Calculation) def parse_scf(iteration, msection): energy_total = iteration.get('energy_total') sec_energy\",\n \"'x_exciting_interstitial_charge': ['interstitial'], 'x_exciting_total_MT_charge': ['total charge in muffin-tins', 'total in muffin-tins'], 'charge_total': ['total charge'],\",\n \"= self.input_xml_parser.get('structure/species/atom') atom_labels = [] for n in range(len(atoms)): atom_labels.extend([species[n].split('.')[0]] * len(atoms[n])) if\",\n \"folder where the mainfile is stored. \\\"\\\"\\\" mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT')\",\n \"= moment_contributions.get(name, None) if val is not None: break if val is None:\",\n \"self.energy_unit is not None: self._energies = self._energies * self.energy_unit return self._energies def _get_dos(self,\",\n \"'optimization_step', r'(Optimization step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n *\\\\n\\\\-{10}|Time spent in this optimization step\\\\s*:\\\\s*[\\\\d\\\\.]+ seconds))', sub_parser=TextParser(quantities=optimization_quantities), repeats=True), Quantity(\",\n \"self._distances is None: dist = np.transpose(self.data)[0] n_k_points = np.where(dist == dist[0])[0][1] self._distances =\",\n \"self.distances[i - 1]: self._nkpts_segment .append(count) count = 1 else: count += 1 self._nkpts_segment.append(count)\",\n \"lm in range(len(partialdos)): for spin in range(len(partialdos[lm])): for atom in range(len(partialdos[lm][spin])): sec_dos_values =\",\n \"'Gaussian': 'gaussian', 'Methfessel-Paxton': 'methfessel-paxton', 'Fermi-Dirac': 'fermi', 'Extended': 'tetrahedra'} sec_smearing = sec_electronic.m_create(Smearing) smearing_kind =\",\n \"potential mixing', repeats=False, flatten=False) ) initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation type[\\\\s\\\\S]+?\\\\n *\\\\n)', sub_parser=TextParser(quantities=[ Quantity('type', r'Exchange-correlation\",\n \"self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy = np.array([ np.transpose(energy)[start:end] for energy in\",\n \"self.band_out_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.value =\",\n \"forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Time', repeats=False, str_operation=str_to_array, convert=False, unit=ureg.hartree / ureg.bohr), Quantity( 'step',\",\n \"start = 0 band_energies = np.zeros(( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, len(self.distances)), dtype=float) for i in\",\n \"self.input_xml_parser.get( 'xs/BSE/nstlxas', [0, 0]) if self.input_xml_parser.get('xs/tddft') is not None: sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get( 'xs/tddft/acont',\",\n \"self.logger self.bandstructure_dat_parser.logger = self.logger self.band_out_parser.logger = self.logger self.info_gw_parser.logger = self.logger self.input_xml_parser.logger = self.logger\",\n \"r'Optimization step\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'method', r'method\\\\s*=\\\\s*(\\\\w+)', repeats=False, dtype=str), Quantity( 'n_scf_iterations', r'Number of\",\n \"default) if not os.path.isfile(filename): file_ext = default.split('.')[-1] mainfile_base = mainfile.rsplit('.', 1)[0].replace('INFO', '') options\",\n \"None self.init_parser() sec_run = self.archive.m_create(Run) sec_run.program = Program( name='exciting', version=self.info_parser.get('program_version', '').strip()) # method\",\n \"val = iteration.get(name) if val is None: continue setattr(msection, name, val) # other\",\n \"am not sure about format for spin-polarized case! I assume it is #\",\n \"+(\\\\S+)'), Quantity( 'name_reference', r'\\\\n *(.+?,.+)', str_operation=lambda x: [v.strip() for v in x.split(':')]), Quantity(\",\n \"rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0) sec_method.x_exciting_xs_bse_rgkmax = rgkmax sec_method.x_exciting_xs_bse_sciavbd = self.input_xml_parser.get( 'xs/BSE/sciavbd', True) sec_method.x_exciting_xs_bse_sciavqbd\",\n \"parse_sigma elif quantity == 'LOSS': parse_function = parse_loss else: continue try: parse_function(data, sec_scc)\",\n \"self.info_parser.get_initialization_parameter('potential_mixing') if potential_mixing is not None: sec_system.x_exciting_potential_mixing = potential_mixing return sec_system def parse_configurations(self):\",\n \"= self.info_gw_parser.get('fermi_energy', None) if fermi_energy is not None: sec_scc.energy = Energy(fermi=fermi_energy) gw_files =\",\n \"self._neigs_segment = int(max(data[0])) return self._neigs_segment class BandOutParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin =\",\n \"# other metainfo for name in self.info_parser._miscellaneous_keys_mapping.keys(): val = iteration.get(name) if val is\",\n \"files = self.get_exciting_files(name) if len(files) > 1: self.logger.warn('Found multiple files. Will read all!',\",\n \"at this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', unit=ureg.hartree, repeats=False, dtype=float), Quantity( 'time', r'Time spent in this\",\n \"_parse_xs_bse(self): sec_run = self.archive.run[-1] # TODO read from xml file def get_files(name): bse_types\",\n \"data]) elif key == 'Znk': return np.array([d[1].get(key, None) for d in data]) else:\",\n \"name, [None, None])[-1] def get_scf_quantity(self, name): n_scf = len(self.get('energy_total_scf_iteration', [])) quantity = self.get('%s_scf_iteration'\",\n \"None), 'x_exciting_number_kpoints': (r'Total number of k\\\\-points', None), 'x_exciting_rgkmax': (r'R\\\\^MT\\\\_min \\\\* \\\\|G\\\\+k\\\\|\\\\_max \\\\(rgkmax\\\\)', None),\",\n \"sec_forces = sec_scc.m_create(Forces) sec_forces.total = ForcesEntry(value=forces) # scf iterations scf_iterations = section.get('scf_iteration', [])\",\n \"@property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: self._neigs_segment = len(self.bands[0]) // self.number_of_spin_channels return\",\n \"= fundamental_band_gap optical_band_gap = self.info_gw_parser.get('optical_band_gap', None) if optical_band_gap is not None: sec_gap.value_optical =\",\n \"range(len(val)): sec_mt_charge_atom = msection.m_create(x_exciting_section_MT_charge_atom) sec_mt_charge_atom.x_exciting_MT_charge_atom_index = n + 1 sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol = val[n][0] sec_mt_charge_atom.x_exciting_MT_charge_atom_value\",\n \"eigs = np.transpose(np.array([v.split() for v in val[2:]], dtype=float)) eigs = {keys[i]: eigs[i] for\",\n \"is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi\",\n \"self._calculation_type = 'xs' # inconsistency in the naming convention for xs input xml\",\n \"return res sec_eigenvalues = sec_scc.m_create(BandEnergies) sec_eigenvalues.kpoints = self.eigval_parser.get('k_points') sec_eigenvalues.occupations = get_data('occupancies') sec_eigenvalues.energies =\",\n \"is not None: res = res * (1 / self.energy_unit) elif key ==\",\n \"self._parse_input_gw elif self._calculation_type == 'xs': parser_function = self._parse_input_xs else: # TODO implement reading\",\n \"charge in muffin-tins', 'total in muffin-tins'], 'charge_total': ['total charge'], 'x_exciting_section_MT_charge_atom': ['atom_resolved'] } self._moment_keys_mapping\",\n \"= self.input_xml_parser.get( 'structure/crystal/basevect', np.eye(3)) species = self.input_xml_parser.get('structure/species/speciesfile') if positions is None or lattice_vectors\",\n \"else start, '\\\\u0393' if end.lower() == 'gamma' else end]) elif key == 'band_segm_start_end':\",\n \"correlation = self.input_xml_parser.get('libxc/correlation', None) xc_functional_names.append(correlation) exchange = self.input_xml_parser.get('libxc/exchange', None) xc_functional_names.append(exchange) sec_xc_functional = sec_dft.m_create(XCFunctional)\",\n \"self._distances = [ point.attrib.get(self._distance_key) for point in self.bands[0][0]] self._distances = np.array(self._distances, dtype=float) return\",\n \"moment contributions moment_contributions = iteration.get('moment_contributions', {}) for key, names in self._moment_keys_mapping.items(): val =\",\n \"in EXCITON and file type', data=dict(file=quantity)) sec_scc = sec_run.m_create(Calculation) if quantity == 'EXCITON':\",\n \"v in val[2:]], dtype=float)) eigs = {keys[i]: eigs[i] for i in range(len(keys))} return\",\n \"to scc # TODO add support for more output files and properties exciting_files\",\n \"for i in range(len(self.bands[n])): band_energies[i % self.number_of_spin_channels][i] = np.array( [e.attrib.get(self._energy_key) for e in\",\n \"self.archive.run[-1] final = section if section.get('energy_total') is not None else section.get('final') if final\",\n \"return # volume optimizations volume_index = 1 while True: info_volume = self.get_exciting_files('run_dir%s/INFO.OUT' %\",\n \"val is not None: break if val is None: continue if key ==\",\n \"return self._neigs_segment def parse(self, key): if self._results is None: self._results = dict() if\",\n \"quantity == 'EPSILON' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field = data[1:3] elif quantity ==\",\n \"key_unit[0], unit=key_unit[1], repeats=False) ) initialization_quantities.append(Quantity( 'species', rf'(Species : *\\\\d+ *\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float} *{re_float} *{re_float}\\\\n\\\\s*\\\\n)', repeats=True,\",\n \"energies = dict() for v in val: v = v.split(':') if len(v) <\",\n \"ureg.elementary_charge * ureg.bohr val = val_in.strip().split('\\\\n') properties = dict() atom_resolved = [] species\",\n \"break for f in exciting_files: self.parse_file(f, sec_scc) # structure optimization structure_optimization = self.info_parser.get('structure_optimization',\",\n \"if final is None: return sec_scc = sec_run.m_create(Calculation) def parse_scf(iteration, msection): energy_total =\",\n \"str_operation=lambda x: [v.strip() for v in x.split(':')]), Quantity( 'parameters', r'\\\\n *(.+?:.+)', repeats=True, str_operation=lambda\",\n \"= self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels = self.info_parser.get_atom_labels(section.get('atomic_positions', {})) input_file = self.get_exciting_files('input.xml') if positions is None:\",\n \"parse_function = parse_sigma elif quantity == 'LOSS': parse_function = parse_loss else: continue try:\",\n \"Quantity( 'atomic_positions', r'(Atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True,\",\n \"= f if self.data_xs_parser.data is None: continue data.append(self.data_xs_parser.data) return data def parse_exciton(data, sec_scc):\",\n \"for n in range(len(self.bands)): res_n = [] start = 0 band_energies = np.zeros((\",\n \"nomad.parsing.file_parser import TextParser, Quantity, XMLParser, DataTextParser from nomad.datamodel.metainfo.simulation.run import Run, Program from nomad.datamodel.metainfo.simulation.method\",\n \"data[0], (n_components, n_sigma)) * ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re = np.reshape( data[1], (n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im =\",\n \"sec_atoms.labels = atom_labels sec_atoms.periodic = [True] * 3 # TODO confirm no cell\",\n \"'GGA_X_PBE_R'], 22: ['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'], 26: ['GGA_C_PBE', 'GGA_X_WC'], 30: ['GGA_C_AM05', 'GGA_C_AM05'], 300: ['GGA_C_BGCP', 'GGA_X_PBE'],\",\n \"{})) return reference.get('scf_iteration', [{}])[-1].get( name, [None, None])[-1] def get_scf_quantity(self, name): n_scf = len(self.get('energy_total_scf_iteration',\",\n \"return self._energy_unit @property def number_of_spin_channels(self): if self._nspin is None: if not self.total_dos: return\",\n \"lattice_vectors *= self.input_xml_parser.get('structure/crystal/scale', 1.0) positions = np.dot(positions, lattice_vectors) * ureg.bohr lattice_vectors = lattice_vectors\",\n \"= frequency_data.get('values') sec_method.gw.frequency_weights = frequency_data.get('weights') fundamental_band_gap = self.info_gw_parser.get('direct_band_gap', None) if fundamental_band_gap is None:\",\n \")) def get_atom_labels(self, section): labels = section.get('symbols') if labels is None: # we\",\n \"for specie in species: positions_format = specie.get('positions_format', None) if positions_format is not None:\",\n \"None self._band_energies = None self._neigs_segment = None self._nkpts_segment = None @property def band_energies(self):\",\n \"labels = [] for specie in species: labels += [specie.get('symbol')] * len(specie.get('positions')) return\",\n \"self._nspin = None self._nkpts_segment = None self._neigs_segment = None self._vertices = None self._distances\",\n \"# the original scale in which also other energies are reported. energy_fermi =\",\n \"'x_exciting_species_rtmin': setattr(sec_system, name, ' '.join([str(v) for v in val])) else: try: setattr(sec_system, name,\",\n \"= None self._neigs_segment = None self._bands = None self._vertices = None self._distances =\",\n \"= spin sec_dos_values.atom_index = atom sec_dos_values.value = partialdos[lm][spin][atom] def _parse_bandstructure(self, sec_scc): # we\",\n \"is None: data = np.transpose(self.data) n_kpoints = np.where(data[0] == data[0][0])[0][1] bands = data[1:]\",\n \"sec_gw.self_energy_c_number_of_empty_states = int(self.input_xml_parser.get( 'gw/selfenergy/nempty', 0)) sec_gw.self_energy_singularity_treatment = self.input_xml_parser.get( 'gw/selfenergy/singularity', 'mpd') sec_gw.self_energy_c_analytical_continuation = self.input_xml_parser.get(\",\n \"parse input xml file, there seems to be two versions, input_gw.xml and input-gw.xml\",\n \"np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates = np.transpose( np.array(data[:3], dtype=float)) sec_system.x_exciting_clathrates_atom_labels = list(data[3]) else: sec_system.x_exciting_clathrates = False\",\n \"None: sec_energy.total = EnergyEntry(value=energy_total) x_exciting_dos_fermi = iteration.get('x_exciting_dos_fermi') if x_exciting_dos_fermi is not None: setattr(msection,\",\n \"(1 / self.energy_unit) elif key == 'energies': return self.energies else: res = None\",\n \"self._bands = [] if bands: self._bands.append(bands) # add atom-resolved bands_atom = self.root.findall('./*/%s' %\",\n \"is None: # This is the case when there is a mismatch between\",\n \"'gaule2') sec_gw.number_of_frequencies = int(self.input_xml_parser.get( 'gw/freqgrid/nomeg', 16)) sec_gw.self_energy_c_number_of_poles = int(self.input_xml_parser.get( 'gw/selfenergy/npol', 0)) sec_gw.self_energy_c_number_of_empty_states =\",\n \"None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface = fermi_energy sec_fermisurface.x_exciting_values_fermi_surface = fermi_surface def _parse_evalqp(self, sec_scc): data = self.evalqp_parser.get('kpoints_eigenvalues')\",\n \"'initialization', r'(?:All units are atomic|Starting initialization)([\\\\s\\\\S]+?)(?:Using|Ending initialization)', repeats=False, sub_parser=TextParser(quantities=initialization_quantities)) ) scf_quantities = [\",\n \"number of G\\\\-vectors', None), 'x_exciting_lmaxapw': (r' APW functions', None), 'x_exciting_nuclear_charge': ('Total nuclear charge',\",\n \"if sec_scc is None: continue # add data to scc # TODO add\",\n \"labels += [specie.get('symbol')] * len(specie.get('positions')) return labels def get_positions_format(self, section): positions_format = section.get('positions_format')\",\n \"dtype=float) vector = np.array([v.split() for v in val[3:6]], dtype=float) return [nbands, mesh, origin,\",\n \"* ureg.hartree eigs_gw = get_data('E_GW') if eigs_gw[0] is None: return nspin = self.info_parser.get_number_of_spin_channels()\",\n \"values for name in self.info_parser._convergence_keys_mapping.keys(): val = iteration.get(name) if val is None: continue\",\n \"self.logger.warn('Found multiple files. Will read all!', data=dict(file=name)) for n in range(len(files)): parser.mainfile =\",\n \"sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution = self.input_xml_parser.get( 'xs/tddft/intraband', False) sec_method.x_exciting_xs_tddft_diagonal_xc_kernel = self.input_xml_parser.get( 'xs/tddft/kerndiag', False) sec_method.x_exciting_xs_tddft_lmax_alda = self.input_xml_parser.get(\",\n \"positions_format positions = specie.get('positions') positions = self.info_parser.get_atom_positions( positions=positions, positions_format=positions_format).to('m') sec_atoms_group.x_exciting_geometry_atom_positions = positions.magnitude #\",\n \"self.energy_unit is not None: res = res * (1 / self.energy_unit) elif key\",\n \"len(occs) // nspin)) data['eigenvalues'] = np.reshape(eigs, (nspin, len(eigs) // nspin)) return data self._quantities.append(\",\n \"False) sec_method.x_exciting_xs_tddft_lmax_alda = self.input_xml_parser.get( 'xs/tddft/lmaxalda', 3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment = self.input_xml_parser.get( 'xs/tddft/mdfqtype', 0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals =\",\n \"else: setattr(msection, key, val) # moment contributions moment_contributions = iteration.get('moment_contributions', {}) for key,\",\n \"= get_files(quantity) for i in range(len(files)): data = get_data(files[i]) if not data: sccs.append(None)\",\n \"in range(len(files)): parser.mainfile = files[n] parser_function(section) # free up memory parser.mainfile = None\",\n \"self._natoms = None self._nspin = None self._nlm = None self._energies = None self._total_dos\",\n \"repeats=False)) def str_to_band_parameters(val_in): val = val_in.strip().split('\\\\n') nbands = int(val[0]) mesh = np.array(val[1].split(), dtype=int)\",\n \"= None @property def energy_unit(self): if self._energy_unit is None: axis = self.root.find('./axis') if\",\n \"self._nlm += 2 * li + 1 return self._nlm @property def total_dos(self): if\",\n \"'Coulomb'], 'energy_exchange': ['Exchange energy', 'exchange'], 'x_exciting_exchange_energy': ['Exchange energy', 'exchange'], 'energy_correlation': ['Correlation energy', 'correlation'],\",\n \"data[2], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_oscillator_strength = np.reshape( data[3], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_re =\",\n \"per unit cell', None), 'x_exciting_spin_treatment': ('Spin treatment', None), 'x_exciting_number_of_bravais_lattice_symmetries': ('Number of Bravais lattice\",\n \"max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)', ureg.hartree / ureg.bohr)} for name, key_unit in self._convergence_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)'\",\n \"['Exchange energy', 'exchange'], 'energy_correlation': ['Correlation energy', 'correlation'], 'x_exciting_correlation_energy': ['Correlation energy', 'correlation'], 'energy_sum_eigenvalues': ['Sum\",\n \"= smearing_kind smearing_width = self.info_parser.get_initialization_parameter('smearing_width') if smearing_width is not None: smearing_width = (smearing_width\",\n \"None self._distances = None self._band_energies = None self._band_k_points = None @property def band_energies(self):\",\n \"is None: self._nspin = np.shape(np.transpose(self.data)[1:])[0] return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is\",\n \"'totaldos' self._partialdos_key = 'partialdos' self._diagram_key = 'diagram' self._l_key = 'l' self._m_key = 'm'\",\n \"concatenating species symbols species = self.get('initialization', {}).get('species', []) labels = [] for specie\",\n \"= res.reshape((len(res), len(data), len(res[0]) // len(data))) if key == 'eigenvalues': res = res\",\n \"sec_method): xstype = self.input_xml_parser.get('xs/xstype', None) if xstype is not None: sec_method.x_exciting_xs_xstype = xstype\",\n \"self._distances @property def number_of_spin_channels(self): if self._nspin is None: self._nspin = np.shape(np.transpose(self.data))[0] - 6\",\n \"smearing_kind_map = { 'Gaussian': 'gaussian', 'Methfessel-Paxton': 'methfessel-paxton', 'Fermi-Dirac': 'fermi', 'Extended': 'tetrahedra'} sec_smearing =\",\n \"setattr(msection, name, val) # energy, moment, charge contributions parse_scf(final, sec_scc) # forces forces\",\n \"change in max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)', ureg.hartree / ureg.bohr)} for name, key_unit in self._convergence_keys_mapping.items(): scf_quantities.append(Quantity( name,\",\n \"'optical_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) class ExcitingEvalqpParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities\",\n \"names for different versions of exciting self._energy_keys_mapping = { 'energy_total': ['Total energy', 'total\",\n \"'hybrids', r'Hybrids module started([\\\\s\\\\S]+?)Hybrids module stopped', sub_parser=TextParser(quantities=module_quantities) )) def get_atom_labels(self, section): labels =\",\n \"TextParser, Quantity, XMLParser, DataTextParser from nomad.datamodel.metainfo.simulation.run import Run, Program from nomad.datamodel.metainfo.simulation.method import (\",\n \"None), 'x_exciting_hamiltonian_size': ('Maximum Hamiltonian size', None), 'x_exciting_pw': (r'Maximum number of plane\\\\-waves', None), 'x_exciting_lo':\",\n \"os.F_OK)] return filenames def file_exists(self, filename): \\\"\\\"\\\"Checks if a the given filename exists\",\n \"range(len(files)): data = get_data(files[i]) if not data: sccs.append(None) continue if quantity == 'EXCITON':\",\n \"= np.reshape( data[1], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies = np.reshape( data[2], (n_components, n_excitons))\",\n \"= self.fermisurf_parser.get('band_parameters', None) if band_parameters is not None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface = band_parameters[0] sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface =\",\n \"energy', 'Coulomb potential'], 'energy_xc_potential': ['xc potential energy', 'xc potential'], 'energy_electrostatic': ['Hartree energy', 'Hartree'],\",\n \"sec_method = sec_run.m_create(Method) sec_method.basis_set.append(BasisSet(type='(L)APW+lo')) sec_dft = sec_method.m_create(DFT) sec_electronic = sec_method.m_create(Electronic) sec_electronic.method = 'DFT'\",\n \"v = v.split(':') if len(v) < 2: continue energies[v[0].strip()] = float(v[1]) * ureg.hartree\",\n \"keys = val[1].split() eigs = np.transpose(np.array([v.split() for v in val[2:]], dtype=float)) eigs =\",\n \"x_exciting_dos_fermi) # energy contributions energy_contributions = iteration.get('energy_contributions', {}) for key, names in self._energy_keys_mapping.items():\",\n \"Exception: self.logger.error('Error setting xs data', data=dict(file=quantity)) def _parse_xs_tddft(self): sec_run = self.archive.run[-1] fxctype =\",\n \"1, 1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk', [1, 1, 1]) rgkmax = self.input_xml_parser.get('xs/rgkmax', None) if\",\n \"mainfile_base = mainfile.rsplit('.', 1)[0].replace('INFO', '') options = [ f for f in os.listdir(\",\n \"files are really correct, i.e. columns are supposed # to be what they\",\n \"force_convergence is not None: sec_scc.x_exciting_maximum_force_magnitude = force_convergence[0] sec_scc.x_exciting_geometry_optimization_threshold_force = force_convergence[1] sec_scc = parse_configuration(structure_optimization)\",\n \"'x_exciting_number_of_crystal_symmetries': ('Number of crystal symmetries', None), 'x_exciting_kpoint_grid': (r'k\\\\-point grid', None), 'x_exciting_kpoint_offset': (r'k\\\\-point offset',\",\n \"kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): # TODO make a parent\",\n \"name == 'time': msection.time_calculation = val else: setattr(msection, name, val) # energy, moment,\",\n \"'x_exciting_number_of_bravais_lattice_symmetries': ('Number of Bravais lattice symmetries', None), 'x_exciting_number_of_crystal_symmetries': ('Number of crystal symmetries', None),\",\n \"None) if val_l is None or val_m is None: lm = i %\",\n \"val = val_in.strip().split('\\\\n') energies = dict() for v in val: v = v.split(':')\",\n \"this step\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity(\",\n \"to be what they are. What is the fourth column in epsilon which\",\n \"and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field = data[1:3] elif quantity == 'LOSS' and ext\",\n \"self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies = [] start = 0 for nkpts_segment in self.number_of_k_points_per_segment: end\",\n \"else: try: setattr(sec_system, name, val) except Exception: self.logger.warn('Error setting metainfo.') # species species\",\n \"sec_scc.x_exciting_xs_tddft_sigma_local_field = data[1:3] elif quantity == 'SIGMA' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_sigma_no_local_field =\",\n \"self.bandstructure_parser.get('band_energies', []) for n in range(len(band_energies)): # Get fermi energy: it is used\",\n \"sec_scc.x_exciting_xs_bse_exciton_oscillator_strength = np.reshape( data[3], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_re = np.reshape( data[4], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_im\",\n \"section.get('symbols') if labels is None: # we get it by concatenating species symbols\",\n \"sec_mt_moment_atom.x_exciting_MT_moment_atom_value = val[n][1] else: setattr(msection, key, val) # convergence values for name in\",\n \"parse_configuration(structure_optimization) if sec_scc is None: return # volume optimizations volume_index = 1 while\",\n \"for potential and density', 1 / ureg.bohr), 'x_exciting_gvector_size': (r'G\\\\-vector grid sizes', None), 'x_exciting_gvector_total':\",\n \"about the format for the spin polarized case # I cannot find an\",\n \"if x_exciting_dos_fermi is not None: setattr(msection, 'x_exciting_dos_fermi', x_exciting_dos_fermi) # energy contributions energy_contributions =\",\n \"= self.archive.run[-1] final = section if section.get('energy_total') is not None else section.get('final') if\",\n \"in the naming convention for xs input xml file sec_method = sec_run.m_create(Method) sec_method_ref\",\n \"sec_method.x_exciting_xs_tetra = self.input_xml_parser.get( 'xs/tetra/tetradf', False) def _parse_xs_bse(self): sec_run = self.archive.run[-1] # TODO read\",\n \"['FXC', 'NLF_FXC']: data = get_data(quantity, ext) if not data[0]: continue if quantity ==\",\n \"if not self.bands: return if key == 'band_energies': # TODO I am not\",\n \"val) # other metainfo for name in self.info_parser._miscellaneous_keys_mapping.keys(): val = iteration.get(name) if val\",\n \"# TODO implement reading of parameters from input.xml for normal calculations # in\",\n \"continue energies[v[0].strip()] = float(v[1]) * ureg.hartree return energies self._quantities = [Quantity( 'program_version', r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\.\",\n \"'x_exciting_empty_states': ('Number of empty states', None), 'x_exciting_valence_states': ('Total number of valence states', None),\",\n \"= val_in.strip().split('\\\\n') properties = dict() atom_resolved = [] species = None for v\",\n \"from one of the files: bs_files = ['bandstructure-qp.dat', 'BAND-QP.OUT'] for fname in bs_files:\",\n \"children of xs if self.input_xml_parser.get('xs/screening') is not None: sec_method.x_exciting_xs_screening_number_of_empty_states = self.input_xml_parser.get( 'xs/screening/nempty', 0)\",\n \"ureg.bohr, repeats=False, dtype=float), Quantity( 'energy_total', r'Total energy at this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', unit=ureg.hartree, repeats=False,\",\n \"= reshape(eigs_gw) sec_gw_eigenvalues.value_exchange = reshape(get_data('Sx')) eigs_gw_C = reshape(get_data('Sc')) if eigs_gw_C is None: eigs_gw_C\",\n \") self._quantities.append( Quantity( 'fermi_energy', r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi [Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s', unit=ureg.hartree, repeats=False) ) self._quantities.append( Quantity( 'direct_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct\",\n \"in range(len(partialdos)): for spin in range(len(partialdos[lm])): for atom in range(len(partialdos[lm][spin])): sec_dos_values = sec_dos.m_create(DosValues,\",\n \"sec_system.x_exciting_clathrates = True self.data_clathrate_parser.mainfile = clathrate_file[0] if self.data_clathrate_parser.data: data = np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates =\",\n \"of parameters from input.xml for normal calculations # in addition to INFO.OUT return\",\n \"= sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters = self.fermisurf_parser.get('band_parameters', None) if band_parameters is not None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface =\",\n \"self.input_xml_parser.mainfile = input_file[0] xstype = self.input_xml_parser.get('xs/xstype', '') if xstype.lower() == 'bse': self._parse_xs_bse() elif\",\n \"# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"name, r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)' % key_unit[0], repeats=False, unit=key_unit[1])) self._convergence_keys_mapping = { 'x_exciting_effective_potential_convergence': ( r'RMS change\",\n \"np.array( self.vertices[i].attrib.get(self._coord_key).split(), dtype=float) end = np.array( self.vertices[i + 1].attrib.get(self._coord_key).split(), dtype=float) res.append(np.linspace(start, end, self.number_of_k_points_per_segment[i]))\",\n \"data = self.dos_out_parser.data if data is None: return # Get fermi energy: it\",\n \"return filenames def file_exists(self, filename): \\\"\\\"\\\"Checks if a the given filename exists and\",\n \"'x_exciting_correlation_energy': ['Correlation energy', 'correlation'], 'energy_sum_eigenvalues': ['Sum of eigenvalues', 'sum of eigenvalues'], 'x_exciting_effective_potential_energy': ['Effective\",\n \"= sec_scc_ref sec_scc.calculations_ref = [sec_scc_ref] # parse properties gw_info_files = self.get_exciting_files(gw_info_file) if len(gw_info_files)\",\n \"nkpts_segment = self.bandstructure_parser.number_of_k_points_per_segment band_seg_labels = self.bandstructure_parser.get('band_segm_labels') for nb in range(len(band_energies[n])): sec_k_band_segment = sec_k_band.m_create(BandEnergies)\",\n \"0) sec_method.x_exciting_xs_bse_rgkmax = rgkmax sec_method.x_exciting_xs_bse_sciavbd = self.input_xml_parser.get( 'xs/BSE/sciavbd', True) sec_method.x_exciting_xs_bse_sciavqbd = self.input_xml_parser.get( 'xs/BSE/sciavqbd',\",\n \"sec_scc.x_exciting_xs_tddft_dielectric_function_local_field = data[1:] elif quantity == 'EPSILON' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field =\",\n \"None else self.get_positions_format(section) if positions_format == 'lattice': cell = self.get_initialization_parameter('lattice_vectors') if cell is\",\n \"= np.transpose(np.vstack(data)) n_epsilon = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_energy_points = n_epsilon sec_scc.x_exciting_xs_bse_epsilon_energies = np.reshape(\",\n \"dict(hartree=ureg.hartree) def init_parameters(self): self._ndos = None self._natoms = None self._nspin = None self._nlm\",\n \"dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces', r'Total atomic forces including IBS\",\n \"if os.path.isfile(filepath) and os.access(filepath, os.F_OK): return True return False def _parse_dos(self, sec_scc): if\",\n \"for f in files_q: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None: continue data_q.append(self.data_xs_parser.data)\",\n \"# TODO I am not certain if screening/BSE are children of xs if\",\n \"Parse GW band structure from one of the files: bs_files = ['bandstructure-qp.dat', 'BAND-QP.OUT']\",\n \"Quantity( 'moment_contributions', r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False)] self._miscellaneous_keys_mapping = { 'x_exciting_gap': (r'Estimated fundamental\",\n \"n_loss = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_loss_energies = np.reshape( data[0], (n_components, n_loss)) * ureg.hartree\",\n \"in self.bands[n][i]]) for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy =\",\n \"n in range(len(atoms)): atom_labels.extend([species[n].split('.')[0]] * len(atoms[n])) if positions is None or atom_labels is\",\n \"@property def band_k_points(self): if self._band_k_points is None: data = np.transpose(self.data) self._band_k_points = []\",\n \"start = self.vertices[i].attrib.get(self._coord_key).split() end = self.vertices[i + 1].attrib.get(self._coord_key).split() res.append([start, end]) else: res =\",\n \"in data]) else: energy = np.array([d[1].get(key, None) for d in data]) if None\",\n \"gmb * gmaxvr pwm = self.input_xml_parser.get('gw/barecoul/pwm', 2.0) sec_gw.bare_coulomb_gmax = pwm * gmb *\",\n \"parser_function = self._parse_band_out elif name.startswith('input') and name.endswith('xml'): parser = self.input_xml_parser if self._calculation_type ==\",\n \"= i // (self.number_of_lm * self.number_of_spin_channels) res[lm][spin][atom] = self._get_dos(self.partial_dos[i]) if self.energy_unit is not\",\n \"if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_rgkmax = rgkmax sec_method.x_exciting_xs_scissor =\",\n \"self.get_exciting_files(gw_info_file) if len(gw_info_files) > 1: self.logger.warn('Found multiple GW info files, will read only\",\n \"in range(len(data)): data[j].append(data_q[j]) return data for quantity in ['EPSILON', 'LOSS', 'SIGMA']: for ext\",\n \"v[1] = v[1][0] if len(v[1]) == 1 else v[1] if species is None:\",\n \"= self.get_initialization_parameter('lattice_vectors') if cell is None: return positions = np.dot(positions, cell.magnitude) return positions\",\n \"sec_gw.mixed_basis_lmax = int(self.input_xml_parser.get( 'gw/mixbasis/lmaxmb', 3)) sec_gw.mixed_basis_tolerance = self.input_xml_parser.get( 'gw/mixbasis/epsmb', 0.0001) gmb = self.input_xml_parser.get('gw/mixbasis/gmb',\",\n \"= self.dos_out_parser parser_function = self._parse_dos_out elif name.startswith('bandstructure') and name.endswith('dat'): parser = self.bandstructure_dat_parser parser_function\",\n \"self._nkpts_segment .append(count) count = 1 else: count += 1 self._nkpts_segment.append(count) return self._nkpts_segment @property\",\n \"sec_gw.mixed_basis_gmax = gmb * gmaxvr pwm = self.input_xml_parser.get('gw/barecoul/pwm', 2.0) sec_gw.bare_coulomb_gmax = pwm *\",\n \"language governing permissions and # limitations under the License. # import numpy as\",\n \"# Get fermi energy: it is used to un-shift the DOS to #\",\n \"KIND, either express or implied. # See the License for the specific language\",\n \"if self._nkpts_segment is None: self._nkpts_segment = [] count = 1 for i in\",\n \"[] for i in range(len(self.number_of_k_points_per_segment)): start = self.vertices[i].attrib.get(self._coord_key).split() end = self.vertices[i + 1].attrib.get(self._coord_key).split()\",\n \"r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'fundamental_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity(\",\n \"# TODO make a parent clas for bandstructure dat and xml self._nspin =\",\n \"for i in range(len(self.vertices) - 1): start = self.vertices[i].attrib.get('label') end = self.vertices[i +\",\n \"weights=val[2]) # TODO Read also input parameters here if input_GW.xml does not exist\",\n \"('Total nuclear charge', ureg.elementary_charge), 'x_exciting_electronic_charge': ('Total electronic charge', ureg.elementary_charge), 'x_exciting_core_charge_initial': ('Total core charge',\",\n \"problematic if some scf steps dont have the quantity # the only thing\",\n \"= frequency_data.get('weights') fundamental_band_gap = self.info_gw_parser.get('direct_band_gap', None) if fundamental_band_gap is None: fundamental_band_gap = self.info_gw_parser.get('fundamental_band_gap',\",\n \"sec_method.gw.number_of_frequencies = len(number) sec_method.gw.frequency_number = number sec_method.gw.frequency_values = frequency_data.get('values') sec_method.gw.frequency_weights = frequency_data.get('weights') fundamental_band_gap\",\n \"1): start = self.vertices[i].attrib.get('label') end = self.vertices[i + 1].attrib.get('label') res.append([ '\\\\u0393' if start.lower()\",\n \"= force_convergence[0] sec_scc.x_exciting_geometry_optimization_threshold_force = force_convergence[1] sec_scc = parse_configuration(structure_optimization) if sec_scc is None: return\",\n \"if start.lower() == 'gamma' else start, '\\\\u0393' if end.lower() == 'gamma' else end])\",\n \"not exist self._quantities.append( Quantity( 'frequency_data', r'frequency list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)', str_operation=str_to_frequency, repeats=False) ) self._quantities.append( Quantity( 'fermi_energy',\",\n \"if name is None: continue if '_X_' in name: sec_xc_functional.exchange.append(Functional(name=name)) elif '_C_' in\",\n \"self._nlm is None: if self.partial_dos is None: return self._nlm = 0 l_list =\",\n \"target[1:]: filename = '%s.%s' % (filename, target[1]) filename = os.path.join(self.info_parser.maindir, filename) if os.path.isfile(filename):\",\n \"if spin is None: spin = (i % (self.number_of_spin_channels * self.number_of_lm)) // self.number_of_lm\",\n \"= self.logger self.data_clathrate_parser.logger = self.logger def reuse_parser(self, parser): self.info_parser.quantities = parser.info_parser.quantities self.eigval_parser.quantities =\",\n \"= self.info_parser.get_initialization_parameter('species', []) for specie in species: sec_atoms_group = sec_system.m_create(x_exciting_section_atoms_group) sec_atoms_group.x_exciting_geometry_atom_labels = specie.get('symbol')\",\n \"parser_function = self._parse_bandstructure elif name.startswith('EIGVAL') and name.endswith('OUT'): parser = self.eigval_parser parser_function = self._parse_eigenvalues\",\n \"self.input_xml_parser.get( 'xs/tddft/aresdf', False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel = self.input_xml_parser.get( 'xs/tddft/aresfxc', True) sec_method.x_exciting_xs_tddft_drude = self.input_xml_parser.get( 'xs/tddft/drude', [0.,\",\n \"bse_types = ['IP', 'singlet', 'triplet', 'RPA'] scr_types = ['full', 'diag', 'noinvdiag', 'longrange'] bse_files\",\n \"name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) initialization_quantities.append(Quantity( 'species', rf'(Species : *\\\\d+ *\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float}\",\n \"# add atom-resolved bands_atom = self.root.findall('./*/%s' % self._atom_key) for band in bands_atom: self._bands.append(band.findall('./%s'\",\n \"parse(self, key): if self._results is None: self._results = dict() if not self.bands: return\",\n \"nomad.datamodel.metainfo.simulation.system import ( System, Atoms ) from nomad.datamodel.metainfo.simulation.calculation import ( Calculation, Dos, DosValues,\",\n \"file_ext_list if e]) # read q points qpoints = self.input_xml_parser.get('xs/qpointset/qpoint') def get_data(quantity, ext):\",\n \"frequency_data.get('weights') fundamental_band_gap = self.info_gw_parser.get('direct_band_gap', None) if fundamental_band_gap is None: fundamental_band_gap = self.info_gw_parser.get('fundamental_band_gap', None)\",\n \"['dos.xml', 'TDOS.OUT'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break # Parse DFT\",\n \"ureg.elementary_charge), 'x_exciting_electronic_charge': ('Total electronic charge', ureg.elementary_charge), 'x_exciting_core_charge_initial': ('Total core charge', ureg.elementary_charge), 'x_exciting_valence_charge_initial': ('Total\",\n \"as GWMethod, Scf, BasisSet ) from nomad.datamodel.metainfo.simulation.system import ( System, Atoms ) from\",\n \"return if key == 'band_energies': # TODO I am not certain about the\",\n \"exciting_files.append(fname) break # Parse DFT band structure from one of the files bs_files\",\n \"= '_'.join([e for e in file_ext_list if e]) # read q points qpoints\",\n \"len(quantity)) + quantity return quantity def get_xc_functional_name(self): # TODO expand list to include\",\n \"Quantity( 'atomic_positions', r'(Atomic positions at this step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions at\",\n \"contributions charge_contributions = iteration.get('charge_contributions', {}) for key, names in self._electron_charge_keys_mapping.items(): val = None\",\n \"['core'], 'x_exciting_core_leakage': ['core leakage'], 'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge': ['interstitial'], 'x_exciting_total_MT_charge': ['total charge in muffin-tins',\",\n \"nspin)) return data self._quantities.append( Quantity( 'eigenvalues_occupancies', r'\\\\(state\\\\, eigenvalue and occupancy below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n *\\\\n))', str_operation=str_to_eigenvalues,\",\n \"None: species = self.get_initialization_parameter('species', []) for specie in species: positions_format = specie.get('positions_format', None)\",\n \"r'Number of (?:total)* scf iterations\\\\s*\\\\:\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'force_convergence', r'Maximum force magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)',\",\n \"r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?' class GWInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] def str_to_frequency(val_in):\",\n \"self._natoms @property def number_of_dos(self): if self._ndos is None: total_dos = self.root.find('./%s/%s' % (self._totaldos_key,\",\n \"0)) sec_gw.ngridq = self.input_xml_parser.get('gw/ngridq', [1, 1, 1]) sec_gw.basis_set = 'mixed' sec_gw.qp_equation_treatment = 'linearization'\",\n \"simply write parameters xc_functional = self.info_parser.get('initialization', {}).get('xc_functional') if xc_functional is not None: sec_xc_functional.name\",\n \"smearing with should have units of energy sec_smearing.width = smearing_width.magnitude for name in\",\n \"def __init__(self): super().__init__(None) def init_quantities(self): re_symbol = re.compile(r'([A-Z][a-z]?)') def str_to_array(val_in): val = [v.split(':')[-1].split()\",\n \"to include other xcf xc_functional_map = { 2: ['LDA_C_PZ', 'LDA_X_PZ'], 3: ['LDA_C_PW', 'LDA_X_PZ'],\",\n \"energy', 'Madelung'], 'x_exciting_core_electron_kinetic_energy': ['Core-electron kinetic energy', 'core electron kinetic'], 'x_exciting_dft_d2_dispersion_correction': ['DFT-D2 dispersion correction']\",\n \"= self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] self.parse_file('input.xml', sec_method) # parse properties\",\n \"self._ndos = None self._natoms = None self._nspin = None self._nlm = None self._energies\",\n \"'%s.%s' % (filename, target[1]) filename = os.path.join(self.info_parser.maindir, filename) if os.path.isfile(filename): return [filename] filename\",\n \"scr_type)) bse_files.append(files) return bse_files def get_data(files): data = [] for f in files:\",\n \"suffix = mainfile.strip('INFO.OUT') target = default.rsplit('.', 1) filename = '%s%s' % (target[0], suffix)\",\n \"int( self.input_xml_parser.get('gw/nempty', 0)) sec_gw.ngridq = self.input_xml_parser.get('gw/ngridq', [1, 1, 1]) sec_gw.basis_set = 'mixed' sec_gw.qp_equation_treatment\",\n \"= optimization_step.get('method') if optimization_step.get('step') is not None: sec_scc.x_exciting_geometry_optimization_step = optimization_step.get('step') force_convergence = optimization_step.get('force_convergence')\",\n \"this is really problematic if some scf steps dont have the quantity #\",\n \"self._partialdos_key = 'partialdos' self._diagram_key = 'diagram' self._l_key = 'l' self._m_key = 'm' self._energy_key\",\n \"start = 0 for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment self._band_k_points.append(\",\n \"structure to # the original scale in which also other energies are reported.\",\n \"v in val[3:6]], dtype=float) return [nbands, mesh, origin, vector] self._quantities.append( Quantity( 'band_parameters', r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)',\",\n \"not None: sec_scc.system_ref = sec_system sec_scc.method_ref = sec_run.method[-1] return sec_scc # groundstate and\",\n \"xc_functional = self.info_parser.get('initialization', {}).get('xc_functional') if xc_functional is not None: sec_xc_functional.name = xc_functional.get('name_reference', [None,\",\n \"None self._distances = None self._band_energies = None self._neigs_segment = None self._nkpts_segment = None\",\n \"What is the fourth column in epsilon which is not parsed? sccs =\",\n \"= band_parameters[1] sec_fermisurface.x_exciting_origin_fermi_surface = band_parameters[2] sec_fermisurface.x_exciting_vectors_fermi_surface = band_parameters[3] fermi_energy = self.fermisurf_parser.get('fermi_energy', None) if\",\n \"reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is None: continue energy_fermi = energy_fermi.to(\\\"hartree\\\") sec_k_band\",\n \"crystal symmetries', None), 'x_exciting_kpoint_grid': (r'k\\\\-point grid', None), 'x_exciting_kpoint_offset': (r'k\\\\-point offset', None), 'x_exciting_number_kpoints': (r'Total\",\n \"in name: sec_xc_functional.hybrid.append(Functional(name=name)) else: sec_xc_functional.contributions.append(Functional(name=name)) if not xc_functional_names: # simply write parameters xc_functional\",\n \"not certain if screening/BSE are children of xs if self.input_xml_parser.get('xs/screening') is not None:\",\n \"'xs/tddft/intraband', False) sec_method.x_exciting_xs_tddft_diagonal_xc_kernel = self.input_xml_parser.get( 'xs/tddft/kerndiag', False) sec_method.x_exciting_xs_tddft_lmax_alda = self.input_xml_parser.get( 'xs/tddft/lmaxalda', 3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment\",\n \"in self._system_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) for name,\",\n \"self._get_dos(self._total_dos[i]) if self.energy_unit is not None: res = res * (1 / self.energy_unit)\",\n \"['EVALQP.DAT', 'EVALQP.TXT', 'TDOS-QP.OUT'] # Parse GW band structure from one of the files:\",\n \"'gamma' else end]) elif key == 'band_segm_start_end': res = [] for i in\",\n \"= val_in[:val_in.rfind('\\\\n \\\\n')].strip() val = np.array([v.split() for v in val.split('\\\\n')], dtype=float) val =\",\n \"# parse properties input_file = self.get_exciting_files('input.xml') if not input_file: return self.input_xml_parser.mainfile = input_file[0]\",\n \"'GGA_X_PBE'], 406: ['HYB_GGA_XC_PBEH'], 408: ['HYB_GGA_XC_HSE03']} xc_functional = self.get('initialization', {}).get('xc_functional', None) if xc_functional is\",\n \"v in val: v = v.split(':') if len(v) < 2: continue energies[v[0].strip()] =\",\n \"% (filename, target[1]) filename = os.path.join(self.info_parser.maindir, filename) if os.path.isfile(filename): return [filename] filename =\",\n \"return self._neigs_segment class BandstructureXMLParser(XMLParser): def __init__(self, **kwargs): # TODO make a parent class\",\n \"GW as GWMethod, Scf, BasisSet ) from nomad.datamodel.metainfo.simulation.system import ( System, Atoms )\",\n \"self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, len(self.distances)), dtype=float) for i in range(len(self.bands[n])): band_energies[i % self.number_of_spin_channels][i] = np.array(\",\n \"smearing_kind = smearing_kind[0] smearing_kind = smearing_kind_map[smearing_kind] sec_smearing.kind = smearing_kind smearing_width = self.info_parser.get_initialization_parameter('smearing_width') if\",\n \"0.0, '1/bohr') sec_method.x_exciting_xs_lmaxapw = self.input_xml_parser.get('xs/lmaxapw', 10) sec_method.x_exciting_xs_number_of_empty_states = self.input_xml_parser.get( 'xs/nempty', 5) sec_method.x_exciting_xs_ngridq =\",\n \"n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies = np.reshape( data[2], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_oscillator_strength =\",\n \"= self.root.findall('./%s' % self._partialdos_key) self._natoms = len(partial_dos) return self._natoms @property def number_of_dos(self): if\",\n \"axis is None: return self._energy_unit = self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1) return self._energy_unit @property def number_of_spin_channels(self):\",\n \"['bandstructure.xml', 'BAND.OUT', 'bandstructure.dat'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break for f\",\n \"at all qpoints files = self.get_exciting_files('%s_%s%s%s.OUT' % (quantity, file_ext, ext, fxctype)) data =\",\n \"frequency_data.get('number') sec_method.gw.number_of_frequencies = len(number) sec_method.gw.frequency_number = number sec_method.gw.frequency_values = frequency_data.get('values') sec_method.gw.frequency_weights = frequency_data.get('weights')\",\n \"'SIGMA']: for ext in ['FXC', 'NLF_FXC']: data = get_data(quantity, ext) if not data[0]:\",\n \"kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None self._nkpts_segment =\",\n \"data[0][0] * ureg.hartree + energy_fermi volume = self.info_parser.get_unit_cell_volume() dos = data[1] * (1\",\n \"'x_exciting_core_leakage': ['core leakage'], 'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge': ['interstitial'], 'x_exciting_total_MT_charge': ['total charge in muffin-tins', 'total\",\n \"= sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value = dos[spin] # TODO add PDOS\",\n \"= 0 for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy =\",\n \"sec_method.x_exciting_xs_screening_ngridk = self.input_xml_parser.get( 'xs/screening/ngridk', [0, 0, 0]) rgkmax = self.input_xml_parser.get('xs/screening/rgkmax', None) if rgkmax\",\n \"None: continue # add data to scc # TODO add support for more\",\n \"energy_fermi volume = self.info_parser.get_unit_cell_volume() totaldos = self.dos_parser.get('totaldos') * volume.to('m**3').magnitude for spin in range(len(totaldos)):\",\n \"repeats=False)) self._quantities.append(Quantity( 'hybrids', r'Hybrids module started([\\\\s\\\\S]+?)Hybrids module stopped', sub_parser=TextParser(quantities=module_quantities) )) def get_atom_labels(self, section):\",\n \"if self._nspin is None: self._nspin = np.shape(np.transpose(self.data)[1:])[0] return self._nspin @property def number_of_k_points_per_segment(self): if\",\n \"sec_scc is None: return sec_system = self.parse_system(section) if sec_system is not None: sec_scc.system_ref\",\n \"self._quantities.append( Quantity( 'fermi_surface', r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n *E*', unit=ureg.hartree, repeats=True)) class ExcitingEigenvalueParser(TextParser): def __init__(self): super().__init__(None) def\",\n \"from .metainfo.exciting import x_exciting_section_MT_charge_atom, x_exciting_section_MT_moment_atom,\\\\ x_exciting_section_spin, x_exciting_section_fermi_surface,\\\\ x_exciting_section_atoms_group re_float = r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?' class GWInfoParser(TextParser):\",\n \"nspin = self.info_parser.get_number_of_spin_channels() sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = len(data) // nspin data\",\n \"is None: self._nspin = 1 return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is\",\n \"number_of_dos(self): if self._ndos is None: total_dos = self.root.find('./%s/%s' % (self._totaldos_key, self._diagram_key)) self._ndos =\",\n \"DFT, Electronic, Smearing, XCFunctional, Functional, GW as GWMethod, Scf, BasisSet ) from nomad.datamodel.metainfo.simulation.system\",\n \"self._method_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) initialization_quantities.append(Quantity( 'species', rf'(Species\",\n \"(?:total)* scf iterations\\\\s*\\\\:\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'force_convergence', r'Maximum force magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)', str_operation=str_to_quantity_tolerances, unit=ureg.hartree\",\n \"self._band_energies.append(band_energy) start = end return self._band_energies @property def band_k_points(self): if self._band_k_points is None:\",\n \"['Fermi energy', 'Fermi'], 'energy_kinetic_electronic': ['Kinetic energy', 'electronic kinetic'], 'energy_coulomb': ['Coulomb energy', 'Coulomb'], 'x_exciting_coulomb_energy':\",\n \"self._calculation_type == 'xs': parser_function = self._parse_input_xs else: # TODO implement reading of parameters\",\n \"= 'nspin' self._totaldos_key = 'totaldos' self._partialdos_key = 'partialdos' self._diagram_key = 'diagram' self._l_key =\",\n \"_parse_xs_tddft(self): sec_run = self.archive.run[-1] fxctype = self.input_xml_parser.get('xs/tddft/fxctype', 'RPA') tetradf = self.input_xml_parser.get('xs/tetra/tetradf', None) nwacont\",\n \"if len(val) == 1 else val return np.array(val, dtype=float) def str_to_atom_properties_dict(val_in): unit =\",\n \"aresdf else None] file_ext = '_'.join([e for e in file_ext_list if e]) #\",\n \"in filenames if os.access(f, os.F_OK)] return filenames def file_exists(self, filename): \\\"\\\"\\\"Checks if a\",\n \"* ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies = np.reshape( data[2], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_oscillator_strength = np.reshape(\",\n \"self.bandstructure_parser.get('band_segm_labels') for nb in range(len(band_energies[n])): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints =\",\n \"step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds', unit=ureg.s, repeats=False, dtype=float) ] self._quantities.append(Quantity( 'structure_optimization', r'Structure\\\\-optimization module started([\\\\s\\\\S]+?)Structure\\\\-optimization module stopped', sub_parser=TextParser(quantities=[\",\n \"Quantity( 'eigenvalues_occupancies', r'\\\\(state\\\\, eigenvalue and occupancy below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n *\\\\n))', str_operation=str_to_eigenvalues, repeats=True)) class ExcitingGWOutParser(TextParser): def\",\n \"if xstype is not None: sec_method.x_exciting_xs_xstype = xstype sec_method.x_exciting_electronic_structure_method = xstype sec_method.x_exciting_xs_broadening =\",\n \"# atom-resolved bandstructure are added as separate section_k_band res = [] for n\",\n \"data self._quantities.append( Quantity( 'eigenvalues_occupancies', r'\\\\(state\\\\, eigenvalue and occupancy below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n *\\\\n))', str_operation=str_to_eigenvalues, repeats=True)) class\",\n \"is None: spin = (i % (self.number_of_spin_channels * self.number_of_lm)) // self.number_of_lm else: spin\",\n \"= self.bandstructure_dat_parser.band_k_points nkpts_segment = self.bandstructure_dat_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints\",\n \"'parameters', r'\\\\n *(.+?:.+)', repeats=True, str_operation=lambda x: [v.strip() for v in x.split(':')])])) ) self._quantities.append(Quantity(\",\n \"['IP', 'singlet', 'triplet', 'RPA'] scr_types = ['full', 'diag', 'noinvdiag', 'longrange'] bse_files = []\",\n \"is not None: sec_method.x_exciting_xs_bse_antiresonant = self.input_xml_parser.get( 'xs/BSE/aresbse', True) sec_method.x_exciting_xs_bse_angular_momentum_cutoff = self.input_xml_parser.get( 'xs/BSE/lmaxdielt', 14)\",\n \"[ Quantity( 'scf_iteration', r'(?:I| i)teration number :([\\\\s\\\\S]+?)(?:\\\\n *\\\\n\\\\+{10}|\\\\+\\\\-{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=True), Quantity( 'final', r'(?:Convergence\",\n \"= 'x_exciting_scf_threshold_%s_change' % name.split('_')[-2] setattr(sec_method, metainfo_name, threshold) # additionally, set threshold to global\",\n \"'geometry_optimization' sec_geometry_opt = sec_worfklow.m_create(GeometryOptimization) threshold_force = structure_optimization.get( 'optimization_step', [{}])[0].get('force_convergence', [0., 0.])[-1] sec_geometry_opt.input_force_maximum_tolerance =\",\n \"Quantity( 'force_convergence', r'Maximum force magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)', str_operation=str_to_quantity_tolerances, unit=ureg.hartree / ureg.bohr, repeats=False, dtype=float), Quantity(\",\n \"= iteration.get(name) if val is None: continue setattr(msection, name, val) # other metainfo\",\n \"= v[0].split() v[1] = [float(vi) for vi in v[1].split()] v[1] = v[1][0] if\",\n \"[] self._quantities.append( Quantity( 'fermi_energy', r'Fermi Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*', unit=ureg.hartree, repeats=False)) def str_to_band_parameters(val_in): val = val_in.strip().split('\\\\n')\",\n \"sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'imized atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions',\",\n \"sec_gw_eigenvalues.energies = reshape(eigs_gw) sec_gw_eigenvalues.value_exchange = reshape(get_data('Sx')) eigs_gw_C = reshape(get_data('Sc')) if eigs_gw_C is None:\",\n \"start = self.vertices[i].attrib.get('label') end = self.vertices[i + 1].attrib.get('label') res.append([ '\\\\u0393' if start.lower() ==\",\n \"n_excitons)) def parse_epsilon(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_epsilon = len(data[0])\",\n \"= BandOutParser(energy_unit=ureg.hartree) self.info_gw_parser = GWInfoParser() self.input_xml_parser = XMLParser() self.data_xs_parser = DataTextParser() self.data_clathrate_parser =\",\n \"self.get('groundstate', self.get('hybrids', {})) return reference.get('scf_iteration', [{}])[-1].get( name, [None, None])[-1] def get_scf_quantity(self, name): n_scf\",\n \"positions=positions, positions_format=positions_format).to('m') sec_atoms_group.x_exciting_geometry_atom_positions = positions.magnitude # clathrate info clathrate_file = self.get_exciting_files('str.out') if clathrate_file:\",\n \"default) class ExcitingParser: def __init__(self): self.info_parser = ExcitingInfoParser() self.dos_parser = DOSXMLParser(energy_unit=ureg.hartree) self.bandstructure_parser =\",\n \"4: ['LDA_C_XALPHA'], 5: ['LDA_C_VBH'], 20: ['GGA_C_PBE', 'GGA_X_PBE'], 21: ['GGA_C_PBE', 'GGA_X_PBE_R'], 22: ['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'],\",\n \"as np import os import re import logging from nomad.units import ureg from\",\n \"self.bandstructure_dat_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints =\",\n \"last scf_iteration or optimization_step final = section.get('scf_iteration', [None])[-1] final = section.get('optimization_step', [None])[-1] if\",\n \"('Total number of valence states', None), 'x_exciting_hamiltonian_size': ('Maximum Hamiltonian size', None), 'x_exciting_pw': (r'Maximum\",\n \"name.endswith('xml'): parser = self.bandstructure_parser parser_function = self._parse_bandstructure elif name.startswith('EIGVAL') and name.endswith('OUT'): parser =\",\n \"by applicable law or agreed to in writing, software # distributed under the\",\n \"name = xc_functional_map.get(xc_functional.type, []) return name @property def n_optimization_steps(self): return len(self.get('structure_optimization', {}).get('optimization_step', []))\",\n \"= len(data[0]) // n_components sec_scc.x_exciting_xs_bse_sigma_energies = np.reshape( data[0], (n_components, n_sigma)) * ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re\",\n \"= os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = default.rsplit('.', 1) filename = '%s%s' %\",\n \"= sec_run.m_create(Calculation) sccs.append(sec_scc) else: sec_scc = sccs[i] if sec_scc is None: # This\",\n \"name, key_unit in self._convergence_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)' % key_unit[0], str_operation=str_to_quantity_tolerances, unit=key_unit[1], repeats=False))\",\n \"pwm = self.input_xml_parser.get('gw/barecoul/pwm', 2.0) sec_gw.bare_coulomb_gmax = pwm * gmb * gmaxvr sec_gw.bare_coulomb_cutofftype =\",\n \"self.input_xml_parser if self._calculation_type == 'gw': parser_function = self._parse_input_gw elif self._calculation_type == 'xs': parser_function\",\n \"sec_run.system: sec_scc.system_ref = sec_run.system[-1] sec_scc_ref = sec_run.calculation[0] sec_scc.starting_calculation_ref = sec_scc_ref sec_scc.calculations_ref = [sec_scc_ref]\",\n \"is None: data = np.transpose(self.data) self._neigs_segment = int(max(data[0])) return self._neigs_segment class BandOutParser(DataTextParser): def\",\n \"is # energy dos_up dos_down nspin = self.info_parser.get_number_of_spin_channels() sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies\",\n \"bands_atom: self._bands.append(band.findall('./%s' % self._band_key)) return self._bands @property def vertices(self): if self._vertices is None:\",\n \"import os import re import logging from nomad.units import ureg from nomad.parsing.file_parser import\",\n \"= r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?' class GWInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] def\",\n \"dtype=int), values=val[1] * ureg.hartree, weights=val[2]) # TODO Read also input parameters here if\",\n \"= DataTextParser(dtype=str) # different names for different versions of exciting self._energy_keys_mapping = {\",\n \"and name.endswith('OUT'): parser = self.dos_out_parser parser_function = self._parse_dos_out elif name.startswith('bandstructure') and name.endswith('dat'): parser\",\n \"# TODO make a parent class for dos and bandstructure super().__init__(None) self._distance_key =\",\n \"is None: continue if name == 'x_exciting_spin_treatment': sub_sec = sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment = val\",\n \"repeats=False) ) initialization_quantities.append(Quantity( 'species', rf'(Species : *\\\\d+ *\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float} *{re_float} *{re_float}\\\\n\\\\s*\\\\n)', repeats=True, sub_parser=TextParser(quantities=[ Quantity('number',\",\n \"energy', 'Hartree'], 'x_exciting_hartree_energy': ['Hartree energy', 'Hartree'], 'x_exciting_electron_nuclear_energy': ['Electron-nuclear energy', 'electron-nuclear '], 'x_exciting_nuclear_nuclear_energy': ['Nuclear-nuclear\",\n \"have units of energy sec_smearing.width = smearing_width.magnitude for name in self.info_parser._convergence_keys_mapping.keys(): threshold =\",\n \"section.get('positions_format') if positions_format is None: species = self.get_initialization_parameter('species', []) for specie in species:\",\n \"1 sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol = val[n][0] sec_mt_moment_atom.x_exciting_MT_moment_atom_value = val[n][1] else: setattr(msection, key, val) # convergence\",\n \"[]) if species: positions = np.vstack([s.get('positions') for s in species]) if positions is\",\n \"= None self.init_parser() sec_run = self.archive.m_create(Run) sec_run.program = Program( name='exciting', version=self.info_parser.get('program_version', '').strip()) #\",\n \"file def get_files(name): bse_types = ['IP', 'singlet', 'triplet', 'RPA'] scr_types = ['full', 'diag',\",\n \"sec_scc): data = self.dos_out_parser.data if data is None: return # Get fermi energy:\",\n \"Quantity( 'fermi_energy', r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi [Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s', unit=ureg.hartree, repeats=False) ) self._quantities.append( Quantity( 'direct_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False)\",\n \"= self.total_dos[i].attrib.get(self._nspin_key, i) res[i] = self._get_dos(self._total_dos[i]) if self.energy_unit is not None: res =\",\n \"res = np.hstack([np.reshape(v[key], (nspin, np.size(v[key]) // nspin)) for v in data]) res =\",\n \"'gw/scrcoul/sciavtype', 'isotropic') sec_gw.screened_Coulomb = self.input_xml_parser.get( 'gw/scrcoul/scrtype', 'rpa') def parse_gw(self): sec_run = self.archive.run[-1] #\",\n \"+ int(val_l) atom = i // (self.number_of_lm * self.number_of_spin_channels) res[lm][spin][atom] = self._get_dos(self.partial_dos[i]) if\",\n \"species is None: species = v[0][2] atom_resolved.append(((species, v[1] * unit))) else: vi =\",\n \"def energy_unit(self): if self._energy_unit is None: axis = self.root.find('./axis') if axis is None:\",\n \"units are atomic|Starting initialization)([\\\\s\\\\S]+?)(?:Using|Ending initialization)', repeats=False, sub_parser=TextParser(quantities=initialization_quantities)) ) scf_quantities = [ Quantity( 'energy_total',\",\n \"{}).get('optimization_step', [])) def get_number_of_spin_channels(self): spin_treatment = self.get('initialization', {}).get( 'x_exciting_spin_treatment', 'spin-unpolarised') n_spin = 1\",\n \"name.endswith('TXT')): parser = self.evalqp_parser parser_function = self._parse_evalqp elif name.startswith('TDOS') and name.endswith('OUT'): parser =\",\n \"val = [v.split() for v in val_in.split('\\\\n')] val = np.transpose(np.array([v for v in\",\n \"'x_exciting_unit_cell_volume': ('Unit cell volume', ureg.bohr ** 3), 'x_exciting_brillouin_zone_volume': ('Brillouin zone volume', 1 /\",\n \"sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk', [1, 1, 1]) rgkmax = self.input_xml_parser.get('xs/rgkmax', None) if rgkmax is\",\n \"= 1 else: count += 1 self._nkpts_segment.append(count) return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if\",\n \"as this is done in parser, however nspin is # determined from occupancies\",\n \"v[-1]).group(1) elif v[0].startswith('atom'): v[0] = v[0].split() v[1] = [float(vi) for vi in v[1].split()]\",\n \"rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_screening_rgkmax = rgkmax sec_method.x_exciting_xs_screening_type = self.input_xml_parser.get( 'xs/screening/screentype', 'full') if\",\n \"return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = self.dos_parser.number_of_dos\",\n \"size', None), 'x_exciting_pw': (r'Maximum number of plane\\\\-waves', None), 'x_exciting_lo': (r'Total number of local\\\\-orbitals',\",\n \"'dos' self._unit_key = 'unit' self._energy_unit = kwargs.get('energy_unit', None) self._units_mapping = dict(hartree=ureg.hartree) def init_parameters(self):\",\n \"may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"str_operation=str_to_band_parameters, repeats=False)) self._quantities.append( Quantity( 'fermi_surface', r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n *E*', unit=ureg.hartree, repeats=True)) class ExcitingEigenvalueParser(TextParser): def __init__(self):\",\n \"None self._neigs_segment = None self._bands = None self._vertices = None self._distances = None\",\n \"energy', 'Coulomb'], 'energy_exchange': ['Exchange energy', 'exchange'], 'x_exciting_exchange_energy': ['Exchange energy', 'exchange'], 'energy_correlation': ['Correlation energy',\",\n \"val = None for name in names: val = energy_contributions.get(name, None) if val\",\n \"case! I assume it is # energy dos_up dos_down nspin = self.info_parser.get_number_of_spin_channels() sec_dos\",\n \"= self.archive.m_create(Run) sec_run.program = Program( name='exciting', version=self.info_parser.get('program_version', '').strip()) # method goes first since\",\n \"'GGA_C_AM05'], 300: ['GGA_C_BGCP', 'GGA_X_PBE'], 406: ['HYB_GGA_XC_PBEH'], 408: ['HYB_GGA_XC_HSE03']} xc_functional = self.get('initialization', {}).get('xc_functional', None)\",\n \"charge_contributions = iteration.get('charge_contributions', {}) for key, names in self._electron_charge_keys_mapping.items(): val = None for\",\n \"len(data[0]) // n_components sec_scc.x_exciting_xs_bse_loss_energies = np.reshape( data[0], (n_components, n_loss)) * ureg.hartree sec_scc.x_exciting_xs_bse_loss =\",\n \"not self.total_dos: return self._nspin = len(self.total_dos) return self._nspin @property def number_of_atoms(self): if self._natoms\",\n \"in self.info_parser._convergence_keys_mapping.keys(): threshold = self.info_parser.get_scf_threshold(name) if threshold is None: continue metainfo_name = 'x_exciting_scf_threshold_%s_change'\",\n \"parser.info_gw_parser.quantities def parse(self, filepath, archive, logger): self.filepath = filepath self.archive = archive self.logger\",\n \"return np.array([d[1].get(key, None) for d in data]) else: energy = np.array([d[1].get(key, None) for\",\n \"1: return sec_system for name in self.info_parser._system_keys_mapping.keys(): val = self.info_parser.get_initialization_parameter(name) if val is\",\n \"spin is None: spin = (i % (self.number_of_spin_channels * self.number_of_lm)) // self.number_of_lm else:\",\n \"is not None: sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get( 'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get( 'xs/tddft/ahc', False)\",\n \"'xs/screening/ngridk', [0, 0, 0]) rgkmax = self.input_xml_parser.get('xs/screening/rgkmax', None) if rgkmax is None: rgkmax\",\n \"sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment = self.input_xml_parser.get( 'xs/tddft/mdfqtype', 0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals = self.input_xml_parser.get( 'xs/tddft/nwacont', 0) sec_method.x_exciting_xs_tetra = self.input_xml_parser.get(\",\n \"*\\\\n))', str_operation=str_to_eigenvalues, repeats=True)) class ExcitingGWOutParser(TextParser): def __init__(self, mainfile, logger): super().__init__(mainfile, logger=logger) def init_quantities(self):\",\n \"electronic charge', ureg.elementary_charge), 'x_exciting_core_charge_initial': ('Total core charge', ureg.elementary_charge), 'x_exciting_valence_charge_initial': ('Total valence charge', ureg.elementary_charge),\",\n \"module stopped', sub_parser=TextParser(quantities=module_quantities) )) def get_atom_labels(self, section): labels = section.get('symbols') if labels is\",\n \"None: sec_system.x_exciting_potential_mixing = potential_mixing return sec_system def parse_configurations(self): sec_run = self.archive.run[-1] def parse_configuration(section):\",\n \"None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface = band_parameters[0] sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface = np.product(band_parameters[1]) sec_fermisurface.x_exciting_grid_fermi_surface = band_parameters[1] sec_fermisurface.x_exciting_origin_fermi_surface = band_parameters[2]\",\n \"def get_unit_cell_volume(self): return self.get('initialization', {}).get('x_exciting_unit_cell_volume', 1.0 * ureg.bohr ** 3) def get_initialization_parameter(self, key,\",\n \"= n_components n_excitons = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_excitons = n_excitons sec_scc.x_exciting_xs_bse_exciton_energies = np.reshape(\",\n \"* gmaxvr sec_gw.bare_coulomb_cutofftype = self.input_xml_parser.get( 'gw/barecoul/cutofftype', 'none') sec_gw.screened_coulomb_volume_average = self.input_xml_parser.get( 'gw/scrcoul/sciavtype', 'isotropic') sec_gw.screened_Coulomb\",\n \"range(len(self.vertices) - 1): start = self.vertices[i].attrib.get('label') end = self.vertices[i + 1].attrib.get('label') res.append([ '\\\\u0393'\",\n \"for n in range(len(val)): sec_mt_moment_atom = msection.m_create(x_exciting_section_MT_moment_atom) sec_mt_moment_atom.x_exciting_MT_moment_atom_index = n + 1 sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol\",\n \"= dict() data['occupancies'] = np.reshape(occs, (nspin, len(occs) // nspin)) data['eigenvalues'] = np.reshape(eigs, (nspin,\",\n \"= int(self.input_xml_parser.get( 'gw/freqgrid/nomeg', 16)) sec_gw.self_energy_c_number_of_poles = int(self.input_xml_parser.get( 'gw/selfenergy/npol', 0)) sec_gw.self_energy_c_number_of_empty_states = int(self.input_xml_parser.get( 'gw/selfenergy/nempty',\",\n \"quantity is None: return # this is really problematic if some scf steps\",\n \"= self.input_xml_parser if self._calculation_type == 'gw': parser_function = self._parse_input_gw elif self._calculation_type == 'xs':\",\n \"return val_in.strip().replace('(', '').replace(')', '').split() def str_to_energy_dict(val_in): val = val_in.strip().split('\\\\n') energies = dict() for\",\n \"sec_gw.screened_Coulomb = self.input_xml_parser.get( 'gw/scrcoul/scrtype', 'rpa') def parse_gw(self): sec_run = self.archive.run[-1] # two versions\",\n \"= section if section.get('energy_total') is not None else section.get('final') if final is None:\",\n \"is not None: break if val is None: continue if key == 'x_exciting_section_MT_charge_atom':\",\n \"specie.get('positions_format') sec_atoms_group.x_exciting_atom_position_format = positions_format positions = specie.get('positions') positions = self.info_parser.get_atom_positions( positions=positions, positions_format=positions_format).to('m') sec_atoms_group.x_exciting_geometry_atom_positions\",\n \"for scr_type in scr_types: files = self.get_exciting_files( '%s_BSE%s_SCR%s.OUT' % (name, bse_type, scr_type)) bse_files.append(files)\",\n \"potential'], 'energy_xc_potential': ['xc potential energy', 'xc potential'], 'energy_electrostatic': ['Hartree energy', 'Hartree'], 'x_exciting_hartree_energy': ['Hartree\",\n \"end = start + nkpts_segment band_energy = np.array([ np.transpose(energy)[start:end] for energy in band_energies])\",\n \"here if input_GW.xml does not exist self._quantities.append( Quantity( 'frequency_data', r'frequency list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)', str_operation=str_to_frequency, repeats=False)\",\n \"class ExcitingInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): re_symbol = re.compile(r'([A-Z][a-z]?)') def str_to_array(val_in): val\",\n \"self.input_xml_parser.get( 'xs/tddft/intraband', False) sec_method.x_exciting_xs_tddft_diagonal_xc_kernel = self.input_xml_parser.get( 'xs/tddft/kerndiag', False) sec_method.x_exciting_xs_tddft_lmax_alda = self.input_xml_parser.get( 'xs/tddft/lmaxalda', 3)\",\n \"optimization_step.get('force_convergence') if force_convergence is not None: sec_scc.x_exciting_maximum_force_magnitude = force_convergence[0] sec_scc.x_exciting_geometry_optimization_threshold_force = force_convergence[1] sec_scc\",\n \"logger): super().__init__(mainfile, logger=logger) def init_quantities(self): self._quantities = [] class ExcitingInfoParser(TextParser): def __init__(self): super().__init__(None)\",\n \"[nbands, mesh, origin, vector] self._quantities.append( Quantity( 'band_parameters', r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)', str_operation=str_to_band_parameters, repeats=False)) self._quantities.append( Quantity( 'fermi_surface',\",\n \"spent in this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds', unit=ureg.s, repeats=False, dtype=float) ] self._quantities.append(Quantity( 'structure_optimization', r'Structure\\\\-optimization module\",\n \"rgkmax sec_method.x_exciting_xs_screening_type = self.input_xml_parser.get( 'xs/screening/screentype', 'full') if self.input_xml_parser.get('xs/BSE') is not None: sec_method.x_exciting_xs_bse_antiresonant =\",\n \"'TDOS-QP.OUT'] # Parse GW band structure from one of the files: bs_files =\",\n \"sec_system.x_exciting_potential_mixing = potential_mixing return sec_system def parse_configurations(self): sec_run = self.archive.run[-1] def parse_configuration(section): if\",\n \"Quantity( 'fermi_energy', r'Fermi Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*', unit=ureg.hartree, repeats=False)) def str_to_band_parameters(val_in): val = val_in.strip().split('\\\\n') nbands =\",\n \"np.array(val, dtype=float) def str_to_atom_properties_dict(val_in): unit = None if 'charge' in val_in: unit =\",\n \"for v in val if len(v) == 3], float)) return dict( number=np.array(val[0], dtype=int),\",\n \"= reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands = len(eigs_gw[0]) sec_gw_eigenvalues.n_kpoints = len(eigs_gw) sec_gw_eigenvalues.kpoints = get_data('k_points') sec_gw_eigenvalues.energies =\",\n \"muffin-tins'], 'charge_total': ['total charge'], 'x_exciting_section_MT_charge_atom': ['atom_resolved'] } self._moment_keys_mapping = { 'x_exciting_interstitial_moment': ['interstitial'], 'x_exciting_total_MT_moment':\",\n \"read q points qpoints = self.input_xml_parser.get('xs/qpointset/qpoint') def get_data(quantity, ext): # all files related\",\n \"self.number_of_lm)) // self.number_of_lm else: spin = int(spin) - 1 val_l = self.partial_dos[i].attrib.get(self._l_key, None)\",\n \"= np.reshape(eigs, (nspin, len(eigs) // nspin)) return data self._quantities.append( Quantity( 'eigenvalues_occupancies', r'\\\\(state\\\\, eigenvalue\",\n \"self._distances is None: data = np.transpose(self.data) self._distances = data[5][:int(max(data[1]))] return self._distances @property def\",\n \"__init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] def str_to_eigenvalue(val_in): val = val_in.strip().split('\\\\n') kpts\",\n \"is not None: sec_system.x_exciting_potential_mixing = potential_mixing return sec_system def parse_configurations(self): sec_run = self.archive.run[-1]\",\n \"sccs.append(sec_scc) else: sec_scc = sccs[i] if sec_scc is None: # This is the\",\n \"dict() data['occupancies'] = np.reshape(occs, (nspin, len(occs) // nspin)) data['eigenvalues'] = np.reshape(eigs, (nspin, len(eigs)\",\n \"energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic)\",\n \"xml file sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref =\",\n \"return def get_data(key): if key == 'k_points': return np.array([d[0][:3] for d in data])\",\n \"None: sec_scc.x_exciting_geometry_optimization_step = optimization_step.get('step') force_convergence = optimization_step.get('force_convergence') if force_convergence is not None: sec_scc.x_exciting_maximum_force_magnitude\",\n \"input_file[0] xstype = self.input_xml_parser.get('xs/xstype', '') if xstype.lower() == 'bse': self._parse_xs_bse() elif xstype.lower() ==\",\n \"v[0] = v[0].split() v[1] = [float(vi) for vi in v[1].split()] v[1] = v[1][0]\",\n \"in this optimization step\\\\s*:\\\\s*[\\\\d\\\\.]+ seconds))', sub_parser=TextParser(quantities=optimization_quantities), repeats=True), Quantity( 'final', r'Force convergence target achieved([\\\\s\\\\S]+?Opt)',\",\n \"atomic|Starting initialization)([\\\\s\\\\S]+?)(?:Using|Ending initialization)', repeats=False, sub_parser=TextParser(quantities=initialization_quantities)) ) scf_quantities = [ Quantity( 'energy_total', r'[Tt]*otal energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)',\",\n \"sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_parser.get('band_k_points') nkpts_segment = self.bandstructure_parser.number_of_k_points_per_segment band_seg_labels = self.bandstructure_parser.get('band_segm_labels') for\",\n \"setting metainfo.') # species species = self.info_parser.get_initialization_parameter('species', []) for specie in species: sec_atoms_group\",\n \"def _get_dos(self, diagram): dos = np.array( [point.attrib.get(self._dos_key) for point in diagram], dtype=float) return\",\n \"if screening/BSE are children of xs if self.input_xml_parser.get('xs/screening') is not None: sec_method.x_exciting_xs_screening_number_of_empty_states =\",\n \"sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = self.dos_parser.number_of_dos sec_dos.energies = self.dos_parser.energies + energy_fermi volume = self.info_parser.get_unit_cell_volume()\",\n \"Scf, BasisSet ) from nomad.datamodel.metainfo.simulation.system import ( System, Atoms ) from nomad.datamodel.metainfo.simulation.calculation import\",\n \"> 1: self.logger.warn('Found multiple files. Will read all!', data=dict(file=name)) for n in range(len(files)):\",\n \"= sec_scc.energy.fermi if energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_dos\",\n \"nwacont else None, 'NAR' if not aresdf else None] file_ext = '_'.join([e for\",\n \"for n in range(len(val)): sec_mt_charge_atom = msection.m_create(x_exciting_section_MT_charge_atom) sec_mt_charge_atom.x_exciting_MT_charge_atom_index = n + 1 sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol\",\n \"dict() atom_resolved = [] species = None for v in val: v =\",\n \"volume_index = 1 while True: info_volume = self.get_exciting_files('run_dir%s/INFO.OUT' % str(volume_index).rjust(2, '0')) if not\",\n \"def number_of_lm(self): if self._nlm is None: if self.partial_dos is None: return self._nlm =\",\n \"def parse_scc(self, section): sec_run = self.archive.run[-1] final = section if section.get('energy_total') is not\",\n \"is None: total_dos = self.root.find('./%s/%s' % (self._totaldos_key, self._diagram_key)) self._ndos = len(total_dos) return self._ndos\",\n \"sec_system sec_scc.method_ref = sec_run.method[-1] return sec_scc # groundstate and hybrids calculation for module\",\n \"Parse DFT band structure from one of the files bs_files = ['bandstructure.xml', 'BAND.OUT',\",\n \"number of plane\\\\-waves', None), 'x_exciting_lo': (r'Total number of local\\\\-orbitals', None)} self._method_keys_mapping = {\",\n \"core charge', ureg.elementary_charge), 'x_exciting_valence_charge_initial': ('Total valence charge', ureg.elementary_charge), 'x_exciting_wigner_radius': (r'Effective Wigner radius, r\\\\_s',\",\n \"repeats=False), Quantity( 'atomic_positions', r'(imized atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'imized atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity(\",\n \"= ExcitingInfoParser() self.dos_parser = DOSXMLParser(energy_unit=ureg.hartree) self.bandstructure_parser = BandstructureXMLParser(energy_unit=ureg.hartree) self.eigval_parser = ExcitingEigenvalueParser() self.fermisurf_parser =\",\n \"self.input_xml_parser.get('gw/mixbasis/gmb', 1.0) sec_gw.mixed_basis_gmax = gmb * gmaxvr pwm = self.input_xml_parser.get('gw/barecoul/pwm', 2.0) sec_gw.bare_coulomb_gmax =\",\n \"= [] def str_to_eigenvalue(val_in): val = val_in.strip().split('\\\\n') kpts = np.array(val[0].split(), dtype=float) keys =\",\n \"f if self.data_xs_parser.data is None: continue data.append(self.data_xs_parser.data) return data def parse_exciton(data, sec_scc): n_components\",\n \"is not None: setattr(msection, 'x_exciting_dos_fermi', x_exciting_dos_fermi) # energy contributions energy_contributions = iteration.get('energy_contributions', {})\",\n \"if key == 'k_points': return np.array([d[0][:3] for d in data]) elif key ==\",\n \"for i in range(1, len(self.distances)): if self.distances[i] == self.distances[i - 1]: self._nkpts_segment.append(count) count\",\n \"self.logger self.band_out_parser.logger = self.logger self.info_gw_parser.logger = self.logger self.input_xml_parser.logger = self.logger self.data_xs_parser.logger = self.logger\",\n \"'tetrahedra'} sec_smearing = sec_electronic.m_create(Smearing) smearing_kind = self.info_parser.get_initialization_parameter('smearing_kind') if smearing_kind is not None: if\",\n \"= optimization_step.get('force_convergence') if force_convergence is not None: sec_scc.x_exciting_maximum_force_magnitude = force_convergence[0] sec_scc.x_exciting_geometry_optimization_threshold_force = force_convergence[1]\",\n \"2: continue elif v[0].startswith('species'): species = re.search(re_symbol, v[-1]).group(1) elif v[0].startswith('atom'): v[0] = v[0].split()\",\n \"sec_scc.x_exciting_xs_bse_sigma_im = np.reshape( data[2], (n_components, n_sigma)) def parse_loss(data, sec_scc): n_components = len(data) data\",\n \"Dos.atom_projected) sec_dos_values.m_kind = 'spherical' sec_dos_values.lm = lm_values[lm] sec_dos_values.spin = spin sec_dos_values.atom_index = atom\",\n \"self.info_gw_parser.mainfile = gw_info_files[0] fermi_energy = self.info_gw_parser.get('fermi_energy', None) if fermi_energy is not None: sec_scc.energy\",\n \"charge'], 'x_exciting_section_MT_charge_atom': ['atom_resolved'] } self._moment_keys_mapping = { 'x_exciting_interstitial_moment': ['interstitial'], 'x_exciting_total_MT_moment': ['total moment in\",\n \"the License for the specific language governing permissions and # limitations under the\",\n \"from nomad.datamodel.metainfo.simulation.calculation import ( Calculation, Dos, DosValues, BandStructure, BandEnergies, Energy, EnergyEntry, Charges, Forces,\",\n \"to un-shift the DOS to # the original scale in which also other\",\n \"Quantity( 'energy_contributions', r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n *(?:DOS|Density)', str_operation=str_to_energy_dict, repeats=False, convert=False), Quantity( 'x_exciting_dos_fermi', r'DOS at Fermi energy\",\n \"len(val) == 1 else val return np.array(val, dtype=float) def str_to_atom_properties_dict(val_in): unit = None\",\n \"+= 1 self._nkpts_segment.append(count) return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data\",\n \"['valence'], 'x_exciting_interstitial_charge': ['interstitial'], 'x_exciting_total_MT_charge': ['total charge in muffin-tins', 'total in muffin-tins'], 'charge_total': ['total\",\n \"= positions sec_atoms.labels = atom_labels sec_atoms.periodic = [True] * 3 # TODO confirm\",\n \"= self.archive.run[-1] xs_info_files = self.get_exciting_files('INFOXS.OUT') if not xs_info_files: return self._calculation_type = 'xs' #\",\n \"in val])) else: try: setattr(sec_system, name, val) except Exception: self.logger.warn('Error setting metainfo.') #\",\n \"== data[0])[0]) return self._neigs_segment class BandstructureXMLParser(XMLParser): def __init__(self, **kwargs): # TODO make a\",\n \"band in bands_atom: self._bands.append(band.findall('./%s' % self._band_key)) return self._bands @property def vertices(self): if self._vertices\",\n \"file_ext = '_'.join([e for e in file_ext_list if e]) # read q points\",\n \"System, Atoms ) from nomad.datamodel.metainfo.simulation.calculation import ( Calculation, Dos, DosValues, BandStructure, BandEnergies, Energy,\",\n \"ext == 'FXC': sec_scc = sec_run.m_create(Calculation) sec_scc.x_exciting_xs_tddft_number_of_epsilon_values = len(data[0][0][0]) sec_scc.x_exciting_xs_tddft_epsilon_energies = data[0][0][0] *\",\n \"= self.info_parser.get_xc_functional_name() if not xc_functional_names: # get it from input.xml input_file = self.get_exciting_files('input.xml')\",\n \"is None: return positions = np.dot(positions, cell.magnitude) return positions * ureg.bohr def get_scf_threshold(self,\",\n \"is None: continue metainfo_name = 'x_exciting_scf_threshold_%s_change' % name.split('_')[-2] setattr(sec_method, metainfo_name, threshold) # additionally,\",\n \"sec_scc.energy = Energy(fermi=fermi_energy) gw_files = ['EVALQP.DAT', 'EVALQP.TXT', 'TDOS-QP.OUT'] # Parse GW band structure\",\n \"'FXC': sec_scc.x_exciting_xs_tddft_sigma_local_field = data[1:3] elif quantity == 'SIGMA' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_sigma_no_local_field\",\n \"if self.input_xml_parser.get('xs/BSE') is not None: sec_method.x_exciting_xs_bse_antiresonant = self.input_xml_parser.get( 'xs/BSE/aresbse', True) sec_method.x_exciting_xs_bse_angular_momentum_cutoff = self.input_xml_parser.get(\",\n \"else None] file_ext = '_'.join([e for e in file_ext_list if e]) # read\",\n \"if labels is None: # we get it by concatenating species symbols species\",\n \"= self._parse_dos elif name.startswith('bandstructure') and name.endswith('xml'): parser = self.bandstructure_parser parser_function = self._parse_bandstructure elif\",\n \"3) def get_initialization_parameter(self, key, default=None): return self.get('initialization', {}).get(key, default) class ExcitingParser: def __init__(self):\",\n \"if band_parameters is not None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface = band_parameters[0] sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface = np.product(band_parameters[1]) sec_fermisurface.x_exciting_grid_fermi_surface =\",\n \"= sec_method if sec_run.system: sec_scc.system_ref = sec_run.system[-1] sec_scc_ref = sec_run.calculation[0] sec_scc.starting_calculation_ref = sec_scc_ref\",\n \"charge', ureg.elementary_charge), 'x_exciting_valence_charge_initial': ('Total valence charge', ureg.elementary_charge), 'x_exciting_wigner_radius': (r'Effective Wigner radius, r\\\\_s', ureg.bohr),\",\n \"= self.get('groundstate', self.get('hybrids', {})) return reference.get('scf_iteration', [{}])[-1].get( name, [None, None])[-1] def get_scf_quantity(self, name):\",\n \"data[2], (n_components, n_sigma)) def parse_loss(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_loss\",\n \"sec_system for name in self.info_parser._system_keys_mapping.keys(): val = self.info_parser.get_initialization_parameter(name) if val is None: continue\",\n \"val is None: continue if key == 'x_exciting_section_MT_moment_atom': for n in range(len(val)): sec_mt_moment_atom\",\n \"a parent class for dos and bandstructure super().__init__(None) self._distance_key = 'distance' self._coord_key =\",\n \"*({re_float})', dtype=np.float64, unit=ureg.electron_mass), Quantity('muffin_tin_radius', rf'muffin-tin radius *: *({re_float})', dtype=np.float64, unit=ureg.bohr), Quantity('radial_points', rf'radial points\",\n \"to un-shift the band structure to # the original scale in which also\",\n \"self._neigs_segment is None: self._neigs_segment = len(self.bands[0]) // self.number_of_spin_channels return self._neigs_segment def parse(self, key):\",\n \"self._atom_key) for band in bands_atom: self._bands.append(band.findall('./%s' % self._band_key)) return self._bands @property def vertices(self):\",\n \"for f in input_file: self.input_xml_parser.mainfile = f positions = self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors = self.input_xml_parser.get(\",\n \"return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data = np.transpose(self.data) self._neigs_segment\",\n \"['GGA_C_PBE', 'GGA_X_WC'], 30: ['GGA_C_AM05', 'GGA_C_AM05'], 300: ['GGA_C_BGCP', 'GGA_X_PBE'], 406: ['HYB_GGA_XC_PBEH'], 408: ['HYB_GGA_XC_HSE03']} xc_functional\",\n \"name.startswith('EVALQP') and (name.endswith('DAT') or name.endswith('TXT')): parser = self.evalqp_parser parser_function = self._parse_evalqp elif name.startswith('TDOS')\",\n \"self._quantities = [Quantity( 'program_version', r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\. ]+)\\\\s*started', repeats=False, dtype=str, flatten=False)] initialization_quantities = [ Quantity(\",\n \"self._band_energies is None: if self.data is None: return data = np.transpose(self.data) n_kpoints =\",\n \"volume.to('m**3').magnitude for spin in range(len(dos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value\",\n \"= get_data('eigenvalues') def _parse_fermisurface(self, sec_scc): fermi_surface = self.fermisurf_parser.get('fermi_surface', [None])[0] if fermi_surface is None:\",\n \"energy', 'correlation'], 'energy_sum_eigenvalues': ['Sum of eigenvalues', 'sum of eigenvalues'], 'x_exciting_effective_potential_energy': ['Effective potential energy'],\",\n \"import logging from nomad.units import ureg from nomad.parsing.file_parser import TextParser, Quantity, XMLParser, DataTextParser\",\n \"XMLParser() self.data_xs_parser = DataTextParser() self.data_clathrate_parser = DataTextParser(dtype=str) # different names for different versions\",\n \"occupancies which is problematic sometimes res = np.hstack([np.reshape(v[key], (nspin, np.size(v[key]) // nspin)) for\",\n \"'eigenvalues': res = res * ureg.hartree return res sec_eigenvalues = sec_scc.m_create(BandEnergies) sec_eigenvalues.kpoints =\",\n \"ExcitingEigenvalueParser() self.fermisurf_parser = ExcitingFermiSurfaceBxsfParser() self.evalqp_parser = ExcitingEvalqpParser() self.dos_out_parser = DataTextParser() self.bandstructure_dat_parser = BandstructureDatParser(energy_unit=ureg.hartree)\",\n \"xc_functional_map.get(xc_functional.type, []) return name @property def n_optimization_steps(self): return len(self.get('structure_optimization', {}).get('optimization_step', [])) def get_number_of_spin_channels(self):\",\n \"in file_ext_list if e]) # read q points qpoints = self.input_xml_parser.get('xs/qpointset/qpoint') def get_data(quantity,\",\n \"parameters xc_functional = self.info_parser.get('initialization', {}).get('xc_functional') if xc_functional is not None: sec_xc_functional.name = xc_functional.get('name_reference',\",\n \"name): n_scf = len(self.get('energy_total_scf_iteration', [])) quantity = self.get('%s_scf_iteration' % name) if quantity is\",\n \"*[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False), Quantity( 'moment_contributions', r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False)] self._miscellaneous_keys_mapping =\",\n \"number_of_spin_channels(self): if self._nspin is None: if not self.total_dos: return self._nspin = len(self.total_dos) return\",\n \"'energy_contributions', r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n *(?:DOS|Density)', str_operation=str_to_energy_dict, repeats=False, convert=False), Quantity( 'x_exciting_dos_fermi', r'DOS at Fermi energy \\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)',\",\n \"key == 'Znk': return np.array([d[1].get(key, None) for d in data]) else: energy =\",\n \"val) if key == 'x_exciting_fermi_energy': sec_energy.fermi = val # charge contributions charge_contributions =\",\n \"BandEnergies, Energy, EnergyEntry, Charges, Forces, ForcesEntry, ScfIteration, BandGap ) from nomad.datamodel.metainfo.workflow import Workflow,\",\n \"diagram): dos = np.array( [point.attrib.get(self._dos_key) for point in diagram], dtype=float) return dos def\",\n \"band structure from one of the files bs_files = ['bandstructure.xml', 'BAND.OUT', 'bandstructure.dat'] for\",\n \"ureg.elementary_charge elif 'moment' in val_in: unit = ureg.elementary_charge * ureg.bohr val = val_in.strip().split('\\\\n')\",\n \"self.input_xml_parser.get( 'xs/gqmax', 0.0, '1/bohr') sec_method.x_exciting_xs_lmaxapw = self.input_xml_parser.get('xs/lmaxapw', 10) sec_method.x_exciting_xs_number_of_empty_states = self.input_xml_parser.get( 'xs/nempty', 5)\",\n \"None: data = np.transpose(self.data) n_kpoints = np.where(data[0] == data[0][0])[0][1] bands = data[1:] bands\",\n \"False potential_mixing = self.info_parser.get_initialization_parameter('potential_mixing') if potential_mixing is not None: sec_system.x_exciting_potential_mixing = potential_mixing return\",\n \"r'(?:Charges|Electron charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False), Quantity( 'moment_contributions', r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False)]\",\n \"fundamental_band_gap is None: fundamental_band_gap = self.info_gw_parser.get('fundamental_band_gap', None) sec_gap = sec_scc.eigenvalues[-1].m_create(BandGap) if fundamental_band_gap is\",\n \"= [] for i in range(len(self.number_of_k_points_per_segment)): start = np.array( self.vertices[i].attrib.get(self._coord_key).split(), dtype=float) end =\",\n \"ureg.bohr), ]), repeats=False)) self._quantities.append(Quantity( 'hybrids', r'Hybrids module started([\\\\s\\\\S]+?)Hybrids module stopped', sub_parser=TextParser(quantities=module_quantities) )) def\",\n \"ureg.hartree), Quantity( 'charge_contributions', r'(?:Charges|Electron charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False), Quantity( 'moment_contributions', r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]',\",\n \"if self._vertices is None: self._vertices = self.root.findall('./%s' % self._vertex_key) return self._vertices @property def\",\n \"(energy_fermi.magnitude * ureg.joule).to('hartree') sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = self.dos_parser.number_of_dos sec_dos.energies = self.dos_parser.energies\",\n \"= self.input_xml_parser.get('xs/vkloff', [0., 0., 0.]) # TODO I am not certain if screening/BSE\",\n \"symmetries', None), 'x_exciting_kpoint_grid': (r'k\\\\-point grid', None), 'x_exciting_kpoint_offset': (r'k\\\\-point offset', None), 'x_exciting_number_kpoints': (r'Total number\",\n \"unit cell', None), 'x_exciting_spin_treatment': ('Spin treatment', None), 'x_exciting_number_of_bravais_lattice_symmetries': ('Number of Bravais lattice symmetries',\",\n \"bandstructure super().__init__(None) self._distance_key = 'distance' self._coord_key = 'coord' self._energy_key = 'eval' self._vertex_key =\",\n \"= val_in.strip().split('\\\\n') nbands = int(val[0]) mesh = np.array(val[1].split(), dtype=int) origin = np.array(val[2].split(), dtype=float)\",\n \"data[j].append(data_q[j]) return data for quantity in ['EPSILON', 'LOSS', 'SIGMA']: for ext in ['FXC',\",\n \"@property def partial_dos(self): if self._partial_dos is None: self._partial_dos = self.root.findall('./%s/%s' % (self._partialdos_key, self._diagram_key))\",\n \"val = [v.split(':')[-1].split() for v in val_in.strip().split('\\\\n')] val = val[0] if len(val) ==\",\n \"the missing quantity if len(quantity) < n_scf: quantity = [None] * (n_scf -\",\n \"r'method\\\\s*=\\\\s*(\\\\w+)', repeats=False, dtype=str), Quantity( 'n_scf_iterations', r'Number of (?:total)* scf iterations\\\\s*\\\\:\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity(\",\n \"val[-1] eigs = val[-2] nspin = 2 if occs[0] == 1. else 1\",\n \"the # ones with the missing quantity if len(quantity) < n_scf: quantity =\",\n \"= rgkmax sec_method.x_exciting_xs_bse_sciavbd = self.input_xml_parser.get( 'xs/BSE/sciavbd', True) sec_method.x_exciting_xs_bse_sciavqbd = self.input_xml_parser.get( 'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd\",\n \"self._quantities.append( Quantity( 'band_parameters', r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)', str_operation=str_to_band_parameters, repeats=False)) self._quantities.append( Quantity( 'fermi_surface', r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n *E*', unit=ureg.hartree, repeats=True))\",\n \"= int(self.input_xml_parser.get( 'gw/selfenergy/npol', 0)) sec_gw.self_energy_c_number_of_empty_states = int(self.input_xml_parser.get( 'gw/selfenergy/nempty', 0)) sec_gw.self_energy_singularity_treatment = self.input_xml_parser.get( 'gw/selfenergy/singularity',\",\n \"self.info_parser.get_xc_functional_name() if not xc_functional_names: # get it from input.xml input_file = self.get_exciting_files('input.xml') for\",\n \"parser): self.info_parser.quantities = parser.info_parser.quantities self.eigval_parser.quantities = parser.eigval_parser.quantities self.fermisurf_parser.quantities = parser.fermisurf_parser.quantities self.evalqp_parser.quantities = parser.evalqp_parser.quantities\",\n \"not None: sec_scc.x_exciting_geometry_optimization_step = optimization_step.get('step') force_convergence = optimization_step.get('force_convergence') if force_convergence is not None:\",\n \"different versions of exciting self._energy_keys_mapping = { 'energy_total': ['Total energy', 'total energy'], 'x_exciting_fermi_energy':\",\n \"initialization_quantities.append(Quantity( 'species', rf'(Species : *\\\\d+ *\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float} *{re_float} *{re_float}\\\\n\\\\s*\\\\n)', repeats=True, sub_parser=TextParser(quantities=[ Quantity('number', r'Species :\",\n \"[] count = 1 for i in range(1, len(self.distances)): if self.distances[i] == self.distances[i\",\n \"self._band_energies = [] start = 0 for nkpts_segment in self.number_of_k_points_per_segment: end = start\",\n \"nspin, len(data[0]))) sec_gw_eigenvalues = sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor = reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands = len(eigs_gw[0]) sec_gw_eigenvalues.n_kpoints =\",\n \"multiple GW info files, will read only first!') self.info_gw_parser.mainfile = gw_info_files[0] fermi_energy =\",\n \"= self.dos_out_parser.data if data is None: return # Get fermi energy: it is\",\n \"sub_parser=TextParser(quantities=initialization_quantities)) ) scf_quantities = [ Quantity( 'energy_total', r'[Tt]*otal energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=ureg.hartree), Quantity(\",\n \"sec_energy.total = EnergyEntry(value=energy_total) x_exciting_dos_fermi = iteration.get('x_exciting_dos_fermi') if x_exciting_dos_fermi is not None: setattr(msection, 'x_exciting_dos_fermi',\",\n \"info.xml, wannier.out if name.startswith('dos') and name.endswith('xml'): parser = self.dos_parser parser_function = self._parse_dos elif\",\n \"= self.info_parser.get_number_of_spin_channels() def get_data(key): data = self.eigval_parser.get('eigenvalues_occupancies') # reshaping is not necessary as\",\n \"(energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi nkpts_segment = self.band_out_parser.number_of_k_points_per_segment\",\n \"for name in self.info_parser._convergence_keys_mapping.keys(): threshold = self.info_parser.get_scf_threshold(name) if threshold is None: continue metainfo_name\",\n \"self.archive.run[-1] fxctype = self.input_xml_parser.get('xs/tddft/fxctype', 'RPA') tetradf = self.input_xml_parser.get('xs/tetra/tetradf', None) nwacont = self.input_xml_parser.get('xs/tddft/nwacont', None)\",\n \"ureg.elementary_charge), 'x_exciting_core_charge_initial': ('Total core charge', ureg.elementary_charge), 'x_exciting_valence_charge_initial': ('Total valence charge', ureg.elementary_charge), 'x_exciting_wigner_radius': (r'Effective\",\n \"in os.listdir( self.info_parser.maindir) if target[0] in f and mainfile_base in f] options =\",\n \"*[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False)] self._miscellaneous_keys_mapping = { 'x_exciting_gap': (r'Estimated fundamental gap', ureg.hartree), 'time':\",\n \"gw_info_file = f break if not self._calculation_type == 'gw': return sec_method = sec_run.m_create(Method)\",\n \"sec_xc_functional.name = xc_functional.get('name_reference', [None, None])[0] sec_xc_functional.reference = xc_functional.get('name_reference', [None, None])[1] sec_electronic.n_spin_channels = self.info_parser.get_number_of_spin_channels()\",\n \"files: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None: continue data.append(self.data_xs_parser.data) return data def\",\n \"parent clas for bandstructure dat and xml self._nspin = None self._nkpts_segment = None\",\n \"for nb in range(len(band_energies[n])): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb]\",\n \"= np.array(lattice_vectors, dtype=float) lattice_vectors *= self.input_xml_parser.get('structure/crystal/scale', 1.0) positions = np.dot(positions, lattice_vectors) * ureg.bohr\",\n \"sec_gw_eigenvalues.value_exchange = reshape(get_data('Sx')) eigs_gw_C = reshape(get_data('Sc')) if eigs_gw_C is None: eigs_gw_C = reshape(get_data('Re(Sc)'))\",\n \"'.join([str(v) for v in val])) else: try: setattr(sec_system, name, val) except Exception: self.logger.warn('Error\",\n \"self.input_xml_parser.get( 'xs/BSE/sciavqwg', False) sec_method.x_exciting_xs_bse_sciavtype = self.input_xml_parser.get( 'xs/BSE/sciavtype', 'spherical') sec_method.x_exciting_xs_bse_xas = self.input_xml_parser.get( 'xs/BSE/xas', False)\",\n \"try: parse_function(data, sec_scc) except Exception: self.logger.error('Error setting xs data', data=dict(file=quantity)) def _parse_xs_tddft(self): sec_run\",\n \"I am not sure about format for spin-polarized case! I assume it is\",\n \"'energies': return self.energies else: res = None self._results[key] = res class ExcitingFermiSurfaceBxsfParser(TextParser): def\",\n \"Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_parser.get('band_k_points') nkpts_segment = self.bandstructure_parser.number_of_k_points_per_segment band_seg_labels = self.bandstructure_parser.get('band_segm_labels')\",\n \"is None: # get it from last scf_iteration or optimization_step final = section.get('scf_iteration',\",\n \"= '%s.%s' % (filepath, target[1]) filepath = os.path.join(self.info_parser.maindir, filepath) if os.path.isfile(filepath) and os.access(filepath,\",\n \"n_kpoints = int(max(data[1])) bands = data[6:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints))\",\n \"the License. # import numpy as np import os import re import logging\",\n \"(1 / ureg.hartree) * volume.to('m**3').magnitude for spin in range(len(dos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total)\",\n \"properties['atom_resolved'] = atom_resolved return properties def str_to_quantity_tolerances(val_in): return val_in.strip().replace('(', '').replace(')', '').split() def str_to_energy_dict(val_in):\",\n \"sec_gw.self_energy_c_number_of_poles = int(self.input_xml_parser.get( 'gw/selfenergy/npol', 0)) sec_gw.self_energy_c_number_of_empty_states = int(self.input_xml_parser.get( 'gw/selfenergy/nempty', 0)) sec_gw.self_energy_singularity_treatment = self.input_xml_parser.get(\",\n \"sec_dos.m_create(DosValues, Dos.atom_projected) sec_dos_values.m_kind = 'spherical' sec_dos_values.lm = lm_values[lm] sec_dos_values.spin = spin sec_dos_values.atom_index =\",\n \"return properties def str_to_quantity_tolerances(val_in): return val_in.strip().replace('(', '').replace(')', '').split() def str_to_energy_dict(val_in): val = val_in.strip().split('\\\\n')\",\n \"@property def total_dos(self): if self._total_dos is None: self._total_dos = self.root.findall('./%s/%s' % (self._totaldos_key, self._diagram_key))\",\n \"rgkmax = self.input_xml_parser.get('xs/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_rgkmax\",\n \"None: return sec_system = self.parse_system(section) if sec_system is not None: sec_scc.system_ref = sec_system\",\n \"None: return self._energy_unit = self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1) return self._energy_unit @property def number_of_spin_channels(self): if self._nspin\",\n \"options = [ f for f in os.listdir( self.info_parser.maindir) if target[0] in f\",\n \"all!', data=dict(file=name)) for n in range(len(files)): parser.mainfile = files[n] parser_function(section) # free up\",\n \"BandstructureDatParser(energy_unit=ureg.hartree) self.band_out_parser = BandOutParser(energy_unit=ureg.hartree) self.info_gw_parser = GWInfoParser() self.input_xml_parser = XMLParser() self.data_xs_parser = DataTextParser()\",\n \"sec_atoms.lattice_vectors = lattice_vectors lattice_vectors_reciprocal = self.info_parser.get_initialization_parameter( 'lattice_vectors_reciprocal') sec_atoms.lattice_vectors_reciprocal = lattice_vectors_reciprocal if len(sec_run.system) >\",\n \"i // (self.number_of_lm * self.number_of_spin_channels) res[lm][spin][atom] = self._get_dos(self.partial_dos[i]) if self.energy_unit is not None:\",\n \"int(val_l) ** 2 + int(val_m) + int(val_l) atom = i // (self.number_of_lm *\",\n \"self.input_xml_parser.get( 'xs/BSE/lmaxdielt', 14) rgkmax = self.input_xml_parser.get('xs/BSE/rgkmax', None) if rgkmax is None: rgkmax =\",\n \"info_volume: break sec_scc.calculations_path.append(info_volume[0]) def init_parser(self): self.info_parser.mainfile = self.filepath self.info_parser.logger = self.logger self.dos_parser.logger =\",\n \"OF ANY KIND, either express or implied. # See the License for the\",\n \"def _parse_eigenvalues(self, sec_scc): if self.eigval_parser.get('eigenvalues_occupancies', None) is None: return nspin = self.info_parser.get_number_of_spin_channels() def\",\n \"None), 'smearing_width': ('Smearing width', None)} for name, key_unit in self._system_keys_mapping.items(): initialization_quantities.append( Quantity( name,\",\n \"+ energy_fermi volume = self.info_parser.get_unit_cell_volume() dos = data[1] * (1 / ureg.hartree) *\",\n \"format for spin-polarized case! I assume it is # energy dos_up dos_down nspin\",\n \"if input_GW.xml does not exist self._quantities.append( Quantity( 'frequency_data', r'frequency list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)', str_operation=str_to_frequency, repeats=False) )\",\n \"None: continue data.append(self.data_xs_parser.data) return data def parse_exciton(data, sec_scc): n_components = len(data) data =\",\n \"\\\"\\\"\\\" mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = filename.rsplit('.', 1) filepath =\",\n \"f in exciting_files: self.parse_file(f, sec_scc) # structure optimization structure_optimization = self.info_parser.get('structure_optimization', {}) for\",\n \"= self.archive.run[-1] sec_method = sec_run.m_create(Method) sec_method.basis_set.append(BasisSet(type='(L)APW+lo')) sec_dft = sec_method.m_create(DFT) sec_electronic = sec_method.m_create(Electronic) sec_electronic.method\",\n \"= np.transpose(self.data) n_kpoints = int(max(data[1])) bands = data[6:] bands = np.reshape(bands, ( self.number_of_spin_channels,\",\n \"'correlation'], 'energy_sum_eigenvalues': ['Sum of eigenvalues', 'sum of eigenvalues'], 'x_exciting_effective_potential_energy': ['Effective potential energy'], 'x_exciting_coulomb_potential_energy':\",\n \"be two versions, input_gw.xml and input-gw.xml for f in ['input_gw.xml', 'input-gw.xml', 'input.xml']: self.parse_file(f,\",\n \"parse properties input_file = self.get_exciting_files('input.xml') if not input_file: return self.input_xml_parser.mainfile = input_file[0] xstype\",\n \"not None: setattr(msection, 'x_exciting_dos_fermi', x_exciting_dos_fermi) # energy contributions energy_contributions = iteration.get('energy_contributions', {}) for\",\n \"names: val = charge_contributions.get(name, None) if val is not None: break if val\",\n \"= len(eigs_gw[0]) sec_gw_eigenvalues.n_kpoints = len(eigs_gw) sec_gw_eigenvalues.kpoints = get_data('k_points') sec_gw_eigenvalues.energies = reshape(eigs_gw) sec_gw_eigenvalues.value_exchange =\",\n \"np.reshape(data, (nspin, len(data) // nspin, len(data[0]))) sec_gw_eigenvalues = sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor = reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands\",\n \"for name in self.info_parser._miscellaneous_keys_mapping.keys(): val = iteration.get(name) if val is None: continue if\",\n \"DOS to # the original scale in which also other energies are reported.\",\n \"1 / ureg.bohr), 'x_exciting_gvector_size': (r'G\\\\-vector grid sizes', None), 'x_exciting_gvector_total': (r'Total number of G\\\\-vectors',\",\n \"break # Parse DFT band structure from one of the files bs_files =\",\n \"np.reshape( data[5], (n_components, n_excitons)) def parse_epsilon(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data))\",\n \"This is killing me! if metainfo_name == 'x_exciting_scf_threshold_energy_change': sec_method.scf = Scf(threshold_energy_change=threshold) xc_functional_names =\",\n \"sec_electronic.method = 'DFT' smearing_kind_map = { 'Gaussian': 'gaussian', 'Methfessel-Paxton': 'methfessel-paxton', 'Fermi-Dirac': 'fermi', 'Extended':\",\n \"2 + int(val_m) + int(val_l) atom = i // (self.number_of_lm * self.number_of_spin_channels) res[lm][spin][atom]\",\n \"positions * ureg.bohr def get_scf_threshold(self, name): reference = self.get('groundstate', self.get('hybrids', {})) return reference.get('scf_iteration',\",\n \"self.input_xml_parser.get('gw/barecoul/pwm', 2.0) sec_gw.bare_coulomb_gmax = pwm * gmb * gmaxvr sec_gw.bare_coulomb_cutofftype = self.input_xml_parser.get( 'gw/barecoul/cutofftype',\",\n \"in effective potential \\\\(target\\\\)', ureg.hartree), 'x_exciting_energy_convergence': ( r'Absolute change in total energy\\\\s*\\\\(target\\\\)', ureg.hartree),\",\n \"time \\\\(seconds\\\\)', ureg.s)} for name, key_unit in self._miscellaneous_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)' % key_unit[0],\",\n \"ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field = data[1:3] elif quantity == 'LOSS' and ext ==\",\n \"sec_eigenvalues.kpoints = self.eigval_parser.get('k_points') sec_eigenvalues.occupations = get_data('occupancies') sec_eigenvalues.energies = get_data('eigenvalues') def _parse_fermisurface(self, sec_scc): fermi_surface\",\n \"under the License. # import numpy as np import os import re import\",\n \"and is accessible in the same folder where the mainfile is stored. \\\"\\\"\\\"\",\n \"# import numpy as np import os import re import logging from nomad.units\",\n \"in epsilon which is not parsed? sccs = [] for quantity in ['EXCITON',\",\n \"for name, key_unit in self._miscellaneous_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)' % key_unit[0], repeats=False, unit=key_unit[1])) self._convergence_keys_mapping\",\n \"scf_iterations = section.get('scf_iteration', []) for scf_iteration in scf_iterations: sec_scf_iteration = sec_scc.m_create(ScfIteration) parse_scf(scf_iteration, sec_scf_iteration)\",\n \"None else section.get('positions') if positions is None: species = self.get_initialization_parameter('species', []) if species:\",\n \"return self._energy_unit = self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1) return self._energy_unit @property def number_of_spin_channels(self): if self._nspin is\",\n \"= Energy(fermi=fermi_energy) gw_files = ['EVALQP.DAT', 'EVALQP.TXT', 'TDOS-QP.OUT'] # Parse GW band structure from\",\n \"or agreed to in writing, software # distributed under the License is distributed\",\n \"sec_mt_moment_atom = msection.m_create(x_exciting_section_MT_moment_atom) sec_mt_moment_atom.x_exciting_MT_moment_atom_index = n + 1 sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol = val[n][0] sec_mt_moment_atom.x_exciting_MT_moment_atom_value =\",\n \"break return positions_format def get_atom_positions(self, section={}, positions=None, positions_format=None): positions = positions if positions\",\n \"sec_xc_functional.exchange.append(Functional(name=name)) elif '_C_' in name: sec_xc_functional.correlation.append(Functional(name=name)) elif 'HYB' in name: sec_xc_functional.hybrid.append(Functional(name=name)) else: sec_xc_functional.contributions.append(Functional(name=name))\",\n \"= self.info_gw_parser.get('frequency_data', None) if frequency_data is not None: number = frequency_data.get('number') sec_method.gw.number_of_frequencies =\",\n \"Hamiltonian size', None), 'x_exciting_pw': (r'Maximum number of plane\\\\-waves', None), 'x_exciting_lo': (r'Total number of\",\n \"vi properties[v[0].strip()] = vi * unit properties['atom_resolved'] = atom_resolved return properties def str_to_quantity_tolerances(val_in):\",\n \"'hartree') sec_method.x_exciting_xs_tddft_xc_kernel = self.input_xml_parser.get( 'xs/tddft/fxctype', 'RPA') sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution = self.input_xml_parser.get( 'xs/tddft/intraband', False) sec_method.x_exciting_xs_tddft_diagonal_xc_kernel =\",\n \"2 return n_spin def get_unit_cell_volume(self): return self.get('initialization', {}).get('x_exciting_unit_cell_volume', 1.0 * ureg.bohr ** 3)\",\n \"Quantity('atomic_mass', rf'atomic mass *: *({re_float})', dtype=np.float64, unit=ureg.electron_mass), Quantity('muffin_tin_radius', rf'muffin-tin radius *: *({re_float})', dtype=np.float64,\",\n \"started)([\\\\s\\\\S]+?)Groundstate module stopped', sub_parser=TextParser(quantities=module_quantities), repeats=False)) optimization_quantities = [ Quantity( 'atomic_positions', r'(Atomic positions at\",\n \"['Correlation energy', 'correlation'], 'energy_sum_eigenvalues': ['Sum of eigenvalues', 'sum of eigenvalues'], 'x_exciting_effective_potential_energy': ['Effective potential\",\n \"= 'e' self._dos_key = 'dos' self._unit_key = 'unit' self._energy_unit = kwargs.get('energy_unit', None) self._units_mapping\",\n \"0, 0]) rgkmax = self.input_xml_parser.get('xs/screening/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax',\",\n \"nwacont = self.input_xml_parser.get('xs/tddft/nwacont', None) aresdf = self.input_xml_parser.get('xs/tddft/aresdf', True) file_ext_list = [ 'TET' if\",\n \"0) sec_method.x_exciting_xs_screening_ngridk = self.input_xml_parser.get( 'xs/screening/ngridk', [0, 0, 0]) rgkmax = self.input_xml_parser.get('xs/screening/rgkmax', None) if\",\n \"'Coulomb potential'], 'energy_xc_potential': ['xc potential energy', 'xc potential'], 'energy_electrostatic': ['Hartree energy', 'Hartree'], 'x_exciting_hartree_energy':\",\n \"dos = np.array( [point.attrib.get(self._dos_key) for point in diagram], dtype=float) return dos def parse(self,\",\n \"// nspin)) return data self._quantities.append( Quantity( 'eigenvalues_occupancies', r'\\\\(state\\\\, eigenvalue and occupancy below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n *\\\\n))',\",\n \"= default.split('.')[-1] mainfile_base = mainfile.rsplit('.', 1)[0].replace('INFO', '') options = [ f for f\",\n \"original scale in which also other energies are reported. energy_fermi = sec_scc.energy.fermi if\",\n \"'EXCITON': sec_scc = sec_run.m_create(Calculation) sccs.append(sec_scc) else: sec_scc = sccs[i] if sec_scc is None:\",\n \"I am not certain if screening/BSE are children of xs if self.input_xml_parser.get('xs/screening') is\",\n \"**kwargs): # TODO make a parent class for dos and bandstructure super().__init__(None) self._distance_key\",\n \"species = re.search(re_symbol, v[-1]).group(1) elif v[0].startswith('atom'): v[0] = v[0].split() v[1] = [float(vi) for\",\n \"sccs[i] if sec_scc is None: # This is the case when there is\",\n \"parser = self.dos_out_parser parser_function = self._parse_dos_out elif name.startswith('bandstructure') and name.endswith('dat'): parser = self.bandstructure_dat_parser\",\n \"None or species is None: continue lattice_vectors = np.array(lattice_vectors, dtype=float) lattice_vectors *= self.input_xml_parser.get('structure/crystal/scale',\",\n \"= data[0][0][0] * ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field = data[1:] elif quantity == 'EPSILON' and ext\",\n \"of crystal symmetries', None), 'x_exciting_kpoint_grid': (r'k\\\\-point grid', None), 'x_exciting_kpoint_offset': (r'k\\\\-point offset', None), 'x_exciting_number_kpoints':\",\n \"width', None)} for name, key_unit in self._system_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0],\",\n \"'band_segm_labels': res = [] for i in range(len(self.vertices) - 1): start = self.vertices[i].attrib.get('label')\",\n \"= self.evalqp_parser parser_function = self._parse_evalqp elif name.startswith('TDOS') and name.endswith('OUT'): parser = self.dos_out_parser parser_function\",\n \"None) if val is not None: break if val is None: continue if\",\n \"if positions_format is None: species = self.get_initialization_parameter('species', []) for specie in species: positions_format\",\n \"self.get_positions_format(section) if positions_format == 'lattice': cell = self.get_initialization_parameter('lattice_vectors') if cell is None: return\",\n \"['interstitial'], 'x_exciting_total_MT_moment': ['total moment in muffin-tins'], 'x_exciting_total_moment': ['total moment'], 'x_exciting_section_MT_moment_atom': ['atom_resolved'] } def\",\n \"the format for the spin polarized case # I cannot find an example\",\n \"'xs/screening/nempty', 0) sec_method.x_exciting_xs_screening_ngridk = self.input_xml_parser.get( 'xs/screening/ngridk', [0, 0, 0]) rgkmax = self.input_xml_parser.get('xs/screening/rgkmax', None)\",\n \"loop started|Groundstate module started)([\\\\s\\\\S]+?)Groundstate module stopped', sub_parser=TextParser(quantities=module_quantities), repeats=False)) optimization_quantities = [ Quantity( 'atomic_positions',\",\n \"1) filename = '%s%s' % (target[0], suffix) if target[1:]: filename = '%s.%s' %\",\n \"or name.startswith('FS')) and name.endswith('bxsf'): parser = self.fermisurf_parser parser_function = self._parse_fermisurface elif name.startswith('EVALQP') and\",\n \"self.get('%s_scf_iteration' % name) if quantity is None: return # this is really problematic\",\n \"val = val_in.strip().split('\\\\n') nbands = int(val[0]) mesh = np.array(val[1].split(), dtype=int) origin = np.array(val[2].split(),\",\n \"xc_functional.get('name_reference', [None, None])[1] sec_electronic.n_spin_channels = self.info_parser.get_number_of_spin_channels() if self._calculation_type == 'volume_optimization': sec_method.x_exciting_volume_optimization = True\",\n \"r'Hybrids module started([\\\\s\\\\S]+?)Hybrids module stopped', sub_parser=TextParser(quantities=module_quantities) )) def get_atom_labels(self, section): labels = section.get('symbols')\",\n \"* ureg.bohr atoms = self.input_xml_parser.get('structure/species/atom') atom_labels = [] for n in range(len(atoms)): atom_labels.extend([species[n].split('.')[0]]\",\n \"'eval' self._vertex_key = 'vertex' self._band_key = 'band' self._atom_key = 'atom' self._nspin = kwargs.get('nspin',\",\n \"len(v) < 2: continue energies[v[0].strip()] = float(v[1]) * ureg.hartree return energies self._quantities =\",\n \"= self.input_xml_parser.get( 'xs/tddft/nwacont', 0) sec_method.x_exciting_xs_tetra = self.input_xml_parser.get( 'xs/tetra/tetradf', False) def _parse_xs_bse(self): sec_run =\",\n \"occs[0] == 1. else 1 data = dict() data['occupancies'] = np.reshape(occs, (nspin, len(occs)\",\n \"maximum)([\\\\s\\\\S]+?)(\\\\n *\\\\n\\\\+{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(Atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions\\\\s*\\\\(([a-z]+)\\\\)'),\",\n \"'spherical') sec_method.x_exciting_xs_bse_xas = self.input_xml_parser.get( 'xs/BSE/xas', False) sec_method.x_exciting_xs_bse_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlbse', [0, 0, 0,\",\n \"self.info_parser.get('initialization', {}).get('xc_functional') if xc_functional is not None: sec_xc_functional.name = xc_functional.get('name_reference', [None, None])[0] sec_xc_functional.reference\",\n \"fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break # Parse DFT band structure from\",\n \"return bse_files def get_data(files): data = [] for f in files: self.data_xs_parser.mainfile =\",\n \"res * (1 / self.energy_unit) elif key == 'energies': return self.energies else: res\",\n \"quantity at all qpoints files = self.get_exciting_files('%s_%s%s%s.OUT' % (quantity, file_ext, ext, fxctype)) data\",\n \"ureg.hartree), 'x_exciting_energy_convergence': ( r'Absolute change in total energy\\\\s*\\\\(target\\\\)', ureg.hartree), 'x_exciting_charge_convergence': ( r'Charge distance\\\\s*\\\\(target\\\\)',\",\n \"is not None: sec_gap.value_fundamental = fundamental_band_gap optical_band_gap = self.info_gw_parser.get('optical_band_gap', None) if optical_band_gap is\",\n \"None: continue if key.startswith('energy_'): sec_energy.m_add_sub_section(getattr( Energy, key.replace('energy_', '')), EnergyEntry(value=val)) else: setattr(msection, key, val)\",\n \"= np.transpose(self.data)[0] n_k_points = np.where(dist == dist[0])[0][1] self._distances = dist[:n_k_points] return self._distances @property\",\n \"len(np.where(data == data[0])[0]) return self._neigs_segment class BandstructureXMLParser(XMLParser): def __init__(self, **kwargs): # TODO make\",\n \"self.get_initialization_parameter('lattice_vectors') if cell is None: return positions = np.dot(positions, cell.magnitude) return positions *\",\n \"is not None else logging self._calculation_type = None self.init_parser() sec_run = self.archive.m_create(Run) sec_run.program\",\n \"[] for specie in species: labels += [specie.get('symbol')] * len(specie.get('positions')) return labels def\",\n \"unit=ureg.bohr), Quantity('radial_points', rf'radial points in muffin-tin *: *({re_float})', dtype=np.int32), Quantity('positions_format', r'atomic positions \\\\((.+?)\\\\)',\",\n \"data = np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components = n_components n_excitons = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_excitons =\",\n \"bands = data[1:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies = []\",\n \"r'(?:Convergence targets achieved\\\\. Performing final SCF iteration|Reached self-consistent loops maximum)([\\\\s\\\\S]+?)(\\\\n *\\\\n\\\\+{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=False),\",\n \"data[0][0][0] * ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field = data[1:] elif quantity == 'EPSILON' and ext ==\",\n \"in range(len(self.bands[n])): band_energies[i % self.number_of_spin_channels][i] = np.array( [e.attrib.get(self._energy_key) for e in self.bands[n][i]]) for\",\n \"'final', r'Force convergence target achieved([\\\\s\\\\S]+?Opt)', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(imized atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[\",\n \"def energies(self): if self._energies is None: if self.total_dos is None: return self._energies =\",\n \"is None: self._results = dict() if 'total' in key: if not self.total_dos: return\",\n \"= ['EIGVAL.OUT', 'FERMISURF.bxsf', 'FS.bxsf'] # Parse DFT DOS from one of the files\",\n \"exists and is accessible in the same folder where the mainfile is stored.\",\n \"get it from last scf_iteration or optimization_step final = section.get('scf_iteration', [None])[-1] final =\",\n \"('Brillouin zone volume', 1 / ureg.bohr ** 3), 'x_exciting_number_of_atoms': ('Total number of atoms\",\n \"spin polarized case # I cannot find an example bandstructure file # atom-resolved\",\n \"= msection.m_create(Charges) sec_charges.value = [ val[n][1].magnitude for n in range(len(val))] * val[0][1].units sec_charges.total\",\n \"quantity == 'LOSS' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_loss_function_local_field = data[1] elif quantity ==\",\n \"* ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi nkpts_segment = self.band_out_parser.number_of_k_points_per_segment for\",\n \"= BandstructureXMLParser(energy_unit=ureg.hartree) self.eigval_parser = ExcitingEigenvalueParser() self.fermisurf_parser = ExcitingFermiSurfaceBxsfParser() self.evalqp_parser = ExcitingEvalqpParser() self.dos_out_parser =\",\n \"sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] self.parse_file('input.xml',\",\n \"'x_exciting_electronic_charge': ('Total electronic charge', ureg.elementary_charge), 'x_exciting_core_charge_initial': ('Total core charge', ureg.elementary_charge), 'x_exciting_valence_charge_initial': ('Total valence\",\n \"np.reshape( data[3], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_re = np.reshape( data[4], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_im = np.reshape(\",\n \"parse_gw(self): sec_run = self.archive.run[-1] # two versions of gw info files gw_info_files =\",\n \"name, val) # other metainfo for name in self.info_parser._miscellaneous_keys_mapping.keys(): val = iteration.get(name) if\",\n \"'moment' in val_in: unit = ureg.elementary_charge * ureg.bohr val = val_in.strip().split('\\\\n') properties =\",\n \"[0, 0, 0, 0]) if sec_method.x_exciting_xs_bse_xas: sec_method.x_exciting_xs_bse_xasatom = self.input_xml_parser.get( 'xs/BSE/xasatom', 0) sec_method.x_exciting_xs_bse_xasedge =\",\n \"= self.eigval_parser parser_function = self._parse_eigenvalues elif (name.startswith('FERMISURF') or name.startswith('FS')) and name.endswith('bxsf'): parser =\",\n \"self.eigval_parser.quantities = parser.eigval_parser.quantities self.fermisurf_parser.quantities = parser.fermisurf_parser.quantities self.evalqp_parser.quantities = parser.evalqp_parser.quantities self.info_gw_parser.quantities = parser.info_gw_parser.quantities def\",\n \"dat and xml self._nspin = None self._nkpts_segment = None self._neigs_segment = None self._vertices\",\n \"'gw/barecoul/cutofftype', 'none') sec_gw.screened_coulomb_volume_average = self.input_xml_parser.get( 'gw/scrcoul/sciavtype', 'isotropic') sec_gw.screened_Coulomb = self.input_xml_parser.get( 'gw/scrcoul/scrtype', 'rpa') def\",\n \"= len(self.get('energy_total_scf_iteration', [])) quantity = self.get('%s_scf_iteration' % name) if quantity is None: return\",\n \"= iteration.get('energy_total') sec_energy = msection.m_create(Energy) if energy_total is not None: sec_energy.total = EnergyEntry(value=energy_total)\",\n \"self.logger.warn('Found multiple GW info files, will read only first!') self.info_gw_parser.mainfile = gw_info_files[0] fermi_energy\",\n \"'hartree') sec_method.x_exciting_xs_gqmax = self.input_xml_parser.get( 'xs/gqmax', 0.0, '1/bohr') sec_method.x_exciting_xs_lmaxapw = self.input_xml_parser.get('xs/lmaxapw', 10) sec_method.x_exciting_xs_number_of_empty_states =\",\n \"ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_loss_function_no_local_field = data[1] elif quantity == 'SIGMA' and ext ==\",\n \"= self.info_parser.get_atom_positions(section.get('atomic_positions', {})) lattice_vectors = self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels = self.info_parser.get_atom_labels(section.get('atomic_positions', {})) input_file = self.get_exciting_files('input.xml')\",\n \"sec_scc.x_exciting_xs_bse_exciton_energies = np.reshape( data[1], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies = np.reshape( data[2], (n_components,\",\n \"= int(max(data[0])) return self._neigs_segment class BandOutParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin',\",\n \"self.info_parser.get_unit_cell_volume() totaldos = self.dos_parser.get('totaldos') * volume.to('m**3').magnitude for spin in range(len(totaldos)): sec_dos_values = sec_dos.m_create(DosValues,\",\n \"what they are. What is the fourth column in epsilon which is not\",\n \"% (self.number_of_spin_channels * self.number_of_lm)) // self.number_of_lm else: spin = int(spin) - 1 val_l\",\n \"np.array(val[1].split(), dtype=int) origin = np.array(val[2].split(), dtype=float) vector = np.array([v.split() for v in val[3:6]],\",\n \"sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value = dos[spin] # TODO add PDOS def\",\n \"val elif name == 'x_exciting_species_rtmin': setattr(sec_system, name, ' '.join([str(v) for v in val]))\",\n \"2: ['LDA_C_PZ', 'LDA_X_PZ'], 3: ['LDA_C_PW', 'LDA_X_PZ'], 4: ['LDA_C_XALPHA'], 5: ['LDA_C_VBH'], 20: ['GGA_C_PBE', 'GGA_X_PBE'],\",\n \"np.array(val[0].split(), dtype=float) keys = val[1].split() eigs = np.transpose(np.array([v.split() for v in val[2:]], dtype=float))\",\n \"or implied. # See the License for the specific language governing permissions and\",\n \"return self._distances = [ point.attrib.get(self._distance_key) for point in self.bands[0][0]] self._distances = np.array(self._distances, dtype=float)\",\n \"of the files bs_files = ['bandstructure.xml', 'BAND.OUT', 'bandstructure.dat'] for fname in bs_files: if\",\n \"= [v.split(':')[-1].split() for v in val_in.strip().split('\\\\n')] val = val[0] if len(val) == 1\",\n \"continue if key.startswith('energy_'): sec_energy.m_add_sub_section(getattr( Energy, key.replace('energy_', '')), EnergyEntry(value=val)) else: setattr(msection, key, val) if\",\n \"= parse_epsilon elif quantity == 'SIGMA': parse_function = parse_sigma elif quantity == 'LOSS':\",\n \"rgkmax = self.input_xml_parser.get('xs/screening/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_screening_rgkmax\",\n \"specie in species: positions_format = specie.get('positions_format', None) if positions_format is not None: break\",\n \"res = res * (1 / self.energy_unit) elif key == 'energies': return self.energies\",\n \"sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.energies = band_energies[nb] + energy_fermi def\",\n \"if self.partial_dos is None: return self._nlm = 0 l_list = set([int(e.attrib.get(self._l_key)) for e\",\n \"and os.access(filepath, os.F_OK): return True return False def _parse_dos(self, sec_scc): if self.dos_parser.get('totaldos', None)\",\n \"sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.endpoints_labels = band_seg_labels[nb] sec_k_band_segment.energies = band_energies[n][nb] +\",\n \"in val_in.strip().split('\\\\n')] val = val[0] if len(val) == 1 else val return np.array(val,\",\n \"not self.total_dos: return res = np.zeros((self.number_of_spin_channels, self.number_of_dos)) for i in range(len(self.total_dos)): spin =\",\n \"elif 'moment' in val_in: unit = ureg.elementary_charge * ureg.bohr val = val_in.strip().split('\\\\n') properties\",\n \"= None self._band_energies = None self._neigs_segment = None self._nkpts_segment = None @property def\",\n \"range(1, len(self.distances)): if self.distances[i] == self.distances[i - 1]: self._nkpts_segment .append(count) count = 1\",\n \"for spin-polarized case! I assume it is # energy dos_up dos_down nspin =\",\n \"sec_system is not None: sec_scc.system_ref = sec_system sec_scc.method_ref = sec_run.method[-1] return sec_scc #\",\n \"# get it from input.xml for f in input_file: self.input_xml_parser.mainfile = f positions\",\n \"1)[0].replace('INFO', '') options = [ f for f in os.listdir( self.info_parser.maindir) if target[0]\",\n \"(filepath, target[1]) filepath = os.path.join(self.info_parser.maindir, filepath) if os.path.isfile(filepath) and os.access(filepath, os.F_OK): return True\",\n \"True def parse_scc(self, section): sec_run = self.archive.run[-1] final = section if section.get('energy_total') is\",\n \"repeats=True, dtype=float)])), Quantity( 'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array,\",\n \"data = np.transpose(self.data)[0] self._neigs_segment = len(np.where(data == data[0])[0]) return self._neigs_segment class BandstructureXMLParser(XMLParser): def\",\n \"for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment self._band_k_points.append( np.transpose(data[2:5])[start:end]) start =\",\n \"convert=False), Quantity( 'moment_contributions', r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False)] self._miscellaneous_keys_mapping = { 'x_exciting_gap': (r'Estimated\",\n \"None: if not self.bands: return self._distances = [ point.attrib.get(self._distance_key) for point in self.bands[0][0]]\",\n \"str_operation=lambda x: [v.strip() for v in x.split(':')])])) ) self._quantities.append(Quantity( 'initialization', r'(?:All units are\",\n \"None: sec_scc.system_ref = sec_system sec_scc.method_ref = sec_run.method[-1] return sec_scc # groundstate and hybrids\",\n \"of the files: bs_files = ['bandstructure-qp.dat', 'BAND-QP.OUT'] for fname in bs_files: if self.file_exists(fname):\",\n \"sec_dos.n_energies = len(data) // nspin data = np.reshape(data, (nspin, len(data) // nspin, 2))\",\n \"name.split('_')[-2] setattr(sec_method, metainfo_name, threshold) # additionally, set threshold to global metainfo. This is\",\n \"parser.evalqp_parser.quantities self.info_gw_parser.quantities = parser.info_gw_parser.quantities def parse(self, filepath, archive, logger): self.filepath = filepath self.archive\",\n \"= sec_worfklow.m_create(GeometryOptimization) threshold_force = structure_optimization.get( 'optimization_step', [{}])[0].get('force_convergence', [0., 0.])[-1] sec_geometry_opt.input_force_maximum_tolerance = threshold_force def\",\n \"'_C_' in name: sec_xc_functional.correlation.append(Functional(name=name)) elif 'HYB' in name: sec_xc_functional.hybrid.append(Functional(name=name)) else: sec_xc_functional.contributions.append(Functional(name=name)) if not\",\n \"lm = int(val_l) ** 2 + int(val_m) + int(val_l) atom = i //\",\n \"# Parse GW band structure from one of the files: bs_files = ['bandstructure-qp.dat',\",\n \"None: sec_method.x_exciting_xs_screening_number_of_empty_states = self.input_xml_parser.get( 'xs/screening/nempty', 0) sec_method.x_exciting_xs_screening_ngridk = self.input_xml_parser.get( 'xs/screening/ngridk', [0, 0, 0])\",\n \"'EXCITON': parse_function = parse_exciton elif quantity == 'EPSILON': parse_function = parse_epsilon elif quantity\",\n \"not parsed? sccs = [] for quantity in ['EXCITON', 'EPSILON', 'SIGMA', 'LOSS']: files\",\n \"'EVALQP.TXT', 'TDOS-QP.OUT'] # Parse GW band structure from one of the files: bs_files\",\n \"(n_components, n_epsilon)) def parse_sigma(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_sigma =\",\n \"r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi [Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s', unit=ureg.hartree, repeats=False) ) self._quantities.append( Quantity( 'direct_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append(\",\n \"f if self.data_xs_parser.data is None: continue data_q.append(self.data_xs_parser.data) if not data_q: continue data_q =\",\n \"energy'], 'x_exciting_fermi_energy': ['Fermi energy', 'Fermi'], 'energy_kinetic_electronic': ['Kinetic energy', 'electronic kinetic'], 'energy_coulomb': ['Coulomb energy',\",\n \"= band_seg_labels[nb] sec_k_band_segment.energies = band_energies[n][nb] + energy_fermi def _parse_eigenvalues(self, sec_scc): if self.eigval_parser.get('eigenvalues_occupancies', None)\",\n \"is None: return def get_data(key): if key == 'k_points': return np.array([d[0][:3] for d\",\n \"This file is part of NOMAD. # See https://nomad-lab.eu for further info. #\",\n \"os import re import logging from nomad.units import ureg from nomad.parsing.file_parser import TextParser,\",\n \"[] for bse_type in bse_types: for scr_type in scr_types: files = self.get_exciting_files( '%s_BSE%s_SCR%s.OUT'\",\n \"return data self._quantities.append( Quantity( 'eigenvalues_occupancies', r'\\\\(state\\\\, eigenvalue and occupancy below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n *\\\\n))', str_operation=str_to_eigenvalues, repeats=True))\",\n \"= dist[:n_k_points] return self._distances @property def number_of_spin_channels(self): if self._nspin is None: self._nspin =\",\n \"f in files: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None: continue data.append(self.data_xs_parser.data) return\",\n \"None: return nspin = self.info_parser.get_number_of_spin_channels() def get_data(key): data = self.eigval_parser.get('eigenvalues_occupancies') # reshaping is\",\n \"self.archive.run[-1] def parse_configuration(section): if not section: return sec_scc = self.parse_scc(section) if sec_scc is\",\n \"len(self.distances)): if self.distances[i] == self.distances[i - 1]: self._nkpts_segment .append(count) count = 1 else:\",\n \"sec_fermisurface.x_exciting_vectors_fermi_surface = band_parameters[3] fermi_energy = self.fermisurf_parser.get('fermi_energy', None) if fermi_energy is not None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface\",\n \"DOS from one of the files bs_files = ['dos.xml', 'TDOS.OUT'] for fname in\",\n \"unit=key_unit[1], repeats=False)) module_quantities = [ Quantity( 'scf_iteration', r'(?:I| i)teration number :([\\\\s\\\\S]+?)(?:\\\\n *\\\\n\\\\+{10}|\\\\+\\\\-{10})', sub_parser=TextParser(quantities=scf_quantities),\",\n \"[1, 1, 1]) rgkmax = self.input_xml_parser.get('xs/rgkmax', None) if rgkmax is None: rgkmax =\",\n \"self.bandstructure_dat_parser.band_energies if band_energies is None: return # Get fermi energy: it is used\",\n \"__init__(self, **kwargs): super().__init__(None) self._nspin_key = 'nspin' self._totaldos_key = 'totaldos' self._partialdos_key = 'partialdos' self._diagram_key\",\n \"None: return positions = np.dot(positions, cell.magnitude) return positions * ureg.bohr def get_scf_threshold(self, name):\",\n \"= band_energies[n][nb] + energy_fermi def _parse_eigenvalues(self, sec_scc): if self.eigval_parser.get('eigenvalues_occupancies', None) is None: return\",\n \"= { 'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage': ['core leakage'], 'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge': ['interstitial'], 'x_exciting_total_MT_charge': ['total\",\n \"Workflow, GeometryOptimization from .metainfo.exciting import x_exciting_section_MT_charge_atom, x_exciting_section_MT_moment_atom,\\\\ x_exciting_section_spin, x_exciting_section_fermi_surface,\\\\ x_exciting_section_atoms_group re_float = r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?'\",\n \"self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data = np.transpose(self.data) self._neigs_segment =\",\n \"positions_format if positions_format is not None else self.get_positions_format(section) if positions_format == 'lattice': cell\",\n \"np.array(positions) positions_format = positions_format if positions_format is not None else self.get_positions_format(section) if positions_format\",\n \"reference = self.get('groundstate', self.get('hybrids', {})) return reference.get('scf_iteration', [{}])[-1].get( name, [None, None])[-1] def get_scf_quantity(self,\",\n \"= self.input_xml_parser.get( 'xs/tddft/lmaxalda', 3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment = self.input_xml_parser.get( 'xs/tddft/mdfqtype', 0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals = self.input_xml_parser.get( 'xs/tddft/nwacont',\",\n \"= 'diagram' self._l_key = 'l' self._m_key = 'm' self._energy_key = 'e' self._dos_key =\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"energy', 'xc potential'], 'energy_electrostatic': ['Hartree energy', 'Hartree'], 'x_exciting_hartree_energy': ['Hartree energy', 'Hartree'], 'x_exciting_electron_nuclear_energy': ['Electron-nuclear\",\n \"sec_method.x_exciting_xs_rgkmax = rgkmax sec_method.x_exciting_xs_scissor = self.input_xml_parser.get('xs/scissor', 0.0) sec_method.x_exciting_xs_vkloff = self.input_xml_parser.get('xs/vkloff', [0., 0., 0.])\",\n \"n_epsilon = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_energy_points = n_epsilon sec_scc.x_exciting_xs_bse_epsilon_energies = np.reshape( data[0], (n_components,\",\n \"= self.partial_dos[i].attrib.get(self._nspin_key, None) if spin is None: spin = (i % (self.number_of_spin_channels *\",\n \"self._vertices @property def number_of_spin_channels(self): if self._nspin is None: self._nspin = 1 return self._nspin\",\n \"i in range(len(self.partial_dos)): spin = self.partial_dos[i].attrib.get(self._nspin_key, None) if spin is None: spin =\",\n \"sec_gw_eigenvalues.n_kpoints = len(eigs_gw) sec_gw_eigenvalues.kpoints = get_data('k_points') sec_gw_eigenvalues.energies = reshape(eigs_gw) sec_gw_eigenvalues.value_exchange = reshape(get_data('Sx')) eigs_gw_C\",\n \"re_float = r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?' class GWInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = []\",\n \"sec_method.x_exciting_xs_screening_rgkmax = rgkmax sec_method.x_exciting_xs_screening_type = self.input_xml_parser.get( 'xs/screening/screentype', 'full') if self.input_xml_parser.get('xs/BSE') is not None:\",\n \"os.path.join(self.info_parser.maindir, default) if not os.path.isfile(filename): file_ext = default.split('.')[-1] mainfile_base = mainfile.rsplit('.', 1)[0].replace('INFO', '')\",\n \"'EPSILON' and ext == 'FXC': sec_scc = sec_run.m_create(Calculation) sec_scc.x_exciting_xs_tddft_number_of_epsilon_values = len(data[0][0][0]) sec_scc.x_exciting_xs_tddft_epsilon_energies =\",\n \"self.info_parser.get_atom_labels(section.get('atomic_positions', {})) input_file = self.get_exciting_files('input.xml') if positions is None: # get it from\",\n \"if data is None: return # Get fermi energy: it is used to\",\n \"['Madelung energy', 'Madelung'], 'x_exciting_core_electron_kinetic_energy': ['Core-electron kinetic energy', 'core electron kinetic'], 'x_exciting_dft_d2_dispersion_correction': ['DFT-D2 dispersion\",\n \"_parse_dos(self, sec_scc): if self.dos_parser.get('totaldos', None) is None: return # Get fermi energy: it\",\n \"= sec_system sec_scc.method_ref = sec_run.method[-1] return sec_scc # groundstate and hybrids calculation for\",\n \"is None: continue data.append(self.data_xs_parser.data) return data def parse_exciton(data, sec_scc): n_components = len(data) data\",\n \"input-gw.xml for f in ['input_gw.xml', 'input-gw.xml', 'input.xml']: self.parse_file(f, sec_method) xc_functional_name = ' '.join(self.info_parser.get_xc_functional_name())\",\n \"band_energy = band_energy * self._energy_unit res_n.append(band_energy) start = end res.append(res_n) elif key ==\",\n \"def get_xc_functional_name(self): # TODO expand list to include other xcf xc_functional_map = {\",\n \"(self._partialdos_key, self._diagram_key)) return self._partial_dos @property def energies(self): if self._energies is None: if self.total_dos\",\n \"* li + 1 return self._nlm @property def total_dos(self): if self._total_dos is None:\",\n \"*: *({re_float})', dtype=np.float64, unit=ureg.bohr), Quantity('radial_points', rf'radial points in muffin-tin *: *({re_float})', dtype=np.int32), Quantity('positions_format',\",\n \"_parse_bandstructure_dat(self, sec_scc): self.bandstructure_dat_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_dat_parser.band_energies if band_energies is None: return\",\n \"self.data_xs_parser = DataTextParser() self.data_clathrate_parser = DataTextParser(dtype=str) # different names for different versions of\",\n \"'x_exciting_lo': (r'Total number of local\\\\-orbitals', None)} self._method_keys_mapping = { 'smearing_kind': ('Smearing scheme', None),\",\n \"sec_scc.x_exciting_xs_bse_number_of_components = n_components n_excitons = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_excitons = n_excitons sec_scc.x_exciting_xs_bse_exciton_energies =\",\n \"r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False,\",\n \"* ureg.hartree + energy_fermi volume = self.info_parser.get_unit_cell_volume() dos = data[1] * (1 /\",\n \"= kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None self._distances\",\n \"val[n][0] sec_mt_charge_atom.x_exciting_MT_charge_atom_value = val[n][1] sec_charges = msection.m_create(Charges) sec_charges.value = [ val[n][1].magnitude for n\",\n \") from nomad.datamodel.metainfo.workflow import Workflow, GeometryOptimization from .metainfo.exciting import x_exciting_section_MT_charge_atom, x_exciting_section_MT_moment_atom,\\\\ x_exciting_section_spin, x_exciting_section_fermi_surface,\\\\\",\n \"parse properties gw_info_files = self.get_exciting_files(gw_info_file) if len(gw_info_files) > 1: self.logger.warn('Found multiple GW info\",\n \"in self._method_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) initialization_quantities.append(Quantity( 'species',\",\n \"'xs/tetra/tetradf', False) def _parse_xs_bse(self): sec_run = self.archive.run[-1] # TODO read from xml file\",\n \"self.info_parser = ExcitingInfoParser() self.dos_parser = DOSXMLParser(energy_unit=ureg.hartree) self.bandstructure_parser = BandstructureXMLParser(energy_unit=ureg.hartree) self.eigval_parser = ExcitingEigenvalueParser() self.fermisurf_parser\",\n \"self.bandstructure_dat_parser.band_k_points nkpts_segment = self.bandstructure_dat_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints =\",\n \"EnergyEntry(value=energy_total) x_exciting_dos_fermi = iteration.get('x_exciting_dos_fermi') if x_exciting_dos_fermi is not None: setattr(msection, 'x_exciting_dos_fermi', x_exciting_dos_fermi) #\",\n \"I am not certain about the format for the spin polarized case #\",\n \"self._parse_eigenvalues elif (name.startswith('FERMISURF') or name.startswith('FS')) and name.endswith('bxsf'): parser = self.fermisurf_parser parser_function = self._parse_fermisurface\",\n \"sec_scc.method_ref = sec_run.method[-1] return sec_scc # groundstate and hybrids calculation for module in\",\n \") self._quantities.append( Quantity( 'fundamental_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'optical_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s',\",\n \"input_file = self.get_exciting_files('input.xml') if not input_file: return self.input_xml_parser.mainfile = input_file[0] xstype = self.input_xml_parser.get('xs/xstype',\",\n \"setattr(msection, key, val) # convergence values for name in self.info_parser._convergence_keys_mapping.keys(): val = iteration.get(name)\",\n \"def parse_miscellaneous(self): sec_worfklow = self.archive.m_create(Workflow) sec_worfklow.type = 'single_point' structure_optimization = self.info_parser.get('structure_optimization') if structure_optimization\",\n \"= sec_method.m_create(Electronic) sec_electronic.method = 'DFT' smearing_kind_map = { 'Gaussian': 'gaussian', 'Methfessel-Paxton': 'methfessel-paxton', 'Fermi-Dirac':\",\n \"def init_parameters(self): self._nspin = None self._distances = None self._band_energies = None self._neigs_segment =\",\n \"is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies\",\n \"final = section if section.get('energy_total') is not None else section.get('final') if final is\",\n \"[ 'TET' if tetradf else None, 'AC' if nwacont else None, 'NAR' if\",\n \"in files: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None: continue data.append(self.data_xs_parser.data) return data\",\n \"Fermi energy \\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=1 / ureg.hartree), Quantity( 'charge_contributions', r'(?:Charges|Electron charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]',\",\n \"in compliance with the License. # You may obtain a copy of the\",\n \"structure_optimization = self.info_parser.get('structure_optimization') if structure_optimization is not None: sec_worfklow.type = 'geometry_optimization' sec_geometry_opt =\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"nkpts_segment = self.band_out_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb]\",\n \"None: self._nkpts_segment = [] count = 1 for i in range(1, len(self.distances)): if\",\n \"sec_scc.x_exciting_xs_bse_exciton_amplitude_im = np.reshape( data[5], (n_components, n_excitons)) def parse_epsilon(data, sec_scc): n_components = len(data) data\",\n \"None: return self._energies = np.array( [float(point.attrib.get(self._energy_key)) for point in self.total_dos[0]]) if self.energy_unit is\",\n \"self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_screening_rgkmax = rgkmax sec_method.x_exciting_xs_screening_type = self.input_xml_parser.get( 'xs/screening/screentype', 'full') if self.input_xml_parser.get('xs/BSE') is\",\n \"species = self.get_initialization_parameter('species', []) for specie in species: positions_format = specie.get('positions_format', None) if\",\n \"None: sec_gap.value_optical = optical_band_gap def parse_miscellaneous(self): sec_worfklow = self.archive.m_create(Workflow) sec_worfklow.type = 'single_point' structure_optimization\",\n \"atom_labels = [] for n in range(len(atoms)): atom_labels.extend([species[n].split('.')[0]] * len(atoms[n])) if positions is\",\n \"return self._distances @property def number_of_spin_channels(self): if self._nspin is None: self._nspin = np.shape(np.transpose(self.data))[0] -\",\n \"repeats=False, convert=False), Quantity( 'moment_contributions', r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False)] self._miscellaneous_keys_mapping = { 'x_exciting_gap':\",\n \"in which also other energies are reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is\",\n \"range(len(self.number_of_k_points_per_segment)): start = self.vertices[i].attrib.get(self._coord_key).split() end = self.vertices[i + 1].attrib.get(self._coord_key).split() res.append([start, end]) else: res\",\n \"'spin-unpolarised' else 2 return n_spin def get_unit_cell_volume(self): return self.get('initialization', {}).get('x_exciting_unit_cell_volume', 1.0 * ureg.bohr\",\n \"('Smearing width', None)} for name, key_unit in self._system_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' %\",\n \"data: sccs.append(None) continue if quantity == 'EXCITON': sec_scc = sec_run.m_create(Calculation) sccs.append(sec_scc) else: sec_scc\",\n \"self._calculation_type == 'gw': return sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref\",\n \"= mainfile.strip('INFO.OUT') target = filename.rsplit('.', 1) filepath = '%s%s' % (target[0], suffix) if\",\n \"= np.transpose(self.data) self._distances = data[5][:int(max(data[1]))] return self._distances @property def number_of_spin_channels(self): if self._nspin is\",\n \"= np.array(positions) positions_format = positions_format if positions_format is not None else self.get_positions_format(section) if\",\n \"class BandOutParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit',\",\n \"self.evalqp_parser parser_function = self._parse_evalqp elif name.startswith('TDOS') and name.endswith('OUT'): parser = self.dos_out_parser parser_function =\",\n \"def number_of_atoms(self): if self._natoms is None: partial_dos = self.root.findall('./%s' % self._partialdos_key) self._natoms =\",\n \"('Total valence charge', ureg.elementary_charge), 'x_exciting_wigner_radius': (r'Effective Wigner radius, r\\\\_s', ureg.bohr), 'x_exciting_empty_states': ('Number of\",\n \"@property def vertices(self): if self._vertices is None: self._vertices = self.root.findall('./%s' % self._vertex_key) return\",\n \"return labels def get_positions_format(self, section): positions_format = section.get('positions_format') if positions_format is None: species\",\n \"self._quantities.append( Quantity( 'direct_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'fundamental_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False)\",\n \"l_list = set([int(e.attrib.get(self._l_key)) for e in self.partial_dos]) for li in l_list: self._nlm +=\",\n \"positions_format=positions_format).to('m') sec_atoms_group.x_exciting_geometry_atom_positions = positions.magnitude # clathrate info clathrate_file = self.get_exciting_files('str.out') if clathrate_file: sec_system.x_exciting_clathrates\",\n \"self.archive.run[-1] positions = self.info_parser.get_atom_positions(section.get('atomic_positions', {})) lattice_vectors = self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels = self.info_parser.get_atom_labels(section.get('atomic_positions', {})) input_file\",\n \"this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds', unit=ureg.s, repeats=False, dtype=float) ] self._quantities.append(Quantity( 'structure_optimization', r'Structure\\\\-optimization module started([\\\\s\\\\S]+?)Structure\\\\-optimization module\",\n \"in range(len(partialdos[lm])): for atom in range(len(partialdos[lm][spin])): sec_dos_values = sec_dos.m_create(DosValues, Dos.atom_projected) sec_dos_values.m_kind = 'spherical'\",\n \"len(self.total_dos) return self._nspin @property def number_of_atoms(self): if self._natoms is None: partial_dos = self.root.findall('./%s'\",\n \"data[1:] elif quantity == 'EPSILON' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field = data[1:3] elif\",\n \"= band_energies[nb] + energy_fermi def _parse_band_out(self, sec_scc): self.band_out_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.band_out_parser.band_energies\",\n \"sec_scc = sec_run.m_create(Calculation) if quantity == 'EXCITON': parse_function = parse_exciton elif quantity ==\",\n \"[e.attrib.get(self._energy_key) for e in self.bands[n][i]]) for nkpts_segment in self.number_of_k_points_per_segment: end = start +\",\n \"self.data_clathrate_parser.mainfile = clathrate_file[0] if self.data_clathrate_parser.data: data = np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates = np.transpose( np.array(data[:3], dtype=float))\",\n \"units of energy sec_smearing.width = smearing_width.magnitude for name in self.info_parser._convergence_keys_mapping.keys(): threshold = self.info_parser.get_scf_threshold(name)\",\n \"if positions_format is not None else self.get_positions_format(section) if positions_format == 'lattice': cell =\",\n \"dont have the quantity # the only thing that we can do is\",\n \"fname in bs_files: if self.file_exists(fname): gw_files.append(fname) break for f in gw_files: self.parse_file(f, sec_scc)\",\n \"not data[0]: continue if quantity == 'EPSILON' and ext == 'FXC': sec_scc =\",\n \"x_exciting_dos_fermi is not None: setattr(msection, 'x_exciting_dos_fermi', x_exciting_dos_fermi) # energy contributions energy_contributions = iteration.get('energy_contributions',\",\n \"assume it is # energy dos_up dos_down nspin = self.info_parser.get_number_of_spin_channels() sec_dos = sec_scc.m_create(Dos,\",\n \"elif key == 'band_segm_labels': res = [] for i in range(len(self.vertices) - 1):\",\n \"if self.energy_unit is not None: res = res * (1 / self.energy_unit) elif\",\n \"Quantity('symbol', r'\\\\((\\\\w+)\\\\)'), Quantity('file', r'parameters loaded from *: *(.+)'), Quantity('name', r'name *: *(.+)'), Quantity('nuclear_charge',\",\n \"= sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = len(data) // nspin data = np.reshape(data, (nspin, len(data)\",\n \"is not None: sec_scc.x_exciting_maximum_force_magnitude = force_convergence[0] sec_scc.x_exciting_geometry_optimization_threshold_force = force_convergence[1] sec_scc = parse_configuration(structure_optimization) if\",\n \"' '.join([str(v) for v in val])) else: try: setattr(sec_system, name, val) except Exception:\",\n \"= kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None self._distances = None self._band_energies =\",\n \"self.get_exciting_files('INFOXS.OUT') if not xs_info_files: return self._calculation_type = 'xs' # inconsistency in the naming\",\n \"name in names: val = energy_contributions.get(name, None) if val is not None: break\",\n \"forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr) ] self._quantities.append(Quantity(\",\n \"self.input_xml_parser.get( 'xs/BSE/aresbse', True) sec_method.x_exciting_xs_bse_angular_momentum_cutoff = self.input_xml_parser.get( 'xs/BSE/lmaxdielt', 14) rgkmax = self.input_xml_parser.get('xs/BSE/rgkmax', None) if\",\n \"self._bands @property def vertices(self): if self._vertices is None: self._vertices = self.root.findall('./%s' % self._vertex_key)\",\n \"if cell is None: return positions = np.dot(positions, cell.magnitude) return positions * ureg.bohr\",\n \"elif name.startswith('EVALQP') and (name.endswith('DAT') or name.endswith('TXT')): parser = self.evalqp_parser parser_function = self._parse_evalqp elif\",\n \"= get_data(files[i]) if not data: sccs.append(None) continue if quantity == 'EXCITON': sec_scc =\",\n \"'\\\\u0393' if start.lower() == 'gamma' else start, '\\\\u0393' if end.lower() == 'gamma' else\",\n \"self._vertex_key) return self._vertices @property def number_of_spin_channels(self): if self._nspin is None: self._nspin = 1\",\n \"nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy = np.array([np.transpose(band)[start:end] for band\",\n \"= self.input_xml_parser.get( 'gw/freqgrid/freqmax', 1.0) sec_gw.frequency_grid_type = self.input_xml_parser.get( 'gw/freqgrid/fgrid', 'gaule2') sec_gw.number_of_frequencies = int(self.input_xml_parser.get( 'gw/freqgrid/nomeg',\",\n \"not None: sec_energy.total = EnergyEntry(value=energy_total) x_exciting_dos_fermi = iteration.get('x_exciting_dos_fermi') if x_exciting_dos_fermi is not None:\",\n \"'RPA') tetradf = self.input_xml_parser.get('xs/tetra/tetradf', None) nwacont = self.input_xml_parser.get('xs/tddft/nwacont', None) aresdf = self.input_xml_parser.get('xs/tddft/aresdf', True)\",\n \"for scf_iteration in scf_iterations: sec_scf_iteration = sec_scc.m_create(ScfIteration) parse_scf(scf_iteration, sec_scf_iteration) return sec_scc def parse_system(self,\",\n \"self._nkpts_segment = None self._neigs_segment = None self._vertices = None self._distances = None self._band_energies\",\n \"Quantity('name', r'name *: *(.+)'), Quantity('nuclear_charge', rf'nuclear charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('electronic_charge', rf'electronic\",\n \"files = self.get_exciting_files('%s_%s%s%s.OUT' % (quantity, file_ext, ext, fxctype)) data = [[], [], []]\",\n \"xc_functional_names.append(exchange) sec_xc_functional = sec_dft.m_create(XCFunctional) for name in xc_functional_names: if name is None: continue\",\n \"ureg.bohr), 'x_exciting_empty_states': ('Number of empty states', None), 'x_exciting_valence_states': ('Total number of valence states',\",\n \"sec_atoms_group.x_exciting_atom_position_format = positions_format positions = specie.get('positions') positions = self.info_parser.get_atom_positions( positions=positions, positions_format=positions_format).to('m') sec_atoms_group.x_exciting_geometry_atom_positions =\",\n \"'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field = data[1:3] elif quantity == 'LOSS' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_loss_function_local_field\",\n \"r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\. ]+)\\\\s*started', repeats=False, dtype=str, flatten=False)] initialization_quantities = [ Quantity( 'lattice_vectors', r'Lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array,\",\n \"is not None: sec_scc.energy = Energy(fermi=fermi_energy) gw_files = ['EVALQP.DAT', 'EVALQP.TXT', 'TDOS-QP.OUT'] # Parse\",\n \"= start + nkpts_segment band_energy = np.array([np.transpose(band)[start:end] for band in bands]) if self._energy_unit:\",\n \"should have units of energy sec_smearing.width = smearing_width.magnitude for name in self.info_parser._convergence_keys_mapping.keys(): threshold\",\n \"section): positions_format = section.get('positions_format') if positions_format is None: species = self.get_initialization_parameter('species', []) for\",\n \"data_q = np.transpose(data_q, axes=(2, 0, 1)) for j in range(len(data)): data[j].append(data_q[j]) return data\",\n \"if val is None: continue if key == 'x_exciting_section_MT_moment_atom': for n in range(len(val)):\",\n \"rgkmax = self.input_xml_parser.get('xs/BSE/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0) sec_method.x_exciting_xs_bse_rgkmax\",\n \"is None: # get it from input.xml for f in input_file: self.input_xml_parser.mainfile =\",\n \"iteration.get('moment_contributions', {}) for key, names in self._moment_keys_mapping.items(): val = None for name in\",\n \"continue energy_fermi = energy_fermi.to(\\\"hartree\\\") sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points =\",\n \"self.band_out_parser.band_energies if band_energies is None: return # Get fermi energy: it is used\",\n \"return n_spin def get_unit_cell_volume(self): return self.get('initialization', {}).get('x_exciting_unit_cell_volume', 1.0 * ureg.bohr ** 3) def\",\n \"None) for d in data]) if None in energy: return energy return np.array([d[1].get(key)\",\n \"= sec_scc.m_create(Forces) sec_forces.total = ForcesEntry(value=forces) # scf iterations scf_iterations = section.get('scf_iteration', []) for\",\n \"ureg.bohr)} for name, key_unit in self._convergence_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)' % key_unit[0], str_operation=str_to_quantity_tolerances,\",\n \"is None: return self._energy_unit = self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1) return self._energy_unit @property def number_of_spin_channels(self): if\",\n \"parse_sigma(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_sigma = len(data[0]) // n_components\",\n \"* 3 # TODO confirm no cell optimization in exciting sec_atoms.lattice_vectors = lattice_vectors\",\n \"'%s%s' % (target[0], suffix) if target[1:]: filename = '%s.%s' % (filename, target[1]) filename\",\n \"# to be what they are. What is the fourth column in epsilon\",\n \"name, key_unit in self._miscellaneous_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)' % key_unit[0], repeats=False, unit=key_unit[1])) self._convergence_keys_mapping =\",\n \"np.reshape( data[0], (n_components, n_sigma)) * ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re = np.reshape( data[1], (n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im\",\n \"'l' self._m_key = 'm' self._energy_key = 'e' self._dos_key = 'dos' self._unit_key = 'unit'\",\n \"* unit properties['atom_resolved'] = atom_resolved return properties def str_to_quantity_tolerances(val_in): return val_in.strip().replace('(', '').replace(')', '').split()\",\n \"in self._miscellaneous_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)' % key_unit[0], repeats=False, unit=key_unit[1])) self._convergence_keys_mapping = { 'x_exciting_effective_potential_convergence':\",\n \"val = iteration.get(name) if val is None: continue if name == 'time': msection.time_calculation\",\n \"len(partial_dos) return self._natoms @property def number_of_dos(self): if self._ndos is None: total_dos = self.root.find('./%s/%s'\",\n \"= self.logger def reuse_parser(self, parser): self.info_parser.quantities = parser.info_parser.quantities self.eigval_parser.quantities = parser.eigval_parser.quantities self.fermisurf_parser.quantities =\",\n \"== 3], float)) return dict( number=np.array(val[0], dtype=int), values=val[1] * ureg.hartree, weights=val[2]) # TODO\",\n \"val: v = v.split(':') if len(v) < 2: continue energies[v[0].strip()] = float(v[1]) *\",\n \"= self._parse_input_xs else: # TODO implement reading of parameters from input.xml for normal\",\n \"if target[1:]: filename = '%s.%s' % (filename, target[1]) filename = os.path.join(self.info_parser.maindir, filename) if\",\n \"threshold = self.info_parser.get_scf_threshold(name) if threshold is None: continue metainfo_name = 'x_exciting_scf_threshold_%s_change' % name.split('_')[-2]\",\n \"self.partial_dos[i].attrib.get(self._nspin_key, None) if spin is None: spin = (i % (self.number_of_spin_channels * self.number_of_lm))\",\n \"sec_electronic.n_spin_channels = self.info_parser.get_number_of_spin_channels() if self._calculation_type == 'volume_optimization': sec_method.x_exciting_volume_optimization = True def parse_scc(self, section):\",\n \"however nspin is # determined from occupancies which is problematic sometimes res =\",\n \"% self.number_of_lm else: lm = int(val_l) ** 2 + int(val_m) + int(val_l) atom\",\n \"r'(?:I| i)teration number :([\\\\s\\\\S]+?)(?:\\\\n *\\\\n\\\\+{10}|\\\\+\\\\-{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=True), Quantity( 'final', r'(?:Convergence targets achieved\\\\. Performing\",\n \"else: setattr(msection, name, val) # energy, moment, charge contributions parse_scf(final, sec_scc) # forces\",\n \"energy_fermi = sec_scc.energy.fermi if energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree')\",\n \"self._quantities.append( Quantity( 'fermi_energy', r'Fermi Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*', unit=ureg.hartree, repeats=False)) def str_to_band_parameters(val_in): val = val_in.strip().split('\\\\n') nbands\",\n \"potential_mixing = self.info_parser.get_initialization_parameter('potential_mixing') if potential_mixing is not None: sec_system.x_exciting_potential_mixing = potential_mixing return sec_system\",\n \"'energy_coulomb': ['Coulomb energy', 'Coulomb'], 'x_exciting_coulomb_energy': ['Coulomb energy', 'Coulomb'], 'energy_exchange': ['Exchange energy', 'exchange'], 'x_exciting_exchange_energy':\",\n \"stored. \\\"\\\"\\\" mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = filename.rsplit('.', 1) filepath\",\n \"= msection.m_create(Energy) if energy_total is not None: sec_energy.total = EnergyEntry(value=energy_total) x_exciting_dos_fermi = iteration.get('x_exciting_dos_fermi')\",\n \"% self._atom_key) for band in bands_atom: self._bands.append(band.findall('./%s' % self._band_key)) return self._bands @property def\",\n \"= self.logger self.bandstructure_dat_parser.logger = self.logger self.band_out_parser.logger = self.logger self.info_gw_parser.logger = self.logger self.input_xml_parser.logger =\",\n \"res * ureg.hartree return res sec_eigenvalues = sec_scc.m_create(BandEnergies) sec_eigenvalues.kpoints = self.eigval_parser.get('k_points') sec_eigenvalues.occupations =\",\n \"filename = '%s.%s' % (filename, target[1]) filename = os.path.join(self.info_parser.maindir, filename) if os.path.isfile(filename): return\",\n \"optimization_step.get('method') if optimization_step.get('step') is not None: sec_scc.x_exciting_geometry_optimization_step = optimization_step.get('step') force_convergence = optimization_step.get('force_convergence') if\",\n \"1 return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment = []\",\n \"not None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface = band_parameters[0] sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface = np.product(band_parameters[1]) sec_fermisurface.x_exciting_grid_fermi_surface = band_parameters[1] sec_fermisurface.x_exciting_origin_fermi_surface =\",\n \"r'DOS at Fermi energy \\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=1 / ureg.hartree), Quantity( 'charge_contributions', r'(?:Charges|Electron\",\n \"val is None: continue setattr(msection, name, val) # other metainfo for name in\",\n \"= self.filepath self.info_parser.logger = self.logger self.dos_parser.logger = self.logger self.bandstructure_parser.logger = self.logger self.eigval_parser.logger =\",\n \"data', data=dict(file=quantity)) def _parse_xs_tddft(self): sec_run = self.archive.run[-1] fxctype = self.input_xml_parser.get('xs/tddft/fxctype', 'RPA') tetradf =\",\n \"potential energy', 'Coulomb potential'], 'energy_xc_potential': ['xc potential energy', 'xc potential'], 'energy_electrostatic': ['Hartree energy',\",\n \"f in input_file: self.input_xml_parser.mainfile = f correlation = self.input_xml_parser.get('libxc/correlation', None) xc_functional_names.append(correlation) exchange =\",\n \"class ExcitingEigenvalueParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'k_points',\",\n \"'full') if self.input_xml_parser.get('xs/BSE') is not None: sec_method.x_exciting_xs_bse_antiresonant = self.input_xml_parser.get( 'xs/BSE/aresbse', True) sec_method.x_exciting_xs_bse_angular_momentum_cutoff =\",\n \"def reshape(data): if data[0] is None: return return np.reshape(data, (nspin, len(data) // nspin,\",\n \"[float(vii) for vii in v[1].split()] vi = vi[0] if len(vi) == 1 else\",\n \"forces = section.get('forces') if forces is not None: sec_forces = sec_scc.m_create(Forces) sec_forces.total =\",\n \"Quantity( 'energy_total', r'[Tt]*otal energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=ureg.hartree), Quantity( 'energy_contributions', r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n *(?:DOS|Density)', str_operation=str_to_energy_dict, repeats=False,\",\n \"key.replace('energy_', '')), EnergyEntry(value=val)) else: setattr(msection, key, val) if key == 'x_exciting_fermi_energy': sec_energy.fermi =\",\n \"n_spin def get_unit_cell_volume(self): return self.get('initialization', {}).get('x_exciting_unit_cell_volume', 1.0 * ureg.bohr ** 3) def get_initialization_parameter(self,\",\n \"given filename exists and is accessible in the same folder where the mainfile\",\n \"potential and density', 1 / ureg.bohr), 'x_exciting_gvector_size': (r'G\\\\-vector grid sizes', None), 'x_exciting_gvector_total': (r'Total\",\n \"sec_geometry_opt.input_force_maximum_tolerance = threshold_force def parse_method(self): sec_run = self.archive.run[-1] sec_method = sec_run.m_create(Method) sec_method.basis_set.append(BasisSet(type='(L)APW+lo')) sec_dft\",\n \"convergence values for name in self.info_parser._convergence_keys_mapping.keys(): val = iteration.get(name) if val is None:\",\n \"column in epsilon which is not parsed? sccs = [] for quantity in\",\n \"name in names: val = moment_contributions.get(name, None) if val is not None: break\",\n \"add data to scc # TODO add support for more output files and\",\n \"which also other energies are reported. energy_fermi = 0.0 * ureg.hartree if sec_scc.energy\",\n \"is None: continue if key == 'x_exciting_section_MT_charge_atom': for n in range(len(val)): sec_mt_charge_atom =\",\n \"NOMAD Authors. # # This file is part of NOMAD. # See https://nomad-lab.eu\",\n \"accessible in the same folder where the mainfile is stored. \\\"\\\"\\\" mainfile =\",\n \"sec_scc.x_exciting_maximum_force_magnitude = force_convergence[0] sec_scc.x_exciting_geometry_optimization_threshold_force = force_convergence[1] sec_scc = parse_configuration(structure_optimization) if sec_scc is None:\",\n \"positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'imized atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity(\",\n \"= charge_contributions.get(name, None) if val is not None: break if val is None:\",\n \"[f for f in files if f.endswith('QMT%s.OUT' % str(i + 1).rjust(3, '0'))] for\",\n \"sccs.append(None) continue if quantity == 'EXCITON': sec_scc = sec_run.m_create(Calculation) sccs.append(sec_scc) else: sec_scc =\",\n \"sec_method.x_exciting_xs_bse_xasatom = self.input_xml_parser.get( 'xs/BSE/xasatom', 0) sec_method.x_exciting_xs_bse_xasedge = self.input_xml_parser.get( 'xs/BSE/xasedge', 'K') sec_method.x_exciting_xs_bse_xasspecies = self.input_xml_parser.get(\",\n \"ureg.bohr ** 3) def get_initialization_parameter(self, key, default=None): return self.get('initialization', {}).get(key, default) class ExcitingParser:\",\n \"# parse properties gw_info_files = self.get_exciting_files(gw_info_file) if len(gw_info_files) > 1: self.logger.warn('Found multiple GW\",\n \"return self._distances @property def number_of_spin_channels(self): if self._nspin is None: self._nspin = np.shape(np.transpose(self.data)[1:])[0] return\",\n \"name.startswith('TDOS') and name.endswith('OUT'): parser = self.dos_out_parser parser_function = self._parse_dos_out elif name.startswith('bandstructure') and name.endswith('dat'):\",\n \"20: ['GGA_C_PBE', 'GGA_X_PBE'], 21: ['GGA_C_PBE', 'GGA_X_PBE_R'], 22: ['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'], 26: ['GGA_C_PBE', 'GGA_X_WC'], 30:\",\n \"if data is None: return def get_data(key): if key == 'k_points': return np.array([d[0][:3]\",\n \"None, 'NAR' if not aresdf else None] file_ext = '_'.join([e for e in\",\n \"f in ['input_gw.xml', 'input-gw.xml', 'input.xml']: self.parse_file(f, sec_method) xc_functional_name = ' '.join(self.info_parser.get_xc_functional_name()) sec_method.gw.starting_point =\",\n \"self.eigval_parser.logger = self.logger self.fermisurf_parser.logger = self.logger self.evalqp_parser.logger = self.logger self.dos_out_parser.logger = self.logger self.bandstructure_dat_parser.logger\",\n \"'LOSS']: files = get_files(quantity) for i in range(len(files)): data = get_data(files[i]) if not\",\n \"Parse DFT DOS from one of the files bs_files = ['dos.xml', 'TDOS.OUT'] for\",\n \"Exception: self.logger.warn('Error setting metainfo.') # species species = self.info_parser.get_initialization_parameter('species', []) for specie in\",\n \"def _parse_xs_bse(self): sec_run = self.archive.run[-1] # TODO read from xml file def get_files(name):\",\n \"is not None: sec_scc.system_ref = sec_system sec_scc.method_ref = sec_run.method[-1] return sec_scc # groundstate\",\n \"== 'bse': self._parse_xs_bse() elif xstype.lower() == 'tddft': self._parse_xs_tddft() def _parse_input_gw(self, sec_method): sec_gw =\",\n \"= os.path.join(self.info_parser.maindir, filename) if os.path.isfile(filename): return [filename] filename = os.path.join(self.info_parser.maindir, default) if not\",\n \"key_unit in self._convergence_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)' % key_unit[0], str_operation=str_to_quantity_tolerances, unit=key_unit[1], repeats=False)) module_quantities\",\n \"self._results is None: self._results = dict() if not self.bands: return if key ==\",\n \"'EPSILON', 'SIGMA', 'LOSS']: files = get_files(quantity) for i in range(len(files)): data = get_data(files[i])\",\n \"'xs/BSE/sciavbd', True) sec_method.x_exciting_xs_bse_sciavqbd = self.input_xml_parser.get( 'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg\",\n \") initialization_quantities.append(Quantity( 'species', rf'(Species : *\\\\d+ *\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float} *{re_float} *{re_float}\\\\n\\\\s*\\\\n)', repeats=True, sub_parser=TextParser(quantities=[ Quantity('number', r'Species\",\n \"'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree /\",\n \"self.file_exists(fname): exciting_files.append(fname) break for f in exciting_files: self.parse_file(f, sec_scc) # structure optimization structure_optimization\",\n \"r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr)\",\n \"else None, 'AC' if nwacont else None, 'NAR' if not aresdf else None]\",\n \"self._units_mapping = dict(hartree=ureg.hartree) def init_parameters(self): self._ndos = None self._natoms = None self._nspin =\",\n \"'input-gw.xml', 'input.xml']: self.parse_file(f, sec_method) xc_functional_name = ' '.join(self.info_parser.get_xc_functional_name()) sec_method.gw.starting_point = xc_functional_name sec_scc =\",\n \"return sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref]\",\n \"['bandstructure-qp.dat', 'BAND-QP.OUT'] for fname in bs_files: if self.file_exists(fname): gw_files.append(fname) break for f in\",\n \"'%s.%s' % (filepath, target[1]) filepath = os.path.join(self.info_parser.maindir, filepath) if os.path.isfile(filepath) and os.access(filepath, os.F_OK):\",\n \"self._energy_unit res_n.append(band_energy) start = end res.append(res_n) elif key == 'band_k_points': res = []\",\n \"'x_exciting_section_MT_charge_atom': ['atom_resolved'] } self._moment_keys_mapping = { 'x_exciting_interstitial_moment': ['interstitial'], 'x_exciting_total_MT_moment': ['total moment in muffin-tins'],\",\n \"def band_energies(self): if self._band_energies is None: data = np.transpose(self.data) n_kpoints = np.where(data[0] ==\",\n \"np.dot(positions, cell.magnitude) return positions * ureg.bohr def get_scf_threshold(self, name): reference = self.get('groundstate', self.get('hybrids',\",\n \"* ureg.hartree if sec_scc.energy is not None: energy_fermi = sec_scc.energy.fermi energy_fermi = (energy_fermi.magnitude\",\n \"None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0) sec_method.x_exciting_xs_bse_rgkmax = rgkmax sec_method.x_exciting_xs_bse_sciavbd\",\n \"force magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)', str_operation=str_to_quantity_tolerances, unit=ureg.hartree / ureg.bohr, repeats=False, dtype=float), Quantity( 'energy_total', r'Total energy\",\n \"'x_exciting_effective_potential_convergence': ( r'RMS change in effective potential \\\\(target\\\\)', ureg.hartree), 'x_exciting_energy_convergence': ( r'Absolute change\",\n \"in muffin-tins'], 'charge_total': ['total charge'], 'x_exciting_section_MT_charge_atom': ['atom_resolved'] } self._moment_keys_mapping = { 'x_exciting_interstitial_moment': ['interstitial'],\",\n \": *(\\\\d+)', dtype=np.int32), Quantity('symbol', r'\\\\((\\\\w+)\\\\)'), Quantity('file', r'parameters loaded from *: *(.+)'), Quantity('name', r'name\",\n \"self._bands.append(band.findall('./%s' % self._band_key)) return self._bands @property def vertices(self): if self._vertices is None: self._vertices\",\n \"sec_method.x_exciting_xs_number_of_empty_states = self.input_xml_parser.get( 'xs/nempty', 5) sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq', [1, 1, 1]) sec_method.x_exciting_xs_ngridk =\",\n \"( Method, DFT, Electronic, Smearing, XCFunctional, Functional, GW as GWMethod, Scf, BasisSet )\",\n \"supposed # to be what they are. What is the fourth column in\",\n \"Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces', r'Total\",\n \"sec_atoms.periodic = [True] * 3 # TODO confirm no cell optimization in exciting\",\n \"for v in val_in.split('\\\\n')] val = np.transpose(np.array([v for v in val if len(v)\",\n \"f in files_q: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None: continue data_q.append(self.data_xs_parser.data) if\",\n \"in input_file: self.input_xml_parser.mainfile = f positions = self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors = self.input_xml_parser.get( 'structure/crystal/basevect', np.eye(3))\",\n \"0.) sec_method.x_exciting_xs_screening_rgkmax = rgkmax sec_method.x_exciting_xs_screening_type = self.input_xml_parser.get( 'xs/screening/screentype', 'full') if self.input_xml_parser.get('xs/BSE') is not\",\n \"== 'spin-unpolarised' else 2 return n_spin def get_unit_cell_volume(self): return self.get('initialization', {}).get('x_exciting_unit_cell_volume', 1.0 *\",\n \"str_to_array(val_in): val = [v.split(':')[-1].split() for v in val_in.strip().split('\\\\n')] val = val[0] if len(val)\",\n \"for name, key_unit in self._convergence_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)' % key_unit[0], str_operation=str_to_quantity_tolerances, unit=key_unit[1],\",\n \"sec_run.m_create(Method) sec_method.basis_set.append(BasisSet(type='(L)APW+lo')) sec_dft = sec_method.m_create(DFT) sec_electronic = sec_method.m_create(Electronic) sec_electronic.method = 'DFT' smearing_kind_map =\",\n \"self._vertices = None self._distances = None self._band_energies = None self._band_k_points = None @property\",\n \"= specie.get('positions') positions = self.info_parser.get_atom_positions( positions=positions, positions_format=positions_format).to('m') sec_atoms_group.x_exciting_geometry_atom_positions = positions.magnitude # clathrate info\",\n \"fermi_energy sec_fermisurface.x_exciting_values_fermi_surface = fermi_surface def _parse_evalqp(self, sec_scc): data = self.evalqp_parser.get('kpoints_eigenvalues') if data is\",\n \"nkpts_segment[nb] sec_k_band_segment.value = band_energies[nb] + energy_fermi def parse_file(self, name, section): # TODO add\",\n \"sec_fermisurface.x_exciting_origin_fermi_surface = band_parameters[2] sec_fermisurface.x_exciting_vectors_fermi_surface = band_parameters[3] fermi_energy = self.fermisurf_parser.get('fermi_energy', None) if fermi_energy is\",\n \"None or lattice_vectors is None or species is None: continue lattice_vectors = np.array(lattice_vectors,\",\n \"return name @property def n_optimization_steps(self): return len(self.get('structure_optimization', {}).get('optimization_step', [])) def get_number_of_spin_channels(self): spin_treatment =\",\n \"== 'x_exciting_section_MT_moment_atom': for n in range(len(val)): sec_mt_moment_atom = msection.m_create(x_exciting_section_MT_moment_atom) sec_mt_moment_atom.x_exciting_MT_moment_atom_index = n +\",\n \"len(self.get('energy_total_scf_iteration', [])) quantity = self.get('%s_scf_iteration' % name) if quantity is None: return #\",\n \"nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment self._band_k_points.append( np.transpose(data[2:5])[start:end]) start = end\",\n \"dtype=float) lattice_vectors *= self.input_xml_parser.get('structure/crystal/scale', 1.0) positions = np.dot(positions, lattice_vectors) * ureg.bohr lattice_vectors =\",\n \"filenames = [os.path.join(self.info_parser.maindir, f) for f in options] else: filenames = [filename] filenames\",\n \"atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr), ]),\",\n \"['Coulomb energy', 'Coulomb'], 'x_exciting_coulomb_energy': ['Coulomb energy', 'Coulomb'], 'energy_exchange': ['Exchange energy', 'exchange'], 'x_exciting_exchange_energy': ['Exchange\",\n \"muffin-tins'], 'x_exciting_total_moment': ['total moment'], 'x_exciting_section_MT_moment_atom': ['atom_resolved'] } def get_exciting_files(self, default): mainfile = os.path.basename(self.info_parser.mainfile)\",\n \"and name.endswith('xml'): parser = self.dos_parser parser_function = self._parse_dos elif name.startswith('bandstructure') and name.endswith('xml'): parser\",\n \"[] for i in range(len(self.vertices) - 1): start = self.vertices[i].attrib.get('label') end = self.vertices[i\",\n \"= f correlation = self.input_xml_parser.get('libxc/correlation', None) xc_functional_names.append(correlation) exchange = self.input_xml_parser.get('libxc/exchange', None) xc_functional_names.append(exchange) sec_xc_functional\",\n \"self._quantities.append( Quantity( 'kpoints_eigenvalues', r'\\\\s*k\\\\-point \\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([ \\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)', str_operation=str_to_eigenvalue, repeats=True)) class BandstructureDatParser(DataTextParser): def __init__(self, **kwargs):\",\n \"for f in gw_files: self.parse_file(f, sec_scc) frequency_data = self.info_gw_parser.get('frequency_data', None) if frequency_data is\",\n \"'partialdos' self._diagram_key = 'diagram' self._l_key = 'l' self._m_key = 'm' self._energy_key = 'e'\",\n \"r'\\\\n *(.+?,.+)', str_operation=lambda x: [v.strip() for v in x.split(':')]), Quantity( 'parameters', r'\\\\n *(.+?:.+)',\",\n \"(self._totaldos_key, self._diagram_key)) return self._total_dos @property def partial_dos(self): if self._partial_dos is None: self._partial_dos =\",\n \"i % self.number_of_lm else: lm = int(val_l) ** 2 + int(val_m) + int(val_l)\",\n \"in x.split(':')]), Quantity( 'parameters', r'\\\\n *(.+?:.+)', repeats=True, str_operation=lambda x: [v.strip() for v in\",\n \"sec_run = self.archive.run[-1] # TODO read from xml file def get_files(name): bse_types =\",\n \"n in range(len(val)): sec_mt_charge_atom = msection.m_create(x_exciting_section_MT_charge_atom) sec_mt_charge_atom.x_exciting_MT_charge_atom_index = n + 1 sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol =\",\n \"sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol = val[n][0] sec_mt_moment_atom.x_exciting_MT_moment_atom_value = val[n][1] else: setattr(msection, key, val) # convergence values\",\n \"*(.+)'), Quantity('name', r'name *: *(.+)'), Quantity('nuclear_charge', rf'nuclear charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('electronic_charge',\",\n \"is not None: break return positions_format def get_atom_positions(self, section={}, positions=None, positions_format=None): positions =\",\n \"in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.energies =\",\n \"np.zeros((self.number_of_spin_channels, self.number_of_dos)) for i in range(len(self.total_dos)): spin = self.total_dos[i].attrib.get(self._nspin_key, i) res[i] = self._get_dos(self._total_dos[i])\",\n \"{ 2: ['LDA_C_PZ', 'LDA_X_PZ'], 3: ['LDA_C_PW', 'LDA_X_PZ'], 4: ['LDA_C_XALPHA'], 5: ['LDA_C_VBH'], 20: ['GGA_C_PBE',\",\n \"also other energies are reported. energy_fermi = 0.0 * ureg.hartree if sec_scc.energy is\",\n \"G\\\\-vectors', None), 'x_exciting_lmaxapw': (r' APW functions', None), 'x_exciting_nuclear_charge': ('Total nuclear charge', ureg.elementary_charge), 'x_exciting_electronic_charge':\",\n \"def parse_scf(iteration, msection): energy_total = iteration.get('energy_total') sec_energy = msection.m_create(Energy) if energy_total is not\",\n \"* ureg.bohr ** 3) def get_initialization_parameter(self, key, default=None): return self.get('initialization', {}).get(key, default) class\",\n \"get it from input.xml for f in input_file: self.input_xml_parser.mainfile = f positions =\",\n \"by concatenating species symbols species = self.get('initialization', {}).get('species', []) labels = [] for\",\n \"os.path.isfile(filename): return [filename] filename = os.path.join(self.info_parser.maindir, default) if not os.path.isfile(filename): file_ext = default.split('.')[-1]\",\n \"if val is None: continue if key.startswith('energy_'): sec_energy.m_add_sub_section(getattr( Energy, key.replace('energy_', '')), EnergyEntry(value=val)) else:\",\n \"= get_data('k_points') sec_gw_eigenvalues.energies = reshape(eigs_gw) sec_gw_eigenvalues.value_exchange = reshape(get_data('Sx')) eigs_gw_C = reshape(get_data('Sc')) if eigs_gw_C\",\n \"if None in energy: return energy return np.array([d[1].get(key) for d in data]) *\",\n \"# # This file is part of NOMAD. # See https://nomad-lab.eu for further\",\n \"DataTextParser() self.data_clathrate_parser = DataTextParser(dtype=str) # different names for different versions of exciting self._energy_keys_mapping\",\n \"'none') sec_gw.screened_coulomb_volume_average = self.input_xml_parser.get( 'gw/scrcoul/sciavtype', 'isotropic') sec_gw.screened_Coulomb = self.input_xml_parser.get( 'gw/scrcoul/scrtype', 'rpa') def parse_gw(self):\",\n \"len(data))) if key == 'eigenvalues': res = res * ureg.hartree return res sec_eigenvalues\",\n \"self._nspin is None: self._nspin = np.shape(np.transpose(self.data)[1:])[0] return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment\",\n \"= msection.m_create(x_exciting_section_MT_moment_atom) sec_mt_moment_atom.x_exciting_MT_moment_atom_index = n + 1 sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol = val[n][0] sec_mt_moment_atom.x_exciting_MT_moment_atom_value = val[n][1]\",\n \"== 'FXC': sec_scc = sec_run.m_create(Calculation) sec_scc.x_exciting_xs_tddft_number_of_epsilon_values = len(data[0][0][0]) sec_scc.x_exciting_xs_tddft_epsilon_energies = data[0][0][0] * ureg.hartree\",\n \"Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_dat_parser.band_k_points nkpts_segment = self.bandstructure_dat_parser.number_of_k_points_per_segment for nb in\",\n \"if fermi_surface is None: return sec_fermisurface = sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters = self.fermisurf_parser.get('band_parameters', None) if\",\n \"None: if not self.total_dos: return self._nspin = len(self.total_dos) return self._nspin @property def number_of_atoms(self):\",\n \"None self._energies = None self._total_dos = None self._partial_dos = None @property def energy_unit(self):\",\n \"positions = positions if positions is not None else section.get('positions') if positions is\",\n \"1.0) sec_gw.mixed_basis_gmax = gmb * gmaxvr pwm = self.input_xml_parser.get('gw/barecoul/pwm', 2.0) sec_gw.bare_coulomb_gmax = pwm\",\n \"name.endswith('dat'): parser = self.bandstructure_dat_parser parser_function = self._parse_bandstructure_dat elif name.startswith('BAND') and name.endswith('OUT'): parser =\",\n \"[] for n in range(len(self.bands)): res_n = [] start = 0 band_energies =\",\n \"self.evalqp_parser.get('kpoints_eigenvalues') if data is None: return def get_data(key): if key == 'k_points': return\",\n \"False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg', False) sec_method.x_exciting_xs_bse_sciavtype = self.input_xml_parser.get( 'xs/BSE/sciavtype', 'spherical') sec_method.x_exciting_xs_bse_xas =\",\n \"'Hartree'], 'x_exciting_electron_nuclear_energy': ['Electron-nuclear energy', 'electron-nuclear '], 'x_exciting_nuclear_nuclear_energy': ['Nuclear-nuclear energy', 'nuclear-nuclear'], 'x_exciting_madelung_energy': ['Madelung energy',\",\n \"at Fermi energy \\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=1 / ureg.hartree), Quantity( 'charge_contributions', r'(?:Charges|Electron charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n\",\n \"TODO add support for more output files and properties exciting_files = ['EIGVAL.OUT', 'FERMISURF.bxsf',\",\n \"sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get( 'xs/tddft/ahc', False) sec_method.x_exciting_xs_tddft_anti_resonant_dielectric = self.input_xml_parser.get( 'xs/tddft/aresdf', False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel = self.input_xml_parser.get(\",\n \"= None self._nlm = None self._energies = None self._total_dos = None self._partial_dos =\",\n \"in val: v = v.split(':') if len(v) < 2: continue energies[v[0].strip()] = float(v[1])\",\n \"'Methfessel-Paxton': 'methfessel-paxton', 'Fermi-Dirac': 'fermi', 'Extended': 'tetrahedra'} sec_smearing = sec_electronic.m_create(Smearing) smearing_kind = self.info_parser.get_initialization_parameter('smearing_kind') if\",\n \"qpoints files = self.get_exciting_files('%s_%s%s%s.OUT' % (quantity, file_ext, ext, fxctype)) data = [[], [],\",\n \"xc_functional_names.append(correlation) exchange = self.input_xml_parser.get('libxc/exchange', None) xc_functional_names.append(exchange) sec_xc_functional = sec_dft.m_create(XCFunctional) for name in xc_functional_names:\",\n \"for normal calculations # in addition to INFO.OUT return else: return files =\",\n \"self.input_xml_parser.get( 'xs/tddft/kerndiag', False) sec_method.x_exciting_xs_tddft_lmax_alda = self.input_xml_parser.get( 'xs/tddft/lmaxalda', 3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment = self.input_xml_parser.get( 'xs/tddft/mdfqtype', 0)\",\n \"None self._results[key] = res class ExcitingFermiSurfaceBxsfParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities =\",\n \"sec_worfklow = self.archive.m_create(Workflow) sec_worfklow.type = 'single_point' structure_optimization = self.info_parser.get('structure_optimization') if structure_optimization is not\",\n \"energy', 'electron-nuclear '], 'x_exciting_nuclear_nuclear_energy': ['Nuclear-nuclear energy', 'nuclear-nuclear'], 'x_exciting_madelung_energy': ['Madelung energy', 'Madelung'], 'x_exciting_core_electron_kinetic_energy': ['Core-electron\",\n \"self.input_xml_parser.get('xs/screening') is not None: sec_method.x_exciting_xs_screening_number_of_empty_states = self.input_xml_parser.get( 'xs/screening/nempty', 0) sec_method.x_exciting_xs_screening_ngridk = self.input_xml_parser.get( 'xs/screening/ngridk',\",\n \"elif v[0].startswith('atom'): v[0] = v[0].split() v[1] = [float(vi) for vi in v[1].split()] v[1]\",\n \"None: res = res * (1 / self.energy_unit) elif key == 'energies': return\",\n \"* (1 / ureg.hartree) * volume.to('m**3').magnitude for spin in range(len(dos)): sec_dos_values = sec_dos.m_create(DosValues,\",\n \"self.band_out_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.band_out_parser.band_energies if band_energies is None: return # Get\",\n \"sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = self.dos_parser.number_of_dos sec_dos.energies = self.dos_parser.energies + energy_fermi volume\",\n \"['total charge in muffin-tins', 'total in muffin-tins'], 'charge_total': ['total charge'], 'x_exciting_section_MT_charge_atom': ['atom_resolved'] }\",\n \"number of atoms per unit cell', None), 'x_exciting_spin_treatment': ('Spin treatment', None), 'x_exciting_number_of_bravais_lattice_symmetries': ('Number\",\n \"sub_parser=TextParser(quantities=[ Quantity( 'optimization_step', r'(Optimization step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n *\\\\n\\\\-{10}|Time spent in this optimization step\\\\s*:\\\\s*[\\\\d\\\\.]+ seconds))', sub_parser=TextParser(quantities=optimization_quantities),\",\n \"('Total electronic charge', ureg.elementary_charge), 'x_exciting_core_charge_initial': ('Total core charge', ureg.elementary_charge), 'x_exciting_valence_charge_initial': ('Total valence charge',\",\n \"sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg', False) sec_method.x_exciting_xs_bse_sciavtype = self.input_xml_parser.get( 'xs/BSE/sciavtype', 'spherical') sec_method.x_exciting_xs_bse_xas = self.input_xml_parser.get(\",\n \"= len(self.bands[0]) // self.number_of_spin_channels return self._neigs_segment def parse(self, key): if self._results is None:\",\n \"('Maximum Hamiltonian size', None), 'x_exciting_pw': (r'Maximum number of plane\\\\-waves', None), 'x_exciting_lo': (r'Total number\",\n \"properties[v[0].strip()] = vi * unit properties['atom_resolved'] = atom_resolved return properties def str_to_quantity_tolerances(val_in): return\",\n \"target[0] in f and mainfile_base in f] options = [f for f in\",\n \"+ energy_fermi volume = self.info_parser.get_unit_cell_volume() totaldos = self.dos_parser.get('totaldos') * volume.to('m**3').magnitude for spin in\",\n \"None: continue energy_fermi = energy_fermi.to(\\\"hartree\\\") sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points\",\n \"r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)', str_operation=str_to_band_parameters, repeats=False)) self._quantities.append( Quantity( 'fermi_surface', r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n *E*', unit=ureg.hartree, repeats=True)) class ExcitingEigenvalueParser(TextParser): def\",\n \"BandGap ) from nomad.datamodel.metainfo.workflow import Workflow, GeometryOptimization from .metainfo.exciting import x_exciting_section_MT_charge_atom, x_exciting_section_MT_moment_atom,\\\\ x_exciting_section_spin,\",\n \"bandstructure dat and xml self._nspin = None self._nkpts_segment = None self._neigs_segment = None\",\n \"if not self._calculation_type == 'gw': return sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref\",\n \"if self.data_clathrate_parser.data: data = np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates = np.transpose( np.array(data[:3], dtype=float)) sec_system.x_exciting_clathrates_atom_labels = list(data[3])\",\n \"sec_method.x_exciting_volume_optimization = True def parse_scc(self, section): sec_run = self.archive.run[-1] final = section if\",\n \"module started)([\\\\s\\\\S]+?)Groundstate module stopped', sub_parser=TextParser(quantities=module_quantities), repeats=False)) optimization_quantities = [ Quantity( 'atomic_positions', r'(Atomic positions\",\n \"band_parameters is not None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface = band_parameters[0] sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface = np.product(band_parameters[1]) sec_fermisurface.x_exciting_grid_fermi_surface = band_parameters[1]\",\n \"can do is to assume that the first steps are the # ones\",\n \"= self.input_xml_parser.get( 'xs/gqmax', 0.0, '1/bohr') sec_method.x_exciting_xs_lmaxapw = self.input_xml_parser.get('xs/lmaxapw', 10) sec_method.x_exciting_xs_number_of_empty_states = self.input_xml_parser.get( 'xs/nempty',\",\n \"energy dos_up dos_down nspin = self.info_parser.get_number_of_spin_channels() sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = len(data)\",\n \"sec_run.m_create(Calculation) sccs.append(sec_scc) else: sec_scc = sccs[i] if sec_scc is None: # This is\",\n \"values=val[1] * ureg.hartree, weights=val[2]) # TODO Read also input parameters here if input_GW.xml\",\n \"sure about format for spin-polarized case! I assume it is # energy dos_up\",\n \"other metainfo for name in self.info_parser._miscellaneous_keys_mapping.keys(): val = iteration.get(name) if val is None:\",\n \"# # Unless required by applicable law or agreed to in writing, software\",\n \"ureg.hartree sec_scc.x_exciting_xs_bse_exciton_oscillator_strength = np.reshape( data[3], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_re = np.reshape( data[4], (n_components, n_excitons))\",\n \"= self.input_xml_parser.get('structure/species/speciesfile') if positions is None or lattice_vectors is None or species is\",\n \"nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.energies\",\n \"positions_format = section.get('positions_format') if positions_format is None: species = self.get_initialization_parameter('species', []) for specie\",\n \"The NOMAD Authors. # # This file is part of NOMAD. # See\",\n \"None self._nkpts_segment = None @property def band_energies(self): if self._band_energies is None: data =\",\n \"elif v[0].startswith('species'): species = re.search(re_symbol, v[-1]).group(1) elif v[0].startswith('atom'): v[0] = v[0].split() v[1] =\",\n \"unit=key_unit[1], repeats=False) ) for name, key_unit in self._method_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' %\",\n \"either express or implied. # See the License for the specific language governing\",\n \"unit=ureg.hartree, repeats=False) ) self._quantities.append( Quantity( 'direct_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'fundamental_band_gap',\",\n \"self.input_xml_parser.get( 'gw/scrcoul/sciavtype', 'isotropic') sec_gw.screened_Coulomb = self.input_xml_parser.get( 'gw/scrcoul/scrtype', 'rpa') def parse_gw(self): sec_run = self.archive.run[-1]\",\n \"self.input_xml_parser.get( 'xs/screening/nempty', 0) sec_method.x_exciting_xs_screening_ngridk = self.input_xml_parser.get( 'xs/screening/ngridk', [0, 0, 0]) rgkmax = self.input_xml_parser.get('xs/screening/rgkmax',\",\n \"'NLF_FXC']: data = get_data(quantity, ext) if not data[0]: continue if quantity == 'EPSILON'\",\n \"5) sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq', [1, 1, 1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk', [1, 1, 1])\",\n \"if positions is None: species = self.get_initialization_parameter('species', []) if species: positions = np.vstack([s.get('positions')\",\n \"return self._nlm = 0 l_list = set([int(e.attrib.get(self._l_key)) for e in self.partial_dos]) for li\",\n \"init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'k_points', r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\- ]+):\\\\s*k\\\\-point', repeats=True)) def str_to_eigenvalues(val_in): val\",\n \"structure_optimization = self.info_parser.get('structure_optimization', {}) for optimization_step in structure_optimization.get('optimization_step', []): sec_scc = parse_configuration(optimization_step) if\",\n \"certain if screening/BSE are children of xs if self.input_xml_parser.get('xs/screening') is not None: sec_method.x_exciting_xs_screening_number_of_empty_states\",\n \"= np.reshape( data[0], (n_components, n_epsilon)) * ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re = np.reshape( data[1], (n_components, n_epsilon))\",\n \"= np.zeros(( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, len(self.distances)), dtype=float) for i in range(len(self.bands[n])): band_energies[i % self.number_of_spin_channels][i]\",\n \"= (energy_fermi.magnitude * ureg.joule).to('hartree') # TODO I am not sure about format for\",\n \"name in self.info_parser._system_keys_mapping.keys(): val = self.info_parser.get_initialization_parameter(name) if val is None: continue if name\",\n \"is None: eigs_gw_C = reshape(get_data('Re(Sc)')) sec_gw_eigenvalues.value_correlation = eigs_gw_C sec_gw_eigenvalues.value_xc_potential = reshape(get_data('Vxc')) def _parse_dos_out(self,\",\n \"n_components = len(data) data = np.transpose(np.vstack(data)) n_loss = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_loss_energies =\",\n \"= val # charge contributions charge_contributions = iteration.get('charge_contributions', {}) for key, names in\",\n \"the License. # You may obtain a copy of the License at #\",\n \"the first steps are the # ones with the missing quantity if len(quantity)\",\n \"xstype sec_method.x_exciting_xs_broadening = self.input_xml_parser.get( 'xs/broad', 0.01, 'hartree') sec_method.x_exciting_xs_gqmax = self.input_xml_parser.get( 'xs/gqmax', 0.0, '1/bohr')\",\n \"sec_scc = parse_configuration(optimization_step) if optimization_step.get('method') is not None: sec_scc.x_exciting_geometry_optimization_method = optimization_step.get('method') if optimization_step.get('step')\",\n \"sec_method_ref sec_method.methods_ref = [sec_method_ref] self.parse_file('input.xml', sec_method) # parse properties input_file = self.get_exciting_files('input.xml') if\",\n \"repeats=False) ) self._quantities.append( Quantity( 'direct_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'fundamental_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental\",\n \"we need to set nspin again as this is overwritten when setting mainfile\",\n \"to assume that the first steps are the # ones with the missing\",\n \"get_data(files): data = [] for f in files: self.data_xs_parser.mainfile = f if self.data_xs_parser.data\",\n \"atoms = self.input_xml_parser.get('structure/species/atom') atom_labels = [] for n in range(len(atoms)): atom_labels.extend([species[n].split('.')[0]] * len(atoms[n]))\",\n \"nkpts_segment band_energy = np.array([np.transpose(band)[start:end] for band in bands]) if self._energy_unit: band_energy = band_energy\",\n \"sec_atoms = sec_system.m_create(Atoms) sec_atoms.positions = positions sec_atoms.labels = atom_labels sec_atoms.periodic = [True] *\",\n \"= self.input_xml_parser.get( 'xs/tddft/drude', [0., 0.]) sec_method.x_exciting_xs_tddft_split_parameter = self.input_xml_parser.get( 'xs/tddft/fxcbsesplit', 0.00001, 'hartree') sec_method.x_exciting_xs_tddft_xc_kernel =\",\n \"'%s%s' % (target[0], suffix) if target[1:]: filepath = '%s.%s' % (filepath, target[1]) filepath\",\n \"= f if self.data_xs_parser.data is None: continue data_q.append(self.data_xs_parser.data) if not data_q: continue data_q\",\n \"val is None: continue if key.startswith('energy_'): sec_energy.m_add_sub_section(getattr( Energy, key.replace('energy_', '')), EnergyEntry(value=val)) else: setattr(msection,\",\n \"data[1], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies = np.reshape( data[2], (n_components, n_excitons)) * ureg.hartree\",\n \"= parser.info_parser.quantities self.eigval_parser.quantities = parser.eigval_parser.quantities self.fermisurf_parser.quantities = parser.fermisurf_parser.quantities self.evalqp_parser.quantities = parser.evalqp_parser.quantities self.info_gw_parser.quantities =\",\n \"is stored. \\\"\\\"\\\" mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = filename.rsplit('.', 1)\",\n \"is None: return partialdos = partialdos.to('1/joule').magnitude lm_values = np.column_stack((np.arange(len(partialdos)), np.zeros(len(partialdos), dtype=np.int32))) for lm\",\n \"= self.input_xml_parser.get('libxc/correlation', None) xc_functional_names.append(correlation) exchange = self.input_xml_parser.get('libxc/exchange', None) xc_functional_names.append(exchange) sec_xc_functional = sec_dft.m_create(XCFunctional) for\",\n \"range(len(data)): data[j].append(data_q[j]) return data for quantity in ['EPSILON', 'LOSS', 'SIGMA']: for ext in\",\n \"self._band_k_points.append( np.transpose(data[2:5])[start:end]) start = end return self._band_k_points @property def distances(self): if self._distances is\",\n \"symbols species = self.get('initialization', {}).get('species', []) labels = [] for specie in species:\",\n \"1]: self._nkpts_segment.append(count) count = 1 else: count += 1 self._nkpts_segment.append(count) return self._nkpts_segment @property\",\n \"self._energy_unit: band_energy = band_energy * self._energy_unit self._band_energies.append(band_energy) start = end return self._band_energies @property\",\n \"= self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] # parse input xml file,\",\n \"x_exciting_section_spin, x_exciting_section_fermi_surface,\\\\ x_exciting_section_atoms_group re_float = r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?' class GWInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self):\",\n \"data = np.transpose(self.data) self._neigs_segment = int(max(data[0])) return self._neigs_segment class BandOutParser(DataTextParser): def __init__(self, **kwargs):\",\n \"'charge_contributions', r'(?:Charges|Electron charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False), Quantity( 'moment_contributions', r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False,\",\n \"input_gw.xml and input-gw.xml for f in ['input_gw.xml', 'input-gw.xml', 'input.xml']: self.parse_file(f, sec_method) xc_functional_name =\",\n \"offset', None), 'x_exciting_number_kpoints': (r'Total number of k\\\\-points', None), 'x_exciting_rgkmax': (r'R\\\\^MT\\\\_min \\\\* \\\\|G\\\\+k\\\\|\\\\_max \\\\(rgkmax\\\\)',\",\n \"val[1].split() eigs = np.transpose(np.array([v.split() for v in val[2:]], dtype=float)) eigs = {keys[i]: eigs[i]\",\n \"lm_values[lm] sec_dos_values.spin = spin sec_dos_values.atom_index = atom sec_dos_values.value = partialdos[lm][spin][atom] def _parse_bandstructure(self, sec_scc):\",\n \"None: sec_xc_functional.name = xc_functional.get('name_reference', [None, None])[0] sec_xc_functional.reference = xc_functional.get('name_reference', [None, None])[1] sec_electronic.n_spin_channels =\",\n \"from nomad.units import ureg from nomad.parsing.file_parser import TextParser, Quantity, XMLParser, DataTextParser from nomad.datamodel.metainfo.simulation.run\",\n \"EnergyEntry, Charges, Forces, ForcesEntry, ScfIteration, BandGap ) from nomad.datamodel.metainfo.workflow import Workflow, GeometryOptimization from\",\n \"repeats=False, convert=False), Quantity( 'lattice_vectors_reciprocal', r'Reciprocal lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=1 / ureg.bohr, repeats=False, convert=False),\",\n \"self.get_initialization_parameter('species', []) for specie in species: positions_format = specie.get('positions_format', None) if positions_format is\",\n \"filepath) if os.path.isfile(filepath) and os.access(filepath, os.F_OK): return True return False def _parse_dos(self, sec_scc):\",\n \"= re.search(re_symbol, v[-1]).group(1) elif v[0].startswith('atom'): v[0] = v[0].split() v[1] = [float(vi) for vi\",\n \"'Mismatch in EXCITON and file type', data=dict(file=quantity)) sec_scc = sec_run.m_create(Calculation) if quantity ==\",\n \"// nspin)) data['eigenvalues'] = np.reshape(eigs, (nspin, len(eigs) // nspin)) return data self._quantities.append( Quantity(\",\n \"init_quantities(self): self._quantities = [] def str_to_frequency(val_in): val = [v.split() for v in val_in.split('\\\\n')]\",\n \"= self.logger self.data_xs_parser.logger = self.logger self.data_clathrate_parser.logger = self.logger def reuse_parser(self, parser): self.info_parser.quantities =\",\n \"= data[1:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies = [] start\",\n \"self._quantities.append( Quantity( 'eigenvalues_occupancies', r'\\\\(state\\\\, eigenvalue and occupancy below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n *\\\\n))', str_operation=str_to_eigenvalues, repeats=True)) class ExcitingGWOutParser(TextParser):\",\n \"forces forces = section.get('forces') if forces is not None: sec_forces = sec_scc.m_create(Forces) sec_forces.total\",\n \"xstype is not None: sec_method.x_exciting_xs_xstype = xstype sec_method.x_exciting_electronic_structure_method = xstype sec_method.x_exciting_xs_broadening = self.input_xml_parser.get(\",\n \"end.lower() == 'gamma' else end]) elif key == 'band_segm_start_end': res = [] for\",\n \"# volume optimizations volume_index = 1 while True: info_volume = self.get_exciting_files('run_dir%s/INFO.OUT' % str(volume_index).rjust(2,\",\n \"= self.archive.run[-1] fxctype = self.input_xml_parser.get('xs/tddft/fxctype', 'RPA') tetradf = self.input_xml_parser.get('xs/tetra/tetradf', None) nwacont = self.input_xml_parser.get('xs/tddft/nwacont',\",\n \"== 'EXCITON': parse_function = parse_exciton elif quantity == 'EPSILON': parse_function = parse_epsilon elif\",\n \"= n_epsilon sec_scc.x_exciting_xs_bse_epsilon_energies = np.reshape( data[0], (n_components, n_epsilon)) * ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re = np.reshape(\",\n \"step\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'method', r'method\\\\s*=\\\\s*(\\\\w+)', repeats=False, dtype=str), Quantity( 'n_scf_iterations', r'Number of (?:total)*\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"'xs/screening/screentype', 'full') if self.input_xml_parser.get('xs/BSE') is not None: sec_method.x_exciting_xs_bse_antiresonant = self.input_xml_parser.get( 'xs/BSE/aresbse', True) sec_method.x_exciting_xs_bse_angular_momentum_cutoff\",\n \"[filename] filenames = [f for f in filenames if os.access(f, os.F_OK)] return filenames\",\n \"ForcesEntry(value=forces) # scf iterations scf_iterations = section.get('scf_iteration', []) for scf_iteration in scf_iterations: sec_scf_iteration\",\n \"3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment = self.input_xml_parser.get( 'xs/tddft/mdfqtype', 0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals = self.input_xml_parser.get( 'xs/tddft/nwacont', 0) sec_method.x_exciting_xs_tetra =\",\n \"= self.input_xml_parser.get( 'gw/mixbasis/epsmb', 0.0001) gmb = self.input_xml_parser.get('gw/mixbasis/gmb', 1.0) sec_gw.mixed_basis_gmax = gmb * gmaxvr\",\n \"*(.+?:.+)', repeats=True, str_operation=lambda x: [v.strip() for v in x.split(':')])])) ) self._quantities.append(Quantity( 'initialization', r'(?:All\",\n \"potential'], 'energy_electrostatic': ['Hartree energy', 'Hartree'], 'x_exciting_hartree_energy': ['Hartree energy', 'Hartree'], 'x_exciting_electron_nuclear_energy': ['Electron-nuclear energy', 'electron-nuclear\",\n \"self.eigval_parser.get('eigenvalues_occupancies', None) is None: return nspin = self.info_parser.get_number_of_spin_channels() def get_data(key): data = self.eigval_parser.get('eigenvalues_occupancies')\",\n \"if self._bands is None: bands = self.root.findall('./%s' % self._band_key) self._bands = [] if\",\n \"quantity in ['EPSILON', 'LOSS', 'SIGMA']: for ext in ['FXC', 'NLF_FXC']: data = get_data(quantity,\",\n \"sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_epsilon = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_energy_points\",\n \"return res = np.zeros((self.number_of_spin_channels, self.number_of_dos)) for i in range(len(self.total_dos)): spin = self.total_dos[i].attrib.get(self._nspin_key, i)\",\n \"sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] self.parse_file('input.xml', sec_method) # parse properties input_file =\",\n \"name: sec_xc_functional.exchange.append(Functional(name=name)) elif '_C_' in name: sec_xc_functional.correlation.append(Functional(name=name)) elif 'HYB' in name: sec_xc_functional.hybrid.append(Functional(name=name)) else:\",\n \"# species species = self.info_parser.get_initialization_parameter('species', []) for specie in species: sec_atoms_group = sec_system.m_create(x_exciting_section_atoms_group)\",\n \"= self.logger self.eigval_parser.logger = self.logger self.fermisurf_parser.logger = self.logger self.evalqp_parser.logger = self.logger self.dos_out_parser.logger =\",\n \"None) aresdf = self.input_xml_parser.get('xs/tddft/aresdf', True) file_ext_list = [ 'TET' if tetradf else None,\",\n \"first steps are the # ones with the missing quantity if len(quantity) <\",\n \"['Hartree energy', 'Hartree'], 'x_exciting_hartree_energy': ['Hartree energy', 'Hartree'], 'x_exciting_electron_nuclear_energy': ['Electron-nuclear energy', 'electron-nuclear '], 'x_exciting_nuclear_nuclear_energy':\",\n \"self.logger self.data_clathrate_parser.logger = self.logger def reuse_parser(self, parser): self.info_parser.quantities = parser.info_parser.quantities self.eigval_parser.quantities = parser.eigval_parser.quantities\",\n \"valence states', None), 'x_exciting_hamiltonian_size': ('Maximum Hamiltonian size', None), 'x_exciting_pw': (r'Maximum number of plane\\\\-waves',\",\n \"in species: sec_atoms_group = sec_system.m_create(x_exciting_section_atoms_group) sec_atoms_group.x_exciting_geometry_atom_labels = specie.get('symbol') sec_atoms_group.x_exciting_geometry_atom_number = str(specie.get('number')) sec_atoms_group.x_exciting_muffin_tin_points =\",\n \"data = [[], [], []] for i in range(len(qpoints)): data_q = [] files_q\",\n \"sec_scc is None: # This is the case when there is a mismatch\",\n \"below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n *\\\\n))', str_operation=str_to_eigenvalues, repeats=True)) class ExcitingGWOutParser(TextParser): def __init__(self, mainfile, logger): super().__init__(mainfile, logger=logger) def\",\n \"is None: continue if key.startswith('energy_'): sec_energy.m_add_sub_section(getattr( Energy, key.replace('energy_', '')), EnergyEntry(value=val)) else: setattr(msection, key,\",\n \"]+):\\\\s*k\\\\-point', repeats=True)) def str_to_eigenvalues(val_in): val = val_in[:val_in.rfind('\\\\n \\\\n')].strip() val = np.array([v.split() for v\",\n \"= f positions = self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors = self.input_xml_parser.get( 'structure/crystal/basevect', np.eye(3)) species = self.input_xml_parser.get('structure/species/speciesfile')\",\n \"= ['bandstructure.xml', 'BAND.OUT', 'bandstructure.dat'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break for\",\n \"data = np.transpose(self.data) n_kpoints = int(max(data[1])) bands = data[6:] bands = np.reshape(bands, (\",\n \"it is used to un-shift the band structure to # the original scale\",\n \"= np.reshape( data[0], (n_components, n_loss)) * ureg.hartree sec_scc.x_exciting_xs_bse_loss = np.reshape( data[1], (n_components, n_loss))\",\n \"parse_epsilon elif quantity == 'SIGMA': parse_function = parse_sigma elif quantity == 'LOSS': parse_function\",\n \"[ Quantity( 'lattice_vectors', r'Lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=ureg.bohr, repeats=False, convert=False), Quantity( 'lattice_vectors_reciprocal', r'Reciprocal lattice\",\n \"int(self.input_xml_parser.get( 'gw/selfenergy/nempty', 0)) sec_gw.self_energy_singularity_treatment = self.input_xml_parser.get( 'gw/selfenergy/singularity', 'mpd') sec_gw.self_energy_c_analytical_continuation = self.input_xml_parser.get( 'gw/selfenergy/actype', 'pade')\",\n \"sec_gw.polarizability_number_of_empty_states = int( self.input_xml_parser.get('gw/nempty', 0)) sec_gw.ngridq = self.input_xml_parser.get('gw/ngridq', [1, 1, 1]) sec_gw.basis_set =\",\n \"type', data=dict(file=quantity)) sec_scc = sec_run.m_create(Calculation) if quantity == 'EXCITON': parse_function = parse_exciton elif\",\n \"not info_volume: break sec_scc.calculations_path.append(info_volume[0]) def init_parser(self): self.info_parser.mainfile = self.filepath self.info_parser.logger = self.logger self.dos_parser.logger\",\n \"sec_atoms_group.x_exciting_muffin_tin_points = specie.get('radial_points') sec_atoms_group.x_exciting_muffin_tin_radius = specie.get('muffin_tin_radius') positions_format = specie.get('positions_format') sec_atoms_group.x_exciting_atom_position_format = positions_format positions\",\n \"= 'mixed' sec_gw.qp_equation_treatment = 'linearization' sec_gw.max_frequency = self.input_xml_parser.get( 'gw/freqgrid/freqmax', 1.0) sec_gw.frequency_grid_type = self.input_xml_parser.get(\",\n \"'RPA'] scr_types = ['full', 'diag', 'noinvdiag', 'longrange'] bse_files = [] for bse_type in\",\n \"which is not parsed? sccs = [] for quantity in ['EXCITON', 'EPSILON', 'SIGMA',\",\n \"may not use this file except in compliance with the License. # You\",\n \"if partialdos is None: return partialdos = partialdos.to('1/joule').magnitude lm_values = np.column_stack((np.arange(len(partialdos)), np.zeros(len(partialdos), dtype=np.int32)))\",\n \"self._band_energies @property def distances(self): if self._distances is None: dist = np.transpose(self.data)[0] n_k_points =\",\n \"not None: if not isinstance(smearing_kind, str): smearing_kind = smearing_kind[0] smearing_kind = smearing_kind_map[smearing_kind] sec_smearing.kind\",\n \"'GGA_X_PBE'], 21: ['GGA_C_PBE', 'GGA_X_PBE_R'], 22: ['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'], 26: ['GGA_C_PBE', 'GGA_X_WC'], 30: ['GGA_C_AM05', 'GGA_C_AM05'],\",\n \"from *: *(.+)'), Quantity('name', r'name *: *(.+)'), Quantity('nuclear_charge', rf'nuclear charge *: *({re_float})', dtype=np.float64,\",\n \"break for f in gw_files: self.parse_file(f, sec_scc) frequency_data = self.info_gw_parser.get('frequency_data', None) if frequency_data\",\n \"xml file def get_files(name): bse_types = ['IP', 'singlet', 'triplet', 'RPA'] scr_types = ['full',\",\n \"r'(?:Self\\\\-consistent loop started|Groundstate module started)([\\\\s\\\\S]+?)Groundstate module stopped', sub_parser=TextParser(quantities=module_quantities), repeats=False)) optimization_quantities = [ Quantity(\",\n \"not None: sec_system.x_exciting_potential_mixing = potential_mixing return sec_system def parse_configurations(self): sec_run = self.archive.run[-1] def\",\n \"i in range(len(files)): data = get_data(files[i]) if not data: sccs.append(None) continue if quantity\",\n \"continue setattr(msection, name, val) # other metainfo for name in self.info_parser._miscellaneous_keys_mapping.keys(): val =\",\n \"None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi =\",\n \"= 1 if spin_treatment.lower() == 'spin-unpolarised' else 2 return n_spin def get_unit_cell_volume(self): return\",\n \"self.logger.warn( 'Mismatch in EXCITON and file type', data=dict(file=quantity)) sec_scc = sec_run.m_create(Calculation) if quantity\",\n \"sec_run.m_create(Calculation) sec_scc.method_ref = sec_method if sec_run.system: sec_scc.system_ref = sec_run.system[-1] sec_scc_ref = sec_run.calculation[0] sec_scc.starting_calculation_ref\",\n \"self._vertices = self.root.findall('./%s' % self._vertex_key) return self._vertices @property def number_of_spin_channels(self): if self._nspin is\",\n \"% (quantity, file_ext, ext, fxctype)) data = [[], [], []] for i in\",\n \"* len(specie.get('positions')) return labels def get_positions_format(self, section): positions_format = section.get('positions_format') if positions_format is\",\n \"density', 1 / ureg.bohr), 'x_exciting_gvector_size': (r'G\\\\-vector grid sizes', None), 'x_exciting_gvector_total': (r'Total number of\",\n \"energy_fermi def _parse_band_out(self, sec_scc): self.band_out_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.band_out_parser.band_energies if band_energies is\",\n \"lattice_vectors = self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels = self.info_parser.get_atom_labels(section.get('atomic_positions', {})) input_file = self.get_exciting_files('input.xml') if positions is\",\n \"vector] self._quantities.append( Quantity( 'band_parameters', r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)', str_operation=str_to_band_parameters, repeats=False)) self._quantities.append( Quantity( 'fermi_surface', r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n *E*', unit=ureg.hartree,\",\n \"data[0])[0]) return self._neigs_segment class BandstructureXMLParser(XMLParser): def __init__(self, **kwargs): # TODO make a parent\",\n \"x: [v.strip() for v in x.split(':')]), Quantity( 'parameters', r'\\\\n *(.+?:.+)', repeats=True, str_operation=lambda x:\",\n \"np.dot(positions, lattice_vectors) * ureg.bohr lattice_vectors = lattice_vectors * ureg.bohr atoms = self.input_xml_parser.get('structure/species/atom') atom_labels\",\n \"in ['groundstate', 'hybrids']: sec_scc = parse_configuration(self.info_parser.get(module)) if sec_scc is None: continue # add\",\n \"# the only thing that we can do is to assume that the\",\n \"for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.value = band_energies[nb]\",\n \"= energy_fermi nkpts_segment = self.band_out_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints\",\n \"e in self.bands[n][i]]) for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy\",\n \"self.input_xml_parser.get('gw/nempty', 0)) sec_gw.ngridq = self.input_xml_parser.get('gw/ngridq', [1, 1, 1]) sec_gw.basis_set = 'mixed' sec_gw.qp_equation_treatment =\",\n \"== 'LOSS' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_loss_function_no_local_field = data[1] elif quantity == 'SIGMA'\",\n \"sec_method.x_exciting_xs_vkloff = self.input_xml_parser.get('xs/vkloff', [0., 0., 0.]) # TODO I am not certain if\",\n \"== 'xs': parser_function = self._parse_input_xs else: # TODO implement reading of parameters from\",\n \"(r'Estimated fundamental gap', ureg.hartree), 'time': (r'Wall time \\\\(seconds\\\\)', ureg.s)} for name, key_unit in\",\n \"unit=ureg.hartree, repeats=False, dtype=float), Quantity( 'time', r'Time spent in this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds', unit=ureg.s, repeats=False,\",\n \"= v[1][0] if len(v[1]) == 1 else v[1] if species is None: species\",\n \"*\\\\d+ *\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float} *{re_float} *{re_float}\\\\n\\\\s*\\\\n)', repeats=True, sub_parser=TextParser(quantities=[ Quantity('number', r'Species : *(\\\\d+)', dtype=np.int32), Quantity('symbol', r'\\\\((\\\\w+)\\\\)'),\",\n \"the band structure to # the original scale in which also other energies\",\n \"= self.input_xml_parser.get( 'xs/tddft/ahc', False) sec_method.x_exciting_xs_tddft_anti_resonant_dielectric = self.input_xml_parser.get( 'xs/tddft/aresdf', False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel = self.input_xml_parser.get( 'xs/tddft/aresfxc',\",\n \"self.number_of_lm, self.number_of_spin_channels, self.number_of_atoms, self.number_of_dos)) for i in range(len(self.partial_dos)): spin = self.partial_dos[i].attrib.get(self._nspin_key, None) if\",\n \"[]): sec_scc = parse_configuration(optimization_step) if optimization_step.get('method') is not None: sec_scc.x_exciting_geometry_optimization_method = optimization_step.get('method') if\",\n \"r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False)] self._miscellaneous_keys_mapping = { 'x_exciting_gap': (r'Estimated fundamental gap', ureg.hartree),\",\n \"= 'DFT' smearing_kind_map = { 'Gaussian': 'gaussian', 'Methfessel-Paxton': 'methfessel-paxton', 'Fermi-Dirac': 'fermi', 'Extended': 'tetrahedra'}\",\n \"return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data = np.transpose(self.data)[0] self._neigs_segment\",\n \"atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr) ]\",\n \"sometimes res = np.hstack([np.reshape(v[key], (nspin, np.size(v[key]) // nspin)) for v in data]) res\",\n \"else self.get_positions_format(section) if positions_format == 'lattice': cell = self.get_initialization_parameter('lattice_vectors') if cell is None:\",\n \"# method goes first since reference needed for sec_scc self.parse_method() self.parse_configurations() self.parse_gw() self.parse_xs()\",\n \"eigs = {keys[i]: eigs[i] for i in range(len(keys))} return [kpts, eigs] self._quantities.append( Quantity(\",\n \"data=dict(file=quantity)) def _parse_xs_tddft(self): sec_run = self.archive.run[-1] fxctype = self.input_xml_parser.get('xs/tddft/fxctype', 'RPA') tetradf = self.input_xml_parser.get('xs/tetra/tetradf',\",\n \"res = res * ureg.hartree return res sec_eigenvalues = sec_scc.m_create(BandEnergies) sec_eigenvalues.kpoints = self.eigval_parser.get('k_points')\",\n \"e]) # read q points qpoints = self.input_xml_parser.get('xs/qpointset/qpoint') def get_data(quantity, ext): # all\",\n \"= int(self.input_xml_parser.get( 'gw/selfenergy/nempty', 0)) sec_gw.self_energy_singularity_treatment = self.input_xml_parser.get( 'gw/selfenergy/singularity', 'mpd') sec_gw.self_energy_c_analytical_continuation = self.input_xml_parser.get( 'gw/selfenergy/actype',\",\n \"if self._neigs_segment is None: self._neigs_segment = len(self.bands[0]) // self.number_of_spin_channels return self._neigs_segment def parse(self,\",\n \"r'(Optimization step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n *\\\\n\\\\-{10}|Time spent in this optimization step\\\\s*:\\\\s*[\\\\d\\\\.]+ seconds))', sub_parser=TextParser(quantities=optimization_quantities), repeats=True), Quantity( 'final',\",\n \"end return self._band_k_points @property def distances(self): if self._distances is None: data = np.transpose(self.data)\",\n \"and (name.endswith('DAT') or name.endswith('TXT')): parser = self.evalqp_parser parser_function = self._parse_evalqp elif name.startswith('TDOS') and\",\n \"= sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_parser.get('band_k_points') nkpts_segment = self.bandstructure_parser.number_of_k_points_per_segment band_seg_labels\",\n \"= get_data(quantity, ext) if not data[0]: continue if quantity == 'EPSILON' and ext\",\n \"energy in band_energies]) if self._energy_unit is not None: band_energy = band_energy * self._energy_unit\",\n \"np.transpose(val) occs = val[-1] eigs = val[-2] nspin = 2 if occs[0] ==\",\n \"(i % (self.number_of_spin_channels * self.number_of_lm)) // self.number_of_lm else: spin = int(spin) - 1\",\n \"== 'gw': parser_function = self._parse_input_gw elif self._calculation_type == 'xs': parser_function = self._parse_input_xs else:\",\n \"= kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): # TODO make a\",\n \"= self._parse_input_gw elif self._calculation_type == 'xs': parser_function = self._parse_input_xs else: # TODO implement\",\n \"sec_system = self.parse_system(section) if sec_system is not None: sec_scc.system_ref = sec_system sec_scc.method_ref =\",\n \"sec_gw_eigenvalues.qp_linearization_prefactor = reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands = len(eigs_gw[0]) sec_gw_eigenvalues.n_kpoints = len(eigs_gw) sec_gw_eigenvalues.kpoints = get_data('k_points') sec_gw_eigenvalues.energies\",\n \"self.input_xml_parser.get( 'gw/freqgrid/freqmax', 1.0) sec_gw.frequency_grid_type = self.input_xml_parser.get( 'gw/freqgrid/fgrid', 'gaule2') sec_gw.number_of_frequencies = int(self.input_xml_parser.get( 'gw/freqgrid/nomeg', 16))\",\n \"'x_exciting_number_kpoints': (r'Total number of k\\\\-points', None), 'x_exciting_rgkmax': (r'R\\\\^MT\\\\_min \\\\* \\\\|G\\\\+k\\\\|\\\\_max \\\\(rgkmax\\\\)', None), 'x_exciting_species_rtmin':\",\n \"+ 1).rjust(3, '0'))] for f in files_q: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is\",\n \"if not info_volume: break sec_scc.calculations_path.append(info_volume[0]) def init_parser(self): self.info_parser.mainfile = self.filepath self.info_parser.logger = self.logger\",\n \"None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None self._nkpts_segment = None\",\n \"\\\\n')].strip() val = np.array([v.split() for v in val.split('\\\\n')], dtype=float) val = np.transpose(val) occs\",\n \"'coord' self._energy_key = 'eval' self._vertex_key = 'vertex' self._band_key = 'band' self._atom_key = 'atom'\",\n \"None: number = frequency_data.get('number') sec_method.gw.number_of_frequencies = len(number) sec_method.gw.frequency_number = number sec_method.gw.frequency_values = frequency_data.get('values')\",\n \"self._distances = data[5][:int(max(data[1]))] return self._distances @property def number_of_spin_channels(self): if self._nspin is None: self._nspin\",\n \"'structure_optimization', r'Structure\\\\-optimization module started([\\\\s\\\\S]+?)Structure\\\\-optimization module stopped', sub_parser=TextParser(quantities=[ Quantity( 'optimization_step', r'(Optimization step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n *\\\\n\\\\-{10}|Time spent\",\n \"= dos[spin] # TODO add PDOS def _parse_bandstructure_dat(self, sec_scc): self.bandstructure_dat_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies\",\n \"= data[5][:int(max(data[1]))] return self._distances @property def number_of_spin_channels(self): if self._nspin is None: self._nspin =\",\n \"Quantity, XMLParser, DataTextParser from nomad.datamodel.metainfo.simulation.run import Run, Program from nomad.datamodel.metainfo.simulation.method import ( Method,\",\n \"(energy_fermi.magnitude * ureg.joule).to('hartree') # TODO I am not sure about format for spin-polarized\",\n \"bs_files: if self.file_exists(fname): exciting_files.append(fname) break for f in exciting_files: self.parse_file(f, sec_scc) # structure\",\n \"sec_run = self.archive.run[-1] xs_info_files = self.get_exciting_files('INFOXS.OUT') if not xs_info_files: return self._calculation_type = 'xs'\",\n \"and hybrids calculation for module in ['groundstate', 'hybrids']: sec_scc = parse_configuration(self.info_parser.get(module)) if sec_scc\",\n \"False) sec_method.x_exciting_xs_bse_sciavtype = self.input_xml_parser.get( 'xs/BSE/sciavtype', 'spherical') sec_method.x_exciting_xs_bse_xas = self.input_xml_parser.get( 'xs/BSE/xas', False) sec_method.x_exciting_xs_bse_number_of_bands =\",\n \"nspin again as this is overwritten when setting mainfile self.bandstructure_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies\",\n \"specie.get('symbol') sec_atoms_group.x_exciting_geometry_atom_number = str(specie.get('number')) sec_atoms_group.x_exciting_muffin_tin_points = specie.get('radial_points') sec_atoms_group.x_exciting_muffin_tin_radius = specie.get('muffin_tin_radius') positions_format = specie.get('positions_format')\",\n \"elif name.startswith('bandstructure') and name.endswith('dat'): parser = self.bandstructure_dat_parser parser_function = self._parse_bandstructure_dat elif name.startswith('BAND') and\",\n \"quantity == 'EXCITON': sec_scc = sec_run.m_create(Calculation) sccs.append(sec_scc) else: sec_scc = sccs[i] if sec_scc\",\n \"atom sec_dos_values.value = partialdos[lm][spin][atom] def _parse_bandstructure(self, sec_scc): # we need to set nspin\",\n \"= section.get('scf_iteration', [None])[-1] final = section.get('optimization_step', [None])[-1] if final is None else final\",\n \"ureg.joule).to('hartree') # TODO I am not sure about format for spin-polarized case! I\",\n \"= sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.value = band_energies[nb] + energy_fermi def parse_file(self, name,\",\n \"n_k_points = np.where(dist == dist[0])[0][1] self._distances = dist[:n_k_points] return self._distances @property def number_of_spin_channels(self):\",\n \"class ExcitingEvalqpParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] def str_to_eigenvalue(val_in): val\",\n \"i in range(len(self.number_of_k_points_per_segment)): start = self.vertices[i].attrib.get(self._coord_key).split() end = self.vertices[i + 1].attrib.get(self._coord_key).split() res.append([start, end])\",\n \"\\\\* \\\\|G\\\\+k\\\\|\\\\_max \\\\(rgkmax\\\\)', None), 'x_exciting_species_rtmin': (r'Species with R\\\\^MT\\\\_min', None), 'x_exciting_gkmax': (r'Maximum \\\\|G\\\\+k\\\\| for\",\n \"n in range(len(val)): sec_mt_moment_atom = msection.m_create(x_exciting_section_MT_moment_atom) sec_mt_moment_atom.x_exciting_MT_moment_atom_index = n + 1 sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol =\",\n \"name: sec_xc_functional.correlation.append(Functional(name=name)) elif 'HYB' in name: sec_xc_functional.hybrid.append(Functional(name=name)) else: sec_xc_functional.contributions.append(Functional(name=name)) if not xc_functional_names: #\",\n \"# TODO confirm no cell optimization in exciting sec_atoms.lattice_vectors = lattice_vectors lattice_vectors_reciprocal =\",\n \"0, 1)) sec_dos.energies = data[0][0] * ureg.hartree + energy_fermi volume = self.info_parser.get_unit_cell_volume() dos\",\n \"data = np.transpose(data, axes=(2, 0, 1)) sec_dos.energies = data[0][0] * ureg.hartree + energy_fermi\",\n \"'x_exciting_effective_potential_energy': ['Effective potential energy'], 'x_exciting_coulomb_potential_energy': ['Coulomb potential energy', 'Coulomb potential'], 'energy_xc_potential': ['xc potential\",\n \"of plane\\\\-waves', None), 'x_exciting_lo': (r'Total number of local\\\\-orbitals', None)} self._method_keys_mapping = { 'smearing_kind':\",\n \"x: [v.strip() for v in x.split(':')])])) ) self._quantities.append(Quantity( 'initialization', r'(?:All units are atomic|Starting\",\n \"= val[0] if len(val) == 1 else val return np.array(val, dtype=float) def str_to_atom_properties_dict(val_in):\",\n \"set([int(e.attrib.get(self._l_key)) for e in self.partial_dos]) for li in l_list: self._nlm += 2 *\",\n \"self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None self._distances = None self._band_energies\",\n \"// nspin, len(data[0]))) sec_gw_eigenvalues = sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor = reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands = len(eigs_gw[0]) sec_gw_eigenvalues.n_kpoints\",\n \"* ureg.joule).to('hartree') sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = self.dos_parser.number_of_dos sec_dos.energies = self.dos_parser.energies +\",\n \"= [[], [], []] for i in range(len(qpoints)): data_q = [] files_q =\",\n \"in self._electron_charge_keys_mapping.items(): val = None for name in names: val = charge_contributions.get(name, None)\",\n \"os.access(f, os.F_OK)] return filenames def file_exists(self, filename): \\\"\\\"\\\"Checks if a the given filename\",\n \"is None: continue lattice_vectors = np.array(lattice_vectors, dtype=float) lattice_vectors *= self.input_xml_parser.get('structure/crystal/scale', 1.0) positions =\",\n \"['LDA_C_VBH'], 20: ['GGA_C_PBE', 'GGA_X_PBE'], 21: ['GGA_C_PBE', 'GGA_X_PBE_R'], 22: ['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'], 26: ['GGA_C_PBE', 'GGA_X_WC'],\",\n \"r'\\\\s*k\\\\-point \\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([ \\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)', str_operation=str_to_eigenvalue, repeats=True)) class BandstructureDatParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin =\",\n \"unit=key_unit[1], repeats=False) ) initialization_quantities.append(Quantity( 'species', rf'(Species : *\\\\d+ *\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float} *{re_float} *{re_float}\\\\n\\\\s*\\\\n)', repeats=True, sub_parser=TextParser(quantities=[\",\n \"np.reshape(data, (nspin, len(data) // nspin, 2)) data = np.transpose(data, axes=(2, 0, 1)) sec_dos.energies\",\n \"= self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_parser.get('band_energies', []) for n in range(len(band_energies)): # Get fermi\",\n \"f break if not self._calculation_type == 'gw': return sec_method = sec_run.m_create(Method) sec_method_ref =\",\n \"sec_k_band_segment.endpoints_labels = band_seg_labels[nb] sec_k_band_segment.energies = band_energies[n][nb] + energy_fermi def _parse_eigenvalues(self, sec_scc): if self.eigval_parser.get('eigenvalues_occupancies',\",\n \"True) sec_method.x_exciting_xs_tddft_drude = self.input_xml_parser.get( 'xs/tddft/drude', [0., 0.]) sec_method.x_exciting_xs_tddft_split_parameter = self.input_xml_parser.get( 'xs/tddft/fxcbsesplit', 0.00001, 'hartree')\",\n \"def _parse_dos_out(self, sec_scc): data = self.dos_out_parser.data if data is None: return # Get\",\n \"not self._calculation_type == 'gw': return sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref =\",\n \"= self.get_exciting_files('INFOXS.OUT') if not xs_info_files: return self._calculation_type = 'xs' # inconsistency in the\",\n \"final if final is None: return sec_scc = sec_run.m_create(Calculation) def parse_scf(iteration, msection): energy_total\",\n \"= dict() if 'total' in key: if not self.total_dos: return res = np.zeros((self.number_of_spin_channels,\",\n \"section if section.get('energy_total') is not None else section.get('final') if final is None: #\",\n \"key, names in self._moment_keys_mapping.items(): val = None for name in names: val =\",\n \"[] start = 0 band_energies = np.zeros(( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, len(self.distances)), dtype=float) for i\",\n \"filenames if os.access(f, os.F_OK)] return filenames def file_exists(self, filename): \\\"\\\"\\\"Checks if a the\",\n \"key, names in self._electron_charge_keys_mapping.items(): val = None for name in names: val =\",\n \"return len(self.get('structure_optimization', {}).get('optimization_step', [])) def get_number_of_spin_channels(self): spin_treatment = self.get('initialization', {}).get( 'x_exciting_spin_treatment', 'spin-unpolarised') n_spin\",\n \"+= 2 * li + 1 return self._nlm @property def total_dos(self): if self._total_dos\",\n \"= totaldos[spin] partialdos = self.dos_parser.get('partialdos') if partialdos is None: return partialdos = partialdos.to('1/joule').magnitude\",\n \"started([\\\\s\\\\S]+?)Structure\\\\-optimization module stopped', sub_parser=TextParser(quantities=[ Quantity( 'optimization_step', r'(Optimization step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n *\\\\n\\\\-{10}|Time spent in this optimization\",\n \"governing permissions and # limitations under the License. # import numpy as np\",\n \"'x_exciting_hamiltonian_size': ('Maximum Hamiltonian size', None), 'x_exciting_pw': (r'Maximum number of plane\\\\-waves', None), 'x_exciting_lo': (r'Total\",\n \"0.) sec_method.x_exciting_xs_rgkmax = rgkmax sec_method.x_exciting_xs_scissor = self.input_xml_parser.get('xs/scissor', 0.0) sec_method.x_exciting_xs_vkloff = self.input_xml_parser.get('xs/vkloff', [0., 0.,\",\n \"band_energy * self._energy_unit res_n.append(band_energy) start = end res.append(res_n) elif key == 'band_k_points': res\",\n \"dtype=float, unit=ureg.hartree / ureg.bohr) ] self._quantities.append(Quantity( 'groundstate', r'(?:Self\\\\-consistent loop started|Groundstate module started)([\\\\s\\\\S]+?)Groundstate module\",\n \"ureg.bohr), Quantity( 'step', r'Optimization step\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'method', r'method\\\\s*=\\\\s*(\\\\w+)', repeats=False, dtype=str), Quantity(\",\n \"energy at this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', unit=ureg.hartree, repeats=False, dtype=float), Quantity( 'time', r'Time spent in\",\n \"get_scf_threshold(self, name): reference = self.get('groundstate', self.get('hybrids', {})) return reference.get('scf_iteration', [{}])[-1].get( name, [None, None])[-1]\",\n \"self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0) sec_method.x_exciting_xs_bse_rgkmax = rgkmax sec_method.x_exciting_xs_bse_sciavbd = self.input_xml_parser.get( 'xs/BSE/sciavbd', True) sec_method.x_exciting_xs_bse_sciavqbd = self.input_xml_parser.get(\",\n \"( r'Abs\\\\. change in max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)', ureg.hartree / ureg.bohr)} for name, key_unit in self._convergence_keys_mapping.items():\",\n \"self.input_xml_parser.get( 'xs/broad', 0.01, 'hartree') sec_method.x_exciting_xs_gqmax = self.input_xml_parser.get( 'xs/gqmax', 0.0, '1/bohr') sec_method.x_exciting_xs_lmaxapw = self.input_xml_parser.get('xs/lmaxapw',\",\n \"class BandstructureXMLParser(XMLParser): def __init__(self, **kwargs): # TODO make a parent class for dos\",\n \"'gw/freqgrid/freqmax', 1.0) sec_gw.frequency_grid_type = self.input_xml_parser.get( 'gw/freqgrid/fgrid', 'gaule2') sec_gw.number_of_frequencies = int(self.input_xml_parser.get( 'gw/freqgrid/nomeg', 16)) sec_gw.self_energy_c_number_of_poles\",\n \"os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = default.rsplit('.', 1) filename = '%s%s' % (target[0],\",\n \"not data_q: continue data_q = np.transpose(data_q, axes=(2, 0, 1)) for j in range(len(data)):\",\n \"not use this file except in compliance with the License. # You may\",\n \"if self.data_xs_parser.data is None: continue data.append(self.data_xs_parser.data) return data def parse_exciton(data, sec_scc): n_components =\",\n \"number of valence states', None), 'x_exciting_hamiltonian_size': ('Maximum Hamiltonian size', None), 'x_exciting_pw': (r'Maximum number\",\n \"self._partialdos_key) self._natoms = len(partial_dos) return self._natoms @property def number_of_dos(self): if self._ndos is None:\",\n \"cell', None), 'x_exciting_spin_treatment': ('Spin treatment', None), 'x_exciting_number_of_bravais_lattice_symmetries': ('Number of Bravais lattice symmetries', None),\",\n \"r'Maximum force magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)', str_operation=str_to_quantity_tolerances, unit=ureg.hartree / ureg.bohr, repeats=False, dtype=float), Quantity( 'energy_total', r'Total\",\n \"fermi_surface def _parse_evalqp(self, sec_scc): data = self.evalqp_parser.get('kpoints_eigenvalues') if data is None: return def\",\n \"radius *: *({re_float})', dtype=np.float64, unit=ureg.bohr), Quantity('radial_points', rf'radial points in muffin-tin *: *({re_float})', dtype=np.int32),\",\n \"self._convergence_keys_mapping = { 'x_exciting_effective_potential_convergence': ( r'RMS change in effective potential \\\\(target\\\\)', ureg.hartree), 'x_exciting_energy_convergence':\",\n \"\\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)', str_operation=str_to_eigenvalue, repeats=True)) class BandstructureDatParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None)\",\n \"False) sec_method.x_exciting_xs_tddft_anti_resonant_dielectric = self.input_xml_parser.get( 'xs/tddft/aresdf', False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel = self.input_xml_parser.get( 'xs/tddft/aresfxc', True) sec_method.x_exciting_xs_tddft_drude =\",\n \"0, 0]) if sec_method.x_exciting_xs_bse_xas: sec_method.x_exciting_xs_bse_xasatom = self.input_xml_parser.get( 'xs/BSE/xasatom', 0) sec_method.x_exciting_xs_bse_xasedge = self.input_xml_parser.get( 'xs/BSE/xasedge',\",\n \"is the case when there is a mismatch between files self.logger.warn( 'Mismatch in\",\n \"if val is not None: break if val is None: continue if key\",\n \"# charge contributions charge_contributions = iteration.get('charge_contributions', {}) for key, names in self._electron_charge_keys_mapping.items(): val\",\n \"parse_scc(self, section): sec_run = self.archive.run[-1] final = section if section.get('energy_total') is not None\",\n \"/ ureg.hartree) * volume.to('m**3').magnitude for spin in range(len(dos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin\",\n \"nspin = self.info_parser.get_number_of_spin_channels() def reshape(data): if data[0] is None: return return np.reshape(data, (nspin,\",\n \"= True def parse_scc(self, section): sec_run = self.archive.run[-1] final = section if section.get('energy_total')\",\n \"agreed to in writing, software # distributed under the License is distributed on\",\n \"i)teration number :([\\\\s\\\\S]+?)(?:\\\\n *\\\\n\\\\+{10}|\\\\+\\\\-{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=True), Quantity( 'final', r'(?:Convergence targets achieved\\\\. Performing final\",\n \"None @property def energy_unit(self): if self._energy_unit is None: axis = self.root.find('./axis') if axis\",\n \"# Get fermi energy: it is used to un-shift the band structure to\",\n \"= np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates = np.transpose( np.array(data[:3], dtype=float)) sec_system.x_exciting_clathrates_atom_labels = list(data[3]) else: sec_system.x_exciting_clathrates =\",\n \"# TODO read from xml file def get_files(name): bse_types = ['IP', 'singlet', 'triplet',\",\n \"def str_to_quantity_tolerances(val_in): return val_in.strip().replace('(', '').replace(')', '').split() def str_to_energy_dict(val_in): val = val_in.strip().split('\\\\n') energies =\",\n \"sec_scc.x_exciting_xs_tddft_loss_function_local_field = data[1] elif quantity == 'LOSS' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_loss_function_no_local_field =\",\n \"cell = self.get_initialization_parameter('lattice_vectors') if cell is None: return positions = np.dot(positions, cell.magnitude) return\",\n \"self.input_xml_parser.get( 'structure/crystal/basevect', np.eye(3)) species = self.input_xml_parser.get('structure/species/speciesfile') if positions is None or lattice_vectors is\",\n \"iterations\\\\s*\\\\:\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'force_convergence', r'Maximum force magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)', str_operation=str_to_quantity_tolerances, unit=ureg.hartree / ureg.bohr,\",\n \"self.number_of_lm else: lm = int(val_l) ** 2 + int(val_m) + int(val_l) atom =\",\n \"self._results[key] = res class DOSXMLParser(XMLParser): def __init__(self, **kwargs): super().__init__(None) self._nspin_key = 'nspin' self._totaldos_key\",\n \"at this step\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])),\",\n \"moment_contributions = iteration.get('moment_contributions', {}) for key, names in self._moment_keys_mapping.items(): val = None for\",\n \"# simply write parameters xc_functional = self.info_parser.get('initialization', {}).get('xc_functional') if xc_functional is not None:\",\n \"'time': msection.time_calculation = val else: setattr(msection, name, val) # energy, moment, charge contributions\",\n \"'xs/BSE/aresbse', True) sec_method.x_exciting_xs_bse_angular_momentum_cutoff = self.input_xml_parser.get( 'xs/BSE/lmaxdielt', 14) rgkmax = self.input_xml_parser.get('xs/BSE/rgkmax', None) if rgkmax\",\n \"sec_run = self.archive.run[-1] positions = self.info_parser.get_atom_positions(section.get('atomic_positions', {})) lattice_vectors = self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels = self.info_parser.get_atom_labels(section.get('atomic_positions',\",\n \"**kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin\",\n \"\\\\|G\\\\| for potential and density', 1 / ureg.bohr), 'x_exciting_gvector_size': (r'G\\\\-vector grid sizes', None),\",\n \"i.e. columns are supposed # to be what they are. What is the\",\n \"self.input_xml_parser.get('structure/crystal/scale', 1.0) positions = np.dot(positions, lattice_vectors) * ureg.bohr lattice_vectors = lattice_vectors * ureg.bohr\",\n \"in val: v = v.strip().split(':') if len(v) < 2: continue elif v[0].startswith('species'): species\",\n \"0.0) sec_method.x_exciting_xs_vkloff = self.input_xml_parser.get('xs/vkloff', [0., 0., 0.]) # TODO I am not certain\",\n \"__init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] def str_to_frequency(val_in): val = [v.split() for\",\n \"dtype=float) end = np.array( self.vertices[i + 1].attrib.get(self._coord_key).split(), dtype=float) res.append(np.linspace(start, end, self.number_of_k_points_per_segment[i])) elif key\",\n \"def number_of_spin_channels(self): if self._nspin is None: self._nspin = np.shape(np.transpose(self.data)[1:])[0] return self._nspin @property def\",\n \"forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr), ]), repeats=False))\",\n \"== 'FXC': sec_scc.x_exciting_xs_tddft_loss_function_local_field = data[1] elif quantity == 'LOSS' and ext == 'NLF_FXC':\",\n \"val[-2] nspin = 2 if occs[0] == 1. else 1 data = dict()\",\n \"unit = ureg.elementary_charge * ureg.bohr val = val_in.strip().split('\\\\n') properties = dict() atom_resolved =\",\n \"val_in.strip().split('\\\\n')] val = val[0] if len(val) == 1 else val return np.array(val, dtype=float)\",\n \"if self.distances[i] == self.distances[i - 1]: self._nkpts_segment.append(count) count = 1 else: count +=\",\n \"= self.info_parser.get_initialization_parameter('smearing_width') if smearing_width is not None: smearing_width = (smearing_width * ureg.hartree).to('joule') #\",\n \"in scf_iterations: sec_scf_iteration = sec_scc.m_create(ScfIteration) parse_scf(scf_iteration, sec_scf_iteration) return sec_scc def parse_system(self, section): sec_run\",\n \"self.get('initialization', {}).get(key, default) class ExcitingParser: def __init__(self): self.info_parser = ExcitingInfoParser() self.dos_parser = DOSXMLParser(energy_unit=ureg.hartree)\",\n \"positions.magnitude # clathrate info clathrate_file = self.get_exciting_files('str.out') if clathrate_file: sec_system.x_exciting_clathrates = True self.data_clathrate_parser.mainfile\",\n \"xc_functional_map = { 2: ['LDA_C_PZ', 'LDA_X_PZ'], 3: ['LDA_C_PW', 'LDA_X_PZ'], 4: ['LDA_C_XALPHA'], 5: ['LDA_C_VBH'],\",\n \"= [float(vii) for vii in v[1].split()] vi = vi[0] if len(vi) == 1\",\n \"TODO add PDOS def _parse_bandstructure_dat(self, sec_scc): self.bandstructure_dat_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_dat_parser.band_energies if\",\n \"and name.endswith('bxsf'): parser = self.fermisurf_parser parser_function = self._parse_fermisurface elif name.startswith('EVALQP') and (name.endswith('DAT') or\",\n \"- 1 val_l = self.partial_dos[i].attrib.get(self._l_key, None) val_m = self.partial_dos[i].attrib.get(self._m_key, None) if val_l is\",\n \"energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') # TODO I am\",\n \"for i in range(len(qpoints)): data_q = [] files_q = [f for f in\",\n \"# energy, moment, charge contributions parse_scf(final, sec_scc) # forces forces = section.get('forces') if\",\n \"* ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_dat_parser.band_k_points nkpts_segment\",\n \"@property def distances(self): if self._distances is None: dist = np.transpose(self.data)[0] n_k_points = np.where(dist\",\n \"= list(data[3]) else: sec_system.x_exciting_clathrates = False potential_mixing = self.info_parser.get_initialization_parameter('potential_mixing') if potential_mixing is not\",\n \"if bands: self._bands.append(bands) # add atom-resolved bands_atom = self.root.findall('./*/%s' % self._atom_key) for band\",\n \"of energy sec_smearing.width = smearing_width.magnitude for name in self.info_parser._convergence_keys_mapping.keys(): threshold = self.info_parser.get_scf_threshold(name) if\",\n \"(name.startswith('FERMISURF') or name.startswith('FS')) and name.endswith('bxsf'): parser = self.fermisurf_parser parser_function = self._parse_fermisurface elif name.startswith('EVALQP')\",\n \"if some scf steps dont have the quantity # the only thing that\",\n \"self.fermisurf_parser parser_function = self._parse_fermisurface elif name.startswith('EVALQP') and (name.endswith('DAT') or name.endswith('TXT')): parser = self.evalqp_parser\",\n \"that we can do is to assume that the first steps are the\",\n \"a mismatch between files self.logger.warn( 'Mismatch in EXCITON and file type', data=dict(file=quantity)) sec_scc\",\n \"a parent clas for bandstructure dat and xml self._nspin = None self._nkpts_segment =\",\n \"sccs = [] for quantity in ['EXCITON', 'EPSILON', 'SIGMA', 'LOSS']: files = get_files(quantity)\",\n \"parser = self.bandstructure_dat_parser parser_function = self._parse_bandstructure_dat elif name.startswith('BAND') and name.endswith('OUT'): parser = self.band_out_parser\",\n \"if eigs_gw[0] is None: return nspin = self.info_parser.get_number_of_spin_channels() def reshape(data): if data[0] is\",\n \"None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None self._distances = None\",\n \"force_convergence[0] sec_scc.x_exciting_geometry_optimization_threshold_force = force_convergence[1] sec_scc = parse_configuration(structure_optimization) if sec_scc is None: return #\",\n \"return True return False def _parse_dos(self, sec_scc): if self.dos_parser.get('totaldos', None) is None: return\",\n \"if not data_q: continue data_q = np.transpose(data_q, axes=(2, 0, 1)) for j in\",\n \"lm = i % self.number_of_lm else: lm = int(val_l) ** 2 + int(val_m)\",\n \"band_energies(self): if self._band_energies is None: data = np.transpose(self.data) n_kpoints = np.where(data[0] == data[0][0])[0][1]\",\n \"None) val_m = self.partial_dos[i].attrib.get(self._m_key, None) if val_l is None or val_m is None:\",\n \"= self.root.findall('./*/%s' % self._atom_key) for band in bands_atom: self._bands.append(band.findall('./%s' % self._band_key)) return self._bands\",\n \"r'(Exchange-correlation type[\\\\s\\\\S]+?\\\\n *\\\\n)', sub_parser=TextParser(quantities=[ Quantity('type', r'Exchange-correlation type +: +(\\\\S+)'), Quantity( 'name_reference', r'\\\\n *(.+?,.+)',\",\n \"for quantity in ['EXCITON', 'EPSILON', 'SIGMA', 'LOSS']: files = get_files(quantity) for i in\",\n \"None: return def get_data(key): if key == 'k_points': return np.array([d[0][:3] for d in\",\n \"parser_function = self._parse_evalqp elif name.startswith('TDOS') and name.endswith('OUT'): parser = self.dos_out_parser parser_function = self._parse_dos_out\",\n \"1, 1]) rgkmax = self.input_xml_parser.get('xs/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax',\",\n \"for spin in range(len(dos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value =\",\n \"ext in ['FXC', 'NLF_FXC']: data = get_data(quantity, ext) if not data[0]: continue if\",\n \"n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_oscillator_strength = np.reshape( data[3], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_re = np.reshape( data[4],\",\n \"Forces, ForcesEntry, ScfIteration, BandGap ) from nomad.datamodel.metainfo.workflow import Workflow, GeometryOptimization from .metainfo.exciting import\",\n \"groundstate and hybrids calculation for module in ['groundstate', 'hybrids']: sec_scc = parse_configuration(self.info_parser.get(module)) if\",\n \"in bse_types: for scr_type in scr_types: files = self.get_exciting_files( '%s_BSE%s_SCR%s.OUT' % (name, bse_type,\",\n \"res = [] for i in range(len(self.number_of_k_points_per_segment)): start = np.array( self.vertices[i].attrib.get(self._coord_key).split(), dtype=float) end\",\n \"iteration.get('charge_contributions', {}) for key, names in self._electron_charge_keys_mapping.items(): val = None for name in\",\n \"self.get_exciting_files('input.xml') if not input_file: return self.input_xml_parser.mainfile = input_file[0] xstype = self.input_xml_parser.get('xs/xstype', '') if\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"if os.path.isfile(filename): return [filename] filename = os.path.join(self.info_parser.maindir, default) if not os.path.isfile(filename): file_ext =\",\n \"]+)', str_operation=str_to_quantity_tolerances, unit=ureg.hartree / ureg.bohr, repeats=False, dtype=float), Quantity( 'energy_total', r'Total energy at this\",\n \"get_data('k_points') sec_gw_eigenvalues.energies = reshape(eigs_gw) sec_gw_eigenvalues.value_exchange = reshape(get_data('Sx')) eigs_gw_C = reshape(get_data('Sc')) if eigs_gw_C is\",\n \"(filename, target[1]) filename = os.path.join(self.info_parser.maindir, filename) if os.path.isfile(filename): return [filename] filename = os.path.join(self.info_parser.maindir,\",\n \"self._neigs_segment is None: data = np.transpose(self.data) self._neigs_segment = int(max(data[0])) return self._neigs_segment class BandOutParser(DataTextParser):\",\n \"= os.path.join(self.info_parser.maindir, default) if not os.path.isfile(filename): file_ext = default.split('.')[-1] mainfile_base = mainfile.rsplit('.', 1)[0].replace('INFO',\",\n \"sec_fermisurface.x_exciting_number_of_bands_fermi_surface = band_parameters[0] sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface = np.product(band_parameters[1]) sec_fermisurface.x_exciting_grid_fermi_surface = band_parameters[1] sec_fermisurface.x_exciting_origin_fermi_surface = band_parameters[2] sec_fermisurface.x_exciting_vectors_fermi_surface\",\n \"n_epsilon sec_scc.x_exciting_xs_bse_epsilon_energies = np.reshape( data[0], (n_components, n_epsilon)) * ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re = np.reshape( data[1],\",\n \"between files self.logger.warn( 'Mismatch in EXCITON and file type', data=dict(file=quantity)) sec_scc = sec_run.m_create(Calculation)\",\n \"None for name in names: val = moment_contributions.get(name, None) if val is not\",\n \"= self.input_xml_parser.get('xs/xstype', '') if xstype.lower() == 'bse': self._parse_xs_bse() elif xstype.lower() == 'tddft': self._parse_xs_tddft()\",\n \"['Sum of eigenvalues', 'sum of eigenvalues'], 'x_exciting_effective_potential_energy': ['Effective potential energy'], 'x_exciting_coulomb_potential_energy': ['Coulomb potential\",\n \"('Smearing scheme', None), 'smearing_width': ('Smearing width', None)} for name, key_unit in self._system_keys_mapping.items(): initialization_quantities.append(\",\n \"'x_exciting_gap': (r'Estimated fundamental gap', ureg.hartree), 'time': (r'Wall time \\\\(seconds\\\\)', ureg.s)} for name, key_unit\",\n \"self._quantities.append(Quantity( 'hybrids', r'Hybrids module started([\\\\s\\\\S]+?)Hybrids module stopped', sub_parser=TextParser(quantities=module_quantities) )) def get_atom_labels(self, section): labels\",\n \"sec_method.gw.starting_point = xc_functional_name sec_scc = sec_run.m_create(Calculation) sec_scc.method_ref = sec_method if sec_run.system: sec_scc.system_ref =\",\n \"key == 'eigenvalues': res = res * ureg.hartree return res sec_eigenvalues = sec_scc.m_create(BandEnergies)\",\n \"== self.distances[i - 1]: self._nkpts_segment .append(count) count = 1 else: count += 1\",\n \"including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr), ]), repeats=False)) self._quantities.append(Quantity(\",\n \"sec_gw.core_treatment = self.input_xml_parser.get( 'gw/coreflag', 'all') sec_gw.polarizability_number_of_empty_states = int( self.input_xml_parser.get('gw/nempty', 0)) sec_gw.ngridq = self.input_xml_parser.get('gw/ngridq',\",\n \"'_X_' in name: sec_xc_functional.exchange.append(Functional(name=name)) elif '_C_' in name: sec_xc_functional.correlation.append(Functional(name=name)) elif 'HYB' in name:\",\n \"'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic',\",\n \"1 for i in range(1, len(self.distances)): if self.distances[i] == self.distances[i - 1]: self._nkpts_segment\",\n \"data = dict() data['occupancies'] = np.reshape(occs, (nspin, len(occs) // nspin)) data['eigenvalues'] = np.reshape(eigs,\",\n \"clathrate_file = self.get_exciting_files('str.out') if clathrate_file: sec_system.x_exciting_clathrates = True self.data_clathrate_parser.mainfile = clathrate_file[0] if self.data_clathrate_parser.data:\",\n \"self._energy_keys_mapping = { 'energy_total': ['Total energy', 'total energy'], 'x_exciting_fermi_energy': ['Fermi energy', 'Fermi'], 'energy_kinetic_electronic':\",\n \"nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.value = band_energies[nb] +\",\n \"section={}, positions=None, positions_format=None): positions = positions if positions is not None else section.get('positions')\",\n \"if self.eigval_parser.get('eigenvalues_occupancies', None) is None: return nspin = self.info_parser.get_number_of_spin_channels() def get_data(key): data =\",\n \"smearing_width is not None: smearing_width = (smearing_width * ureg.hartree).to('joule') # TODO smearing with\",\n \"r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)' % key_unit[0], repeats=False, unit=key_unit[1])) self._convergence_keys_mapping = { 'x_exciting_effective_potential_convergence': ( r'RMS change in\",\n \"float)) return dict( number=np.array(val[0], dtype=int), values=val[1] * ureg.hartree, weights=val[2]) # TODO Read also\",\n \"end = start + nkpts_segment self._band_k_points.append( np.transpose(data[2:5])[start:end]) start = end return self._band_k_points @property\",\n \"is None: continue # add data to scc # TODO add support for\",\n \"init_parameters(self): self._ndos = None self._natoms = None self._nspin = None self._nlm = None\",\n \"= self.input_xml_parser.get( 'xs/screening/screentype', 'full') if self.input_xml_parser.get('xs/BSE') is not None: sec_method.x_exciting_xs_bse_antiresonant = self.input_xml_parser.get( 'xs/BSE/aresbse',\",\n \") initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation type[\\\\s\\\\S]+?\\\\n *\\\\n)', sub_parser=TextParser(quantities=[ Quantity('type', r'Exchange-correlation type +: +(\\\\S+)'), Quantity(\",\n \"def parse_system(self, section): sec_run = self.archive.run[-1] positions = self.info_parser.get_atom_positions(section.get('atomic_positions', {})) lattice_vectors = self.info_parser.get_initialization_parameter('lattice_vectors')\",\n \"positions = self.info_parser.get_atom_positions(section.get('atomic_positions', {})) lattice_vectors = self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels = self.info_parser.get_atom_labels(section.get('atomic_positions', {})) input_file =\",\n \"self._nspin = None self._nlm = None self._energies = None self._total_dos = None self._partial_dos\",\n \"[point.attrib.get(self._dos_key) for point in diagram], dtype=float) return dos def parse(self, key): if self._results\",\n \"volume', 1 / ureg.bohr ** 3), 'x_exciting_number_of_atoms': ('Total number of atoms per unit\",\n \"% (self._partialdos_key, self._diagram_key)) return self._partial_dos @property def energies(self): if self._energies is None: if\",\n \"not None: sec_gap.value_optical = optical_band_gap def parse_miscellaneous(self): sec_worfklow = self.archive.m_create(Workflow) sec_worfklow.type = 'single_point'\",\n \"not None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface = fermi_energy sec_fermisurface.x_exciting_values_fermi_surface = fermi_surface def _parse_evalqp(self, sec_scc): data =\",\n \"str(i + 1).rjust(3, '0'))] for f in files_q: self.data_xs_parser.mainfile = f if self.data_xs_parser.data\",\n \"repeats=True)) class ExcitingGWOutParser(TextParser): def __init__(self, mainfile, logger): super().__init__(mainfile, logger=logger) def init_quantities(self): self._quantities =\",\n \"in ['EPSILON', 'LOSS', 'SIGMA']: for ext in ['FXC', 'NLF_FXC']: data = get_data(quantity, ext)\",\n \"* unit))) else: vi = [float(vii) for vii in v[1].split()] vi = vi[0]\",\n \"count = 1 else: count += 1 self._nkpts_segment.append(count) return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self):\",\n \"target = filename.rsplit('.', 1) filepath = '%s%s' % (target[0], suffix) if target[1:]: filepath\",\n \"= fermi_surface def _parse_evalqp(self, sec_scc): data = self.evalqp_parser.get('kpoints_eigenvalues') if data is None: return\",\n \"/ ureg.bohr, repeats=False, dtype=float), Quantity( 'energy_total', r'Total energy at this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', unit=ureg.hartree,\",\n \"positions_format = specie.get('positions_format', None) if positions_format is not None: break return positions_format def\",\n \"'scf_iteration', r'(?:I| i)teration number :([\\\\s\\\\S]+?)(?:\\\\n *\\\\n\\\\+{10}|\\\\+\\\\-{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=True), Quantity( 'final', r'(?:Convergence targets achieved\\\\.\",\n \"= len(self.total_dos) return self._nspin @property def number_of_atoms(self): if self._natoms is None: partial_dos =\",\n \"'energy_correlation': ['Correlation energy', 'correlation'], 'x_exciting_correlation_energy': ['Correlation energy', 'correlation'], 'energy_sum_eigenvalues': ['Sum of eigenvalues', 'sum\",\n \"APW functions', 1 / ureg.bohr), 'x_exciting_gmaxvr': (r'Maximum \\\\|G\\\\| for potential and density', 1\",\n \"(nspin, len(data) // nspin, 2)) data = np.transpose(data, axes=(2, 0, 1)) sec_dos.energies =\",\n \"elif quantity == 'LOSS' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_loss_function_local_field = data[1] elif quantity\",\n \"'xc_functional', r'(Exchange-correlation type[\\\\s\\\\S]+?\\\\n *\\\\n)', sub_parser=TextParser(quantities=[ Quantity('type', r'Exchange-correlation type +: +(\\\\S+)'), Quantity( 'name_reference', r'\\\\n\",\n \"None)} for name, key_unit in self._system_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1],\",\n \"= np.transpose(data, axes=(2, 0, 1)) sec_dos.energies = data[0][0] * ureg.hartree + energy_fermi volume\",\n \"bands(self): if self._bands is None: bands = self.root.findall('./%s' % self._band_key) self._bands = []\",\n \"= val_in.strip().split('\\\\n') energies = dict() for v in val: v = v.split(':') if\",\n \"== 'eigenvalues': res = res * ureg.hartree return res sec_eigenvalues = sec_scc.m_create(BandEnergies) sec_eigenvalues.kpoints\",\n \"free up memory parser.mainfile = None def _parse_input_xs(self, sec_method): xstype = self.input_xml_parser.get('xs/xstype', None)\",\n \"'xs/tddft/mdfqtype', 0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals = self.input_xml_parser.get( 'xs/tddft/nwacont', 0) sec_method.x_exciting_xs_tetra = self.input_xml_parser.get( 'xs/tetra/tetradf', False) def\",\n \"atom_labels.extend([species[n].split('.')[0]] * len(atoms[n])) if positions is None or atom_labels is None: return sec_system\",\n \"self._nspin = np.shape(np.transpose(self.data)[1:])[0] return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment\",\n \"'nspin' self._totaldos_key = 'totaldos' self._partialdos_key = 'partialdos' self._diagram_key = 'diagram' self._l_key = 'l'\",\n \"**kwargs): super().__init__(None) self._nspin_key = 'nspin' self._totaldos_key = 'totaldos' self._partialdos_key = 'partialdos' self._diagram_key =\",\n \"partialdos[lm][spin][atom] def _parse_bandstructure(self, sec_scc): # we need to set nspin again as this\",\n \"forces is not None: sec_forces = sec_scc.m_create(Forces) sec_forces.total = ForcesEntry(value=forces) # scf iterations\",\n \"species: labels += [specie.get('symbol')] * len(specie.get('positions')) return labels def get_positions_format(self, section): positions_format =\",\n \"= '%s%s' % (target[0], suffix) if target[1:]: filepath = '%s.%s' % (filepath, target[1])\",\n \"= mainfile.rsplit('.', 1)[0].replace('INFO', '') options = [ f for f in os.listdir( self.info_parser.maindir)\",\n \"if self._energies is None: if self.total_dos is None: return self._energies = np.array( [float(point.attrib.get(self._energy_key))\",\n \"atom = i // (self.number_of_lm * self.number_of_spin_channels) res[lm][spin][atom] = self._get_dos(self.partial_dos[i]) if self.energy_unit is\",\n \"dtype=float) val = np.transpose(val) occs = val[-1] eigs = val[-2] nspin = 2\",\n \"} self._electron_charge_keys_mapping = { 'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage': ['core leakage'], 'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge': ['interstitial'],\",\n \"sec_method.x_exciting_xs_bse_xas = self.input_xml_parser.get( 'xs/BSE/xas', False) sec_method.x_exciting_xs_bse_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlbse', [0, 0, 0, 0])\",\n \"len(eigs_gw) sec_gw_eigenvalues.kpoints = get_data('k_points') sec_gw_eigenvalues.energies = reshape(eigs_gw) sec_gw_eigenvalues.value_exchange = reshape(get_data('Sx')) eigs_gw_C = reshape(get_data('Sc'))\",\n \"x.split(':')])])) ) self._quantities.append(Quantity( 'initialization', r'(?:All units are atomic|Starting initialization)([\\\\s\\\\S]+?)(?:Using|Ending initialization)', repeats=False, sub_parser=TextParser(quantities=initialization_quantities)) )\",\n \"= 'm' self._energy_key = 'e' self._dos_key = 'dos' self._unit_key = 'unit' self._energy_unit =\",\n \"val[0][1].units sec_charges.total = charge_contributions.get('total charge') elif key == 'charge_total': pass else: setattr(msection, key,\",\n \"self.parse_file(f, sec_scc) frequency_data = self.info_gw_parser.get('frequency_data', None) if frequency_data is not None: number =\",\n \"0)) sec_gw.self_energy_c_number_of_empty_states = int(self.input_xml_parser.get( 'gw/selfenergy/nempty', 0)) sec_gw.self_energy_singularity_treatment = self.input_xml_parser.get( 'gw/selfenergy/singularity', 'mpd') sec_gw.self_energy_c_analytical_continuation =\",\n \"does not exist self._quantities.append( Quantity( 'frequency_data', r'frequency list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)', str_operation=str_to_frequency, repeats=False) ) self._quantities.append( Quantity(\",\n \"ureg.hartree eigs_gw = get_data('E_GW') if eigs_gw[0] is None: return nspin = self.info_parser.get_number_of_spin_channels() def\",\n \"np.transpose(np.array([v.split() for v in val[2:]], dtype=float)) eigs = {keys[i]: eigs[i] for i in\",\n \"elif name == 'x_exciting_species_rtmin': setattr(sec_system, name, ' '.join([str(v) for v in val])) else:\",\n \"* ureg.hartree).to('joule') # TODO smearing with should have units of energy sec_smearing.width =\",\n \"self.dos_parser.energies + energy_fermi volume = self.info_parser.get_unit_cell_volume() totaldos = self.dos_parser.get('totaldos') * volume.to('m**3').magnitude for spin\",\n \"def number_of_spin_channels(self): if self._nspin is None: self._nspin = np.shape(np.transpose(self.data))[0] - 6 return self._nspin\",\n \"= xstype sec_method.x_exciting_xs_broadening = self.input_xml_parser.get( 'xs/broad', 0.01, 'hartree') sec_method.x_exciting_xs_gqmax = self.input_xml_parser.get( 'xs/gqmax', 0.0,\",\n \"gmb * gmaxvr sec_gw.bare_coulomb_cutofftype = self.input_xml_parser.get( 'gw/barecoul/cutofftype', 'none') sec_gw.screened_coulomb_volume_average = self.input_xml_parser.get( 'gw/scrcoul/sciavtype', 'isotropic')\",\n \"return self._vertices @property def number_of_spin_channels(self): if self._nspin is None: self._nspin = 1 return\",\n \"@property def distances(self): if self._distances is None: if not self.bands: return self._distances =\",\n \"vi in v[1].split()] v[1] = v[1][0] if len(v[1]) == 1 else v[1] if\",\n \"name, val) # energy, moment, charge contributions parse_scf(final, sec_scc) # forces forces =\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"(self.number_of_spin_channels * self.number_of_lm)) // self.number_of_lm else: spin = int(spin) - 1 val_l =\",\n \"dtype=np.dtype(np.float64))]))) initialization_quantities.append(Quantity( 'potential_mixing', r'Using ([\\\\w ]+) potential mixing', repeats=False, flatten=False) ) initialization_quantities.append(Quantity( 'xc_functional',\",\n \"'core electron kinetic'], 'x_exciting_dft_d2_dispersion_correction': ['DFT-D2 dispersion correction'] } self._electron_charge_keys_mapping = { 'x_exciting_core_charge': ['core'],\",\n \"if self.dos_parser.get('totaldos', None) is None: return # Get fermi energy: it is used\",\n \"this step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions at this step\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)',\",\n \"None) xc_functional_names.append(exchange) sec_xc_functional = sec_dft.m_create(XCFunctional) for name in xc_functional_names: if name is None:\",\n \"def _parse_evalqp(self, sec_scc): data = self.evalqp_parser.get('kpoints_eigenvalues') if data is None: return def get_data(key):\",\n \"res sec_eigenvalues = sec_scc.m_create(BandEnergies) sec_eigenvalues.kpoints = self.eigval_parser.get('k_points') sec_eigenvalues.occupations = get_data('occupancies') sec_eigenvalues.energies = get_data('eigenvalues')\",\n \"if not self.bands: return self._distances = [ point.attrib.get(self._distance_key) for point in self.bands[0][0]] self._distances\",\n \"repeats=False) ) self._quantities.append( Quantity( 'optical_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) class ExcitingEvalqpParser(TextParser): def __init__(self):\",\n \"if xstype.lower() == 'bse': self._parse_xs_bse() elif xstype.lower() == 'tddft': self._parse_xs_tddft() def _parse_input_gw(self, sec_method):\",\n \"band_k_points = self.bandstructure_parser.get('band_k_points') nkpts_segment = self.bandstructure_parser.number_of_k_points_per_segment band_seg_labels = self.bandstructure_parser.get('band_segm_labels') for nb in range(len(band_energies[n])):\",\n \"// n_components sec_scc.x_exciting_xs_bse_sigma_energies = np.reshape( data[0], (n_components, n_sigma)) * ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re = np.reshape(\",\n \"]+) potential mixing', repeats=False, flatten=False) ) initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation type[\\\\s\\\\S]+?\\\\n *\\\\n)', sub_parser=TextParser(quantities=[ Quantity('type',\",\n \"1. else 1 data = dict() data['occupancies'] = np.reshape(occs, (nspin, len(occs) // nspin))\",\n \"if tetradf else None, 'AC' if nwacont else None, 'NAR' if not aresdf\",\n \"in self.number_of_k_points_per_segment: end = start + nkpts_segment self._band_k_points.append( np.transpose(data[2:5])[start:end]) start = end return\",\n \"== 'x_exciting_spin_treatment': sub_sec = sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment = val elif name == 'x_exciting_species_rtmin': setattr(sec_system,\",\n \"sec_scc.x_exciting_xs_tddft_epsilon_energies = data[0][0][0] * ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field = data[1:] elif quantity == 'EPSILON' and\",\n \"if energy_fermi is None: continue energy_fermi = energy_fermi.to(\\\"hartree\\\") sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi\",\n \"exciting_files.append(fname) break for f in exciting_files: self.parse_file(f, sec_scc) # structure optimization structure_optimization =\",\n \"None: bands = self.root.findall('./%s' % self._band_key) self._bands = [] if bands: self._bands.append(bands) #\",\n \"[sec_method_ref] self.parse_file('input.xml', sec_method) # parse properties input_file = self.get_exciting_files('input.xml') if not input_file: return\",\n \"data = np.transpose(self.data) self._band_k_points = [] start = 0 for nkpts_segment in self.number_of_k_points_per_segment:\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"if quantity == 'EXCITON': parse_function = parse_exciton elif quantity == 'EPSILON': parse_function =\",\n \"= vi * unit properties['atom_resolved'] = atom_resolved return properties def str_to_quantity_tolerances(val_in): return val_in.strip().replace('(',\",\n \"= np.array([d[1].get(key, None) for d in data]) if None in energy: return energy\",\n \"*E*', unit=ureg.hartree, repeats=True)) class ExcitingEigenvalueParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = []\",\n \"vi[0] if len(vi) == 1 else vi properties[v[0].strip()] = vi * unit properties['atom_resolved']\",\n \"self.get('hybrids', {})) return reference.get('scf_iteration', [{}])[-1].get( name, [None, None])[-1] def get_scf_quantity(self, name): n_scf =\",\n \"% (target[0], suffix) if target[1:]: filepath = '%s.%s' % (filepath, target[1]) filepath =\",\n \"sec_method.gw.frequency_number = number sec_method.gw.frequency_values = frequency_data.get('values') sec_method.gw.frequency_weights = frequency_data.get('weights') fundamental_band_gap = self.info_gw_parser.get('direct_band_gap', None)\",\n \"Quantity( 'name_reference', r'\\\\n *(.+?,.+)', str_operation=lambda x: [v.strip() for v in x.split(':')]), Quantity( 'parameters',\",\n \"'x_exciting_gkmax': (r'Maximum \\\\|G\\\\+k\\\\| for APW functions', 1 / ureg.bohr), 'x_exciting_gmaxvr': (r'Maximum \\\\|G\\\\| for\",\n \"+ nkpts_segment self._band_k_points.append( np.transpose(data[2:5])[start:end]) start = end return self._band_k_points @property def distances(self): if\",\n \"sec_dos_values.spin = spin sec_dos_values.atom_index = atom sec_dos_values.value = partialdos[lm][spin][atom] def _parse_bandstructure(self, sec_scc): #\",\n \"key == 'charge_total': pass else: setattr(msection, key, val) # moment contributions moment_contributions =\",\n \"sec_method.x_exciting_xs_bse_sciavbd = self.input_xml_parser.get( 'xs/BSE/sciavbd', True) sec_method.x_exciting_xs_bse_sciavqbd = self.input_xml_parser.get( 'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get(\",\n \"self.info_parser.get_number_of_spin_channels() def get_data(key): data = self.eigval_parser.get('eigenvalues_occupancies') # reshaping is not necessary as this\",\n \"= None self._total_dos = None self._partial_dos = None @property def energy_unit(self): if self._energy_unit\",\n \"'x_exciting_kpoint_grid': (r'k\\\\-point grid', None), 'x_exciting_kpoint_offset': (r'k\\\\-point offset', None), 'x_exciting_number_kpoints': (r'Total number of k\\\\-points',\",\n \"in options if f.endswith(file_ext)] options.sort() filenames = [os.path.join(self.info_parser.maindir, f) for f in options]\",\n \"not None else logging self._calculation_type = None self.init_parser() sec_run = self.archive.m_create(Run) sec_run.program =\",\n \"def parse_loss(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_loss = len(data[0]) //\",\n \"s in species]) if positions is None: return positions = np.array(positions) positions_format =\",\n \"None: # This is the case when there is a mismatch between files\",\n \"val = val_in.strip().split('\\\\n') properties = dict() atom_resolved = [] species = None for\",\n \"structure optimization structure_optimization = self.info_parser.get('structure_optimization', {}) for optimization_step in structure_optimization.get('optimization_step', []): sec_scc =\",\n \"= self._parse_band_out elif name.startswith('input') and name.endswith('xml'): parser = self.input_xml_parser if self._calculation_type == 'gw':\",\n \"lattice_vectors_reciprocal = self.info_parser.get_initialization_parameter( 'lattice_vectors_reciprocal') sec_atoms.lattice_vectors_reciprocal = lattice_vectors_reciprocal if len(sec_run.system) > 1: return sec_system\",\n \"[None, None])[0] sec_xc_functional.reference = xc_functional.get('name_reference', [None, None])[1] sec_electronic.n_spin_channels = self.info_parser.get_number_of_spin_channels() if self._calculation_type ==\",\n \"{})) input_file = self.get_exciting_files('input.xml') if positions is None: # get it from input.xml\",\n \"distances(self): if self._distances is None: if not self.bands: return self._distances = [ point.attrib.get(self._distance_key)\",\n \"is not None: sec_xc_functional.name = xc_functional.get('name_reference', [None, None])[0] sec_xc_functional.reference = xc_functional.get('name_reference', [None, None])[1]\",\n \"= self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_screening_rgkmax = rgkmax sec_method.x_exciting_xs_screening_type = self.input_xml_parser.get( 'xs/screening/screentype', 'full') if self.input_xml_parser.get('xs/BSE')\",\n \"fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break for f in exciting_files: self.parse_file(f, sec_scc)\",\n \"sec_mt_charge_atom = msection.m_create(x_exciting_section_MT_charge_atom) sec_mt_charge_atom.x_exciting_MT_charge_atom_index = n + 1 sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol = val[n][0] sec_mt_charge_atom.x_exciting_MT_charge_atom_value =\",\n \"elif quantity == 'SIGMA' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_sigma_no_local_field = data[1:3] def parse_xs(self):\",\n \"metainfo_name, threshold) # additionally, set threshold to global metainfo. This is killing me!\",\n \"input.xml for normal calculations # in addition to INFO.OUT return else: return files\",\n \"= [None] * (n_scf - len(quantity)) + quantity return quantity def get_xc_functional_name(self): #\",\n \"= np.array( [point.attrib.get(self._dos_key) for point in diagram], dtype=float) return dos def parse(self, key):\",\n \"mainfile self.bandstructure_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_parser.get('band_energies', []) for n in range(len(band_energies)): #\",\n \"'GGA_X_WC'], 30: ['GGA_C_AM05', 'GGA_C_AM05'], 300: ['GGA_C_BGCP', 'GGA_X_PBE'], 406: ['HYB_GGA_XC_PBEH'], 408: ['HYB_GGA_XC_HSE03']} xc_functional =\",\n \"f) for f in options] else: filenames = [filename] filenames = [f for\",\n \"= self.info_parser.get_number_of_spin_channels() band_energies = self.band_out_parser.band_energies if band_energies is None: return # Get fermi\",\n \"of empty states', None), 'x_exciting_valence_states': ('Total number of valence states', None), 'x_exciting_hamiltonian_size': ('Maximum\",\n \"sec_method.methods_ref = [sec_method_ref] self.parse_file('input.xml', sec_method) # parse properties input_file = self.get_exciting_files('input.xml') if not\",\n \"self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_dat_parser.band_energies if band_energies is None: return # Get fermi energy:\",\n \"versions of exciting self._energy_keys_mapping = { 'energy_total': ['Total energy', 'total energy'], 'x_exciting_fermi_energy': ['Fermi\",\n \"self.band_out_parser parser_function = self._parse_band_out elif name.startswith('input') and name.endswith('xml'): parser = self.input_xml_parser if self._calculation_type\",\n \"file sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref]\",\n \"atom_resolved return properties def str_to_quantity_tolerances(val_in): return val_in.strip().replace('(', '').replace(')', '').split() def str_to_energy_dict(val_in): val =\",\n \"f.endswith('QMT%s.OUT' % str(i + 1).rjust(3, '0'))] for f in files_q: self.data_xs_parser.mainfile = f\",\n \"'gw/coreflag', 'all') sec_gw.polarizability_number_of_empty_states = int( self.input_xml_parser.get('gw/nempty', 0)) sec_gw.ngridq = self.input_xml_parser.get('gw/ngridq', [1, 1, 1])\",\n \"sec_gw.bare_coulomb_cutofftype = self.input_xml_parser.get( 'gw/barecoul/cutofftype', 'none') sec_gw.screened_coulomb_volume_average = self.input_xml_parser.get( 'gw/scrcoul/sciavtype', 'isotropic') sec_gw.screened_Coulomb = self.input_xml_parser.get(\",\n \"i in range(len(self.vertices) - 1): start = self.vertices[i].attrib.get('label') end = self.vertices[i + 1].attrib.get('label')\",\n \"get it by concatenating species symbols species = self.get('initialization', {}).get('species', []) labels =\",\n \"str_operation=str_to_array, unit=1 / ureg.bohr, repeats=False, convert=False), ] self._system_keys_mapping = { 'x_exciting_unit_cell_volume': ('Unit cell\",\n \"for specie in species: labels += [specie.get('symbol')] * len(specie.get('positions')) return labels def get_positions_format(self,\",\n \"def parse_sigma(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_sigma = len(data[0]) //\",\n \"self.archive.run[-1] sec_method = sec_run.m_create(Method) sec_method.basis_set.append(BasisSet(type='(L)APW+lo')) sec_dft = sec_method.m_create(DFT) sec_electronic = sec_method.m_create(Electronic) sec_electronic.method =\",\n \"if self.file_exists(fname): exciting_files.append(fname) break # Parse DFT band structure from one of the\",\n \"'singlet', 'triplet', 'RPA'] scr_types = ['full', 'diag', 'noinvdiag', 'longrange'] bse_files = [] for\",\n \"get_files(quantity) for i in range(len(files)): data = get_data(files[i]) if not data: sccs.append(None) continue\",\n \"f in filenames if os.access(f, os.F_OK)] return filenames def file_exists(self, filename): \\\"\\\"\\\"Checks if\",\n \"self.number_of_dos)) for i in range(len(self.total_dos)): spin = self.total_dos[i].attrib.get(self._nspin_key, i) res[i] = self._get_dos(self._total_dos[i]) if\",\n \"['total charge'], 'x_exciting_section_MT_charge_atom': ['atom_resolved'] } self._moment_keys_mapping = { 'x_exciting_interstitial_moment': ['interstitial'], 'x_exciting_total_MT_moment': ['total moment\",\n \"format for the spin polarized case # I cannot find an example bandstructure\",\n \"['total moment'], 'x_exciting_section_MT_moment_atom': ['atom_resolved'] } def get_exciting_files(self, default): mainfile = os.path.basename(self.info_parser.mainfile) suffix =\",\n \"self.info_parser.get_scf_threshold(name) if threshold is None: continue metainfo_name = 'x_exciting_scf_threshold_%s_change' % name.split('_')[-2] setattr(sec_method, metainfo_name,\",\n \"ExcitingEvalqpParser() self.dos_out_parser = DataTextParser() self.bandstructure_dat_parser = BandstructureDatParser(energy_unit=ureg.hartree) self.band_out_parser = BandOutParser(energy_unit=ureg.hartree) self.info_gw_parser = GWInfoParser()\",\n \"filepath = os.path.join(self.info_parser.maindir, filepath) if os.path.isfile(filepath) and os.access(filepath, os.F_OK): return True return False\",\n \"/ ureg.bohr), 'x_exciting_gvector_size': (r'G\\\\-vector grid sizes', None), 'x_exciting_gvector_total': (r'Total number of G\\\\-vectors', None),\",\n \"res = [] for i in range(len(self.number_of_k_points_per_segment)): start = self.vertices[i].attrib.get(self._coord_key).split() end = self.vertices[i\",\n \"continue try: parse_function(data, sec_scc) except Exception: self.logger.error('Error setting xs data', data=dict(file=quantity)) def _parse_xs_tddft(self):\",\n \"= rgkmax sec_method.x_exciting_xs_scissor = self.input_xml_parser.get('xs/scissor', 0.0) sec_method.x_exciting_xs_vkloff = self.input_xml_parser.get('xs/vkloff', [0., 0., 0.]) #\",\n \"1]: self._nkpts_segment .append(count) count = 1 else: count += 1 self._nkpts_segment.append(count) return self._nkpts_segment\",\n \"further info. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"in parser, however nspin is # determined from occupancies which is problematic sometimes\",\n \"= self.input_xml_parser.get( 'xs/BSE/sciavbd', True) sec_method.x_exciting_xs_bse_sciavqbd = self.input_xml_parser.get( 'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get( 'xs/BSE/sciavqhd',\",\n \"None, 'AC' if nwacont else None, 'NAR' if not aresdf else None] file_ext\",\n \"energy_fermi = 0.0 * ureg.hartree if sec_scc.energy is not None: energy_fermi = sec_scc.energy.fermi\",\n \"[None])[-1] if final is None else final if final is None: return sec_scc\",\n \"1 data = dict() data['occupancies'] = np.reshape(occs, (nspin, len(occs) // nspin)) data['eigenvalues'] =\",\n \"n_scf: quantity = [None] * (n_scf - len(quantity)) + quantity return quantity def\",\n \"self.input_xml_parser.get('libxc/correlation', None) xc_functional_names.append(correlation) exchange = self.input_xml_parser.get('libxc/exchange', None) xc_functional_names.append(exchange) sec_xc_functional = sec_dft.m_create(XCFunctional) for name\",\n \"= self.input_xml_parser.get( 'xs/BSE/xas', False) sec_method.x_exciting_xs_bse_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlbse', [0, 0, 0, 0]) if\",\n \"sec_run = self.archive.m_create(Run) sec_run.program = Program( name='exciting', version=self.info_parser.get('program_version', '').strip()) # method goes first\",\n \"= self.get_initialization_parameter('species', []) if species: positions = np.vstack([s.get('positions') for s in species]) if\",\n \"def get_atom_labels(self, section): labels = section.get('symbols') if labels is None: # we get\",\n \"= parse_configuration(optimization_step) if optimization_step.get('method') is not None: sec_scc.x_exciting_geometry_optimization_method = optimization_step.get('method') if optimization_step.get('step') is\",\n \"else: return files = self.get_exciting_files(name) if len(files) > 1: self.logger.warn('Found multiple files. Will\",\n \"names: val = moment_contributions.get(name, None) if val is not None: break if val\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See\",\n \"if not aresdf else None] file_ext = '_'.join([e for e in file_ext_list if\",\n \"= band_energy * self._energy_unit self._band_energies.append(band_energy) start = end return self._band_energies @property def band_k_points(self):\",\n \"if self._distances is None: data = np.transpose(self.data) self._distances = data[5][:int(max(data[1]))] return self._distances @property\",\n \"@property def number_of_spin_channels(self): if self._nspin is None: self._nspin = 1 return self._nspin @property\",\n \"sec_dos_values.value = dos[spin] # TODO add PDOS def _parse_bandstructure_dat(self, sec_scc): self.bandstructure_dat_parser._nspin = self.info_parser.get_number_of_spin_channels()\",\n \"= atom_resolved return properties def str_to_quantity_tolerances(val_in): return val_in.strip().replace('(', '').replace(')', '').split() def str_to_energy_dict(val_in): val\",\n \"the spin polarized case # I cannot find an example bandstructure file #\",\n \"gmaxvr = self.info_parser.get_initialization_parameter('x_exciting_gmaxvr', 0) sec_gw.core_treatment = self.input_xml_parser.get( 'gw/coreflag', 'all') sec_gw.polarizability_number_of_empty_states = int( self.input_xml_parser.get('gw/nempty',\",\n \"np.reshape(eigs, (nspin, len(eigs) // nspin)) return data self._quantities.append( Quantity( 'eigenvalues_occupancies', r'\\\\(state\\\\, eigenvalue and\",\n \"= len(partial_dos) return self._natoms @property def number_of_dos(self): if self._ndos is None: total_dos =\",\n \"[] name = xc_functional_map.get(xc_functional.type, []) return name @property def n_optimization_steps(self): return len(self.get('structure_optimization', {}).get('optimization_step',\",\n \"val = charge_contributions.get(name, None) if val is not None: break if val is\",\n \"= self.root.findall('./%s' % self._vertex_key) return self._vertices @property def number_of_spin_channels(self): if self._nspin is None:\",\n \"fundamental gap', ureg.hartree), 'time': (r'Wall time \\\\(seconds\\\\)', ureg.s)} for name, key_unit in self._miscellaneous_keys_mapping.items():\",\n \"species = v[0][2] atom_resolved.append(((species, v[1] * unit))) else: vi = [float(vii) for vii\",\n \"None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies =\",\n \"screening/BSE are children of xs if self.input_xml_parser.get('xs/screening') is not None: sec_method.x_exciting_xs_screening_number_of_empty_states = self.input_xml_parser.get(\",\n \"range(len(self.total_dos)): spin = self.total_dos[i].attrib.get(self._nspin_key, i) res[i] = self._get_dos(self._total_dos[i]) if self.energy_unit is not None:\",\n \"atoms per unit cell', None), 'x_exciting_spin_treatment': ('Spin treatment', None), 'x_exciting_number_of_bravais_lattice_symmetries': ('Number of Bravais\",\n \"'TDOS.OUT'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break # Parse DFT band\",\n \"= int( self.input_xml_parser.get('gw/nempty', 0)) sec_gw.ngridq = self.input_xml_parser.get('gw/ngridq', [1, 1, 1]) sec_gw.basis_set = 'mixed'\",\n \"self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data = np.transpose(self.data)[0] self._neigs_segment =\",\n \"sec_gw.self_energy_c_analytical_continuation = self.input_xml_parser.get( 'gw/selfenergy/actype', 'pade') sec_gw.mixed_basis_lmax = int(self.input_xml_parser.get( 'gw/mixbasis/lmaxmb', 3)) sec_gw.mixed_basis_tolerance = self.input_xml_parser.get(\",\n \"= self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_rgkmax = rgkmax sec_method.x_exciting_xs_scissor = self.input_xml_parser.get('xs/scissor', 0.0) sec_method.x_exciting_xs_vkloff = self.input_xml_parser.get('xs/vkloff',\",\n \"{ 'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage': ['core leakage'], 'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge': ['interstitial'], 'x_exciting_total_MT_charge': ['total charge\",\n \"nspin = 2 if occs[0] == 1. else 1 data = dict() data['occupancies']\",\n \"sec_scc.x_exciting_geometry_optimization_method = optimization_step.get('method') if optimization_step.get('step') is not None: sec_scc.x_exciting_geometry_optimization_step = optimization_step.get('step') force_convergence =\",\n \"super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'fermi_energy', r'Fermi Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*', unit=ureg.hartree, repeats=False))\",\n \"Program from nomad.datamodel.metainfo.simulation.method import ( Method, DFT, Electronic, Smearing, XCFunctional, Functional, GW as\",\n \"self.vertices[i + 1].attrib.get(self._coord_key).split(), dtype=float) res.append(np.linspace(start, end, self.number_of_k_points_per_segment[i])) elif key == 'band_segm_labels': res =\",\n \"* ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re = np.reshape( data[1], (n_components, n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im = np.reshape( data[2], (n_components,\",\n \"return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: self._neigs_segment = len(self.bands[0]) //\",\n \"/ ureg.bohr ** 3), 'x_exciting_number_of_atoms': ('Total number of atoms per unit cell', None),\",\n \"]+)' % key_unit[0], str_operation=str_to_quantity_tolerances, unit=key_unit[1], repeats=False)) module_quantities = [ Quantity( 'scf_iteration', r'(?:I| i)teration\",\n \"Quantity( 'optimization_step', r'(Optimization step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n *\\\\n\\\\-{10}|Time spent in this optimization step\\\\s*:\\\\s*[\\\\d\\\\.]+ seconds))', sub_parser=TextParser(quantities=optimization_quantities), repeats=True),\",\n \"= self.vertices[i + 1].attrib.get('label') res.append([ '\\\\u0393' if start.lower() == 'gamma' else start, '\\\\u0393'\",\n \"for v in data]) res = res.reshape((len(res), len(data), len(res[0]) // len(data))) if key\",\n \"* volume.to('m**3').magnitude for spin in range(len(dos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin\",\n \"= self.dos_parser.energies + energy_fermi volume = self.info_parser.get_unit_cell_volume() totaldos = self.dos_parser.get('totaldos') * volume.to('m**3').magnitude for\",\n \"sec_gw_eigenvalues = sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor = reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands = len(eigs_gw[0]) sec_gw_eigenvalues.n_kpoints = len(eigs_gw) sec_gw_eigenvalues.kpoints\",\n \"def str_to_energy_dict(val_in): val = val_in.strip().split('\\\\n') energies = dict() for v in val: v\",\n \"xml self._nspin = None self._nkpts_segment = None self._neigs_segment = None self._vertices = None\",\n \"not input_file: return self.input_xml_parser.mainfile = input_file[0] xstype = self.input_xml_parser.get('xs/xstype', '') if xstype.lower() ==\",\n \"spin in range(len(dos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value = dos[spin]\",\n \"['Total energy', 'total energy'], 'x_exciting_fermi_energy': ['Fermi energy', 'Fermi'], 'energy_kinetic_electronic': ['Kinetic energy', 'electronic kinetic'],\",\n \"key_unit in self._miscellaneous_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)' % key_unit[0], repeats=False, unit=key_unit[1])) self._convergence_keys_mapping = {\",\n \"'x_exciting_electron_nuclear_energy': ['Electron-nuclear energy', 'electron-nuclear '], 'x_exciting_nuclear_nuclear_energy': ['Nuclear-nuclear energy', 'nuclear-nuclear'], 'x_exciting_madelung_energy': ['Madelung energy', 'Madelung'],\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_rgkmax = rgkmax sec_method.x_exciting_xs_scissor = self.input_xml_parser.get('xs/scissor', 0.0)\",\n \"val) # energy, moment, charge contributions parse_scf(final, sec_scc) # forces forces = section.get('forces')\",\n \"None if 'charge' in val_in: unit = ureg.elementary_charge elif 'moment' in val_in: unit\",\n \"val = val_in[:val_in.rfind('\\\\n \\\\n')].strip() val = np.array([v.split() for v in val.split('\\\\n')], dtype=float) val\",\n \"= self.get_exciting_files('input.xml') if positions is None: # get it from input.xml for f\",\n \"self.info_parser.get_number_of_spin_channels() band_energies = self.band_out_parser.band_energies if band_energies is None: return # Get fermi energy:\",\n \"30: ['GGA_C_AM05', 'GGA_C_AM05'], 300: ['GGA_C_BGCP', 'GGA_X_PBE'], 406: ['HYB_GGA_XC_PBEH'], 408: ['HYB_GGA_XC_HSE03']} xc_functional = self.get('initialization',\",\n \"* gmaxvr pwm = self.input_xml_parser.get('gw/barecoul/pwm', 2.0) sec_gw.bare_coulomb_gmax = pwm * gmb * gmaxvr\",\n \"BasisSet ) from nomad.datamodel.metainfo.simulation.system import ( System, Atoms ) from nomad.datamodel.metainfo.simulation.calculation import (\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) class ExcitingEvalqpParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = []\",\n \"else: res = None self._results[key] = res class ExcitingFermiSurfaceBxsfParser(TextParser): def __init__(self): super().__init__(None) def\",\n \"parser_function(section) # free up memory parser.mainfile = None def _parse_input_xs(self, sec_method): xstype =\",\n \"from nomad.datamodel.metainfo.simulation.run import Run, Program from nomad.datamodel.metainfo.simulation.method import ( Method, DFT, Electronic, Smearing,\",\n \"@property def bands(self): if self._bands is None: bands = self.root.findall('./%s' % self._band_key) self._bands\",\n \"self.input_xml_parser.get( 'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get( 'xs/tddft/ahc', False) sec_method.x_exciting_xs_tddft_anti_resonant_dielectric = self.input_xml_parser.get( 'xs/tddft/aresdf', False)\",\n \"self._energy_unit self._band_energies.append(band_energy) start = end return self._band_energies @property def distances(self): if self._distances is\",\n \"scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)' % key_unit[0], repeats=False, unit=key_unit[1])) self._convergence_keys_mapping = { 'x_exciting_effective_potential_convergence': ( r'RMS\",\n \"self.number_of_k_points_per_segment[i])) elif key == 'band_segm_labels': res = [] for i in range(len(self.vertices) -\",\n \"example bandstructure file # atom-resolved bandstructure are added as separate section_k_band res =\",\n \"val = moment_contributions.get(name, None) if val is not None: break if val is\",\n \"elif quantity == 'EPSILON': parse_function = parse_epsilon elif quantity == 'SIGMA': parse_function =\",\n \"CONDITIONS OF ANY KIND, either express or implied. # See the License for\",\n \"1 self._nkpts_segment.append(count) return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: self._neigs_segment =\",\n \"'RPA') sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution = self.input_xml_parser.get( 'xs/tddft/intraband', False) sec_method.x_exciting_xs_tddft_diagonal_xc_kernel = self.input_xml_parser.get( 'xs/tddft/kerndiag', False) sec_method.x_exciting_xs_tddft_lmax_alda =\",\n \"self.partial_dos[i].attrib.get(self._m_key, None) if val_l is None or val_m is None: lm = i\",\n \"self.get('initialization', {}).get( 'x_exciting_spin_treatment', 'spin-unpolarised') n_spin = 1 if spin_treatment.lower() == 'spin-unpolarised' else 2\",\n \"np.transpose(self.data) n_kpoints = int(max(data[1])) bands = data[6:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues,\",\n \"mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = filename.rsplit('.', 1) filepath = '%s%s'\",\n \"None: energy_fermi = sec_scc.energy.fermi energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic)\",\n \"- 1]: self._nkpts_segment.append(count) count = 1 else: count += 1 self._nkpts_segment.append(count) return self._nkpts_segment\",\n \"it from last scf_iteration or optimization_step final = section.get('scf_iteration', [None])[-1] final = section.get('optimization_step',\",\n \"= val_in.strip().split('\\\\n') kpts = np.array(val[0].split(), dtype=float) keys = val[1].split() eigs = np.transpose(np.array([v.split() for\",\n \"compliance with the License. # You may obtain a copy of the License\",\n \"is None: self._total_dos = self.root.findall('./%s/%s' % (self._totaldos_key, self._diagram_key)) return self._total_dos @property def partial_dos(self):\",\n \"'gw' gw_info_file = f break if not self._calculation_type == 'gw': return sec_method =\",\n \"self.input_xml_parser.get('xs/xstype', '') if xstype.lower() == 'bse': self._parse_xs_bse() elif xstype.lower() == 'tddft': self._parse_xs_tddft() def\",\n \"*: *(.+)'), Quantity('name', r'name *: *(.+)'), Quantity('nuclear_charge', rf'nuclear charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge),\",\n \"is None else final if final is None: return sec_scc = sec_run.m_create(Calculation) def\",\n \"positions = np.array(positions) positions_format = positions_format if positions_format is not None else self.get_positions_format(section)\",\n \"energy: return energy return np.array([d[1].get(key) for d in data]) * ureg.hartree eigs_gw =\",\n \"os.path.isfile(filepath) and os.access(filepath, os.F_OK): return True return False def _parse_dos(self, sec_scc): if self.dos_parser.get('totaldos',\",\n \"np.reshape( data[0], (n_components, n_epsilon)) * ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re = np.reshape( data[1], (n_components, n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im\",\n \"optical_band_gap def parse_miscellaneous(self): sec_worfklow = self.archive.m_create(Workflow) sec_worfklow.type = 'single_point' structure_optimization = self.info_parser.get('structure_optimization') if\",\n \"number=np.array(val[0], dtype=int), values=val[1] * ureg.hartree, weights=val[2]) # TODO Read also input parameters here\",\n \"'FXC': sec_scc.x_exciting_xs_tddft_loss_function_local_field = data[1] elif quantity == 'LOSS' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_loss_function_no_local_field\",\n \"= data[1:] elif quantity == 'EPSILON' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field = data[1:3]\",\n \"'gw/selfenergy/singularity', 'mpd') sec_gw.self_energy_c_analytical_continuation = self.input_xml_parser.get( 'gw/selfenergy/actype', 'pade') sec_gw.mixed_basis_lmax = int(self.input_xml_parser.get( 'gw/mixbasis/lmaxmb', 3)) sec_gw.mixed_basis_tolerance\",\n \"= len(total_dos) return self._ndos @property def number_of_lm(self): if self._nlm is None: if self.partial_dos\",\n \"= [f for f in filenames if os.access(f, os.F_OK)] return filenames def file_exists(self,\",\n \"start = end return self._band_energies @property def band_k_points(self): if self._band_k_points is None: data\",\n \"if self._distances is None: if not self.bands: return self._distances = [ point.attrib.get(self._distance_key) for\",\n \"elif name.startswith('EIGVAL') and name.endswith('OUT'): parser = self.eigval_parser parser_function = self._parse_eigenvalues elif (name.startswith('FERMISURF') or\",\n \"repeats=False, convert=False), Quantity( 'x_exciting_dos_fermi', r'DOS at Fermi energy \\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=1 /\",\n \"for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb]\",\n \"v in x.split(':')]), Quantity( 'parameters', r'\\\\n *(.+?:.+)', repeats=True, str_operation=lambda x: [v.strip() for v\",\n \"[])) def get_number_of_spin_channels(self): spin_treatment = self.get('initialization', {}).get( 'x_exciting_spin_treatment', 'spin-unpolarised') n_spin = 1 if\",\n \"nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.energies = band_energies[nb] + energy_fermi def _parse_band_out(self, sec_scc): self.band_out_parser._nspin\",\n \"'positions_format', r'Atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])),\",\n \"species = None for v in val: v = v.strip().split(':') if len(v) <\",\n \"mainfile.rsplit('.', 1)[0].replace('INFO', '') options = [ f for f in os.listdir( self.info_parser.maindir) if\",\n \"for name, key_unit in self._system_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False)\",\n \"parser_function = self._parse_eigenvalues elif (name.startswith('FERMISURF') or name.startswith('FS')) and name.endswith('bxsf'): parser = self.fermisurf_parser parser_function\",\n \"self._distances = None self._species = None @property def distances(self): if self._distances is None:\",\n \"symmetries', None), 'x_exciting_number_of_crystal_symmetries': ('Number of crystal symmetries', None), 'x_exciting_kpoint_grid': (r'k\\\\-point grid', None), 'x_exciting_kpoint_offset':\",\n \"= atom sec_dos_values.value = partialdos[lm][spin][atom] def _parse_bandstructure(self, sec_scc): # we need to set\",\n \"def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data = np.transpose(self.data)[0] self._neigs_segment = len(np.where(data ==\",\n \"res.reshape((len(res), len(data), len(res[0]) // len(data))) if key == 'eigenvalues': res = res *\",\n \"parser_function = self._parse_input_gw elif self._calculation_type == 'xs': parser_function = self._parse_input_xs else: # TODO\",\n \"_parse_band_out(self, sec_scc): self.band_out_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.band_out_parser.band_energies if band_energies is None: return\",\n \"else: sec_system.x_exciting_clathrates = False potential_mixing = self.info_parser.get_initialization_parameter('potential_mixing') if potential_mixing is not None: sec_system.x_exciting_potential_mixing\",\n \"self.vertices[i].attrib.get(self._coord_key).split() end = self.vertices[i + 1].attrib.get(self._coord_key).split() res.append([start, end]) else: res = None self._results[key]\",\n \"of G\\\\-vectors', None), 'x_exciting_lmaxapw': (r' APW functions', None), 'x_exciting_nuclear_charge': ('Total nuclear charge', ureg.elementary_charge),\",\n \"'SIGMA': parse_function = parse_sigma elif quantity == 'LOSS': parse_function = parse_loss else: continue\",\n \": *({re_float}) *({re_float}) *({re_float})', repeats=True, dtype=np.dtype(np.float64))]))) initialization_quantities.append(Quantity( 'potential_mixing', r'Using ([\\\\w ]+) potential mixing',\",\n \"np.shape(np.transpose(self.data)[1:])[0] return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment = []\",\n \"{ 'x_exciting_unit_cell_volume': ('Unit cell volume', ureg.bohr ** 3), 'x_exciting_brillouin_zone_volume': ('Brillouin zone volume', 1\",\n \"band structure from one of the files: bs_files = ['bandstructure-qp.dat', 'BAND-QP.OUT'] for fname\",\n \"None)} self._method_keys_mapping = { 'smearing_kind': ('Smearing scheme', None), 'smearing_width': ('Smearing width', None)} for\",\n \"sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel = self.input_xml_parser.get( 'xs/tddft/aresfxc', True) sec_method.x_exciting_xs_tddft_drude = self.input_xml_parser.get( 'xs/tddft/drude', [0., 0.]) sec_method.x_exciting_xs_tddft_split_parameter =\",\n \"dtype=np.int32), Quantity('positions_format', r'atomic positions \\\\((.+?)\\\\)', flatten=False), Quantity( 'positions', rf'\\\\d+ : *({re_float}) *({re_float}) *({re_float})',\",\n \"'LOSS': parse_function = parse_loss else: continue try: parse_function(data, sec_scc) except Exception: self.logger.error('Error setting\",\n \"'gw/selfenergy/npol', 0)) sec_gw.self_energy_c_number_of_empty_states = int(self.input_xml_parser.get( 'gw/selfenergy/nempty', 0)) sec_gw.self_energy_singularity_treatment = self.input_xml_parser.get( 'gw/selfenergy/singularity', 'mpd') sec_gw.self_energy_c_analytical_continuation\",\n \"logger): self.filepath = filepath self.archive = archive self.logger = logger if logger is\",\n \"properties gw_info_files = self.get_exciting_files(gw_info_file) if len(gw_info_files) > 1: self.logger.warn('Found multiple GW info files,\",\n \"# Unless required by applicable law or agreed to in writing, software #\",\n \"@property def band_energies(self): if self._band_energies is None: if self.data is None: return data\",\n \"dtype=float)])), Quantity( 'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float,\",\n \"* (1 / self.energy_unit) elif 'partial' in key: if not self.partial_dos: return res\",\n \"= [f for f in files if f.endswith('QMT%s.OUT' % str(i + 1).rjust(3, '0'))]\",\n \"sec_scc) frequency_data = self.info_gw_parser.get('frequency_data', None) if frequency_data is not None: number = frequency_data.get('number')\",\n \"@property def energy_unit(self): if self._energy_unit is None: axis = self.root.find('./axis') if axis is\",\n \"/ ureg.bohr)} for name, key_unit in self._convergence_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)' % key_unit[0],\",\n \"'kpoints_eigenvalues', r'\\\\s*k\\\\-point \\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([ \\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)', str_operation=str_to_eigenvalue, repeats=True)) class BandstructureDatParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin\",\n \"= self._parse_fermisurface elif name.startswith('EVALQP') and (name.endswith('DAT') or name.endswith('TXT')): parser = self.evalqp_parser parser_function =\",\n \"'x_exciting_gvector_total': (r'Total number of G\\\\-vectors', None), 'x_exciting_lmaxapw': (r' APW functions', None), 'x_exciting_nuclear_charge': ('Total\",\n \"return files = self.get_exciting_files(name) if len(files) > 1: self.logger.warn('Found multiple files. Will read\",\n \"or optimization_step final = section.get('scf_iteration', [None])[-1] final = section.get('optimization_step', [None])[-1] if final is\",\n \"return [kpts, eigs] self._quantities.append( Quantity( 'kpoints_eigenvalues', r'\\\\s*k\\\\-point \\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([ \\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)', str_operation=str_to_eigenvalue, repeats=True)) class BandstructureDatParser(DataTextParser):\",\n \"unit=ureg.hartree / ureg.bohr), ]), repeats=False)) self._quantities.append(Quantity( 'hybrids', r'Hybrids module started([\\\\s\\\\S]+?)Hybrids module stopped', sub_parser=TextParser(quantities=module_quantities)\",\n \"(nspin, len(occs) // nspin)) data['eigenvalues'] = np.reshape(eigs, (nspin, len(eigs) // nspin)) return data\",\n \"# scf iterations scf_iterations = section.get('scf_iteration', []) for scf_iteration in scf_iterations: sec_scf_iteration =\",\n \"data[1:3] elif quantity == 'SIGMA' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_sigma_no_local_field = data[1:3] def\",\n \"Quantity( 'method', r'method\\\\s*=\\\\s*(\\\\w+)', repeats=False, dtype=str), Quantity( 'n_scf_iterations', r'Number of (?:total)* scf iterations\\\\s*\\\\:\\\\s*(\\\\d+)', repeats=False,\",\n \"** 3) def get_initialization_parameter(self, key, default=None): return self.get('initialization', {}).get(key, default) class ExcitingParser: def\",\n \"np.transpose(energy)[start:end] for energy in band_energies]) if self._energy_unit is not None: band_energy = band_energy\",\n \"['total moment in muffin-tins'], 'x_exciting_total_moment': ['total moment'], 'x_exciting_section_MT_moment_atom': ['atom_resolved'] } def get_exciting_files(self, default):\",\n \"is None: self._results = dict() if not self.bands: return if key == 'band_energies':\",\n \"rf'muffin-tin radius *: *({re_float})', dtype=np.float64, unit=ureg.bohr), Quantity('radial_points', rf'radial points in muffin-tin *: *({re_float})',\",\n \"TODO make a parent clas for bandstructure dat and xml self._nspin = None\",\n \"in bs_files: if self.file_exists(fname): exciting_files.append(fname) break for f in exciting_files: self.parse_file(f, sec_scc) #\",\n \"not None: number = frequency_data.get('number') sec_method.gw.number_of_frequencies = len(number) sec_method.gw.frequency_number = number sec_method.gw.frequency_values =\",\n \"e in self.partial_dos]) for li in l_list: self._nlm += 2 * li +\",\n \"sec_scc.x_exciting_xs_bse_epsilon_energies = np.reshape( data[0], (n_components, n_epsilon)) * ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re = np.reshape( data[1], (n_components,\",\n \"v in x.split(':')])])) ) self._quantities.append(Quantity( 'initialization', r'(?:All units are atomic|Starting initialization)([\\\\s\\\\S]+?)(?:Using|Ending initialization)', repeats=False,\",\n \"of (?:total)* scf iterations\\\\s*\\\\:\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'force_convergence', r'Maximum force magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)', str_operation=str_to_quantity_tolerances,\",\n \"self.filepath = filepath self.archive = archive self.logger = logger if logger is not\",\n \"if self._energy_unit is None: axis = self.root.find('./axis') if axis is None: return self._energy_unit\",\n \"read only first!') self.info_gw_parser.mainfile = gw_info_files[0] fermi_energy = self.info_gw_parser.get('fermi_energy', None) if fermi_energy is\",\n \"np.shape(np.transpose(self.data))[0] - 6 return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment\",\n \"% (name, bse_type, scr_type)) bse_files.append(files) return bse_files def get_data(files): data = [] for\",\n \"['Coulomb potential energy', 'Coulomb potential'], 'energy_xc_potential': ['xc potential energy', 'xc potential'], 'energy_electrostatic': ['Hartree\",\n \"unit=ureg.bohr, repeats=False, convert=False), Quantity( 'lattice_vectors_reciprocal', r'Reciprocal lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=1 / ureg.bohr, repeats=False,\",\n \"module_quantities = [ Quantity( 'scf_iteration', r'(?:I| i)teration number :([\\\\s\\\\S]+?)(?:\\\\n *\\\\n\\\\+{10}|\\\\+\\\\-{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=True), Quantity(\",\n \"energy_unit(self): if self._energy_unit is None: axis = self.root.find('./axis') if axis is None: return\",\n \"self._nkpts_segment = None self._neigs_segment = None self._bands = None self._vertices = None self._distances\",\n \"sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_sigma = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_sigma_energies\",\n \"is not None: if not isinstance(smearing_kind, str): smearing_kind = smearing_kind[0] smearing_kind = smearing_kind_map[smearing_kind]\",\n \"np.array( [float(point.attrib.get(self._energy_key)) for point in self.total_dos[0]]) if self.energy_unit is not None: self._energies =\",\n \"ureg.bohr lattice_vectors = lattice_vectors * ureg.bohr atoms = self.input_xml_parser.get('structure/species/atom') atom_labels = [] for\",\n \"= ' '.join(self.info_parser.get_xc_functional_name()) sec_method.gw.starting_point = xc_functional_name sec_scc = sec_run.m_create(Calculation) sec_scc.method_ref = sec_method if\",\n \"= self.evalqp_parser.get('kpoints_eigenvalues') if data is None: return def get_data(key): if key == 'k_points':\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. # See the License\",\n \"['GGA_C_AM05', 'GGA_C_AM05'], 300: ['GGA_C_BGCP', 'GGA_X_PBE'], 406: ['HYB_GGA_XC_PBEH'], 408: ['HYB_GGA_XC_HSE03']} xc_functional = self.get('initialization', {}).get('xc_functional',\",\n \"sec_system = sec_run.m_create(System) sec_atoms = sec_system.m_create(Atoms) sec_atoms.positions = positions sec_atoms.labels = atom_labels sec_atoms.periodic\",\n \"# forces forces = section.get('forces') if forces is not None: sec_forces = sec_scc.m_create(Forces)\",\n \"= self._parse_dos_out elif name.startswith('bandstructure') and name.endswith('dat'): parser = self.bandstructure_dat_parser parser_function = self._parse_bandstructure_dat elif\",\n \"'FS.bxsf'] # Parse DFT DOS from one of the files bs_files = ['dos.xml',\",\n \"if self._neigs_segment is None: data = np.transpose(self.data)[0] self._neigs_segment = len(np.where(data == data[0])[0]) return\",\n \"self.total_dos is None: return self._energies = np.array( [float(point.attrib.get(self._energy_key)) for point in self.total_dos[0]]) if\",\n \"= 'gw' gw_info_file = f break if not self._calculation_type == 'gw': return sec_method\",\n \"sec_method.scf = Scf(threshold_energy_change=threshold) xc_functional_names = self.info_parser.get_xc_functional_name() if not xc_functional_names: # get it from\",\n \"sub_parser=TextParser(quantities=module_quantities), repeats=False)) optimization_quantities = [ Quantity( 'atomic_positions', r'(Atomic positions at this step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[\",\n \"(r'Total number of G\\\\-vectors', None), 'x_exciting_lmaxapw': (r' APW functions', None), 'x_exciting_nuclear_charge': ('Total nuclear\",\n \"parser = self.bandstructure_parser parser_function = self._parse_bandstructure elif name.startswith('EIGVAL') and name.endswith('OUT'): parser = self.eigval_parser\",\n \"'energy_exchange': ['Exchange energy', 'exchange'], 'x_exciting_exchange_energy': ['Exchange energy', 'exchange'], 'energy_correlation': ['Correlation energy', 'correlation'], 'x_exciting_correlation_energy':\",\n \"energies are reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is None: continue energy_fermi =\",\n \"parse_miscellaneous(self): sec_worfklow = self.archive.m_create(Workflow) sec_worfklow.type = 'single_point' structure_optimization = self.info_parser.get('structure_optimization') if structure_optimization is\",\n \"= self._energies * self.energy_unit return self._energies def _get_dos(self, diagram): dos = np.array( [point.attrib.get(self._dos_key)\",\n \"self._energies = None self._total_dos = None self._partial_dos = None @property def energy_unit(self): if\",\n \"[] class ExcitingInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): re_symbol = re.compile(r'([A-Z][a-z]?)') def str_to_array(val_in):\",\n \"res = res.reshape((len(res), len(data), len(res[0]) // len(data))) if key == 'eigenvalues': res =\",\n \"self.eigval_parser parser_function = self._parse_eigenvalues elif (name.startswith('FERMISURF') or name.startswith('FS')) and name.endswith('bxsf'): parser = self.fermisurf_parser\",\n \"sec_scc = sec_run.m_create(Calculation) sec_scc.method_ref = sec_method if sec_run.system: sec_scc.system_ref = sec_run.system[-1] sec_scc_ref =\",\n \"None: fundamental_band_gap = self.info_gw_parser.get('fundamental_band_gap', None) sec_gap = sec_scc.eigenvalues[-1].m_create(BandGap) if fundamental_band_gap is not None:\",\n \"self.archive.run[-1] # TODO read from xml file def get_files(name): bse_types = ['IP', 'singlet',\",\n \"self.dos_out_parser parser_function = self._parse_dos_out elif name.startswith('bandstructure') and name.endswith('dat'): parser = self.bandstructure_dat_parser parser_function =\",\n \"band_energies]) if self._energy_unit is not None: band_energy = band_energy * self._energy_unit res_n.append(band_energy) start\",\n \"== 'x_exciting_fermi_energy': sec_energy.fermi = val # charge contributions charge_contributions = iteration.get('charge_contributions', {}) for\",\n \"in files_q: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None: continue data_q.append(self.data_xs_parser.data) if not\",\n \"sec_scc = sec_run.m_create(Calculation) sec_scc.x_exciting_xs_tddft_number_of_epsilon_values = len(data[0][0][0]) sec_scc.x_exciting_xs_tddft_epsilon_energies = data[0][0][0] * ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field =\",\n \"if self._energy_unit is not None: band_energy = band_energy * self._energy_unit res_n.append(band_energy) start =\",\n \"range(len(qpoints)): data_q = [] files_q = [f for f in files if f.endswith('QMT%s.OUT'\",\n \"f] options = [f for f in options if f.endswith(file_ext)] options.sort() filenames =\",\n \"= self.archive.run[-1] positions = self.info_parser.get_atom_positions(section.get('atomic_positions', {})) lattice_vectors = self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels = self.info_parser.get_atom_labels(section.get('atomic_positions', {}))\",\n \"{}).get('x_exciting_unit_cell_volume', 1.0 * ureg.bohr ** 3) def get_initialization_parameter(self, key, default=None): return self.get('initialization', {}).get(key,\",\n \"n_sigma)) def parse_loss(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_loss = len(data[0])\",\n \"= self._parse_bandstructure elif name.startswith('EIGVAL') and name.endswith('OUT'): parser = self.eigval_parser parser_function = self._parse_eigenvalues elif\",\n \"= optimization_step.get('step') force_convergence = optimization_step.get('force_convergence') if force_convergence is not None: sec_scc.x_exciting_maximum_force_magnitude = force_convergence[0]\",\n \"data = np.transpose(self.data) n_kpoints = np.where(data[0] == data[0][0])[0][1] bands = data[1:] bands =\",\n \"for v in val[3:6]], dtype=float) return [nbands, mesh, origin, vector] self._quantities.append( Quantity( 'band_parameters',\",\n \"continue data_q.append(self.data_xs_parser.data) if not data_q: continue data_q = np.transpose(data_q, axes=(2, 0, 1)) for\",\n \"is not None: break if val is None: continue if key == 'x_exciting_section_MT_moment_atom':\",\n \"msection): energy_total = iteration.get('energy_total') sec_energy = msection.m_create(Energy) if energy_total is not None: sec_energy.total\",\n \"* self._energy_unit self._band_energies.append(band_energy) start = end return self._band_energies @property def band_k_points(self): if self._band_k_points\",\n \"sec_gw.number_of_frequencies = int(self.input_xml_parser.get( 'gw/freqgrid/nomeg', 16)) sec_gw.self_energy_c_number_of_poles = int(self.input_xml_parser.get( 'gw/selfenergy/npol', 0)) sec_gw.self_energy_c_number_of_empty_states = int(self.input_xml_parser.get(\",\n \"is not None else section.get('final') if final is None: # get it from\",\n \"]+)\\\\s*started', repeats=False, dtype=str, flatten=False)] initialization_quantities = [ Quantity( 'lattice_vectors', r'Lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=ureg.bohr,\",\n \"in data]) if None in energy: return energy return np.array([d[1].get(key) for d in\",\n \"i) res[i] = self._get_dos(self._total_dos[i]) if self.energy_unit is not None: res = res *\",\n \"int(self.input_xml_parser.get( 'gw/mixbasis/lmaxmb', 3)) sec_gw.mixed_basis_tolerance = self.input_xml_parser.get( 'gw/mixbasis/epsmb', 0.0001) gmb = self.input_xml_parser.get('gw/mixbasis/gmb', 1.0) sec_gw.mixed_basis_gmax\",\n \"None) sec_gap = sec_scc.eigenvalues[-1].m_create(BandGap) if fundamental_band_gap is not None: sec_gap.value_fundamental = fundamental_band_gap optical_band_gap\",\n \"parse_scf(scf_iteration, sec_scf_iteration) return sec_scc def parse_system(self, section): sec_run = self.archive.run[-1] positions = self.info_parser.get_atom_positions(section.get('atomic_positions',\",\n \"0]) if self.input_xml_parser.get('xs/tddft') is not None: sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get( 'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity =\",\n \"from occupancies which is problematic sometimes res = np.hstack([np.reshape(v[key], (nspin, np.size(v[key]) // nspin))\",\n \"*({re_float})', dtype=np.int32), Quantity('positions_format', r'atomic positions \\\\((.+?)\\\\)', flatten=False), Quantity( 'positions', rf'\\\\d+ : *({re_float}) *({re_float})\",\n \"including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Time', repeats=False, str_operation=str_to_array, convert=False, unit=ureg.hartree / ureg.bohr), Quantity( 'step', r'Optimization\",\n \"for i in range(len(self.number_of_k_points_per_segment)): start = np.array( self.vertices[i].attrib.get(self._coord_key).split(), dtype=float) end = np.array( self.vertices[i\",\n \"range(len(totaldos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value = totaldos[spin] partialdos =\",\n \"file # atom-resolved bandstructure are added as separate section_k_band res = [] for\",\n \"r'Fermi Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*', unit=ureg.hartree, repeats=False)) def str_to_band_parameters(val_in): val = val_in.strip().split('\\\\n') nbands = int(val[0]) mesh\",\n \"__init__(self): super().__init__(None) def init_quantities(self): re_symbol = re.compile(r'([A-Z][a-z]?)') def str_to_array(val_in): val = [v.split(':')[-1].split() for\",\n \"self.bandstructure_dat_parser parser_function = self._parse_bandstructure_dat elif name.startswith('BAND') and name.endswith('OUT'): parser = self.band_out_parser parser_function =\",\n \"['HYB_GGA_XC_HSE03']} xc_functional = self.get('initialization', {}).get('xc_functional', None) if xc_functional is None: return [] name\",\n \"band_parameters[2] sec_fermisurface.x_exciting_vectors_fermi_surface = band_parameters[3] fermi_energy = self.fermisurf_parser.get('fermi_energy', None) if fermi_energy is not None:\",\n \"3)) sec_gw.mixed_basis_tolerance = self.input_xml_parser.get( 'gw/mixbasis/epsmb', 0.0001) gmb = self.input_xml_parser.get('gw/mixbasis/gmb', 1.0) sec_gw.mixed_basis_gmax = gmb\",\n \"super().__init__(mainfile, logger=logger) def init_quantities(self): self._quantities = [] class ExcitingInfoParser(TextParser): def __init__(self): super().__init__(None) def\",\n \"= XMLParser() self.data_xs_parser = DataTextParser() self.data_clathrate_parser = DataTextParser(dtype=str) # different names for different\",\n \"= parse_configuration(structure_optimization) if sec_scc is None: return # volume optimizations volume_index = 1\",\n \"+ 1].attrib.get('label') res.append([ '\\\\u0393' if start.lower() == 'gamma' else start, '\\\\u0393' if end.lower()\",\n \"in v[1].split()] v[1] = v[1][0] if len(v[1]) == 1 else v[1] if species\",\n \"n + 1 sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol = val[n][0] sec_mt_charge_atom.x_exciting_MT_charge_atom_value = val[n][1] sec_charges = msection.m_create(Charges) sec_charges.value\",\n \"def parse(self, key): if self._results is None: self._results = dict() if not self.bands:\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi nkpts_segment = self.band_out_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment\",\n \"This is the case when there is a mismatch between files self.logger.warn( 'Mismatch\",\n \"really correct, i.e. columns are supposed # to be what they are. What\",\n \"len(data) data = np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components = n_components n_excitons = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_excitons\",\n \"logger if logger is not None else logging self._calculation_type = None self.init_parser() sec_run\",\n \"sec_gw.qp_equation_treatment = 'linearization' sec_gw.max_frequency = self.input_xml_parser.get( 'gw/freqgrid/freqmax', 1.0) sec_gw.frequency_grid_type = self.input_xml_parser.get( 'gw/freqgrid/fgrid', 'gaule2')\",\n \"'EPSILON': parse_function = parse_epsilon elif quantity == 'SIGMA': parse_function = parse_sigma elif quantity\",\n \"sec_scc.x_exciting_xs_tddft_sigma_no_local_field = data[1:3] def parse_xs(self): sec_run = self.archive.run[-1] xs_info_files = self.get_exciting_files('INFOXS.OUT') if not\",\n \"the original scale in which also other energies are reported. energy_fermi = 0.0\",\n \"not None: sec_method.x_exciting_xs_bse_antiresonant = self.input_xml_parser.get( 'xs/BSE/aresbse', True) sec_method.x_exciting_xs_bse_angular_momentum_cutoff = self.input_xml_parser.get( 'xs/BSE/lmaxdielt', 14) rgkmax\",\n \"self.number_of_spin_channels][i] = np.array( [e.attrib.get(self._energy_key) for e in self.bands[n][i]]) for nkpts_segment in self.number_of_k_points_per_segment: end\",\n \"* ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field = data[1:] elif quantity == 'EPSILON' and ext == 'NLF_FXC':\",\n \"= np.dot(positions, lattice_vectors) * ureg.bohr lattice_vectors = lattice_vectors * ureg.bohr atoms = self.input_xml_parser.get('structure/species/atom')\",\n \"sec_method.basis_set.append(BasisSet(type='(L)APW+lo')) sec_dft = sec_method.m_create(DFT) sec_electronic = sec_method.m_create(Electronic) sec_electronic.method = 'DFT' smearing_kind_map = {\",\n \"as separate section_k_band res = [] for n in range(len(self.bands)): res_n = []\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"n_components sec_scc.x_exciting_xs_bse_loss_energies = np.reshape( data[0], (n_components, n_loss)) * ureg.hartree sec_scc.x_exciting_xs_bse_loss = np.reshape( data[1],\",\n \"self._neigs_segment def parse(self, key): if self._results is None: self._results = dict() if not\",\n \"return energies self._quantities = [Quantity( 'program_version', r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\. ]+)\\\\s*started', repeats=False, dtype=str, flatten=False)] initialization_quantities =\",\n \"else: continue try: parse_function(data, sec_scc) except Exception: self.logger.error('Error setting xs data', data=dict(file=quantity)) def\",\n \"labels = section.get('symbols') if labels is None: # we get it by concatenating\",\n \"repeats=True), Quantity( 'final', r'Force convergence target achieved([\\\\s\\\\S]+?Opt)', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(imized atomic\",\n \"sec_charges = msection.m_create(Charges) sec_charges.value = [ val[n][1].magnitude for n in range(len(val))] * val[0][1].units\",\n \"data[0][0])[0][1] bands = data[1:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies =\",\n \"= np.shape(np.transpose(self.data))[0] - 6 return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None:\",\n \"sec_gw_eigenvalues.value_correlation = eigs_gw_C sec_gw_eigenvalues.value_xc_potential = reshape(get_data('Vxc')) def _parse_dos_out(self, sec_scc): data = self.dos_out_parser.data if\",\n \"for n in range(len(files)): parser.mainfile = files[n] parser_function(section) # free up memory parser.mainfile\",\n \"electron kinetic'], 'x_exciting_dft_d2_dispersion_correction': ['DFT-D2 dispersion correction'] } self._electron_charge_keys_mapping = { 'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage':\",\n \"convention for xs input xml file sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref\",\n \"charge', ureg.elementary_charge), 'x_exciting_wigner_radius': (r'Effective Wigner radius, r\\\\_s', ureg.bohr), 'x_exciting_empty_states': ('Number of empty states',\",\n \"positions is not None else section.get('positions') if positions is None: species = self.get_initialization_parameter('species',\",\n \"ureg.bohr) ] self._quantities.append(Quantity( 'groundstate', r'(?:Self\\\\-consistent loop started|Groundstate module started)([\\\\s\\\\S]+?)Groundstate module stopped', sub_parser=TextParser(quantities=module_quantities), repeats=False))\",\n \"repeats=False, convert=False)] self._miscellaneous_keys_mapping = { 'x_exciting_gap': (r'Estimated fundamental gap', ureg.hartree), 'time': (r'Wall time\",\n \"one of the files bs_files = ['dos.xml', 'TDOS.OUT'] for fname in bs_files: if\",\n \"= np.reshape( data[5], (n_components, n_excitons)) def parse_epsilon(data, sec_scc): n_components = len(data) data =\",\n \"not None: sec_scc.x_exciting_geometry_optimization_method = optimization_step.get('method') if optimization_step.get('step') is not None: sec_scc.x_exciting_geometry_optimization_step = optimization_step.get('step')\",\n \"['LDA_C_PW', 'LDA_X_PZ'], 4: ['LDA_C_XALPHA'], 5: ['LDA_C_VBH'], 20: ['GGA_C_PBE', 'GGA_X_PBE'], 21: ['GGA_C_PBE', 'GGA_X_PBE_R'], 22:\",\n \"bands = data[6:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies = []\",\n \"IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr), ]), repeats=False)) self._quantities.append(Quantity( 'hybrids',\",\n \"kwargs.get('energy_unit', None) self._units_mapping = dict(hartree=ureg.hartree) def init_parameters(self): self._ndos = None self._natoms = None\",\n \"options] else: filenames = [filename] filenames = [f for f in filenames if\",\n \"None self._neigs_segment = None self._nkpts_segment = None @property def band_energies(self): if self._band_energies is\",\n \"vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=1 / ureg.bohr, repeats=False, convert=False), ] self._system_keys_mapping = { 'x_exciting_unit_cell_volume': ('Unit\",\n \"up memory parser.mainfile = None def _parse_input_xs(self, sec_method): xstype = self.input_xml_parser.get('xs/xstype', None) if\",\n \"sec_method.x_exciting_xs_bse_rgkmax = rgkmax sec_method.x_exciting_xs_bse_sciavbd = self.input_xml_parser.get( 'xs/BSE/sciavbd', True) sec_method.x_exciting_xs_bse_sciavqbd = self.input_xml_parser.get( 'xs/BSE/sciavqbd', False)\",\n \"if len(v) < 2: continue elif v[0].startswith('species'): species = re.search(re_symbol, v[-1]).group(1) elif v[0].startswith('atom'):\",\n \"= np.transpose(self.data) self._neigs_segment = int(max(data[0])) return self._neigs_segment class BandOutParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs)\",\n \"energy_total is not None: sec_energy.total = EnergyEntry(value=energy_total) x_exciting_dos_fermi = iteration.get('x_exciting_dos_fermi') if x_exciting_dos_fermi is\",\n \"xstype.lower() == 'bse': self._parse_xs_bse() elif xstype.lower() == 'tddft': self._parse_xs_tddft() def _parse_input_gw(self, sec_method): sec_gw\",\n \"None: break if val is None: continue if key == 'x_exciting_section_MT_moment_atom': for n\",\n \"else: res = None self._results[key] = res class DOSXMLParser(XMLParser): def __init__(self, **kwargs): super().__init__(None)\",\n \"__init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self):\",\n \"in scr_types: files = self.get_exciting_files( '%s_BSE%s_SCR%s.OUT' % (name, bse_type, scr_type)) bse_files.append(files) return bse_files\",\n \"if self._total_dos is None: self._total_dos = self.root.findall('./%s/%s' % (self._totaldos_key, self._diagram_key)) return self._total_dos @property\",\n \"i in range(len(self.total_dos)): spin = self.total_dos[i].attrib.get(self._nspin_key, i) res[i] = self._get_dos(self._total_dos[i]) if self.energy_unit is\",\n \"val if len(v) == 3], float)) return dict( number=np.array(val[0], dtype=int), values=val[1] * ureg.hartree,\",\n \"== 'lattice': cell = self.get_initialization_parameter('lattice_vectors') if cell is None: return positions = np.dot(positions,\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #\",\n \"in muffin-tins', 'total in muffin-tins'], 'charge_total': ['total charge'], 'x_exciting_section_MT_charge_atom': ['atom_resolved'] } self._moment_keys_mapping =\",\n \"setattr(msection, 'x_exciting_dos_fermi', x_exciting_dos_fermi) # energy contributions energy_contributions = iteration.get('energy_contributions', {}) for key, names\",\n \"ureg from nomad.parsing.file_parser import TextParser, Quantity, XMLParser, DataTextParser from nomad.datamodel.metainfo.simulation.run import Run, Program\",\n \"sec_dos_values.value = totaldos[spin] partialdos = self.dos_parser.get('partialdos') if partialdos is None: return partialdos =\",\n \"= sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_dat_parser.band_k_points nkpts_segment = self.bandstructure_dat_parser.number_of_k_points_per_segment for\",\n \"None: eigs_gw_C = reshape(get_data('Re(Sc)')) sec_gw_eigenvalues.value_correlation = eigs_gw_C sec_gw_eigenvalues.value_xc_potential = reshape(get_data('Vxc')) def _parse_dos_out(self, sec_scc):\",\n \"and ext == 'FXC': sec_scc = sec_run.m_create(Calculation) sec_scc.x_exciting_xs_tddft_number_of_epsilon_values = len(data[0][0][0]) sec_scc.x_exciting_xs_tddft_epsilon_energies = data[0][0][0]\",\n \"return self.get('initialization', {}).get('x_exciting_unit_cell_volume', 1.0 * ureg.bohr ** 3) def get_initialization_parameter(self, key, default=None): return\",\n \"len(total_dos) return self._ndos @property def number_of_lm(self): if self._nlm is None: if self.partial_dos is\",\n \"in range(len(self.bands)): res_n = [] start = 0 band_energies = np.zeros(( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues,\",\n \"if sec_scc is None: # This is the case when there is a\",\n \"initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) for name, key_unit in\",\n \"band_energies = np.zeros(( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, len(self.distances)), dtype=float) for i in range(len(self.bands[n])): band_energies[i %\",\n \"partialdos is None: return partialdos = partialdos.to('1/joule').magnitude lm_values = np.column_stack((np.arange(len(partialdos)), np.zeros(len(partialdos), dtype=np.int32))) for\",\n \"'energy_total', r'Total energy at this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', unit=ureg.hartree, repeats=False, dtype=float), Quantity( 'time', r'Time\",\n \"plane\\\\-waves', None), 'x_exciting_lo': (r'Total number of local\\\\-orbitals', None)} self._method_keys_mapping = { 'smearing_kind': ('Smearing\",\n \"* ureg.hartree sec_scc.x_exciting_xs_bse_exciton_oscillator_strength = np.reshape( data[3], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_re = np.reshape( data[4], (n_components,\",\n \"def _parse_input_xs(self, sec_method): xstype = self.input_xml_parser.get('xs/xstype', None) if xstype is not None: sec_method.x_exciting_xs_xstype\",\n \"for spin in range(len(partialdos[lm])): for atom in range(len(partialdos[lm][spin])): sec_dos_values = sec_dos.m_create(DosValues, Dos.atom_projected) sec_dos_values.m_kind\",\n \"'positions', rf'\\\\d+ : *({re_float}) *({re_float}) *({re_float})', repeats=True, dtype=np.dtype(np.float64))]))) initialization_quantities.append(Quantity( 'potential_mixing', r'Using ([\\\\w ]+)\",\n \"= np.array( [e.attrib.get(self._energy_key) for e in self.bands[n][i]]) for nkpts_segment in self.number_of_k_points_per_segment: end =\",\n \"self._energies = np.array( [float(point.attrib.get(self._energy_key)) for point in self.total_dos[0]]) if self.energy_unit is not None:\",\n \"optimization_step.get('step') force_convergence = optimization_step.get('force_convergence') if force_convergence is not None: sec_scc.x_exciting_maximum_force_magnitude = force_convergence[0] sec_scc.x_exciting_geometry_optimization_threshold_force\",\n \"self._quantities = [] class ExcitingInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): re_symbol = re.compile(r'([A-Z][a-z]?)')\",\n \"parse_configuration(section): if not section: return sec_scc = self.parse_scc(section) if sec_scc is None: return\",\n \"= reshape(get_data('Sx')) eigs_gw_C = reshape(get_data('Sc')) if eigs_gw_C is None: eigs_gw_C = reshape(get_data('Re(Sc)')) sec_gw_eigenvalues.value_correlation\",\n \"expand list to include other xcf xc_functional_map = { 2: ['LDA_C_PZ', 'LDA_X_PZ'], 3:\",\n \"gw_files.append(fname) break for f in gw_files: self.parse_file(f, sec_scc) frequency_data = self.info_gw_parser.get('frequency_data', None) if\",\n \"(target[0], suffix) if target[1:]: filepath = '%s.%s' % (filepath, target[1]) filepath = os.path.join(self.info_parser.maindir,\",\n \"['HYB_GGA_XC_PBEH'], 408: ['HYB_GGA_XC_HSE03']} xc_functional = self.get('initialization', {}).get('xc_functional', None) if xc_functional is None: return\",\n \"vector = np.array([v.split() for v in val[3:6]], dtype=float) return [nbands, mesh, origin, vector]\",\n \"= np.array(self._distances, dtype=float) return self._distances @property def bands(self): if self._bands is None: bands\",\n \"('Number of empty states', None), 'x_exciting_valence_states': ('Total number of valence states', None), 'x_exciting_hamiltonian_size':\",\n \"v.strip().split(':') if len(v) < 2: continue elif v[0].startswith('species'): species = re.search(re_symbol, v[-1]).group(1) elif\",\n \"self.input_xml_parser.get( 'xs/BSE/xas', False) sec_method.x_exciting_xs_bse_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlbse', [0, 0, 0, 0]) if sec_method.x_exciting_xs_bse_xas:\",\n \"= ['full', 'diag', 'noinvdiag', 'longrange'] bse_files = [] for bse_type in bse_types: for\",\n \"nomad.units import ureg from nomad.parsing.file_parser import TextParser, Quantity, XMLParser, DataTextParser from nomad.datamodel.metainfo.simulation.run import\",\n \"= [sec_scc_ref] # parse properties gw_info_files = self.get_exciting_files(gw_info_file) if len(gw_info_files) > 1: self.logger.warn('Found\",\n \"= f break if not self._calculation_type == 'gw': return sec_method = sec_run.m_create(Method) sec_method_ref\",\n \"number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment = [] count = 1 for i\",\n \"# determined from occupancies which is problematic sometimes res = np.hstack([np.reshape(v[key], (nspin, np.size(v[key])\",\n \"self.dos_parser.number_of_dos sec_dos.energies = self.dos_parser.energies + energy_fermi volume = self.info_parser.get_unit_cell_volume() totaldos = self.dos_parser.get('totaldos') *\",\n \"self.fermisurf_parser.quantities = parser.fermisurf_parser.quantities self.evalqp_parser.quantities = parser.evalqp_parser.quantities self.info_gw_parser.quantities = parser.info_gw_parser.quantities def parse(self, filepath, archive,\",\n \"achieved([\\\\s\\\\S]+?Opt)', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(imized atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'imized atomic\",\n \"sec_scc.x_exciting_xs_bse_loss_energies = np.reshape( data[0], (n_components, n_loss)) * ureg.hartree sec_scc.x_exciting_xs_bse_loss = np.reshape( data[1], (n_components,\",\n \"= os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = filename.rsplit('.', 1) filepath = '%s%s' %\",\n \"None: return # volume optimizations volume_index = 1 while True: info_volume = self.get_exciting_files('run_dir%s/INFO.OUT'\",\n \"0.01, 'hartree') sec_method.x_exciting_xs_gqmax = self.input_xml_parser.get( 'xs/gqmax', 0.0, '1/bohr') sec_method.x_exciting_xs_lmaxapw = self.input_xml_parser.get('xs/lmaxapw', 10) sec_method.x_exciting_xs_number_of_empty_states\",\n \"positions_format=None): positions = positions if positions is not None else section.get('positions') if positions\",\n \"atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Time', repeats=False, str_operation=str_to_array, convert=False, unit=ureg.hartree / ureg.bohr), Quantity(\",\n \"parser_function = self._parse_bandstructure_dat elif name.startswith('BAND') and name.endswith('OUT'): parser = self.band_out_parser parser_function = self._parse_band_out\",\n \"smearing_kind = self.info_parser.get_initialization_parameter('smearing_kind') if smearing_kind is not None: if not isinstance(smearing_kind, str): smearing_kind\",\n \"1) filepath = '%s%s' % (target[0], suffix) if target[1:]: filepath = '%s.%s' %\",\n \"def bands(self): if self._bands is None: bands = self.root.findall('./%s' % self._band_key) self._bands =\",\n \"not None: sec_worfklow.type = 'geometry_optimization' sec_geometry_opt = sec_worfklow.m_create(GeometryOptimization) threshold_force = structure_optimization.get( 'optimization_step', [{}])[0].get('force_convergence',\",\n \"val = np.transpose(val) occs = val[-1] eigs = val[-2] nspin = 2 if\",\n \"None: sec_scc.energy = Energy(fermi=fermi_energy) gw_files = ['EVALQP.DAT', 'EVALQP.TXT', 'TDOS-QP.OUT'] # Parse GW band\",\n \"self._diagram_key)) self._ndos = len(total_dos) return self._ndos @property def number_of_lm(self): if self._nlm is None:\",\n \"band_energies[nb] + energy_fermi def parse_file(self, name, section): # TODO add support for info.xml,\",\n \"and properties exciting_files = ['EIGVAL.OUT', 'FERMISURF.bxsf', 'FS.bxsf'] # Parse DFT DOS from one\",\n \"= archive self.logger = logger if logger is not None else logging self._calculation_type\",\n \"self.get_exciting_files('input.xml') if positions is None: # get it from input.xml for f in\",\n \"self.logger self.info_gw_parser.logger = self.logger self.input_xml_parser.logger = self.logger self.data_xs_parser.logger = self.logger self.data_clathrate_parser.logger = self.logger\",\n \"repeats=False, sub_parser=TextParser(quantities=initialization_quantities)) ) scf_quantities = [ Quantity( 'energy_total', r'[Tt]*otal energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=ureg.hartree),\",\n \"end = start + nkpts_segment band_energy = np.array([np.transpose(band)[start:end] for band in bands]) if\",\n \"= { 'x_exciting_gap': (r'Estimated fundamental gap', ureg.hartree), 'time': (r'Wall time \\\\(seconds\\\\)', ureg.s)} for\",\n \"nspin = self.info_parser.get_number_of_spin_channels() def get_data(key): data = self.eigval_parser.get('eigenvalues_occupancies') # reshaping is not necessary\",\n \"ureg.s)} for name, key_unit in self._miscellaneous_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)' % key_unit[0], repeats=False, unit=key_unit[1]))\",\n \"return self._calculation_type = 'xs' # inconsistency in the naming convention for xs input\",\n \"f in input_file: self.input_xml_parser.mainfile = f positions = self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors = self.input_xml_parser.get( 'structure/crystal/basevect',\",\n \"positions is None: species = self.get_initialization_parameter('species', []) if species: positions = np.vstack([s.get('positions') for\",\n \"rf'radial points in muffin-tin *: *({re_float})', dtype=np.int32), Quantity('positions_format', r'atomic positions \\\\((.+?)\\\\)', flatten=False), Quantity(\",\n \"sec_scc.system_ref = sec_run.system[-1] sec_scc_ref = sec_run.calculation[0] sec_scc.starting_calculation_ref = sec_scc_ref sec_scc.calculations_ref = [sec_scc_ref] #\",\n \"from input.xml input_file = self.get_exciting_files('input.xml') for f in input_file: self.input_xml_parser.mainfile = f correlation\",\n \"'xs/BSE/xasspecies', 0) sec_method.x_exciting_xs_bse_xas_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlxas', [0, 0]) if self.input_xml_parser.get('xs/tddft') is not None:\",\n \"= self.input_xml_parser.get( 'gw/barecoul/cutofftype', 'none') sec_gw.screened_coulomb_volume_average = self.input_xml_parser.get( 'gw/scrcoul/sciavtype', 'isotropic') sec_gw.screened_Coulomb = self.input_xml_parser.get( 'gw/scrcoul/scrtype',\",\n \"'x_exciting_section_MT_moment_atom': for n in range(len(val)): sec_mt_moment_atom = msection.m_create(x_exciting_section_MT_moment_atom) sec_mt_moment_atom.x_exciting_MT_moment_atom_index = n + 1\",\n \"this is done in parser, however nspin is # determined from occupancies which\",\n \"= self.info_parser.get_initialization_parameter('smearing_kind') if smearing_kind is not None: if not isinstance(smearing_kind, str): smearing_kind =\",\n \"= [sec_method_ref] self.parse_file('input.xml', sec_method) # parse properties input_file = self.get_exciting_files('input.xml') if not input_file:\",\n \"* ureg.hartree sec_scc.x_exciting_xs_bse_loss = np.reshape( data[1], (n_components, n_loss)) # TODO check if format\",\n \"= self.partial_dos[i].attrib.get(self._m_key, None) if val_l is None or val_m is None: lm =\",\n \"nspin is # determined from occupancies which is problematic sometimes res = np.hstack([np.reshape(v[key],\",\n \"* ureg.hartree return energies self._quantities = [Quantity( 'program_version', r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\. ]+)\\\\s*started', repeats=False, dtype=str, flatten=False)]\",\n \"range(len(band_energies[n])): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.endpoints_labels = band_seg_labels[nb]\",\n \"sec_scc) # structure optimization structure_optimization = self.info_parser.get('structure_optimization', {}) for optimization_step in structure_optimization.get('optimization_step', []):\",\n \"self._distance_key = 'distance' self._coord_key = 'coord' self._energy_key = 'eval' self._vertex_key = 'vertex' self._band_key\",\n \"start + nkpts_segment self._band_k_points.append( np.transpose(data[2:5])[start:end]) start = end return self._band_k_points @property def distances(self):\",\n \"module in ['groundstate', 'hybrids']: sec_scc = parse_configuration(self.info_parser.get(module)) if sec_scc is None: continue #\",\n \"== 'SIGMA' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_sigma_no_local_field = data[1:3] def parse_xs(self): sec_run =\",\n \"xc_functional_name sec_scc = sec_run.m_create(Calculation) sec_scc.method_ref = sec_method if sec_run.system: sec_scc.system_ref = sec_run.system[-1] sec_scc_ref\",\n \"len(eigs_gw[0]) sec_gw_eigenvalues.n_kpoints = len(eigs_gw) sec_gw_eigenvalues.kpoints = get_data('k_points') sec_gw_eigenvalues.energies = reshape(eigs_gw) sec_gw_eigenvalues.value_exchange = reshape(get_data('Sx'))\",\n \"= val else: setattr(msection, name, val) # energy, moment, charge contributions parse_scf(final, sec_scc)\",\n \"return dict( number=np.array(val[0], dtype=int), values=val[1] * ureg.hartree, weights=val[2]) # TODO Read also input\",\n \"res = res * (1 / self.energy_unit) elif 'partial' in key: if not\",\n \"sec_method.gw.frequency_values = frequency_data.get('values') sec_method.gw.frequency_weights = frequency_data.get('weights') fundamental_band_gap = self.info_gw_parser.get('direct_band_gap', None) if fundamental_band_gap is\",\n \"sec_method.x_exciting_xs_xstype = xstype sec_method.x_exciting_electronic_structure_method = xstype sec_method.x_exciting_xs_broadening = self.input_xml_parser.get( 'xs/broad', 0.01, 'hartree') sec_method.x_exciting_xs_gqmax\",\n \"dict() if 'total' in key: if not self.total_dos: return res = np.zeros((self.number_of_spin_channels, self.number_of_dos))\",\n \"{ 'x_exciting_interstitial_moment': ['interstitial'], 'x_exciting_total_MT_moment': ['total moment in muffin-tins'], 'x_exciting_total_moment': ['total moment'], 'x_exciting_section_MT_moment_atom': ['atom_resolved']\",\n \"= [] self._quantities.append( Quantity( 'fermi_energy', r'Fermi Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*', unit=ureg.hartree, repeats=False)) def str_to_band_parameters(val_in): val =\",\n \"None) is None: return nspin = self.info_parser.get_number_of_spin_channels() def get_data(key): data = self.eigval_parser.get('eigenvalues_occupancies') #\",\n \"else: setattr(msection, key, val) if key == 'x_exciting_fermi_energy': sec_energy.fermi = val # charge\",\n \"= ureg.elementary_charge * ureg.bohr val = val_in.strip().split('\\\\n') properties = dict() atom_resolved = []\",\n \"len(sec_run.system) > 1: return sec_system for name in self.info_parser._system_keys_mapping.keys(): val = self.info_parser.get_initialization_parameter(name) if\",\n \"(r'Maximum number of plane\\\\-waves', None), 'x_exciting_lo': (r'Total number of local\\\\-orbitals', None)} self._method_keys_mapping =\",\n \"they are. What is the fourth column in epsilon which is not parsed?\",\n \"energy_fermi band_k_points = self.bandstructure_dat_parser.band_k_points nkpts_segment = self.bandstructure_dat_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment =\",\n \"= [True] * 3 # TODO confirm no cell optimization in exciting sec_atoms.lattice_vectors\",\n \"return self._band_k_points @property def distances(self): if self._distances is None: data = np.transpose(self.data) self._distances\",\n \"def partial_dos(self): if self._partial_dos is None: self._partial_dos = self.root.findall('./%s/%s' % (self._partialdos_key, self._diagram_key)) return\",\n \"distances(self): if self._distances is None: dist = np.transpose(self.data)[0] n_k_points = np.where(dist == dist[0])[0][1]\",\n \"np.transpose(self.data) n_kpoints = np.where(data[0] == data[0][0])[0][1] bands = data[1:] bands = np.reshape(bands, (\",\n \"quantity def get_xc_functional_name(self): # TODO expand list to include other xcf xc_functional_map =\",\n \"% str(i + 1).rjust(3, '0'))] for f in files_q: self.data_xs_parser.mainfile = f if\",\n \"reshape(get_data('Vxc')) def _parse_dos_out(self, sec_scc): data = self.dos_out_parser.data if data is None: return #\",\n \"in v[1].split()] vi = vi[0] if len(vi) == 1 else vi properties[v[0].strip()] =\",\n \"target achieved([\\\\s\\\\S]+?Opt)', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(imized atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'imized\",\n \"is None: continue if '_X_' in name: sec_xc_functional.exchange.append(Functional(name=name)) elif '_C_' in name: sec_xc_functional.correlation.append(Functional(name=name))\",\n \"'x_exciting_section_MT_moment_atom': ['atom_resolved'] } def get_exciting_files(self, default): mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target\",\n \"ureg.bohr atoms = self.input_xml_parser.get('structure/species/atom') atom_labels = [] for n in range(len(atoms)): atom_labels.extend([species[n].split('.')[0]] *\",\n \"self.input_xml_parser.get('xs/ngridq', [1, 1, 1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk', [1, 1, 1]) rgkmax = self.input_xml_parser.get('xs/rgkmax',\",\n \"sec_gw.ngridq = self.input_xml_parser.get('gw/ngridq', [1, 1, 1]) sec_gw.basis_set = 'mixed' sec_gw.qp_equation_treatment = 'linearization' sec_gw.max_frequency\",\n \"and density', 1 / ureg.bohr), 'x_exciting_gvector_size': (r'G\\\\-vector grid sizes', None), 'x_exciting_gvector_total': (r'Total number\",\n \"= self.input_xml_parser.get( 'xs/BSE/nstlxas', [0, 0]) if self.input_xml_parser.get('xs/tddft') is not None: sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get(\",\n \"'AC' if nwacont else None, 'NAR' if not aresdf else None] file_ext =\",\n \"def _parse_bandstructure_dat(self, sec_scc): self.bandstructure_dat_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_dat_parser.band_energies if band_energies is None:\",\n \"= 'atom' self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin\",\n \"get_exciting_files(self, default): mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = default.rsplit('.', 1) filename\",\n \"atom_resolved = [] species = None for v in val: v = v.strip().split(':')\",\n \"self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors = self.input_xml_parser.get( 'structure/crystal/basevect', np.eye(3)) species = self.input_xml_parser.get('structure/species/speciesfile') if positions is None\",\n \"name, val) except Exception: self.logger.warn('Error setting metainfo.') # species species = self.info_parser.get_initialization_parameter('species', [])\",\n \"thing that we can do is to assume that the first steps are\",\n \"self.info_parser._convergence_keys_mapping.keys(): val = iteration.get(name) if val is None: continue setattr(msection, name, val) #\",\n \"val_in.strip().split('\\\\n') kpts = np.array(val[0].split(), dtype=float) keys = val[1].split() eigs = np.transpose(np.array([v.split() for v\",\n \"sec_scc.m_create(ScfIteration) parse_scf(scf_iteration, sec_scf_iteration) return sec_scc def parse_system(self, section): sec_run = self.archive.run[-1] positions =\",\n \"+ energy_fermi def parse_file(self, name, section): # TODO add support for info.xml, wannier.out\",\n \"get_positions_format(self, section): positions_format = section.get('positions_format') if positions_format is None: species = self.get_initialization_parameter('species', [])\",\n \"self._diagram_key = 'diagram' self._l_key = 'l' self._m_key = 'm' self._energy_key = 'e' self._dos_key\",\n \"is None: self._partial_dos = self.root.findall('./%s/%s' % (self._partialdos_key, self._diagram_key)) return self._partial_dos @property def energies(self):\",\n \"None self._natoms = None self._nspin = None self._nlm = None self._energies = None\",\n \"muffin-tins', 'total in muffin-tins'], 'charge_total': ['total charge'], 'x_exciting_section_MT_charge_atom': ['atom_resolved'] } self._moment_keys_mapping = {\",\n \"self.number_of_spin_channels, self.number_of_atoms, self.number_of_dos)) for i in range(len(self.partial_dos)): spin = self.partial_dos[i].attrib.get(self._nspin_key, None) if spin\",\n \"f.endswith(file_ext)] options.sort() filenames = [os.path.join(self.info_parser.maindir, f) for f in options] else: filenames =\",\n \"if self._nspin is None: self._nspin = np.shape(np.transpose(self.data))[0] - 6 return self._nspin @property def\",\n \"self._vertex_key = 'vertex' self._band_key = 'band' self._atom_key = 'atom' self._nspin = kwargs.get('nspin', None)\",\n \"self._band_k_points is None: data = np.transpose(self.data) self._band_k_points = [] start = 0 for\",\n \"number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: self._neigs_segment = len(self.bands[0]) // self.number_of_spin_channels return self._neigs_segment def\",\n \"= DOSXMLParser(energy_unit=ureg.hartree) self.bandstructure_parser = BandstructureXMLParser(energy_unit=ureg.hartree) self.eigval_parser = ExcitingEigenvalueParser() self.fermisurf_parser = ExcitingFermiSurfaceBxsfParser() self.evalqp_parser =\",\n \"step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions at this step\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True,\",\n \"INFO.OUT return else: return files = self.get_exciting_files(name) if len(files) > 1: self.logger.warn('Found multiple\",\n \"dtype=float) res.append(np.linspace(start, end, self.number_of_k_points_per_segment[i])) elif key == 'band_segm_labels': res = [] for i\",\n \"sec_gw.self_energy_singularity_treatment = self.input_xml_parser.get( 'gw/selfenergy/singularity', 'mpd') sec_gw.self_energy_c_analytical_continuation = self.input_xml_parser.get( 'gw/selfenergy/actype', 'pade') sec_gw.mixed_basis_lmax = int(self.input_xml_parser.get(\",\n \"numpy as np import os import re import logging from nomad.units import ureg\",\n \"repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr), ]), repeats=False)) self._quantities.append(Quantity( 'hybrids', r'Hybrids module started([\\\\s\\\\S]+?)Hybrids\",\n \"None])[0] sec_xc_functional.reference = xc_functional.get('name_reference', [None, None])[1] sec_electronic.n_spin_channels = self.info_parser.get_number_of_spin_channels() if self._calculation_type == 'volume_optimization':\",\n \"import x_exciting_section_MT_charge_atom, x_exciting_section_MT_moment_atom,\\\\ x_exciting_section_spin, x_exciting_section_fermi_surface,\\\\ x_exciting_section_atoms_group re_float = r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?' class GWInfoParser(TextParser): def __init__(self):\",\n \"self._calculation_type = None self.init_parser() sec_run = self.archive.m_create(Run) sec_run.program = Program( name='exciting', version=self.info_parser.get('program_version', '').strip())\",\n \"support for info.xml, wannier.out if name.startswith('dos') and name.endswith('xml'): parser = self.dos_parser parser_function =\",\n \"r'Lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=ureg.bohr, repeats=False, convert=False), Quantity( 'lattice_vectors_reciprocal', r'Reciprocal lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=1\",\n \"elif quantity == 'SIGMA' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_sigma_local_field = data[1:3] elif quantity\",\n \"sec_k_band_segment.value = band_energies[nb] + energy_fermi def parse_file(self, name, section): # TODO add support\",\n \"structure_optimization.get('optimization_step', []): sec_scc = parse_configuration(optimization_step) if optimization_step.get('method') is not None: sec_scc.x_exciting_geometry_optimization_method = optimization_step.get('method')\",\n \"self.band_out_parser.logger = self.logger self.info_gw_parser.logger = self.logger self.input_xml_parser.logger = self.logger self.data_xs_parser.logger = self.logger self.data_clathrate_parser.logger\",\n \"continue if name == 'time': msection.time_calculation = val else: setattr(msection, name, val) #\",\n \"key_unit in self._system_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) for\",\n \"info files, will read only first!') self.info_gw_parser.mainfile = gw_info_files[0] fermi_energy = self.info_gw_parser.get('fermi_energy', None)\",\n \"is None: return # this is really problematic if some scf steps dont\",\n \"np.reshape( data[1], (n_components, n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im = np.reshape( data[2], (n_components, n_epsilon)) def parse_sigma(data, sec_scc):\",\n \"self.bandstructure_parser parser_function = self._parse_bandstructure elif name.startswith('EIGVAL') and name.endswith('OUT'): parser = self.eigval_parser parser_function =\",\n \"def __init__(self, mainfile, logger): super().__init__(mainfile, logger=logger) def init_quantities(self): self._quantities = [] class ExcitingInfoParser(TextParser):\",\n \"key == 'band_k_points': res = [] for i in range(len(self.number_of_k_points_per_segment)): start = np.array(\",\n \"np.transpose(np.array([v for v in val if len(v) == 3], float)) return dict( number=np.array(val[0],\",\n \"sec_method.gw.frequency_weights = frequency_data.get('weights') fundamental_band_gap = self.info_gw_parser.get('direct_band_gap', None) if fundamental_band_gap is None: fundamental_band_gap =\",\n \"= np.array(val[0].split(), dtype=float) keys = val[1].split() eigs = np.transpose(np.array([v.split() for v in val[2:]],\",\n \"'').strip()) # method goes first since reference needed for sec_scc self.parse_method() self.parse_configurations() self.parse_gw()\",\n \"clathrate_file[0] if self.data_clathrate_parser.data: data = np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates = np.transpose( np.array(data[:3], dtype=float)) sec_system.x_exciting_clathrates_atom_labels =\",\n \"n_components = len(data) data = np.transpose(np.vstack(data)) n_sigma = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_sigma_energies =\",\n \"def distances(self): if self._distances is None: data = np.transpose(self.data) self._distances = data[5][:int(max(data[1]))] return\",\n \"quantity = [None] * (n_scf - len(quantity)) + quantity return quantity def get_xc_functional_name(self):\",\n \"val) # convergence values for name in self.info_parser._convergence_keys_mapping.keys(): val = iteration.get(name) if val\",\n \"'e' self._dos_key = 'dos' self._unit_key = 'unit' self._energy_unit = kwargs.get('energy_unit', None) self._units_mapping =\",\n \"kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None self._nkpts_segment = None self._neigs_segment = None\",\n \"= [ Quantity( 'lattice_vectors', r'Lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=ureg.bohr, repeats=False, convert=False), Quantity( 'lattice_vectors_reciprocal', r'Reciprocal\",\n \"sec_eigenvalues = sec_scc.m_create(BandEnergies) sec_eigenvalues.kpoints = self.eigval_parser.get('k_points') sec_eigenvalues.occupations = get_data('occupancies') sec_eigenvalues.energies = get_data('eigenvalues') def\",\n \"lattice_vectors is None or species is None: continue lattice_vectors = np.array(lattice_vectors, dtype=float) lattice_vectors\",\n \"return np.array([d[1].get(key) for d in data]) * ureg.hartree eigs_gw = get_data('E_GW') if eigs_gw[0]\",\n \"'atom' self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin =\",\n \"if self._nlm is None: if self.partial_dos is None: return self._nlm = 0 l_list\",\n \"repeats=False, str_operation=str_to_array, convert=False, unit=ureg.hartree / ureg.bohr), Quantity( 'step', r'Optimization step\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity(\",\n \"continue # add data to scc # TODO add support for more output\",\n \"stopped', sub_parser=TextParser(quantities=module_quantities) )) def get_atom_labels(self, section): labels = section.get('symbols') if labels is None:\",\n \"[]) labels = [] for specie in species: labels += [specie.get('symbol')] * len(specie.get('positions'))\",\n \"gw info files gw_info_files = ['GW_INFO.OUT', 'GWINFO.OUT'] for f in gw_info_files: if self.get_exciting_files(f):\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"with should have units of energy sec_smearing.width = smearing_width.magnitude for name in self.info_parser._convergence_keys_mapping.keys():\",\n \"def str_to_frequency(val_in): val = [v.split() for v in val_in.split('\\\\n')] val = np.transpose(np.array([v for\",\n \"distances(self): if self._distances is None: data = np.transpose(self.data) self._distances = data[5][:int(max(data[1]))] return self._distances\",\n \"sec_method.x_exciting_xs_broadening = self.input_xml_parser.get( 'xs/broad', 0.01, 'hartree') sec_method.x_exciting_xs_gqmax = self.input_xml_parser.get( 'xs/gqmax', 0.0, '1/bohr') sec_method.x_exciting_xs_lmaxapw\",\n \"'LOSS' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_loss_function_local_field = data[1] elif quantity == 'LOSS' and\",\n \"in this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds', unit=ureg.s, repeats=False, dtype=float) ] self._quantities.append(Quantity( 'structure_optimization', r'Structure\\\\-optimization module started([\\\\s\\\\S]+?)Structure\\\\-optimization\",\n \"dtype=float) return dos def parse(self, key): if self._results is None: self._results = dict()\",\n \"if self._results is None: self._results = dict() if 'total' in key: if not\",\n \"ureg.bohr val = val_in.strip().split('\\\\n') properties = dict() atom_resolved = [] species = None\",\n \"'x_exciting_lmaxapw': (r' APW functions', None), 'x_exciting_nuclear_charge': ('Total nuclear charge', ureg.elementary_charge), 'x_exciting_electronic_charge': ('Total electronic\",\n \"['EPSILON', 'LOSS', 'SIGMA']: for ext in ['FXC', 'NLF_FXC']: data = get_data(quantity, ext) if\",\n \"self.bandstructure_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_parser.get('band_energies', []) for n in range(len(band_energies)): # Get\",\n \"lattice_vectors) * ureg.bohr lattice_vectors = lattice_vectors * ureg.bohr atoms = self.input_xml_parser.get('structure/species/atom') atom_labels =\",\n \"= partialdos.to('1/joule').magnitude lm_values = np.column_stack((np.arange(len(partialdos)), np.zeros(len(partialdos), dtype=np.int32))) for lm in range(len(partialdos)): for spin\",\n \"sec_gw.screened_coulomb_volume_average = self.input_xml_parser.get( 'gw/scrcoul/sciavtype', 'isotropic') sec_gw.screened_Coulomb = self.input_xml_parser.get( 'gw/scrcoul/scrtype', 'rpa') def parse_gw(self): sec_run\",\n \"= parse_loss else: continue try: parse_function(data, sec_scc) except Exception: self.logger.error('Error setting xs data',\",\n \"parser.eigval_parser.quantities self.fermisurf_parser.quantities = parser.fermisurf_parser.quantities self.evalqp_parser.quantities = parser.evalqp_parser.quantities self.info_gw_parser.quantities = parser.info_gw_parser.quantities def parse(self, filepath,\",\n \"n_components sec_scc.x_exciting_xs_bse_number_of_excitons = n_excitons sec_scc.x_exciting_xs_bse_exciton_energies = np.reshape( data[1], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies\",\n \"None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') # TODO I am not sure\",\n \"= [] for quantity in ['EXCITON', 'EPSILON', 'SIGMA', 'LOSS']: files = get_files(quantity) for\",\n \"(r'Maximum \\\\|G\\\\| for potential and density', 1 / ureg.bohr), 'x_exciting_gvector_size': (r'G\\\\-vector grid sizes',\",\n \"self.info_parser.get_number_of_spin_channels() sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = len(data) // nspin data = np.reshape(data,\",\n \"'gw/selfenergy/nempty', 0)) sec_gw.self_energy_singularity_treatment = self.input_xml_parser.get( 'gw/selfenergy/singularity', 'mpd') sec_gw.self_energy_c_analytical_continuation = self.input_xml_parser.get( 'gw/selfenergy/actype', 'pade') sec_gw.mixed_basis_lmax\",\n \"self._diagram_key)) return self._partial_dos @property def energies(self): if self._energies is None: if self.total_dos is\",\n \"range(len(partialdos[lm])): for atom in range(len(partialdos[lm][spin])): sec_dos_values = sec_dos.m_create(DosValues, Dos.atom_projected) sec_dos_values.m_kind = 'spherical' sec_dos_values.lm\",\n \"get it from input.xml input_file = self.get_exciting_files('input.xml') for f in input_file: self.input_xml_parser.mainfile =\",\n \"is None: self._neigs_segment = len(self.bands[0]) // self.number_of_spin_channels return self._neigs_segment def parse(self, key): if\",\n \"kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None self._distances = None self._band_energies = None\",\n \"multiple files. Will read all!', data=dict(file=name)) for n in range(len(files)): parser.mainfile = files[n]\",\n \"= self.vertices[i + 1].attrib.get(self._coord_key).split() res.append([start, end]) else: res = None self._results[key] = res\",\n \"'gw/freqgrid/nomeg', 16)) sec_gw.self_energy_c_number_of_poles = int(self.input_xml_parser.get( 'gw/selfenergy/npol', 0)) sec_gw.self_energy_c_number_of_empty_states = int(self.input_xml_parser.get( 'gw/selfenergy/nempty', 0)) sec_gw.self_energy_singularity_treatment\",\n \"'x_exciting_scf_threshold_%s_change' % name.split('_')[-2] setattr(sec_method, metainfo_name, threshold) # additionally, set threshold to global metainfo.\",\n \"setting mainfile self.bandstructure_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_parser.get('band_energies', []) for n in range(len(band_energies)):\",\n \"self._vertices = None self._distances = None self._species = None @property def distances(self): if\",\n \"band_energies[n][nb] + energy_fermi def _parse_eigenvalues(self, sec_scc): if self.eigval_parser.get('eigenvalues_occupancies', None) is None: return nspin\",\n \"kinetic energy', 'core electron kinetic'], 'x_exciting_dft_d2_dispersion_correction': ['DFT-D2 dispersion correction'] } self._electron_charge_keys_mapping = {\",\n \"data is None: return def get_data(key): if key == 'k_points': return np.array([d[0][:3] for\",\n \"@property def number_of_lm(self): if self._nlm is None: if self.partial_dos is None: return self._nlm\",\n \"sec_gw.max_frequency = self.input_xml_parser.get( 'gw/freqgrid/freqmax', 1.0) sec_gw.frequency_grid_type = self.input_xml_parser.get( 'gw/freqgrid/fgrid', 'gaule2') sec_gw.number_of_frequencies = int(self.input_xml_parser.get(\",\n \"return False def _parse_dos(self, sec_scc): if self.dos_parser.get('totaldos', None) is None: return # Get\",\n \"sec_fermisurface.x_exciting_grid_fermi_surface = band_parameters[1] sec_fermisurface.x_exciting_origin_fermi_surface = band_parameters[2] sec_fermisurface.x_exciting_vectors_fermi_surface = band_parameters[3] fermi_energy = self.fermisurf_parser.get('fermi_energy', None)\",\n \"= vi[0] if len(vi) == 1 else vi properties[v[0].strip()] = vi * unit\",\n \"in x.split(':')])])) ) self._quantities.append(Quantity( 'initialization', r'(?:All units are atomic|Starting initialization)([\\\\s\\\\S]+?)(?:Using|Ending initialization)', repeats=False, sub_parser=TextParser(quantities=initialization_quantities))\",\n \"for i in range(len(self.total_dos)): spin = self.total_dos[i].attrib.get(self._nspin_key, i) res[i] = self._get_dos(self._total_dos[i]) if self.energy_unit\",\n \"{ 'energy_total': ['Total energy', 'total energy'], 'x_exciting_fermi_energy': ['Fermi energy', 'Fermi'], 'energy_kinetic_electronic': ['Kinetic energy',\",\n \"in val_in.split('\\\\n')] val = np.transpose(np.array([v for v in val if len(v) == 3],\",\n \"is None: return self._nlm = 0 l_list = set([int(e.attrib.get(self._l_key)) for e in self.partial_dos])\",\n \"'gaussian', 'Methfessel-Paxton': 'methfessel-paxton', 'Fermi-Dirac': 'fermi', 'Extended': 'tetrahedra'} sec_smearing = sec_electronic.m_create(Smearing) smearing_kind = self.info_parser.get_initialization_parameter('smearing_kind')\",\n \"treatment', None), 'x_exciting_number_of_bravais_lattice_symmetries': ('Number of Bravais lattice symmetries', None), 'x_exciting_number_of_crystal_symmetries': ('Number of crystal\",\n \"f in options if f.endswith(file_ext)] options.sort() filenames = [os.path.join(self.info_parser.maindir, f) for f in\",\n \"self.input_xml_parser.get('xs/tddft/nwacont', None) aresdf = self.input_xml_parser.get('xs/tddft/aresdf', True) file_ext_list = [ 'TET' if tetradf else\",\n \"% name.split('_')[-2] setattr(sec_method, metainfo_name, threshold) # additionally, set threshold to global metainfo. This\",\n \"'gamma' else start, '\\\\u0393' if end.lower() == 'gamma' else end]) elif key ==\",\n \"parser = self.input_xml_parser if self._calculation_type == 'gw': parser_function = self._parse_input_gw elif self._calculation_type ==\",\n \"l_list: self._nlm += 2 * li + 1 return self._nlm @property def total_dos(self):\",\n \"1].attrib.get(self._coord_key).split(), dtype=float) res.append(np.linspace(start, end, self.number_of_k_points_per_segment[i])) elif key == 'band_segm_labels': res = [] for\",\n \"key: if not self.partial_dos: return res = np.zeros(( self.number_of_lm, self.number_of_spin_channels, self.number_of_atoms, self.number_of_dos)) for\",\n \"get_data(files[i]) if not data: sccs.append(None) continue if quantity == 'EXCITON': sec_scc = sec_run.m_create(Calculation)\",\n \"['GGA_C_PBE', 'GGA_X_PBE_R'], 22: ['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'], 26: ['GGA_C_PBE', 'GGA_X_WC'], 30: ['GGA_C_AM05', 'GGA_C_AM05'], 300: ['GGA_C_BGCP',\",\n \"DOSXMLParser(energy_unit=ureg.hartree) self.bandstructure_parser = BandstructureXMLParser(energy_unit=ureg.hartree) self.eigval_parser = ExcitingEigenvalueParser() self.fermisurf_parser = ExcitingFermiSurfaceBxsfParser() self.evalqp_parser = ExcitingEvalqpParser()\",\n \"if self._nspin is None: if not self.total_dos: return self._nspin = len(self.total_dos) return self._nspin\",\n \"is not None: sec_worfklow.type = 'geometry_optimization' sec_geometry_opt = sec_worfklow.m_create(GeometryOptimization) threshold_force = structure_optimization.get( 'optimization_step',\",\n \"1]) rgkmax = self.input_xml_parser.get('xs/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.)\",\n \"TODO check if format of files are really correct, i.e. columns are supposed\",\n \"return positions = np.array(positions) positions_format = positions_format if positions_format is not None else\",\n \"initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation type[\\\\s\\\\S]+?\\\\n *\\\\n)', sub_parser=TextParser(quantities=[ Quantity('type', r'Exchange-correlation type +: +(\\\\S+)'), Quantity( 'name_reference',\",\n \"self._calculation_type == 'gw': parser_function = self._parse_input_gw elif self._calculation_type == 'xs': parser_function = self._parse_input_xs\",\n \"'K') sec_method.x_exciting_xs_bse_xasspecies = self.input_xml_parser.get( 'xs/BSE/xasspecies', 0) sec_method.x_exciting_xs_bse_xas_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlxas', [0, 0]) if\",\n \"nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy = np.array([ np.transpose(energy)[start:end] for\",\n \"'x_exciting_gvector_size': (r'G\\\\-vector grid sizes', None), 'x_exciting_gvector_total': (r'Total number of G\\\\-vectors', None), 'x_exciting_lmaxapw': (r'\",\n \"np.reshape( data[0], (n_components, n_loss)) * ureg.hartree sec_scc.x_exciting_xs_bse_loss = np.reshape( data[1], (n_components, n_loss)) #\",\n \"if val is None: continue if name == 'time': msection.time_calculation = val else:\",\n \"== 'volume_optimization': sec_method.x_exciting_volume_optimization = True def parse_scc(self, section): sec_run = self.archive.run[-1] final =\",\n \"['EXCITON', 'EPSILON', 'SIGMA', 'LOSS']: files = get_files(quantity) for i in range(len(files)): data =\",\n \"[ point.attrib.get(self._distance_key) for point in self.bands[0][0]] self._distances = np.array(self._distances, dtype=float) return self._distances @property\",\n \"# # Copyright The NOMAD Authors. # # This file is part of\",\n \"energy_fermi def _parse_eigenvalues(self, sec_scc): if self.eigval_parser.get('eigenvalues_occupancies', None) is None: return nspin = self.info_parser.get_number_of_spin_channels()\",\n \"== 'x_exciting_section_MT_charge_atom': for n in range(len(val)): sec_mt_charge_atom = msection.m_create(x_exciting_section_MT_charge_atom) sec_mt_charge_atom.x_exciting_MT_charge_atom_index = n +\",\n \"np.array(val[2].split(), dtype=float) vector = np.array([v.split() for v in val[3:6]], dtype=float) return [nbands, mesh,\",\n \"li + 1 return self._nlm @property def total_dos(self): if self._total_dos is None: self._total_dos\",\n \"% key_unit[0], unit=key_unit[1], repeats=False) ) initialization_quantities.append(Quantity( 'species', rf'(Species : *\\\\d+ *\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float} *{re_float} *{re_float}\\\\n\\\\s*\\\\n)',\",\n \"self.root.findall('./*/%s' % self._atom_key) for band in bands_atom: self._bands.append(band.findall('./%s' % self._band_key)) return self._bands @property\",\n \"sec_scc): # we need to set nspin again as this is overwritten when\",\n \"if self._band_energies is None: if self.data is None: return data = np.transpose(self.data) n_kpoints\",\n \"self._distances is None: if not self.bands: return self._distances = [ point.attrib.get(self._distance_key) for point\",\n \"np.array([v.split() for v in val[3:6]], dtype=float) return [nbands, mesh, origin, vector] self._quantities.append( Quantity(\",\n \"other energies are reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is None: return energy_fermi\",\n \"i in range(len(self.number_of_k_points_per_segment)): start = np.array( self.vertices[i].attrib.get(self._coord_key).split(), dtype=float) end = np.array( self.vertices[i +\",\n \"Smearing, XCFunctional, Functional, GW as GWMethod, Scf, BasisSet ) from nomad.datamodel.metainfo.simulation.system import (\",\n \"[None] * (n_scf - len(quantity)) + quantity return quantity def get_xc_functional_name(self): # TODO\",\n \"(n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im = np.reshape( data[2], (n_components, n_sigma)) def parse_loss(data, sec_scc): n_components =\",\n \"= 'xs' # inconsistency in the naming convention for xs input xml file\",\n \"if len(files) > 1: self.logger.warn('Found multiple files. Will read all!', data=dict(file=name)) for n\",\n \"energy_contributions.get(name, None) if val is not None: break if val is None: continue\",\n \"number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data = np.transpose(self.data) self._neigs_segment = int(max(data[0])) return self._neigs_segment\",\n \"in structure_optimization.get('optimization_step', []): sec_scc = parse_configuration(optimization_step) if optimization_step.get('method') is not None: sec_scc.x_exciting_geometry_optimization_method =\",\n \"[]] for i in range(len(qpoints)): data_q = [] files_q = [f for f\",\n \"sec_smearing.kind = smearing_kind smearing_width = self.info_parser.get_initialization_parameter('smearing_width') if smearing_width is not None: smearing_width =\",\n \"'x_exciting_coulomb_potential_energy': ['Coulomb potential energy', 'Coulomb potential'], 'energy_xc_potential': ['xc potential energy', 'xc potential'], 'energy_electrostatic':\",\n \"= None @property def band_energies(self): if self._band_energies is None: if self.data is None:\",\n \"'x_exciting_number_of_atoms': ('Total number of atoms per unit cell', None), 'x_exciting_spin_treatment': ('Spin treatment', None),\",\n \"key == 'band_segm_start_end': res = [] for i in range(len(self.number_of_k_points_per_segment)): start = self.vertices[i].attrib.get(self._coord_key).split()\",\n \"cell is None: return positions = np.dot(positions, cell.magnitude) return positions * ureg.bohr def\",\n \"reshaping is not necessary as this is done in parser, however nspin is\",\n \"if energy_total is not None: sec_energy.total = EnergyEntry(value=energy_total) x_exciting_dos_fermi = iteration.get('x_exciting_dos_fermi') if x_exciting_dos_fermi\",\n \"= self.info_parser.get_atom_labels(section.get('atomic_positions', {})) input_file = self.get_exciting_files('input.xml') if positions is None: # get it\",\n \"(r'R\\\\^MT\\\\_min \\\\* \\\\|G\\\\+k\\\\|\\\\_max \\\\(rgkmax\\\\)', None), 'x_exciting_species_rtmin': (r'Species with R\\\\^MT\\\\_min', None), 'x_exciting_gkmax': (r'Maximum \\\\|G\\\\+k\\\\|\",\n \"parse_system(self, section): sec_run = self.archive.run[-1] positions = self.info_parser.get_atom_positions(section.get('atomic_positions', {})) lattice_vectors = self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels\",\n \"self.root.find('./axis') if axis is None: return self._energy_unit = self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1) return self._energy_unit @property\",\n \"section.get('scf_iteration', []) for scf_iteration in scf_iterations: sec_scf_iteration = sec_scc.m_create(ScfIteration) parse_scf(scf_iteration, sec_scf_iteration) return sec_scc\",\n \"to set nspin again as this is overwritten when setting mainfile self.bandstructure_parser._nspin =\",\n \"iteration.get('x_exciting_dos_fermi') if x_exciting_dos_fermi is not None: setattr(msection, 'x_exciting_dos_fermi', x_exciting_dos_fermi) # energy contributions energy_contributions\",\n \"= self.input_xml_parser.get( 'xs/tddft/fxctype', 'RPA') sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution = self.input_xml_parser.get( 'xs/tddft/intraband', False) sec_method.x_exciting_xs_tddft_diagonal_xc_kernel = self.input_xml_parser.get( 'xs/tddft/kerndiag',\",\n \"= 'single_point' structure_optimization = self.info_parser.get('structure_optimization') if structure_optimization is not None: sec_worfklow.type = 'geometry_optimization'\",\n \"== 'gw': return sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref\",\n \"= 'linearization' sec_gw.max_frequency = self.input_xml_parser.get( 'gw/freqgrid/freqmax', 1.0) sec_gw.frequency_grid_type = self.input_xml_parser.get( 'gw/freqgrid/fgrid', 'gaule2') sec_gw.number_of_frequencies\",\n \"= np.array( [float(point.attrib.get(self._energy_key)) for point in self.total_dos[0]]) if self.energy_unit is not None: self._energies\",\n \"self.distances[i - 1]: self._nkpts_segment.append(count) count = 1 else: count += 1 self._nkpts_segment.append(count) return\",\n \"sec_gap.value_optical = optical_band_gap def parse_miscellaneous(self): sec_worfklow = self.archive.m_create(Workflow) sec_worfklow.type = 'single_point' structure_optimization =\",\n \"specie in species: sec_atoms_group = sec_system.m_create(x_exciting_section_atoms_group) sec_atoms_group.x_exciting_geometry_atom_labels = specie.get('symbol') sec_atoms_group.x_exciting_geometry_atom_number = str(specie.get('number')) sec_atoms_group.x_exciting_muffin_tin_points\",\n \"for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy = np.array([np.transpose(band)[start:end] for\",\n \"= self.info_parser.get_initialization_parameter(name) if val is None: continue if name == 'x_exciting_spin_treatment': sub_sec =\",\n \"= self.partial_dos[i].attrib.get(self._l_key, None) val_m = self.partial_dos[i].attrib.get(self._m_key, None) if val_l is None or val_m\",\n \"sec_scc.x_exciting_xs_bse_exciton_amplitude_re = np.reshape( data[4], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_im = np.reshape( data[5], (n_components, n_excitons)) def\",\n \"effective potential \\\\(target\\\\)', ureg.hartree), 'x_exciting_energy_convergence': ( r'Absolute change in total energy\\\\s*\\\\(target\\\\)', ureg.hartree), 'x_exciting_charge_convergence':\",\n \"*({re_float}) *({re_float}) *({re_float})', repeats=True, dtype=np.dtype(np.float64))]))) initialization_quantities.append(Quantity( 'potential_mixing', r'Using ([\\\\w ]+) potential mixing', repeats=False,\",\n \"filename = '%s%s' % (target[0], suffix) if target[1:]: filename = '%s.%s' % (filename,\",\n \"unit=ureg.hartree / ureg.bohr), Quantity( 'step', r'Optimization step\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'method', r'method\\\\s*=\\\\s*(\\\\w+)', repeats=False,\",\n \"for the specific language governing permissions and # limitations under the License. #\",\n \"= None self._vertices = None self._distances = None self._band_energies = None self._band_k_points =\",\n \"exciting_files: self.parse_file(f, sec_scc) # structure optimization structure_optimization = self.info_parser.get('structure_optimization', {}) for optimization_step in\",\n \"sec_scc): fermi_surface = self.fermisurf_parser.get('fermi_surface', [None])[0] if fermi_surface is None: return sec_fermisurface = sec_scc.m_create(x_exciting_section_fermi_surface)\",\n \"def parse_configurations(self): sec_run = self.archive.run[-1] def parse_configuration(section): if not section: return sec_scc =\",\n \"self._energies def _get_dos(self, diagram): dos = np.array( [point.attrib.get(self._dos_key) for point in diagram], dtype=float)\",\n \"spin sec_dos_values.value = dos[spin] # TODO add PDOS def _parse_bandstructure_dat(self, sec_scc): self.bandstructure_dat_parser._nspin =\",\n \"if e]) # read q points qpoints = self.input_xml_parser.get('xs/qpointset/qpoint') def get_data(quantity, ext): #\",\n \"names in self._electron_charge_keys_mapping.items(): val = None for name in names: val = charge_contributions.get(name,\",\n \"used to un-shift the band structure to # the original scale in which\",\n \"(r'Effective Wigner radius, r\\\\_s', ureg.bohr), 'x_exciting_empty_states': ('Number of empty states', None), 'x_exciting_valence_states': ('Total\",\n \"in total energy\\\\s*\\\\(target\\\\)', ureg.hartree), 'x_exciting_charge_convergence': ( r'Charge distance\\\\s*\\\\(target\\\\)', ureg.elementary_charge), 'x_exciting_IBS_force_convergence': ( r'Abs\\\\. change\",\n \"initialization_quantities = [ Quantity( 'lattice_vectors', r'Lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=ureg.bohr, repeats=False, convert=False), Quantity( 'lattice_vectors_reciprocal',\",\n \"get_data('E_GW') if eigs_gw[0] is None: return nspin = self.info_parser.get_number_of_spin_channels() def reshape(data): if data[0]\",\n \"= self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1) return self._energy_unit @property def number_of_spin_channels(self): if self._nspin is None: if\",\n \"def parse_epsilon(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_epsilon = len(data[0]) //\",\n \"sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_dat_parser.band_k_points nkpts_segment = self.bandstructure_dat_parser.number_of_k_points_per_segment for nb\",\n \"energy_fermi.to(\\\"hartree\\\") sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_parser.get('band_k_points') nkpts_segment =\",\n \"None: continue if '_X_' in name: sec_xc_functional.exchange.append(Functional(name=name)) elif '_C_' in name: sec_xc_functional.correlation.append(Functional(name=name)) elif\",\n \"[{}])[-1].get( name, [None, None])[-1] def get_scf_quantity(self, name): n_scf = len(self.get('energy_total_scf_iteration', [])) quantity =\",\n \"None), 'x_exciting_spin_treatment': ('Spin treatment', None), 'x_exciting_number_of_bravais_lattice_symmetries': ('Number of Bravais lattice symmetries', None), 'x_exciting_number_of_crystal_symmetries':\",\n \"= len(np.where(data == data[0])[0]) return self._neigs_segment class BandstructureXMLParser(XMLParser): def __init__(self, **kwargs): # TODO\",\n \"== 'Znk': return np.array([d[1].get(key, None) for d in data]) else: energy = np.array([d[1].get(key,\",\n \"parser, however nspin is # determined from occupancies which is problematic sometimes res\",\n \"sec_k_band_segment.energies = band_energies[n][nb] + energy_fermi def _parse_eigenvalues(self, sec_scc): if self.eigval_parser.get('eigenvalues_occupancies', None) is None:\",\n \"['LDA_C_XALPHA'], 5: ['LDA_C_VBH'], 20: ['GGA_C_PBE', 'GGA_X_PBE'], 21: ['GGA_C_PBE', 'GGA_X_PBE_R'], 22: ['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'], 26:\",\n \"fundamental_band_gap = self.info_gw_parser.get('direct_band_gap', None) if fundamental_band_gap is None: fundamental_band_gap = self.info_gw_parser.get('fundamental_band_gap', None) sec_gap\",\n \"clathrate info clathrate_file = self.get_exciting_files('str.out') if clathrate_file: sec_system.x_exciting_clathrates = True self.data_clathrate_parser.mainfile = clathrate_file[0]\",\n \"= None @property def band_energies(self): if self._band_energies is None: data = np.transpose(self.data) n_kpoints\",\n \"np.reshape( data[2], (n_components, n_epsilon)) def parse_sigma(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data))\",\n \"ureg.hartree) * volume.to('m**3').magnitude for spin in range(len(dos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin =\",\n \"'eigenvalues_occupancies', r'\\\\(state\\\\, eigenvalue and occupancy below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n *\\\\n))', str_operation=str_to_eigenvalues, repeats=True)) class ExcitingGWOutParser(TextParser): def __init__(self,\",\n \"spin_treatment.lower() == 'spin-unpolarised' else 2 return n_spin def get_unit_cell_volume(self): return self.get('initialization', {}).get('x_exciting_unit_cell_volume', 1.0\",\n \"'xs' # inconsistency in the naming convention for xs input xml file sec_method\",\n \"files = get_files(quantity) for i in range(len(files)): data = get_data(files[i]) if not data:\",\n \"'').replace(')', '').split() def str_to_energy_dict(val_in): val = val_in.strip().split('\\\\n') energies = dict() for v in\",\n \"if positions is None: return positions = np.array(positions) positions_format = positions_format if positions_format\",\n \"'x_exciting_coulomb_energy': ['Coulomb energy', 'Coulomb'], 'energy_exchange': ['Exchange energy', 'exchange'], 'x_exciting_exchange_energy': ['Exchange energy', 'exchange'], 'energy_correlation':\",\n \"in self.info_parser._system_keys_mapping.keys(): val = self.info_parser.get_initialization_parameter(name) if val is None: continue if name ==\",\n \"< 2: continue elif v[0].startswith('species'): species = re.search(re_symbol, v[-1]).group(1) elif v[0].startswith('atom'): v[0] =\",\n \"'Extended': 'tetrahedra'} sec_smearing = sec_electronic.m_create(Smearing) smearing_kind = self.info_parser.get_initialization_parameter('smearing_kind') if smearing_kind is not None:\",\n \"sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq', [1, 1, 1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk', [1, 1, 1]) rgkmax\",\n \"for i in range(1, len(self.distances)): if self.distances[i] == self.distances[i - 1]: self._nkpts_segment .append(count)\",\n \"XMLParser, DataTextParser from nomad.datamodel.metainfo.simulation.run import Run, Program from nomad.datamodel.metainfo.simulation.method import ( Method, DFT,\",\n \"= self.root.findall('./%s/%s' % (self._partialdos_key, self._diagram_key)) return self._partial_dos @property def energies(self): if self._energies is\",\n \"potential_mixing return sec_system def parse_configurations(self): sec_run = self.archive.run[-1] def parse_configuration(section): if not section:\",\n \"= (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points =\",\n \"elif 'HYB' in name: sec_xc_functional.hybrid.append(Functional(name=name)) else: sec_xc_functional.contributions.append(Functional(name=name)) if not xc_functional_names: # simply write\",\n \"options = [f for f in options if f.endswith(file_ext)] options.sort() filenames = [os.path.join(self.info_parser.maindir,\",\n \"and name.endswith('xml'): parser = self.bandstructure_parser parser_function = self._parse_bandstructure elif name.startswith('EIGVAL') and name.endswith('OUT'): parser\",\n \"[] start = 0 for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment\",\n \"from nomad.datamodel.metainfo.workflow import Workflow, GeometryOptimization from .metainfo.exciting import x_exciting_section_MT_charge_atom, x_exciting_section_MT_moment_atom,\\\\ x_exciting_section_spin, x_exciting_section_fermi_surface,\\\\ x_exciting_section_atoms_group\",\n \"in diagram], dtype=float) return dos def parse(self, key): if self._results is None: self._results\",\n \"parse_scf(iteration, msection): energy_total = iteration.get('energy_total') sec_energy = msection.m_create(Energy) if energy_total is not None:\",\n \"bs_files = ['bandstructure-qp.dat', 'BAND-QP.OUT'] for fname in bs_files: if self.file_exists(fname): gw_files.append(fname) break for\",\n \"self._quantities.append(Quantity( 'initialization', r'(?:All units are atomic|Starting initialization)([\\\\s\\\\S]+?)(?:Using|Ending initialization)', repeats=False, sub_parser=TextParser(quantities=initialization_quantities)) ) scf_quantities =\",\n \"name) if quantity is None: return # this is really problematic if some\",\n \"= DataTextParser() self.data_clathrate_parser = DataTextParser(dtype=str) # different names for different versions of exciting\",\n \"= self.info_parser.get('structure_optimization', {}) for optimization_step in structure_optimization.get('optimization_step', []): sec_scc = parse_configuration(optimization_step) if optimization_step.get('method')\",\n \"None: sec_scc.x_exciting_geometry_optimization_method = optimization_step.get('method') if optimization_step.get('step') is not None: sec_scc.x_exciting_geometry_optimization_step = optimization_step.get('step') force_convergence\",\n \"end res.append(res_n) elif key == 'band_k_points': res = [] for i in range(len(self.number_of_k_points_per_segment)):\",\n \"= iteration.get(name) if val is None: continue if name == 'time': msection.time_calculation =\",\n \"None else logging self._calculation_type = None self.init_parser() sec_run = self.archive.m_create(Run) sec_run.program = Program(\",\n \"type[\\\\s\\\\S]+?\\\\n *\\\\n)', sub_parser=TextParser(quantities=[ Quantity('type', r'Exchange-correlation type +: +(\\\\S+)'), Quantity( 'name_reference', r'\\\\n *(.+?,.+)', str_operation=lambda\",\n \"moment, charge contributions parse_scf(final, sec_scc) # forces forces = section.get('forces') if forces is\",\n \"self.input_xml_parser.mainfile = f positions = self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors = self.input_xml_parser.get( 'structure/crystal/basevect', np.eye(3)) species =\",\n \"= [ f for f in os.listdir( self.info_parser.maindir) if target[0] in f and\",\n \"*Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr) ] self._quantities.append(Quantity( 'groundstate', r'(?:Self\\\\-consistent loop started|Groundstate\",\n \"1 / ureg.bohr ** 3), 'x_exciting_number_of_atoms': ('Total number of atoms per unit cell',\",\n \"/ ureg.bohr), Quantity( 'step', r'Optimization step\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'method', r'method\\\\s*=\\\\s*(\\\\w+)', repeats=False, dtype=str),\",\n \"band_seg_labels = self.bandstructure_parser.get('band_segm_labels') for nb in range(len(band_energies[n])): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb]\",\n \"= np.dot(positions, cell.magnitude) return positions * ureg.bohr def get_scf_threshold(self, name): reference = self.get('groundstate',\",\n \"TODO I am not certain if screening/BSE are children of xs if self.input_xml_parser.get('xs/screening')\",\n \"*({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic mass *: *({re_float})', dtype=np.float64, unit=ureg.electron_mass), Quantity('muffin_tin_radius', rf'muffin-tin radius\",\n \"parser_function = self._parse_dos elif name.startswith('bandstructure') and name.endswith('xml'): parser = self.bandstructure_parser parser_function = self._parse_bandstructure\",\n \"is not None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface = band_parameters[0] sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface = np.product(band_parameters[1]) sec_fermisurface.x_exciting_grid_fermi_surface = band_parameters[1] sec_fermisurface.x_exciting_origin_fermi_surface\",\n \"elif quantity == 'SIGMA': parse_function = parse_sigma elif quantity == 'LOSS': parse_function =\",\n \"positions_format == 'lattice': cell = self.get_initialization_parameter('lattice_vectors') if cell is None: return positions =\",\n \"% key_unit[0], str_operation=str_to_quantity_tolerances, unit=key_unit[1], repeats=False)) module_quantities = [ Quantity( 'scf_iteration', r'(?:I| i)teration number\",\n \"if name == 'time': msection.time_calculation = val else: setattr(msection, name, val) # energy,\",\n \"nomad.datamodel.metainfo.simulation.method import ( Method, DFT, Electronic, Smearing, XCFunctional, Functional, GW as GWMethod, Scf,\",\n \"1.0) sec_gw.frequency_grid_type = self.input_xml_parser.get( 'gw/freqgrid/fgrid', 'gaule2') sec_gw.number_of_frequencies = int(self.input_xml_parser.get( 'gw/freqgrid/nomeg', 16)) sec_gw.self_energy_c_number_of_poles =\",\n \"(nspin, np.size(v[key]) // nspin)) for v in data]) res = res.reshape((len(res), len(data), len(res[0])\",\n \"sec_scc # groundstate and hybrids calculation for module in ['groundstate', 'hybrids']: sec_scc =\",\n \"labels is None: # we get it by concatenating species symbols species =\",\n \"vi * unit properties['atom_resolved'] = atom_resolved return properties def str_to_quantity_tolerances(val_in): return val_in.strip().replace('(', '').replace(')',\",\n \"parse_method(self): sec_run = self.archive.run[-1] sec_method = sec_run.m_create(Method) sec_method.basis_set.append(BasisSet(type='(L)APW+lo')) sec_dft = sec_method.m_create(DFT) sec_electronic =\",\n \"info clathrate_file = self.get_exciting_files('str.out') if clathrate_file: sec_system.x_exciting_clathrates = True self.data_clathrate_parser.mainfile = clathrate_file[0] if\",\n \"files bs_files = ['dos.xml', 'TDOS.OUT'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break\",\n \"axes=(2, 0, 1)) sec_dos.energies = data[0][0] * ureg.hartree + energy_fermi volume = self.info_parser.get_unit_cell_volume()\",\n \"not None: res = res * (1 / self.energy_unit) elif key == 'energies':\",\n \"*{re_float}\\\\n\\\\s*\\\\n)', repeats=True, sub_parser=TextParser(quantities=[ Quantity('number', r'Species : *(\\\\d+)', dtype=np.int32), Quantity('symbol', r'\\\\((\\\\w+)\\\\)'), Quantity('file', r'parameters loaded\",\n \"find an example bandstructure file # atom-resolved bandstructure are added as separate section_k_band\",\n \"= None self._results[key] = res class ExcitingFermiSurfaceBxsfParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities\",\n \"data def parse_exciton(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components = n_components\",\n \"self.energies else: res = None self._results[key] = res class ExcitingFermiSurfaceBxsfParser(TextParser): def __init__(self): super().__init__(None)\",\n \"# TODO I am not certain about the format for the spin polarized\",\n \"if val is None: continue if name == 'x_exciting_spin_treatment': sub_sec = sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment\",\n \"if data[0] is None: return return np.reshape(data, (nspin, len(data) // nspin, len(data[0]))) sec_gw_eigenvalues\",\n \"number of local\\\\-orbitals', None)} self._method_keys_mapping = { 'smearing_kind': ('Smearing scheme', None), 'smearing_width': ('Smearing\",\n \"def file_exists(self, filename): \\\"\\\"\\\"Checks if a the given filename exists and is accessible\",\n \"= self.input_xml_parser.get('xs/lmaxapw', 10) sec_method.x_exciting_xs_number_of_empty_states = self.input_xml_parser.get( 'xs/nempty', 5) sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq', [1, 1,\",\n \"def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: self._neigs_segment = len(self.bands[0]) // self.number_of_spin_channels return self._neigs_segment\",\n \"sec_scc) except Exception: self.logger.error('Error setting xs data', data=dict(file=quantity)) def _parse_xs_tddft(self): sec_run = self.archive.run[-1]\",\n \"= None self._vertices = None self._distances = None self._species = None @property def\",\n \"frequency_data = self.info_gw_parser.get('frequency_data', None) if frequency_data is not None: number = frequency_data.get('number') sec_method.gw.number_of_frequencies\",\n \"is None: return self._energies = np.array( [float(point.attrib.get(self._energy_key)) for point in self.total_dos[0]]) if self.energy_unit\",\n \"to quantity at all qpoints files = self.get_exciting_files('%s_%s%s%s.OUT' % (quantity, file_ext, ext, fxctype))\",\n \"sec_xc_functional = sec_dft.m_create(XCFunctional) for name in xc_functional_names: if name is None: continue if\",\n \"* ureg.bohr val = val_in.strip().split('\\\\n') properties = dict() atom_resolved = [] species =\",\n \"https://nomad-lab.eu for further info. # # Licensed under the Apache License, Version 2.0\",\n \"convert=False, unit=ureg.hartree / ureg.bohr), Quantity( 'step', r'Optimization step\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'method', r'method\\\\s*=\\\\s*(\\\\w+)',\",\n \"('Number of crystal symmetries', None), 'x_exciting_kpoint_grid': (r'k\\\\-point grid', None), 'x_exciting_kpoint_offset': (r'k\\\\-point offset', None),\",\n \"different names for different versions of exciting self._energy_keys_mapping = { 'energy_total': ['Total energy',\",\n \"files and properties exciting_files = ['EIGVAL.OUT', 'FERMISURF.bxsf', 'FS.bxsf'] # Parse DFT DOS from\",\n \"= energy_fermi.to(\\\"hartree\\\") sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_parser.get('band_k_points') nkpts_segment\",\n \"* ureg.hartree return res sec_eigenvalues = sec_scc.m_create(BandEnergies) sec_eigenvalues.kpoints = self.eigval_parser.get('k_points') sec_eigenvalues.occupations = get_data('occupancies')\",\n \"not necessary as this is done in parser, however nspin is # determined\",\n \"= self.input_xml_parser.get('libxc/exchange', None) xc_functional_names.append(exchange) sec_xc_functional = sec_dft.m_create(XCFunctional) for name in xc_functional_names: if name\",\n \"'rpa') def parse_gw(self): sec_run = self.archive.run[-1] # two versions of gw info files\",\n \"set threshold to global metainfo. This is killing me! if metainfo_name == 'x_exciting_scf_threshold_energy_change':\",\n \"'x_exciting_pw': (r'Maximum number of plane\\\\-waves', None), 'x_exciting_lo': (r'Total number of local\\\\-orbitals', None)} self._method_keys_mapping\",\n \"for point in diagram], dtype=float) return dos def parse(self, key): if self._results is\",\n \"problematic sometimes res = np.hstack([np.reshape(v[key], (nspin, np.size(v[key]) // nspin)) for v in data])\",\n \"self.logger self.dos_out_parser.logger = self.logger self.bandstructure_dat_parser.logger = self.logger self.band_out_parser.logger = self.logger self.info_gw_parser.logger = self.logger\",\n \"sec_gw_eigenvalues.value_xc_potential = reshape(get_data('Vxc')) def _parse_dos_out(self, sec_scc): data = self.dos_out_parser.data if data is None:\",\n \"= len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_energy_points = n_epsilon sec_scc.x_exciting_xs_bse_epsilon_energies = np.reshape( data[0], (n_components, n_epsilon))\",\n \"None) if frequency_data is not None: number = frequency_data.get('number') sec_method.gw.number_of_frequencies = len(number) sec_method.gw.frequency_number\",\n \"if fermi_energy is not None: sec_scc.energy = Energy(fermi=fermi_energy) gw_files = ['EVALQP.DAT', 'EVALQP.TXT', 'TDOS-QP.OUT']\",\n \"in names: val = charge_contributions.get(name, None) if val is not None: break if\",\n \"return self._bands @property def vertices(self): if self._vertices is None: self._vertices = self.root.findall('./%s' %\",\n \"of the files bs_files = ['dos.xml', 'TDOS.OUT'] for fname in bs_files: if self.file_exists(fname):\",\n \"for d in data]) * ureg.hartree eigs_gw = get_data('E_GW') if eigs_gw[0] is None:\",\n \"self._miscellaneous_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)' % key_unit[0], repeats=False, unit=key_unit[1])) self._convergence_keys_mapping = { 'x_exciting_effective_potential_convergence': (\",\n \"# TODO add PDOS def _parse_bandstructure_dat(self, sec_scc): self.bandstructure_dat_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_dat_parser.band_energies\",\n \"'diagram' self._l_key = 'l' self._m_key = 'm' self._energy_key = 'e' self._dos_key = 'dos'\",\n \"energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points\",\n \"// n_components sec_scc.x_exciting_xs_bse_loss_energies = np.reshape( data[0], (n_components, n_loss)) * ureg.hartree sec_scc.x_exciting_xs_bse_loss = np.reshape(\",\n \"self.info_parser.get_unit_cell_volume() dos = data[1] * (1 / ureg.hartree) * volume.to('m**3').magnitude for spin in\",\n \"= parse_exciton elif quantity == 'EPSILON': parse_function = parse_epsilon elif quantity == 'SIGMA':\",\n \"or lattice_vectors is None or species is None: continue lattice_vectors = np.array(lattice_vectors, dtype=float)\",\n \"__init__(self): self.info_parser = ExcitingInfoParser() self.dos_parser = DOSXMLParser(energy_unit=ureg.hartree) self.bandstructure_parser = BandstructureXMLParser(energy_unit=ureg.hartree) self.eigval_parser = ExcitingEigenvalueParser()\",\n \"1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk', [1, 1, 1]) rgkmax = self.input_xml_parser.get('xs/rgkmax', None) if rgkmax\",\n \"in data]) * ureg.hartree eigs_gw = get_data('E_GW') if eigs_gw[0] is None: return nspin\",\n \"== self.distances[i - 1]: self._nkpts_segment.append(count) count = 1 else: count += 1 self._nkpts_segment.append(count)\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"= self.parse_scc(section) if sec_scc is None: return sec_system = self.parse_system(section) if sec_system is\",\n \"sec_dos_values.m_kind = 'spherical' sec_dos_values.lm = lm_values[lm] sec_dos_values.spin = spin sec_dos_values.atom_index = atom sec_dos_values.value\",\n \"sec_dos.energies = data[0][0] * ureg.hartree + energy_fermi volume = self.info_parser.get_unit_cell_volume() dos = data[1]\",\n \"if energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_dos = sec_scc.m_create(Dos,\",\n \"for more output files and properties exciting_files = ['EIGVAL.OUT', 'FERMISURF.bxsf', 'FS.bxsf'] # Parse\",\n \"None) if spin is None: spin = (i % (self.number_of_spin_channels * self.number_of_lm)) //\",\n \"= self.root.find('./axis') if axis is None: return self._energy_unit = self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1) return self._energy_unit\",\n \"from nomad.datamodel.metainfo.simulation.method import ( Method, DFT, Electronic, Smearing, XCFunctional, Functional, GW as GWMethod,\",\n \"BandOutParser(energy_unit=ureg.hartree) self.info_gw_parser = GWInfoParser() self.input_xml_parser = XMLParser() self.data_xs_parser = DataTextParser() self.data_clathrate_parser = DataTextParser(dtype=str)\",\n \"point.attrib.get(self._distance_key) for point in self.bands[0][0]] self._distances = np.array(self._distances, dtype=float) return self._distances @property def\",\n \"= files[n] parser_function(section) # free up memory parser.mainfile = None def _parse_input_xs(self, sec_method):\",\n \"'x_exciting_dos_fermi', x_exciting_dos_fermi) # energy contributions energy_contributions = iteration.get('energy_contributions', {}) for key, names in\",\n \"files bs_files = ['bandstructure.xml', 'BAND.OUT', 'bandstructure.dat'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname)\",\n \"separate section_k_band res = [] for n in range(len(self.bands)): res_n = [] start\",\n \"0) sec_method.x_exciting_xs_bse_xas_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlxas', [0, 0]) if self.input_xml_parser.get('xs/tddft') is not None: sec_method.x_exciting_xs_tddft_analytic_continuation\",\n \"break if val is None: continue if key.startswith('energy_'): sec_energy.m_add_sub_section(getattr( Energy, key.replace('energy_', '')), EnergyEntry(value=val))\",\n \"name.startswith('dos') and name.endswith('xml'): parser = self.dos_parser parser_function = self._parse_dos elif name.startswith('bandstructure') and name.endswith('xml'):\",\n \"Functional, GW as GWMethod, Scf, BasisSet ) from nomad.datamodel.metainfo.simulation.system import ( System, Atoms\",\n \"start = end return self._band_k_points @property def distances(self): if self._distances is None: data\",\n \"* ureg.joule).to('hartree') # TODO I am not sure about format for spin-polarized case!\",\n \"sec_atoms_group.x_exciting_geometry_atom_labels = specie.get('symbol') sec_atoms_group.x_exciting_geometry_atom_number = str(specie.get('number')) sec_atoms_group.x_exciting_muffin_tin_points = specie.get('radial_points') sec_atoms_group.x_exciting_muffin_tin_radius = specie.get('muffin_tin_radius') positions_format\",\n \"bse_files def get_data(files): data = [] for f in files: self.data_xs_parser.mainfile = f\",\n \"['Hartree energy', 'Hartree'], 'x_exciting_electron_nuclear_energy': ['Electron-nuclear energy', 'electron-nuclear '], 'x_exciting_nuclear_nuclear_energy': ['Nuclear-nuclear energy', 'nuclear-nuclear'], 'x_exciting_madelung_energy':\",\n \"= self.logger self.dos_out_parser.logger = self.logger self.bandstructure_dat_parser.logger = self.logger self.band_out_parser.logger = self.logger self.info_gw_parser.logger =\",\n \"return sec_scc = self.parse_scc(section) if sec_scc is None: return sec_system = self.parse_system(section) if\",\n \"BandStructure, BandEnergies, Energy, EnergyEntry, Charges, Forces, ForcesEntry, ScfIteration, BandGap ) from nomad.datamodel.metainfo.workflow import\",\n \"states', None), 'x_exciting_valence_states': ('Total number of valence states', None), 'x_exciting_hamiltonian_size': ('Maximum Hamiltonian size',\",\n \"- len(quantity)) + quantity return quantity def get_xc_functional_name(self): # TODO expand list to\",\n \"License. # import numpy as np import os import re import logging from\",\n \"mesh = np.array(val[1].split(), dtype=int) origin = np.array(val[2].split(), dtype=float) vector = np.array([v.split() for v\",\n \"msection.time_calculation = val else: setattr(msection, name, val) # energy, moment, charge contributions parse_scf(final,\",\n \"'band_segm_start_end': res = [] for i in range(len(self.number_of_k_points_per_segment)): start = self.vertices[i].attrib.get(self._coord_key).split() end =\",\n \"quantity if len(quantity) < n_scf: quantity = [None] * (n_scf - len(quantity)) +\",\n \"not section: return sec_scc = self.parse_scc(section) if sec_scc is None: return sec_system =\",\n \"0.00001, 'hartree') sec_method.x_exciting_xs_tddft_xc_kernel = self.input_xml_parser.get( 'xs/tddft/fxctype', 'RPA') sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution = self.input_xml_parser.get( 'xs/tddft/intraband', False) sec_method.x_exciting_xs_tddft_diagonal_xc_kernel\",\n \"for e in self.partial_dos]) for li in l_list: self._nlm += 2 * li\",\n \"sec_smearing = sec_electronic.m_create(Smearing) smearing_kind = self.info_parser.get_initialization_parameter('smearing_kind') if smearing_kind is not None: if not\",\n \"start = np.array( self.vertices[i].attrib.get(self._coord_key).split(), dtype=float) end = np.array( self.vertices[i + 1].attrib.get(self._coord_key).split(), dtype=float) res.append(np.linspace(start,\",\n \"structure from one of the files bs_files = ['bandstructure.xml', 'BAND.OUT', 'bandstructure.dat'] for fname\",\n \"= np.reshape( data[2], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_oscillator_strength = np.reshape( data[3], (n_components, n_excitons))\",\n \"we get it by concatenating species symbols species = self.get('initialization', {}).get('species', []) labels\",\n \"is None: continue if key == 'x_exciting_section_MT_moment_atom': for n in range(len(val)): sec_mt_moment_atom =\",\n \"= xstype sec_method.x_exciting_electronic_structure_method = xstype sec_method.x_exciting_xs_broadening = self.input_xml_parser.get( 'xs/broad', 0.01, 'hartree') sec_method.x_exciting_xs_gqmax =\",\n \"else v[1] if species is None: species = v[0][2] atom_resolved.append(((species, v[1] * unit)))\",\n \"None), 'x_exciting_kpoint_offset': (r'k\\\\-point offset', None), 'x_exciting_number_kpoints': (r'Total number of k\\\\-points', None), 'x_exciting_rgkmax': (r'R\\\\^MT\\\\_min\",\n \"str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr), ]), repeats=False)) self._quantities.append(Quantity( 'hybrids', r'Hybrids module started([\\\\s\\\\S]+?)Hybrids module\",\n \"nspin data = np.reshape(data, (nspin, len(data) // nspin, 2)) data = np.transpose(data, axes=(2,\",\n \"len(self.distances)), dtype=float) for i in range(len(self.bands[n])): band_energies[i % self.number_of_spin_channels][i] = np.array( [e.attrib.get(self._energy_key) for\",\n \"end return self._band_energies @property def distances(self): if self._distances is None: dist = np.transpose(self.data)[0]\",\n \"mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = default.rsplit('.', 1) filename = '%s%s'\",\n \"def init_parameters(self): self._ndos = None self._natoms = None self._nspin = None self._nlm =\",\n \"0, 0, 0]) if sec_method.x_exciting_xs_bse_xas: sec_method.x_exciting_xs_bse_xasatom = self.input_xml_parser.get( 'xs/BSE/xasatom', 0) sec_method.x_exciting_xs_bse_xasedge = self.input_xml_parser.get(\",\n \"in species: positions_format = specie.get('positions_format', None) if positions_format is not None: break return\",\n \"'TET' if tetradf else None, 'AC' if nwacont else None, 'NAR' if not\",\n \"setattr(sec_system, name, ' '.join([str(v) for v in val])) else: try: setattr(sec_system, name, val)\",\n \"write parameters xc_functional = self.info_parser.get('initialization', {}).get('xc_functional') if xc_functional is not None: sec_xc_functional.name =\",\n \"ureg.elementary_charge), 'x_exciting_wigner_radius': (r'Effective Wigner radius, r\\\\_s', ureg.bohr), 'x_exciting_empty_states': ('Number of empty states', None),\",\n \"DosValues, BandStructure, BandEnergies, Energy, EnergyEntry, Charges, Forces, ForcesEntry, ScfIteration, BandGap ) from nomad.datamodel.metainfo.workflow\",\n \"= [] def str_to_frequency(val_in): val = [v.split() for v in val_in.split('\\\\n')] val =\",\n \"else 2 return n_spin def get_unit_cell_volume(self): return self.get('initialization', {}).get('x_exciting_unit_cell_volume', 1.0 * ureg.bohr **\",\n \"= None self._distances = None self._species = None @property def distances(self): if self._distances\",\n \"\\\\((.+?)\\\\)', flatten=False), Quantity( 'positions', rf'\\\\d+ : *({re_float}) *({re_float}) *({re_float})', repeats=True, dtype=np.dtype(np.float64))]))) initialization_quantities.append(Quantity( 'potential_mixing',\",\n \"self.eigval_parser.get('k_points') sec_eigenvalues.occupations = get_data('occupancies') sec_eigenvalues.energies = get_data('eigenvalues') def _parse_fermisurface(self, sec_scc): fermi_surface = self.fermisurf_parser.get('fermi_surface',\",\n \"r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\- ]+):\\\\s*k\\\\-point', repeats=True)) def str_to_eigenvalues(val_in): val = val_in[:val_in.rfind('\\\\n \\\\n')].strip() val = np.array([v.split() for\",\n \"in max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)', ureg.hartree / ureg.bohr)} for name, key_unit in self._convergence_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee\",\n \"'NLF_FXC': sec_scc.x_exciting_xs_tddft_loss_function_no_local_field = data[1] elif quantity == 'SIGMA' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_sigma_local_field\",\n \"(n_components, n_loss)) # TODO check if format of files are really correct, i.e.\",\n \"total_dos(self): if self._total_dos is None: self._total_dos = self.root.findall('./%s/%s' % (self._totaldos_key, self._diagram_key)) return self._total_dos\",\n \"else section.get('positions') if positions is None: species = self.get_initialization_parameter('species', []) if species: positions\",\n \"sec_fermisurface = sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters = self.fermisurf_parser.get('band_parameters', None) if band_parameters is not None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface\",\n \"str_operation=str_to_quantity_tolerances, unit=ureg.hartree / ureg.bohr, repeats=False, dtype=float), Quantity( 'energy_total', r'Total energy at this optimization\",\n \"sec_dos.energies = self.dos_parser.energies + energy_fermi volume = self.info_parser.get_unit_cell_volume() totaldos = self.dos_parser.get('totaldos') * volume.to('m**3').magnitude\",\n \"('Total core charge', ureg.elementary_charge), 'x_exciting_valence_charge_initial': ('Total valence charge', ureg.elementary_charge), 'x_exciting_wigner_radius': (r'Effective Wigner radius,\",\n \"= [] for n in range(len(self.bands)): res_n = [] start = 0 band_energies\",\n \"str_operation=str_to_array, unit=ureg.bohr, repeats=False, convert=False), Quantity( 'lattice_vectors_reciprocal', r'Reciprocal lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=1 / ureg.bohr,\",\n \"'FXC': sec_scc = sec_run.m_create(Calculation) sec_scc.x_exciting_xs_tddft_number_of_epsilon_values = len(data[0][0][0]) sec_scc.x_exciting_xs_tddft_epsilon_energies = data[0][0][0] * ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field\",\n \"[None])[-1] final = section.get('optimization_step', [None])[-1] if final is None else final if final\",\n \"= np.array([ np.transpose(energy)[start:end] for energy in band_energies]) if self._energy_unit is not None: band_energy\",\n \"quantity == 'LOSS': parse_function = parse_loss else: continue try: parse_function(data, sec_scc) except Exception:\",\n \"dist[0])[0][1] self._distances = dist[:n_k_points] return self._distances @property def number_of_spin_channels(self): if self._nspin is None:\",\n \"while True: info_volume = self.get_exciting_files('run_dir%s/INFO.OUT' % str(volume_index).rjust(2, '0')) if not info_volume: break sec_scc.calculations_path.append(info_volume[0])\",\n \"**kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): #\",\n \"self._m_key = 'm' self._energy_key = 'e' self._dos_key = 'dos' self._unit_key = 'unit' self._energy_unit\",\n \"([\\\\w ]+) potential mixing', repeats=False, flatten=False) ) initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation type[\\\\s\\\\S]+?\\\\n *\\\\n)', sub_parser=TextParser(quantities=[\",\n \"setattr(sec_method, metainfo_name, threshold) # additionally, set threshold to global metainfo. This is killing\",\n \"self._total_dos is None: self._total_dos = self.root.findall('./%s/%s' % (self._totaldos_key, self._diagram_key)) return self._total_dos @property def\",\n \"np.transpose(self.data)[0] self._neigs_segment = len(np.where(data == data[0])[0]) return self._neigs_segment class BandstructureXMLParser(XMLParser): def __init__(self, **kwargs):\",\n \"r'atomic positions \\\\((.+?)\\\\)', flatten=False), Quantity( 'positions', rf'\\\\d+ : *({re_float}) *({re_float}) *({re_float})', repeats=True, dtype=np.dtype(np.float64))])))\",\n \"return self._energies = np.array( [float(point.attrib.get(self._energy_key)) for point in self.total_dos[0]]) if self.energy_unit is not\",\n \"self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: self._neigs_segment = len(self.bands[0]) // self.number_of_spin_channels\",\n \"{}).get('xc_functional', None) if xc_functional is None: return [] name = xc_functional_map.get(xc_functional.type, []) return\",\n \"for v in val[2:]], dtype=float)) eigs = {keys[i]: eigs[i] for i in range(len(keys))}\",\n \"and ext == 'FXC': sec_scc.x_exciting_xs_tddft_loss_function_local_field = data[1] elif quantity == 'LOSS' and ext\",\n \"np.size(v[key]) // nspin)) for v in data]) res = res.reshape((len(res), len(data), len(res[0]) //\",\n \"= xc_functional_map.get(xc_functional.type, []) return name @property def n_optimization_steps(self): return len(self.get('structure_optimization', {}).get('optimization_step', [])) def\",\n \"% self.number_of_spin_channels][i] = np.array( [e.attrib.get(self._energy_key) for e in self.bands[n][i]]) for nkpts_segment in self.number_of_k_points_per_segment:\",\n \"sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.energies = band_energies[nb] +\",\n \"= energy_contributions.get(name, None) if val is not None: break if val is None:\",\n \"lattice_vectors = np.array(lattice_vectors, dtype=float) lattice_vectors *= self.input_xml_parser.get('structure/crystal/scale', 1.0) positions = np.dot(positions, lattice_vectors) *\",\n \"= { 'smearing_kind': ('Smearing scheme', None), 'smearing_width': ('Smearing width', None)} for name, key_unit\",\n \"' '.join(self.info_parser.get_xc_functional_name()) sec_method.gw.starting_point = xc_functional_name sec_scc = sec_run.m_create(Calculation) sec_scc.method_ref = sec_method if sec_run.system:\",\n \"species = self.get('initialization', {}).get('species', []) labels = [] for specie in species: labels\",\n \"== 'band_energies': # TODO I am not certain about the format for the\",\n \"if structure_optimization is not None: sec_worfklow.type = 'geometry_optimization' sec_geometry_opt = sec_worfklow.m_create(GeometryOptimization) threshold_force =\",\n \"self.info_parser.get_initialization_parameter('species', []) for specie in species: sec_atoms_group = sec_system.m_create(x_exciting_section_atoms_group) sec_atoms_group.x_exciting_geometry_atom_labels = specie.get('symbol') sec_atoms_group.x_exciting_geometry_atom_number\",\n \"= np.array([v.split() for v in val.split('\\\\n')], dtype=float) val = np.transpose(val) occs = val[-1]\",\n \"= self.logger self.info_gw_parser.logger = self.logger self.input_xml_parser.logger = self.logger self.data_xs_parser.logger = self.logger self.data_clathrate_parser.logger =\",\n \"leakage'], 'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge': ['interstitial'], 'x_exciting_total_MT_charge': ['total charge in muffin-tins', 'total in muffin-tins'],\",\n \"name: sec_xc_functional.hybrid.append(Functional(name=name)) else: sec_xc_functional.contributions.append(Functional(name=name)) if not xc_functional_names: # simply write parameters xc_functional =\",\n \"sec_method.m_create(Electronic) sec_electronic.method = 'DFT' smearing_kind_map = { 'Gaussian': 'gaussian', 'Methfessel-Paxton': 'methfessel-paxton', 'Fermi-Dirac': 'fermi',\",\n \"self.info_parser.get_initialization_parameter(name) if val is None: continue if name == 'x_exciting_spin_treatment': sub_sec = sec_system.m_create(x_exciting_section_spin)\",\n \"sec_scc): self.band_out_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.band_out_parser.band_energies if band_energies is None: return #\",\n \"= self.fermisurf_parser parser_function = self._parse_fermisurface elif name.startswith('EVALQP') and (name.endswith('DAT') or name.endswith('TXT')): parser =\",\n \"in bands_atom: self._bands.append(band.findall('./%s' % self._band_key)) return self._bands @property def vertices(self): if self._vertices is\",\n \"self.root.findall('./%s' % self._vertex_key) return self._vertices @property def number_of_spin_channels(self): if self._nspin is None: self._nspin\",\n \"really problematic if some scf steps dont have the quantity # the only\",\n \"parser_function = self._parse_fermisurface elif name.startswith('EVALQP') and (name.endswith('DAT') or name.endswith('TXT')): parser = self.evalqp_parser parser_function\",\n \"= None self._results[key] = res class DOSXMLParser(XMLParser): def __init__(self, **kwargs): super().__init__(None) self._nspin_key =\",\n \"including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr) ] self._quantities.append(Quantity( 'groundstate',\",\n \"= len(data[0]) // n_components sec_scc.x_exciting_xs_bse_loss_energies = np.reshape( data[0], (n_components, n_loss)) * ureg.hartree sec_scc.x_exciting_xs_bse_loss\",\n \"filename exists and is accessible in the same folder where the mainfile is\",\n \"'k_points', r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\- ]+):\\\\s*k\\\\-point', repeats=True)) def str_to_eigenvalues(val_in): val = val_in[:val_in.rfind('\\\\n \\\\n')].strip() val = np.array([v.split()\",\n \"(r'Wall time \\\\(seconds\\\\)', ureg.s)} for name, key_unit in self._miscellaneous_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)' %\",\n \"['core leakage'], 'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge': ['interstitial'], 'x_exciting_total_MT_charge': ['total charge in muffin-tins', 'total in\",\n \"parse_function = parse_exciton elif quantity == 'EPSILON': parse_function = parse_epsilon elif quantity ==\",\n \"'xs/BSE/sciavtype', 'spherical') sec_method.x_exciting_xs_bse_xas = self.input_xml_parser.get( 'xs/BSE/xas', False) sec_method.x_exciting_xs_bse_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlbse', [0, 0,\",\n \"band_k_points[nb] sec_k_band_segment.energies = band_energies[nb] + energy_fermi def _parse_band_out(self, sec_scc): self.band_out_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies\",\n \"try: setattr(sec_system, name, val) except Exception: self.logger.warn('Error setting metainfo.') # species species =\",\n \"[])) quantity = self.get('%s_scf_iteration' % name) if quantity is None: return # this\",\n \"None: return positions = np.array(positions) positions_format = positions_format if positions_format is not None\",\n \"'') if xstype.lower() == 'bse': self._parse_xs_bse() elif xstype.lower() == 'tddft': self._parse_xs_tddft() def _parse_input_gw(self,\",\n \"number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data = np.transpose(self.data)[0] self._neigs_segment = len(np.where(data == data[0])[0])\",\n \"def parse_file(self, name, section): # TODO add support for info.xml, wannier.out if name.startswith('dos')\",\n \"sec_mt_moment_atom.x_exciting_MT_moment_atom_index = n + 1 sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol = val[n][0] sec_mt_moment_atom.x_exciting_MT_moment_atom_value = val[n][1] else: setattr(msection,\",\n \"= sec_electronic.m_create(Smearing) smearing_kind = self.info_parser.get_initialization_parameter('smearing_kind') if smearing_kind is not None: if not isinstance(smearing_kind,\",\n \"dtype=int) origin = np.array(val[2].split(), dtype=float) vector = np.array([v.split() for v in val[3:6]], dtype=float)\",\n \"certain about the format for the spin polarized case # I cannot find\",\n \"= np.transpose(val) occs = val[-1] eigs = val[-2] nspin = 2 if occs[0]\",\n \"spin sec_dos_values.atom_index = atom sec_dos_values.value = partialdos[lm][spin][atom] def _parse_bandstructure(self, sec_scc): # we need\",\n \"range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.value = band_energies[nb] + energy_fermi def\",\n \"= None self._neigs_segment = None self._vertices = None self._distances = None self._band_energies =\",\n \"= iteration.get('x_exciting_dos_fermi') if x_exciting_dos_fermi is not None: setattr(msection, 'x_exciting_dos_fermi', x_exciting_dos_fermi) # energy contributions\",\n \"filename = os.path.join(self.info_parser.maindir, filename) if os.path.isfile(filename): return [filename] filename = os.path.join(self.info_parser.maindir, default) if\",\n \"lattice_vectors lattice_vectors_reciprocal = self.info_parser.get_initialization_parameter( 'lattice_vectors_reciprocal') sec_atoms.lattice_vectors_reciprocal = lattice_vectors_reciprocal if len(sec_run.system) > 1: return\",\n \"sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_parser.get('band_k_points') nkpts_segment = self.bandstructure_parser.number_of_k_points_per_segment\",\n \"if not xc_functional_names: # get it from input.xml input_file = self.get_exciting_files('input.xml') for f\",\n \"n_components sec_scc.x_exciting_xs_bse_sigma_energies = np.reshape( data[0], (n_components, n_sigma)) * ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re = np.reshape( data[1],\",\n \"files gw_info_files = ['GW_INFO.OUT', 'GWINFO.OUT'] for f in gw_info_files: if self.get_exciting_files(f): self._calculation_type =\",\n \"in self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy = np.array([ np.transpose(energy)[start:end] for energy\",\n \"bands = self.root.findall('./%s' % self._band_key) self._bands = [] if bands: self._bands.append(bands) # add\",\n \"= self.input_xml_parser.get( 'xs/BSE/xasspecies', 0) sec_method.x_exciting_xs_bse_xas_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlxas', [0, 0]) if self.input_xml_parser.get('xs/tddft') is\",\n \"[v.split(':')[-1].split() for v in val_in.strip().split('\\\\n')] val = val[0] if len(val) == 1 else\",\n \"self.input_xml_parser.get('xs/ngridk', [1, 1, 1]) rgkmax = self.input_xml_parser.get('xs/rgkmax', None) if rgkmax is None: rgkmax\",\n \"for name in self.info_parser._convergence_keys_mapping.keys(): val = iteration.get(name) if val is None: continue setattr(msection,\",\n \"sec_method.x_exciting_xs_gqmax = self.input_xml_parser.get( 'xs/gqmax', 0.0, '1/bohr') sec_method.x_exciting_xs_lmaxapw = self.input_xml_parser.get('xs/lmaxapw', 10) sec_method.x_exciting_xs_number_of_empty_states = self.input_xml_parser.get(\",\n \"sec_system.x_exciting_clathrates_atom_labels = list(data[3]) else: sec_system.x_exciting_clathrates = False potential_mixing = self.info_parser.get_initialization_parameter('potential_mixing') if potential_mixing is\",\n \"self._nkpts_segment.append(count) count = 1 else: count += 1 self._nkpts_segment.append(count) return self._nkpts_segment @property def\",\n \"frequency_data.get('values') sec_method.gw.frequency_weights = frequency_data.get('weights') fundamental_band_gap = self.info_gw_parser.get('direct_band_gap', None) if fundamental_band_gap is None: fundamental_band_gap\",\n \"self.logger.error('Error setting xs data', data=dict(file=quantity)) def _parse_xs_tddft(self): sec_run = self.archive.run[-1] fxctype = self.input_xml_parser.get('xs/tddft/fxctype',\",\n \"elif '_C_' in name: sec_xc_functional.correlation.append(Functional(name=name)) elif 'HYB' in name: sec_xc_functional.hybrid.append(Functional(name=name)) else: sec_xc_functional.contributions.append(Functional(name=name)) if\",\n \"band_parameters = self.fermisurf_parser.get('band_parameters', None) if band_parameters is not None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface = band_parameters[0] sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface\",\n \"= self.input_xml_parser.get( 'xs/BSE/xasedge', 'K') sec_method.x_exciting_xs_bse_xasspecies = self.input_xml_parser.get( 'xs/BSE/xasspecies', 0) sec_method.x_exciting_xs_bse_xas_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlxas',\",\n \"= xc_functional_name sec_scc = sec_run.m_create(Calculation) sec_scc.method_ref = sec_method if sec_run.system: sec_scc.system_ref = sec_run.system[-1]\",\n \"iteration.get(name) if val is None: continue if name == 'time': msection.time_calculation = val\",\n \"sec_atoms_group.x_exciting_geometry_atom_positions = positions.magnitude # clathrate info clathrate_file = self.get_exciting_files('str.out') if clathrate_file: sec_system.x_exciting_clathrates =\",\n \"return self._nlm @property def total_dos(self): if self._total_dos is None: self._total_dos = self.root.findall('./%s/%s' %\",\n \"setting xs data', data=dict(file=quantity)) def _parse_xs_tddft(self): sec_run = self.archive.run[-1] fxctype = self.input_xml_parser.get('xs/tddft/fxctype', 'RPA')\",\n \"* self._energy_unit self._band_energies.append(band_energy) start = end return self._band_energies @property def distances(self): if self._distances\",\n \"= iteration.get('moment_contributions', {}) for key, names in self._moment_keys_mapping.items(): val = None for name\",\n \"eigs_gw = get_data('E_GW') if eigs_gw[0] is None: return nspin = self.info_parser.get_number_of_spin_channels() def reshape(data):\",\n \"in muffin-tins'], 'x_exciting_total_moment': ['total moment'], 'x_exciting_section_MT_moment_atom': ['atom_resolved'] } def get_exciting_files(self, default): mainfile =\",\n \"files if f.endswith('QMT%s.OUT' % str(i + 1).rjust(3, '0'))] for f in files_q: self.data_xs_parser.mainfile\",\n \"None) is None: return # Get fermi energy: it is used to un-shift\",\n \"self.input_xml_parser.get( 'xs/tddft/ahc', False) sec_method.x_exciting_xs_tddft_anti_resonant_dielectric = self.input_xml_parser.get( 'xs/tddft/aresdf', False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel = self.input_xml_parser.get( 'xs/tddft/aresfxc', True)\",\n \"= self.get('initialization', {}).get('xc_functional', None) if xc_functional is None: return [] name = xc_functional_map.get(xc_functional.type,\",\n \"Quantity( 'band_parameters', r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)', str_operation=str_to_band_parameters, repeats=False)) self._quantities.append( Quantity( 'fermi_surface', r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n *E*', unit=ureg.hartree, repeats=True)) class\",\n \"not None: sec_method.x_exciting_xs_screening_number_of_empty_states = self.input_xml_parser.get( 'xs/screening/nempty', 0) sec_method.x_exciting_xs_screening_ngridk = self.input_xml_parser.get( 'xs/screening/ngridk', [0, 0,\",\n \"and file type', data=dict(file=quantity)) sec_scc = sec_run.m_create(Calculation) if quantity == 'EXCITON': parse_function =\",\n \"continue metainfo_name = 'x_exciting_scf_threshold_%s_change' % name.split('_')[-2] setattr(sec_method, metainfo_name, threshold) # additionally, set threshold\",\n \": *\\\\d+ *\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float} *{re_float} *{re_float}\\\\n\\\\s*\\\\n)', repeats=True, sub_parser=TextParser(quantities=[ Quantity('number', r'Species : *(\\\\d+)', dtype=np.int32), Quantity('symbol',\",\n \"None), 'x_exciting_lmaxapw': (r' APW functions', None), 'x_exciting_nuclear_charge': ('Total nuclear charge', ureg.elementary_charge), 'x_exciting_electronic_charge': ('Total\",\n \"if positions is None or lattice_vectors is None or species is None: continue\",\n \"= input_file[0] xstype = self.input_xml_parser.get('xs/xstype', '') if xstype.lower() == 'bse': self._parse_xs_bse() elif xstype.lower()\",\n \"sec_mt_charge_atom.x_exciting_MT_charge_atom_index = n + 1 sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol = val[n][0] sec_mt_charge_atom.x_exciting_MT_charge_atom_value = val[n][1] sec_charges =\",\n \"'LOSS' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_loss_function_no_local_field = data[1] elif quantity == 'SIGMA' and\",\n \"val])) else: try: setattr(sec_system, name, val) except Exception: self.logger.warn('Error setting metainfo.') # species\",\n \"\\\"License\\\"); # you may not use this file except in compliance with the\",\n \"if not data[0]: continue if quantity == 'EPSILON' and ext == 'FXC': sec_scc\",\n \"'x_exciting_energy_convergence': ( r'Absolute change in total energy\\\\s*\\\\(target\\\\)', ureg.hartree), 'x_exciting_charge_convergence': ( r'Charge distance\\\\s*\\\\(target\\\\)', ureg.elementary_charge),\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"False) def _parse_xs_bse(self): sec_run = self.archive.run[-1] # TODO read from xml file def\",\n \"sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value = dos[spin] # TODO add\",\n \"if final is None else final if final is None: return sec_scc =\",\n \"GeometryOptimization from .metainfo.exciting import x_exciting_section_MT_charge_atom, x_exciting_section_MT_moment_atom,\\\\ x_exciting_section_spin, x_exciting_section_fermi_surface,\\\\ x_exciting_section_atoms_group re_float = r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?' class\",\n \"[0, 0]) if self.input_xml_parser.get('xs/tddft') is not None: sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get( 'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity\",\n \"(n_components, n_sigma)) def parse_loss(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_loss =\",\n \"repeats=False, dtype=float), Quantity( 'time', r'Time spent in this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds', unit=ureg.s, repeats=False, dtype=float)\",\n \"self._energy_key = 'e' self._dos_key = 'dos' self._unit_key = 'unit' self._energy_unit = kwargs.get('energy_unit', None)\",\n \"self.band_out_parser = BandOutParser(energy_unit=ureg.hartree) self.info_gw_parser = GWInfoParser() self.input_xml_parser = XMLParser() self.data_xs_parser = DataTextParser() self.data_clathrate_parser\",\n \"data to scc # TODO add support for more output files and properties\",\n \"added as separate section_k_band res = [] for n in range(len(self.bands)): res_n =\",\n \"('Unit cell volume', ureg.bohr ** 3), 'x_exciting_brillouin_zone_volume': ('Brillouin zone volume', 1 / ureg.bohr\",\n \"= self.input_xml_parser.get( 'gw/freqgrid/fgrid', 'gaule2') sec_gw.number_of_frequencies = int(self.input_xml_parser.get( 'gw/freqgrid/nomeg', 16)) sec_gw.self_energy_c_number_of_poles = int(self.input_xml_parser.get( 'gw/selfenergy/npol',\",\n \"data = np.transpose(np.vstack(data)) n_epsilon = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_energy_points = n_epsilon sec_scc.x_exciting_xs_bse_epsilon_energies =\",\n \"scf_quantities = [ Quantity( 'energy_total', r'[Tt]*otal energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=ureg.hartree), Quantity( 'energy_contributions', r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n\",\n \"return data = np.transpose(self.data) n_kpoints = int(max(data[1])) bands = data[6:] bands = np.reshape(bands,\",\n \"= { 'x_exciting_unit_cell_volume': ('Unit cell volume', ureg.bohr ** 3), 'x_exciting_brillouin_zone_volume': ('Brillouin zone volume',\",\n \"continue if quantity == 'EXCITON': sec_scc = sec_run.m_create(Calculation) sccs.append(sec_scc) else: sec_scc = sccs[i]\",\n \"['full', 'diag', 'noinvdiag', 'longrange'] bse_files = [] for bse_type in bse_types: for scr_type\",\n \"if sec_scc.energy is not None: energy_fermi = sec_scc.energy.fermi energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree')\",\n \"elif name.startswith('TDOS') and name.endswith('OUT'): parser = self.dos_out_parser parser_function = self._parse_dos_out elif name.startswith('bandstructure') and\",\n \"file type', data=dict(file=quantity)) sec_scc = sec_run.m_create(Calculation) if quantity == 'EXCITON': parse_function = parse_exciton\",\n \"in ['input_gw.xml', 'input-gw.xml', 'input.xml']: self.parse_file(f, sec_method) xc_functional_name = ' '.join(self.info_parser.get_xc_functional_name()) sec_method.gw.starting_point = xc_functional_name\",\n \"necessary as this is done in parser, however nspin is # determined from\",\n \"str(volume_index).rjust(2, '0')) if not info_volume: break sec_scc.calculations_path.append(info_volume[0]) def init_parser(self): self.info_parser.mainfile = self.filepath self.info_parser.logger\",\n \"filenames = [filename] filenames = [f for f in filenames if os.access(f, os.F_OK)]\",\n \"positions is None or lattice_vectors is None or species is None: continue lattice_vectors\",\n \"in range(len(self.vertices) - 1): start = self.vertices[i].attrib.get('label') end = self.vertices[i + 1].attrib.get('label') res.append([\",\n \"not self.bands: return if key == 'band_energies': # TODO I am not certain\",\n \"sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_parser.get('band_k_points') nkpts_segment = self.bandstructure_parser.number_of_k_points_per_segment band_seg_labels =\",\n \"= self.input_xml_parser.get( 'xs/BSE/nstlbse', [0, 0, 0, 0]) if sec_method.x_exciting_xs_bse_xas: sec_method.x_exciting_xs_bse_xasatom = self.input_xml_parser.get( 'xs/BSE/xasatom',\",\n \"self.parse_file('input.xml', sec_method) # parse properties input_file = self.get_exciting_files('input.xml') if not input_file: return self.input_xml_parser.mainfile\",\n \"ureg.bohr ** 3), 'x_exciting_brillouin_zone_volume': ('Brillouin zone volume', 1 / ureg.bohr ** 3), 'x_exciting_number_of_atoms':\",\n \"number_of_spin_channels(self): if self._nspin is None: self._nspin = np.shape(np.transpose(self.data)[1:])[0] return self._nspin @property def number_of_k_points_per_segment(self):\",\n \"self.info_parser.quantities = parser.info_parser.quantities self.eigval_parser.quantities = parser.eigval_parser.quantities self.fermisurf_parser.quantities = parser.fermisurf_parser.quantities self.evalqp_parser.quantities = parser.evalqp_parser.quantities self.info_gw_parser.quantities\",\n \"for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break # Parse DFT band structure\",\n \"_parse_evalqp(self, sec_scc): data = self.evalqp_parser.get('kpoints_eigenvalues') if data is None: return def get_data(key): if\",\n \"'') options = [ f for f in os.listdir( self.info_parser.maindir) if target[0] in\",\n \"= sec_scc.energy.fermi if energy_fermi is None: continue energy_fermi = energy_fermi.to(\\\"hartree\\\") sec_k_band = sec_scc.m_create(BandStructure,\",\n \"are atomic|Starting initialization)([\\\\s\\\\S]+?)(?:Using|Ending initialization)', repeats=False, sub_parser=TextParser(quantities=initialization_quantities)) ) scf_quantities = [ Quantity( 'energy_total', r'[Tt]*otal\",\n \"def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'fermi_energy', r'Fermi Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*',\",\n \"= self.get_exciting_files('input.xml') for f in input_file: self.input_xml_parser.mainfile = f correlation = self.input_xml_parser.get('libxc/correlation', None)\",\n \"is not None: sec_forces = sec_scc.m_create(Forces) sec_forces.total = ForcesEntry(value=forces) # scf iterations scf_iterations\",\n \"self._get_dos(self.partial_dos[i]) if self.energy_unit is not None: res = res * (1 / self.energy_unit)\",\n \"'atomic_positions', r'(imized atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'imized atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)',\",\n \"# reshaping is not necessary as this is done in parser, however nspin\",\n \"n in range(len(files)): parser.mainfile = files[n] parser_function(section) # free up memory parser.mainfile =\",\n \"= len(number) sec_method.gw.frequency_number = number sec_method.gw.frequency_values = frequency_data.get('values') sec_method.gw.frequency_weights = frequency_data.get('weights') fundamental_band_gap =\",\n \"= default.rsplit('.', 1) filename = '%s%s' % (target[0], suffix) if target[1:]: filename =\",\n \"= np.reshape( data[4], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_im = np.reshape( data[5], (n_components, n_excitons)) def parse_epsilon(data,\",\n \"cannot find an example bandstructure file # atom-resolved bandstructure are added as separate\",\n \"% str(volume_index).rjust(2, '0')) if not info_volume: break sec_scc.calculations_path.append(info_volume[0]) def init_parser(self): self.info_parser.mainfile = self.filepath\",\n \"== 'EPSILON' and ext == 'FXC': sec_scc = sec_run.m_create(Calculation) sec_scc.x_exciting_xs_tddft_number_of_epsilon_values = len(data[0][0][0]) sec_scc.x_exciting_xs_tddft_epsilon_energies\",\n \"[] self._quantities.append( Quantity( 'k_points', r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\- ]+):\\\\s*k\\\\-point', repeats=True)) def str_to_eigenvalues(val_in): val = val_in[:val_in.rfind('\\\\n \\\\n')].strip()\",\n \"'x_exciting_spin_treatment': ('Spin treatment', None), 'x_exciting_number_of_bravais_lattice_symmetries': ('Number of Bravais lattice symmetries', None), 'x_exciting_number_of_crystal_symmetries': ('Number\",\n \"( r'Charge distance\\\\s*\\\\(target\\\\)', ureg.elementary_charge), 'x_exciting_IBS_force_convergence': ( r'Abs\\\\. change in max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)', ureg.hartree / ureg.bohr)}\",\n \"= 'coord' self._energy_key = 'eval' self._vertex_key = 'vertex' self._band_key = 'band' self._atom_key =\",\n \"filename) if os.path.isfile(filename): return [filename] filename = os.path.join(self.info_parser.maindir, default) if not os.path.isfile(filename): file_ext\",\n \"import ureg from nomad.parsing.file_parser import TextParser, Quantity, XMLParser, DataTextParser from nomad.datamodel.metainfo.simulation.run import Run,\",\n \"= [float(vi) for vi in v[1].split()] v[1] = v[1][0] if len(v[1]) == 1\",\n \"xs_info_files: return self._calculation_type = 'xs' # inconsistency in the naming convention for xs\",\n \"None: species = self.get_initialization_parameter('species', []) if species: positions = np.vstack([s.get('positions') for s in\",\n \"band_energies = self.band_out_parser.band_energies if band_energies is None: return # Get fermi energy: it\",\n \"'x_exciting_total_moment': ['total moment'], 'x_exciting_section_MT_moment_atom': ['atom_resolved'] } def get_exciting_files(self, default): mainfile = os.path.basename(self.info_parser.mainfile) suffix\",\n \"is part of NOMAD. # See https://nomad-lab.eu for further info. # # Licensed\",\n \"in muffin-tin *: *({re_float})', dtype=np.int32), Quantity('positions_format', r'atomic positions \\\\((.+?)\\\\)', flatten=False), Quantity( 'positions', rf'\\\\d+\",\n \"is None: # we get it by concatenating species symbols species = self.get('initialization',\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the\",\n \"def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] def str_to_eigenvalue(val_in): val = val_in.strip().split('\\\\n')\",\n \"'xs/broad', 0.01, 'hartree') sec_method.x_exciting_xs_gqmax = self.input_xml_parser.get( 'xs/gqmax', 0.0, '1/bohr') sec_method.x_exciting_xs_lmaxapw = self.input_xml_parser.get('xs/lmaxapw', 10)\",\n \"self.number_of_band_segment_eigenvalues, len(self.distances)), dtype=float) for i in range(len(self.bands[n])): band_energies[i % self.number_of_spin_channels][i] = np.array( [e.attrib.get(self._energy_key)\",\n \"is killing me! if metainfo_name == 'x_exciting_scf_threshold_energy_change': sec_method.scf = Scf(threshold_energy_change=threshold) xc_functional_names = self.info_parser.get_xc_functional_name()\",\n \"dos_up dos_down nspin = self.info_parser.get_number_of_spin_channels() sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = len(data) //\",\n \"clathrate_file: sec_system.x_exciting_clathrates = True self.data_clathrate_parser.mainfile = clathrate_file[0] if self.data_clathrate_parser.data: data = np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates\",\n \"'total energy'], 'x_exciting_fermi_energy': ['Fermi energy', 'Fermi'], 'energy_kinetic_electronic': ['Kinetic energy', 'electronic kinetic'], 'energy_coulomb': ['Coulomb\",\n \"r'RMS change in effective potential \\\\(target\\\\)', ureg.hartree), 'x_exciting_energy_convergence': ( r'Absolute change in total\",\n \"{}) for key, names in self._moment_keys_mapping.items(): val = None for name in names:\",\n \"if clathrate_file: sec_system.x_exciting_clathrates = True self.data_clathrate_parser.mainfile = clathrate_file[0] if self.data_clathrate_parser.data: data = np.transpose(self.data_clathrate_parser.data)\",\n \"str_to_frequency(val_in): val = [v.split() for v in val_in.split('\\\\n')] val = np.transpose(np.array([v for v\",\n \"['atom_resolved'] } def get_exciting_files(self, default): mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target =\",\n \"is None: continue data_q.append(self.data_xs_parser.data) if not data_q: continue data_q = np.transpose(data_q, axes=(2, 0,\",\n \"0.])[-1] sec_geometry_opt.input_force_maximum_tolerance = threshold_force def parse_method(self): sec_run = self.archive.run[-1] sec_method = sec_run.m_create(Method) sec_method.basis_set.append(BasisSet(type='(L)APW+lo'))\",\n \"def get_data(key): if key == 'k_points': return np.array([d[0][:3] for d in data]) elif\",\n \"= self.bandstructure_dat_parser.band_energies if band_energies is None: return # Get fermi energy: it is\",\n \"self.fermisurf_parser = ExcitingFermiSurfaceBxsfParser() self.evalqp_parser = ExcitingEvalqpParser() self.dos_out_parser = DataTextParser() self.bandstructure_dat_parser = BandstructureDatParser(energy_unit=ureg.hartree) self.band_out_parser\",\n \"= self.info_parser.get_number_of_spin_channels() sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = len(data) // nspin data =\",\n \"self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None\",\n \"0) sec_method.x_exciting_xs_bse_xasedge = self.input_xml_parser.get( 'xs/BSE/xasedge', 'K') sec_method.x_exciting_xs_bse_xasspecies = self.input_xml_parser.get( 'xs/BSE/xasspecies', 0) sec_method.x_exciting_xs_bse_xas_number_of_bands =\",\n \"self.get('initialization', {}).get('xc_functional', None) if xc_functional is None: return [] name = xc_functional_map.get(xc_functional.type, [])\",\n \"for n in range(len(val))] * val[0][1].units sec_charges.total = charge_contributions.get('total charge') elif key ==\",\n \"'x_exciting_species_rtmin': (r'Species with R\\\\^MT\\\\_min', None), 'x_exciting_gkmax': (r'Maximum \\\\|G\\\\+k\\\\| for APW functions', 1 /\",\n \"= self.input_xml_parser.get( 'xs/tetra/tetradf', False) def _parse_xs_bse(self): sec_run = self.archive.run[-1] # TODO read from\",\n \"= self.input_xml_parser.get('gw/mixbasis/gmb', 1.0) sec_gw.mixed_basis_gmax = gmb * gmaxvr pwm = self.input_xml_parser.get('gw/barecoul/pwm', 2.0) sec_gw.bare_coulomb_gmax\",\n \"self.dos_parser parser_function = self._parse_dos elif name.startswith('bandstructure') and name.endswith('xml'): parser = self.bandstructure_parser parser_function =\",\n \"= [] start = 0 for nkpts_segment in self.number_of_k_points_per_segment: end = start +\",\n \"= sccs[i] if sec_scc is None: # This is the case when there\",\n \"= DataTextParser() self.bandstructure_dat_parser = BandstructureDatParser(energy_unit=ureg.hartree) self.band_out_parser = BandOutParser(energy_unit=ureg.hartree) self.info_gw_parser = GWInfoParser() self.input_xml_parser =\",\n \"self.input_xml_parser.get( 'xs/screening/ngridk', [0, 0, 0]) rgkmax = self.input_xml_parser.get('xs/screening/rgkmax', None) if rgkmax is None:\",\n \"r'Reciprocal lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=1 / ureg.bohr, repeats=False, convert=False), ] self._system_keys_mapping = {\",\n \"if end.lower() == 'gamma' else end]) elif key == 'band_segm_start_end': res = []\",\n \"self._energy_unit @property def number_of_spin_channels(self): if self._nspin is None: if not self.total_dos: return self._nspin\",\n \"for i in range(len(self.number_of_k_points_per_segment)): start = self.vertices[i].attrib.get(self._coord_key).split() end = self.vertices[i + 1].attrib.get(self._coord_key).split() res.append([start,\",\n \"self.number_of_spin_channels return self._neigs_segment def parse(self, key): if self._results is None: self._results = dict()\",\n \"sec_dos_values.value = partialdos[lm][spin][atom] def _parse_bandstructure(self, sec_scc): # we need to set nspin again\",\n \"'SIGMA' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_sigma_local_field = data[1:3] elif quantity == 'SIGMA' and\",\n \"= get_data('occupancies') sec_eigenvalues.energies = get_data('eigenvalues') def _parse_fermisurface(self, sec_scc): fermi_surface = self.fermisurf_parser.get('fermi_surface', [None])[0] if\",\n \"= ['GW_INFO.OUT', 'GWINFO.OUT'] for f in gw_info_files: if self.get_exciting_files(f): self._calculation_type = 'gw' gw_info_file\",\n \"EnergyEntry(value=val)) else: setattr(msection, key, val) if key == 'x_exciting_fermi_energy': sec_energy.fermi = val #\",\n \"= np.product(band_parameters[1]) sec_fermisurface.x_exciting_grid_fermi_surface = band_parameters[1] sec_fermisurface.x_exciting_origin_fermi_surface = band_parameters[2] sec_fermisurface.x_exciting_vectors_fermi_surface = band_parameters[3] fermi_energy =\",\n \"self.info_parser.logger = self.logger self.dos_parser.logger = self.logger self.bandstructure_parser.logger = self.logger self.eigval_parser.logger = self.logger self.fermisurf_parser.logger\",\n \"range(len(self.bands[n])): band_energies[i % self.number_of_spin_channels][i] = np.array( [e.attrib.get(self._energy_key) for e in self.bands[n][i]]) for nkpts_segment\",\n \"xc_functional.get('name_reference', [None, None])[0] sec_xc_functional.reference = xc_functional.get('name_reference', [None, None])[1] sec_electronic.n_spin_channels = self.info_parser.get_number_of_spin_channels() if self._calculation_type\",\n \"= self.info_parser.get_unit_cell_volume() totaldos = self.dos_parser.get('totaldos') * volume.to('m**3').magnitude for spin in range(len(totaldos)): sec_dos_values =\",\n \"which also other energies are reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is None:\",\n \"convert=False), Quantity( 'lattice_vectors_reciprocal', r'Reciprocal lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=1 / ureg.bohr, repeats=False, convert=False), ]\",\n \"the quantity # the only thing that we can do is to assume\",\n \"= charge_contributions.get('total charge') elif key == 'charge_total': pass else: setattr(msection, key, val) #\",\n \"seconds))', sub_parser=TextParser(quantities=optimization_quantities), repeats=True), Quantity( 'final', r'Force convergence target achieved([\\\\s\\\\S]+?Opt)', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions',\",\n \"msection.m_create(Charges) sec_charges.value = [ val[n][1].magnitude for n in range(len(val))] * val[0][1].units sec_charges.total =\",\n \"res class DOSXMLParser(XMLParser): def __init__(self, **kwargs): super().__init__(None) self._nspin_key = 'nspin' self._totaldos_key = 'totaldos'\",\n \"self.partial_dos[i].attrib.get(self._l_key, None) val_m = self.partial_dos[i].attrib.get(self._m_key, None) if val_l is None or val_m is\",\n \"v[1] = [float(vi) for vi in v[1].split()] v[1] = v[1][0] if len(v[1]) ==\",\n \"started|Groundstate module started)([\\\\s\\\\S]+?)Groundstate module stopped', sub_parser=TextParser(quantities=module_quantities), repeats=False)) optimization_quantities = [ Quantity( 'atomic_positions', r'(Atomic\",\n \"sec_method.x_exciting_xs_tddft_lmax_alda = self.input_xml_parser.get( 'xs/tddft/lmaxalda', 3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment = self.input_xml_parser.get( 'xs/tddft/mdfqtype', 0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals = self.input_xml_parser.get(\",\n \"r'imized atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])),\",\n \"None: if not isinstance(smearing_kind, str): smearing_kind = smearing_kind[0] smearing_kind = smearing_kind_map[smearing_kind] sec_smearing.kind =\",\n \"data[1] elif quantity == 'LOSS' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_loss_function_no_local_field = data[1] elif\",\n \"# read q points qpoints = self.input_xml_parser.get('xs/qpointset/qpoint') def get_data(quantity, ext): # all files\",\n \"val_in.strip().split('\\\\n') nbands = int(val[0]) mesh = np.array(val[1].split(), dtype=int) origin = np.array(val[2].split(), dtype=float) vector\",\n \"= sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor = reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands = len(eigs_gw[0]) sec_gw_eigenvalues.n_kpoints = len(eigs_gw) sec_gw_eigenvalues.kpoints =\",\n \"self.energy_unit return self._energies def _get_dos(self, diagram): dos = np.array( [point.attrib.get(self._dos_key) for point in\",\n \"number_of_spin_channels(self): if self._nspin is None: self._nspin = np.shape(np.transpose(self.data))[0] - 6 return self._nspin @property\",\n \"volume = self.info_parser.get_unit_cell_volume() totaldos = self.dos_parser.get('totaldos') * volume.to('m**3').magnitude for spin in range(len(totaldos)): sec_dos_values\",\n \"sec_method.x_exciting_xs_tddft_drude = self.input_xml_parser.get( 'xs/tddft/drude', [0., 0.]) sec_method.x_exciting_xs_tddft_split_parameter = self.input_xml_parser.get( 'xs/tddft/fxcbsesplit', 0.00001, 'hartree') sec_method.x_exciting_xs_tddft_xc_kernel\",\n \"'sum of eigenvalues'], 'x_exciting_effective_potential_energy': ['Effective potential energy'], 'x_exciting_coulomb_potential_energy': ['Coulomb potential energy', 'Coulomb potential'],\",\n \"if sec_scc is None: return sec_system = self.parse_system(section) if sec_system is not None:\",\n \"sec_method.m_create(DFT) sec_electronic = sec_method.m_create(Electronic) sec_electronic.method = 'DFT' smearing_kind_map = { 'Gaussian': 'gaussian', 'Methfessel-Paxton':\",\n \"else: vi = [float(vii) for vii in v[1].split()] vi = vi[0] if len(vi)\",\n \"= reshape(get_data('Re(Sc)')) sec_gw_eigenvalues.value_correlation = eigs_gw_C sec_gw_eigenvalues.value_xc_potential = reshape(get_data('Vxc')) def _parse_dos_out(self, sec_scc): data =\",\n \"== 'EPSILON' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field = data[1:3] elif quantity == 'LOSS'\",\n \"not None: sec_xc_functional.name = xc_functional.get('name_reference', [None, None])[0] sec_xc_functional.reference = xc_functional.get('name_reference', [None, None])[1] sec_electronic.n_spin_channels\",\n \"sec_system.x_exciting_clathrates = False potential_mixing = self.info_parser.get_initialization_parameter('potential_mixing') if potential_mixing is not None: sec_system.x_exciting_potential_mixing =\",\n \"xstype sec_method.x_exciting_electronic_structure_method = xstype sec_method.x_exciting_xs_broadening = self.input_xml_parser.get( 'xs/broad', 0.01, 'hartree') sec_method.x_exciting_xs_gqmax = self.input_xml_parser.get(\",\n \"= np.array(val[2].split(), dtype=float) vector = np.array([v.split() for v in val[3:6]], dtype=float) return [nbands,\",\n \"os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = filename.rsplit('.', 1) filepath = '%s%s' % (target[0],\",\n \"reshape(get_data('Re(Sc)')) sec_gw_eigenvalues.value_correlation = eigs_gw_C sec_gw_eigenvalues.value_xc_potential = reshape(get_data('Vxc')) def _parse_dos_out(self, sec_scc): data = self.dos_out_parser.data\",\n \"gw_info_files = ['GW_INFO.OUT', 'GWINFO.OUT'] for f in gw_info_files: if self.get_exciting_files(f): self._calculation_type = 'gw'\",\n \"'xs/BSE/sciavqwg', False) sec_method.x_exciting_xs_bse_sciavtype = self.input_xml_parser.get( 'xs/BSE/sciavtype', 'spherical') sec_method.x_exciting_xs_bse_xas = self.input_xml_parser.get( 'xs/BSE/xas', False) sec_method.x_exciting_xs_bse_number_of_bands\",\n \"self._neigs_segment class BandOutParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit =\",\n \"= self.input_xml_parser.get( 'xs/tddft/mdfqtype', 0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals = self.input_xml_parser.get( 'xs/tddft/nwacont', 0) sec_method.x_exciting_xs_tetra = self.input_xml_parser.get( 'xs/tetra/tetradf',\",\n \"charge contributions parse_scf(final, sec_scc) # forces forces = section.get('forces') if forces is not\",\n \"at this step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions at this step\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols',\",\n \"% self._vertex_key) return self._vertices @property def number_of_spin_channels(self): if self._nspin is None: self._nspin =\",\n \"= positions.magnitude # clathrate info clathrate_file = self.get_exciting_files('str.out') if clathrate_file: sec_system.x_exciting_clathrates = True\",\n \"= self.logger self.fermisurf_parser.logger = self.logger self.evalqp_parser.logger = self.logger self.dos_out_parser.logger = self.logger self.bandstructure_dat_parser.logger =\",\n \"else final if final is None: return sec_scc = sec_run.m_create(Calculation) def parse_scf(iteration, msection):\",\n \"def distances(self): if self._distances is None: if not self.bands: return self._distances = [\",\n \"n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_im = np.reshape( data[5], (n_components, n_excitons)) def parse_epsilon(data, sec_scc): n_components = len(data)\",\n \"scf iterations scf_iterations = section.get('scf_iteration', []) for scf_iteration in scf_iterations: sec_scf_iteration = sec_scc.m_create(ScfIteration)\",\n \"_parse_fermisurface(self, sec_scc): fermi_surface = self.fermisurf_parser.get('fermi_surface', [None])[0] if fermi_surface is None: return sec_fermisurface =\",\n \"= [sec_method_ref] # parse input xml file, there seems to be two versions,\",\n \"val_in.strip().replace('(', '').replace(')', '').split() def str_to_energy_dict(val_in): val = val_in.strip().split('\\\\n') energies = dict() for v\",\n \"def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] def str_to_frequency(val_in): val = [v.split()\",\n \"NOMAD. # See https://nomad-lab.eu for further info. # # Licensed under the Apache\",\n \"ureg.hartree), 'x_exciting_charge_convergence': ( r'Charge distance\\\\s*\\\\(target\\\\)', ureg.elementary_charge), 'x_exciting_IBS_force_convergence': ( r'Abs\\\\. change in max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)', ureg.hartree\",\n \"sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg', False) sec_method.x_exciting_xs_bse_sciavtype = self.input_xml_parser.get(\",\n \"dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic mass *: *({re_float})', dtype=np.float64, unit=ureg.electron_mass), Quantity('muffin_tin_radius', rf'muffin-tin radius *:\",\n \"band in bands]) if self._energy_unit: band_energy = band_energy * self._energy_unit self._band_energies.append(band_energy) start =\",\n \"self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy = np.array([np.transpose(band)[start:end] for band in bands])\",\n \"continue elif v[0].startswith('species'): species = re.search(re_symbol, v[-1]).group(1) elif v[0].startswith('atom'): v[0] = v[0].split() v[1]\",\n \"positions_format = specie.get('positions_format') sec_atoms_group.x_exciting_atom_position_format = positions_format positions = specie.get('positions') positions = self.info_parser.get_atom_positions( positions=positions,\",\n \"setattr(msection, key, val) # moment contributions moment_contributions = iteration.get('moment_contributions', {}) for key, names\",\n \"sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface = np.product(band_parameters[1]) sec_fermisurface.x_exciting_grid_fermi_surface = band_parameters[1] sec_fermisurface.x_exciting_origin_fermi_surface = band_parameters[2] sec_fermisurface.x_exciting_vectors_fermi_surface = band_parameters[3] fermi_energy\",\n \"'HYB' in name: sec_xc_functional.hybrid.append(Functional(name=name)) else: sec_xc_functional.contributions.append(Functional(name=name)) if not xc_functional_names: # simply write parameters\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"= band_energy * self._energy_unit self._band_energies.append(band_energy) start = end return self._band_energies @property def distances(self):\",\n \"filename = os.path.join(self.info_parser.maindir, default) if not os.path.isfile(filename): file_ext = default.split('.')[-1] mainfile_base = mainfile.rsplit('.',\",\n \"'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg', False) sec_method.x_exciting_xs_bse_sciavtype = self.input_xml_parser.get( 'xs/BSE/sciavtype', 'spherical') sec_method.x_exciting_xs_bse_xas\",\n \"+= 1 self._nkpts_segment.append(count) return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: self._neigs_segment\",\n \"specie.get('positions') positions = self.info_parser.get_atom_positions( positions=positions, positions_format=positions_format).to('m') sec_atoms_group.x_exciting_geometry_atom_positions = positions.magnitude # clathrate info clathrate_file\",\n \"self.info_gw_parser.logger = self.logger self.input_xml_parser.logger = self.logger self.data_xs_parser.logger = self.logger self.data_clathrate_parser.logger = self.logger def\",\n \"if positions_format is not None: break return positions_format def get_atom_positions(self, section={}, positions=None, positions_format=None):\",\n \"name.endswith('xml'): parser = self.dos_parser parser_function = self._parse_dos elif name.startswith('bandstructure') and name.endswith('xml'): parser =\",\n \"0 band_energies = np.zeros(( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, len(self.distances)), dtype=float) for i in range(len(self.bands[n])): band_energies[i\",\n \"% key_unit[0], unit=key_unit[1], repeats=False) ) for name, key_unit in self._method_keys_mapping.items(): initialization_quantities.append( Quantity( name,\",\n \"sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi nkpts_segment = self.band_out_parser.number_of_k_points_per_segment for nb in\",\n \"repeats=True)) def str_to_eigenvalues(val_in): val = val_in[:val_in.rfind('\\\\n \\\\n')].strip() val = np.array([v.split() for v in\",\n \"= np.transpose(np.array([v.split() for v in val[2:]], dtype=float)) eigs = {keys[i]: eigs[i] for i\",\n \"two versions, input_gw.xml and input-gw.xml for f in ['input_gw.xml', 'input-gw.xml', 'input.xml']: self.parse_file(f, sec_method)\",\n \"spin = int(spin) - 1 val_l = self.partial_dos[i].attrib.get(self._l_key, None) val_m = self.partial_dos[i].attrib.get(self._m_key, None)\",\n \"*{re_float} *{re_float}\\\\n\\\\s*\\\\n)', repeats=True, sub_parser=TextParser(quantities=[ Quantity('number', r'Species : *(\\\\d+)', dtype=np.int32), Quantity('symbol', r'\\\\((\\\\w+)\\\\)'), Quantity('file', r'parameters\",\n \"data[1], (n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im = np.reshape( data[2], (n_components, n_sigma)) def parse_loss(data, sec_scc): n_components\",\n \"spin_treatment = self.get('initialization', {}).get( 'x_exciting_spin_treatment', 'spin-unpolarised') n_spin = 1 if spin_treatment.lower() == 'spin-unpolarised'\",\n \"__init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'k_points', r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\- ]+):\\\\s*k\\\\-point', repeats=True))\",\n \"lattice_vectors = lattice_vectors * ureg.bohr atoms = self.input_xml_parser.get('structure/species/atom') atom_labels = [] for n\",\n \"suffix = mainfile.strip('INFO.OUT') target = filename.rsplit('.', 1) filepath = '%s%s' % (target[0], suffix)\",\n \"self._results = dict() if 'total' in key: if not self.total_dos: return res =\",\n \"res = np.zeros((self.number_of_spin_channels, self.number_of_dos)) for i in range(len(self.total_dos)): spin = self.total_dos[i].attrib.get(self._nspin_key, i) res[i]\",\n \"= self.band_out_parser.band_energies if band_energies is None: return # Get fermi energy: it is\",\n \"= self.input_xml_parser.get('gw/barecoul/pwm', 2.0) sec_gw.bare_coulomb_gmax = pwm * gmb * gmaxvr sec_gw.bare_coulomb_cutofftype = self.input_xml_parser.get(\",\n \"(n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_re = np.reshape( data[4], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_im = np.reshape( data[5], (n_components,\",\n \"have the quantity # the only thing that we can do is to\",\n \"3: ['LDA_C_PW', 'LDA_X_PZ'], 4: ['LDA_C_XALPHA'], 5: ['LDA_C_VBH'], 20: ['GGA_C_PBE', 'GGA_X_PBE'], 21: ['GGA_C_PBE', 'GGA_X_PBE_R'],\",\n \"self._band_energies.append(band_energy) start = end return self._band_energies @property def distances(self): if self._distances is None:\",\n \"species: positions_format = specie.get('positions_format', None) if positions_format is not None: break return positions_format\",\n \"self.data is None: return data = np.transpose(self.data) n_kpoints = int(max(data[1])) bands = data[6:]\",\n \"self.bands: return self._distances = [ point.attrib.get(self._distance_key) for point in self.bands[0][0]] self._distances = np.array(self._distances,\",\n \"repeats=False, dtype=str), Quantity( 'n_scf_iterations', r'Number of (?:total)* scf iterations\\\\s*\\\\:\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'force_convergence',\",\n \"sec_scc.energy is not None: energy_fermi = sec_scc.energy.fermi energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band\",\n \"np.array(self._distances, dtype=float) return self._distances @property def bands(self): if self._bands is None: bands =\",\n \"[ f for f in os.listdir( self.info_parser.maindir) if target[0] in f and mainfile_base\",\n \"convert=False), Quantity( 'x_exciting_dos_fermi', r'DOS at Fermi energy \\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=1 / ureg.hartree),\",\n \"['GGA_C_BGCP', 'GGA_X_PBE'], 406: ['HYB_GGA_XC_PBEH'], 408: ['HYB_GGA_XC_HSE03']} xc_functional = self.get('initialization', {}).get('xc_functional', None) if xc_functional\",\n \"key_unit[0], str_operation=str_to_quantity_tolerances, unit=key_unit[1], repeats=False)) module_quantities = [ Quantity( 'scf_iteration', r'(?:I| i)teration number :([\\\\s\\\\S]+?)(?:\\\\n\",\n \"self.vertices[i + 1].attrib.get('label') res.append([ '\\\\u0393' if start.lower() == 'gamma' else start, '\\\\u0393' if\",\n \"self.input_xml_parser.get( 'xs/BSE/xasatom', 0) sec_method.x_exciting_xs_bse_xasedge = self.input_xml_parser.get( 'xs/BSE/xasedge', 'K') sec_method.x_exciting_xs_bse_xasspecies = self.input_xml_parser.get( 'xs/BSE/xasspecies', 0)\",\n \"'x_exciting_dft_d2_dispersion_correction': ['DFT-D2 dispersion correction'] } self._electron_charge_keys_mapping = { 'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage': ['core leakage'],\",\n \"str_operation=str_to_quantity_tolerances, unit=key_unit[1], repeats=False)) module_quantities = [ Quantity( 'scf_iteration', r'(?:I| i)teration number :([\\\\s\\\\S]+?)(?:\\\\n *\\\\n\\\\+{10}|\\\\+\\\\-{10})',\",\n \"self._calculation_type == 'volume_optimization': sec_method.x_exciting_volume_optimization = True def parse_scc(self, section): sec_run = self.archive.run[-1] final\",\n \"self.logger self.bandstructure_parser.logger = self.logger self.eigval_parser.logger = self.logger self.fermisurf_parser.logger = self.logger self.evalqp_parser.logger = self.logger\",\n \"= dict() if not self.bands: return if key == 'band_energies': # TODO I\",\n \"Unless required by applicable law or agreed to in writing, software # distributed\",\n \"nomad.datamodel.metainfo.workflow import Workflow, GeometryOptimization from .metainfo.exciting import x_exciting_section_MT_charge_atom, x_exciting_section_MT_moment_atom,\\\\ x_exciting_section_spin, x_exciting_section_fermi_surface,\\\\ x_exciting_section_atoms_group re_float\",\n \"def str_to_eigenvalue(val_in): val = val_in.strip().split('\\\\n') kpts = np.array(val[0].split(), dtype=float) keys = val[1].split() eigs\",\n \"files_q = [f for f in files if f.endswith('QMT%s.OUT' % str(i + 1).rjust(3,\",\n \"more output files and properties exciting_files = ['EIGVAL.OUT', 'FERMISURF.bxsf', 'FS.bxsf'] # Parse DFT\",\n \"v[1][0] if len(v[1]) == 1 else v[1] if species is None: species =\",\n \"wannier.out if name.startswith('dos') and name.endswith('xml'): parser = self.dos_parser parser_function = self._parse_dos elif name.startswith('bandstructure')\",\n \"'x_exciting_hartree_energy': ['Hartree energy', 'Hartree'], 'x_exciting_electron_nuclear_energy': ['Electron-nuclear energy', 'electron-nuclear '], 'x_exciting_nuclear_nuclear_energy': ['Nuclear-nuclear energy', 'nuclear-nuclear'],\",\n \"def parse(self, key): if self._results is None: self._results = dict() if 'total' in\",\n \"return else: return files = self.get_exciting_files(name) if len(files) > 1: self.logger.warn('Found multiple files.\",\n \"sec_system.m_create(x_exciting_section_atoms_group) sec_atoms_group.x_exciting_geometry_atom_labels = specie.get('symbol') sec_atoms_group.x_exciting_geometry_atom_number = str(specie.get('number')) sec_atoms_group.x_exciting_muffin_tin_points = specie.get('radial_points') sec_atoms_group.x_exciting_muffin_tin_radius = specie.get('muffin_tin_radius')\",\n \"parser.info_parser.quantities self.eigval_parser.quantities = parser.eigval_parser.quantities self.fermisurf_parser.quantities = parser.fermisurf_parser.quantities self.evalqp_parser.quantities = parser.evalqp_parser.quantities self.info_gw_parser.quantities = parser.info_gw_parser.quantities\",\n \"for name in xc_functional_names: if name is None: continue if '_X_' in name:\",\n \"= 'dos' self._unit_key = 'unit' self._energy_unit = kwargs.get('energy_unit', None) self._units_mapping = dict(hartree=ureg.hartree) def\",\n \"mass *: *({re_float})', dtype=np.float64, unit=ureg.electron_mass), Quantity('muffin_tin_radius', rf'muffin-tin radius *: *({re_float})', dtype=np.float64, unit=ureg.bohr), Quantity('radial_points',\",\n \"self._system_keys_mapping = { 'x_exciting_unit_cell_volume': ('Unit cell volume', ureg.bohr ** 3), 'x_exciting_brillouin_zone_volume': ('Brillouin zone\",\n \"fxctype)) data = [[], [], []] for i in range(len(qpoints)): data_q = []\",\n \"method goes first since reference needed for sec_scc self.parse_method() self.parse_configurations() self.parse_gw() self.parse_xs() self.parse_miscellaneous()\",\n \"os.path.join(self.info_parser.maindir, filename) if os.path.isfile(filename): return [filename] filename = os.path.join(self.info_parser.maindir, default) if not os.path.isfile(filename):\",\n \"[os.path.join(self.info_parser.maindir, f) for f in options] else: filenames = [filename] filenames = [f\",\n \") self._quantities.append( Quantity( 'direct_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'fundamental_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s',\",\n \"file_ext, ext, fxctype)) data = [[], [], []] for i in range(len(qpoints)): data_q\",\n \"val_in: unit = ureg.elementary_charge elif 'moment' in val_in: unit = ureg.elementary_charge * ureg.bohr\",\n \"name is None: continue if '_X_' in name: sec_xc_functional.exchange.append(Functional(name=name)) elif '_C_' in name:\",\n \"mainfile_base in f] options = [f for f in options if f.endswith(file_ext)] options.sort()\",\n \"r'Atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity(\",\n \"sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.value = band_energies[nb] + energy_fermi def parse_file(self, name, section):\",\n \"self._neigs_segment = len(np.where(data == data[0])[0]) return self._neigs_segment class BandstructureXMLParser(XMLParser): def __init__(self, **kwargs): #\",\n \"occs = val[-1] eigs = val[-2] nspin = 2 if occs[0] == 1.\",\n \"data_q.append(self.data_xs_parser.data) if not data_q: continue data_q = np.transpose(data_q, axes=(2, 0, 1)) for j\",\n \"self.input_xml_parser.get('libxc/exchange', None) xc_functional_names.append(exchange) sec_xc_functional = sec_dft.m_create(XCFunctional) for name in xc_functional_names: if name is\",\n \"def parse_method(self): sec_run = self.archive.run[-1] sec_method = sec_run.m_create(Method) sec_method.basis_set.append(BasisSet(type='(L)APW+lo')) sec_dft = sec_method.m_create(DFT) sec_electronic\",\n \"sec_run.system[-1] sec_scc_ref = sec_run.calculation[0] sec_scc.starting_calculation_ref = sec_scc_ref sec_scc.calculations_ref = [sec_scc_ref] # parse properties\",\n \"list(data[3]) else: sec_system.x_exciting_clathrates = False potential_mixing = self.info_parser.get_initialization_parameter('potential_mixing') if potential_mixing is not None:\",\n \"val[3:6]], dtype=float) return [nbands, mesh, origin, vector] self._quantities.append( Quantity( 'band_parameters', r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)', str_operation=str_to_band_parameters, repeats=False))\",\n \"if self._band_k_points is None: data = np.transpose(self.data) self._band_k_points = [] start = 0\",\n \"'gw/freqgrid/fgrid', 'gaule2') sec_gw.number_of_frequencies = int(self.input_xml_parser.get( 'gw/freqgrid/nomeg', 16)) sec_gw.self_energy_c_number_of_poles = int(self.input_xml_parser.get( 'gw/selfenergy/npol', 0)) sec_gw.self_energy_c_number_of_empty_states\",\n \"sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value = totaldos[spin] partialdos = self.dos_parser.get('partialdos') if partialdos\",\n \"TODO add support for info.xml, wannier.out if name.startswith('dos') and name.endswith('xml'): parser = self.dos_parser\",\n \"self.input_xml_parser.get( 'gw/selfenergy/singularity', 'mpd') sec_gw.self_energy_c_analytical_continuation = self.input_xml_parser.get( 'gw/selfenergy/actype', 'pade') sec_gw.mixed_basis_lmax = int(self.input_xml_parser.get( 'gw/mixbasis/lmaxmb', 3))\",\n \"'x_exciting_fermi_energy': sec_energy.fermi = val # charge contributions charge_contributions = iteration.get('charge_contributions', {}) for key,\",\n \"done in parser, however nspin is # determined from occupancies which is problematic\",\n \"ext) if not data[0]: continue if quantity == 'EPSILON' and ext == 'FXC':\",\n \"get_data('occupancies') sec_eigenvalues.energies = get_data('eigenvalues') def _parse_fermisurface(self, sec_scc): fermi_surface = self.fermisurf_parser.get('fermi_surface', [None])[0] if fermi_surface\",\n \"= self.info_parser.get('structure_optimization') if structure_optimization is not None: sec_worfklow.type = 'geometry_optimization' sec_geometry_opt = sec_worfklow.m_create(GeometryOptimization)\",\n \"xc_functional_names: if name is None: continue if '_X_' in name: sec_xc_functional.exchange.append(Functional(name=name)) elif '_C_'\",\n \"= [] for bse_type in bse_types: for scr_type in scr_types: files = self.get_exciting_files(\",\n \"== 'FXC': sec_scc.x_exciting_xs_tddft_sigma_local_field = data[1:3] elif quantity == 'SIGMA' and ext == 'NLF_FXC':\",\n \"sec_worfklow.type = 'geometry_optimization' sec_geometry_opt = sec_worfklow.m_create(GeometryOptimization) threshold_force = structure_optimization.get( 'optimization_step', [{}])[0].get('force_convergence', [0., 0.])[-1]\",\n \"self.input_xml_parser.get( 'xs/tddft/lmaxalda', 3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment = self.input_xml_parser.get( 'xs/tddft/mdfqtype', 0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals = self.input_xml_parser.get( 'xs/tddft/nwacont', 0)\",\n \"in self.info_parser._miscellaneous_keys_mapping.keys(): val = iteration.get(name) if val is None: continue if name ==\",\n \"*: *({re_float})', dtype=np.int32), Quantity('positions_format', r'atomic positions \\\\((.+?)\\\\)', flatten=False), Quantity( 'positions', rf'\\\\d+ : *({re_float})\",\n \"def _parse_band_out(self, sec_scc): self.band_out_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.band_out_parser.band_energies if band_energies is None:\",\n \"potential \\\\(target\\\\)', ureg.hartree), 'x_exciting_energy_convergence': ( r'Absolute change in total energy\\\\s*\\\\(target\\\\)', ureg.hartree), 'x_exciting_charge_convergence': (\",\n \"self.dos_out_parser.logger = self.logger self.bandstructure_dat_parser.logger = self.logger self.band_out_parser.logger = self.logger self.info_gw_parser.logger = self.logger self.input_xml_parser.logger\",\n \"def n_optimization_steps(self): return len(self.get('structure_optimization', {}).get('optimization_step', [])) def get_number_of_spin_channels(self): spin_treatment = self.get('initialization', {}).get( 'x_exciting_spin_treatment',\",\n \"data[0], (n_components, n_loss)) * ureg.hartree sec_scc.x_exciting_xs_bse_loss = np.reshape( data[1], (n_components, n_loss)) # TODO\",\n \"np.vstack([s.get('positions') for s in species]) if positions is None: return positions = np.array(positions)\",\n \"positions is None: # get it from input.xml for f in input_file: self.input_xml_parser.mainfile\",\n \"== 'EXCITON': sec_scc = sec_run.m_create(Calculation) sccs.append(sec_scc) else: sec_scc = sccs[i] if sec_scc is\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"name == 'x_exciting_spin_treatment': sub_sec = sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment = val elif name == 'x_exciting_species_rtmin':\",\n \"'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get( 'xs/tddft/ahc', False) sec_method.x_exciting_xs_tddft_anti_resonant_dielectric = self.input_xml_parser.get( 'xs/tddft/aresdf', False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel\",\n \"end]) elif key == 'band_segm_start_end': res = [] for i in range(len(self.number_of_k_points_per_segment)): start\",\n \"dispersion correction'] } self._electron_charge_keys_mapping = { 'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage': ['core leakage'], 'x_exciting_valence_charge': ['valence'],\",\n \"else section.get('final') if final is None: # get it from last scf_iteration or\",\n \"'x_exciting_madelung_energy': ['Madelung energy', 'Madelung'], 'x_exciting_core_electron_kinetic_energy': ['Core-electron kinetic energy', 'core electron kinetic'], 'x_exciting_dft_d2_dispersion_correction': ['DFT-D2\",\n \"= self.root.findall('./%s/%s' % (self._totaldos_key, self._diagram_key)) return self._total_dos @property def partial_dos(self): if self._partial_dos is\",\n \"= partialdos[lm][spin][atom] def _parse_bandstructure(self, sec_scc): # we need to set nspin again as\",\n \"= self.parse_system(section) if sec_system is not None: sec_scc.system_ref = sec_system sec_scc.method_ref = sec_run.method[-1]\",\n \"repeats=False, convert=False), ] self._system_keys_mapping = { 'x_exciting_unit_cell_volume': ('Unit cell volume', ureg.bohr ** 3),\",\n \"= len(data) data = np.transpose(np.vstack(data)) n_loss = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_loss_energies = np.reshape(\",\n \"repeats=True)) class ExcitingEigenvalueParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity(\",\n \"r'Charge distance\\\\s*\\\\(target\\\\)', ureg.elementary_charge), 'x_exciting_IBS_force_convergence': ( r'Abs\\\\. change in max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)', ureg.hartree / ureg.bohr)} for\",\n \"if potential_mixing is not None: sec_system.x_exciting_potential_mixing = potential_mixing return sec_system def parse_configurations(self): sec_run\",\n \"= self.input_xml_parser.get( 'xs/tddft/aresdf', False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel = self.input_xml_parser.get( 'xs/tddft/aresfxc', True) sec_method.x_exciting_xs_tddft_drude = self.input_xml_parser.get( 'xs/tddft/drude',\",\n \"'m' self._energy_key = 'e' self._dos_key = 'dos' self._unit_key = 'unit' self._energy_unit = kwargs.get('energy_unit',\",\n \"super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin =\",\n \"np.where(data[0] == data[0][0])[0][1] bands = data[1:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints))\",\n \"= [] self._quantities.append( Quantity( 'k_points', r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\- ]+):\\\\s*k\\\\-point', repeats=True)) def str_to_eigenvalues(val_in): val = val_in[:val_in.rfind('\\\\n\",\n \"3), 'x_exciting_number_of_atoms': ('Total number of atoms per unit cell', None), 'x_exciting_spin_treatment': ('Spin treatment',\",\n \"is a mismatch between files self.logger.warn( 'Mismatch in EXCITON and file type', data=dict(file=quantity))\",\n \"repeats=True, sub_parser=TextParser(quantities=[ Quantity('number', r'Species : *(\\\\d+)', dtype=np.int32), Quantity('symbol', r'\\\\((\\\\w+)\\\\)'), Quantity('file', r'parameters loaded from\",\n \"energy_total = iteration.get('energy_total') sec_energy = msection.m_create(Energy) if energy_total is not None: sec_energy.total =\",\n \"gap', ureg.hartree), 'time': (r'Wall time \\\\(seconds\\\\)', ureg.s)} for name, key_unit in self._miscellaneous_keys_mapping.items(): scf_quantities.append(Quantity(\",\n \"if len(v[1]) == 1 else v[1] if species is None: species = v[0][2]\",\n \"charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False), Quantity( 'moment_contributions', r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False)] self._miscellaneous_keys_mapping\",\n \"* self.energy_unit return self._energies def _get_dos(self, diagram): dos = np.array( [point.attrib.get(self._dos_key) for point\",\n \"'energy_total': ['Total energy', 'total energy'], 'x_exciting_fermi_energy': ['Fermi energy', 'Fermi'], 'energy_kinetic_electronic': ['Kinetic energy', 'electronic\",\n \"f in files if f.endswith('QMT%s.OUT' % str(i + 1).rjust(3, '0'))] for f in\",\n \"len(self.bands[0]) // self.number_of_spin_channels return self._neigs_segment def parse(self, key): if self._results is None: self._results\",\n \"= [] for specie in species: labels += [specie.get('symbol')] * len(specie.get('positions')) return labels\",\n \"in options] else: filenames = [filename] filenames = [f for f in filenames\",\n \"qpoints = self.input_xml_parser.get('xs/qpointset/qpoint') def get_data(quantity, ext): # all files related to quantity at\",\n \"ScfIteration, BandGap ) from nomad.datamodel.metainfo.workflow import Workflow, GeometryOptimization from .metainfo.exciting import x_exciting_section_MT_charge_atom, x_exciting_section_MT_moment_atom,\\\\\",\n \"re.search(re_symbol, v[-1]).group(1) elif v[0].startswith('atom'): v[0] = v[0].split() v[1] = [float(vi) for vi in\",\n \"eigenvalue and occupancy below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n *\\\\n))', str_operation=str_to_eigenvalues, repeats=True)) class ExcitingGWOutParser(TextParser): def __init__(self, mainfile, logger):\",\n \"'NLF_FXC': sec_scc.x_exciting_xs_tddft_sigma_no_local_field = data[1:3] def parse_xs(self): sec_run = self.archive.run[-1] xs_info_files = self.get_exciting_files('INFOXS.OUT') if\",\n \"= int(spin) - 1 val_l = self.partial_dos[i].attrib.get(self._l_key, None) val_m = self.partial_dos[i].attrib.get(self._m_key, None) if\",\n \"np.array( [point.attrib.get(self._dos_key) for point in diagram], dtype=float) return dos def parse(self, key): if\",\n \"sec_method) # parse properties input_file = self.get_exciting_files('input.xml') if not input_file: return self.input_xml_parser.mainfile =\",\n \"\\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=1 / ureg.hartree), Quantity( 'charge_contributions', r'(?:Charges|Electron charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False,\",\n \"('Number of Bravais lattice symmetries', None), 'x_exciting_number_of_crystal_symmetries': ('Number of crystal symmetries', None), 'x_exciting_kpoint_grid':\",\n \"repeats=False)) optimization_quantities = [ Quantity( 'atomic_positions', r'(Atomic positions at this step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity(\",\n \"for v in val: v = v.strip().split(':') if len(v) < 2: continue elif\",\n \"dtype=np.int32))) for lm in range(len(partialdos)): for spin in range(len(partialdos[lm])): for atom in range(len(partialdos[lm][spin])):\",\n \"positions_format is None: species = self.get_initialization_parameter('species', []) for specie in species: positions_format =\",\n \"threshold_force = structure_optimization.get( 'optimization_step', [{}])[0].get('force_convergence', [0., 0.])[-1] sec_geometry_opt.input_force_maximum_tolerance = threshold_force def parse_method(self): sec_run\",\n \"sec_method.x_exciting_xs_bse_xas: sec_method.x_exciting_xs_bse_xasatom = self.input_xml_parser.get( 'xs/BSE/xasatom', 0) sec_method.x_exciting_xs_bse_xasedge = self.input_xml_parser.get( 'xs/BSE/xasedge', 'K') sec_method.x_exciting_xs_bse_xasspecies =\",\n \"self.input_xml_parser.get( 'xs/BSE/nstlbse', [0, 0, 0, 0]) if sec_method.x_exciting_xs_bse_xas: sec_method.x_exciting_xs_bse_xasatom = self.input_xml_parser.get( 'xs/BSE/xasatom', 0)\",\n \"unit))) else: vi = [float(vii) for vii in v[1].split()] vi = vi[0] if\",\n \"DataTextParser(dtype=str) # different names for different versions of exciting self._energy_keys_mapping = { 'energy_total':\",\n \"self.info_parser.get_atom_positions( positions=positions, positions_format=positions_format).to('m') sec_atoms_group.x_exciting_geometry_atom_positions = positions.magnitude # clathrate info clathrate_file = self.get_exciting_files('str.out') if\",\n \"sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = len(data) // nspin data = np.reshape(data, (nspin,\",\n \"'xs/tddft/aresdf', False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel = self.input_xml_parser.get( 'xs/tddft/aresfxc', True) sec_method.x_exciting_xs_tddft_drude = self.input_xml_parser.get( 'xs/tddft/drude', [0., 0.])\",\n \"ureg.elementary_charge), 'x_exciting_IBS_force_convergence': ( r'Abs\\\\. change in max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)', ureg.hartree / ureg.bohr)} for name, key_unit\",\n \"also other energies are reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is None: continue\",\n \"x_exciting_section_atoms_group re_float = r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?' class GWInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities =\",\n \"spin = self.partial_dos[i].attrib.get(self._nspin_key, None) if spin is None: spin = (i % (self.number_of_spin_channels\",\n \"Copyright The NOMAD Authors. # # This file is part of NOMAD. #\",\n \"= self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors = self.input_xml_parser.get( 'structure/crystal/basevect', np.eye(3)) species = self.input_xml_parser.get('structure/species/speciesfile') if positions is\",\n \"energy_fermi volume = self.info_parser.get_unit_cell_volume() dos = data[1] * (1 / ureg.hartree) * volume.to('m**3').magnitude\",\n \"Energy(fermi=fermi_energy) gw_files = ['EVALQP.DAT', 'EVALQP.TXT', 'TDOS-QP.OUT'] # Parse GW band structure from one\",\n \"None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_rgkmax = rgkmax sec_method.x_exciting_xs_scissor\",\n \"f and mainfile_base in f] options = [f for f in options if\",\n \"ExcitingEvalqpParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] def str_to_eigenvalue(val_in): val =\",\n \"self.input_xml_parser.get('xs/tddft/aresdf', True) file_ext_list = [ 'TET' if tetradf else None, 'AC' if nwacont\",\n \"to # the original scale in which also other energies are reported. energy_fermi\",\n \"energy', 'exchange'], 'energy_correlation': ['Correlation energy', 'correlation'], 'x_exciting_correlation_energy': ['Correlation energy', 'correlation'], 'energy_sum_eigenvalues': ['Sum of\",\n \"for f in files if f.endswith('QMT%s.OUT' % str(i + 1).rjust(3, '0'))] for f\",\n \"key == 'x_exciting_section_MT_charge_atom': for n in range(len(val)): sec_mt_charge_atom = msection.m_create(x_exciting_section_MT_charge_atom) sec_mt_charge_atom.x_exciting_MT_charge_atom_index = n\",\n \"- 6 return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment =\",\n \"if quantity == 'EXCITON': sec_scc = sec_run.m_create(Calculation) sccs.append(sec_scc) else: sec_scc = sccs[i] if\",\n \"[0., 0.]) sec_method.x_exciting_xs_tddft_split_parameter = self.input_xml_parser.get( 'xs/tddft/fxcbsesplit', 0.00001, 'hartree') sec_method.x_exciting_xs_tddft_xc_kernel = self.input_xml_parser.get( 'xs/tddft/fxctype', 'RPA')\",\n \"1: self.logger.warn('Found multiple files. Will read all!', data=dict(file=name)) for n in range(len(files)): parser.mainfile\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"am not certain about the format for the spin polarized case # I\",\n \"if species: positions = np.vstack([s.get('positions') for s in species]) if positions is None:\",\n \"that the first steps are the # ones with the missing quantity if\",\n \"= self.input_xml_parser.get('xs/tddft/fxctype', 'RPA') tetradf = self.input_xml_parser.get('xs/tetra/tetradf', None) nwacont = self.input_xml_parser.get('xs/tddft/nwacont', None) aresdf =\",\n \"Quantity( 'charge_contributions', r'(?:Charges|Electron charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False), Quantity( 'moment_contributions', r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict,\",\n \"the given filename exists and is accessible in the same folder where the\",\n \"self.input_xml_parser.get('xs/scissor', 0.0) sec_method.x_exciting_xs_vkloff = self.input_xml_parser.get('xs/vkloff', [0., 0., 0.]) # TODO I am not\",\n \"is None: continue energy_fermi = energy_fermi.to(\\\"hartree\\\") sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi\",\n \"mixing', repeats=False, flatten=False) ) initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation type[\\\\s\\\\S]+?\\\\n *\\\\n)', sub_parser=TextParser(quantities=[ Quantity('type', r'Exchange-correlation type\",\n \"= smearing_kind_map[smearing_kind] sec_smearing.kind = smearing_kind smearing_width = self.info_parser.get_initialization_parameter('smearing_width') if smearing_width is not None:\",\n \"* self._energy_unit res_n.append(band_energy) start = end res.append(res_n) elif key == 'band_k_points': res =\",\n \"['Correlation energy', 'correlation'], 'x_exciting_correlation_energy': ['Correlation energy', 'correlation'], 'energy_sum_eigenvalues': ['Sum of eigenvalues', 'sum of\",\n \"self.input_xml_parser.get('gw/ngridq', [1, 1, 1]) sec_gw.basis_set = 'mixed' sec_gw.qp_equation_treatment = 'linearization' sec_gw.max_frequency = self.input_xml_parser.get(\",\n \"res[i] = self._get_dos(self._total_dos[i]) if self.energy_unit is not None: res = res * (1\",\n \"self.eigval_parser = ExcitingEigenvalueParser() self.fermisurf_parser = ExcitingFermiSurfaceBxsfParser() self.evalqp_parser = ExcitingEvalqpParser() self.dos_out_parser = DataTextParser() self.bandstructure_dat_parser\",\n \"v in val_in.split('\\\\n')] val = np.transpose(np.array([v for v in val if len(v) ==\",\n \"= self.info_parser.get_initialization_parameter('potential_mixing') if potential_mixing is not None: sec_system.x_exciting_potential_mixing = potential_mixing return sec_system def\",\n \"'energy_electrostatic': ['Hartree energy', 'Hartree'], 'x_exciting_hartree_energy': ['Hartree energy', 'Hartree'], 'x_exciting_electron_nuclear_energy': ['Electron-nuclear energy', 'electron-nuclear '],\",\n \"2 * li + 1 return self._nlm @property def total_dos(self): if self._total_dos is\",\n \"val = self.info_parser.get_initialization_parameter(name) if val is None: continue if name == 'x_exciting_spin_treatment': sub_sec\",\n \"self._results = dict() if not self.bands: return if key == 'band_energies': # TODO\",\n \"charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('electronic_charge', rf'electronic charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass',\",\n \"= len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_excitons = n_excitons sec_scc.x_exciting_xs_bse_exciton_energies = np.reshape( data[1], (n_components, n_excitons))\",\n \"r'[Tt]*otal energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=ureg.hartree), Quantity( 'energy_contributions', r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n *(?:DOS|Density)', str_operation=str_to_energy_dict, repeats=False, convert=False), Quantity(\",\n \"= 'G0W0' gmaxvr = self.info_parser.get_initialization_parameter('x_exciting_gmaxvr', 0) sec_gw.core_treatment = self.input_xml_parser.get( 'gw/coreflag', 'all') sec_gw.polarizability_number_of_empty_states =\",\n \"add atom-resolved bands_atom = self.root.findall('./*/%s' % self._atom_key) for band in bands_atom: self._bands.append(band.findall('./%s' %\",\n \"is None: species = self.get_initialization_parameter('species', []) if species: positions = np.vstack([s.get('positions') for s\",\n \"= 0 l_list = set([int(e.attrib.get(self._l_key)) for e in self.partial_dos]) for li in l_list:\",\n \"is None: return nspin = self.info_parser.get_number_of_spin_channels() def get_data(key): data = self.eigval_parser.get('eigenvalues_occupancies') # reshaping\",\n \"= self.vertices[i].attrib.get(self._coord_key).split() end = self.vertices[i + 1].attrib.get(self._coord_key).split() res.append([start, end]) else: res = None\",\n \"self._quantities.append( Quantity( 'k_points', r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\- ]+):\\\\s*k\\\\-point', repeats=True)) def str_to_eigenvalues(val_in): val = val_in[:val_in.rfind('\\\\n \\\\n')].strip() val\",\n \"n_components sec_scc.x_exciting_xs_bse_number_of_energy_points = n_epsilon sec_scc.x_exciting_xs_bse_epsilon_energies = np.reshape( data[0], (n_components, n_epsilon)) * ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re\",\n \"else 1 data = dict() data['occupancies'] = np.reshape(occs, (nspin, len(occs) // nspin)) data['eigenvalues']\",\n \"self.dos_out_parser = DataTextParser() self.bandstructure_dat_parser = BandstructureDatParser(energy_unit=ureg.hartree) self.band_out_parser = BandOutParser(energy_unit=ureg.hartree) self.info_gw_parser = GWInfoParser() self.input_xml_parser\",\n \"self._miscellaneous_keys_mapping = { 'x_exciting_gap': (r'Estimated fundamental gap', ureg.hartree), 'time': (r'Wall time \\\\(seconds\\\\)', ureg.s)}\",\n \"int(max(data[1])) bands = data[6:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies =\",\n \"= val elif name == 'x_exciting_species_rtmin': setattr(sec_system, name, ' '.join([str(v) for v in\",\n \"name, ' '.join([str(v) for v in val])) else: try: setattr(sec_system, name, val) except\",\n \"*Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr), ]), repeats=False)) self._quantities.append(Quantity( 'hybrids', r'Hybrids module\",\n \"1 else v[1] if species is None: species = v[0][2] atom_resolved.append(((species, v[1] *\",\n \"'x_exciting_fermi_energy': ['Fermi energy', 'Fermi'], 'energy_kinetic_electronic': ['Kinetic energy', 'electronic kinetic'], 'energy_coulomb': ['Coulomb energy', 'Coulomb'],\",\n \"to in writing, software # distributed under the License is distributed on an\",\n \"dtype=float) keys = val[1].split() eigs = np.transpose(np.array([v.split() for v in val[2:]], dtype=float)) eigs\",\n \"self._partial_dos = None @property def energy_unit(self): if self._energy_unit is None: axis = self.root.find('./axis')\",\n \"= self.input_xml_parser.get('xs/BSE/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0) sec_method.x_exciting_xs_bse_rgkmax =\",\n \"@property def number_of_spin_channels(self): if self._nspin is None: if not self.total_dos: return self._nspin =\",\n \"self.bandstructure_dat_parser = BandstructureDatParser(energy_unit=ureg.hartree) self.band_out_parser = BandOutParser(energy_unit=ureg.hartree) self.info_gw_parser = GWInfoParser() self.input_xml_parser = XMLParser() self.data_xs_parser\",\n \"is None: if not self.bands: return self._distances = [ point.attrib.get(self._distance_key) for point in\",\n \"v[0][2] atom_resolved.append(((species, v[1] * unit))) else: vi = [float(vii) for vii in v[1].split()]\",\n \"in range(len(dos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value = dos[spin] #\",\n \"np.product(band_parameters[1]) sec_fermisurface.x_exciting_grid_fermi_surface = band_parameters[1] sec_fermisurface.x_exciting_origin_fermi_surface = band_parameters[2] sec_fermisurface.x_exciting_vectors_fermi_surface = band_parameters[3] fermi_energy = self.fermisurf_parser.get('fermi_energy',\",\n \"self.bands: return if key == 'band_energies': # TODO I am not certain about\",\n \"if eigs_gw_C is None: eigs_gw_C = reshape(get_data('Re(Sc)')) sec_gw_eigenvalues.value_correlation = eigs_gw_C sec_gw_eigenvalues.value_xc_potential = reshape(get_data('Vxc'))\",\n \"in val_in: unit = ureg.elementary_charge elif 'moment' in val_in: unit = ureg.elementary_charge *\",\n \"= self.get_exciting_files( '%s_BSE%s_SCR%s.OUT' % (name, bse_type, scr_type)) bse_files.append(files) return bse_files def get_data(files): data\",\n \"'fermi', 'Extended': 'tetrahedra'} sec_smearing = sec_electronic.m_create(Smearing) smearing_kind = self.info_parser.get_initialization_parameter('smearing_kind') if smearing_kind is not\",\n \"self.logger self.fermisurf_parser.logger = self.logger self.evalqp_parser.logger = self.logger self.dos_out_parser.logger = self.logger self.bandstructure_dat_parser.logger = self.logger\",\n \"= n + 1 sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol = val[n][0] sec_mt_charge_atom.x_exciting_MT_charge_atom_value = val[n][1] sec_charges = msection.m_create(Charges)\",\n \"self._distances @property def number_of_spin_channels(self): if self._nspin is None: self._nspin = np.shape(np.transpose(self.data)[1:])[0] return self._nspin\",\n \"= structure_optimization.get( 'optimization_step', [{}])[0].get('force_convergence', [0., 0.])[-1] sec_geometry_opt.input_force_maximum_tolerance = threshold_force def parse_method(self): sec_run =\",\n \"in self.bands[0][0]] self._distances = np.array(self._distances, dtype=float) return self._distances @property def bands(self): if self._bands\",\n \"= sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value = totaldos[spin] partialdos = self.dos_parser.get('partialdos') if\",\n \"// nspin, 2)) data = np.transpose(data, axes=(2, 0, 1)) sec_dos.energies = data[0][0] *\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this file\",\n \"None: sec_forces = sec_scc.m_create(Forces) sec_forces.total = ForcesEntry(value=forces) # scf iterations scf_iterations = section.get('scf_iteration',\",\n \"parser = self.fermisurf_parser parser_function = self._parse_fermisurface elif name.startswith('EVALQP') and (name.endswith('DAT') or name.endswith('TXT')): parser\",\n \"_parse_input_xs(self, sec_method): xstype = self.input_xml_parser.get('xs/xstype', None) if xstype is not None: sec_method.x_exciting_xs_xstype =\",\n \"else None, 'NAR' if not aresdf else None] file_ext = '_'.join([e for e\",\n \"'').split() def str_to_energy_dict(val_in): val = val_in.strip().split('\\\\n') energies = dict() for v in val:\",\n \"None: return # Get fermi energy: it is used to un-shift the DOS\",\n \"for lm in range(len(partialdos)): for spin in range(len(partialdos[lm])): for atom in range(len(partialdos[lm][spin])): sec_dos_values\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"partialdos.to('1/joule').magnitude lm_values = np.column_stack((np.arange(len(partialdos)), np.zeros(len(partialdos), dtype=np.int32))) for lm in range(len(partialdos)): for spin in\",\n \"= sec_system.m_create(x_exciting_section_atoms_group) sec_atoms_group.x_exciting_geometry_atom_labels = specie.get('symbol') sec_atoms_group.x_exciting_geometry_atom_number = str(specie.get('number')) sec_atoms_group.x_exciting_muffin_tin_points = specie.get('radial_points') sec_atoms_group.x_exciting_muffin_tin_radius =\",\n \"unit=ureg.hartree / ureg.bohr, repeats=False, dtype=float), Quantity( 'energy_total', r'Total energy at this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)',\",\n \"rf'nuclear charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('electronic_charge', rf'electronic charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge),\",\n \"sec_gw.mixed_basis_tolerance = self.input_xml_parser.get( 'gw/mixbasis/epsmb', 0.0001) gmb = self.input_xml_parser.get('gw/mixbasis/gmb', 1.0) sec_gw.mixed_basis_gmax = gmb *\",\n \"self._partial_dos @property def energies(self): if self._energies is None: if self.total_dos is None: return\",\n \"potential energy', 'xc potential'], 'energy_electrostatic': ['Hartree energy', 'Hartree'], 'x_exciting_hartree_energy': ['Hartree energy', 'Hartree'], 'x_exciting_electron_nuclear_energy':\",\n \"end]) else: res = None self._results[key] = res class DOSXMLParser(XMLParser): def __init__(self, **kwargs):\",\n \"input_file = self.get_exciting_files('input.xml') if positions is None: # get it from input.xml for\",\n \"nomad.datamodel.metainfo.simulation.calculation import ( Calculation, Dos, DosValues, BandStructure, BandEnergies, Energy, EnergyEntry, Charges, Forces, ForcesEntry,\",\n \"range(len(val)): sec_mt_moment_atom = msection.m_create(x_exciting_section_MT_moment_atom) sec_mt_moment_atom.x_exciting_MT_moment_atom_index = n + 1 sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol = val[n][0] sec_mt_moment_atom.x_exciting_MT_moment_atom_value\",\n \"we can do is to assume that the first steps are the #\",\n \"r'(Atomic positions at this step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions at this step\\\\s*\\\\(([a-z]+)\\\\)'),\",\n \"you may not use this file except in compliance with the License. #\",\n \"polarized case # I cannot find an example bandstructure file # atom-resolved bandstructure\",\n \"== 'gamma' else start, '\\\\u0393' if end.lower() == 'gamma' else end]) elif key\",\n \"False) sec_method.x_exciting_xs_tddft_diagonal_xc_kernel = self.input_xml_parser.get( 'xs/tddft/kerndiag', False) sec_method.x_exciting_xs_tddft_lmax_alda = self.input_xml_parser.get( 'xs/tddft/lmaxalda', 3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment =\",\n \"import re import logging from nomad.units import ureg from nomad.parsing.file_parser import TextParser, Quantity,\",\n \"data = self.evalqp_parser.get('kpoints_eigenvalues') if data is None: return def get_data(key): if key ==\",\n \"sizes', None), 'x_exciting_gvector_total': (r'Total number of G\\\\-vectors', None), 'x_exciting_lmaxapw': (r' APW functions', None),\",\n \"= self.info_parser.get_number_of_spin_channels() if self._calculation_type == 'volume_optimization': sec_method.x_exciting_volume_optimization = True def parse_scc(self, section): sec_run\",\n \"parser = self.eigval_parser parser_function = self._parse_eigenvalues elif (name.startswith('FERMISURF') or name.startswith('FS')) and name.endswith('bxsf'): parser\",\n \"len(data), len(res[0]) // len(data))) if key == 'eigenvalues': res = res * ureg.hartree\",\n \"self.data_clathrate_parser.data: data = np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates = np.transpose( np.array(data[:3], dtype=float)) sec_system.x_exciting_clathrates_atom_labels = list(data[3]) else:\",\n \"return nspin = self.info_parser.get_number_of_spin_channels() def get_data(key): data = self.eigval_parser.get('eigenvalues_occupancies') # reshaping is not\",\n \"= self.info_parser.get_initialization_parameter('x_exciting_gmaxvr', 0) sec_gw.core_treatment = self.input_xml_parser.get( 'gw/coreflag', 'all') sec_gw.polarizability_number_of_empty_states = int( self.input_xml_parser.get('gw/nempty', 0))\",\n \"= self.info_gw_parser.get('direct_band_gap', None) if fundamental_band_gap is None: fundamental_band_gap = self.info_gw_parser.get('fundamental_band_gap', None) sec_gap =\",\n \"Quantity( 'atomic_positions', r'(imized atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'imized atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols',\",\n \"TODO I am not certain about the format for the spin polarized case\",\n \"self.dos_parser.logger = self.logger self.bandstructure_parser.logger = self.logger self.eigval_parser.logger = self.logger self.fermisurf_parser.logger = self.logger self.evalqp_parser.logger\",\n \"None else section.get('final') if final is None: # get it from last scf_iteration\",\n \"if forces is not None: sec_forces = sec_scc.m_create(Forces) sec_forces.total = ForcesEntry(value=forces) # scf\",\n \"check if format of files are really correct, i.e. columns are supposed #\",\n \"is None: return # volume optimizations volume_index = 1 while True: info_volume =\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"None: # get it from input.xml for f in input_file: self.input_xml_parser.mainfile = f\",\n \"'LOSS', 'SIGMA']: for ext in ['FXC', 'NLF_FXC']: data = get_data(quantity, ext) if not\",\n \"# inconsistency in the naming convention for xs input xml file sec_method =\",\n \"# different names for different versions of exciting self._energy_keys_mapping = { 'energy_total': ['Total\",\n \"sec_dos_values.atom_index = atom sec_dos_values.value = partialdos[lm][spin][atom] def _parse_bandstructure(self, sec_scc): # we need to\",\n \"sec_run.calculation[0] sec_scc.starting_calculation_ref = sec_scc_ref sec_scc.calculations_ref = [sec_scc_ref] # parse properties gw_info_files = self.get_exciting_files(gw_info_file)\",\n \"additionally, set threshold to global metainfo. This is killing me! if metainfo_name ==\",\n \"Scf(threshold_energy_change=threshold) xc_functional_names = self.info_parser.get_xc_functional_name() if not xc_functional_names: # get it from input.xml input_file\",\n \"np.transpose(data_q, axes=(2, 0, 1)) for j in range(len(data)): data[j].append(data_q[j]) return data for quantity\",\n \"options.sort() filenames = [os.path.join(self.info_parser.maindir, f) for f in options] else: filenames = [filename]\",\n \"gw_info_files = self.get_exciting_files(gw_info_file) if len(gw_info_files) > 1: self.logger.warn('Found multiple GW info files, will\",\n \"sec_method.x_exciting_xs_bse_xasspecies = self.input_xml_parser.get( 'xs/BSE/xasspecies', 0) sec_method.x_exciting_xs_bse_xas_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlxas', [0, 0]) if self.input_xml_parser.get('xs/tddft')\",\n \"if final is None: # get it from last scf_iteration or optimization_step final\",\n \"0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals = self.input_xml_parser.get( 'xs/tddft/nwacont', 0) sec_method.x_exciting_xs_tetra = self.input_xml_parser.get( 'xs/tetra/tetradf', False) def _parse_xs_bse(self):\",\n \"break if val is None: continue if key == 'x_exciting_section_MT_charge_atom': for n in\",\n \"// self.number_of_spin_channels return self._neigs_segment def parse(self, key): if self._results is None: self._results =\",\n \"'gw/scrcoul/scrtype', 'rpa') def parse_gw(self): sec_run = self.archive.run[-1] # two versions of gw info\",\n \"sec_scc.x_exciting_geometry_optimization_threshold_force = force_convergence[1] sec_scc = parse_configuration(structure_optimization) if sec_scc is None: return # volume\",\n \"# we get it by concatenating species symbols species = self.get('initialization', {}).get('species', [])\",\n \"= res class DOSXMLParser(XMLParser): def __init__(self, **kwargs): super().__init__(None) self._nspin_key = 'nspin' self._totaldos_key =\",\n \"= [f for f in options if f.endswith(file_ext)] options.sort() filenames = [os.path.join(self.info_parser.maindir, f)\",\n \"(r'Total number of k\\\\-points', None), 'x_exciting_rgkmax': (r'R\\\\^MT\\\\_min \\\\* \\\\|G\\\\+k\\\\|\\\\_max \\\\(rgkmax\\\\)', None), 'x_exciting_species_rtmin': (r'Species\",\n \"xs data', data=dict(file=quantity)) def _parse_xs_tddft(self): sec_run = self.archive.run[-1] fxctype = self.input_xml_parser.get('xs/tddft/fxctype', 'RPA') tetradf\",\n \"optimization structure_optimization = self.info_parser.get('structure_optimization', {}) for optimization_step in structure_optimization.get('optimization_step', []): sec_scc = parse_configuration(optimization_step)\",\n \"atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity(\",\n \"parse_epsilon(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_epsilon = len(data[0]) // n_components\",\n \"self.input_xml_parser.get('xs/tddft/fxctype', 'RPA') tetradf = self.input_xml_parser.get('xs/tetra/tetradf', None) nwacont = self.input_xml_parser.get('xs/tddft/nwacont', None) aresdf = self.input_xml_parser.get('xs/tddft/aresdf',\",\n \"self._parse_xs_tddft() def _parse_input_gw(self, sec_method): sec_gw = sec_method.m_create(GWMethod) sec_gw.type = 'G0W0' gmaxvr = self.info_parser.get_initialization_parameter('x_exciting_gmaxvr',\",\n \"if xc_functional is not None: sec_xc_functional.name = xc_functional.get('name_reference', [None, None])[0] sec_xc_functional.reference = xc_functional.get('name_reference',\",\n \"[0., 0.])[-1] sec_geometry_opt.input_force_maximum_tolerance = threshold_force def parse_method(self): sec_run = self.archive.run[-1] sec_method = sec_run.m_create(Method)\",\n \"len(data) // nspin data = np.reshape(data, (nspin, len(data) // nspin, 2)) data =\",\n \"1: self.logger.warn('Found multiple GW info files, will read only first!') self.info_gw_parser.mainfile = gw_info_files[0]\",\n \"also input parameters here if input_GW.xml does not exist self._quantities.append( Quantity( 'frequency_data', r'frequency\",\n \"data = np.transpose(self.data) self._distances = data[5][:int(max(data[1]))] return self._distances @property def number_of_spin_channels(self): if self._nspin\",\n \"26: ['GGA_C_PBE', 'GGA_X_WC'], 30: ['GGA_C_AM05', 'GGA_C_AM05'], 300: ['GGA_C_BGCP', 'GGA_X_PBE'], 406: ['HYB_GGA_XC_PBEH'], 408: ['HYB_GGA_XC_HSE03']}\",\n \"(n_components, n_excitons)) def parse_epsilon(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_epsilon =\",\n \"= dict(hartree=ureg.hartree) def init_parameters(self): self._ndos = None self._natoms = None self._nspin = None\",\n \"xc_functional_names: # get it from input.xml input_file = self.get_exciting_files('input.xml') for f in input_file:\",\n \"self._energy_unit = kwargs.get('energy_unit', None) self._units_mapping = dict(hartree=ureg.hartree) def init_parameters(self): self._ndos = None self._natoms\",\n \"self.data_xs_parser.data is None: continue data_q.append(self.data_xs_parser.data) if not data_q: continue data_q = np.transpose(data_q, axes=(2,\",\n \"files, will read only first!') self.info_gw_parser.mainfile = gw_info_files[0] fermi_energy = self.info_gw_parser.get('fermi_energy', None) if\",\n \"return self._partial_dos @property def energies(self): if self._energies is None: if self.total_dos is None:\",\n \"= '%s.%s' % (filename, target[1]) filename = os.path.join(self.info_parser.maindir, filename) if os.path.isfile(filename): return [filename]\",\n \"sec_dos_values = sec_dos.m_create(DosValues, Dos.atom_projected) sec_dos_values.m_kind = 'spherical' sec_dos_values.lm = lm_values[lm] sec_dos_values.spin = spin\",\n \"v in val if len(v) == 3], float)) return dict( number=np.array(val[0], dtype=int), values=val[1]\",\n \"= self.input_xml_parser.get( 'xs/tddft/kerndiag', False) sec_method.x_exciting_xs_tddft_lmax_alda = self.input_xml_parser.get( 'xs/tddft/lmaxalda', 3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment = self.input_xml_parser.get( 'xs/tddft/mdfqtype',\",\n \"= self.get('initialization', {}).get('species', []) labels = [] for specie in species: labels +=\",\n \"= None self._band_k_points = None @property def band_energies(self): if self._band_energies is None: if\",\n \"self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies = [] start = 0 for nkpts_segment in self.number_of_k_points_per_segment:\",\n \"dtype=float) for i in range(len(self.bands[n])): band_energies[i % self.number_of_spin_channels][i] = np.array( [e.attrib.get(self._energy_key) for e\",\n \"positions is None: return positions = np.array(positions) positions_format = positions_format if positions_format is\",\n \"(n_scf - len(quantity)) + quantity return quantity def get_xc_functional_name(self): # TODO expand list\",\n \"[0, 0, 0]) rgkmax = self.input_xml_parser.get('xs/screening/rgkmax', None) if rgkmax is None: rgkmax =\",\n \"= sec_run.m_create(Method) sec_method.basis_set.append(BasisSet(type='(L)APW+lo')) sec_dft = sec_method.m_create(DFT) sec_electronic = sec_method.m_create(Electronic) sec_electronic.method = 'DFT' smearing_kind_map\",\n \"read all!', data=dict(file=name)) for n in range(len(files)): parser.mainfile = files[n] parser_function(section) # free\",\n \"= self.get_exciting_files('%s_%s%s%s.OUT' % (quantity, file_ext, ext, fxctype)) data = [[], [], []] for\",\n \"None: continue data_q.append(self.data_xs_parser.data) if not data_q: continue data_q = np.transpose(data_q, axes=(2, 0, 1))\",\n \"for the spin polarized case # I cannot find an example bandstructure file\",\n \"nbands = int(val[0]) mesh = np.array(val[1].split(), dtype=int) origin = np.array(val[2].split(), dtype=float) vector =\",\n \"nspin, 2)) data = np.transpose(data, axes=(2, 0, 1)) sec_dos.energies = data[0][0] * ureg.hartree\",\n \"[] if bands: self._bands.append(bands) # add atom-resolved bands_atom = self.root.findall('./*/%s' % self._atom_key) for\",\n \"= dict() for v in val: v = v.split(':') if len(v) < 2:\",\n \"# get it from input.xml input_file = self.get_exciting_files('input.xml') for f in input_file: self.input_xml_parser.mainfile\",\n \"= self.fermisurf_parser.get('fermi_surface', [None])[0] if fermi_surface is None: return sec_fermisurface = sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters =\",\n \"'x_exciting_rgkmax': (r'R\\\\^MT\\\\_min \\\\* \\\\|G\\\\+k\\\\|\\\\_max \\\\(rgkmax\\\\)', None), 'x_exciting_species_rtmin': (r'Species with R\\\\^MT\\\\_min', None), 'x_exciting_gkmax': (r'Maximum\",\n \"energy: it is used to un-shift the band structure to # the original\",\n \"sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.value = band_energies[nb] + energy_fermi def parse_file(self, name, section): #\",\n \"count += 1 self._nkpts_segment.append(count) return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None:\",\n \"= int(val_l) ** 2 + int(val_m) + int(val_l) atom = i // (self.number_of_lm\",\n \"def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data = np.transpose(self.data) self._neigs_segment = int(max(data[0])) return\",\n \"'isotropic') sec_gw.screened_Coulomb = self.input_xml_parser.get( 'gw/scrcoul/scrtype', 'rpa') def parse_gw(self): sec_run = self.archive.run[-1] # two\",\n \"(quantity, file_ext, ext, fxctype)) data = [[], [], []] for i in range(len(qpoints)):\",\n \"[{}])[0].get('force_convergence', [0., 0.])[-1] sec_geometry_opt.input_force_maximum_tolerance = threshold_force def parse_method(self): sec_run = self.archive.run[-1] sec_method =\",\n \"continue if name == 'x_exciting_spin_treatment': sub_sec = sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment = val elif name\",\n \"= self.archive.run[-1] # TODO read from xml file def get_files(name): bse_types = ['IP',\",\n \"energy_fermi def parse_file(self, name, section): # TODO add support for info.xml, wannier.out if\",\n \"key_unit[0], repeats=False, unit=key_unit[1])) self._convergence_keys_mapping = { 'x_exciting_effective_potential_convergence': ( r'RMS change in effective potential\",\n \"(name, bse_type, scr_type)) bse_files.append(files) return bse_files def get_data(files): data = [] for f\",\n \"len(self.get('structure_optimization', {}).get('optimization_step', [])) def get_number_of_spin_channels(self): spin_treatment = self.get('initialization', {}).get( 'x_exciting_spin_treatment', 'spin-unpolarised') n_spin =\",\n \"def str_to_band_parameters(val_in): val = val_in.strip().split('\\\\n') nbands = int(val[0]) mesh = np.array(val[1].split(), dtype=int) origin\",\n \"'moment_contributions', r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False)] self._miscellaneous_keys_mapping = { 'x_exciting_gap': (r'Estimated fundamental gap',\",\n \"self._nkpts_segment = None @property def band_energies(self): if self._band_energies is None: data = np.transpose(self.data)\",\n \"self._neigs_segment is None: data = np.transpose(self.data)[0] self._neigs_segment = len(np.where(data == data[0])[0]) return self._neigs_segment\",\n \"optimization step\\\\s*:\\\\s*[\\\\d\\\\.]+ seconds))', sub_parser=TextParser(quantities=optimization_quantities), repeats=True), Quantity( 'final', r'Force convergence target achieved([\\\\s\\\\S]+?Opt)', sub_parser=TextParser(quantities=scf_quantities), repeats=False),\",\n \"= self.dos_parser.number_of_dos sec_dos.energies = self.dos_parser.energies + energy_fermi volume = self.info_parser.get_unit_cell_volume() totaldos = self.dos_parser.get('totaldos')\",\n \"= sec_dft.m_create(XCFunctional) for name in xc_functional_names: if name is None: continue if '_X_'\",\n \"if occs[0] == 1. else 1 data = dict() data['occupancies'] = np.reshape(occs, (nspin,\",\n \"return self._natoms @property def number_of_dos(self): if self._ndos is None: total_dos = self.root.find('./%s/%s' %\",\n \"band_energies(self): if self._band_energies is None: if self.data is None: return data = np.transpose(self.data)\",\n \"self._band_energies = None self._neigs_segment = None self._nkpts_segment = None @property def band_energies(self): if\",\n \"species symbols species = self.get('initialization', {}).get('species', []) labels = [] for specie in\",\n \"(n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies = np.reshape( data[2], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_oscillator_strength\",\n \") scf_quantities = [ Quantity( 'energy_total', r'[Tt]*otal energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=ureg.hartree), Quantity( 'energy_contributions',\",\n \"in self._energy_keys_mapping.items(): val = None for name in names: val = energy_contributions.get(name, None)\",\n \"seems to be two versions, input_gw.xml and input-gw.xml for f in ['input_gw.xml', 'input-gw.xml',\",\n \"val = val[0] if len(val) == 1 else val return np.array(val, dtype=float) def\",\n \"None: axis = self.root.find('./axis') if axis is None: return self._energy_unit = self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1)\",\n \"(r'k\\\\-point grid', None), 'x_exciting_kpoint_offset': (r'k\\\\-point offset', None), 'x_exciting_number_kpoints': (r'Total number of k\\\\-points', None),\",\n \"np.transpose(np.vstack(data)) n_sigma = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_sigma_energies = np.reshape( data[0], (n_components, n_sigma)) *\",\n \"version=self.info_parser.get('program_version', '').strip()) # method goes first since reference needed for sec_scc self.parse_method() self.parse_configurations()\",\n \"positions=None, positions_format=None): positions = positions if positions is not None else section.get('positions') if\",\n \"if section.get('energy_total') is not None else section.get('final') if final is None: # get\",\n \"See the License for the specific language governing permissions and # limitations under\",\n \"None for name in names: val = charge_contributions.get(name, None) if val is not\",\n \"= specie.get('muffin_tin_radius') positions_format = specie.get('positions_format') sec_atoms_group.x_exciting_atom_position_format = positions_format positions = specie.get('positions') positions =\",\n \"None self._vertices = None self._distances = None self._species = None @property def distances(self):\",\n \"[float(point.attrib.get(self._energy_key)) for point in self.total_dos[0]]) if self.energy_unit is not None: self._energies = self._energies\",\n \"Get fermi energy: it is used to un-shift the band structure to #\",\n \"= logger if logger is not None else logging self._calculation_type = None self.init_parser()\",\n \"energies are reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is None: return energy_fermi =\",\n \"*(\\\\d+)', dtype=np.int32), Quantity('symbol', r'\\\\((\\\\w+)\\\\)'), Quantity('file', r'parameters loaded from *: *(.+)'), Quantity('name', r'name *:\",\n \"quantity == 'SIGMA' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_sigma_local_field = data[1:3] elif quantity ==\",\n \"data = np.transpose(np.vstack(data)) n_loss = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_loss_energies = np.reshape( data[0], (n_components,\",\n \"get_xc_functional_name(self): # TODO expand list to include other xcf xc_functional_map = { 2:\",\n \"structure from one of the files: bs_files = ['bandstructure-qp.dat', 'BAND-QP.OUT'] for fname in\",\n \"volume.to('m**3').magnitude for spin in range(len(totaldos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value\",\n \"@property def n_optimization_steps(self): return len(self.get('structure_optimization', {}).get('optimization_step', [])) def get_number_of_spin_channels(self): spin_treatment = self.get('initialization', {}).get(\",\n \"repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr) ] self._quantities.append(Quantity( 'groundstate', r'(?:Self\\\\-consistent loop started|Groundstate module\",\n \"total energy\\\\s*\\\\(target\\\\)', ureg.hartree), 'x_exciting_charge_convergence': ( r'Charge distance\\\\s*\\\\(target\\\\)', ureg.elementary_charge), 'x_exciting_IBS_force_convergence': ( r'Abs\\\\. change in\",\n \"sec_scf_iteration) return sec_scc def parse_system(self, section): sec_run = self.archive.run[-1] positions = self.info_parser.get_atom_positions(section.get('atomic_positions', {}))\",\n \"not None: sec_scc.energy = Energy(fermi=fermi_energy) gw_files = ['EVALQP.DAT', 'EVALQP.TXT', 'TDOS-QP.OUT'] # Parse GW\",\n \"= (energy_fermi.magnitude * ureg.joule).to('hartree') sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = self.dos_parser.number_of_dos sec_dos.energies =\",\n \"stopped', sub_parser=TextParser(quantities=[ Quantity( 'optimization_step', r'(Optimization step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n *\\\\n\\\\-{10}|Time spent in this optimization step\\\\s*:\\\\s*[\\\\d\\\\.]+ seconds))',\",\n \"# add data to scc # TODO add support for more output files\",\n \"positions = np.vstack([s.get('positions') for s in species]) if positions is None: return positions\",\n \"{ 'x_exciting_effective_potential_convergence': ( r'RMS change in effective potential \\\\(target\\\\)', ureg.hartree), 'x_exciting_energy_convergence': ( r'Absolute\",\n \"r'(Atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity(\",\n \"= xc_functional.get('name_reference', [None, None])[0] sec_xc_functional.reference = xc_functional.get('name_reference', [None, None])[1] sec_electronic.n_spin_channels = self.info_parser.get_number_of_spin_channels() if\",\n \"nkpts_segment = self.bandstructure_dat_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb]\",\n \"return sec_system = sec_run.m_create(System) sec_atoms = sec_system.m_create(Atoms) sec_atoms.positions = positions sec_atoms.labels = atom_labels\",\n \"sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.energies = band_energies[nb] + energy_fermi def _parse_band_out(self, sec_scc): self.band_out_parser._nspin =\",\n \"if nwacont else None, 'NAR' if not aresdf else None] file_ext = '_'.join([e\",\n \"for v in x.split(':')]), Quantity( 'parameters', r'\\\\n *(.+?:.+)', repeats=True, str_operation=lambda x: [v.strip() for\",\n \"return sec_system for name in self.info_parser._system_keys_mapping.keys(): val = self.info_parser.get_initialization_parameter(name) if val is None:\",\n \"bandstructure are added as separate section_k_band res = [] for n in range(len(self.bands)):\",\n \"gw_files = ['EVALQP.DAT', 'EVALQP.TXT', 'TDOS-QP.OUT'] # Parse GW band structure from one of\",\n \"elif key == 'Znk': return np.array([d[1].get(key, None) for d in data]) else: energy\",\n \"sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol = val[n][0] sec_mt_charge_atom.x_exciting_MT_charge_atom_value = val[n][1] sec_charges = msection.m_create(Charges) sec_charges.value = [ val[n][1].magnitude\",\n \"end return self._band_energies @property def band_k_points(self): if self._band_k_points is None: data = np.transpose(self.data)\",\n \"reference.get('scf_iteration', [{}])[-1].get( name, [None, None])[-1] def get_scf_quantity(self, name): n_scf = len(self.get('energy_total_scf_iteration', [])) quantity\",\n \"self._totaldos_key = 'totaldos' self._partialdos_key = 'partialdos' self._diagram_key = 'diagram' self._l_key = 'l' self._m_key\",\n \"self._band_k_points = [] start = 0 for nkpts_segment in self.number_of_k_points_per_segment: end = start\",\n \"sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.endpoints_labels = band_seg_labels[nb] sec_k_band_segment.energies\",\n \"'band_k_points': res = [] for i in range(len(self.number_of_k_points_per_segment)): start = np.array( self.vertices[i].attrib.get(self._coord_key).split(), dtype=float)\",\n \"'single_point' structure_optimization = self.info_parser.get('structure_optimization') if structure_optimization is not None: sec_worfklow.type = 'geometry_optimization' sec_geometry_opt\",\n \"li in l_list: self._nlm += 2 * li + 1 return self._nlm @property\",\n \"'xs/BSE/lmaxdielt', 14) rgkmax = self.input_xml_parser.get('xs/BSE/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax',\",\n \"# TODO Read also input parameters here if input_GW.xml does not exist self._quantities.append(\",\n \"for v in val_in.strip().split('\\\\n')] val = val[0] if len(val) == 1 else val\",\n \"potential energy'], 'x_exciting_coulomb_potential_energy': ['Coulomb potential energy', 'Coulomb potential'], 'energy_xc_potential': ['xc potential energy', 'xc\",\n \"continue if '_X_' in name: sec_xc_functional.exchange.append(Functional(name=name)) elif '_C_' in name: sec_xc_functional.correlation.append(Functional(name=name)) elif 'HYB'\",\n \"default.split('.')[-1] mainfile_base = mainfile.rsplit('.', 1)[0].replace('INFO', '') options = [ f for f in\",\n \"= None self._nkpts_segment = None self._neigs_segment = None self._vertices = None self._distances =\",\n \"= self.input_xml_parser.get( 'gw/scrcoul/sciavtype', 'isotropic') sec_gw.screened_Coulomb = self.input_xml_parser.get( 'gw/scrcoul/scrtype', 'rpa') def parse_gw(self): sec_run =\",\n \"case # I cannot find an example bandstructure file # atom-resolved bandstructure are\",\n \"] self._quantities.append(Quantity( 'groundstate', r'(?:Self\\\\-consistent loop started|Groundstate module started)([\\\\s\\\\S]+?)Groundstate module stopped', sub_parser=TextParser(quantities=module_quantities), repeats=False)) optimization_quantities\",\n \"= 'spherical' sec_dos_values.lm = lm_values[lm] sec_dos_values.spin = spin sec_dos_values.atom_index = atom sec_dos_values.value =\",\n \"+ nkpts_segment band_energy = np.array([np.transpose(band)[start:end] for band in bands]) if self._energy_unit: band_energy =\",\n \"['Core-electron kinetic energy', 'core electron kinetic'], 'x_exciting_dft_d2_dispersion_correction': ['DFT-D2 dispersion correction'] } self._electron_charge_keys_mapping =\",\n \"sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_dat_parser.band_k_points nkpts_segment = self.bandstructure_dat_parser.number_of_k_points_per_segment\",\n \"unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic mass *: *({re_float})', dtype=np.float64, unit=ureg.electron_mass), Quantity('muffin_tin_radius', rf'muffin-tin radius *: *({re_float})',\",\n \"threshold_force def parse_method(self): sec_run = self.archive.run[-1] sec_method = sec_run.m_create(Method) sec_method.basis_set.append(BasisSet(type='(L)APW+lo')) sec_dft = sec_method.m_create(DFT)\",\n \"quantity in ['EXCITON', 'EPSILON', 'SIGMA', 'LOSS']: files = get_files(quantity) for i in range(len(files)):\",\n \"{keys[i]: eigs[i] for i in range(len(keys))} return [kpts, eigs] self._quantities.append( Quantity( 'kpoints_eigenvalues', r'\\\\s*k\\\\-point\",\n \"'k_points': return np.array([d[0][:3] for d in data]) elif key == 'Znk': return np.array([d[1].get(key,\",\n \"[float(vi) for vi in v[1].split()] v[1] = v[1][0] if len(v[1]) == 1 else\",\n \"energy, moment, charge contributions parse_scf(final, sec_scc) # forces forces = section.get('forces') if forces\",\n \"sec_scc.energy.fermi if energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') # TODO\",\n \"> 1: return sec_system for name in self.info_parser._system_keys_mapping.keys(): val = self.info_parser.get_initialization_parameter(name) if val\",\n \"'energy_sum_eigenvalues': ['Sum of eigenvalues', 'sum of eigenvalues'], 'x_exciting_effective_potential_energy': ['Effective potential energy'], 'x_exciting_coulomb_potential_energy': ['Coulomb\",\n \"( System, Atoms ) from nomad.datamodel.metainfo.simulation.calculation import ( Calculation, Dos, DosValues, BandStructure, BandEnergies,\",\n \"fermi_energy is not None: sec_scc.energy = Energy(fermi=fermi_energy) gw_files = ['EVALQP.DAT', 'EVALQP.TXT', 'TDOS-QP.OUT'] #\",\n \"len(number) sec_method.gw.frequency_number = number sec_method.gw.frequency_values = frequency_data.get('values') sec_method.gw.frequency_weights = frequency_data.get('weights') fundamental_band_gap = self.info_gw_parser.get('direct_band_gap',\",\n \"= parser.fermisurf_parser.quantities self.evalqp_parser.quantities = parser.evalqp_parser.quantities self.info_gw_parser.quantities = parser.info_gw_parser.quantities def parse(self, filepath, archive, logger):\",\n \"except Exception: self.logger.warn('Error setting metainfo.') # species species = self.info_parser.get_initialization_parameter('species', []) for specie\",\n \"species = self.input_xml_parser.get('structure/species/speciesfile') if positions is None or lattice_vectors is None or species\",\n \"else: filenames = [filename] filenames = [f for f in filenames if os.access(f,\",\n \"Quantity( 'direct_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'fundamental_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) )\",\n \"*(.+)'), Quantity('nuclear_charge', rf'nuclear charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('electronic_charge', rf'electronic charge *: *({re_float})',\",\n \"name in self.info_parser._miscellaneous_keys_mapping.keys(): val = iteration.get(name) if val is None: continue if name\",\n \"self._total_dos = self.root.findall('./%s/%s' % (self._totaldos_key, self._diagram_key)) return self._total_dos @property def partial_dos(self): if self._partial_dos\",\n \"= 'l' self._m_key = 'm' self._energy_key = 'e' self._dos_key = 'dos' self._unit_key =\",\n \"is problematic sometimes res = np.hstack([np.reshape(v[key], (nspin, np.size(v[key]) // nspin)) for v in\",\n \"self.input_xml_parser.get( 'xs/tddft/aresfxc', True) sec_method.x_exciting_xs_tddft_drude = self.input_xml_parser.get( 'xs/tddft/drude', [0., 0.]) sec_method.x_exciting_xs_tddft_split_parameter = self.input_xml_parser.get( 'xs/tddft/fxcbsesplit',\",\n \"v = v.strip().split(':') if len(v) < 2: continue elif v[0].startswith('species'): species = re.search(re_symbol,\",\n \"are. What is the fourth column in epsilon which is not parsed? sccs\",\n \"range(len(dos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value = dos[spin] # TODO\",\n \"sec_method.x_exciting_xs_bse_sciavtype = self.input_xml_parser.get( 'xs/BSE/sciavtype', 'spherical') sec_method.x_exciting_xs_bse_xas = self.input_xml_parser.get( 'xs/BSE/xas', False) sec_method.x_exciting_xs_bse_number_of_bands = self.input_xml_parser.get(\",\n \"energy sec_smearing.width = smearing_width.magnitude for name in self.info_parser._convergence_keys_mapping.keys(): threshold = self.info_parser.get_scf_threshold(name) if threshold\",\n \"self.info_parser.get_atom_positions(section.get('atomic_positions', {})) lattice_vectors = self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels = self.info_parser.get_atom_labels(section.get('atomic_positions', {})) input_file = self.get_exciting_files('input.xml') if\",\n \"n in range(len(val))] * val[0][1].units sec_charges.total = charge_contributions.get('total charge') elif key == 'charge_total':\",\n \"sec_method) xc_functional_name = ' '.join(self.info_parser.get_xc_functional_name()) sec_method.gw.starting_point = xc_functional_name sec_scc = sec_run.m_create(Calculation) sec_scc.method_ref =\",\n \"iteration.get('energy_contributions', {}) for key, names in self._energy_keys_mapping.items(): val = None for name in\",\n \"name.endswith('OUT'): parser = self.band_out_parser parser_function = self._parse_band_out elif name.startswith('input') and name.endswith('xml'): parser =\",\n \"def init_parser(self): self.info_parser.mainfile = self.filepath self.info_parser.logger = self.logger self.dos_parser.logger = self.logger self.bandstructure_parser.logger =\",\n \"BandstructureXMLParser(XMLParser): def __init__(self, **kwargs): # TODO make a parent class for dos and\",\n \"ANY KIND, either express or implied. # See the License for the specific\",\n \"killing me! if metainfo_name == 'x_exciting_scf_threshold_energy_change': sec_method.scf = Scf(threshold_energy_change=threshold) xc_functional_names = self.info_parser.get_xc_functional_name() if\",\n \"i in range(len(qpoints)): data_q = [] files_q = [f for f in files\",\n \"sec_run.m_create(Calculation) def parse_scf(iteration, msection): energy_total = iteration.get('energy_total') sec_energy = msection.m_create(Energy) if energy_total is\",\n \"spent in this optimization step\\\\s*:\\\\s*[\\\\d\\\\.]+ seconds))', sub_parser=TextParser(quantities=optimization_quantities), repeats=True), Quantity( 'final', r'Force convergence target\",\n \"= lm_values[lm] sec_dos_values.spin = spin sec_dos_values.atom_index = atom sec_dos_values.value = partialdos[lm][spin][atom] def _parse_bandstructure(self,\",\n \"in name: sec_xc_functional.correlation.append(Functional(name=name)) elif 'HYB' in name: sec_xc_functional.hybrid.append(Functional(name=name)) else: sec_xc_functional.contributions.append(Functional(name=name)) if not xc_functional_names:\",\n \"self.energy_unit) elif key == 'energies': return self.energies else: res = None self._results[key] =\",\n \"self.dos_parser.get('totaldos', None) is None: return # Get fermi energy: it is used to\",\n \"self.file_exists(fname): exciting_files.append(fname) break # Parse DFT band structure from one of the files\",\n \"tetradf else None, 'AC' if nwacont else None, 'NAR' if not aresdf else\",\n \"except Exception: self.logger.error('Error setting xs data', data=dict(file=quantity)) def _parse_xs_tddft(self): sec_run = self.archive.run[-1] fxctype\",\n \"= spin sec_dos_values.value = totaldos[spin] partialdos = self.dos_parser.get('partialdos') if partialdos is None: return\",\n \"self.info_gw_parser = GWInfoParser() self.input_xml_parser = XMLParser() self.data_xs_parser = DataTextParser() self.data_clathrate_parser = DataTextParser(dtype=str) #\",\n \"'band' self._atom_key = 'atom' self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def\",\n \"steps are the # ones with the missing quantity if len(quantity) < n_scf:\",\n \"] self._quantities.append(Quantity( 'structure_optimization', r'Structure\\\\-optimization module started([\\\\s\\\\S]+?)Structure\\\\-optimization module stopped', sub_parser=TextParser(quantities=[ Quantity( 'optimization_step', r'(Optimization step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n\",\n \"= sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.energies = band_energies[nb] + energy_fermi\",\n \"self._quantities.append(Quantity( 'structure_optimization', r'Structure\\\\-optimization module started([\\\\s\\\\S]+?)Structure\\\\-optimization module stopped', sub_parser=TextParser(quantities=[ Quantity( 'optimization_step', r'(Optimization step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n *\\\\n\\\\-{10}|Time\",\n \"is # determined from occupancies which is problematic sometimes res = np.hstack([np.reshape(v[key], (nspin,\",\n \"data]) else: energy = np.array([d[1].get(key, None) for d in data]) if None in\",\n \"missing quantity if len(quantity) < n_scf: quantity = [None] * (n_scf - len(quantity))\",\n \"is None: if self.total_dos is None: return self._energies = np.array( [float(point.attrib.get(self._energy_key)) for point\",\n \"scf steps dont have the quantity # the only thing that we can\",\n \"energy_fermi = sec_scc.energy.fermi if energy_fermi is None: continue energy_fermi = energy_fermi.to(\\\"hartree\\\") sec_k_band =\",\n \"self._energies * self.energy_unit return self._energies def _get_dos(self, diagram): dos = np.array( [point.attrib.get(self._dos_key) for\",\n \"are reported. energy_fermi = 0.0 * ureg.hartree if sec_scc.energy is not None: energy_fermi\",\n \"[filename] filename = os.path.join(self.info_parser.maindir, default) if not os.path.isfile(filename): file_ext = default.split('.')[-1] mainfile_base =\",\n \"= val[-2] nspin = 2 if occs[0] == 1. else 1 data =\",\n \"def _parse_fermisurface(self, sec_scc): fermi_surface = self.fermisurf_parser.get('fermi_surface', [None])[0] if fermi_surface is None: return sec_fermisurface\",\n \"'xs/BSE/xas', False) sec_method.x_exciting_xs_bse_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlbse', [0, 0, 0, 0]) if sec_method.x_exciting_xs_bse_xas: sec_method.x_exciting_xs_bse_xasatom\",\n \"target[1]) filename = os.path.join(self.info_parser.maindir, filename) if os.path.isfile(filename): return [filename] filename = os.path.join(self.info_parser.maindir, default)\",\n \"is not None: sec_method.x_exciting_xs_xstype = xstype sec_method.x_exciting_electronic_structure_method = xstype sec_method.x_exciting_xs_broadening = self.input_xml_parser.get( 'xs/broad',\",\n \"= sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment = val elif name == 'x_exciting_species_rtmin': setattr(sec_system, name, ' '.join([str(v)\",\n \"sec_electronic = sec_method.m_create(Electronic) sec_electronic.method = 'DFT' smearing_kind_map = { 'Gaussian': 'gaussian', 'Methfessel-Paxton': 'methfessel-paxton',\",\n \"val else: setattr(msection, name, val) # energy, moment, charge contributions parse_scf(final, sec_scc) #\",\n \"None: self._nspin = np.shape(np.transpose(self.data)[1:])[0] return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None:\",\n \"is overwritten when setting mainfile self.bandstructure_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_parser.get('band_energies', []) for\",\n \"and input-gw.xml for f in ['input_gw.xml', 'input-gw.xml', 'input.xml']: self.parse_file(f, sec_method) xc_functional_name = '\",\n \"def vertices(self): if self._vertices is None: self._vertices = self.root.findall('./%s' % self._vertex_key) return self._vertices\",\n \"None) self._units_mapping = dict(hartree=ureg.hartree) def init_parameters(self): self._ndos = None self._natoms = None self._nspin\",\n \"support for more output files and properties exciting_files = ['EIGVAL.OUT', 'FERMISURF.bxsf', 'FS.bxsf'] #\",\n \"sec_atoms_group = sec_system.m_create(x_exciting_section_atoms_group) sec_atoms_group.x_exciting_geometry_atom_labels = specie.get('symbol') sec_atoms_group.x_exciting_geometry_atom_number = str(specie.get('number')) sec_atoms_group.x_exciting_muffin_tin_points = specie.get('radial_points') sec_atoms_group.x_exciting_muffin_tin_radius\",\n \"energy', 'correlation'], 'x_exciting_correlation_energy': ['Correlation energy', 'correlation'], 'energy_sum_eigenvalues': ['Sum of eigenvalues', 'sum of eigenvalues'],\",\n \"sec_scc.method_ref = sec_method if sec_run.system: sec_scc.system_ref = sec_run.system[-1] sec_scc_ref = sec_run.calculation[0] sec_scc.starting_calculation_ref =\",\n \"band structure to # the original scale in which also other energies are\",\n \"for info.xml, wannier.out if name.startswith('dos') and name.endswith('xml'): parser = self.dos_parser parser_function = self._parse_dos\",\n \"super().__init__(None) self._nspin_key = 'nspin' self._totaldos_key = 'totaldos' self._partialdos_key = 'partialdos' self._diagram_key = 'diagram'\",\n \"Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi nkpts_segment = self.band_out_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment =\",\n \"@property def number_of_spin_channels(self): if self._nspin is None: self._nspin = np.shape(np.transpose(self.data))[0] - 6 return\",\n \"'x_exciting_interstitial_moment': ['interstitial'], 'x_exciting_total_MT_moment': ['total moment in muffin-tins'], 'x_exciting_total_moment': ['total moment'], 'x_exciting_section_MT_moment_atom': ['atom_resolved'] }\",\n \"self._quantities.append( Quantity( 'fermi_energy', r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi [Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s', unit=ureg.hartree, repeats=False) ) self._quantities.append( Quantity( 'direct_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s',\",\n \"None) if fermi_energy is not None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface = fermi_energy sec_fermisurface.x_exciting_values_fermi_surface = fermi_surface def\",\n \"n in range(len(self.bands)): res_n = [] start = 0 band_energies = np.zeros(( self.number_of_spin_channels,\",\n \"if band_energies is None: return # Get fermi energy: it is used to\",\n \"= frequency_data.get('number') sec_method.gw.number_of_frequencies = len(number) sec_method.gw.frequency_number = number sec_method.gw.frequency_values = frequency_data.get('values') sec_method.gw.frequency_weights =\",\n \"'%s_BSE%s_SCR%s.OUT' % (name, bse_type, scr_type)) bse_files.append(files) return bse_files def get_data(files): data = []\",\n \"return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment = [] count\",\n \"( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies = [] start = 0 for nkpts_segment in\",\n \"energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = self.dos_parser.number_of_dos sec_dos.energies\",\n \"are reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is None: return energy_fermi = (energy_fermi.magnitude\",\n \"assume that the first steps are the # ones with the missing quantity\",\n \"self.number_of_atoms, self.number_of_dos)) for i in range(len(self.partial_dos)): spin = self.partial_dos[i].attrib.get(self._nspin_key, None) if spin is\",\n \"origin = np.array(val[2].split(), dtype=float) vector = np.array([v.split() for v in val[3:6]], dtype=float) return\",\n \"is None: partial_dos = self.root.findall('./%s' % self._partialdos_key) self._natoms = len(partial_dos) return self._natoms @property\",\n \"self.distances[i] == self.distances[i - 1]: self._nkpts_segment .append(count) count = 1 else: count +=\",\n \"data[1:3] elif quantity == 'LOSS' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_loss_function_local_field = data[1] elif\",\n \"optimization_step.get('step') is not None: sec_scc.x_exciting_geometry_optimization_step = optimization_step.get('step') force_convergence = optimization_step.get('force_convergence') if force_convergence is\",\n \"self._calculation_type = 'gw' gw_info_file = f break if not self._calculation_type == 'gw': return\",\n \"init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'fermi_energy', r'Fermi Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*', unit=ureg.hartree, repeats=False)) def str_to_band_parameters(val_in):\",\n \"repeats=False, dtype=float), Quantity( 'energy_total', r'Total energy at this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', unit=ureg.hartree, repeats=False, dtype=float),\",\n \"moment_contributions.get(name, None) if val is not None: break if val is None: continue\",\n \"of local\\\\-orbitals', None)} self._method_keys_mapping = { 'smearing_kind': ('Smearing scheme', None), 'smearing_width': ('Smearing width',\",\n \"sec_scc = sccs[i] if sec_scc is None: # This is the case when\",\n \"# structure optimization structure_optimization = self.info_parser.get('structure_optimization', {}) for optimization_step in structure_optimization.get('optimization_step', []): sec_scc\",\n \"= pwm * gmb * gmaxvr sec_gw.bare_coulomb_cutofftype = self.input_xml_parser.get( 'gw/barecoul/cutofftype', 'none') sec_gw.screened_coulomb_volume_average =\",\n \"eigs = val[-2] nspin = 2 if occs[0] == 1. else 1 data\",\n \"is None: dist = np.transpose(self.data)[0] n_k_points = np.where(dist == dist[0])[0][1] self._distances = dist[:n_k_points]\",\n \"= None self._energies = None self._total_dos = None self._partial_dos = None @property def\",\n \"None: if self.data is None: return data = np.transpose(self.data) n_kpoints = int(max(data[1])) bands\",\n \"section: return sec_scc = self.parse_scc(section) if sec_scc is None: return sec_system = self.parse_system(section)\",\n \"str(specie.get('number')) sec_atoms_group.x_exciting_muffin_tin_points = specie.get('radial_points') sec_atoms_group.x_exciting_muffin_tin_radius = specie.get('muffin_tin_radius') positions_format = specie.get('positions_format') sec_atoms_group.x_exciting_atom_position_format = positions_format\",\n \"= val[-1] eigs = val[-2] nspin = 2 if occs[0] == 1. else\",\n \"dtype=float, unit=ureg.hartree), Quantity( 'energy_contributions', r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n *(?:DOS|Density)', str_operation=str_to_energy_dict, repeats=False, convert=False), Quantity( 'x_exciting_dos_fermi', r'DOS at\",\n \"number :([\\\\s\\\\S]+?)(?:\\\\n *\\\\n\\\\+{10}|\\\\+\\\\-{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=True), Quantity( 'final', r'(?:Convergence targets achieved\\\\. Performing final SCF\",\n \"'lattice_vectors', r'Lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=ureg.bohr, repeats=False, convert=False), Quantity( 'lattice_vectors_reciprocal', r'Reciprocal lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array,\",\n \"writing, software # distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"gw_files: self.parse_file(f, sec_scc) frequency_data = self.info_gw_parser.get('frequency_data', None) if frequency_data is not None: number\",\n \"properties def str_to_quantity_tolerances(val_in): return val_in.strip().replace('(', '').replace(')', '').split() def str_to_energy_dict(val_in): val = val_in.strip().split('\\\\n') energies\",\n \"self.get_exciting_files('input.xml') for f in input_file: self.input_xml_parser.mainfile = f correlation = self.input_xml_parser.get('libxc/correlation', None) xc_functional_names.append(correlation)\",\n \"= self.get('%s_scf_iteration' % name) if quantity is None: return # this is really\",\n \"if force_convergence is not None: sec_scc.x_exciting_maximum_force_magnitude = force_convergence[0] sec_scc.x_exciting_geometry_optimization_threshold_force = force_convergence[1] sec_scc =\",\n \"are the # ones with the missing quantity if len(quantity) < n_scf: quantity\",\n \"not certain about the format for the spin polarized case # I cannot\",\n \"* (n_scf - len(quantity)) + quantity return quantity def get_xc_functional_name(self): # TODO expand\",\n \"smearing_kind[0] smearing_kind = smearing_kind_map[smearing_kind] sec_smearing.kind = smearing_kind smearing_width = self.info_parser.get_initialization_parameter('smearing_width') if smearing_width is\",\n \"= band_parameters[0] sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface = np.product(band_parameters[1]) sec_fermisurface.x_exciting_grid_fermi_surface = band_parameters[1] sec_fermisurface.x_exciting_origin_fermi_surface = band_parameters[2] sec_fermisurface.x_exciting_vectors_fermi_surface =\",\n \"parser.fermisurf_parser.quantities self.evalqp_parser.quantities = parser.evalqp_parser.quantities self.info_gw_parser.quantities = parser.info_gw_parser.quantities def parse(self, filepath, archive, logger): self.filepath\",\n \"sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals = self.input_xml_parser.get( 'xs/tddft/nwacont', 0) sec_method.x_exciting_xs_tetra = self.input_xml_parser.get( 'xs/tetra/tetradf', False) def _parse_xs_bse(self): sec_run\",\n \"Quantity( 'final', r'(?:Convergence targets achieved\\\\. Performing final SCF iteration|Reached self-consistent loops maximum)([\\\\s\\\\S]+?)(\\\\n *\\\\n\\\\+{10})',\",\n \"r'Total energy at this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', unit=ureg.hartree, repeats=False, dtype=float), Quantity( 'time', r'Time spent\",\n \"the naming convention for xs input xml file sec_method = sec_run.m_create(Method) sec_method_ref =\",\n \"continue if quantity == 'EPSILON' and ext == 'FXC': sec_scc = sec_run.m_create(Calculation) sec_scc.x_exciting_xs_tddft_number_of_epsilon_values\",\n \"None: species = v[0][2] atom_resolved.append(((species, v[1] * unit))) else: vi = [float(vii) for\",\n \"v[0].split() v[1] = [float(vi) for vi in v[1].split()] v[1] = v[1][0] if len(v[1])\",\n \"= sec_method_ref sec_method.methods_ref = [sec_method_ref] self.parse_file('input.xml', sec_method) # parse properties input_file = self.get_exciting_files('input.xml')\",\n \"if quantity == 'EPSILON' and ext == 'FXC': sec_scc = sec_run.m_create(Calculation) sec_scc.x_exciting_xs_tddft_number_of_epsilon_values =\",\n \"val_in.split('\\\\n')] val = np.transpose(np.array([v for v in val if len(v) == 3], float))\",\n \"None: sec_gap.value_fundamental = fundamental_band_gap optical_band_gap = self.info_gw_parser.get('optical_band_gap', None) if optical_band_gap is not None:\",\n \"# energy dos_up dos_down nspin = self.info_parser.get_number_of_spin_channels() sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies =\",\n \"class ExcitingFermiSurfaceBxsfParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'fermi_energy',\",\n \"sec_method.x_exciting_xs_bse_xas_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlxas', [0, 0]) if self.input_xml_parser.get('xs/tddft') is not None: sec_method.x_exciting_xs_tddft_analytic_continuation =\",\n \"= ForcesEntry(value=forces) # scf iterations scf_iterations = section.get('scf_iteration', []) for scf_iteration in scf_iterations:\",\n \"= (i % (self.number_of_spin_channels * self.number_of_lm)) // self.number_of_lm else: spin = int(spin) -\",\n \"1 if spin_treatment.lower() == 'spin-unpolarised' else 2 return n_spin def get_unit_cell_volume(self): return self.get('initialization',\",\n \"band_energy = np.array([ np.transpose(energy)[start:end] for energy in band_energies]) if self._energy_unit is not None:\",\n \"= sec_method.m_create(DFT) sec_electronic = sec_method.m_create(Electronic) sec_electronic.method = 'DFT' smearing_kind_map = { 'Gaussian': 'gaussian',\",\n \"self._bands = None self._vertices = None self._distances = None self._species = None @property\",\n \"** 3), 'x_exciting_number_of_atoms': ('Total number of atoms per unit cell', None), 'x_exciting_spin_treatment': ('Spin\",\n \"if not isinstance(smearing_kind, str): smearing_kind = smearing_kind[0] smearing_kind = smearing_kind_map[smearing_kind] sec_smearing.kind = smearing_kind\",\n \"self.number_of_dos)) for i in range(len(self.partial_dos)): spin = self.partial_dos[i].attrib.get(self._nspin_key, None) if spin is None:\",\n \"'total' in key: if not self.total_dos: return res = np.zeros((self.number_of_spin_channels, self.number_of_dos)) for i\",\n \"@property def energies(self): if self._energies is None: if self.total_dos is None: return self._energies\",\n \"= [] count = 1 for i in range(1, len(self.distances)): if self.distances[i] ==\",\n \"in range(len(val)): sec_mt_charge_atom = msection.m_create(x_exciting_section_MT_charge_atom) sec_mt_charge_atom.x_exciting_MT_charge_atom_index = n + 1 sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol = val[n][0]\",\n \"if self.total_dos is None: return self._energies = np.array( [float(point.attrib.get(self._energy_key)) for point in self.total_dos[0]])\",\n \"len(data[0][0][0]) sec_scc.x_exciting_xs_tddft_epsilon_energies = data[0][0][0] * ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field = data[1:] elif quantity == 'EPSILON'\",\n \"gw_info_files: if self.get_exciting_files(f): self._calculation_type = 'gw' gw_info_file = f break if not self._calculation_type\",\n \"if energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') # TODO I\",\n \"1 / ureg.bohr), 'x_exciting_gmaxvr': (r'Maximum \\\\|G\\\\| for potential and density', 1 / ureg.bohr),\",\n \"sec_scc.energy.fermi if energy_fermi is None: continue energy_fermi = energy_fermi.to(\\\"hartree\\\") sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic)\",\n \"False) sec_method.x_exciting_xs_bse_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlbse', [0, 0, 0, 0]) if sec_method.x_exciting_xs_bse_xas: sec_method.x_exciting_xs_bse_xasatom =\",\n \"DataTextParser() self.bandstructure_dat_parser = BandstructureDatParser(energy_unit=ureg.hartree) self.band_out_parser = BandOutParser(energy_unit=ureg.hartree) self.info_gw_parser = GWInfoParser() self.input_xml_parser = XMLParser()\",\n \"= data[1] * (1 / ureg.hartree) * volume.to('m**3').magnitude for spin in range(len(dos)): sec_dos_values\",\n \"# in addition to INFO.OUT return else: return files = self.get_exciting_files(name) if len(files)\",\n \"when setting mainfile self.bandstructure_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_parser.get('band_energies', []) for n in\",\n \"in data]) elif key == 'Znk': return np.array([d[1].get(key, None) for d in data])\",\n \"number sec_method.gw.frequency_values = frequency_data.get('values') sec_method.gw.frequency_weights = frequency_data.get('weights') fundamental_band_gap = self.info_gw_parser.get('direct_band_gap', None) if fundamental_band_gap\",\n \"self._method_keys_mapping = { 'smearing_kind': ('Smearing scheme', None), 'smearing_width': ('Smearing width', None)} for name,\",\n \"r'Abs\\\\. change in max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)', ureg.hartree / ureg.bohr)} for name, key_unit in self._convergence_keys_mapping.items(): scf_quantities.append(Quantity(\",\n \"[1, 1, 1]) sec_gw.basis_set = 'mixed' sec_gw.qp_equation_treatment = 'linearization' sec_gw.max_frequency = self.input_xml_parser.get( 'gw/freqgrid/freqmax',\",\n \"band_energies is None: return # Get fermi energy: it is used to un-shift\",\n \"= number sec_method.gw.frequency_values = frequency_data.get('values') sec_method.gw.frequency_weights = frequency_data.get('weights') fundamental_band_gap = self.info_gw_parser.get('direct_band_gap', None) if\",\n \"super().__init__(None) def init_quantities(self): re_symbol = re.compile(r'([A-Z][a-z]?)') def str_to_array(val_in): val = [v.split(':')[-1].split() for v\",\n \"file except in compliance with the License. # You may obtain a copy\",\n \"# parse input xml file, there seems to be two versions, input_gw.xml and\",\n \"def init_quantities(self): re_symbol = re.compile(r'([A-Z][a-z]?)') def str_to_array(val_in): val = [v.split(':')[-1].split() for v in\",\n \"if positions is None: # get it from input.xml for f in input_file:\",\n \"distance\\\\s*\\\\(target\\\\)', ureg.elementary_charge), 'x_exciting_IBS_force_convergence': ( r'Abs\\\\. change in max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)', ureg.hartree / ureg.bohr)} for name,\",\n \"Quantity('nuclear_charge', rf'nuclear charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('electronic_charge', rf'electronic charge *: *({re_float})', dtype=np.float64,\",\n \"None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_screening_rgkmax = rgkmax sec_method.x_exciting_xs_screening_type = self.input_xml_parser.get( 'xs/screening/screentype', 'full')\",\n \"is not None: smearing_width = (smearing_width * ureg.hartree).to('joule') # TODO smearing with should\",\n \"is done in parser, however nspin is # determined from occupancies which is\",\n \"band_parameters[3] fermi_energy = self.fermisurf_parser.get('fermi_energy', None) if fermi_energy is not None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface = fermi_energy\",\n \"lm_values = np.column_stack((np.arange(len(partialdos)), np.zeros(len(partialdos), dtype=np.int32))) for lm in range(len(partialdos)): for spin in range(len(partialdos[lm])):\",\n \"other energies are reported. energy_fermi = 0.0 * ureg.hartree if sec_scc.energy is not\",\n \"repeats=False), Quantity( 'atomic_positions', r'(Atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)',\",\n \"= atom_labels sec_atoms.periodic = [True] * 3 # TODO confirm no cell optimization\",\n \"'x_exciting_kpoint_offset': (r'k\\\\-point offset', None), 'x_exciting_number_kpoints': (r'Total number of k\\\\-points', None), 'x_exciting_rgkmax': (r'R\\\\^MT\\\\_min \\\\*\",\n \"number_of_spin_channels(self): if self._nspin is None: self._nspin = 1 return self._nspin @property def number_of_k_points_per_segment(self):\",\n \"/ self.energy_unit) elif 'partial' in key: if not self.partial_dos: return res = np.zeros((\",\n \"if self.data_xs_parser.data is None: continue data_q.append(self.data_xs_parser.data) if not data_q: continue data_q = np.transpose(data_q,\",\n \"- 1]: self._nkpts_segment .append(count) count = 1 else: count += 1 self._nkpts_segment.append(count) return\",\n \"len(data[0]))) sec_gw_eigenvalues = sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor = reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands = len(eigs_gw[0]) sec_gw_eigenvalues.n_kpoints = len(eigs_gw)\",\n \"is None or species is None: continue lattice_vectors = np.array(lattice_vectors, dtype=float) lattice_vectors *=\",\n \"{ 'Gaussian': 'gaussian', 'Methfessel-Paxton': 'methfessel-paxton', 'Fermi-Dirac': 'fermi', 'Extended': 'tetrahedra'} sec_smearing = sec_electronic.m_create(Smearing) smearing_kind\",\n \"[specie.get('symbol')] * len(specie.get('positions')) return labels def get_positions_format(self, section): positions_format = section.get('positions_format') if positions_format\",\n \"None: continue if key == 'x_exciting_section_MT_charge_atom': for n in range(len(val)): sec_mt_charge_atom = msection.m_create(x_exciting_section_MT_charge_atom)\",\n \"data[5], (n_components, n_excitons)) def parse_epsilon(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_epsilon\",\n \"and mainfile_base in f] options = [f for f in options if f.endswith(file_ext)]\",\n \"sec_fermisurface.x_exciting_fermi_energy_fermi_surface = fermi_energy sec_fermisurface.x_exciting_values_fermi_surface = fermi_surface def _parse_evalqp(self, sec_scc): data = self.evalqp_parser.get('kpoints_eigenvalues') if\",\n \"False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel = self.input_xml_parser.get( 'xs/tddft/aresfxc', True) sec_method.x_exciting_xs_tddft_drude = self.input_xml_parser.get( 'xs/tddft/drude', [0., 0.]) sec_method.x_exciting_xs_tddft_split_parameter\",\n \"self.input_xml_parser.get( 'xs/screening/screentype', 'full') if self.input_xml_parser.get('xs/BSE') is not None: sec_method.x_exciting_xs_bse_antiresonant = self.input_xml_parser.get( 'xs/BSE/aresbse', True)\",\n \"quantity == 'EXCITON': parse_function = parse_exciton elif quantity == 'EPSILON': parse_function = parse_epsilon\",\n \"cell.magnitude) return positions * ureg.bohr def get_scf_threshold(self, name): reference = self.get('groundstate', self.get('hybrids', {}))\",\n \"return self._distances @property def bands(self): if self._bands is None: bands = self.root.findall('./%s' %\",\n \"( r'Absolute change in total energy\\\\s*\\\\(target\\\\)', ureg.hartree), 'x_exciting_charge_convergence': ( r'Charge distance\\\\s*\\\\(target\\\\)', ureg.elementary_charge), 'x_exciting_IBS_force_convergence':\",\n \"'longrange'] bse_files = [] for bse_type in bse_types: for scr_type in scr_types: files\",\n \"= self.input_xml_parser.get( 'xs/broad', 0.01, 'hartree') sec_method.x_exciting_xs_gqmax = self.input_xml_parser.get( 'xs/gqmax', 0.0, '1/bohr') sec_method.x_exciting_xs_lmaxapw =\",\n \"[]) for specie in species: positions_format = specie.get('positions_format', None) if positions_format is not\",\n \"'hybrids']: sec_scc = parse_configuration(self.info_parser.get(module)) if sec_scc is None: continue # add data to\",\n \"def parse(self, filepath, archive, logger): self.filepath = filepath self.archive = archive self.logger =\",\n \"= res * (1 / self.energy_unit) elif key == 'energies': return self.energies else:\",\n \"ureg.hartree + energy_fermi volume = self.info_parser.get_unit_cell_volume() dos = data[1] * (1 / ureg.hartree)\",\n \"self.evalqp_parser.quantities = parser.evalqp_parser.quantities self.info_gw_parser.quantities = parser.info_gw_parser.quantities def parse(self, filepath, archive, logger): self.filepath =\",\n \"return [] name = xc_functional_map.get(xc_functional.type, []) return name @property def n_optimization_steps(self): return len(self.get('structure_optimization',\",\n \"['groundstate', 'hybrids']: sec_scc = parse_configuration(self.info_parser.get(module)) if sec_scc is None: continue # add data\",\n \"os.path.join(self.info_parser.maindir, filepath) if os.path.isfile(filepath) and os.access(filepath, os.F_OK): return True return False def _parse_dos(self,\",\n \"sub_parser=TextParser(quantities=optimization_quantities), repeats=True), Quantity( 'final', r'Force convergence target achieved([\\\\s\\\\S]+?Opt)', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(imized\",\n \"% name) if quantity is None: return # this is really problematic if\",\n \"self.partial_dos: return res = np.zeros(( self.number_of_lm, self.number_of_spin_channels, self.number_of_atoms, self.number_of_dos)) for i in range(len(self.partial_dos)):\",\n \"def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'k_points', r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\- ]+):\\\\s*k\\\\-point',\",\n \"= energy_fermi band_k_points = self.bandstructure_dat_parser.band_k_points nkpts_segment = self.bandstructure_dat_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment\",\n \"eigs[i] for i in range(len(keys))} return [kpts, eigs] self._quantities.append( Quantity( 'kpoints_eigenvalues', r'\\\\s*k\\\\-point \\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([\",\n \"target[1:]: filepath = '%s.%s' % (filepath, target[1]) filepath = os.path.join(self.info_parser.maindir, filepath) if os.path.isfile(filepath)\",\n \"continue if key == 'x_exciting_section_MT_moment_atom': for n in range(len(val)): sec_mt_moment_atom = msection.m_create(x_exciting_section_MT_moment_atom) sec_mt_moment_atom.x_exciting_MT_moment_atom_index\",\n \"end = np.array( self.vertices[i + 1].attrib.get(self._coord_key).split(), dtype=float) res.append(np.linspace(start, end, self.number_of_k_points_per_segment[i])) elif key ==\",\n \"x.split(':')]), Quantity( 'parameters', r'\\\\n *(.+?:.+)', repeats=True, str_operation=lambda x: [v.strip() for v in x.split(':')])]))\",\n \"name.startswith('bandstructure') and name.endswith('xml'): parser = self.bandstructure_parser parser_function = self._parse_bandstructure elif name.startswith('EIGVAL') and name.endswith('OUT'):\",\n \"len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_energy_points = n_epsilon sec_scc.x_exciting_xs_bse_epsilon_energies = np.reshape( data[0], (n_components, n_epsilon)) *\",\n \"self.info_parser.get_number_of_spin_channels() if self._calculation_type == 'volume_optimization': sec_method.x_exciting_volume_optimization = True def parse_scc(self, section): sec_run =\",\n \"None), 'x_exciting_gvector_total': (r'Total number of G\\\\-vectors', None), 'x_exciting_lmaxapw': (r' APW functions', None), 'x_exciting_nuclear_charge':\",\n \"* len(atoms[n])) if positions is None or atom_labels is None: return sec_system =\",\n \"radius, r\\\\_s', ureg.bohr), 'x_exciting_empty_states': ('Number of empty states', None), 'x_exciting_valence_states': ('Total number of\",\n \"= np.transpose(self.data) self._band_k_points = [] start = 0 for nkpts_segment in self.number_of_k_points_per_segment: end\",\n \"np.zeros(( self.number_of_lm, self.number_of_spin_channels, self.number_of_atoms, self.number_of_dos)) for i in range(len(self.partial_dos)): spin = self.partial_dos[i].attrib.get(self._nspin_key, None)\",\n \"self._nspin = None self._distances = None self._band_energies = None self._neigs_segment = None self._nkpts_segment\",\n \"= ['IP', 'singlet', 'triplet', 'RPA'] scr_types = ['full', 'diag', 'noinvdiag', 'longrange'] bse_files =\",\n \"1).rjust(3, '0'))] for f in files_q: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None:\",\n \"@property def number_of_atoms(self): if self._natoms is None: partial_dos = self.root.findall('./%s' % self._partialdos_key) self._natoms\",\n \"def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'k_points', r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\- ]+):\\\\s*k\\\\-point', repeats=True)) def str_to_eigenvalues(val_in):\",\n \"sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.endpoints_labels = band_seg_labels[nb] sec_k_band_segment.energies = band_energies[n][nb]\",\n \"self._total_dos = None self._partial_dos = None @property def energy_unit(self): if self._energy_unit is None:\",\n \"default=None): return self.get('initialization', {}).get(key, default) class ExcitingParser: def __init__(self): self.info_parser = ExcitingInfoParser() self.dos_parser\",\n \"data['occupancies'] = np.reshape(occs, (nspin, len(occs) // nspin)) data['eigenvalues'] = np.reshape(eigs, (nspin, len(eigs) //\",\n \"moment in muffin-tins'], 'x_exciting_total_moment': ['total moment'], 'x_exciting_section_MT_moment_atom': ['atom_resolved'] } def get_exciting_files(self, default): mainfile\",\n \"(name.endswith('DAT') or name.endswith('TXT')): parser = self.evalqp_parser parser_function = self._parse_evalqp elif name.startswith('TDOS') and name.endswith('OUT'):\",\n \"if threshold is None: continue metainfo_name = 'x_exciting_scf_threshold_%s_change' % name.split('_')[-2] setattr(sec_method, metainfo_name, threshold)\",\n \"= [] for i in range(len(self.number_of_k_points_per_segment)): start = self.vertices[i].attrib.get(self._coord_key).split() end = self.vertices[i +\",\n \"= self.archive.run[-1] def parse_configuration(section): if not section: return sec_scc = self.parse_scc(section) if sec_scc\",\n \"# Parse DFT DOS from one of the files bs_files = ['dos.xml', 'TDOS.OUT']\",\n \"len(v[1]) == 1 else v[1] if species is None: species = v[0][2] atom_resolved.append(((species,\",\n \"GWInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] def str_to_frequency(val_in): val =\",\n \"None: self._vertices = self.root.findall('./%s' % self._vertex_key) return self._vertices @property def number_of_spin_channels(self): if self._nspin\",\n \"spin = (i % (self.number_of_spin_channels * self.number_of_lm)) // self.number_of_lm else: spin = int(spin)\",\n \"'triplet', 'RPA'] scr_types = ['full', 'diag', 'noinvdiag', 'longrange'] bse_files = [] for bse_type\",\n \"[f for f in options if f.endswith(file_ext)] options.sort() filenames = [os.path.join(self.info_parser.maindir, f) for\",\n \"sec_method.x_exciting_xs_bse_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlbse', [0, 0, 0, 0]) if sec_method.x_exciting_xs_bse_xas: sec_method.x_exciting_xs_bse_xasatom = self.input_xml_parser.get(\",\n \"* self.number_of_spin_channels) res[lm][spin][atom] = self._get_dos(self.partial_dos[i]) if self.energy_unit is not None: res = res\",\n \"contributions parse_scf(final, sec_scc) # forces forces = section.get('forces') if forces is not None:\",\n \"None: data = np.transpose(self.data)[0] self._neigs_segment = len(np.where(data == data[0])[0]) return self._neigs_segment class BandstructureXMLParser(XMLParser):\",\n \"0.]) sec_method.x_exciting_xs_tddft_split_parameter = self.input_xml_parser.get( 'xs/tddft/fxcbsesplit', 0.00001, 'hartree') sec_method.x_exciting_xs_tddft_xc_kernel = self.input_xml_parser.get( 'xs/tddft/fxctype', 'RPA') sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution\",\n \"eigs_gw_C = reshape(get_data('Sc')) if eigs_gw_C is None: eigs_gw_C = reshape(get_data('Re(Sc)')) sec_gw_eigenvalues.value_correlation = eigs_gw_C\",\n \"sec_charges.value = [ val[n][1].magnitude for n in range(len(val))] * val[0][1].units sec_charges.total = charge_contributions.get('total\",\n \"count = 1 for i in range(1, len(self.distances)): if self.distances[i] == self.distances[i -\",\n \"= np.array( self.vertices[i].attrib.get(self._coord_key).split(), dtype=float) end = np.array( self.vertices[i + 1].attrib.get(self._coord_key).split(), dtype=float) res.append(np.linspace(start, end,\",\n \"dos_down nspin = self.info_parser.get_number_of_spin_channels() sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = len(data) // nspin\",\n \"= kwargs.get('energy_unit', None) def init_parameters(self): # TODO make a parent clas for bandstructure\",\n \"len(specie.get('positions')) return labels def get_positions_format(self, section): positions_format = section.get('positions_format') if positions_format is None:\",\n \"if target[1:]: filepath = '%s.%s' % (filepath, target[1]) filepath = os.path.join(self.info_parser.maindir, filepath) if\",\n \"the DOS to # the original scale in which also other energies are\",\n \"+ 1].attrib.get(self._coord_key).split() res.append([start, end]) else: res = None self._results[key] = res class DOSXMLParser(XMLParser):\",\n \"*\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float} *{re_float} *{re_float}\\\\n\\\\s*\\\\n)', repeats=True, sub_parser=TextParser(quantities=[ Quantity('number', r'Species : *(\\\\d+)', dtype=np.int32), Quantity('symbol', r'\\\\((\\\\w+)\\\\)'), Quantity('file',\",\n \"xcf xc_functional_map = { 2: ['LDA_C_PZ', 'LDA_X_PZ'], 3: ['LDA_C_PW', 'LDA_X_PZ'], 4: ['LDA_C_XALPHA'], 5:\",\n \"sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] self.parse_file('input.xml', sec_method) # parse\",\n \"Dos, DosValues, BandStructure, BandEnergies, Energy, EnergyEntry, Charges, Forces, ForcesEntry, ScfIteration, BandGap ) from\",\n \"not isinstance(smearing_kind, str): smearing_kind = smearing_kind[0] smearing_kind = smearing_kind_map[smearing_kind] sec_smearing.kind = smearing_kind smearing_width\",\n \"val_m is None: lm = i % self.number_of_lm else: lm = int(val_l) **\",\n \"2)) data = np.transpose(data, axes=(2, 0, 1)) sec_dos.energies = data[0][0] * ureg.hartree +\",\n \"= specie.get('positions_format', None) if positions_format is not None: break return positions_format def get_atom_positions(self,\",\n \"np.reshape( data[2], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_oscillator_strength = np.reshape( data[3], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_re\",\n \"return positions = np.dot(positions, cell.magnitude) return positions * ureg.bohr def get_scf_threshold(self, name): reference\",\n \"sec_gw_eigenvalues.n_bands = len(eigs_gw[0]) sec_gw_eigenvalues.n_kpoints = len(eigs_gw) sec_gw_eigenvalues.kpoints = get_data('k_points') sec_gw_eigenvalues.energies = reshape(eigs_gw) sec_gw_eigenvalues.value_exchange\",\n \"*Time', repeats=False, str_operation=str_to_array, convert=False, unit=ureg.hartree / ureg.bohr), Quantity( 'step', r'Optimization step\\\\s*(\\\\d+)', repeats=False, dtype=int),\",\n \"[] species = None for v in val: v = v.strip().split(':') if len(v)\",\n \"repeats=False, dtype=str, flatten=False)] initialization_quantities = [ Quantity( 'lattice_vectors', r'Lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=ureg.bohr, repeats=False,\",\n \"input_GW.xml does not exist self._quantities.append( Quantity( 'frequency_data', r'frequency list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)', str_operation=str_to_frequency, repeats=False) ) self._quantities.append(\",\n \"not None: sec_scc.x_exciting_maximum_force_magnitude = force_convergence[0] sec_scc.x_exciting_geometry_optimization_threshold_force = force_convergence[1] sec_scc = parse_configuration(structure_optimization) if sec_scc\",\n \"if not self.partial_dos: return res = np.zeros(( self.number_of_lm, self.number_of_spin_channels, self.number_of_atoms, self.number_of_dos)) for i\",\n \"is not None: number = frequency_data.get('number') sec_method.gw.number_of_frequencies = len(number) sec_method.gw.frequency_number = number sec_method.gw.frequency_values\",\n \"self._parse_input_xs else: # TODO implement reading of parameters from input.xml for normal calculations\",\n \"/ ureg.bohr, repeats=False, convert=False), ] self._system_keys_mapping = { 'x_exciting_unit_cell_volume': ('Unit cell volume', ureg.bohr\",\n \"positions = self.info_parser.get_atom_positions( positions=positions, positions_format=positions_format).to('m') sec_atoms_group.x_exciting_geometry_atom_positions = positions.magnitude # clathrate info clathrate_file =\",\n \"= sec_run.m_create(System) sec_atoms = sec_system.m_create(Atoms) sec_atoms.positions = positions sec_atoms.labels = atom_labels sec_atoms.periodic =\",\n \"sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] # parse input\",\n \"== 'gamma' else end]) elif key == 'band_segm_start_end': res = [] for i\",\n \"parse_exciton(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components = n_components n_excitons =\",\n \"break sec_scc.calculations_path.append(info_volume[0]) def init_parser(self): self.info_parser.mainfile = self.filepath self.info_parser.logger = self.logger self.dos_parser.logger = self.logger\",\n \"else: sec_xc_functional.contributions.append(Functional(name=name)) if not xc_functional_names: # simply write parameters xc_functional = self.info_parser.get('initialization', {}).get('xc_functional')\",\n \"ext == 'FXC': sec_scc.x_exciting_xs_tddft_loss_function_local_field = data[1] elif quantity == 'LOSS' and ext ==\",\n \"val = None for name in names: val = moment_contributions.get(name, None) if val\",\n \"/ ureg.bohr), 'x_exciting_gmaxvr': (r'Maximum \\\\|G\\\\| for potential and density', 1 / ureg.bohr), 'x_exciting_gvector_size':\",\n \"else vi properties[v[0].strip()] = vi * unit properties['atom_resolved'] = atom_resolved return properties def\",\n \"# this is really problematic if some scf steps dont have the quantity\",\n \"= np.transpose(data_q, axes=(2, 0, 1)) for j in range(len(data)): data[j].append(data_q[j]) return data for\",\n \"sec_system.x_exciting_clathrates_atom_coordinates = np.transpose( np.array(data[:3], dtype=float)) sec_system.x_exciting_clathrates_atom_labels = list(data[3]) else: sec_system.x_exciting_clathrates = False potential_mixing\",\n \"sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions at this step\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str),\",\n \"zone volume', 1 / ureg.bohr ** 3), 'x_exciting_number_of_atoms': ('Total number of atoms per\",\n \"[ val[n][1].magnitude for n in range(len(val))] * val[0][1].units sec_charges.total = charge_contributions.get('total charge') elif\",\n \"spin in range(len(partialdos[lm])): for atom in range(len(partialdos[lm][spin])): sec_dos_values = sec_dos.m_create(DosValues, Dos.atom_projected) sec_dos_values.m_kind =\",\n \"get_data('eigenvalues') def _parse_fermisurface(self, sec_scc): fermi_surface = self.fermisurf_parser.get('fermi_surface', [None])[0] if fermi_surface is None: return\",\n \"np.transpose(self.data) self._neigs_segment = int(max(data[0])) return self._neigs_segment class BandOutParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin\",\n \"metainfo for name in self.info_parser._miscellaneous_keys_mapping.keys(): val = iteration.get(name) if val is None: continue\",\n \"for dos and bandstructure super().__init__(None) self._distance_key = 'distance' self._coord_key = 'coord' self._energy_key =\",\n \"self.archive.run[-1] # two versions of gw info files gw_info_files = ['GW_INFO.OUT', 'GWINFO.OUT'] for\",\n \"res = [] for i in range(len(self.vertices) - 1): start = self.vertices[i].attrib.get('label') end\",\n \"= self.get_initialization_parameter('species', []) for specie in species: positions_format = specie.get('positions_format', None) if positions_format\",\n \"1.0 * ureg.bohr ** 3) def get_initialization_parameter(self, key, default=None): return self.get('initialization', {}).get(key, default)\",\n \"sec_dos_values.lm = lm_values[lm] sec_dos_values.spin = spin sec_dos_values.atom_index = atom sec_dos_values.value = partialdos[lm][spin][atom] def\",\n \"repeats=False) ) class ExcitingEvalqpParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] def\",\n \"self.data_clathrate_parser = DataTextParser(dtype=str) # different names for different versions of exciting self._energy_keys_mapping =\",\n \"= np.reshape( data[2], (n_components, n_sigma)) def parse_loss(data, sec_scc): n_components = len(data) data =\",\n \"where the mainfile is stored. \\\"\\\"\\\" mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target\",\n \"self.input_xml_parser.get( 'gw/scrcoul/scrtype', 'rpa') def parse_gw(self): sec_run = self.archive.run[-1] # two versions of gw\",\n \"Quantity( 'fermi_surface', r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n *E*', unit=ureg.hartree, repeats=True)) class ExcitingEigenvalueParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self):\",\n \"self._parse_dos elif name.startswith('bandstructure') and name.endswith('xml'): parser = self.bandstructure_parser parser_function = self._parse_bandstructure elif name.startswith('EIGVAL')\",\n \"'x_exciting_dos_fermi', r'DOS at Fermi energy \\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=1 / ureg.hartree), Quantity( 'charge_contributions',\",\n \"np.transpose(self.data)[0] n_k_points = np.where(dist == dist[0])[0][1] self._distances = dist[:n_k_points] return self._distances @property def\",\n \"sec_run = self.archive.run[-1] sec_method = sec_run.m_create(Method) sec_method.basis_set.append(BasisSet(type='(L)APW+lo')) sec_dft = sec_method.m_create(DFT) sec_electronic = sec_method.m_create(Electronic)\",\n \"= sec_run.method[-1] return sec_scc # groundstate and hybrids calculation for module in ['groundstate',\",\n \"R\\\\^MT\\\\_min', None), 'x_exciting_gkmax': (r'Maximum \\\\|G\\\\+k\\\\| for APW functions', 1 / ureg.bohr), 'x_exciting_gmaxvr': (r'Maximum\",\n \"= positions_format if positions_format is not None else self.get_positions_format(section) if positions_format == 'lattice':\",\n \"1.0) positions = np.dot(positions, lattice_vectors) * ureg.bohr lattice_vectors = lattice_vectors * ureg.bohr atoms\",\n \"of gw info files gw_info_files = ['GW_INFO.OUT', 'GWINFO.OUT'] for f in gw_info_files: if\",\n \"to INFO.OUT return else: return files = self.get_exciting_files(name) if len(files) > 1: self.logger.warn('Found\",\n \"return data def parse_exciton(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components =\",\n \"charge') elif key == 'charge_total': pass else: setattr(msection, key, val) # moment contributions\",\n \"sec_scc_ref sec_scc.calculations_ref = [sec_scc_ref] # parse properties gw_info_files = self.get_exciting_files(gw_info_file) if len(gw_info_files) >\",\n \"def get_positions_format(self, section): positions_format = section.get('positions_format') if positions_format is None: species = self.get_initialization_parameter('species',\",\n \"1)) for j in range(len(data)): data[j].append(data_q[j]) return data for quantity in ['EPSILON', 'LOSS',\",\n \"moment'], 'x_exciting_section_MT_moment_atom': ['atom_resolved'] } def get_exciting_files(self, default): mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT')\",\n \"self._nkpts_segment.append(count) return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: self._neigs_segment = len(self.bands[0])\",\n \"% (self._totaldos_key, self._diagram_key)) return self._total_dos @property def partial_dos(self): if self._partial_dos is None: self._partial_dos\",\n \"import ( Calculation, Dos, DosValues, BandStructure, BandEnergies, Energy, EnergyEntry, Charges, Forces, ForcesEntry, ScfIteration,\",\n \"None), 'x_exciting_nuclear_charge': ('Total nuclear charge', ureg.elementary_charge), 'x_exciting_electronic_charge': ('Total electronic charge', ureg.elementary_charge), 'x_exciting_core_charge_initial': ('Total\",\n \"in range(len(band_energies[n])): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.endpoints_labels =\",\n \"* self.number_of_lm)) // self.number_of_lm else: spin = int(spin) - 1 val_l = self.partial_dos[i].attrib.get(self._l_key,\",\n \"val = energy_contributions.get(name, None) if val is not None: break if val is\",\n \"energy contributions energy_contributions = iteration.get('energy_contributions', {}) for key, names in self._energy_keys_mapping.items(): val =\",\n \"logger is not None else logging self._calculation_type = None self.init_parser() sec_run = self.archive.m_create(Run)\",\n \"am not certain if screening/BSE are children of xs if self.input_xml_parser.get('xs/screening') is not\",\n \"step\\\\s*:\\\\s*[\\\\d\\\\.]+ seconds))', sub_parser=TextParser(quantities=optimization_quantities), repeats=True), Quantity( 'final', r'Force convergence target achieved([\\\\s\\\\S]+?Opt)', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity(\",\n \"= 'band' self._atom_key = 'atom' self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None)\",\n \"= self.input_xml_parser.get( 'xs/tddft/intraband', False) sec_method.x_exciting_xs_tddft_diagonal_xc_kernel = self.input_xml_parser.get( 'xs/tddft/kerndiag', False) sec_method.x_exciting_xs_tddft_lmax_alda = self.input_xml_parser.get( 'xs/tddft/lmaxalda',\",\n \"_parse_bandstructure(self, sec_scc): # we need to set nspin again as this is overwritten\",\n \"charge_contributions.get('total charge') elif key == 'charge_total': pass else: setattr(msection, key, val) # moment\",\n \"# moment contributions moment_contributions = iteration.get('moment_contributions', {}) for key, names in self._moment_keys_mapping.items(): val\",\n \"name.startswith('EIGVAL') and name.endswith('OUT'): parser = self.eigval_parser parser_function = self._parse_eigenvalues elif (name.startswith('FERMISURF') or name.startswith('FS'))\",\n \"self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels = self.info_parser.get_atom_labels(section.get('atomic_positions', {})) input_file = self.get_exciting_files('input.xml') if positions is None: #\",\n \"self.input_xml_parser.get( 'xs/tddft/fxctype', 'RPA') sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution = self.input_xml_parser.get( 'xs/tddft/intraband', False) sec_method.x_exciting_xs_tddft_diagonal_xc_kernel = self.input_xml_parser.get( 'xs/tddft/kerndiag', False)\",\n \"not None: sec_gap.value_fundamental = fundamental_band_gap optical_band_gap = self.info_gw_parser.get('optical_band_gap', None) if optical_band_gap is not\",\n \"fermi energy: it is used to un-shift the DOS to # the original\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this file except in\",\n \"str_operation=str_to_atom_properties_dict, repeats=False, convert=False), Quantity( 'moment_contributions', r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False)] self._miscellaneous_keys_mapping = {\",\n \"sec_scc = sec_run.m_create(Calculation) def parse_scf(iteration, msection): energy_total = iteration.get('energy_total') sec_energy = msection.m_create(Energy) if\",\n \"not None: break if val is None: continue if key == 'x_exciting_section_MT_charge_atom': for\",\n \"return self._band_energies @property def band_k_points(self): if self._band_k_points is None: data = np.transpose(self.data) self._band_k_points\",\n \"= np.where(dist == dist[0])[0][1] self._distances = dist[:n_k_points] return self._distances @property def number_of_spin_channels(self): if\",\n \"continue lattice_vectors = np.array(lattice_vectors, dtype=float) lattice_vectors *= self.input_xml_parser.get('structure/crystal/scale', 1.0) positions = np.dot(positions, lattice_vectors)\",\n \"and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_sigma_no_local_field = data[1:3] def parse_xs(self): sec_run = self.archive.run[-1] xs_info_files\",\n \"self._moment_keys_mapping.items(): val = None for name in names: val = moment_contributions.get(name, None) if\",\n \"return sec_fermisurface = sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters = self.fermisurf_parser.get('band_parameters', None) if band_parameters is not None:\",\n \"== 'energies': return self.energies else: res = None self._results[key] = res class ExcitingFermiSurfaceBxsfParser(TextParser):\",\n \"eigs_gw[0] is None: return nspin = self.info_parser.get_number_of_spin_channels() def reshape(data): if data[0] is None:\",\n \"super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): # TODO\",\n \"n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im = np.reshape( data[2], (n_components, n_sigma)) def parse_loss(data, sec_scc): n_components = len(data)\",\n \"vii in v[1].split()] vi = vi[0] if len(vi) == 1 else vi properties[v[0].strip()]\",\n \"= sec_scc.energy.fermi if energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band\",\n \"parse_file(self, name, section): # TODO add support for info.xml, wannier.out if name.startswith('dos') and\",\n \"'Znk': return np.array([d[1].get(key, None) for d in data]) else: energy = np.array([d[1].get(key, None)\",\n \"and xml self._nspin = None self._nkpts_segment = None self._neigs_segment = None self._vertices =\",\n \"self.fermisurf_parser.get('fermi_surface', [None])[0] if fermi_surface is None: return sec_fermisurface = sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters = self.fermisurf_parser.get('band_parameters',\",\n \"dtype=float) return self._distances @property def bands(self): if self._bands is None: bands = self.root.findall('./%s'\",\n \"rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_rgkmax = rgkmax sec_method.x_exciting_xs_scissor = self.input_xml_parser.get('xs/scissor',\",\n \"'vertex' self._band_key = 'band' self._atom_key = 'atom' self._nspin = kwargs.get('nspin', None) self._energy_unit =\",\n \"if quantity is None: return # this is really problematic if some scf\",\n \"reuse_parser(self, parser): self.info_parser.quantities = parser.info_parser.quantities self.eigval_parser.quantities = parser.eigval_parser.quantities self.fermisurf_parser.quantities = parser.fermisurf_parser.quantities self.evalqp_parser.quantities =\",\n \"'x_exciting_wigner_radius': (r'Effective Wigner radius, r\\\\_s', ureg.bohr), 'x_exciting_empty_states': ('Number of empty states', None), 'x_exciting_valence_states':\",\n \"APW functions', None), 'x_exciting_nuclear_charge': ('Total nuclear charge', ureg.elementary_charge), 'x_exciting_electronic_charge': ('Total electronic charge', ureg.elementary_charge),\",\n \"sec_scc.energy.fermi energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi\",\n \"EXCITON and file type', data=dict(file=quantity)) sec_scc = sec_run.m_create(Calculation) if quantity == 'EXCITON': parse_function\",\n \"repeats=True, str_operation=lambda x: [v.strip() for v in x.split(':')])])) ) self._quantities.append(Quantity( 'initialization', r'(?:All units\",\n \"self.logger = logger if logger is not None else logging self._calculation_type = None\",\n \"point in diagram], dtype=float) return dos def parse(self, key): if self._results is None:\",\n \"def _parse_input_gw(self, sec_method): sec_gw = sec_method.m_create(GWMethod) sec_gw.type = 'G0W0' gmaxvr = self.info_parser.get_initialization_parameter('x_exciting_gmaxvr', 0)\",\n \"files: bs_files = ['bandstructure-qp.dat', 'BAND-QP.OUT'] for fname in bs_files: if self.file_exists(fname): gw_files.append(fname) break\",\n \"self._atom_key = 'atom' self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self):\",\n \"mainfile.strip('INFO.OUT') target = filename.rsplit('.', 1) filepath = '%s%s' % (target[0], suffix) if target[1:]:\",\n \"= np.transpose(self.data)[0] self._neigs_segment = len(np.where(data == data[0])[0]) return self._neigs_segment class BandstructureXMLParser(XMLParser): def __init__(self,\",\n \"= v.split(':') if len(v) < 2: continue energies[v[0].strip()] = float(v[1]) * ureg.hartree return\",\n \"None for v in val: v = v.strip().split(':') if len(v) < 2: continue\",\n \"408: ['HYB_GGA_XC_HSE03']} xc_functional = self.get('initialization', {}).get('xc_functional', None) if xc_functional is None: return []\",\n \"name @property def n_optimization_steps(self): return len(self.get('structure_optimization', {}).get('optimization_step', [])) def get_number_of_spin_channels(self): spin_treatment = self.get('initialization',\",\n \"data[3], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_re = np.reshape( data[4], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_im = np.reshape( data[5],\",\n \"repeats=False) ) self._quantities.append( Quantity( 'fundamental_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'optical_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical\",\n \"number_of_lm(self): if self._nlm is None: if self.partial_dos is None: return self._nlm = 0\",\n \"= (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi nkpts_segment =\",\n \"when there is a mismatch between files self.logger.warn( 'Mismatch in EXCITON and file\",\n \"self.logger def reuse_parser(self, parser): self.info_parser.quantities = parser.info_parser.quantities self.eigval_parser.quantities = parser.eigval_parser.quantities self.fermisurf_parser.quantities = parser.fermisurf_parser.quantities\",\n \"# clathrate info clathrate_file = self.get_exciting_files('str.out') if clathrate_file: sec_system.x_exciting_clathrates = True self.data_clathrate_parser.mainfile =\",\n \"= [ point.attrib.get(self._distance_key) for point in self.bands[0][0]] self._distances = np.array(self._distances, dtype=float) return self._distances\",\n \"species = self.get_initialization_parameter('species', []) if species: positions = np.vstack([s.get('positions') for s in species])\",\n \"in range(len(val))] * val[0][1].units sec_charges.total = charge_contributions.get('total charge') elif key == 'charge_total': pass\",\n \"os.F_OK): return True return False def _parse_dos(self, sec_scc): if self.dos_parser.get('totaldos', None) is None:\",\n \"return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi\",\n \"flatten=False)] initialization_quantities = [ Quantity( 'lattice_vectors', r'Lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=ureg.bohr, repeats=False, convert=False), Quantity(\",\n \"is not None else self.get_positions_format(section) if positions_format == 'lattice': cell = self.get_initialization_parameter('lattice_vectors') if\",\n \"v[1].split()] v[1] = v[1][0] if len(v[1]) == 1 else v[1] if species is\",\n \"= len(data) data = np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components = n_components n_excitons = len(data[0]) // n_components\",\n \"positions = specie.get('positions') positions = self.info_parser.get_atom_positions( positions=positions, positions_format=positions_format).to('m') sec_atoms_group.x_exciting_geometry_atom_positions = positions.magnitude # clathrate\",\n \"not self.partial_dos: return res = np.zeros(( self.number_of_lm, self.number_of_spin_channels, self.number_of_atoms, self.number_of_dos)) for i in\",\n \"['input_gw.xml', 'input-gw.xml', 'input.xml']: self.parse_file(f, sec_method) xc_functional_name = ' '.join(self.info_parser.get_xc_functional_name()) sec_method.gw.starting_point = xc_functional_name sec_scc\",\n \"def get_data(key): data = self.eigval_parser.get('eigenvalues_occupancies') # reshaping is not necessary as this is\",\n \"if self._distances is None: dist = np.transpose(self.data)[0] n_k_points = np.where(dist == dist[0])[0][1] self._distances\",\n \"res[lm][spin][atom] = self._get_dos(self.partial_dos[i]) if self.energy_unit is not None: res = res * (1\",\n \"key, val) # moment contributions moment_contributions = iteration.get('moment_contributions', {}) for key, names in\",\n \"None self._band_energies = None self._band_k_points = None @property def band_energies(self): if self._band_energies is\",\n \"= int(val[0]) mesh = np.array(val[1].split(), dtype=int) origin = np.array(val[2].split(), dtype=float) vector = np.array([v.split()\",\n \"== 'LOSS': parse_function = parse_loss else: continue try: parse_function(data, sec_scc) except Exception: self.logger.error('Error\",\n \"ext == 'FXC': sec_scc.x_exciting_xs_tddft_sigma_local_field = data[1:3] elif quantity == 'SIGMA' and ext ==\",\n \"= None for name in names: val = moment_contributions.get(name, None) if val is\",\n \"sec_run.method[-1] return sec_scc # groundstate and hybrids calculation for module in ['groundstate', 'hybrids']:\",\n \"sec_xc_functional.contributions.append(Functional(name=name)) if not xc_functional_names: # simply write parameters xc_functional = self.info_parser.get('initialization', {}).get('xc_functional') if\",\n \"energies(self): if self._energies is None: if self.total_dos is None: return self._energies = np.array(\",\n \"r'Exchange-correlation type +: +(\\\\S+)'), Quantity( 'name_reference', r'\\\\n *(.+?,.+)', str_operation=lambda x: [v.strip() for v\",\n \"1)) sec_dos.energies = data[0][0] * ureg.hartree + energy_fermi volume = self.info_parser.get_unit_cell_volume() dos =\",\n \".append(count) count = 1 else: count += 1 self._nkpts_segment.append(count) return self._nkpts_segment @property def\",\n \"TODO implement reading of parameters from input.xml for normal calculations # in addition\",\n \"= end res.append(res_n) elif key == 'band_k_points': res = [] for i in\",\n \"for v in x.split(':')])])) ) self._quantities.append(Quantity( 'initialization', r'(?:All units are atomic|Starting initialization)([\\\\s\\\\S]+?)(?:Using|Ending initialization)',\",\n \"if species is None: species = v[0][2] atom_resolved.append(((species, v[1] * unit))) else: vi\",\n \"target = default.rsplit('.', 1) filename = '%s%s' % (target[0], suffix) if target[1:]: filename\",\n \"if a the given filename exists and is accessible in the same folder\",\n \"None) if fermi_energy is not None: sec_scc.energy = Energy(fermi=fermi_energy) gw_files = ['EVALQP.DAT', 'EVALQP.TXT',\",\n \"n_excitons = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_excitons = n_excitons sec_scc.x_exciting_xs_bse_exciton_energies = np.reshape( data[1], (n_components,\",\n \"xstype = self.input_xml_parser.get('xs/xstype', '') if xstype.lower() == 'bse': self._parse_xs_bse() elif xstype.lower() == 'tddft':\",\n \"True: info_volume = self.get_exciting_files('run_dir%s/INFO.OUT' % str(volume_index).rjust(2, '0')) if not info_volume: break sec_scc.calculations_path.append(info_volume[0]) def\",\n \"sec_run.m_create(Calculation) sec_scc.x_exciting_xs_tddft_number_of_epsilon_values = len(data[0][0][0]) sec_scc.x_exciting_xs_tddft_epsilon_energies = data[0][0][0] * ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field = data[1:] elif\",\n \"= sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] self.parse_file('input.xml', sec_method)\",\n \"self.info_parser._miscellaneous_keys_mapping.keys(): val = iteration.get(name) if val is None: continue if name == 'time':\",\n \"parser.mainfile = files[n] parser_function(section) # free up memory parser.mainfile = None def _parse_input_xs(self,\",\n \"r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n *(?:DOS|Density)', str_operation=str_to_energy_dict, repeats=False, convert=False), Quantity( 'x_exciting_dos_fermi', r'DOS at Fermi energy \\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False,\",\n \"= None for name in names: val = charge_contributions.get(name, None) if val is\",\n \"# convergence values for name in self.info_parser._convergence_keys_mapping.keys(): val = iteration.get(name) if val is\",\n \"sec_scc is None: continue # add data to scc # TODO add support\",\n \"Dos.total) sec_dos_values.spin = spin sec_dos_values.value = dos[spin] # TODO add PDOS def _parse_bandstructure_dat(self,\",\n \"str_operation=str_to_energy_dict, repeats=False, convert=False), Quantity( 'x_exciting_dos_fermi', r'DOS at Fermi energy \\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=1\",\n \"start = end return self._band_energies @property def distances(self): if self._distances is None: dist\",\n \"contributions energy_contributions = iteration.get('energy_contributions', {}) for key, names in self._energy_keys_mapping.items(): val = None\",\n \"val_in.strip().split('\\\\n') properties = dict() atom_resolved = [] species = None for v in\",\n \"self.root.findall('./%s/%s' % (self._totaldos_key, self._diagram_key)) return self._total_dos @property def partial_dos(self): if self._partial_dos is None:\",\n \"def str_to_atom_properties_dict(val_in): unit = None if 'charge' in val_in: unit = ureg.elementary_charge elif\",\n \"sec_scc.x_exciting_xs_bse_exciton_binding_energies = np.reshape( data[2], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_oscillator_strength = np.reshape( data[3], (n_components,\",\n \"if key == 'band_energies': # TODO I am not certain about the format\",\n \"21: ['GGA_C_PBE', 'GGA_X_PBE_R'], 22: ['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'], 26: ['GGA_C_PBE', 'GGA_X_WC'], 30: ['GGA_C_AM05', 'GGA_C_AM05'], 300:\",\n \"atom in range(len(partialdos[lm][spin])): sec_dos_values = sec_dos.m_create(DosValues, Dos.atom_projected) sec_dos_values.m_kind = 'spherical' sec_dos_values.lm = lm_values[lm]\",\n \"'time': (r'Wall time \\\\(seconds\\\\)', ureg.s)} for name, key_unit in self._miscellaneous_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)'\",\n \"['GGA_C_PBE', 'GGA_X_PBE'], 21: ['GGA_C_PBE', 'GGA_X_PBE_R'], 22: ['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'], 26: ['GGA_C_PBE', 'GGA_X_WC'], 30: ['GGA_C_AM05',\",\n \"'NAR' if not aresdf else None] file_ext = '_'.join([e for e in file_ext_list\",\n \"dtype=float)) sec_system.x_exciting_clathrates_atom_labels = list(data[3]) else: sec_system.x_exciting_clathrates = False potential_mixing = self.info_parser.get_initialization_parameter('potential_mixing') if potential_mixing\",\n \"self.partial_dos is None: return self._nlm = 0 l_list = set([int(e.attrib.get(self._l_key)) for e in\",\n \"'name_reference', r'\\\\n *(.+?,.+)', str_operation=lambda x: [v.strip() for v in x.split(':')]), Quantity( 'parameters', r'\\\\n\",\n \"'x_exciting_gmaxvr': (r'Maximum \\\\|G\\\\| for potential and density', 1 / ureg.bohr), 'x_exciting_gvector_size': (r'G\\\\-vector grid\",\n \"'x_exciting_valence_charge_initial': ('Total valence charge', ureg.elementary_charge), 'x_exciting_wigner_radius': (r'Effective Wigner radius, r\\\\_s', ureg.bohr), 'x_exciting_empty_states': ('Number\",\n \"= energy_fermi band_k_points = self.bandstructure_parser.get('band_k_points') nkpts_segment = self.bandstructure_parser.number_of_k_points_per_segment band_seg_labels = self.bandstructure_parser.get('band_segm_labels') for nb\",\n \"str_operation=str_to_atom_properties_dict, repeats=False, convert=False)] self._miscellaneous_keys_mapping = { 'x_exciting_gap': (r'Estimated fundamental gap', ureg.hartree), 'time': (r'Wall\",\n \"\\\\(rgkmax\\\\)', None), 'x_exciting_species_rtmin': (r'Species with R\\\\^MT\\\\_min', None), 'x_exciting_gkmax': (r'Maximum \\\\|G\\\\+k\\\\| for APW functions',\",\n \"if f.endswith('QMT%s.OUT' % str(i + 1).rjust(3, '0'))] for f in files_q: self.data_xs_parser.mainfile =\",\n \"energy', 'exchange'], 'x_exciting_exchange_energy': ['Exchange energy', 'exchange'], 'energy_correlation': ['Correlation energy', 'correlation'], 'x_exciting_correlation_energy': ['Correlation energy',\",\n \"= section.get('positions_format') if positions_format is None: species = self.get_initialization_parameter('species', []) for specie in\",\n \"['Electron-nuclear energy', 'electron-nuclear '], 'x_exciting_nuclear_nuclear_energy': ['Nuclear-nuclear energy', 'nuclear-nuclear'], 'x_exciting_madelung_energy': ['Madelung energy', 'Madelung'], 'x_exciting_core_electron_kinetic_energy':\",\n \"self.info_gw_parser.quantities = parser.info_gw_parser.quantities def parse(self, filepath, archive, logger): self.filepath = filepath self.archive =\",\n \"+ nkpts_segment band_energy = np.array([ np.transpose(energy)[start:end] for energy in band_energies]) if self._energy_unit is\",\n \"self.input_xml_parser.get( 'gw/mixbasis/epsmb', 0.0001) gmb = self.input_xml_parser.get('gw/mixbasis/gmb', 1.0) sec_gw.mixed_basis_gmax = gmb * gmaxvr pwm\",\n \"r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Time', repeats=False, str_operation=str_to_array, convert=False, unit=ureg.hartree / ureg.bohr),\",\n \"def distances(self): if self._distances is None: dist = np.transpose(self.data)[0] n_k_points = np.where(dist ==\",\n \"quantity == 'LOSS' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_loss_function_no_local_field = data[1] elif quantity ==\",\n \"def get_data(quantity, ext): # all files related to quantity at all qpoints files\",\n \"16)) sec_gw.self_energy_c_number_of_poles = int(self.input_xml_parser.get( 'gw/selfenergy/npol', 0)) sec_gw.self_energy_c_number_of_empty_states = int(self.input_xml_parser.get( 'gw/selfenergy/nempty', 0)) sec_gw.self_energy_singularity_treatment =\",\n \"14) rgkmax = self.input_xml_parser.get('xs/BSE/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0)\",\n \"3), 'x_exciting_brillouin_zone_volume': ('Brillouin zone volume', 1 / ureg.bohr ** 3), 'x_exciting_number_of_atoms': ('Total number\",\n \"sec_scc def parse_system(self, section): sec_run = self.archive.run[-1] positions = self.info_parser.get_atom_positions(section.get('atomic_positions', {})) lattice_vectors =\",\n \"@property def distances(self): if self._distances is None: data = np.transpose(self.data) self._distances = data[5][:int(max(data[1]))]\",\n \"or species is None: continue lattice_vectors = np.array(lattice_vectors, dtype=float) lattice_vectors *= self.input_xml_parser.get('structure/crystal/scale', 1.0)\",\n \"v[1].split()] vi = vi[0] if len(vi) == 1 else vi properties[v[0].strip()] = vi\",\n \"energy_fermi = sec_scc.energy.fermi energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi\",\n \"repeats=False, dtype=float, unit=ureg.hartree), Quantity( 'energy_contributions', r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n *(?:DOS|Density)', str_operation=str_to_energy_dict, repeats=False, convert=False), Quantity( 'x_exciting_dos_fermi', r'DOS\",\n \"contributions moment_contributions = iteration.get('moment_contributions', {}) for key, names in self._moment_keys_mapping.items(): val = None\",\n \"self.info_parser.get_initialization_parameter('smearing_kind') if smearing_kind is not None: if not isinstance(smearing_kind, str): smearing_kind = smearing_kind[0]\",\n \"= positions_format positions = specie.get('positions') positions = self.info_parser.get_atom_positions( positions=positions, positions_format=positions_format).to('m') sec_atoms_group.x_exciting_geometry_atom_positions = positions.magnitude\",\n \"in bs_files: if self.file_exists(fname): exciting_files.append(fname) break # Parse DFT band structure from one\",\n \"None) if positions_format is not None: break return positions_format def get_atom_positions(self, section={}, positions=None,\",\n \"ureg.hartree if sec_scc.energy is not None: energy_fermi = sec_scc.energy.fermi energy_fermi = (energy_fermi.magnitude *\",\n \"BandstructureDatParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None)\",\n \"if fermi_energy is not None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface = fermi_energy sec_fermisurface.x_exciting_values_fermi_surface = fermi_surface def _parse_evalqp(self,\",\n \"bse_types: for scr_type in scr_types: files = self.get_exciting_files( '%s_BSE%s_SCR%s.OUT' % (name, bse_type, scr_type))\",\n \"'structure/crystal/basevect', np.eye(3)) species = self.input_xml_parser.get('structure/species/speciesfile') if positions is None or lattice_vectors is None\",\n \"val = None for name in names: val = charge_contributions.get(name, None) if val\",\n \"= 1 while True: info_volume = self.get_exciting_files('run_dir%s/INFO.OUT' % str(volume_index).rjust(2, '0')) if not info_volume:\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use\",\n \"for e in self.bands[n][i]]) for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment\",\n \"if smearing_kind is not None: if not isinstance(smearing_kind, str): smearing_kind = smearing_kind[0] smearing_kind\",\n \"// nspin)) for v in data]) res = res.reshape((len(res), len(data), len(res[0]) // len(data)))\",\n \"data[6:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies = [] start =\",\n \"nuclear charge', ureg.elementary_charge), 'x_exciting_electronic_charge': ('Total electronic charge', ureg.elementary_charge), 'x_exciting_core_charge_initial': ('Total core charge', ureg.elementary_charge),\",\n \"sec_scc.calculations_ref = [sec_scc_ref] # parse properties gw_info_files = self.get_exciting_files(gw_info_file) if len(gw_info_files) > 1:\",\n \"determined from occupancies which is problematic sometimes res = np.hstack([np.reshape(v[key], (nspin, np.size(v[key]) //\",\n \"[True] * 3 # TODO confirm no cell optimization in exciting sec_atoms.lattice_vectors =\",\n \"mainfile, logger): super().__init__(mainfile, logger=logger) def init_quantities(self): self._quantities = [] class ExcitingInfoParser(TextParser): def __init__(self):\",\n \"__init__(self, **kwargs): # TODO make a parent class for dos and bandstructure super().__init__(None)\",\n \"self.root.find('./%s/%s' % (self._totaldos_key, self._diagram_key)) self._ndos = len(total_dos) return self._ndos @property def number_of_lm(self): if\",\n \"None: setattr(msection, 'x_exciting_dos_fermi', x_exciting_dos_fermi) # energy contributions energy_contributions = iteration.get('energy_contributions', {}) for key,\",\n \"np.zeros(( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, len(self.distances)), dtype=float) for i in range(len(self.bands[n])): band_energies[i % self.number_of_spin_channels][i] =\",\n \"f in os.listdir( self.info_parser.maindir) if target[0] in f and mainfile_base in f] options\",\n \"'energy_kinetic_electronic': ['Kinetic energy', 'electronic kinetic'], 'energy_coulomb': ['Coulomb energy', 'Coulomb'], 'x_exciting_coulomb_energy': ['Coulomb energy', 'Coulomb'],\",\n \"= self.eigval_parser.get('k_points') sec_eigenvalues.occupations = get_data('occupancies') sec_eigenvalues.energies = get_data('eigenvalues') def _parse_fermisurface(self, sec_scc): fermi_surface =\",\n \"// (self.number_of_lm * self.number_of_spin_channels) res[lm][spin][atom] = self._get_dos(self.partial_dos[i]) if self.energy_unit is not None: res\",\n \"in energy: return energy return np.array([d[1].get(key) for d in data]) * ureg.hartree eigs_gw\",\n \"sec_scc.x_exciting_xs_tddft_number_of_epsilon_values = len(data[0][0][0]) sec_scc.x_exciting_xs_tddft_epsilon_energies = data[0][0][0] * ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field = data[1:] elif quantity\",\n \"states', None), 'x_exciting_hamiltonian_size': ('Maximum Hamiltonian size', None), 'x_exciting_pw': (r'Maximum number of plane\\\\-waves', None),\",\n \"sec_scc = self.parse_scc(section) if sec_scc is None: return sec_system = self.parse_system(section) if sec_system\",\n \"smearing_kind = smearing_kind_map[smearing_kind] sec_smearing.kind = smearing_kind smearing_width = self.info_parser.get_initialization_parameter('smearing_width') if smearing_width is not\",\n \"data[1:3] def parse_xs(self): sec_run = self.archive.run[-1] xs_info_files = self.get_exciting_files('INFOXS.OUT') if not xs_info_files: return\",\n \"= self.get_exciting_files('run_dir%s/INFO.OUT' % str(volume_index).rjust(2, '0')) if not info_volume: break sec_scc.calculations_path.append(info_volume[0]) def init_parser(self): self.info_parser.mainfile\",\n \"grid', None), 'x_exciting_kpoint_offset': (r'k\\\\-point offset', None), 'x_exciting_number_kpoints': (r'Total number of k\\\\-points', None), 'x_exciting_rgkmax':\",\n \"self._moment_keys_mapping = { 'x_exciting_interstitial_moment': ['interstitial'], 'x_exciting_total_MT_moment': ['total moment in muffin-tins'], 'x_exciting_total_moment': ['total moment'],\",\n \"in range(1, len(self.distances)): if self.distances[i] == self.distances[i - 1]: self._nkpts_segment.append(count) count = 1\",\n \"== 'charge_total': pass else: setattr(msection, key, val) # moment contributions moment_contributions = iteration.get('moment_contributions',\",\n \"rf'\\\\d+ : *({re_float}) *({re_float}) *({re_float})', repeats=True, dtype=np.dtype(np.float64))]))) initialization_quantities.append(Quantity( 'potential_mixing', r'Using ([\\\\w ]+) potential\",\n \"get_atom_positions(self, section={}, positions=None, positions_format=None): positions = positions if positions is not None else\",\n \"need to set nspin again as this is overwritten when setting mainfile self.bandstructure_parser._nspin\",\n \"def get_exciting_files(self, default): mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = default.rsplit('.', 1)\",\n \"None: smearing_width = (smearing_width * ureg.hartree).to('joule') # TODO smearing with should have units\",\n \"final SCF iteration|Reached self-consistent loops maximum)([\\\\s\\\\S]+?)(\\\\n *\\\\n\\\\+{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(Atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n',\",\n \"other xcf xc_functional_map = { 2: ['LDA_C_PZ', 'LDA_X_PZ'], 3: ['LDA_C_PW', 'LDA_X_PZ'], 4: ['LDA_C_XALPHA'],\",\n \"def parse_exciton(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components = n_components n_excitons\",\n \"positions_format def get_atom_positions(self, section={}, positions=None, positions_format=None): positions = positions if positions is not\",\n \"sec_scc.x_exciting_xs_bse_epsilon_im = np.reshape( data[2], (n_components, n_epsilon)) def parse_sigma(data, sec_scc): n_components = len(data) data\",\n \"== dist[0])[0][1] self._distances = dist[:n_k_points] return self._distances @property def number_of_spin_channels(self): if self._nspin is\",\n \"data = [] for f in files: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is\",\n \"sec_energy.m_add_sub_section(getattr( Energy, key.replace('energy_', '')), EnergyEntry(value=val)) else: setattr(msection, key, val) if key == 'x_exciting_fermi_energy':\",\n \"n_epsilon)) * ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re = np.reshape( data[1], (n_components, n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im = np.reshape( data[2],\",\n \"= EnergyEntry(value=energy_total) x_exciting_dos_fermi = iteration.get('x_exciting_dos_fermi') if x_exciting_dos_fermi is not None: setattr(msection, 'x_exciting_dos_fermi', x_exciting_dos_fermi)\",\n \"name, key_unit in self._system_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) )\",\n \"len(self.distances)): if self.distances[i] == self.distances[i - 1]: self._nkpts_segment.append(count) count = 1 else: count\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use this file except\",\n \"\\\\(target\\\\)', ureg.hartree), 'x_exciting_energy_convergence': ( r'Absolute change in total energy\\\\s*\\\\(target\\\\)', ureg.hartree), 'x_exciting_charge_convergence': ( r'Charge\",\n \"force_convergence[1] sec_scc = parse_configuration(structure_optimization) if sec_scc is None: return # volume optimizations volume_index\",\n \"= self.input_xml_parser.get('gw/ngridq', [1, 1, 1]) sec_gw.basis_set = 'mixed' sec_gw.qp_equation_treatment = 'linearization' sec_gw.max_frequency =\",\n \"self.vertices[i].attrib.get('label') end = self.vertices[i + 1].attrib.get('label') res.append([ '\\\\u0393' if start.lower() == 'gamma' else\",\n \"setattr(msection, key, val) if key == 'x_exciting_fermi_energy': sec_energy.fermi = val # charge contributions\",\n \"mainfile is stored. \\\"\\\"\\\" mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = filename.rsplit('.',\",\n \"energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic)\",\n \"np.array(data[:3], dtype=float)) sec_system.x_exciting_clathrates_atom_labels = list(data[3]) else: sec_system.x_exciting_clathrates = False potential_mixing = self.info_parser.get_initialization_parameter('potential_mixing') if\",\n \"section.get('optimization_step', [None])[-1] if final is None else final if final is None: return\",\n \"is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') # TODO I am not\",\n \"3 # TODO confirm no cell optimization in exciting sec_atoms.lattice_vectors = lattice_vectors lattice_vectors_reciprocal\",\n \"v.split(':') if len(v) < 2: continue energies[v[0].strip()] = float(v[1]) * ureg.hartree return energies\",\n \"= [] files_q = [f for f in files if f.endswith('QMT%s.OUT' % str(i\",\n \"else: # TODO implement reading of parameters from input.xml for normal calculations #\",\n \"specie.get('muffin_tin_radius') positions_format = specie.get('positions_format') sec_atoms_group.x_exciting_atom_position_format = positions_format positions = specie.get('positions') positions = self.info_parser.get_atom_positions(\",\n \"def get_files(name): bse_types = ['IP', 'singlet', 'triplet', 'RPA'] scr_types = ['full', 'diag', 'noinvdiag',\",\n \"= self._parse_evalqp elif name.startswith('TDOS') and name.endswith('OUT'): parser = self.dos_out_parser parser_function = self._parse_dos_out elif\",\n \"kwargs.get('energy_unit', None) def init_parameters(self): # TODO make a parent clas for bandstructure dat\",\n \"self._neigs_segment = None self._vertices = None self._distances = None self._band_energies = None self._band_k_points\",\n \"in data]) res = res.reshape((len(res), len(data), len(res[0]) // len(data))) if key == 'eigenvalues':\",\n \"volume = self.info_parser.get_unit_cell_volume() dos = data[1] * (1 / ureg.hartree) * volume.to('m**3').magnitude for\",\n \"[[], [], []] for i in range(len(qpoints)): data_q = [] files_q = [f\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"scr_type in scr_types: files = self.get_exciting_files( '%s_BSE%s_SCR%s.OUT' % (name, bse_type, scr_type)) bse_files.append(files) return\",\n \"charge', ureg.elementary_charge), 'x_exciting_electronic_charge': ('Total electronic charge', ureg.elementary_charge), 'x_exciting_core_charge_initial': ('Total core charge', ureg.elementary_charge), 'x_exciting_valence_charge_initial':\",\n \"charge_contributions.get(name, None) if val is not None: break if val is None: continue\",\n \"self._nkpts_segment.append(count) return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data = np.transpose(self.data)[0]\",\n \"name in names: val = charge_contributions.get(name, None) if val is not None: break\",\n \"data = np.reshape(data, (nspin, len(data) // nspin, 2)) data = np.transpose(data, axes=(2, 0,\",\n \"= np.reshape( data[1], (n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im = np.reshape( data[2], (n_components, n_sigma)) def parse_loss(data,\",\n \"dos[spin] # TODO add PDOS def _parse_bandstructure_dat(self, sec_scc): self.bandstructure_dat_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies =\",\n \"scf iterations\\\\s*\\\\:\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'force_convergence', r'Maximum force magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)', str_operation=str_to_quantity_tolerances, unit=ureg.hartree /\",\n \"ExcitingParser: def __init__(self): self.info_parser = ExcitingInfoParser() self.dos_parser = DOSXMLParser(energy_unit=ureg.hartree) self.bandstructure_parser = BandstructureXMLParser(energy_unit=ureg.hartree) self.eigval_parser\",\n \"= self.info_parser.get_atom_positions( positions=positions, positions_format=positions_format).to('m') sec_atoms_group.x_exciting_geometry_atom_positions = positions.magnitude # clathrate info clathrate_file = self.get_exciting_files('str.out')\",\n \"in self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy = np.array([np.transpose(band)[start:end] for band in\",\n \"os.access(filepath, os.F_OK): return True return False def _parse_dos(self, sec_scc): if self.dos_parser.get('totaldos', None) is\",\n \"= self.archive.m_create(Workflow) sec_worfklow.type = 'single_point' structure_optimization = self.info_parser.get('structure_optimization') if structure_optimization is not None:\",\n \"self.vertices[i].attrib.get(self._coord_key).split(), dtype=float) end = np.array( self.vertices[i + 1].attrib.get(self._coord_key).split(), dtype=float) res.append(np.linspace(start, end, self.number_of_k_points_per_segment[i])) elif\",\n \"1]) sec_gw.basis_set = 'mixed' sec_gw.qp_equation_treatment = 'linearization' sec_gw.max_frequency = self.input_xml_parser.get( 'gw/freqgrid/freqmax', 1.0) sec_gw.frequency_grid_type\",\n \"1].attrib.get(self._coord_key).split() res.append([start, end]) else: res = None self._results[key] = res class DOSXMLParser(XMLParser): def\",\n \"0]) if sec_method.x_exciting_xs_bse_xas: sec_method.x_exciting_xs_bse_xasatom = self.input_xml_parser.get( 'xs/BSE/xasatom', 0) sec_method.x_exciting_xs_bse_xasedge = self.input_xml_parser.get( 'xs/BSE/xasedge', 'K')\",\n \"None: return partialdos = partialdos.to('1/joule').magnitude lm_values = np.column_stack((np.arange(len(partialdos)), np.zeros(len(partialdos), dtype=np.int32))) for lm in\",\n \"= np.reshape( data[1], (n_components, n_loss)) # TODO check if format of files are\",\n \"sec_dft = sec_method.m_create(DFT) sec_electronic = sec_method.m_create(Electronic) sec_electronic.method = 'DFT' smearing_kind_map = { 'Gaussian':\",\n \"= set([int(e.attrib.get(self._l_key)) for e in self.partial_dos]) for li in l_list: self._nlm += 2\",\n \"* ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re = np.reshape( data[1], (n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im = np.reshape( data[2], (n_components,\",\n \"energy', 'Fermi'], 'energy_kinetic_electronic': ['Kinetic energy', 'electronic kinetic'], 'energy_coulomb': ['Coulomb energy', 'Coulomb'], 'x_exciting_coulomb_energy': ['Coulomb\",\n \"or name.endswith('TXT')): parser = self.evalqp_parser parser_function = self._parse_evalqp elif name.startswith('TDOS') and name.endswith('OUT'): parser\",\n \"val) # moment contributions moment_contributions = iteration.get('moment_contributions', {}) for key, names in self._moment_keys_mapping.items():\",\n \"'x_exciting_section_MT_charge_atom': for n in range(len(val)): sec_mt_charge_atom = msection.m_create(x_exciting_section_MT_charge_atom) sec_mt_charge_atom.x_exciting_MT_charge_atom_index = n + 1\",\n \"self.init_parser() sec_run = self.archive.m_create(Run) sec_run.program = Program( name='exciting', version=self.info_parser.get('program_version', '').strip()) # method goes\",\n \"= res * ureg.hartree return res sec_eigenvalues = sec_scc.m_create(BandEnergies) sec_eigenvalues.kpoints = self.eigval_parser.get('k_points') sec_eigenvalues.occupations\",\n \"*({re_float})', repeats=True, dtype=np.dtype(np.float64))]))) initialization_quantities.append(Quantity( 'potential_mixing', r'Using ([\\\\w ]+) potential mixing', repeats=False, flatten=False) )\",\n \"None) xc_functional_names.append(correlation) exchange = self.input_xml_parser.get('libxc/exchange', None) xc_functional_names.append(exchange) sec_xc_functional = sec_dft.m_create(XCFunctional) for name in\",\n \"'band_parameters', r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)', str_operation=str_to_band_parameters, repeats=False)) self._quantities.append( Quantity( 'fermi_surface', r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n *E*', unit=ureg.hartree, repeats=True)) class ExcitingEigenvalueParser(TextParser):\",\n \"the case when there is a mismatch between files self.logger.warn( 'Mismatch in EXCITON\",\n \"{}) for optimization_step in structure_optimization.get('optimization_step', []): sec_scc = parse_configuration(optimization_step) if optimization_step.get('method') is not\",\n \"None: return sec_fermisurface = sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters = self.fermisurf_parser.get('band_parameters', None) if band_parameters is not\",\n \"mismatch between files self.logger.warn( 'Mismatch in EXCITON and file type', data=dict(file=quantity)) sec_scc =\",\n \"key_unit in self._method_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) initialization_quantities.append(Quantity(\",\n \"ext, fxctype)) data = [[], [], []] for i in range(len(qpoints)): data_q =\",\n \"1) return self._energy_unit @property def number_of_spin_channels(self): if self._nspin is None: if not self.total_dos:\",\n \"in val[3:6]], dtype=float) return [nbands, mesh, origin, vector] self._quantities.append( Quantity( 'band_parameters', r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)', str_operation=str_to_band_parameters,\",\n \"name in self.info_parser._convergence_keys_mapping.keys(): threshold = self.info_parser.get_scf_threshold(name) if threshold is None: continue metainfo_name =\",\n \"[v.split() for v in val_in.split('\\\\n')] val = np.transpose(np.array([v for v in val if\",\n \"end = self.vertices[i + 1].attrib.get(self._coord_key).split() res.append([start, end]) else: res = None self._results[key] =\",\n \"key, val) # convergence values for name in self.info_parser._convergence_keys_mapping.keys(): val = iteration.get(name) if\",\n \"'linearization' sec_gw.max_frequency = self.input_xml_parser.get( 'gw/freqgrid/freqmax', 1.0) sec_gw.frequency_grid_type = self.input_xml_parser.get( 'gw/freqgrid/fgrid', 'gaule2') sec_gw.number_of_frequencies =\",\n \"= [] for i in range(len(self.vertices) - 1): start = self.vertices[i].attrib.get('label') end =\",\n \"energy_fermi band_k_points = self.bandstructure_parser.get('band_k_points') nkpts_segment = self.bandstructure_parser.number_of_k_points_per_segment band_seg_labels = self.bandstructure_parser.get('band_segm_labels') for nb in\",\n \"labels def get_positions_format(self, section): positions_format = section.get('positions_format') if positions_format is None: species =\",\n \"eigenvalues', 'sum of eigenvalues'], 'x_exciting_effective_potential_energy': ['Effective potential energy'], 'x_exciting_coulomb_potential_energy': ['Coulomb potential energy', 'Coulomb\",\n \"np.zeros(len(partialdos), dtype=np.int32))) for lm in range(len(partialdos)): for spin in range(len(partialdos[lm])): for atom in\",\n \"range(len(band_energies)): # Get fermi energy: it is used to un-shift the band structure\",\n \"None: total_dos = self.root.find('./%s/%s' % (self._totaldos_key, self._diagram_key)) self._ndos = len(total_dos) return self._ndos @property\",\n \"int(self.input_xml_parser.get( 'gw/freqgrid/nomeg', 16)) sec_gw.self_energy_c_number_of_poles = int(self.input_xml_parser.get( 'gw/selfenergy/npol', 0)) sec_gw.self_energy_c_number_of_empty_states = int(self.input_xml_parser.get( 'gw/selfenergy/nempty', 0))\",\n \"in range(len(totaldos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value = totaldos[spin] partialdos\",\n \"[]) for scf_iteration in scf_iterations: sec_scf_iteration = sec_scc.m_create(ScfIteration) parse_scf(scf_iteration, sec_scf_iteration) return sec_scc def\",\n \"init_quantities(self): re_symbol = re.compile(r'([A-Z][a-z]?)') def str_to_array(val_in): val = [v.split(':')[-1].split() for v in val_in.strip().split('\\\\n')]\",\n \"self._energy_key = 'eval' self._vertex_key = 'vertex' self._band_key = 'band' self._atom_key = 'atom' self._nspin\",\n \"Quantity( 'time', r'Time spent in this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds', unit=ureg.s, repeats=False, dtype=float) ] self._quantities.append(Quantity(\",\n \"parse_exciton elif quantity == 'EPSILON': parse_function = parse_epsilon elif quantity == 'SIGMA': parse_function\",\n \"= None self._nspin = None self._nlm = None self._energies = None self._total_dos =\",\n \"is None: bands = self.root.findall('./%s' % self._band_key) self._bands = [] if bands: self._bands.append(bands)\",\n \"'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Time', repeats=False, str_operation=str_to_array, convert=False, unit=ureg.hartree /\",\n \"class ExcitingParser: def __init__(self): self.info_parser = ExcitingInfoParser() self.dos_parser = DOSXMLParser(energy_unit=ureg.hartree) self.bandstructure_parser = BandstructureXMLParser(energy_unit=ureg.hartree)\",\n \"it from input.xml input_file = self.get_exciting_files('input.xml') for f in input_file: self.input_xml_parser.mainfile = f\",\n \"sec_run = self.archive.run[-1] # two versions of gw info files gw_info_files = ['GW_INFO.OUT',\",\n \"Will read all!', data=dict(file=name)) for n in range(len(files)): parser.mainfile = files[n] parser_function(section) #\",\n \"ureg.joule).to('hartree') sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = self.dos_parser.number_of_dos sec_dos.energies = self.dos_parser.energies + energy_fermi\",\n \"'xs/BSE/xasatom', 0) sec_method.x_exciting_xs_bse_xasedge = self.input_xml_parser.get( 'xs/BSE/xasedge', 'K') sec_method.x_exciting_xs_bse_xasspecies = self.input_xml_parser.get( 'xs/BSE/xasspecies', 0) sec_method.x_exciting_xs_bse_xas_number_of_bands\",\n \"# ones with the missing quantity if len(quantity) < n_scf: quantity = [None]\",\n \"self.input_xml_parser.get('xs/BSE/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0) sec_method.x_exciting_xs_bse_rgkmax = rgkmax\",\n \"quantity == 'SIGMA': parse_function = parse_sigma elif quantity == 'LOSS': parse_function = parse_loss\",\n \"if not input_file: return self.input_xml_parser.mainfile = input_file[0] xstype = self.input_xml_parser.get('xs/xstype', '') if xstype.lower()\",\n \"archive self.logger = logger if logger is not None else logging self._calculation_type =\",\n \"ureg.elementary_charge), 'x_exciting_valence_charge_initial': ('Total valence charge', ureg.elementary_charge), 'x_exciting_wigner_radius': (r'Effective Wigner radius, r\\\\_s', ureg.bohr), 'x_exciting_empty_states':\",\n \"self._energy_keys_mapping.items(): val = None for name in names: val = energy_contributions.get(name, None) if\",\n \"energies[v[0].strip()] = float(v[1]) * ureg.hartree return energies self._quantities = [Quantity( 'program_version', r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\. ]+)\\\\s*started',\",\n \"steps dont have the quantity # the only thing that we can do\",\n \"for s in species]) if positions is None: return positions = np.array(positions) positions_format\",\n \"of NOMAD. # See https://nomad-lab.eu for further info. # # Licensed under the\",\n \"ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_dat_parser.band_k_points nkpts_segment =\",\n \"in f] options = [f for f in options if f.endswith(file_ext)] options.sort() filenames\",\n \"return self.input_xml_parser.mainfile = input_file[0] xstype = self.input_xml_parser.get('xs/xstype', '') if xstype.lower() == 'bse': self._parse_xs_bse()\",\n \"mesh, origin, vector] self._quantities.append( Quantity( 'band_parameters', r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)', str_operation=str_to_band_parameters, repeats=False)) self._quantities.append( Quantity( 'fermi_surface', r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n\",\n \"else: setattr(msection, key, val) # convergence values for name in self.info_parser._convergence_keys_mapping.keys(): val =\",\n \"input.xml input_file = self.get_exciting_files('input.xml') for f in input_file: self.input_xml_parser.mainfile = f correlation =\",\n \"self.input_xml_parser.get( 'xs/BSE/sciavtype', 'spherical') sec_method.x_exciting_xs_bse_xas = self.input_xml_parser.get( 'xs/BSE/xas', False) sec_method.x_exciting_xs_bse_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlbse', [0,\",\n \"re.compile(r'([A-Z][a-z]?)') def str_to_array(val_in): val = [v.split(':')[-1].split() for v in val_in.strip().split('\\\\n')] val = val[0]\",\n \"return self._total_dos @property def partial_dos(self): if self._partial_dos is None: self._partial_dos = self.root.findall('./%s/%s' %\",\n \"(n_components, n_loss)) * ureg.hartree sec_scc.x_exciting_xs_bse_loss = np.reshape( data[1], (n_components, n_loss)) # TODO check\",\n \"if len(gw_info_files) > 1: self.logger.warn('Found multiple GW info files, will read only first!')\",\n \"'spherical' sec_dos_values.lm = lm_values[lm] sec_dos_values.spin = spin sec_dos_values.atom_index = atom sec_dos_values.value = partialdos[lm][spin][atom]\",\n \"= { 'x_exciting_effective_potential_convergence': ( r'RMS change in effective potential \\\\(target\\\\)', ureg.hartree), 'x_exciting_energy_convergence': (\",\n \"'FERMISURF.bxsf', 'FS.bxsf'] # Parse DFT DOS from one of the files bs_files =\",\n \"exist self._quantities.append( Quantity( 'frequency_data', r'frequency list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)', str_operation=str_to_frequency, repeats=False) ) self._quantities.append( Quantity( 'fermi_energy', r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi\",\n \"if fundamental_band_gap is not None: sec_gap.value_fundamental = fundamental_band_gap optical_band_gap = self.info_gw_parser.get('optical_band_gap', None) if\",\n \"= data[1:3] elif quantity == 'LOSS' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_loss_function_local_field = data[1]\",\n \"vertices(self): if self._vertices is None: self._vertices = self.root.findall('./%s' % self._vertex_key) return self._vertices @property\",\n \"= np.reshape( data[2], (n_components, n_epsilon)) def parse_sigma(data, sec_scc): n_components = len(data) data =\",\n \"return dos def parse(self, key): if self._results is None: self._results = dict() if\",\n \"atom_labels sec_atoms.periodic = [True] * 3 # TODO confirm no cell optimization in\",\n \"= np.vstack([s.get('positions') for s in species]) if positions is None: return positions =\",\n \"smearing_width = (smearing_width * ureg.hartree).to('joule') # TODO smearing with should have units of\",\n \"input_file = self.get_exciting_files('input.xml') for f in input_file: self.input_xml_parser.mainfile = f correlation = self.input_xml_parser.get('libxc/correlation',\",\n \"[None, None])[-1] def get_scf_quantity(self, name): n_scf = len(self.get('energy_total_scf_iteration', [])) quantity = self.get('%s_scf_iteration' %\",\n \"repeats=False) ) self._quantities.append( Quantity( 'fermi_energy', r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi [Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s', unit=ureg.hartree, repeats=False) ) self._quantities.append( Quantity( 'direct_band_gap',\",\n \"import TextParser, Quantity, XMLParser, DataTextParser from nomad.datamodel.metainfo.simulation.run import Run, Program from nomad.datamodel.metainfo.simulation.method import\",\n \"def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment = [] count = 1 for\",\n \"= nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.energies = band_energies[nb] + energy_fermi def _parse_band_out(self, sec_scc):\",\n \"{})) lattice_vectors = self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels = self.info_parser.get_atom_labels(section.get('atomic_positions', {})) input_file = self.get_exciting_files('input.xml') if positions\",\n \"from one of the files bs_files = ['bandstructure.xml', 'BAND.OUT', 'bandstructure.dat'] for fname in\",\n \"in range(1, len(self.distances)): if self.distances[i] == self.distances[i - 1]: self._nkpts_segment .append(count) count =\",\n \"np.array([d[1].get(key, None) for d in data]) else: energy = np.array([d[1].get(key, None) for d\",\n \"Quantity( 'scf_iteration', r'(?:I| i)teration number :([\\\\s\\\\S]+?)(?:\\\\n *\\\\n\\\\+{10}|\\\\+\\\\-{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=True), Quantity( 'final', r'(?:Convergence targets\",\n \"unit=ureg.electron_mass), Quantity('muffin_tin_radius', rf'muffin-tin radius *: *({re_float})', dtype=np.float64, unit=ureg.bohr), Quantity('radial_points', rf'radial points in muffin-tin\",\n \"1 self._nkpts_segment.append(count) return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data =\",\n \"= self.get_exciting_files(gw_info_file) if len(gw_info_files) > 1: self.logger.warn('Found multiple GW info files, will read\",\n \"calculations # in addition to INFO.OUT return else: return files = self.get_exciting_files(name) if\",\n \"with the License. # You may obtain a copy of the License at\",\n \"from input.xml for normal calculations # in addition to INFO.OUT return else: return\",\n \"= parser.evalqp_parser.quantities self.info_gw_parser.quantities = parser.info_gw_parser.quantities def parse(self, filepath, archive, logger): self.filepath = filepath\",\n \"filename.rsplit('.', 1) filepath = '%s%s' % (target[0], suffix) if target[1:]: filepath = '%s.%s'\",\n \"section): labels = section.get('symbols') if labels is None: # we get it by\",\n \"start = end res.append(res_n) elif key == 'band_k_points': res = [] for i\",\n \"data = np.transpose(np.vstack(data)) n_sigma = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_sigma_energies = np.reshape( data[0], (n_components,\",\n \"as this is overwritten when setting mainfile self.bandstructure_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_parser.get('band_energies',\",\n \"law or agreed to in writing, software # distributed under the License is\",\n \"Energy, key.replace('energy_', '')), EnergyEntry(value=val)) else: setattr(msection, key, val) if key == 'x_exciting_fermi_energy': sec_energy.fermi\",\n \"= reshape(get_data('Vxc')) def _parse_dos_out(self, sec_scc): data = self.dos_out_parser.data if data is None: return\",\n \"val is None: continue if name == 'x_exciting_spin_treatment': sub_sec = sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment =\",\n \"positions = np.dot(positions, cell.magnitude) return positions * ureg.bohr def get_scf_threshold(self, name): reference =\",\n \"for ext in ['FXC', 'NLF_FXC']: data = get_data(quantity, ext) if not data[0]: continue\",\n \"dtype=float, unit=1 / ureg.hartree), Quantity( 'charge_contributions', r'(?:Charges|Electron charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False), Quantity(\",\n \"is None: return nspin = self.info_parser.get_number_of_spin_channels() def reshape(data): if data[0] is None: return\",\n \") from nomad.datamodel.metainfo.simulation.system import ( System, Atoms ) from nomad.datamodel.metainfo.simulation.calculation import ( Calculation,\",\n \"name, key_unit in self._method_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) )\",\n \"= {keys[i]: eigs[i] for i in range(len(keys))} return [kpts, eigs] self._quantities.append( Quantity( 'kpoints_eigenvalues',\",\n \"section.get('forces') if forces is not None: sec_forces = sec_scc.m_create(Forces) sec_forces.total = ForcesEntry(value=forces) #\",\n \"self.get_exciting_files('run_dir%s/INFO.OUT' % str(volume_index).rjust(2, '0')) if not info_volume: break sec_scc.calculations_path.append(info_volume[0]) def init_parser(self): self.info_parser.mainfile =\",\n \"atom_labels = self.info_parser.get_atom_labels(section.get('atomic_positions', {})) input_file = self.get_exciting_files('input.xml') if positions is None: # get\",\n \"return np.reshape(data, (nspin, len(data) // nspin, len(data[0]))) sec_gw_eigenvalues = sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor = reshape(get_data('Znk'))\",\n \"reshape(data): if data[0] is None: return return np.reshape(data, (nspin, len(data) // nspin, len(data[0])))\",\n \"threshold to global metainfo. This is killing me! if metainfo_name == 'x_exciting_scf_threshold_energy_change': sec_method.scf\",\n \"specie.get('positions_format', None) if positions_format is not None: break return positions_format def get_atom_positions(self, section={},\",\n \"add support for info.xml, wannier.out if name.startswith('dos') and name.endswith('xml'): parser = self.dos_parser parser_function\",\n \"total_dos = self.root.find('./%s/%s' % (self._totaldos_key, self._diagram_key)) self._ndos = len(total_dos) return self._ndos @property def\",\n \"None self._band_k_points = None @property def band_energies(self): if self._band_energies is None: if self.data\",\n \"data[5][:int(max(data[1]))] return self._distances @property def number_of_spin_channels(self): if self._nspin is None: self._nspin = np.shape(np.transpose(self.data))[0]\",\n \"functions', None), 'x_exciting_nuclear_charge': ('Total nuclear charge', ureg.elementary_charge), 'x_exciting_electronic_charge': ('Total electronic charge', ureg.elementary_charge), 'x_exciting_core_charge_initial':\",\n \"totaldos = self.dos_parser.get('totaldos') * volume.to('m**3').magnitude for spin in range(len(totaldos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total)\",\n \"species is None: continue lattice_vectors = np.array(lattice_vectors, dtype=float) lattice_vectors *= self.input_xml_parser.get('structure/crystal/scale', 1.0) positions\",\n \"'')), EnergyEntry(value=val)) else: setattr(msection, key, val) if key == 'x_exciting_fermi_energy': sec_energy.fermi = val\",\n \"ureg.hartree), 'time': (r'Wall time \\\\(seconds\\\\)', ureg.s)} for name, key_unit in self._miscellaneous_keys_mapping.items(): scf_quantities.append(Quantity( name,\",\n \"'n_scf_iterations', r'Number of (?:total)* scf iterations\\\\s*\\\\:\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'force_convergence', r'Maximum force magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee\",\n \"origin, vector] self._quantities.append( Quantity( 'band_parameters', r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)', str_operation=str_to_band_parameters, repeats=False)) self._quantities.append( Quantity( 'fermi_surface', r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n *E*',\",\n \"of eigenvalues'], 'x_exciting_effective_potential_energy': ['Effective potential energy'], 'x_exciting_coulomb_potential_energy': ['Coulomb potential energy', 'Coulomb potential'], 'energy_xc_potential':\",\n \"parse(self, key): if self._results is None: self._results = dict() if 'total' in key:\",\n \"BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'fundamental_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'optical_band_gap',\",\n \"(n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_oscillator_strength = np.reshape( data[3], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_re = np.reshape(\",\n \"= kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None self._nkpts_segment = None self._neigs_segment =\",\n \"empty states', None), 'x_exciting_valence_states': ('Total number of valence states', None), 'x_exciting_hamiltonian_size': ('Maximum Hamiltonian\",\n \"class DOSXMLParser(XMLParser): def __init__(self, **kwargs): super().__init__(None) self._nspin_key = 'nspin' self._totaldos_key = 'totaldos' self._partialdos_key\",\n \"'xs/tddft/aresfxc', True) sec_method.x_exciting_xs_tddft_drude = self.input_xml_parser.get( 'xs/tddft/drude', [0., 0.]) sec_method.x_exciting_xs_tddft_split_parameter = self.input_xml_parser.get( 'xs/tddft/fxcbsesplit', 0.00001,\",\n \"% key_unit[0], repeats=False, unit=key_unit[1])) self._convergence_keys_mapping = { 'x_exciting_effective_potential_convergence': ( r'RMS change in effective\",\n \"self.info_parser.get('structure_optimization', {}) for optimization_step in structure_optimization.get('optimization_step', []): sec_scc = parse_configuration(optimization_step) if optimization_step.get('method') is\",\n \"'lattice': cell = self.get_initialization_parameter('lattice_vectors') if cell is None: return positions = np.dot(positions, cell.magnitude)\",\n \"properties = dict() atom_resolved = [] species = None for v in val:\",\n \"= 'partialdos' self._diagram_key = 'diagram' self._l_key = 'l' self._m_key = 'm' self._energy_key =\",\n \"parser_function = self._parse_dos_out elif name.startswith('bandstructure') and name.endswith('dat'): parser = self.bandstructure_dat_parser parser_function = self._parse_bandstructure_dat\",\n \"= self.input_xml_parser.get( 'xs/screening/nempty', 0) sec_method.x_exciting_xs_screening_ngridk = self.input_xml_parser.get( 'xs/screening/ngridk', [0, 0, 0]) rgkmax =\",\n \"ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies = np.reshape( data[2], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_oscillator_strength = np.reshape( data[3],\",\n \"start.lower() == 'gamma' else start, '\\\\u0393' if end.lower() == 'gamma' else end]) elif\",\n \"res = np.zeros(( self.number_of_lm, self.number_of_spin_channels, self.number_of_atoms, self.number_of_dos)) for i in range(len(self.partial_dos)): spin =\",\n \"IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Time', repeats=False, str_operation=str_to_array, convert=False, unit=ureg.hartree / ureg.bohr), Quantity( 'step', r'Optimization step\\\\s*(\\\\d+)',\",\n \"unit=ureg.hartree, repeats=True)) class ExcitingEigenvalueParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append(\",\n \"r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Time', repeats=False,\",\n \"= band_k_points[nb] sec_k_band_segment.endpoints_labels = band_seg_labels[nb] sec_k_band_segment.energies = band_energies[n][nb] + energy_fermi def _parse_eigenvalues(self, sec_scc):\",\n \"reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands = len(eigs_gw[0]) sec_gw_eigenvalues.n_kpoints = len(eigs_gw) sec_gw_eigenvalues.kpoints = get_data('k_points') sec_gw_eigenvalues.energies = reshape(eigs_gw)\",\n \"0.]) # TODO I am not certain if screening/BSE are children of xs\",\n \"= { 'x_exciting_interstitial_moment': ['interstitial'], 'x_exciting_total_MT_moment': ['total moment in muffin-tins'], 'x_exciting_total_moment': ['total moment'], 'x_exciting_section_MT_moment_atom':\",\n \"sec_worfklow.m_create(GeometryOptimization) threshold_force = structure_optimization.get( 'optimization_step', [{}])[0].get('force_convergence', [0., 0.])[-1] sec_geometry_opt.input_force_maximum_tolerance = threshold_force def parse_method(self):\",\n \"def _parse_xs_tddft(self): sec_run = self.archive.run[-1] fxctype = self.input_xml_parser.get('xs/tddft/fxctype', 'RPA') tetradf = self.input_xml_parser.get('xs/tetra/tetradf', None)\",\n \"'exchange'], 'x_exciting_exchange_energy': ['Exchange energy', 'exchange'], 'energy_correlation': ['Correlation energy', 'correlation'], 'x_exciting_correlation_energy': ['Correlation energy', 'correlation'],\",\n \"self._nspin = np.shape(np.transpose(self.data))[0] - 6 return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is\",\n \"for f in os.listdir( self.info_parser.maindir) if target[0] in f and mainfile_base in f]\",\n \"parse_loss else: continue try: parse_function(data, sec_scc) except Exception: self.logger.error('Error setting xs data', data=dict(file=quantity))\",\n \"= ['dos.xml', 'TDOS.OUT'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break # Parse\",\n \"fermi_surface is None: return sec_fermisurface = sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters = self.fermisurf_parser.get('band_parameters', None) if band_parameters\",\n \"['xc potential energy', 'xc potential'], 'energy_electrostatic': ['Hartree energy', 'Hartree'], 'x_exciting_hartree_energy': ['Hartree energy', 'Hartree'],\",\n \"self._neigs_segment = None self._nkpts_segment = None @property def band_energies(self): if self._band_energies is None:\",\n \"xs_info_files = self.get_exciting_files('INFOXS.OUT') if not xs_info_files: return self._calculation_type = 'xs' # inconsistency in\",\n \"elif name.startswith('BAND') and name.endswith('OUT'): parser = self.band_out_parser parser_function = self._parse_band_out elif name.startswith('input') and\",\n \"ureg.bohr ** 3), 'x_exciting_number_of_atoms': ('Total number of atoms per unit cell', None), 'x_exciting_spin_treatment':\",\n \"res class ExcitingFermiSurfaceBxsfParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity(\",\n \"self._band_key) self._bands = [] if bands: self._bands.append(bands) # add atom-resolved bands_atom = self.root.findall('./*/%s'\",\n \"sub_parser=TextParser(quantities=scf_quantities), repeats=True), Quantity( 'final', r'(?:Convergence targets achieved\\\\. Performing final SCF iteration|Reached self-consistent loops\",\n \"self._electron_charge_keys_mapping = { 'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage': ['core leakage'], 'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge': ['interstitial'], 'x_exciting_total_MT_charge':\",\n \"= BandstructureDatParser(energy_unit=ureg.hartree) self.band_out_parser = BandOutParser(energy_unit=ureg.hartree) self.info_gw_parser = GWInfoParser() self.input_xml_parser = XMLParser() self.data_xs_parser =\",\n \"of eigenvalues', 'sum of eigenvalues'], 'x_exciting_effective_potential_energy': ['Effective potential energy'], 'x_exciting_coulomb_potential_energy': ['Coulomb potential energy',\",\n \"sec_mt_charge_atom.x_exciting_MT_charge_atom_value = val[n][1] sec_charges = msection.m_create(Charges) sec_charges.value = [ val[n][1].magnitude for n in\",\n \"[Quantity( 'program_version', r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\. ]+)\\\\s*started', repeats=False, dtype=str, flatten=False)] initialization_quantities = [ Quantity( 'lattice_vectors', r'Lattice\",\n \"= self.bandstructure_parser.get('band_segm_labels') for nb in range(len(band_energies[n])): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints\",\n \"get_atom_labels(self, section): labels = section.get('symbols') if labels is None: # we get it\",\n \"['Exchange energy', 'exchange'], 'x_exciting_exchange_energy': ['Exchange energy', 'exchange'], 'energy_correlation': ['Correlation energy', 'correlation'], 'x_exciting_correlation_energy': ['Correlation\",\n \"sec_method.x_exciting_electronic_structure_method = xstype sec_method.x_exciting_xs_broadening = self.input_xml_parser.get( 'xs/broad', 0.01, 'hartree') sec_method.x_exciting_xs_gqmax = self.input_xml_parser.get( 'xs/gqmax',\",\n \"sec_fermisurface.x_exciting_values_fermi_surface = fermi_surface def _parse_evalqp(self, sec_scc): data = self.evalqp_parser.get('kpoints_eigenvalues') if data is None:\",\n \"if 'charge' in val_in: unit = ureg.elementary_charge elif 'moment' in val_in: unit =\",\n \"ureg.hartree sec_scc.x_exciting_xs_bse_loss = np.reshape( data[1], (n_components, n_loss)) # TODO check if format of\",\n \"sec_method if sec_run.system: sec_scc.system_ref = sec_run.system[-1] sec_scc_ref = sec_run.calculation[0] sec_scc.starting_calculation_ref = sec_scc_ref sec_scc.calculations_ref\",\n \"len(eigs) // nspin)) return data self._quantities.append( Quantity( 'eigenvalues_occupancies', r'\\\\(state\\\\, eigenvalue and occupancy below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n\",\n \"np.transpose( np.array(data[:3], dtype=float)) sec_system.x_exciting_clathrates_atom_labels = list(data[3]) else: sec_system.x_exciting_clathrates = False potential_mixing = self.info_parser.get_initialization_parameter('potential_mixing')\",\n \"None: return sec_scc = sec_run.m_create(Calculation) def parse_scf(iteration, msection): energy_total = iteration.get('energy_total') sec_energy =\",\n \"for name in names: val = charge_contributions.get(name, None) if val is not None:\",\n \"= '%s%s' % (target[0], suffix) if target[1:]: filename = '%s.%s' % (filename, target[1])\",\n \"r'frequency list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)', str_operation=str_to_frequency, repeats=False) ) self._quantities.append( Quantity( 'fermi_energy', r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi [Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s', unit=ureg.hartree, repeats=False) )\",\n \"the same folder where the mainfile is stored. \\\"\\\"\\\" mainfile = os.path.basename(self.info_parser.mainfile) suffix\",\n \"def band_k_points(self): if self._band_k_points is None: data = np.transpose(self.data) self._band_k_points = [] start\",\n \"/ self.energy_unit) elif key == 'energies': return self.energies else: res = None self._results[key]\",\n \"data[1:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies = [] start =\",\n \"(n_components, n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im = np.reshape( data[2], (n_components, n_epsilon)) def parse_sigma(data, sec_scc): n_components =\",\n \"= val[n][0] sec_mt_charge_atom.x_exciting_MT_charge_atom_value = val[n][1] sec_charges = msection.m_create(Charges) sec_charges.value = [ val[n][1].magnitude for\",\n \"in exciting sec_atoms.lattice_vectors = lattice_vectors lattice_vectors_reciprocal = self.info_parser.get_initialization_parameter( 'lattice_vectors_reciprocal') sec_atoms.lattice_vectors_reciprocal = lattice_vectors_reciprocal if\",\n \"self._parse_evalqp elif name.startswith('TDOS') and name.endswith('OUT'): parser = self.dos_out_parser parser_function = self._parse_dos_out elif name.startswith('bandstructure')\",\n \"*\\\\n\\\\+{10}|\\\\+\\\\-{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=True), Quantity( 'final', r'(?:Convergence targets achieved\\\\. Performing final SCF iteration|Reached self-consistent\",\n \"memory parser.mainfile = None def _parse_input_xs(self, sec_method): xstype = self.input_xml_parser.get('xs/xstype', None) if xstype\",\n \"(energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_dat_parser.band_k_points\",\n \"this file except in compliance with the License. # You may obtain a\",\n \"in range(len(band_energies)): # Get fermi energy: it is used to un-shift the band\",\n \"fermi_energy = self.info_gw_parser.get('fermi_energy', None) if fermi_energy is not None: sec_scc.energy = Energy(fermi=fermi_energy) gw_files\",\n \"reshape(eigs_gw) sec_gw_eigenvalues.value_exchange = reshape(get_data('Sx')) eigs_gw_C = reshape(get_data('Sc')) if eigs_gw_C is None: eigs_gw_C =\",\n \"points in muffin-tin *: *({re_float})', dtype=np.int32), Quantity('positions_format', r'atomic positions \\\\((.+?)\\\\)', flatten=False), Quantity( 'positions',\",\n \"self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None: continue data_q.append(self.data_xs_parser.data) if not data_q: continue\",\n \"= ExcitingEigenvalueParser() self.fermisurf_parser = ExcitingFermiSurfaceBxsfParser() self.evalqp_parser = ExcitingEvalqpParser() self.dos_out_parser = DataTextParser() self.bandstructure_dat_parser =\",\n \"\\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Time', repeats=False, str_operation=str_to_array, convert=False, unit=ureg.hartree / ureg.bohr), Quantity( 'step', r'Optimization step\\\\s*(\\\\d+)', repeats=False,\",\n \"== data[0][0])[0][1] bands = data[1:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies\",\n \"if self._nspin is None: self._nspin = 1 return self._nspin @property def number_of_k_points_per_segment(self): if\",\n \"sec_system def parse_configurations(self): sec_run = self.archive.run[-1] def parse_configuration(section): if not section: return sec_scc\",\n \"section.get('scf_iteration', [None])[-1] final = section.get('optimization_step', [None])[-1] if final is None else final if\",\n \"(r'Total number of local\\\\-orbitals', None)} self._method_keys_mapping = { 'smearing_kind': ('Smearing scheme', None), 'smearing_width':\",\n \"not None: break if val is None: continue if key.startswith('energy_'): sec_energy.m_add_sub_section(getattr( Energy, key.replace('energy_',\",\n \"= specie.get('radial_points') sec_atoms_group.x_exciting_muffin_tin_radius = specie.get('muffin_tin_radius') positions_format = specie.get('positions_format') sec_atoms_group.x_exciting_atom_position_format = positions_format positions =\",\n \"in which also other energies are reported. energy_fermi = 0.0 * ureg.hartree if\",\n \"ExcitingInfoParser() self.dos_parser = DOSXMLParser(energy_unit=ureg.hartree) self.bandstructure_parser = BandstructureXMLParser(energy_unit=ureg.hartree) self.eigval_parser = ExcitingEigenvalueParser() self.fermisurf_parser = ExcitingFermiSurfaceBxsfParser()\",\n \"n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im = np.reshape( data[2], (n_components, n_epsilon)) def parse_sigma(data, sec_scc): n_components = len(data)\",\n \"[Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s', unit=ureg.hartree, repeats=False) ) self._quantities.append( Quantity( 'direct_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity(\",\n \"start + nkpts_segment band_energy = np.array([np.transpose(band)[start:end] for band in bands]) if self._energy_unit: band_energy\",\n \"res.append(res_n) elif key == 'band_k_points': res = [] for i in range(len(self.number_of_k_points_per_segment)): start\",\n \"in gw_files: self.parse_file(f, sec_scc) frequency_data = self.info_gw_parser.get('frequency_data', None) if frequency_data is not None:\",\n \"ExcitingEigenvalueParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'k_points', r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\-\",\n \"quantity = self.get('%s_scf_iteration' % name) if quantity is None: return # this is\",\n \"val = val_in.strip().split('\\\\n') kpts = np.array(val[0].split(), dtype=float) keys = val[1].split() eigs = np.transpose(np.array([v.split()\",\n \"self.get_exciting_files('%s_%s%s%s.OUT' % (quantity, file_ext, ext, fxctype)) data = [[], [], []] for i\",\n \"bandstructure file # atom-resolved bandstructure are added as separate section_k_band res = []\",\n \"= np.reshape( data[1], (n_components, n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im = np.reshape( data[2], (n_components, n_epsilon)) def parse_sigma(data,\",\n \"self._parse_fermisurface elif name.startswith('EVALQP') and (name.endswith('DAT') or name.endswith('TXT')): parser = self.evalqp_parser parser_function = self._parse_evalqp\",\n \"gmb = self.input_xml_parser.get('gw/mixbasis/gmb', 1.0) sec_gw.mixed_basis_gmax = gmb * gmaxvr pwm = self.input_xml_parser.get('gw/barecoul/pwm', 2.0)\",\n \"# get it from last scf_iteration or optimization_step final = section.get('scf_iteration', [None])[-1] final\",\n \"fundamental_band_gap is not None: sec_gap.value_fundamental = fundamental_band_gap optical_band_gap = self.info_gw_parser.get('optical_band_gap', None) if optical_band_gap\",\n \"self._natoms = len(partial_dos) return self._natoms @property def number_of_dos(self): if self._ndos is None: total_dos\",\n \"DFT band structure from one of the files bs_files = ['bandstructure.xml', 'BAND.OUT', 'bandstructure.dat']\",\n \"self.number_of_spin_channels) res[lm][spin][atom] = self._get_dos(self.partial_dos[i]) if self.energy_unit is not None: res = res *\",\n \"atom-resolved bands_atom = self.root.findall('./*/%s' % self._atom_key) for band in bands_atom: self._bands.append(band.findall('./%s' % self._band_key))\",\n \"val_in.strip().split('\\\\n') energies = dict() for v in val: v = v.split(':') if len(v)\",\n \"f in options] else: filenames = [filename] filenames = [f for f in\",\n \"class GWInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] def str_to_frequency(val_in): val\",\n \"val = np.transpose(np.array([v for v in val if len(v) == 3], float)) return\",\n \"None @property def band_energies(self): if self._band_energies is None: data = np.transpose(self.data) n_kpoints =\",\n \"same folder where the mainfile is stored. \\\"\\\"\\\" mainfile = os.path.basename(self.info_parser.mainfile) suffix =\",\n \"if not xs_info_files: return self._calculation_type = 'xs' # inconsistency in the naming convention\",\n \"= [os.path.join(self.info_parser.maindir, f) for f in options] else: filenames = [filename] filenames =\",\n \"self.input_xml_parser.get('xs/qpointset/qpoint') def get_data(quantity, ext): # all files related to quantity at all qpoints\",\n \"'force_convergence', r'Maximum force magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)', str_operation=str_to_quantity_tolerances, unit=ureg.hartree / ureg.bohr, repeats=False, dtype=float), Quantity( 'energy_total',\",\n \"'xc potential'], 'energy_electrostatic': ['Hartree energy', 'Hartree'], 'x_exciting_hartree_energy': ['Hartree energy', 'Hartree'], 'x_exciting_electron_nuclear_energy': ['Electron-nuclear energy',\",\n \"None: res = res * (1 / self.energy_unit) elif 'partial' in key: if\",\n \"band_k_points[nb] sec_k_band_segment.endpoints_labels = band_seg_labels[nb] sec_k_band_segment.energies = band_energies[n][nb] + energy_fermi def _parse_eigenvalues(self, sec_scc): if\",\n \"required by applicable law or agreed to in writing, software # distributed under\",\n \"== 'tddft': self._parse_xs_tddft() def _parse_input_gw(self, sec_method): sec_gw = sec_method.m_create(GWMethod) sec_gw.type = 'G0W0' gmaxvr\",\n \"Quantity( 'parameters', r'\\\\n *(.+?:.+)', repeats=True, str_operation=lambda x: [v.strip() for v in x.split(':')])])) )\",\n \"'smearing_kind': ('Smearing scheme', None), 'smearing_width': ('Smearing width', None)} for name, key_unit in self._system_keys_mapping.items():\",\n \"parse_function(data, sec_scc) except Exception: self.logger.error('Error setting xs data', data=dict(file=quantity)) def _parse_xs_tddft(self): sec_run =\",\n \"continue data.append(self.data_xs_parser.data) return data def parse_exciton(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data))\",\n \"optimizations volume_index = 1 while True: info_volume = self.get_exciting_files('run_dir%s/INFO.OUT' % str(volume_index).rjust(2, '0')) if\",\n \"= val[n][0] sec_mt_moment_atom.x_exciting_MT_moment_atom_value = val[n][1] else: setattr(msection, key, val) # convergence values for\",\n \"= self.bandstructure_dat_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints\",\n \"'noinvdiag', 'longrange'] bse_files = [] for bse_type in bse_types: for scr_type in scr_types:\",\n \"= start + nkpts_segment self._band_k_points.append( np.transpose(data[2:5])[start:end]) start = end return self._band_k_points @property def\",\n \"n_loss)) * ureg.hartree sec_scc.x_exciting_xs_bse_loss = np.reshape( data[1], (n_components, n_loss)) # TODO check if\",\n \"int(spin) - 1 val_l = self.partial_dos[i].attrib.get(self._l_key, None) val_m = self.partial_dos[i].attrib.get(self._m_key, None) if val_l\",\n \"parameters here if input_GW.xml does not exist self._quantities.append( Quantity( 'frequency_data', r'frequency list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)', str_operation=str_to_frequency,\",\n \"*({re_float})', dtype=np.float64, unit=ureg.bohr), Quantity('radial_points', rf'radial points in muffin-tin *: *({re_float})', dtype=np.int32), Quantity('positions_format', r'atomic\",\n \"sec_scc.system_ref = sec_system sec_scc.method_ref = sec_run.method[-1] return sec_scc # groundstate and hybrids calculation\",\n \"not xc_functional_names: # get it from input.xml input_file = self.get_exciting_files('input.xml') for f in\",\n \"(nspin, len(eigs) // nspin)) return data self._quantities.append( Quantity( 'eigenvalues_occupancies', r'\\\\(state\\\\, eigenvalue and occupancy\",\n \"= os.path.join(self.info_parser.maindir, filepath) if os.path.isfile(filepath) and os.access(filepath, os.F_OK): return True return False def\",\n \"self.parse_file(f, sec_method) xc_functional_name = ' '.join(self.info_parser.get_xc_functional_name()) sec_method.gw.starting_point = xc_functional_name sec_scc = sec_run.m_create(Calculation) sec_scc.method_ref\",\n \"Quantity('file', r'parameters loaded from *: *(.+)'), Quantity('name', r'name *: *(.+)'), Quantity('nuclear_charge', rf'nuclear charge\",\n \"partialdos = self.dos_parser.get('partialdos') if partialdos is None: return partialdos = partialdos.to('1/joule').magnitude lm_values =\",\n \"or atom_labels is None: return sec_system = sec_run.m_create(System) sec_atoms = sec_system.m_create(Atoms) sec_atoms.positions =\",\n \"= sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] # parse\",\n \"species = self.info_parser.get_initialization_parameter('species', []) for specie in species: sec_atoms_group = sec_system.m_create(x_exciting_section_atoms_group) sec_atoms_group.x_exciting_geometry_atom_labels =\",\n \"init_parser(self): self.info_parser.mainfile = self.filepath self.info_parser.logger = self.logger self.dos_parser.logger = self.logger self.bandstructure_parser.logger = self.logger\",\n \"= self.info_parser.get('initialization', {}).get('xc_functional') if xc_functional is not None: sec_xc_functional.name = xc_functional.get('name_reference', [None, None])[0]\",\n \"super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'k_points', r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\- ]+):\\\\s*k\\\\-point', repeats=True)) def\",\n \"= float(v[1]) * ureg.hartree return energies self._quantities = [Quantity( 'program_version', r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\. ]+)\\\\s*started', repeats=False,\",\n \"'exchange'], 'energy_correlation': ['Correlation energy', 'correlation'], 'x_exciting_correlation_energy': ['Correlation energy', 'correlation'], 'energy_sum_eigenvalues': ['Sum of eigenvalues',\",\n \"Program( name='exciting', version=self.info_parser.get('program_version', '').strip()) # method goes first since reference needed for sec_scc\",\n \"spin-polarized case! I assume it is # energy dos_up dos_down nspin = self.info_parser.get_number_of_spin_channels()\",\n \"None: self._partial_dos = self.root.findall('./%s/%s' % (self._partialdos_key, self._diagram_key)) return self._partial_dos @property def energies(self): if\",\n \"val # charge contributions charge_contributions = iteration.get('charge_contributions', {}) for key, names in self._electron_charge_keys_mapping.items():\",\n \"'xs/tddft/ahc', False) sec_method.x_exciting_xs_tddft_anti_resonant_dielectric = self.input_xml_parser.get( 'xs/tddft/aresdf', False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel = self.input_xml_parser.get( 'xs/tddft/aresfxc', True) sec_method.x_exciting_xs_tddft_drude\",\n \"= [] start = 0 band_energies = np.zeros(( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, len(self.distances)), dtype=float) for\",\n \"if self.file_exists(fname): exciting_files.append(fname) break for f in exciting_files: self.parse_file(f, sec_scc) # structure optimization\",\n \"gw_info_files[0] fermi_energy = self.info_gw_parser.get('fermi_energy', None) if fermi_energy is not None: sec_scc.energy = Energy(fermi=fermi_energy)\",\n \"of atoms per unit cell', None), 'x_exciting_spin_treatment': ('Spin treatment', None), 'x_exciting_number_of_bravais_lattice_symmetries': ('Number of\",\n \"default.rsplit('.', 1) filename = '%s%s' % (target[0], suffix) if target[1:]: filename = '%s.%s'\",\n \"unit=1 / ureg.bohr, repeats=False, convert=False), ] self._system_keys_mapping = { 'x_exciting_unit_cell_volume': ('Unit cell volume',\",\n \"(smearing_width * ureg.hartree).to('joule') # TODO smearing with should have units of energy sec_smearing.width\",\n \"continue data_q = np.transpose(data_q, axes=(2, 0, 1)) for j in range(len(data)): data[j].append(data_q[j]) return\",\n \"= gw_info_files[0] fermi_energy = self.info_gw_parser.get('fermi_energy', None) if fermi_energy is not None: sec_scc.energy =\",\n \"= self._parse_eigenvalues elif (name.startswith('FERMISURF') or name.startswith('FS')) and name.endswith('bxsf'): parser = self.fermisurf_parser parser_function =\",\n \"flatten=False), Quantity( 'positions', rf'\\\\d+ : *({re_float}) *({re_float}) *({re_float})', repeats=True, dtype=np.dtype(np.float64))]))) initialization_quantities.append(Quantity( 'potential_mixing', r'Using\",\n \"if optimization_step.get('method') is not None: sec_scc.x_exciting_geometry_optimization_method = optimization_step.get('method') if optimization_step.get('step') is not None:\",\n \"BandstructureXMLParser(energy_unit=ureg.hartree) self.eigval_parser = ExcitingEigenvalueParser() self.fermisurf_parser = ExcitingFermiSurfaceBxsfParser() self.evalqp_parser = ExcitingEvalqpParser() self.dos_out_parser = DataTextParser()\",\n \"module started([\\\\s\\\\S]+?)Structure\\\\-optimization module stopped', sub_parser=TextParser(quantities=[ Quantity( 'optimization_step', r'(Optimization step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n *\\\\n\\\\-{10}|Time spent in this\",\n \"'.join(self.info_parser.get_xc_functional_name()) sec_method.gw.starting_point = xc_functional_name sec_scc = sec_run.m_create(Calculation) sec_scc.method_ref = sec_method if sec_run.system: sec_scc.system_ref\",\n \"0 l_list = set([int(e.attrib.get(self._l_key)) for e in self.partial_dos]) for li in l_list: self._nlm\",\n \"= self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg', False) sec_method.x_exciting_xs_bse_sciavtype = self.input_xml_parser.get( 'xs/BSE/sciavtype',\",\n \"not None else section.get('final') if final is None: # get it from last\",\n \"fourth column in epsilon which is not parsed? sccs = [] for quantity\",\n \"name.startswith('bandstructure') and name.endswith('dat'): parser = self.bandstructure_dat_parser parser_function = self._parse_bandstructure_dat elif name.startswith('BAND') and name.endswith('OUT'):\",\n \"r'(?:All units are atomic|Starting initialization)([\\\\s\\\\S]+?)(?:Using|Ending initialization)', repeats=False, sub_parser=TextParser(quantities=initialization_quantities)) ) scf_quantities = [ Quantity(\",\n \"names in self._energy_keys_mapping.items(): val = None for name in names: val = energy_contributions.get(name,\",\n \"'positions_format', r'imized atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True,\",\n \"is not None: sec_gap.value_optical = optical_band_gap def parse_miscellaneous(self): sec_worfklow = self.archive.m_create(Workflow) sec_worfklow.type =\",\n \"self.bandstructure_parser = BandstructureXMLParser(energy_unit=ureg.hartree) self.eigval_parser = ExcitingEigenvalueParser() self.fermisurf_parser = ExcitingFermiSurfaceBxsfParser() self.evalqp_parser = ExcitingEvalqpParser() self.dos_out_parser\",\n \"len(res[0]) // len(data))) if key == 'eigenvalues': res = res * ureg.hartree return\",\n \"True) file_ext_list = [ 'TET' if tetradf else None, 'AC' if nwacont else\",\n \"Calculation.dos_electronic) sec_dos.n_energies = self.dos_parser.number_of_dos sec_dos.energies = self.dos_parser.energies + energy_fermi volume = self.info_parser.get_unit_cell_volume() totaldos\",\n \"scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)' % key_unit[0], str_operation=str_to_quantity_tolerances, unit=key_unit[1], repeats=False)) module_quantities = [ Quantity(\",\n \"sec_charges.total = charge_contributions.get('total charge') elif key == 'charge_total': pass else: setattr(msection, key, val)\",\n \"len(v) < 2: continue elif v[0].startswith('species'): species = re.search(re_symbol, v[-1]).group(1) elif v[0].startswith('atom'): v[0]\",\n \"r'Structure\\\\-optimization module started([\\\\s\\\\S]+?)Structure\\\\-optimization module stopped', sub_parser=TextParser(quantities=[ Quantity( 'optimization_step', r'(Optimization step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n *\\\\n\\\\-{10}|Time spent in\",\n \"is None: if self.partial_dos is None: return self._nlm = 0 l_list = set([int(e.attrib.get(self._l_key))\",\n \"'time', r'Time spent in this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds', unit=ureg.s, repeats=False, dtype=float) ] self._quantities.append(Quantity( 'structure_optimization',\",\n \"self.info_gw_parser.get('optical_band_gap', None) if optical_band_gap is not None: sec_gap.value_optical = optical_band_gap def parse_miscellaneous(self): sec_worfklow\",\n \"self.input_xml_parser.get('xs/tetra/tetradf', None) nwacont = self.input_xml_parser.get('xs/tddft/nwacont', None) aresdf = self.input_xml_parser.get('xs/tddft/aresdf', True) file_ext_list = [\",\n \"key: if not self.total_dos: return res = np.zeros((self.number_of_spin_channels, self.number_of_dos)) for i in range(len(self.total_dos)):\",\n \"sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_dat_parser.band_k_points nkpts_segment = self.bandstructure_dat_parser.number_of_k_points_per_segment for nb in range(len(band_energies)):\",\n \"sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get( 'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get( 'xs/tddft/ahc', False) sec_method.x_exciting_xs_tddft_anti_resonant_dielectric = self.input_xml_parser.get(\",\n \"structure_optimization is not None: sec_worfklow.type = 'geometry_optimization' sec_geometry_opt = sec_worfklow.m_create(GeometryOptimization) threshold_force = structure_optimization.get(\",\n \"for v in val: v = v.split(':') if len(v) < 2: continue energies[v[0].strip()]\",\n \"filename): \\\"\\\"\\\"Checks if a the given filename exists and is accessible in the\",\n \"parse_xs(self): sec_run = self.archive.run[-1] xs_info_files = self.get_exciting_files('INFOXS.OUT') if not xs_info_files: return self._calculation_type =\",\n \"self._nspin_key = 'nspin' self._totaldos_key = 'totaldos' self._partialdos_key = 'partialdos' self._diagram_key = 'diagram' self._l_key\",\n \"v[1] * unit))) else: vi = [float(vii) for vii in v[1].split()] vi =\",\n \"only thing that we can do is to assume that the first steps\",\n \"sec_forces.total = ForcesEntry(value=forces) # scf iterations scf_iterations = section.get('scf_iteration', []) for scf_iteration in\",\n \"not os.path.isfile(filename): file_ext = default.split('.')[-1] mainfile_base = mainfile.rsplit('.', 1)[0].replace('INFO', '') options = [\",\n \"if key == 'x_exciting_section_MT_charge_atom': for n in range(len(val)): sec_mt_charge_atom = msection.m_create(x_exciting_section_MT_charge_atom) sec_mt_charge_atom.x_exciting_MT_charge_atom_index =\",\n \"def init_parameters(self): self._nspin = None self._nkpts_segment = None self._neigs_segment = None self._bands =\",\n \"is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_screening_rgkmax = rgkmax sec_method.x_exciting_xs_screening_type = self.input_xml_parser.get( 'xs/screening/screentype',\",\n \"exciting_files = ['EIGVAL.OUT', 'FERMISURF.bxsf', 'FS.bxsf'] # Parse DFT DOS from one of the\",\n \"structure_optimization.get( 'optimization_step', [{}])[0].get('force_convergence', [0., 0.])[-1] sec_geometry_opt.input_force_maximum_tolerance = threshold_force def parse_method(self): sec_run = self.archive.run[-1]\",\n \"magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)', str_operation=str_to_quantity_tolerances, unit=ureg.hartree / ureg.bohr, repeats=False, dtype=float), Quantity( 'energy_total', r'Total energy at\",\n \"@property def number_of_spin_channels(self): if self._nspin is None: self._nspin = np.shape(np.transpose(self.data)[1:])[0] return self._nspin @property\",\n \":([\\\\s\\\\S]+?)(?:\\\\n *\\\\n\\\\+{10}|\\\\+\\\\-{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=True), Quantity( 'final', r'(?:Convergence targets achieved\\\\. Performing final SCF iteration|Reached\",\n \"n + 1 sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol = val[n][0] sec_mt_moment_atom.x_exciting_MT_moment_atom_value = val[n][1] else: setattr(msection, key, val)\",\n \"self.archive.run[-1] xs_info_files = self.get_exciting_files('INFOXS.OUT') if not xs_info_files: return self._calculation_type = 'xs' # inconsistency\",\n \"i in range(len(self.bands[n])): band_energies[i % self.number_of_spin_channels][i] = np.array( [e.attrib.get(self._energy_key) for e in self.bands[n][i]])\",\n \"self.parse_file(f, sec_scc) # structure optimization structure_optimization = self.info_parser.get('structure_optimization', {}) for optimization_step in structure_optimization.get('optimization_step',\",\n \"= iteration.get('charge_contributions', {}) for key, names in self._electron_charge_keys_mapping.items(): val = None for name\",\n \"0.0 * ureg.hartree if sec_scc.energy is not None: energy_fermi = sec_scc.energy.fermi energy_fermi =\",\n \"r'Absolute change in total energy\\\\s*\\\\(target\\\\)', ureg.hartree), 'x_exciting_charge_convergence': ( r'Charge distance\\\\s*\\\\(target\\\\)', ureg.elementary_charge), 'x_exciting_IBS_force_convergence': (\",\n \"self._band_energies is None: data = np.transpose(self.data) n_kpoints = np.where(data[0] == data[0][0])[0][1] bands =\",\n \"def number_of_dos(self): if self._ndos is None: total_dos = self.root.find('./%s/%s' % (self._totaldos_key, self._diagram_key)) self._ndos\",\n \"= data[1:3] elif quantity == 'SIGMA' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_sigma_no_local_field = data[1:3]\",\n \"bse_type, scr_type)) bse_files.append(files) return bse_files def get_data(files): data = [] for f in\",\n \"# energy contributions energy_contributions = iteration.get('energy_contributions', {}) for key, names in self._energy_keys_mapping.items(): val\",\n \"not None: sec_method.x_exciting_xs_xstype = xstype sec_method.x_exciting_electronic_structure_method = xstype sec_method.x_exciting_xs_broadening = self.input_xml_parser.get( 'xs/broad', 0.01,\",\n \"is not None: sec_scc.x_exciting_geometry_optimization_method = optimization_step.get('method') if optimization_step.get('step') is not None: sec_scc.x_exciting_geometry_optimization_step =\",\n \"Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) for name, key_unit in self._method_keys_mapping.items():\",\n \"and name.endswith('OUT'): parser = self.band_out_parser parser_function = self._parse_band_out elif name.startswith('input') and name.endswith('xml'): parser\",\n \"= np.reshape(occs, (nspin, len(occs) // nspin)) data['eigenvalues'] = np.reshape(eigs, (nspin, len(eigs) // nspin))\",\n \"sec_gw.basis_set = 'mixed' sec_gw.qp_equation_treatment = 'linearization' sec_gw.max_frequency = self.input_xml_parser.get( 'gw/freqgrid/freqmax', 1.0) sec_gw.frequency_grid_type =\",\n \"if self._calculation_type == 'volume_optimization': sec_method.x_exciting_volume_optimization = True def parse_scc(self, section): sec_run = self.archive.run[-1]\",\n \"for point in self.total_dos[0]]) if self.energy_unit is not None: self._energies = self._energies *\",\n \"Quantity('muffin_tin_radius', rf'muffin-tin radius *: *({re_float})', dtype=np.float64, unit=ureg.bohr), Quantity('radial_points', rf'radial points in muffin-tin *:\",\n \"return self._nspin = len(self.total_dos) return self._nspin @property def number_of_atoms(self): if self._natoms is None:\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"= GWInfoParser() self.input_xml_parser = XMLParser() self.data_xs_parser = DataTextParser() self.data_clathrate_parser = DataTextParser(dtype=str) # different\",\n \"return positions * ureg.bohr def get_scf_threshold(self, name): reference = self.get('groundstate', self.get('hybrids', {})) return\",\n \"in writing, software # distributed under the License is distributed on an \\\"AS\",\n \"Run, Program from nomad.datamodel.metainfo.simulation.method import ( Method, DFT, Electronic, Smearing, XCFunctional, Functional, GW\",\n \"self._band_k_points = None @property def band_energies(self): if self._band_energies is None: if self.data is\",\n \"self.bandstructure_parser.get('band_k_points') nkpts_segment = self.bandstructure_parser.number_of_k_points_per_segment band_seg_labels = self.bandstructure_parser.get('band_segm_labels') for nb in range(len(band_energies[n])): sec_k_band_segment =\",\n \"= np.reshape(data, (nspin, len(data) // nspin, 2)) data = np.transpose(data, axes=(2, 0, 1))\",\n \"= specie.get('symbol') sec_atoms_group.x_exciting_geometry_atom_number = str(specie.get('number')) sec_atoms_group.x_exciting_muffin_tin_points = specie.get('radial_points') sec_atoms_group.x_exciting_muffin_tin_radius = specie.get('muffin_tin_radius') positions_format =\",\n \"Quantity( 'optical_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) class ExcitingEvalqpParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self):\",\n \"energy'], 'x_exciting_coulomb_potential_energy': ['Coulomb potential energy', 'Coulomb potential'], 'energy_xc_potential': ['xc potential energy', 'xc potential'],\",\n \"= self.fermisurf_parser.get('fermi_energy', None) if fermi_energy is not None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface = fermi_energy sec_fermisurface.x_exciting_values_fermi_surface =\",\n \"cell optimization in exciting sec_atoms.lattice_vectors = lattice_vectors lattice_vectors_reciprocal = self.info_parser.get_initialization_parameter( 'lattice_vectors_reciprocal') sec_atoms.lattice_vectors_reciprocal =\",\n \"'SIGMA', 'LOSS']: files = get_files(quantity) for i in range(len(files)): data = get_data(files[i]) if\",\n \"sec_atoms_group.x_exciting_geometry_atom_number = str(specie.get('number')) sec_atoms_group.x_exciting_muffin_tin_points = specie.get('radial_points') sec_atoms_group.x_exciting_muffin_tin_radius = specie.get('muffin_tin_radius') positions_format = specie.get('positions_format') sec_atoms_group.x_exciting_atom_position_format\",\n \"band_energy = band_energy * self._energy_unit self._band_energies.append(band_energy) start = end return self._band_energies @property def\",\n \"TODO make a parent class for dos and bandstructure super().__init__(None) self._distance_key = 'distance'\",\n \"BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'optical_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) class ExcitingEvalqpParser(TextParser): def\",\n \"= fermi_energy sec_fermisurface.x_exciting_values_fermi_surface = fermi_surface def _parse_evalqp(self, sec_scc): data = self.evalqp_parser.get('kpoints_eigenvalues') if data\",\n \"positions \\\\((.+?)\\\\)', flatten=False), Quantity( 'positions', rf'\\\\d+ : *({re_float}) *({re_float}) *({re_float})', repeats=True, dtype=np.dtype(np.float64))]))) initialization_quantities.append(Quantity(\",\n \"overwritten when setting mainfile self.bandstructure_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_parser.get('band_energies', []) for n\",\n \"and name.endswith('OUT'): parser = self.eigval_parser parser_function = self._parse_eigenvalues elif (name.startswith('FERMISURF') or name.startswith('FS')) and\",\n \"self.input_xml_parser.get( 'gw/freqgrid/fgrid', 'gaule2') sec_gw.number_of_frequencies = int(self.input_xml_parser.get( 'gw/freqgrid/nomeg', 16)) sec_gw.self_energy_c_number_of_poles = int(self.input_xml_parser.get( 'gw/selfenergy/npol', 0))\",\n \"self._nspin @property def number_of_atoms(self): if self._natoms is None: partial_dos = self.root.findall('./%s' % self._partialdos_key)\",\n \"axes=(2, 0, 1)) for j in range(len(data)): data[j].append(data_q[j]) return data for quantity in\",\n \"= ['EVALQP.DAT', 'EVALQP.TXT', 'TDOS-QP.OUT'] # Parse GW band structure from one of the\",\n \"self.input_xml_parser.get('xs/lmaxapw', 10) sec_method.x_exciting_xs_number_of_empty_states = self.input_xml_parser.get( 'xs/nempty', 5) sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq', [1, 1, 1])\",\n \"'EPSILON' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field = data[1:3] elif quantity == 'LOSS' and\",\n \"self._distances = None self._band_energies = None self._neigs_segment = None self._nkpts_segment = None @property\",\n \"self.info_parser.get_initialization_parameter('smearing_width') if smearing_width is not None: smearing_width = (smearing_width * ureg.hartree).to('joule') # TODO\",\n \"= [] for f in files: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None:\",\n \"Quantity( 'frequency_data', r'frequency list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)', str_operation=str_to_frequency, repeats=False) ) self._quantities.append( Quantity( 'fermi_energy', r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi [Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s', unit=ureg.hartree,\",\n \"self.input_xml_parser.get('xs/vkloff', [0., 0., 0.]) # TODO I am not certain if screening/BSE are\",\n \"convert=False), ] self._system_keys_mapping = { 'x_exciting_unit_cell_volume': ('Unit cell volume', ureg.bohr ** 3), 'x_exciting_brillouin_zone_volume':\",\n \"return [nbands, mesh, origin, vector] self._quantities.append( Quantity( 'band_parameters', r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)', str_operation=str_to_band_parameters, repeats=False)) self._quantities.append( Quantity(\",\n \"else: lm = int(val_l) ** 2 + int(val_m) + int(val_l) atom = i\",\n \"sec_method.x_exciting_xs_bse_sciavqbd = self.input_xml_parser.get( 'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get(\",\n \"positions if positions is not None else section.get('positions') if positions is None: species\",\n \"self.get_exciting_files(name) if len(files) > 1: self.logger.warn('Found multiple files. Will read all!', data=dict(file=name)) for\",\n \"dos = data[1] * (1 / ureg.hartree) * volume.to('m**3').magnitude for spin in range(len(dos)):\",\n \"== 'k_points': return np.array([d[0][:3] for d in data]) elif key == 'Znk': return\",\n \"= self.input_xml_parser.get( 'xs/screening/ngridk', [0, 0, 0]) rgkmax = self.input_xml_parser.get('xs/screening/rgkmax', None) if rgkmax is\",\n \"eigenvalues'], 'x_exciting_effective_potential_energy': ['Effective potential energy'], 'x_exciting_coulomb_potential_energy': ['Coulomb potential energy', 'Coulomb potential'], 'energy_xc_potential': ['xc\",\n \"output files and properties exciting_files = ['EIGVAL.OUT', 'FERMISURF.bxsf', 'FS.bxsf'] # Parse DFT DOS\",\n \"np.transpose(np.vstack(data)) n_loss = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_loss_energies = np.reshape( data[0], (n_components, n_loss)) *\",\n \"self._quantities = [] def str_to_eigenvalue(val_in): val = val_in.strip().split('\\\\n') kpts = np.array(val[0].split(), dtype=float) keys\",\n \"np.transpose(data[2:5])[start:end]) start = end return self._band_k_points @property def distances(self): if self._distances is None:\",\n \"is accessible in the same folder where the mainfile is stored. \\\"\\\"\\\" mainfile\",\n \"name.endswith('OUT'): parser = self.dos_out_parser parser_function = self._parse_dos_out elif name.startswith('bandstructure') and name.endswith('dat'): parser =\",\n \"repeats=True, dtype=float)])), Quantity( 'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Time', repeats=False, str_operation=str_to_array,\",\n \"= end return self._band_energies @property def distances(self): if self._distances is None: dist =\",\n \"number_of_atoms(self): if self._natoms is None: partial_dos = self.root.findall('./%s' % self._partialdos_key) self._natoms = len(partial_dos)\",\n \"np.array([d[1].get(key, None) for d in data]) if None in energy: return energy return\",\n \"== 1 else val return np.array(val, dtype=float) def str_to_atom_properties_dict(val_in): unit = None if\",\n \"None: self._neigs_segment = len(self.bands[0]) // self.number_of_spin_channels return self._neigs_segment def parse(self, key): if self._results\",\n \"= self.get_exciting_files('input.xml') if not input_file: return self.input_xml_parser.mainfile = input_file[0] xstype = self.input_xml_parser.get('xs/xstype', '')\",\n \"2.0 (the \\\"License\\\"); # you may not use this file except in compliance\",\n \"and # limitations under the License. # import numpy as np import os\",\n \"True) sec_method.x_exciting_xs_bse_sciavqbd = self.input_xml_parser.get( 'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg =\",\n \"import Workflow, GeometryOptimization from .metainfo.exciting import x_exciting_section_MT_charge_atom, x_exciting_section_MT_moment_atom,\\\\ x_exciting_section_spin, x_exciting_section_fermi_surface,\\\\ x_exciting_section_atoms_group re_float =\",\n \"section.get('positions') if positions is None: species = self.get_initialization_parameter('species', []) if species: positions =\",\n \"volume optimizations volume_index = 1 while True: info_volume = self.get_exciting_files('run_dir%s/INFO.OUT' % str(volume_index).rjust(2, '0'))\",\n \"sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] # parse input xml\",\n \"logger=logger) def init_quantities(self): self._quantities = [] class ExcitingInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self):\",\n \"data]) * ureg.hartree eigs_gw = get_data('E_GW') if eigs_gw[0] is None: return nspin =\",\n \"to global metainfo. This is killing me! if metainfo_name == 'x_exciting_scf_threshold_energy_change': sec_method.scf =\",\n \"is not necessary as this is done in parser, however nspin is #\",\n \"for spin in range(len(totaldos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value =\",\n \"one of the files bs_files = ['bandstructure.xml', 'BAND.OUT', 'bandstructure.dat'] for fname in bs_files:\",\n \"np.reshape( data[1], (n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im = np.reshape( data[2], (n_components, n_sigma)) def parse_loss(data, sec_scc):\",\n \"[] def str_to_eigenvalue(val_in): val = val_in.strip().split('\\\\n') kpts = np.array(val[0].split(), dtype=float) keys = val[1].split()\",\n \"str): smearing_kind = smearing_kind[0] smearing_kind = smearing_kind_map[smearing_kind] sec_smearing.kind = smearing_kind smearing_width = self.info_parser.get_initialization_parameter('smearing_width')\",\n \"express or implied. # See the License for the specific language governing permissions\",\n \"self._parse_bandstructure elif name.startswith('EIGVAL') and name.endswith('OUT'): parser = self.eigval_parser parser_function = self._parse_eigenvalues elif (name.startswith('FERMISURF')\",\n \"DFT DOS from one of the files bs_files = ['dos.xml', 'TDOS.OUT'] for fname\",\n \"'xs/gqmax', 0.0, '1/bohr') sec_method.x_exciting_xs_lmaxapw = self.input_xml_parser.get('xs/lmaxapw', 10) sec_method.x_exciting_xs_number_of_empty_states = self.input_xml_parser.get( 'xs/nempty', 5) sec_method.x_exciting_xs_ngridq\",\n \"= self.info_parser.get_unit_cell_volume() dos = data[1] * (1 / ureg.hartree) * volume.to('m**3').magnitude for spin\",\n \"sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(Atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols',\",\n \"('Spin treatment', None), 'x_exciting_number_of_bravais_lattice_symmetries': ('Number of Bravais lattice symmetries', None), 'x_exciting_number_of_crystal_symmetries': ('Number of\",\n \"dtype=str, flatten=False)] initialization_quantities = [ Quantity( 'lattice_vectors', r'Lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=ureg.bohr, repeats=False, convert=False),\",\n \"band_k_points(self): if self._band_k_points is None: data = np.transpose(self.data) self._band_k_points = [] start =\",\n \"1 return self._nlm @property def total_dos(self): if self._total_dos is None: self._total_dos = self.root.findall('./%s/%s'\",\n \"= self.bandstructure_parser.number_of_k_points_per_segment band_seg_labels = self.bandstructure_parser.get('band_segm_labels') for nb in range(len(band_energies[n])): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints\",\n \"self._results is None: self._results = dict() if 'total' in key: if not self.total_dos:\",\n \"self.bands[n][i]]) for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy = np.array([\",\n \"start = 0 for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy\",\n \"class BandstructureDatParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit',\",\n \"if frequency_data is not None: number = frequency_data.get('number') sec_method.gw.number_of_frequencies = len(number) sec_method.gw.frequency_number =\",\n \"band_seg_labels[nb] sec_k_band_segment.energies = band_energies[n][nb] + energy_fermi def _parse_eigenvalues(self, sec_scc): if self.eigval_parser.get('eigenvalues_occupancies', None) is\",\n \"for vi in v[1].split()] v[1] = v[1][0] if len(v[1]) == 1 else v[1]\",\n \"final = section.get('scf_iteration', [None])[-1] final = section.get('optimization_step', [None])[-1] if final is None else\",\n \"quantity == 'SIGMA' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_sigma_no_local_field = data[1:3] def parse_xs(self): sec_run\",\n \"= 2 if occs[0] == 1. else 1 data = dict() data['occupancies'] =\",\n \"filepath = '%s.%s' % (filepath, target[1]) filepath = os.path.join(self.info_parser.maindir, filepath) if os.path.isfile(filepath) and\",\n \"break if val is None: continue if key == 'x_exciting_section_MT_moment_atom': for n in\",\n \"reading of parameters from input.xml for normal calculations # in addition to INFO.OUT\",\n \"== 'NLF_FXC': sec_scc.x_exciting_xs_tddft_sigma_no_local_field = data[1:3] def parse_xs(self): sec_run = self.archive.run[-1] xs_info_files = self.get_exciting_files('INFOXS.OUT')\",\n \"= [] if bands: self._bands.append(bands) # add atom-resolved bands_atom = self.root.findall('./*/%s' % self._atom_key)\",\n \"in range(len(self.partial_dos)): spin = self.partial_dos[i].attrib.get(self._nspin_key, None) if spin is None: spin = (i\",\n \"return energy return np.array([d[1].get(key) for d in data]) * ureg.hartree eigs_gw = get_data('E_GW')\",\n \"'x_exciting_total_MT_moment': ['total moment in muffin-tins'], 'x_exciting_total_moment': ['total moment'], 'x_exciting_section_MT_moment_atom': ['atom_resolved'] } def get_exciting_files(self,\",\n \"key == 'k_points': return np.array([d[0][:3] for d in data]) elif key == 'Znk':\",\n \"'x_exciting_spin_treatment': sub_sec = sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment = val elif name == 'x_exciting_species_rtmin': setattr(sec_system, name,\",\n \"files related to quantity at all qpoints files = self.get_exciting_files('%s_%s%s%s.OUT' % (quantity, file_ext,\",\n \"= sec_run.calculation[0] sec_scc.starting_calculation_ref = sec_scc_ref sec_scc.calculations_ref = [sec_scc_ref] # parse properties gw_info_files =\",\n \"unit = None if 'charge' in val_in: unit = ureg.elementary_charge elif 'moment' in\",\n \"\\\\|G\\\\+k\\\\| for APW functions', 1 / ureg.bohr), 'x_exciting_gmaxvr': (r'Maximum \\\\|G\\\\| for potential and\",\n \"for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy = np.array([ np.transpose(energy)[start:end]\",\n \"if len(vi) == 1 else vi properties[v[0].strip()] = vi * unit properties['atom_resolved'] =\",\n \"input xml file sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref\",\n \"'charge' in val_in: unit = ureg.elementary_charge elif 'moment' in val_in: unit = ureg.elementary_charge\",\n \"in species: labels += [specie.get('symbol')] * len(specie.get('positions')) return labels def get_positions_format(self, section): positions_format\",\n \"'0'))] for f in files_q: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None: continue\",\n \"if val is not None: break if val is None: continue if key.startswith('energy_'):\",\n \"= self.input_xml_parser.get( 'gw/coreflag', 'all') sec_gw.polarizability_number_of_empty_states = int( self.input_xml_parser.get('gw/nempty', 0)) sec_gw.ngridq = self.input_xml_parser.get('gw/ngridq', [1,\",\n \"return self._energies def _get_dos(self, diagram): dos = np.array( [point.attrib.get(self._dos_key) for point in diagram],\",\n \"None), 'x_exciting_lo': (r'Total number of local\\\\-orbitals', None)} self._method_keys_mapping = { 'smearing_kind': ('Smearing scheme',\",\n \"None), 'x_exciting_gkmax': (r'Maximum \\\\|G\\\\+k\\\\| for APW functions', 1 / ureg.bohr), 'x_exciting_gmaxvr': (r'Maximum \\\\|G\\\\|\",\n \"self.bandstructure_parser.number_of_k_points_per_segment band_seg_labels = self.bandstructure_parser.get('band_segm_labels') for nb in range(len(band_energies[n])): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints =\",\n \"# # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"= False potential_mixing = self.info_parser.get_initialization_parameter('potential_mixing') if potential_mixing is not None: sec_system.x_exciting_potential_mixing = potential_mixing\",\n \"None), 'x_exciting_number_of_crystal_symmetries': ('Number of crystal symmetries', None), 'x_exciting_kpoint_grid': (r'k\\\\-point grid', None), 'x_exciting_kpoint_offset': (r'k\\\\-point\",\n \"for name, key_unit in self._method_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False)\",\n \") self._quantities.append(Quantity( 'initialization', r'(?:All units are atomic|Starting initialization)([\\\\s\\\\S]+?)(?:Using|Ending initialization)', repeats=False, sub_parser=TextParser(quantities=initialization_quantities)) ) scf_quantities\",\n \"return np.array(val, dtype=float) def str_to_atom_properties_dict(val_in): unit = None if 'charge' in val_in: unit\",\n \"None: self._results = dict() if 'total' in key: if not self.total_dos: return res\",\n \"if not section: return sec_scc = self.parse_scc(section) if sec_scc is None: return sec_system\",\n \"dict() for v in val: v = v.split(':') if len(v) < 2: continue\",\n \"unit=ureg.hartree), Quantity( 'energy_contributions', r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n *(?:DOS|Density)', str_operation=str_to_energy_dict, repeats=False, convert=False), Quantity( 'x_exciting_dos_fermi', r'DOS at Fermi\",\n \"= np.zeros((self.number_of_spin_channels, self.number_of_dos)) for i in range(len(self.total_dos)): spin = self.total_dos[i].attrib.get(self._nspin_key, i) res[i] =\",\n \"else: spin = int(spin) - 1 val_l = self.partial_dos[i].attrib.get(self._l_key, None) val_m = self.partial_dos[i].attrib.get(self._m_key,\",\n \"'fermi_energy', r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi [Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s', unit=ureg.hartree, repeats=False) ) self._quantities.append( Quantity( 'direct_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) )\",\n \"'bse': self._parse_xs_bse() elif xstype.lower() == 'tddft': self._parse_xs_tddft() def _parse_input_gw(self, sec_method): sec_gw = sec_method.m_create(GWMethod)\",\n \"def str_to_eigenvalues(val_in): val = val_in[:val_in.rfind('\\\\n \\\\n')].strip() val = np.array([v.split() for v in val.split('\\\\n')],\",\n \"self.input_xml_parser.get( 'xs/tddft/nwacont', 0) sec_method.x_exciting_xs_tetra = self.input_xml_parser.get( 'xs/tetra/tetradf', False) def _parse_xs_bse(self): sec_run = self.archive.run[-1]\",\n \"'total in muffin-tins'], 'charge_total': ['total charge'], 'x_exciting_section_MT_charge_atom': ['atom_resolved'] } self._moment_keys_mapping = { 'x_exciting_interstitial_moment':\",\n \"for quantity in ['EPSILON', 'LOSS', 'SIGMA']: for ext in ['FXC', 'NLF_FXC']: data =\",\n \"GWInfoParser() self.input_xml_parser = XMLParser() self.data_xs_parser = DataTextParser() self.data_clathrate_parser = DataTextParser(dtype=str) # different names\",\n \"= data[6:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies = [] start\",\n \"nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.endpoints_labels = band_seg_labels[nb] sec_k_band_segment.energies = band_energies[n][nb] + energy_fermi def\",\n \"part of NOMAD. # See https://nomad-lab.eu for further info. # # Licensed under\",\n \"len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_excitons = n_excitons sec_scc.x_exciting_xs_bse_exciton_energies = np.reshape( data[1], (n_components, n_excitons)) *\",\n \"None: dist = np.transpose(self.data)[0] n_k_points = np.where(dist == dist[0])[0][1] self._distances = dist[:n_k_points] return\",\n \"info. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"is None: self._nspin = np.shape(np.transpose(self.data))[0] - 6 return self._nspin @property def number_of_k_points_per_segment(self): if\",\n \"= np.shape(np.transpose(self.data)[1:])[0] return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment =\",\n \"= lattice_vectors lattice_vectors_reciprocal = self.info_parser.get_initialization_parameter( 'lattice_vectors_reciprocal') sec_atoms.lattice_vectors_reciprocal = lattice_vectors_reciprocal if len(sec_run.system) > 1:\",\n \"]), repeats=False)) self._quantities.append(Quantity( 'hybrids', r'Hybrids module started([\\\\s\\\\S]+?)Hybrids module stopped', sub_parser=TextParser(quantities=module_quantities) )) def get_atom_labels(self,\",\n \"in exciting_files: self.parse_file(f, sec_scc) # structure optimization structure_optimization = self.info_parser.get('structure_optimization', {}) for optimization_step\",\n \"input parameters here if input_GW.xml does not exist self._quantities.append( Quantity( 'frequency_data', r'frequency list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)',\",\n \"self._ndos @property def number_of_lm(self): if self._nlm is None: if self.partial_dos is None: return\",\n \"*: *(.+)'), Quantity('nuclear_charge', rf'nuclear charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('electronic_charge', rf'electronic charge *:\",\n \"IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr) ] self._quantities.append(Quantity( 'groundstate', r'(?:Self\\\\-consistent\",\n \"if positions is None or atom_labels is None: return sec_system = sec_run.m_create(System) sec_atoms\",\n \"repeats=False)) self._quantities.append( Quantity( 'fermi_surface', r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n *E*', unit=ureg.hartree, repeats=True)) class ExcitingEigenvalueParser(TextParser): def __init__(self): super().__init__(None)\",\n \"Quantity( 'energy_total', r'Total energy at this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', unit=ureg.hartree, repeats=False, dtype=float), Quantity( 'time',\",\n \"band_energies = self.bandstructure_parser.get('band_energies', []) for n in range(len(band_energies)): # Get fermi energy: it\",\n \"suffix) if target[1:]: filepath = '%s.%s' % (filepath, target[1]) filepath = os.path.join(self.info_parser.maindir, filepath)\",\n \"TODO read from xml file def get_files(name): bse_types = ['IP', 'singlet', 'triplet', 'RPA']\",\n \"= dict() atom_resolved = [] species = None for v in val: v\",\n \"return data for quantity in ['EPSILON', 'LOSS', 'SIGMA']: for ext in ['FXC', 'NLF_FXC']:\",\n \"elif quantity == 'EPSILON' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field = data[1:3] elif quantity\",\n \"points qpoints = self.input_xml_parser.get('xs/qpointset/qpoint') def get_data(quantity, ext): # all files related to quantity\",\n \"self.input_xml_parser.get( 'xs/BSE/xasedge', 'K') sec_method.x_exciting_xs_bse_xasspecies = self.input_xml_parser.get( 'xs/BSE/xasspecies', 0) sec_method.x_exciting_xs_bse_xas_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlxas', [0,\",\n \"'partial' in key: if not self.partial_dos: return res = np.zeros(( self.number_of_lm, self.number_of_spin_channels, self.number_of_atoms,\",\n \"the mainfile is stored. \\\"\\\"\\\" mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target =\",\n \"def get_atom_positions(self, section={}, positions=None, positions_format=None): positions = positions if positions is not None\",\n \"ureg.hartree, weights=val[2]) # TODO Read also input parameters here if input_GW.xml does not\",\n \"\\\\(seconds\\\\)', ureg.s)} for name, key_unit in self._miscellaneous_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\-\\\\d\\\\.Ee]+)' % key_unit[0], repeats=False,\",\n \"sec_gw_eigenvalues.kpoints = get_data('k_points') sec_gw_eigenvalues.energies = reshape(eigs_gw) sec_gw_eigenvalues.value_exchange = reshape(get_data('Sx')) eigs_gw_C = reshape(get_data('Sc')) if\",\n \"= self.input_xml_parser.get( 'xs/BSE/xasatom', 0) sec_method.x_exciting_xs_bse_xasedge = self.input_xml_parser.get( 'xs/BSE/xasedge', 'K') sec_method.x_exciting_xs_bse_xasspecies = self.input_xml_parser.get( 'xs/BSE/xasspecies',\",\n \"* val[0][1].units sec_charges.total = charge_contributions.get('total charge') elif key == 'charge_total': pass else: setattr(msection,\",\n \"xc_functional_names = self.info_parser.get_xc_functional_name() if not xc_functional_names: # get it from input.xml input_file =\",\n \"*= self.input_xml_parser.get('structure/crystal/scale', 1.0) positions = np.dot(positions, lattice_vectors) * ureg.bohr lattice_vectors = lattice_vectors *\",\n \"bse_files = [] for bse_type in bse_types: for scr_type in scr_types: files =\",\n \"except in compliance with the License. # You may obtain a copy of\",\n \"self._neigs_segment = None self._bands = None self._vertices = None self._distances = None self._species\",\n \"sec_electronic.m_create(Smearing) smearing_kind = self.info_parser.get_initialization_parameter('smearing_kind') if smearing_kind is not None: if not isinstance(smearing_kind, str):\",\n \"if self._neigs_segment is None: data = np.transpose(self.data) self._neigs_segment = int(max(data[0])) return self._neigs_segment class\",\n \"dtype=float), Quantity( 'energy_total', r'Total energy at this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', unit=ureg.hartree, repeats=False, dtype=float), Quantity(\",\n \"metainfo. This is killing me! if metainfo_name == 'x_exciting_scf_threshold_energy_change': sec_method.scf = Scf(threshold_energy_change=threshold) xc_functional_names\",\n \"and name.endswith('dat'): parser = self.bandstructure_dat_parser parser_function = self._parse_bandstructure_dat elif name.startswith('BAND') and name.endswith('OUT'): parser\",\n \"self.input_xml_parser.get( 'xs/BSE/xasspecies', 0) sec_method.x_exciting_xs_bse_xas_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlxas', [0, 0]) if self.input_xml_parser.get('xs/tddft') is not\",\n \"# I cannot find an example bandstructure file # atom-resolved bandstructure are added\",\n \"['Coulomb energy', 'Coulomb'], 'energy_exchange': ['Exchange energy', 'exchange'], 'x_exciting_exchange_energy': ['Exchange energy', 'exchange'], 'energy_correlation': ['Correlation\",\n \"self._energy_unit = self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1) return self._energy_unit @property def number_of_spin_channels(self): if self._nspin is None:\",\n \"sec_scc.eigenvalues[-1].m_create(BandGap) if fundamental_band_gap is not None: sec_gap.value_fundamental = fundamental_band_gap optical_band_gap = self.info_gw_parser.get('optical_band_gap', None)\",\n \"np.transpose(data, axes=(2, 0, 1)) sec_dos.energies = data[0][0] * ureg.hartree + energy_fermi volume =\",\n \"= len(eigs_gw) sec_gw_eigenvalues.kpoints = get_data('k_points') sec_gw_eigenvalues.energies = reshape(eigs_gw) sec_gw_eigenvalues.value_exchange = reshape(get_data('Sx')) eigs_gw_C =\",\n \"None self._results[key] = res class DOSXMLParser(XMLParser): def __init__(self, **kwargs): super().__init__(None) self._nspin_key = 'nspin'\",\n \"if len(v) == 3], float)) return dict( number=np.array(val[0], dtype=int), values=val[1] * ureg.hartree, weights=val[2])\",\n \"info files gw_info_files = ['GW_INFO.OUT', 'GWINFO.OUT'] for f in gw_info_files: if self.get_exciting_files(f): self._calculation_type\",\n \"self.input_xml_parser.get( 'gw/barecoul/cutofftype', 'none') sec_gw.screened_coulomb_volume_average = self.input_xml_parser.get( 'gw/scrcoul/sciavtype', 'isotropic') sec_gw.screened_Coulomb = self.input_xml_parser.get( 'gw/scrcoul/scrtype', 'rpa')\",\n \"0) sec_method.x_exciting_xs_tetra = self.input_xml_parser.get( 'xs/tetra/tetradf', False) def _parse_xs_bse(self): sec_run = self.archive.run[-1] # TODO\",\n \"= 'unit' self._energy_unit = kwargs.get('energy_unit', None) self._units_mapping = dict(hartree=ureg.hartree) def init_parameters(self): self._ndos =\",\n \"ureg.hartree / ureg.bohr)} for name, key_unit in self._convergence_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)' %\",\n \"= parse_configuration(self.info_parser.get(module)) if sec_scc is None: continue # add data to scc #\",\n \"sec_method.x_exciting_xs_lmaxapw = self.input_xml_parser.get('xs/lmaxapw', 10) sec_method.x_exciting_xs_number_of_empty_states = self.input_xml_parser.get( 'xs/nempty', 5) sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq', [1,\",\n \"logging from nomad.units import ureg from nomad.parsing.file_parser import TextParser, Quantity, XMLParser, DataTextParser from\",\n \"self.input_xml_parser.get( 'gw/selfenergy/actype', 'pade') sec_gw.mixed_basis_lmax = int(self.input_xml_parser.get( 'gw/mixbasis/lmaxmb', 3)) sec_gw.mixed_basis_tolerance = self.input_xml_parser.get( 'gw/mixbasis/epsmb', 0.0001)\",\n \"self._nlm @property def total_dos(self): if self._total_dos is None: self._total_dos = self.root.findall('./%s/%s' % (self._totaldos_key,\",\n \"_parse_input_gw(self, sec_method): sec_gw = sec_method.m_create(GWMethod) sec_gw.type = 'G0W0' gmaxvr = self.info_parser.get_initialization_parameter('x_exciting_gmaxvr', 0) sec_gw.core_treatment\",\n \"not xs_info_files: return self._calculation_type = 'xs' # inconsistency in the naming convention for\",\n \"'fermi_surface', r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n *E*', unit=ureg.hartree, repeats=True)) class ExcitingEigenvalueParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities\",\n \"filepath = '%s%s' % (target[0], suffix) if target[1:]: filepath = '%s.%s' % (filepath,\",\n \"read from xml file def get_files(name): bse_types = ['IP', 'singlet', 'triplet', 'RPA'] scr_types\",\n \"sec_method.m_create(GWMethod) sec_gw.type = 'G0W0' gmaxvr = self.info_parser.get_initialization_parameter('x_exciting_gmaxvr', 0) sec_gw.core_treatment = self.input_xml_parser.get( 'gw/coreflag', 'all')\",\n \"if '_X_' in name: sec_xc_functional.exchange.append(Functional(name=name)) elif '_C_' in name: sec_xc_functional.correlation.append(Functional(name=name)) elif 'HYB' in\",\n \"sec_energy = msection.m_create(Energy) if energy_total is not None: sec_energy.total = EnergyEntry(value=energy_total) x_exciting_dos_fermi =\",\n \"False) sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg', False) sec_method.x_exciting_xs_bse_sciavtype =\",\n \"v in data]) res = res.reshape((len(res), len(data), len(res[0]) // len(data))) if key ==\",\n \"and name.endswith('xml'): parser = self.input_xml_parser if self._calculation_type == 'gw': parser_function = self._parse_input_gw elif\",\n \"sec_xc_functional.reference = xc_functional.get('name_reference', [None, None])[1] sec_electronic.n_spin_channels = self.info_parser.get_number_of_spin_channels() if self._calculation_type == 'volume_optimization': sec_method.x_exciting_volume_optimization\",\n \"sec_method.x_exciting_xs_screening_number_of_empty_states = self.input_xml_parser.get( 'xs/screening/nempty', 0) sec_method.x_exciting_xs_screening_ngridk = self.input_xml_parser.get( 'xs/screening/ngridk', [0, 0, 0]) rgkmax\",\n \"= sec_run.m_create(Calculation) if quantity == 'EXCITON': parse_function = parse_exciton elif quantity == 'EPSILON':\",\n \"sec_scc.x_exciting_xs_bse_number_of_energy_points = n_epsilon sec_scc.x_exciting_xs_bse_epsilon_energies = np.reshape( data[0], (n_components, n_epsilon)) * ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re =\",\n \"if not self.total_dos: return res = np.zeros((self.number_of_spin_channels, self.number_of_dos)) for i in range(len(self.total_dos)): spin\",\n \"again as this is overwritten when setting mainfile self.bandstructure_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies =\",\n \"ExcitingGWOutParser(TextParser): def __init__(self, mainfile, logger): super().__init__(mainfile, logger=logger) def init_quantities(self): self._quantities = [] class\",\n \"None: break if val is None: continue if key == 'x_exciting_section_MT_charge_atom': for n\",\n \"self._partial_dos is None: self._partial_dos = self.root.findall('./%s/%s' % (self._partialdos_key, self._diagram_key)) return self._partial_dos @property def\",\n \"/ ureg.bohr), ]), repeats=False)) self._quantities.append(Quantity( 'hybrids', r'Hybrids module started([\\\\s\\\\S]+?)Hybrids module stopped', sub_parser=TextParser(quantities=module_quantities) ))\",\n \"+ energy_fermi def _parse_band_out(self, sec_scc): self.band_out_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.band_out_parser.band_energies if band_energies\",\n \"self.info_gw_parser.get('direct_band_gap', None) if fundamental_band_gap is None: fundamental_band_gap = self.info_gw_parser.get('fundamental_band_gap', None) sec_gap = sec_scc.eigenvalues[-1].m_create(BandGap)\",\n \"versions of gw info files gw_info_files = ['GW_INFO.OUT', 'GWINFO.OUT'] for f in gw_info_files:\",\n \"Bravais lattice symmetries', None), 'x_exciting_number_of_crystal_symmetries': ('Number of crystal symmetries', None), 'x_exciting_kpoint_grid': (r'k\\\\-point grid',\",\n \"energy_fermi = energy_fermi.to(\\\"hartree\\\") sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi band_k_points = self.bandstructure_parser.get('band_k_points')\",\n \"name.endswith('OUT'): parser = self.eigval_parser parser_function = self._parse_eigenvalues elif (name.startswith('FERMISURF') or name.startswith('FS')) and name.endswith('bxsf'):\",\n \"for d in data]) else: energy = np.array([d[1].get(key, None) for d in data])\",\n \"final is None else final if final is None: return sec_scc = sec_run.m_create(Calculation)\",\n \"'potential_mixing', r'Using ([\\\\w ]+) potential mixing', repeats=False, flatten=False) ) initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation type[\\\\s\\\\S]+?\\\\n\",\n \"= self.bandstructure_dat_parser parser_function = self._parse_bandstructure_dat elif name.startswith('BAND') and name.endswith('OUT'): parser = self.band_out_parser parser_function\",\n \"in band_energies]) if self._energy_unit is not None: band_energy = band_energy * self._energy_unit res_n.append(band_energy)\",\n \"= { 'energy_total': ['Total energy', 'total energy'], 'x_exciting_fermi_energy': ['Fermi energy', 'Fermi'], 'energy_kinetic_electronic': ['Kinetic\",\n \"for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break for f in exciting_files: self.parse_file(f,\",\n \"sec_scc.m_create(Forces) sec_forces.total = ForcesEntry(value=forces) # scf iterations scf_iterations = section.get('scf_iteration', []) for scf_iteration\",\n \"x_exciting_section_MT_charge_atom, x_exciting_section_MT_moment_atom,\\\\ x_exciting_section_spin, x_exciting_section_fermi_surface,\\\\ x_exciting_section_atoms_group re_float = r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?' class GWInfoParser(TextParser): def __init__(self): super().__init__(None)\",\n \"sec_scc.energy.fermi if energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band =\",\n \"repeats=True)) class BandstructureDatParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit =\",\n \"n_loss)) # TODO check if format of files are really correct, i.e. columns\",\n \"return res = np.zeros(( self.number_of_lm, self.number_of_spin_channels, self.number_of_atoms, self.number_of_dos)) for i in range(len(self.partial_dos)): spin\",\n \"positions = np.dot(positions, lattice_vectors) * ureg.bohr lattice_vectors = lattice_vectors * ureg.bohr atoms =\",\n \"self._dos_key = 'dos' self._unit_key = 'unit' self._energy_unit = kwargs.get('energy_unit', None) self._units_mapping = dict(hartree=ureg.hartree)\",\n \"= kwargs.get('energy_unit', None) self._units_mapping = dict(hartree=ureg.hartree) def init_parameters(self): self._ndos = None self._natoms =\",\n \"r'BAND:\\\\s*\\\\d+\\\\s*([\\\\d\\\\-\\\\+\\\\.Ee\\\\s]+)\\\\n *E*', unit=ureg.hartree, repeats=True)) class ExcitingEigenvalueParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities =\",\n \"band_energy * self._energy_unit self._band_energies.append(band_energy) start = end return self._band_energies @property def band_k_points(self): if\",\n \"partial_dos = self.root.findall('./%s' % self._partialdos_key) self._natoms = len(partial_dos) return self._natoms @property def number_of_dos(self):\",\n \"setattr(sec_system, name, val) except Exception: self.logger.warn('Error setting metainfo.') # species species = self.info_parser.get_initialization_parameter('species',\",\n \"scf_iteration in scf_iterations: sec_scf_iteration = sec_scc.m_create(ScfIteration) parse_scf(scf_iteration, sec_scf_iteration) return sec_scc def parse_system(self, section):\",\n \"None) if optical_band_gap is not None: sec_gap.value_optical = optical_band_gap def parse_miscellaneous(self): sec_worfklow =\",\n \"= [ val[n][1].magnitude for n in range(len(val))] * val[0][1].units sec_charges.total = charge_contributions.get('total charge')\",\n \"for f in input_file: self.input_xml_parser.mainfile = f correlation = self.input_xml_parser.get('libxc/correlation', None) xc_functional_names.append(correlation) exchange\",\n \"self._quantities.append( Quantity( 'optical_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) class ExcitingEvalqpParser(TextParser): def __init__(self): super().__init__(None) def\",\n \"do is to assume that the first steps are the # ones with\",\n \"with the missing quantity if len(quantity) < n_scf: quantity = [None] * (n_scf\",\n \"= [] for n in range(len(atoms)): atom_labels.extend([species[n].split('.')[0]] * len(atoms[n])) if positions is None\",\n \"np.transpose(self.data) self._distances = data[5][:int(max(data[1]))] return self._distances @property def number_of_spin_channels(self): if self._nspin is None:\",\n \"if os.access(f, os.F_OK)] return filenames def file_exists(self, filename): \\\"\\\"\\\"Checks if a the given\",\n \"global metainfo. This is killing me! if metainfo_name == 'x_exciting_scf_threshold_energy_change': sec_method.scf = Scf(threshold_energy_change=threshold)\",\n \"self.info_gw_parser.get('fermi_energy', None) if fermi_energy is not None: sec_scc.energy = Energy(fermi=fermi_energy) gw_files = ['EVALQP.DAT',\",\n \"str_operation=str_to_frequency, repeats=False) ) self._quantities.append( Quantity( 'fermi_energy', r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi [Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s', unit=ureg.hartree, repeats=False) ) self._quantities.append( Quantity(\",\n \"def init_quantities(self): self._quantities = [] class ExcitingInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): re_symbol\",\n \"self.logger self.eigval_parser.logger = self.logger self.fermisurf_parser.logger = self.logger self.evalqp_parser.logger = self.logger self.dos_out_parser.logger = self.logger\",\n \".metainfo.exciting import x_exciting_section_MT_charge_atom, x_exciting_section_MT_moment_atom,\\\\ x_exciting_section_spin, x_exciting_section_fermi_surface,\\\\ x_exciting_section_atoms_group re_float = r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?' class GWInfoParser(TextParser): def\",\n \"val[n][0] sec_mt_moment_atom.x_exciting_MT_moment_atom_value = val[n][1] else: setattr(msection, key, val) # convergence values for name\",\n \"== 'band_k_points': res = [] for i in range(len(self.number_of_k_points_per_segment)): start = np.array( self.vertices[i].attrib.get(self._coord_key).split(),\",\n \"self.bandstructure_dat_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_dat_parser.band_energies if band_energies is None: return # Get\",\n \"v in val.split('\\\\n')], dtype=float) val = np.transpose(val) occs = val[-1] eigs = val[-2]\",\n \"get_data(quantity, ext) if not data[0]: continue if quantity == 'EPSILON' and ext ==\",\n \"= [ 'TET' if tetradf else None, 'AC' if nwacont else None, 'NAR'\",\n \"# TODO add support for more output files and properties exciting_files = ['EIGVAL.OUT',\",\n \"= self._get_dos(self.partial_dos[i]) if self.energy_unit is not None: res = res * (1 /\",\n \"'electronic kinetic'], 'energy_coulomb': ['Coulomb energy', 'Coulomb'], 'x_exciting_coulomb_energy': ['Coulomb energy', 'Coulomb'], 'energy_exchange': ['Exchange energy',\",\n \"atom_resolved.append(((species, v[1] * unit))) else: vi = [float(vii) for vii in v[1].split()] vi\",\n \"= self.bandstructure_parser.get('band_energies', []) for n in range(len(band_energies)): # Get fermi energy: it is\",\n \"spin in range(len(totaldos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value = totaldos[spin]\",\n \"np.transpose(self.data) self._band_k_points = [] start = 0 for nkpts_segment in self.number_of_k_points_per_segment: end =\",\n \"0.0001) gmb = self.input_xml_parser.get('gw/mixbasis/gmb', 1.0) sec_gw.mixed_basis_gmax = gmb * gmaxvr pwm = self.input_xml_parser.get('gw/barecoul/pwm',\",\n \"unit=ureg.s, repeats=False, dtype=float) ] self._quantities.append(Quantity( 'structure_optimization', r'Structure\\\\-optimization module started([\\\\s\\\\S]+?)Structure\\\\-optimization module stopped', sub_parser=TextParser(quantities=[ Quantity(\",\n \"parsed? sccs = [] for quantity in ['EXCITON', 'EPSILON', 'SIGMA', 'LOSS']: files =\",\n \"n_kpoints)) self._band_energies = [] start = 0 for nkpts_segment in self.number_of_k_points_per_segment: end =\",\n \"not None: sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get( 'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get( 'xs/tddft/ahc', False) sec_method.x_exciting_xs_tddft_anti_resonant_dielectric\",\n \"change in total energy\\\\s*\\\\(target\\\\)', ureg.hartree), 'x_exciting_charge_convergence': ( r'Charge distance\\\\s*\\\\(target\\\\)', ureg.elementary_charge), 'x_exciting_IBS_force_convergence': ( r'Abs\\\\.\",\n \"sec_eigenvalues.energies = get_data('eigenvalues') def _parse_fermisurface(self, sec_scc): fermi_surface = self.fermisurf_parser.get('fermi_surface', [None])[0] if fermi_surface is\",\n \"== 'EPSILON': parse_function = parse_epsilon elif quantity == 'SIGMA': parse_function = parse_sigma elif\",\n \"self.distances[i] == self.distances[i - 1]: self._nkpts_segment.append(count) count = 1 else: count += 1\",\n \"of files are really correct, i.e. columns are supposed # to be what\",\n \"TODO Read also input parameters here if input_GW.xml does not exist self._quantities.append( Quantity(\",\n \"sec_dos_values.spin = spin sec_dos_values.value = dos[spin] # TODO add PDOS def _parse_bandstructure_dat(self, sec_scc):\",\n \"\\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([ \\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)', str_operation=str_to_eigenvalue, repeats=True)) class BandstructureDatParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin',\",\n \"if sec_scc is None: return # volume optimizations volume_index = 1 while True:\",\n \"= smearing_width.magnitude for name in self.info_parser._convergence_keys_mapping.keys(): threshold = self.info_parser.get_scf_threshold(name) if threshold is None:\",\n \"d in data]) * ureg.hartree eigs_gw = get_data('E_GW') if eigs_gw[0] is None: return\",\n \"self.input_xml_parser.logger = self.logger self.data_xs_parser.logger = self.logger self.data_clathrate_parser.logger = self.logger def reuse_parser(self, parser): self.info_parser.quantities\",\n \"sub_parser=TextParser(quantities=module_quantities) )) def get_atom_labels(self, section): labels = section.get('symbols') if labels is None: #\",\n \"self.vertices[i + 1].attrib.get(self._coord_key).split() res.append([start, end]) else: res = None self._results[key] = res class\",\n \"is None: return sec_system = sec_run.m_create(System) sec_atoms = sec_system.m_create(Atoms) sec_atoms.positions = positions sec_atoms.labels\",\n \"[] for i in range(len(self.number_of_k_points_per_segment)): start = np.array( self.vertices[i].attrib.get(self._coord_key).split(), dtype=float) end = np.array(\",\n \"not None: res = res * (1 / self.energy_unit) elif 'partial' in key:\",\n \"= data[0][0] * ureg.hartree + energy_fermi volume = self.info_parser.get_unit_cell_volume() dos = data[1] *\",\n \"bs_files = ['bandstructure.xml', 'BAND.OUT', 'bandstructure.dat'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break\",\n \"return self.energies else: res = None self._results[key] = res class ExcitingFermiSurfaceBxsfParser(TextParser): def __init__(self):\",\n \"spin = self.total_dos[i].attrib.get(self._nspin_key, i) res[i] = self._get_dos(self._total_dos[i]) if self.energy_unit is not None: res\",\n \"threshold) # additionally, set threshold to global metainfo. This is killing me! if\",\n \"kpts = np.array(val[0].split(), dtype=float) keys = val[1].split() eigs = np.transpose(np.array([v.split() for v in\",\n \"also other energies are reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is None: return\",\n \"= start + nkpts_segment band_energy = np.array([ np.transpose(energy)[start:end] for energy in band_energies]) if\",\n \"software # distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"for e in file_ext_list if e]) # read q points qpoints = self.input_xml_parser.get('xs/qpointset/qpoint')\",\n \"if val is None: continue setattr(msection, name, val) # other metainfo for name\",\n \"else val return np.array(val, dtype=float) def str_to_atom_properties_dict(val_in): unit = None if 'charge' in\",\n \"None), 'x_exciting_rgkmax': (r'R\\\\^MT\\\\_min \\\\* \\\\|G\\\\+k\\\\|\\\\_max \\\\(rgkmax\\\\)', None), 'x_exciting_species_rtmin': (r'Species with R\\\\^MT\\\\_min', None), 'x_exciting_gkmax':\",\n \"1 sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol = val[n][0] sec_mt_charge_atom.x_exciting_MT_charge_atom_value = val[n][1] sec_charges = msection.m_create(Charges) sec_charges.value = [\",\n \"Charges, Forces, ForcesEntry, ScfIteration, BandGap ) from nomad.datamodel.metainfo.workflow import Workflow, GeometryOptimization from .metainfo.exciting\",\n \"repeats=False, dtype=int), Quantity( 'force_convergence', r'Maximum force magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)', str_operation=str_to_quantity_tolerances, unit=ureg.hartree / ureg.bohr, repeats=False,\",\n \"filepath, archive, logger): self.filepath = filepath self.archive = archive self.logger = logger if\",\n \") for name, key_unit in self._method_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1],\",\n \"= self.input_xml_parser.get( 'gw/selfenergy/actype', 'pade') sec_gw.mixed_basis_lmax = int(self.input_xml_parser.get( 'gw/mixbasis/lmaxmb', 3)) sec_gw.mixed_basis_tolerance = self.input_xml_parser.get( 'gw/mixbasis/epsmb',\",\n \"self.root.findall('./%s/%s' % (self._partialdos_key, self._diagram_key)) return self._partial_dos @property def energies(self): if self._energies is None:\",\n \"for f in filenames if os.access(f, os.F_OK)] return filenames def file_exists(self, filename): \\\"\\\"\\\"Checks\",\n \"elif self._calculation_type == 'xs': parser_function = self._parse_input_xs else: # TODO implement reading of\",\n \"None: return [] name = xc_functional_map.get(xc_functional.type, []) return name @property def n_optimization_steps(self): return\",\n \"= self.input_xml_parser.get( 'gw/selfenergy/singularity', 'mpd') sec_gw.self_energy_c_analytical_continuation = self.input_xml_parser.get( 'gw/selfenergy/actype', 'pade') sec_gw.mixed_basis_lmax = int(self.input_xml_parser.get( 'gw/mixbasis/lmaxmb',\",\n \"exchange = self.input_xml_parser.get('libxc/exchange', None) xc_functional_names.append(exchange) sec_xc_functional = sec_dft.m_create(XCFunctional) for name in xc_functional_names: if\",\n \"for further info. # # Licensed under the Apache License, Version 2.0 (the\",\n \"nb in range(len(band_energies[n])): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.endpoints_labels\",\n \"self._energy_unit is None: axis = self.root.find('./axis') if axis is None: return self._energy_unit =\",\n \"band_k_points = self.bandstructure_dat_parser.band_k_points nkpts_segment = self.bandstructure_dat_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies)\",\n \"'x_exciting_IBS_force_convergence': ( r'Abs\\\\. change in max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)', ureg.hartree / ureg.bohr)} for name, key_unit in\",\n \"Wigner radius, r\\\\_s', ureg.bohr), 'x_exciting_empty_states': ('Number of empty states', None), 'x_exciting_valence_states': ('Total number\",\n \"1 else: count += 1 self._nkpts_segment.append(count) return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment\",\n \"of k\\\\-points', None), 'x_exciting_rgkmax': (r'R\\\\^MT\\\\_min \\\\* \\\\|G\\\\+k\\\\|\\\\_max \\\\(rgkmax\\\\)', None), 'x_exciting_species_rtmin': (r'Species with R\\\\^MT\\\\_min',\",\n \"species: positions = np.vstack([s.get('positions') for s in species]) if positions is None: return\",\n \"continue if key == 'x_exciting_section_MT_charge_atom': for n in range(len(val)): sec_mt_charge_atom = msection.m_create(x_exciting_section_MT_charge_atom) sec_mt_charge_atom.x_exciting_MT_charge_atom_index\",\n \"get_data(quantity, ext): # all files related to quantity at all qpoints files =\",\n \"self._nspin = 1 return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment\",\n \"charge contributions charge_contributions = iteration.get('charge_contributions', {}) for key, names in self._electron_charge_keys_mapping.items(): val =\",\n \"= val[1].split() eigs = np.transpose(np.array([v.split() for v in val[2:]], dtype=float)) eigs = {keys[i]:\",\n \"is None: return sec_fermisurface = sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters = self.fermisurf_parser.get('band_parameters', None) if band_parameters is\",\n \"'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces', r'Total atomic\",\n \"name in self.info_parser._convergence_keys_mapping.keys(): val = iteration.get(name) if val is None: continue setattr(msection, name,\",\n \"None: return self._nlm = 0 l_list = set([int(e.attrib.get(self._l_key)) for e in self.partial_dos]) for\",\n \"dtype=float) return [nbands, mesh, origin, vector] self._quantities.append( Quantity( 'band_parameters', r'BANDGRID_3D_BANDS\\\\s*([\\\\d\\\\.\\\\-Ee\\\\s]+)', str_operation=str_to_band_parameters, repeats=False)) self._quantities.append(\",\n \"only first!') self.info_gw_parser.mainfile = gw_info_files[0] fermi_energy = self.info_gw_parser.get('fermi_energy', None) if fermi_energy is not\",\n \"str_to_band_parameters(val_in): val = val_in.strip().split('\\\\n') nbands = int(val[0]) mesh = np.array(val[1].split(), dtype=int) origin =\",\n \"sec_run = self.archive.run[-1] def parse_configuration(section): if not section: return sec_scc = self.parse_scc(section) if\",\n \"return self._nspin @property def number_of_atoms(self): if self._natoms is None: partial_dos = self.root.findall('./%s' %\",\n \"n_optimization_steps(self): return len(self.get('structure_optimization', {}).get('optimization_step', [])) def get_number_of_spin_channels(self): spin_treatment = self.get('initialization', {}).get( 'x_exciting_spin_treatment', 'spin-unpolarised')\",\n \"for key, names in self._energy_keys_mapping.items(): val = None for name in names: val\",\n \"None: return sec_system = sec_run.m_create(System) sec_atoms = sec_system.m_create(Atoms) sec_atoms.positions = positions sec_atoms.labels =\",\n \"None) def init_parameters(self): self._nspin = None self._nkpts_segment = None self._neigs_segment = None self._bands\",\n \"= self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0) sec_method.x_exciting_xs_bse_rgkmax = rgkmax sec_method.x_exciting_xs_bse_sciavbd = self.input_xml_parser.get( 'xs/BSE/sciavbd', True) sec_method.x_exciting_xs_bse_sciavqbd =\",\n \"repeats=True, dtype=np.dtype(np.float64))]))) initialization_quantities.append(Quantity( 'potential_mixing', r'Using ([\\\\w ]+) potential mixing', repeats=False, flatten=False) ) initialization_quantities.append(Quantity(\",\n \"return # Get fermi energy: it is used to un-shift the DOS to\",\n \"not None: break if val is None: continue if key == 'x_exciting_section_MT_moment_atom': for\",\n \"kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None self._distances =\",\n \"None self._total_dos = None self._partial_dos = None @property def energy_unit(self): if self._energy_unit is\",\n \"self._bands is None: bands = self.root.findall('./%s' % self._band_key) self._bands = [] if bands:\",\n \"self.dos_parser.get('totaldos') * volume.to('m**3').magnitude for spin in range(len(totaldos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin =\",\n \"= sec_scc.m_create(ScfIteration) parse_scf(scf_iteration, sec_scf_iteration) return sec_scc def parse_system(self, section): sec_run = self.archive.run[-1] positions\",\n \"DOSXMLParser(XMLParser): def __init__(self, **kwargs): super().__init__(None) self._nspin_key = 'nspin' self._totaldos_key = 'totaldos' self._partialdos_key =\",\n \"sec_geometry_opt = sec_worfklow.m_create(GeometryOptimization) threshold_force = structure_optimization.get( 'optimization_step', [{}])[0].get('force_convergence', [0., 0.])[-1] sec_geometry_opt.input_force_maximum_tolerance = threshold_force\",\n \"eigs] self._quantities.append( Quantity( 'kpoints_eigenvalues', r'\\\\s*k\\\\-point \\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([ \\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)', str_operation=str_to_eigenvalue, repeats=True)) class BandstructureDatParser(DataTextParser): def __init__(self,\",\n \"data[4], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_im = np.reshape( data[5], (n_components, n_excitons)) def parse_epsilon(data, sec_scc): n_components\",\n \"def parse_gw(self): sec_run = self.archive.run[-1] # two versions of gw info files gw_info_files\",\n \"key.startswith('energy_'): sec_energy.m_add_sub_section(getattr( Energy, key.replace('energy_', '')), EnergyEntry(value=val)) else: setattr(msection, key, val) if key ==\",\n \"None: continue if name == 'x_exciting_spin_treatment': sub_sec = sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment = val elif\",\n \"= self.input_xml_parser.get('xs/xstype', None) if xstype is not None: sec_method.x_exciting_xs_xstype = xstype sec_method.x_exciting_electronic_structure_method =\",\n \"data_q: continue data_q = np.transpose(data_q, axes=(2, 0, 1)) for j in range(len(data)): data[j].append(data_q[j])\",\n \"= n + 1 sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol = val[n][0] sec_mt_moment_atom.x_exciting_MT_moment_atom_value = val[n][1] else: setattr(msection, key,\",\n \"= force_convergence[1] sec_scc = parse_configuration(structure_optimization) if sec_scc is None: return # volume optimizations\",\n \"= np.transpose(np.vstack(data)) n_loss = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_loss_energies = np.reshape( data[0], (n_components, n_loss))\",\n \"is None: lm = i % self.number_of_lm else: lm = int(val_l) ** 2\",\n \"return sec_scc = sec_run.m_create(Calculation) def parse_scf(iteration, msection): energy_total = iteration.get('energy_total') sec_energy = msection.m_create(Energy)\",\n \"* ureg.bohr def get_scf_threshold(self, name): reference = self.get('groundstate', self.get('hybrids', {})) return reference.get('scf_iteration', [{}])[-1].get(\",\n \"in key: if not self.partial_dos: return res = np.zeros(( self.number_of_lm, self.number_of_spin_channels, self.number_of_atoms, self.number_of_dos))\",\n \"// nspin data = np.reshape(data, (nspin, len(data) // nspin, 2)) data = np.transpose(data,\",\n \"val_in[:val_in.rfind('\\\\n \\\\n')].strip() val = np.array([v.split() for v in val.split('\\\\n')], dtype=float) val = np.transpose(val)\",\n \"3], float)) return dict( number=np.array(val[0], dtype=int), values=val[1] * ureg.hartree, weights=val[2]) # TODO Read\",\n \"self.energy_unit) elif 'partial' in key: if not self.partial_dos: return res = np.zeros(( self.number_of_lm,\",\n \"if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0) sec_method.x_exciting_xs_bse_rgkmax = rgkmax sec_method.x_exciting_xs_bse_sciavbd =\",\n \"input_file: return self.input_xml_parser.mainfile = input_file[0] xstype = self.input_xml_parser.get('xs/xstype', '') if xstype.lower() == 'bse':\",\n \"# limitations under the License. # import numpy as np import os import\",\n \"if name == 'x_exciting_spin_treatment': sub_sec = sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment = val elif name ==\",\n \"if smearing_width is not None: smearing_width = (smearing_width * ureg.hartree).to('joule') # TODO smearing\",\n \"Quantity( 'step', r'Optimization step\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'method', r'method\\\\s*=\\\\s*(\\\\w+)', repeats=False, dtype=str), Quantity( 'n_scf_iterations',\",\n \"convergence target achieved([\\\\s\\\\S]+?Opt)', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(imized atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format',\",\n \"is None: return return np.reshape(data, (nspin, len(data) // nspin, len(data[0]))) sec_gw_eigenvalues = sec_scc.m_create(BandEnergies)\",\n \"def total_dos(self): if self._total_dos is None: self._total_dos = self.root.findall('./%s/%s' % (self._totaldos_key, self._diagram_key)) return\",\n \"specific language governing permissions and # limitations under the License. # import numpy\",\n \"@property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data = np.transpose(self.data) self._neigs_segment = int(max(data[0]))\",\n \"= self.logger self.bandstructure_parser.logger = self.logger self.eigval_parser.logger = self.logger self.fermisurf_parser.logger = self.logger self.evalqp_parser.logger =\",\n \"key == 'energies': return self.energies else: res = None self._results[key] = res class\",\n \"or val_m is None: lm = i % self.number_of_lm else: lm = int(val_l)\",\n \"= clathrate_file[0] if self.data_clathrate_parser.data: data = np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates = np.transpose( np.array(data[:3], dtype=float)) sec_system.x_exciting_clathrates_atom_labels\",\n \"self._system_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) for name, key_unit\",\n \"if format of files are really correct, i.e. columns are supposed # to\",\n \"[] files_q = [f for f in files if f.endswith('QMT%s.OUT' % str(i +\",\n \"init_quantities(self): self._quantities = [] class ExcitingInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): re_symbol =\",\n \"(n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_im = np.reshape( data[5], (n_components, n_excitons)) def parse_epsilon(data, sec_scc): n_components =\",\n \"elif (name.startswith('FERMISURF') or name.startswith('FS')) and name.endswith('bxsf'): parser = self.fermisurf_parser parser_function = self._parse_fermisurface elif\",\n \"= Scf(threshold_energy_change=threshold) xc_functional_names = self.info_parser.get_xc_functional_name() if not xc_functional_names: # get it from input.xml\",\n \"sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.value = band_energies[nb] + energy_fermi def parse_file(self,\",\n \"v[0].startswith('species'): species = re.search(re_symbol, v[-1]).group(1) elif v[0].startswith('atom'): v[0] = v[0].split() v[1] = [float(vi)\",\n \"None), 'x_exciting_number_of_bravais_lattice_symmetries': ('Number of Bravais lattice symmetries', None), 'x_exciting_number_of_crystal_symmetries': ('Number of crystal symmetries',\",\n \"TODO smearing with should have units of energy sec_smearing.width = smearing_width.magnitude for name\",\n \"= self.input_xml_parser.get( 'xs/BSE/lmaxdielt', 14) rgkmax = self.input_xml_parser.get('xs/BSE/rgkmax', None) if rgkmax is None: rgkmax\",\n \"point in self.bands[0][0]] self._distances = np.array(self._distances, dtype=float) return self._distances @property def bands(self): if\",\n \"self.logger self.evalqp_parser.logger = self.logger self.dos_out_parser.logger = self.logger self.bandstructure_dat_parser.logger = self.logger self.band_out_parser.logger = self.logger\",\n \"self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None: continue data.append(self.data_xs_parser.data) return data def parse_exciton(data,\",\n \"range(len(partialdos[lm][spin])): sec_dos_values = sec_dos.m_create(DosValues, Dos.atom_projected) sec_dos_values.m_kind = 'spherical' sec_dos_values.lm = lm_values[lm] sec_dos_values.spin =\",\n \"= sec_run.m_create(Calculation) def parse_scf(iteration, msection): energy_total = iteration.get('energy_total') sec_energy = msection.m_create(Energy) if energy_total\",\n \"elif xstype.lower() == 'tddft': self._parse_xs_tddft() def _parse_input_gw(self, sec_method): sec_gw = sec_method.m_create(GWMethod) sec_gw.type =\",\n \"self.info_gw_parser.get('fundamental_band_gap', None) sec_gap = sec_scc.eigenvalues[-1].m_create(BandGap) if fundamental_band_gap is not None: sec_gap.value_fundamental = fundamental_band_gap\",\n \"= 1 for i in range(1, len(self.distances)): if self.distances[i] == self.distances[i - 1]:\",\n \"None: sec_method.x_exciting_xs_bse_antiresonant = self.input_xml_parser.get( 'xs/BSE/aresbse', True) sec_method.x_exciting_xs_bse_angular_momentum_cutoff = self.input_xml_parser.get( 'xs/BSE/lmaxdielt', 14) rgkmax =\",\n \"= self.info_parser.get_number_of_spin_channels() def reshape(data): if data[0] is None: return return np.reshape(data, (nspin, len(data)\",\n \"10) sec_method.x_exciting_xs_number_of_empty_states = self.input_xml_parser.get( 'xs/nempty', 5) sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq', [1, 1, 1]) sec_method.x_exciting_xs_ngridk\",\n \"case when there is a mismatch between files self.logger.warn( 'Mismatch in EXCITON and\",\n \"key == 'x_exciting_section_MT_moment_atom': for n in range(len(val)): sec_mt_moment_atom = msection.m_create(x_exciting_section_MT_moment_atom) sec_mt_moment_atom.x_exciting_MT_moment_atom_index = n\",\n \"= np.array( self.vertices[i + 1].attrib.get(self._coord_key).split(), dtype=float) res.append(np.linspace(start, end, self.number_of_k_points_per_segment[i])) elif key == 'band_segm_labels':\",\n \"optimization_step.get('method') is not None: sec_scc.x_exciting_geometry_optimization_method = optimization_step.get('method') if optimization_step.get('step') is not None: sec_scc.x_exciting_geometry_optimization_step\",\n \"sec_gw.frequency_grid_type = self.input_xml_parser.get( 'gw/freqgrid/fgrid', 'gaule2') sec_gw.number_of_frequencies = int(self.input_xml_parser.get( 'gw/freqgrid/nomeg', 16)) sec_gw.self_energy_c_number_of_poles = int(self.input_xml_parser.get(\",\n \"*(?:DOS|Density)', str_operation=str_to_energy_dict, repeats=False, convert=False), Quantity( 'x_exciting_dos_fermi', r'DOS at Fermi energy \\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float,\",\n \"'\\\\u0393' if end.lower() == 'gamma' else end]) elif key == 'band_segm_start_end': res =\",\n \"= np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies = [] start = 0 for\",\n \"= ['bandstructure-qp.dat', 'BAND-QP.OUT'] for fname in bs_files: if self.file_exists(fname): gw_files.append(fname) break for f\",\n \"force_convergence = optimization_step.get('force_convergence') if force_convergence is not None: sec_scc.x_exciting_maximum_force_magnitude = force_convergence[0] sec_scc.x_exciting_geometry_optimization_threshold_force =\",\n \"implied. # See the License for the specific language governing permissions and #\",\n \"= data[1:3] def parse_xs(self): sec_run = self.archive.run[-1] xs_info_files = self.get_exciting_files('INFOXS.OUT') if not xs_info_files:\",\n \"data_q = [] files_q = [f for f in files if f.endswith('QMT%s.OUT' %\",\n \"self._energy_unit is not None: band_energy = band_energy * self._energy_unit res_n.append(band_energy) start = end\",\n \"self._energies = self._energies * self.energy_unit return self._energies def _get_dos(self, diagram): dos = np.array(\",\n \"be what they are. What is the fourth column in epsilon which is\",\n \"if f.endswith(file_ext)] options.sort() filenames = [os.path.join(self.info_parser.maindir, f) for f in options] else: filenames\",\n \"sec_xc_functional.correlation.append(Functional(name=name)) elif 'HYB' in name: sec_xc_functional.hybrid.append(Functional(name=name)) else: sec_xc_functional.contributions.append(Functional(name=name)) if not xc_functional_names: # simply\",\n \"sec_scc.m_create(BandEnergies) sec_eigenvalues.kpoints = self.eigval_parser.get('k_points') sec_eigenvalues.occupations = get_data('occupancies') sec_eigenvalues.energies = get_data('eigenvalues') def _parse_fermisurface(self, sec_scc):\",\n \"== 'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field = data[1:3] elif quantity == 'LOSS' and ext == 'FXC':\",\n \"{ 'smearing_kind': ('Smearing scheme', None), 'smearing_width': ('Smearing width', None)} for name, key_unit in\",\n \"xstype = self.input_xml_parser.get('xs/xstype', None) if xstype is not None: sec_method.x_exciting_xs_xstype = xstype sec_method.x_exciting_electronic_structure_method\",\n \"sec_atoms_group.x_exciting_muffin_tin_radius = specie.get('muffin_tin_radius') positions_format = specie.get('positions_format') sec_atoms_group.x_exciting_atom_position_format = positions_format positions = specie.get('positions') positions\",\n \"val = np.array([v.split() for v in val.split('\\\\n')], dtype=float) val = np.transpose(val) occs =\",\n \"*(.+?,.+)', str_operation=lambda x: [v.strip() for v in x.split(':')]), Quantity( 'parameters', r'\\\\n *(.+?:.+)', repeats=True,\",\n \"\\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr) ] self._quantities.append(Quantity( 'groundstate', r'(?:Self\\\\-consistent loop\",\n \"+: +(\\\\S+)'), Quantity( 'name_reference', r'\\\\n *(.+?,.+)', str_operation=lambda x: [v.strip() for v in x.split(':')]),\",\n \"if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_screening_rgkmax = rgkmax sec_method.x_exciting_xs_screening_type =\",\n \"None: sec_scc.x_exciting_maximum_force_magnitude = force_convergence[0] sec_scc.x_exciting_geometry_optimization_threshold_force = force_convergence[1] sec_scc = parse_configuration(structure_optimization) if sec_scc is\",\n \"two versions of gw info files gw_info_files = ['GW_INFO.OUT', 'GWINFO.OUT'] for f in\",\n \"== 'band_segm_start_end': res = [] for i in range(len(self.number_of_k_points_per_segment)): start = self.vertices[i].attrib.get(self._coord_key).split() end\",\n \"# See the License for the specific language governing permissions and # limitations\",\n \"np.reshape( data[2], (n_components, n_sigma)) def parse_loss(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data))\",\n \"in range(len(self.total_dos)): spin = self.total_dos[i].attrib.get(self._nspin_key, i) res[i] = self._get_dos(self._total_dos[i]) if self.energy_unit is not\",\n \"get_number_of_spin_channels(self): spin_treatment = self.get('initialization', {}).get( 'x_exciting_spin_treatment', 'spin-unpolarised') n_spin = 1 if spin_treatment.lower() ==\",\n \"name.startswith('BAND') and name.endswith('OUT'): parser = self.band_out_parser parser_function = self._parse_band_out elif name.startswith('input') and name.endswith('xml'):\",\n \"properties exciting_files = ['EIGVAL.OUT', 'FERMISURF.bxsf', 'FS.bxsf'] # Parse DFT DOS from one of\",\n \"f in gw_files: self.parse_file(f, sec_scc) frequency_data = self.info_gw_parser.get('frequency_data', None) if frequency_data is not\",\n \"= [] species = None for v in val: v = v.strip().split(':') if\",\n \"sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)',\",\n \"% (target[0], suffix) if target[1:]: filename = '%s.%s' % (filename, target[1]) filename =\",\n \"is None: return # Get fermi energy: it is used to un-shift the\",\n \"data[1], (n_components, n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im = np.reshape( data[2], (n_components, n_epsilon)) def parse_sigma(data, sec_scc): n_components\",\n \"r'Atomic positions at this step\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)',\",\n \"is None: continue if name == 'time': msection.time_calculation = val else: setattr(msection, name,\",\n \"repeats=False, dtype=float, unit=1 / ureg.hartree), Quantity( 'charge_contributions', r'(?:Charges|Electron charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False),\",\n \"if self._energy_unit: band_energy = band_energy * self._energy_unit self._band_energies.append(band_energy) start = end return self._band_energies\",\n \"sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] self.parse_file('input.xml', sec_method) #\",\n \"sec_gap.value_fundamental = fundamental_band_gap optical_band_gap = self.info_gw_parser.get('optical_band_gap', None) if optical_band_gap is not None: sec_gap.value_optical\",\n \"+ 1 sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol = val[n][0] sec_mt_charge_atom.x_exciting_MT_charge_atom_value = val[n][1] sec_charges = msection.m_create(Charges) sec_charges.value =\",\n \"'correlation'], 'x_exciting_correlation_energy': ['Correlation energy', 'correlation'], 'energy_sum_eigenvalues': ['Sum of eigenvalues', 'sum of eigenvalues'], 'x_exciting_effective_potential_energy':\",\n \"dtype=float, unit=ureg.hartree / ureg.bohr), ]), repeats=False)) self._quantities.append(Quantity( 'hybrids', r'Hybrids module started([\\\\s\\\\S]+?)Hybrids module stopped',\",\n \"val return np.array(val, dtype=float) def str_to_atom_properties_dict(val_in): unit = None if 'charge' in val_in:\",\n \"= (smearing_width * ureg.hartree).to('joule') # TODO smearing with should have units of energy\",\n \"self.evalqp_parser.logger = self.logger self.dos_out_parser.logger = self.logger self.bandstructure_dat_parser.logger = self.logger self.band_out_parser.logger = self.logger self.info_gw_parser.logger\",\n \"Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*', unit=ureg.hartree, repeats=False)) def str_to_band_parameters(val_in): val = val_in.strip().split('\\\\n') nbands = int(val[0]) mesh =\",\n \"self.input_xml_parser.get('xs/BSE') is not None: sec_method.x_exciting_xs_bse_antiresonant = self.input_xml_parser.get( 'xs/BSE/aresbse', True) sec_method.x_exciting_xs_bse_angular_momentum_cutoff = self.input_xml_parser.get( 'xs/BSE/lmaxdielt',\",\n \"TODO confirm no cell optimization in exciting sec_atoms.lattice_vectors = lattice_vectors lattice_vectors_reciprocal = self.info_parser.get_initialization_parameter(\",\n \"is None: return [] name = xc_functional_map.get(xc_functional.type, []) return name @property def n_optimization_steps(self):\",\n \"is None: data = np.transpose(self.data) self._distances = data[5][:int(max(data[1]))] return self._distances @property def number_of_spin_channels(self):\",\n \"include other xcf xc_functional_map = { 2: ['LDA_C_PZ', 'LDA_X_PZ'], 3: ['LDA_C_PW', 'LDA_X_PZ'], 4:\",\n \"bse_files.append(files) return bse_files def get_data(files): data = [] for f in files: self.data_xs_parser.mainfile\",\n \"limitations under the License. # import numpy as np import os import re\",\n \"sec_gw.bare_coulomb_gmax = pwm * gmb * gmaxvr sec_gw.bare_coulomb_cutofftype = self.input_xml_parser.get( 'gw/barecoul/cutofftype', 'none') sec_gw.screened_coulomb_volume_average\",\n \"energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=ureg.hartree), Quantity( 'energy_contributions', r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n *(?:DOS|Density)', str_operation=str_to_energy_dict, repeats=False, convert=False), Quantity( 'x_exciting_dos_fermi',\",\n \"names: val = energy_contributions.get(name, None) if val is not None: break if val\",\n \"len(data[0]) // n_components sec_scc.x_exciting_xs_bse_sigma_energies = np.reshape( data[0], (n_components, n_sigma)) * ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re =\",\n \"fermi_energy = self.fermisurf_parser.get('fermi_energy', None) if fermi_energy is not None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface = fermi_energy sec_fermisurface.x_exciting_values_fermi_surface\",\n \"band_parameters[1] sec_fermisurface.x_exciting_origin_fermi_surface = band_parameters[2] sec_fermisurface.x_exciting_vectors_fermi_surface = band_parameters[3] fermi_energy = self.fermisurf_parser.get('fermi_energy', None) if fermi_energy\",\n \"repeats=False, dtype=float) ] self._quantities.append(Quantity( 'structure_optimization', r'Structure\\\\-optimization module started([\\\\s\\\\S]+?)Structure\\\\-optimization module stopped', sub_parser=TextParser(quantities=[ Quantity( 'optimization_step',\",\n \"for optimization_step in structure_optimization.get('optimization_step', []): sec_scc = parse_configuration(optimization_step) if optimization_step.get('method') is not None:\",\n \"dtype=float)) eigs = {keys[i]: eigs[i] for i in range(len(keys))} return [kpts, eigs] self._quantities.append(\",\n \"unit = ureg.elementary_charge elif 'moment' in val_in: unit = ureg.elementary_charge * ureg.bohr val\",\n \"r'name *: *(.+)'), Quantity('nuclear_charge', rf'nuclear charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('electronic_charge', rf'electronic charge\",\n \"if sec_method.x_exciting_xs_bse_xas: sec_method.x_exciting_xs_bse_xasatom = self.input_xml_parser.get( 'xs/BSE/xasatom', 0) sec_method.x_exciting_xs_bse_xasedge = self.input_xml_parser.get( 'xs/BSE/xasedge', 'K') sec_method.x_exciting_xs_bse_xasspecies\",\n \"None: self._total_dos = self.root.findall('./%s/%s' % (self._totaldos_key, self._diagram_key)) return self._total_dos @property def partial_dos(self): if\",\n \"clas for bandstructure dat and xml self._nspin = None self._nkpts_segment = None self._neigs_segment\",\n \"of valence states', None), 'x_exciting_hamiltonian_size': ('Maximum Hamiltonian size', None), 'x_exciting_pw': (r'Maximum number of\",\n \"kinetic'], 'x_exciting_dft_d2_dispersion_correction': ['DFT-D2 dispersion correction'] } self._electron_charge_keys_mapping = { 'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage': ['core\",\n \"sec_scc.x_exciting_xs_bse_epsilon_re = np.reshape( data[1], (n_components, n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im = np.reshape( data[2], (n_components, n_epsilon)) def\",\n \"not sure about format for spin-polarized case! I assume it is # energy\",\n \"True) sec_method.x_exciting_xs_bse_angular_momentum_cutoff = self.input_xml_parser.get( 'xs/BSE/lmaxdielt', 14) rgkmax = self.input_xml_parser.get('xs/BSE/rgkmax', None) if rgkmax is\",\n \"for i in range(len(keys))} return [kpts, eigs] self._quantities.append( Quantity( 'kpoints_eigenvalues', r'\\\\s*k\\\\-point \\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([ \\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)',\",\n \"v in val_in.strip().split('\\\\n')] val = val[0] if len(val) == 1 else val return\",\n \"[] for n in range(len(atoms)): atom_labels.extend([species[n].split('.')[0]] * len(atoms[n])) if positions is None or\",\n \"dict() if not self.bands: return if key == 'band_energies': # TODO I am\",\n \"specie in species: labels += [specie.get('symbol')] * len(specie.get('positions')) return labels def get_positions_format(self, section):\",\n \"files_q: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None: continue data_q.append(self.data_xs_parser.data) if not data_q:\",\n \"Quantity('radial_points', rf'radial points in muffin-tin *: *({re_float})', dtype=np.int32), Quantity('positions_format', r'atomic positions \\\\((.+?)\\\\)', flatten=False),\",\n \"['Nuclear-nuclear energy', 'nuclear-nuclear'], 'x_exciting_madelung_energy': ['Madelung energy', 'Madelung'], 'x_exciting_core_electron_kinetic_energy': ['Core-electron kinetic energy', 'core electron\",\n \"np.array([v.split() for v in val.split('\\\\n')], dtype=float) val = np.transpose(val) occs = val[-1] eigs\",\n \"dtype=int), Quantity( 'force_convergence', r'Maximum force magnitude\\\\s*\\\\(target\\\\)\\\\s*\\\\:(\\\\s*[\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)', str_operation=str_to_quantity_tolerances, unit=ureg.hartree / ureg.bohr, repeats=False, dtype=float),\",\n \"file_ext_list = [ 'TET' if tetradf else None, 'AC' if nwacont else None,\",\n \"of exciting self._energy_keys_mapping = { 'energy_total': ['Total energy', 'total energy'], 'x_exciting_fermi_energy': ['Fermi energy',\",\n \"self._nspin is None: self._nspin = 1 return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment\",\n \"self._coord_key = 'coord' self._energy_key = 'eval' self._vertex_key = 'vertex' self._band_key = 'band' self._atom_key\",\n \"key == 'band_segm_labels': res = [] for i in range(len(self.vertices) - 1): start\",\n \"= np.transpose(np.array([v for v in val if len(v) == 3], float)) return dict(\",\n \"# Copyright The NOMAD Authors. # # This file is part of NOMAD.\",\n \"return sec_system def parse_configurations(self): sec_run = self.archive.run[-1] def parse_configuration(section): if not section: return\",\n \"positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces',\",\n \"xstype.lower() == 'tddft': self._parse_xs_tddft() def _parse_input_gw(self, sec_method): sec_gw = sec_method.m_create(GWMethod) sec_gw.type = 'G0W0'\",\n \"energy', 'electronic kinetic'], 'energy_coulomb': ['Coulomb energy', 'Coulomb'], 'x_exciting_coulomb_energy': ['Coulomb energy', 'Coulomb'], 'energy_exchange': ['Exchange\",\n \"'xs': parser_function = self._parse_input_xs else: # TODO implement reading of parameters from input.xml\",\n \"self.input_xml_parser.get( 'xs/tetra/tetradf', False) def _parse_xs_bse(self): sec_run = self.archive.run[-1] # TODO read from xml\",\n \"return quantity def get_xc_functional_name(self): # TODO expand list to include other xcf xc_functional_map\",\n \"val) except Exception: self.logger.warn('Error setting metainfo.') # species species = self.info_parser.get_initialization_parameter('species', []) for\",\n \"(nspin, len(data) // nspin, len(data[0]))) sec_gw_eigenvalues = sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor = reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands =\",\n \"'groundstate', r'(?:Self\\\\-consistent loop started|Groundstate module started)([\\\\s\\\\S]+?)Groundstate module stopped', sub_parser=TextParser(quantities=module_quantities), repeats=False)) optimization_quantities = [\",\n \"dos def parse(self, key): if self._results is None: self._results = dict() if 'total'\",\n \"for f in ['input_gw.xml', 'input-gw.xml', 'input.xml']: self.parse_file(f, sec_method) xc_functional_name = ' '.join(self.info_parser.get_xc_functional_name()) sec_method.gw.starting_point\",\n \"self.partial_dos]) for li in l_list: self._nlm += 2 * li + 1 return\",\n \"I assume it is # energy dos_up dos_down nspin = self.info_parser.get_number_of_spin_channels() sec_dos =\",\n \"self._nkpts_segment.append(count) return self._nkpts_segment @property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data = np.transpose(self.data)\",\n \"[kpts, eigs] self._quantities.append( Quantity( 'kpoints_eigenvalues', r'\\\\s*k\\\\-point \\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([ \\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)', str_operation=str_to_eigenvalue, repeats=True)) class BandstructureDatParser(DataTextParser): def\",\n \"'x_exciting_scf_threshold_energy_change': sec_method.scf = Scf(threshold_energy_change=threshold) xc_functional_names = self.info_parser.get_xc_functional_name() if not xc_functional_names: # get it\",\n \"np.eye(3)) species = self.input_xml_parser.get('structure/species/speciesfile') if positions is None or lattice_vectors is None or\",\n \"ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_sigma_no_local_field = data[1:3] def parse_xs(self): sec_run = self.archive.run[-1] xs_info_files =\",\n \"sec_system.m_create(Atoms) sec_atoms.positions = positions sec_atoms.labels = atom_labels sec_atoms.periodic = [True] * 3 #\",\n \"the original scale in which also other energies are reported. energy_fermi = sec_scc.energy.fermi\",\n \"positions = self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors = self.input_xml_parser.get( 'structure/crystal/basevect', np.eye(3)) species = self.input_xml_parser.get('structure/species/speciesfile') if positions\",\n \"sub_parser=TextParser(quantities=[ Quantity('type', r'Exchange-correlation type +: +(\\\\S+)'), Quantity( 'name_reference', r'\\\\n *(.+?,.+)', str_operation=lambda x: [v.strip()\",\n \"dtype=int), Quantity( 'method', r'method\\\\s*=\\\\s*(\\\\w+)', repeats=False, dtype=str), Quantity( 'n_scf_iterations', r'Number of (?:total)* scf iterations\\\\s*\\\\:\\\\s*(\\\\d+)',\",\n \"if metainfo_name == 'x_exciting_scf_threshold_energy_change': sec_method.scf = Scf(threshold_energy_change=threshold) xc_functional_names = self.info_parser.get_xc_functional_name() if not xc_functional_names:\",\n \"data[2], (n_components, n_epsilon)) def parse_sigma(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_sigma\",\n \"iteration.get(name) if val is None: continue setattr(msection, name, val) # other metainfo for\",\n \"info_volume = self.get_exciting_files('run_dir%s/INFO.OUT' % str(volume_index).rjust(2, '0')) if not info_volume: break sec_scc.calculations_path.append(info_volume[0]) def init_parser(self):\",\n \"range(len(partialdos)): for spin in range(len(partialdos[lm])): for atom in range(len(partialdos[lm][spin])): sec_dos_values = sec_dos.m_create(DosValues, Dos.atom_projected)\",\n \"and occupancy below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n *\\\\n))', str_operation=str_to_eigenvalues, repeats=True)) class ExcitingGWOutParser(TextParser): def __init__(self, mainfile, logger): super().__init__(mainfile,\",\n \"{ 'x_exciting_gap': (r'Estimated fundamental gap', ureg.hartree), 'time': (r'Wall time \\\\(seconds\\\\)', ureg.s)} for name,\",\n \"# we need to set nspin again as this is overwritten when setting\",\n \"len(data) data = np.transpose(np.vstack(data)) n_sigma = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_sigma_energies = np.reshape( data[0],\",\n \"'G0W0' gmaxvr = self.info_parser.get_initialization_parameter('x_exciting_gmaxvr', 0) sec_gw.core_treatment = self.input_xml_parser.get( 'gw/coreflag', 'all') sec_gw.polarizability_number_of_empty_states = int(\",\n \"repeats=False)) module_quantities = [ Quantity( 'scf_iteration', r'(?:I| i)teration number :([\\\\s\\\\S]+?)(?:\\\\n *\\\\n\\\\+{10}|\\\\+\\\\-{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=True),\",\n \"section_k_band res = [] for n in range(len(self.bands)): res_n = [] start =\",\n \"np.array( self.vertices[i + 1].attrib.get(self._coord_key).split(), dtype=float) res.append(np.linspace(start, end, self.number_of_k_points_per_segment[i])) elif key == 'band_segm_labels': res\",\n \"len(quantity) < n_scf: quantity = [None] * (n_scf - len(quantity)) + quantity return\",\n \"np.array([d[1].get(key) for d in data]) * ureg.hartree eigs_gw = get_data('E_GW') if eigs_gw[0] is\",\n \"'LDA_X_PZ'], 4: ['LDA_C_XALPHA'], 5: ['LDA_C_VBH'], 20: ['GGA_C_PBE', 'GGA_X_PBE'], 21: ['GGA_C_PBE', 'GGA_X_PBE_R'], 22: ['GGA_C_PBE_SOL',\",\n \"are reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is None: continue energy_fermi = energy_fermi.to(\\\"hartree\\\")\",\n \"np.reshape(occs, (nspin, len(occs) // nspin)) data['eigenvalues'] = np.reshape(eigs, (nspin, len(eigs) // nspin)) return\",\n \"get_unit_cell_volume(self): return self.get('initialization', {}).get('x_exciting_unit_cell_volume', 1.0 * ureg.bohr ** 3) def get_initialization_parameter(self, key, default=None):\",\n \"'0')) if not info_volume: break sec_scc.calculations_path.append(info_volume[0]) def init_parser(self): self.info_parser.mainfile = self.filepath self.info_parser.logger =\",\n \"parameters from input.xml for normal calculations # in addition to INFO.OUT return else:\",\n \"self.dos_parser.get('partialdos') if partialdos is None: return partialdos = partialdos.to('1/joule').magnitude lm_values = np.column_stack((np.arange(len(partialdos)), np.zeros(len(partialdos),\",\n \"= sec_scc.eigenvalues[-1].m_create(BandGap) if fundamental_band_gap is not None: sec_gap.value_fundamental = fundamental_band_gap optical_band_gap = self.info_gw_parser.get('optical_band_gap',\",\n \"range(len(self.bands)): res_n = [] start = 0 band_energies = np.zeros(( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, len(self.distances)),\",\n \"for j in range(len(data)): data[j].append(data_q[j]) return data for quantity in ['EPSILON', 'LOSS', 'SIGMA']:\",\n \"d in data]) if None in energy: return energy return np.array([d[1].get(key) for d\",\n \"None or val_m is None: lm = i % self.number_of_lm else: lm =\",\n \"x_exciting_section_MT_moment_atom,\\\\ x_exciting_section_spin, x_exciting_section_fermi_surface,\\\\ x_exciting_section_atoms_group re_float = r'[-+]?\\\\d+\\\\.\\\\d*(?:[Ee][-+]\\\\d+)?' class GWInfoParser(TextParser): def __init__(self): super().__init__(None) def\",\n \"sec_scc) # forces forces = section.get('forces') if forces is not None: sec_forces =\",\n \"= n_excitons sec_scc.x_exciting_xs_bse_exciton_energies = np.reshape( data[1], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies = np.reshape(\",\n \"file_exists(self, filename): \\\"\\\"\\\"Checks if a the given filename exists and is accessible in\",\n \"[]) for specie in species: sec_atoms_group = sec_system.m_create(x_exciting_section_atoms_group) sec_atoms_group.x_exciting_geometry_atom_labels = specie.get('symbol') sec_atoms_group.x_exciting_geometry_atom_number =\",\n \"n_sigma)) * ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re = np.reshape( data[1], (n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im = np.reshape( data[2],\",\n \"sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.endpoints_labels = band_seg_labels[nb] sec_k_band_segment.energies = band_energies[n][nb] + energy_fermi def _parse_eigenvalues(self,\",\n \"section): # TODO add support for info.xml, wannier.out if name.startswith('dos') and name.endswith('xml'): parser\",\n \"None self._bands = None self._vertices = None self._distances = None self._species = None\",\n \"np.array( [e.attrib.get(self._energy_key) for e in self.bands[n][i]]) for nkpts_segment in self.number_of_k_points_per_segment: end = start\",\n \"if optimization_step.get('step') is not None: sec_scc.x_exciting_geometry_optimization_step = optimization_step.get('step') force_convergence = optimization_step.get('force_convergence') if force_convergence\",\n \"ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi nkpts_segment = self.band_out_parser.number_of_k_points_per_segment for nb\",\n \"str_to_energy_dict(val_in): val = val_in.strip().split('\\\\n') energies = dict() for v in val: v =\",\n \"'xs/tddft/drude', [0., 0.]) sec_method.x_exciting_xs_tddft_split_parameter = self.input_xml_parser.get( 'xs/tddft/fxcbsesplit', 0.00001, 'hartree') sec_method.x_exciting_xs_tddft_xc_kernel = self.input_xml_parser.get( 'xs/tddft/fxctype',\",\n \"{}).get(key, default) class ExcitingParser: def __init__(self): self.info_parser = ExcitingInfoParser() self.dos_parser = DOSXMLParser(energy_unit=ureg.hartree) self.bandstructure_parser\",\n \"= [ Quantity( 'energy_total', r'[Tt]*otal energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=ureg.hartree), Quantity( 'energy_contributions', r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n *(?:DOS|Density)',\",\n \"TODO expand list to include other xcf xc_functional_map = { 2: ['LDA_C_PZ', 'LDA_X_PZ'],\",\n \"'Madelung'], 'x_exciting_core_electron_kinetic_energy': ['Core-electron kinetic energy', 'core electron kinetic'], 'x_exciting_dft_d2_dispersion_correction': ['DFT-D2 dispersion correction'] }\",\n \"Calculation.dos_electronic) sec_dos.n_energies = len(data) // nspin data = np.reshape(data, (nspin, len(data) // nspin,\",\n \"= self._get_dos(self._total_dos[i]) if self.energy_unit is not None: res = res * (1 /\",\n \"input_file: self.input_xml_parser.mainfile = f correlation = self.input_xml_parser.get('libxc/correlation', None) xc_functional_names.append(correlation) exchange = self.input_xml_parser.get('libxc/exchange', None)\",\n \"self.get('initialization', {}).get('x_exciting_unit_cell_volume', 1.0 * ureg.bohr ** 3) def get_initialization_parameter(self, key, default=None): return self.get('initialization',\",\n \"format of files are really correct, i.e. columns are supposed # to be\",\n \"the specific language governing permissions and # limitations under the License. # import\",\n \"filenames def file_exists(self, filename): \\\"\\\"\\\"Checks if a the given filename exists and is\",\n \"= len(data) data = np.transpose(np.vstack(data)) n_sigma = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_sigma_energies = np.reshape(\",\n \"parse_function = parse_loss else: continue try: parse_function(data, sec_scc) except Exception: self.logger.error('Error setting xs\",\n \"None) def init_parameters(self): self._nspin = None self._distances = None self._band_energies = None self._neigs_segment\",\n \"species]) if positions is None: return positions = np.array(positions) positions_format = positions_format if\",\n \"r'(imized atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'imized atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True,\",\n \"= i % self.number_of_lm else: lm = int(val_l) ** 2 + int(val_m) +\",\n \"i in range(1, len(self.distances)): if self.distances[i] == self.distances[i - 1]: self._nkpts_segment.append(count) count =\",\n \"if xc_functional is None: return [] name = xc_functional_map.get(xc_functional.type, []) return name @property\",\n \"None: spin = (i % (self.number_of_spin_channels * self.number_of_lm)) // self.number_of_lm else: spin =\",\n \"len(gw_info_files) > 1: self.logger.warn('Found multiple GW info files, will read only first!') self.info_gw_parser.mainfile\",\n \"None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_screening_rgkmax = rgkmax sec_method.x_exciting_xs_screening_type\",\n \"= [ Quantity( 'atomic_positions', r'(Atomic positions at this step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic\",\n \"list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)', str_operation=str_to_frequency, repeats=False) ) self._quantities.append( Quantity( 'fermi_energy', r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi [Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s', unit=ureg.hartree, repeats=False) ) self._quantities.append(\",\n \"for fname in bs_files: if self.file_exists(fname): gw_files.append(fname) break for f in gw_files: self.parse_file(f,\",\n \"r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) class ExcitingEvalqpParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities =\",\n \"None self._nlm = None self._energies = None self._total_dos = None self._partial_dos = None\",\n \"data[1] elif quantity == 'SIGMA' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_sigma_local_field = data[1:3] elif\",\n \"parse_scf(final, sec_scc) # forces forces = section.get('forces') if forces is not None: sec_forces\",\n \"300: ['GGA_C_BGCP', 'GGA_X_PBE'], 406: ['HYB_GGA_XC_PBEH'], 408: ['HYB_GGA_XC_HSE03']} xc_functional = self.get('initialization', {}).get('xc_functional', None) if\",\n \"un-shift the DOS to # the original scale in which also other energies\",\n \"// len(data))) if key == 'eigenvalues': res = res * ureg.hartree return res\",\n \"is not None: sec_energy.total = EnergyEntry(value=energy_total) x_exciting_dos_fermi = iteration.get('x_exciting_dos_fermi') if x_exciting_dos_fermi is not\",\n \"# Parse DFT band structure from one of the files bs_files = ['bandstructure.xml',\",\n \"pwm * gmb * gmaxvr sec_gw.bare_coulomb_cutofftype = self.input_xml_parser.get( 'gw/barecoul/cutofftype', 'none') sec_gw.screened_coulomb_volume_average = self.input_xml_parser.get(\",\n \"data[0] is None: return return np.reshape(data, (nspin, len(data) // nspin, len(data[0]))) sec_gw_eigenvalues =\",\n \"= section.get('scf_iteration', []) for scf_iteration in scf_iterations: sec_scf_iteration = sec_scc.m_create(ScfIteration) parse_scf(scf_iteration, sec_scf_iteration) return\",\n \"parser.mainfile = None def _parse_input_xs(self, sec_method): xstype = self.input_xml_parser.get('xs/xstype', None) if xstype is\",\n \"elif name.startswith('bandstructure') and name.endswith('xml'): parser = self.bandstructure_parser parser_function = self._parse_bandstructure elif name.startswith('EIGVAL') and\",\n \"sec_scc = parse_configuration(structure_optimization) if sec_scc is None: return # volume optimizations volume_index =\",\n \"= [filename] filenames = [f for f in filenames if os.access(f, os.F_OK)] return\",\n \"sec_scc = sec_run.m_create(Calculation) sccs.append(sec_scc) else: sec_scc = sccs[i] if sec_scc is None: #\",\n \"def _parse_bandstructure(self, sec_scc): # we need to set nspin again as this is\",\n \"= sec_scc.m_create(BandEnergies) sec_eigenvalues.kpoints = self.eigval_parser.get('k_points') sec_eigenvalues.occupations = get_data('occupancies') sec_eigenvalues.energies = get_data('eigenvalues') def _parse_fermisurface(self,\",\n \"is the fourth column in epsilon which is not parsed? sccs = []\",\n \"str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr) ] self._quantities.append(Quantity( 'groundstate', r'(?:Self\\\\-consistent loop started|Groundstate module started)([\\\\s\\\\S]+?)Groundstate\",\n \"xs if self.input_xml_parser.get('xs/screening') is not None: sec_method.x_exciting_xs_screening_number_of_empty_states = self.input_xml_parser.get( 'xs/screening/nempty', 0) sec_method.x_exciting_xs_screening_ngridk =\",\n \"self._distances = None self._band_energies = None self._band_k_points = None @property def band_energies(self): if\",\n \"r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces', r'Total atomic forces\",\n \"self._species = None @property def distances(self): if self._distances is None: if not self.bands:\",\n \"'lattice_vectors_reciprocal', r'Reciprocal lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=1 / ureg.bohr, repeats=False, convert=False), ] self._system_keys_mapping =\",\n \"# groundstate and hybrids calculation for module in ['groundstate', 'hybrids']: sec_scc = parse_configuration(self.info_parser.get(module))\",\n \"Quantity( 'positions_format', r'Atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True,\",\n \"in gw_info_files: if self.get_exciting_files(f): self._calculation_type = 'gw' gw_info_file = f break if not\",\n \"= None self._distances = None self._band_energies = None self._band_k_points = None @property def\",\n \"started([\\\\s\\\\S]+?)Hybrids module stopped', sub_parser=TextParser(quantities=module_quantities) )) def get_atom_labels(self, section): labels = section.get('symbols') if labels\",\n \"self._parse_band_out elif name.startswith('input') and name.endswith('xml'): parser = self.input_xml_parser if self._calculation_type == 'gw': parser_function\",\n \"f in gw_info_files: if self.get_exciting_files(f): self._calculation_type = 'gw' gw_info_file = f break if\",\n \"exciting sec_atoms.lattice_vectors = lattice_vectors lattice_vectors_reciprocal = self.info_parser.get_initialization_parameter( 'lattice_vectors_reciprocal') sec_atoms.lattice_vectors_reciprocal = lattice_vectors_reciprocal if len(sec_run.system)\",\n \"is not None: band_energy = band_energy * self._energy_unit res_n.append(band_energy) start = end res.append(res_n)\",\n \"'frequency_data', r'frequency list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)', str_operation=str_to_frequency, repeats=False) ) self._quantities.append( Quantity( 'fermi_energy', r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi [Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s', unit=ureg.hartree, repeats=False)\",\n \"self.evalqp_parser = ExcitingEvalqpParser() self.dos_out_parser = DataTextParser() self.bandstructure_dat_parser = BandstructureDatParser(energy_unit=ureg.hartree) self.band_out_parser = BandOutParser(energy_unit=ureg.hartree) self.info_gw_parser\",\n \"- 1): start = self.vertices[i].attrib.get('label') end = self.vertices[i + 1].attrib.get('label') res.append([ '\\\\u0393' if\",\n \"None] file_ext = '_'.join([e for e in file_ext_list if e]) # read q\",\n \"def _parse_dos(self, sec_scc): if self.dos_parser.get('totaldos', None) is None: return # Get fermi energy:\",\n \"self._nlm = None self._energies = None self._total_dos = None self._partial_dos = None @property\",\n \"final is None: return sec_scc = sec_run.m_create(Calculation) def parse_scf(iteration, msection): energy_total = iteration.get('energy_total')\",\n \"self.bands[0][0]] self._distances = np.array(self._distances, dtype=float) return self._distances @property def bands(self): if self._bands is\",\n \"self.input_xml_parser.get('xs/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_rgkmax = rgkmax\",\n \"band_energy * self._energy_unit self._band_energies.append(band_energy) start = end return self._band_energies @property def distances(self): if\",\n \"self._total_dos @property def partial_dos(self): if self._partial_dos is None: self._partial_dos = self.root.findall('./%s/%s' % (self._partialdos_key,\",\n \"positions_format = positions_format if positions_format is not None else self.get_positions_format(section) if positions_format ==\",\n \"optical_band_gap = self.info_gw_parser.get('optical_band_gap', None) if optical_band_gap is not None: sec_gap.value_optical = optical_band_gap def\",\n \"i in range(len(keys))} return [kpts, eigs] self._quantities.append( Quantity( 'kpoints_eigenvalues', r'\\\\s*k\\\\-point \\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([ \\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)', str_operation=str_to_eigenvalue,\",\n \"'xs/BSE/xasedge', 'K') sec_method.x_exciting_xs_bse_xasspecies = self.input_xml_parser.get( 'xs/BSE/xasspecies', 0) sec_method.x_exciting_xs_bse_xas_number_of_bands = self.input_xml_parser.get( 'xs/BSE/nstlxas', [0, 0])\",\n \"res.append(np.linspace(start, end, self.number_of_k_points_per_segment[i])) elif key == 'band_segm_labels': res = [] for i in\",\n \"# TODO check if format of files are really correct, i.e. columns are\",\n \"'x_exciting_charge_convergence': ( r'Charge distance\\\\s*\\\\(target\\\\)', ureg.elementary_charge), 'x_exciting_IBS_force_convergence': ( r'Abs\\\\. change in max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)', ureg.hartree /\",\n \"val_m = self.partial_dos[i].attrib.get(self._m_key, None) if val_l is None or val_m is None: lm\",\n \"I cannot find an example bandstructure file # atom-resolved bandstructure are added as\",\n \"None else final if final is None: return sec_scc = sec_run.m_create(Calculation) def parse_scf(iteration,\",\n \"def get_number_of_spin_channels(self): spin_treatment = self.get('initialization', {}).get( 'x_exciting_spin_treatment', 'spin-unpolarised') n_spin = 1 if spin_treatment.lower()\",\n \"= spin sec_dos_values.value = dos[spin] # TODO add PDOS def _parse_bandstructure_dat(self, sec_scc): self.bandstructure_dat_parser._nspin\",\n \"sec_method.x_exciting_xs_tddft_xc_kernel = self.input_xml_parser.get( 'xs/tddft/fxctype', 'RPA') sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution = self.input_xml_parser.get( 'xs/tddft/intraband', False) sec_method.x_exciting_xs_tddft_diagonal_xc_kernel = self.input_xml_parser.get(\",\n \"in l_list: self._nlm += 2 * li + 1 return self._nlm @property def\",\n \"len(v) == 3], float)) return dict( number=np.array(val[0], dtype=int), values=val[1] * ureg.hartree, weights=val[2]) #\",\n \"r'Species : *(\\\\d+)', dtype=np.int32), Quantity('symbol', r'\\\\((\\\\w+)\\\\)'), Quantity('file', r'parameters loaded from *: *(.+)'), Quantity('name',\",\n \"GW info files, will read only first!') self.info_gw_parser.mainfile = gw_info_files[0] fermi_energy = self.info_gw_parser.get('fermi_energy',\",\n \"initialization)([\\\\s\\\\S]+?)(?:Using|Ending initialization)', repeats=False, sub_parser=TextParser(quantities=initialization_quantities)) ) scf_quantities = [ Quantity( 'energy_total', r'[Tt]*otal energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False,\",\n \"= str(specie.get('number')) sec_atoms_group.x_exciting_muffin_tin_points = specie.get('radial_points') sec_atoms_group.x_exciting_muffin_tin_radius = specie.get('muffin_tin_radius') positions_format = specie.get('positions_format') sec_atoms_group.x_exciting_atom_position_format =\",\n \"in species]) if positions is None: return positions = np.array(positions) positions_format = positions_format\",\n \"smearing_kind smearing_width = self.info_parser.get_initialization_parameter('smearing_width') if smearing_width is not None: smearing_width = (smearing_width *\",\n \"related to quantity at all qpoints files = self.get_exciting_files('%s_%s%s%s.OUT' % (quantity, file_ext, ext,\",\n \"0., 0.]) # TODO I am not certain if screening/BSE are children of\",\n \"'fermi_energy', r'Fermi Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*', unit=ureg.hartree, repeats=False)) def str_to_band_parameters(val_in): val = val_in.strip().split('\\\\n') nbands = int(val[0])\",\n \"if self.get_exciting_files(f): self._calculation_type = 'gw' gw_info_file = f break if not self._calculation_type ==\",\n \"is used to un-shift the band structure to # the original scale in\",\n \"[f for f in filenames if os.access(f, os.F_OK)] return filenames def file_exists(self, filename):\",\n \"sec_method): sec_gw = sec_method.m_create(GWMethod) sec_gw.type = 'G0W0' gmaxvr = self.info_parser.get_initialization_parameter('x_exciting_gmaxvr', 0) sec_gw.core_treatment =\",\n \"(self.number_of_lm * self.number_of_spin_channels) res[lm][spin][atom] = self._get_dos(self.partial_dos[i]) if self.energy_unit is not None: res =\",\n \"int(val_l) atom = i // (self.number_of_lm * self.number_of_spin_channels) res[lm][spin][atom] = self._get_dos(self.partial_dos[i]) if self.energy_unit\",\n \"dtype=float) def str_to_atom_properties_dict(val_in): unit = None if 'charge' in val_in: unit = ureg.elementary_charge\",\n \"def init_parameters(self): # TODO make a parent clas for bandstructure dat and xml\",\n \"'gw/selfenergy/actype', 'pade') sec_gw.mixed_basis_lmax = int(self.input_xml_parser.get( 'gw/mixbasis/lmaxmb', 3)) sec_gw.mixed_basis_tolerance = self.input_xml_parser.get( 'gw/mixbasis/epsmb', 0.0001) gmb\",\n \"Read also input parameters here if input_GW.xml does not exist self._quantities.append( Quantity( 'frequency_data',\",\n \"= self._parse_bandstructure_dat elif name.startswith('BAND') and name.endswith('OUT'): parser = self.band_out_parser parser_function = self._parse_band_out elif\",\n \"step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', unit=ureg.hartree, repeats=False, dtype=float), Quantity( 'time', r'Time spent in this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds', unit=ureg.s,\",\n \"str_to_atom_properties_dict(val_in): unit = None if 'charge' in val_in: unit = ureg.elementary_charge elif 'moment'\",\n \"lattice symmetries', None), 'x_exciting_number_of_crystal_symmetries': ('Number of crystal symmetries', None), 'x_exciting_kpoint_grid': (r'k\\\\-point grid', None),\",\n \"['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'], 26: ['GGA_C_PBE', 'GGA_X_WC'], 30: ['GGA_C_AM05', 'GGA_C_AM05'], 300: ['GGA_C_BGCP', 'GGA_X_PBE'], 406: ['HYB_GGA_XC_PBEH'],\",\n \"# TODO smearing with should have units of energy sec_smearing.width = smearing_width.magnitude for\",\n \"= val[n][1] else: setattr(msection, key, val) # convergence values for name in self.info_parser._convergence_keys_mapping.keys():\",\n \"atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'imized atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str),\",\n \"else logging self._calculation_type = None self.init_parser() sec_run = self.archive.m_create(Run) sec_run.program = Program( name='exciting',\",\n \"msection.m_create(x_exciting_section_MT_charge_atom) sec_mt_charge_atom.x_exciting_MT_charge_atom_index = n + 1 sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol = val[n][0] sec_mt_charge_atom.x_exciting_MT_charge_atom_value = val[n][1] sec_charges\",\n \"def get_initialization_parameter(self, key, default=None): return self.get('initialization', {}).get(key, default) class ExcitingParser: def __init__(self): self.info_parser\",\n \"fundamental_band_gap = self.info_gw_parser.get('fundamental_band_gap', None) sec_gap = sec_scc.eigenvalues[-1].m_create(BandGap) if fundamental_band_gap is not None: sec_gap.value_fundamental\",\n \"ExcitingFermiSurfaceBxsfParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'fermi_energy', r'Fermi\",\n \"dtype=float) ] self._quantities.append(Quantity( 'structure_optimization', r'Structure\\\\-optimization module started([\\\\s\\\\S]+?)Structure\\\\-optimization module stopped', sub_parser=TextParser(quantities=[ Quantity( 'optimization_step', r'(Optimization\",\n \"% self._band_key)) return self._bands @property def vertices(self): if self._vertices is None: self._vertices =\",\n \"input.xml for f in input_file: self.input_xml_parser.mainfile = f positions = self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors =\",\n \"n_scf = len(self.get('energy_total_scf_iteration', [])) quantity = self.get('%s_scf_iteration' % name) if quantity is None:\",\n \"0) sec_gw.core_treatment = self.input_xml_parser.get( 'gw/coreflag', 'all') sec_gw.polarizability_number_of_empty_states = int( self.input_xml_parser.get('gw/nempty', 0)) sec_gw.ngridq =\",\n \"= 1 return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment =\",\n \"optimization_step in structure_optimization.get('optimization_step', []): sec_scc = parse_configuration(optimization_step) if optimization_step.get('method') is not None: sec_scc.x_exciting_geometry_optimization_method\",\n \"parse(self, filepath, archive, logger): self.filepath = filepath self.archive = archive self.logger = logger\",\n \"add PDOS def _parse_bandstructure_dat(self, sec_scc): self.bandstructure_dat_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_dat_parser.band_energies if band_energies\",\n \"msection.m_create(Energy) if energy_total is not None: sec_energy.total = EnergyEntry(value=energy_total) x_exciting_dos_fermi = iteration.get('x_exciting_dos_fermi') if\",\n \"= end return self._band_energies @property def band_k_points(self): if self._band_k_points is None: data =\",\n \"if len(sec_run.system) > 1: return sec_system for name in self.info_parser._system_keys_mapping.keys(): val = self.info_parser.get_initialization_parameter(name)\",\n \"= { 'Gaussian': 'gaussian', 'Methfessel-Paxton': 'methfessel-paxton', 'Fermi-Dirac': 'fermi', 'Extended': 'tetrahedra'} sec_smearing = sec_electronic.m_create(Smearing)\",\n \"this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', unit=ureg.hartree, repeats=False, dtype=float), Quantity( 'time', r'Time spent in this optimization\",\n \"self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment = [] count =\",\n \"files[n] parser_function(section) # free up memory parser.mainfile = None def _parse_input_xs(self, sec_method): xstype\",\n \"final = section.get('optimization_step', [None])[-1] if final is None else final if final is\",\n \"# This file is part of NOMAD. # See https://nomad-lab.eu for further info.\",\n \"an example bandstructure file # atom-resolved bandstructure are added as separate section_k_band res\",\n \"if not os.path.isfile(filename): file_ext = default.split('.')[-1] mainfile_base = mainfile.rsplit('.', 1)[0].replace('INFO', '') options =\",\n \"len(data) data = np.transpose(np.vstack(data)) n_loss = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_loss_energies = np.reshape( data[0],\",\n \"if val_l is None or val_m is None: lm = i % self.number_of_lm\",\n \"file_ext = default.split('.')[-1] mainfile_base = mainfile.rsplit('.', 1)[0].replace('INFO', '') options = [ f for\",\n \"'1/bohr') sec_method.x_exciting_xs_lmaxapw = self.input_xml_parser.get('xs/lmaxapw', 10) sec_method.x_exciting_xs_number_of_empty_states = self.input_xml_parser.get( 'xs/nempty', 5) sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq',\",\n \"it from input.xml for f in input_file: self.input_xml_parser.mainfile = f positions = self.input_xml_parser.get('structure/species/atom/coord')\",\n \"= v.strip().split(':') if len(v) < 2: continue elif v[0].startswith('species'): species = re.search(re_symbol, v[-1]).group(1)\",\n \"= sec_scc.energy.fermi if energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') #\",\n \"diagram], dtype=float) return dos def parse(self, key): if self._results is None: self._results =\",\n \"Quantity( 'kpoints_eigenvalues', r'\\\\s*k\\\\-point \\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([ \\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)', str_operation=str_to_eigenvalue, repeats=True)) class BandstructureDatParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs)\",\n \"val[0] if len(val) == 1 else val return np.array(val, dtype=float) def str_to_atom_properties_dict(val_in): unit\",\n \"not self.bands: return self._distances = [ point.attrib.get(self._distance_key) for point in self.bands[0][0]] self._distances =\",\n \"sec_scc.energy.fermi if energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_dos =\",\n \"one of the files: bs_files = ['bandstructure-qp.dat', 'BAND-QP.OUT'] for fname in bs_files: if\",\n \"applicable law or agreed to in writing, software # distributed under the License\",\n \"unit=ureg.hartree / ureg.bohr) ] self._quantities.append(Quantity( 'groundstate', r'(?:Self\\\\-consistent loop started|Groundstate module started)([\\\\s\\\\S]+?)Groundstate module stopped',\",\n \"atom-resolved bandstructure are added as separate section_k_band res = [] for n in\",\n \"self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] self.parse_file('input.xml', sec_method) # parse properties input_file\",\n \"['GW_INFO.OUT', 'GWINFO.OUT'] for f in gw_info_files: if self.get_exciting_files(f): self._calculation_type = 'gw' gw_info_file =\",\n \"def __init__(self, **kwargs): # TODO make a parent class for dos and bandstructure\",\n \"= np.transpose(self.data) n_kpoints = np.where(data[0] == data[0][0])[0][1] bands = data[1:] bands = np.reshape(bands,\",\n \"sec_run.m_create(Calculation) if quantity == 'EXCITON': parse_function = parse_exciton elif quantity == 'EPSILON': parse_function\",\n \"import numpy as np import os import re import logging from nomad.units import\",\n \"no cell optimization in exciting sec_atoms.lattice_vectors = lattice_vectors lattice_vectors_reciprocal = self.info_parser.get_initialization_parameter( 'lattice_vectors_reciprocal') sec_atoms.lattice_vectors_reciprocal\",\n \"r'\\\\(state\\\\, eigenvalue and occupancy below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n *\\\\n))', str_operation=str_to_eigenvalues, repeats=True)) class ExcitingGWOutParser(TextParser): def __init__(self, mainfile,\",\n \"n_components n_excitons = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_excitons = n_excitons sec_scc.x_exciting_xs_bse_exciton_energies = np.reshape( data[1],\",\n \"all files related to quantity at all qpoints files = self.get_exciting_files('%s_%s%s%s.OUT' % (quantity,\",\n \"@property def number_of_band_segment_eigenvalues(self): if self._neigs_segment is None: data = np.transpose(self.data)[0] self._neigs_segment = len(np.where(data\",\n \"if axis is None: return self._energy_unit = self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1) return self._energy_unit @property def\",\n \"*({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('electronic_charge', rf'electronic charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic mass\",\n \"k\\\\-points', None), 'x_exciting_rgkmax': (r'R\\\\^MT\\\\_min \\\\* \\\\|G\\\\+k\\\\|\\\\_max \\\\(rgkmax\\\\)', None), 'x_exciting_species_rtmin': (r'Species with R\\\\^MT\\\\_min', None),\",\n \"# two versions of gw info files gw_info_files = ['GW_INFO.OUT', 'GWINFO.OUT'] for f\",\n \"scr_types = ['full', 'diag', 'noinvdiag', 'longrange'] bse_files = [] for bse_type in bse_types:\",\n \"sec_method_ref sec_method.methods_ref = [sec_method_ref] # parse input xml file, there seems to be\",\n \"'distance' self._coord_key = 'coord' self._energy_key = 'eval' self._vertex_key = 'vertex' self._band_key = 'band'\",\n \"range(len(val))] * val[0][1].units sec_charges.total = charge_contributions.get('total charge') elif key == 'charge_total': pass else:\",\n \"of xs if self.input_xml_parser.get('xs/screening') is not None: sec_method.x_exciting_xs_screening_number_of_empty_states = self.input_xml_parser.get( 'xs/screening/nempty', 0) sec_method.x_exciting_xs_screening_ngridk\",\n \"return sec_system = self.parse_system(section) if sec_system is not None: sec_scc.system_ref = sec_system sec_scc.method_ref\",\n \"band_energies[nb] + energy_fermi def _parse_band_out(self, sec_scc): self.band_out_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.band_out_parser.band_energies if\",\n \"elif key == 'energies': return self.energies else: res = None self._results[key] = res\",\n \"sec_scc.calculations_path.append(info_volume[0]) def init_parser(self): self.info_parser.mainfile = self.filepath self.info_parser.logger = self.logger self.dos_parser.logger = self.logger self.bandstructure_parser.logger\",\n \"res = None self._results[key] = res class ExcitingFermiSurfaceBxsfParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self):\",\n \"return # this is really problematic if some scf steps dont have the\",\n \"# TODO I am not sure about format for spin-polarized case! I assume\",\n \"self.total_dos: return self._nspin = len(self.total_dos) return self._nspin @property def number_of_atoms(self): if self._natoms is\",\n \"os.listdir( self.info_parser.maindir) if target[0] in f and mainfile_base in f] options = [f\",\n \"for bse_type in bse_types: for scr_type in scr_types: files = self.get_exciting_files( '%s_BSE%s_SCR%s.OUT' %\",\n \"if self.data is None: return data = np.transpose(self.data) n_kpoints = int(max(data[1])) bands =\",\n \"data = self.eigval_parser.get('eigenvalues_occupancies') # reshaping is not necessary as this is done in\",\n \"[] for quantity in ['EXCITON', 'EPSILON', 'SIGMA', 'LOSS']: files = get_files(quantity) for i\",\n \"energy_contributions = iteration.get('energy_contributions', {}) for key, names in self._energy_keys_mapping.items(): val = None for\",\n \"sec_eigenvalues.occupations = get_data('occupancies') sec_eigenvalues.energies = get_data('eigenvalues') def _parse_fermisurface(self, sec_scc): fermi_surface = self.fermisurf_parser.get('fermi_surface', [None])[0]\",\n \"unit=key_unit[1])) self._convergence_keys_mapping = { 'x_exciting_effective_potential_convergence': ( r'RMS change in effective potential \\\\(target\\\\)', ureg.hartree),\",\n \"def __init__(self, **kwargs): super().__init__(None) self._nspin_key = 'nspin' self._totaldos_key = 'totaldos' self._partialdos_key = 'partialdos'\",\n \"all qpoints files = self.get_exciting_files('%s_%s%s%s.OUT' % (quantity, file_ext, ext, fxctype)) data = [[],\",\n \"start, '\\\\u0393' if end.lower() == 'gamma' else end]) elif key == 'band_segm_start_end': res\",\n \"fermi energy: it is used to un-shift the band structure to # the\",\n \"sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin sec_dos_values.value = totaldos[spin] partialdos = self.dos_parser.get('partialdos')\",\n \"Calculation, Dos, DosValues, BandStructure, BandEnergies, Energy, EnergyEntry, Charges, Forces, ForcesEntry, ScfIteration, BandGap )\",\n \"('Total number of atoms per unit cell', None), 'x_exciting_spin_treatment': ('Spin treatment', None), 'x_exciting_number_of_bravais_lattice_symmetries':\",\n \"len(vi) == 1 else vi properties[v[0].strip()] = vi * unit properties['atom_resolved'] = atom_resolved\",\n \"from nomad.datamodel.metainfo.simulation.system import ( System, Atoms ) from nomad.datamodel.metainfo.simulation.calculation import ( Calculation, Dos,\",\n \"'x_exciting_total_MT_charge': ['total charge in muffin-tins', 'total in muffin-tins'], 'charge_total': ['total charge'], 'x_exciting_section_MT_charge_atom': ['atom_resolved']\",\n \"if 'total' in key: if not self.total_dos: return res = np.zeros((self.number_of_spin_channels, self.number_of_dos)) for\",\n \"energy', 'core electron kinetic'], 'x_exciting_dft_d2_dispersion_correction': ['DFT-D2 dispersion correction'] } self._electron_charge_keys_mapping = { 'x_exciting_core_charge':\",\n \"def init_quantities(self): self._quantities = [] def str_to_frequency(val_in): val = [v.split() for v in\",\n \"np.reshape( data[1], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies = np.reshape( data[2], (n_components, n_excitons)) *\",\n \"self.filepath self.info_parser.logger = self.logger self.dos_parser.logger = self.logger self.bandstructure_parser.logger = self.logger self.eigval_parser.logger = self.logger\",\n \"if self._ndos is None: total_dos = self.root.find('./%s/%s' % (self._totaldos_key, self._diagram_key)) self._ndos = len(total_dos)\",\n \"and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_loss_function_no_local_field = data[1] elif quantity == 'SIGMA' and ext\",\n \"self.bandstructure_parser.logger = self.logger self.eigval_parser.logger = self.logger self.fermisurf_parser.logger = self.logger self.evalqp_parser.logger = self.logger self.dos_out_parser.logger\",\n \"[ Quantity( 'energy_total', r'[Tt]*otal energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=ureg.hartree), Quantity( 'energy_contributions', r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n *(?:DOS|Density)', str_operation=str_to_energy_dict,\",\n \"if len(quantity) < n_scf: quantity = [None] * (n_scf - len(quantity)) + quantity\",\n \"band_parameters[0] sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface = np.product(band_parameters[1]) sec_fermisurface.x_exciting_grid_fermi_surface = band_parameters[1] sec_fermisurface.x_exciting_origin_fermi_surface = band_parameters[2] sec_fermisurface.x_exciting_vectors_fermi_surface = band_parameters[3]\",\n \"in val[2:]], dtype=float)) eigs = {keys[i]: eigs[i] for i in range(len(keys))} return [kpts,\",\n \"= np.array([np.transpose(band)[start:end] for band in bands]) if self._energy_unit: band_energy = band_energy * self._energy_unit\",\n \"def get_scf_quantity(self, name): n_scf = len(self.get('energy_total_scf_iteration', [])) quantity = self.get('%s_scf_iteration' % name) if\",\n \"None: self._nspin = 1 return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None:\",\n \"in range(len(keys))} return [kpts, eigs] self._quantities.append( Quantity( 'kpoints_eigenvalues', r'\\\\s*k\\\\-point \\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([ \\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)', str_operation=str_to_eigenvalue, repeats=True))\",\n \"return nspin = self.info_parser.get_number_of_spin_channels() def reshape(data): if data[0] is None: return return np.reshape(data,\",\n \"= None self._nkpts_segment = None @property def band_energies(self): if self._band_energies is None: data\",\n \"= 'totaldos' self._partialdos_key = 'partialdos' self._diagram_key = 'diagram' self._l_key = 'l' self._m_key =\",\n \"in the same folder where the mainfile is stored. \\\"\\\"\\\" mainfile = os.path.basename(self.info_parser.mainfile)\",\n \"val[n][1] else: setattr(msection, key, val) # convergence values for name in self.info_parser._convergence_keys_mapping.keys(): val\",\n \"default): mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = default.rsplit('.', 1) filename =\",\n \"( r'RMS change in effective potential \\\\(target\\\\)', ureg.hartree), 'x_exciting_energy_convergence': ( r'Absolute change in\",\n \"optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', unit=ureg.hartree, repeats=False, dtype=float), Quantity( 'time', r'Time spent in this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds',\",\n \"muffin-tin *: *({re_float})', dtype=np.int32), Quantity('positions_format', r'atomic positions \\\\((.+?)\\\\)', flatten=False), Quantity( 'positions', rf'\\\\d+ :\",\n \"(the \\\"License\\\"); # you may not use this file except in compliance with\",\n \"cell volume', ureg.bohr ** 3), 'x_exciting_brillouin_zone_volume': ('Brillouin zone volume', 1 / ureg.bohr **\",\n \"{}).get('xc_functional') if xc_functional is not None: sec_xc_functional.name = xc_functional.get('name_reference', [None, None])[0] sec_xc_functional.reference =\",\n \"species: sec_atoms_group = sec_system.m_create(x_exciting_section_atoms_group) sec_atoms_group.x_exciting_geometry_atom_labels = specie.get('symbol') sec_atoms_group.x_exciting_geometry_atom_number = str(specie.get('number')) sec_atoms_group.x_exciting_muffin_tin_points = specie.get('radial_points')\",\n \"v[0].startswith('atom'): v[0] = v[0].split() v[1] = [float(vi) for vi in v[1].split()] v[1] =\",\n \"end = self.vertices[i + 1].attrib.get('label') res.append([ '\\\\u0393' if start.lower() == 'gamma' else start,\",\n \"None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_rgkmax = rgkmax sec_method.x_exciting_xs_scissor = self.input_xml_parser.get('xs/scissor', 0.0) sec_method.x_exciting_xs_vkloff\",\n \"'charge_total': ['total charge'], 'x_exciting_section_MT_charge_atom': ['atom_resolved'] } self._moment_keys_mapping = { 'x_exciting_interstitial_moment': ['interstitial'], 'x_exciting_total_MT_moment': ['total\",\n \"= self.get_exciting_files(name) if len(files) > 1: self.logger.warn('Found multiple files. Will read all!', data=dict(file=name))\",\n \"== 1. else 1 data = dict() data['occupancies'] = np.reshape(occs, (nspin, len(occs) //\",\n \"'step', r'Optimization step\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'method', r'method\\\\s*=\\\\s*(\\\\w+)', repeats=False, dtype=str), Quantity( 'n_scf_iterations', r'Number\",\n \"gmaxvr pwm = self.input_xml_parser.get('gw/barecoul/pwm', 2.0) sec_gw.bare_coulomb_gmax = pwm * gmb * gmaxvr sec_gw.bare_coulomb_cutofftype\",\n \"the files: bs_files = ['bandstructure-qp.dat', 'BAND-QP.OUT'] for fname in bs_files: if self.file_exists(fname): gw_files.append(fname)\",\n \"bse_type in bse_types: for scr_type in scr_types: files = self.get_exciting_files( '%s_BSE%s_SCR%s.OUT' % (name,\",\n \"'atomic_positions', r'(Atomic positions at this step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions at this\",\n \"dos and bandstructure super().__init__(None) self._distance_key = 'distance' self._coord_key = 'coord' self._energy_key = 'eval'\",\n \"get_data(key): data = self.eigval_parser.get('eigenvalues_occupancies') # reshaping is not necessary as this is done\",\n \"energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi nkpts_segment\",\n \"xml file, there seems to be two versions, input_gw.xml and input-gw.xml for f\",\n \"dtype=np.float64, unit=ureg.bohr), Quantity('radial_points', rf'radial points in muffin-tin *: *({re_float})', dtype=np.int32), Quantity('positions_format', r'atomic positions\",\n \"None @property def band_energies(self): if self._band_energies is None: if self.data is None: return\",\n \"*: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic mass *: *({re_float})', dtype=np.float64, unit=ureg.electron_mass), Quantity('muffin_tin_radius', rf'muffin-tin\",\n \"quantity == 'EPSILON': parse_function = parse_epsilon elif quantity == 'SIGMA': parse_function = parse_sigma\",\n \"= 0 band_energies = np.zeros(( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, len(self.distances)), dtype=float) for i in range(len(self.bands[n])):\",\n \"is None: fundamental_band_gap = self.info_gw_parser.get('fundamental_band_gap', None) sec_gap = sec_scc.eigenvalues[-1].m_create(BandGap) if fundamental_band_gap is not\",\n \"un-shift the band structure to # the original scale in which also other\",\n \"rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0) sec_method.x_exciting_xs_bse_rgkmax = rgkmax sec_method.x_exciting_xs_bse_sciavbd = self.input_xml_parser.get(\",\n \"exciting self._energy_keys_mapping = { 'energy_total': ['Total energy', 'total energy'], 'x_exciting_fermi_energy': ['Fermi energy', 'Fermi'],\",\n \"in name: sec_xc_functional.exchange.append(Functional(name=name)) elif '_C_' in name: sec_xc_functional.correlation.append(Functional(name=name)) elif 'HYB' in name: sec_xc_functional.hybrid.append(Functional(name=name))\",\n \"nkpts_segment band_energy = np.array([ np.transpose(energy)[start:end] for energy in band_energies]) if self._energy_unit is not\",\n \"name.endswith('xml'): parser = self.input_xml_parser if self._calculation_type == 'gw': parser_function = self._parse_input_gw elif self._calculation_type\",\n \"self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] # parse input xml file, there\",\n \"= 'geometry_optimization' sec_geometry_opt = sec_worfklow.m_create(GeometryOptimization) threshold_force = structure_optimization.get( 'optimization_step', [{}])[0].get('force_convergence', [0., 0.])[-1] sec_geometry_opt.input_force_maximum_tolerance\",\n \"def parse_xs(self): sec_run = self.archive.run[-1] xs_info_files = self.get_exciting_files('INFOXS.OUT') if not xs_info_files: return self._calculation_type\",\n \"np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies = [] start = 0 for nkpts_segment\",\n \"= self.eigval_parser.get('eigenvalues_occupancies') # reshaping is not necessary as this is done in parser,\",\n \"name): reference = self.get('groundstate', self.get('hybrids', {})) return reference.get('scf_iteration', [{}])[-1].get( name, [None, None])[-1] def\",\n \"in range(len(self.number_of_k_points_per_segment)): start = self.vertices[i].attrib.get(self._coord_key).split() end = self.vertices[i + 1].attrib.get(self._coord_key).split() res.append([start, end]) else:\",\n \"filenames = [f for f in filenames if os.access(f, os.F_OK)] return filenames def\",\n \"sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_loss = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_loss_energies\",\n \"name.endswith('bxsf'): parser = self.fermisurf_parser parser_function = self._parse_fermisurface elif name.startswith('EVALQP') and (name.endswith('DAT') or name.endswith('TXT')):\",\n \"= [Quantity( 'program_version', r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\. ]+)\\\\s*started', repeats=False, dtype=str, flatten=False)] initialization_quantities = [ Quantity( 'lattice_vectors',\",\n \"sec_xc_functional.hybrid.append(Functional(name=name)) else: sec_xc_functional.contributions.append(Functional(name=name)) if not xc_functional_names: # simply write parameters xc_functional = self.info_parser.get('initialization',\",\n \"self.data_xs_parser.logger = self.logger self.data_clathrate_parser.logger = self.logger def reuse_parser(self, parser): self.info_parser.quantities = parser.info_parser.quantities self.eigval_parser.quantities\",\n \"species species = self.info_parser.get_initialization_parameter('species', []) for specie in species: sec_atoms_group = sec_system.m_create(x_exciting_section_atoms_group) sec_atoms_group.x_exciting_geometry_atom_labels\",\n \"[] def str_to_frequency(val_in): val = [v.split() for v in val_in.split('\\\\n')] val = np.transpose(np.array([v\",\n \"bs_files: if self.file_exists(fname): exciting_files.append(fname) break # Parse DFT band structure from one of\",\n \"self.dos_out_parser.data if data is None: return # Get fermi energy: it is used\",\n \"elif key == 'band_k_points': res = [] for i in range(len(self.number_of_k_points_per_segment)): start =\",\n \"None: sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get( 'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get( 'xs/tddft/ahc', False) sec_method.x_exciting_xs_tddft_anti_resonant_dielectric =\",\n \"self._energy_unit self._band_energies.append(band_energy) start = end return self._band_energies @property def band_k_points(self): if self._band_k_points is\",\n \"'tddft': self._parse_xs_tddft() def _parse_input_gw(self, sec_method): sec_gw = sec_method.m_create(GWMethod) sec_gw.type = 'G0W0' gmaxvr =\",\n \"# additionally, set threshold to global metainfo. This is killing me! if metainfo_name\",\n \"type +: +(\\\\S+)'), Quantity( 'name_reference', r'\\\\n *(.+?,.+)', str_operation=lambda x: [v.strip() for v in\",\n \"not None: sec_forces = sec_scc.m_create(Forces) sec_forces.total = ForcesEntry(value=forces) # scf iterations scf_iterations =\",\n \"* ureg.bohr lattice_vectors = lattice_vectors * ureg.bohr atoms = self.input_xml_parser.get('structure/species/atom') atom_labels = []\",\n \"self._nlm = 0 l_list = set([int(e.attrib.get(self._l_key)) for e in self.partial_dos]) for li in\",\n \"is None: self._vertices = self.root.findall('./%s' % self._vertex_key) return self._vertices @property def number_of_spin_channels(self): if\",\n \"from nomad.parsing.file_parser import TextParser, Quantity, XMLParser, DataTextParser from nomad.datamodel.metainfo.simulation.run import Run, Program from\",\n \"self._parse_bandstructure_dat elif name.startswith('BAND') and name.endswith('OUT'): parser = self.band_out_parser parser_function = self._parse_band_out elif name.startswith('input')\",\n \"['LDA_C_PZ', 'LDA_X_PZ'], 3: ['LDA_C_PW', 'LDA_X_PZ'], 4: ['LDA_C_XALPHA'], 5: ['LDA_C_VBH'], 20: ['GGA_C_PBE', 'GGA_X_PBE'], 21:\",\n \"self._convergence_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)' % key_unit[0], str_operation=str_to_quantity_tolerances, unit=key_unit[1], repeats=False)) module_quantities = [\",\n \"None self._nkpts_segment = None self._neigs_segment = None self._bands = None self._vertices = None\",\n \"None), 'x_exciting_valence_states': ('Total number of valence states', None), 'x_exciting_hamiltonian_size': ('Maximum Hamiltonian size', None),\",\n \"sec_scc.x_exciting_xs_bse_loss = np.reshape( data[1], (n_components, n_loss)) # TODO check if format of files\",\n \"= sec_dos.m_create(DosValues, Dos.atom_projected) sec_dos_values.m_kind = 'spherical' sec_dos_values.lm = lm_values[lm] sec_dos_values.spin = spin sec_dos_values.atom_index\",\n \"= specie.get('positions_format') sec_atoms_group.x_exciting_atom_position_format = positions_format positions = specie.get('positions') positions = self.info_parser.get_atom_positions( positions=positions, positions_format=positions_format).to('m')\",\n \"r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'optical_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) class ExcitingEvalqpParser(TextParser):\",\n \"is to assume that the first steps are the # ones with the\",\n \"'band_energies': # TODO I am not certain about the format for the spin\",\n \"key, default=None): return self.get('initialization', {}).get(key, default) class ExcitingParser: def __init__(self): self.info_parser = ExcitingInfoParser()\",\n \"= sec_run.m_create(Calculation) sec_scc.x_exciting_xs_tddft_number_of_epsilon_values = len(data[0][0][0]) sec_scc.x_exciting_xs_tddft_epsilon_energies = data[0][0][0] * ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field = data[1:]\",\n \"target[1]) filepath = os.path.join(self.info_parser.maindir, filepath) if os.path.isfile(filepath) and os.access(filepath, os.F_OK): return True return\",\n \"in range(len(qpoints)): data_q = [] files_q = [f for f in files if\",\n \"= mainfile.strip('INFO.OUT') target = default.rsplit('.', 1) filename = '%s%s' % (target[0], suffix) if\",\n \"'SIGMA' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_sigma_no_local_field = data[1:3] def parse_xs(self): sec_run = self.archive.run[-1]\",\n \"str_to_eigenvalues(val_in): val = val_in[:val_in.rfind('\\\\n \\\\n')].strip() val = np.array([v.split() for v in val.split('\\\\n')], dtype=float)\",\n \"in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.value = band_energies[nb] + energy_fermi\",\n \"= get_data('E_GW') if eigs_gw[0] is None: return nspin = self.info_parser.get_number_of_spin_channels() def reshape(data): if\",\n \"None: break if val is None: continue if key.startswith('energy_'): sec_energy.m_add_sub_section(getattr( Energy, key.replace('energy_', '')),\",\n \"= [ Quantity( 'scf_iteration', r'(?:I| i)teration number :([\\\\s\\\\S]+?)(?:\\\\n *\\\\n\\\\+{10}|\\\\+\\\\-{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=True), Quantity( 'final',\",\n \"PDOS def _parse_bandstructure_dat(self, sec_scc): self.bandstructure_dat_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_dat_parser.band_energies if band_energies is\",\n \"vi = [float(vii) for vii in v[1].split()] vi = vi[0] if len(vi) ==\",\n \"energy', 'total energy'], 'x_exciting_fermi_energy': ['Fermi energy', 'Fermi'], 'energy_kinetic_electronic': ['Kinetic energy', 'electronic kinetic'], 'energy_coulomb':\",\n \"a the given filename exists and is accessible in the same folder where\",\n \"self.logger self.dos_parser.logger = self.logger self.bandstructure_parser.logger = self.logger self.eigval_parser.logger = self.logger self.fermisurf_parser.logger = self.logger\",\n \"None: partial_dos = self.root.findall('./%s' % self._partialdos_key) self._natoms = len(partial_dos) return self._natoms @property def\",\n \"name in xc_functional_names: if name is None: continue if '_X_' in name: sec_xc_functional.exchange.append(Functional(name=name))\",\n \"Get fermi energy: it is used to un-shift the DOS to # the\",\n \"flatten=False) ) initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation type[\\\\s\\\\S]+?\\\\n *\\\\n)', sub_parser=TextParser(quantities=[ Quantity('type', r'Exchange-correlation type +: +(\\\\S+)'),\",\n \"= data[1] elif quantity == 'LOSS' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_loss_function_no_local_field = data[1]\",\n \"self.total_dos[0]]) if self.energy_unit is not None: self._energies = self._energies * self.energy_unit return self._energies\",\n \"// n_components sec_scc.x_exciting_xs_bse_number_of_excitons = n_excitons sec_scc.x_exciting_xs_bse_exciton_energies = np.reshape( data[1], (n_components, n_excitons)) * ureg.hartree\",\n \"v in val: v = v.strip().split(':') if len(v) < 2: continue elif v[0].startswith('species'):\",\n \"= positions if positions is not None else section.get('positions') if positions is None:\",\n \"'LDA_X_PZ'], 3: ['LDA_C_PW', 'LDA_X_PZ'], 4: ['LDA_C_XALPHA'], 5: ['LDA_C_VBH'], 20: ['GGA_C_PBE', 'GGA_X_PBE'], 21: ['GGA_C_PBE',\",\n \"sec_scc): data = self.evalqp_parser.get('kpoints_eigenvalues') if data is None: return def get_data(key): if key\",\n \"1 for i in range(1, len(self.distances)): if self.distances[i] == self.distances[i - 1]: self._nkpts_segment.append(count)\",\n \"naming convention for xs input xml file sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0]\",\n \"= smearing_kind[0] smearing_kind = smearing_kind_map[smearing_kind] sec_smearing.kind = smearing_kind smearing_width = self.info_parser.get_initialization_parameter('smearing_width') if smearing_width\",\n \"class for dos and bandstructure super().__init__(None) self._distance_key = 'distance' self._coord_key = 'coord' self._energy_key\",\n \"sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(imized atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'imized atomic positions\\\\s*\\\\(([a-z]+)\\\\)'),\",\n \"Quantity('positions_format', r'atomic positions \\\\((.+?)\\\\)', flatten=False), Quantity( 'positions', rf'\\\\d+ : *({re_float}) *({re_float}) *({re_float})', repeats=True,\",\n \"scf_iterations: sec_scf_iteration = sec_scc.m_create(ScfIteration) parse_scf(scf_iteration, sec_scf_iteration) return sec_scc def parse_system(self, section): sec_run =\",\n \"= end return self._band_k_points @property def distances(self): if self._distances is None: data =\",\n \"in range(len(files)): data = get_data(files[i]) if not data: sccs.append(None) continue if quantity ==\",\n \"sec_scc is None: return # volume optimizations volume_index = 1 while True: info_volume\",\n \"tetradf = self.input_xml_parser.get('xs/tetra/tetradf', None) nwacont = self.input_xml_parser.get('xs/tddft/nwacont', None) aresdf = self.input_xml_parser.get('xs/tddft/aresdf', True) file_ext_list\",\n \"def __init__(self): self.info_parser = ExcitingInfoParser() self.dos_parser = DOSXMLParser(energy_unit=ureg.hartree) self.bandstructure_parser = BandstructureXMLParser(energy_unit=ureg.hartree) self.eigval_parser =\",\n \"for f in files: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None: continue data.append(self.data_xs_parser.data)\",\n \"None])[1] sec_electronic.n_spin_channels = self.info_parser.get_number_of_spin_channels() if self._calculation_type == 'volume_optimization': sec_method.x_exciting_volume_optimization = True def parse_scc(self,\",\n \"= self.input_xml_parser.get( 'gw/scrcoul/scrtype', 'rpa') def parse_gw(self): sec_run = self.archive.run[-1] # two versions of\",\n \"{}) for key, names in self._energy_keys_mapping.items(): val = None for name in names:\",\n \"['EIGVAL.OUT', 'FERMISURF.bxsf', 'FS.bxsf'] # Parse DFT DOS from one of the files bs_files\",\n \"not data: sccs.append(None) continue if quantity == 'EXCITON': sec_scc = sec_run.m_create(Calculation) sccs.append(sec_scc) else:\",\n \"1 else val return np.array(val, dtype=float) def str_to_atom_properties_dict(val_in): unit = None if 'charge'\",\n \"return return np.reshape(data, (nspin, len(data) // nspin, len(data[0]))) sec_gw_eigenvalues = sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor =\",\n \"'x_exciting_spin_treatment', 'spin-unpolarised') n_spin = 1 if spin_treatment.lower() == 'spin-unpolarised' else 2 return n_spin\",\n \"self.data_clathrate_parser.logger = self.logger def reuse_parser(self, parser): self.info_parser.quantities = parser.info_parser.quantities self.eigval_parser.quantities = parser.eigval_parser.quantities self.fermisurf_parser.quantities\",\n \"lattice_vectors_reciprocal if len(sec_run.system) > 1: return sec_system for name in self.info_parser._system_keys_mapping.keys(): val =\",\n \"self._bands.append(bands) # add atom-resolved bands_atom = self.root.findall('./*/%s' % self._atom_key) for band in bands_atom:\",\n \"= parser.info_gw_parser.quantities def parse(self, filepath, archive, logger): self.filepath = filepath self.archive = archive\",\n \"np.reshape( data[1], (n_components, n_loss)) # TODO check if format of files are really\",\n \"} def get_exciting_files(self, default): mainfile = os.path.basename(self.info_parser.mainfile) suffix = mainfile.strip('INFO.OUT') target = default.rsplit('.',\",\n \"if key.startswith('energy_'): sec_energy.m_add_sub_section(getattr( Energy, key.replace('energy_', '')), EnergyEntry(value=val)) else: setattr(msection, key, val) if key\",\n \"res_n.append(band_energy) start = end res.append(res_n) elif key == 'band_k_points': res = [] for\",\n \"fxctype = self.input_xml_parser.get('xs/tddft/fxctype', 'RPA') tetradf = self.input_xml_parser.get('xs/tetra/tetradf', None) nwacont = self.input_xml_parser.get('xs/tddft/nwacont', None) aresdf\",\n \"parser = self.band_out_parser parser_function = self._parse_band_out elif name.startswith('input') and name.endswith('xml'): parser = self.input_xml_parser\",\n \"input xml file, there seems to be two versions, input_gw.xml and input-gw.xml for\",\n \"/ ureg.bohr) ] self._quantities.append(Quantity( 'groundstate', r'(?:Self\\\\-consistent loop started|Groundstate module started)([\\\\s\\\\S]+?)Groundstate module stopped', sub_parser=TextParser(quantities=module_quantities),\",\n \"None for name in names: val = energy_contributions.get(name, None) if val is not\",\n \"in self.info_parser._convergence_keys_mapping.keys(): val = iteration.get(name) if val is None: continue setattr(msection, name, val)\",\n \"r'Using ([\\\\w ]+) potential mixing', repeats=False, flatten=False) ) initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation type[\\\\s\\\\S]+?\\\\n *\\\\n)',\",\n \"normal calculations # in addition to INFO.OUT return else: return files = self.get_exciting_files(name)\",\n \"smearing_width = self.info_parser.get_initialization_parameter('smearing_width') if smearing_width is not None: smearing_width = (smearing_width * ureg.hartree).to('joule')\",\n \"calculation for module in ['groundstate', 'hybrids']: sec_scc = parse_configuration(self.info_parser.get(module)) if sec_scc is None:\",\n \"'input.xml']: self.parse_file(f, sec_method) xc_functional_name = ' '.join(self.info_parser.get_xc_functional_name()) sec_method.gw.starting_point = xc_functional_name sec_scc = sec_run.m_create(Calculation)\",\n \"np.hstack([np.reshape(v[key], (nspin, np.size(v[key]) // nspin)) for v in data]) res = res.reshape((len(res), len(data),\",\n \"= val[n][1] sec_charges = msection.m_create(Charges) sec_charges.value = [ val[n][1].magnitude for n in range(len(val))]\",\n \"np.array([ np.transpose(energy)[start:end] for energy in band_energies]) if self._energy_unit is not None: band_energy =\",\n \"f for f in os.listdir( self.info_parser.maindir) if target[0] in f and mainfile_base in\",\n \"if logger is not None else logging self._calculation_type = None self.init_parser() sec_run =\",\n \"in range(len(val)): sec_mt_moment_atom = msection.m_create(x_exciting_section_MT_moment_atom) sec_mt_moment_atom.x_exciting_MT_moment_atom_index = n + 1 sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol = val[n][0]\",\n \"if key == 'x_exciting_fermi_energy': sec_energy.fermi = val # charge contributions charge_contributions = iteration.get('charge_contributions',\",\n \"for xs input xml file sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref =\",\n \"self.parse_scc(section) if sec_scc is None: return sec_system = self.parse_system(section) if sec_system is not\",\n \"self.input_xml_parser.get('xs/xstype', None) if xstype is not None: sec_method.x_exciting_xs_xstype = xstype sec_method.x_exciting_electronic_structure_method = xstype\",\n \"in range(len(self.number_of_k_points_per_segment)): start = np.array( self.vertices[i].attrib.get(self._coord_key).split(), dtype=float) end = np.array( self.vertices[i + 1].attrib.get(self._coord_key).split(),\",\n \"XCFunctional, Functional, GW as GWMethod, Scf, BasisSet ) from nomad.datamodel.metainfo.simulation.system import ( System,\",\n \"'gw': return sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref =\",\n \"loops maximum)([\\\\s\\\\S]+?)(\\\\n *\\\\n\\\\+{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(Atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic\",\n \"self._ndos = len(total_dos) return self._ndos @property def number_of_lm(self): if self._nlm is None: if\",\n \"not None else section.get('positions') if positions is None: species = self.get_initialization_parameter('species', []) if\",\n \"is not None: energy_fermi = sec_scc.energy.fermi energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band =\",\n \"sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment = val elif name == 'x_exciting_species_rtmin': setattr(sec_system, name, ' '.join([str(v) for\",\n \"is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0) sec_method.x_exciting_xs_bse_rgkmax = rgkmax sec_method.x_exciting_xs_bse_sciavbd = self.input_xml_parser.get( 'xs/BSE/sciavbd',\",\n \"'xs/BSE/nstlbse', [0, 0, 0, 0]) if sec_method.x_exciting_xs_bse_xas: sec_method.x_exciting_xs_bse_xasatom = self.input_xml_parser.get( 'xs/BSE/xasatom', 0) sec_method.x_exciting_xs_bse_xasedge\",\n \"str_operation=str_to_eigenvalue, repeats=True)) class BandstructureDatParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit\",\n \"lattice_vectors * ureg.bohr atoms = self.input_xml_parser.get('structure/species/atom') atom_labels = [] for n in range(len(atoms)):\",\n \"columns are supposed # to be what they are. What is the fourth\",\n \"== 'x_exciting_species_rtmin': setattr(sec_system, name, ' '.join([str(v) for v in val])) else: try: setattr(sec_system,\",\n \"self._quantities.append( Quantity( 'frequency_data', r'frequency list:\\\\s*\\\\<\\\\s*#\\\\s*freqs\\\\s*weight\\\\s*>\\\\s*([\\\\d\\\\.Ee\\\\s\\\\-]+)', str_operation=str_to_frequency, repeats=False) ) self._quantities.append( Quantity( 'fermi_energy', r'\\\\-\\\\s*G0W0.+\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fermi [Ee]nergy\\\\s*[:=](\\\\s*-?[\\\\d\\\\.]+)\\\\s',\",\n \"[] for f in files: self.data_xs_parser.mainfile = f if self.data_xs_parser.data is None: continue\",\n \"== 'NLF_FXC': sec_scc.x_exciting_xs_tddft_loss_function_no_local_field = data[1] elif quantity == 'SIGMA' and ext == 'FXC':\",\n \"used to un-shift the DOS to # the original scale in which also\",\n \"are children of xs if self.input_xml_parser.get('xs/screening') is not None: sec_method.x_exciting_xs_screening_number_of_empty_states = self.input_xml_parser.get( 'xs/screening/nempty',\",\n \"are supposed # to be what they are. What is the fourth column\",\n \"= band_k_points[nb] sec_k_band_segment.energies = band_energies[nb] + energy_fermi def _parse_band_out(self, sec_scc): self.band_out_parser._nspin = self.info_parser.get_number_of_spin_channels()\",\n \"None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0) sec_method.x_exciting_xs_bse_rgkmax = rgkmax sec_method.x_exciting_xs_bse_sciavbd = self.input_xml_parser.get( 'xs/BSE/sciavbd', True)\",\n \"'x_exciting_exchange_energy': ['Exchange energy', 'exchange'], 'energy_correlation': ['Correlation energy', 'correlation'], 'x_exciting_correlation_energy': ['Correlation energy', 'correlation'], 'energy_sum_eigenvalues':\",\n \"(r' APW functions', None), 'x_exciting_nuclear_charge': ('Total nuclear charge', ureg.elementary_charge), 'x_exciting_electronic_charge': ('Total electronic charge',\",\n \"if val is None: continue if key == 'x_exciting_section_MT_charge_atom': for n in range(len(val)):\",\n \"is None: data = np.transpose(self.data) self._band_k_points = [] start = 0 for nkpts_segment\",\n \") self._quantities.append( Quantity( 'optical_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) class ExcitingEvalqpParser(TextParser): def __init__(self): super().__init__(None)\",\n \"is None: return data = np.transpose(self.data) n_kpoints = int(max(data[1])) bands = data[6:] bands\",\n \"Quantity('type', r'Exchange-correlation type +: +(\\\\S+)'), Quantity( 'name_reference', r'\\\\n *(.+?,.+)', str_operation=lambda x: [v.strip() for\",\n \"self.input_xml_parser.mainfile = f correlation = self.input_xml_parser.get('libxc/correlation', None) xc_functional_names.append(correlation) exchange = self.input_xml_parser.get('libxc/exchange', None) xc_functional_names.append(exchange)\",\n \"2.0) sec_gw.bare_coulomb_gmax = pwm * gmb * gmaxvr sec_gw.bare_coulomb_cutofftype = self.input_xml_parser.get( 'gw/barecoul/cutofftype', 'none')\",\n \"set nspin again as this is overwritten when setting mainfile self.bandstructure_parser._nspin = self.info_parser.get_number_of_spin_channels()\",\n \"self._nkpts_segment = [] count = 1 for i in range(1, len(self.distances)): if self.distances[i]\",\n \"r'\\\\((\\\\w+)\\\\)'), Quantity('file', r'parameters loaded from *: *(.+)'), Quantity('name', r'name *: *(.+)'), Quantity('nuclear_charge', rf'nuclear\",\n \"rf'(Species : *\\\\d+ *\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float} *{re_float} *{re_float}\\\\n\\\\s*\\\\n)', repeats=True, sub_parser=TextParser(quantities=[ Quantity('number', r'Species : *(\\\\d+)', dtype=np.int32),\",\n \"r'Force convergence target achieved([\\\\s\\\\S]+?Opt)', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(imized atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity(\",\n \"self._parse_xs_bse() elif xstype.lower() == 'tddft': self._parse_xs_tddft() def _parse_input_gw(self, sec_method): sec_gw = sec_method.m_create(GWMethod) sec_gw.type\",\n \"it is used to un-shift the DOS to # the original scale in\",\n \"*\\\\n\\\\+{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(Atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity(\",\n \"'program_version', r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\. ]+)\\\\s*started', repeats=False, dtype=str, flatten=False)] initialization_quantities = [ Quantity( 'lattice_vectors', r'Lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n',\",\n \"logging self._calculation_type = None self.init_parser() sec_run = self.archive.m_create(Run) sec_run.program = Program( name='exciting', version=self.info_parser.get('program_version',\",\n \"{}) for key, names in self._electron_charge_keys_mapping.items(): val = None for name in names:\",\n \"'GWINFO.OUT'] for f in gw_info_files: if self.get_exciting_files(f): self._calculation_type = 'gw' gw_info_file = f\",\n \"repeats=False) ) for name, key_unit in self._method_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0],\",\n \"end, self.number_of_k_points_per_segment[i])) elif key == 'band_segm_labels': res = [] for i in range(len(self.vertices)\",\n \"= gmb * gmaxvr pwm = self.input_xml_parser.get('gw/barecoul/pwm', 2.0) sec_gw.bare_coulomb_gmax = pwm * gmb\",\n \"and ext == 'FXC': sec_scc.x_exciting_xs_tddft_sigma_local_field = data[1:3] elif quantity == 'SIGMA' and ext\",\n \"super().__init__(None) def init_quantities(self): self._quantities = [] def str_to_eigenvalue(val_in): val = val_in.strip().split('\\\\n') kpts =\",\n \"in self.partial_dos]) for li in l_list: self._nlm += 2 * li + 1\",\n \"self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_parser.get('band_energies', []) for n in range(len(band_energies)): # Get fermi energy:\",\n \"[]) for n in range(len(band_energies)): # Get fermi energy: it is used to\",\n \"self.dos_parser = DOSXMLParser(energy_unit=ureg.hartree) self.bandstructure_parser = BandstructureXMLParser(energy_unit=ureg.hartree) self.eigval_parser = ExcitingEigenvalueParser() self.fermisurf_parser = ExcitingFermiSurfaceBxsfParser() self.evalqp_parser\",\n \"nkpts_segment self._band_k_points.append( np.transpose(data[2:5])[start:end]) start = end return self._band_k_points @property def distances(self): if self._distances\",\n \"None: # we get it by concatenating species symbols species = self.get('initialization', {}).get('species',\",\n \"def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def\",\n \"== 'x_exciting_scf_threshold_energy_change': sec_method.scf = Scf(threshold_energy_change=threshold) xc_functional_names = self.info_parser.get_xc_functional_name() if not xc_functional_names: # get\",\n \"metainfo.') # species species = self.info_parser.get_initialization_parameter('species', []) for specie in species: sec_atoms_group =\",\n \"= sec_scc.energy.fermi energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi =\",\n \"self.info_parser.mainfile = self.filepath self.info_parser.logger = self.logger self.dos_parser.logger = self.logger self.bandstructure_parser.logger = self.logger self.eigval_parser.logger\",\n \"= np.transpose( np.array(data[:3], dtype=float)) sec_system.x_exciting_clathrates_atom_labels = list(data[3]) else: sec_system.x_exciting_clathrates = False potential_mixing =\",\n \"'pade') sec_gw.mixed_basis_lmax = int(self.input_xml_parser.get( 'gw/mixbasis/lmaxmb', 3)) sec_gw.mixed_basis_tolerance = self.input_xml_parser.get( 'gw/mixbasis/epsmb', 0.0001) gmb =\",\n \"self._quantities = [] self._quantities.append( Quantity( 'k_points', r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\- ]+):\\\\s*k\\\\-point', repeats=True)) def str_to_eigenvalues(val_in): val =\",\n \"reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is None: return energy_fermi = (energy_fermi.magnitude *\",\n \"optimization_step final = section.get('scf_iteration', [None])[-1] final = section.get('optimization_step', [None])[-1] if final is None\",\n \"Quantity( 'lattice_vectors_reciprocal', r'Reciprocal lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=1 / ureg.bohr, repeats=False, convert=False), ] self._system_keys_mapping\",\n \"= None if 'charge' in val_in: unit = ureg.elementary_charge elif 'moment' in val_in:\",\n \"in val if len(v) == 3], float)) return dict( number=np.array(val[0], dtype=int), values=val[1] *\",\n \"return self._ndos @property def number_of_lm(self): if self._nlm is None: if self.partial_dos is None:\",\n \"return positions_format def get_atom_positions(self, section={}, positions=None, positions_format=None): positions = positions if positions is\",\n \"+ int(val_m) + int(val_l) atom = i // (self.number_of_lm * self.number_of_spin_channels) res[lm][spin][atom] =\",\n \"= data[1] elif quantity == 'SIGMA' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_sigma_local_field = data[1:3]\",\n \"= np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components = n_components n_excitons = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_excitons = n_excitons\",\n \"dict( number=np.array(val[0], dtype=int), values=val[1] * ureg.hartree, weights=val[2]) # TODO Read also input parameters\",\n \"GWMethod, Scf, BasisSet ) from nomad.datamodel.metainfo.simulation.system import ( System, Atoms ) from nomad.datamodel.metainfo.simulation.calculation\",\n \"def get_data(files): data = [] for f in files: self.data_xs_parser.mainfile = f if\",\n \"energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') # TODO I am not sure about format\",\n \"sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] #\",\n \"self.get('initialization', {}).get('species', []) labels = [] for specie in species: labels += [specie.get('symbol')]\",\n \"None: continue if key == 'x_exciting_section_MT_moment_atom': for n in range(len(val)): sec_mt_moment_atom = msection.m_create(x_exciting_section_MT_moment_atom)\",\n \"iteration.get('energy_total') sec_energy = msection.m_create(Energy) if energy_total is not None: sec_energy.total = EnergyEntry(value=energy_total) x_exciting_dos_fermi\",\n \"= section.get('symbols') if labels is None: # we get it by concatenating species\",\n \"metainfo_name == 'x_exciting_scf_threshold_energy_change': sec_method.scf = Scf(threshold_energy_change=threshold) xc_functional_names = self.info_parser.get_xc_functional_name() if not xc_functional_names: #\",\n \"def number_of_spin_channels(self): if self._nspin is None: self._nspin = 1 return self._nspin @property def\",\n \"= potential_mixing return sec_system def parse_configurations(self): sec_run = self.archive.run[-1] def parse_configuration(section): if not\",\n \"for APW functions', 1 / ureg.bohr), 'x_exciting_gmaxvr': (r'Maximum \\\\|G\\\\| for potential and density',\",\n \"None: return # Get fermi energy: it is used to un-shift the band\",\n \"specie.get('radial_points') sec_atoms_group.x_exciting_muffin_tin_radius = specie.get('muffin_tin_radius') positions_format = specie.get('positions_format') sec_atoms_group.x_exciting_atom_position_format = positions_format positions = specie.get('positions')\",\n \"] self._system_keys_mapping = { 'x_exciting_unit_cell_volume': ('Unit cell volume', ureg.bohr ** 3), 'x_exciting_brillouin_zone_volume': ('Brillouin\",\n \"from one of the files bs_files = ['dos.xml', 'TDOS.OUT'] for fname in bs_files:\",\n \"sec_method.x_exciting_xs_tddft_diagonal_xc_kernel = self.input_xml_parser.get( 'xs/tddft/kerndiag', False) sec_method.x_exciting_xs_tddft_lmax_alda = self.input_xml_parser.get( 'xs/tddft/lmaxalda', 3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment = self.input_xml_parser.get(\",\n \"# TODO add support for info.xml, wannier.out if name.startswith('dos') and name.endswith('xml'): parser =\",\n \"for n in range(len(atoms)): atom_labels.extend([species[n].split('.')[0]] * len(atoms[n])) if positions is None or atom_labels\",\n \"quantity # the only thing that we can do is to assume that\",\n \"np.array(lattice_vectors, dtype=float) lattice_vectors *= self.input_xml_parser.get('structure/crystal/scale', 1.0) positions = np.dot(positions, lattice_vectors) * ureg.bohr lattice_vectors\",\n \"self._quantities.append(Quantity( 'groundstate', r'(?:Self\\\\-consistent loop started|Groundstate module started)([\\\\s\\\\S]+?)Groundstate module stopped', sub_parser=TextParser(quantities=module_quantities), repeats=False)) optimization_quantities =\",\n \"if self._partial_dos is None: self._partial_dos = self.root.findall('./%s/%s' % (self._partialdos_key, self._diagram_key)) return self._partial_dos @property\",\n \"= 'vertex' self._band_key = 'band' self._atom_key = 'atom' self._nspin = kwargs.get('nspin', None) self._energy_unit\",\n \"self._quantities.append( Quantity( 'fundamental_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'optical_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False)\",\n \"point in self.total_dos[0]]) if self.energy_unit is not None: self._energies = self._energies * self.energy_unit\",\n \"for atom in range(len(partialdos[lm][spin])): sec_dos_values = sec_dos.m_create(DosValues, Dos.atom_projected) sec_dos_values.m_kind = 'spherical' sec_dos_values.lm =\",\n \"val: v = v.strip().split(':') if len(v) < 2: continue elif v[0].startswith('species'): species =\",\n \"// self.number_of_lm else: spin = int(spin) - 1 val_l = self.partial_dos[i].attrib.get(self._l_key, None) val_m\",\n \"Quantity( 'x_exciting_dos_fermi', r'DOS at Fermi energy \\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=1 / ureg.hartree), Quantity(\",\n \"= None self._bands = None self._vertices = None self._distances = None self._species =\",\n \"= np.where(data[0] == data[0][0])[0][1] bands = data[1:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues,\",\n \"+ 1].attrib.get(self._coord_key).split(), dtype=float) res.append(np.linspace(start, end, self.number_of_k_points_per_segment[i])) elif key == 'band_segm_labels': res = []\",\n \"in val_in: unit = ureg.elementary_charge * ureg.bohr val = val_in.strip().split('\\\\n') properties = dict()\",\n \"elif key == 'band_segm_start_end': res = [] for i in range(len(self.number_of_k_points_per_segment)): start =\",\n \"Atoms ) from nomad.datamodel.metainfo.simulation.calculation import ( Calculation, Dos, DosValues, BandStructure, BandEnergies, Energy, EnergyEntry,\",\n \"= self.logger self.band_out_parser.logger = self.logger self.info_gw_parser.logger = self.logger self.input_xml_parser.logger = self.logger self.data_xs_parser.logger =\",\n \"None: self._energies = self._energies * self.energy_unit return self._energies def _get_dos(self, diagram): dos =\",\n \"module started([\\\\s\\\\S]+?)Hybrids module stopped', sub_parser=TextParser(quantities=module_quantities) )) def get_atom_labels(self, section): labels = section.get('symbols') if\",\n \"dtype=np.float64, unit=ureg.elementary_charge), Quantity('electronic_charge', rf'electronic charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic mass *:\",\n \"super().__init__(None) def init_quantities(self): self._quantities = [] def str_to_frequency(val_in): val = [v.split() for v\",\n \"self._distances @property def bands(self): if self._bands is None: bands = self.root.findall('./%s' % self._band_key)\",\n \"= None self._partial_dos = None @property def energy_unit(self): if self._energy_unit is None: axis\",\n \"data = get_data(files[i]) if not data: sccs.append(None) continue if quantity == 'EXCITON': sec_scc\",\n \"is None: return sec_system = self.parse_system(section) if sec_system is not None: sec_scc.system_ref =\",\n \"rf'electronic charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic mass *: *({re_float})', dtype=np.float64, unit=ureg.electron_mass),\",\n \"parse_loss(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) n_loss = len(data[0]) // n_components\",\n \"for name in self.info_parser._system_keys_mapping.keys(): val = self.info_parser.get_initialization_parameter(name) if val is None: continue if\",\n \"ureg.hartree return energies self._quantities = [Quantity( 'program_version', r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\. ]+)\\\\s*started', repeats=False, dtype=str, flatten=False)] initialization_quantities\",\n \"key): if self._results is None: self._results = dict() if not self.bands: return if\",\n \"= iteration.get('energy_contributions', {}) for key, names in self._energy_keys_mapping.items(): val = None for name\",\n \"= sec_method_ref sec_method.methods_ref = [sec_method_ref] # parse input xml file, there seems to\",\n \"for name in names: val = energy_contributions.get(name, None) if val is not None:\",\n \"data.append(self.data_xs_parser.data) return data def parse_exciton(data, sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components\",\n \"== 1 else vi properties[v[0].strip()] = vi * unit properties['atom_resolved'] = atom_resolved return\",\n \"406: ['HYB_GGA_XC_PBEH'], 408: ['HYB_GGA_XC_HSE03']} xc_functional = self.get('initialization', {}).get('xc_functional', None) if xc_functional is None:\",\n \"= np.hstack([np.reshape(v[key], (nspin, np.size(v[key]) // nspin)) for v in data]) res = res.reshape((len(res),\",\n \"None: data = np.transpose(self.data) self._band_k_points = [] start = 0 for nkpts_segment in\",\n \"= ureg.elementary_charge elif 'moment' in val_in: unit = ureg.elementary_charge * ureg.bohr val =\",\n \"for energy in band_energies]) if self._energy_unit is not None: band_energy = band_energy *\",\n \"sec_scc.x_exciting_xs_bse_number_of_excitons = n_excitons sec_scc.x_exciting_xs_bse_exciton_energies = np.reshape( data[1], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies =\",\n \"= 0 for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment self._band_k_points.append( np.transpose(data[2:5])[start:end])\",\n \"= None for v in val: v = v.strip().split(':') if len(v) < 2:\",\n \"= self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_dat_parser.band_energies if band_energies is None: return # Get fermi\",\n \"scc # TODO add support for more output files and properties exciting_files =\",\n \"file, there seems to be two versions, input_gw.xml and input-gw.xml for f in\",\n \"not xc_functional_names: # simply write parameters xc_functional = self.info_parser.get('initialization', {}).get('xc_functional') if xc_functional is\",\n \"Quantity( 'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree\",\n \"= reshape(get_data('Sc')) if eigs_gw_C is None: eigs_gw_C = reshape(get_data('Re(Sc)')) sec_gw_eigenvalues.value_correlation = eigs_gw_C sec_gw_eigenvalues.value_xc_potential\",\n \"class ExcitingGWOutParser(TextParser): def __init__(self, mainfile, logger): super().__init__(mainfile, logger=logger) def init_quantities(self): self._quantities = []\",\n \"val[n][1].magnitude for n in range(len(val))] * val[0][1].units sec_charges.total = charge_contributions.get('total charge') elif key\",\n \"for band in bands_atom: self._bands.append(band.findall('./%s' % self._band_key)) return self._bands @property def vertices(self): if\",\n \"name, r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)' % key_unit[0], str_operation=str_to_quantity_tolerances, unit=key_unit[1], repeats=False)) module_quantities = [ Quantity( 'scf_iteration',\",\n \"'BAND-QP.OUT'] for fname in bs_files: if self.file_exists(fname): gw_files.append(fname) break for f in gw_files:\",\n \"res.append([ '\\\\u0393' if start.lower() == 'gamma' else start, '\\\\u0393' if end.lower() == 'gamma'\",\n \"{}).get( 'x_exciting_spin_treatment', 'spin-unpolarised') n_spin = 1 if spin_treatment.lower() == 'spin-unpolarised' else 2 return\",\n \"= len(data) // nspin data = np.reshape(data, (nspin, len(data) // nspin, 2)) data\",\n \"nomad.datamodel.metainfo.simulation.run import Run, Program from nomad.datamodel.metainfo.simulation.method import ( Method, DFT, Electronic, Smearing, XCFunctional,\",\n \"= len(data) data = np.transpose(np.vstack(data)) n_epsilon = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_energy_points = n_epsilon\",\n \"from input.xml for f in input_file: self.input_xml_parser.mainfile = f positions = self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors\",\n \"= None self._neigs_segment = None self._nkpts_segment = None @property def band_energies(self): if self._band_energies\",\n \"for d in data]) if None in energy: return energy return np.array([d[1].get(key) for\",\n \"['DFT-D2 dispersion correction'] } self._electron_charge_keys_mapping = { 'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage': ['core leakage'], 'x_exciting_valence_charge':\",\n \"len(data) data = np.transpose(np.vstack(data)) n_epsilon = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_energy_points = n_epsilon sec_scc.x_exciting_xs_bse_epsilon_energies\",\n \"loaded from *: *(.+)'), Quantity('name', r'name *: *(.+)'), Quantity('nuclear_charge', rf'nuclear charge *: *({re_float})',\",\n \"e in file_ext_list if e]) # read q points qpoints = self.input_xml_parser.get('xs/qpointset/qpoint') def\",\n \"is None: self._nkpts_segment = [] count = 1 for i in range(1, len(self.distances)):\",\n \"is None or lattice_vectors is None or species is None: continue lattice_vectors =\",\n \"elif 'partial' in key: if not self.partial_dos: return res = np.zeros(( self.number_of_lm, self.number_of_spin_channels,\",\n \"22: ['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'], 26: ['GGA_C_PBE', 'GGA_X_WC'], 30: ['GGA_C_AM05', 'GGA_C_AM05'], 300: ['GGA_C_BGCP', 'GGA_X_PBE'], 406:\",\n \"self.info_parser._system_keys_mapping.keys(): val = self.info_parser.get_initialization_parameter(name) if val is None: continue if name == 'x_exciting_spin_treatment':\",\n \"License. # You may obtain a copy of the License at # #\",\n \"for d in data]) elif key == 'Znk': return np.array([d[1].get(key, None) for d\",\n \"return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') # TODO I am not sure about\",\n \"None: return # this is really problematic if some scf steps dont have\",\n \"axis = self.root.find('./axis') if axis is None: return self._energy_unit = self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1) return\",\n \"number = frequency_data.get('number') sec_method.gw.number_of_frequencies = len(number) sec_method.gw.frequency_number = number sec_method.gw.frequency_values = frequency_data.get('values') sec_method.gw.frequency_weights\",\n \"r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) initialization_quantities.append(Quantity( 'species', rf'(Species : *\\\\d+ *\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float} *{re_float}\",\n \"sec_method.x_exciting_xs_bse_angular_momentum_cutoff = self.input_xml_parser.get( 'xs/BSE/lmaxdielt', 14) rgkmax = self.input_xml_parser.get('xs/BSE/rgkmax', None) if rgkmax is None:\",\n \"*\\\\n)', sub_parser=TextParser(quantities=[ Quantity('type', r'Exchange-correlation type +: +(\\\\S+)'), Quantity( 'name_reference', r'\\\\n *(.+?,.+)', str_operation=lambda x:\",\n \"sec_run = self.archive.run[-1] fxctype = self.input_xml_parser.get('xs/tddft/fxctype', 'RPA') tetradf = self.input_xml_parser.get('xs/tetra/tetradf', None) nwacont =\",\n \"n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_re = np.reshape( data[4], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_im = np.reshape( data[5], (n_components, n_excitons))\",\n \"pass else: setattr(msection, key, val) # moment contributions moment_contributions = iteration.get('moment_contributions', {}) for\",\n \"1, 1]) sec_gw.basis_set = 'mixed' sec_gw.qp_equation_treatment = 'linearization' sec_gw.max_frequency = self.input_xml_parser.get( 'gw/freqgrid/freqmax', 1.0)\",\n \"implement reading of parameters from input.xml for normal calculations # in addition to\",\n \"scf_iteration or optimization_step final = section.get('scf_iteration', [None])[-1] final = section.get('optimization_step', [None])[-1] if final\",\n \"is None: if self.data is None: return data = np.transpose(self.data) n_kpoints = int(max(data[1]))\",\n \"dtype=float), Quantity( 'time', r'Time spent in this optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds', unit=ureg.s, repeats=False, dtype=float) ]\",\n \"[sec_method_ref] # parse input xml file, there seems to be two versions, input_gw.xml\",\n \"None])[-1] def get_scf_quantity(self, name): n_scf = len(self.get('energy_total_scf_iteration', [])) quantity = self.get('%s_scf_iteration' % name)\",\n \"self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None self._nkpts_segment = None self._neigs_segment\",\n \"volume', ureg.bohr ** 3), 'x_exciting_brillouin_zone_volume': ('Brillouin zone volume', 1 / ureg.bohr ** 3),\",\n \"q points qpoints = self.input_xml_parser.get('xs/qpointset/qpoint') def get_data(quantity, ext): # all files related to\",\n \"confirm no cell optimization in exciting sec_atoms.lattice_vectors = lattice_vectors lattice_vectors_reciprocal = self.info_parser.get_initialization_parameter( 'lattice_vectors_reciprocal')\",\n \"is None or val_m is None: lm = i % self.number_of_lm else: lm\",\n \"'Coulomb'], 'x_exciting_coulomb_energy': ['Coulomb energy', 'Coulomb'], 'energy_exchange': ['Exchange energy', 'exchange'], 'x_exciting_exchange_energy': ['Exchange energy', 'exchange'],\",\n \"energy', 'nuclear-nuclear'], 'x_exciting_madelung_energy': ['Madelung energy', 'Madelung'], 'x_exciting_core_electron_kinetic_energy': ['Core-electron kinetic energy', 'core electron kinetic'],\",\n \"(r'k\\\\-point offset', None), 'x_exciting_number_kpoints': (r'Total number of k\\\\-points', None), 'x_exciting_rgkmax': (r'R\\\\^MT\\\\_min \\\\* \\\\|G\\\\+k\\\\|\\\\_max\",\n \"*\\\\n\\\\-{10}|Time spent in this optimization step\\\\s*:\\\\s*[\\\\d\\\\.]+ seconds))', sub_parser=TextParser(quantities=optimization_quantities), repeats=True), Quantity( 'final', r'Force convergence\",\n \"\\\"\\\"\\\"Checks if a the given filename exists and is accessible in the same\",\n \"** 3), 'x_exciting_brillouin_zone_volume': ('Brillouin zone volume', 1 / ureg.bohr ** 3), 'x_exciting_number_of_atoms': ('Total\",\n \"None self._nkpts_segment = None self._neigs_segment = None self._vertices = None self._distances = None\",\n \"energy \\\\(states\\\\/Ha\\\\/cell\\\\)\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=1 / ureg.hartree), Quantity( 'charge_contributions', r'(?:Charges|Electron charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict,\",\n \"= np.transpose(np.vstack(data)) n_sigma = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_sigma_energies = np.reshape( data[0], (n_components, n_sigma))\",\n \"None: self._results = dict() if not self.bands: return if key == 'band_energies': #\",\n \"key, val) if key == 'x_exciting_fermi_energy': sec_energy.fermi = val # charge contributions charge_contributions\",\n \"np.reshape( data[4], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_im = np.reshape( data[5], (n_components, n_excitons)) def parse_epsilon(data, sec_scc):\",\n \"= None self._natoms = None self._nspin = None self._nlm = None self._energies =\",\n \"atom_labels is None: return sec_system = sec_run.m_create(System) sec_atoms = sec_system.m_create(Atoms) sec_atoms.positions = positions\",\n \"module stopped', sub_parser=TextParser(quantities=module_quantities), repeats=False)) optimization_quantities = [ Quantity( 'atomic_positions', r'(Atomic positions at this\",\n \"for v in val])) else: try: setattr(sec_system, name, val) except Exception: self.logger.warn('Error setting\",\n \"'species', rf'(Species : *\\\\d+ *\\\\(\\\\w+\\\\)[\\\\s\\\\S]+?{re_float} *{re_float} *{re_float}\\\\n\\\\s*\\\\n)', repeats=True, sub_parser=TextParser(quantities=[ Quantity('number', r'Species : *(\\\\d+)',\",\n \"'energy_total', r'[Tt]*otal energy\\\\s*:\\\\s*([\\\\-\\\\d\\\\.Ee]+)', repeats=False, dtype=float, unit=ureg.hartree), Quantity( 'energy_contributions', r'(?:Energies|_)([\\\\+\\\\-\\\\s\\\\w\\\\.\\\\:]+?)\\\\n *(?:DOS|Density)', str_operation=str_to_energy_dict, repeats=False, convert=False),\",\n \"self._results[key] = res class ExcitingFermiSurfaceBxsfParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = []\",\n \"files self.logger.warn( 'Mismatch in EXCITON and file type', data=dict(file=quantity)) sec_scc = sec_run.m_create(Calculation) if\",\n \"data]) res = res.reshape((len(res), len(data), len(res[0]) // len(data))) if key == 'eigenvalues': res\",\n \"0 for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment band_energy = np.array([np.transpose(band)[start:end]\",\n \"np.where(dist == dist[0])[0][1] self._distances = dist[:n_k_points] return self._distances @property def number_of_spin_channels(self): if self._nspin\",\n \"** 2 + int(val_m) + int(val_l) atom = i // (self.number_of_lm * self.number_of_spin_channels)\",\n \"self.eigval_parser.get('eigenvalues_occupancies') # reshaping is not necessary as this is done in parser, however\",\n \"in ['EXCITON', 'EPSILON', 'SIGMA', 'LOSS']: files = get_files(quantity) for i in range(len(files)): data\",\n \"not None: break return positions_format def get_atom_positions(self, section={}, positions=None, positions_format=None): positions = positions\",\n \"(r'Maximum \\\\|G\\\\+k\\\\| for APW functions', 1 / ureg.bohr), 'x_exciting_gmaxvr': (r'Maximum \\\\|G\\\\| for potential\",\n \"= [] class ExcitingInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): re_symbol = re.compile(r'([A-Z][a-z]?)') def\",\n \"data[1] * (1 / ureg.hartree) * volume.to('m**3').magnitude for spin in range(len(dos)): sec_dos_values =\",\n \"sec_scc = parse_configuration(self.info_parser.get(module)) if sec_scc is None: continue # add data to scc\",\n \"n in range(len(band_energies)): # Get fermi energy: it is used to un-shift the\",\n \"self._unit_key = 'unit' self._energy_unit = kwargs.get('energy_unit', None) self._units_mapping = dict(hartree=ureg.hartree) def init_parameters(self): self._ndos\",\n \"is not None: break if val is None: continue if key.startswith('energy_'): sec_energy.m_add_sub_section(getattr( Energy,\",\n \"positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions',\",\n \"n_components = len(data) data = np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components = n_components n_excitons = len(data[0]) //\",\n \"= sec_run.system[-1] sec_scc_ref = sec_run.calculation[0] sec_scc.starting_calculation_ref = sec_scc_ref sec_scc.calculations_ref = [sec_scc_ref] # parse\",\n \"dtype=np.float64, unit=ureg.electron_mass), Quantity('muffin_tin_radius', rf'muffin-tin radius *: *({re_float})', dtype=np.float64, unit=ureg.bohr), Quantity('radial_points', rf'radial points in\",\n \"repeats=False, unit=key_unit[1])) self._convergence_keys_mapping = { 'x_exciting_effective_potential_convergence': ( r'RMS change in effective potential \\\\(target\\\\)',\",\n \"self.logger self.data_xs_parser.logger = self.logger self.data_clathrate_parser.logger = self.logger def reuse_parser(self, parser): self.info_parser.quantities = parser.info_parser.quantities\",\n \"if self._results is None: self._results = dict() if not self.bands: return if key\",\n \"some scf steps dont have the quantity # the only thing that we\",\n \"files. Will read all!', data=dict(file=name)) for n in range(len(files)): parser.mainfile = files[n] parser_function(section)\",\n \"range(len(self.number_of_k_points_per_segment)): start = np.array( self.vertices[i].attrib.get(self._coord_key).split(), dtype=float) end = np.array( self.vertices[i + 1].attrib.get(self._coord_key).split(), dtype=float)\",\n \"data=dict(file=quantity)) sec_scc = sec_run.m_create(Calculation) if quantity == 'EXCITON': parse_function = parse_exciton elif quantity\",\n \"module stopped', sub_parser=TextParser(quantities=[ Quantity( 'optimization_step', r'(Optimization step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n *\\\\n\\\\-{10}|Time spent in this optimization step\\\\s*:\\\\s*[\\\\d\\\\.]+\",\n \"scheme', None), 'smearing_width': ('Smearing width', None)} for name, key_unit in self._system_keys_mapping.items(): initialization_quantities.append( Quantity(\",\n \"it by concatenating species symbols species = self.get('initialization', {}).get('species', []) labels = []\",\n \"'xs/nempty', 5) sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq', [1, 1, 1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk', [1, 1,\",\n \"* ureg.hartree, weights=val[2]) # TODO Read also input parameters here if input_GW.xml does\",\n \"occupancy below\\\\)\\\\s*([\\\\d\\\\.Ee\\\\-\\\\s]+?(?:\\\\n *\\\\n))', str_operation=str_to_eigenvalues, repeats=True)) class ExcitingGWOutParser(TextParser): def __init__(self, mainfile, logger): super().__init__(mainfile, logger=logger)\",\n \"self._nspin is None: self._nspin = np.shape(np.transpose(self.data))[0] - 6 return self._nspin @property def number_of_k_points_per_segment(self):\",\n \"xc_functional_name = ' '.join(self.info_parser.get_xc_functional_name()) sec_method.gw.starting_point = xc_functional_name sec_scc = sec_run.m_create(Calculation) sec_scc.method_ref = sec_method\",\n \"name='exciting', version=self.info_parser.get('program_version', '').strip()) # method goes first since reference needed for sec_scc self.parse_method()\",\n \"sec_run.m_create(System) sec_atoms = sec_system.m_create(Atoms) sec_atoms.positions = positions sec_atoms.labels = atom_labels sec_atoms.periodic = [True]\",\n \"dtype=float)])), Quantity( 'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Time', repeats=False, str_operation=str_to_array, convert=False,\",\n \"in range(len(partialdos[lm][spin])): sec_dos_values = sec_dos.m_create(DosValues, Dos.atom_projected) sec_dos_values.m_kind = 'spherical' sec_dos_values.lm = lm_values[lm] sec_dos_values.spin\",\n \"data]) if None in energy: return energy return np.array([d[1].get(key) for d in data])\",\n \"True self.data_clathrate_parser.mainfile = clathrate_file[0] if self.data_clathrate_parser.data: data = np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates = np.transpose( np.array(data[:3],\",\n \"= threshold_force def parse_method(self): sec_run = self.archive.run[-1] sec_method = sec_run.m_create(Method) sec_method.basis_set.append(BasisSet(type='(L)APW+lo')) sec_dft =\",\n \"positions_format is not None: break return positions_format def get_atom_positions(self, section={}, positions=None, positions_format=None): positions\",\n \"f positions = self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors = self.input_xml_parser.get( 'structure/crystal/basevect', np.eye(3)) species = self.input_xml_parser.get('structure/species/speciesfile') if\",\n \"is None: return positions = np.array(positions) positions_format = positions_format if positions_format is not\",\n \"None) for d in data]) else: energy = np.array([d[1].get(key, None) for d in\",\n \"self._band_energies @property def band_k_points(self): if self._band_k_points is None: data = np.transpose(self.data) self._band_k_points =\",\n \"str_to_quantity_tolerances(val_in): return val_in.strip().replace('(', '').replace(')', '').split() def str_to_energy_dict(val_in): val = val_in.strip().split('\\\\n') energies = dict()\",\n \"val[n][1] sec_charges = msection.m_create(Charges) sec_charges.value = [ val[n][1].magnitude for n in range(len(val))] *\",\n \"rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_rgkmax = rgkmax sec_method.x_exciting_xs_scissor = self.input_xml_parser.get('xs/scissor', 0.0) sec_method.x_exciting_xs_vkloff =\",\n \"for different versions of exciting self._energy_keys_mapping = { 'energy_total': ['Total energy', 'total energy'],\",\n \"None def _parse_input_xs(self, sec_method): xstype = self.input_xml_parser.get('xs/xstype', None) if xstype is not None:\",\n \"msection.m_create(x_exciting_section_MT_moment_atom) sec_mt_moment_atom.x_exciting_MT_moment_atom_index = n + 1 sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol = val[n][0] sec_mt_moment_atom.x_exciting_MT_moment_atom_value = val[n][1] else:\",\n \"sec_method.methods_ref = [sec_method_ref] # parse input xml file, there seems to be two\",\n \"0, 1)) for j in range(len(data)): data[j].append(data_q[j]) return data for quantity in ['EPSILON',\",\n \"self-consistent loops maximum)([\\\\s\\\\S]+?)(\\\\n *\\\\n\\\\+{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(Atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format',\",\n \"self.info_parser.get_number_of_spin_channels() def reshape(data): if data[0] is None: return return np.reshape(data, (nspin, len(data) //\",\n \"if energy_fermi is None: return energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure,\",\n \"return sec_scc def parse_system(self, section): sec_run = self.archive.run[-1] positions = self.info_parser.get_atom_positions(section.get('atomic_positions', {})) lattice_vectors\",\n \"sec_energy.fermi = val # charge contributions charge_contributions = iteration.get('charge_contributions', {}) for key, names\",\n \"scr_types: files = self.get_exciting_files( '%s_BSE%s_SCR%s.OUT' % (name, bse_type, scr_type)) bse_files.append(files) return bse_files def\",\n \"res * (1 / self.energy_unit) elif 'partial' in key: if not self.partial_dos: return\",\n \"in bs_files: if self.file_exists(fname): gw_files.append(fname) break for f in gw_files: self.parse_file(f, sec_scc) frequency_data\",\n \"parser_function = self._parse_input_xs else: # TODO implement reading of parameters from input.xml for\",\n \"sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor = reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands = len(eigs_gw[0]) sec_gw_eigenvalues.n_kpoints = len(eigs_gw) sec_gw_eigenvalues.kpoints = get_data('k_points')\",\n \"self.info_parser._convergence_keys_mapping.keys(): threshold = self.info_parser.get_scf_threshold(name) if threshold is None: continue metainfo_name = 'x_exciting_scf_threshold_%s_change' %\",\n \"= self.info_gw_parser.get('fundamental_band_gap', None) sec_gap = sec_scc.eigenvalues[-1].m_create(BandGap) if fundamental_band_gap is not None: sec_gap.value_fundamental =\",\n \"'xs/tddft/kerndiag', False) sec_method.x_exciting_xs_tddft_lmax_alda = self.input_xml_parser.get( 'xs/tddft/lmaxalda', 3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment = self.input_xml_parser.get( 'xs/tddft/mdfqtype', 0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals\",\n \"'mixed' sec_gw.qp_equation_treatment = 'linearization' sec_gw.max_frequency = self.input_xml_parser.get( 'gw/freqgrid/freqmax', 1.0) sec_gw.frequency_grid_type = self.input_xml_parser.get( 'gw/freqgrid/fgrid',\",\n \"band_energy = np.array([np.transpose(band)[start:end] for band in bands]) if self._energy_unit: band_energy = band_energy *\",\n \"ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re = np.reshape( data[1], (n_components, n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im = np.reshape( data[2], (n_components, n_epsilon))\",\n \"data['eigenvalues'] = np.reshape(eigs, (nspin, len(eigs) // nspin)) return data self._quantities.append( Quantity( 'eigenvalues_occupancies', r'\\\\(state\\\\,\",\n \"eigs_gw_C is None: eigs_gw_C = reshape(get_data('Re(Sc)')) sec_gw_eigenvalues.value_correlation = eigs_gw_C sec_gw_eigenvalues.value_xc_potential = reshape(get_data('Vxc')) def\",\n \"i in range(1, len(self.distances)): if self.distances[i] == self.distances[i - 1]: self._nkpts_segment .append(count) count\",\n \"start + nkpts_segment band_energy = np.array([ np.transpose(energy)[start:end] for energy in band_energies]) if self._energy_unit\",\n \"None: if self.partial_dos is None: return self._nlm = 0 l_list = set([int(e.attrib.get(self._l_key)) for\",\n \"is really problematic if some scf steps dont have the quantity # the\",\n \"charge', ureg.elementary_charge), 'x_exciting_core_charge_initial': ('Total core charge', ureg.elementary_charge), 'x_exciting_valence_charge_initial': ('Total valence charge', ureg.elementary_charge), 'x_exciting_wigner_radius':\",\n \"use this file except in compliance with the License. # You may obtain\",\n \"= self.logger self.dos_parser.logger = self.logger self.bandstructure_parser.logger = self.logger self.eigval_parser.logger = self.logger self.fermisurf_parser.logger =\",\n \"charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic mass *: *({re_float})', dtype=np.float64, unit=ureg.electron_mass), Quantity('muffin_tin_radius',\",\n \"2 if occs[0] == 1. else 1 data = dict() data['occupancies'] = np.reshape(occs,\",\n \"self.info_parser.maindir) if target[0] in f and mainfile_base in f] options = [f for\",\n \"init_parameters(self): self._nspin = None self._distances = None self._band_energies = None self._neigs_segment = None\",\n \"= None self._band_energies = None self._band_k_points = None @property def band_energies(self): if self._band_energies\",\n \"return self._band_energies @property def distances(self): if self._distances is None: dist = np.transpose(self.data)[0] n_k_points\",\n \"1 val_l = self.partial_dos[i].attrib.get(self._l_key, None) val_m = self.partial_dos[i].attrib.get(self._m_key, None) if val_l is None\",\n \"= self.input_xml_parser.get('xs/screening/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_screening_rgkmax =\",\n \"'x_exciting_nuclear_nuclear_energy': ['Nuclear-nuclear energy', 'nuclear-nuclear'], 'x_exciting_madelung_energy': ['Madelung energy', 'Madelung'], 'x_exciting_core_electron_kinetic_energy': ['Core-electron kinetic energy', 'core\",\n \"DataTextParser from nomad.datamodel.metainfo.simulation.run import Run, Program from nomad.datamodel.metainfo.simulation.method import ( Method, DFT, Electronic,\",\n \"band_energies = self.bandstructure_dat_parser.band_energies if band_energies is None: return # Get fermi energy: it\",\n \"there is a mismatch between files self.logger.warn( 'Mismatch in EXCITON and file type',\",\n \"None: continue lattice_vectors = np.array(lattice_vectors, dtype=float) lattice_vectors *= self.input_xml_parser.get('structure/crystal/scale', 1.0) positions = np.dot(positions,\",\n \"self.input_xml_parser = XMLParser() self.data_xs_parser = DataTextParser() self.data_clathrate_parser = DataTextParser(dtype=str) # different names for\",\n \"for n in range(len(band_energies)): # Get fermi energy: it is used to un-shift\",\n \"nspin)) data['eigenvalues'] = np.reshape(eigs, (nspin, len(eigs) // nspin)) return data self._quantities.append( Quantity( 'eigenvalues_occupancies',\",\n \"sec_scc): if self.dos_parser.get('totaldos', None) is None: return # Get fermi energy: it is\",\n \"= sec_system.m_create(Atoms) sec_atoms.positions = positions sec_atoms.labels = atom_labels sec_atoms.periodic = [True] * 3\",\n \"Dos.total) sec_dos_values.spin = spin sec_dos_values.value = totaldos[spin] partialdos = self.dos_parser.get('partialdos') if partialdos is\",\n \"sec_method.starting_method_ref = sec_method_ref sec_method.methods_ref = [sec_method_ref] # parse input xml file, there seems\",\n \"__init__(self, mainfile, logger): super().__init__(mainfile, logger=logger) def init_quantities(self): self._quantities = [] class ExcitingInfoParser(TextParser): def\",\n \"= self.input_xml_parser.get('xs/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_rgkmax =\",\n \"elif quantity == 'LOSS' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_loss_function_no_local_field = data[1] elif quantity\",\n \"in range(len(atoms)): atom_labels.extend([species[n].split('.')[0]] * len(atoms[n])) if positions is None or atom_labels is None:\",\n \"= 'eval' self._vertex_key = 'vertex' self._band_key = 'band' self._atom_key = 'atom' self._nspin =\",\n \"self.input_xml_parser.get('structure/species/atom') atom_labels = [] for n in range(len(atoms)): atom_labels.extend([species[n].split('.')[0]] * len(atoms[n])) if positions\",\n \"bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies = [] start = 0\",\n \"for f in gw_info_files: if self.get_exciting_files(f): self._calculation_type = 'gw' gw_info_file = f break\",\n \"val_in: unit = ureg.elementary_charge * ureg.bohr val = val_in.strip().split('\\\\n') properties = dict() atom_resolved\",\n \"if positions is not None else section.get('positions') if positions is None: species =\",\n \"if self.input_xml_parser.get('xs/screening') is not None: sec_method.x_exciting_xs_screening_number_of_empty_states = self.input_xml_parser.get( 'xs/screening/nempty', 0) sec_method.x_exciting_xs_screening_ngridk = self.input_xml_parser.get(\",\n \"self.info_gw_parser.get('frequency_data', None) if frequency_data is not None: number = frequency_data.get('number') sec_method.gw.number_of_frequencies = len(number)\",\n \"@property def band_energies(self): if self._band_energies is None: data = np.transpose(self.data) n_kpoints = np.where(data[0]\",\n \"def reuse_parser(self, parser): self.info_parser.quantities = parser.info_parser.quantities self.eigval_parser.quantities = parser.eigval_parser.quantities self.fermisurf_parser.quantities = parser.fermisurf_parser.quantities self.evalqp_parser.quantities\",\n \"'lattice_vectors_reciprocal') sec_atoms.lattice_vectors_reciprocal = lattice_vectors_reciprocal if len(sec_run.system) > 1: return sec_system for name in\",\n \"= np.column_stack((np.arange(len(partialdos)), np.zeros(len(partialdos), dtype=np.int32))) for lm in range(len(partialdos)): for spin in range(len(partialdos[lm])): for\",\n \"for f in options if f.endswith(file_ext)] options.sort() filenames = [os.path.join(self.info_parser.maindir, f) for f\",\n \"step\\\\s*\\\\d+[\\\\s\\\\S]+?(?:\\\\n *\\\\n\\\\-{10}|Time spent in this optimization step\\\\s*:\\\\s*[\\\\d\\\\.]+ seconds))', sub_parser=TextParser(quantities=optimization_quantities), repeats=True), Quantity( 'final', r'Force\",\n \"break if not self._calculation_type == 'gw': return sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0]\",\n \"energy return np.array([d[1].get(key) for d in data]) * ureg.hartree eigs_gw = get_data('E_GW') if\",\n \"= self.info_parser.get_initialization_parameter( 'lattice_vectors_reciprocal') sec_atoms.lattice_vectors_reciprocal = lattice_vectors_reciprocal if len(sec_run.system) > 1: return sec_system for\",\n \"+ quantity return quantity def get_xc_functional_name(self): # TODO expand list to include other\",\n \"there seems to be two versions, input_gw.xml and input-gw.xml for f in ['input_gw.xml',\",\n \"self._energies is None: if self.total_dos is None: return self._energies = np.array( [float(point.attrib.get(self._energy_key)) for\",\n \"sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = len(data) // nspin data = np.reshape(data, (nspin, len(data) //\",\n \"re import logging from nomad.units import ureg from nomad.parsing.file_parser import TextParser, Quantity, XMLParser,\",\n \"= band_energy * self._energy_unit res_n.append(band_energy) start = end res.append(res_n) elif key == 'band_k_points':\",\n \"self._distances = np.array(self._distances, dtype=float) return self._distances @property def bands(self): if self._bands is None:\",\n \"self.bandstructure_dat_parser.logger = self.logger self.band_out_parser.logger = self.logger self.info_gw_parser.logger = self.logger self.input_xml_parser.logger = self.logger self.data_xs_parser.logger\",\n \"True return False def _parse_dos(self, sec_scc): if self.dos_parser.get('totaldos', None) is None: return #\",\n \"_parse_dos_out(self, sec_scc): data = self.dos_out_parser.data if data is None: return # Get fermi\",\n \"repeats=False, dtype=int), Quantity( 'method', r'method\\\\s*=\\\\s*(\\\\w+)', repeats=False, dtype=str), Quantity( 'n_scf_iterations', r'Number of (?:total)* scf\",\n \"'x_exciting_core_charge_initial': ('Total core charge', ureg.elementary_charge), 'x_exciting_valence_charge_initial': ('Total valence charge', ureg.elementary_charge), 'x_exciting_wigner_radius': (r'Effective Wigner\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"parse_configurations(self): sec_run = self.archive.run[-1] def parse_configuration(section): if not section: return sec_scc = self.parse_scc(section)\",\n \"init_parameters(self): # TODO make a parent clas for bandstructure dat and xml self._nspin\",\n \"SCF iteration|Reached self-consistent loops maximum)([\\\\s\\\\S]+?)(\\\\n *\\\\n\\\\+{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(Atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[\",\n \"'xs/tddft/fxctype', 'RPA') sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution = self.input_xml_parser.get( 'xs/tddft/intraband', False) sec_method.x_exciting_xs_tddft_diagonal_xc_kernel = self.input_xml_parser.get( 'xs/tddft/kerndiag', False) sec_method.x_exciting_xs_tddft_lmax_alda\",\n \"ExcitingInfoParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): re_symbol = re.compile(r'([A-Z][a-z]?)') def str_to_array(val_in): val =\",\n \"for li in l_list: self._nlm += 2 * li + 1 return self._nlm\",\n \"self.root.findall('./%s' % self._partialdos_key) self._natoms = len(partial_dos) return self._natoms @property def number_of_dos(self): if self._ndos\",\n \"positions is None or atom_labels is None: return sec_system = sec_run.m_create(System) sec_atoms =\",\n \"= self.info_gw_parser.get('optical_band_gap', None) if optical_band_gap is not None: sec_gap.value_optical = optical_band_gap def parse_miscellaneous(self):\",\n \"self.get_exciting_files('str.out') if clathrate_file: sec_system.x_exciting_clathrates = True self.data_clathrate_parser.mainfile = clathrate_file[0] if self.data_clathrate_parser.data: data =\",\n \"int(val_m) + int(val_l) atom = i // (self.number_of_lm * self.number_of_spin_channels) res[lm][spin][atom] = self._get_dos(self.partial_dos[i])\",\n \"'fundamental_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'optical_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) class\",\n \"= re.compile(r'([A-Z][a-z]?)') def str_to_array(val_in): val = [v.split(':')[-1].split() for v in val_in.strip().split('\\\\n')] val =\",\n \"= self.input_xml_parser.get( 'xs/BSE/sciavqwg', False) sec_method.x_exciting_xs_bse_sciavtype = self.input_xml_parser.get( 'xs/BSE/sciavtype', 'spherical') sec_method.x_exciting_xs_bse_xas = self.input_xml_parser.get( 'xs/BSE/xas',\",\n \"= eigs_gw_C sec_gw_eigenvalues.value_xc_potential = reshape(get_data('Vxc')) def _parse_dos_out(self, sec_scc): data = self.dos_out_parser.data if data\",\n \"name == 'x_exciting_species_rtmin': setattr(sec_system, name, ' '.join([str(v) for v in val])) else: try:\",\n \"if positions_format == 'lattice': cell = self.get_initialization_parameter('lattice_vectors') if cell is None: return positions\",\n \"self._nspin is None: if not self.total_dos: return self._nspin = len(self.total_dos) return self._nspin @property\",\n \"None), 'x_exciting_kpoint_grid': (r'k\\\\-point grid', None), 'x_exciting_kpoint_offset': (r'k\\\\-point offset', None), 'x_exciting_number_kpoints': (r'Total number of\",\n \"not None: smearing_width = (smearing_width * ureg.hartree).to('joule') # TODO smearing with should have\",\n \"[v.strip() for v in x.split(':')])])) ) self._quantities.append(Quantity( 'initialization', r'(?:All units are atomic|Starting initialization)([\\\\s\\\\S]+?)(?:Using|Ending\",\n \"ureg.bohr def get_scf_threshold(self, name): reference = self.get('groundstate', self.get('hybrids', {})) return reference.get('scf_iteration', [{}])[-1].get( name,\",\n \"x_exciting_dos_fermi = iteration.get('x_exciting_dos_fermi') if x_exciting_dos_fermi is not None: setattr(msection, 'x_exciting_dos_fermi', x_exciting_dos_fermi) # energy\",\n \"data[0]: continue if quantity == 'EPSILON' and ext == 'FXC': sec_scc = sec_run.m_create(Calculation)\",\n \"sec_run.program = Program( name='exciting', version=self.info_parser.get('program_version', '').strip()) # method goes first since reference needed\",\n \"'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg', False) sec_method.x_exciting_xs_bse_sciavtype\",\n \"len(data) // nspin, 2)) data = np.transpose(data, axes=(2, 0, 1)) sec_dos.energies = data[0][0]\",\n \"None: return data = np.transpose(self.data) n_kpoints = int(max(data[1])) bands = data[6:] bands =\",\n \"self.get_initialization_parameter('species', []) if species: positions = np.vstack([s.get('positions') for s in species]) if positions\",\n \"len(atoms[n])) if positions is None or atom_labels is None: return sec_system = sec_run.m_create(System)\",\n \"sec_k_band_segment.energies = band_energies[nb] + energy_fermi def _parse_band_out(self, sec_scc): self.band_out_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies =\",\n \"% (self._totaldos_key, self._diagram_key)) self._ndos = len(total_dos) return self._ndos @property def number_of_lm(self): if self._nlm\",\n \"self.input_xml_parser.get( 'gw/coreflag', 'all') sec_gw.polarizability_number_of_empty_states = int( self.input_xml_parser.get('gw/nempty', 0)) sec_gw.ngridq = self.input_xml_parser.get('gw/ngridq', [1, 1,\",\n \"v in val])) else: try: setattr(sec_system, name, val) except Exception: self.logger.warn('Error setting metainfo.')\",\n \"self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_rgkmax = rgkmax sec_method.x_exciting_xs_scissor = self.input_xml_parser.get('xs/scissor', 0.0) sec_method.x_exciting_xs_vkloff = self.input_xml_parser.get('xs/vkloff', [0.,\",\n \"targets achieved\\\\. Performing final SCF iteration|Reached self-consistent loops maximum)([\\\\s\\\\S]+?)(\\\\n *\\\\n\\\\+{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity(\",\n \"None: self._nspin = np.shape(np.transpose(self.data))[0] - 6 return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment\",\n \"name.startswith('FS')) and name.endswith('bxsf'): parser = self.fermisurf_parser parser_function = self._parse_fermisurface elif name.startswith('EVALQP') and (name.endswith('DAT')\",\n \"None) if xc_functional is None: return [] name = xc_functional_map.get(xc_functional.type, []) return name\",\n \"for vii in v[1].split()] vi = vi[0] if len(vi) == 1 else vi\",\n \"'nuclear-nuclear'], 'x_exciting_madelung_energy': ['Madelung energy', 'Madelung'], 'x_exciting_core_electron_kinetic_energy': ['Core-electron kinetic energy', 'core electron kinetic'], 'x_exciting_dft_d2_dispersion_correction':\",\n \"not None else self.get_positions_format(section) if positions_format == 'lattice': cell = self.get_initialization_parameter('lattice_vectors') if cell\",\n \"local\\\\-orbitals', None)} self._method_keys_mapping = { 'smearing_kind': ('Smearing scheme', None), 'smearing_width': ('Smearing width', None)}\",\n \"'_'.join([e for e in file_ext_list if e]) # read q points qpoints =\",\n \"quantity return quantity def get_xc_functional_name(self): # TODO expand list to include other xcf\",\n \"energy = np.array([d[1].get(key, None) for d in data]) if None in energy: return\",\n \"if not data: sccs.append(None) continue if quantity == 'EXCITON': sec_scc = sec_run.m_create(Calculation) sccs.append(sec_scc)\",\n \"key == 'band_energies': # TODO I am not certain about the format for\",\n \"str_operation=str_to_eigenvalues, repeats=True)) class ExcitingGWOutParser(TextParser): def __init__(self, mainfile, logger): super().__init__(mainfile, logger=logger) def init_quantities(self): self._quantities\",\n \"= v[0][2] atom_resolved.append(((species, v[1] * unit))) else: vi = [float(vii) for vii in\",\n \"None self._species = None @property def distances(self): if self._distances is None: if not\",\n \"gmaxvr sec_gw.bare_coulomb_cutofftype = self.input_xml_parser.get( 'gw/barecoul/cutofftype', 'none') sec_gw.screened_coulomb_volume_average = self.input_xml_parser.get( 'gw/scrcoul/sciavtype', 'isotropic') sec_gw.screened_Coulomb =\",\n \"if self.file_exists(fname): gw_files.append(fname) break for f in gw_files: self.parse_file(f, sec_scc) frequency_data = self.info_gw_parser.get('frequency_data',\",\n \"r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)' % key_unit[0], str_operation=str_to_quantity_tolerances, unit=key_unit[1], repeats=False)) module_quantities = [ Quantity( 'scf_iteration', r'(?:I|\",\n \"= np.zeros(( self.number_of_lm, self.number_of_spin_channels, self.number_of_atoms, self.number_of_dos)) for i in range(len(self.partial_dos)): spin = self.partial_dos[i].attrib.get(self._nspin_key,\",\n \"scale in which also other energies are reported. energy_fermi = sec_scc.energy.fermi if energy_fermi\",\n \"self.logger self.input_xml_parser.logger = self.logger self.data_xs_parser.logger = self.logger self.data_clathrate_parser.logger = self.logger def reuse_parser(self, parser):\",\n \"None), 'x_exciting_species_rtmin': (r'Species with R\\\\^MT\\\\_min', None), 'x_exciting_gkmax': (r'Maximum \\\\|G\\\\+k\\\\| for APW functions', 1\",\n \"None: continue if name == 'time': msection.time_calculation = val else: setattr(msection, name, val)\",\n \"frequency_data is not None: number = frequency_data.get('number') sec_method.gw.number_of_frequencies = len(number) sec_method.gw.frequency_number = number\",\n \"= band_parameters[3] fermi_energy = self.fermisurf_parser.get('fermi_energy', None) if fermi_energy is not None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface =\",\n \"archive, logger): self.filepath = filepath self.archive = archive self.logger = logger if logger\",\n \"sec_scc.starting_calculation_ref = sec_scc_ref sec_scc.calculations_ref = [sec_scc_ref] # parse properties gw_info_files = self.get_exciting_files(gw_info_file) if\",\n \"@property def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment = [] count = 1\",\n \"for name in names: val = moment_contributions.get(name, None) if val is not None:\",\n \"None in energy: return energy return np.array([d[1].get(key) for d in data]) * ureg.hartree\",\n \"in addition to INFO.OUT return else: return files = self.get_exciting_files(name) if len(files) >\",\n \"import ( System, Atoms ) from nomad.datamodel.metainfo.simulation.calculation import ( Calculation, Dos, DosValues, BandStructure,\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"['interstitial'], 'x_exciting_total_MT_charge': ['total charge in muffin-tins', 'total in muffin-tins'], 'charge_total': ['total charge'], 'x_exciting_section_MT_charge_atom':\",\n \"self.info_parser.get_initialization_parameter( 'lattice_vectors_reciprocal') sec_atoms.lattice_vectors_reciprocal = lattice_vectors_reciprocal if len(sec_run.system) > 1: return sec_system for name\",\n \"'diag', 'noinvdiag', 'longrange'] bse_files = [] for bse_type in bse_types: for scr_type in\",\n \"= self.get('initialization', {}).get( 'x_exciting_spin_treatment', 'spin-unpolarised') n_spin = 1 if spin_treatment.lower() == 'spin-unpolarised' else\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"data = get_data(quantity, ext) if not data[0]: continue if quantity == 'EPSILON' and\",\n \"grid sizes', None), 'x_exciting_gvector_total': (r'Total number of G\\\\-vectors', None), 'x_exciting_lmaxapw': (r' APW functions',\",\n \"Quantity( 'final', r'Force convergence target achieved([\\\\s\\\\S]+?Opt)', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(imized atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n',\",\n \"self._quantities = [] def str_to_frequency(val_in): val = [v.split() for v in val_in.split('\\\\n')] val\",\n \"= None self._nkpts_segment = None self._neigs_segment = None self._bands = None self._vertices =\",\n \"sec_gw.type = 'G0W0' gmaxvr = self.info_parser.get_initialization_parameter('x_exciting_gmaxvr', 0) sec_gw.core_treatment = self.input_xml_parser.get( 'gw/coreflag', 'all') sec_gw.polarizability_number_of_empty_states\",\n \"of Bravais lattice symmetries', None), 'x_exciting_number_of_crystal_symmetries': ('Number of crystal symmetries', None), 'x_exciting_kpoint_grid': (r'k\\\\-point\",\n \"self._band_energies = None self._band_k_points = None @property def band_energies(self): if self._band_energies is None:\",\n \"else: sec_scc = sccs[i] if sec_scc is None: # This is the case\",\n \"sec_scc.x_exciting_xs_bse_sigma_energies = np.reshape( data[0], (n_components, n_sigma)) * ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re = np.reshape( data[1], (n_components,\",\n \"+ energy_fermi def _parse_eigenvalues(self, sec_scc): if self.eigval_parser.get('eigenvalues_occupancies', None) is None: return nspin =\",\n \"sec_dos_values.spin = spin sec_dos_values.value = totaldos[spin] partialdos = self.dos_parser.get('partialdos') if partialdos is None:\",\n \"totaldos[spin] partialdos = self.dos_parser.get('partialdos') if partialdos is None: return partialdos = partialdos.to('1/joule').magnitude lm_values\",\n \"self._nkpts_segment is None: self._nkpts_segment = [] count = 1 for i in range(1,\",\n \"'mpd') sec_gw.self_energy_c_analytical_continuation = self.input_xml_parser.get( 'gw/selfenergy/actype', 'pade') sec_gw.mixed_basis_lmax = int(self.input_xml_parser.get( 'gw/mixbasis/lmaxmb', 3)) sec_gw.mixed_basis_tolerance =\",\n \"= None self._species = None @property def distances(self): if self._distances is None: if\",\n \"not aresdf else None] file_ext = '_'.join([e for e in file_ext_list if e])\",\n \"r'\\\\n *(.+?:.+)', repeats=True, str_operation=lambda x: [v.strip() for v in x.split(':')])])) ) self._quantities.append(Quantity( 'initialization',\",\n \"rgkmax sec_method.x_exciting_xs_scissor = self.input_xml_parser.get('xs/scissor', 0.0) sec_method.x_exciting_xs_vkloff = self.input_xml_parser.get('xs/vkloff', [0., 0., 0.]) # TODO\",\n \"= self.input_xml_parser.get('xs/tddft/nwacont', None) aresdf = self.input_xml_parser.get('xs/tddft/aresdf', True) file_ext_list = [ 'TET' if tetradf\",\n \"None) if band_parameters is not None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface = band_parameters[0] sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface = np.product(band_parameters[1]) sec_fermisurface.x_exciting_grid_fermi_surface\",\n \"None self._partial_dos = None @property def energy_unit(self): if self._energy_unit is None: axis =\",\n \"(n_components, n_sigma)) * ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re = np.reshape( data[1], (n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im = np.reshape(\",\n \"energy: it is used to un-shift the DOS to # the original scale\",\n \"iteration|Reached self-consistent loops maximum)([\\\\s\\\\S]+?)(\\\\n *\\\\n\\\\+{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions', r'(Atomic positions\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity(\",\n \"achieved\\\\. Performing final SCF iteration|Reached self-consistent loops maximum)([\\\\s\\\\S]+?)(\\\\n *\\\\n\\\\+{10})', sub_parser=TextParser(quantities=scf_quantities), repeats=False), Quantity( 'atomic_positions',\",\n \"(self._totaldos_key, self._diagram_key)) self._ndos = len(total_dos) return self._ndos @property def number_of_lm(self): if self._nlm is\",\n \"return reference.get('scf_iteration', [{}])[-1].get( name, [None, None])[-1] def get_scf_quantity(self, name): n_scf = len(self.get('energy_total_scf_iteration', []))\",\n \"self.input_xml_parser.get( 'xs/tddft/mdfqtype', 0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals = self.input_xml_parser.get( 'xs/tddft/nwacont', 0) sec_method.x_exciting_xs_tetra = self.input_xml_parser.get( 'xs/tetra/tetradf', False)\",\n \"get_initialization_parameter(self, key, default=None): return self.get('initialization', {}).get(key, default) class ExcitingParser: def __init__(self): self.info_parser =\",\n \"range(len(keys))} return [kpts, eigs] self._quantities.append( Quantity( 'kpoints_eigenvalues', r'\\\\s*k\\\\-point \\\\#\\\\s*\\\\d+:\\\\s*([\\\\d\\\\s\\\\.\\\\-]+)([ \\\\w\\\\(\\\\)]+\\\\n)([\\\\s\\\\d\\\\.\\\\-Ee]+)', str_operation=str_to_eigenvalue, repeats=True)) class\",\n \"= { 2: ['LDA_C_PZ', 'LDA_X_PZ'], 3: ['LDA_C_PW', 'LDA_X_PZ'], 4: ['LDA_C_XALPHA'], 5: ['LDA_C_VBH'], 20:\",\n \"Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n\",\n \"ext): # all files related to quantity at all qpoints files = self.get_exciting_files('%s_%s%s%s.OUT'\",\n \"= self.input_xml_parser.get( 'xs/BSE/aresbse', True) sec_method.x_exciting_xs_bse_angular_momentum_cutoff = self.input_xml_parser.get( 'xs/BSE/lmaxdielt', 14) rgkmax = self.input_xml_parser.get('xs/BSE/rgkmax', None)\",\n \"= xc_functional.get('name_reference', [None, None])[1] sec_electronic.n_spin_channels = self.info_parser.get_number_of_spin_channels() if self._calculation_type == 'volume_optimization': sec_method.x_exciting_volume_optimization =\",\n \"for f in options] else: filenames = [filename] filenames = [f for f\",\n \"} self._moment_keys_mapping = { 'x_exciting_interstitial_moment': ['interstitial'], 'x_exciting_total_MT_moment': ['total moment in muffin-tins'], 'x_exciting_total_moment': ['total\",\n \"data[1], (n_components, n_loss)) # TODO check if format of files are really correct,\",\n \"str_operation=str_to_array, convert=False, unit=ureg.hartree / ureg.bohr), Quantity( 'step', r'Optimization step\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'method',\",\n \"def init_quantities(self): self._quantities = [] def str_to_eigenvalue(val_in): val = val_in.strip().split('\\\\n') kpts = np.array(val[0].split(),\",\n \"for module in ['groundstate', 'hybrids']: sec_scc = parse_configuration(self.info_parser.get(module)) if sec_scc is None: continue\",\n \"[None, None])[1] sec_electronic.n_spin_channels = self.info_parser.get_number_of_spin_channels() if self._calculation_type == 'volume_optimization': sec_method.x_exciting_volume_optimization = True def\",\n \"reshape(get_data('Sx')) eigs_gw_C = reshape(get_data('Sc')) if eigs_gw_C is None: eigs_gw_C = reshape(get_data('Re(Sc)')) sec_gw_eigenvalues.value_correlation =\",\n \"'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge': ['interstitial'], 'x_exciting_total_MT_charge': ['total charge in muffin-tins', 'total in muffin-tins'], 'charge_total':\",\n \"Quantity('number', r'Species : *(\\\\d+)', dtype=np.int32), Quantity('symbol', r'\\\\((\\\\w+)\\\\)'), Quantity('file', r'parameters loaded from *: *(.+)'),\",\n \"= self.input_xml_parser.get( 'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get( 'xs/tddft/ahc', False) sec_method.x_exciting_xs_tddft_anti_resonant_dielectric = self.input_xml_parser.get( 'xs/tddft/aresdf',\",\n \"sub_parser=TextParser(quantities=[ Quantity('number', r'Species : *(\\\\d+)', dtype=np.int32), Quantity('symbol', r'\\\\((\\\\w+)\\\\)'), Quantity('file', r'parameters loaded from *:\",\n \"GW band structure from one of the files: bs_files = ['bandstructure-qp.dat', 'BAND-QP.OUT'] for\",\n \"= self.root.find('./%s/%s' % (self._totaldos_key, self._diagram_key)) self._ndos = len(total_dos) return self._ndos @property def number_of_lm(self):\",\n \"str_to_eigenvalue(val_in): val = val_in.strip().split('\\\\n') kpts = np.array(val[0].split(), dtype=float) keys = val[1].split() eigs =\",\n \"lattice_vectors = self.input_xml_parser.get( 'structure/crystal/basevect', np.eye(3)) species = self.input_xml_parser.get('structure/species/speciesfile') if positions is None or\",\n \"# all files related to quantity at all qpoints files = self.get_exciting_files('%s_%s%s%s.OUT' %\",\n \"repeats=True), Quantity( 'final', r'(?:Convergence targets achieved\\\\. Performing final SCF iteration|Reached self-consistent loops maximum)([\\\\s\\\\S]+?)(\\\\n\",\n \"for specie in species: sec_atoms_group = sec_system.m_create(x_exciting_section_atoms_group) sec_atoms_group.x_exciting_geometry_atom_labels = specie.get('symbol') sec_atoms_group.x_exciting_geometry_atom_number = str(specie.get('number'))\",\n \"= sec_method.m_create(GWMethod) sec_gw.type = 'G0W0' gmaxvr = self.info_parser.get_initialization_parameter('x_exciting_gmaxvr', 0) sec_gw.core_treatment = self.input_xml_parser.get( 'gw/coreflag',\",\n \"== 1 else v[1] if species is None: species = v[0][2] atom_resolved.append(((species, v[1]\",\n \"addition to INFO.OUT return else: return files = self.get_exciting_files(name) if len(files) > 1:\",\n \"sec_method.x_exciting_xs_screening_type = self.input_xml_parser.get( 'xs/screening/screentype', 'full') if self.input_xml_parser.get('xs/BSE') is not None: sec_method.x_exciting_xs_bse_antiresonant = self.input_xml_parser.get(\",\n \"'energy_xc_potential': ['xc potential energy', 'xc potential'], 'energy_electrostatic': ['Hartree energy', 'Hartree'], 'x_exciting_hartree_energy': ['Hartree energy',\",\n \"Method, DFT, Electronic, Smearing, XCFunctional, Functional, GW as GWMethod, Scf, BasisSet ) from\",\n \"self.archive = archive self.logger = logger if logger is not None else logging\",\n \"return sec_scc # groundstate and hybrids calculation for module in ['groundstate', 'hybrids']: sec_scc\",\n \"Quantity( 'positions', rf'\\\\d+ : *({re_float}) *({re_float}) *({re_float})', repeats=True, dtype=np.dtype(np.float64))]))) initialization_quantities.append(Quantity( 'potential_mixing', r'Using ([\\\\w\",\n \"'Hartree'], 'x_exciting_hartree_energy': ['Hartree energy', 'Hartree'], 'x_exciting_electron_nuclear_energy': ['Electron-nuclear energy', 'electron-nuclear '], 'x_exciting_nuclear_nuclear_energy': ['Nuclear-nuclear energy',\",\n \"optical_band_gap is not None: sec_gap.value_optical = optical_band_gap def parse_miscellaneous(self): sec_worfklow = self.archive.m_create(Workflow) sec_worfklow.type\",\n \"is None: continue setattr(msection, name, val) # other metainfo for name in self.info_parser._miscellaneous_keys_mapping.keys():\",\n \"is None: species = self.get_initialization_parameter('species', []) for specie in species: positions_format = specie.get('positions_format',\",\n \"= filename.rsplit('.', 1) filepath = '%s%s' % (target[0], suffix) if target[1:]: filepath =\",\n \"= self.bandstructure_parser.get('band_k_points') nkpts_segment = self.bandstructure_parser.number_of_k_points_per_segment band_seg_labels = self.bandstructure_parser.get('band_segm_labels') for nb in range(len(band_energies[n])): sec_k_band_segment\",\n \"self._band_k_points @property def distances(self): if self._distances is None: data = np.transpose(self.data) self._distances =\",\n \"= filepath self.archive = archive self.logger = logger if logger is not None\",\n \"sec_scc.x_exciting_geometry_optimization_step = optimization_step.get('step') force_convergence = optimization_step.get('force_convergence') if force_convergence is not None: sec_scc.x_exciting_maximum_force_magnitude =\",\n \"0]) rgkmax = self.input_xml_parser.get('xs/screening/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.)\",\n \"'optimization_step', [{}])[0].get('force_convergence', [0., 0.])[-1] sec_geometry_opt.input_force_maximum_tolerance = threshold_force def parse_method(self): sec_run = self.archive.run[-1] sec_method\",\n \"vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=ureg.bohr, repeats=False, convert=False), Quantity( 'lattice_vectors_reciprocal', r'Reciprocal lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=1 /\",\n \"= None for name in names: val = energy_contributions.get(name, None) if val is\",\n \"2: continue energies[v[0].strip()] = float(v[1]) * ureg.hartree return energies self._quantities = [Quantity( 'program_version',\",\n \"None: sec_worfklow.type = 'geometry_optimization' sec_geometry_opt = sec_worfklow.m_create(GeometryOptimization) threshold_force = structure_optimization.get( 'optimization_step', [{}])[0].get('force_convergence', [0.,\",\n \"are added as separate section_k_band res = [] for n in range(len(self.bands)): res_n\",\n \"* (1 / self.energy_unit) elif key == 'energies': return self.energies else: res =\",\n \"[None])[0] if fermi_surface is None: return sec_fermisurface = sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters = self.fermisurf_parser.get('band_parameters', None)\",\n \"Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) initialization_quantities.append(Quantity( 'species', rf'(Species : *\\\\d+\",\n \"'method', r'method\\\\s*=\\\\s*(\\\\w+)', repeats=False, dtype=str), Quantity( 'n_scf_iterations', r'Number of (?:total)* scf iterations\\\\s*\\\\:\\\\s*(\\\\d+)', repeats=False, dtype=int),\",\n \"['Kinetic energy', 'electronic kinetic'], 'energy_coulomb': ['Coulomb energy', 'Coulomb'], 'x_exciting_coulomb_energy': ['Coulomb energy', 'Coulomb'], 'energy_exchange':\",\n \") class ExcitingEvalqpParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] def str_to_eigenvalue(val_in):\",\n \"= self.info_parser.get_scf_threshold(name) if threshold is None: continue metainfo_name = 'x_exciting_scf_threshold_%s_change' % name.split('_')[-2] setattr(sec_method,\",\n \"number of k\\\\-points', None), 'x_exciting_rgkmax': (r'R\\\\^MT\\\\_min \\\\* \\\\|G\\\\+k\\\\|\\\\_max \\\\(rgkmax\\\\)', None), 'x_exciting_species_rtmin': (r'Species with\",\n \"make a parent clas for bandstructure dat and xml self._nspin = None self._nkpts_segment\",\n \"'Fermi-Dirac': 'fermi', 'Extended': 'tetrahedra'} sec_smearing = sec_electronic.m_create(Smearing) smearing_kind = self.info_parser.get_initialization_parameter('smearing_kind') if smearing_kind is\",\n \"'xs/tddft/nwacont', 0) sec_method.x_exciting_xs_tetra = self.input_xml_parser.get( 'xs/tetra/tetradf', False) def _parse_xs_bse(self): sec_run = self.archive.run[-1] #\",\n \"eigs_gw_C = reshape(get_data('Re(Sc)')) sec_gw_eigenvalues.value_correlation = eigs_gw_C sec_gw_eigenvalues.value_xc_potential = reshape(get_data('Vxc')) def _parse_dos_out(self, sec_scc): data\",\n \"= sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.endpoints_labels = band_seg_labels[nb] sec_k_band_segment.energies =\",\n \"self.fermisurf_parser.logger = self.logger self.evalqp_parser.logger = self.logger self.dos_out_parser.logger = self.logger self.bandstructure_dat_parser.logger = self.logger self.band_out_parser.logger\",\n \"self.info_parser.get('structure_optimization') if structure_optimization is not None: sec_worfklow.type = 'geometry_optimization' sec_geometry_opt = sec_worfklow.m_create(GeometryOptimization) threshold_force\",\n \"for i in range(len(files)): data = get_data(files[i]) if not data: sccs.append(None) continue if\",\n \"os.path.isfile(filename): file_ext = default.split('.')[-1] mainfile_base = mainfile.rsplit('.', 1)[0].replace('INFO', '') options = [ f\",\n \"self.input_xml_parser.get( 'xs/tddft/fxcbsesplit', 0.00001, 'hartree') sec_method.x_exciting_xs_tddft_xc_kernel = self.input_xml_parser.get( 'xs/tddft/fxctype', 'RPA') sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution = self.input_xml_parser.get( 'xs/tddft/intraband',\",\n \"= lattice_vectors_reciprocal if len(sec_run.system) > 1: return sec_system for name in self.info_parser._system_keys_mapping.keys(): val\",\n \"epsilon which is not parsed? sccs = [] for quantity in ['EXCITON', 'EPSILON',\",\n \"functions', 1 / ureg.bohr), 'x_exciting_gmaxvr': (r'Maximum \\\\|G\\\\| for potential and density', 1 /\",\n \"is None: if not self.total_dos: return self._nspin = len(self.total_dos) return self._nspin @property def\",\n \"sec_method.x_exciting_xs_bse_xasedge = self.input_xml_parser.get( 'xs/BSE/xasedge', 'K') sec_method.x_exciting_xs_bse_xasspecies = self.input_xml_parser.get( 'xs/BSE/xasspecies', 0) sec_method.x_exciting_xs_bse_xas_number_of_bands = self.input_xml_parser.get(\",\n \"= section.get('forces') if forces is not None: sec_forces = sec_scc.m_create(Forces) sec_forces.total = ForcesEntry(value=forces)\",\n \"rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_screening_rgkmax = rgkmax sec_method.x_exciting_xs_screening_type = self.input_xml_parser.get(\",\n \"= self.input_xml_parser.get('xs/tddft/aresdf', True) file_ext_list = [ 'TET' if tetradf else None, 'AC' if\",\n \"bands_atom = self.root.findall('./*/%s' % self._atom_key) for band in bands_atom: self._bands.append(band.findall('./%s' % self._band_key)) return\",\n \"unit properties['atom_resolved'] = atom_resolved return properties def str_to_quantity_tolerances(val_in): return val_in.strip().replace('(', '').replace(')', '').split() def\",\n \"# See https://nomad-lab.eu for further info. # # Licensed under the Apache License,\",\n \"= np.reshape( data[3], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_re = np.reshape( data[4], (n_components, n_excitons)) sec_scc.x_exciting_xs_bse_exciton_amplitude_im =\",\n \"sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field = data[1:3] elif quantity == 'LOSS' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_loss_function_local_field =\",\n \"sec_scc.x_exciting_xs_tddft_loss_function_no_local_field = data[1] elif quantity == 'SIGMA' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_sigma_local_field =\",\n \"energy_fermi nkpts_segment = self.band_out_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints =\",\n \"= res * (1 / self.energy_unit) elif 'partial' in key: if not self.partial_dos:\",\n \"sec_method.x_exciting_xs_bse_antiresonant = self.input_xml_parser.get( 'xs/BSE/aresbse', True) sec_method.x_exciting_xs_bse_angular_momentum_cutoff = self.input_xml_parser.get( 'xs/BSE/lmaxdielt', 14) rgkmax = self.input_xml_parser.get('xs/BSE/rgkmax',\",\n \"self.archive.m_create(Run) sec_run.program = Program( name='exciting', version=self.info_parser.get('program_version', '').strip()) # method goes first since reference\",\n \"for band in bands]) if self._energy_unit: band_energy = band_energy * self._energy_unit self._band_energies.append(band_energy) start\",\n \"metainfo_name = 'x_exciting_scf_threshold_%s_change' % name.split('_')[-2] setattr(sec_method, metainfo_name, threshold) # additionally, set threshold to\",\n \"(n_components, n_epsilon)) * ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re = np.reshape( data[1], (n_components, n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im = np.reshape(\",\n \"= int(self.input_xml_parser.get( 'gw/mixbasis/lmaxmb', 3)) sec_gw.mixed_basis_tolerance = self.input_xml_parser.get( 'gw/mixbasis/epsmb', 0.0001) gmb = self.input_xml_parser.get('gw/mixbasis/gmb', 1.0)\",\n \"_get_dos(self, diagram): dos = np.array( [point.attrib.get(self._dos_key) for point in diagram], dtype=float) return dos\",\n \"r\\\\_s', ureg.bohr), 'x_exciting_empty_states': ('Number of empty states', None), 'x_exciting_valence_states': ('Total number of valence\",\n \"in files if f.endswith('QMT%s.OUT' % str(i + 1).rjust(3, '0'))] for f in files_q:\",\n \"'x_exciting_valence_states': ('Total number of valence states', None), 'x_exciting_hamiltonian_size': ('Maximum Hamiltonian size', None), 'x_exciting_pw':\",\n \"None: continue metainfo_name = 'x_exciting_scf_threshold_%s_change' % name.split('_')[-2] setattr(sec_method, metainfo_name, threshold) # additionally, set\",\n \"= int(max(data[1])) bands = data[6:] bands = np.reshape(bands, ( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, n_kpoints)) self._band_energies\",\n \"= res class ExcitingFermiSurfaceBxsfParser(TextParser): def __init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append(\",\n \"in self._convergence_keys_mapping.items(): scf_quantities.append(Quantity( name, r'%s\\\\s*\\\\:*\\\\s*([\\\\(\\\\)\\\\d\\\\.\\\\-\\\\+Ee ]+)' % key_unit[0], str_operation=str_to_quantity_tolerances, unit=key_unit[1], repeats=False)) module_quantities =\",\n \"/ ureg.hartree), Quantity( 'charge_contributions', r'(?:Charges|Electron charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False), Quantity( 'moment_contributions', r'(?:Moments\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n\",\n \"if self._band_energies is None: data = np.transpose(self.data) n_kpoints = np.where(data[0] == data[0][0])[0][1] bands\",\n \"names in self._moment_keys_mapping.items(): val = None for name in names: val = moment_contributions.get(name,\",\n \"Quantity( 'positions_format', r'imized atomic positions\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)',\",\n \"not None: self._energies = self._energies * self.energy_unit return self._energies def _get_dos(self, diagram): dos\",\n \"setattr(msection, name, val) # other metainfo for name in self.info_parser._miscellaneous_keys_mapping.keys(): val = iteration.get(name)\",\n \"bs_files: if self.file_exists(fname): gw_files.append(fname) break for f in gw_files: self.parse_file(f, sec_scc) frequency_data =\",\n \"in f and mainfile_base in f] options = [f for f in options\",\n \"properties input_file = self.get_exciting_files('input.xml') if not input_file: return self.input_xml_parser.mainfile = input_file[0] xstype =\",\n \"(r'G\\\\-vector grid sizes', None), 'x_exciting_gvector_total': (r'Total number of G\\\\-vectors', None), 'x_exciting_lmaxapw': (r' APW\",\n \"optimization_quantities = [ Quantity( 'atomic_positions', r'(Atomic positions at this step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format',\",\n \"TODO I am not sure about format for spin-polarized case! I assume it\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"'BAND.OUT', 'bandstructure.dat'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break for f in\",\n \"filepath self.archive = archive self.logger = logger if logger is not None else\",\n \"range(1, len(self.distances)): if self.distances[i] == self.distances[i - 1]: self._nkpts_segment.append(count) count = 1 else:\",\n \"for key, names in self._electron_charge_keys_mapping.items(): val = None for name in names: val\",\n \"in bands]) if self._energy_unit: band_energy = band_energy * self._energy_unit self._band_energies.append(band_energy) start = end\",\n \"xc_functional is not None: sec_xc_functional.name = xc_functional.get('name_reference', [None, None])[0] sec_xc_functional.reference = xc_functional.get('name_reference', [None,\",\n \"ureg.hartree return res sec_eigenvalues = sec_scc.m_create(BandEnergies) sec_eigenvalues.kpoints = self.eigval_parser.get('k_points') sec_eigenvalues.occupations = get_data('occupancies') sec_eigenvalues.energies\",\n \"stopped', sub_parser=TextParser(quantities=module_quantities), repeats=False)) optimization_quantities = [ Quantity( 'atomic_positions', r'(Atomic positions at this step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n',\",\n \"False def _parse_dos(self, sec_scc): if self.dos_parser.get('totaldos', None) is None: return # Get fermi\",\n \"final is None: # get it from last scf_iteration or optimization_step final =\",\n \"np.array([np.transpose(band)[start:end] for band in bands]) if self._energy_unit: band_energy = band_energy * self._energy_unit self._band_energies.append(band_energy)\",\n \"sec_gap = sec_scc.eigenvalues[-1].m_create(BandGap) if fundamental_band_gap is not None: sec_gap.value_fundamental = fundamental_band_gap optical_band_gap =\",\n \"= self.band_out_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.value\",\n \"self._distances = dist[:n_k_points] return self._distances @property def number_of_spin_channels(self): if self._nspin is None: self._nspin\",\n \"self._nspin = len(self.total_dos) return self._nspin @property def number_of_atoms(self): if self._natoms is None: partial_dos\",\n \"will read only first!') self.info_gw_parser.mainfile = gw_info_files[0] fermi_energy = self.info_gw_parser.get('fermi_energy', None) if fermi_energy\",\n \"is not None else section.get('positions') if positions is None: species = self.get_initialization_parameter('species', [])\",\n \"and bandstructure super().__init__(None) self._distance_key = 'distance' self._coord_key = 'coord' self._energy_key = 'eval' self._vertex_key\",\n \"np import os import re import logging from nomad.units import ureg from nomad.parsing.file_parser\",\n \"['Effective potential energy'], 'x_exciting_coulomb_potential_energy': ['Coulomb potential energy', 'Coulomb potential'], 'energy_xc_potential': ['xc potential energy',\",\n \"self._quantities = [] self._quantities.append( Quantity( 'fermi_energy', r'Fermi Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*', unit=ureg.hartree, repeats=False)) def str_to_band_parameters(val_in): val\",\n \"self.file_exists(fname): gw_files.append(fname) break for f in gw_files: self.parse_file(f, sec_scc) frequency_data = self.info_gw_parser.get('frequency_data', None)\",\n \"lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=1 / ureg.bohr, repeats=False, convert=False), ] self._system_keys_mapping = { 'x_exciting_unit_cell_volume':\",\n \"positions sec_atoms.labels = atom_labels sec_atoms.periodic = [True] * 3 # TODO confirm no\",\n \"unit=1 / ureg.hartree), Quantity( 'charge_contributions', r'(?:Charges|Electron charges\\\\s*\\\\:*\\\\s*)([\\\\-\\\\s\\\\w\\\\.\\\\:\\\\(\\\\)]+?)\\\\n *[A-Z\\\\+]', str_operation=str_to_atom_properties_dict, repeats=False, convert=False), Quantity( 'moment_contributions',\",\n \"def str_to_array(val_in): val = [v.split(':')[-1].split() for v in val_in.strip().split('\\\\n')] val = val[0] if\",\n \"if name.startswith('dos') and name.endswith('xml'): parser = self.dos_parser parser_function = self._parse_dos elif name.startswith('bandstructure') and\",\n \"sec_atoms.lattice_vectors_reciprocal = lattice_vectors_reciprocal if len(sec_run.system) > 1: return sec_system for name in self.info_parser._system_keys_mapping.keys():\",\n \"'unit' self._energy_unit = kwargs.get('energy_unit', None) self._units_mapping = dict(hartree=ureg.hartree) def init_parameters(self): self._ndos = None\",\n \"BandOutParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None)\",\n \"res_n = [] start = 0 band_energies = np.zeros(( self.number_of_spin_channels, self.number_of_band_segment_eigenvalues, len(self.distances)), dtype=float)\",\n \"'gw/mixbasis/epsmb', 0.0001) gmb = self.input_xml_parser.get('gw/mixbasis/gmb', 1.0) sec_gw.mixed_basis_gmax = gmb * gmaxvr pwm =\",\n \"None self._vertices = None self._distances = None self._band_energies = None self._band_k_points = None\",\n \"= self.archive.run[-1] # two versions of gw info files gw_info_files = ['GW_INFO.OUT', 'GWINFO.OUT']\",\n \"parser = self.dos_parser parser_function = self._parse_dos elif name.startswith('bandstructure') and name.endswith('xml'): parser = self.bandstructure_parser\",\n \"return np.array([d[0][:3] for d in data]) elif key == 'Znk': return np.array([d[1].get(key, None)\",\n \"self.fermisurf_parser.get('band_parameters', None) if band_parameters is not None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface = band_parameters[0] sec_fermisurface.x_exciting_number_of_mesh_points_fermi_surface = np.product(band_parameters[1])\",\n \"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"init_quantities(self): self._quantities = [] def str_to_eigenvalue(val_in): val = val_in.strip().split('\\\\n') kpts = np.array(val[0].split(), dtype=float)\",\n \"the only thing that we can do is to assume that the first\",\n \"sec_dft.m_create(XCFunctional) for name in xc_functional_names: if name is None: continue if '_X_' in\",\n \"quantity == 'EPSILON' and ext == 'FXC': sec_scc = sec_run.m_create(Calculation) sec_scc.x_exciting_xs_tddft_number_of_epsilon_values = len(data[0][0][0])\",\n \"= lattice_vectors * ureg.bohr atoms = self.input_xml_parser.get('structure/species/atom') atom_labels = [] for n in\",\n \"data is None: return # Get fermi energy: it is used to un-shift\",\n \"def band_energies(self): if self._band_energies is None: if self.data is None: return data =\",\n \"self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): # TODO make\",\n \"= self.input_xml_parser.get('xs/tetra/tetradf', None) nwacont = self.input_xml_parser.get('xs/tddft/nwacont', None) aresdf = self.input_xml_parser.get('xs/tddft/aresdf', True) file_ext_list =\",\n \"not None: energy_fermi = sec_scc.energy.fermi energy_fermi = (energy_fermi.magnitude * ureg.joule).to('hartree') sec_k_band = sec_scc.m_create(BandStructure,\",\n \"self.fermisurf_parser.get('fermi_energy', None) if fermi_energy is not None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface = fermi_energy sec_fermisurface.x_exciting_values_fermi_surface = fermi_surface\",\n \"= msection.m_create(x_exciting_section_MT_charge_atom) sec_mt_charge_atom.x_exciting_MT_charge_atom_index = n + 1 sec_mt_charge_atom.x_exciting_MT_charge_atom_symbol = val[n][0] sec_mt_charge_atom.x_exciting_MT_charge_atom_value = val[n][1]\",\n \"import ( Method, DFT, Electronic, Smearing, XCFunctional, Functional, GW as GWMethod, Scf, BasisSet\",\n \"smearing_kind_map[smearing_kind] sec_smearing.kind = smearing_kind smearing_width = self.info_parser.get_initialization_parameter('smearing_width') if smearing_width is not None: smearing_width\",\n \"self._neigs_segment = len(self.bands[0]) // self.number_of_spin_channels return self._neigs_segment def parse(self, key): if self._results is\",\n \"fundamental_band_gap optical_band_gap = self.info_gw_parser.get('optical_band_gap', None) if optical_band_gap is not None: sec_gap.value_optical = optical_band_gap\",\n \"xc_functional_names: # simply write parameters xc_functional = self.info_parser.get('initialization', {}).get('xc_functional') if xc_functional is not\",\n \"self.parse_system(section) if sec_system is not None: sec_scc.system_ref = sec_system sec_scc.method_ref = sec_run.method[-1] return\",\n \"correct, i.e. columns are supposed # to be what they are. What is\",\n \"def number_of_spin_channels(self): if self._nspin is None: if not self.total_dos: return self._nspin = len(self.total_dos)\",\n \"in self.total_dos[0]]) if self.energy_unit is not None: self._energies = self._energies * self.energy_unit return\",\n \"is used to un-shift the DOS to # the original scale in which\",\n \"'spin-unpolarised') n_spin = 1 if spin_treatment.lower() == 'spin-unpolarised' else 2 return n_spin def\",\n \"+ 1 return self._nlm @property def total_dos(self): if self._total_dos is None: self._total_dos =\",\n \"iterations scf_iterations = section.get('scf_iteration', []) for scf_iteration in scf_iterations: sec_scf_iteration = sec_scc.m_create(ScfIteration) parse_scf(scf_iteration,\",\n \"= self.band_out_parser parser_function = self._parse_band_out elif name.startswith('input') and name.endswith('xml'): parser = self.input_xml_parser if\",\n \"me! if metainfo_name == 'x_exciting_scf_threshold_energy_change': sec_method.scf = Scf(threshold_energy_change=threshold) xc_functional_names = self.info_parser.get_xc_functional_name() if not\",\n \"files = self.get_exciting_files( '%s_BSE%s_SCR%s.OUT' % (name, bse_type, scr_type)) bse_files.append(files) return bse_files def get_data(files):\",\n \"= True self.data_clathrate_parser.mainfile = clathrate_file[0] if self.data_clathrate_parser.data: data = np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates = np.transpose(\",\n \"int(max(data[0])) return self._neigs_segment class BandOutParser(DataTextParser): def __init__(self, **kwargs): super().__init__(**kwargs) self._nspin = kwargs.get('nspin', None)\",\n \"reported. energy_fermi = 0.0 * ureg.hartree if sec_scc.energy is not None: energy_fermi =\",\n \"suffix) if target[1:]: filename = '%s.%s' % (filename, target[1]) filename = os.path.join(self.info_parser.maindir, filename)\",\n \"about format for spin-polarized case! I assume it is # energy dos_up dos_down\",\n \"# This is the case when there is a mismatch between files self.logger.warn(\",\n \"v[1] if species is None: species = v[0][2] atom_resolved.append(((species, v[1] * unit))) else:\",\n \"np.transpose(np.vstack(data)) n_epsilon = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_energy_points = n_epsilon sec_scc.x_exciting_xs_bse_epsilon_energies = np.reshape( data[0],\",\n \"for key, names in self._moment_keys_mapping.items(): val = None for name in names: val\",\n \"{}).get('species', []) labels = [] for specie in species: labels += [specie.get('symbol')] *\",\n \"self.input_xml_parser.get( 'xs/tddft/drude', [0., 0.]) sec_method.x_exciting_xs_tddft_split_parameter = self.input_xml_parser.get( 'xs/tddft/fxcbsesplit', 0.00001, 'hartree') sec_method.x_exciting_xs_tddft_xc_kernel = self.input_xml_parser.get(\",\n \"if fundamental_band_gap is None: fundamental_band_gap = self.info_gw_parser.get('fundamental_band_gap', None) sec_gap = sec_scc.eigenvalues[-1].m_create(BandGap) if fundamental_band_gap\",\n \"r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr),\",\n \"# you may not use this file except in compliance with the License.\",\n \"first!') self.info_gw_parser.mainfile = gw_info_files[0] fermi_energy = self.info_gw_parser.get('fermi_energy', None) if fermi_energy is not None:\",\n \"= ExcitingEvalqpParser() self.dos_out_parser = DataTextParser() self.bandstructure_dat_parser = BandstructureDatParser(energy_unit=ureg.hartree) self.band_out_parser = BandOutParser(energy_unit=ureg.hartree) self.info_gw_parser =\",\n \"super().__init__(None) self._distance_key = 'distance' self._coord_key = 'coord' self._energy_key = 'eval' self._vertex_key = 'vertex'\",\n \"rgkmax sec_method.x_exciting_xs_bse_sciavbd = self.input_xml_parser.get( 'xs/BSE/sciavbd', True) sec_method.x_exciting_xs_bse_sciavqbd = self.input_xml_parser.get( 'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd =\",\n \"to be two versions, input_gw.xml and input-gw.xml for f in ['input_gw.xml', 'input-gw.xml', 'input.xml']:\",\n \"'electron-nuclear '], 'x_exciting_nuclear_nuclear_energy': ['Nuclear-nuclear energy', 'nuclear-nuclear'], 'x_exciting_madelung_energy': ['Madelung energy', 'Madelung'], 'x_exciting_core_electron_kinetic_energy': ['Core-electron kinetic\",\n \"res = [] for n in range(len(self.bands)): res_n = [] start = 0\",\n \"None: continue setattr(msection, name, val) # other metainfo for name in self.info_parser._miscellaneous_keys_mapping.keys(): val\",\n \"mainfile.strip('INFO.OUT') target = default.rsplit('.', 1) filename = '%s%s' % (target[0], suffix) if target[1:]:\",\n \"[]) return name @property def n_optimization_steps(self): return len(self.get('structure_optimization', {}).get('optimization_step', [])) def get_number_of_spin_channels(self): spin_treatment\",\n \"= self.input_xml_parser.get( 'xs/tddft/fxcbsesplit', 0.00001, 'hartree') sec_method.x_exciting_xs_tddft_xc_kernel = self.input_xml_parser.get( 'xs/tddft/fxctype', 'RPA') sec_method.x_exciting_xs_tddft_finite_q_intraband_contribution = self.input_xml_parser.get(\",\n \"key, names in self._energy_keys_mapping.items(): val = None for name in names: val =\",\n \"% (filepath, target[1]) filepath = os.path.join(self.info_parser.maindir, filepath) if os.path.isfile(filepath) and os.access(filepath, os.F_OK): return\",\n \"in key: if not self.total_dos: return res = np.zeros((self.number_of_spin_channels, self.number_of_dos)) for i in\",\n \"threshold is None: continue metainfo_name = 'x_exciting_scf_threshold_%s_change' % name.split('_')[-2] setattr(sec_method, metainfo_name, threshold) #\",\n \"it is # energy dos_up dos_down nspin = self.info_parser.get_number_of_spin_channels() sec_dos = sec_scc.m_create(Dos, Calculation.dos_electronic)\",\n \"partial_dos(self): if self._partial_dos is None: self._partial_dos = self.root.findall('./%s/%s' % (self._partialdos_key, self._diagram_key)) return self._partial_dos\",\n \"r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) for name, key_unit in self._method_keys_mapping.items(): initialization_quantities.append( Quantity(\",\n \"xc_functional is None: return [] name = xc_functional_map.get(xc_functional.type, []) return name @property def\",\n \"if self.distances[i] == self.distances[i - 1]: self._nkpts_segment .append(count) count = 1 else: count\",\n \"parent class for dos and bandstructure super().__init__(None) self._distance_key = 'distance' self._coord_key = 'coord'\",\n \"aresdf = self.input_xml_parser.get('xs/tddft/aresdf', True) file_ext_list = [ 'TET' if tetradf else None, 'AC'\",\n \"= kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): self._nspin = None self._nkpts_segment\",\n \"self.input_xml_parser.get( 'xs/BSE/sciavbd', True) sec_method.x_exciting_xs_bse_sciavqbd = self.input_xml_parser.get( 'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get( 'xs/BSE/sciavqhd', False)\",\n \"= None @property def distances(self): if self._distances is None: if not self.bands: return\",\n \"is not parsed? sccs = [] for quantity in ['EXCITON', 'EPSILON', 'SIGMA', 'LOSS']:\",\n \"init_parameters(self): self._nspin = None self._nkpts_segment = None self._neigs_segment = None self._bands = None\",\n \"sec_scc): n_components = len(data) data = np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components = n_components n_excitons = len(data[0])\",\n \"self.archive.m_create(Workflow) sec_worfklow.type = 'single_point' structure_optimization = self.info_parser.get('structure_optimization') if structure_optimization is not None: sec_worfklow.type\",\n \"License for the specific language governing permissions and # limitations under the License.\",\n \"val_l = self.partial_dos[i].attrib.get(self._l_key, None) val_m = self.partial_dos[i].attrib.get(self._m_key, None) if val_l is None or\",\n \"if key == 'x_exciting_section_MT_moment_atom': for n in range(len(val)): sec_mt_moment_atom = msection.m_create(x_exciting_section_MT_moment_atom) sec_mt_moment_atom.x_exciting_MT_moment_atom_index =\",\n \"range(len(files)): parser.mainfile = files[n] parser_function(section) # free up memory parser.mainfile = None def\",\n \"np.array([d[0][:3] for d in data]) elif key == 'Znk': return np.array([d[1].get(key, None) for\",\n \"np.column_stack((np.arange(len(partialdos)), np.zeros(len(partialdos), dtype=np.int32))) for lm in range(len(partialdos)): for spin in range(len(partialdos[lm])): for atom\",\n \"'smearing_width': ('Smearing width', None)} for name, key_unit in self._system_keys_mapping.items(): initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n'\",\n \"self.get_exciting_files( '%s_BSE%s_SCR%s.OUT' % (name, bse_type, scr_type)) bse_files.append(files) return bse_files def get_data(files): data =\",\n \"= self.dos_parser.get('totaldos') * volume.to('m**3').magnitude for spin in range(len(totaldos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin\",\n \"fermi_energy is not None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface = fermi_energy sec_fermisurface.x_exciting_values_fermi_surface = fermi_surface def _parse_evalqp(self, sec_scc):\",\n \"= self.dos_parser.get('partialdos') if partialdos is None: return partialdos = partialdos.to('1/joule').magnitude lm_values = np.column_stack((np.arange(len(partialdos)),\",\n \"None self._neigs_segment = None self._vertices = None self._distances = None self._band_energies = None\",\n \"= self.get_exciting_files('str.out') if clathrate_file: sec_system.x_exciting_clathrates = True self.data_clathrate_parser.mainfile = clathrate_file[0] if self.data_clathrate_parser.data: data\",\n \"is not None: res = res * (1 / self.energy_unit) elif 'partial' in\",\n \"in val.split('\\\\n')], dtype=float) val = np.transpose(val) occs = val[-1] eigs = val[-2] nspin\",\n \"'], 'x_exciting_nuclear_nuclear_energy': ['Nuclear-nuclear energy', 'nuclear-nuclear'], 'x_exciting_madelung_energy': ['Madelung energy', 'Madelung'], 'x_exciting_core_electron_kinetic_energy': ['Core-electron kinetic energy',\",\n \"'gw/mixbasis/lmaxmb', 3)) sec_gw.mixed_basis_tolerance = self.input_xml_parser.get( 'gw/mixbasis/epsmb', 0.0001) gmb = self.input_xml_parser.get('gw/mixbasis/gmb', 1.0) sec_gw.mixed_basis_gmax =\",\n \"not None: band_energy = band_energy * self._energy_unit res_n.append(band_energy) start = end res.append(res_n) elif\",\n \"= self.bandstructure_parser parser_function = self._parse_bandstructure elif name.startswith('EIGVAL') and name.endswith('OUT'): parser = self.eigval_parser parser_function\",\n \"'volume_optimization': sec_method.x_exciting_volume_optimization = True def parse_scc(self, section): sec_run = self.archive.run[-1] final = section\",\n \"is None: species = v[0][2] atom_resolved.append(((species, v[1] * unit))) else: vi = [float(vii)\",\n \"positions at this step\\\\s*\\\\([\\\\s\\\\S]+?)\\\\n\\\\n', sub_parser=TextParser(quantities=[ Quantity( 'positions_format', r'Atomic positions at this step\\\\s*\\\\(([a-z]+)\\\\)'), Quantity(\",\n \"repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces', r'Total atomic forces including\",\n \"+ 1 sec_mt_moment_atom.x_exciting_MT_moment_atom_symbol = val[n][0] sec_mt_moment_atom.x_exciting_MT_moment_atom_value = val[n][1] else: setattr(msection, key, val) #\",\n \"convert=False)] self._miscellaneous_keys_mapping = { 'x_exciting_gap': (r'Estimated fundamental gap', ureg.hartree), 'time': (r'Wall time \\\\(seconds\\\\)',\",\n \"permissions and # limitations under the License. # import numpy as np import\",\n \"None) def init_parameters(self): # TODO make a parent clas for bandstructure dat and\",\n \"sec_method.x_exciting_xs_scissor = self.input_xml_parser.get('xs/scissor', 0.0) sec_method.x_exciting_xs_vkloff = self.input_xml_parser.get('xs/vkloff', [0., 0., 0.]) # TODO I\",\n \"reshape(get_data('Sc')) if eigs_gw_C is None: eigs_gw_C = reshape(get_data('Re(Sc)')) sec_gw_eigenvalues.value_correlation = eigs_gw_C sec_gw_eigenvalues.value_xc_potential =\",\n \"is None or atom_labels is None: return sec_system = sec_run.m_create(System) sec_atoms = sec_system.m_create(Atoms)\",\n \"get_scf_quantity(self, name): n_scf = len(self.get('energy_total_scf_iteration', [])) quantity = self.get('%s_scf_iteration' % name) if quantity\",\n \"6 return self._nspin @property def number_of_k_points_per_segment(self): if self._nkpts_segment is None: self._nkpts_segment = []\",\n \"sec_run = self.archive.run[-1] final = section if section.get('energy_total') is not None else section.get('final')\",\n \"[1, 1, 1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk', [1, 1, 1]) rgkmax = self.input_xml_parser.get('xs/rgkmax', None)\",\n \"Authors. # # This file is part of NOMAD. # See https://nomad-lab.eu for\",\n \"nspin)) for v in data]) res = res.reshape((len(res), len(data), len(res[0]) // len(data))) if\",\n \"self._l_key = 'l' self._m_key = 'm' self._energy_key = 'e' self._dos_key = 'dos' self._unit_key\",\n \"for bandstructure dat and xml self._nspin = None self._nkpts_segment = None self._neigs_segment =\",\n \"self.info_parser.get_initialization_parameter('x_exciting_gmaxvr', 0) sec_gw.core_treatment = self.input_xml_parser.get( 'gw/coreflag', 'all') sec_gw.polarizability_number_of_empty_states = int( self.input_xml_parser.get('gw/nempty', 0)) sec_gw.ngridq\",\n \"dist[:n_k_points] return self._distances @property def number_of_spin_channels(self): if self._nspin is None: self._nspin = np.shape(np.transpose(self.data)[1:])[0]\",\n \"['atom_resolved'] } self._moment_keys_mapping = { 'x_exciting_interstitial_moment': ['interstitial'], 'x_exciting_total_MT_moment': ['total moment in muffin-tins'], 'x_exciting_total_moment':\",\n \"*: *({re_float})', dtype=np.float64, unit=ureg.electron_mass), Quantity('muffin_tin_radius', rf'muffin-tin radius *: *({re_float})', dtype=np.float64, unit=ureg.bohr), Quantity('radial_points', rf'radial\",\n \"[], []] for i in range(len(qpoints)): data_q = [] files_q = [f for\",\n \"get_files(name): bse_types = ['IP', 'singlet', 'triplet', 'RPA'] scr_types = ['full', 'diag', 'noinvdiag', 'longrange']\",\n \"Quantity( 'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Time', repeats=False, str_operation=str_to_array, convert=False, unit=ureg.hartree\",\n \"sec_k_band.energy_fermi = energy_fermi nkpts_segment = self.band_out_parser.number_of_k_points_per_segment for nb in range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies)\",\n \"this optimization step\\\\s*:\\\\s*[\\\\d\\\\.]+ seconds))', sub_parser=TextParser(quantities=optimization_quantities), repeats=True), Quantity( 'final', r'Force convergence target achieved([\\\\s\\\\S]+?Opt)', sub_parser=TextParser(quantities=scf_quantities),\",\n \"self.input_xml_parser.get('structure/species/speciesfile') if positions is None or lattice_vectors is None or species is None:\",\n \"[v.strip() for v in x.split(':')]), Quantity( 'parameters', r'\\\\n *(.+?:.+)', repeats=True, str_operation=lambda x: [v.strip()\",\n \"elif key == 'charge_total': pass else: setattr(msection, key, val) # moment contributions moment_contributions\",\n \"ForcesEntry, ScfIteration, BandGap ) from nomad.datamodel.metainfo.workflow import Workflow, GeometryOptimization from .metainfo.exciting import x_exciting_section_MT_charge_atom,\",\n \"int(self.input_xml_parser.get( 'gw/selfenergy/npol', 0)) sec_gw.self_energy_c_number_of_empty_states = int(self.input_xml_parser.get( 'gw/selfenergy/nempty', 0)) sec_gw.self_energy_singularity_treatment = self.input_xml_parser.get( 'gw/selfenergy/singularity', 'mpd')\",\n \"f correlation = self.input_xml_parser.get('libxc/correlation', None) xc_functional_names.append(correlation) exchange = self.input_xml_parser.get('libxc/exchange', None) xc_functional_names.append(exchange) sec_xc_functional =\",\n \"energy_fermi is None: continue energy_fermi = energy_fermi.to(\\\"hartree\\\") sec_k_band = sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi =\",\n \"* volume.to('m**3').magnitude for spin in range(len(totaldos)): sec_dos_values = sec_dos.m_create(DosValues, Dos.total) sec_dos_values.spin = spin\",\n \"xs input xml file sec_method = sec_run.m_create(Method) sec_method_ref = self.archive.run[-1].method[0] sec_method.starting_method_ref = sec_method_ref\",\n \"if key == 'eigenvalues': res = res * ureg.hartree return res sec_eigenvalues =\",\n \"See https://nomad-lab.eu for further info. # # Licensed under the Apache License, Version\",\n \"= self.input_xml_parser.get( 'xs/tddft/aresfxc', True) sec_method.x_exciting_xs_tddft_drude = self.input_xml_parser.get( 'xs/tddft/drude', [0., 0.]) sec_method.x_exciting_xs_tddft_split_parameter = self.input_xml_parser.get(\",\n \"spin sec_dos_values.value = totaldos[spin] partialdos = self.dos_parser.get('partialdos') if partialdos is None: return partialdos\",\n \"'positions_format', r'Atomic positions at this step\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions',\",\n \"*({re_float}) *({re_float})', repeats=True, dtype=np.dtype(np.float64))]))) initialization_quantities.append(Quantity( 'potential_mixing', r'Using ([\\\\w ]+) potential mixing', repeats=False, flatten=False)\",\n \"Quantity( 'n_scf_iterations', r'Number of (?:total)* scf iterations\\\\s*\\\\:\\\\s*(\\\\d+)', repeats=False, dtype=int), Quantity( 'force_convergence', r'Maximum force\",\n \"= ExcitingFermiSurfaceBxsfParser() self.evalqp_parser = ExcitingEvalqpParser() self.dos_out_parser = DataTextParser() self.bandstructure_dat_parser = BandstructureDatParser(energy_unit=ureg.hartree) self.band_out_parser =\",\n \"== 'SIGMA' and ext == 'FXC': sec_scc.x_exciting_xs_tddft_sigma_local_field = data[1:3] elif quantity == 'SIGMA'\",\n \"(1 / self.energy_unit) elif 'partial' in key: if not self.partial_dos: return res =\",\n \"self._band_key = 'band' self._atom_key = 'atom' self._nspin = kwargs.get('nspin', None) self._energy_unit = kwargs.get('energy_unit',\",\n \"< 2: continue energies[v[0].strip()] = float(v[1]) * ureg.hartree return energies self._quantities = [Quantity(\",\n \"if not self.total_dos: return self._nspin = len(self.total_dos) return self._nspin @property def number_of_atoms(self): if\",\n \"Quantity( 'k_points', r'\\\\s*\\\\d+\\\\s*([\\\\d\\\\.Ee\\\\- ]+):\\\\s*k\\\\-point', repeats=True)) def str_to_eigenvalues(val_in): val = val_in[:val_in.rfind('\\\\n \\\\n')].strip() val =\",\n \"if sec_system is not None: sec_scc.system_ref = sec_system sec_scc.method_ref = sec_run.method[-1] return sec_scc\",\n \"in self._moment_keys_mapping.items(): val = None for name in names: val = moment_contributions.get(name, None)\",\n \"Quantity( 'fundamental_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'optical_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Optical BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) )\",\n \"for v in val.split('\\\\n')], dtype=float) val = np.transpose(val) occs = val[-1] eigs =\",\n \"sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.energies = band_energies[nb] + energy_fermi def _parse_band_out(self,\",\n \"val is None: continue if name == 'time': msection.time_calculation = val else: setattr(msection,\",\n \"= nkpts_segment[nb] sec_k_band_segment.value = band_energies[nb] + energy_fermi def parse_file(self, name, section): # TODO\",\n \"self._units_mapping.get(axis.attrib.get(self._unit_key).lower(), 1) return self._energy_unit @property def number_of_spin_channels(self): if self._nspin is None: if not\",\n \"= np.array([v.split() for v in val[3:6]], dtype=float) return [nbands, mesh, origin, vector] self._quantities.append(\",\n \"bs_files = ['dos.xml', 'TDOS.OUT'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break #\",\n \"= sec_scc.m_create(BandStructure, Calculation.band_structure_electronic) sec_k_band.energy_fermi = energy_fermi nkpts_segment = self.band_out_parser.number_of_k_points_per_segment for nb in range(len(band_energies)):\",\n \"vi = vi[0] if len(vi) == 1 else vi properties[v[0].strip()] = vi *\",\n \"sec_dos.n_energies = self.dos_parser.number_of_dos sec_dos.energies = self.dos_parser.energies + energy_fermi volume = self.info_parser.get_unit_cell_volume() totaldos =\",\n \"xc_functional = self.get('initialization', {}).get('xc_functional', None) if xc_functional is None: return [] name =\",\n \"None or atom_labels is None: return sec_system = sec_run.m_create(System) sec_atoms = sec_system.m_create(Atoms) sec_atoms.positions\",\n \"d in data]) elif key == 'Znk': return np.array([d[1].get(key, None) for d in\",\n \"self._diagram_key)) return self._total_dos @property def partial_dos(self): if self._partial_dos is None: self._partial_dos = self.root.findall('./%s/%s'\",\n \"range(len(self.partial_dos)): spin = self.partial_dos[i].attrib.get(self._nspin_key, None) if spin is None: spin = (i %\",\n \"sec_scc.m_create(x_exciting_section_fermi_surface) band_parameters = self.fermisurf_parser.get('band_parameters', None) if band_parameters is not None: sec_fermisurface.x_exciting_number_of_bands_fermi_surface = band_parameters[0]\",\n \"range(len(band_energies)): sec_k_band_segment = sec_k_band.m_create(BandEnergies) sec_k_band_segment.n_kpoints = nkpts_segment[nb] sec_k_band_segment.kpoints = band_k_points[nb] sec_k_band_segment.energies = band_energies[nb]\",\n \"initialization_quantities.append( Quantity( name, r'%s\\\\s*:\\\\s*([\\\\s\\\\S]*?)\\\\n' % key_unit[0], unit=key_unit[1], repeats=False) ) initialization_quantities.append(Quantity( 'species', rf'(Species :\",\n \"in ['FXC', 'NLF_FXC']: data = get_data(quantity, ext) if not data[0]: continue if quantity\",\n \"== 'time': msection.time_calculation = val else: setattr(msection, name, val) # energy, moment, charge\",\n \"ureg.hartree sec_scc.x_exciting_xs_bse_sigma_re = np.reshape( data[1], (n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im = np.reshape( data[2], (n_components, n_sigma))\",\n \"(r'Species with R\\\\^MT\\\\_min', None), 'x_exciting_gkmax': (r'Maximum \\\\|G\\\\+k\\\\| for APW functions', 1 / ureg.bohr),\",\n \"self.data_xs_parser.data is None: continue data.append(self.data_xs_parser.data) return data def parse_exciton(data, sec_scc): n_components = len(data)\",\n \"np.transpose(np.vstack(data)) sec_scc.x_exciting_xs_bse_number_of_components = n_components n_excitons = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_excitons = n_excitons sec_scc.x_exciting_xs_bse_exciton_energies\",\n \"eigs_gw_C sec_gw_eigenvalues.value_xc_potential = reshape(get_data('Vxc')) def _parse_dos_out(self, sec_scc): data = self.dos_out_parser.data if data is\",\n \"sec_scc.x_exciting_xs_bse_sigma_re = np.reshape( data[1], (n_components, n_sigma)) sec_scc.x_exciting_xs_bse_sigma_im = np.reshape( data[2], (n_components, n_sigma)) def\",\n \"bands: self._bands.append(bands) # add atom-resolved bands_atom = self.root.findall('./*/%s' % self._atom_key) for band in\",\n \"d in data]) else: energy = np.array([d[1].get(key, None) for d in data]) if\",\n \"the files bs_files = ['dos.xml', 'TDOS.OUT'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname)\",\n \"re_symbol = re.compile(r'([A-Z][a-z]?)') def str_to_array(val_in): val = [v.split(':')[-1].split() for v in val_in.strip().split('\\\\n')] val\",\n \"'Fermi'], 'energy_kinetic_electronic': ['Kinetic energy', 'electronic kinetic'], 'energy_coulomb': ['Coulomb energy', 'Coulomb'], 'x_exciting_coulomb_energy': ['Coulomb energy',\",\n \"= self.input_xml_parser.get( 'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg',\",\n \"% self._band_key) self._bands = [] if bands: self._bands.append(bands) # add atom-resolved bands_atom =\",\n \"self.energy_unit is not None: res = res * (1 / self.energy_unit) elif 'partial'\",\n \"sec_scc): self.bandstructure_dat_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_dat_parser.band_energies if band_energies is None: return #\",\n \"elif quantity == 'LOSS': parse_function = parse_loss else: continue try: parse_function(data, sec_scc) except\",\n \"Energy, EnergyEntry, Charges, Forces, ForcesEntry, ScfIteration, BandGap ) from nomad.datamodel.metainfo.workflow import Workflow, GeometryOptimization\",\n \"= self.input_xml_parser.get('xs/ngridq', [1, 1, 1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk', [1, 1, 1]) rgkmax =\",\n \"\\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Atomic', repeats=False, str_operation=str_to_array, dtype=float, unit=ureg.hartree / ureg.bohr), ]), repeats=False)) self._quantities.append(Quantity( 'hybrids', r'Hybrids\",\n \"parse_configuration(optimization_step) if optimization_step.get('method') is not None: sec_scc.x_exciting_geometry_optimization_method = optimization_step.get('method') if optimization_step.get('step') is not\",\n \"n_excitons sec_scc.x_exciting_xs_bse_exciton_energies = np.reshape( data[1], (n_components, n_excitons)) * ureg.hartree sec_scc.x_exciting_xs_bse_exciton_binding_energies = np.reshape( data[2],\",\n \"None: band_energy = band_energy * self._energy_unit res_n.append(band_energy) start = end res.append(res_n) elif key\",\n \"% self._partialdos_key) self._natoms = len(partial_dos) return self._natoms @property def number_of_dos(self): if self._ndos is\",\n \"= self.logger self.evalqp_parser.logger = self.logger self.dos_out_parser.logger = self.logger self.bandstructure_dat_parser.logger = self.logger self.band_out_parser.logger =\",\n \"'x_exciting_brillouin_zone_volume': ('Brillouin zone volume', 1 / ureg.bohr ** 3), 'x_exciting_number_of_atoms': ('Total number of\",\n \"unit=ureg.elementary_charge), Quantity('electronic_charge', rf'electronic charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic mass *: *({re_float})',\",\n \"5: ['LDA_C_VBH'], 20: ['GGA_C_PBE', 'GGA_X_PBE'], 21: ['GGA_C_PBE', 'GGA_X_PBE_R'], 22: ['GGA_C_PBE_SOL', 'GGA_X_PBE_SOL'], 26: ['GGA_C_PBE',\",\n \"len(files) > 1: self.logger.warn('Found multiple files. Will read all!', data=dict(file=name)) for n in\",\n \"return [filename] filename = os.path.join(self.info_parser.maindir, default) if not os.path.isfile(filename): file_ext = default.split('.')[-1] mainfile_base\",\n \"= rgkmax sec_method.x_exciting_xs_screening_type = self.input_xml_parser.get( 'xs/screening/screentype', 'full') if self.input_xml_parser.get('xs/BSE') is not None: sec_method.x_exciting_xs_bse_antiresonant\",\n \"'DFT' smearing_kind_map = { 'Gaussian': 'gaussian', 'Methfessel-Paxton': 'methfessel-paxton', 'Fermi-Dirac': 'fermi', 'Extended': 'tetrahedra'} sec_smearing\",\n \"data[0], (n_components, n_epsilon)) * ureg.hartree sec_scc.x_exciting_xs_bse_epsilon_re = np.reshape( data[1], (n_components, n_epsilon)) sec_scc.x_exciting_xs_bse_epsilon_im =\",\n \"band_energies[i % self.number_of_spin_channels][i] = np.array( [e.attrib.get(self._energy_key) for e in self.bands[n][i]]) for nkpts_segment in\",\n \"n_kpoints = np.where(data[0] == data[0][0])[0][1] bands = data[1:] bands = np.reshape(bands, ( self.number_of_spin_channels,\",\n \"[0., 0., 0.]) # TODO I am not certain if screening/BSE are children\",\n \"self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg', False) sec_method.x_exciting_xs_bse_sciavtype = self.input_xml_parser.get( 'xs/BSE/sciavtype', 'spherical')\",\n \"input_file: self.input_xml_parser.mainfile = f positions = self.input_xml_parser.get('structure/species/atom/coord') lattice_vectors = self.input_xml_parser.get( 'structure/crystal/basevect', np.eye(3)) species\",\n \"None: break return positions_format def get_atom_positions(self, section={}, positions=None, positions_format=None): positions = positions if\",\n \"'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces', r'Total atomic forces including IBS \\\\(\\\\w+\\\\)\\\\s*\\\\:(\\\\s*atom[\\\\-\\\\s\\\\w\\\\.\\\\:]*?)\\\\n *Time',\",\n \"val[2:]], dtype=float)) eigs = {keys[i]: eigs[i] for i in range(len(keys))} return [kpts, eigs]\",\n \"== 'band_segm_labels': res = [] for i in range(len(self.vertices) - 1): start =\",\n \"section.get('final') if final is None: # get it from last scf_iteration or optimization_step\",\n \"None self._distances = None self._species = None @property def distances(self): if self._distances is\",\n \"rf'atomic mass *: *({re_float})', dtype=np.float64, unit=ureg.electron_mass), Quantity('muffin_tin_radius', rf'muffin-tin radius *: *({re_float})', dtype=np.float64, unit=ureg.bohr),\",\n \"= self.input_xml_parser.get( 'xs/nempty', 5) sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq', [1, 1, 1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk',\",\n \"val is None: continue if key == 'x_exciting_section_MT_charge_atom': for n in range(len(val)): sec_mt_charge_atom\",\n \"sub_sec = sec_system.m_create(x_exciting_section_spin) sub_sec.x_exciting_spin_treatment = val elif name == 'x_exciting_species_rtmin': setattr(sec_system, name, '\",\n \"self.root.findall('./%s' % self._band_key) self._bands = [] if bands: self._bands.append(bands) # add atom-resolved bands_atom\",\n \"= self.input_xml_parser.get('xs/ngridk', [1, 1, 1]) rgkmax = self.input_xml_parser.get('xs/rgkmax', None) if rgkmax is None:\",\n \"self._nspin = None self._nkpts_segment = None self._neigs_segment = None self._bands = None self._vertices\",\n \"= parse_sigma elif quantity == 'LOSS': parse_function = parse_loss else: continue try: parse_function(data,\",\n \"inconsistency in the naming convention for xs input xml file sec_method = sec_run.m_create(Method)\",\n \"is None: data = np.transpose(self.data)[0] self._neigs_segment = len(np.where(data == data[0])[0]) return self._neigs_segment class\",\n \"self.input_xml_parser.get( 'xs/nempty', 5) sec_method.x_exciting_xs_ngridq = self.input_xml_parser.get('xs/ngridq', [1, 1, 1]) sec_method.x_exciting_xs_ngridk = self.input_xml_parser.get('xs/ngridk', [1,\",\n \"= sec_scc.m_create(Dos, Calculation.dos_electronic) sec_dos.n_energies = self.dos_parser.number_of_dos sec_dos.energies = self.dos_parser.energies + energy_fermi volume =\",\n \"def parse_configuration(section): if not section: return sec_scc = self.parse_scc(section) if sec_scc is None:\",\n \"None), 'x_exciting_pw': (r'Maximum number of plane\\\\-waves', None), 'x_exciting_lo': (r'Total number of local\\\\-orbitals', None)}\",\n \"res = None self._results[key] = res class DOSXMLParser(XMLParser): def __init__(self, **kwargs): super().__init__(None) self._nspin_key\",\n \"in input_file: self.input_xml_parser.mainfile = f correlation = self.input_xml_parser.get('libxc/correlation', None) xc_functional_names.append(correlation) exchange = self.input_xml_parser.get('libxc/exchange',\",\n \"self._partial_dos = self.root.findall('./%s/%s' % (self._partialdos_key, self._diagram_key)) return self._partial_dos @property def energies(self): if self._energies\",\n \"self.total_dos: return res = np.zeros((self.number_of_spin_channels, self.number_of_dos)) for i in range(len(self.total_dos)): spin = self.total_dos[i].attrib.get(self._nspin_key,\",\n \"section.get('energy_total') is not None else section.get('final') if final is None: # get it\",\n \"ureg.hartree sec_scc.x_exciting_xs_tddft_dielectric_function_local_field = data[1:] elif quantity == 'EPSILON' and ext == 'NLF_FXC': sec_scc.x_exciting_xs_tddft_dielectric_function_no_local_field\",\n \"None: if self.total_dos is None: return self._energies = np.array( [float(point.attrib.get(self._energy_key)) for point in\",\n \"= section.get('optimization_step', [None])[-1] if final is None else final if final is None:\",\n \"correction'] } self._electron_charge_keys_mapping = { 'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage': ['core leakage'], 'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge':\",\n \"'final', r'(?:Convergence targets achieved\\\\. Performing final SCF iteration|Reached self-consistent loops maximum)([\\\\s\\\\S]+?)(\\\\n *\\\\n\\\\+{10})', sub_parser=TextParser(quantities=scf_quantities),\",\n \"original scale in which also other energies are reported. energy_fermi = 0.0 *\",\n \"= parser.eigval_parser.quantities self.fermisurf_parser.quantities = parser.fermisurf_parser.quantities self.evalqp_parser.quantities = parser.evalqp_parser.quantities self.info_gw_parser.quantities = parser.info_gw_parser.quantities def parse(self,\",\n \"repeats=False, flatten=False) ) initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation type[\\\\s\\\\S]+?\\\\n *\\\\n)', sub_parser=TextParser(quantities=[ Quantity('type', r'Exchange-correlation type +:\",\n \"n_sigma = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_sigma_energies = np.reshape( data[0], (n_components, n_sigma)) * ureg.hartree\",\n \"self._electron_charge_keys_mapping.items(): val = None for name in names: val = charge_contributions.get(name, None) if\",\n \"potential_mixing is not None: sec_system.x_exciting_potential_mixing = potential_mixing return sec_system def parse_configurations(self): sec_run =\",\n \"if self._natoms is None: partial_dos = self.root.findall('./%s' % self._partialdos_key) self._natoms = len(partial_dos) return\",\n \"is None: axis = self.root.find('./axis') if axis is None: return self._energy_unit = self._units_mapping.get(axis.attrib.get(self._unit_key).lower(),\",\n \"'x_exciting_core_charge': ['core'], 'x_exciting_core_leakage': ['core leakage'], 'x_exciting_valence_charge': ['valence'], 'x_exciting_interstitial_charge': ['interstitial'], 'x_exciting_total_MT_charge': ['total charge in\",\n \"'bandstructure.dat'] for fname in bs_files: if self.file_exists(fname): exciting_files.append(fname) break for f in exciting_files:\",\n \"[sec_scc_ref] # parse properties gw_info_files = self.get_exciting_files(gw_info_file) if len(gw_info_files) > 1: self.logger.warn('Found multiple\",\n \"self.input_xml_parser.get('xs/screening/rgkmax', None) if rgkmax is None: rgkmax = self.info_parser.get_initialization_parameter('x_exciting_rgkmax', 0.) sec_method.x_exciting_xs_screening_rgkmax = rgkmax\",\n \"self.input_xml_parser.get( 'xs/BSE/sciavqbd', False) sec_method.x_exciting_xs_bse_sciavqhd = self.input_xml_parser.get( 'xs/BSE/sciavqhd', False) sec_method.x_exciting_xs_bse_sciavqwg = self.input_xml_parser.get( 'xs/BSE/sciavqwg', False)\",\n \"initialization_quantities.append(Quantity( 'potential_mixing', r'Using ([\\\\w ]+) potential mixing', repeats=False, flatten=False) ) initialization_quantities.append(Quantity( 'xc_functional', r'(Exchange-correlation\",\n \"r'parameters loaded from *: *(.+)'), Quantity('name', r'name *: *(.+)'), Quantity('nuclear_charge', rf'nuclear charge *:\",\n \"sec_scf_iteration = sec_scc.m_create(ScfIteration) parse_scf(scf_iteration, sec_scf_iteration) return sec_scc def parse_system(self, section): sec_run = self.archive.run[-1]\",\n \"'gw': parser_function = self._parse_input_gw elif self._calculation_type == 'xs': parser_function = self._parse_input_xs else: #\",\n \"self.number_of_lm else: spin = int(spin) - 1 val_l = self.partial_dos[i].attrib.get(self._l_key, None) val_m =\",\n \"Quantity('electronic_charge', rf'electronic charge *: *({re_float})', dtype=np.float64, unit=ureg.elementary_charge), Quantity('atomic_mass', rf'atomic mass *: *({re_float})', dtype=np.float64,\",\n \"= [v.split() for v in val_in.split('\\\\n')] val = np.transpose(np.array([v for v in val\",\n \"change in effective potential \\\\(target\\\\)', ureg.hartree), 'x_exciting_energy_convergence': ( r'Absolute change in total energy\\\\s*\\\\(target\\\\)',\",\n \"ureg.hartree).to('joule') # TODO smearing with should have units of energy sec_smearing.width = smearing_width.magnitude\",\n \"is not None: sec_fermisurface.x_exciting_fermi_energy_fermi_surface = fermi_energy sec_fermisurface.x_exciting_values_fermi_surface = fermi_surface def _parse_evalqp(self, sec_scc): data\",\n \"0)) sec_gw.self_energy_singularity_treatment = self.input_xml_parser.get( 'gw/selfenergy/singularity', 'mpd') sec_gw.self_energy_c_analytical_continuation = self.input_xml_parser.get( 'gw/selfenergy/actype', 'pade') sec_gw.mixed_basis_lmax =\",\n \"0 for nkpts_segment in self.number_of_k_points_per_segment: end = start + nkpts_segment self._band_k_points.append( np.transpose(data[2:5])[start:end]) start\",\n \"return # Get fermi energy: it is used to un-shift the band structure\",\n \"res.append([start, end]) else: res = None self._results[key] = res class DOSXMLParser(XMLParser): def __init__(self,\",\n \"# free up memory parser.mainfile = None def _parse_input_xs(self, sec_method): xstype = self.input_xml_parser.get('xs/xstype',\",\n \"the fourth column in epsilon which is not parsed? sccs = [] for\",\n \"'x_exciting_core_electron_kinetic_energy': ['Core-electron kinetic energy', 'core electron kinetic'], 'x_exciting_dft_d2_dispersion_correction': ['DFT-D2 dispersion correction'] } self._electron_charge_keys_mapping\",\n \"for point in self.bands[0][0]] self._distances = np.array(self._distances, dtype=float) return self._distances @property def bands(self):\",\n \"energy\\\\s*\\\\(target\\\\)', ureg.hartree), 'x_exciting_charge_convergence': ( r'Charge distance\\\\s*\\\\(target\\\\)', ureg.elementary_charge), 'x_exciting_IBS_force_convergence': ( r'Abs\\\\. change in max\\\\-nonIBS\\\\-force\\\\s*\\\\(target\\\\)',\",\n \"optimization step\\\\s*\\\\:\\\\s*([\\\\-\\\\d\\\\.Ee]+)\\\\s*seconds', unit=ureg.s, repeats=False, dtype=float) ] self._quantities.append(Quantity( 'structure_optimization', r'Structure\\\\-optimization module started([\\\\s\\\\S]+?)Structure\\\\-optimization module stopped',\",\n \"file is part of NOMAD. # See https://nomad-lab.eu for further info. # #\",\n \"get_data(key): if key == 'k_points': return np.array([d[0][:3] for d in data]) elif key\",\n \"val is not None: break if val is None: continue if key.startswith('energy_'): sec_energy.m_add_sub_section(getattr(\",\n \"= self.input_xml_parser.get('xs/scissor', 0.0) sec_method.x_exciting_xs_vkloff = self.input_xml_parser.get('xs/vkloff', [0., 0., 0.]) # TODO I am\",\n \"data = np.transpose(self.data_clathrate_parser.data) sec_system.x_exciting_clathrates_atom_coordinates = np.transpose( np.array(data[:3], dtype=float)) sec_system.x_exciting_clathrates_atom_labels = list(data[3]) else: sec_system.x_exciting_clathrates\",\n \"= self.dos_parser parser_function = self._parse_dos elif name.startswith('bandstructure') and name.endswith('xml'): parser = self.bandstructure_parser parser_function\",\n \"'direct_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Direct BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append( Quantity( 'fundamental_band_gap', r'\\\\-\\\\s*G0W0\\\\s*\\\\-\\\\s*\\\\-+\\\\s*[\\\\s\\\\S]*?Fundamental BandGap\\\\s*\\\\((?P<__unit>\\\\w+)\\\\)\\\\s*\\\\:(\\\\s*[\\\\d\\\\.]+)\\\\s', repeats=False) ) self._quantities.append(\",\n \"= None def _parse_input_xs(self, sec_method): xstype = self.input_xml_parser.get('xs/xstype', None) if xstype is not\",\n \"energies self._quantities = [Quantity( 'program_version', r'\\\\s*EXCITING\\\\s*([\\\\w\\\\-\\\\(\\\\)\\\\. ]+)\\\\s*started', repeats=False, dtype=str, flatten=False)] initialization_quantities = [\",\n \"= self.input_xml_parser.get('xs/qpointset/qpoint') def get_data(quantity, ext): # all files related to quantity at all\",\n \"val_l is None or val_m is None: lm = i % self.number_of_lm else:\",\n \"ones with the missing quantity if len(quantity) < n_scf: quantity = [None] *\",\n \"None: return nspin = self.info_parser.get_number_of_spin_channels() def reshape(data): if data[0] is None: return return\",\n \"parse_configuration(self.info_parser.get(module)) if sec_scc is None: continue # add data to scc # TODO\",\n \"bands]) if self._energy_unit: band_energy = band_energy * self._energy_unit self._band_energies.append(band_energy) start = end return\",\n \"if self.input_xml_parser.get('xs/tddft') is not None: sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get( 'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get(\",\n \"versions, input_gw.xml and input-gw.xml for f in ['input_gw.xml', 'input-gw.xml', 'input.xml']: self.parse_file(f, sec_method) xc_functional_name\",\n \"len(data) // nspin, len(data[0]))) sec_gw_eigenvalues = sec_scc.m_create(BandEnergies) sec_gw_eigenvalues.qp_linearization_prefactor = reshape(get_data('Znk')) sec_gw_eigenvalues.n_bands = len(eigs_gw[0])\",\n \"parse_function = parse_epsilon elif quantity == 'SIGMA': parse_function = parse_sigma elif quantity ==\",\n \"= optical_band_gap def parse_miscellaneous(self): sec_worfklow = self.archive.m_create(Workflow) sec_worfklow.type = 'single_point' structure_optimization = self.info_parser.get('structure_optimization')\",\n \"make a parent class for dos and bandstructure super().__init__(None) self._distance_key = 'distance' self._coord_key\",\n \"if sec_run.system: sec_scc.system_ref = sec_run.system[-1] sec_scc_ref = sec_run.calculation[0] sec_scc.starting_calculation_ref = sec_scc_ref sec_scc.calculations_ref =\",\n \"self._energy_unit = kwargs.get('energy_unit', None) def init_parameters(self): # TODO make a parent clas for\",\n \"Quantity( 'positions_format', r'Atomic positions at this step\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity(\",\n \"__init__(self): super().__init__(None) def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'fermi_energy', r'Fermi Energy:\\\\s*([\\\\d\\\\.]+)\\\\s*', unit=ureg.hartree,\",\n \"sec_method.x_exciting_xs_tddft_anti_resonant_dielectric = self.input_xml_parser.get( 'xs/tddft/aresdf', False) sec_method.x_exciting_xs_tddft_anti_resonant_xc_kernel = self.input_xml_parser.get( 'xs/tddft/aresfxc', True) sec_method.x_exciting_xs_tddft_drude = self.input_xml_parser.get(\",\n \"valence charge', ureg.elementary_charge), 'x_exciting_wigner_radius': (r'Effective Wigner radius, r\\\\_s', ureg.bohr), 'x_exciting_empty_states': ('Number of empty\",\n \"# TODO expand list to include other xcf xc_functional_map = { 2: ['LDA_C_PZ',\",\n \"n_components = len(data) data = np.transpose(np.vstack(data)) n_epsilon = len(data[0]) // n_components sec_scc.x_exciting_xs_bse_number_of_energy_points =\",\n \"this is overwritten when setting mainfile self.bandstructure_parser._nspin = self.info_parser.get_number_of_spin_channels() band_energies = self.bandstructure_parser.get('band_energies', [])\",\n \"val.split('\\\\n')], dtype=float) val = np.transpose(val) occs = val[-1] eigs = val[-2] nspin =\",\n \"'xs/tddft/lmaxalda', 3) sec_method.x_exciting_xs_tddft_macroscopic_dielectric_function_q_treatment = self.input_xml_parser.get( 'xs/tddft/mdfqtype', 0) sec_method.x_exciting_xs_tddft_analytic_continuation_number_of_intervals = self.input_xml_parser.get( 'xs/tddft/nwacont', 0) sec_method.x_exciting_xs_tetra\",\n \"ureg.bohr), 'x_exciting_gmaxvr': (r'Maximum \\\\|G\\\\| for potential and density', 1 / ureg.bohr), 'x_exciting_gvector_size': (r'G\\\\-vector\",\n \"scale in which also other energies are reported. energy_fermi = 0.0 * ureg.hartree\",\n \"Quantity( 'lattice_vectors', r'Lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n', str_operation=str_to_array, unit=ureg.bohr, repeats=False, convert=False), Quantity( 'lattice_vectors_reciprocal', r'Reciprocal lattice vectors\\\\s*[\\\\(cartesian\\\\)]*\\\\s*:\\\\s*([\\\\-0-9\\\\.\\\\s]+)\\\\n',\",\n \"None) if xstype is not None: sec_method.x_exciting_xs_xstype = xstype sec_method.x_exciting_electronic_structure_method = xstype sec_method.x_exciting_xs_broadening\",\n \"step\\\\s*\\\\(([a-z]+)\\\\)'), Quantity( 'symbols', r'atom\\\\s*\\\\d+\\\\s*(\\\\w+)', repeats=True, dtype=str), Quantity( 'positions', r'\\\\s*:\\\\s*([\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+\\\\s*[\\\\d\\\\.\\\\-]+)', repeats=True, dtype=float)])), Quantity( 'forces',\",\n \"data=dict(file=name)) for n in range(len(files)): parser.mainfile = files[n] parser_function(section) # free up memory\",\n \"section): sec_run = self.archive.run[-1] positions = self.info_parser.get_atom_positions(section.get('atomic_positions', {})) lattice_vectors = self.info_parser.get_initialization_parameter('lattice_vectors') atom_labels =\",\n \"for i in range(len(self.partial_dos)): spin = self.partial_dos[i].attrib.get(self._nspin_key, None) if spin is None: spin\",\n \"'x_exciting_nuclear_charge': ('Total nuclear charge', ureg.elementary_charge), 'x_exciting_electronic_charge': ('Total electronic charge', ureg.elementary_charge), 'x_exciting_core_charge_initial': ('Total core\",\n \"in xc_functional_names: if name is None: continue if '_X_' in name: sec_xc_functional.exchange.append(Functional(name=name)) elif\",\n \"dist = np.transpose(self.data)[0] n_k_points = np.where(dist == dist[0])[0][1] self._distances = dist[:n_k_points] return self._distances\",\n \"self.input_xml_parser.get('xs/tddft') is not None: sec_method.x_exciting_xs_tddft_analytic_continuation = self.input_xml_parser.get( 'xs/tddft/acont', False) sec_method.x_exciting_xs_tddft_anomalous_Hall_conductivity = self.input_xml_parser.get( 'xs/tddft/ahc',\",\n \"sec_scc_ref = sec_run.calculation[0] sec_scc.starting_calculation_ref = sec_scc_ref sec_scc.calculations_ref = [sec_scc_ref] # parse properties gw_info_files\"\n]","isConversational":false}}},{"rowIdx":2981,"cells":{"ngram":{"kind":"list like","value":["required parameter `user_id` when calling `get_file_metadata`\") # noqa: E501 collection_formats = {} path_params","noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped',","= thread.get() :param async_req bool :return: HealthCheckEnveloped If the method is called asynchronously,","locals() all_params = ['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')","unexpected keyword argument '%s'\" \" to method check_action_post\" % key ) local_var_params[key] =","= locals() all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')","request thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source']","`get_file_metadata`\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id' in","pass async_req=True >>> thread = api.check_action_post(action, async_req=True) >>> result = thread.get() :param async_req","to method get_files_metadata\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify","get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get Files Metadata # noqa: E501","noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data def","_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa:","self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501","'fake_type' in local_var_params: body_params = local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept(","self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501",">>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req","= {} form_params = [] local_var_files = {} body_params = None if 'fake_type'","parameter 'file_id' is set if ('file_id' not in local_var_params or local_var_params['file_id'] is None):","True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) #","user_id, **kwargs) # noqa: E501 return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs):","asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file(file_id, location_id, user_id, async_req=True)","an unexpected keyword argument '%s'\" \" to method get_files_metadata\" % key ) local_var_params[key]","\"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501","= [] # noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params,","method is called asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if",") local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'file_id' is","noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json'])","raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method download_file\" %","file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns download link for requested file","called asynchronously, returns the request thread. \"\"\" local_var_params = locals() all_params = ['file_id',","# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT',","noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs): #","\"\"\"Get Files Metadata # noqa: E501 This method makes a synchronous HTTP request","local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling `upload_file`\") #","thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id'] # noqa: E501","`Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication","# noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data","unexpected keyword argument '%s'\" \" to method delete_file\" % key ) local_var_params[key] =","str action: (required) :param str data: :param FakeType fake_type: :return: FakeEnveloped If the","`file_id` when calling `update_file_meta_data`\") # noqa: E501 # verify the required parameter 'location_id'","keyword argument '%s'\" \" to method get_files_metadata\" % key ) local_var_params[key] = val","thread. \"\"\" local_var_params = locals() all_params = ['action', 'data', 'fake_type'] # noqa: E501","FileMetaDataEnveloped If the method is called asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only']","[] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in","None): raise ValueError(\"Missing the required parameter `location_id` when calling `upload_file`\") # noqa: E501","FakeEnveloped If the method is called asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only']","pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool","local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params =","set if ('file_id' not in local_var_params or local_var_params['file_id'] is None): raise ValueError(\"Missing the","'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept(","async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req","the request thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id'] #","auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params,","pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param async_req","requested file # noqa: E501 This method makes a synchronous HTTP request by","please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get()","files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),","query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #","data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \"\"\"Get available storage locations #","None): raise ValueError(\"Missing the required parameter `location_id` when calling `get_files_metadata`\") # noqa: E501","argument '%s'\" \" to method upload_file\" % key ) local_var_params[key] = val del","request thread. \"\"\" local_var_params = locals() all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa:","\"\"\"Returns download link for requested file # noqa: E501 This method makes a","when calling `upload_file`\") # noqa: E501 # verify the required parameter 'user_id' is","'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in","E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', #","called asynchronously, returns the request thread. \"\"\" local_var_params = locals() all_params = ['user_id']","collection_formats = {} path_params = {} if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id']","if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) =","unexpected keyword argument '%s'\" \" to method update_file_meta_data\" % key ) local_var_params[key] =","# verify the required parameter 'location_id' is set if ('location_id' not in local_var_params","user_id, **kwargs): # noqa: E501 \"\"\"Get File Metadata # noqa: E501 This method",">>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool","service # noqa: E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech","raise ValueError(\"Missing the required parameter `user_id` when calling `delete_file`\") # noqa: E501 collection_formats","is set if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ValueError(\"Missing","method get_storage_locations\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the","E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self,","the method is called asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True","from __future__ import absolute_import import re # noqa: F401 # python 2 and","argument '%s'\" \" to method get_files_metadata\" % key ) local_var_params[key] = val del","[] # noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params,","HTTP request, please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result","_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa: E501 \"\"\"Get available storage locations","collection_formats = {} path_params = {} if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id']","collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update File Metadata #","_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id,","raise ValueError(\"Missing the required parameter `location_id` when calling `delete_file`\") # noqa: E501 #","locals() all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')","request, please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file(file_id, location_id,","bool :param str file_id: (required) :param str location_id: (required) :param FileMetaDataType file_meta_data_type: :return:","async_req=True) >>> result = thread.get() :param async_req bool :param str file_id: (required) :param","= api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id:","self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def delete_file_with_http_info(self, file_id, location_id,","user_id, **kwargs): # noqa: E501 \"\"\"Deletes File # noqa: E501 This method makes","thread = api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","raise ValueError(\"Missing the required parameter `location_id` when calling `update_file_meta_data`\") # noqa: E501 collection_formats","to fail or echo back the transmitted data # noqa: E501 This method","setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params,","when calling `get_file_metadata`\") # noqa: E501 # verify the required parameter 'location_id' is","`download_file`\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id' in","This method makes a synchronous HTTP request by default. To make an asynchronous","simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \"\"\"NOTE: This class is auto generated by OpenAPI","if key not in all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\"","**kwargs) # noqa: E501 return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): #","transmitted data # noqa: E501 This method makes a synchronous HTTP request by","= self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return data def get_files_metadata_with_http_info(self, location_id, user_id,","please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param","{} if 'action' in local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501 query_params =","async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id,","(required) :param str user_id: (required) :return: PresignedLinkEnveloped If the method is called asynchronously,","'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in","**kwargs): # noqa: E501 \"\"\"Service health-check endpoint # noqa: E501 Some general information","= api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param","bool :param str location_id: (required) :param str user_id: (required) :param str uuid_filter: :return:","setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params,","# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): #","# noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = []","'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,","# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs):","returns the request thread. \"\"\" local_var_params = locals() all_params = [] # noqa:",":param str location_id: (required) :param str user_id: (required) :return: None If the method","\"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:","True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns","or local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling `upload_file`\")","upload_file\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required","https://openapi-generator.tech \"\"\" from __future__ import absolute_import import re # noqa: F401 # python",":param str uuid_filter: :return: FileMetaDataArrayEnveloped If the method is called asynchronously, returns the","= {} query_params = [] header_params = {} form_params = [] local_var_files =","= self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id,","all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError(","request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa:","E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data","information on the API and state of the service behind # noqa: E501","location_id: (required) :param str user_id: (required) :return: PresignedLinkEnveloped If the method is called","thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501","= api.upload_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param","Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params,","noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped',","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id,","`location_id` when calling `upload_file`\") # noqa: E501 # verify the required parameter 'user_id'","in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] header_params =","% key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter","E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params = {}","setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params,","[] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params,","{} body_params = None if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP","Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/', 'GET', path_params,","def health_check_with_http_info(self, **kwargs): # noqa: E501 \"\"\"Service health-check endpoint # noqa: E501 Some","returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id,","in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json'])","`get_files_metadata`\") # noqa: E501 # verify the required parameter 'user_id' is set if","user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str file_id: (required)","FileLocationArrayEnveloped If the method is called asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only']","\"\"\"Get available storage locations # noqa: E501 This method makes a synchronous HTTP","pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get() :param","else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return data def update_file_meta_data_with_http_info(self,","**kwargs): # noqa: E501 \"\"\"Test checkpoint to ask server to fail or echo","the transmitted data # noqa: E501 This method makes a synchronous HTTP request","_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get File","_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update File Metadata","all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method download_file\"","noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped',","keyword argument '%s'\" \" to method health_check\" % key ) local_var_params[key] = val","echo back the transmitted data # noqa: E501 This method makes a synchronous","get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get File Metadata # noqa:","True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data) = self.check_action_post_with_http_info(action,","ValueError(\"Missing the required parameter `file_id` when calling `delete_file`\") # noqa: E501 # verify","HTTP request, please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>>","thread. \"\"\" local_var_params = locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')","= self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def get_file_metadata_with_http_info(self, file_id,","noqa: E501 \"\"\"Get Files Metadata # noqa: E501 This method makes a synchronous","= self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings","E501 \"\"\"Update File Metadata # noqa: E501 This method makes a synchronous HTTP","def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns download link for","True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get Files","request thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id'] # noqa:","back the transmitted data # noqa: E501 This method makes a synchronous HTTP","# HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication","(required) :param str user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If the method","= thread.get() :param async_req bool :param str user_id: (required) :return: FileLocationArrayEnveloped If the","Ref: https://openapi-generator.tech Do not edit the class manually. \"\"\" def __init__(self, api_client=None): if","is None): raise ValueError(\"Missing the required parameter `file_id` when calling `delete_file`\") # noqa:","# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs):","str file_id: (required) :param str location_id: (required) :param str user_id: (required) :return: FileMetaDataEnveloped","a synchronous HTTP request by default. To make an asynchronous HTTP request, please","# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations', 'GET',","the required parameter `location_id` when calling `update_file_meta_data`\") # noqa: E501 collection_formats = {}","[] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] =","E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self,","Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params,","thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501","api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: (required)","request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa:","operation to datcore # noqa: E501 This method makes a synchronous HTTP request","= api.check_action_post(action, async_req=True) >>> result = thread.get() :param async_req bool :param str action:","`file_id` when calling `download_file`\") # noqa: E501 # verify the required parameter 'location_id'","auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id,","noqa: E501 \"\"\"Update File Metadata # noqa: E501 This method makes a synchronous","`file_id` when calling `get_file_metadata`\") # noqa: E501 # verify the required parameter 'location_id'","all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method check_action_post\"","asynchronously, returns the request thread. \"\"\" local_var_params = locals() all_params = [] #","del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params =","api.check_action_post(action, async_req=True) >>> result = thread.get() :param async_req bool :param str action: (required)","in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params = {} form_params = []","user_id, **kwargs) # noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) #","called asynchronously, returns the request thread. \"\"\" local_var_params = locals() all_params = ['action',","local_var_params['uuid_filter'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {}","simcore-service-storage API API definition for simcore-service-storage service # noqa: E501 OpenAPI spec version:","None): raise ValueError(\"Missing the required parameter `file_id` when calling `download_file`\") # noqa: E501","thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) #","health_check\" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params","class manually. \"\"\" def __init__(self, api_client=None): if api_client is None: api_client = ApiClient()","noqa: E501 query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa:","E501 This method makes a synchronous HTTP request by default. To make an","return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa:","parameter `user_id` when calling `get_file_metadata`\") # noqa: E501 collection_formats = {} path_params =","the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id,","verify the required parameter 'location_id' is set if ('location_id' not in local_var_params or","parameter `user_id` when calling `upload_file`\") # noqa: E501 collection_formats = {} path_params =","E501 \"\"\"Test checkpoint to ask server to fail or echo back the transmitted","= val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = []","= ApiClient() self.api_client = api_client def check_action_post(self, action, **kwargs): # noqa: E501 \"\"\"Test","is None): raise ValueError(\"Missing the required parameter `file_id` when calling `download_file`\") # noqa:","self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id,","please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result =","path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),","calling `check_action_post`\") # noqa: E501 collection_formats = {} path_params = {} if 'action'","makes a synchronous HTTP request by default. To make an asynchronous HTTP request,","noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source'","the request thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] #","noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None,","# noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data","query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa:","asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check(async_req=True) >>> result =",":param str extra_source: :return: PresignedLinkEnveloped If the method is called asynchronously, returns the","E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self,","_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501","E501 \"\"\"Deletes File # noqa: E501 This method makes a synchronous HTTP request","API definition for simcore-service-storage service # noqa: E501 OpenAPI spec version: 0.1.0 Contact:","E501 query_params = [] if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501","\"\"\"Test checkpoint to ask server to fail or echo back the transmitted data","(required) :param str extra_location: :param str extra_source: :return: PresignedLinkEnveloped If the method is","E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', #","return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id,","thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool","noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data def","local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling `delete_file`\") #","# noqa: E501 \"\"\"Get Files Metadata # noqa: E501 This method makes a","kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.download_file_with_http_info(file_id,",":return: None If the method is called asynchronously, returns the request thread. \"\"\"","when calling `check_action_post`\") # noqa: E501 collection_formats = {} path_params = {} if","HTTP request, please pass async_req=True >>> thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>>","'/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings,","post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',","noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api(","an asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>>","raise ValueError(\"Missing the required parameter `file_id` when calling `get_file_metadata`\") # noqa: E501 #","__future__ import absolute_import import re # noqa: F401 # python 2 and python","query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #","(required) :param str location_id: (required) :param str user_id: (required) :return: FileMetaDataEnveloped If the","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id,","(required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If the method is called asynchronously, returns","asynchronously, returns the request thread. \"\"\" local_var_params = locals() all_params = ['location_id', 'user_id',","noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params =","_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get","['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val","api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If the method","bool :param str user_id: (required) :return: FileLocationArrayEnveloped If the method is called asynchronously,","'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),","= [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params =","or local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id` when calling `delete_file`\")","'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in","PresignedLinkEnveloped If the method is called asynchronously, returns the request thread. \"\"\" local_var_params","to method get_storage_locations\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify","raise ValueError(\"Missing the required parameter `user_id` when calling `get_files_metadata`\") # noqa: E501 collection_formats","`delete_file`\") # noqa: E501 # verify the required parameter 'user_id' is set if","the required parameter `location_id` when calling `delete_file`\") # noqa: E501 # verify the","required parameter `location_id` when calling `upload_file`\") # noqa: E501 # verify the required","pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get()","E501 return data def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \"\"\"Test checkpoint to","raise ValueError(\"Missing the required parameter `location_id` when calling `get_files_metadata`\") # noqa: E501 #","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file(file_id, location_id,","File # noqa: E501 This method makes a synchronous HTTP request by default.","path_params = {} if 'action' in local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501","body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501","['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for","please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get()","noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped',","`file_id` when calling `delete_file`\") # noqa: E501 # verify the required parameter 'location_id'","pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get()","= {} if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params","files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),","pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req","compatibility library import six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \"\"\"NOTE: This class","local_var_params['kwargs'] # verify the required parameter 'location_id' is set if ('location_id' not in","is None): raise ValueError(\"Missing the required parameter `location_id` when calling `download_file`\") # noqa:","str location_id: (required) :param str user_id: (required) :param str extra_location: :param str extra_source:","local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling `get_files_metadata`\") #","noqa: E501 # verify the required parameter 'user_id' is set if ('user_id' not","# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET',","an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True)","not in all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to","local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling `get_file_metadata`\") #","noqa: E501 \"\"\"Service health-check endpoint # noqa: E501 Some general information on the","when calling `get_files_metadata`\") # noqa: E501 # verify the required parameter 'user_id' is","please pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get()","async_req=True) >>> result = thread.get() :param async_req bool :param str action: (required) :param","= [] header_params = {} form_params = [] local_var_files = {} body_params =","ValueError(\"Missing the required parameter `location_id` when calling `download_file`\") # noqa: E501 # verify","local_var_params['kwargs'] # verify the required parameter 'action' is set if ('action' not in","all_params = ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val","TypeError( \"Got an unexpected keyword argument '%s'\" \" to method upload_file\" % key","required parameter `user_id` when calling `delete_file`\") # noqa: E501 collection_formats = {} path_params","files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),","action, **kwargs): # noqa: E501 \"\"\"Test checkpoint to ask server to fail or","import re # noqa: F401 # python 2 and python 3 compatibility library","else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def","required parameter `location_id` when calling `delete_file`\") # noqa: E501 # verify the required","`user_id` when calling `get_storage_locations`\") # noqa: E501 collection_formats = {} path_params = {}","performs copy operation to datcore # noqa: E501 This method makes a synchronous","action: (required) :param str data: :param FakeType fake_type: :return: FakeEnveloped If the method","return data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns download","file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Deletes File # noqa: E501 This","get_storage_locations(self, user_id, **kwargs): # noqa: E501 \"\"\"Get available storage locations # noqa: E501","= [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params,","body_params = None if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header","str user_id: (required) :param str extra_location: :param str extra_source: :return: PresignedLinkEnveloped If the","True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get","path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),","pass async_req=True >>> thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get()","user_id, **kwargs) # noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) #","query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params = {} form_params = [] local_var_files =","local_var_params['action'] # noqa: E501 query_params = [] if 'data' in local_var_params: query_params.append(('data', local_var_params['data']))","user_id: (required) :return: None If the method is called asynchronously, returns the request","from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \"\"\"NOTE: This class is auto generated by","please pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result =","the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa:","asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>>","location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs)","not in local_var_params or local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id`","asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs)","`update_file_meta_data`\") # noqa: E501 # verify the required parameter 'location_id' is set if","HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True","\" to method download_file\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #","body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501",">>> thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req","is None): raise ValueError(\"Missing the required parameter `action` when calling `check_action_post`\") # noqa:","in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if 'location_id' in local_var_params: path_params['location_id']","synchronous HTTP request by default. To make an asynchronous HTTP request, please pass","raise ValueError(\"Missing the required parameter `user_id` when calling `get_storage_locations`\") # noqa: E501 collection_formats","= [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params,","= ['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,","an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check(async_req=True) >>> result","noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs): #","asynchronously, returns the request thread. \"\"\" local_var_params = locals() all_params = ['action', 'data',","method get_file_metadata\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the","user_id, **kwargs) # noqa: E501 return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): #","\"\"\"Returns upload link or performs copy operation to datcore # noqa: E501 This","E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in","= {} form_params = [] local_var_files = {} body_params = None # HTTP","method download_file\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the","{} path_params = {} query_params = [] header_params = {} form_params = []","setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params,","in local_var_params or local_var_params['action'] is None): raise ValueError(\"Missing the required parameter `action` when","when calling `update_file_meta_data`\") # noqa: E501 collection_formats = {} path_params = {} if","E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa:","returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id,",") local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'action' is","the API and state of the service behind # noqa: E501 This method","an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id,","the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) #","request, please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result =","= local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501","result = thread.get() :param async_req bool :param str file_id: (required) :param str location_id:","if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id,","= locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for","result = thread.get() :param async_req bool :param str location_id: (required) :param str user_id:","E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) #","local_var_params['kwargs'] # verify the required parameter 'user_id' is set if ('user_id' not in",":param str extra_location: :param str extra_source: :return: PresignedLinkEnveloped If the method is called","method delete_file\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the","local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id` when calling `download_file`\") #","in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params = {} form_params = []","to method upload_file\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify","asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file(file_id, location_id, user_id, async_req=True)","async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param","(required) :return: FileLocationArrayEnveloped If the method is called asynchronously, returns the request thread.","is called asynchronously, returns the request thread. \"\"\" local_var_params = locals() all_params =","None): raise ValueError(\"Missing the required parameter `file_id` when calling `update_file_meta_data`\") # noqa: E501","required parameter `location_id` when calling `update_file_meta_data`\") # noqa: E501 collection_formats = {} path_params","True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns","collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns download link","delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Deletes File # noqa: E501","the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id,","noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501 \"\"\"Service","= api.health_check(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If the","noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped',","async_req bool :return: HealthCheckEnveloped If the method is called asynchronously, returns the request","str user_id: (required) :return: PresignedLinkEnveloped If the method is called asynchronously, returns the","post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',","(required) :param str user_id: (required) :return: FileMetaDataEnveloped If the method is called asynchronously,","= [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept']","header_params = {} form_params = [] local_var_files = {} body_params = None if",">>> thread = api.health_check(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped","header_params = {} form_params = [] local_var_files = {} body_params = None #","'%s'\" \" to method download_file\" % key ) local_var_params[key] = val del local_var_params['kwargs']","return data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \"\"\"Get available storage locations","asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post(action, async_req=True) >>> result","[] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params,","E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', #","thread = api.health_check(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If",") local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params","user_id, **kwargs) # noqa: E501 return data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs):","**kwargs): # noqa: E501 \"\"\"Update File Metadata # noqa: E501 This method makes","query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa:","= [] # noqa: E501 return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params,","`location_id` when calling `update_file_meta_data`\") # noqa: E501 collection_formats = {} path_params = {}","self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501","unexpected keyword argument '%s'\" \" to method get_storage_locations\" % key ) local_var_params[key] =","calling `get_storage_locations`\") # noqa: E501 collection_formats = {} path_params = {} query_params =","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id,","raise ValueError(\"Missing the required parameter `location_id` when calling `get_file_metadata`\") # noqa: E501 #","'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val",":param async_req bool :param str file_id: (required) :param str location_id: (required) :param str","# noqa: E501 This method makes a synchronous HTTP request by default. To","`location_id` when calling `download_file`\") # noqa: E501 # verify the required parameter 'user_id'","**kwargs) # noqa: E501 return data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): #","async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req","_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501","set if ('action' not in local_var_params or local_var_params['action'] is None): raise ValueError(\"Missing the","None): raise ValueError(\"Missing the required parameter `user_id` when calling `delete_file`\") # noqa: E501","self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) #","argument '%s'\" \" to method health_check\" % key ) local_var_params[key] = val del","str file_id: (required) :param str location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If","self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa:","E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs): # noqa:","keyword argument '%s'\" \" to method delete_file\" % key ) local_var_params[key] = val","E501 return data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns","an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file(file_id, location_id, user_id,","response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),","please pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get()","keyword argument '%s'\" \" to method get_storage_locations\" % key ) local_var_params[key] = val","= ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in","or local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling `download_file`\")","# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/', 'GET',","'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] if","= [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params,","when calling `delete_file`\") # noqa: E501 # verify the required parameter 'user_id' is","local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 #","available storage locations # noqa: E501 This method makes a synchronous HTTP request","\"Got an unexpected keyword argument '%s'\" \" to method check_action_post\" % key )","E501 return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', #","location_id: (required) :param str user_id: (required) :param str extra_location: :param str extra_source: :return:","= val del local_var_params['kwargs'] # verify the required parameter 'user_id' is set if","= self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type(","or local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id` when calling `download_file`\")","`upload_file`\") # noqa: E501 # verify the required parameter 'location_id' is set if","setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params,","_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501 \"\"\"Service health-check endpoint # noqa:","is None): raise ValueError(\"Missing the required parameter `location_id` when calling `get_files_metadata`\") # noqa:","returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) #","# noqa: E501 # verify the required parameter 'location_id' is set if ('location_id'",">>> result = thread.get() :param async_req bool :param str file_id: (required) :param str","# python 2 and python 3 compatibility library import six from simcore_service_storage_sdk.api_client import","Generated by: https://openapi-generator.tech \"\"\" from __future__ import absolute_import import re # noqa: F401","local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params = {} form_params = [] local_var_files","async_req=True >>> thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param","asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id,","raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method update_file_meta_data\" %","user_id: (required) :param str extra_location: :param str extra_source: :return: PresignedLinkEnveloped If the method","\"Got an unexpected keyword argument '%s'\" \" to method get_storage_locations\" % key )","the request thread. \"\"\" local_var_params = locals() all_params = ['user_id'] # noqa: E501","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True)","parameter `file_id` when calling `upload_file`\") # noqa: E501 # verify the required parameter","= self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data def check_action_post_with_http_info(self, action, **kwargs): #","thread.get() :param async_req bool :return: HealthCheckEnveloped If the method is called asynchronously, returns","storage locations # noqa: E501 This method makes a synchronous HTTP request by",":param str location_id: (required) :param str user_id: (required) :return: FileMetaDataEnveloped If the method","thread = api.check_action_post(action, async_req=True) >>> result = thread.get() :param async_req bool :param str","(data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def delete_file_with_http_info(self,","check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \"\"\"Test checkpoint to ask server to fail",":param str file_id: (required) :param str location_id: (required) :param str user_id: (required) :return:","\"\"\"Update File Metadata # noqa: E501 This method makes a synchronous HTTP request","request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs)","file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update File Metadata # noqa: E501 This","None): raise ValueError(\"Missing the required parameter `action` when calling `check_action_post`\") # noqa: E501","delete_file\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required","default. To make an asynchronous HTTP request, please pass async_req=True >>> thread =","local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if 'location_id' in local_var_params: path_params['location_id'] =",":param str user_id: (required) :return: PresignedLinkEnveloped If the method is called asynchronously, returns","asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result","method update_file_meta_data\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the","\"Got an unexpected keyword argument '%s'\" \" to method delete_file\" % key )","pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get() :param","fake_type: :return: FakeEnveloped If the method is called asynchronously, returns the request thread.","request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs)","kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501","None If the method is called asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only']","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id,","HTTP request, please pass async_req=True >>> thread = api.check_action_post(action, async_req=True) >>> result =","E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata',","= {} path_params = {} if 'action' in local_var_params: path_params['action'] = local_var_params['action'] #","**kwargs) # noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:","raise ValueError(\"Missing the required parameter `file_id` when calling `update_file_meta_data`\") # noqa: E501 #","str file_id: (required) :param str location_id: (required) :param str user_id: (required) :return: PresignedLinkEnveloped","on the API and state of the service behind # noqa: E501 This","= None if 'fake_type' in local_var_params: body_params = local_var_params['fake_type'] # HTTP header `Accept`","user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str location_id: (required)","# noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501","path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),","def get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get File Metadata #","HTTP request, please pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>> result =","api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def check_action_post(self,","local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'location_id' is set","required parameter `location_id` when calling `get_file_metadata`\") # noqa: E501 # verify the required","setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params,","{} form_params = [] local_var_files = {} body_params = None # HTTP header","[] local_var_files = {} body_params = None if 'fake_type' in local_var_params: body_params =","keyword argument '%s'\" \" to method get_file_metadata\" % key ) local_var_params[key] = val","= {} form_params = [] local_var_files = {} body_params = None if 'file_meta_data_type'","E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data def check_action_post_with_http_info(self,","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id,","when calling `download_file`\") # noqa: E501 collection_formats = {} path_params = {} if","HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header","in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter']))","key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {}","service behind # noqa: E501 This method makes a synchronous HTTP request by","val del local_var_params['kwargs'] # verify the required parameter 'file_id' is set if ('file_id'","or performs copy operation to datcore # noqa: E501 This method makes a","'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),","response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),","the class manually. \"\"\" def __init__(self, api_client=None): if api_client is None: api_client =","`user_id` when calling `delete_file`\") # noqa: E501 collection_formats = {} path_params = {}","file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get File Metadata # noqa: E501","method is called asynchronously, returns the request thread. \"\"\" local_var_params = locals() all_params","auth_settings = [] # noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params,","thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa:","local_var_params['action'] is None): raise ValueError(\"Missing the required parameter `action` when calling `check_action_post`\") #","required parameter 'action' is set if ('action' not in local_var_params or local_var_params['action'] is","kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id,","is None): raise ValueError(\"Missing the required parameter `location_id` when calling `get_file_metadata`\") # noqa:",":param str action: (required) :param str data: :param FakeType fake_type: :return: FakeEnveloped If","when calling `download_file`\") # noqa: E501 # verify the required parameter 'user_id' is","HTTP request, please pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>>","\"\"\" local_var_params = locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')","path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),","is None): raise ValueError(\"Missing the required parameter `location_id` when calling `upload_file`\") # noqa:","kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501","data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get Files Metadata #","ValueError(\"Missing the required parameter `location_id` when calling `upload_file`\") # noqa: E501 # verify","E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata',","# noqa: F401 # python 2 and python 3 compatibility library import six","when calling `update_file_meta_data`\") # noqa: E501 # verify the required parameter 'location_id' is","raise ValueError(\"Missing the required parameter `user_id` when calling `get_file_metadata`\") # noqa: E501 collection_formats","'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location',","noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped',","locals() all_params = ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,","thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] #","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id,","asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id,","setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params,","ValueError(\"Missing the required parameter `user_id` when calling `get_storage_locations`\") # noqa: E501 collection_formats =","is None): raise ValueError(\"Missing the required parameter `location_id` when calling `update_file_meta_data`\") # noqa:","TypeError( \"Got an unexpected keyword argument '%s'\" \" to method update_file_meta_data\" % key","_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns download","an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>>","**kwargs) # noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:","when calling `get_storage_locations`\") # noqa: E501 collection_formats = {} path_params = {} query_params","# noqa: E501 return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa:","TypeError( \"Got an unexpected keyword argument '%s'\" \" to method get_storage_locations\" % key","def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client","**kwargs): # noqa: E501 \"\"\"Get Files Metadata # noqa: E501 This method makes","\"\"\" local_var_params = locals() all_params = ['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req')","required parameter `file_id` when calling `get_file_metadata`\") # noqa: E501 # verify the required","**kwargs) # noqa: E501 return data def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501","noqa: E501 return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped',","E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data","= api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param","query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #","noqa: E501 \"\"\"Get File Metadata # noqa: E501 This method makes a synchronous","Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params,","parameter `location_id` when calling `upload_file`\") # noqa: E501 # verify the required parameter","when calling `get_file_metadata`\") # noqa: E501 collection_formats = {} path_params = {} if","3 compatibility library import six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \"\"\"NOTE: This","local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling `delete_file`\") #","check_action_post\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required","def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get Files Metadata # noqa:","= {} path_params = {} if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] #","spec version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\" from __future__ import absolute_import","thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501","ValueError(\"Missing the required parameter `action` when calling `check_action_post`\") # noqa: E501 collection_formats =","Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params,","download_file\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required","self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings =","please pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result =","= {} path_params = {} if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] #","E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self,","('user_id' not in local_var_params or local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter","self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs):","files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),","return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return",">>> thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped","HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id,","True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data)","= True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else:","kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501","return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa:","an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>>","state of the service behind # noqa: E501 This method makes a synchronous","# HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP","api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: (required)","# noqa: E501 return data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa:","calling `update_file_meta_data`\") # noqa: E501 # verify the required parameter 'location_id' is set","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True)","self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501","= ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')","`download_file`\") # noqa: E501 # verify the required parameter 'user_id' is set if","_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): #","if ('action' not in local_var_params or local_var_params['action'] is None): raise ValueError(\"Missing the required","# noqa: E501 if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501","noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped',","raise ValueError(\"Missing the required parameter `location_id` when calling `download_file`\") # noqa: E501 #","self.api_client = api_client def check_action_post(self, action, **kwargs): # noqa: E501 \"\"\"Test checkpoint to","required parameter `file_id` when calling `upload_file`\") # noqa: E501 # verify the required","{} body_params = None if 'fake_type' in local_var_params: body_params = local_var_params['fake_type'] # HTTP","del local_var_params['kwargs'] # verify the required parameter 'location_id' is set if ('location_id' not","is None): raise ValueError(\"Missing the required parameter `location_id` when calling `delete_file`\") # noqa:","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id,","the required parameter `user_id` when calling `get_files_metadata`\") # noqa: E501 collection_formats = {}","locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,","return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa:","# noqa: E501 \"\"\"Returns download link for requested file # noqa: E501 This","api.upload_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","**kwargs) # noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:","parameter `user_id` when calling `get_files_metadata`\") # noqa: E501 collection_formats = {} path_params =","# noqa: E501 return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file(file_id,","unexpected keyword argument '%s'\" \" to method health_check\" % key ) local_var_params[key] =","request, please pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result =","str user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If the method is called","local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling `get_files_metadata`\") #","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>>","health_check_with_http_info(self, **kwargs): # noqa: E501 \"\"\"Service health-check endpoint # noqa: E501 Some general",":param str user_id: (required) :return: FileMetaDataEnveloped If the method is called asynchronously, returns","# noqa: E501 \"\"\"Get File Metadata # noqa: E501 This method makes a","collection_formats = {} path_params = {} query_params = [] if 'user_id' in local_var_params:","str extra_location: :param str extra_source: :return: PresignedLinkEnveloped If the method is called asynchronously,","or local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling `get_files_metadata`\")","E501 collection_formats = {} path_params = {} query_params = [] if 'user_id' in","data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get File Metadata","thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) #","form_params = [] local_var_files = {} body_params = None if 'file_meta_data_type' in local_var_params:","parameter 'user_id' is set if ('user_id' not in local_var_params or local_var_params['user_id'] is None):","if api_client is None: api_client = ApiClient() self.api_client = api_client def check_action_post(self, action,","file # noqa: E501 This method makes a synchronous HTTP request by default.","the method is called asynchronously, returns the request thread. \"\"\" local_var_params = locals()","coding: utf-8 \"\"\" simcore-service-storage API API definition for simcore-service-storage service # noqa: E501","# noqa: E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \"\"\"Get","calling `upload_file`\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id'","the required parameter `location_id` when calling `get_file_metadata`\") # noqa: E501 # verify the","all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method delete_file\"","form_params = [] local_var_files = {} body_params = None # HTTP header `Accept`","the required parameter `file_id` when calling `upload_file`\") # noqa: E501 # verify the","result = thread.get() :param async_req bool :return: HealthCheckEnveloped If the method is called",":param str file_id: (required) :param str location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped","request thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa:","None: api_client = ApiClient() self.api_client = api_client def check_action_post(self, action, **kwargs): # noqa:","E501 \"\"\"Get File Metadata # noqa: E501 This method makes a synchronous HTTP","async_req bool :param str action: (required) :param str data: :param FakeType fake_type: :return:","library import six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \"\"\"NOTE: This class is","api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str location_id:","= {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json'])","local_var_params or local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling","the required parameter 'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id']","Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params,","noqa: E501 \"\"\"Get available storage locations # noqa: E501 This method makes a","thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool","kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else:","E501 return data def health_check_with_http_info(self, **kwargs): # noqa: E501 \"\"\"Service health-check endpoint #","= True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else:","parameter `user_id` when calling `delete_file`\") # noqa: E501 collection_formats = {} path_params =","returns the request thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'file_meta_data_type']","not in local_var_params or local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id`","local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params = {} form_params = [] local_var_files","pass async_req=True >>> thread = api.health_check(async_req=True) >>> result = thread.get() :param async_req bool","val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( \"Got an unexpected","parameter `location_id` when calling `delete_file`\") # noqa: E501 # verify the required parameter","local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params = {} form_params = [] local_var_files","simcore-service-storage service # noqa: E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated by:","`delete_file`\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id' in","E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self,","\"\"\" local_var_params = locals() all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req')","(required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method is called asynchronously, returns","if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params = {} form_params","body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501","the required parameter 'file_id' is set if ('file_id' not in local_var_params or local_var_params['file_id']","HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501","post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',","required parameter `location_id` when calling `get_files_metadata`\") # noqa: E501 # verify the required","True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501 \"\"\"Service health-check endpoint #","= api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If the","the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id,","asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True)","noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params =","'action' is set if ('action' not in local_var_params or local_var_params['action'] is None): raise","thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param","# noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params","# noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,","noqa: E501 return data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501","val del local_var_params['kwargs'] # verify the required parameter 'user_id' is set if ('user_id'","# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST',","asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True)","noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params =","'%s'\" \" to method get_storage_locations\" % key ) local_var_params[key] = val del local_var_params['kwargs']","'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):","[] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params,","argument '%s'\" \" to method get_file_metadata\" % key ) local_var_params[key] = val del","self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id,","file_meta_data_type: :return: FileMetaDataEnveloped If the method is called asynchronously, returns the request thread.","del local_var_params['kwargs'] # verify the required parameter 'file_id' is set if ('file_id' not","calling `download_file`\") # noqa: E501 # verify the required parameter 'location_id' is set","[] # noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params,","None): raise ValueError(\"Missing the required parameter `location_id` when calling `update_file_meta_data`\") # noqa: E501","is None): raise ValueError(\"Missing the required parameter `user_id` when calling `get_files_metadata`\") # noqa:","'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] header_params","\"\"\" simcore-service-storage API API definition for simcore-service-storage service # noqa: E501 OpenAPI spec","# noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501",">>> result = thread.get() :param async_req bool :param str location_id: (required) :param str","location_id, **kwargs) # noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): #","noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return","`user_id` when calling `get_files_metadata`\") # noqa: E501 collection_formats = {} path_params = {}","else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def","self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def upload_file_with_http_info(self, file_id, location_id,","HTTP request, please pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result","self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check(async_req=True)","E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not","argument '%s'\" \" to method check_action_post\" % key ) local_var_params[key] = val del","E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs): # noqa:","when calling `download_file`\") # noqa: E501 # verify the required parameter 'location_id' is","\" to method get_storage_locations\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #","async_req=True) >>> result = thread.get() :param async_req bool :param str location_id: (required) :param","ApiClient() self.api_client = api_client def check_action_post(self, action, **kwargs): # noqa: E501 \"\"\"Test checkpoint","E501 return data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Deletes","E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/check/{action}',","api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id,","path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),","calling `update_file_meta_data`\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id'","E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', #","collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa: E501 \"\"\"Get available storage locations #","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id,","is None): raise ValueError(\"Missing the required parameter `user_id` when calling `get_file_metadata`\") # noqa:","{} form_params = [] local_var_files = {} body_params = None if 'file_meta_data_type' in","E501 \"\"\"Returns upload link or performs copy operation to datcore # noqa: E501","data: :param FakeType fake_type: :return: FakeEnveloped If the method is called asynchronously, returns","an unexpected keyword argument '%s'\" \" to method download_file\" % key ) local_var_params[key]","for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( \"Got","else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data def get_storage_locations_with_http_info(self, user_id,","user_id: (required) :return: PresignedLinkEnveloped If the method is called asynchronously, returns the request","self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) #","https://openapi-generator.tech Do not edit the class manually. \"\"\" def __init__(self, api_client=None): if api_client","all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise","thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param","files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),","all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method health_check\"","def check_action_post(self, action, **kwargs): # noqa: E501 \"\"\"Test checkpoint to ask server to","noqa: E501 This method makes a synchronous HTTP request by default. To make","'/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings,","del local_var_params['kwargs'] # verify the required parameter 'user_id' is set if ('user_id' not","file_id: (required) :param str location_id: (required) :param str user_id: (required) :return: None If","'/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings,","= api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","TypeError( \"Got an unexpected keyword argument '%s'\" \" to method check_action_post\" % key","required parameter `user_id` when calling `upload_file`\") # noqa: E501 collection_formats = {} path_params","Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params,","ValueError(\"Missing the required parameter `user_id` when calling `upload_file`\") # noqa: E501 collection_formats =","# noqa: E501 return data def health_check_with_http_info(self, **kwargs): # noqa: E501 \"\"\"Service health-check","E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/',","= api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param","all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for","{} path_params = {} if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa:","TypeError( \"Got an unexpected keyword argument '%s'\" \" to method get_file_metadata\" % key","the request thread. \"\"\" local_var_params = locals() all_params = ['action', 'data', 'fake_type'] #","raise ValueError(\"Missing the required parameter `location_id` when calling `upload_file`\") # noqa: E501 #","all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method get_files_metadata\"","# verify the required parameter 'user_id' is set if ('user_id' not in local_var_params","E501 header_params = {} form_params = [] local_var_files = {} body_params = None","endpoint # noqa: E501 Some general information on the API and state of","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post(action, async_req=True)","'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params = {} form_params =","download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns download link for requested","= self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def upload_file_with_http_info(self, file_id,","E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update File",":param FakeType fake_type: :return: FakeEnveloped If the method is called asynchronously, returns the","an asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file(file_id, location_id, user_id,","'/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings,","True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id,","import absolute_import import re # noqa: F401 # python 2 and python 3","return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id,","= thread.get() :param async_req bool :param str action: (required) :param str data: :param","post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',","calling `download_file`\") # noqa: E501 # verify the required parameter 'user_id' is set","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data(file_id,","argument '%s'\" \" to method delete_file\" % key ) local_var_params[key] = val del","body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501","in local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501 query_params = [] if 'data'","by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. \"\"\" def","`download_file`\") # noqa: E501 # verify the required parameter 'location_id' is set if","If the method is called asynchronously, returns the request thread. \"\"\" local_var_params =","= api_client def check_action_post(self, action, **kwargs): # noqa: E501 \"\"\"Test checkpoint to ask","= api.delete_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param","response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),","HTTP request, please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get()","= ['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,","2 and python 3 compatibility library import six from simcore_service_storage_sdk.api_client import ApiClient class","or local_var_params['action'] is None): raise ValueError(\"Missing the required parameter `action` when calling `check_action_post`\")","# noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if","E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data def get_storage_locations_with_http_info(self,","api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool :param str file_id:","# noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,","def delete_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Deletes File # noqa:","local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling `update_file_meta_data`\") #","local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 #",":param str file_id: (required) :param str location_id: (required) :param str user_id: (required) :param","<EMAIL> Generated by: https://openapi-generator.tech \"\"\" from __future__ import absolute_import import re # noqa:","or local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling `get_files_metadata`\")","local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) #","= True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else:","E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self,","locals() all_params = ['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')","noqa: E501 collection_formats = {} path_params = {} if 'file_id' in local_var_params: path_params['fileId']","HealthCheckEnveloped If the method is called asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only']","local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] header_params = {}","`get_file_metadata`\") # noqa: E501 # verify the required parameter 'location_id' is set if","= api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","(required) :param str location_id: (required) :param str user_id: (required) :return: None If the","local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id` when calling `update_file_meta_data`\") #","self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def get_file_metadata_with_http_info(self, file_id, location_id,","request, please pass async_req=True >>> thread = api.check_action_post(action, async_req=True) >>> result = thread.get()","result = thread.get() :param async_req bool :param str user_id: (required) :return: FileLocationArrayEnveloped If","api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str location_id:","async_req=True >>> thread = api.check_action_post(action, async_req=True) >>> result = thread.get() :param async_req bool","Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params,","the request thread. \"\"\" local_var_params = locals() all_params = [] # noqa: E501","all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in","def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update File Metadata # noqa:","E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations',","auth_settings = [] # noqa: E501 return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params,","`get_file_metadata`\") # noqa: E501 # verify the required parameter 'user_id' is set if","'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in","parameter 'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id'] is None):","None If the method is called asynchronously, returns the request thread. \"\"\" local_var_params","asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id,","noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files =","'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params = {} form_params =","server to fail or echo back the transmitted data # noqa: E501 This","\"Got an unexpected keyword argument '%s'\" \" to method get_files_metadata\" % key )","extra_source: :return: PresignedLinkEnveloped If the method is called asynchronously, returns the request thread.","unexpected keyword argument '%s'\" \" to method upload_file\" % key ) local_var_params[key] =","# noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,","upload link or performs copy operation to datcore # noqa: E501 This method",">>> result = thread.get() :param async_req bool :param str action: (required) :param str","local_var_params['user_id'])) # noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501","noqa: E501 \"\"\"Test checkpoint to ask server to fail or echo back the","E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return data def","the required parameter 'user_id' is set if ('user_id' not in local_var_params or local_var_params['user_id']","asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True)","FileMetaDataArrayEnveloped If the method is called asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only']","# noqa: E501 collection_formats = {} path_params = {} if 'action' in local_var_params:","user_id, **kwargs) # noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) #","request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501","calling `get_file_metadata`\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id'","edit the class manually. \"\"\" def __init__(self, api_client=None): if api_client is None: api_client","parameter `file_id` when calling `get_file_metadata`\") # noqa: E501 # verify the required parameter","local_var_params or local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id` when calling","query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params = {} form_params = [] local_var_files =","download link for requested file # noqa: E501 This method makes a synchronous","def get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get Files Metadata # noqa:","header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:","files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),","an unexpected keyword argument '%s'\" \" to method delete_file\" % key ) local_var_params[key]","request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) #","file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns upload link or performs copy","location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs)","thread = api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param async_req bool :param str","API API definition for simcore-service-storage service # noqa: E501 OpenAPI spec version: 0.1.0","keyword argument '%s'\" \" to method check_action_post\" % key ) local_var_params[key] = val","method upload_file\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the","query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #","\"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:","= thread.get() :param async_req bool :param str file_id: (required) :param str location_id: (required)","api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param async_req bool :param str action: (required)","async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id,","request thread. \"\"\" local_var_params = locals() all_params = ['action', 'data', 'fake_type'] # noqa:","raise ValueError(\"Missing the required parameter `file_id` when calling `delete_file`\") # noqa: E501 #","async_req bool :param str file_id: (required) :param str location_id: (required) :param str user_id:","body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501","HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result","None if 'fake_type' in local_var_params: body_params = local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept']","an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id,","# noqa: E501 return data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa:","\" to method delete_file\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #","key not in all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\" \"","val del local_var_params['kwargs'] # verify the required parameter 'location_id' is set if ('location_id'","{} query_params = [] header_params = {} form_params = [] local_var_files = {}","the required parameter `user_id` when calling `download_file`\") # noqa: E501 collection_formats = {}","request thread. \"\"\" local_var_params = locals() all_params = ['user_id'] # noqa: E501 all_params.append('async_req')","`delete_file`\") # noqa: E501 # verify the required parameter 'location_id' is set if","E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params = {}","is None): raise ValueError(\"Missing the required parameter `user_id` when calling `download_file`\") # noqa:","required parameter `location_id` when calling `download_file`\") # noqa: E501 # verify the required","auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params,","asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>>","E501 query_params = [] header_params = {} form_params = [] local_var_files = {}","E501 Some general information on the API and state of the service behind","bool :param str file_id: (required) :param str location_id: (required) :param str user_id: (required)","six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( \"Got an unexpected keyword argument","an unexpected keyword argument '%s'\" \" to method check_action_post\" % key ) local_var_params[key]","request, please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param",">>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req","return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa:","E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \"\"\"Get available storage","locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')","auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id,","'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise","to method delete_file\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify","None): raise ValueError(\"Missing the required parameter `location_id` when calling `delete_file`\") # noqa: E501","\"Got an unexpected keyword argument '%s'\" \" to method health_check\" % key )","the required parameter `user_id` when calling `get_storage_locations`\") # noqa: E501 collection_formats = {}","collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns upload link","or local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling `get_file_metadata`\")","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file(file_id, location_id,","local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')","noqa: E501 return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501","E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data","_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id,","**kwargs) # noqa: E501 return data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): #","behind # noqa: E501 This method makes a synchronous HTTP request by default.",":param async_req bool :param str action: (required) :param str data: :param FakeType fake_type:","(required) :return: FileMetaDataEnveloped If the method is called asynchronously, returns the request thread.","FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method is called asynchronously, returns the request","'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter',","= api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param","local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'user_id' is set","ApiClient class UsersApi(object): \"\"\"NOTE: This class is auto generated by OpenAPI Generator Ref:","api_client = ApiClient() self.api_client = api_client def check_action_post(self, action, **kwargs): # noqa: E501","# noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,","= True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else: (data)","path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] if 'user_id' in local_var_params:",":return: FileMetaDataEnveloped If the method is called asynchronously, returns the request thread. \"\"\"","to method check_action_post\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify","local_var_params['location_id'] # noqa: E501 query_params = [] header_params = {} form_params = []","the required parameter `user_id` when calling `delete_file`\") # noqa: E501 collection_formats = {}","user_id, **kwargs): # noqa: E501 \"\"\"Returns upload link or performs copy operation to","(required) :return: None If the method is called asynchronously, returns the request thread.","F401 # python 2 and python 3 compatibility library import six from simcore_service_storage_sdk.api_client","request, please pass async_req=True >>> thread = api.health_check(async_req=True) >>> result = thread.get() :param","if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in local_var_params:","None): raise ValueError(\"Missing the required parameter `file_id` when calling `upload_file`\") # noqa: E501","ValueError(\"Missing the required parameter `location_id` when calling `get_file_metadata`\") # noqa: E501 # verify","post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',","returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs)","else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return data def get_files_metadata_with_http_info(self,","local_var_params = locals() all_params = ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')","kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501","location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs)","'%s'\" \" to method delete_file\" % key ) local_var_params[key] = val del local_var_params['kwargs']","local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] if 'user_id' in","to method get_file_metadata\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify","request, please pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get()","the required parameter `user_id` when calling `get_file_metadata`\") # noqa: E501 collection_formats = {}","else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def","= [] local_var_files = {} body_params = None if 'fake_type' in local_var_params: body_params","['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val","\" to method update_file_meta_data\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #","'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):","required parameter `user_id` when calling `get_storage_locations`\") # noqa: E501 collection_formats = {} path_params","str location_id: (required) :param str user_id: (required) :return: None If the method is","method check_action_post\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the","body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa:","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check(async_req=True) >>>","location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get Files Metadata # noqa: E501 This","please pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param","all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params:","api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool :param str file_id:","key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'user_id'","or local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling `get_file_metadata`\")","None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 #","api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id,","an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True)","ValueError(\"Missing the required parameter `user_id` when calling `download_file`\") # noqa: E501 collection_formats =","`check_action_post`\") # noqa: E501 collection_formats = {} path_params = {} if 'action' in","keyword argument '%s'\" \" to method update_file_meta_data\" % key ) local_var_params[key] = val","__init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def","raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method check_action_post\" %","None): raise ValueError(\"Missing the required parameter `user_id` when calling `download_file`\") # noqa: E501","# noqa: E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\"","or local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling `delete_file`\")","= [] # noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params,","E501 return data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns","class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the","\"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:","local_var_files = {} body_params = None if 'fake_type' in local_var_params: body_params = local_var_params['fake_type']","get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get File Metadata # noqa:","self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501","noqa: E501 \"\"\"Returns upload link or performs copy operation to datcore # noqa:","location_id: (required) :param str user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If the","self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data def check_action_post_with_http_info(self, action, **kwargs): # noqa:","returns the request thread. \"\"\" local_var_params = locals() all_params = ['user_id'] # noqa:","= val del local_var_params['kwargs'] # verify the required parameter 'file_id' is set if","all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val","request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) #","E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id,","local_var_files = {} body_params = None if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type']","`location_id` when calling `get_files_metadata`\") # noqa: E501 # verify the required parameter 'user_id'","locations # noqa: E501 This method makes a synchronous HTTP request by default.","Generator Ref: https://openapi-generator.tech Do not edit the class manually. \"\"\" def __init__(self, api_client=None):","returns the request thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id',","(required) :param str location_id: (required) :param str user_id: (required) :param str extra_location: :param","ValueError(\"Missing the required parameter `user_id` when calling `delete_file`\") # noqa: E501 collection_formats =","auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id,","is None): raise ValueError(\"Missing the required parameter `user_id` when calling `delete_file`\") # noqa:","(data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs):","None): raise ValueError(\"Missing the required parameter `file_id` when calling `get_file_metadata`\") # noqa: E501",":param str user_id: (required) :return: None If the method is called asynchronously, returns","E501 \"\"\"Service health-check endpoint # noqa: E501 Some general information on the API","an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True)","thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else:","local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) #","return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get File","\"\"\" from __future__ import absolute_import import re # noqa: F401 # python 2","True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Deletes","data def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \"\"\"Test checkpoint to ask server","to datcore # noqa: E501 This method makes a synchronous HTTP request by","thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool","{} query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501","local_var_params = locals() all_params = ['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')","`user_id` when calling `download_file`\") # noqa: E501 collection_formats = {} path_params = {}","def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get File Metadata #","header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = []","return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa:","in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source']))","returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id,","the required parameter `location_id` when calling `download_file`\") # noqa: E501 # verify the","(data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data def check_action_post_with_http_info(self, action, **kwargs):","= local_var_params['file_id'] # noqa: E501 if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] #","noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return data def health_check_with_http_info(self,","None): raise ValueError(\"Missing the required parameter `location_id` when calling `download_file`\") # noqa: E501","'%s'\" \" to method upload_file\" % key ) local_var_params[key] = val del local_var_params['kwargs']","async_req=True >>> thread = api.health_check(async_req=True) >>> result = thread.get() :param async_req bool :return:","update_file_meta_data\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required","val del local_var_params['kwargs'] # verify the required parameter 'action' is set if ('action'","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id,","is None): raise ValueError(\"Missing the required parameter `file_id` when calling `update_file_meta_data`\") # noqa:","when calling `get_file_metadata`\") # noqa: E501 # verify the required parameter 'user_id' is","data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update File Metadata #","E501 \"\"\"Get Files Metadata # noqa: E501 This method makes a synchronous HTTP","E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\" from __future__","`user_id` when calling `get_file_metadata`\") # noqa: E501 collection_formats = {} path_params = {}","auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs):","if ('user_id' not in local_var_params or local_var_params['user_id'] is None): raise ValueError(\"Missing the required","asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result =","local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id` when calling `get_file_metadata`\") #","required parameter `user_id` when calling `get_files_metadata`\") # noqa: E501 collection_formats = {} path_params","**kwargs) # noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return","an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id,","Metadata # noqa: E501 This method makes a synchronous HTTP request by default.","please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result =","upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns upload link or performs","= True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data) =","in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location']))","uuid_filter: :return: FileMetaDataArrayEnveloped If the method is called asynchronously, returns the request thread.","request, please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result","TypeError( \"Got an unexpected keyword argument '%s'\" \" to method health_check\" % key","the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs)","is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class","the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id,","def download_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns download link for","local_var_params['location_id'] # noqa: E501 query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id']))","'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):","`action` when calling `check_action_post`\") # noqa: E501 collection_formats = {} path_params = {}","post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',","and python 3 compatibility library import six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object):","str file_id: (required) :param str location_id: (required) :param str user_id: (required) :param str","\"Got an unexpected keyword argument '%s'\" \" to method update_file_meta_data\" % key )","'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),","local_var_params or local_var_params['action'] is None): raise ValueError(\"Missing the required parameter `action` when calling","for requested file # noqa: E501 This method makes a synchronous HTTP request","{} if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if 'location_id'","local_var_params['file_id'] # noqa: E501 if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa:","file_id: (required) :param str location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the","path_params = {} query_params = [] header_params = {} form_params = [] local_var_files","FakeType fake_type: :return: FakeEnveloped If the method is called asynchronously, returns the request","raise ValueError(\"Missing the required parameter `user_id` when calling `download_file`\") # noqa: E501 collection_formats","argument '%s'\" \" to method update_file_meta_data\" % key ) local_var_params[key] = val del","E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501 \"\"\"Service health-check","[] local_var_files = {} body_params = None if 'file_meta_data_type' in local_var_params: body_params =","in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( \"Got an unexpected keyword","raise ValueError(\"Missing the required parameter `user_id` when calling `upload_file`\") # noqa: E501 collection_formats",">>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param","# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE',","data # noqa: E501 This method makes a synchronous HTTP request by default.","utf-8 \"\"\" simcore-service-storage API API definition for simcore-service-storage service # noqa: E501 OpenAPI",">>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req","if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs)","header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:","E501 # verify the required parameter 'location_id' is set if ('location_id' not in","_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa: E501","E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self,","E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return data def health_check_with_http_info(self, **kwargs):","'/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings,","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id,","an unexpected keyword argument '%s'\" \" to method update_file_meta_data\" % key ) local_var_params[key]","= locals() all_params = ['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file(file_id,","local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept(","if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa:","location_id, **kwargs): # noqa: E501 \"\"\"Update File Metadata # noqa: E501 This method","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file_with_http_info(file_id,","E501 collection_formats = {} path_params = {} if 'location_id' in local_var_params: path_params['location_id'] =","header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:","(required) :return: PresignedLinkEnveloped If the method is called asynchronously, returns the request thread.","the required parameter `file_id` when calling `get_file_metadata`\") # noqa: E501 # verify the","str extra_source: :return: PresignedLinkEnveloped If the method is called asynchronously, returns the request","response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),","async_req bool :param str location_id: (required) :param str user_id: (required) :param str uuid_filter:","# noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501","or local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling `update_file_meta_data`\")","request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs)","path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id']","request thread. \"\"\" local_var_params = locals() all_params = [] # noqa: E501 all_params.append('async_req')","the required parameter `file_id` when calling `download_file`\") # noqa: E501 # verify the","absolute_import import re # noqa: F401 # python 2 and python 3 compatibility","calling `delete_file`\") # noqa: E501 # verify the required parameter 'user_id' is set","path_params['action'] = local_var_params['action'] # noqa: E501 query_params = [] if 'data' in local_var_params:","def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns upload link or","# noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return","['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val","noqa: E501 query_params = [] if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa:","E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}',","of the service behind # noqa: E501 This method makes a synchronous HTTP","async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): #","user_id, **kwargs): # noqa: E501 \"\"\"Returns download link for requested file # noqa:","is set if ('action' not in local_var_params or local_var_params['action'] is None): raise ValueError(\"Missing","local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'file_id' is set","None if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept']","noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key",":param async_req bool :param str location_id: (required) :param str user_id: (required) :param str","= {} query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa:","API and state of the service behind # noqa: E501 This method makes","or local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id` when calling `upload_file`\")","kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data)","bool :param str action: (required) :param str data: :param FakeType fake_type: :return: FakeEnveloped","in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] if 'user_id'","= {} if 'action' in local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501 query_params","locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')","\"\"\"Service health-check endpoint # noqa: E501 Some general information on the API and","or local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling `download_file`\")","del local_var_params['kwargs'] # verify the required parameter 'action' is set if ('action' not","an unexpected keyword argument '%s'\" \" to method health_check\" % key ) local_var_params[key]","True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else: (data) =","file_id: (required) :param str location_id: (required) :param str user_id: (required) :return: FileMetaDataEnveloped If","noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa:",":return: PresignedLinkEnveloped If the method is called asynchronously, returns the request thread. \"\"\"",":return: FileLocationArrayEnveloped If the method is called asynchronously, returns the request thread. \"\"\"","'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),","= locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req')","HTTP request, please pass async_req=True >>> thread = api.download_file(file_id, location_id, user_id, async_req=True) >>>","_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501","by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread","# noqa: E501 \"\"\"Test checkpoint to ask server to fail or echo back","# noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,","kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) #","the required parameter `user_id` when calling `upload_file`\") # noqa: E501 collection_formats = {}","str user_id: (required) :return: None If the method is called asynchronously, returns the","E501 query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501","'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):","local_var_params['user_id'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {}","# noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501","ValueError(\"Missing the required parameter `file_id` when calling `upload_file`\") # noqa: E501 # verify","0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\" from __future__ import absolute_import import re","async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: (required) :return:",":param str location_id: (required) :param str user_id: (required) :param str extra_location: :param str","['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val","(data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return data def health_check_with_http_info(self, **kwargs): # noqa:","_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs):","calling `get_files_metadata`\") # noqa: E501 collection_formats = {} path_params = {} if 'location_id'","or echo back the transmitted data # noqa: E501 This method makes a","noqa: E501 # verify the required parameter 'location_id' is set if ('location_id' not","(data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def upload_file_with_http_info(self,","header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:","'%s'\" \" to method get_file_metadata\" % key ) local_var_params[key] = val del local_var_params['kwargs']","kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) =","asynchronously, returns the request thread. \"\"\" local_var_params = locals() all_params = ['user_id'] #","called asynchronously, returns the request thread. \"\"\" local_var_params = locals() all_params = []","an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file(file_id, location_id, user_id,","request, please pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result","\"\"\"Deletes File # noqa: E501 This method makes a synchronous HTTP request by","local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {}","the required parameter 'action' is set if ('action' not in local_var_params or local_var_params['action']","body_params = None if 'fake_type' in local_var_params: body_params = local_var_params['fake_type'] # HTTP header","= self.health_check_with_http_info(**kwargs) # noqa: E501 return data def health_check_with_http_info(self, **kwargs): # noqa: E501","the service behind # noqa: E501 This method makes a synchronous HTTP request","an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result","async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req","None): raise ValueError(\"Missing the required parameter `user_id` when calling `get_storage_locations`\") # noqa: E501","self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id,",":param async_req bool :param str file_id: (required) :param str location_id: (required) :param FileMetaDataType","the required parameter `action` when calling `check_action_post`\") # noqa: E501 collection_formats = {}","thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool :param","= self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs): #","'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),","FileMetaDataArrayEnveloped If the method is called asynchronously, returns the request thread. \"\"\" local_var_params","async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id,","kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.upload_file_with_http_info(file_id,","`get_files_metadata`\") # noqa: E501 collection_formats = {} path_params = {} if 'location_id' in","argument '%s'\" \" to method get_storage_locations\" % key ) local_var_params[key] = val del","ValueError(\"Missing the required parameter `location_id` when calling `get_files_metadata`\") # noqa: E501 # verify","return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get Files Metadata","E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', #","# noqa: E501 return data def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \"\"\"Test","_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns upload","location_id, user_id, **kwargs) # noqa: E501 return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id,","get_file_metadata\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required","= local_var_params['location_id'] # noqa: E501 query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id',","calling `upload_file`\") # noqa: E501 # verify the required parameter 'user_id' is set","pass async_req=True >>> thread = api.download_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get()","location_id: (required) :param str user_id: (required) :return: FileMetaDataEnveloped If the method is called","= self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def delete_file_with_http_info(self, file_id,","HTTP request, please pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result =","{} path_params = {} if 'action' in local_var_params: path_params['action'] = local_var_params['action'] # noqa:","when calling `delete_file`\") # noqa: E501 # verify the required parameter 'location_id' is","HTTP request, please pass async_req=True >>> thread = api.health_check(async_req=True) >>> result = thread.get()","_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Deletes File","# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): #","thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool","thread.get() :param async_req bool :param str user_id: (required) :return: FileLocationArrayEnveloped If the method","**kwargs): # noqa: E501 \"\"\"Returns upload link or performs copy operation to datcore","called asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return","noqa: E501 return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get","thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If","location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns upload link or performs copy operation","or local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling `get_storage_locations`\")","or local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling `delete_file`\")","PresignedLinkEnveloped If the method is called asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only']","= self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def download_file_with_http_info(self, file_id,","Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\" from __future__ import absolute_import import re #","`upload_file`\") # noqa: E501 # verify the required parameter 'user_id' is set if","`location_id` when calling `get_file_metadata`\") # noqa: E501 # verify the required parameter 'user_id'","asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file(file_id, location_id, user_id, async_req=True)","[] if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params = {}","pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get()","import ApiClient class UsersApi(object): \"\"\"NOTE: This class is auto generated by OpenAPI Generator","async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param","def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \"\"\"Get available storage locations # noqa:","self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501","'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),","# noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if","return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa:","**kwargs): # noqa: E501 \"\"\"Returns download link for requested file # noqa: E501","file_id: (required) :param str location_id: (required) :param str user_id: (required) :return: PresignedLinkEnveloped If","asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True)","self.health_check_with_http_info(**kwargs) # noqa: E501 return data def health_check_with_http_info(self, **kwargs): # noqa: E501 \"\"\"Service","when calling `upload_file`\") # noqa: E501 # verify the required parameter 'location_id' is","pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param async_req","val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params","(required) :param str data: :param FakeType fake_type: :return: FakeEnveloped If the method is","= api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param","body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa:","pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result = thread.get()","# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa:","post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',","asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>>","str data: :param FakeType fake_type: :return: FakeEnveloped If the method is called asynchronously,","in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params = {} form_params = []","async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param async_req bool","request, please pass async_req=True >>> thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>> result","all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method upload_file\"","{} path_params = {} if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa:","str location_id: (required) :param str user_id: (required) :return: FileMetaDataEnveloped If the method is","in local_var_params: body_params = local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json'])","E501 return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get Files","'%s'\" \" to method health_check\" % key ) local_var_params[key] = val del local_var_params['kwargs']","(data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id,","download_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns download link for requested","'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in","async_req=True >>> thread = api.download_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param","noqa: E501 return data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501","returns the request thread. \"\"\" local_var_params = locals() all_params = ['location_id', 'user_id', 'uuid_filter']","auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params,","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post_with_http_info(action,","the required parameter `file_id` when calling `delete_file`\") # noqa: E501 # verify the","local_var_params or local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling","import six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \"\"\"NOTE: This class is auto","= local_var_params['location_id'] # noqa: E501 query_params = [] header_params = {} form_params =","if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location' in local_var_params:","an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id,",":param str location_id: (required) :param str user_id: (required) :return: PresignedLinkEnveloped If the method","six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \"\"\"NOTE: This class is auto generated","= ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,","True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data)","(required) :param str location_id: (required) :param str user_id: (required) :return: PresignedLinkEnveloped If the","= {} path_params = {} query_params = [] header_params = {} form_params =","keyword argument '%s'\" \" to method upload_file\" % key ) local_var_params[key] = val","True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update File","in local_var_params or local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id` when","= local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501","header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:","'user_id' is set if ('user_id' not in local_var_params or local_var_params['user_id'] is None): raise",">>> result = thread.get() :param async_req bool :param str user_id: (required) :return: FileLocationArrayEnveloped","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True)","request by default. To make an asynchronous HTTP request, please pass async_req=True >>>","# noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,","set if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ValueError(\"Missing the","key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'action'","[] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params,","re # noqa: F401 # python 2 and python 3 compatibility library import","method health_check\" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {}","noqa: E501 Some general information on the API and state of the service","an unexpected keyword argument '%s'\" \" to method upload_file\" % key ) local_var_params[key]","self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id,","'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if 'location_id' in local_var_params:","**kwargs) # noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501","or local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id` when calling `get_file_metadata`\")","an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True)","# noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats)","path_params = {} if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501","local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling `download_file`\") #","query_params = [] header_params = {} form_params = [] local_var_files = {} body_params","delete_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Deletes File # noqa: E501","user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If the method is called asynchronously,","E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa: E501 \"\"\"Get","= [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter'","parameter `location_id` when calling `update_file_meta_data`\") # noqa: E501 collection_formats = {} path_params =","str uuid_filter: :return: FileMetaDataArrayEnveloped If the method is called asynchronously, returns the request","if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] =","# noqa: E501 collection_formats = {} path_params = {} if 'file_id' in local_var_params:","local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'action' is set","by: https://openapi-generator.tech \"\"\" from __future__ import absolute_import import re # noqa: F401 #","self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa:","if 'action' in local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501 query_params = []",">>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool","**kwargs) # noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa:","'action' in local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501 query_params = [] if","method get_files_metadata\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the","to method health_check\" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats =","query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #","(required) :param str location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method","auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id,","E501 if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params =","required parameter `file_id` when calling `update_file_meta_data`\") # noqa: E501 # verify the required","asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result","collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Deletes File #","**kwargs): # noqa: E501 \"\"\"Get available storage locations # noqa: E501 This method","all_params = ['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for","header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:","async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param","thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa:","str user_id: (required) :return: FileLocationArrayEnveloped If the method is called asynchronously, returns the","'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):","parameter `user_id` when calling `get_storage_locations`\") # noqa: E501 collection_formats = {} path_params =","= [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location'","# noqa: E501 return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,","local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling `download_file`\") #","noqa: E501 \"\"\"Deletes File # noqa: E501 This method makes a synchronous HTTP","# coding: utf-8 \"\"\" simcore-service-storage API API definition for simcore-service-storage service # noqa:","if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params = {} form_params","please pass async_req=True >>> thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>> result =","not in local_var_params or local_var_params['action'] is None): raise ValueError(\"Missing the required parameter `action`","query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params = {} form_params = [] local_var_files =","collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get File Metadata","[] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):","local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling `get_storage_locations`\") #","This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit","return data def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \"\"\"Test checkpoint to ask","= api.download_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param","request, please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get()","location_id, user_id, **kwargs): # noqa: E501 \"\"\"Deletes File # noqa: E501 This method","copy operation to datcore # noqa: E501 This method makes a synchronous HTTP",">>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool","method makes a synchronous HTTP request by default. To make an asynchronous HTTP","`Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings =","_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs):","for simcore-service-storage service # noqa: E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated","local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501","verify the required parameter 'user_id' is set if ('user_id' not in local_var_params or","= {} path_params = {} query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id',",">>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param async_req bool :param","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations(user_id,","E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', #","# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs):","OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\" from __future__ import","request, please pass async_req=True >>> thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>> result","kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501","= self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] #","raise ValueError(\"Missing the required parameter `action` when calling `check_action_post`\") # noqa: E501 collection_formats","returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id,","str location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method is called","async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id,","api.delete_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","to method update_file_meta_data\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify","# noqa: E501 query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) #","header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 #","self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( #","= {} if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if","header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting","= api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id:","'/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings,","= api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","request, please pass async_req=True >>> thread = api.download_file(file_id, location_id, user_id, async_req=True) >>> result","= None if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept`","kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id,","E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501","local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) #",":param str user_id: (required) :param str extra_location: :param str extra_source: :return: PresignedLinkEnveloped If","noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return","kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.delete_file_with_http_info(file_id,","thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) #","E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return data def","return data def health_check_with_http_info(self, **kwargs): # noqa: E501 \"\"\"Service health-check endpoint # noqa:","link or performs copy operation to datcore # noqa: E501 This method makes","result = thread.get() :param async_req bool :param str action: (required) :param str data:","body_params = local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa:","required parameter `user_id` when calling `download_file`\") # noqa: E501 collection_formats = {} path_params","please pass async_req=True >>> thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>> result =","in local_var_params or local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when","is None): raise ValueError(\"Missing the required parameter `user_id` when calling `get_storage_locations`\") # noqa:","# noqa: E501 query_params = [] if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) #","ValueError(\"Missing the required parameter `user_id` when calling `get_file_metadata`\") # noqa: E501 collection_formats =","form_params = [] local_var_files = {} body_params = None if 'fake_type' in local_var_params:","collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get Files Metadata #","location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str file_id:","# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET',","local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params = {} form_params = [] local_var_files",") local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'user_id' is","= True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs)","ValueError(\"Missing the required parameter `file_id` when calling `update_file_meta_data`\") # noqa: E501 # verify","is None): raise ValueError(\"Missing the required parameter `user_id` when calling `upload_file`\") # noqa:","parameter `action` when calling `check_action_post`\") # noqa: E501 collection_formats = {} path_params =","the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs)","async_req bool :param str user_id: (required) :return: FileLocationArrayEnveloped If the method is called","setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params,","request, please pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result =","parameter `location_id` when calling `get_file_metadata`\") # noqa: E501 # verify the required parameter","user_id, **kwargs): # noqa: E501 \"\"\"Get Files Metadata # noqa: E501 This method","self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501","query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params = {} form_params = [] local_var_files =","setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/', 'GET', path_params, query_params,","collection_formats = {} path_params = {} if 'action' in local_var_params: path_params['action'] = local_var_params['action']","self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file(file_id,","parameter `location_id` when calling `get_files_metadata`\") # noqa: E501 # verify the required parameter","# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs):","**kwargs) # noqa: E501 return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa:","if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ValueError(\"Missing the required","noqa: F401 # python 2 and python 3 compatibility library import six from","'%s'\" \" to method get_files_metadata\" % key ) local_var_params[key] = val del local_var_params['kwargs']","local_var_params['kwargs'] # verify the required parameter 'file_id' is set if ('file_id' not in","'/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings,","ValueError(\"Missing the required parameter `location_id` when calling `update_file_meta_data`\") # noqa: E501 collection_formats =","data def health_check_with_http_info(self, **kwargs): # noqa: E501 \"\"\"Service health-check endpoint # noqa: E501","return data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns upload",">>> thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req","E501 collection_formats = {} path_params = {} if 'file_id' in local_var_params: path_params['fileId'] =","\" to method check_action_post\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata(file_id,","api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501 \"\"\"Service health-check endpoint # noqa: E501","in local_var_params or local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when","location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method is called asynchronously,","# verify the required parameter 'action' is set if ('action' not in local_var_params",":return: FileMetaDataArrayEnveloped If the method is called asynchronously, returns the request thread. \"\"\"","local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling `upload_file`\") #","(data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def get_file_metadata_with_http_info(self,","please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req","api_client def check_action_post(self, action, **kwargs): # noqa: E501 \"\"\"Test checkpoint to ask server","= api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param async_req bool :param str action:","self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501","True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else: (data) =","`update_file_meta_data`\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id' in","HTTP request, please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result =","str user_id: (required) :return: FileMetaDataEnveloped If the method is called asynchronously, returns the","local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501 query_params = [] if 'data' in","`location_id` when calling `delete_file`\") # noqa: E501 # verify the required parameter 'user_id'","update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update File Metadata # noqa: E501","manually. \"\"\" def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client","# noqa: E501 \"\"\"Update File Metadata # noqa: E501 This method makes a","async_req bool :param str file_id: (required) :param str location_id: (required) :param FileMetaDataType file_meta_data_type:","('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter","required parameter 'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id'] is","the required parameter `location_id` when calling `get_files_metadata`\") # noqa: E501 # verify the","noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'user_id'","E501 # verify the required parameter 'user_id' is set if ('user_id' not in","if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = []","please pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param","\" to method get_file_metadata\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #",":return: HealthCheckEnveloped If the method is called asynchronously, returns the request thread. \"\"\"","raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method health_check\" %","{} if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params =","async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param","['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):","pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get() :param","ask server to fail or echo back the transmitted data # noqa: E501","(required) :param str user_id: (required) :return: None If the method is called asynchronously,","returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id,","location_id, user_id, **kwargs) # noqa: E501 return data def upload_file_with_http_info(self, file_id, location_id, user_id,","data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns upload link","path_params = {} if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501","return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs)","\"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501","raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method delete_file\" %","data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns download link","[] header_params = {} form_params = [] local_var_files = {} body_params = None","('file_id' not in local_var_params or local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter","path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] header_params = {} form_params","\"Got an unexpected keyword argument '%s'\" \" to method download_file\" % key )","api.health_check(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If the method","location_id, async_req=True) >>> result = thread.get() :param async_req bool :param str file_id: (required)","extra_location: :param str extra_source: :return: PresignedLinkEnveloped If the method is called asynchronously, returns","None): raise ValueError(\"Missing the required parameter `user_id` when calling `get_files_metadata`\") # noqa: E501",":return: FakeEnveloped If the method is called asynchronously, returns the request thread. \"\"\"","\"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:","ValueError(\"Missing the required parameter `user_id` when calling `get_files_metadata`\") # noqa: E501 collection_formats =","returns the request thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id']","def health_check(self, **kwargs): # noqa: E501 \"\"\"Service health-check endpoint # noqa: E501 Some","please pass async_req=True >>> thread = api.health_check(async_req=True) >>> result = thread.get() :param async_req","pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get() :param","required parameter `file_id` when calling `delete_file`\") # noqa: E501 # verify the required","FileMetaDataEnveloped If the method is called asynchronously, returns the request thread. \"\"\" local_var_params","asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True)","auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params,","self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs):","query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #","called asynchronously, returns the request thread. \"\"\" local_var_params = locals() all_params = ['location_id',","# noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params","parameter `file_id` when calling `download_file`\") # noqa: E501 # verify the required parameter","noqa: E501 \"\"\"Returns download link for requested file # noqa: E501 This method","str location_id: (required) :param str user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If",":param str location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method is","[] # noqa: E501 return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params,","_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id,","# noqa: E501 return data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa:","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id,","user_id, **kwargs) # noqa: E501 return data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs):","auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id,",">>> thread = api.check_action_post(action, async_req=True) >>> result = thread.get() :param async_req bool :param","query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params","not edit the class manually. \"\"\" def __init__(self, api_client=None): if api_client is None:","header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings","path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),","user_id, **kwargs): # noqa: E501 \"\"\"Get available storage locations # noqa: E501 This","True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data)","_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa: E501 \"\"\"Get available","header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type`","else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def","def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update File Metadata # noqa:","{} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) #","location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs)","# noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files","local_var_params['extra_source'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {}","calling `get_files_metadata`\") # noqa: E501 # verify the required parameter 'user_id' is set","get_storage_locations\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required","file_id: (required) :param str location_id: (required) :param str user_id: (required) :param str extra_location:","api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","when calling `get_files_metadata`\") # noqa: E501 collection_formats = {} path_params = {} if","pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get()","kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id,","python 2 and python 3 compatibility library import six from simcore_service_storage_sdk.api_client import ApiClient","thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) #","str location_id: (required) :param str user_id: (required) :return: PresignedLinkEnveloped If the method is","required parameter 'user_id' is set if ('user_id' not in local_var_params or local_var_params['user_id'] is","raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method get_file_metadata\" %","`user_id` when calling `upload_file`\") # noqa: E501 collection_formats = {} path_params = {}","an unexpected keyword argument '%s'\" \" to method get_storage_locations\" % key ) local_var_params[key]","**kwargs): # noqa: E501 \"\"\"Get File Metadata # noqa: E501 This method makes","query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #","'file_id' is set if ('file_id' not in local_var_params or local_var_params['file_id'] is None): raise","= True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else: (data)","thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool","noqa: E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \"\"\"Get available","asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>> result","when calling `upload_file`\") # noqa: E501 collection_formats = {} path_params = {} if","None): raise ValueError(\"Missing the required parameter `user_id` when calling `get_file_metadata`\") # noqa: E501","auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id,","noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs): #","= [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params,","return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update File Metadata","to method download_file\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify","not in local_var_params or local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id`","is called asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'):","path_params = {} query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) #","E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs): # noqa:","async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs):","if 'fake_type' in local_var_params: body_params = local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] =","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True)","path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),","noqa: E501 return data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501","request, please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result =",":param async_req bool :return: HealthCheckEnveloped If the method is called asynchronously, returns the","if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) =","response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),","'/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings,","local_var_params['data'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {}",":param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method is called asynchronously, returns the","in all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method","asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id,","or local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id` when calling `update_file_meta_data`\")","'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params = {} form_params =","# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH',","user_id, **kwargs) # noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa:","async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req bool","unexpected keyword argument '%s'\" \" to method get_files_metadata\" % key ) local_var_params[key] =","= [] if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params =","# noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return","asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id,","if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params = {} form_params","noqa: E501 return data def health_check_with_http_info(self, **kwargs): # noqa: E501 \"\"\"Service health-check endpoint","collection_formats = {} path_params = {} query_params = [] header_params = {} form_params","E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, #","Some general information on the API and state of the service behind #","pass async_req=True >>> thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get()","Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params,","**kwargs) # noqa: E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501","required parameter `file_id` when calling `download_file`\") # noqa: E501 # verify the required","str file_id: (required) :param str location_id: (required) :param str user_id: (required) :return: None","parameter 'action' is set if ('action' not in local_var_params or local_var_params['action'] is None):","raise ValueError(\"Missing the required parameter `file_id` when calling `download_file`\") # noqa: E501 #","# noqa: E501 \"\"\"Returns upload link or performs copy operation to datcore #","**kwargs) # noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return","returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs)","when calling `delete_file`\") # noqa: E501 collection_formats = {} path_params = {} if","keyword argument '%s'\" \" to method download_file\" % key ) local_var_params[key] = val","class UsersApi(object): \"\"\"NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech","TypeError( \"Got an unexpected keyword argument '%s'\" \" to method get_files_metadata\" % key","parameter `file_id` when calling `update_file_meta_data`\") # noqa: E501 # verify the required parameter",":param str user_id: (required) :return: FileLocationArrayEnveloped If the method is called asynchronously, returns","else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return data def health_check_with_http_info(self, **kwargs): #","general information on the API and state of the service behind # noqa:","_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get Files Metadata","E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501","thread. \"\"\" local_var_params = locals() all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa: E501","ValueError(\"Missing the required parameter `file_id` when calling `download_file`\") # noqa: E501 # verify","in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params = {} form_params = []","= [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params,","key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'location_id'","**kwargs) # noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:","('action' not in local_var_params or local_var_params['action'] is None): raise ValueError(\"Missing the required parameter","key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( \"Got an","local_var_params['user_id'] is None): raise ValueError(\"Missing the required parameter `user_id` when calling `get_file_metadata`\") #","noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update","`upload_file`\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id' in","= {} body_params = None if 'fake_type' in local_var_params: body_params = local_var_params['fake_type'] #","raise ValueError(\"Missing the required parameter `file_id` when calling `upload_file`\") # noqa: E501 #","'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source',","if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) =","\"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req')","an unexpected keyword argument '%s'\" \" to method get_file_metadata\" % key ) local_var_params[key]",">>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If the method is","verify the required parameter 'action' is set if ('action' not in local_var_params or","E501 collection_formats = {} path_params = {} if 'action' in local_var_params: path_params['action'] =","api.download_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","`Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type']","to ask server to fail or echo back the transmitted data # noqa:","if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) =","# noqa: E501 Some general information on the API and state of the","user_id: (required) :return: FileMetaDataEnveloped If the method is called asynchronously, returns the request","raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method get_files_metadata\" %","raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method get_storage_locations\" %","\" to method upload_file\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #","calling `delete_file`\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id'","local_var_params = locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')","return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa:","thread = api.download_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool","noqa: E501 if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params",":param str data: :param FakeType fake_type: :return: FakeEnveloped If the method is called","\"Got an unexpected keyword argument '%s'\" \" to method get_file_metadata\" % key )","calling `get_file_metadata`\") # noqa: E501 # verify the required parameter 'user_id' is set","_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501 \"\"\"Service health-check endpoint","# noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,","the request thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location',","if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id,","thread. \"\"\" local_var_params = locals() all_params = ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')","\"\"\" def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client =","`get_storage_locations`\") # noqa: E501 collection_formats = {} path_params = {} query_params = []","# noqa: E501 collection_formats = {} path_params = {} if 'location_id' in local_var_params:","is None): raise ValueError(\"Missing the required parameter `file_id` when calling `upload_file`\") # noqa:","noqa: E501 return data def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \"\"\"Test checkpoint","an asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post(action, async_req=True) >>>","= True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else:","= api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param","please pass async_req=True >>> thread = api.download_file(file_id, location_id, user_id, async_req=True) >>> result =","= locals() all_params = ['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')","auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params,","# noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501","link for requested file # noqa: E501 This method makes a synchronous HTTP","return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa:","= thread.get() :param async_req bool :param str location_id: (required) :param str user_id: (required)","auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params,","(data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def download_file_with_http_info(self,","async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id,","fail or echo back the transmitted data # noqa: E501 This method makes","returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id,","'/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings,","return data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Deletes File","location_id, user_id, **kwargs) # noqa: E501 return data def delete_file_with_http_info(self, file_id, location_id, user_id,","the required parameter `location_id` when calling `upload_file`\") # noqa: E501 # verify the","def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Deletes File # noqa:","{} form_params = [] local_var_files = {} body_params = None if 'fake_type' in","python 3 compatibility library import six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \"\"\"NOTE:","header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] =","\"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data)","health-check endpoint # noqa: E501 Some general information on the API and state","= {} body_params = None if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] #","[] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params,","E501 \"\"\"Returns download link for requested file # noqa: E501 This method makes","the required parameter `file_id` when calling `update_file_meta_data`\") # noqa: E501 # verify the","\"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa:","= val del local_var_params['kwargs'] # verify the required parameter 'action' is set if","= local_var_params['action'] # noqa: E501 query_params = [] if 'data' in local_var_params: query_params.append(('data',","user_id: (required) :return: FileLocationArrayEnveloped If the method is called asynchronously, returns the request","async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id,","if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs)","noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return","thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501","response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),","['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa:","all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')","None): raise ValueError(\"Missing the required parameter `location_id` when calling `get_file_metadata`\") # noqa: E501","# noqa: E501 \"\"\"Service health-check endpoint # noqa: E501 Some general information on","files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),","FileLocationArrayEnveloped If the method is called asynchronously, returns the request thread. \"\"\" local_var_params","api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str","return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id,","noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return","**kwargs) # noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501","{} path_params = {} query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id']))","\"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else:","noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): # noqa:","# noqa: E501 # verify the required parameter 'user_id' is set if ('user_id'","or local_var_params['location_id'] is None): raise ValueError(\"Missing the required parameter `location_id` when calling `upload_file`\")","File Metadata # noqa: E501 This method makes a synchronous HTTP request by","location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns download link for requested file #","local_var_params = locals() all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')","local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id` when calling `upload_file`\") #","None): raise ValueError(\"Missing the required parameter `user_id` when calling `upload_file`\") # noqa: E501","E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa:",") local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'location_id' is","= [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params,",":param async_req bool :param str user_id: (required) :return: FileLocationArrayEnveloped If the method is","(data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return data def get_files_metadata_with_http_info(self, location_id,","all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for","version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\" from __future__ import absolute_import import","request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs)","local_var_params['file_id'] is None): raise ValueError(\"Missing the required parameter `file_id` when calling `delete_file`\") #","# noqa: E501 collection_formats = {} path_params = {} query_params = [] if","else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data def check_action_post_with_http_info(self, action,","True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data)","**kwargs) # noqa: E501 return data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): #","thread.get() :param async_req bool :param str file_id: (required) :param str location_id: (required) :param","parameter `location_id` when calling `download_file`\") # noqa: E501 # verify the required parameter","asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action,","[] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params = {}","location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get File Metadata # noqa: E501 This","E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', #","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post(action,","required parameter 'file_id' is set if ('file_id' not in local_var_params or local_var_params['file_id'] is",":param str user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If the method is",">>> thread = api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param","user_id, **kwargs) # noqa: E501 return data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs):","local_var_params = locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')","If the method is called asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] =","upload_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns upload link or performs","the request thread. \"\"\" local_var_params = locals() all_params = ['location_id', 'user_id', 'uuid_filter'] #","datcore # noqa: E501 This method makes a synchronous HTTP request by default.","thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool","# noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501","To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata(location_id,","location_id, **kwargs) # noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa:","local_var_params: body_params = local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) #","returns the request thread. \"\"\" local_var_params = locals() all_params = ['action', 'data', 'fake_type']","key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'file_id'","if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if 'location_id' in","query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa:","def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \"\"\"Test checkpoint to ask server to","async_req=True >>> thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param","= [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in","return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs)","Do not edit the class manually. \"\"\" def __init__(self, api_client=None): if api_client is","= val del local_var_params['kwargs'] # verify the required parameter 'location_id' is set if","body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501","calling `delete_file`\") # noqa: E501 # verify the required parameter 'location_id' is set","[] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location' in","api_client is None: api_client = ApiClient() self.api_client = api_client def check_action_post(self, action, **kwargs):","return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa:","= None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501",">>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req","ValueError(\"Missing the required parameter `file_id` when calling `get_file_metadata`\") # noqa: E501 # verify","noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] #","HTTP request, please pass async_req=True >>> thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>>","self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return data","calling `get_file_metadata`\") # noqa: E501 # verify the required parameter 'location_id' is set","response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),","all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method get_file_metadata\"","health_check(self, **kwargs): # noqa: E501 \"\"\"Service health-check endpoint # noqa: E501 Some general","get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get Files Metadata # noqa: E501","thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool","= locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')","is set if ('file_id' not in local_var_params or local_var_params['file_id'] is None): raise ValueError(\"Missing","the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) #","'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),","return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa:","= True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) =","bool :return: HealthCheckEnveloped If the method is called asynchronously, returns the request thread.","asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>>","# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501","request, please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result","auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id,","auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params,","location_id, user_id, **kwargs) # noqa: E501 return data def download_file_with_http_info(self, file_id, location_id, user_id,","please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result =","_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id,","definition for simcore-service-storage service # noqa: E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL>","raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method upload_file\" %","E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self,","all_params = ['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for","_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update","# HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa:","header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:","if ('file_id' not in local_var_params or local_var_params['file_id'] is None): raise ValueError(\"Missing the required","update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501 \"\"\"Update File Metadata # noqa: E501","OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. \"\"\" def __init__(self,","auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually.","an asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id,","# noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return data def","noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return data","generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. \"\"\"","async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req","query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if","\"\"\"NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not","'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),","all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method get_storage_locations\"","noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def","UsersApi(object): \"\"\"NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do",":param str location_id: (required) :param str user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped","query_params = [] if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params","local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501","\" to method health_check\" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats","TypeError( \"Got an unexpected keyword argument '%s'\" \" to method download_file\" % key","\"\"\" local_var_params = locals() all_params = ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')",">>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool","please pass async_req=True >>> thread = api.check_action_post(action, async_req=True) >>> result = thread.get() :param","'%s'\" \" to method check_action_post\" % key ) local_var_params[key] = val del local_var_params['kwargs']","request, please pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result","unexpected keyword argument '%s'\" \" to method get_file_metadata\" % key ) local_var_params[key] =","noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs): #","(required) :param str user_id: (required) :param str extra_location: :param str extra_source: :return: PresignedLinkEnveloped","verify the required parameter 'file_id' is set if ('file_id' not in local_var_params or","kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data)","# noqa: E501 \"\"\"Deletes File # noqa: E501 This method makes a synchronous","calling `download_file`\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id'","E501 \"\"\"Get available storage locations # noqa: E501 This method makes a synchronous","location_id: (required) :param str user_id: (required) :return: None If the method is called","['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501","'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params = {} form_params =","make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id,","async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param async_req bool","# noqa: E501 \"\"\"Get available storage locations # noqa: E501 This method makes","\" to method get_files_metadata\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #","check_action_post(self, action, **kwargs): # noqa: E501 \"\"\"Test checkpoint to ask server to fail","# noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return","\"\"\"Get File Metadata # noqa: E501 This method makes a synchronous HTTP request","and state of the service behind # noqa: E501 This method makes a","HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting","noqa: E501 collection_formats = {} path_params = {} if 'action' in local_var_params: path_params['action']","local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) #","`file_id` when calling `upload_file`\") # noqa: E501 # verify the required parameter 'location_id'","True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa: E501 \"\"\"Get available storage","asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id,","return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa:","async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return:","argument '%s'\" \" to method download_file\" % key ) local_var_params[key] = val del","None): raise ValueError(\"Missing the required parameter `file_id` when calling `delete_file`\") # noqa: E501","# noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params","\"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else:","def get_storage_locations(self, user_id, **kwargs): # noqa: E501 \"\"\"Get available storage locations # noqa:","user_id, **kwargs) # noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) #","'%s'\" \" to method update_file_meta_data\" % key ) local_var_params[key] = val del local_var_params['kwargs']","thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool :param","noqa: E501 collection_formats = {} path_params = {} if 'location_id' in local_var_params: path_params['location_id']","TypeError( \"Got an unexpected keyword argument '%s'\" \" to method delete_file\" % key","checkpoint to ask server to fail or echo back the transmitted data #","parameter `user_id` when calling `download_file`\") # noqa: E501 collection_formats = {} path_params =","kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) #","parameter `file_id` when calling `delete_file`\") # noqa: E501 # verify the required parameter","if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in local_var_params:","required parameter `action` when calling `check_action_post`\") # noqa: E501 collection_formats = {} path_params","thread.get() :param async_req bool :param str location_id: (required) :param str user_id: (required) :param","noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return data","noqa: E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \"\"\" from","[] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params,","asynchronously, returns the request thread. \"\"\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id,","E501 return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Get","if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params = {} form_params","local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501","kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else:","def upload_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Returns upload link or","request, please pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>> result = thread.get()","is None): raise ValueError(\"Missing the required parameter `file_id` when calling `get_file_metadata`\") # noqa:","data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \"\"\"Deletes File #","\"Got an unexpected keyword argument '%s'\" \" to method upload_file\" % key )","HTTP request, please pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>>","set if ('user_id' not in local_var_params or local_var_params['user_id'] is None): raise ValueError(\"Missing the",">>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req","HTTP request, please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>>","calling `upload_file`\") # noqa: E501 # verify the required parameter 'location_id' is set","ValueError(\"Missing the required parameter `location_id` when calling `delete_file`\") # noqa: E501 # verify","FakeEnveloped If the method is called asynchronously, returns the request thread. \"\"\" local_var_params","= ['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,","is None: api_client = ApiClient() self.api_client = api_client def check_action_post(self, action, **kwargs): #","Files Metadata # noqa: E501 This method makes a synchronous HTTP request by","get_files_metadata\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required","% key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params =","HealthCheckEnveloped If the method is called asynchronously, returns the request thread. \"\"\" local_var_params","**kwargs): # noqa: E501 \"\"\"Deletes File # noqa: E501 This method makes a","is set if ('user_id' not in local_var_params or local_var_params['user_id'] is None): raise ValueError(\"Missing","'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),","HTTP request, please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>>","asynchronously, returns the request thread. \"\"\" local_var_params = locals() all_params = ['file_id', 'location_id',","# noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,","= [] local_var_files = {} body_params = None if 'file_meta_data_type' in local_var_params: body_params","\"\"\" local_var_params = locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req')","thread.get() :param async_req bool :param str action: (required) :param str data: :param FakeType","self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def download_file_with_http_info(self, file_id, location_id,",">>> thread = api.download_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req","all_params: raise TypeError( \"Got an unexpected keyword argument '%s'\" \" to method update_file_meta_data\"","get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \"\"\"Get available storage locations # noqa: E501","# verify the required parameter 'file_id' is set if ('file_id' not in local_var_params","# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET',","unexpected keyword argument '%s'\" \" to method download_file\" % key ) local_var_params[key] =","= locals() all_params = ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for","noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa: E501"],"string":"[\n \"required parameter `user_id` when calling `get_file_metadata`\\\") # noqa: E501 collection_formats = {} path_params\",\n \"noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped',\",\n \"= thread.get() :param async_req bool :return: HealthCheckEnveloped If the method is called asynchronously,\",\n \"locals() all_params = ['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')\",\n \"unexpected keyword argument '%s'\\\" \\\" to method check_action_post\\\" % key ) local_var_params[key] =\",\n \"= locals() all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')\",\n \"request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source']\",\n \"`get_file_metadata`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id' in\",\n \"pass async_req=True >>> thread = api.check_action_post(action, async_req=True) >>> result = thread.get() :param async_req\",\n \"to method get_files_metadata\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify\",\n \"get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get Files Metadata # noqa: E501\",\n \"noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data def\",\n \"_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa:\",\n \"self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501\",\n \"'fake_type' in local_var_params: body_params = local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept(\",\n \"self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501\",\n \">>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"= {} form_params = [] local_var_files = {} body_params = None if 'fake_type'\",\n \"parameter 'file_id' is set if ('file_id' not in local_var_params or local_var_params['file_id'] is None):\",\n \"True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) #\",\n \"user_id, **kwargs) # noqa: E501 return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs):\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file(file_id, location_id, user_id, async_req=True)\",\n \"an unexpected keyword argument '%s'\\\" \\\" to method get_files_metadata\\\" % key ) local_var_params[key]\",\n \"\\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501\",\n \"= [] # noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params,\",\n \"method is called asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if\",\n \") local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'file_id' is\",\n \"noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json'])\",\n \"raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method download_file\\\" %\",\n \"file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns download link for requested file\",\n \"called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id',\",\n \"# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT',\",\n \"noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs): #\",\n \"\\\"\\\"\\\"Get Files Metadata # noqa: E501 This method makes a synchronous HTTP request\",\n \"local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `upload_file`\\\") #\",\n \"thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id'] # noqa: E501\",\n \"`Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication\",\n \"# noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data\",\n \"unexpected keyword argument '%s'\\\" \\\" to method delete_file\\\" % key ) local_var_params[key] =\",\n \"str action: (required) :param str data: :param FakeType fake_type: :return: FakeEnveloped If the\",\n \"`file_id` when calling `update_file_meta_data`\\\") # noqa: E501 # verify the required parameter 'location_id'\",\n \"keyword argument '%s'\\\" \\\" to method get_files_metadata\\\" % key ) local_var_params[key] = val\",\n \"thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['action', 'data', 'fake_type'] # noqa: E501\",\n \"FileMetaDataEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only']\",\n \"[] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in\",\n \"None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `upload_file`\\\") # noqa: E501\",\n \"FakeEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only']\",\n \"pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool\",\n \"local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params =\",\n \"set if ('file_id' not in local_var_params or local_var_params['file_id'] is None): raise ValueError(\\\"Missing the\",\n \"'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept(\",\n \"async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id'] #\",\n \"auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params,\",\n \"pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param async_req\",\n \"requested file # noqa: E501 This method makes a synchronous HTTP request by\",\n \"please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get()\",\n \"files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),\",\n \"query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #\",\n \"data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available storage locations #\",\n \"None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `get_files_metadata`\\\") # noqa: E501\",\n \"argument '%s'\\\" \\\" to method upload_file\\\" % key ) local_var_params[key] = val del\",\n \"request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa:\",\n \"\\\"\\\"\\\"Returns download link for requested file # noqa: E501 This method makes a\",\n \"when calling `upload_file`\\\") # noqa: E501 # verify the required parameter 'user_id' is\",\n \"'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in\",\n \"E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', #\",\n \"called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['user_id']\",\n \"collection_formats = {} path_params = {} if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id']\",\n \"if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) =\",\n \"unexpected keyword argument '%s'\\\" \\\" to method update_file_meta_data\\\" % key ) local_var_params[key] =\",\n \"# verify the required parameter 'location_id' is set if ('location_id' not in local_var_params\",\n \"user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get File Metadata # noqa: E501 This method\",\n \">>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"service # noqa: E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech\",\n \"raise ValueError(\\\"Missing the required parameter `user_id` when calling `delete_file`\\\") # noqa: E501 collection_formats\",\n \"is set if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ValueError(\\\"Missing\",\n \"method get_storage_locations\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the\",\n \"E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self,\",\n \"the method is called asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True\",\n \"from __future__ import absolute_import import re # noqa: F401 # python 2 and\",\n \"argument '%s'\\\" \\\" to method get_files_metadata\\\" % key ) local_var_params[key] = val del\",\n \"[] # noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params,\",\n \"HTTP request, please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result\",\n \"_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available storage locations\",\n \"collection_formats = {} path_params = {} if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id']\",\n \"collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update File Metadata #\",\n \"_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id,\",\n \"raise ValueError(\\\"Missing the required parameter `location_id` when calling `delete_file`\\\") # noqa: E501 #\",\n \"locals() all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')\",\n \"request, please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file(file_id, location_id,\",\n \"bool :param str file_id: (required) :param str location_id: (required) :param FileMetaDataType file_meta_data_type: :return:\",\n \"async_req=True) >>> result = thread.get() :param async_req bool :param str file_id: (required) :param\",\n \"= api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id:\",\n \"self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def delete_file_with_http_info(self, file_id, location_id,\",\n \"user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes File # noqa: E501 This method makes\",\n \"thread = api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"raise ValueError(\\\"Missing the required parameter `location_id` when calling `update_file_meta_data`\\\") # noqa: E501 collection_formats\",\n \"to fail or echo back the transmitted data # noqa: E501 This method\",\n \"setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params,\",\n \"when calling `get_file_metadata`\\\") # noqa: E501 # verify the required parameter 'location_id' is\",\n \"`download_file`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id' in\",\n \"This method makes a synchronous HTTP request by default. To make an asynchronous\",\n \"simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \\\"\\\"\\\"NOTE: This class is auto generated by OpenAPI\",\n \"if key not in all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\"\",\n \"**kwargs) # noqa: E501 return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): #\",\n \"transmitted data # noqa: E501 This method makes a synchronous HTTP request by\",\n \"= self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return data def get_files_metadata_with_http_info(self, location_id, user_id,\",\n \"please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param\",\n \"{} if 'action' in local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501 query_params =\",\n \"async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id,\",\n \"(required) :param str user_id: (required) :return: PresignedLinkEnveloped If the method is called asynchronously,\",\n \"'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in\",\n \"**kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint # noqa: E501 Some general information\",\n \"= api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"bool :param str location_id: (required) :param str user_id: (required) :param str uuid_filter: :return:\",\n \"setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params,\",\n \"# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): #\",\n \"# noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = []\",\n \"'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,\",\n \"# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs):\",\n \"returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = [] # noqa:\",\n \":param str location_id: (required) :param str user_id: (required) :return: None If the method\",\n \"\\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:\",\n \"True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns\",\n \"or local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `upload_file`\\\")\",\n \"upload_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required\",\n \"https://openapi-generator.tech \\\"\\\"\\\" from __future__ import absolute_import import re # noqa: F401 # python\",\n \":param str uuid_filter: :return: FileMetaDataArrayEnveloped If the method is called asynchronously, returns the\",\n \"= {} query_params = [] header_params = {} form_params = [] local_var_files =\",\n \"= self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id,\",\n \"all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError(\",\n \"request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa:\",\n \"E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data\",\n \"information on the API and state of the service behind # noqa: E501\",\n \"location_id: (required) :param str user_id: (required) :return: PresignedLinkEnveloped If the method is called\",\n \"thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501\",\n \"= api.upload_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params,\",\n \"noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped',\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id,\",\n \"`location_id` when calling `upload_file`\\\") # noqa: E501 # verify the required parameter 'user_id'\",\n \"in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] header_params =\",\n \"% key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter\",\n \"E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params = {}\",\n \"setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params,\",\n \"[] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params,\",\n \"{} body_params = None if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP\",\n \"Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/', 'GET', path_params,\",\n \"def health_check_with_http_info(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint # noqa: E501 Some\",\n \"returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id,\",\n \"in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json'])\",\n \"`get_files_metadata`\\\") # noqa: E501 # verify the required parameter 'user_id' is set if\",\n \"user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str file_id: (required)\",\n \"FileLocationArrayEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only']\",\n \"\\\"\\\"\\\"Get available storage locations # noqa: E501 This method makes a synchronous HTTP\",\n \"pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get() :param\",\n \"else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return data def update_file_meta_data_with_http_info(self,\",\n \"**kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint to ask server to fail or echo\",\n \"the transmitted data # noqa: E501 This method makes a synchronous HTTP request\",\n \"_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get File\",\n \"_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update File Metadata\",\n \"all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method download_file\\\"\",\n \"noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped',\",\n \"keyword argument '%s'\\\" \\\" to method health_check\\\" % key ) local_var_params[key] = val\",\n \"echo back the transmitted data # noqa: E501 This method makes a synchronous\",\n \"get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get File Metadata # noqa:\",\n \"True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data) = self.check_action_post_with_http_info(action,\",\n \"ValueError(\\\"Missing the required parameter `file_id` when calling `delete_file`\\\") # noqa: E501 # verify\",\n \"HTTP request, please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>>\",\n \"thread. \\\"\\\"\\\" local_var_params = locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')\",\n \"= self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def get_file_metadata_with_http_info(self, file_id,\",\n \"noqa: E501 \\\"\\\"\\\"Get Files Metadata # noqa: E501 This method makes a synchronous\",\n \"= self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings\",\n \"E501 \\\"\\\"\\\"Update File Metadata # noqa: E501 This method makes a synchronous HTTP\",\n \"def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns download link for\",\n \"True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get Files\",\n \"request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id'] # noqa:\",\n \"back the transmitted data # noqa: E501 This method makes a synchronous HTTP\",\n \"# HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication\",\n \"(required) :param str user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If the method\",\n \"= thread.get() :param async_req bool :param str user_id: (required) :return: FileLocationArrayEnveloped If the\",\n \"Ref: https://openapi-generator.tech Do not edit the class manually. \\\"\\\"\\\" def __init__(self, api_client=None): if\",\n \"is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `delete_file`\\\") # noqa:\",\n \"# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs):\",\n \"str file_id: (required) :param str location_id: (required) :param str user_id: (required) :return: FileMetaDataEnveloped\",\n \"a synchronous HTTP request by default. To make an asynchronous HTTP request, please\",\n \"# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations', 'GET',\",\n \"the required parameter `location_id` when calling `update_file_meta_data`\\\") # noqa: E501 collection_formats = {}\",\n \"[] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] =\",\n \"E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self,\",\n \"Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params,\",\n \"thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501\",\n \"api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: (required)\",\n \"request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa:\",\n \"operation to datcore # noqa: E501 This method makes a synchronous HTTP request\",\n \"= api.check_action_post(action, async_req=True) >>> result = thread.get() :param async_req bool :param str action:\",\n \"`file_id` when calling `download_file`\\\") # noqa: E501 # verify the required parameter 'location_id'\",\n \"auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id,\",\n \"noqa: E501 \\\"\\\"\\\"Update File Metadata # noqa: E501 This method makes a synchronous\",\n \"`file_id` when calling `get_file_metadata`\\\") # noqa: E501 # verify the required parameter 'location_id'\",\n \"all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method check_action_post\\\"\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = [] #\",\n \"del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params =\",\n \"api.check_action_post(action, async_req=True) >>> result = thread.get() :param async_req bool :param str action: (required)\",\n \"in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params = {} form_params = []\",\n \"user_id, **kwargs) # noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) #\",\n \"called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['action',\",\n \"local_var_params['uuid_filter'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {}\",\n \"simcore-service-storage API API definition for simcore-service-storage service # noqa: E501 OpenAPI spec version:\",\n \"None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `download_file`\\\") # noqa: E501\",\n \"thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) #\",\n \"health_check\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params\",\n \"class manually. \\\"\\\"\\\" def __init__(self, api_client=None): if api_client is None: api_client = ApiClient()\",\n \"noqa: E501 query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa:\",\n \"E501 This method makes a synchronous HTTP request by default. To make an\",\n \"return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa:\",\n \"parameter `user_id` when calling `get_file_metadata`\\\") # noqa: E501 collection_formats = {} path_params =\",\n \"the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id,\",\n \"verify the required parameter 'location_id' is set if ('location_id' not in local_var_params or\",\n \"parameter `user_id` when calling `upload_file`\\\") # noqa: E501 collection_formats = {} path_params =\",\n \"E501 \\\"\\\"\\\"Test checkpoint to ask server to fail or echo back the transmitted\",\n \"= val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = []\",\n \"= ApiClient() self.api_client = api_client def check_action_post(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test\",\n \"is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `download_file`\\\") # noqa:\",\n \"self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id,\",\n \"please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result =\",\n \"path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),\",\n \"calling `check_action_post`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'action'\",\n \"makes a synchronous HTTP request by default. To make an asynchronous HTTP request,\",\n \"noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source'\",\n \"the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] #\",\n \"noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None,\",\n \"# noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data\",\n \"query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa:\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check(async_req=True) >>> result =\",\n \":param str extra_source: :return: PresignedLinkEnveloped If the method is called asynchronously, returns the\",\n \"E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self,\",\n \"_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501\",\n \"E501 \\\"\\\"\\\"Deletes File # noqa: E501 This method makes a synchronous HTTP request\",\n \"API definition for simcore-service-storage service # noqa: E501 OpenAPI spec version: 0.1.0 Contact:\",\n \"E501 query_params = [] if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501\",\n \"\\\"\\\"\\\"Test checkpoint to ask server to fail or echo back the transmitted data\",\n \"(required) :param str extra_location: :param str extra_source: :return: PresignedLinkEnveloped If the method is\",\n \"E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', #\",\n \"return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id,\",\n \"thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data def\",\n \"local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `delete_file`\\\") #\",\n \"# noqa: E501 \\\"\\\"\\\"Get Files Metadata # noqa: E501 This method makes a\",\n \"kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.download_file_with_http_info(file_id,\",\n \":return: None If the method is called asynchronously, returns the request thread. \\\"\\\"\\\"\",\n \"when calling `check_action_post`\\\") # noqa: E501 collection_formats = {} path_params = {} if\",\n \"HTTP request, please pass async_req=True >>> thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>>\",\n \"'/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings,\",\n \"post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',\",\n \"noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api(\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>>\",\n \"raise ValueError(\\\"Missing the required parameter `file_id` when calling `get_file_metadata`\\\") # noqa: E501 #\",\n \"__future__ import absolute_import import re # noqa: F401 # python 2 and python\",\n \"query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #\",\n \"(required) :param str location_id: (required) :param str user_id: (required) :return: FileMetaDataEnveloped If the\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id,\",\n \"(required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If the method is called asynchronously, returns\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['location_id', 'user_id',\",\n \"noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params =\",\n \"_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get\",\n \"['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val\",\n \"api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If the method\",\n \"bool :param str user_id: (required) :return: FileLocationArrayEnveloped If the method is called asynchronously,\",\n \"'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),\",\n \"= [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params =\",\n \"or local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `delete_file`\\\")\",\n \"'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in\",\n \"PresignedLinkEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params\",\n \"to method get_storage_locations\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify\",\n \"raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_files_metadata`\\\") # noqa: E501 collection_formats\",\n \"`delete_file`\\\") # noqa: E501 # verify the required parameter 'user_id' is set if\",\n \"the required parameter `location_id` when calling `delete_file`\\\") # noqa: E501 # verify the\",\n \"required parameter `location_id` when calling `upload_file`\\\") # noqa: E501 # verify the required\",\n \"pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get()\",\n \"E501 return data def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint to\",\n \"raise ValueError(\\\"Missing the required parameter `location_id` when calling `get_files_metadata`\\\") # noqa: E501 #\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file(file_id, location_id,\",\n \"File # noqa: E501 This method makes a synchronous HTTP request by default.\",\n \"path_params = {} if 'action' in local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501\",\n \"body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501\",\n \"['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for\",\n \"please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get()\",\n \"noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped',\",\n \"`file_id` when calling `delete_file`\\\") # noqa: E501 # verify the required parameter 'location_id'\",\n \"pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get()\",\n \"= {} if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params\",\n \"files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),\",\n \"pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"compatibility library import six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \\\"\\\"\\\"NOTE: This class\",\n \"local_var_params['kwargs'] # verify the required parameter 'location_id' is set if ('location_id' not in\",\n \"is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `download_file`\\\") # noqa:\",\n \"str location_id: (required) :param str user_id: (required) :param str extra_location: :param str extra_source:\",\n \"local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `get_files_metadata`\\\") #\",\n \"noqa: E501 # verify the required parameter 'user_id' is set if ('user_id' not\",\n \"# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET',\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True)\",\n \"not in all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to\",\n \"local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `get_file_metadata`\\\") #\",\n \"noqa: E501 \\\"\\\"\\\"Service health-check endpoint # noqa: E501 Some general information on the\",\n \"when calling `get_files_metadata`\\\") # noqa: E501 # verify the required parameter 'user_id' is\",\n \"please pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get()\",\n \"async_req=True) >>> result = thread.get() :param async_req bool :param str action: (required) :param\",\n \"= [] header_params = {} form_params = [] local_var_files = {} body_params =\",\n \"ValueError(\\\"Missing the required parameter `location_id` when calling `download_file`\\\") # noqa: E501 # verify\",\n \"local_var_params['kwargs'] # verify the required parameter 'action' is set if ('action' not in\",\n \"all_params = ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val\",\n \"TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method upload_file\\\" % key\",\n \"required parameter `user_id` when calling `delete_file`\\\") # noqa: E501 collection_formats = {} path_params\",\n \"files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),\",\n \"action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint to ask server to fail or\",\n \"import re # noqa: F401 # python 2 and python 3 compatibility library\",\n \"else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def\",\n \"required parameter `location_id` when calling `delete_file`\\\") # noqa: E501 # verify the required\",\n \"`user_id` when calling `get_storage_locations`\\\") # noqa: E501 collection_formats = {} path_params = {}\",\n \"performs copy operation to datcore # noqa: E501 This method makes a synchronous\",\n \"action: (required) :param str data: :param FakeType fake_type: :return: FakeEnveloped If the method\",\n \"return data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns download\",\n \"file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes File # noqa: E501 This\",\n \"get_storage_locations(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available storage locations # noqa: E501\",\n \"= [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params,\",\n \"body_params = None if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header\",\n \"str user_id: (required) :param str extra_location: :param str extra_source: :return: PresignedLinkEnveloped If the\",\n \"True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get\",\n \"path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),\",\n \"pass async_req=True >>> thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get()\",\n \"user_id, **kwargs) # noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) #\",\n \"query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params = {} form_params = [] local_var_files =\",\n \"local_var_params['action'] # noqa: E501 query_params = [] if 'data' in local_var_params: query_params.append(('data', local_var_params['data']))\",\n \"user_id: (required) :return: None If the method is called asynchronously, returns the request\",\n \"from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \\\"\\\"\\\"NOTE: This class is auto generated by\",\n \"please pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result =\",\n \"the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa:\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>>\",\n \"location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs)\",\n \"not in local_var_params or local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id`\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs)\",\n \"`update_file_meta_data`\\\") # noqa: E501 # verify the required parameter 'location_id' is set if\",\n \"HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True\",\n \"\\\" to method download_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #\",\n \"body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501\",\n \">>> thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"is None): raise ValueError(\\\"Missing the required parameter `action` when calling `check_action_post`\\\") # noqa:\",\n \"in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if 'location_id' in local_var_params: path_params['location_id']\",\n \"synchronous HTTP request by default. To make an asynchronous HTTP request, please pass\",\n \"raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_storage_locations`\\\") # noqa: E501 collection_formats\",\n \"= [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params,\",\n \"= ['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check(async_req=True) >>> result\",\n \"noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs): #\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['action', 'data',\",\n \"method get_file_metadata\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the\",\n \"user_id, **kwargs) # noqa: E501 return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): #\",\n \"\\\"\\\"\\\"Returns upload link or performs copy operation to datcore # noqa: E501 This\",\n \"E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in\",\n \"= {} form_params = [] local_var_files = {} body_params = None # HTTP\",\n \"method download_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the\",\n \"{} path_params = {} query_params = [] header_params = {} form_params = []\",\n \"setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params,\",\n \"in local_var_params or local_var_params['action'] is None): raise ValueError(\\\"Missing the required parameter `action` when\",\n \"when calling `update_file_meta_data`\\\") # noqa: E501 collection_formats = {} path_params = {} if\",\n \"E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa:\",\n \"returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id,\",\n \") local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'action' is\",\n \"the API and state of the service behind # noqa: E501 This method\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id,\",\n \"the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) #\",\n \"request, please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result =\",\n \"= local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501\",\n \"result = thread.get() :param async_req bool :param str file_id: (required) :param str location_id:\",\n \"if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id,\",\n \"= locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for\",\n \"result = thread.get() :param async_req bool :param str location_id: (required) :param str user_id:\",\n \"E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) #\",\n \"local_var_params['kwargs'] # verify the required parameter 'user_id' is set if ('user_id' not in\",\n \":param str extra_location: :param str extra_source: :return: PresignedLinkEnveloped If the method is called\",\n \"method delete_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the\",\n \"local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `download_file`\\\") #\",\n \"in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params = {} form_params = []\",\n \"to method upload_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file(file_id, location_id, user_id, async_req=True)\",\n \"async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param\",\n \"(required) :return: FileLocationArrayEnveloped If the method is called asynchronously, returns the request thread.\",\n \"is called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params =\",\n \"None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `update_file_meta_data`\\\") # noqa: E501\",\n \"required parameter `location_id` when calling `update_file_meta_data`\\\") # noqa: E501 collection_formats = {} path_params\",\n \"True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns\",\n \"collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns download link\",\n \"delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes File # noqa: E501\",\n \"the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id,\",\n \"noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service\",\n \"= api.health_check(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If the\",\n \"noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped',\",\n \"async_req bool :return: HealthCheckEnveloped If the method is called asynchronously, returns the request\",\n \"str user_id: (required) :return: PresignedLinkEnveloped If the method is called asynchronously, returns the\",\n \"post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',\",\n \"(required) :param str user_id: (required) :return: FileMetaDataEnveloped If the method is called asynchronously,\",\n \"= [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept']\",\n \"header_params = {} form_params = [] local_var_files = {} body_params = None if\",\n \">>> thread = api.health_check(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped\",\n \"header_params = {} form_params = [] local_var_files = {} body_params = None #\",\n \"'%s'\\\" \\\" to method download_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs']\",\n \"return data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available storage locations\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post(action, async_req=True) >>> result\",\n \"[] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params,\",\n \"E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', #\",\n \"thread = api.health_check(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If\",\n \") local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params\",\n \"user_id, **kwargs) # noqa: E501 return data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs):\",\n \"**kwargs): # noqa: E501 \\\"\\\"\\\"Update File Metadata # noqa: E501 This method makes\",\n \"query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa:\",\n \"= [] # noqa: E501 return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params,\",\n \"`location_id` when calling `update_file_meta_data`\\\") # noqa: E501 collection_formats = {} path_params = {}\",\n \"self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501\",\n \"unexpected keyword argument '%s'\\\" \\\" to method get_storage_locations\\\" % key ) local_var_params[key] =\",\n \"calling `get_storage_locations`\\\") # noqa: E501 collection_formats = {} path_params = {} query_params =\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id,\",\n \"raise ValueError(\\\"Missing the required parameter `location_id` when calling `get_file_metadata`\\\") # noqa: E501 #\",\n \"'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val\",\n \":param async_req bool :param str file_id: (required) :param str location_id: (required) :param str\",\n \"# noqa: E501 This method makes a synchronous HTTP request by default. To\",\n \"`location_id` when calling `download_file`\\\") # noqa: E501 # verify the required parameter 'user_id'\",\n \"**kwargs) # noqa: E501 return data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): #\",\n \"async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501\",\n \"set if ('action' not in local_var_params or local_var_params['action'] is None): raise ValueError(\\\"Missing the\",\n \"None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `delete_file`\\\") # noqa: E501\",\n \"self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) #\",\n \"argument '%s'\\\" \\\" to method health_check\\\" % key ) local_var_params[key] = val del\",\n \"str file_id: (required) :param str location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If\",\n \"self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa:\",\n \"E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs): # noqa:\",\n \"keyword argument '%s'\\\" \\\" to method delete_file\\\" % key ) local_var_params[key] = val\",\n \"E501 return data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file(file_id, location_id, user_id,\",\n \"response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),\",\n \"please pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get()\",\n \"keyword argument '%s'\\\" \\\" to method get_storage_locations\\\" % key ) local_var_params[key] = val\",\n \"= ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in\",\n \"or local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `download_file`\\\")\",\n \"# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/', 'GET',\",\n \"'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] if\",\n \"= [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params,\",\n \"when calling `delete_file`\\\") # noqa: E501 # verify the required parameter 'user_id' is\",\n \"local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 #\",\n \"available storage locations # noqa: E501 This method makes a synchronous HTTP request\",\n \"\\\"Got an unexpected keyword argument '%s'\\\" \\\" to method check_action_post\\\" % key )\",\n \"E501 return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', #\",\n \"location_id: (required) :param str user_id: (required) :param str extra_location: :param str extra_source: :return:\",\n \"= val del local_var_params['kwargs'] # verify the required parameter 'user_id' is set if\",\n \"= self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type(\",\n \"or local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `download_file`\\\")\",\n \"`upload_file`\\\") # noqa: E501 # verify the required parameter 'location_id' is set if\",\n \"setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params,\",\n \"_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint # noqa:\",\n \"is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `get_files_metadata`\\\") # noqa:\",\n \"returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) #\",\n \"# noqa: E501 # verify the required parameter 'location_id' is set if ('location_id'\",\n \">>> result = thread.get() :param async_req bool :param str file_id: (required) :param str\",\n \"# python 2 and python 3 compatibility library import six from simcore_service_storage_sdk.api_client import\",\n \"Generated by: https://openapi-generator.tech \\\"\\\"\\\" from __future__ import absolute_import import re # noqa: F401\",\n \"local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params = {} form_params = [] local_var_files\",\n \"async_req=True >>> thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id,\",\n \"raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method update_file_meta_data\\\" %\",\n \"user_id: (required) :param str extra_location: :param str extra_source: :return: PresignedLinkEnveloped If the method\",\n \"\\\"Got an unexpected keyword argument '%s'\\\" \\\" to method get_storage_locations\\\" % key )\",\n \"the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['user_id'] # noqa: E501\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True)\",\n \"parameter `file_id` when calling `upload_file`\\\") # noqa: E501 # verify the required parameter\",\n \"= self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data def check_action_post_with_http_info(self, action, **kwargs): #\",\n \"thread.get() :param async_req bool :return: HealthCheckEnveloped If the method is called asynchronously, returns\",\n \"storage locations # noqa: E501 This method makes a synchronous HTTP request by\",\n \":param str location_id: (required) :param str user_id: (required) :return: FileMetaDataEnveloped If the method\",\n \"thread = api.check_action_post(action, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"(data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def delete_file_with_http_info(self,\",\n \"check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint to ask server to fail\",\n \":param str file_id: (required) :param str location_id: (required) :param str user_id: (required) :return:\",\n \"\\\"\\\"\\\"Update File Metadata # noqa: E501 This method makes a synchronous HTTP request\",\n \"request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs)\",\n \"file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update File Metadata # noqa: E501 This\",\n \"None): raise ValueError(\\\"Missing the required parameter `action` when calling `check_action_post`\\\") # noqa: E501\",\n \"delete_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required\",\n \"default. To make an asynchronous HTTP request, please pass async_req=True >>> thread =\",\n \"local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if 'location_id' in local_var_params: path_params['location_id'] =\",\n \":param str user_id: (required) :return: PresignedLinkEnveloped If the method is called asynchronously, returns\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result\",\n \"method update_file_meta_data\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the\",\n \"\\\"Got an unexpected keyword argument '%s'\\\" \\\" to method delete_file\\\" % key )\",\n \"pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get() :param\",\n \"fake_type: :return: FakeEnveloped If the method is called asynchronously, returns the request thread.\",\n \"request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs)\",\n \"kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501\",\n \"None If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only']\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id,\",\n \"HTTP request, please pass async_req=True >>> thread = api.check_action_post(action, async_req=True) >>> result =\",\n \"E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata',\",\n \"= {} path_params = {} if 'action' in local_var_params: path_params['action'] = local_var_params['action'] #\",\n \"**kwargs) # noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:\",\n \"raise ValueError(\\\"Missing the required parameter `file_id` when calling `update_file_meta_data`\\\") # noqa: E501 #\",\n \"str file_id: (required) :param str location_id: (required) :param str user_id: (required) :return: PresignedLinkEnveloped\",\n \"on the API and state of the service behind # noqa: E501 This\",\n \"= None if 'fake_type' in local_var_params: body_params = local_var_params['fake_type'] # HTTP header `Accept`\",\n \"user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str location_id: (required)\",\n \"# noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501\",\n \"path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),\",\n \"def get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get File Metadata #\",\n \"HTTP request, please pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>> result =\",\n \"api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def check_action_post(self,\",\n \"local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'location_id' is set\",\n \"required parameter `location_id` when calling `get_file_metadata`\\\") # noqa: E501 # verify the required\",\n \"setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params,\",\n \"{} form_params = [] local_var_files = {} body_params = None # HTTP header\",\n \"[] local_var_files = {} body_params = None if 'fake_type' in local_var_params: body_params =\",\n \"keyword argument '%s'\\\" \\\" to method get_file_metadata\\\" % key ) local_var_params[key] = val\",\n \"= {} form_params = [] local_var_files = {} body_params = None if 'file_meta_data_type'\",\n \"E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data def check_action_post_with_http_info(self,\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id,\",\n \"when calling `download_file`\\\") # noqa: E501 collection_formats = {} path_params = {} if\",\n \"HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header\",\n \"in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter']))\",\n \"key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {}\",\n \"service behind # noqa: E501 This method makes a synchronous HTTP request by\",\n \"val del local_var_params['kwargs'] # verify the required parameter 'file_id' is set if ('file_id'\",\n \"or performs copy operation to datcore # noqa: E501 This method makes a\",\n \"'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),\",\n \"response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),\",\n \"the class manually. \\\"\\\"\\\" def __init__(self, api_client=None): if api_client is None: api_client =\",\n \"`user_id` when calling `delete_file`\\\") # noqa: E501 collection_formats = {} path_params = {}\",\n \"file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get File Metadata # noqa: E501\",\n \"method is called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params\",\n \"auth_settings = [] # noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params,\",\n \"thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa:\",\n \"local_var_params['action'] is None): raise ValueError(\\\"Missing the required parameter `action` when calling `check_action_post`\\\") #\",\n \"required parameter 'action' is set if ('action' not in local_var_params or local_var_params['action'] is\",\n \"kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id,\",\n \"is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `get_file_metadata`\\\") # noqa:\",\n \":param str action: (required) :param str data: :param FakeType fake_type: :return: FakeEnveloped If\",\n \"when calling `download_file`\\\") # noqa: E501 # verify the required parameter 'user_id' is\",\n \"HTTP request, please pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>>\",\n \"\\\"\\\"\\\" local_var_params = locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')\",\n \"path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),\",\n \"is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `upload_file`\\\") # noqa:\",\n \"kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501\",\n \"data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get Files Metadata #\",\n \"ValueError(\\\"Missing the required parameter `location_id` when calling `upload_file`\\\") # noqa: E501 # verify\",\n \"E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata',\",\n \"# noqa: F401 # python 2 and python 3 compatibility library import six\",\n \"when calling `update_file_meta_data`\\\") # noqa: E501 # verify the required parameter 'location_id' is\",\n \"raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_file_metadata`\\\") # noqa: E501 collection_formats\",\n \"'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location',\",\n \"noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped',\",\n \"locals() all_params = ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,\",\n \"thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] #\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id,\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id,\",\n \"setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params,\",\n \"ValueError(\\\"Missing the required parameter `user_id` when calling `get_storage_locations`\\\") # noqa: E501 collection_formats =\",\n \"is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `update_file_meta_data`\\\") # noqa:\",\n \"TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method update_file_meta_data\\\" % key\",\n \"_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns download\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>>\",\n \"**kwargs) # noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:\",\n \"when calling `get_storage_locations`\\\") # noqa: E501 collection_formats = {} path_params = {} query_params\",\n \"# noqa: E501 return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa:\",\n \"TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method get_storage_locations\\\" % key\",\n \"def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client\",\n \"**kwargs): # noqa: E501 \\\"\\\"\\\"Get Files Metadata # noqa: E501 This method makes\",\n \"\\\"\\\"\\\" local_var_params = locals() all_params = ['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req')\",\n \"required parameter `file_id` when calling `get_file_metadata`\\\") # noqa: E501 # verify the required\",\n \"**kwargs) # noqa: E501 return data def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501\",\n \"noqa: E501 return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped',\",\n \"E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data\",\n \"= api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #\",\n \"noqa: E501 \\\"\\\"\\\"Get File Metadata # noqa: E501 This method makes a synchronous\",\n \"Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params,\",\n \"parameter `location_id` when calling `upload_file`\\\") # noqa: E501 # verify the required parameter\",\n \"when calling `get_file_metadata`\\\") # noqa: E501 collection_formats = {} path_params = {} if\",\n \"3 compatibility library import six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \\\"\\\"\\\"NOTE: This\",\n \"local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `delete_file`\\\") #\",\n \"check_action_post\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required\",\n \"def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get Files Metadata # noqa:\",\n \"= {} path_params = {} if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] #\",\n \"spec version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from __future__ import absolute_import\",\n \"thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501\",\n \"ValueError(\\\"Missing the required parameter `action` when calling `check_action_post`\\\") # noqa: E501 collection_formats =\",\n \"Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params,\",\n \"download_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required\",\n \"self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings =\",\n \"please pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result =\",\n \"= {} path_params = {} if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] #\",\n \"E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self,\",\n \"('user_id' not in local_var_params or local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter\",\n \"self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs):\",\n \"files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),\",\n \"return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return\",\n \">>> thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped\",\n \"HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id,\",\n \"True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data)\",\n \"= True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else:\",\n \"kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501\",\n \"return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa:\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>>\",\n \"state of the service behind # noqa: E501 This method makes a synchronous\",\n \"# HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP\",\n \"api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: (required)\",\n \"# noqa: E501 return data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa:\",\n \"calling `update_file_meta_data`\\\") # noqa: E501 # verify the required parameter 'location_id' is set\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True)\",\n \"self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501\",\n \"= ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')\",\n \"`download_file`\\\") # noqa: E501 # verify the required parameter 'user_id' is set if\",\n \"_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): #\",\n \"if ('action' not in local_var_params or local_var_params['action'] is None): raise ValueError(\\\"Missing the required\",\n \"# noqa: E501 if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501\",\n \"noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped',\",\n \"raise ValueError(\\\"Missing the required parameter `location_id` when calling `download_file`\\\") # noqa: E501 #\",\n \"self.api_client = api_client def check_action_post(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint to\",\n \"required parameter `file_id` when calling `upload_file`\\\") # noqa: E501 # verify the required\",\n \"{} body_params = None if 'fake_type' in local_var_params: body_params = local_var_params['fake_type'] # HTTP\",\n \"del local_var_params['kwargs'] # verify the required parameter 'location_id' is set if ('location_id' not\",\n \"is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `delete_file`\\\") # noqa:\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id,\",\n \"the required parameter `user_id` when calling `get_files_metadata`\\\") # noqa: E501 collection_formats = {}\",\n \"locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,\",\n \"return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa:\",\n \"# noqa: E501 \\\"\\\"\\\"Returns download link for requested file # noqa: E501 This\",\n \"api.upload_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"**kwargs) # noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:\",\n \"parameter `user_id` when calling `get_files_metadata`\\\") # noqa: E501 collection_formats = {} path_params =\",\n \"# noqa: E501 return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file(file_id,\",\n \"unexpected keyword argument '%s'\\\" \\\" to method health_check\\\" % key ) local_var_params[key] =\",\n \"request, please pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result =\",\n \"str user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If the method is called\",\n \"local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_files_metadata`\\\") #\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>>\",\n \"health_check_with_http_info(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint # noqa: E501 Some general\",\n \":param str user_id: (required) :return: FileMetaDataEnveloped If the method is called asynchronously, returns\",\n \"# noqa: E501 \\\"\\\"\\\"Get File Metadata # noqa: E501 This method makes a\",\n \"collection_formats = {} path_params = {} query_params = [] if 'user_id' in local_var_params:\",\n \"str extra_location: :param str extra_source: :return: PresignedLinkEnveloped If the method is called asynchronously,\",\n \"or local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `get_files_metadata`\\\")\",\n \"E501 collection_formats = {} path_params = {} query_params = [] if 'user_id' in\",\n \"data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get File Metadata\",\n \"thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) #\",\n \"form_params = [] local_var_files = {} body_params = None if 'file_meta_data_type' in local_var_params:\",\n \"parameter 'user_id' is set if ('user_id' not in local_var_params or local_var_params['user_id'] is None):\",\n \"if api_client is None: api_client = ApiClient() self.api_client = api_client def check_action_post(self, action,\",\n \"file # noqa: E501 This method makes a synchronous HTTP request by default.\",\n \"the method is called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals()\",\n \"coding: utf-8 \\\"\\\"\\\" simcore-service-storage API API definition for simcore-service-storage service # noqa: E501\",\n \"# noqa: E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get\",\n \"calling `upload_file`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id'\",\n \"the required parameter `location_id` when calling `get_file_metadata`\\\") # noqa: E501 # verify the\",\n \"all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method delete_file\\\"\",\n \"form_params = [] local_var_files = {} body_params = None # HTTP header `Accept`\",\n \"the required parameter `file_id` when calling `upload_file`\\\") # noqa: E501 # verify the\",\n \"result = thread.get() :param async_req bool :return: HealthCheckEnveloped If the method is called\",\n \":param str file_id: (required) :param str location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped\",\n \"request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa:\",\n \"None: api_client = ApiClient() self.api_client = api_client def check_action_post(self, action, **kwargs): # noqa:\",\n \"E501 \\\"\\\"\\\"Get File Metadata # noqa: E501 This method makes a synchronous HTTP\",\n \"async_req bool :param str action: (required) :param str data: :param FakeType fake_type: :return:\",\n \"library import six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \\\"\\\"\\\"NOTE: This class is\",\n \"api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str location_id:\",\n \"= {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json'])\",\n \"local_var_params or local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling\",\n \"the required parameter 'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id']\",\n \"Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params,\",\n \"noqa: E501 \\\"\\\"\\\"Get available storage locations # noqa: E501 This method makes a\",\n \"thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else:\",\n \"E501 return data def health_check_with_http_info(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint #\",\n \"= True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else:\",\n \"parameter `user_id` when calling `delete_file`\\\") # noqa: E501 collection_formats = {} path_params =\",\n \"returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'file_meta_data_type']\",\n \"not in local_var_params or local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id`\",\n \"local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params = {} form_params = [] local_var_files\",\n \"pass async_req=True >>> thread = api.health_check(async_req=True) >>> result = thread.get() :param async_req bool\",\n \"val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( \\\"Got an unexpected\",\n \"parameter `location_id` when calling `delete_file`\\\") # noqa: E501 # verify the required parameter\",\n \"local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params = {} form_params = [] local_var_files\",\n \"simcore-service-storage service # noqa: E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated by:\",\n \"`delete_file`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id' in\",\n \"E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self,\",\n \"\\\"\\\"\\\" local_var_params = locals() all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req')\",\n \"(required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method is called asynchronously, returns\",\n \"if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params = {} form_params\",\n \"body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501\",\n \"the required parameter 'file_id' is set if ('file_id' not in local_var_params or local_var_params['file_id']\",\n \"HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501\",\n \"post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',\",\n \"required parameter `location_id` when calling `get_files_metadata`\\\") # noqa: E501 # verify the required\",\n \"True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint #\",\n \"= api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If the\",\n \"the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id,\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True)\",\n \"noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params =\",\n \"'action' is set if ('action' not in local_var_params or local_var_params['action'] is None): raise\",\n \"thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"# noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params\",\n \"# noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,\",\n \"noqa: E501 return data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501\",\n \"val del local_var_params['kwargs'] # verify the required parameter 'user_id' is set if ('user_id'\",\n \"# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST',\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True)\",\n \"noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params =\",\n \"'%s'\\\" \\\" to method get_storage_locations\\\" % key ) local_var_params[key] = val del local_var_params['kwargs']\",\n \"'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):\",\n \"[] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params,\",\n \"argument '%s'\\\" \\\" to method get_file_metadata\\\" % key ) local_var_params[key] = val del\",\n \"self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id,\",\n \"file_meta_data_type: :return: FileMetaDataEnveloped If the method is called asynchronously, returns the request thread.\",\n \"del local_var_params['kwargs'] # verify the required parameter 'file_id' is set if ('file_id' not\",\n \"calling `download_file`\\\") # noqa: E501 # verify the required parameter 'location_id' is set\",\n \"[] # noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params,\",\n \"None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `update_file_meta_data`\\\") # noqa: E501\",\n \"is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_files_metadata`\\\") # noqa:\",\n \"'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] header_params\",\n \"\\\"\\\"\\\" simcore-service-storage API API definition for simcore-service-storage service # noqa: E501 OpenAPI spec\",\n \"# noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501\",\n \">>> result = thread.get() :param async_req bool :param str location_id: (required) :param str\",\n \"location_id, **kwargs) # noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): #\",\n \"noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return\",\n \"`user_id` when calling `get_files_metadata`\\\") # noqa: E501 collection_formats = {} path_params = {}\",\n \"else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def\",\n \"self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def upload_file_with_http_info(self, file_id, location_id,\",\n \"HTTP request, please pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result\",\n \"self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check(async_req=True)\",\n \"E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not\",\n \"argument '%s'\\\" \\\" to method check_action_post\\\" % key ) local_var_params[key] = val del\",\n \"E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs): # noqa:\",\n \"when calling `download_file`\\\") # noqa: E501 # verify the required parameter 'location_id' is\",\n \"\\\" to method get_storage_locations\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #\",\n \"async_req=True) >>> result = thread.get() :param async_req bool :param str location_id: (required) :param\",\n \"ApiClient() self.api_client = api_client def check_action_post(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint\",\n \"E501 return data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes\",\n \"E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/check/{action}',\",\n \"api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id,\",\n \"path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),\",\n \"calling `update_file_meta_data`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id'\",\n \"E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', #\",\n \"collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available storage locations #\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id,\",\n \"is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_file_metadata`\\\") # noqa:\",\n \"{} form_params = [] local_var_files = {} body_params = None if 'file_meta_data_type' in\",\n \"E501 \\\"\\\"\\\"Returns upload link or performs copy operation to datcore # noqa: E501\",\n \"data: :param FakeType fake_type: :return: FakeEnveloped If the method is called asynchronously, returns\",\n \"an unexpected keyword argument '%s'\\\" \\\" to method download_file\\\" % key ) local_var_params[key]\",\n \"for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( \\\"Got\",\n \"else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data def get_storage_locations_with_http_info(self, user_id,\",\n \"user_id: (required) :return: PresignedLinkEnveloped If the method is called asynchronously, returns the request\",\n \"self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) #\",\n \"https://openapi-generator.tech Do not edit the class manually. \\\"\\\"\\\" def __init__(self, api_client=None): if api_client\",\n \"all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise\",\n \"thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),\",\n \"all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method health_check\\\"\",\n \"def check_action_post(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint to ask server to\",\n \"noqa: E501 This method makes a synchronous HTTP request by default. To make\",\n \"'/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings,\",\n \"del local_var_params['kwargs'] # verify the required parameter 'user_id' is set if ('user_id' not\",\n \"file_id: (required) :param str location_id: (required) :param str user_id: (required) :return: None If\",\n \"'/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings,\",\n \"= api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method check_action_post\\\" % key\",\n \"required parameter `user_id` when calling `upload_file`\\\") # noqa: E501 collection_formats = {} path_params\",\n \"Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params,\",\n \"ValueError(\\\"Missing the required parameter `user_id` when calling `upload_file`\\\") # noqa: E501 collection_formats =\",\n \"# noqa: E501 return data def health_check_with_http_info(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check\",\n \"E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/',\",\n \"= api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param\",\n \"all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for\",\n \"{} path_params = {} if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa:\",\n \"TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method get_file_metadata\\\" % key\",\n \"the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['action', 'data', 'fake_type'] #\",\n \"raise ValueError(\\\"Missing the required parameter `location_id` when calling `upload_file`\\\") # noqa: E501 #\",\n \"all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method get_files_metadata\\\"\",\n \"# verify the required parameter 'user_id' is set if ('user_id' not in local_var_params\",\n \"E501 header_params = {} form_params = [] local_var_files = {} body_params = None\",\n \"endpoint # noqa: E501 Some general information on the API and state of\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post(action, async_req=True)\",\n \"'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params = {} form_params =\",\n \"download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns download link for requested\",\n \"= self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def upload_file_with_http_info(self, file_id,\",\n \"E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update File\",\n \":param FakeType fake_type: :return: FakeEnveloped If the method is called asynchronously, returns the\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file(file_id, location_id, user_id,\",\n \"'/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings,\",\n \"True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id,\",\n \"import absolute_import import re # noqa: F401 # python 2 and python 3\",\n \"return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id,\",\n \"= thread.get() :param async_req bool :param str action: (required) :param str data: :param\",\n \"post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',\",\n \"calling `download_file`\\\") # noqa: E501 # verify the required parameter 'user_id' is set\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data(file_id,\",\n \"argument '%s'\\\" \\\" to method delete_file\\\" % key ) local_var_params[key] = val del\",\n \"body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501\",\n \"in local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501 query_params = [] if 'data'\",\n \"by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. \\\"\\\"\\\" def\",\n \"`download_file`\\\") # noqa: E501 # verify the required parameter 'location_id' is set if\",\n \"If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params =\",\n \"= api_client def check_action_post(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint to ask\",\n \"= api.delete_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),\",\n \"HTTP request, please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get()\",\n \"= ['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,\",\n \"2 and python 3 compatibility library import six from simcore_service_storage_sdk.api_client import ApiClient class\",\n \"or local_var_params['action'] is None): raise ValueError(\\\"Missing the required parameter `action` when calling `check_action_post`\\\")\",\n \"# noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if\",\n \"E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data def get_storage_locations_with_http_info(self,\",\n \"api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool :param str file_id:\",\n \"# noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,\",\n \"def delete_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes File # noqa:\",\n \"local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `update_file_meta_data`\\\") #\",\n \"local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 #\",\n \":param str file_id: (required) :param str location_id: (required) :param str user_id: (required) :param\",\n \"<EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from __future__ import absolute_import import re # noqa:\",\n \"or local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_files_metadata`\\\")\",\n \"local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) #\",\n \"= True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else:\",\n \"E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self,\",\n \"locals() all_params = ['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')\",\n \"noqa: E501 collection_formats = {} path_params = {} if 'file_id' in local_var_params: path_params['fileId']\",\n \"HealthCheckEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only']\",\n \"local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] header_params = {}\",\n \"`get_file_metadata`\\\") # noqa: E501 # verify the required parameter 'location_id' is set if\",\n \"= api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"(required) :param str location_id: (required) :param str user_id: (required) :return: None If the\",\n \"local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `update_file_meta_data`\\\") #\",\n \"self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def get_file_metadata_with_http_info(self, file_id, location_id,\",\n \"request, please pass async_req=True >>> thread = api.check_action_post(action, async_req=True) >>> result = thread.get()\",\n \"result = thread.get() :param async_req bool :param str user_id: (required) :return: FileLocationArrayEnveloped If\",\n \"api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str location_id:\",\n \"async_req=True >>> thread = api.check_action_post(action, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params,\",\n \"the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = [] # noqa: E501\",\n \"all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in\",\n \"def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update File Metadata # noqa:\",\n \"E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations',\",\n \"auth_settings = [] # noqa: E501 return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params,\",\n \"`get_file_metadata`\\\") # noqa: E501 # verify the required parameter 'user_id' is set if\",\n \"'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in\",\n \"parameter 'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id'] is None):\",\n \"None If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id,\",\n \"noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files =\",\n \"'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params = {} form_params =\",\n \"server to fail or echo back the transmitted data # noqa: E501 This\",\n \"\\\"Got an unexpected keyword argument '%s'\\\" \\\" to method get_files_metadata\\\" % key )\",\n \"extra_source: :return: PresignedLinkEnveloped If the method is called asynchronously, returns the request thread.\",\n \"unexpected keyword argument '%s'\\\" \\\" to method upload_file\\\" % key ) local_var_params[key] =\",\n \"# noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,\",\n \"upload link or performs copy operation to datcore # noqa: E501 This method\",\n \">>> result = thread.get() :param async_req bool :param str action: (required) :param str\",\n \"local_var_params['user_id'])) # noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501\",\n \"noqa: E501 \\\"\\\"\\\"Test checkpoint to ask server to fail or echo back the\",\n \"E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return data def\",\n \"the required parameter 'user_id' is set if ('user_id' not in local_var_params or local_var_params['user_id']\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True)\",\n \"FileMetaDataArrayEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only']\",\n \"# noqa: E501 collection_formats = {} path_params = {} if 'action' in local_var_params:\",\n \"user_id, **kwargs) # noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) #\",\n \"request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501\",\n \"calling `get_file_metadata`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id'\",\n \"edit the class manually. \\\"\\\"\\\" def __init__(self, api_client=None): if api_client is None: api_client\",\n \"parameter `file_id` when calling `get_file_metadata`\\\") # noqa: E501 # verify the required parameter\",\n \"local_var_params or local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling\",\n \"query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params = {} form_params = [] local_var_files =\",\n \"download link for requested file # noqa: E501 This method makes a synchronous\",\n \"def get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get Files Metadata # noqa:\",\n \"header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:\",\n \"files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),\",\n \"an unexpected keyword argument '%s'\\\" \\\" to method delete_file\\\" % key ) local_var_params[key]\",\n \"request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) #\",\n \"file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns upload link or performs copy\",\n \"location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs)\",\n \"thread = api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"API API definition for simcore-service-storage service # noqa: E501 OpenAPI spec version: 0.1.0\",\n \"keyword argument '%s'\\\" \\\" to method check_action_post\\\" % key ) local_var_params[key] = val\",\n \"method upload_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the\",\n \"query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #\",\n \"\\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:\",\n \"= thread.get() :param async_req bool :param str file_id: (required) :param str location_id: (required)\",\n \"api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param async_req bool :param str action: (required)\",\n \"async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id,\",\n \"request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['action', 'data', 'fake_type'] # noqa:\",\n \"raise ValueError(\\\"Missing the required parameter `file_id` when calling `delete_file`\\\") # noqa: E501 #\",\n \"async_req bool :param str file_id: (required) :param str location_id: (required) :param str user_id:\",\n \"body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501\",\n \"HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result\",\n \"None if 'fake_type' in local_var_params: body_params = local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept']\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id,\",\n \"# noqa: E501 return data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa:\",\n \"\\\" to method delete_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #\",\n \"key not in all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\"\",\n \"val del local_var_params['kwargs'] # verify the required parameter 'location_id' is set if ('location_id'\",\n \"{} query_params = [] header_params = {} form_params = [] local_var_files = {}\",\n \"the required parameter `user_id` when calling `download_file`\\\") # noqa: E501 collection_formats = {}\",\n \"request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['user_id'] # noqa: E501 all_params.append('async_req')\",\n \"`delete_file`\\\") # noqa: E501 # verify the required parameter 'location_id' is set if\",\n \"E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params = {}\",\n \"is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `download_file`\\\") # noqa:\",\n \"required parameter `location_id` when calling `download_file`\\\") # noqa: E501 # verify the required\",\n \"auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params,\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>>\",\n \"E501 query_params = [] header_params = {} form_params = [] local_var_files = {}\",\n \"E501 Some general information on the API and state of the service behind\",\n \"bool :param str file_id: (required) :param str location_id: (required) :param str user_id: (required)\",\n \"six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( \\\"Got an unexpected keyword argument\",\n \"an unexpected keyword argument '%s'\\\" \\\" to method check_action_post\\\" % key ) local_var_params[key]\",\n \"request, please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param\",\n \">>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa:\",\n \"E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available storage\",\n \"locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')\",\n \"auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id,\",\n \"'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise\",\n \"to method delete_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify\",\n \"None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `delete_file`\\\") # noqa: E501\",\n \"\\\"Got an unexpected keyword argument '%s'\\\" \\\" to method health_check\\\" % key )\",\n \"the required parameter `user_id` when calling `get_storage_locations`\\\") # noqa: E501 collection_formats = {}\",\n \"collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns upload link\",\n \"or local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `get_file_metadata`\\\")\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file(file_id, location_id,\",\n \"local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')\",\n \"noqa: E501 return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501\",\n \"E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data\",\n \"_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id,\",\n \"**kwargs) # noqa: E501 return data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): #\",\n \"behind # noqa: E501 This method makes a synchronous HTTP request by default.\",\n \":param async_req bool :param str action: (required) :param str data: :param FakeType fake_type:\",\n \"(required) :return: FileMetaDataEnveloped If the method is called asynchronously, returns the request thread.\",\n \"FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method is called asynchronously, returns the request\",\n \"'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter',\",\n \"= api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'user_id' is set\",\n \"ApiClient class UsersApi(object): \\\"\\\"\\\"NOTE: This class is auto generated by OpenAPI Generator Ref:\",\n \"api_client = ApiClient() self.api_client = api_client def check_action_post(self, action, **kwargs): # noqa: E501\",\n \"# noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,\",\n \"= True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else: (data)\",\n \"path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] if 'user_id' in local_var_params:\",\n \":return: FileMetaDataEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\"\",\n \"to method check_action_post\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify\",\n \"local_var_params['location_id'] # noqa: E501 query_params = [] header_params = {} form_params = []\",\n \"the required parameter `user_id` when calling `delete_file`\\\") # noqa: E501 collection_formats = {}\",\n \"user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns upload link or performs copy operation to\",\n \"(required) :return: None If the method is called asynchronously, returns the request thread.\",\n \"F401 # python 2 and python 3 compatibility library import six from simcore_service_storage_sdk.api_client\",\n \"request, please pass async_req=True >>> thread = api.health_check(async_req=True) >>> result = thread.get() :param\",\n \"if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in local_var_params:\",\n \"None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `upload_file`\\\") # noqa: E501\",\n \"ValueError(\\\"Missing the required parameter `location_id` when calling `get_file_metadata`\\\") # noqa: E501 # verify\",\n \"post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',\",\n \"returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs)\",\n \"else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return data def get_files_metadata_with_http_info(self,\",\n \"local_var_params = locals() all_params = ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')\",\n \"kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501\",\n \"location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs)\",\n \"'%s'\\\" \\\" to method delete_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs']\",\n \"local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] if 'user_id' in\",\n \"to method get_file_metadata\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify\",\n \"request, please pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get()\",\n \"the required parameter `user_id` when calling `get_file_metadata`\\\") # noqa: E501 collection_formats = {}\",\n \"else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def\",\n \"= [] local_var_files = {} body_params = None if 'fake_type' in local_var_params: body_params\",\n \"['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val\",\n \"\\\" to method update_file_meta_data\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #\",\n \"'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):\",\n \"required parameter `user_id` when calling `get_storage_locations`\\\") # noqa: E501 collection_formats = {} path_params\",\n \"str location_id: (required) :param str user_id: (required) :return: None If the method is\",\n \"method check_action_post\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the\",\n \"body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa:\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check(async_req=True) >>>\",\n \"location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get Files Metadata # noqa: E501 This\",\n \"please pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param\",\n \"all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params:\",\n \"api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool :param str file_id:\",\n \"key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'user_id'\",\n \"or local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_file_metadata`\\\")\",\n \"None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 #\",\n \"api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id,\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True)\",\n \"ValueError(\\\"Missing the required parameter `user_id` when calling `download_file`\\\") # noqa: E501 collection_formats =\",\n \"`check_action_post`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'action' in\",\n \"keyword argument '%s'\\\" \\\" to method update_file_meta_data\\\" % key ) local_var_params[key] = val\",\n \"__init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def\",\n \"raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method check_action_post\\\" %\",\n \"None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `download_file`\\\") # noqa: E501\",\n \"# noqa: E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\"\",\n \"or local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `delete_file`\\\")\",\n \"= [] # noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params,\",\n \"E501 return data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns\",\n \"class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the\",\n \"\\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:\",\n \"local_var_files = {} body_params = None if 'fake_type' in local_var_params: body_params = local_var_params['fake_type']\",\n \"get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get File Metadata # noqa:\",\n \"self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501\",\n \"noqa: E501 \\\"\\\"\\\"Returns upload link or performs copy operation to datcore # noqa:\",\n \"location_id: (required) :param str user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If the\",\n \"self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data def check_action_post_with_http_info(self, action, **kwargs): # noqa:\",\n \"returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['user_id'] # noqa:\",\n \"= val del local_var_params['kwargs'] # verify the required parameter 'file_id' is set if\",\n \"all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val\",\n \"request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) #\",\n \"E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id,\",\n \"local_var_files = {} body_params = None if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type']\",\n \"`location_id` when calling `get_files_metadata`\\\") # noqa: E501 # verify the required parameter 'user_id'\",\n \"locations # noqa: E501 This method makes a synchronous HTTP request by default.\",\n \"Generator Ref: https://openapi-generator.tech Do not edit the class manually. \\\"\\\"\\\" def __init__(self, api_client=None):\",\n \"returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id',\",\n \"(required) :param str location_id: (required) :param str user_id: (required) :param str extra_location: :param\",\n \"ValueError(\\\"Missing the required parameter `user_id` when calling `delete_file`\\\") # noqa: E501 collection_formats =\",\n \"auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id,\",\n \"is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `delete_file`\\\") # noqa:\",\n \"(data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs):\",\n \"None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `get_file_metadata`\\\") # noqa: E501\",\n \":param str user_id: (required) :return: None If the method is called asynchronously, returns\",\n \"E501 \\\"\\\"\\\"Service health-check endpoint # noqa: E501 Some general information on the API\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True)\",\n \"thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else:\",\n \"local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) #\",\n \"return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get File\",\n \"\\\"\\\"\\\" from __future__ import absolute_import import re # noqa: F401 # python 2\",\n \"True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes\",\n \"data def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint to ask server\",\n \"to datcore # noqa: E501 This method makes a synchronous HTTP request by\",\n \"thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"{} query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501\",\n \"local_var_params = locals() all_params = ['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')\",\n \"`user_id` when calling `download_file`\\\") # noqa: E501 collection_formats = {} path_params = {}\",\n \"def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get File Metadata #\",\n \"header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = []\",\n \"return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa:\",\n \"in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source']))\",\n \"returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id,\",\n \"the required parameter `location_id` when calling `download_file`\\\") # noqa: E501 # verify the\",\n \"(data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data def check_action_post_with_http_info(self, action, **kwargs):\",\n \"= local_var_params['file_id'] # noqa: E501 if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] #\",\n \"noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return data def health_check_with_http_info(self,\",\n \"None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `download_file`\\\") # noqa: E501\",\n \"'%s'\\\" \\\" to method upload_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs']\",\n \"async_req=True >>> thread = api.health_check(async_req=True) >>> result = thread.get() :param async_req bool :return:\",\n \"update_file_meta_data\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required\",\n \"val del local_var_params['kwargs'] # verify the required parameter 'action' is set if ('action'\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id,\",\n \"is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `update_file_meta_data`\\\") # noqa:\",\n \"when calling `get_file_metadata`\\\") # noqa: E501 # verify the required parameter 'user_id' is\",\n \"data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update File Metadata #\",\n \"E501 \\\"\\\"\\\"Get Files Metadata # noqa: E501 This method makes a synchronous HTTP\",\n \"E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from __future__\",\n \"`user_id` when calling `get_file_metadata`\\\") # noqa: E501 collection_formats = {} path_params = {}\",\n \"auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs):\",\n \"if ('user_id' not in local_var_params or local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result =\",\n \"local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `get_file_metadata`\\\") #\",\n \"required parameter `user_id` when calling `get_files_metadata`\\\") # noqa: E501 collection_formats = {} path_params\",\n \"**kwargs) # noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id,\",\n \"Metadata # noqa: E501 This method makes a synchronous HTTP request by default.\",\n \"please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result =\",\n \"upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns upload link or performs\",\n \"= True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data) =\",\n \"in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location']))\",\n \"uuid_filter: :return: FileMetaDataArrayEnveloped If the method is called asynchronously, returns the request thread.\",\n \"request, please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result\",\n \"TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method health_check\\\" % key\",\n \"the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs)\",\n \"is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class\",\n \"the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id,\",\n \"def download_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns download link for\",\n \"local_var_params['location_id'] # noqa: E501 query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id']))\",\n \"'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):\",\n \"`action` when calling `check_action_post`\\\") # noqa: E501 collection_formats = {} path_params = {}\",\n \"post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',\",\n \"and python 3 compatibility library import six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object):\",\n \"str file_id: (required) :param str location_id: (required) :param str user_id: (required) :param str\",\n \"\\\"Got an unexpected keyword argument '%s'\\\" \\\" to method update_file_meta_data\\\" % key )\",\n \"'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),\",\n \"local_var_params or local_var_params['action'] is None): raise ValueError(\\\"Missing the required parameter `action` when calling\",\n \"for requested file # noqa: E501 This method makes a synchronous HTTP request\",\n \"{} if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if 'location_id'\",\n \"local_var_params['file_id'] # noqa: E501 if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa:\",\n \"file_id: (required) :param str location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the\",\n \"path_params = {} query_params = [] header_params = {} form_params = [] local_var_files\",\n \"FakeType fake_type: :return: FakeEnveloped If the method is called asynchronously, returns the request\",\n \"raise ValueError(\\\"Missing the required parameter `user_id` when calling `download_file`\\\") # noqa: E501 collection_formats\",\n \"argument '%s'\\\" \\\" to method update_file_meta_data\\\" % key ) local_var_params[key] = val del\",\n \"E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check\",\n \"[] local_var_files = {} body_params = None if 'file_meta_data_type' in local_var_params: body_params =\",\n \"in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( \\\"Got an unexpected keyword\",\n \"raise ValueError(\\\"Missing the required parameter `user_id` when calling `upload_file`\\\") # noqa: E501 collection_formats\",\n \">>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE',\",\n \"data # noqa: E501 This method makes a synchronous HTTP request by default.\",\n \"utf-8 \\\"\\\"\\\" simcore-service-storage API API definition for simcore-service-storage service # noqa: E501 OpenAPI\",\n \">>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs)\",\n \"header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:\",\n \"E501 # verify the required parameter 'location_id' is set if ('location_id' not in\",\n \"_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa: E501\",\n \"E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self,\",\n \"E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return data def health_check_with_http_info(self, **kwargs):\",\n \"'/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings,\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id,\",\n \"an unexpected keyword argument '%s'\\\" \\\" to method update_file_meta_data\\\" % key ) local_var_params[key]\",\n \"= locals() all_params = ['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file(file_id,\",\n \"local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept(\",\n \"if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa:\",\n \"location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update File Metadata # noqa: E501 This method\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file_with_http_info(file_id,\",\n \"E501 collection_formats = {} path_params = {} if 'location_id' in local_var_params: path_params['location_id'] =\",\n \"header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:\",\n \"(required) :return: PresignedLinkEnveloped If the method is called asynchronously, returns the request thread.\",\n \"the required parameter `file_id` when calling `get_file_metadata`\\\") # noqa: E501 # verify the\",\n \"str extra_source: :return: PresignedLinkEnveloped If the method is called asynchronously, returns the request\",\n \"response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),\",\n \"async_req bool :param str location_id: (required) :param str user_id: (required) :param str uuid_filter:\",\n \"# noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501\",\n \"or local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `update_file_meta_data`\\\")\",\n \"request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs)\",\n \"path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id']\",\n \"request thread. \\\"\\\"\\\" local_var_params = locals() all_params = [] # noqa: E501 all_params.append('async_req')\",\n \"the required parameter `file_id` when calling `download_file`\\\") # noqa: E501 # verify the\",\n \"absolute_import import re # noqa: F401 # python 2 and python 3 compatibility\",\n \"calling `delete_file`\\\") # noqa: E501 # verify the required parameter 'user_id' is set\",\n \"path_params['action'] = local_var_params['action'] # noqa: E501 query_params = [] if 'data' in local_var_params:\",\n \"def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns upload link or\",\n \"# noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return\",\n \"['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val\",\n \"noqa: E501 query_params = [] if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa:\",\n \"E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}',\",\n \"of the service behind # noqa: E501 This method makes a synchronous HTTP\",\n \"async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): #\",\n \"user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns download link for requested file # noqa:\",\n \"is set if ('action' not in local_var_params or local_var_params['action'] is None): raise ValueError(\\\"Missing\",\n \"local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'file_id' is set\",\n \"None if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept']\",\n \"noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key\",\n \":param async_req bool :param str location_id: (required) :param str user_id: (required) :param str\",\n \"= {} query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa:\",\n \"API and state of the service behind # noqa: E501 This method makes\",\n \"or local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `upload_file`\\\")\",\n \"kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data)\",\n \"bool :param str action: (required) :param str data: :param FakeType fake_type: :return: FakeEnveloped\",\n \"in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] if 'user_id'\",\n \"= {} if 'action' in local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501 query_params\",\n \"locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')\",\n \"\\\"\\\"\\\"Service health-check endpoint # noqa: E501 Some general information on the API and\",\n \"or local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `download_file`\\\")\",\n \"del local_var_params['kwargs'] # verify the required parameter 'action' is set if ('action' not\",\n \"an unexpected keyword argument '%s'\\\" \\\" to method health_check\\\" % key ) local_var_params[key]\",\n \"True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else: (data) =\",\n \"file_id: (required) :param str location_id: (required) :param str user_id: (required) :return: FileMetaDataEnveloped If\",\n \"noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa:\",\n \":return: PresignedLinkEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\"\",\n \":return: FileLocationArrayEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\"\",\n \"'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),\",\n \"= locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req')\",\n \"HTTP request, please pass async_req=True >>> thread = api.download_file(file_id, location_id, user_id, async_req=True) >>>\",\n \"_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501\",\n \"by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread\",\n \"# noqa: E501 \\\"\\\"\\\"Test checkpoint to ask server to fail or echo back\",\n \"# noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,\",\n \"kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) #\",\n \"the required parameter `user_id` when calling `upload_file`\\\") # noqa: E501 collection_formats = {}\",\n \"str user_id: (required) :return: None If the method is called asynchronously, returns the\",\n \"E501 query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501\",\n \"'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):\",\n \"local_var_params['user_id'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {}\",\n \"# noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501\",\n \"ValueError(\\\"Missing the required parameter `file_id` when calling `upload_file`\\\") # noqa: E501 # verify\",\n \"0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from __future__ import absolute_import import re\",\n \"async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: (required) :return:\",\n \":param str location_id: (required) :param str user_id: (required) :param str extra_location: :param str\",\n \"['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val\",\n \"(data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return data def health_check_with_http_info(self, **kwargs): # noqa:\",\n \"_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs):\",\n \"calling `get_files_metadata`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'location_id'\",\n \"or echo back the transmitted data # noqa: E501 This method makes a\",\n \"noqa: E501 # verify the required parameter 'location_id' is set if ('location_id' not\",\n \"(data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def upload_file_with_http_info(self,\",\n \"header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:\",\n \"'%s'\\\" \\\" to method get_file_metadata\\\" % key ) local_var_params[key] = val del local_var_params['kwargs']\",\n \"kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) =\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['user_id'] #\",\n \"called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = []\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file(file_id, location_id, user_id,\",\n \"request, please pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result\",\n \"\\\"\\\"\\\"Deletes File # noqa: E501 This method makes a synchronous HTTP request by\",\n \"local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {}\",\n \"the required parameter 'action' is set if ('action' not in local_var_params or local_var_params['action']\",\n \"body_params = None if 'fake_type' in local_var_params: body_params = local_var_params['fake_type'] # HTTP header\",\n \"= self.health_check_with_http_info(**kwargs) # noqa: E501 return data def health_check_with_http_info(self, **kwargs): # noqa: E501\",\n \"the service behind # noqa: E501 This method makes a synchronous HTTP request\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result\",\n \"async_req=True >>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_storage_locations`\\\") # noqa: E501\",\n \"self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id,\",\n \":param async_req bool :param str file_id: (required) :param str location_id: (required) :param FileMetaDataType\",\n \"the required parameter `action` when calling `check_action_post`\\\") # noqa: E501 collection_formats = {}\",\n \"thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"= self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs): #\",\n \"'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),\",\n \"FileMetaDataArrayEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params\",\n \"async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id,\",\n \"kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.upload_file_with_http_info(file_id,\",\n \"`get_files_metadata`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'location_id' in\",\n \"argument '%s'\\\" \\\" to method get_storage_locations\\\" % key ) local_var_params[key] = val del\",\n \"ValueError(\\\"Missing the required parameter `location_id` when calling `get_files_metadata`\\\") # noqa: E501 # verify\",\n \"return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get Files Metadata\",\n \"E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', #\",\n \"# noqa: E501 return data def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test\",\n \"_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns upload\",\n \"location_id, user_id, **kwargs) # noqa: E501 return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id,\",\n \"get_file_metadata\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required\",\n \"= local_var_params['location_id'] # noqa: E501 query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id',\",\n \"calling `upload_file`\\\") # noqa: E501 # verify the required parameter 'user_id' is set\",\n \"pass async_req=True >>> thread = api.download_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get()\",\n \"location_id: (required) :param str user_id: (required) :return: FileMetaDataEnveloped If the method is called\",\n \"= self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def delete_file_with_http_info(self, file_id,\",\n \"HTTP request, please pass async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result =\",\n \"{} path_params = {} if 'action' in local_var_params: path_params['action'] = local_var_params['action'] # noqa:\",\n \"when calling `delete_file`\\\") # noqa: E501 # verify the required parameter 'location_id' is\",\n \"HTTP request, please pass async_req=True >>> thread = api.health_check(async_req=True) >>> result = thread.get()\",\n \"_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes File\",\n \"# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): #\",\n \"thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"thread.get() :param async_req bool :param str user_id: (required) :return: FileLocationArrayEnveloped If the method\",\n \"**kwargs): # noqa: E501 \\\"\\\"\\\"Returns upload link or performs copy operation to datcore\",\n \"called asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return\",\n \"noqa: E501 return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get\",\n \"thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If\",\n \"location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns upload link or performs copy operation\",\n \"or local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_storage_locations`\\\")\",\n \"or local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `delete_file`\\\")\",\n \"PresignedLinkEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only']\",\n \"= self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def download_file_with_http_info(self, file_id,\",\n \"Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from __future__ import absolute_import import re #\",\n \"`upload_file`\\\") # noqa: E501 # verify the required parameter 'user_id' is set if\",\n \"`location_id` when calling `get_file_metadata`\\\") # noqa: E501 # verify the required parameter 'user_id'\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file(file_id, location_id, user_id, async_req=True)\",\n \"[] if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params = {}\",\n \"pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get()\",\n \"import ApiClient class UsersApi(object): \\\"\\\"\\\"NOTE: This class is auto generated by OpenAPI Generator\",\n \"async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param\",\n \"def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available storage locations # noqa:\",\n \"self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501\",\n \"'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),\",\n \"# noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if\",\n \"return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa:\",\n \"**kwargs): # noqa: E501 \\\"\\\"\\\"Returns download link for requested file # noqa: E501\",\n \"file_id: (required) :param str location_id: (required) :param str user_id: (required) :return: PresignedLinkEnveloped If\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True)\",\n \"self.health_check_with_http_info(**kwargs) # noqa: E501 return data def health_check_with_http_info(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service\",\n \"when calling `upload_file`\\\") # noqa: E501 # verify the required parameter 'location_id' is\",\n \"pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params\",\n \"(required) :param str data: :param FakeType fake_type: :return: FakeEnveloped If the method is\",\n \"= api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa:\",\n \"pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result = thread.get()\",\n \"# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa:\",\n \"post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content',\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>>\",\n \"str data: :param FakeType fake_type: :return: FakeEnveloped If the method is called asynchronously,\",\n \"in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params = {} form_params = []\",\n \"async_req=True >>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"request, please pass async_req=True >>> thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>> result\",\n \"all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method upload_file\\\"\",\n \"{} path_params = {} if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa:\",\n \"str location_id: (required) :param str user_id: (required) :return: FileMetaDataEnveloped If the method is\",\n \"in local_var_params: body_params = local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json'])\",\n \"E501 return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get Files\",\n \"'%s'\\\" \\\" to method health_check\\\" % key ) local_var_params[key] = val del local_var_params['kwargs']\",\n \"(data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id,\",\n \"download_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns download link for requested\",\n \"'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in\",\n \"async_req=True >>> thread = api.download_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param\",\n \"noqa: E501 return data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501\",\n \"returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['location_id', 'user_id', 'uuid_filter']\",\n \"auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params,\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post_with_http_info(action,\",\n \"the required parameter `file_id` when calling `delete_file`\\\") # noqa: E501 # verify the\",\n \"local_var_params or local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling\",\n \"import six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \\\"\\\"\\\"NOTE: This class is auto\",\n \"= local_var_params['location_id'] # noqa: E501 query_params = [] header_params = {} form_params =\",\n \"if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location' in local_var_params:\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id,\",\n \":param str location_id: (required) :param str user_id: (required) :return: PresignedLinkEnveloped If the method\",\n \"six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \\\"\\\"\\\"NOTE: This class is auto generated\",\n \"= ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,\",\n \"True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data)\",\n \"(required) :param str location_id: (required) :param str user_id: (required) :return: PresignedLinkEnveloped If the\",\n \"= {} path_params = {} query_params = [] header_params = {} form_params =\",\n \"keyword argument '%s'\\\" \\\" to method upload_file\\\" % key ) local_var_params[key] = val\",\n \"True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update File\",\n \"in local_var_params or local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id` when\",\n \"= local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501\",\n \"header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:\",\n \"'user_id' is set if ('user_id' not in local_var_params or local_var_params['user_id'] is None): raise\",\n \">>> result = thread.get() :param async_req bool :param str user_id: (required) :return: FileLocationArrayEnveloped\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True)\",\n \"request by default. To make an asynchronous HTTP request, please pass async_req=True >>>\",\n \"# noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,\",\n \"set if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ValueError(\\\"Missing the\",\n \"key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'action'\",\n \"[] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params,\",\n \"re # noqa: F401 # python 2 and python 3 compatibility library import\",\n \"method health_check\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {}\",\n \"noqa: E501 Some general information on the API and state of the service\",\n \"an unexpected keyword argument '%s'\\\" \\\" to method upload_file\\\" % key ) local_var_params[key]\",\n \"self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id,\",\n \"'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if 'location_id' in local_var_params:\",\n \"**kwargs) # noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501\",\n \"or local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `get_file_metadata`\\\")\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True)\",\n \"# noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats)\",\n \"path_params = {} if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501\",\n \"local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `download_file`\\\") #\",\n \"query_params = [] header_params = {} form_params = [] local_var_files = {} body_params\",\n \"delete_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes File # noqa: E501\",\n \"user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If the method is called asynchronously,\",\n \"E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get\",\n \"= [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'uuid_filter'\",\n \"parameter `location_id` when calling `update_file_meta_data`\\\") # noqa: E501 collection_formats = {} path_params =\",\n \"str uuid_filter: :return: FileMetaDataArrayEnveloped If the method is called asynchronously, returns the request\",\n \"if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept` header_params['Accept'] =\",\n \"# noqa: E501 collection_formats = {} path_params = {} if 'file_id' in local_var_params:\",\n \"local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'action' is set\",\n \"by: https://openapi-generator.tech \\\"\\\"\\\" from __future__ import absolute_import import re # noqa: F401 #\",\n \"self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa:\",\n \"if 'action' in local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501 query_params = []\",\n \">>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"**kwargs) # noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa:\",\n \"'action' in local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501 query_params = [] if\",\n \"method get_files_metadata\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the\",\n \"to method health_check\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats =\",\n \"query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #\",\n \"(required) :param str location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method\",\n \"auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id,\",\n \"E501 if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params =\",\n \"required parameter `file_id` when calling `update_file_meta_data`\\\") # noqa: E501 # verify the required\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result\",\n \"collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes File #\",\n \"**kwargs): # noqa: E501 \\\"\\\"\\\"Get available storage locations # noqa: E501 This method\",\n \"all_params = ['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for\",\n \"header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:\",\n \"async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param\",\n \"thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa:\",\n \"str user_id: (required) :return: FileLocationArrayEnveloped If the method is called asynchronously, returns the\",\n \"'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):\",\n \"parameter `user_id` when calling `get_storage_locations`\\\") # noqa: E501 collection_formats = {} path_params =\",\n \"= [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location'\",\n \"# noqa: E501 return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,\",\n \"local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `download_file`\\\") #\",\n \"noqa: E501 \\\"\\\"\\\"Deletes File # noqa: E501 This method makes a synchronous HTTP\",\n \"# coding: utf-8 \\\"\\\"\\\" simcore-service-storage API API definition for simcore-service-storage service # noqa:\",\n \"if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params = {} form_params\",\n \"please pass async_req=True >>> thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>> result =\",\n \"not in local_var_params or local_var_params['action'] is None): raise ValueError(\\\"Missing the required parameter `action`\",\n \"query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params = {} form_params = [] local_var_files =\",\n \"collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get File Metadata\",\n \"[] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):\",\n \"local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_storage_locations`\\\") #\",\n \"This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit\",\n \"return data def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint to ask\",\n \"= api.download_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"request, please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get()\",\n \"location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes File # noqa: E501 This method\",\n \"copy operation to datcore # noqa: E501 This method makes a synchronous HTTP\",\n \">>> thread = api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"method makes a synchronous HTTP request by default. To make an asynchronous HTTP\",\n \"`Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings =\",\n \"_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs):\",\n \"for simcore-service-storage service # noqa: E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated\",\n \"local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501\",\n \"verify the required parameter 'user_id' is set if ('user_id' not in local_var_params or\",\n \"= {} path_params = {} query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id',\",\n \">>> thread = api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations(user_id,\",\n \"E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', #\",\n \"# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id, **kwargs):\",\n \"OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from __future__ import\",\n \"request, please pass async_req=True >>> thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>> result\",\n \"kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501\",\n \"= self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] #\",\n \"raise ValueError(\\\"Missing the required parameter `action` when calling `check_action_post`\\\") # noqa: E501 collection_formats\",\n \"returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id,\",\n \"str location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method is called\",\n \"async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id,\",\n \"api.delete_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"to method update_file_meta_data\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify\",\n \"# noqa: E501 query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) #\",\n \"header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 #\",\n \"self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( #\",\n \"= {} if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if\",\n \"header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting\",\n \"= api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id:\",\n \"'/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings,\",\n \"= api.update_file_meta_data_with_http_info(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"request, please pass async_req=True >>> thread = api.download_file(file_id, location_id, user_id, async_req=True) >>> result\",\n \"= None if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] # HTTP header `Accept`\",\n \"kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id,\",\n \"E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501\",\n \"local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) #\",\n \":param str user_id: (required) :param str extra_location: :param str extra_source: :return: PresignedLinkEnveloped If\",\n \"noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return\",\n \"kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.delete_file_with_http_info(file_id,\",\n \"thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) #\",\n \"E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return data def\",\n \"return data def health_check_with_http_info(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint # noqa:\",\n \"link or performs copy operation to datcore # noqa: E501 This method makes\",\n \"result = thread.get() :param async_req bool :param str action: (required) :param str data:\",\n \"body_params = local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa:\",\n \"required parameter `user_id` when calling `download_file`\\\") # noqa: E501 collection_formats = {} path_params\",\n \"please pass async_req=True >>> thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>> result =\",\n \"in local_var_params or local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when\",\n \"is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_storage_locations`\\\") # noqa:\",\n \"# noqa: E501 query_params = [] if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) #\",\n \"ValueError(\\\"Missing the required parameter `user_id` when calling `get_file_metadata`\\\") # noqa: E501 collection_formats =\",\n \"form_params = [] local_var_files = {} body_params = None if 'fake_type' in local_var_params:\",\n \"collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get Files Metadata #\",\n \"location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str file_id:\",\n \"# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET',\",\n \"local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params = {} form_params = [] local_var_files\",\n \") local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'user_id' is\",\n \"= True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs)\",\n \"ValueError(\\\"Missing the required parameter `file_id` when calling `update_file_meta_data`\\\") # noqa: E501 # verify\",\n \"is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `upload_file`\\\") # noqa:\",\n \"parameter `action` when calling `check_action_post`\\\") # noqa: E501 collection_formats = {} path_params =\",\n \"the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs)\",\n \"async_req bool :param str user_id: (required) :return: FileLocationArrayEnveloped If the method is called\",\n \"setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params,\",\n \"request, please pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result =\",\n \"parameter `location_id` when calling `get_file_metadata`\\\") # noqa: E501 # verify the required parameter\",\n \"user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get Files Metadata # noqa: E501 This method\",\n \"self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501\",\n \"query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params = {} form_params = [] local_var_files =\",\n \"setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/', 'GET', path_params, query_params,\",\n \"collection_formats = {} path_params = {} if 'action' in local_var_params: path_params['action'] = local_var_params['action']\",\n \"self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file(file_id,\",\n \"parameter `location_id` when calling `get_files_metadata`\\\") # noqa: E501 # verify the required parameter\",\n \"# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs):\",\n \"**kwargs) # noqa: E501 return data def get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa:\",\n \"if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required\",\n \"noqa: F401 # python 2 and python 3 compatibility library import six from\",\n \"'%s'\\\" \\\" to method get_files_metadata\\\" % key ) local_var_params[key] = val del local_var_params['kwargs']\",\n \"local_var_params['kwargs'] # verify the required parameter 'file_id' is set if ('file_id' not in\",\n \"'/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings,\",\n \"ValueError(\\\"Missing the required parameter `location_id` when calling `update_file_meta_data`\\\") # noqa: E501 collection_formats =\",\n \"data def health_check_with_http_info(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint # noqa: E501\",\n \"return data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns upload\",\n \">>> thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"E501 collection_formats = {} path_params = {} if 'file_id' in local_var_params: path_params['fileId'] =\",\n \"\\\" to method check_action_post\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata(file_id,\",\n \"api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint # noqa: E501\",\n \"in local_var_params or local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when\",\n \"location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method is called asynchronously,\",\n \"# verify the required parameter 'action' is set if ('action' not in local_var_params\",\n \":return: FileMetaDataArrayEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\"\",\n \"local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `upload_file`\\\") #\",\n \"(data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def get_file_metadata_with_http_info(self,\",\n \"please pass async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req\",\n \"api_client def check_action_post(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint to ask server\",\n \"= api.check_action_post_with_http_info(action, async_req=True) >>> result = thread.get() :param async_req bool :param str action:\",\n \"self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501\",\n \"True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else: (data) =\",\n \"`update_file_meta_data`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id' in\",\n \"HTTP request, please pass async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result =\",\n \"str user_id: (required) :return: FileMetaDataEnveloped If the method is called asynchronously, returns the\",\n \"local_var_params: path_params['action'] = local_var_params['action'] # noqa: E501 query_params = [] if 'data' in\",\n \"`location_id` when calling `delete_file`\\\") # noqa: E501 # verify the required parameter 'user_id'\",\n \"update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update File Metadata # noqa: E501\",\n \"manually. \\\"\\\"\\\" def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client\",\n \"# noqa: E501 \\\"\\\"\\\"Update File Metadata # noqa: E501 This method makes a\",\n \"async_req bool :param str file_id: (required) :param str location_id: (required) :param FileMetaDataType file_meta_data_type:\",\n \"('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter\",\n \"required parameter 'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id'] is\",\n \"the required parameter `location_id` when calling `get_files_metadata`\\\") # noqa: E501 # verify the\",\n \"noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'user_id'\",\n \"E501 # verify the required parameter 'user_id' is set if ('user_id' not in\",\n \"if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = []\",\n \"please pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param\",\n \"\\\" to method get_file_metadata\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #\",\n \":return: HealthCheckEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\"\",\n \"raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method health_check\\\" %\",\n \"{} if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params =\",\n \"async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param\",\n \"['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']):\",\n \"pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result = thread.get() :param\",\n \"ask server to fail or echo back the transmitted data # noqa: E501\",\n \"(required) :param str user_id: (required) :return: None If the method is called asynchronously,\",\n \"returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id,\",\n \"location_id, user_id, **kwargs) # noqa: E501 return data def upload_file_with_http_info(self, file_id, location_id, user_id,\",\n \"data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns upload link\",\n \"path_params = {} if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501\",\n \"return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs)\",\n \"\\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501\",\n \"raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method delete_file\\\" %\",\n \"data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns download link\",\n \"[] header_params = {} form_params = [] local_var_files = {} body_params = None\",\n \"('file_id' not in local_var_params or local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter\",\n \"path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params = [] header_params = {} form_params\",\n \"\\\"Got an unexpected keyword argument '%s'\\\" \\\" to method download_file\\\" % key )\",\n \"api.health_check(async_req=True) >>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If the method\",\n \"location_id, async_req=True) >>> result = thread.get() :param async_req bool :param str file_id: (required)\",\n \"extra_location: :param str extra_source: :return: PresignedLinkEnveloped If the method is called asynchronously, returns\",\n \"None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_files_metadata`\\\") # noqa: E501\",\n \":return: FakeEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:\",\n \"ValueError(\\\"Missing the required parameter `user_id` when calling `get_files_metadata`\\\") # noqa: E501 collection_formats =\",\n \"returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id']\",\n \"def health_check(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint # noqa: E501 Some\",\n \"please pass async_req=True >>> thread = api.health_check(async_req=True) >>> result = thread.get() :param async_req\",\n \"pass async_req=True >>> thread = api.get_files_metadata(location_id, user_id, async_req=True) >>> result = thread.get() :param\",\n \"required parameter `file_id` when calling `delete_file`\\\") # noqa: E501 # verify the required\",\n \"FileMetaDataEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True)\",\n \"auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations', 'GET', path_params, query_params, header_params,\",\n \"self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs):\",\n \"query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #\",\n \"called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['location_id',\",\n \"# noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params\",\n \"parameter `file_id` when calling `download_file`\\\") # noqa: E501 # verify the required parameter\",\n \"noqa: E501 \\\"\\\"\\\"Returns download link for requested file # noqa: E501 This method\",\n \"str location_id: (required) :param str user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If\",\n \":param str location_id: (required) :param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method is\",\n \"[] # noqa: E501 return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params,\",\n \"_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id,\",\n \"# noqa: E501 return data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa:\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id,\",\n \"user_id, **kwargs) # noqa: E501 return data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs):\",\n \"auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id,\",\n \">>> thread = api.check_action_post(action, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params\",\n \"not edit the class manually. \\\"\\\"\\\" def __init__(self, api_client=None): if api_client is None:\",\n \"header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings\",\n \"path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),\",\n \"user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available storage locations # noqa: E501 This\",\n \"True if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data)\",\n \"_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available\",\n \"header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type`\",\n \"else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def\",\n \"def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update File Metadata # noqa:\",\n \"{} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) #\",\n \"location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs)\",\n \"# noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files\",\n \"local_var_params['extra_source'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {}\",\n \"calling `get_files_metadata`\\\") # noqa: E501 # verify the required parameter 'user_id' is set\",\n \"get_storage_locations\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required\",\n \"file_id: (required) :param str location_id: (required) :param str user_id: (required) :param str extra_location:\",\n \"api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"when calling `get_files_metadata`\\\") # noqa: E501 collection_formats = {} path_params = {} if\",\n \"pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get()\",\n \"kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id,\",\n \"python 2 and python 3 compatibility library import six from simcore_service_storage_sdk.api_client import ApiClient\",\n \"thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) #\",\n \"str location_id: (required) :param str user_id: (required) :return: PresignedLinkEnveloped If the method is\",\n \"required parameter 'user_id' is set if ('user_id' not in local_var_params or local_var_params['user_id'] is\",\n \"raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method get_file_metadata\\\" %\",\n \"`user_id` when calling `upload_file`\\\") # noqa: E501 collection_formats = {} path_params = {}\",\n \"an unexpected keyword argument '%s'\\\" \\\" to method get_storage_locations\\\" % key ) local_var_params[key]\",\n \"**kwargs): # noqa: E501 \\\"\\\"\\\"Get File Metadata # noqa: E501 This method makes\",\n \"query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), #\",\n \"'file_id' is set if ('file_id' not in local_var_params or local_var_params['file_id'] is None): raise\",\n \"= True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else: (data)\",\n \"thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"noqa: E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>> result\",\n \"when calling `upload_file`\\\") # noqa: E501 collection_formats = {} path_params = {} if\",\n \"None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_file_metadata`\\\") # noqa: E501\",\n \"auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id,\",\n \"noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs): #\",\n \"= [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params,\",\n \"return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update File Metadata\",\n \"to method download_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify\",\n \"not in local_var_params or local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id`\",\n \"is called asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'):\",\n \"path_params = {} query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) #\",\n \"E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self, file_id, location_id, user_id, **kwargs): # noqa:\",\n \"async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs):\",\n \"if 'fake_type' in local_var_params: body_params = local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] =\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True)\",\n \"path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'),\",\n \"noqa: E501 return data def download_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501\",\n \"request, please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id, async_req=True) >>> result =\",\n \":param async_req bool :return: HealthCheckEnveloped If the method is called asynchronously, returns the\",\n \"if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) =\",\n \"response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),\",\n \"'/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa: E501 auth_settings=auth_settings,\",\n \"local_var_params['data'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {}\",\n \":param FileMetaDataType file_meta_data_type: :return: FileMetaDataEnveloped If the method is called asynchronously, returns the\",\n \"in all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id,\",\n \"or local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `update_file_meta_data`\\\")\",\n \"'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params = {} form_params =\",\n \"# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}/metadata', 'PATCH',\",\n \"user_id, **kwargs) # noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa:\",\n \"async_req=True >>> thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"unexpected keyword argument '%s'\\\" \\\" to method get_files_metadata\\\" % key ) local_var_params[key] =\",\n \"= [] if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params =\",\n \"# noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id,\",\n \"if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params = {} form_params\",\n \"noqa: E501 return data def health_check_with_http_info(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint\",\n \"collection_formats = {} path_params = {} query_params = [] header_params = {} form_params\",\n \"E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, #\",\n \"Some general information on the API and state of the service behind #\",\n \"pass async_req=True >>> thread = api.delete_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get()\",\n \"Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params,\",\n \"**kwargs) # noqa: E501 return data def get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501\",\n \"required parameter `file_id` when calling `download_file`\\\") # noqa: E501 # verify the required\",\n \"str file_id: (required) :param str location_id: (required) :param str user_id: (required) :return: None\",\n \"parameter 'action' is set if ('action' not in local_var_params or local_var_params['action'] is None):\",\n \"raise ValueError(\\\"Missing the required parameter `file_id` when calling `download_file`\\\") # noqa: E501 #\",\n \"# noqa: E501 \\\"\\\"\\\"Returns upload link or performs copy operation to datcore #\",\n \"**kwargs) # noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return\",\n \"returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs)\",\n \"when calling `delete_file`\\\") # noqa: E501 collection_formats = {} path_params = {} if\",\n \"keyword argument '%s'\\\" \\\" to method download_file\\\" % key ) local_var_params[key] = val\",\n \"class UsersApi(object): \\\"\\\"\\\"NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech\",\n \"TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method get_files_metadata\\\" % key\",\n \"parameter `file_id` when calling `update_file_meta_data`\\\") # noqa: E501 # verify the required parameter\",\n \":param str user_id: (required) :return: FileLocationArrayEnveloped If the method is called asynchronously, returns\",\n \"else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return data def health_check_with_http_info(self, **kwargs): #\",\n \"general information on the API and state of the service behind # noqa:\",\n \"_request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get Files Metadata\",\n \"E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501\",\n \"thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa: E501\",\n \"ValueError(\\\"Missing the required parameter `file_id` when calling `download_file`\\\") # noqa: E501 # verify\",\n \"in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params = {} form_params = []\",\n \"= [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params,\",\n \"key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'location_id'\",\n \"**kwargs) # noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa:\",\n \"('action' not in local_var_params or local_var_params['action'] is None): raise ValueError(\\\"Missing the required parameter\",\n \"key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( \\\"Got an\",\n \"local_var_params['user_id'] is None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `get_file_metadata`\\\") #\",\n \"noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update\",\n \"`upload_file`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id' in\",\n \"= {} body_params = None if 'fake_type' in local_var_params: body_params = local_var_params['fake_type'] #\",\n \"raise ValueError(\\\"Missing the required parameter `file_id` when calling `upload_file`\\\") # noqa: E501 #\",\n \"'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source',\",\n \"if kwargs.get('async_req'): return self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) =\",\n \"\\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req')\",\n \"an unexpected keyword argument '%s'\\\" \\\" to method get_file_metadata\\\" % key ) local_var_params[key]\",\n \">>> result = thread.get() :param async_req bool :return: HealthCheckEnveloped If the method is\",\n \"verify the required parameter 'action' is set if ('action' not in local_var_params or\",\n \"E501 collection_formats = {} path_params = {} if 'action' in local_var_params: path_params['action'] =\",\n \"api.download_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"`Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type']\",\n \"to ask server to fail or echo back the transmitted data # noqa:\",\n \"if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) =\",\n \"# noqa: E501 Some general information on the API and state of the\",\n \"user_id: (required) :return: FileMetaDataEnveloped If the method is called asynchronously, returns the request\",\n \"raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method get_files_metadata\\\" %\",\n \"raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method get_storage_locations\\\" %\",\n \"\\\" to method upload_file\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #\",\n \"calling `delete_file`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id'\",\n \"local_var_params = locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout')\",\n \"return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa:\",\n \"thread = api.download_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"noqa: E501 if 'location_id' in local_var_params: path_params['location_id'] = local_var_params['location_id'] # noqa: E501 query_params\",\n \":param str data: :param FakeType fake_type: :return: FakeEnveloped If the method is called\",\n \"\\\"Got an unexpected keyword argument '%s'\\\" \\\" to method get_file_metadata\\\" % key )\",\n \"calling `get_file_metadata`\\\") # noqa: E501 # verify the required parameter 'user_id' is set\",\n \"_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint\",\n \"# noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,\",\n \"the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location',\",\n \"if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id,\",\n \"thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')\",\n \"\\\"\\\"\\\" def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client =\",\n \"`get_storage_locations`\\\") # noqa: E501 collection_formats = {} path_params = {} query_params = []\",\n \"# noqa: E501 collection_formats = {} path_params = {} if 'location_id' in local_var_params:\",\n \"is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `upload_file`\\\") # noqa:\",\n \"noqa: E501 return data def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post(action, async_req=True) >>>\",\n \"= True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else:\",\n \"= api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"please pass async_req=True >>> thread = api.download_file(file_id, location_id, user_id, async_req=True) >>> result =\",\n \"= locals() all_params = ['action', 'data', 'fake_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')\",\n \"auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params,\",\n \"# noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501\",\n \"link for requested file # noqa: E501 This method makes a synchronous HTTP\",\n \"return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa:\",\n \"= thread.get() :param async_req bool :param str location_id: (required) :param str user_id: (required)\",\n \"auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'DELETE', path_params, query_params, header_params,\",\n \"(data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def download_file_with_http_info(self,\",\n \"async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id,\",\n \"fail or echo back the transmitted data # noqa: E501 This method makes\",\n \"returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.upload_file_with_http_info(file_id, location_id,\",\n \"'/locations/{location_id}/files/{fileId}/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings,\",\n \"return data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes File\",\n \"location_id, user_id, **kwargs) # noqa: E501 return data def delete_file_with_http_info(self, file_id, location_id, user_id,\",\n \"the required parameter `location_id` when calling `upload_file`\\\") # noqa: E501 # verify the\",\n \"def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes File # noqa:\",\n \"{} form_params = [] local_var_files = {} body_params = None if 'fake_type' in\",\n \"python 3 compatibility library import six from simcore_service_storage_sdk.api_client import ApiClient class UsersApi(object): \\\"\\\"\\\"NOTE:\",\n \"header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] =\",\n \"\\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data)\",\n \"health-check endpoint # noqa: E501 Some general information on the API and state\",\n \"= {} body_params = None if 'file_meta_data_type' in local_var_params: body_params = local_var_params['file_meta_data_type'] #\",\n \"[] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params,\",\n \"E501 \\\"\\\"\\\"Returns download link for requested file # noqa: E501 This method makes\",\n \"the required parameter `file_id` when calling `update_file_meta_data`\\\") # noqa: E501 # verify the\",\n \"\\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa:\",\n \"= val del local_var_params['kwargs'] # verify the required parameter 'action' is set if\",\n \"= local_var_params['action'] # noqa: E501 query_params = [] if 'data' in local_var_params: query_params.append(('data',\",\n \"user_id: (required) :return: FileLocationArrayEnveloped If the method is called asynchronously, returns the request\",\n \"async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id,\",\n \"if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs)\",\n \"noqa: E501 else: (data) = self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return\",\n \"thread = api.get_storage_locations_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501\",\n \"response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),\",\n \"['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa:\",\n \"all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')\",\n \"None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `get_file_metadata`\\\") # noqa: E501\",\n \"# noqa: E501 \\\"\\\"\\\"Service health-check endpoint # noqa: E501 Some general information on\",\n \"files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True),\",\n \"FileLocationArrayEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params\",\n \"api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str\",\n \"return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id,\",\n \"noqa: E501 else: (data) = self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return\",\n \"**kwargs) # noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501\",\n \"{} path_params = {} query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id']))\",\n \"\\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 else:\",\n \"noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_files_metadata(self, location_id, user_id, **kwargs): # noqa:\",\n \"# noqa: E501 # verify the required parameter 'user_id' is set if ('user_id'\",\n \"or local_var_params['location_id'] is None): raise ValueError(\\\"Missing the required parameter `location_id` when calling `upload_file`\\\")\",\n \"File Metadata # noqa: E501 This method makes a synchronous HTTP request by\",\n \"location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns download link for requested file #\",\n \"local_var_params = locals() all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')\",\n \"local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `upload_file`\\\") #\",\n \"None): raise ValueError(\\\"Missing the required parameter `user_id` when calling `upload_file`\\\") # noqa: E501\",\n \"E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_file_metadata(self, file_id, location_id, user_id, **kwargs): # noqa:\",\n \") local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'location_id' is\",\n \"= [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params,\",\n \":param async_req bool :param str user_id: (required) :return: FileLocationArrayEnveloped If the method is\",\n \"(data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return data def get_files_metadata_with_http_info(self, location_id,\",\n \"all_params = ['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for\",\n \"version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from __future__ import absolute_import import\",\n \"request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs)\",\n \"local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `delete_file`\\\") #\",\n \"# noqa: E501 collection_formats = {} path_params = {} query_params = [] if\",\n \"else: (data) = self.check_action_post_with_http_info(action, **kwargs) # noqa: E501 return data def check_action_post_with_http_info(self, action,\",\n \"True if kwargs.get('async_req'): return self.get_file_metadata_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 else: (data)\",\n \"**kwargs) # noqa: E501 return data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): #\",\n \"thread.get() :param async_req bool :param str file_id: (required) :param str location_id: (required) :param\",\n \"parameter `location_id` when calling `download_file`\\\") # noqa: E501 # verify the required parameter\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action,\",\n \"[] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 header_params = {}\",\n \"location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get File Metadata # noqa: E501 This\",\n \"E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', #\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.check_action_post(action,\",\n \"required parameter 'file_id' is set if ('file_id' not in local_var_params or local_var_params['file_id'] is\",\n \":param str user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped If the method is\",\n \">>> thread = api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"user_id, **kwargs) # noqa: E501 return data def upload_file_with_http_info(self, file_id, location_id, user_id, **kwargs):\",\n \"local_var_params = locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only')\",\n \"If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] =\",\n \"upload_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns upload link or performs\",\n \"the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['location_id', 'user_id', 'uuid_filter'] #\",\n \"datcore # noqa: E501 This method makes a synchronous HTTP request by default.\",\n \"thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"# noqa: E501 return data def update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501\",\n \"To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata(location_id,\",\n \"location_id, **kwargs) # noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa:\",\n \"local_var_params: body_params = local_var_params['fake_type'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) #\",\n \"returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['action', 'data', 'fake_type']\",\n \"key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'file_id'\",\n \"if 'file_id' in local_var_params: path_params['fileId'] = local_var_params['file_id'] # noqa: E501 if 'location_id' in\",\n \"query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa:\",\n \"def check_action_post_with_http_info(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint to ask server to\",\n \"async_req=True >>> thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param\",\n \"= [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in\",\n \"return self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs)\",\n \"Do not edit the class manually. \\\"\\\"\\\" def __init__(self, api_client=None): if api_client is\",\n \"= val del local_var_params['kwargs'] # verify the required parameter 'location_id' is set if\",\n \"body=body_params, post_params=form_params, files=local_var_files, response_type='FileLocationArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501\",\n \"calling `delete_file`\\\") # noqa: E501 # verify the required parameter 'location_id' is set\",\n \"[] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location' in\",\n \"api_client is None: api_client = ApiClient() self.api_client = api_client def check_action_post(self, action, **kwargs):\",\n \"return self.api_client.call_api( '/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa:\",\n \"= None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501\",\n \">>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"ValueError(\\\"Missing the required parameter `file_id` when calling `get_file_metadata`\\\") # noqa: E501 # verify\",\n \"noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] #\",\n \"HTTP request, please pass async_req=True >>> thread = api.upload_file(file_id, location_id, user_id, async_req=True) >>>\",\n \"self.health_check_with_http_info(**kwargs) # noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return data\",\n \"calling `get_file_metadata`\\\") # noqa: E501 # verify the required parameter 'location_id' is set\",\n \"response_type='FakeEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'),\",\n \"all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method get_file_metadata\\\"\",\n \"health_check(self, **kwargs): # noqa: E501 \\\"\\\"\\\"Service health-check endpoint # noqa: E501 Some general\",\n \"get_files_metadata_with_http_info(self, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get Files Metadata # noqa: E501\",\n \"thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"= locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')\",\n \"is set if ('file_id' not in local_var_params or local_var_params['file_id'] is None): raise ValueError(\\\"Missing\",\n \"the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.check_action_post_with_http_info(action, **kwargs) #\",\n \"'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),\",\n \"return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa:\",\n \"= True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) =\",\n \"bool :return: HealthCheckEnveloped If the method is called asynchronously, returns the request thread.\",\n \"asynchronous HTTP request, please pass async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>>\",\n \"# noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def health_check(self, **kwargs): # noqa: E501\",\n \"request, please pass async_req=True >>> thread = api.upload_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result\",\n \"auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id,\",\n \"auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/metadata', 'GET', path_params, query_params, header_params,\",\n \"location_id, user_id, **kwargs) # noqa: E501 return data def download_file_with_http_info(self, file_id, location_id, user_id,\",\n \"please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result =\",\n \"_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def upload_file(self, file_id, location_id, user_id,\",\n \"definition for simcore-service-storage service # noqa: E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL>\",\n \"raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method upload_file\\\" %\",\n \"E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_file(self,\",\n \"all_params = ['file_id', 'location_id', 'user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for\",\n \"_preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def update_file_meta_data(self, file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update\",\n \"# HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa:\",\n \"header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='HealthCheckEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa:\",\n \"if ('file_id' not in local_var_params or local_var_params['file_id'] is None): raise ValueError(\\\"Missing the required\",\n \"update_file_meta_data_with_http_info(self, file_id, location_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Update File Metadata # noqa: E501\",\n \"OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. \\\"\\\"\\\" def __init__(self,\",\n \"auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually.\",\n \"an asynchronous HTTP request, please pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id,\",\n \"# noqa: E501 else: (data) = self.health_check_with_http_info(**kwargs) # noqa: E501 return data def\",\n \"noqa: E501 else: (data) = self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 return data\",\n \"generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. \\\"\\\"\\\"\",\n \"async_req=True >>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"query_params = [] if 'user_id' in local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if\",\n \"\\\"\\\"\\\"NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not\",\n \"'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),\",\n \"all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method get_storage_locations\\\"\",\n \"noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def\",\n \"UsersApi(object): \\\"\\\"\\\"NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do\",\n \":param str location_id: (required) :param str user_id: (required) :param str uuid_filter: :return: FileMetaDataArrayEnveloped\",\n \"query_params = [] if 'data' in local_var_params: query_params.append(('data', local_var_params['data'])) # noqa: E501 header_params\",\n \"local_var_params['user_id'])) # noqa: E501 if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501\",\n \"\\\" to method health_check\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats\",\n \"TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method download_file\\\" % key\",\n \"\\\"\\\"\\\" local_var_params = locals() all_params = ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content')\",\n \">>> thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"please pass async_req=True >>> thread = api.check_action_post(action, async_req=True) >>> result = thread.get() :param\",\n \"'%s'\\\" \\\" to method check_action_post\\\" % key ) local_var_params[key] = val del local_var_params['kwargs']\",\n \"request, please pass async_req=True >>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result\",\n \"unexpected keyword argument '%s'\\\" \\\" to method get_file_metadata\\\" % key ) local_var_params[key] =\",\n \"noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def download_file(self, file_id, location_id, user_id, **kwargs): #\",\n \"(required) :param str user_id: (required) :param str extra_location: :param str extra_source: :return: PresignedLinkEnveloped\",\n \"verify the required parameter 'file_id' is set if ('file_id' not in local_var_params or\",\n \"kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data)\",\n \"# noqa: E501 \\\"\\\"\\\"Deletes File # noqa: E501 This method makes a synchronous\",\n \"calling `download_file`\\\") # noqa: E501 collection_formats = {} path_params = {} if 'file_id'\",\n \"E501 \\\"\\\"\\\"Get available storage locations # noqa: E501 This method makes a synchronous\",\n \"location_id: (required) :param str user_id: (required) :return: None If the method is called\",\n \"['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501\",\n \"'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params = {} form_params =\",\n \"make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_files_metadata_with_http_info(location_id, user_id,\",\n \"async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>> result = thread.get() :param async_req bool\",\n \"# noqa: E501 \\\"\\\"\\\"Get available storage locations # noqa: E501 This method makes\",\n \"\\\" to method get_files_metadata\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] #\",\n \"check_action_post(self, action, **kwargs): # noqa: E501 \\\"\\\"\\\"Test checkpoint to ask server to fail\",\n \"# noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return\",\n \"\\\"\\\"\\\"Get File Metadata # noqa: E501 This method makes a synchronous HTTP request\",\n \"and state of the service behind # noqa: E501 This method makes a\",\n \"HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting\",\n \"noqa: E501 collection_formats = {} path_params = {} if 'action' in local_var_params: path_params['action']\",\n \"local_var_params: query_params.append(('user_id', local_var_params['user_id'])) # noqa: E501 if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) #\",\n \"`file_id` when calling `upload_file`\\\") # noqa: E501 # verify the required parameter 'location_id'\",\n \"True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available storage\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_file_with_http_info(file_id,\",\n \"return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PresignedLinkEnveloped', # noqa:\",\n \"async_req=True >>> thread = api.health_check_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return:\",\n \"argument '%s'\\\" \\\" to method download_file\\\" % key ) local_var_params[key] = val del\",\n \"None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `delete_file`\\\") # noqa: E501\",\n \"# noqa: E501 if 'extra_source' in local_var_params: query_params.append(('extra_source', local_var_params['extra_source'])) # noqa: E501 header_params\",\n \"\\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else:\",\n \"def get_storage_locations(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available storage locations # noqa:\",\n \"user_id, **kwargs) # noqa: E501 else: (data) = self.delete_file_with_http_info(file_id, location_id, user_id, **kwargs) #\",\n \"'%s'\\\" \\\" to method update_file_meta_data\\\" % key ) local_var_params[key] = val del local_var_params['kwargs']\",\n \"thread = api.update_file_meta_data(file_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool :param\",\n \"noqa: E501 collection_formats = {} path_params = {} if 'location_id' in local_var_params: path_params['location_id']\",\n \"TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method delete_file\\\" % key\",\n \"checkpoint to ask server to fail or echo back the transmitted data #\",\n \"parameter `user_id` when calling `download_file`\\\") # noqa: E501 collection_formats = {} path_params =\",\n \"kwargs.get('async_req'): return self.get_storage_locations_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) = self.get_storage_locations_with_http_info(user_id, **kwargs) #\",\n \"parameter `file_id` when calling `delete_file`\\\") # noqa: E501 # verify the required parameter\",\n \"if 'extra_location' in local_var_params: query_params.append(('extra_location', local_var_params['extra_location'])) # noqa: E501 if 'extra_source' in local_var_params:\",\n \"required parameter `action` when calling `check_action_post`\\\") # noqa: E501 collection_formats = {} path_params\",\n \"thread.get() :param async_req bool :param str location_id: (required) :param str user_id: (required) :param\",\n \"noqa: E501 else: (data) = self.update_file_meta_data_with_http_info(file_id, location_id, **kwargs) # noqa: E501 return data\",\n \"noqa: E501 OpenAPI spec version: 0.1.0 Contact: <EMAIL> Generated by: https://openapi-generator.tech \\\"\\\"\\\" from\",\n \"[] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params,\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_file_meta_data_with_http_info(file_id,\",\n \"E501 return data def get_file_metadata_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get\",\n \"if 'uuid_filter' in local_var_params: query_params.append(('uuid_filter', local_var_params['uuid_filter'])) # noqa: E501 header_params = {} form_params\",\n \"local_var_params = locals() all_params = ['file_id', 'location_id', 'user_id', 'extra_location', 'extra_source'] # noqa: E501\",\n \"kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_files_metadata_with_http_info(location_id, user_id, **kwargs) # noqa: E501 else:\",\n \"def upload_file(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Returns upload link or\",\n \"request, please pass async_req=True >>> thread = api.get_storage_locations(user_id, async_req=True) >>> result = thread.get()\",\n \"is None): raise ValueError(\\\"Missing the required parameter `file_id` when calling `get_file_metadata`\\\") # noqa:\",\n \"data def delete_file_with_http_info(self, file_id, location_id, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Deletes File #\",\n \"\\\"Got an unexpected keyword argument '%s'\\\" \\\" to method upload_file\\\" % key )\",\n \"HTTP request, please pass async_req=True >>> thread = api.get_file_metadata(file_id, location_id, user_id, async_req=True) >>>\",\n \"set if ('user_id' not in local_var_params or local_var_params['user_id'] is None): raise ValueError(\\\"Missing the\",\n \">>> thread = api.download_file_with_http_info(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"HTTP request, please pass async_req=True >>> thread = api.get_file_metadata_with_http_info(file_id, location_id, user_id, async_req=True) >>>\",\n \"calling `upload_file`\\\") # noqa: E501 # verify the required parameter 'location_id' is set\",\n \"ValueError(\\\"Missing the required parameter `location_id` when calling `delete_file`\\\") # noqa: E501 # verify\",\n \"FakeEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params\",\n \"= ['location_id', 'user_id', 'uuid_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key,\",\n \"is None: api_client = ApiClient() self.api_client = api_client def check_action_post(self, action, **kwargs): #\",\n \"Files Metadata # noqa: E501 This method makes a synchronous HTTP request by\",\n \"get_files_metadata\\\" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required\",\n \"% key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params =\",\n \"HealthCheckEnveloped If the method is called asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params\",\n \"**kwargs): # noqa: E501 \\\"\\\"\\\"Deletes File # noqa: E501 This method makes a\",\n \"is set if ('user_id' not in local_var_params or local_var_params['user_id'] is None): raise ValueError(\\\"Missing\",\n \"'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileMetaDataArrayEnveloped', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'),\",\n \"HTTP request, please pass async_req=True >>> thread = api.delete_file_with_http_info(file_id, location_id, user_id, async_req=True) >>>\",\n \"asynchronously, returns the request thread. \\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id',\",\n \"# noqa: E501 return self.api_client.call_api( '/check/{action}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files,\",\n \"= [] local_var_files = {} body_params = None if 'file_meta_data_type' in local_var_params: body_params\",\n \"\\\"\\\"\\\" local_var_params = locals() all_params = ['file_id', 'location_id', 'file_meta_data_type'] # noqa: E501 all_params.append('async_req')\",\n \"thread.get() :param async_req bool :param str action: (required) :param str data: :param FakeType\",\n \"self.download_file_with_http_info(file_id, location_id, user_id, **kwargs) # noqa: E501 return data def download_file_with_http_info(self, file_id, location_id,\",\n \">>> thread = api.download_file(file_id, location_id, user_id, async_req=True) >>> result = thread.get() :param async_req\",\n \"all_params: raise TypeError( \\\"Got an unexpected keyword argument '%s'\\\" \\\" to method update_file_meta_data\\\"\",\n \"get_storage_locations_with_http_info(self, user_id, **kwargs): # noqa: E501 \\\"\\\"\\\"Get available storage locations # noqa: E501\",\n \"# verify the required parameter 'file_id' is set if ('file_id' not in local_var_params\",\n \"# Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'GET',\",\n \"unexpected keyword argument '%s'\\\" \\\" to method download_file\\\" % key ) local_var_params[key] =\",\n \"= locals() all_params = ['user_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for\",\n \"noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_storage_locations(self, user_id, **kwargs): # noqa: E501\"\n]","isConversational":false}}},{"rowIdx":2982,"cells":{"ngram":{"kind":"list like","value":["2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'),","# Generated by Django 3.2.7 on 2021-10-22 14:23 from django.db import migrations class","dependencies = [ ('reservation_management', '0020_greenpass'), ] operations = [ migrations.DeleteModel( name='GreenPass', ), ]","Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ] operations = [ migrations.DeleteModel( name='GreenPass', ),","<filename>reservation_management/migrations/0021_delete_greenpass.py # Generated by Django 3.2.7 on 2021-10-22 14:23 from django.db import migrations","by Django 3.2.7 on 2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration): dependencies","3.2.7 on 2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration): dependencies = [","Generated by Django 3.2.7 on 2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration):","from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ] operations","django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ] operations =","import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ] operations = [","migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ] operations = [ migrations.DeleteModel(","class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ] operations = [ migrations.DeleteModel( name='GreenPass',","on 2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management',","Django 3.2.7 on 2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration): dependencies =","14:23 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ]"],"string":"[\n \"2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'),\",\n \"# Generated by Django 3.2.7 on 2021-10-22 14:23 from django.db import migrations class\",\n \"dependencies = [ ('reservation_management', '0020_greenpass'), ] operations = [ migrations.DeleteModel( name='GreenPass', ), ]\",\n \"Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ] operations = [ migrations.DeleteModel( name='GreenPass', ),\",\n \"<filename>reservation_management/migrations/0021_delete_greenpass.py # Generated by Django 3.2.7 on 2021-10-22 14:23 from django.db import migrations\",\n \"by Django 3.2.7 on 2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration): dependencies\",\n \"3.2.7 on 2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration): dependencies = [\",\n \"Generated by Django 3.2.7 on 2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration):\",\n \"from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ] operations\",\n \"django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ] operations =\",\n \"import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ] operations = [\",\n \"migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ] operations = [ migrations.DeleteModel(\",\n \"class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ] operations = [ migrations.DeleteModel( name='GreenPass',\",\n \"on 2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management',\",\n \"Django 3.2.7 on 2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration): dependencies =\",\n \"14:23 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'), ]\"\n]","isConversational":false}}},{"rowIdx":2983,"cells":{"ngram":{"kind":"list like","value":["bw = 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power = None if","output_ext) output_count += 1 fig_title = 'Signal Decoded Using Leaky IAF Population Decoder'","a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as","+ output_ext) # Test ideal IAF algorithms: b1 = 3.5 # bias d1","3.4 # bias d2 = 0.8 # threshold R2 = 9.0 # resistance","b1 = 3.5 # bias d1 = 0.7 # threshold R1 = 10.0","under the terms of the BSD license: # http://www.opensource.org/licenses/bsd-license import sys import numpy","b_list = [b1, b2] d_list = [d1, d2] R_list = [R1, R2] C_list","BSD license: # http://www.opensource.org/licenses/bsd-license import sys import numpy as np # Set matplotlib","ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4 # bias d2 = 0.8","as iaf # For determining output plot file names: output_name = 'iaf_pop_demo_' output_count","0.7 # threshold R1 = np.inf # resistance C1 = 0.01 # capacitance","output_count += 1 fig_title = 'Signal Decoded Using Leaky IAF Population Decoder' print","output_ext) # Test leaky IAF algorithms: b1 = 3.5 # bias d1 =","For determining output plot file names: output_name = 'iaf_pop_demo_' output_count = 0 output_ext","Signal with %d dB of Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur,","3.4 # bias d2 = 0.8 # threshold R2 = np.inf # resistance","Test ideal IAF algorithms: b1 = 3.5 # bias d1 = 0.7 #","dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title + ' #1', output_name","dB of Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power)","Leaky IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list,","= 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction","b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = [b1,","fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u,","fig_title + ' #2', output_name + str(output_count) + output_ext) output_count += 1 fig_title","output_count = 0 output_ext = '.png' # Define algorithm parameters and input signal:","sys.exit() b2 = 3.4 # bias d2 = 0.8 # threshold R2 =","sys.exit() b_list = [b1, b2] d_list = [d1, d2] R_list = [R1, R2]","and input signal: dur = 0.1 dt = 1e-6 f = 32 bw","condition not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1, d2]) R_list","Using Ideal IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list,","str(output_count) + output_ext) # Test leaky IAF algorithms: b1 = 3.5 # bias","= func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title,","# Test ideal IAF algorithms: b1 = 3.5 # bias d1 = 0.7","import bionet.ted.iaf as iaf # For determining output plot file names: output_name =","= 'Signal Encoded Using Ideal IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u],","threshold R1 = 10.0 # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u,","without a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited","\"\"\" # Copyright (c) 2009-2015, <NAME> # All rights reserved. # Distributed under","# resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2,","<reponame>bionet/ted.python #!/usr/bin/env python \"\"\" Demos of encoding and decoding algorithms using populations of","# Set matplotlib backend so that plots can be generated without a #","dur, dt) np.random.seed(0) noise_power = None if noise_power == None: fig_title = 'IAF","# resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1,","No Noise' else: fig_title = 'IAF Input Signal with %d dB of Noise'","output_name = 'iaf_pop_demo_' output_count = 0 output_ext = '.png' # Define algorithm parameters","that plots can be generated without a # display: import matplotlib matplotlib.use('AGG') from","file names: output_name = 'iaf_pop_demo_' output_count = 0 output_ext = '.png' # Define","10.0 # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1,","bias d2 = 0.8 # threshold R2 = 9.0 # resistance C2 =","= 0.7 # threshold R1 = np.inf # resistance C1 = 0.01 #","algorithms: b1 = 3.5 # bias d1 = 0.7 # threshold R1 =","1 pl.plot_encoded(t, u, s_list[1], fig_title + ' #2', output_name + str(output_count) + output_ext)","= '.png' # Define algorithm parameters and input signal: dur = 0.1 dt","output_count += 1 fig_title = 'Signal Encoded Using Leaky IAF Encoder' print fig_title","terms of the BSD license: # http://www.opensource.org/licenses/bsd-license import sys import numpy as np","threshold R2 = np.inf # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u,","= np.array([C1, C2]) output_count += 1 fig_title = 'Signal Encoded Using Leaky IAF","Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list, C_list)","the BSD license: # http://www.opensource.org/licenses/bsd-license import sys import numpy as np # Set","# threshold R2 = np.inf # resistance C2 = 0.01 # capacitance try:","d1, R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4 #","except ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4 # bias d2 =","<NAME> # All rights reserved. # Distributed under the terms of the BSD","Using Leaky IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list,","# threshold R1 = 10.0 # resistance C1 = 0.01 # capacitance try:","Define algorithm parameters and input signal: dur = 0.1 dt = 1e-6 f","IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list,","python \"\"\" Demos of encoding and decoding algorithms using populations of IAF neurons.","9.0 # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2,","u, s_list[0], fig_title + ' #1', output_name + str(output_count) + output_ext) output_count +=","satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1, d2]) R_list = np.array([R1,","R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title + ' #1', output_name + str(output_count) +","with No Noise' else: fig_title = 'IAF Input Signal with %d dB of","C2] output_count += 1 fig_title = 'Signal Encoded Using Ideal IAF Encoder' print","Input Signal with No Noise' else: fig_title = 'IAF Input Signal with %d","Using Ideal IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw,","determining output plot file names: output_name = 'iaf_pop_demo_' output_count = 0 output_ext =","bionet.utils.plotting as pl import bionet.ted.iaf as iaf # For determining output plot file","b2] d_list = [d1, d2] R_list = [R1, R2] C_list = [C1, C2]","== None: fig_title = 'IAF Input Signal with No Noise' else: fig_title =","algorithm parameters and input signal: dur = 0.1 dt = 1e-6 f =","output plot file names: output_name = 'iaf_pop_demo_' output_count = 0 output_ext = '.png'","try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit()","0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction condition","algorithms using populations of IAF neurons. \"\"\" # Copyright (c) 2009-2015, <NAME> #","matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting as pl","pl.plot_encoded(t, u, s_list[0], fig_title + ' #1', output_name + str(output_count) + output_ext) output_count","' #1', output_name + str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1],","= 'Signal Decoded Using Leaky IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list,","3.5 # bias d1 = 0.7 # threshold R1 = 10.0 # resistance","u_rec, fig_title, output_name + str(output_count) + output_ext) # Test ideal IAF algorithms: b1","C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4 # bias d2","matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting as","3.5 # bias d1 = 0.7 # threshold R1 = np.inf # resistance","= 10.0 # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1,","fig_title = 'Signal Decoded Using Ideal IAF Population Decoder' print fig_title u_rec =","0.8 # threshold R2 = 9.0 # resistance C2 = 0.01 # capacitance","b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1,","IAF neurons. \"\"\" # Copyright (c) 2009-2015, <NAME> # All rights reserved. #","pl import bionet.ted.iaf as iaf # For determining output plot file names: output_name","Input Signal with %d dB of Noise' % noise_power print fig_title u =","http://www.opensource.org/licenses/bsd-license import sys import numpy as np # Set matplotlib backend so that","= np.array([d1, d2]) R_list = np.array([R1, R2]) C_list = np.array([C1, C2]) output_count +=","u, s_list[1], fig_title + ' #2', output_name + str(output_count) + output_ext) output_count +=","+= 1 fig_title = 'Signal Decoded Using Ideal IAF Population Decoder' print fig_title","= [d1, d2] R_list = [R1, R2] C_list = [C1, C2] output_count +=","import func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting as pl import bionet.ted.iaf as","ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1, d2])","None: fig_title = 'IAF Input Signal with No Noise' else: fig_title = 'IAF","np.random.seed(0) noise_power = None if noise_power == None: fig_title = 'IAF Input Signal","numpy as np # Set matplotlib backend so that plots can be generated","fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count)","C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except","Leaky IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list,","np.inf # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2,","bionet.utils.band_limited as bl import bionet.utils.plotting as pl import bionet.ted.iaf as iaf # For","= [R1, R2] C_list = [C1, C2] output_count += 1 fig_title = 'Signal","fig_title + ' #1', output_name + str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t,","[b1, b2] d_list = [d1, d2] R_list = [R1, R2] C_list = [C1,","dt = 1e-6 f = 32 bw = 2*np.pi*f t = np.arange(0, dur,","'Signal Encoded Using Leaky IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt,","sys import numpy as np # Set matplotlib backend so that plots can","s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title","d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title + ' #1', output_name + str(output_count)","b1 = 3.5 # bias d1 = 0.7 # threshold R1 = np.inf","generated without a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import","= [b1, b2] d_list = [d1, d2] R_list = [R1, R2] C_list =","with %d dB of Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt,","= 0.8 # threshold R2 = np.inf # resistance C2 = 0.01 #","#1', output_name + str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title","pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) # Test leaky IAF algorithms:","import numpy as np # Set matplotlib backend so that plots can be","resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2)","R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list","pl.plot_encoded(t, u, s_list[1], fig_title + ' #2', output_name + str(output_count) + output_ext) output_count","d2 = 0.8 # threshold R2 = np.inf # resistance C2 = 0.01","# threshold R2 = 9.0 # resistance C2 = 0.01 # capacitance try:","1 fig_title = 'Signal Decoded Using Leaky IAF Population Decoder' print fig_title u_rec","resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1)","= 3.5 # bias d1 = 0.7 # threshold R1 = 10.0 #","dur, dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name +","bionet.ted.iaf as iaf # For determining output plot file names: output_name = 'iaf_pop_demo_'","f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) # Test leaky","fig_title = 'Signal Decoded Using Leaky IAF Population Decoder' print fig_title u_rec =","'IAF Input Signal with No Noise' else: fig_title = 'IAF Input Signal with","= 'Signal Decoded Using Ideal IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list,","str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title + ' #2',","+ ' #1', output_name + str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t, u,","u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title + ' #1',","C_list) pl.plot_encoded(t, u, s_list[0], fig_title + ' #1', output_name + str(output_count) + output_ext)","0.8 # threshold R2 = np.inf # resistance C2 = 0.01 # capacitance","fig_title = 'Signal Encoded Using Leaky IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u,","# bias d1 = 0.7 # threshold R1 = np.inf # resistance C1","leaky IAF algorithms: b1 = 3.5 # bias d1 = 0.7 # threshold","d1 = 0.7 # threshold R1 = np.inf # resistance C1 = 0.01","populations of IAF neurons. \"\"\" # Copyright (c) 2009-2015, <NAME> # All rights","Ideal IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list,","1e-6 f = 32 bw = 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0)","t = np.arange(0, dur, dt) np.random.seed(0) noise_power = None if noise_power == None:","+ output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title + ' #2', output_name","R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = [b1, b2] d_list","np.inf # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1,","output_name + str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title +","Decoded Using Leaky IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt,","# Distributed under the terms of the BSD license: # http://www.opensource.org/licenses/bsd-license import sys","fig_title = 'IAF Input Signal with %d dB of Noise' % noise_power print","condition not satisfied'): sys.exit() b2 = 3.4 # bias d2 = 0.8 #","= 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power = None if noise_power","of IAF neurons. \"\"\" # Copyright (c) 2009-2015, <NAME> # All rights reserved.","print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name +","d2] R_list = [R1, R2] C_list = [C1, C2] output_count += 1 fig_title","plot file names: output_name = 'iaf_pop_demo_' output_count = 0 output_ext = '.png' #","d2]) R_list = np.array([R1, R2]) C_list = np.array([C1, C2]) output_count += 1 fig_title","Test leaky IAF algorithms: b1 = 3.5 # bias d1 = 0.7 #","u, u_rec, fig_title, output_name + str(output_count) + output_ext) # Test ideal IAF algorithms:","output_name + str(output_count) + output_ext) output_count += 1 fig_title = 'Signal Decoded Using","import bionet.utils.band_limited as bl import bionet.utils.plotting as pl import bionet.ted.iaf as iaf #","'Signal Decoded Using Ideal IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur,","= 32 bw = 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power =","[C1, C2] output_count += 1 fig_title = 'Signal Encoded Using Ideal IAF Encoder'","R2 = np.inf # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw,","noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name","display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as bl import","= np.array([b1, b2]) d_list = np.array([d1, d2]) R_list = np.array([R1, R2]) C_list =","' #2', output_name + str(output_count) + output_ext) output_count += 1 fig_title = 'Signal","so that plots can be generated without a # display: import matplotlib matplotlib.use('AGG')","C_list = np.array([C1, C2]) output_count += 1 fig_title = 'Signal Encoded Using Leaky","np.array([C1, C2]) output_count += 1 fig_title = 'Signal Encoded Using Leaky IAF Encoder'","not satisfied'): sys.exit() b_list = [b1, b2] d_list = [d1, d2] R_list =","fig_title, output_name + str(output_count) + output_ext) # Test leaky IAF algorithms: b1 =","= 'iaf_pop_demo_' output_count = 0 output_ext = '.png' # Define algorithm parameters and","if noise_power == None: fig_title = 'IAF Input Signal with No Noise' else:","dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) # Test","else: fig_title = 'IAF Input Signal with %d dB of Noise' % noise_power","1 fig_title = 'Signal Encoded Using Ideal IAF Encoder' print fig_title s_list =","bw, b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list =","C2]) output_count += 1 fig_title = 'Signal Encoded Using Leaky IAF Encoder' print","output_ext) output_count += 1 fig_title = 'Signal Decoded Using Ideal IAF Population Decoder'","0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction condition","= np.array([R1, R2]) C_list = np.array([C1, C2]) output_count += 1 fig_title = 'Signal","noise_power = None if noise_power == None: fig_title = 'IAF Input Signal with","b2 = 3.4 # bias d2 = 0.8 # threshold R2 = np.inf","# bias d2 = 0.8 # threshold R2 = 9.0 # resistance C2","as pl import bionet.ted.iaf as iaf # For determining output plot file names:","d1 = 0.7 # threshold R1 = 10.0 # resistance C1 = 0.01","+ str(output_count) + output_ext) output_count += 1 fig_title = 'Signal Decoded Using Leaky","+ str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title + '","parameters and input signal: dur = 0.1 dt = 1e-6 f = 32","signal: dur = 0.1 dt = 1e-6 f = 32 bw = 2*np.pi*f","Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u,","[R1, R2] C_list = [C1, C2] output_count += 1 fig_title = 'Signal Encoded","sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1, d2]) R_list = np.array([R1, R2])","str(output_count) + output_ext) output_count += 1 fig_title = 'Signal Decoded Using Leaky IAF","0.7 # threshold R1 = 10.0 # resistance C1 = 0.01 # capacitance","# Define algorithm parameters and input signal: dur = 0.1 dt = 1e-6","+ ' #2', output_name + str(output_count) + output_ext) output_count += 1 fig_title =","+= 1 fig_title = 'Signal Encoded Using Ideal IAF Encoder' print fig_title s_list","matplotlib backend so that plots can be generated without a # display: import","threshold R2 = 9.0 # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u,","= 0.8 # threshold R2 = 9.0 # resistance C2 = 0.01 #","neurons. \"\"\" # Copyright (c) 2009-2015, <NAME> # All rights reserved. # Distributed","str(output_count) + output_ext) output_count += 1 fig_title = 'Signal Decoded Using Ideal IAF","Distributed under the terms of the BSD license: # http://www.opensource.org/licenses/bsd-license import sys import","noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) # Test leaky IAF","# capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction condition not","b2]) d_list = np.array([d1, d2]) R_list = np.array([R1, R2]) C_list = np.array([C1, C2])","= 3.4 # bias d2 = 0.8 # threshold R2 = 9.0 #","= np.inf # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1,","b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count) + output_ext)","reserved. # Distributed under the terms of the BSD license: # http://www.opensource.org/licenses/bsd-license import","0.1 dt = 1e-6 f = 32 bw = 2*np.pi*f t = np.arange(0,","+ output_ext) output_count += 1 fig_title = 'Signal Decoded Using Ideal IAF Population","# display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as bl","be generated without a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer","Ideal IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list,","2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power = None if noise_power ==","names: output_name = 'iaf_pop_demo_' output_count = 0 output_ext = '.png' # Define algorithm","# bias d2 = 0.8 # threshold R2 = np.inf # resistance C2","# Test leaky IAF algorithms: b1 = 3.5 # bias d1 = 0.7","+ str(output_count) + output_ext) # Test ideal IAF algorithms: b1 = 3.5 #","Copyright (c) 2009-2015, <NAME> # All rights reserved. # Distributed under the terms","output_count += 1 fig_title = 'Signal Decoded Using Ideal IAF Population Decoder' print","= 0.7 # threshold R1 = 10.0 # resistance C1 = 0.01 #","output_name + str(output_count) + output_ext) # Test ideal IAF algorithms: b1 = 3.5","= 9.0 # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2,","dt) np.random.seed(0) noise_power = None if noise_power == None: fig_title = 'IAF Input","Set matplotlib backend so that plots can be generated without a # display:","\"\"\" Demos of encoding and decoding algorithms using populations of IAF neurons. \"\"\"","of the BSD license: # http://www.opensource.org/licenses/bsd-license import sys import numpy as np #","= 0.1 dt = 1e-6 f = 32 bw = 2*np.pi*f t =","decoding algorithms using populations of IAF neurons. \"\"\" # Copyright (c) 2009-2015, <NAME>","2009-2015, <NAME> # All rights reserved. # Distributed under the terms of the","bias d2 = 0.8 # threshold R2 = np.inf # resistance C2 =","b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title + ' #1', output_name +","import bionet.utils.plotting as pl import bionet.ted.iaf as iaf # For determining output plot","R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count) + output_ext) # Test","+ str(output_count) + output_ext) # Test leaky IAF algorithms: b1 = 3.5 #","plots can be generated without a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc","output_ext = '.png' # Define algorithm parameters and input signal: dur = 0.1","pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count) + output_ext) # Test ideal IAF","func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title + '","as np # Set matplotlib backend so that plots can be generated without","+= 1 fig_title = 'Signal Encoded Using Leaky IAF Encoder' print fig_title s_list","rights reserved. # Distributed under the terms of the BSD license: # http://www.opensource.org/licenses/bsd-license","% noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title,","output_name + str(output_count) + output_ext) # Test leaky IAF algorithms: b1 = 3.5","1 fig_title = 'Signal Decoded Using Ideal IAF Population Decoder' print fig_title u_rec","#2', output_name + str(output_count) + output_ext) output_count += 1 fig_title = 'Signal Decoded","input signal: dur = 0.1 dt = 1e-6 f = 32 bw =","u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) +","d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1, b2])","fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0],","'.png' # Define algorithm parameters and input signal: dur = 0.1 dt =","+= 1 fig_title = 'Signal Decoded Using Leaky IAF Population Decoder' print fig_title","C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list =","Decoded Using Ideal IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt,","d2 = 0.8 # threshold R2 = 9.0 # resistance C2 = 0.01","can be generated without a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import","import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting","u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec,","using populations of IAF neurons. \"\"\" # Copyright (c) 2009-2015, <NAME> # All","of Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t,","str(output_count) + output_ext) # Test ideal IAF algorithms: b1 = 3.5 # bias","= 0 output_ext = '.png' # Define algorithm parameters and input signal: dur","d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = [b1, b2]","fig_title, output_name + str(output_count) + output_ext) # Test ideal IAF algorithms: b1 =","= np.arange(0, dur, dt) np.random.seed(0) noise_power = None if noise_power == None: fig_title","satisfied'): sys.exit() b2 = 3.4 # bias d2 = 0.8 # threshold R2","np.array([d1, d2]) R_list = np.array([R1, R2]) C_list = np.array([C1, C2]) output_count += 1","print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u,","bias d1 = 0.7 # threshold R1 = 10.0 # resistance C1 =","+= 1 pl.plot_encoded(t, u, s_list[1], fig_title + ' #2', output_name + str(output_count) +","= 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction","R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4 # bias","= np.inf # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2,","b2 = 3.4 # bias d2 = 0.8 # threshold R2 = 9.0","not satisfied'): sys.exit() b2 = 3.4 # bias d2 = 0.8 # threshold","= 'IAF Input Signal with No Noise' else: fig_title = 'IAF Input Signal","R1 = np.inf # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw,","= 'IAF Input Signal with %d dB of Noise' % noise_power print fig_title","(c) 2009-2015, <NAME> # All rights reserved. # Distributed under the terms of","'IAF Input Signal with %d dB of Noise' % noise_power print fig_title u","import sys import numpy as np # Set matplotlib backend so that plots","fig_title = 'IAF Input Signal with No Noise' else: fig_title = 'IAF Input","R2 = 9.0 # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw,","Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t,","encoding and decoding algorithms using populations of IAF neurons. \"\"\" # Copyright (c)","condition not satisfied'): sys.exit() b_list = [b1, b2] d_list = [d1, d2] R_list","C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count) + output_ext) # Test ideal","capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction condition not satisfied'):","s_list[0], fig_title + ' #1', output_name + str(output_count) + output_ext) output_count += 1","func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) #","ValueError('reconstruction condition not satisfied'): sys.exit() b_list = [b1, b2] d_list = [d1, d2]","satisfied'): sys.exit() b_list = [b1, b2] d_list = [d1, d2] R_list = [R1,","np # Set matplotlib backend so that plots can be generated without a","noise_power == None: fig_title = 'IAF Input Signal with No Noise' else: fig_title","threshold R1 = np.inf # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u,","C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except","from bionet.utils.misc import func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting as pl import","R_list = [R1, R2] C_list = [C1, C2] output_count += 1 fig_title =","backend so that plots can be generated without a # display: import matplotlib","IAF algorithms: b1 = 3.5 # bias d1 = 0.7 # threshold R1","except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = [b1, b2] d_list = [d1,","Signal with No Noise' else: fig_title = 'IAF Input Signal with %d dB","# threshold R1 = np.inf # resistance C1 = 0.01 # capacitance try:","d_list = np.array([d1, d2]) R_list = np.array([R1, R2]) C_list = np.array([C1, C2]) output_count","%d dB of Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f,","d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count) + output_ext) #","capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'):","# http://www.opensource.org/licenses/bsd-license import sys import numpy as np # Set matplotlib backend so","= None if noise_power == None: fig_title = 'IAF Input Signal with No","= func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title +","license: # http://www.opensource.org/licenses/bsd-license import sys import numpy as np # Set matplotlib backend","+ output_ext) output_count += 1 fig_title = 'Signal Decoded Using Leaky IAF Population","the terms of the BSD license: # http://www.opensource.org/licenses/bsd-license import sys import numpy as","All rights reserved. # Distributed under the terms of the BSD license: #","= 3.4 # bias d2 = 0.8 # threshold R2 = np.inf #","try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit()","d_list = [d1, d2] R_list = [R1, R2] C_list = [C1, C2] output_count","= func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext)","b_list = np.array([b1, b2]) d_list = np.array([d1, d2]) R_list = np.array([R1, R2]) C_list","not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1, d2]) R_list =","[d1, d2] R_list = [R1, R2] C_list = [C1, C2] output_count += 1","output_count += 1 fig_title = 'Signal Encoded Using Ideal IAF Encoder' print fig_title","# For determining output plot file names: output_name = 'iaf_pop_demo_' output_count = 0","of encoding and decoding algorithms using populations of IAF neurons. \"\"\" # Copyright","b1, d1, R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4","C_list = [C1, C2] output_count += 1 fig_title = 'Signal Encoded Using Ideal","func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting as pl import bionet.ted.iaf as iaf","print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t,","bl import bionet.utils.plotting as pl import bionet.ted.iaf as iaf # For determining output","= 3.5 # bias d1 = 0.7 # threshold R1 = np.inf #","dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count)","dur = 0.1 dt = 1e-6 f = 32 bw = 2*np.pi*f t","1 fig_title = 'Signal Encoded Using Leaky IAF Encoder' print fig_title s_list =","except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1,","func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name","+ str(output_count) + output_ext) output_count += 1 fig_title = 'Signal Decoded Using Ideal","np.array([b1, b2]) d_list = np.array([d1, d2]) R_list = np.array([R1, R2]) C_list = np.array([C1,","# bias d1 = 0.7 # threshold R1 = 10.0 # resistance C1","# capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction condition not","0 output_ext = '.png' # Define algorithm parameters and input signal: dur =","None if noise_power == None: fig_title = 'IAF Input Signal with No Noise'","bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count) +","Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list,","R_list = np.array([R1, R2]) C_list = np.array([C1, C2]) output_count += 1 fig_title =","R2]) C_list = np.array([C1, C2]) output_count += 1 fig_title = 'Signal Encoded Using","C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = [b1, b2] d_list =","fig_title = 'Signal Encoded Using Ideal IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u,","#!/usr/bin/env python \"\"\" Demos of encoding and decoding algorithms using populations of IAF","'Signal Encoded Using Ideal IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt,","output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title + ' #2', output_name + str(output_count)","R2] C_list = [C1, C2] output_count += 1 fig_title = 'Signal Encoded Using","'Signal Decoded Using Leaky IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur,","R1 = 10.0 # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw,","Encoded Using Leaky IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list,","and decoding algorithms using populations of IAF neurons. \"\"\" # Copyright (c) 2009-2015,","u, fig_title, output_name + str(output_count) + output_ext) # Test leaky IAF algorithms: b1","f = 32 bw = 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power","Noise' else: fig_title = 'IAF Input Signal with %d dB of Noise' %","Encoded Using Ideal IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list,","np.arange(0, dur, dt) np.random.seed(0) noise_power = None if noise_power == None: fig_title =","ideal IAF algorithms: b1 = 3.5 # bias d1 = 0.7 # threshold","as bl import bionet.utils.plotting as pl import bionet.ted.iaf as iaf # For determining","iaf # For determining output plot file names: output_name = 'iaf_pop_demo_' output_count =","Using Leaky IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw,","IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list)","32 bw = 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power = None","iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list","output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title + ' #2', output_name +","# All rights reserved. # Distributed under the terms of the BSD license:","bw, b1, d1, R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2 =","output_ext) # Test ideal IAF algorithms: b1 = 3.5 # bias d1 =","'iaf_pop_demo_' output_count = 0 output_ext = '.png' # Define algorithm parameters and input","s_list[1], fig_title + ' #2', output_name + str(output_count) + output_ext) output_count += 1","= 'Signal Encoded Using Leaky IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u],","+ output_ext) # Test leaky IAF algorithms: b1 = 3.5 # bias d1","= [C1, C2] output_count += 1 fig_title = 'Signal Encoded Using Ideal IAF","iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2","Demos of encoding and decoding algorithms using populations of IAF neurons. \"\"\" #","np.array([R1, R2]) C_list = np.array([C1, C2]) output_count += 1 fig_title = 'Signal Encoded","bionet.utils.misc import func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting as pl import bionet.ted.iaf","# Copyright (c) 2009-2015, <NAME> # All rights reserved. # Distributed under the","= 1e-6 f = 32 bw = 2*np.pi*f t = np.arange(0, dur, dt)","bias d1 = 0.7 # threshold R1 = np.inf # resistance C1 ="],"string":"[\n \"bw = 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power = None if\",\n \"output_ext) output_count += 1 fig_title = 'Signal Decoded Using Leaky IAF Population Decoder'\",\n \"a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as\",\n \"+ output_ext) # Test ideal IAF algorithms: b1 = 3.5 # bias d1\",\n \"3.4 # bias d2 = 0.8 # threshold R2 = 9.0 # resistance\",\n \"b1 = 3.5 # bias d1 = 0.7 # threshold R1 = 10.0\",\n \"under the terms of the BSD license: # http://www.opensource.org/licenses/bsd-license import sys import numpy\",\n \"b_list = [b1, b2] d_list = [d1, d2] R_list = [R1, R2] C_list\",\n \"BSD license: # http://www.opensource.org/licenses/bsd-license import sys import numpy as np # Set matplotlib\",\n \"ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4 # bias d2 = 0.8\",\n \"as iaf # For determining output plot file names: output_name = 'iaf_pop_demo_' output_count\",\n \"0.7 # threshold R1 = np.inf # resistance C1 = 0.01 # capacitance\",\n \"output_count += 1 fig_title = 'Signal Decoded Using Leaky IAF Population Decoder' print\",\n \"output_ext) # Test leaky IAF algorithms: b1 = 3.5 # bias d1 =\",\n \"For determining output plot file names: output_name = 'iaf_pop_demo_' output_count = 0 output_ext\",\n \"Signal with %d dB of Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur,\",\n \"3.4 # bias d2 = 0.8 # threshold R2 = np.inf # resistance\",\n \"Test ideal IAF algorithms: b1 = 3.5 # bias d1 = 0.7 #\",\n \"dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title + ' #1', output_name\",\n \"dB of Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power)\",\n \"Leaky IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list,\",\n \"= 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction\",\n \"b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = [b1,\",\n \"fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u,\",\n \"fig_title + ' #2', output_name + str(output_count) + output_ext) output_count += 1 fig_title\",\n \"output_count = 0 output_ext = '.png' # Define algorithm parameters and input signal:\",\n \"sys.exit() b2 = 3.4 # bias d2 = 0.8 # threshold R2 =\",\n \"sys.exit() b_list = [b1, b2] d_list = [d1, d2] R_list = [R1, R2]\",\n \"and input signal: dur = 0.1 dt = 1e-6 f = 32 bw\",\n \"condition not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1, d2]) R_list\",\n \"Using Ideal IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list,\",\n \"str(output_count) + output_ext) # Test leaky IAF algorithms: b1 = 3.5 # bias\",\n \"= func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title,\",\n \"# Test ideal IAF algorithms: b1 = 3.5 # bias d1 = 0.7\",\n \"import bionet.ted.iaf as iaf # For determining output plot file names: output_name =\",\n \"= 'Signal Encoded Using Ideal IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u],\",\n \"threshold R1 = 10.0 # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u,\",\n \"without a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited\",\n \"\\\"\\\"\\\" # Copyright (c) 2009-2015, <NAME> # All rights reserved. # Distributed under\",\n \"# resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2,\",\n \"<reponame>bionet/ted.python #!/usr/bin/env python \\\"\\\"\\\" Demos of encoding and decoding algorithms using populations of\",\n \"# Set matplotlib backend so that plots can be generated without a #\",\n \"dur, dt) np.random.seed(0) noise_power = None if noise_power == None: fig_title = 'IAF\",\n \"# resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1,\",\n \"No Noise' else: fig_title = 'IAF Input Signal with %d dB of Noise'\",\n \"output_name = 'iaf_pop_demo_' output_count = 0 output_ext = '.png' # Define algorithm parameters\",\n \"that plots can be generated without a # display: import matplotlib matplotlib.use('AGG') from\",\n \"file names: output_name = 'iaf_pop_demo_' output_count = 0 output_ext = '.png' # Define\",\n \"10.0 # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1,\",\n \"bias d2 = 0.8 # threshold R2 = 9.0 # resistance C2 =\",\n \"= 0.7 # threshold R1 = np.inf # resistance C1 = 0.01 #\",\n \"algorithms: b1 = 3.5 # bias d1 = 0.7 # threshold R1 =\",\n \"1 pl.plot_encoded(t, u, s_list[1], fig_title + ' #2', output_name + str(output_count) + output_ext)\",\n \"= '.png' # Define algorithm parameters and input signal: dur = 0.1 dt\",\n \"output_count += 1 fig_title = 'Signal Encoded Using Leaky IAF Encoder' print fig_title\",\n \"terms of the BSD license: # http://www.opensource.org/licenses/bsd-license import sys import numpy as np\",\n \"threshold R2 = np.inf # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u,\",\n \"= np.array([C1, C2]) output_count += 1 fig_title = 'Signal Encoded Using Leaky IAF\",\n \"Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list, C_list)\",\n \"the BSD license: # http://www.opensource.org/licenses/bsd-license import sys import numpy as np # Set\",\n \"# threshold R2 = np.inf # resistance C2 = 0.01 # capacitance try:\",\n \"d1, R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4 #\",\n \"except ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4 # bias d2 =\",\n \"<NAME> # All rights reserved. # Distributed under the terms of the BSD\",\n \"Using Leaky IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list,\",\n \"# threshold R1 = 10.0 # resistance C1 = 0.01 # capacitance try:\",\n \"Define algorithm parameters and input signal: dur = 0.1 dt = 1e-6 f\",\n \"IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list,\",\n \"python \\\"\\\"\\\" Demos of encoding and decoding algorithms using populations of IAF neurons.\",\n \"9.0 # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2,\",\n \"u, s_list[0], fig_title + ' #1', output_name + str(output_count) + output_ext) output_count +=\",\n \"satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1, d2]) R_list = np.array([R1,\",\n \"R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title + ' #1', output_name + str(output_count) +\",\n \"with No Noise' else: fig_title = 'IAF Input Signal with %d dB of\",\n \"C2] output_count += 1 fig_title = 'Signal Encoded Using Ideal IAF Encoder' print\",\n \"Input Signal with No Noise' else: fig_title = 'IAF Input Signal with %d\",\n \"Using Ideal IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw,\",\n \"determining output plot file names: output_name = 'iaf_pop_demo_' output_count = 0 output_ext =\",\n \"bionet.utils.plotting as pl import bionet.ted.iaf as iaf # For determining output plot file\",\n \"b2] d_list = [d1, d2] R_list = [R1, R2] C_list = [C1, C2]\",\n \"== None: fig_title = 'IAF Input Signal with No Noise' else: fig_title =\",\n \"algorithm parameters and input signal: dur = 0.1 dt = 1e-6 f =\",\n \"output plot file names: output_name = 'iaf_pop_demo_' output_count = 0 output_ext = '.png'\",\n \"try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit()\",\n \"0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction condition\",\n \"algorithms using populations of IAF neurons. \\\"\\\"\\\" # Copyright (c) 2009-2015, <NAME> #\",\n \"matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting as pl\",\n \"pl.plot_encoded(t, u, s_list[0], fig_title + ' #1', output_name + str(output_count) + output_ext) output_count\",\n \"' #1', output_name + str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1],\",\n \"= 'Signal Decoded Using Leaky IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list,\",\n \"3.5 # bias d1 = 0.7 # threshold R1 = 10.0 # resistance\",\n \"u_rec, fig_title, output_name + str(output_count) + output_ext) # Test ideal IAF algorithms: b1\",\n \"C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4 # bias d2\",\n \"matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting as\",\n \"3.5 # bias d1 = 0.7 # threshold R1 = np.inf # resistance\",\n \"= 10.0 # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1,\",\n \"fig_title = 'Signal Decoded Using Ideal IAF Population Decoder' print fig_title u_rec =\",\n \"0.8 # threshold R2 = 9.0 # resistance C2 = 0.01 # capacitance\",\n \"b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1,\",\n \"IAF neurons. \\\"\\\"\\\" # Copyright (c) 2009-2015, <NAME> # All rights reserved. #\",\n \"pl import bionet.ted.iaf as iaf # For determining output plot file names: output_name\",\n \"Input Signal with %d dB of Noise' % noise_power print fig_title u =\",\n \"http://www.opensource.org/licenses/bsd-license import sys import numpy as np # Set matplotlib backend so that\",\n \"= np.array([d1, d2]) R_list = np.array([R1, R2]) C_list = np.array([C1, C2]) output_count +=\",\n \"u, s_list[1], fig_title + ' #2', output_name + str(output_count) + output_ext) output_count +=\",\n \"+= 1 fig_title = 'Signal Decoded Using Ideal IAF Population Decoder' print fig_title\",\n \"= [d1, d2] R_list = [R1, R2] C_list = [C1, C2] output_count +=\",\n \"import func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting as pl import bionet.ted.iaf as\",\n \"ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1, d2])\",\n \"None: fig_title = 'IAF Input Signal with No Noise' else: fig_title = 'IAF\",\n \"np.random.seed(0) noise_power = None if noise_power == None: fig_title = 'IAF Input Signal\",\n \"numpy as np # Set matplotlib backend so that plots can be generated\",\n \"fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count)\",\n \"C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except\",\n \"Leaky IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list,\",\n \"np.inf # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2,\",\n \"bionet.utils.band_limited as bl import bionet.utils.plotting as pl import bionet.ted.iaf as iaf # For\",\n \"= [R1, R2] C_list = [C1, C2] output_count += 1 fig_title = 'Signal\",\n \"fig_title + ' #1', output_name + str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t,\",\n \"[b1, b2] d_list = [d1, d2] R_list = [R1, R2] C_list = [C1,\",\n \"dt = 1e-6 f = 32 bw = 2*np.pi*f t = np.arange(0, dur,\",\n \"'Signal Encoded Using Leaky IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt,\",\n \"sys import numpy as np # Set matplotlib backend so that plots can\",\n \"s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title\",\n \"d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title + ' #1', output_name + str(output_count)\",\n \"b1 = 3.5 # bias d1 = 0.7 # threshold R1 = np.inf\",\n \"generated without a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import\",\n \"= [b1, b2] d_list = [d1, d2] R_list = [R1, R2] C_list =\",\n \"with %d dB of Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt,\",\n \"= 0.8 # threshold R2 = np.inf # resistance C2 = 0.01 #\",\n \"#1', output_name + str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title\",\n \"pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) # Test leaky IAF algorithms:\",\n \"import numpy as np # Set matplotlib backend so that plots can be\",\n \"resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2)\",\n \"R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list\",\n \"pl.plot_encoded(t, u, s_list[1], fig_title + ' #2', output_name + str(output_count) + output_ext) output_count\",\n \"d2 = 0.8 # threshold R2 = np.inf # resistance C2 = 0.01\",\n \"# threshold R2 = 9.0 # resistance C2 = 0.01 # capacitance try:\",\n \"1 fig_title = 'Signal Decoded Using Leaky IAF Population Decoder' print fig_title u_rec\",\n \"resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1)\",\n \"= 3.5 # bias d1 = 0.7 # threshold R1 = 10.0 #\",\n \"dur, dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name +\",\n \"bionet.ted.iaf as iaf # For determining output plot file names: output_name = 'iaf_pop_demo_'\",\n \"f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) # Test leaky\",\n \"fig_title = 'Signal Decoded Using Leaky IAF Population Decoder' print fig_title u_rec =\",\n \"'IAF Input Signal with No Noise' else: fig_title = 'IAF Input Signal with\",\n \"= 'Signal Decoded Using Ideal IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list,\",\n \"str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title + ' #2',\",\n \"+ ' #1', output_name + str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t, u,\",\n \"u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title + ' #1',\",\n \"C_list) pl.plot_encoded(t, u, s_list[0], fig_title + ' #1', output_name + str(output_count) + output_ext)\",\n \"0.8 # threshold R2 = np.inf # resistance C2 = 0.01 # capacitance\",\n \"fig_title = 'Signal Encoded Using Leaky IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u,\",\n \"# bias d1 = 0.7 # threshold R1 = np.inf # resistance C1\",\n \"leaky IAF algorithms: b1 = 3.5 # bias d1 = 0.7 # threshold\",\n \"d1 = 0.7 # threshold R1 = np.inf # resistance C1 = 0.01\",\n \"populations of IAF neurons. \\\"\\\"\\\" # Copyright (c) 2009-2015, <NAME> # All rights\",\n \"Ideal IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list,\",\n \"1e-6 f = 32 bw = 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0)\",\n \"t = np.arange(0, dur, dt) np.random.seed(0) noise_power = None if noise_power == None:\",\n \"+ output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title + ' #2', output_name\",\n \"R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = [b1, b2] d_list\",\n \"np.inf # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1,\",\n \"output_name + str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title +\",\n \"Decoded Using Leaky IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt,\",\n \"# Distributed under the terms of the BSD license: # http://www.opensource.org/licenses/bsd-license import sys\",\n \"fig_title = 'IAF Input Signal with %d dB of Noise' % noise_power print\",\n \"condition not satisfied'): sys.exit() b2 = 3.4 # bias d2 = 0.8 #\",\n \"= 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power = None if noise_power\",\n \"of IAF neurons. \\\"\\\"\\\" # Copyright (c) 2009-2015, <NAME> # All rights reserved.\",\n \"print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name +\",\n \"d2] R_list = [R1, R2] C_list = [C1, C2] output_count += 1 fig_title\",\n \"plot file names: output_name = 'iaf_pop_demo_' output_count = 0 output_ext = '.png' #\",\n \"d2]) R_list = np.array([R1, R2]) C_list = np.array([C1, C2]) output_count += 1 fig_title\",\n \"Test leaky IAF algorithms: b1 = 3.5 # bias d1 = 0.7 #\",\n \"u, u_rec, fig_title, output_name + str(output_count) + output_ext) # Test ideal IAF algorithms:\",\n \"output_name + str(output_count) + output_ext) output_count += 1 fig_title = 'Signal Decoded Using\",\n \"import bionet.utils.band_limited as bl import bionet.utils.plotting as pl import bionet.ted.iaf as iaf #\",\n \"'Signal Decoded Using Ideal IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur,\",\n \"= 32 bw = 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power =\",\n \"[C1, C2] output_count += 1 fig_title = 'Signal Encoded Using Ideal IAF Encoder'\",\n \"R2 = np.inf # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw,\",\n \"noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name\",\n \"display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as bl import\",\n \"= np.array([b1, b2]) d_list = np.array([d1, d2]) R_list = np.array([R1, R2]) C_list =\",\n \"' #2', output_name + str(output_count) + output_ext) output_count += 1 fig_title = 'Signal\",\n \"so that plots can be generated without a # display: import matplotlib matplotlib.use('AGG')\",\n \"C_list = np.array([C1, C2]) output_count += 1 fig_title = 'Signal Encoded Using Leaky\",\n \"np.array([C1, C2]) output_count += 1 fig_title = 'Signal Encoded Using Leaky IAF Encoder'\",\n \"not satisfied'): sys.exit() b_list = [b1, b2] d_list = [d1, d2] R_list =\",\n \"fig_title, output_name + str(output_count) + output_ext) # Test leaky IAF algorithms: b1 =\",\n \"= 'iaf_pop_demo_' output_count = 0 output_ext = '.png' # Define algorithm parameters and\",\n \"if noise_power == None: fig_title = 'IAF Input Signal with No Noise' else:\",\n \"dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) # Test\",\n \"else: fig_title = 'IAF Input Signal with %d dB of Noise' % noise_power\",\n \"1 fig_title = 'Signal Encoded Using Ideal IAF Encoder' print fig_title s_list =\",\n \"bw, b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list =\",\n \"C2]) output_count += 1 fig_title = 'Signal Encoded Using Leaky IAF Encoder' print\",\n \"output_ext) output_count += 1 fig_title = 'Signal Decoded Using Ideal IAF Population Decoder'\",\n \"0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction condition\",\n \"= np.array([R1, R2]) C_list = np.array([C1, C2]) output_count += 1 fig_title = 'Signal\",\n \"noise_power = None if noise_power == None: fig_title = 'IAF Input Signal with\",\n \"b2 = 3.4 # bias d2 = 0.8 # threshold R2 = np.inf\",\n \"# bias d2 = 0.8 # threshold R2 = 9.0 # resistance C2\",\n \"as pl import bionet.ted.iaf as iaf # For determining output plot file names:\",\n \"d1 = 0.7 # threshold R1 = 10.0 # resistance C1 = 0.01\",\n \"+ str(output_count) + output_ext) output_count += 1 fig_title = 'Signal Decoded Using Leaky\",\n \"+ str(output_count) + output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title + '\",\n \"parameters and input signal: dur = 0.1 dt = 1e-6 f = 32\",\n \"signal: dur = 0.1 dt = 1e-6 f = 32 bw = 2*np.pi*f\",\n \"Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u,\",\n \"[R1, R2] C_list = [C1, C2] output_count += 1 fig_title = 'Signal Encoded\",\n \"sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1, d2]) R_list = np.array([R1, R2])\",\n \"str(output_count) + output_ext) output_count += 1 fig_title = 'Signal Decoded Using Leaky IAF\",\n \"0.7 # threshold R1 = 10.0 # resistance C1 = 0.01 # capacitance\",\n \"# Define algorithm parameters and input signal: dur = 0.1 dt = 1e-6\",\n \"+ ' #2', output_name + str(output_count) + output_ext) output_count += 1 fig_title =\",\n \"+= 1 fig_title = 'Signal Encoded Using Ideal IAF Encoder' print fig_title s_list\",\n \"matplotlib backend so that plots can be generated without a # display: import\",\n \"threshold R2 = 9.0 # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u,\",\n \"= 0.8 # threshold R2 = 9.0 # resistance C2 = 0.01 #\",\n \"neurons. \\\"\\\"\\\" # Copyright (c) 2009-2015, <NAME> # All rights reserved. # Distributed\",\n \"str(output_count) + output_ext) output_count += 1 fig_title = 'Signal Decoded Using Ideal IAF\",\n \"Distributed under the terms of the BSD license: # http://www.opensource.org/licenses/bsd-license import sys import\",\n \"noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) # Test leaky IAF\",\n \"# capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction condition not\",\n \"b2]) d_list = np.array([d1, d2]) R_list = np.array([R1, R2]) C_list = np.array([C1, C2])\",\n \"= 3.4 # bias d2 = 0.8 # threshold R2 = 9.0 #\",\n \"= np.inf # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1,\",\n \"b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count) + output_ext)\",\n \"reserved. # Distributed under the terms of the BSD license: # http://www.opensource.org/licenses/bsd-license import\",\n \"0.1 dt = 1e-6 f = 32 bw = 2*np.pi*f t = np.arange(0,\",\n \"+ output_ext) output_count += 1 fig_title = 'Signal Decoded Using Ideal IAF Population\",\n \"# display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as bl\",\n \"be generated without a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer\",\n \"Ideal IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list,\",\n \"2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power = None if noise_power ==\",\n \"names: output_name = 'iaf_pop_demo_' output_count = 0 output_ext = '.png' # Define algorithm\",\n \"# bias d2 = 0.8 # threshold R2 = np.inf # resistance C2\",\n \"# Test leaky IAF algorithms: b1 = 3.5 # bias d1 = 0.7\",\n \"+ str(output_count) + output_ext) # Test ideal IAF algorithms: b1 = 3.5 #\",\n \"Copyright (c) 2009-2015, <NAME> # All rights reserved. # Distributed under the terms\",\n \"output_count += 1 fig_title = 'Signal Decoded Using Ideal IAF Population Decoder' print\",\n \"= 0.7 # threshold R1 = 10.0 # resistance C1 = 0.01 #\",\n \"output_name + str(output_count) + output_ext) # Test ideal IAF algorithms: b1 = 3.5\",\n \"= 9.0 # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2,\",\n \"dt) np.random.seed(0) noise_power = None if noise_power == None: fig_title = 'IAF Input\",\n \"Set matplotlib backend so that plots can be generated without a # display:\",\n \"\\\"\\\"\\\" Demos of encoding and decoding algorithms using populations of IAF neurons. \\\"\\\"\\\"\",\n \"of the BSD license: # http://www.opensource.org/licenses/bsd-license import sys import numpy as np #\",\n \"= 0.1 dt = 1e-6 f = 32 bw = 2*np.pi*f t =\",\n \"decoding algorithms using populations of IAF neurons. \\\"\\\"\\\" # Copyright (c) 2009-2015, <NAME>\",\n \"2009-2015, <NAME> # All rights reserved. # Distributed under the terms of the\",\n \"bias d2 = 0.8 # threshold R2 = np.inf # resistance C2 =\",\n \"b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title + ' #1', output_name +\",\n \"import bionet.utils.plotting as pl import bionet.ted.iaf as iaf # For determining output plot\",\n \"R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count) + output_ext) # Test\",\n \"+ str(output_count) + output_ext) # Test leaky IAF algorithms: b1 = 3.5 #\",\n \"plots can be generated without a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc\",\n \"output_ext = '.png' # Define algorithm parameters and input signal: dur = 0.1\",\n \"pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count) + output_ext) # Test ideal IAF\",\n \"func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title + '\",\n \"as np # Set matplotlib backend so that plots can be generated without\",\n \"+= 1 fig_title = 'Signal Encoded Using Leaky IAF Encoder' print fig_title s_list\",\n \"rights reserved. # Distributed under the terms of the BSD license: # http://www.opensource.org/licenses/bsd-license\",\n \"% noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title,\",\n \"output_name + str(output_count) + output_ext) # Test leaky IAF algorithms: b1 = 3.5\",\n \"1 fig_title = 'Signal Decoded Using Ideal IAF Population Decoder' print fig_title u_rec\",\n \"#2', output_name + str(output_count) + output_ext) output_count += 1 fig_title = 'Signal Decoded\",\n \"input signal: dur = 0.1 dt = 1e-6 f = 32 bw =\",\n \"u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) +\",\n \"d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1, b2])\",\n \"fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0],\",\n \"'.png' # Define algorithm parameters and input signal: dur = 0.1 dt =\",\n \"+= 1 fig_title = 'Signal Decoded Using Leaky IAF Population Decoder' print fig_title\",\n \"C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list =\",\n \"Decoded Using Ideal IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt,\",\n \"d2 = 0.8 # threshold R2 = 9.0 # resistance C2 = 0.01\",\n \"can be generated without a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import\",\n \"import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting\",\n \"u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec,\",\n \"using populations of IAF neurons. \\\"\\\"\\\" # Copyright (c) 2009-2015, <NAME> # All\",\n \"of Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t,\",\n \"str(output_count) + output_ext) # Test ideal IAF algorithms: b1 = 3.5 # bias\",\n \"= 0 output_ext = '.png' # Define algorithm parameters and input signal: dur\",\n \"d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = [b1, b2]\",\n \"fig_title, output_name + str(output_count) + output_ext) # Test ideal IAF algorithms: b1 =\",\n \"= np.arange(0, dur, dt) np.random.seed(0) noise_power = None if noise_power == None: fig_title\",\n \"satisfied'): sys.exit() b2 = 3.4 # bias d2 = 0.8 # threshold R2\",\n \"np.array([d1, d2]) R_list = np.array([R1, R2]) C_list = np.array([C1, C2]) output_count += 1\",\n \"print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u,\",\n \"bias d1 = 0.7 # threshold R1 = 10.0 # resistance C1 =\",\n \"+= 1 pl.plot_encoded(t, u, s_list[1], fig_title + ' #2', output_name + str(output_count) +\",\n \"= 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction\",\n \"R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4 # bias\",\n \"= np.inf # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2,\",\n \"b2 = 3.4 # bias d2 = 0.8 # threshold R2 = 9.0\",\n \"not satisfied'): sys.exit() b2 = 3.4 # bias d2 = 0.8 # threshold\",\n \"= 'IAF Input Signal with No Noise' else: fig_title = 'IAF Input Signal\",\n \"R1 = np.inf # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw,\",\n \"= 'IAF Input Signal with %d dB of Noise' % noise_power print fig_title\",\n \"(c) 2009-2015, <NAME> # All rights reserved. # Distributed under the terms of\",\n \"'IAF Input Signal with %d dB of Noise' % noise_power print fig_title u\",\n \"import sys import numpy as np # Set matplotlib backend so that plots\",\n \"fig_title = 'IAF Input Signal with No Noise' else: fig_title = 'IAF Input\",\n \"R2 = 9.0 # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw,\",\n \"Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t,\",\n \"encoding and decoding algorithms using populations of IAF neurons. \\\"\\\"\\\" # Copyright (c)\",\n \"condition not satisfied'): sys.exit() b_list = [b1, b2] d_list = [d1, d2] R_list\",\n \"C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count) + output_ext) # Test ideal\",\n \"capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction condition not satisfied'):\",\n \"s_list[0], fig_title + ' #1', output_name + str(output_count) + output_ext) output_count += 1\",\n \"func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) #\",\n \"ValueError('reconstruction condition not satisfied'): sys.exit() b_list = [b1, b2] d_list = [d1, d2]\",\n \"satisfied'): sys.exit() b_list = [b1, b2] d_list = [d1, d2] R_list = [R1,\",\n \"np # Set matplotlib backend so that plots can be generated without a\",\n \"noise_power == None: fig_title = 'IAF Input Signal with No Noise' else: fig_title\",\n \"threshold R1 = np.inf # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u,\",\n \"C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except\",\n \"from bionet.utils.misc import func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting as pl import\",\n \"R_list = [R1, R2] C_list = [C1, C2] output_count += 1 fig_title =\",\n \"backend so that plots can be generated without a # display: import matplotlib\",\n \"IAF algorithms: b1 = 3.5 # bias d1 = 0.7 # threshold R1\",\n \"except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = [b1, b2] d_list = [d1,\",\n \"Signal with No Noise' else: fig_title = 'IAF Input Signal with %d dB\",\n \"# threshold R1 = np.inf # resistance C1 = 0.01 # capacitance try:\",\n \"d_list = np.array([d1, d2]) R_list = np.array([R1, R2]) C_list = np.array([C1, C2]) output_count\",\n \"%d dB of Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f,\",\n \"d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count) + output_ext) #\",\n \"capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'):\",\n \"# http://www.opensource.org/licenses/bsd-license import sys import numpy as np # Set matplotlib backend so\",\n \"= None if noise_power == None: fig_title = 'IAF Input Signal with No\",\n \"= func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list) pl.plot_encoded(t, u, s_list[0], fig_title +\",\n \"license: # http://www.opensource.org/licenses/bsd-license import sys import numpy as np # Set matplotlib backend\",\n \"+ output_ext) output_count += 1 fig_title = 'Signal Decoded Using Leaky IAF Population\",\n \"the terms of the BSD license: # http://www.opensource.org/licenses/bsd-license import sys import numpy as\",\n \"All rights reserved. # Distributed under the terms of the BSD license: #\",\n \"= 3.4 # bias d2 = 0.8 # threshold R2 = np.inf #\",\n \"try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit()\",\n \"d_list = [d1, d2] R_list = [R1, R2] C_list = [C1, C2] output_count\",\n \"= func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext)\",\n \"b_list = np.array([b1, b2]) d_list = np.array([d1, d2]) R_list = np.array([R1, R2]) C_list\",\n \"not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1, d2]) R_list =\",\n \"[d1, d2] R_list = [R1, R2] C_list = [C1, C2] output_count += 1\",\n \"output_count += 1 fig_title = 'Signal Encoded Using Ideal IAF Encoder' print fig_title\",\n \"# For determining output plot file names: output_name = 'iaf_pop_demo_' output_count = 0\",\n \"of encoding and decoding algorithms using populations of IAF neurons. \\\"\\\"\\\" # Copyright\",\n \"b1, d1, R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4\",\n \"C_list = [C1, C2] output_count += 1 fig_title = 'Signal Encoded Using Ideal\",\n \"func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting as pl import bionet.ted.iaf as iaf\",\n \"print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t,\",\n \"bl import bionet.utils.plotting as pl import bionet.ted.iaf as iaf # For determining output\",\n \"= 3.5 # bias d1 = 0.7 # threshold R1 = np.inf #\",\n \"dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count)\",\n \"dur = 0.1 dt = 1e-6 f = 32 bw = 2*np.pi*f t\",\n \"1 fig_title = 'Signal Encoded Using Leaky IAF Encoder' print fig_title s_list =\",\n \"except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1,\",\n \"func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name\",\n \"+ str(output_count) + output_ext) output_count += 1 fig_title = 'Signal Decoded Using Ideal\",\n \"np.array([b1, b2]) d_list = np.array([d1, d2]) R_list = np.array([R1, R2]) C_list = np.array([C1,\",\n \"# bias d1 = 0.7 # threshold R1 = 10.0 # resistance C1\",\n \"# capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction condition not\",\n \"0 output_ext = '.png' # Define algorithm parameters and input signal: dur =\",\n \"None if noise_power == None: fig_title = 'IAF Input Signal with No Noise'\",\n \"bw, b_list, d_list, R_list, C_list) pl.plot_compare(t, u, u_rec, fig_title, output_name + str(output_count) +\",\n \"Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw, b_list, d_list, R_list,\",\n \"R_list = np.array([R1, R2]) C_list = np.array([C1, C2]) output_count += 1 fig_title =\",\n \"R2]) C_list = np.array([C1, C2]) output_count += 1 fig_title = 'Signal Encoded Using\",\n \"C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = [b1, b2] d_list =\",\n \"fig_title = 'Signal Encoded Using Ideal IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u,\",\n \"#!/usr/bin/env python \\\"\\\"\\\" Demos of encoding and decoding algorithms using populations of IAF\",\n \"'Signal Encoded Using Ideal IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt,\",\n \"output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title + ' #2', output_name + str(output_count)\",\n \"R2] C_list = [C1, C2] output_count += 1 fig_title = 'Signal Encoded Using\",\n \"'Signal Decoded Using Leaky IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur,\",\n \"R1 = 10.0 # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw,\",\n \"Encoded Using Leaky IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list,\",\n \"and decoding algorithms using populations of IAF neurons. \\\"\\\"\\\" # Copyright (c) 2009-2015,\",\n \"u, fig_title, output_name + str(output_count) + output_ext) # Test leaky IAF algorithms: b1\",\n \"f = 32 bw = 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power\",\n \"Noise' else: fig_title = 'IAF Input Signal with %d dB of Noise' %\",\n \"Encoded Using Ideal IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list,\",\n \"np.arange(0, dur, dt) np.random.seed(0) noise_power = None if noise_power == None: fig_title =\",\n \"ideal IAF algorithms: b1 = 3.5 # bias d1 = 0.7 # threshold\",\n \"as bl import bionet.utils.plotting as pl import bionet.ted.iaf as iaf # For determining\",\n \"iaf # For determining output plot file names: output_name = 'iaf_pop_demo_' output_count =\",\n \"Using Leaky IAF Population Decoder' print fig_title u_rec = func_timer(iaf.iaf_decode_pop)(s_list, dur, dt, bw,\",\n \"IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u], dt, b_list, d_list, R_list, C_list)\",\n \"32 bw = 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power = None\",\n \"iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list\",\n \"output_ext) output_count += 1 pl.plot_encoded(t, u, s_list[1], fig_title + ' #2', output_name +\",\n \"# All rights reserved. # Distributed under the terms of the BSD license:\",\n \"bw, b1, d1, R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2 =\",\n \"output_ext) # Test ideal IAF algorithms: b1 = 3.5 # bias d1 =\",\n \"'iaf_pop_demo_' output_count = 0 output_ext = '.png' # Define algorithm parameters and input\",\n \"s_list[1], fig_title + ' #2', output_name + str(output_count) + output_ext) output_count += 1\",\n \"= 'Signal Encoded Using Leaky IAF Encoder' print fig_title s_list = func_timer(iaf.iaf_encode_pop)([u, u],\",\n \"+ output_ext) # Test leaky IAF algorithms: b1 = 3.5 # bias d1\",\n \"= [C1, C2] output_count += 1 fig_title = 'Signal Encoded Using Ideal IAF\",\n \"iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2\",\n \"Demos of encoding and decoding algorithms using populations of IAF neurons. \\\"\\\"\\\" #\",\n \"np.array([R1, R2]) C_list = np.array([C1, C2]) output_count += 1 fig_title = 'Signal Encoded\",\n \"bionet.utils.misc import func_timer import bionet.utils.band_limited as bl import bionet.utils.plotting as pl import bionet.ted.iaf\",\n \"# Copyright (c) 2009-2015, <NAME> # All rights reserved. # Distributed under the\",\n \"= 1e-6 f = 32 bw = 2*np.pi*f t = np.arange(0, dur, dt)\",\n \"bias d1 = 0.7 # threshold R1 = np.inf # resistance C1 =\"\n]","isConversational":false}}},{"rowIdx":2984,"cells":{"ngram":{"kind":"list like","value":["= function( x,y ): return x+y def main(): TestError( F(1,2) == 3 )","expr\"\"\" F = function( x,y ): return x+y def main(): TestError( F(1,2) ==","\"\"\"func expr\"\"\" F = function( x,y ): return x+y def main(): TestError( F(1,2)","F = function( x,y ): return x+y def main(): TestError( F(1,2) == 3"],"string":"[\n \"= function( x,y ): return x+y def main(): TestError( F(1,2) == 3 )\",\n \"expr\\\"\\\"\\\" F = function( x,y ): return x+y def main(): TestError( F(1,2) ==\",\n \"\\\"\\\"\\\"func expr\\\"\\\"\\\" F = function( x,y ): return x+y def main(): TestError( F(1,2)\",\n \"F = function( x,y ): return x+y def main(): TestError( F(1,2) == 3\"\n]","isConversational":false}}},{"rowIdx":2985,"cells":{"ngram":{"kind":"list like","value":["return media # Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass #","sortable_fields = ['sort'] # Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media","= self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh,","def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return","self.vendor('nadmin.plugin.sortable.js') return media # Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass","'_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def get_media(self, media): if self.sortable_fields","# 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{ #","# 'refresh_times': [{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r)","= '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def get_media(self, media): if","'refresh_times': [{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) ==","current_refresh, # 'refresh_times': [{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected':","[{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh,","self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh, # } for r in self.refresh_times],","str(r) == current_refresh, # } for r in self.refresh_times], # }) # nodes.append(loader.render_to_string('nadmin/blocks/refresh.html',","import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media","self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return media # Block Views","'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{ # 'time': r, # 'url':","media + self.vendor('nadmin.plugin.sortable.js') return media # Block Views def block_top_toolbar(self, context, nodes): if","media = media + self.vendor('nadmin.plugin.sortable.js') return media # Block Views def block_top_toolbar(self, context,","BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def","self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url':","from nadmin.sites import site from nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class","def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({","context, nodes): if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh':","# 'selected': str(r) == current_refresh, # } for r in self.refresh_times], # })","Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js')","r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh, # } for","media # Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass # current_refresh","== current_refresh, # } for r in self.refresh_times], # }) # nodes.append(loader.render_to_string('nadmin/blocks/refresh.html', context_instance=context))","r}), # 'selected': str(r) == current_refresh, # } for r in self.refresh_times], #","'current_refresh': current_refresh, # 'refresh_times': [{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), #","# 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{ # 'time': r, #","current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh':","# 'current_refresh': current_refresh, # 'refresh_times': [{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}),","current_refresh, # } for r in self.refresh_times], # }) # nodes.append(loader.render_to_string('nadmin/blocks/refresh.html', context_instance=context)) site.register_plugin(SortablePlugin,","nadmin.sites import site from nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin):","import site from nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields","'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh, # } for r in","# context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times':","+ self.vendor('nadmin.plugin.sortable.js') return media # Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields:","ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def get_media(self,","'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{ # 'time':","and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return media # Block Views def","= media + self.vendor('nadmin.plugin.sortable.js') return media # Block Views def block_top_toolbar(self, context, nodes):","if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), #","'selected': str(r) == current_refresh, # } for r in self.refresh_times], # }) #","# } for r in self.refresh_times], # }) # nodes.append(loader.render_to_string('nadmin/blocks/refresh.html', context_instance=context)) site.register_plugin(SortablePlugin, ListAdminView)","# Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass # current_refresh =","pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)),","['sort'] # Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media","self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR:","get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return media","media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return media #","SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR):","Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR)","#coding:utf-8 from nadmin.sites import site from nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by'","= ['sort'] # Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media =","# 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh, #","from nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort']","if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return media # Block","SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def get_media(self, media):","block_top_toolbar(self, context, nodes): if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ #","nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] #","nodes): if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh),","# Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media +","# current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), #","context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{","self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, #","self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return media # Block Views def block_top_toolbar(self,","bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{ # 'time': r,","'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh, # }","class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def get_media(self, media): if self.sortable_fields and","# 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh, # } for r","site from nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields =","Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) #"],"string":"[\n \"return media # Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass #\",\n \"sortable_fields = ['sort'] # Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media\",\n \"= self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh,\",\n \"def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return\",\n \"self.vendor('nadmin.plugin.sortable.js') return media # Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass\",\n \"'_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def get_media(self, media): if self.sortable_fields\",\n \"# 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{ #\",\n \"# 'refresh_times': [{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r)\",\n \"= '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def get_media(self, media): if\",\n \"'refresh_times': [{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) ==\",\n \"current_refresh, # 'refresh_times': [{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected':\",\n \"[{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh,\",\n \"self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh, # } for r in self.refresh_times],\",\n \"str(r) == current_refresh, # } for r in self.refresh_times], # }) # nodes.append(loader.render_to_string('nadmin/blocks/refresh.html',\",\n \"import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media\",\n \"self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return media # Block Views\",\n \"'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{ # 'time': r, # 'url':\",\n \"media + self.vendor('nadmin.plugin.sortable.js') return media # Block Views def block_top_toolbar(self, context, nodes): if\",\n \"media = media + self.vendor('nadmin.plugin.sortable.js') return media # Block Views def block_top_toolbar(self, context,\",\n \"BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def\",\n \"self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url':\",\n \"from nadmin.sites import site from nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class\",\n \"def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({\",\n \"context, nodes): if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh':\",\n \"# 'selected': str(r) == current_refresh, # } for r in self.refresh_times], # })\",\n \"Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js')\",\n \"r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh, # } for\",\n \"media # Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass # current_refresh\",\n \"== current_refresh, # } for r in self.refresh_times], # }) # nodes.append(loader.render_to_string('nadmin/blocks/refresh.html', context_instance=context))\",\n \"r}), # 'selected': str(r) == current_refresh, # } for r in self.refresh_times], #\",\n \"'current_refresh': current_refresh, # 'refresh_times': [{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), #\",\n \"# 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{ # 'time': r, #\",\n \"current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh':\",\n \"# 'current_refresh': current_refresh, # 'refresh_times': [{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}),\",\n \"current_refresh, # } for r in self.refresh_times], # }) # nodes.append(loader.render_to_string('nadmin/blocks/refresh.html', context_instance=context)) site.register_plugin(SortablePlugin,\",\n \"nadmin.sites import site from nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin):\",\n \"import site from nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields\",\n \"'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh, # } for r in\",\n \"# context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times':\",\n \"+ self.vendor('nadmin.plugin.sortable.js') return media # Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields:\",\n \"ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def get_media(self,\",\n \"'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{ # 'time':\",\n \"and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return media # Block Views def\",\n \"= media + self.vendor('nadmin.plugin.sortable.js') return media # Block Views def block_top_toolbar(self, context, nodes):\",\n \"if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), #\",\n \"'selected': str(r) == current_refresh, # } for r in self.refresh_times], # }) #\",\n \"# } for r in self.refresh_times], # }) # nodes.append(loader.render_to_string('nadmin/blocks/refresh.html', context_instance=context)) site.register_plugin(SortablePlugin, ListAdminView)\",\n \"# Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass # current_refresh =\",\n \"pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)),\",\n \"['sort'] # Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media\",\n \"self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{ # 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR:\",\n \"get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return media\",\n \"media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return media #\",\n \"SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR):\",\n \"Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR)\",\n \"#coding:utf-8 from nadmin.sites import site from nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by'\",\n \"= ['sort'] # Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media =\",\n \"# 'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh, #\",\n \"from nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort']\",\n \"if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return media # Block\",\n \"SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def get_media(self, media):\",\n \"block_top_toolbar(self, context, nodes): if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ #\",\n \"nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] #\",\n \"nodes): if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh),\",\n \"# Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media = media +\",\n \"# current_refresh = self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), #\",\n \"context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{\",\n \"self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh': bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, #\",\n \"self.request.GET.get(SORTBY_VAR): media = media + self.vendor('nadmin.plugin.sortable.js') return media # Block Views def block_top_toolbar(self,\",\n \"bool(current_refresh), # 'clean_refresh_url': self.admin_view.get_query_string(remove=(REFRESH_VAR,)), # 'current_refresh': current_refresh, # 'refresh_times': [{ # 'time': r,\",\n \"'time': r, # 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh, # }\",\n \"class SortablePlugin(BaseAdminPlugin): sortable_fields = ['sort'] # Media def get_media(self, media): if self.sortable_fields and\",\n \"# 'url': self.admin_view.get_query_string({REFRESH_VAR: r}), # 'selected': str(r) == current_refresh, # } for r\",\n \"site from nadmin.views import BaseAdminPlugin, ListAdminView SORTBY_VAR = '_sort_by' class SortablePlugin(BaseAdminPlugin): sortable_fields =\",\n \"Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass # current_refresh = self.request.GET.get(REFRESH_VAR) #\"\n]","isConversational":false}}},{"rowIdx":2986,"cells":{"ngram":{"kind":"list like","value":["@celery.task() def charge_cards(): lock = Lock(key=\"charge-cards-lock\") lock.acquire() log = Log() log.it(\"---Starting batch job...\")","pass class Lock(object): \"\"\" Claim an exclusive lock. Using Redis. \"\"\" def __init__(self,","today = datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\"---Processing charges...\") log.it(f\"Found {len(opportunities)} opportunities available","root.setLevel(log_level) class Log(object): \"\"\" This encapulates sending to the console/stdout and email all","recipient = ACCOUNTING_MAIL_RECIPIENT subject = \"Batch run\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \"\"\"","Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\" ) try: charge(opportunity) except ChargeException as e: logging.info(\"Batch","ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime, timedelta from pytz import timezone","sending this email and just rely on Sentry and logs? @celery.task() def charge_cards():","Redis. \"\"\" def __init__(self, key): self.key = key self.connection = redis.from_url(REDIS_URL) def acquire(self):","running. \"\"\" pass class Lock(object): \"\"\" Claim an exclusive lock. Using Redis. \"\"\"","available to process.\") for opportunity in opportunities: if not opportunity.stripe_customer: continue amount =","{len(opportunities)} opportunities available to process.\") for opportunity in opportunities: if not opportunity.stripe_customer: continue","except ChargeException as e: logging.info(\"Batch charge error\") e.send_slack_notification() log.send() lock.release() if __name__ ==","time=1200) def release(self): self.connection.delete(self.key) # TODO stop sending this email and just rely","logging.debug(string) self.log.append(string) def send(self): \"\"\" Send the assembled log out as an email.","if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\"---- Charging ${amount} to {opportunity.stripe_customer}","try: charge(opportunity) except ChargeException as e: logging.info(\"Batch charge error\") e.send_slack_notification() log.send() lock.release() if","util import send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level)","logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object): \"\"\" This encapulates sending to the","ChargeException as e: logging.info(\"Batch charge error\") e.send_slack_notification() log.send() lock.release() if __name__ == \"__main__\":","amount = amount_to_charge(opportunity) log.it( f\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\" ) try: charge(opportunity)","encapulates sending to the console/stdout and email all in one. \"\"\" def __init__(self):","ACCOUNTING_MAIL_RECIPIENT subject = \"Batch run\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \"\"\" Here to","on Sentry and logs? @celery.task() def charge_cards(): lock = Lock(key=\"charge-cards-lock\") lock.acquire() log =","__init__(self): self.log = list() def it(self, string): \"\"\" Add something to the log.","from config import ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime, timedelta from","log.it(\"---Starting batch job...\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\"%Y-%m-%d\") today = datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities =","{opportunity.stripe_customer} ({opportunity.name})\" ) try: charge(opportunity) except ChargeException as e: logging.info(\"Batch charge error\") e.send_slack_notification()","email. \"\"\" body = \"\\n\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \"Batch run\" send_email(body=body,","the log. \"\"\" logging.debug(string) self.log.append(string) def send(self): \"\"\" Send the assembled log out","self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\"bar\", time=1200) def","self.connection.delete(self.key) # TODO stop sending this email and just rely on Sentry and","from pytz import timezone import celery import redis from charges import amount_to_charge, charge,","ChargeException from npsp import Opportunity from util import send_email zone = timezone(TIMEZONE) log_level","batch job...\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\"%Y-%m-%d\") today = datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities = Opportunity.list(begin=three_days_ago,","body = \"\\n\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \"Batch run\" send_email(body=body, recipient=recipient, subject=subject)","three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\"%Y-%m-%d\") today = datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\"---Processing","as e: logging.info(\"Batch charge error\") e.send_slack_notification() log.send() lock.release() if __name__ == \"__main__\": charge_cards()","\"\"\" pass class Lock(object): \"\"\" Claim an exclusive lock. Using Redis. \"\"\" def","\"Batch run\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \"\"\" Here to show when more","__init__(self, key): self.key = key self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise",") try: charge(opportunity) except ChargeException as e: logging.info(\"Batch charge error\") e.send_slack_notification() log.send() lock.release()","and just rely on Sentry and logs? @celery.task() def charge_cards(): lock = Lock(key=\"charge-cards-lock\")","self.connection.setex(name=self.key, value=\"bar\", time=1200) def release(self): self.connection.delete(self.key) # TODO stop sending this email and","opportunities: if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\"---- Charging ${amount} to","redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\"bar\", time=1200) def release(self): self.connection.delete(self.key)","Log() log.it(\"---Starting batch job...\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\"%Y-%m-%d\") today = datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities","import celery import redis from charges import amount_to_charge, charge, ChargeException from npsp import","console/stdout and email all in one. \"\"\" def __init__(self): self.log = list() def","timezone import celery import redis from charges import amount_to_charge, charge, ChargeException from npsp","key): self.key = key self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting","log.it(f\"Found {len(opportunities)} opportunities available to process.\") for opportunity in opportunities: if not opportunity.stripe_customer:","# TODO stop sending this email and just rely on Sentry and logs?","assembled log out as an email. \"\"\" body = \"\\n\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT","all in one. \"\"\" def __init__(self): self.log = list() def it(self, string): \"\"\"","rely on Sentry and logs? @celery.task() def charge_cards(): lock = Lock(key=\"charge-cards-lock\") lock.acquire() log","datetime import datetime, timedelta from pytz import timezone import celery import redis from","more than one job of the same type is running. \"\"\" pass class","This encapulates sending to the console/stdout and email all in one. \"\"\" def","def charge_cards(): lock = Lock(key=\"charge-cards-lock\") lock.acquire() log = Log() log.it(\"---Starting batch job...\") three_days_ago","zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object): \"\"\"","Opportunity.list(begin=three_days_ago, end=today) log.it(\"---Processing charges...\") log.it(f\"Found {len(opportunities)} opportunities available to process.\") for opportunity in","import amount_to_charge, charge, ChargeException from npsp import Opportunity from util import send_email zone","show when more than one job of the same type is running. \"\"\"","AlreadyExecuting(Exception): \"\"\" Here to show when more than one job of the same","and logs? @celery.task() def charge_cards(): lock = Lock(key=\"charge-cards-lock\") lock.acquire() log = Log() log.it(\"---Starting","log.it(\"---Processing charges...\") log.it(f\"Found {len(opportunities)} opportunities available to process.\") for opportunity in opportunities: if","opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\"---Processing charges...\") log.it(f\"Found {len(opportunities)} opportunities available to process.\") for","email and just rely on Sentry and logs? @celery.task() def charge_cards(): lock =","out as an email. \"\"\" body = \"\\n\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject =","list() def it(self, string): \"\"\" Add something to the log. \"\"\" logging.debug(string) self.log.append(string)","to {opportunity.stripe_customer} ({opportunity.name})\" ) try: charge(opportunity) except ChargeException as e: logging.info(\"Batch charge error\")","acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\"bar\", time=1200) def release(self): self.connection.delete(self.key) # TODO","the same type is running. \"\"\" pass class Lock(object): \"\"\" Claim an exclusive","import ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime, timedelta from pytz import","AlreadyExecuting self.connection.setex(name=self.key, value=\"bar\", time=1200) def release(self): self.connection.delete(self.key) # TODO stop sending this email","in one. \"\"\" def __init__(self): self.log = list() def it(self, string): \"\"\" Add","lock.acquire() log = Log() log.it(\"---Starting batch job...\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\"%Y-%m-%d\") today","log.it( f\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\" ) try: charge(opportunity) except ChargeException as","is running. \"\"\" pass class Lock(object): \"\"\" Claim an exclusive lock. Using Redis.","an exclusive lock. Using Redis. \"\"\" def __init__(self, key): self.key = key self.connection","to process.\") for opportunity in opportunities: if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity)","logs? @celery.task() def charge_cards(): lock = Lock(key=\"charge-cards-lock\") lock.acquire() log = Log() log.it(\"---Starting batch","key self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\"bar\", time=1200)","it(self, string): \"\"\" Add something to the log. \"\"\" logging.debug(string) self.log.append(string) def send(self):","import timezone import celery import redis from charges import amount_to_charge, charge, ChargeException from","Lock(object): \"\"\" Claim an exclusive lock. Using Redis. \"\"\" def __init__(self, key): self.key","timedelta(days=10)).strftime(\"%Y-%m-%d\") today = datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\"---Processing charges...\") log.it(f\"Found {len(opportunities)} opportunities","- timedelta(days=10)).strftime(\"%Y-%m-%d\") today = datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\"---Processing charges...\") log.it(f\"Found {len(opportunities)}","import send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class","log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object): \"\"\" This encapulates sending","raise AlreadyExecuting self.connection.setex(name=self.key, value=\"bar\", time=1200) def release(self): self.connection.delete(self.key) # TODO stop sending this","not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\"","f\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\" ) try: charge(opportunity) except ChargeException as e:","redis from charges import amount_to_charge, charge, ChargeException from npsp import Opportunity from util","to show when more than one job of the same type is running.","Add something to the log. \"\"\" logging.debug(string) self.log.append(string) def send(self): \"\"\" Send the","= \"\\n\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \"Batch run\" send_email(body=body, recipient=recipient, subject=subject) class","\"\"\" body = \"\\n\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \"Batch run\" send_email(body=body, recipient=recipient,","than one job of the same type is running. \"\"\" pass class Lock(object):","charge(opportunity) except ChargeException as e: logging.info(\"Batch charge error\") e.send_slack_notification() log.send() lock.release() if __name__","= redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\"bar\", time=1200) def release(self):","value=\"bar\", time=1200) def release(self): self.connection.delete(self.key) # TODO stop sending this email and just","TIMEZONE from datetime import datetime, timedelta from pytz import timezone import celery import","import datetime, timedelta from pytz import timezone import celery import redis from charges","Log(object): \"\"\" This encapulates sending to the console/stdout and email all in one.","logging.getLogger() root.setLevel(log_level) class Log(object): \"\"\" This encapulates sending to the console/stdout and email","def __init__(self): self.log = list() def it(self, string): \"\"\" Add something to the","lock = Lock(key=\"charge-cards-lock\") lock.acquire() log = Log() log.it(\"---Starting batch job...\") three_days_ago = (datetime.now(tz=zone)","Sentry and logs? @celery.task() def charge_cards(): lock = Lock(key=\"charge-cards-lock\") lock.acquire() log = Log()","to the console/stdout and email all in one. \"\"\" def __init__(self): self.log =","job of the same type is running. \"\"\" pass class Lock(object): \"\"\" Claim","process.\") for opportunity in opportunities: if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it(","same type is running. \"\"\" pass class Lock(object): \"\"\" Claim an exclusive lock.","email all in one. \"\"\" def __init__(self): self.log = list() def it(self, string):","subject=subject) class AlreadyExecuting(Exception): \"\"\" Here to show when more than one job of","timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object): \"\"\" This encapulates","subject = \"Batch run\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \"\"\" Here to show","from util import send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger()","import logging from config import ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime,","of the same type is running. \"\"\" pass class Lock(object): \"\"\" Claim an","= logging.getLogger() root.setLevel(log_level) class Log(object): \"\"\" This encapulates sending to the console/stdout and","from npsp import Opportunity from util import send_email zone = timezone(TIMEZONE) log_level =","and email all in one. \"\"\" def __init__(self): self.log = list() def it(self,","job...\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\"%Y-%m-%d\") today = datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities = Opportunity.list(begin=three_days_ago, end=today)","opportunity in opportunities: if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\"---- Charging","to the log. \"\"\" logging.debug(string) self.log.append(string) def send(self): \"\"\" Send the assembled log","Send the assembled log out as an email. \"\"\" body = \"\\n\".join(self.log) recipient","import Opportunity from util import send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root","= list() def it(self, string): \"\"\" Add something to the log. \"\"\" logging.debug(string)","exclusive lock. Using Redis. \"\"\" def __init__(self, key): self.key = key self.connection =","def __init__(self, key): self.key = key self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key):","one. \"\"\" def __init__(self): self.log = list() def it(self, string): \"\"\" Add something","send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \"\"\" Here to show when more than one","Here to show when more than one job of the same type is","send(self): \"\"\" Send the assembled log out as an email. \"\"\" body =","(datetime.now(tz=zone) - timedelta(days=10)).strftime(\"%Y-%m-%d\") today = datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\"---Processing charges...\") log.it(f\"Found","def release(self): self.connection.delete(self.key) # TODO stop sending this email and just rely on","charges import amount_to_charge, charge, ChargeException from npsp import Opportunity from util import send_email","= logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object): \"\"\" This encapulates sending to","= key self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\"bar\",","logging from config import ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime, timedelta","class Lock(object): \"\"\" Claim an exclusive lock. Using Redis. \"\"\" def __init__(self, key):","\"\"\" Claim an exclusive lock. Using Redis. \"\"\" def __init__(self, key): self.key =","npsp import Opportunity from util import send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL)","send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object):","this email and just rely on Sentry and logs? @celery.task() def charge_cards(): lock","def send(self): \"\"\" Send the assembled log out as an email. \"\"\" body","amount_to_charge, charge, ChargeException from npsp import Opportunity from util import send_email zone =","class Log(object): \"\"\" This encapulates sending to the console/stdout and email all in","log out as an email. \"\"\" body = \"\\n\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject","lock. Using Redis. \"\"\" def __init__(self, key): self.key = key self.connection = redis.from_url(REDIS_URL)","just rely on Sentry and logs? @celery.task() def charge_cards(): lock = Lock(key=\"charge-cards-lock\") lock.acquire()","= (datetime.now(tz=zone) - timedelta(days=10)).strftime(\"%Y-%m-%d\") today = datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\"---Processing charges...\")","charge, ChargeException from npsp import Opportunity from util import send_email zone = timezone(TIMEZONE)","root = logging.getLogger() root.setLevel(log_level) class Log(object): \"\"\" This encapulates sending to the console/stdout","stop sending this email and just rely on Sentry and logs? @celery.task() def","the assembled log out as an email. \"\"\" body = \"\\n\".join(self.log) recipient =","run\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \"\"\" Here to show when more than","self.key = key self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key,","charges...\") log.it(f\"Found {len(opportunities)} opportunities available to process.\") for opportunity in opportunities: if not","an email. \"\"\" body = \"\\n\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \"Batch run\"","log = Log() log.it(\"---Starting batch job...\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\"%Y-%m-%d\") today =","as an email. \"\"\" body = \"\\n\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \"Batch","one job of the same type is running. \"\"\" pass class Lock(object): \"\"\"","= Lock(key=\"charge-cards-lock\") lock.acquire() log = Log() log.it(\"---Starting batch job...\") three_days_ago = (datetime.now(tz=zone) -","def it(self, string): \"\"\" Add something to the log. \"\"\" logging.debug(string) self.log.append(string) def","Lock(key=\"charge-cards-lock\") lock.acquire() log = Log() log.it(\"---Starting batch job...\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\"%Y-%m-%d\")","datetime, timedelta from pytz import timezone import celery import redis from charges import","self.log.append(string) def send(self): \"\"\" Send the assembled log out as an email. \"\"\"","datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\"---Processing charges...\") log.it(f\"Found {len(opportunities)} opportunities available to process.\")","self.log = list() def it(self, string): \"\"\" Add something to the log. \"\"\"","from charges import amount_to_charge, charge, ChargeException from npsp import Opportunity from util import","from datetime import datetime, timedelta from pytz import timezone import celery import redis","\"\"\" def __init__(self): self.log = list() def it(self, string): \"\"\" Add something to","= datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\"---Processing charges...\") log.it(f\"Found {len(opportunities)} opportunities available to","Claim an exclusive lock. Using Redis. \"\"\" def __init__(self, key): self.key = key","\"\"\" def __init__(self, key): self.key = key self.connection = redis.from_url(REDIS_URL) def acquire(self): if","in opportunities: if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\"---- Charging ${amount}","timedelta from pytz import timezone import celery import redis from charges import amount_to_charge,","LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime, timedelta from pytz import timezone import","REDIS_URL, TIMEZONE from datetime import datetime, timedelta from pytz import timezone import celery","pytz import timezone import celery import redis from charges import amount_to_charge, charge, ChargeException","({opportunity.name})\" ) try: charge(opportunity) except ChargeException as e: logging.info(\"Batch charge error\") e.send_slack_notification() log.send()","type is running. \"\"\" pass class Lock(object): \"\"\" Claim an exclusive lock. Using","= timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object): \"\"\" This","class AlreadyExecuting(Exception): \"\"\" Here to show when more than one job of the","Using Redis. \"\"\" def __init__(self, key): self.key = key self.connection = redis.from_url(REDIS_URL) def","continue amount = amount_to_charge(opportunity) log.it( f\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\" ) try:","\"\"\" Send the assembled log out as an email. \"\"\" body = \"\\n\".join(self.log)","<filename>batch-tmp.py import logging from config import ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import","string): \"\"\" Add something to the log. \"\"\" logging.debug(string) self.log.append(string) def send(self): \"\"\"","TODO stop sending this email and just rely on Sentry and logs? @celery.task()","if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\"bar\", time=1200) def release(self): self.connection.delete(self.key) # TODO stop","when more than one job of the same type is running. \"\"\" pass","for opportunity in opportunities: if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\"----","release(self): self.connection.delete(self.key) # TODO stop sending this email and just rely on Sentry","end=today) log.it(\"---Processing charges...\") log.it(f\"Found {len(opportunities)} opportunities available to process.\") for opportunity in opportunities:","${amount} to {opportunity.stripe_customer} ({opportunity.name})\" ) try: charge(opportunity) except ChargeException as e: logging.info(\"Batch charge","sending to the console/stdout and email all in one. \"\"\" def __init__(self): self.log","= Log() log.it(\"---Starting batch job...\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\"%Y-%m-%d\") today = datetime.now(tz=zone).strftime(\"%Y-%m-%d\")","the console/stdout and email all in one. \"\"\" def __init__(self): self.log = list()","\"\"\" logging.debug(string) self.log.append(string) def send(self): \"\"\" Send the assembled log out as an","log. \"\"\" logging.debug(string) self.log.append(string) def send(self): \"\"\" Send the assembled log out as","def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\"bar\", time=1200) def release(self): self.connection.delete(self.key) #","self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\"bar\", time=1200) def release(self): self.connection.delete(self.key) # TODO stop sending","= ACCOUNTING_MAIL_RECIPIENT subject = \"Batch run\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \"\"\" Here","\"\"\" Here to show when more than one job of the same type","config import ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime, timedelta from pytz","something to the log. \"\"\" logging.debug(string) self.log.append(string) def send(self): \"\"\" Send the assembled","\"\"\" This encapulates sending to the console/stdout and email all in one. \"\"\"","\"\\n\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \"Batch run\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception):","import redis from charges import amount_to_charge, charge, ChargeException from npsp import Opportunity from","= \"Batch run\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \"\"\" Here to show when","amount_to_charge(opportunity) log.it( f\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\" ) try: charge(opportunity) except ChargeException","charge_cards(): lock = Lock(key=\"charge-cards-lock\") lock.acquire() log = Log() log.it(\"---Starting batch job...\") three_days_ago =","= Opportunity.list(begin=three_days_ago, end=today) log.it(\"---Processing charges...\") log.it(f\"Found {len(opportunities)} opportunities available to process.\") for opportunity","\"\"\" Add something to the log. \"\"\" logging.debug(string) self.log.append(string) def send(self): \"\"\" Send","celery import redis from charges import amount_to_charge, charge, ChargeException from npsp import Opportunity","Opportunity from util import send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root =","opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\" )","= amount_to_charge(opportunity) log.it( f\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\" ) try: charge(opportunity) except","opportunities available to process.\") for opportunity in opportunities: if not opportunity.stripe_customer: continue amount","recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \"\"\" Here to show when more than one job"],"string":"[\n \"@celery.task() def charge_cards(): lock = Lock(key=\\\"charge-cards-lock\\\") lock.acquire() log = Log() log.it(\\\"---Starting batch job...\\\")\",\n \"pass class Lock(object): \\\"\\\"\\\" Claim an exclusive lock. Using Redis. \\\"\\\"\\\" def __init__(self,\",\n \"today = datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\\\"---Processing charges...\\\") log.it(f\\\"Found {len(opportunities)} opportunities available\",\n \"root.setLevel(log_level) class Log(object): \\\"\\\"\\\" This encapulates sending to the console/stdout and email all\",\n \"recipient = ACCOUNTING_MAIL_RECIPIENT subject = \\\"Batch run\\\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \\\"\\\"\\\"\",\n \"Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\\\" ) try: charge(opportunity) except ChargeException as e: logging.info(\\\"Batch\",\n \"ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime, timedelta from pytz import timezone\",\n \"sending this email and just rely on Sentry and logs? @celery.task() def charge_cards():\",\n \"Redis. \\\"\\\"\\\" def __init__(self, key): self.key = key self.connection = redis.from_url(REDIS_URL) def acquire(self):\",\n \"running. \\\"\\\"\\\" pass class Lock(object): \\\"\\\"\\\" Claim an exclusive lock. Using Redis. \\\"\\\"\\\"\",\n \"available to process.\\\") for opportunity in opportunities: if not opportunity.stripe_customer: continue amount =\",\n \"{len(opportunities)} opportunities available to process.\\\") for opportunity in opportunities: if not opportunity.stripe_customer: continue\",\n \"except ChargeException as e: logging.info(\\\"Batch charge error\\\") e.send_slack_notification() log.send() lock.release() if __name__ ==\",\n \"time=1200) def release(self): self.connection.delete(self.key) # TODO stop sending this email and just rely\",\n \"logging.debug(string) self.log.append(string) def send(self): \\\"\\\"\\\" Send the assembled log out as an email.\",\n \"if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\\\"---- Charging ${amount} to {opportunity.stripe_customer}\",\n \"try: charge(opportunity) except ChargeException as e: logging.info(\\\"Batch charge error\\\") e.send_slack_notification() log.send() lock.release() if\",\n \"util import send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level)\",\n \"logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object): \\\"\\\"\\\" This encapulates sending to the\",\n \"ChargeException as e: logging.info(\\\"Batch charge error\\\") e.send_slack_notification() log.send() lock.release() if __name__ == \\\"__main__\\\":\",\n \"amount = amount_to_charge(opportunity) log.it( f\\\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\\\" ) try: charge(opportunity)\",\n \"encapulates sending to the console/stdout and email all in one. \\\"\\\"\\\" def __init__(self):\",\n \"ACCOUNTING_MAIL_RECIPIENT subject = \\\"Batch run\\\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \\\"\\\"\\\" Here to\",\n \"on Sentry and logs? @celery.task() def charge_cards(): lock = Lock(key=\\\"charge-cards-lock\\\") lock.acquire() log =\",\n \"__init__(self): self.log = list() def it(self, string): \\\"\\\"\\\" Add something to the log.\",\n \"from config import ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime, timedelta from\",\n \"log.it(\\\"---Starting batch job...\\\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\") today = datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\") opportunities =\",\n \"{opportunity.stripe_customer} ({opportunity.name})\\\" ) try: charge(opportunity) except ChargeException as e: logging.info(\\\"Batch charge error\\\") e.send_slack_notification()\",\n \"email. \\\"\\\"\\\" body = \\\"\\\\n\\\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \\\"Batch run\\\" send_email(body=body,\",\n \"the log. \\\"\\\"\\\" logging.debug(string) self.log.append(string) def send(self): \\\"\\\"\\\" Send the assembled log out\",\n \"self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\\\"bar\\\", time=1200) def\",\n \"self.connection.delete(self.key) # TODO stop sending this email and just rely on Sentry and\",\n \"from pytz import timezone import celery import redis from charges import amount_to_charge, charge,\",\n \"ChargeException from npsp import Opportunity from util import send_email zone = timezone(TIMEZONE) log_level\",\n \"batch job...\\\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\") today = datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\") opportunities = Opportunity.list(begin=three_days_ago,\",\n \"body = \\\"\\\\n\\\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \\\"Batch run\\\" send_email(body=body, recipient=recipient, subject=subject)\",\n \"three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\") today = datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\\\"---Processing\",\n \"as e: logging.info(\\\"Batch charge error\\\") e.send_slack_notification() log.send() lock.release() if __name__ == \\\"__main__\\\": charge_cards()\",\n \"\\\"\\\"\\\" pass class Lock(object): \\\"\\\"\\\" Claim an exclusive lock. Using Redis. \\\"\\\"\\\" def\",\n \"\\\"Batch run\\\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \\\"\\\"\\\" Here to show when more\",\n \"__init__(self, key): self.key = key self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise\",\n \") try: charge(opportunity) except ChargeException as e: logging.info(\\\"Batch charge error\\\") e.send_slack_notification() log.send() lock.release()\",\n \"and just rely on Sentry and logs? @celery.task() def charge_cards(): lock = Lock(key=\\\"charge-cards-lock\\\")\",\n \"self.connection.setex(name=self.key, value=\\\"bar\\\", time=1200) def release(self): self.connection.delete(self.key) # TODO stop sending this email and\",\n \"opportunities: if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\\\"---- Charging ${amount} to\",\n \"redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\\\"bar\\\", time=1200) def release(self): self.connection.delete(self.key)\",\n \"Log() log.it(\\\"---Starting batch job...\\\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\") today = datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\") opportunities\",\n \"import celery import redis from charges import amount_to_charge, charge, ChargeException from npsp import\",\n \"console/stdout and email all in one. \\\"\\\"\\\" def __init__(self): self.log = list() def\",\n \"timezone import celery import redis from charges import amount_to_charge, charge, ChargeException from npsp\",\n \"key): self.key = key self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting\",\n \"log.it(f\\\"Found {len(opportunities)} opportunities available to process.\\\") for opportunity in opportunities: if not opportunity.stripe_customer:\",\n \"# TODO stop sending this email and just rely on Sentry and logs?\",\n \"assembled log out as an email. \\\"\\\"\\\" body = \\\"\\\\n\\\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT\",\n \"all in one. \\\"\\\"\\\" def __init__(self): self.log = list() def it(self, string): \\\"\\\"\\\"\",\n \"rely on Sentry and logs? @celery.task() def charge_cards(): lock = Lock(key=\\\"charge-cards-lock\\\") lock.acquire() log\",\n \"datetime import datetime, timedelta from pytz import timezone import celery import redis from\",\n \"more than one job of the same type is running. \\\"\\\"\\\" pass class\",\n \"This encapulates sending to the console/stdout and email all in one. \\\"\\\"\\\" def\",\n \"def charge_cards(): lock = Lock(key=\\\"charge-cards-lock\\\") lock.acquire() log = Log() log.it(\\\"---Starting batch job...\\\") three_days_ago\",\n \"zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object): \\\"\\\"\\\"\",\n \"Opportunity.list(begin=three_days_ago, end=today) log.it(\\\"---Processing charges...\\\") log.it(f\\\"Found {len(opportunities)} opportunities available to process.\\\") for opportunity in\",\n \"import amount_to_charge, charge, ChargeException from npsp import Opportunity from util import send_email zone\",\n \"show when more than one job of the same type is running. \\\"\\\"\\\"\",\n \"AlreadyExecuting(Exception): \\\"\\\"\\\" Here to show when more than one job of the same\",\n \"and logs? @celery.task() def charge_cards(): lock = Lock(key=\\\"charge-cards-lock\\\") lock.acquire() log = Log() log.it(\\\"---Starting\",\n \"log.it(\\\"---Processing charges...\\\") log.it(f\\\"Found {len(opportunities)} opportunities available to process.\\\") for opportunity in opportunities: if\",\n \"opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\\\"---Processing charges...\\\") log.it(f\\\"Found {len(opportunities)} opportunities available to process.\\\") for\",\n \"email and just rely on Sentry and logs? @celery.task() def charge_cards(): lock =\",\n \"out as an email. \\\"\\\"\\\" body = \\\"\\\\n\\\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject =\",\n \"list() def it(self, string): \\\"\\\"\\\" Add something to the log. \\\"\\\"\\\" logging.debug(string) self.log.append(string)\",\n \"to {opportunity.stripe_customer} ({opportunity.name})\\\" ) try: charge(opportunity) except ChargeException as e: logging.info(\\\"Batch charge error\\\")\",\n \"acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\\\"bar\\\", time=1200) def release(self): self.connection.delete(self.key) # TODO\",\n \"the same type is running. \\\"\\\"\\\" pass class Lock(object): \\\"\\\"\\\" Claim an exclusive\",\n \"import ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime, timedelta from pytz import\",\n \"AlreadyExecuting self.connection.setex(name=self.key, value=\\\"bar\\\", time=1200) def release(self): self.connection.delete(self.key) # TODO stop sending this email\",\n \"in one. \\\"\\\"\\\" def __init__(self): self.log = list() def it(self, string): \\\"\\\"\\\" Add\",\n \"lock.acquire() log = Log() log.it(\\\"---Starting batch job...\\\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\") today\",\n \"log.it( f\\\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\\\" ) try: charge(opportunity) except ChargeException as\",\n \"is running. \\\"\\\"\\\" pass class Lock(object): \\\"\\\"\\\" Claim an exclusive lock. Using Redis.\",\n \"an exclusive lock. Using Redis. \\\"\\\"\\\" def __init__(self, key): self.key = key self.connection\",\n \"to process.\\\") for opportunity in opportunities: if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity)\",\n \"logs? @celery.task() def charge_cards(): lock = Lock(key=\\\"charge-cards-lock\\\") lock.acquire() log = Log() log.it(\\\"---Starting batch\",\n \"key self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\\\"bar\\\", time=1200)\",\n \"it(self, string): \\\"\\\"\\\" Add something to the log. \\\"\\\"\\\" logging.debug(string) self.log.append(string) def send(self):\",\n \"import timezone import celery import redis from charges import amount_to_charge, charge, ChargeException from\",\n \"Lock(object): \\\"\\\"\\\" Claim an exclusive lock. Using Redis. \\\"\\\"\\\" def __init__(self, key): self.key\",\n \"timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\") today = datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\\\"---Processing charges...\\\") log.it(f\\\"Found {len(opportunities)} opportunities\",\n \"- timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\") today = datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\\\"---Processing charges...\\\") log.it(f\\\"Found {len(opportunities)}\",\n \"import send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class\",\n \"log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object): \\\"\\\"\\\" This encapulates sending\",\n \"raise AlreadyExecuting self.connection.setex(name=self.key, value=\\\"bar\\\", time=1200) def release(self): self.connection.delete(self.key) # TODO stop sending this\",\n \"not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\\\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\\\"\",\n \"f\\\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\\\" ) try: charge(opportunity) except ChargeException as e:\",\n \"redis from charges import amount_to_charge, charge, ChargeException from npsp import Opportunity from util\",\n \"to show when more than one job of the same type is running.\",\n \"Add something to the log. \\\"\\\"\\\" logging.debug(string) self.log.append(string) def send(self): \\\"\\\"\\\" Send the\",\n \"= \\\"\\\\n\\\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \\\"Batch run\\\" send_email(body=body, recipient=recipient, subject=subject) class\",\n \"\\\"\\\"\\\" body = \\\"\\\\n\\\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \\\"Batch run\\\" send_email(body=body, recipient=recipient,\",\n \"than one job of the same type is running. \\\"\\\"\\\" pass class Lock(object):\",\n \"charge(opportunity) except ChargeException as e: logging.info(\\\"Batch charge error\\\") e.send_slack_notification() log.send() lock.release() if __name__\",\n \"= redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\\\"bar\\\", time=1200) def release(self):\",\n \"value=\\\"bar\\\", time=1200) def release(self): self.connection.delete(self.key) # TODO stop sending this email and just\",\n \"TIMEZONE from datetime import datetime, timedelta from pytz import timezone import celery import\",\n \"import datetime, timedelta from pytz import timezone import celery import redis from charges\",\n \"Log(object): \\\"\\\"\\\" This encapulates sending to the console/stdout and email all in one.\",\n \"logging.getLogger() root.setLevel(log_level) class Log(object): \\\"\\\"\\\" This encapulates sending to the console/stdout and email\",\n \"def __init__(self): self.log = list() def it(self, string): \\\"\\\"\\\" Add something to the\",\n \"lock = Lock(key=\\\"charge-cards-lock\\\") lock.acquire() log = Log() log.it(\\\"---Starting batch job...\\\") three_days_ago = (datetime.now(tz=zone)\",\n \"Sentry and logs? @celery.task() def charge_cards(): lock = Lock(key=\\\"charge-cards-lock\\\") lock.acquire() log = Log()\",\n \"to the console/stdout and email all in one. \\\"\\\"\\\" def __init__(self): self.log =\",\n \"job of the same type is running. \\\"\\\"\\\" pass class Lock(object): \\\"\\\"\\\" Claim\",\n \"process.\\\") for opportunity in opportunities: if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it(\",\n \"same type is running. \\\"\\\"\\\" pass class Lock(object): \\\"\\\"\\\" Claim an exclusive lock.\",\n \"email all in one. \\\"\\\"\\\" def __init__(self): self.log = list() def it(self, string):\",\n \"subject=subject) class AlreadyExecuting(Exception): \\\"\\\"\\\" Here to show when more than one job of\",\n \"timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object): \\\"\\\"\\\" This encapulates\",\n \"subject = \\\"Batch run\\\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \\\"\\\"\\\" Here to show\",\n \"from util import send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger()\",\n \"import logging from config import ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime,\",\n \"of the same type is running. \\\"\\\"\\\" pass class Lock(object): \\\"\\\"\\\" Claim an\",\n \"= logging.getLogger() root.setLevel(log_level) class Log(object): \\\"\\\"\\\" This encapulates sending to the console/stdout and\",\n \"from npsp import Opportunity from util import send_email zone = timezone(TIMEZONE) log_level =\",\n \"and email all in one. \\\"\\\"\\\" def __init__(self): self.log = list() def it(self,\",\n \"job...\\\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\") today = datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\") opportunities = Opportunity.list(begin=three_days_ago, end=today)\",\n \"opportunity in opportunities: if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\\\"---- Charging\",\n \"to the log. \\\"\\\"\\\" logging.debug(string) self.log.append(string) def send(self): \\\"\\\"\\\" Send the assembled log\",\n \"Send the assembled log out as an email. \\\"\\\"\\\" body = \\\"\\\\n\\\".join(self.log) recipient\",\n \"import Opportunity from util import send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root\",\n \"= list() def it(self, string): \\\"\\\"\\\" Add something to the log. \\\"\\\"\\\" logging.debug(string)\",\n \"exclusive lock. Using Redis. \\\"\\\"\\\" def __init__(self, key): self.key = key self.connection =\",\n \"def __init__(self, key): self.key = key self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key):\",\n \"one. \\\"\\\"\\\" def __init__(self): self.log = list() def it(self, string): \\\"\\\"\\\" Add something\",\n \"send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \\\"\\\"\\\" Here to show when more than one\",\n \"Here to show when more than one job of the same type is\",\n \"send(self): \\\"\\\"\\\" Send the assembled log out as an email. \\\"\\\"\\\" body =\",\n \"(datetime.now(tz=zone) - timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\") today = datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\\\"---Processing charges...\\\") log.it(f\\\"Found\",\n \"def release(self): self.connection.delete(self.key) # TODO stop sending this email and just rely on\",\n \"charges import amount_to_charge, charge, ChargeException from npsp import Opportunity from util import send_email\",\n \"= logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object): \\\"\\\"\\\" This encapulates sending to\",\n \"= key self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\\\"bar\\\",\",\n \"logging from config import ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime, timedelta\",\n \"class Lock(object): \\\"\\\"\\\" Claim an exclusive lock. Using Redis. \\\"\\\"\\\" def __init__(self, key):\",\n \"\\\"\\\"\\\" Claim an exclusive lock. Using Redis. \\\"\\\"\\\" def __init__(self, key): self.key =\",\n \"npsp import Opportunity from util import send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL)\",\n \"send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object):\",\n \"this email and just rely on Sentry and logs? @celery.task() def charge_cards(): lock\",\n \"def send(self): \\\"\\\"\\\" Send the assembled log out as an email. \\\"\\\"\\\" body\",\n \"amount_to_charge, charge, ChargeException from npsp import Opportunity from util import send_email zone =\",\n \"class Log(object): \\\"\\\"\\\" This encapulates sending to the console/stdout and email all in\",\n \"log out as an email. \\\"\\\"\\\" body = \\\"\\\\n\\\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject\",\n \"lock. Using Redis. \\\"\\\"\\\" def __init__(self, key): self.key = key self.connection = redis.from_url(REDIS_URL)\",\n \"just rely on Sentry and logs? @celery.task() def charge_cards(): lock = Lock(key=\\\"charge-cards-lock\\\") lock.acquire()\",\n \"= (datetime.now(tz=zone) - timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\") today = datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\\\"---Processing charges...\\\")\",\n \"charge, ChargeException from npsp import Opportunity from util import send_email zone = timezone(TIMEZONE)\",\n \"root = logging.getLogger() root.setLevel(log_level) class Log(object): \\\"\\\"\\\" This encapulates sending to the console/stdout\",\n \"stop sending this email and just rely on Sentry and logs? @celery.task() def\",\n \"the assembled log out as an email. \\\"\\\"\\\" body = \\\"\\\\n\\\".join(self.log) recipient =\",\n \"run\\\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \\\"\\\"\\\" Here to show when more than\",\n \"self.key = key self.connection = redis.from_url(REDIS_URL) def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key,\",\n \"charges...\\\") log.it(f\\\"Found {len(opportunities)} opportunities available to process.\\\") for opportunity in opportunities: if not\",\n \"an email. \\\"\\\"\\\" body = \\\"\\\\n\\\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \\\"Batch run\\\"\",\n \"log = Log() log.it(\\\"---Starting batch job...\\\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\") today =\",\n \"as an email. \\\"\\\"\\\" body = \\\"\\\\n\\\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \\\"Batch\",\n \"one job of the same type is running. \\\"\\\"\\\" pass class Lock(object): \\\"\\\"\\\"\",\n \"= Lock(key=\\\"charge-cards-lock\\\") lock.acquire() log = Log() log.it(\\\"---Starting batch job...\\\") three_days_ago = (datetime.now(tz=zone) -\",\n \"def it(self, string): \\\"\\\"\\\" Add something to the log. \\\"\\\"\\\" logging.debug(string) self.log.append(string) def\",\n \"Lock(key=\\\"charge-cards-lock\\\") lock.acquire() log = Log() log.it(\\\"---Starting batch job...\\\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\")\",\n \"datetime, timedelta from pytz import timezone import celery import redis from charges import\",\n \"self.log.append(string) def send(self): \\\"\\\"\\\" Send the assembled log out as an email. \\\"\\\"\\\"\",\n \"datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\\\"---Processing charges...\\\") log.it(f\\\"Found {len(opportunities)} opportunities available to process.\\\")\",\n \"self.log = list() def it(self, string): \\\"\\\"\\\" Add something to the log. \\\"\\\"\\\"\",\n \"from charges import amount_to_charge, charge, ChargeException from npsp import Opportunity from util import\",\n \"from datetime import datetime, timedelta from pytz import timezone import celery import redis\",\n \"\\\"\\\"\\\" def __init__(self): self.log = list() def it(self, string): \\\"\\\"\\\" Add something to\",\n \"= datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\") opportunities = Opportunity.list(begin=three_days_ago, end=today) log.it(\\\"---Processing charges...\\\") log.it(f\\\"Found {len(opportunities)} opportunities available to\",\n \"Claim an exclusive lock. Using Redis. \\\"\\\"\\\" def __init__(self, key): self.key = key\",\n \"\\\"\\\"\\\" def __init__(self, key): self.key = key self.connection = redis.from_url(REDIS_URL) def acquire(self): if\",\n \"in opportunities: if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\\\"---- Charging ${amount}\",\n \"timedelta from pytz import timezone import celery import redis from charges import amount_to_charge,\",\n \"LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime, timedelta from pytz import timezone import\",\n \"REDIS_URL, TIMEZONE from datetime import datetime, timedelta from pytz import timezone import celery\",\n \"pytz import timezone import celery import redis from charges import amount_to_charge, charge, ChargeException\",\n \"({opportunity.name})\\\" ) try: charge(opportunity) except ChargeException as e: logging.info(\\\"Batch charge error\\\") e.send_slack_notification() log.send()\",\n \"type is running. \\\"\\\"\\\" pass class Lock(object): \\\"\\\"\\\" Claim an exclusive lock. Using\",\n \"= timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root = logging.getLogger() root.setLevel(log_level) class Log(object): \\\"\\\"\\\" This\",\n \"class AlreadyExecuting(Exception): \\\"\\\"\\\" Here to show when more than one job of the\",\n \"Using Redis. \\\"\\\"\\\" def __init__(self, key): self.key = key self.connection = redis.from_url(REDIS_URL) def\",\n \"continue amount = amount_to_charge(opportunity) log.it( f\\\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\\\" ) try:\",\n \"\\\"\\\"\\\" Send the assembled log out as an email. \\\"\\\"\\\" body = \\\"\\\\n\\\".join(self.log)\",\n \"<filename>batch-tmp.py import logging from config import ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import\",\n \"string): \\\"\\\"\\\" Add something to the log. \\\"\\\"\\\" logging.debug(string) self.log.append(string) def send(self): \\\"\\\"\\\"\",\n \"TODO stop sending this email and just rely on Sentry and logs? @celery.task()\",\n \"if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\\\"bar\\\", time=1200) def release(self): self.connection.delete(self.key) # TODO stop\",\n \"when more than one job of the same type is running. \\\"\\\"\\\" pass\",\n \"for opportunity in opportunities: if not opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\\\"----\",\n \"release(self): self.connection.delete(self.key) # TODO stop sending this email and just rely on Sentry\",\n \"end=today) log.it(\\\"---Processing charges...\\\") log.it(f\\\"Found {len(opportunities)} opportunities available to process.\\\") for opportunity in opportunities:\",\n \"${amount} to {opportunity.stripe_customer} ({opportunity.name})\\\" ) try: charge(opportunity) except ChargeException as e: logging.info(\\\"Batch charge\",\n \"sending to the console/stdout and email all in one. \\\"\\\"\\\" def __init__(self): self.log\",\n \"= Log() log.it(\\\"---Starting batch job...\\\") three_days_ago = (datetime.now(tz=zone) - timedelta(days=10)).strftime(\\\"%Y-%m-%d\\\") today = datetime.now(tz=zone).strftime(\\\"%Y-%m-%d\\\")\",\n \"the console/stdout and email all in one. \\\"\\\"\\\" def __init__(self): self.log = list()\",\n \"\\\"\\\"\\\" logging.debug(string) self.log.append(string) def send(self): \\\"\\\"\\\" Send the assembled log out as an\",\n \"log. \\\"\\\"\\\" logging.debug(string) self.log.append(string) def send(self): \\\"\\\"\\\" Send the assembled log out as\",\n \"def acquire(self): if self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\\\"bar\\\", time=1200) def release(self): self.connection.delete(self.key) #\",\n \"self.connection.get(self.key): raise AlreadyExecuting self.connection.setex(name=self.key, value=\\\"bar\\\", time=1200) def release(self): self.connection.delete(self.key) # TODO stop sending\",\n \"= ACCOUNTING_MAIL_RECIPIENT subject = \\\"Batch run\\\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \\\"\\\"\\\" Here\",\n \"\\\"\\\"\\\" Here to show when more than one job of the same type\",\n \"config import ACCOUNTING_MAIL_RECIPIENT, LOG_LEVEL, REDIS_URL, TIMEZONE from datetime import datetime, timedelta from pytz\",\n \"something to the log. \\\"\\\"\\\" logging.debug(string) self.log.append(string) def send(self): \\\"\\\"\\\" Send the assembled\",\n \"\\\"\\\"\\\" This encapulates sending to the console/stdout and email all in one. \\\"\\\"\\\"\",\n \"\\\"\\\\n\\\".join(self.log) recipient = ACCOUNTING_MAIL_RECIPIENT subject = \\\"Batch run\\\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception):\",\n \"import redis from charges import amount_to_charge, charge, ChargeException from npsp import Opportunity from\",\n \"= \\\"Batch run\\\" send_email(body=body, recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \\\"\\\"\\\" Here to show when\",\n \"amount_to_charge(opportunity) log.it( f\\\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\\\" ) try: charge(opportunity) except ChargeException\",\n \"charge_cards(): lock = Lock(key=\\\"charge-cards-lock\\\") lock.acquire() log = Log() log.it(\\\"---Starting batch job...\\\") three_days_ago =\",\n \"= Opportunity.list(begin=three_days_ago, end=today) log.it(\\\"---Processing charges...\\\") log.it(f\\\"Found {len(opportunities)} opportunities available to process.\\\") for opportunity\",\n \"\\\"\\\"\\\" Add something to the log. \\\"\\\"\\\" logging.debug(string) self.log.append(string) def send(self): \\\"\\\"\\\" Send\",\n \"celery import redis from charges import amount_to_charge, charge, ChargeException from npsp import Opportunity\",\n \"Opportunity from util import send_email zone = timezone(TIMEZONE) log_level = logging.getLevelName(LOG_LEVEL) root =\",\n \"opportunity.stripe_customer: continue amount = amount_to_charge(opportunity) log.it( f\\\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\\\" )\",\n \"= amount_to_charge(opportunity) log.it( f\\\"---- Charging ${amount} to {opportunity.stripe_customer} ({opportunity.name})\\\" ) try: charge(opportunity) except\",\n \"opportunities available to process.\\\") for opportunity in opportunities: if not opportunity.stripe_customer: continue amount\",\n \"recipient=recipient, subject=subject) class AlreadyExecuting(Exception): \\\"\\\"\\\" Here to show when more than one job\"\n]","isConversational":false}}},{"rowIdx":2987,"cells":{"ngram":{"kind":"list like","value":["# pylint: disable=unused-argument ): \"\"\"TODO for this test. Either: 1. Not use own","been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: #","not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as","import operator import os from unittest.mock import patch import pytest import requests from","= '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get,","new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth","testing running with geth in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def","the eth-scan contract there. But probably (1) makes more sense \"\"\" msg =","addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501","first time all balances were queried every time. That slowed everything down (https://github.com/rotki/rotki/issues/678).","msg = 'Should be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None,","regression test for that behaviour TODO: Is this still needed? Shouldn't it just","'<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[],","os from unittest.mock import patch import pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName","{'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH',","from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not","reason='Not testing running with geth in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True])","sense \"\"\" msg = 'Should be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is","ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], )","should not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances')","is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \"\"\"Due to a programming mistake at","eth-scan contract there. But probably (1) makes more sense \"\"\" msg = 'Should","\"\"\" msg = 'Should be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not","blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is not","\"\"\"TODO for this test. Either: 1. Not use own chain but use a","at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument","after the first time all balances were queried every time. That slowed everything","[True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ): \"\"\"TODO for this test.","normal open node for this test. 2. If we use own chain, deploy","in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not","addr1 not in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should not have been","this test. 2. If we use own chain, deploy the eth-scan contract there.","test for that behaviour TODO: Is this still needed? Shouldn't it just be","etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], )","{'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with","(1) makes more sense \"\"\" msg = 'Should be connected to ethereum node'","add lots of mocks to make it not be a slow test \"\"\"","accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock:","with geth in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain,","blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should not have been called' addr2 =","test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \"\"\"Due to a programming mistake at addition and removal of blockchain accounts","== 'nt', reason='Not testing running with geth in windows at the moment', )","test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add)","'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in","eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch(","not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account =","everything down (https://github.com/rotki/rotki/issues/678). This is a regression test for that behaviour TODO: Is","That slowed everything down (https://github.com/rotki/rotki/issues/678). This is a regression test for that behaviour","windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint:","Either: 1. Not use own chain but use a normal open node for","test. 2. If we use own chain, deploy the eth-scan contract there. But","assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in","for that behaviour TODO: Is this still needed? Shouldn't it just be removed?","rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing running with geth in","os.name == 'nt', reason='Not testing running with geth in windows at the moment',","2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch,","If we use own chain, deploy the eth-scan contract there. But probably (1)","at addition and removal of blockchain accounts after the first time all balances","to a programming mistake at addition and removal of blockchain accounts after the","A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt',","own chain but use a normal open node for this test. 2. If","just be removed? Had to add lots of mocks to make it not","'blockchain.query_balances() should not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain,","behaviour TODO: Is this still needed? Shouldn't it just be removed? Had to","'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not","blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None","blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances()","blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \"\"\"Due to a programming mistake","original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts(","@pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ): \"\"\"TODO for this","time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This is a regression test for that","But probably (1) makes more sense \"\"\" msg = 'Should be connected to","inquirer, # pylint: disable=unused-argument ): \"\"\"TODO for this test. Either: 1. Not use","None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \"\"\"Due to a","def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append',","addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2:","ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain,","mock.call_count == 0, 'blockchain.query_balances() should not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with","not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is","account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert","time all balances were queried every time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This","needed? Shouldn't it just be removed? Had to add lots of mocks to","this still needed? Shouldn't it just be removed? Had to add lots of","but use a normal open node for this test. 2. If we use","for this test. 2. If we use own chain, deploy the eth-scan contract","blockchain, inquirer, # pylint: disable=unused-argument ): \"\"\"TODO for this test. Either: 1. Not","in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should not have been called' addr2","a normal open node for this test. 2. If we use own chain,","rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing","0, 'blockchain.query_balances() should not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch,","with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1],","not in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should not have been called'","as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in","and removal of blockchain accounts after the first time all balances were queried","balances were queried every time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This is a","[0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \"\"\"Due to a programming mistake at addition and removal of","Not use own chain but use a normal open node for this test.","pylint: disable=unused-argument ): \"\"\"TODO for this test. Either: 1. Not use own chain","blockchain accounts after the first time all balances were queried every time. That","blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals","blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances()","import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing running","chain but use a normal open node for this test. 2. If we","etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch,","running with geth in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances(","original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM,","moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ): \"\"\"TODO","own chain, deploy the eth-scan contract there. But probably (1) makes more sense","): \"\"\"TODO for this test. Either: 1. Not use own chain but use","import os from unittest.mock import patch import pytest import requests from rotkehlchen.chain.ethereum.manager import","mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth","accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should","test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ): \"\"\"TODO for this test. Either: 1.","'BTC' not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value","operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts',","patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1","1. Not use own chain but use a normal open node for this","@pytest.mark.skipif( os.name == 'nt', reason='Not testing running with geth in windows at the","import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing running with geth in windows","from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing running with geth","1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800,","ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC'","open node for this test. 2. If we use own chain, deploy the","removed? Had to add lots of mocks to make it not be a","use a normal open node for this test. 2. If we use own","etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], )","to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert","called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa:","= 'Should be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg","to make it not be a slow test \"\"\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2","node for this test. 2. If we use own chain, deploy the eth-scan","the first time all balances were queried every time. That slowed everything down","Is this still needed? Shouldn't it just be removed? Had to add lots","TODO: Is this still needed? Shouldn't it just be removed? Had to add","patch import pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC","test. Either: 1. Not use own chain but use a normal open node","programming mistake at addition and removal of blockchain accounts after the first time","connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances()","it just be removed? Had to add lots of mocks to make it","= mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch","deploy the eth-scan contract there. But probably (1) makes more sense \"\"\" msg","this test. Either: 1. Not use own chain but use a normal open","etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch:","a regression test for that behaviour TODO: Is this still needed? Shouldn't it","of mocks to make it not be a slow test \"\"\" addr1 =","in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, #","blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0])","addition and removal of blockchain accounts after the first time all balances were","import NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import","makes more sense \"\"\" msg = 'Should be connected to ethereum node' assert","import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name ==",") assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: #","make it not be a slow test \"\"\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 =","a programming mistake at addition and removal of blockchain accounts after the first","be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain):","assert addr1 not in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should not have","every time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This is a regression test for","mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch =","\"\"\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1},","SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing running with geth in windows at","blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth assert mock.call_count == 0,","assert mock.call_count == 0, 'blockchain.query_balances() should not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2'","etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr2], )","there. But probably (1) makes more sense \"\"\" msg = 'Should be connected","2. If we use own chain, deploy the eth-scan contract there. But probably","test \"\"\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH':","= patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert","more sense \"\"\" msg = 'Should be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN)","'0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM,","with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr2],","addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan,","probably (1) makes more sense \"\"\" msg = 'Should be connected to ethereum","have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock:","= '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH':","the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ):","for this test. Either: 1. Not use own chain but use a normal","assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def","noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth assert mock.call_count","not None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \"\"\"Due to","import patch import pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import","mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing running with","None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account = '<KEY>'","be removed? Had to add lots of mocks to make it not be","it not be a slow test \"\"\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>'","is a regression test for that behaviour TODO: Is this still needed? Shouldn't","'0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}},","from unittest.mock import patch import pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName from","we use own chain, deploy the eth-scan contract there. But probably (1) makes","blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as","E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth assert mock.call_count ==","def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \"\"\"Due to a programming mistake at addition and removal of blockchain","== 0, 'blockchain.query_balances() should not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch,",") @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ): \"\"\"TODO for","with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with etherscan_patch,","mocks to make it not be a slow test \"\"\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c'","not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \"\"\"Due to a programming mistake at addition","'nt', reason='Not testing running with geth in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend',","mistake at addition and removal of blockchain accounts after the first time all","unittest.mock import patch import pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets",") with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with","None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \"\"\"Due to a programming mistake at addition and","be a slow test \"\"\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch =","slow test \"\"\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1:",") assert addr1 not in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should not","of blockchain accounts after the first time all balances were queried every time.","msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account,","addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501","from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif(","patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1","still needed? Shouldn't it just be removed? Had to add lots of mocks","accounts after the first time all balances were queried every time. That slowed","use own chain but use a normal open node for this test. 2.","import pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC from","Shouldn't it just be removed? Had to add lots of mocks to make","use own chain, deploy the eth-scan contract there. But probably (1) makes more","@pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \"\"\"Due to a programming mistake at addition and removal","def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ): \"\"\"TODO for this test. Either:","NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain","blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain):","rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing","blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM,","down (https://github.com/rotki/rotki/issues/678). This is a regression test for that behaviour TODO: Is this","blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances')","lots of mocks to make it not be a slow test \"\"\" addr1","slowed everything down (https://github.com/rotki/rotki/issues/678). This is a regression test for that behaviour TODO:","queried every time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This is a regression test","'<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is","# noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth assert","\"\"\"Due to a programming mistake at addition and removal of blockchain accounts after","This is a regression test for that behaviour TODO: Is this still needed?","'Should be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def","assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \"\"\"Due to a programming","not be a slow test \"\"\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch","to add lots of mocks to make it not be a slow test","'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is not None","import requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import","is not None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \"\"\"Due","= '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.add_blockchain_accounts(","is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account","that behaviour TODO: Is this still needed? Shouldn't it just be removed? Had","ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert","requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query","a slow test \"\"\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query(","all balances were queried every time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This is","contract there. But probably (1) makes more sense \"\"\" msg = 'Should be","assert 'BTC' not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert","chain, deploy the eth-scan contract there. But probably (1) makes more sense \"\"\"","node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not","in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts(","geth in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer,","disable=unused-argument ): \"\"\"TODO for this test. Either: 1. Not use own chain but","= '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount","operator import os from unittest.mock import patch import pytest import requests from rotkehlchen.chain.ethereum.manager","rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name","were queried every time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This is a regression",") ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1],","removal of blockchain accounts after the first time all balances were queried every","assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa:","from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from","(https://github.com/rotki/rotki/issues/678). This is a regression test for that behaviour TODO: Is this still","pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain","Had to add lots of mocks to make it not be a slow","addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, )"],"string":"[\n \"# pylint: disable=unused-argument ): \\\"\\\"\\\"TODO for this test. Either: 1. Not use own\",\n \"been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: #\",\n \"not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as\",\n \"import operator import os from unittest.mock import patch import pytest import requests from\",\n \"= '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get,\",\n \"new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth\",\n \"testing running with geth in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def\",\n \"the eth-scan contract there. But probably (1) makes more sense \\\"\\\"\\\" msg =\",\n \"addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501\",\n \"first time all balances were queried every time. That slowed everything down (https://github.com/rotki/rotki/issues/678).\",\n \"msg = 'Should be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None,\",\n \"regression test for that behaviour TODO: Is this still needed? Shouldn't it just\",\n \"'<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[],\",\n \"os from unittest.mock import patch import pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName\",\n \"{'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH',\",\n \"from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not\",\n \"reason='Not testing running with geth in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True])\",\n \"sense \\\"\\\"\\\" msg = 'Should be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is\",\n \"ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], )\",\n \"should not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances')\",\n \"is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \\\"\\\"\\\"Due to a programming mistake at\",\n \"eth-scan contract there. But probably (1) makes more sense \\\"\\\"\\\" msg = 'Should\",\n \"\\\"\\\"\\\" msg = 'Should be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not\",\n \"blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is not\",\n \"\\\"\\\"\\\"TODO for this test. Either: 1. Not use own chain but use a\",\n \"at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument\",\n \"after the first time all balances were queried every time. That slowed everything\",\n \"[True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ): \\\"\\\"\\\"TODO for this test.\",\n \"normal open node for this test. 2. If we use own chain, deploy\",\n \"in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not\",\n \"addr1 not in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should not have been\",\n \"this test. 2. If we use own chain, deploy the eth-scan contract there.\",\n \"test for that behaviour TODO: Is this still needed? Shouldn't it just be\",\n \"etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], )\",\n \"{'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with\",\n \"(1) makes more sense \\\"\\\"\\\" msg = 'Should be connected to ethereum node'\",\n \"add lots of mocks to make it not be a slow test \\\"\\\"\\\"\",\n \"accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock:\",\n \"with geth in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain,\",\n \"blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should not have been called' addr2 =\",\n \"test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \\\"\\\"\\\"Due to a programming mistake at addition and removal of blockchain accounts\",\n \"== 'nt', reason='Not testing running with geth in windows at the moment', )\",\n \"test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add)\",\n \"'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in\",\n \"eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch(\",\n \"not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account =\",\n \"everything down (https://github.com/rotki/rotki/issues/678). This is a regression test for that behaviour TODO: Is\",\n \"That slowed everything down (https://github.com/rotki/rotki/issues/678). This is a regression test for that behaviour\",\n \"windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint:\",\n \"Either: 1. Not use own chain but use a normal open node for\",\n \"test. 2. If we use own chain, deploy the eth-scan contract there. But\",\n \"assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in\",\n \"for that behaviour TODO: Is this still needed? Shouldn't it just be removed?\",\n \"rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing running with geth in\",\n \"os.name == 'nt', reason='Not testing running with geth in windows at the moment',\",\n \"2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch,\",\n \"If we use own chain, deploy the eth-scan contract there. But probably (1)\",\n \"at addition and removal of blockchain accounts after the first time all balances\",\n \"to a programming mistake at addition and removal of blockchain accounts after the\",\n \"A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt',\",\n \"own chain but use a normal open node for this test. 2. If\",\n \"just be removed? Had to add lots of mocks to make it not\",\n \"'blockchain.query_balances() should not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain,\",\n \"behaviour TODO: Is this still needed? Shouldn't it just be removed? Had to\",\n \"'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not\",\n \"blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None\",\n \"blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances()\",\n \"blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \\\"\\\"\\\"Due to a programming mistake\",\n \"original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts(\",\n \"@pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ): \\\"\\\"\\\"TODO for this\",\n \"time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This is a regression test for that\",\n \"But probably (1) makes more sense \\\"\\\"\\\" msg = 'Should be connected to\",\n \"inquirer, # pylint: disable=unused-argument ): \\\"\\\"\\\"TODO for this test. Either: 1. Not use\",\n \"None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \\\"\\\"\\\"Due to a\",\n \"def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append',\",\n \"addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2:\",\n \"ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain,\",\n \"mock.call_count == 0, 'blockchain.query_balances() should not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with\",\n \"not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is\",\n \"account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert\",\n \"time all balances were queried every time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This\",\n \"needed? Shouldn't it just be removed? Had to add lots of mocks to\",\n \"this still needed? Shouldn't it just be removed? Had to add lots of\",\n \"but use a normal open node for this test. 2. If we use\",\n \"for this test. 2. If we use own chain, deploy the eth-scan contract\",\n \"blockchain, inquirer, # pylint: disable=unused-argument ): \\\"\\\"\\\"TODO for this test. Either: 1. Not\",\n \"in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should not have been called' addr2\",\n \"a normal open node for this test. 2. If we use own chain,\",\n \"rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing\",\n \"0, 'blockchain.query_balances() should not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch,\",\n \"with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1],\",\n \"not in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should not have been called'\",\n \"as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in\",\n \"and removal of blockchain accounts after the first time all balances were queried\",\n \"balances were queried every time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This is a\",\n \"[0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \\\"\\\"\\\"Due to a programming mistake at addition and removal of\",\n \"Not use own chain but use a normal open node for this test.\",\n \"pylint: disable=unused-argument ): \\\"\\\"\\\"TODO for this test. Either: 1. Not use own chain\",\n \"blockchain accounts after the first time all balances were queried every time. That\",\n \"blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals\",\n \"blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances()\",\n \"import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing running\",\n \"chain but use a normal open node for this test. 2. If we\",\n \"etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch,\",\n \"running with geth in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances(\",\n \"original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM,\",\n \"moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ): \\\"\\\"\\\"TODO\",\n \"own chain, deploy the eth-scan contract there. But probably (1) makes more sense\",\n \"): \\\"\\\"\\\"TODO for this test. Either: 1. Not use own chain but use\",\n \"import os from unittest.mock import patch import pytest import requests from rotkehlchen.chain.ethereum.manager import\",\n \"mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth\",\n \"accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should\",\n \"test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ): \\\"\\\"\\\"TODO for this test. Either: 1.\",\n \"'BTC' not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value\",\n \"operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts',\",\n \"patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1\",\n \"1. Not use own chain but use a normal open node for this\",\n \"@pytest.mark.skipif( os.name == 'nt', reason='Not testing running with geth in windows at the\",\n \"import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing running with geth in windows\",\n \"from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing running with geth\",\n \"1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800,\",\n \"ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC'\",\n \"open node for this test. 2. If we use own chain, deploy the\",\n \"removed? Had to add lots of mocks to make it not be a\",\n \"use a normal open node for this test. 2. If we use own\",\n \"etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], )\",\n \"to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert\",\n \"called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa:\",\n \"= 'Should be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg\",\n \"to make it not be a slow test \\\"\\\"\\\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2\",\n \"node for this test. 2. If we use own chain, deploy the eth-scan\",\n \"the first time all balances were queried every time. That slowed everything down\",\n \"Is this still needed? Shouldn't it just be removed? Had to add lots\",\n \"TODO: Is this still needed? Shouldn't it just be removed? Had to add\",\n \"patch import pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC\",\n \"test. Either: 1. Not use own chain but use a normal open node\",\n \"programming mistake at addition and removal of blockchain accounts after the first time\",\n \"connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances()\",\n \"it just be removed? Had to add lots of mocks to make it\",\n \"= mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch\",\n \"deploy the eth-scan contract there. But probably (1) makes more sense \\\"\\\"\\\" msg\",\n \"this test. Either: 1. Not use own chain but use a normal open\",\n \"etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch:\",\n \"a regression test for that behaviour TODO: Is this still needed? Shouldn't it\",\n \"of mocks to make it not be a slow test \\\"\\\"\\\" addr1 =\",\n \"in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, #\",\n \"blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0])\",\n \"addition and removal of blockchain accounts after the first time all balances were\",\n \"import NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import\",\n \"makes more sense \\\"\\\"\\\" msg = 'Should be connected to ethereum node' assert\",\n \"import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name ==\",\n \") assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: #\",\n \"make it not be a slow test \\\"\\\"\\\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 =\",\n \"a programming mistake at addition and removal of blockchain accounts after the first\",\n \"be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain):\",\n \"assert addr1 not in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should not have\",\n \"every time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This is a regression test for\",\n \"mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch =\",\n \"\\\"\\\"\\\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1},\",\n \"SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing running with geth in windows at\",\n \"blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth assert mock.call_count == 0,\",\n \"assert mock.call_count == 0, 'blockchain.query_balances() should not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2'\",\n \"etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr2], )\",\n \"there. But probably (1) makes more sense \\\"\\\"\\\" msg = 'Should be connected\",\n \"2. If we use own chain, deploy the eth-scan contract there. But probably\",\n \"test \\\"\\\"\\\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH':\",\n \"= patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert\",\n \"more sense \\\"\\\"\\\" msg = 'Should be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN)\",\n \"'0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM,\",\n \"with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr2],\",\n \"addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan,\",\n \"probably (1) makes more sense \\\"\\\"\\\" msg = 'Should be connected to ethereum\",\n \"have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock:\",\n \"= '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH':\",\n \"the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ):\",\n \"for this test. Either: 1. Not use own chain but use a normal\",\n \"assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def\",\n \"noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth assert mock.call_count\",\n \"not None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \\\"\\\"\\\"Due to\",\n \"import patch import pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import\",\n \"mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name == 'nt', reason='Not testing running with\",\n \"None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account = '<KEY>'\",\n \"be removed? Had to add lots of mocks to make it not be\",\n \"it not be a slow test \\\"\\\"\\\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>'\",\n \"is a regression test for that behaviour TODO: Is this still needed? Shouldn't\",\n \"'0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1: {'ETH': 1}, addr2: {'ETH': 2}},\",\n \"from unittest.mock import patch import pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName from\",\n \"we use own chain, deploy the eth-scan contract there. But probably (1) makes\",\n \"blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as\",\n \"E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth assert mock.call_count ==\",\n \"def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \\\"\\\"\\\"Due to a programming mistake at addition and removal of blockchain\",\n \"== 0, 'blockchain.query_balances() should not have been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch,\",\n \") @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ): \\\"\\\"\\\"TODO for\",\n \"with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with etherscan_patch,\",\n \"mocks to make it not be a slow test \\\"\\\"\\\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c'\",\n \"not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \\\"\\\"\\\"Due to a programming mistake at addition\",\n \"'nt', reason='Not testing running with geth in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend',\",\n \"mistake at addition and removal of blockchain accounts after the first time all\",\n \"unittest.mock import patch import pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets\",\n \") with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with\",\n \"None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \\\"\\\"\\\"Due to a programming mistake at addition and\",\n \"be a slow test \\\"\\\"\\\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch =\",\n \"slow test \\\"\\\"\\\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query( eth_map={addr1:\",\n \") assert addr1 not in blockchain.accounts.eth assert mock.call_count == 0, 'blockchain.query_balances() should not\",\n \"of blockchain accounts after the first time all balances were queried every time.\",\n \"msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account,\",\n \"addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501\",\n \"from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif(\",\n \"patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1\",\n \"still needed? Shouldn't it just be removed? Had to add lots of mocks\",\n \"accounts after the first time all balances were queried every time. That slowed\",\n \"use own chain but use a normal open node for this test. 2.\",\n \"import pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC from\",\n \"Shouldn't it just be removed? Had to add lots of mocks to make\",\n \"use own chain, deploy the eth-scan contract there. But probably (1) makes more\",\n \"@pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \\\"\\\"\\\"Due to a programming mistake at addition and removal\",\n \"def test_eth_connection_initial_balances( blockchain, inquirer, # pylint: disable=unused-argument ): \\\"\\\"\\\"TODO for this test. Either:\",\n \"NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain\",\n \"blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain):\",\n \"rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing\",\n \"blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM,\",\n \"down (https://github.com/rotki/rotki/issues/678). This is a regression test for that behaviour TODO: Is this\",\n \"blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances')\",\n \"lots of mocks to make it not be a slow test \\\"\\\"\\\" addr1\",\n \"slowed everything down (https://github.com/rotki/rotki/issues/678). This is a regression test for that behaviour TODO:\",\n \"queried every time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This is a regression test\",\n \"'<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is\",\n \"# noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert addr1 not in blockchain.accounts.eth assert\",\n \"\\\"\\\"\\\"Due to a programming mistake at addition and removal of blockchain accounts after\",\n \"This is a regression test for that behaviour TODO: Is this still needed?\",\n \"'Should be connected to ethereum node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def\",\n \"assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \\\"\\\"\\\"Due to a programming\",\n \"not be a slow test \\\"\\\"\\\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch\",\n \"to add lots of mocks to make it not be a slow test\",\n \"'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount is not None\",\n \"import requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import\",\n \"is not None assert blockchain.totals[A_BTC].amount is not None @pytest.mark.parametrize('number_of_eth_accounts', [0]) def test_add_remove_account_assure_all_balances_not_always_queried(blockchain): \\\"\\\"\\\"Due\",\n \"= '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.add_blockchain_accounts(\",\n \"is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not in blockchain.totals account\",\n \"that behaviour TODO: Is this still needed? Shouldn't it just be removed? Had\",\n \"ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1], ) assert\",\n \"requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query\",\n \"a slow test \\\"\\\"\\\" addr1 = '0xe188c6BEBB81b96A65aa20dDB9e2aef62627fa4c' addr2 = '<KEY>' etherscan_patch = mock_etherscan_query(\",\n \"all balances were queried every time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This is\",\n \"contract there. But probably (1) makes more sense \\\"\\\"\\\" msg = 'Should be\",\n \"assert 'BTC' not in blockchain.totals account = '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert\",\n \"chain, deploy the eth-scan contract there. But probably (1) makes more sense \\\"\\\"\\\"\",\n \"node' assert blockchain.ethereum.web3_mapping.get(NodeName.OWN) is not None, msg def test_query_btc_balances(blockchain): blockchain.query_btc_balances() assert 'BTC' not\",\n \"in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa: E501 blockchain.remove_blockchain_accounts(\",\n \"geth in windows at the moment', ) @pytest.mark.parametrize('have_blockchain_backend', [True]) def test_eth_connection_initial_balances( blockchain, inquirer,\",\n \"disable=unused-argument ): \\\"\\\"\\\"TODO for this test. Either: 1. Not use own chain but\",\n \"= '<KEY>' blockchain.modify_btc_account(account, 'append', operator.add) blockchain.query_btc_balances() assert blockchain.totals[A_BTC].usd_value is not None assert blockchain.totals[A_BTC].amount\",\n \"operator import os from unittest.mock import patch import pytest import requests from rotkehlchen.chain.ethereum.manager\",\n \"rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from rotkehlchen.typing import SupportedBlockchain @pytest.mark.skipif( os.name\",\n \"were queried every time. That slowed everything down (https://github.com/rotki/rotki/issues/678). This is a regression\",\n \") ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, ) with etherscan_patch, ethtokens_max_chunks_patch: blockchain.add_blockchain_accounts( blockchain=SupportedBlockchain.ETHEREUM, accounts=[addr1],\",\n \"removal of blockchain accounts after the first time all balances were queried every\",\n \"assert addr1 in blockchain.accounts.eth with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: # noqa:\",\n \"from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain import mock_etherscan_query from\",\n \"(https://github.com/rotki/rotki/issues/678). This is a regression test for that behaviour TODO: Is this still\",\n \"pytest import requests from rotkehlchen.chain.ethereum.manager import NodeName from rotkehlchen.constants.assets import A_BTC from rotkehlchen.tests.utils.blockchain\",\n \"Had to add lots of mocks to make it not be a slow\",\n \"addr2: {'ETH': 2}}, etherscan=blockchain.ethereum.etherscan, original_requests_get=requests.get, original_queries=[], ) ethtokens_max_chunks_patch = patch( 'rotkehlchen.chain.ethereum.tokens.ETHERSCAN_MAX_TOKEN_CHUNK_LENGTH', new=800, )\"\n]","isConversational":false}}},{"rowIdx":2988,"cells":{"ngram":{"kind":"list like","value":["(c) I am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass if","Created: 02/11/2017 # Copyright: (c) I am 2017 # Licence: <your licence> #-------------------------------------------------------------------------------","02/11/2017 # Copyright: (c) I am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def","module1 # Purpose: # # Author: I am # # Created: 02/11/2017 #","# Purpose: # # Author: I am # # Created: 02/11/2017 # Copyright:","am # # Created: 02/11/2017 # Copyright: (c) I am 2017 # Licence:","am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass if __name__ ==","# Created: 02/11/2017 # Copyright: (c) I am 2017 # Licence: <your licence>","# Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass if __name__ == '__main__': main()","Name: module1 # Purpose: # # Author: I am # # Created: 02/11/2017","# Author: I am # # Created: 02/11/2017 # Copyright: (c) I am","# Copyright: (c) I am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main():","Copyright: (c) I am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass","#------------------------------------------------------------------------------- # Name: module1 # Purpose: # # Author: I am # #","# Name: module1 # Purpose: # # Author: I am # # Created:","Purpose: # # Author: I am # # Created: 02/11/2017 # Copyright: (c)","2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass if __name__ == '__main__':","I am # # Created: 02/11/2017 # Copyright: (c) I am 2017 #","I am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass if __name__","# # Author: I am # # Created: 02/11/2017 # Copyright: (c) I","# # Created: 02/11/2017 # Copyright: (c) I am 2017 # Licence: <your","Author: I am # # Created: 02/11/2017 # Copyright: (c) I am 2017"],"string":"[\n \"(c) I am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass if\",\n \"Created: 02/11/2017 # Copyright: (c) I am 2017 # Licence: <your licence> #-------------------------------------------------------------------------------\",\n \"02/11/2017 # Copyright: (c) I am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def\",\n \"module1 # Purpose: # # Author: I am # # Created: 02/11/2017 #\",\n \"# Purpose: # # Author: I am # # Created: 02/11/2017 # Copyright:\",\n \"am # # Created: 02/11/2017 # Copyright: (c) I am 2017 # Licence:\",\n \"am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass if __name__ ==\",\n \"# Created: 02/11/2017 # Copyright: (c) I am 2017 # Licence: <your licence>\",\n \"# Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass if __name__ == '__main__': main()\",\n \"Name: module1 # Purpose: # # Author: I am # # Created: 02/11/2017\",\n \"# Author: I am # # Created: 02/11/2017 # Copyright: (c) I am\",\n \"# Copyright: (c) I am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main():\",\n \"Copyright: (c) I am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass\",\n \"#------------------------------------------------------------------------------- # Name: module1 # Purpose: # # Author: I am # #\",\n \"# Name: module1 # Purpose: # # Author: I am # # Created:\",\n \"Purpose: # # Author: I am # # Created: 02/11/2017 # Copyright: (c)\",\n \"2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass if __name__ == '__main__':\",\n \"I am # # Created: 02/11/2017 # Copyright: (c) I am 2017 #\",\n \"I am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass if __name__\",\n \"# # Author: I am # # Created: 02/11/2017 # Copyright: (c) I\",\n \"# # Created: 02/11/2017 # Copyright: (c) I am 2017 # Licence: <your\",\n \"Author: I am # # Created: 02/11/2017 # Copyright: (c) I am 2017\"\n]","isConversational":false}}},{"rowIdx":2989,"cells":{"ngram":{"kind":"list like","value":["SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view = \"auth.login\" moment = Moment()","# migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp","import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\ if app.config['ELASTICSEARCH_URL'] else None","\"uq_%(table_name)s_%(column_0_name)s\", \"ck\": \"ck_%(table_name)s_%(constraint_name)s\", \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\", \"pk\": \"pk_%(table_name)s\" } metadata = MetaData(naming_convention=convention) db =","'ix_%(column_0_label)s', \"uq\": \"uq_%(table_name)s_%(column_0_name)s\", \"ck\": \"ck_%(table_name)s_%(constraint_name)s\", \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\", \"pk\": \"pk_%(table_name)s\" } metadata = MetaData(naming_convention=convention)","flask_login import LoginManager from flask_moment import Moment from flask_misaka import Misaka from flask_bootstrap","from app.main import bp as main_bp app.register_blueprint(main_bp) from app.cli import bp as cli_bp","db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as","import logging from logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch convention = {","from elasticsearch import Elasticsearch convention = { \"ix\": 'ix_%(column_0_label)s', \"uq\": \"uq_%(table_name)s_%(column_0_name)s\", \"ck\": \"ck_%(table_name)s_%(constraint_name)s\",","\"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\" ) ) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler) app.logger.setLevel(logging.INFO) app.logger.info(\"Moviedb startup\") return","import Elasticsearch convention = { \"ix\": 'ix_%(column_0_label)s', \"uq\": \"uq_%(table_name)s_%(column_0_name)s\", \"ck\": \"ck_%(table_name)s_%(constraint_name)s\", \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\",","Config from sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate","from logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch convention = { \"ix\": 'ix_%(column_0_label)s',","= MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view =","LoginManager from flask_moment import Moment from flask_misaka import Misaka from flask_bootstrap import Bootstrap","\"pk_%(table_name)s\" } metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate() login =","bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\ if app.config['ELASTICSEARCH_URL'] else None from","else None from app import models if not app.debug and not app.testing: if","Migrate from flask_login import LoginManager from flask_moment import Moment from flask_misaka import Misaka","from app.errors import bp as errors_bp app.register_blueprint(errors_bp) from app.auth import bp as auth_bp","with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) #","login = LoginManager() login.login_view = \"auth.login\" moment = Moment() md = Misaka() bootstrap","from config import Config from sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy from","import Misaka from flask_bootstrap import Bootstrap import os import logging from logging.handlers import","file_handler.setFormatter( logging.Formatter( \"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\" ) ) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler) app.logger.setLevel(logging.INFO) app.logger.info(\"Moviedb","from app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\ if app.config['ELASTICSEARCH_URL']","db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db)","app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app,","app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\ if app.config['ELASTICSEARCH_URL'] else None from app import models","url_prefix='/auth') from app.main import bp as main_bp app.register_blueprint(main_bp) from app.cli import bp as","as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\ if app.config['ELASTICSEARCH_URL'] else None from app","maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\" ) ) file_handler.setLevel(logging.INFO)","flask_bootstrap import Bootstrap import os import logging from logging.handlers import RotatingFileHandler from elasticsearch","metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view","Bootstrap import os import logging from logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch","{ \"ix\": 'ix_%(column_0_label)s', \"uq\": \"uq_%(table_name)s_%(column_0_name)s\", \"ck\": \"ck_%(table_name)s_%(constraint_name)s\", \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\", \"pk\": \"pk_%(table_name)s\" } metadata","models if not app.debug and not app.testing: if not os.path.exists(\"logs\"): os.mkdir(\"logs\") file_handler =","Migrate() login = LoginManager() login.login_view = \"auth.login\" moment = Moment() md = Misaka()","Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else:","\"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\", \"pk\": \"pk_%(table_name)s\" } metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate =","login.login_view = \"auth.login\" moment = Moment() md = Misaka() bootstrap = Bootstrap() def","cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\ if app.config['ELASTICSEARCH_URL'] else None from app import","file_handler = RotatingFileHandler( \"logs/moviedb.log\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \"%(asctime)s %(levelname)s: %(message)s [in","migrate = Migrate() login = LoginManager() login.login_view = \"auth.login\" moment = Moment() md","import Flask from config import Config from sqlalchemy import MetaData from flask_sqlalchemy import","bp as main_bp app.register_blueprint(main_bp) from app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch =","flask_moment import Moment from flask_misaka import Misaka from flask_bootstrap import Bootstrap import os","SQLAlchemy from flask_migrate import Migrate from flask_login import LoginManager from flask_moment import Moment","app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app,","app.register_blueprint(errors_bp) from app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp","None from app import models if not app.debug and not app.testing: if not","elasticsearch import Elasticsearch convention = { \"ix\": 'ix_%(column_0_label)s', \"uq\": \"uq_%(table_name)s_%(column_0_name)s\", \"ck\": \"ck_%(table_name)s_%(constraint_name)s\", \"fk\":","config import Config from sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate","os import logging from logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch convention =","as errors_bp app.register_blueprint(errors_bp) from app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main","= Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with","app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\ if app.config['ELASTICSEARCH_URL'] else","import bp as errors_bp app.register_blueprint(errors_bp) from app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth')","import Migrate from flask_login import LoginManager from flask_moment import Moment from flask_misaka import","\"ck_%(table_name)s_%(constraint_name)s\", \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\", \"pk\": \"pk_%(table_name)s\" } metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate","app.testing: if not os.path.exists(\"logs\"): os.mkdir(\"logs\") file_handler = RotatingFileHandler( \"logs/moviedb.log\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter(","Moment from flask_misaka import Misaka from flask_bootstrap import Bootstrap import os import logging","%(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\" ) ) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler) app.logger.setLevel(logging.INFO) app.logger.info(\"Moviedb startup\") return app","Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername ==","Flask from config import Config from sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy","db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp app.register_blueprint(errors_bp) from","bootstrap = Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if","from flask_misaka import Misaka from flask_bootstrap import Bootstrap import os import logging from","md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp app.register_blueprint(errors_bp) from app.auth import bp","\"pk\": \"pk_%(table_name)s\" } metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate() login","app import models if not app.debug and not app.testing: if not os.path.exists(\"logs\"): os.mkdir(\"logs\")","flask_misaka import Misaka from flask_bootstrap import Bootstrap import os import logging from logging.handlers","'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app)","app.main import bp as main_bp app.register_blueprint(main_bp) from app.cli import bp as cli_bp app.register_blueprint(cli_bp)","} metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager()","from app import models if not app.debug and not app.testing: if not os.path.exists(\"logs\"):","\"logs/moviedb.log\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\" ) )","import models if not app.debug and not app.testing: if not os.path.exists(\"logs\"): os.mkdir(\"logs\") file_handler","from sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from","app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\ if app.config['ELASTICSEARCH_URL'] else None from app import models if","as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as main_bp app.register_blueprint(main_bp) from app.cli","Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\ if app.config['ELASTICSEARCH_URL'] else None from app import models if not app.debug","moment = Moment() md = Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app =","not app.testing: if not os.path.exists(\"logs\"): os.mkdir(\"logs\") file_handler = RotatingFileHandler( \"logs/moviedb.log\", maxBytes=10240, backupCount=10 )","LoginManager() login.login_view = \"auth.login\" moment = Moment() md = Misaka() bootstrap = Bootstrap()","def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite':","flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_login import LoginManager from flask_moment",") file_handler.setFormatter( logging.Formatter( \"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\" ) ) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler) app.logger.setLevel(logging.INFO)","import MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_login import","import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as main_bp app.register_blueprint(main_bp)","= Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True)","app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as main_bp","app.errors import bp as errors_bp app.register_blueprint(errors_bp) from app.auth import bp as auth_bp app.register_blueprint(auth_bp,","from flask import Flask from config import Config from sqlalchemy import MetaData from","Misaka from flask_bootstrap import Bootstrap import os import logging from logging.handlers import RotatingFileHandler","from flask_moment import Moment from flask_misaka import Misaka from flask_bootstrap import Bootstrap import","import RotatingFileHandler from elasticsearch import Elasticsearch convention = { \"ix\": 'ix_%(column_0_label)s', \"uq\": \"uq_%(table_name)s_%(column_0_name)s\",","\"ix\": 'ix_%(column_0_label)s', \"uq\": \"uq_%(table_name)s_%(column_0_name)s\", \"ck\": \"ck_%(table_name)s_%(constraint_name)s\", \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\", \"pk\": \"pk_%(table_name)s\" } metadata =","and not app.testing: if not os.path.exists(\"logs\"): os.mkdir(\"logs\") file_handler = RotatingFileHandler( \"logs/moviedb.log\", maxBytes=10240, backupCount=10","\"ck\": \"ck_%(table_name)s_%(constraint_name)s\", \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\", \"pk\": \"pk_%(table_name)s\" } metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata)","= Moment() md = Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app = Flask(__name__)","Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context():","= Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\ if app.config['ELASTICSEARCH_URL'] else None from app import models if not","not os.path.exists(\"logs\"): os.mkdir(\"logs\") file_handler = RotatingFileHandler( \"logs/moviedb.log\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \"%(asctime)s","os.mkdir(\"logs\") file_handler = RotatingFileHandler( \"logs/moviedb.log\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \"%(asctime)s %(levelname)s: %(message)s","bp as errors_bp app.register_blueprint(errors_bp) from app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from","backupCount=10 ) file_handler.setFormatter( logging.Formatter( \"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\" ) ) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler)","migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app)","import os import logging from logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch convention","db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from","from flask_migrate import Migrate from flask_login import LoginManager from flask_moment import Moment from","MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_login import LoginManager","Moment() md = Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config)","moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp app.register_blueprint(errors_bp) from app.auth import","login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp app.register_blueprint(errors_bp) from app.auth","if not os.path.exists(\"logs\"): os.mkdir(\"logs\") file_handler = RotatingFileHandler( \"logs/moviedb.log\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter(","from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_login import LoginManager from","= SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view = \"auth.login\" moment =","render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors","errors_bp app.register_blueprint(errors_bp) from app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import","from app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as","= RotatingFileHandler( \"logs/moviedb.log\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\"","else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import","RotatingFileHandler( \"logs/moviedb.log\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\" )","import bp as main_bp app.register_blueprint(main_bp) from app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch","app.config['ELASTICSEARCH_URL'] else None from app import models if not app.debug and not app.testing:","Elasticsearch convention = { \"ix\": 'ix_%(column_0_label)s', \"uq\": \"uq_%(table_name)s_%(column_0_name)s\", \"ck\": \"ck_%(table_name)s_%(constraint_name)s\", \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\", \"pk\":","sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_login","= \"auth.login\" moment = Moment() md = Misaka() bootstrap = Bootstrap() def create_app(config_class=Config):","= Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername","if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db)","migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp","auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as main_bp app.register_blueprint(main_bp) from app.cli import","bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as main_bp app.register_blueprint(main_bp) from","if not app.debug and not app.testing: if not os.path.exists(\"logs\"): os.mkdir(\"logs\") file_handler = RotatingFileHandler(","logging.Formatter( \"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\" ) ) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler) app.logger.setLevel(logging.INFO) app.logger.info(\"Moviedb startup\")","bootstrap.init_app(app) from app.errors import bp as errors_bp app.register_blueprint(errors_bp) from app.auth import bp as","= { \"ix\": 'ix_%(column_0_label)s', \"uq\": \"uq_%(table_name)s_%(column_0_name)s\", \"ck\": \"ck_%(table_name)s_%(constraint_name)s\", \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\", \"pk\": \"pk_%(table_name)s\" }","\"auth.login\" moment = Moment() md = Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app","RotatingFileHandler from elasticsearch import Elasticsearch convention = { \"ix\": 'ix_%(column_0_label)s', \"uq\": \"uq_%(table_name)s_%(column_0_name)s\", \"ck\":","create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app,","logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch convention = { \"ix\": 'ix_%(column_0_label)s', \"uq\":","from flask_login import LoginManager from flask_moment import Moment from flask_misaka import Misaka from","db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app)","migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp app.register_blueprint(errors_bp)","\"uq\": \"uq_%(table_name)s_%(column_0_name)s\", \"ck\": \"ck_%(table_name)s_%(constraint_name)s\", \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\", \"pk\": \"pk_%(table_name)s\" } metadata = MetaData(naming_convention=convention) db","== 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app)","= Migrate() login = LoginManager() login.login_view = \"auth.login\" moment = Moment() md =","os.path.exists(\"logs\"): os.mkdir(\"logs\") file_handler = RotatingFileHandler( \"logs/moviedb.log\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \"%(asctime)s %(levelname)s:","flask import Flask from config import Config from sqlalchemy import MetaData from flask_sqlalchemy","MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view = \"auth.login\"","app.debug and not app.testing: if not os.path.exists(\"logs\"): os.mkdir(\"logs\") file_handler = RotatingFileHandler( \"logs/moviedb.log\", maxBytes=10240,","\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\", \"pk\": \"pk_%(table_name)s\" } metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate()","app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as main_bp app.register_blueprint(main_bp) from app.cli import bp","logging from logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch convention = { \"ix\":","md = Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app)","flask_migrate import Migrate from flask_login import LoginManager from flask_moment import Moment from flask_misaka","= LoginManager() login.login_view = \"auth.login\" moment = Moment() md = Misaka() bootstrap =","app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db,","main_bp app.register_blueprint(main_bp) from app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\","import Bootstrap import os import logging from logging.handlers import RotatingFileHandler from elasticsearch import","as main_bp app.register_blueprint(main_bp) from app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']])","import LoginManager from flask_moment import Moment from flask_misaka import Misaka from flask_bootstrap import","convention = { \"ix\": 'ix_%(column_0_label)s', \"uq\": \"uq_%(table_name)s_%(column_0_name)s\", \"ck\": \"ck_%(table_name)s_%(constraint_name)s\", \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\", \"pk\": \"pk_%(table_name)s\"","not app.debug and not app.testing: if not os.path.exists(\"logs\"): os.mkdir(\"logs\") file_handler = RotatingFileHandler( \"logs/moviedb.log\",","from flask_bootstrap import Bootstrap import os import logging from logging.handlers import RotatingFileHandler from","import Moment from flask_misaka import Misaka from flask_bootstrap import Bootstrap import os import","app.register_blueprint(main_bp) from app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\ if","\\ if app.config['ELASTICSEARCH_URL'] else None from app import models if not app.debug and","import Config from sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate import","import SQLAlchemy from flask_migrate import Migrate from flask_login import LoginManager from flask_moment import","if app.config['ELASTICSEARCH_URL'] else None from app import models if not app.debug and not","db = SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view = \"auth.login\" moment"],"string":"[\n \"SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view = \\\"auth.login\\\" moment = Moment()\",\n \"# migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp\",\n \"import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\\ if app.config['ELASTICSEARCH_URL'] else None\",\n \"\\\"uq_%(table_name)s_%(column_0_name)s\\\", \\\"ck\\\": \\\"ck_%(table_name)s_%(constraint_name)s\\\", \\\"fk\\\": \\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\", \\\"pk\\\": \\\"pk_%(table_name)s\\\" } metadata = MetaData(naming_convention=convention) db =\",\n \"'ix_%(column_0_label)s', \\\"uq\\\": \\\"uq_%(table_name)s_%(column_0_name)s\\\", \\\"ck\\\": \\\"ck_%(table_name)s_%(constraint_name)s\\\", \\\"fk\\\": \\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\", \\\"pk\\\": \\\"pk_%(table_name)s\\\" } metadata = MetaData(naming_convention=convention)\",\n \"flask_login import LoginManager from flask_moment import Moment from flask_misaka import Misaka from flask_bootstrap\",\n \"from app.main import bp as main_bp app.register_blueprint(main_bp) from app.cli import bp as cli_bp\",\n \"db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as\",\n \"import logging from logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch convention = {\",\n \"from elasticsearch import Elasticsearch convention = { \\\"ix\\\": 'ix_%(column_0_label)s', \\\"uq\\\": \\\"uq_%(table_name)s_%(column_0_name)s\\\", \\\"ck\\\": \\\"ck_%(table_name)s_%(constraint_name)s\\\",\",\n \"\\\"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\\\" ) ) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler) app.logger.setLevel(logging.INFO) app.logger.info(\\\"Moviedb startup\\\") return\",\n \"import Elasticsearch convention = { \\\"ix\\\": 'ix_%(column_0_label)s', \\\"uq\\\": \\\"uq_%(table_name)s_%(column_0_name)s\\\", \\\"ck\\\": \\\"ck_%(table_name)s_%(constraint_name)s\\\", \\\"fk\\\": \\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\",\",\n \"Config from sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate\",\n \"from logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch convention = { \\\"ix\\\": 'ix_%(column_0_label)s',\",\n \"= MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view =\",\n \"LoginManager from flask_moment import Moment from flask_misaka import Misaka from flask_bootstrap import Bootstrap\",\n \"\\\"pk_%(table_name)s\\\" } metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate() login =\",\n \"bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\\ if app.config['ELASTICSEARCH_URL'] else None from\",\n \"else None from app import models if not app.debug and not app.testing: if\",\n \"Migrate from flask_login import LoginManager from flask_moment import Moment from flask_misaka import Misaka\",\n \"from app.errors import bp as errors_bp app.register_blueprint(errors_bp) from app.auth import bp as auth_bp\",\n \"with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) #\",\n \"login = LoginManager() login.login_view = \\\"auth.login\\\" moment = Moment() md = Misaka() bootstrap\",\n \"from config import Config from sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy from\",\n \"import Misaka from flask_bootstrap import Bootstrap import os import logging from logging.handlers import\",\n \"file_handler.setFormatter( logging.Formatter( \\\"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\\\" ) ) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler) app.logger.setLevel(logging.INFO) app.logger.info(\\\"Moviedb\",\n \"from app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\\ if app.config['ELASTICSEARCH_URL']\",\n \"db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db)\",\n \"app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app,\",\n \"app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\\ if app.config['ELASTICSEARCH_URL'] else None from app import models\",\n \"url_prefix='/auth') from app.main import bp as main_bp app.register_blueprint(main_bp) from app.cli import bp as\",\n \"as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\\ if app.config['ELASTICSEARCH_URL'] else None from app\",\n \"maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \\\"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\\\" ) ) file_handler.setLevel(logging.INFO)\",\n \"flask_bootstrap import Bootstrap import os import logging from logging.handlers import RotatingFileHandler from elasticsearch\",\n \"metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view\",\n \"Bootstrap import os import logging from logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch\",\n \"{ \\\"ix\\\": 'ix_%(column_0_label)s', \\\"uq\\\": \\\"uq_%(table_name)s_%(column_0_name)s\\\", \\\"ck\\\": \\\"ck_%(table_name)s_%(constraint_name)s\\\", \\\"fk\\\": \\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\", \\\"pk\\\": \\\"pk_%(table_name)s\\\" } metadata\",\n \"models if not app.debug and not app.testing: if not os.path.exists(\\\"logs\\\"): os.mkdir(\\\"logs\\\") file_handler =\",\n \"Migrate() login = LoginManager() login.login_view = \\\"auth.login\\\" moment = Moment() md = Misaka()\",\n \"Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else:\",\n \"\\\"fk\\\": \\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\", \\\"pk\\\": \\\"pk_%(table_name)s\\\" } metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate =\",\n \"login.login_view = \\\"auth.login\\\" moment = Moment() md = Misaka() bootstrap = Bootstrap() def\",\n \"cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\\ if app.config['ELASTICSEARCH_URL'] else None from app import\",\n \"file_handler = RotatingFileHandler( \\\"logs/moviedb.log\\\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \\\"%(asctime)s %(levelname)s: %(message)s [in\",\n \"migrate = Migrate() login = LoginManager() login.login_view = \\\"auth.login\\\" moment = Moment() md\",\n \"import Flask from config import Config from sqlalchemy import MetaData from flask_sqlalchemy import\",\n \"bp as main_bp app.register_blueprint(main_bp) from app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch =\",\n \"flask_moment import Moment from flask_misaka import Misaka from flask_bootstrap import Bootstrap import os\",\n \"SQLAlchemy from flask_migrate import Migrate from flask_login import LoginManager from flask_moment import Moment\",\n \"app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app,\",\n \"app.register_blueprint(errors_bp) from app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp\",\n \"None from app import models if not app.debug and not app.testing: if not\",\n \"elasticsearch import Elasticsearch convention = { \\\"ix\\\": 'ix_%(column_0_label)s', \\\"uq\\\": \\\"uq_%(table_name)s_%(column_0_name)s\\\", \\\"ck\\\": \\\"ck_%(table_name)s_%(constraint_name)s\\\", \\\"fk\\\":\",\n \"config import Config from sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate\",\n \"os import logging from logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch convention =\",\n \"as errors_bp app.register_blueprint(errors_bp) from app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main\",\n \"= Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with\",\n \"app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\\ if app.config['ELASTICSEARCH_URL'] else\",\n \"import bp as errors_bp app.register_blueprint(errors_bp) from app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth')\",\n \"import Migrate from flask_login import LoginManager from flask_moment import Moment from flask_misaka import\",\n \"\\\"ck_%(table_name)s_%(constraint_name)s\\\", \\\"fk\\\": \\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\", \\\"pk\\\": \\\"pk_%(table_name)s\\\" } metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate\",\n \"app.testing: if not os.path.exists(\\\"logs\\\"): os.mkdir(\\\"logs\\\") file_handler = RotatingFileHandler( \\\"logs/moviedb.log\\\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter(\",\n \"Moment from flask_misaka import Misaka from flask_bootstrap import Bootstrap import os import logging\",\n \"%(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\\\" ) ) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler) app.logger.setLevel(logging.INFO) app.logger.info(\\\"Moviedb startup\\\") return app\",\n \"Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername ==\",\n \"Flask from config import Config from sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy\",\n \"db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp app.register_blueprint(errors_bp) from\",\n \"bootstrap = Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if\",\n \"from flask_misaka import Misaka from flask_bootstrap import Bootstrap import os import logging from\",\n \"md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp app.register_blueprint(errors_bp) from app.auth import bp\",\n \"\\\"pk\\\": \\\"pk_%(table_name)s\\\" } metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate() login\",\n \"app import models if not app.debug and not app.testing: if not os.path.exists(\\\"logs\\\"): os.mkdir(\\\"logs\\\")\",\n \"flask_misaka import Misaka from flask_bootstrap import Bootstrap import os import logging from logging.handlers\",\n \"'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app)\",\n \"app.main import bp as main_bp app.register_blueprint(main_bp) from app.cli import bp as cli_bp app.register_blueprint(cli_bp)\",\n \"} metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager()\",\n \"from app import models if not app.debug and not app.testing: if not os.path.exists(\\\"logs\\\"):\",\n \"\\\"logs/moviedb.log\\\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \\\"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\\\" ) )\",\n \"import models if not app.debug and not app.testing: if not os.path.exists(\\\"logs\\\"): os.mkdir(\\\"logs\\\") file_handler\",\n \"from sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from\",\n \"app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\\ if app.config['ELASTICSEARCH_URL'] else None from app import models if\",\n \"as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as main_bp app.register_blueprint(main_bp) from app.cli\",\n \"Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\\ if app.config['ELASTICSEARCH_URL'] else None from app import models if not app.debug\",\n \"moment = Moment() md = Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app =\",\n \"not app.testing: if not os.path.exists(\\\"logs\\\"): os.mkdir(\\\"logs\\\") file_handler = RotatingFileHandler( \\\"logs/moviedb.log\\\", maxBytes=10240, backupCount=10 )\",\n \"LoginManager() login.login_view = \\\"auth.login\\\" moment = Moment() md = Misaka() bootstrap = Bootstrap()\",\n \"def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite':\",\n \"flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_login import LoginManager from flask_moment\",\n \") file_handler.setFormatter( logging.Formatter( \\\"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\\\" ) ) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler) app.logger.setLevel(logging.INFO)\",\n \"import MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_login import\",\n \"import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as main_bp app.register_blueprint(main_bp)\",\n \"= Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True)\",\n \"app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as main_bp\",\n \"app.errors import bp as errors_bp app.register_blueprint(errors_bp) from app.auth import bp as auth_bp app.register_blueprint(auth_bp,\",\n \"from flask import Flask from config import Config from sqlalchemy import MetaData from\",\n \"Misaka from flask_bootstrap import Bootstrap import os import logging from logging.handlers import RotatingFileHandler\",\n \"from flask_moment import Moment from flask_misaka import Misaka from flask_bootstrap import Bootstrap import\",\n \"import RotatingFileHandler from elasticsearch import Elasticsearch convention = { \\\"ix\\\": 'ix_%(column_0_label)s', \\\"uq\\\": \\\"uq_%(table_name)s_%(column_0_name)s\\\",\",\n \"\\\"ix\\\": 'ix_%(column_0_label)s', \\\"uq\\\": \\\"uq_%(table_name)s_%(column_0_name)s\\\", \\\"ck\\\": \\\"ck_%(table_name)s_%(constraint_name)s\\\", \\\"fk\\\": \\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\", \\\"pk\\\": \\\"pk_%(table_name)s\\\" } metadata =\",\n \"and not app.testing: if not os.path.exists(\\\"logs\\\"): os.mkdir(\\\"logs\\\") file_handler = RotatingFileHandler( \\\"logs/moviedb.log\\\", maxBytes=10240, backupCount=10\",\n \"\\\"ck\\\": \\\"ck_%(table_name)s_%(constraint_name)s\\\", \\\"fk\\\": \\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\", \\\"pk\\\": \\\"pk_%(table_name)s\\\" } metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata)\",\n \"= Moment() md = Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app = Flask(__name__)\",\n \"Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context():\",\n \"= Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\\ if app.config['ELASTICSEARCH_URL'] else None from app import models if not\",\n \"not os.path.exists(\\\"logs\\\"): os.mkdir(\\\"logs\\\") file_handler = RotatingFileHandler( \\\"logs/moviedb.log\\\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \\\"%(asctime)s\",\n \"os.mkdir(\\\"logs\\\") file_handler = RotatingFileHandler( \\\"logs/moviedb.log\\\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \\\"%(asctime)s %(levelname)s: %(message)s\",\n \"bp as errors_bp app.register_blueprint(errors_bp) from app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from\",\n \"backupCount=10 ) file_handler.setFormatter( logging.Formatter( \\\"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\\\" ) ) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler)\",\n \"migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app)\",\n \"import os import logging from logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch convention\",\n \"db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from\",\n \"from flask_migrate import Migrate from flask_login import LoginManager from flask_moment import Moment from\",\n \"MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_login import LoginManager\",\n \"Moment() md = Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config)\",\n \"moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp app.register_blueprint(errors_bp) from app.auth import\",\n \"login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp app.register_blueprint(errors_bp) from app.auth\",\n \"if not os.path.exists(\\\"logs\\\"): os.mkdir(\\\"logs\\\") file_handler = RotatingFileHandler( \\\"logs/moviedb.log\\\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter(\",\n \"from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_login import LoginManager from\",\n \"= SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view = \\\"auth.login\\\" moment =\",\n \"render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors\",\n \"errors_bp app.register_blueprint(errors_bp) from app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import\",\n \"from app.auth import bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as\",\n \"= RotatingFileHandler( \\\"logs/moviedb.log\\\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \\\"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\\\"\",\n \"else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import\",\n \"RotatingFileHandler( \\\"logs/moviedb.log\\\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \\\"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\\\" )\",\n \"import bp as main_bp app.register_blueprint(main_bp) from app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch\",\n \"app.config['ELASTICSEARCH_URL'] else None from app import models if not app.debug and not app.testing:\",\n \"Elasticsearch convention = { \\\"ix\\\": 'ix_%(column_0_label)s', \\\"uq\\\": \\\"uq_%(table_name)s_%(column_0_name)s\\\", \\\"ck\\\": \\\"ck_%(table_name)s_%(constraint_name)s\\\", \\\"fk\\\": \\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\", \\\"pk\\\":\",\n \"sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_login\",\n \"= \\\"auth.login\\\" moment = Moment() md = Misaka() bootstrap = Bootstrap() def create_app(config_class=Config):\",\n \"= Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername\",\n \"if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db)\",\n \"migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp\",\n \"auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as main_bp app.register_blueprint(main_bp) from app.cli import\",\n \"bp as auth_bp app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as main_bp app.register_blueprint(main_bp) from\",\n \"if not app.debug and not app.testing: if not os.path.exists(\\\"logs\\\"): os.mkdir(\\\"logs\\\") file_handler = RotatingFileHandler(\",\n \"logging.Formatter( \\\"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\\\" ) ) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler) app.logger.setLevel(logging.INFO) app.logger.info(\\\"Moviedb startup\\\")\",\n \"bootstrap.init_app(app) from app.errors import bp as errors_bp app.register_blueprint(errors_bp) from app.auth import bp as\",\n \"= { \\\"ix\\\": 'ix_%(column_0_label)s', \\\"uq\\\": \\\"uq_%(table_name)s_%(column_0_name)s\\\", \\\"ck\\\": \\\"ck_%(table_name)s_%(constraint_name)s\\\", \\\"fk\\\": \\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\", \\\"pk\\\": \\\"pk_%(table_name)s\\\" }\",\n \"\\\"auth.login\\\" moment = Moment() md = Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app\",\n \"RotatingFileHandler from elasticsearch import Elasticsearch convention = { \\\"ix\\\": 'ix_%(column_0_label)s', \\\"uq\\\": \\\"uq_%(table_name)s_%(column_0_name)s\\\", \\\"ck\\\":\",\n \"create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app,\",\n \"logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch convention = { \\\"ix\\\": 'ix_%(column_0_label)s', \\\"uq\\\":\",\n \"from flask_login import LoginManager from flask_moment import Moment from flask_misaka import Misaka from\",\n \"db.engine.url.drivername == 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app)\",\n \"migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp app.register_blueprint(errors_bp)\",\n \"\\\"uq\\\": \\\"uq_%(table_name)s_%(column_0_name)s\\\", \\\"ck\\\": \\\"ck_%(table_name)s_%(constraint_name)s\\\", \\\"fk\\\": \\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\", \\\"pk\\\": \\\"pk_%(table_name)s\\\" } metadata = MetaData(naming_convention=convention) db\",\n \"== 'sqlite': migrate.init_app(app, db, render_as_batch=True) else: migrate.init_app(app, db) # migrate.init_app(app, db) login.init_app(app) moment.init_app(app)\",\n \"= Migrate() login = LoginManager() login.login_view = \\\"auth.login\\\" moment = Moment() md =\",\n \"os.path.exists(\\\"logs\\\"): os.mkdir(\\\"logs\\\") file_handler = RotatingFileHandler( \\\"logs/moviedb.log\\\", maxBytes=10240, backupCount=10 ) file_handler.setFormatter( logging.Formatter( \\\"%(asctime)s %(levelname)s:\",\n \"flask import Flask from config import Config from sqlalchemy import MetaData from flask_sqlalchemy\",\n \"MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view = \\\"auth.login\\\"\",\n \"app.debug and not app.testing: if not os.path.exists(\\\"logs\\\"): os.mkdir(\\\"logs\\\") file_handler = RotatingFileHandler( \\\"logs/moviedb.log\\\", maxBytes=10240,\",\n \"\\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\", \\\"pk\\\": \\\"pk_%(table_name)s\\\" } metadata = MetaData(naming_convention=convention) db = SQLAlchemy(metadata=metadata) migrate = Migrate()\",\n \"app.register_blueprint(auth_bp, url_prefix='/auth') from app.main import bp as main_bp app.register_blueprint(main_bp) from app.cli import bp\",\n \"logging from logging.handlers import RotatingFileHandler from elasticsearch import Elasticsearch convention = { \\\"ix\\\":\",\n \"md = Misaka() bootstrap = Bootstrap() def create_app(config_class=Config): app = Flask(__name__) app.config.from_object(Config) db.init_app(app)\",\n \"flask_migrate import Migrate from flask_login import LoginManager from flask_moment import Moment from flask_misaka\",\n \"= LoginManager() login.login_view = \\\"auth.login\\\" moment = Moment() md = Misaka() bootstrap =\",\n \"app = Flask(__name__) app.config.from_object(Config) db.init_app(app) with app.app_context(): if db.engine.url.drivername == 'sqlite': migrate.init_app(app, db,\",\n \"main_bp app.register_blueprint(main_bp) from app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\\\",\n \"import Bootstrap import os import logging from logging.handlers import RotatingFileHandler from elasticsearch import\",\n \"as main_bp app.register_blueprint(main_bp) from app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']])\",\n \"import LoginManager from flask_moment import Moment from flask_misaka import Misaka from flask_bootstrap import\",\n \"convention = { \\\"ix\\\": 'ix_%(column_0_label)s', \\\"uq\\\": \\\"uq_%(table_name)s_%(column_0_name)s\\\", \\\"ck\\\": \\\"ck_%(table_name)s_%(constraint_name)s\\\", \\\"fk\\\": \\\"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\\\", \\\"pk\\\": \\\"pk_%(table_name)s\\\"\",\n \"not app.debug and not app.testing: if not os.path.exists(\\\"logs\\\"): os.mkdir(\\\"logs\\\") file_handler = RotatingFileHandler( \\\"logs/moviedb.log\\\",\",\n \"from flask_bootstrap import Bootstrap import os import logging from logging.handlers import RotatingFileHandler from\",\n \"import Moment from flask_misaka import Misaka from flask_bootstrap import Bootstrap import os import\",\n \"app.register_blueprint(main_bp) from app.cli import bp as cli_bp app.register_blueprint(cli_bp) app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \\\\ if\",\n \"\\\\ if app.config['ELASTICSEARCH_URL'] else None from app import models if not app.debug and\",\n \"import Config from sqlalchemy import MetaData from flask_sqlalchemy import SQLAlchemy from flask_migrate import\",\n \"import SQLAlchemy from flask_migrate import Migrate from flask_login import LoginManager from flask_moment import\",\n \"if app.config['ELASTICSEARCH_URL'] else None from app import models if not app.debug and not\",\n \"db = SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view = \\\"auth.login\\\" moment\"\n]","isConversational":false}}},{"rowIdx":2990,"cells":{"ngram":{"kind":"list like","value":["sampled (s,pi,z)'s from the buffer # ~ 0.1s # TODO: if error, set","== config.period_token: new_array[i, :j + 1] = 1 break elif array[i, j] ==","np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time) # decompresses sampled pi's #","(s,pi,z)'s from the buffer # ~ 0.1s # TODO: if error, set a","for i in range(len(x)): if x[i] > 0.01: print(i, x[i], translate[i]) break''' if","= np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i in range((config.batch_size // 2)): for","will be modified appropriately _, pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch =","latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt = config.model_path + '/model-454.pth'","mask will be modified appropriately _, pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch","for a dramatic speedup. num_segments = 8 for i in range(num_segments): tmp.append( r[(config.batch_size","betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*') latest_file = None","start_iter = int(start_iter) else: model.apply(initialize) start_iter = 0 count = 0 for iter","np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi # creating a mask","import rankdata from lstm import Model, initialize from Optim import ScheduledOptim # import","config.total_iterations): print('iteration: %s' % iter) #if (iter + 1) % 100000 == 0:","[] pi_tmp = [] z_tmp = [] df = cur_row - count '''x","for i in range((config.batch_size // 2)): for j in range(config.max_length): if pi[i, j,","= int(start_iter) else: model.apply(initialize) start_iter = 0 count = 0 for iter in","df) t_inf = time() count = cur_row if cur_row >= config.buffer_size: r =","== 0: # meaning the padding; place -1 padding new_pi[i, j, :] =","minibatch def generate_mask(array): new_array = np.zeros_like(array) for i in range(len(array)): for j in","np.zeros_like(array) for i in range(len(array)): for j in range(len(array[i])): if j == len(array[i])","t_inf) / df) t_inf = time() count = cur_row if cur_row >= config.buffer_size:","meaning the padding; place -1 padding new_pi[i, j, :] = -1 else: #","if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt = config.model_path","(time() - start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path +",":].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi # creating a mask for loss","= model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2)","nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\"grad update: %s seconds\" % (time() - t2))","eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*') latest_file = None if list_of_files:","len(array[i]) - 1: new_array[i, :] = 1 elif array[i, j] == config.period_token: new_array[i,","new_array = np.zeros_like(array) for i in range(len(array)): for j in range(len(array[i])): if j","from lstm import Model, initialize from Optim import ScheduledOptim # import _pickle as","for j in range(config.max_length): if pi[i, j, 0] == -1: # meaning the","t_inf = time() count = cur_row if cur_row >= config.buffer_size: r = np.sort(","2) // num_segments * i:(config.batch_size // 2) // num_segments * (i + 1)])","+ 1) % 100000 == 0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) #","j, :]) == 0: # meaning the padding; place -1 padding new_pi[i, j,","= max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt)","time() if count != 0 and df >= 1000: print('time required for 32","0 elif pi[i, j, 0] == -2 or sum(pi[i, j, :]) == 0:","// config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v,","else: model.apply(initialize) start_iter = 0 count = 0 for iter in range(start_iter, config.total_iterations):","cache = [] for i in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device),","p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*')","%s' % iter) #if (iter + 1) % 100000 == 0: # lr_scheduler.step()","model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*') latest_file","new_pi[i, j, :] = -1 else: # Beware that np.bincount's bin is [0,1,...min_length-1]","p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path +","if cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False))","count != 0 and df >= 1000: print('time required for 32 self-play games:","',time() - start_time) # decompresses sampled pi's # takes about 0.005s new_pi =","config.blank_token: new_array[i, :j] = 1 break return new_array def pi_mask(array): array = array[:,","(0, 1)), 'constant') return generate_mask(array) # pi_tmp isn't modified here, since the mask","0: # meaning the padding; place -1 padding new_pi[i, j, :] = -1","/ 500000 for i in range(len(x)): if x[i] > 0.01: print(i, x[i], translate[i])","padding; place -1 padding new_pi[i, j, :] = -1 else: # Beware that","= 1 break elif array[i, j] == config.blank_token: new_array[i, :j] = 1 break","8 times for a dramatic speedup. num_segments = 8 for i in range(num_segments):","'constant') return generate_mask(array) # pi_tmp isn't modified here, since the mask will be","* i:(config.batch_size // 2) // num_segments * (i + 1)]) for i in","pi_mask(array): array = array[:, 1:] array = np.pad(array, ((0, 0), (0, 1)), 'constant')","iter) #if (iter + 1) % 100000 == 0: # lr_scheduler.step() start_time =","v) * z_mask , 1) / torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip)","* pi_mask + loss_value(z, v) * z_mask , 1) / torch.sum(z_mask, 1)) loss.backward()","set a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\"buffer\", \"r\") as","convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true *","(-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time()","+= [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache = model(s_batch,","step_size=200, gamma=0.1) # 20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()),","#lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda","-2 or sum(pi[i, j, :]) == 0: # meaning the padding; place -1","z_buffer = f['/z'] s_tmp = [] pi_tmp = [] z_tmp = [] df","-1 padding new_pi[i, j, :] = -1 else: # Beware that np.bincount's bin","start_time) # decompresses sampled pi's # takes about 0.005s new_pi = np.zeros(((config.batch_size //","model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-' + str(iter +","config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient update model.train()","cPickle # np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model = model.to(config.device) #optim =","# 20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9,","TODO: if error, set a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with","elif array[i, j] == config.period_token: new_array[i, :j + 1] = 1 break elif","print('time required for 32 self-play games: ', 32 * (time() - t_inf) /","time: ',time() - start_time) # decompresses sampled pi's # takes about 0.005s new_pi","or sum(pi[i, j, :]) == 0: # meaning the padding; place -1 padding","-1 else: # Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :] =","np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i in range((config.batch_size // 2)): for j","be modified appropriately _, pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate(","torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true - y_pred)","the buffer # ~ 0.1s # TODO: if error, set a lock #","device=config.device) t2 = time() # gradient update model.train() cache = [] for i","np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() #","= convert(pi_mask) z_mask = convert(z_mask) z = convert(z) pi = convert(pi) loss =","tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true,","config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache","#optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M","= np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)): if x[i] > 0.01: print(i,","model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200,","= 1 break return new_array def pi_mask(array): array = array[:, 1:] array =","i in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch =","loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\"grad update: %s seconds\" %","print(\"grad update: %s seconds\" % (time() - t2)) print(\"iteration: %s seconds\" % (time()","time import time, sleep import h5py import torch import torch.nn as nn import","= [] z_tmp = [] df = cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int))","% 100000 == 0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads the","** 2 pi_mask = convert(pi_mask) z_mask = convert(z_mask) z = convert(z) pi =",":] = -1 else: # Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j,","= model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter = 0 count = 0","time() optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s from the buffer # ~","np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x):","import random from time import time, sleep import h5py import torch import torch.nn","20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98),","model = Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler","def generate_mask(array): new_array = np.zeros_like(array) for i in range(len(array)): for j in range(len(array[i])):","+ 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...])","creating a mask for loss function and preparing a minibatch def generate_mask(array): new_array","lstm import Model, initialize from Optim import ScheduledOptim # import _pickle as cPickle","t2)) print(\"iteration: %s seconds\" % (time() - start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer':","r[(config.batch_size // 2) // num_segments * i:(config.batch_size // 2) // num_segments * (i","return (y_true - y_pred) ** 2 pi_mask = convert(pi_mask) z_mask = convert(z_mask) z","= int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp =","= [] df = cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for","1)), 'constant') return generate_mask(array) # pi_tmp isn't modified here, since the mask will","new_array[i, :j] = 1 break return new_array def pi_mask(array): array = array[:, 1:]","/ torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\"grad update:","num_segments * i:(config.batch_size // 2) // num_segments * (i + 1)]) for i","np import random from time import time, sleep import h5py import torch import","np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) /","as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model = model.to(config.device) #optim","the padding; place -1 padding new_pi[i, j, :] = -1 else: # Beware","sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-' + str(iter + 1) + '.pth')","optimizer import glob import os #from scipy.stats import rankdata from lstm import Model,","import os #from scipy.stats import rankdata from lstm import Model, initialize from Optim","config.clip) print(gn) optim.step() optim.zero_grad() print(\"grad update: %s seconds\" % (time() - t2)) print(\"iteration:","== len(array[i]) - 1: new_array[i, :] = 1 elif array[i, j] == config.period_token:","as nn import torch.optim as optimizer import glob import os #from scipy.stats import","s_batch = convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return","ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files","// 2), replace=False)) tmp = [] # randomly sample rows 8 times for","seconds\" % (time() - start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint,","lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\"buffer\", \"r\") as f: cur_row","model.apply(initialize) start_iter = 0 count = 0 for iter in range(start_iter, config.total_iterations): print('iteration:","for loss function and preparing a minibatch def generate_mask(array): new_array = np.zeros_like(array) for","checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize)","f['/z'] s_tmp = [] pi_tmp = [] z_tmp = [] df = cur_row","# reads the randomly sampled (s,pi,z)'s from the buffer # ~ 0.1s #","print('io time: ',time() - start_time) # decompresses sampled pi's # takes about 0.005s","'rb')) with h5py.File(\"buffer\", \"r\") as f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer","0 and df >= 1000: print('time required for 32 self-play games: ', 32","np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp = [] # randomly sample rows","x[i], translate[i]) break''' if count == 0: count = cur_row t_inf = time()","= array[:, 1:] array = np.pad(array, ((0, 0), (0, 1)), 'constant') return generate_mask(array)","numpy as np import random from time import time, sleep import h5py import","32 * (time() - t_inf) / df) t_inf = time() count = cur_row","import time, sleep import h5py import torch import torch.nn as nn import torch.optim","= cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)):","# TODO: if error, set a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb'))","return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient update model.train() cache = []","+ '/*') latest_file = None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt =","latest_file = None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file #","2), replace=False)) tmp = [] # randomly sample rows 8 times for a","0) z = np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time) # decompresses","# ~ 0.1s # TODO: if error, set a lock # translate, _","minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi # creating a mask for loss function","new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi","i in range(len(array)): for j in range(len(array[i])): if j == len(array[i]) - 1:","= [] pi_tmp = [] z_tmp = [] df = cur_row - count","i in range(len(x)): if x[i] > 0.01: print(i, x[i], translate[i]) break''' if count","config.simulation_num_per_move pi = new_pi # creating a mask for loss function and preparing","iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09),","j] == config.blank_token: new_array[i, :j] = 1 break return new_array def pi_mask(array): array","function and preparing a minibatch def generate_mask(array): new_array = np.zeros_like(array) for i in","np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)),",":] = 1 elif array[i, j] == config.period_token: new_array[i, :j + 1] =","cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8),","and df >= 1000: print('time required for 32 self-play games: ', 32 *","= np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else: r = np.sort( np.random.choice(list(range(0,","loss_value(y_true, y_pred): return (y_true - y_pred) ** 2 pi_mask = convert(pi_mask) z_mask =","model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def","% (time() - start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path","cur_row if cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2),","optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s from the buffer # ~ 0.1s","0) pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) # print('io time: ',time()","gamma=0.1) # 20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr,","'/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter","policy, v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred","- y_pred) ** 2 pi_mask = convert(pi_mask) z_mask = convert(z_mask) z = convert(z)","return new_array def pi_mask(array): array = array[:, 1:] array = np.pad(array, ((0, 0),","print('iteration: %s' % iter) #if (iter + 1) % 100000 == 0: #","int(start_iter) else: model.apply(initialize) start_iter = 0 count = 0 for iter in range(start_iter,","else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp = []","None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt =","0.01: print(i, x[i], translate[i]) break''' if count == 0: count = cur_row t_inf","import torch.optim as optimizer import glob import os #from scipy.stats import rankdata from","# randomly sample rows 8 times for a dramatic speedup. num_segments = 8","in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0)","t2 = time() # gradient update model.train() cache = [] for i in","0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*') latest_file = None if","array[i, j] == config.period_token: new_array[i, :j + 1] = 1 break elif array[i,","start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' +","in range(len(x)): if x[i] > 0.01: print(i, x[i], translate[i]) break''' if count ==","= 8 for i in range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments *","* torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true - y_pred) **","lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim =","= cur_row if cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size //","((0, 0), (0, 1)), 'constant') return generate_mask(array) # pi_tmp isn't modified here, since","since the mask will be modified appropriately _, pi_mask = pi_mask(s) z_mask =","pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) # print('io time: ',time() -","0 for iter in range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if (iter +","break return new_array def pi_mask(array): array = array[:, 1:] array = np.pad(array, ((0,","+ 1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true - y_pred) ** 2 pi_mask","array[i, j] == config.blank_token: new_array[i, :j] = 1 break return new_array def pi_mask(array):","_ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\"buffer\", \"r\") as f: cur_row = int(f['/cur_row'][0]) s_buffer","count = cur_row if cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size",":config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z","if error, set a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\"buffer\",","break''' if count == 0: count = cur_row t_inf = time() if count","optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim,","// 2), replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False))","as np import random from time import time, sleep import h5py import torch","sample rows 8 times for a dramatic speedup. num_segments = 8 for i","%s seconds\" % (time() - start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time)","1) % 100000 == 0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads","for 32 self-play games: ', 32 * (time() - t_inf) / df) t_inf","i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp,","convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v) * z_mask ,","y_pred): return (y_true - y_pred) ** 2 pi_mask = convert(pi_mask) z_mask = convert(z_mask)","np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4,","%s seconds\" % (time() - t2)) print(\"iteration: %s seconds\" % (time() - start_time))","glob.glob(config.model_path + '/*') latest_file = None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt","= f['/z'] s_tmp = [] pi_tmp = [] z_tmp = [] df =","= time() optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s from the buffer #","_, pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2),","= pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) *","# meaning the padding; place -1 padding new_pi[i, j, :] = -1 else:","= cur_row t_inf = time() if count != 0 and df >= 1000:","mask for loss function and preparing a minibatch def generate_mask(array): new_array = np.zeros_like(array)","for iter in range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if (iter + 1)","is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi","torch import torch.nn as nn import torch.optim as optimizer import glob import os",":] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi # creating","y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true","convert(z_mask) z = convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask","{'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-' + str(iter","appropriately _, pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size //","= np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time) # decompresses sampled pi's","'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-' + str(iter + 1)","array = np.pad(array, ((0, 0), (0, 1)), 'constant') return generate_mask(array) # pi_tmp isn't","array[:, 1:] array = np.pad(array, ((0, 0), (0, 1)), 'constant') return generate_mask(array) #","pi[i, j, 0] == -1: # meaning the terminal state; pi=0 new_pi[i, j,","(iter + 1) % 100000 == 0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter)","new_array[i, :j + 1] = 1 break elif array[i, j] == config.blank_token: new_array[i,","(config.batch_size // 2), replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2),","# creating a mask for loss function and preparing a minibatch def generate_mask(array):","np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time) #","padding new_pi[i, j, :] = -1 else: # Beware that np.bincount's bin is","a minibatch def generate_mask(array): new_array = np.zeros_like(array) for i in range(len(array)): for j","optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files =","takes about 0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i in","= torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p:","range(len(x)): if x[i] > 0.01: print(i, x[i], translate[i]) break''' if count == 0:","start_time = time() optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s from the buffer","randomly sampled (s,pi,z)'s from the buffer # ~ 0.1s # TODO: if error,","momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim = ScheduledOptim(","= {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-' +","import h5py import torch import torch.nn as nn import torch.optim as optimizer import","start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter = 0 count =","j in range(len(array[i])): if j == len(array[i]) - 1: new_array[i, :] = 1","df >= 1000: print('time required for 32 self-play games: ', 32 * (time()","checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-'","range((config.batch_size // 2)): for j in range(config.max_length): if pi[i, j, 0] == -1:","f['/pi'] z_buffer = f['/z'] s_tmp = [] pi_tmp = [] z_tmp = []","#from scipy.stats import rankdata from lstm import Model, initialize from Optim import ScheduledOptim","loss function and preparing a minibatch def generate_mask(array): new_array = np.zeros_like(array) for i","2 pi_mask = convert(pi_mask) z_mask = convert(z_mask) z = convert(z) pi = convert(pi)","decompresses sampled pi's # takes about 0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length,","break elif array[i, j] == config.blank_token: new_array[i, :j] = 1 break return new_array","'/*') latest_file = None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file","import ScheduledOptim # import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model =","1:] array = np.pad(array, ((0, 0), (0, 1)), 'constant') return generate_mask(array) # pi_tmp","torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient update model.train() cache = [] for","# model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict'])","randomly sample rows 8 times for a dramatic speedup. num_segments = 8 for","convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient update model.train() cache =",":j] = 1 break return new_array def pi_mask(array): array = array[:, 1:] array","torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v) * z_mask , 1) / torch.sum(z_mask,","= np.pad(array, ((0, 0), (0, 1)), 'constant') return generate_mask(array) # pi_tmp isn't modified","Model, initialize from Optim import ScheduledOptim # import _pickle as cPickle # np.set_printoptions(threshold=np.nan)","num_segments * (i + 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length,","s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi =","loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v) * z_mask , 1)","from the buffer # ~ 0.1s # TODO: if error, set a lock","// 2)): for j in range(config.max_length): if pi[i, j, 0] == -1: #","/ df) t_inf = time() count = cur_row if cur_row >= config.buffer_size: r","(y_true - y_pred) ** 2 pi_mask = convert(pi_mask) z_mask = convert(z_mask) z =","== config.blank_token: new_array[i, :j] = 1 break return new_array def pi_mask(array): array =","1 break elif array[i, j] == config.blank_token: new_array[i, :j] = 1 break return","= torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter","pi_tmp = [] z_tmp = [] df = cur_row - count '''x =","[] df = cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i","new_array[i, :] = 1 elif array[i, j] == config.period_token: new_array[i, :j + 1]","[] for i in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)]","v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred +","state; pi=0 new_pi[i, j, :] = 0 elif pi[i, j, 0] == -2","pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1),","import glob import os #from scipy.stats import rankdata from lstm import Model, initialize","config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*') latest_file = None if list_of_files: latest_file","y_pred) ** 2 pi_mask = convert(pi_mask) z_mask = convert(z_mask) z = convert(z) pi","(config.batch_size // 2), replace=False)) tmp = [] # randomly sample rows 8 times","// 2), config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32),","model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter = 0","torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true,","0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i in range((config.batch_size //","rankdata from lstm import Model, initialize from Optim import ScheduledOptim # import _pickle","replace=False)) tmp = [] # randomly sample rows 8 times for a dramatic","the randomly sampled (s,pi,z)'s from the buffer # ~ 0.1s # TODO: if","r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else: r = np.sort(","// num_segments * (i + 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i],","in range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if (iter + 1) % 100000","[np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return","// 2), config.max_length, config.vocab_size)) for i in range((config.batch_size // 2)): for j in","', 32 * (time() - t_inf) / df) t_inf = time() count =","preparing a minibatch def generate_mask(array): new_array = np.zeros_like(array) for i in range(len(array)): for","= [] for i in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size,","optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter = 0 count","torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter =","(time() - t2)) print(\"iteration: %s seconds\" % (time() - start_time)) checkpoint = {'state_dict':","if count != 0 and df >= 1000: print('time required for 32 self-play","else: # Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i,","range(config.max_length): if pi[i, j, 0] == -1: # meaning the terminal state; pi=0","1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s","j, 0] == -2 or sum(pi[i, j, :]) == 0: # meaning the","# decompresses sampled pi's # takes about 0.005s new_pi = np.zeros(((config.batch_size // 2),","new_array def pi_mask(array): array = array[:, 1:] array = np.pad(array, ((0, 0), (0,","list_of_files = glob.glob(config.model_path + '/*') latest_file = None if list_of_files: latest_file = max(list_of_files,","gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\"grad update: %s seconds\" % (time()","required for 32 self-play games: ', 32 * (time() - t_inf) / df)","// num_segments * i:(config.batch_size // 2) // num_segments * (i + 1)]) for","== -1: # meaning the terminal state; pi=0 new_pi[i, j, :] = 0","1) / torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\"grad","# lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s from","def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient update model.train() cache","tmp = [] # randomly sample rows 8 times for a dramatic speedup.","cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache =","start(config): model = Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c)","return generate_mask(array) # pi_tmp isn't modified here, since the mask will be modified","i in range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments * i:(config.batch_size // 2)","config.period_token: new_array[i, :j + 1] = 1 break elif array[i, j] == config.blank_token:","df = cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in","count == 0: count = cur_row t_inf = time() if count != 0","r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp = [] #","* (i + 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...])","convert(pi_mask) z_mask = convert(z_mask) z = convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi,","0 count = 0 for iter in range(start_iter, config.total_iterations): print('iteration: %s' % iter)","count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)): if x[i] >","1 elif array[i, j] == config.period_token: new_array[i, :j + 1] = 1 break","- start_time) # decompresses sampled pi's # takes about 0.005s new_pi = np.zeros(((config.batch_size","import torch.nn as nn import torch.optim as optimizer import glob import os #from","range(len(array)): for j in range(len(array[i])): if j == len(array[i]) - 1: new_array[i, :]","~ 0.1s # TODO: if error, set a lock # translate, _ =","j, :] = -1 else: # Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i,","= convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z,","32 self-play games: ', 32 * (time() - t_inf) / df) t_inf =","elif array[i, j] == config.blank_token: new_array[i, :j] = 1 break return new_array def","times for a dramatic speedup. num_segments = 8 for i in range(num_segments): tmp.append(","z = convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask +","import torch import torch.nn as nn import torch.optim as optimizer import glob import","- t_inf) / df) t_inf = time() count = cur_row if cur_row >=","in range(config.max_length): if pi[i, j, 0] == -1: # meaning the terminal state;","for i in range(len(array)): for j in range(len(array[i])): if j == len(array[i]) -","time() # gradient update model.train() cache = [] for i in range(config.depth //","# np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(),","config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2","> 0.01: print(i, x[i], translate[i]) break''' if count == 0: count = cur_row","for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s =","update: %s seconds\" % (time() - t2)) print(\"iteration: %s seconds\" % (time() -","8 for i in range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments * i:(config.batch_size","pi_mask = convert(pi_mask) z_mask = convert(z_mask) z = convert(z) pi = convert(pi) loss","pi's # takes about 0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for","os #from scipy.stats import rankdata from lstm import Model, initialize from Optim import","# takes about 0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i","generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size // 2),","j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi #","torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\"grad update: %s","config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else: r =","// 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient","a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\"buffer\", \"r\") as f:","= convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true","100000 == 0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads the randomly","about 0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i in range((config.batch_size","t_inf = time() if count != 0 and df >= 1000: print('time required","2000) list_of_files = glob.glob(config.model_path + '/*') latest_file = None if list_of_files: latest_file =","for i in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch","count = cur_row t_inf = time() if count != 0 and df >=","# print('io time: ',time() - start_time) # decompresses sampled pi's # takes about","j] == config.period_token: new_array[i, :j + 1] = 1 break elif array[i, j]","with h5py.File(\"buffer\", \"r\") as f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer =","// 2) // num_segments * (i + 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i],","z_tmp = [] df = cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000","sum(pi[i, j, :]) == 0: # meaning the padding; place -1 padding new_pi[i,","modified here, since the mask will be modified appropriately _, pi_mask = pi_mask(s)","== 0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads the randomly sampled","= None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt","filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path","as optimizer import glob import os #from scipy.stats import rankdata from lstm import",":config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp,","= convert(z_mask) z = convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) *","the terminal state; pi=0 new_pi[i, j, :] = 0 elif pi[i, j, 0]","= f['/pi'] z_buffer = f['/z'] s_tmp = [] pi_tmp = [] z_tmp =","latest_file # model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt)","0] == -2 or sum(pi[i, j, :]) == 0: # meaning the padding;","- start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model'","sleep import h5py import torch import torch.nn as nn import torch.optim as optimizer",">= 1000: print('time required for 32 self-play games: ', 32 * (time() -","if j == len(array[i]) - 1: new_array[i, :] = 1 elif array[i, j]","= model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1)","print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter =","in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long()","\"r\") as f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer","loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred): return","key=os.path.getctime) model_ckpt = latest_file # model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt:","= latest_file # model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint =","= cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\"buffer\", \"r\") as f: cur_row = int(f['/cur_row'][0]) s_buffer =","pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0)","= ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000)","elif pi[i, j, 0] == -2 or sum(pi[i, j, :]) == 0: #","500000 for i in range(len(x)): if x[i] > 0.01: print(i, x[i], translate[i]) break'''","np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) # print('io time:","np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size","[torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache))","- count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)): if x[i]","that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size)","seconds\" % (time() - t2)) print(\"iteration: %s seconds\" % (time() - start_time)) checkpoint","error, set a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\"buffer\", \"r\")","= time() if count != 0 and df >= 1000: print('time required for","print(gn) optim.step() optim.zero_grad() print(\"grad update: %s seconds\" % (time() - t2)) print(\"iteration: %s","2) def loss_value(y_true, y_pred): return (y_true - y_pred) ** 2 pi_mask = convert(pi_mask)","1] = 1 break elif array[i, j] == config.blank_token: new_array[i, :j] = 1","torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad,","as f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer =","= nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\"grad update: %s seconds\" % (time() -","+ 1] = 1 break elif array[i, j] == config.blank_token: new_array[i, :j] =","loss_value(z, v) * z_mask , 1) / torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(),","- 1: new_array[i, :] = 1 elif array[i, j] == config.period_token: new_array[i, :j","= torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v) * z_mask , 1) /","z_mask , 1) / torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step()","model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) #","= 1 elif array[i, j] == config.period_token: new_array[i, :j + 1] = 1","torch.nn as nn import torch.optim as optimizer import glob import os #from scipy.stats","optim.zero_grad() print(\"grad update: %s seconds\" % (time() - t2)) print(\"iteration: %s seconds\" %","random from time import time, sleep import h5py import torch import torch.nn as","#if (iter + 1) % 100000 == 0: # lr_scheduler.step() start_time = time()","iter in range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if (iter + 1) %","= np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time)","list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt = config.model_path +","== 0: count = cur_row t_inf = time() if count != 0 and","+ '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0]","def start(config): model = Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9,","def pi_mask(array): array = array[:, 1:] array = np.pad(array, ((0, 0), (0, 1)),","model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer'])","cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else:","= time() # gradient update model.train() cache = [] for i in range(config.depth","s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) #","sampled pi's # takes about 0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size))","2), replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp","translate[i]) break''' if count == 0: count = cur_row t_inf = time() if","h5py import torch import torch.nn as nn import torch.optim as optimizer import glob","z = np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time) # decompresses sampled",":] = 0 elif pi[i, j, 0] == -2 or sum(pi[i, j, :])","generate_mask(array) # pi_tmp isn't modified here, since the mask will be modified appropriately","isn't modified here, since the mask will be modified appropriately _, pi_mask =","0: count = cur_row t_inf = time() if count != 0 and df","modified appropriately _, pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size","= optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters","speedup. num_segments = 8 for i in range(num_segments): tmp.append( r[(config.batch_size // 2) //","int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp = []","# translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\"buffer\", \"r\") as f: cur_row =","* (-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 =","0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s",", 1) / torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad()","= np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])]) def","range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if (iter + 1) % 100000 ==","* (time() - t_inf) / df) t_inf = time() count = cur_row if","config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache)) def","range(len(array[i])): if j == len(array[i]) - 1: new_array[i, :] = 1 elif array[i,","import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model =","= 0 count = 0 for iter in range(start_iter, config.total_iterations): print('iteration: %s' %","i in range((config.batch_size // 2)): for j in range(config.max_length): if pi[i, j, 0]","...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z =","pi_mask + loss_value(z, v) * z_mask , 1) / torch.sum(z_mask, 1)) loss.backward() gn","cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)): if","array = array[:, 1:] array = np.pad(array, ((0, 0), (0, 1)), 'constant') return","optim.step() optim.zero_grad() print(\"grad update: %s seconds\" % (time() - t2)) print(\"iteration: %s seconds\"","= 0 elif pi[i, j, 0] == -2 or sum(pi[i, j, :]) ==","buffer # ~ 0.1s # TODO: if error, set a lock # translate,","torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true - y_pred) ** 2","2), config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device)","/ config.simulation_num_per_move pi = new_pi # creating a mask for loss function and","= np.zeros_like(array) for i in range(len(array)): for j in range(len(array[i])): if j ==","np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp = [] # randomly sample","% (time() - t2)) print(\"iteration: %s seconds\" % (time() - start_time)) checkpoint =","= convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v) * z_mask","config.vocab_size)) for i in range((config.batch_size // 2)): for j in range(config.max_length): if pi[i,","new_pi[i, j, :] = 0 elif pi[i, j, 0] == -2 or sum(pi[i,","z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp,","model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter = 0 count = 0 for","config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter =","config.buffer_size)), (config.batch_size // 2), replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size //","h5py.File(\"buffer\", \"r\") as f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi']","0) # print('io time: ',time() - start_time) # decompresses sampled pi's # takes","= new_pi # creating a mask for loss function and preparing a minibatch","new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i in range((config.batch_size // 2)):","if x[i] > 0.01: print(i, x[i], translate[i]) break''' if count == 0: count","_pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model = model.to(config.device)","np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)): if x[i] > 0.01: print(i, x[i],","print(i, x[i], translate[i]) break''' if count == 0: count = cur_row t_inf =","# pi_tmp isn't modified here, since the mask will be modified appropriately _,","0), (0, 1)), 'constant') return generate_mask(array) # pi_tmp isn't modified here, since the","# import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model","2) // num_segments * (i + 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length])","and preparing a minibatch def generate_mask(array): new_array = np.zeros_like(array) for i in range(len(array)):","range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy,","== -2 or sum(pi[i, j, :]) == 0: # meaning the padding; place","in range(len(array)): for j in range(len(array[i])): if j == len(array[i]) - 1: new_array[i,","s_tmp = [] pi_tmp = [] z_tmp = [] df = cur_row -","new_pi # creating a mask for loss function and preparing a minibatch def","time, sleep import h5py import torch import torch.nn as nn import torch.optim as",">= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else: r","rows 8 times for a dramatic speedup. num_segments = 8 for i in","x[i] > 0.01: print(i, x[i], translate[i]) break''' if count == 0: count =","terminal state; pi=0 new_pi[i, j, :] = 0 elif pi[i, j, 0] ==","[0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi =","def loss_value(y_true, y_pred): return (y_true - y_pred) ** 2 pi_mask = convert(pi_mask) z_mask","initialize from Optim import ScheduledOptim # import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def","# gradient update model.train() cache = [] for i in range(config.depth // config.unit_depth):","<gh_stars>1-10 import numpy as np import random from time import time, sleep import","% iter) #if (iter + 1) % 100000 == 0: # lr_scheduler.step() start_time","from Optim import ScheduledOptim # import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config):","optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim","i:(config.batch_size // 2) // num_segments * (i + 1)]) for i in range(num_segments):","translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\"buffer\", \"r\") as f: cur_row = int(f['/cur_row'][0])","scipy.stats import rankdata from lstm import Model, initialize from Optim import ScheduledOptim #","count = 0 for iter in range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if","cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\"buffer\", \"r\") as f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s']","policy) * pi_mask + loss_value(z, v) * z_mask , 1) / torch.sum(z_mask, 1))","= f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp = [] pi_tmp =","# Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j,","a dramatic speedup. num_segments = 8 for i in range(num_segments): tmp.append( r[(config.batch_size //","in range((config.batch_size // 2)): for j in range(config.max_length): if pi[i, j, 0] ==","glob import os #from scipy.stats import rankdata from lstm import Model, initialize from","[] # randomly sample rows 8 times for a dramatic speedup. num_segments =","j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi # creating a mask for","+ loss_value(z, v) * z_mask , 1) / torch.sum(z_mask, 1)) loss.backward() gn =","...]) s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0)","ScheduledOptim # import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model = Model(config)","if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter)","config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred):","z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])])","if count == 0: count = cur_row t_inf = time() if count !=","the mask will be modified appropriately _, pi_mask = pi_mask(s) z_mask = generate_mask(s)","j in range(config.max_length): if pi[i, j, 0] == -1: # meaning the terminal",":]) == 0: # meaning the padding; place -1 padding new_pi[i, j, :]","f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z']","Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim,","(i + 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i],","print(\"iteration: %s seconds\" % (time() - start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()}","lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*') latest_file =","= config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter","for j in range(len(array[i])): if j == len(array[i]) - 1: new_array[i, :] =","dramatic speedup. num_segments = 8 for i in range(num_segments): tmp.append( r[(config.batch_size // 2)","np.pad(array, ((0, 0), (0, 1)), 'constant') return generate_mask(array) # pi_tmp isn't modified here,","for i in range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments * i:(config.batch_size //","pi[i, j, 0] == -2 or sum(pi[i, j, :]) == 0: # meaning","here, since the mask will be modified appropriately _, pi_mask = pi_mask(s) z_mask","Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int),","= glob.glob(config.model_path + '/*') latest_file = None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime)","2), config.max_length, config.vocab_size)) for i in range((config.batch_size // 2)): for j in range(config.max_length):","generate_mask(array): new_array = np.zeros_like(array) for i in range(len(array)): for j in range(len(array[i])): if","1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\"grad update: %s seconds\"","2)): for j in range(config.max_length): if pi[i, j, 0] == -1: # meaning","reads the randomly sampled (s,pi,z)'s from the buffer # ~ 0.1s # TODO:","games: ', 32 * (time() - t_inf) / df) t_inf = time() count","cur_row)), (config.batch_size // 2), replace=False)) tmp = [] # randomly sample rows 8","# meaning the terminal state; pi=0 new_pi[i, j, :] = 0 elif pi[i,","= np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi # creating a","model_ckpt = latest_file # model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint","= Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler =","j, :] = 0 elif pi[i, j, 0] == -2 or sum(pi[i, j,","torch.optim as optimizer import glob import os #from scipy.stats import rankdata from lstm","j, 0] == -1: # meaning the terminal state; pi=0 new_pi[i, j, :]","cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp","= generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size //","import numpy as np import random from time import time, sleep import h5py","* z_mask , 1) / torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn)","in range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments * i:(config.batch_size // 2) //","nn import torch.optim as optimizer import glob import os #from scipy.stats import rankdata","return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true -","= -1 else: # Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :]","1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true - y_pred) ** 2 pi_mask =","[] z_tmp = [] df = cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) /","= [] # randomly sample rows 8 times for a dramatic speedup. num_segments","= 0 for iter in range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if (iter","start_iter = 0 count = 0 for iter in range(start_iter, config.total_iterations): print('iteration: %s'","update model.train() cache = [] for i in range(config.depth // config.unit_depth): cache +=","def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred):","(time() - t_inf) / df) t_inf = time() count = cur_row if cur_row","0.1s # TODO: if error, set a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl',","z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size","import Model, initialize from Optim import ScheduledOptim # import _pickle as cPickle #","model.train() cache = [] for i in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size,","optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-' + str(iter + 1) +","in range(len(array[i])): if j == len(array[i]) - 1: new_array[i, :] = 1 elif","1000: print('time required for 32 self-play games: ', 32 * (time() - t_inf)","a mask for loss function and preparing a minibatch def generate_mask(array): new_array =","cur_row t_inf = time() if count != 0 and df >= 1000: print('time","Optim import ScheduledOptim # import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model","weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim = ScheduledOptim( optimizer.Adam(","place -1 padding new_pi[i, j, :] = -1 else: # Beware that np.bincount's","j == len(array[i]) - 1: new_array[i, :] = 1 elif array[i, j] ==","0] == -1: # meaning the terminal state; pi=0 new_pi[i, j, :] =","meaning the terminal state; pi=0 new_pi[i, j, :] = 0 elif pi[i, j,","range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi",":j + 1] = 1 break elif array[i, j] == config.blank_token: new_array[i, :j]","- t2)) print(\"iteration: %s seconds\" % (time() - start_time)) checkpoint = {'state_dict': model.state_dict(),","config.max_length, config.vocab_size)) for i in range((config.batch_size // 2)): for j in range(config.max_length): if","2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient update","pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v) *","lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s from the","time() count = cur_row if cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)),","max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if","pi_tmp isn't modified here, since the mask will be modified appropriately _, pi_mask","pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length])","// 2) // num_segments * i:(config.batch_size // 2) // num_segments * (i +","num_segments = 8 for i in range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments","bin is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move","1 break return new_array def pi_mask(array): array = array[:, 1:] array = np.pad(array,","model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else:","gradient update model.train() cache = [] for i in range(config.depth // config.unit_depth): cache","= np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) # print('io","from time import time, sleep import h5py import torch import torch.nn as nn","!= 0 and df >= 1000: print('time required for 32 self-play games: ',","pi=0 new_pi[i, j, :] = 0 elif pi[i, j, 0] == -2 or","f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp = [] pi_tmp = []","'''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)): if x[i] > 0.01:","if pi[i, j, 0] == -1: # meaning the terminal state; pi=0 new_pi[i,","tmp.append( r[(config.batch_size // 2) // num_segments * i:(config.batch_size // 2) // num_segments *","replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp =","-1: # meaning the terminal state; pi=0 new_pi[i, j, :] = 0 elif","= time() count = cur_row if cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0,","pi = new_pi # creating a mask for loss function and preparing a","z_mask = convert(z_mask) z = convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy)","pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp = [] pi_tmp = [] z_tmp","convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v)","1: new_array[i, :] = 1 elif array[i, j] == config.period_token: new_array[i, :j +","range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments * i:(config.batch_size // 2) // num_segments","self-play games: ', 32 * (time() - t_inf) / df) t_inf = time()","= np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp = [] # randomly","s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp = [] pi_tmp"],"string":"[\n \"sampled (s,pi,z)'s from the buffer # ~ 0.1s # TODO: if error, set\",\n \"== config.period_token: new_array[i, :j + 1] = 1 break elif array[i, j] ==\",\n \"np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time) # decompresses sampled pi's #\",\n \"(s,pi,z)'s from the buffer # ~ 0.1s # TODO: if error, set a\",\n \"for i in range(len(x)): if x[i] > 0.01: print(i, x[i], translate[i]) break''' if\",\n \"= np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i in range((config.batch_size // 2)): for\",\n \"will be modified appropriately _, pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch =\",\n \"latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt = config.model_path + '/model-454.pth'\",\n \"mask will be modified appropriately _, pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch\",\n \"for a dramatic speedup. num_segments = 8 for i in range(num_segments): tmp.append( r[(config.batch_size\",\n \"betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*') latest_file = None\",\n \"start_iter = int(start_iter) else: model.apply(initialize) start_iter = 0 count = 0 for iter\",\n \"np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi # creating a mask\",\n \"import rankdata from lstm import Model, initialize from Optim import ScheduledOptim # import\",\n \"config.total_iterations): print('iteration: %s' % iter) #if (iter + 1) % 100000 == 0:\",\n \"[] pi_tmp = [] z_tmp = [] df = cur_row - count '''x\",\n \"for i in range((config.batch_size // 2)): for j in range(config.max_length): if pi[i, j,\",\n \"= int(start_iter) else: model.apply(initialize) start_iter = 0 count = 0 for iter in\",\n \"df) t_inf = time() count = cur_row if cur_row >= config.buffer_size: r =\",\n \"== 0: # meaning the padding; place -1 padding new_pi[i, j, :] =\",\n \"minibatch def generate_mask(array): new_array = np.zeros_like(array) for i in range(len(array)): for j in\",\n \"np.zeros_like(array) for i in range(len(array)): for j in range(len(array[i])): if j == len(array[i])\",\n \"t_inf) / df) t_inf = time() count = cur_row if cur_row >= config.buffer_size:\",\n \"meaning the padding; place -1 padding new_pi[i, j, :] = -1 else: #\",\n \"if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt = config.model_path\",\n \"(time() - start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path +\",\n \":].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi # creating a mask for loss\",\n \"= model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2)\",\n \"nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\\\"grad update: %s seconds\\\" % (time() - t2))\",\n \"eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*') latest_file = None if list_of_files:\",\n \"len(array[i]) - 1: new_array[i, :] = 1 elif array[i, j] == config.period_token: new_array[i,\",\n \"new_array = np.zeros_like(array) for i in range(len(array)): for j in range(len(array[i])): if j\",\n \"from lstm import Model, initialize from Optim import ScheduledOptim # import _pickle as\",\n \"for j in range(config.max_length): if pi[i, j, 0] == -1: # meaning the\",\n \"t_inf = time() count = cur_row if cur_row >= config.buffer_size: r = np.sort(\",\n \"2) // num_segments * i:(config.batch_size // 2) // num_segments * (i + 1)])\",\n \"+ 1) % 100000 == 0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) #\",\n \"j, :]) == 0: # meaning the padding; place -1 padding new_pi[i, j,\",\n \"= max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt)\",\n \"time() if count != 0 and df >= 1000: print('time required for 32\",\n \"0 elif pi[i, j, 0] == -2 or sum(pi[i, j, :]) == 0:\",\n \"// config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v,\",\n \"else: model.apply(initialize) start_iter = 0 count = 0 for iter in range(start_iter, config.total_iterations):\",\n \"cache = [] for i in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device),\",\n \"p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*')\",\n \"%s' % iter) #if (iter + 1) % 100000 == 0: # lr_scheduler.step()\",\n \"model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*') latest_file\",\n \"new_pi[i, j, :] = -1 else: # Beware that np.bincount's bin is [0,1,...min_length-1]\",\n \"p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path +\",\n \"if cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False))\",\n \"count != 0 and df >= 1000: print('time required for 32 self-play games:\",\n \"',time() - start_time) # decompresses sampled pi's # takes about 0.005s new_pi =\",\n \"config.blank_token: new_array[i, :j] = 1 break return new_array def pi_mask(array): array = array[:,\",\n \"(0, 1)), 'constant') return generate_mask(array) # pi_tmp isn't modified here, since the mask\",\n \"0: # meaning the padding; place -1 padding new_pi[i, j, :] = -1\",\n \"/ 500000 for i in range(len(x)): if x[i] > 0.01: print(i, x[i], translate[i])\",\n \"padding; place -1 padding new_pi[i, j, :] = -1 else: # Beware that\",\n \"= 1 break elif array[i, j] == config.blank_token: new_array[i, :j] = 1 break\",\n \"8 times for a dramatic speedup. num_segments = 8 for i in range(num_segments):\",\n \"'constant') return generate_mask(array) # pi_tmp isn't modified here, since the mask will be\",\n \"* i:(config.batch_size // 2) // num_segments * (i + 1)]) for i in\",\n \"pi_mask(array): array = array[:, 1:] array = np.pad(array, ((0, 0), (0, 1)), 'constant')\",\n \"iter) #if (iter + 1) % 100000 == 0: # lr_scheduler.step() start_time =\",\n \"v) * z_mask , 1) / torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip)\",\n \"* pi_mask + loss_value(z, v) * z_mask , 1) / torch.sum(z_mask, 1)) loss.backward()\",\n \"set a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\\\"buffer\\\", \\\"r\\\") as\",\n \"convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true *\",\n \"(-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time()\",\n \"+= [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache = model(s_batch,\",\n \"step_size=200, gamma=0.1) # 20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()),\",\n \"#lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda\",\n \"-2 or sum(pi[i, j, :]) == 0: # meaning the padding; place -1\",\n \"z_buffer = f['/z'] s_tmp = [] pi_tmp = [] z_tmp = [] df\",\n \"-1 padding new_pi[i, j, :] = -1 else: # Beware that np.bincount's bin\",\n \"start_time) # decompresses sampled pi's # takes about 0.005s new_pi = np.zeros(((config.batch_size //\",\n \"model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-' + str(iter +\",\n \"config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient update model.train()\",\n \"cPickle # np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model = model.to(config.device) #optim =\",\n \"# 20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9,\",\n \"TODO: if error, set a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with\",\n \"elif array[i, j] == config.period_token: new_array[i, :j + 1] = 1 break elif\",\n \"print('time required for 32 self-play games: ', 32 * (time() - t_inf) /\",\n \"time: ',time() - start_time) # decompresses sampled pi's # takes about 0.005s new_pi\",\n \"or sum(pi[i, j, :]) == 0: # meaning the padding; place -1 padding\",\n \"-1 else: # Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :] =\",\n \"np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i in range((config.batch_size // 2)): for j\",\n \"be modified appropriately _, pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate(\",\n \"torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true - y_pred)\",\n \"the buffer # ~ 0.1s # TODO: if error, set a lock #\",\n \"device=config.device) t2 = time() # gradient update model.train() cache = [] for i\",\n \"np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() #\",\n \"= convert(pi_mask) z_mask = convert(z_mask) z = convert(z) pi = convert(pi) loss =\",\n \"tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true,\",\n \"config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache\",\n \"#optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M\",\n \"= np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)): if x[i] > 0.01: print(i,\",\n \"model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200,\",\n \"= 1 break return new_array def pi_mask(array): array = array[:, 1:] array =\",\n \"i in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch =\",\n \"loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\\\"grad update: %s seconds\\\" %\",\n \"print(\\\"grad update: %s seconds\\\" % (time() - t2)) print(\\\"iteration: %s seconds\\\" % (time()\",\n \"time import time, sleep import h5py import torch import torch.nn as nn import\",\n \"= [] z_tmp = [] df = cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int))\",\n \"% 100000 == 0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads the\",\n \"** 2 pi_mask = convert(pi_mask) z_mask = convert(z_mask) z = convert(z) pi =\",\n \":] = -1 else: # Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j,\",\n \"= model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter = 0 count = 0\",\n \"time() optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s from the buffer # ~\",\n \"np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x):\",\n \"import random from time import time, sleep import h5py import torch import torch.nn\",\n \"20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98),\",\n \"model = Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler\",\n \"def generate_mask(array): new_array = np.zeros_like(array) for i in range(len(array)): for j in range(len(array[i])):\",\n \"+ 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...])\",\n \"creating a mask for loss function and preparing a minibatch def generate_mask(array): new_array\",\n \"lstm import Model, initialize from Optim import ScheduledOptim # import _pickle as cPickle\",\n \"t2)) print(\\\"iteration: %s seconds\\\" % (time() - start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer':\",\n \"r[(config.batch_size // 2) // num_segments * i:(config.batch_size // 2) // num_segments * (i\",\n \"return (y_true - y_pred) ** 2 pi_mask = convert(pi_mask) z_mask = convert(z_mask) z\",\n \"= int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp =\",\n \"= [] df = cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for\",\n \"1)), 'constant') return generate_mask(array) # pi_tmp isn't modified here, since the mask will\",\n \"new_array[i, :j] = 1 break return new_array def pi_mask(array): array = array[:, 1:]\",\n \"/ torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\\\"grad update:\",\n \"num_segments * i:(config.batch_size // 2) // num_segments * (i + 1)]) for i\",\n \"np import random from time import time, sleep import h5py import torch import\",\n \"np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) /\",\n \"as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model = model.to(config.device) #optim\",\n \"the padding; place -1 padding new_pi[i, j, :] = -1 else: # Beware\",\n \"sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-' + str(iter + 1) + '.pth')\",\n \"optimizer import glob import os #from scipy.stats import rankdata from lstm import Model,\",\n \"import os #from scipy.stats import rankdata from lstm import Model, initialize from Optim\",\n \"config.clip) print(gn) optim.step() optim.zero_grad() print(\\\"grad update: %s seconds\\\" % (time() - t2)) print(\\\"iteration:\",\n \"== len(array[i]) - 1: new_array[i, :] = 1 elif array[i, j] == config.period_token:\",\n \"as nn import torch.optim as optimizer import glob import os #from scipy.stats import\",\n \"s_batch = convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return\",\n \"ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files\",\n \"// 2), replace=False)) tmp = [] # randomly sample rows 8 times for\",\n \"seconds\\\" % (time() - start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint,\",\n \"lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\\\"buffer\\\", \\\"r\\\") as f: cur_row\",\n \"model.apply(initialize) start_iter = 0 count = 0 for iter in range(start_iter, config.total_iterations): print('iteration:\",\n \"for loss function and preparing a minibatch def generate_mask(array): new_array = np.zeros_like(array) for\",\n \"checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize)\",\n \"f['/z'] s_tmp = [] pi_tmp = [] z_tmp = [] df = cur_row\",\n \"# reads the randomly sampled (s,pi,z)'s from the buffer # ~ 0.1s #\",\n \"print('io time: ',time() - start_time) # decompresses sampled pi's # takes about 0.005s\",\n \"'rb')) with h5py.File(\\\"buffer\\\", \\\"r\\\") as f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer\",\n \"0 and df >= 1000: print('time required for 32 self-play games: ', 32\",\n \"np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp = [] # randomly sample rows\",\n \"x[i], translate[i]) break''' if count == 0: count = cur_row t_inf = time()\",\n \"= array[:, 1:] array = np.pad(array, ((0, 0), (0, 1)), 'constant') return generate_mask(array)\",\n \"numpy as np import random from time import time, sleep import h5py import\",\n \"32 * (time() - t_inf) / df) t_inf = time() count = cur_row\",\n \"import time, sleep import h5py import torch import torch.nn as nn import torch.optim\",\n \"= cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)):\",\n \"# TODO: if error, set a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb'))\",\n \"return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient update model.train() cache = []\",\n \"+ '/*') latest_file = None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt =\",\n \"latest_file = None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file #\",\n \"2), replace=False)) tmp = [] # randomly sample rows 8 times for a\",\n \"0) z = np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time) # decompresses\",\n \"# ~ 0.1s # TODO: if error, set a lock # translate, _\",\n \"minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi # creating a mask for loss function\",\n \"new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi\",\n \"i in range(len(array)): for j in range(len(array[i])): if j == len(array[i]) - 1:\",\n \"= [] pi_tmp = [] z_tmp = [] df = cur_row - count\",\n \"i in range(len(x)): if x[i] > 0.01: print(i, x[i], translate[i]) break''' if count\",\n \"config.simulation_num_per_move pi = new_pi # creating a mask for loss function and preparing\",\n \"iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09),\",\n \"j] == config.blank_token: new_array[i, :j] = 1 break return new_array def pi_mask(array): array\",\n \"function and preparing a minibatch def generate_mask(array): new_array = np.zeros_like(array) for i in\",\n \"np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)),\",\n \":] = 1 elif array[i, j] == config.period_token: new_array[i, :j + 1] =\",\n \"cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8),\",\n \"and df >= 1000: print('time required for 32 self-play games: ', 32 *\",\n \"= np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else: r = np.sort( np.random.choice(list(range(0,\",\n \"loss_value(y_true, y_pred): return (y_true - y_pred) ** 2 pi_mask = convert(pi_mask) z_mask =\",\n \"model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def\",\n \"% (time() - start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path\",\n \"cur_row if cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2),\",\n \"optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s from the buffer # ~ 0.1s\",\n \"0) pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) # print('io time: ',time()\",\n \"gamma=0.1) # 20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr,\",\n \"'/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter\",\n \"policy, v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred\",\n \"- y_pred) ** 2 pi_mask = convert(pi_mask) z_mask = convert(z_mask) z = convert(z)\",\n \"return new_array def pi_mask(array): array = array[:, 1:] array = np.pad(array, ((0, 0),\",\n \"print('iteration: %s' % iter) #if (iter + 1) % 100000 == 0: #\",\n \"int(start_iter) else: model.apply(initialize) start_iter = 0 count = 0 for iter in range(start_iter,\",\n \"else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp = []\",\n \"None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt =\",\n \"0.01: print(i, x[i], translate[i]) break''' if count == 0: count = cur_row t_inf\",\n \"import torch.optim as optimizer import glob import os #from scipy.stats import rankdata from\",\n \"# randomly sample rows 8 times for a dramatic speedup. num_segments = 8\",\n \"in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0)\",\n \"t2 = time() # gradient update model.train() cache = [] for i in\",\n \"0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*') latest_file = None if\",\n \"array[i, j] == config.period_token: new_array[i, :j + 1] = 1 break elif array[i,\",\n \"start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' +\",\n \"in range(len(x)): if x[i] > 0.01: print(i, x[i], translate[i]) break''' if count ==\",\n \"= 8 for i in range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments *\",\n \"* torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true - y_pred) **\",\n \"lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim =\",\n \"= cur_row if cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size //\",\n \"((0, 0), (0, 1)), 'constant') return generate_mask(array) # pi_tmp isn't modified here, since\",\n \"since the mask will be modified appropriately _, pi_mask = pi_mask(s) z_mask =\",\n \"pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) # print('io time: ',time() -\",\n \"0 for iter in range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if (iter +\",\n \"break return new_array def pi_mask(array): array = array[:, 1:] array = np.pad(array, ((0,\",\n \"+ 1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true - y_pred) ** 2 pi_mask\",\n \"array[i, j] == config.blank_token: new_array[i, :j] = 1 break return new_array def pi_mask(array):\",\n \"_ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\\\"buffer\\\", \\\"r\\\") as f: cur_row = int(f['/cur_row'][0]) s_buffer\",\n \"count = cur_row if cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size\",\n \":config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z\",\n \"if error, set a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\\\"buffer\\\",\",\n \"break''' if count == 0: count = cur_row t_inf = time() if count\",\n \"optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim,\",\n \"// 2), replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False))\",\n \"as np import random from time import time, sleep import h5py import torch\",\n \"sample rows 8 times for a dramatic speedup. num_segments = 8 for i\",\n \"%s seconds\\\" % (time() - start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time)\",\n \"1) % 100000 == 0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads\",\n \"for 32 self-play games: ', 32 * (time() - t_inf) / df) t_inf\",\n \"i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp,\",\n \"convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v) * z_mask ,\",\n \"y_pred): return (y_true - y_pred) ** 2 pi_mask = convert(pi_mask) z_mask = convert(z_mask)\",\n \"np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4,\",\n \"%s seconds\\\" % (time() - t2)) print(\\\"iteration: %s seconds\\\" % (time() - start_time))\",\n \"glob.glob(config.model_path + '/*') latest_file = None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt\",\n \"= f['/z'] s_tmp = [] pi_tmp = [] z_tmp = [] df =\",\n \"= time() optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s from the buffer #\",\n \"_, pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2),\",\n \"= pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) *\",\n \"# meaning the padding; place -1 padding new_pi[i, j, :] = -1 else:\",\n \"= cur_row t_inf = time() if count != 0 and df >= 1000:\",\n \"mask for loss function and preparing a minibatch def generate_mask(array): new_array = np.zeros_like(array)\",\n \"for iter in range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if (iter + 1)\",\n \"is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi\",\n \"torch import torch.nn as nn import torch.optim as optimizer import glob import os\",\n \":] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi # creating\",\n \"y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true\",\n \"convert(z_mask) z = convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask\",\n \"{'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-' + str(iter\",\n \"appropriately _, pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size //\",\n \"= np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time) # decompresses sampled pi's\",\n \"'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-' + str(iter + 1)\",\n \"array = np.pad(array, ((0, 0), (0, 1)), 'constant') return generate_mask(array) # pi_tmp isn't\",\n \"array[:, 1:] array = np.pad(array, ((0, 0), (0, 1)), 'constant') return generate_mask(array) #\",\n \"pi[i, j, 0] == -1: # meaning the terminal state; pi=0 new_pi[i, j,\",\n \"(iter + 1) % 100000 == 0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter)\",\n \"new_array[i, :j + 1] = 1 break elif array[i, j] == config.blank_token: new_array[i,\",\n \"(config.batch_size // 2), replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2),\",\n \"# creating a mask for loss function and preparing a minibatch def generate_mask(array):\",\n \"np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time) #\",\n \"padding new_pi[i, j, :] = -1 else: # Beware that np.bincount's bin is\",\n \"a minibatch def generate_mask(array): new_array = np.zeros_like(array) for i in range(len(array)): for j\",\n \"optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files =\",\n \"takes about 0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i in\",\n \"= torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p:\",\n \"range(len(x)): if x[i] > 0.01: print(i, x[i], translate[i]) break''' if count == 0:\",\n \"start_time = time() optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s from the buffer\",\n \"randomly sampled (s,pi,z)'s from the buffer # ~ 0.1s # TODO: if error,\",\n \"momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim = ScheduledOptim(\",\n \"= {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-' +\",\n \"import h5py import torch import torch.nn as nn import torch.optim as optimizer import\",\n \"start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter = 0 count =\",\n \"j in range(len(array[i])): if j == len(array[i]) - 1: new_array[i, :] = 1\",\n \"df >= 1000: print('time required for 32 self-play games: ', 32 * (time()\",\n \"checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-'\",\n \"range((config.batch_size // 2)): for j in range(config.max_length): if pi[i, j, 0] == -1:\",\n \"f['/pi'] z_buffer = f['/z'] s_tmp = [] pi_tmp = [] z_tmp = []\",\n \"#from scipy.stats import rankdata from lstm import Model, initialize from Optim import ScheduledOptim\",\n \"loss function and preparing a minibatch def generate_mask(array): new_array = np.zeros_like(array) for i\",\n \"2 pi_mask = convert(pi_mask) z_mask = convert(z_mask) z = convert(z) pi = convert(pi)\",\n \"decompresses sampled pi's # takes about 0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length,\",\n \"break elif array[i, j] == config.blank_token: new_array[i, :j] = 1 break return new_array\",\n \"'/*') latest_file = None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file\",\n \"import ScheduledOptim # import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model =\",\n \"1:] array = np.pad(array, ((0, 0), (0, 1)), 'constant') return generate_mask(array) # pi_tmp\",\n \"torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient update model.train() cache = [] for\",\n \"# model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict'])\",\n \"randomly sample rows 8 times for a dramatic speedup. num_segments = 8 for\",\n \"convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient update model.train() cache =\",\n \":j] = 1 break return new_array def pi_mask(array): array = array[:, 1:] array\",\n \"torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v) * z_mask , 1) / torch.sum(z_mask,\",\n \"= np.pad(array, ((0, 0), (0, 1)), 'constant') return generate_mask(array) # pi_tmp isn't modified\",\n \"Model, initialize from Optim import ScheduledOptim # import _pickle as cPickle # np.set_printoptions(threshold=np.nan)\",\n \"num_segments * (i + 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length,\",\n \"s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi =\",\n \"loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v) * z_mask , 1)\",\n \"from the buffer # ~ 0.1s # TODO: if error, set a lock\",\n \"// 2)): for j in range(config.max_length): if pi[i, j, 0] == -1: #\",\n \"/ df) t_inf = time() count = cur_row if cur_row >= config.buffer_size: r\",\n \"(y_true - y_pred) ** 2 pi_mask = convert(pi_mask) z_mask = convert(z_mask) z =\",\n \"== config.blank_token: new_array[i, :j] = 1 break return new_array def pi_mask(array): array =\",\n \"1 break elif array[i, j] == config.blank_token: new_array[i, :j] = 1 break return\",\n \"= torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter\",\n \"pi_tmp = [] z_tmp = [] df = cur_row - count '''x =\",\n \"[] df = cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i\",\n \"new_array[i, :] = 1 elif array[i, j] == config.period_token: new_array[i, :j + 1]\",\n \"[] for i in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)]\",\n \"v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred +\",\n \"state; pi=0 new_pi[i, j, :] = 0 elif pi[i, j, 0] == -2\",\n \"pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1),\",\n \"import glob import os #from scipy.stats import rankdata from lstm import Model, initialize\",\n \"config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*') latest_file = None if list_of_files: latest_file\",\n \"y_pred) ** 2 pi_mask = convert(pi_mask) z_mask = convert(z_mask) z = convert(z) pi\",\n \"(config.batch_size // 2), replace=False)) tmp = [] # randomly sample rows 8 times\",\n \"// 2), config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32),\",\n \"model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter = 0\",\n \"torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true,\",\n \"0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i in range((config.batch_size //\",\n \"rankdata from lstm import Model, initialize from Optim import ScheduledOptim # import _pickle\",\n \"replace=False)) tmp = [] # randomly sample rows 8 times for a dramatic\",\n \"the randomly sampled (s,pi,z)'s from the buffer # ~ 0.1s # TODO: if\",\n \"r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else: r = np.sort(\",\n \"// num_segments * (i + 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i],\",\n \"in range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if (iter + 1) % 100000\",\n \"[np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return\",\n \"// 2), config.max_length, config.vocab_size)) for i in range((config.batch_size // 2)): for j in\",\n \"', 32 * (time() - t_inf) / df) t_inf = time() count =\",\n \"preparing a minibatch def generate_mask(array): new_array = np.zeros_like(array) for i in range(len(array)): for\",\n \"= [] for i in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size,\",\n \"optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter = 0 count\",\n \"torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter =\",\n \"(time() - t2)) print(\\\"iteration: %s seconds\\\" % (time() - start_time)) checkpoint = {'state_dict':\",\n \"if count != 0 and df >= 1000: print('time required for 32 self-play\",\n \"else: # Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i,\",\n \"range(config.max_length): if pi[i, j, 0] == -1: # meaning the terminal state; pi=0\",\n \"1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s\",\n \"j, 0] == -2 or sum(pi[i, j, :]) == 0: # meaning the\",\n \"# decompresses sampled pi's # takes about 0.005s new_pi = np.zeros(((config.batch_size // 2),\",\n \"new_array def pi_mask(array): array = array[:, 1:] array = np.pad(array, ((0, 0), (0,\",\n \"list_of_files = glob.glob(config.model_path + '/*') latest_file = None if list_of_files: latest_file = max(list_of_files,\",\n \"gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\\\"grad update: %s seconds\\\" % (time()\",\n \"required for 32 self-play games: ', 32 * (time() - t_inf) / df)\",\n \"// num_segments * i:(config.batch_size // 2) // num_segments * (i + 1)]) for\",\n \"== -1: # meaning the terminal state; pi=0 new_pi[i, j, :] = 0\",\n \"1) / torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\\\"grad\",\n \"# lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s from\",\n \"def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient update model.train() cache\",\n \"tmp = [] # randomly sample rows 8 times for a dramatic speedup.\",\n \"cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache =\",\n \"start(config): model = Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c)\",\n \"return generate_mask(array) # pi_tmp isn't modified here, since the mask will be modified\",\n \"i in range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments * i:(config.batch_size // 2)\",\n \"config.period_token: new_array[i, :j + 1] = 1 break elif array[i, j] == config.blank_token:\",\n \"df = cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in\",\n \"count == 0: count = cur_row t_inf = time() if count != 0\",\n \"r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp = [] #\",\n \"* (i + 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...])\",\n \"convert(pi_mask) z_mask = convert(z_mask) z = convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi,\",\n \"0 count = 0 for iter in range(start_iter, config.total_iterations): print('iteration: %s' % iter)\",\n \"count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)): if x[i] >\",\n \"1 elif array[i, j] == config.period_token: new_array[i, :j + 1] = 1 break\",\n \"- start_time) # decompresses sampled pi's # takes about 0.005s new_pi = np.zeros(((config.batch_size\",\n \"import torch.nn as nn import torch.optim as optimizer import glob import os #from\",\n \"range(len(array)): for j in range(len(array[i])): if j == len(array[i]) - 1: new_array[i, :]\",\n \"~ 0.1s # TODO: if error, set a lock # translate, _ =\",\n \"j, :] = -1 else: # Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i,\",\n \"= convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z,\",\n \"32 self-play games: ', 32 * (time() - t_inf) / df) t_inf =\",\n \"elif array[i, j] == config.blank_token: new_array[i, :j] = 1 break return new_array def\",\n \"times for a dramatic speedup. num_segments = 8 for i in range(num_segments): tmp.append(\",\n \"z = convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask +\",\n \"import torch import torch.nn as nn import torch.optim as optimizer import glob import\",\n \"- t_inf) / df) t_inf = time() count = cur_row if cur_row >=\",\n \"in range(config.max_length): if pi[i, j, 0] == -1: # meaning the terminal state;\",\n \"for i in range(len(array)): for j in range(len(array[i])): if j == len(array[i]) -\",\n \"time() # gradient update model.train() cache = [] for i in range(config.depth //\",\n \"# np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(),\",\n \"config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2\",\n \"> 0.01: print(i, x[i], translate[i]) break''' if count == 0: count = cur_row\",\n \"for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s =\",\n \"update: %s seconds\\\" % (time() - t2)) print(\\\"iteration: %s seconds\\\" % (time() -\",\n \"8 for i in range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments * i:(config.batch_size\",\n \"pi_mask = convert(pi_mask) z_mask = convert(z_mask) z = convert(z) pi = convert(pi) loss\",\n \"pi's # takes about 0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for\",\n \"os #from scipy.stats import rankdata from lstm import Model, initialize from Optim import\",\n \"# takes about 0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i\",\n \"generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size // 2),\",\n \"j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi #\",\n \"torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\\\"grad update: %s\",\n \"config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else: r =\",\n \"// 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient\",\n \"a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\\\"buffer\\\", \\\"r\\\") as f:\",\n \"= convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred): return torch.sum(-y_true\",\n \"100000 == 0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads the randomly\",\n \"about 0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i in range((config.batch_size\",\n \"t_inf = time() if count != 0 and df >= 1000: print('time required\",\n \"2000) list_of_files = glob.glob(config.model_path + '/*') latest_file = None if list_of_files: latest_file =\",\n \"for i in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch\",\n \"count = cur_row t_inf = time() if count != 0 and df >=\",\n \"# print('io time: ',time() - start_time) # decompresses sampled pi's # takes about\",\n \"j] == config.period_token: new_array[i, :j + 1] = 1 break elif array[i, j]\",\n \"with h5py.File(\\\"buffer\\\", \\\"r\\\") as f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer =\",\n \"// 2) // num_segments * (i + 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i],\",\n \"z_tmp = [] df = cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000\",\n \"sum(pi[i, j, :]) == 0: # meaning the padding; place -1 padding new_pi[i,\",\n \"modified here, since the mask will be modified appropriately _, pi_mask = pi_mask(s)\",\n \"== 0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads the randomly sampled\",\n \"= None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt\",\n \"filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path\",\n \"as optimizer import glob import os #from scipy.stats import rankdata from lstm import\",\n \":config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp,\",\n \"= convert(z_mask) z = convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) *\",\n \"the terminal state; pi=0 new_pi[i, j, :] = 0 elif pi[i, j, 0]\",\n \"= f['/pi'] z_buffer = f['/z'] s_tmp = [] pi_tmp = [] z_tmp =\",\n \"latest_file # model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt)\",\n \"0] == -2 or sum(pi[i, j, :]) == 0: # meaning the padding;\",\n \"- start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model'\",\n \"sleep import h5py import torch import torch.nn as nn import torch.optim as optimizer\",\n \">= 1000: print('time required for 32 self-play games: ', 32 * (time() -\",\n \"if j == len(array[i]) - 1: new_array[i, :] = 1 elif array[i, j]\",\n \"= model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1)\",\n \"print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter =\",\n \"in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long()\",\n \"\\\"r\\\") as f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer\",\n \"loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred): return\",\n \"key=os.path.getctime) model_ckpt = latest_file # model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt:\",\n \"= latest_file # model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint =\",\n \"= cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\\\"buffer\\\", \\\"r\\\") as f: cur_row = int(f['/cur_row'][0]) s_buffer =\",\n \"pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0)\",\n \"= ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000)\",\n \"elif pi[i, j, 0] == -2 or sum(pi[i, j, :]) == 0: #\",\n \"500000 for i in range(len(x)): if x[i] > 0.01: print(i, x[i], translate[i]) break'''\",\n \"np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) # print('io time:\",\n \"np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size\",\n \"[torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache))\",\n \"- count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)): if x[i]\",\n \"that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size)\",\n \"seconds\\\" % (time() - t2)) print(\\\"iteration: %s seconds\\\" % (time() - start_time)) checkpoint\",\n \"error, set a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\\\"buffer\\\", \\\"r\\\")\",\n \"= time() if count != 0 and df >= 1000: print('time required for\",\n \"print(gn) optim.step() optim.zero_grad() print(\\\"grad update: %s seconds\\\" % (time() - t2)) print(\\\"iteration: %s\",\n \"2) def loss_value(y_true, y_pred): return (y_true - y_pred) ** 2 pi_mask = convert(pi_mask)\",\n \"1] = 1 break elif array[i, j] == config.blank_token: new_array[i, :j] = 1\",\n \"torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim = ScheduledOptim( optimizer.Adam( filter(lambda p: p.requires_grad,\",\n \"as f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer =\",\n \"= nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\\\"grad update: %s seconds\\\" % (time() -\",\n \"+ 1] = 1 break elif array[i, j] == config.blank_token: new_array[i, :j] =\",\n \"loss_value(z, v) * z_mask , 1) / torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(),\",\n \"- 1: new_array[i, :] = 1 elif array[i, j] == config.period_token: new_array[i, :j\",\n \"= torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v) * z_mask , 1) /\",\n \"z_mask , 1) / torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step()\",\n \"model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) #\",\n \"= 1 elif array[i, j] == config.period_token: new_array[i, :j + 1] = 1\",\n \"torch.nn as nn import torch.optim as optimizer import glob import os #from scipy.stats\",\n \"optim.zero_grad() print(\\\"grad update: %s seconds\\\" % (time() - t2)) print(\\\"iteration: %s seconds\\\" %\",\n \"random from time import time, sleep import h5py import torch import torch.nn as\",\n \"#if (iter + 1) % 100000 == 0: # lr_scheduler.step() start_time = time()\",\n \"iter in range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if (iter + 1) %\",\n \"= np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time)\",\n \"list_of_files: latest_file = max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt = config.model_path +\",\n \"== 0: count = cur_row t_inf = time() if count != 0 and\",\n \"+ '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0]\",\n \"def start(config): model = Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9,\",\n \"def pi_mask(array): array = array[:, 1:] array = np.pad(array, ((0, 0), (0, 1)),\",\n \"model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer'])\",\n \"cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else:\",\n \"= time() # gradient update model.train() cache = [] for i in range(config.depth\",\n \"s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) #\",\n \"sampled pi's # takes about 0.005s new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size))\",\n \"2), replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp\",\n \"translate[i]) break''' if count == 0: count = cur_row t_inf = time() if\",\n \"h5py import torch import torch.nn as nn import torch.optim as optimizer import glob\",\n \"z = np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time) # decompresses sampled\",\n \":] = 0 elif pi[i, j, 0] == -2 or sum(pi[i, j, :])\",\n \"generate_mask(array) # pi_tmp isn't modified here, since the mask will be modified appropriately\",\n \"isn't modified here, since the mask will be modified appropriately _, pi_mask =\",\n \"0: count = cur_row t_inf = time() if count != 0 and df\",\n \"modified appropriately _, pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size\",\n \"= optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters\",\n \"speedup. num_segments = 8 for i in range(num_segments): tmp.append( r[(config.batch_size // 2) //\",\n \"int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp = []\",\n \"# translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\\\"buffer\\\", \\\"r\\\") as f: cur_row =\",\n \"* (-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 =\",\n \"0: # lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s\",\n \", 1) / torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad()\",\n \"= np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])]) def\",\n \"range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if (iter + 1) % 100000 ==\",\n \"* (time() - t_inf) / df) t_inf = time() count = cur_row if\",\n \"config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache)) def\",\n \"range(len(array[i])): if j == len(array[i]) - 1: new_array[i, :] = 1 elif array[i,\",\n \"import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model =\",\n \"= 0 count = 0 for iter in range(start_iter, config.total_iterations): print('iteration: %s' %\",\n \"i in range((config.batch_size // 2)): for j in range(config.max_length): if pi[i, j, 0]\",\n \"...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z =\",\n \"pi_mask + loss_value(z, v) * z_mask , 1) / torch.sum(z_mask, 1)) loss.backward() gn\",\n \"cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)): if\",\n \"array = array[:, 1:] array = np.pad(array, ((0, 0), (0, 1)), 'constant') return\",\n \"optim.step() optim.zero_grad() print(\\\"grad update: %s seconds\\\" % (time() - t2)) print(\\\"iteration: %s seconds\\\"\",\n \"= 0 elif pi[i, j, 0] == -2 or sum(pi[i, j, :]) ==\",\n \"buffer # ~ 0.1s # TODO: if error, set a lock # translate,\",\n \"torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true - y_pred) ** 2\",\n \"2), config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device)\",\n \"/ config.simulation_num_per_move pi = new_pi # creating a mask for loss function and\",\n \"= np.zeros_like(array) for i in range(len(array)): for j in range(len(array[i])): if j ==\",\n \"np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp = [] # randomly sample\",\n \"% (time() - t2)) print(\\\"iteration: %s seconds\\\" % (time() - start_time)) checkpoint =\",\n \"= convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v) * z_mask\",\n \"config.vocab_size)) for i in range((config.batch_size // 2)): for j in range(config.max_length): if pi[i,\",\n \"new_pi[i, j, :] = 0 elif pi[i, j, 0] == -2 or sum(pi[i,\",\n \"z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp,\",\n \"model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else: model.apply(initialize) start_iter = 0 count = 0 for\",\n \"config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter =\",\n \"config.buffer_size)), (config.batch_size // 2), replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size //\",\n \"h5py.File(\\\"buffer\\\", \\\"r\\\") as f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi']\",\n \"0) # print('io time: ',time() - start_time) # decompresses sampled pi's # takes\",\n \"= new_pi # creating a mask for loss function and preparing a minibatch\",\n \"new_pi = np.zeros(((config.batch_size // 2), config.max_length, config.vocab_size)) for i in range((config.batch_size // 2)):\",\n \"if x[i] > 0.01: print(i, x[i], translate[i]) break''' if count == 0: count\",\n \"_pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model = model.to(config.device)\",\n \"np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)): if x[i] > 0.01: print(i, x[i],\",\n \"print(i, x[i], translate[i]) break''' if count == 0: count = cur_row t_inf =\",\n \"# pi_tmp isn't modified here, since the mask will be modified appropriately _,\",\n \"0), (0, 1)), 'constant') return generate_mask(array) # pi_tmp isn't modified here, since the\",\n \"# import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model = Model(config) model\",\n \"2) // num_segments * (i + 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length])\",\n \"and preparing a minibatch def generate_mask(array): new_array = np.zeros_like(array) for i in range(len(array)):\",\n \"range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size, config.hidden_dim,device=config.device), torch.zeros(config.batch_size, config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy,\",\n \"== -2 or sum(pi[i, j, :]) == 0: # meaning the padding; place\",\n \"in range(len(array)): for j in range(len(array[i])): if j == len(array[i]) - 1: new_array[i,\",\n \"s_tmp = [] pi_tmp = [] z_tmp = [] df = cur_row -\",\n \"new_pi # creating a mask for loss function and preparing a minibatch def\",\n \"time, sleep import h5py import torch import torch.nn as nn import torch.optim as\",\n \">= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)), (config.batch_size // 2), replace=False)) else: r\",\n \"rows 8 times for a dramatic speedup. num_segments = 8 for i in\",\n \"x[i] > 0.01: print(i, x[i], translate[i]) break''' if count == 0: count =\",\n \"terminal state; pi=0 new_pi[i, j, :] = 0 elif pi[i, j, 0] ==\",\n \"[0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi =\",\n \"def loss_value(y_true, y_pred): return (y_true - y_pred) ** 2 pi_mask = convert(pi_mask) z_mask\",\n \"initialize from Optim import ScheduledOptim # import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def\",\n \"# gradient update model.train() cache = [] for i in range(config.depth // config.unit_depth):\",\n \"<gh_stars>1-10 import numpy as np import random from time import time, sleep import\",\n \"% iter) #if (iter + 1) % 100000 == 0: # lr_scheduler.step() start_time\",\n \"from Optim import ScheduledOptim # import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config):\",\n \"optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim\",\n \"i:(config.batch_size // 2) // num_segments * (i + 1)]) for i in range(num_segments):\",\n \"translate, _ = cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\\\"buffer\\\", \\\"r\\\") as f: cur_row = int(f['/cur_row'][0])\",\n \"scipy.stats import rankdata from lstm import Model, initialize from Optim import ScheduledOptim #\",\n \"count = 0 for iter in range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if\",\n \"cPickle.load(open('save/vocab_cotra.pkl', 'rb')) with h5py.File(\\\"buffer\\\", \\\"r\\\") as f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s']\",\n \"policy) * pi_mask + loss_value(z, v) * z_mask , 1) / torch.sum(z_mask, 1))\",\n \"= f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp = [] pi_tmp =\",\n \"# Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j,\",\n \"a dramatic speedup. num_segments = 8 for i in range(num_segments): tmp.append( r[(config.batch_size //\",\n \"in range((config.batch_size // 2)): for j in range(config.max_length): if pi[i, j, 0] ==\",\n \"glob import os #from scipy.stats import rankdata from lstm import Model, initialize from\",\n \"[] # randomly sample rows 8 times for a dramatic speedup. num_segments =\",\n \"j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi # creating a mask for\",\n \"+ loss_value(z, v) * z_mask , 1) / torch.sum(z_mask, 1)) loss.backward() gn =\",\n \"...]) s = np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0)\",\n \"ScheduledOptim # import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model = Model(config)\",\n \"if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter)\",\n \"config.hidden_dim,device=config.device)] s_batch = convert(np.array(s)).long() policy, v, cache = model(s_batch, tuple(cache)) def loss_policy(y_true, y_pred):\",\n \"z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size // 2), config.max_length])])\",\n \"if count == 0: count = cur_row t_inf = time() if count !=\",\n \"the mask will be modified appropriately _, pi_mask = pi_mask(s) z_mask = generate_mask(s)\",\n \"j in range(config.max_length): if pi[i, j, 0] == -1: # meaning the terminal\",\n \":]) == 0: # meaning the padding; place -1 padding new_pi[i, j, :]\",\n \"f: cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z']\",\n \"Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim,\",\n \"(i + 1)]) for i in range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i],\",\n \"print(\\\"iteration: %s seconds\\\" % (time() - start_time)) checkpoint = {'state_dict': model.state_dict(), 'optimizer': optim.optimizer.state_dict()}\",\n \"lr=config.lr, betas=(0.9, 0.98), eps=1e-09), config.hidden_dim, 2000) list_of_files = glob.glob(config.model_path + '/*') latest_file =\",\n \"= config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter\",\n \"for j in range(len(array[i])): if j == len(array[i]) - 1: new_array[i, :] =\",\n \"dramatic speedup. num_segments = 8 for i in range(num_segments): tmp.append( r[(config.batch_size // 2)\",\n \"np.pad(array, ((0, 0), (0, 1)), 'constant') return generate_mask(array) # pi_tmp isn't modified here,\",\n \"for i in range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments * i:(config.batch_size //\",\n \"pi[i, j, 0] == -2 or sum(pi[i, j, :]) == 0: # meaning\",\n \"here, since the mask will be modified appropriately _, pi_mask = pi_mask(s) z_mask\",\n \"Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int),\",\n \"= glob.glob(config.model_path + '/*') latest_file = None if list_of_files: latest_file = max(list_of_files, key=os.path.getctime)\",\n \"2), config.max_length, config.vocab_size)) for i in range((config.batch_size // 2)): for j in range(config.max_length):\",\n \"generate_mask(array): new_array = np.zeros_like(array) for i in range(len(array)): for j in range(len(array[i])): if\",\n \"1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn) optim.step() optim.zero_grad() print(\\\"grad update: %s seconds\\\"\",\n \"2)): for j in range(config.max_length): if pi[i, j, 0] == -1: # meaning\",\n \"reads the randomly sampled (s,pi,z)'s from the buffer # ~ 0.1s # TODO:\",\n \"games: ', 32 * (time() - t_inf) / df) t_inf = time() count\",\n \"cur_row)), (config.batch_size // 2), replace=False)) tmp = [] # randomly sample rows 8\",\n \"# meaning the terminal state; pi=0 new_pi[i, j, :] = 0 elif pi[i,\",\n \"= np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move pi = new_pi # creating a\",\n \"model_ckpt = latest_file # model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if model_ckpt: checkpoint\",\n \"= Model(config) model = model.to(config.device) #optim = optimizer.SGD(model.parameters(), lr=2e-4, momentum=0.9, weight_decay=config.c) #lr_scheduler =\",\n \"j, :] = 0 elif pi[i, j, 0] == -2 or sum(pi[i, j,\",\n \"torch.optim as optimizer import glob import os #from scipy.stats import rankdata from lstm\",\n \"j, 0] == -1: # meaning the terminal state; pi=0 new_pi[i, j, :]\",\n \"cur_row = int(f['/cur_row'][0]) s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp\",\n \"= generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size //\",\n \"import numpy as np import random from time import time, sleep import h5py\",\n \"* z_mask , 1) / torch.sum(z_mask, 1)) loss.backward() gn = nn.utils.clip_grad_norm(model.parameters(), config.clip) print(gn)\",\n \"in range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments * i:(config.batch_size // 2) //\",\n \"nn import torch.optim as optimizer import glob import os #from scipy.stats import rankdata\",\n \"return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true -\",\n \"= -1 else: # Beware that np.bincount's bin is [0,1,...min_length-1] new_pi[i, j, :]\",\n \"1.0e-8), 2) def loss_value(y_true, y_pred): return (y_true - y_pred) ** 2 pi_mask =\",\n \"[] z_tmp = [] df = cur_row - count '''x = np.bincount(s_buffer[:,1].astype(int)) /\",\n \"= [] # randomly sample rows 8 times for a dramatic speedup. num_segments\",\n \"= 0 for iter in range(start_iter, config.total_iterations): print('iteration: %s' % iter) #if (iter\",\n \"start_iter = 0 count = 0 for iter in range(start_iter, config.total_iterations): print('iteration: %s'\",\n \"update model.train() cache = [] for i in range(config.depth // config.unit_depth): cache +=\",\n \"def loss_policy(y_true, y_pred): return torch.sum(-y_true * torch.log(y_pred + 1.0e-8), 2) def loss_value(y_true, y_pred):\",\n \"(time() - t_inf) / df) t_inf = time() count = cur_row if cur_row\",\n \"0.1s # TODO: if error, set a lock # translate, _ = cPickle.load(open('save/vocab_cotra.pkl',\",\n \"z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length]) * (-1), np.ones([(config.batch_size\",\n \"import Model, initialize from Optim import ScheduledOptim # import _pickle as cPickle #\",\n \"model.train() cache = [] for i in range(config.depth // config.unit_depth): cache += [torch.zeros(config.batch_size,\",\n \"optim.optimizer.state_dict()} sleep(config.training_sleep_time) torch.save(checkpoint, config.model_path + '/model' + '-' + str(iter + 1) +\",\n \"in range(len(array[i])): if j == len(array[i]) - 1: new_array[i, :] = 1 elif\",\n \"1000: print('time required for 32 self-play games: ', 32 * (time() - t_inf)\",\n \"a mask for loss function and preparing a minibatch def generate_mask(array): new_array =\",\n \"cur_row t_inf = time() if count != 0 and df >= 1000: print('time\",\n \"Optim import ScheduledOptim # import _pickle as cPickle # np.set_printoptions(threshold=np.nan) def start(config): model\",\n \"weight_decay=config.c) #lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=200, gamma=0.1) # 20M iters optim = ScheduledOptim( optimizer.Adam(\",\n \"place -1 padding new_pi[i, j, :] = -1 else: # Beware that np.bincount's\",\n \"j == len(array[i]) - 1: new_array[i, :] = 1 elif array[i, j] ==\",\n \"0] == -1: # meaning the terminal state; pi=0 new_pi[i, j, :] =\",\n \"meaning the terminal state; pi=0 new_pi[i, j, :] = 0 elif pi[i, j,\",\n \"range(num_segments): s_tmp.append(s_buffer[tmp[i], :config.max_length]) pi_tmp.append(pi_buffer[tmp[i], :config.max_length, ...]) z_tmp.append(z_buffer[tmp[i], ...]) s = np.concatenate(s_tmp, 0) pi\",\n \":j + 1] = 1 break elif array[i, j] == config.blank_token: new_array[i, :j]\",\n \"- t2)) print(\\\"iteration: %s seconds\\\" % (time() - start_time)) checkpoint = {'state_dict': model.state_dict(),\",\n \"config.max_length, config.vocab_size)) for i in range((config.batch_size // 2)): for j in range(config.max_length): if\",\n \"2), config.max_length])]) def convert(x): return torch.tensor(x.astype(np.float32), device=config.device) t2 = time() # gradient update\",\n \"pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v) *\",\n \"lr_scheduler.step() start_time = time() optim.update_learning_rate(iter) # reads the randomly sampled (s,pi,z)'s from the\",\n \"time() count = cur_row if cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0, config.buffer_size)),\",\n \"max(list_of_files, key=os.path.getctime) model_ckpt = latest_file # model_ckpt = config.model_path + '/model-454.pth' print(model_ckpt) if\",\n \"pi_tmp isn't modified here, since the mask will be modified appropriately _, pi_mask\",\n \"pi_mask = pi_mask(s) z_mask = generate_mask(s) z_batch = np.concatenate( [np.ones([(config.batch_size // 2), config.max_length])\",\n \"// 2) // num_segments * i:(config.batch_size // 2) // num_segments * (i +\",\n \"num_segments = 8 for i in range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments\",\n \"bin is [0,1,...min_length-1] new_pi[i, j, :] = np.bincount(pi[i, j, :].astype(int), minlength=config.vocab_size) / config.simulation_num_per_move\",\n \"1 break return new_array def pi_mask(array): array = array[:, 1:] array = np.pad(array,\",\n \"model_ckpt: checkpoint = torch.load(model_ckpt) model.load_state_dict(checkpoint['state_dict']) optim.optimizer.load_state_dict(checkpoint['optimizer']) start_iter = model_ckpt.split('-')[-1].split('.')[0] start_iter = int(start_iter) else:\",\n \"gradient update model.train() cache = [] for i in range(config.depth // config.unit_depth): cache\",\n \"= np.concatenate(s_tmp, 0) pi = np.concatenate(pi_tmp, 0) z = np.concatenate(z_tmp, 0) # print('io\",\n \"from time import time, sleep import h5py import torch import torch.nn as nn\",\n \"!= 0 and df >= 1000: print('time required for 32 self-play games: ',\",\n \"pi=0 new_pi[i, j, :] = 0 elif pi[i, j, 0] == -2 or\",\n \"f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp = [] pi_tmp = []\",\n \"'''x = np.bincount(s_buffer[:,1].astype(int)) / 500000 for i in range(len(x)): if x[i] > 0.01:\",\n \"if pi[i, j, 0] == -1: # meaning the terminal state; pi=0 new_pi[i,\",\n \"tmp.append( r[(config.batch_size // 2) // num_segments * i:(config.batch_size // 2) // num_segments *\",\n \"replace=False)) else: r = np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp =\",\n \"-1: # meaning the terminal state; pi=0 new_pi[i, j, :] = 0 elif\",\n \"= time() count = cur_row if cur_row >= config.buffer_size: r = np.sort( np.random.choice(list(range(0,\",\n \"pi = new_pi # creating a mask for loss function and preparing a\",\n \"z_mask = convert(z_mask) z = convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy)\",\n \"pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp = [] pi_tmp = [] z_tmp\",\n \"convert(z) pi = convert(pi) loss = torch.mean(torch.sum(loss_policy(pi, policy) * pi_mask + loss_value(z, v)\",\n \"1: new_array[i, :] = 1 elif array[i, j] == config.period_token: new_array[i, :j +\",\n \"range(num_segments): tmp.append( r[(config.batch_size // 2) // num_segments * i:(config.batch_size // 2) // num_segments\",\n \"self-play games: ', 32 * (time() - t_inf) / df) t_inf = time()\",\n \"= np.sort( np.random.choice(list(range(0, cur_row)), (config.batch_size // 2), replace=False)) tmp = [] # randomly\",\n \"s_buffer = f['/s'] pi_buffer = f['/pi'] z_buffer = f['/z'] s_tmp = [] pi_tmp\"\n]","isConversational":false}}},{"rowIdx":2991,"cells":{"ngram":{"kind":"list like","value":["code += f'execute if score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' else:","+= c1 code += t1.cast(ot, tt1, target) code += c2 code += ot.binary(expression.op,","= Int() if target is None or target == []: target = [get_temp()","ttt in tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot = cast(t1, t2)","in range(ot.size)] used_temps.extend(target) code = '' if expression.op in ['&&', '||']: if expression.op","f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute if score {target[0]} {NAMESPACE} matches 0","== t1: code += c1 code += c2 code += t2.cast(ot, tt2, target)","if expression.op in ['&&', '||']: if expression.op == '&&': code += c1 code","casting import cast from globals_consts import NAMESPACE from temps import used_temps, get_temp, get_temp_func","{NAMESPACE}:{f2}\\n' else: if ot == t1: code += c1 code += c2 code","+= c2 code += t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1, target, target)","import Int from casting import cast from globals_consts import NAMESPACE from temps import","ot = cast(t1, t2) rt = ot if expression.op in ['<', '>', '<=',","in tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt = ot if expression.op in","run function {NAMESPACE}:{f2}\\n' elif expression.op == '||': code += c1 code += t1.cast(ot,","tt2, target) code += ot.binary(expression.op, tt1, target, target) else: code += c1 code","else: if ot == t1: code += c1 code += c2 code +=","code += ot.binary(expression.op, tt1, target, target) else: code += c1 code += t1.cast(ot,","expression import generate_expression c1, t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False)","code += c2 code += ot.binary(expression.op, target, tt2, target) return code, rt, target","'||']: if expression.op == '&&': code += c1 code += t1.cast(ot, tt1, target)","in ['<', '>', '<=', '>=', '==', '!=', '&&']: rt = Int() if target","c2 code += t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1, target, target) else:","copy_strings, False) for ttt in tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot","rt = ot if expression.op in ['<', '>', '<=', '>=', '==', '!=', '&&']:","f'execute unless score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' elif expression.op ==","code += c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func() f2h =","open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute unless score {target[0]}","used_temps.remove(ttt) ot = cast(t1, t2) rt = ot if expression.op in ['<', '>',","{NAMESPACE}:{f2}\\n' elif expression.op == '||': code += c1 code += t1.cast(ot, tt1, target)","target) code += c2 code += ot.binary(expression.op, target, tt2, target) return code, rt,","= open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute if score","target) else: code += c1 code += t1.cast(ot, tt1, target) code += c2","{target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' else: if ot == t1: code","tt1, target) code += c2 code += ot.binary(expression.op, target, tt2, target) return code,","+= t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1, target, target) else: code +=","rt = Int() if target is None or target == []: target =","if score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' else: if ot ==","get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression import generate_expression c1,","= generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2, t2, tt2 = generate_expression(None, expression.right,","matches 0 run function {NAMESPACE}:{f2}\\n' elif expression.op == '||': code += c1 code","expression, target, variables_name, vtypes): from expression import generate_expression c1, t1, tt1 = generate_expression(None,","+= f'execute unless score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' elif expression.op","copy_strings, False) c2, t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for","open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute if score {target[0]}","used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt = ot","import NAMESPACE from temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name,","code = '' if expression.op in ['&&', '||']: if expression.op == '&&': code","c1 code += c2 code += t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1,","from expression import generate_expression c1, t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings,","globals_consts import NAMESPACE from temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target,","target = [get_temp() for _ in range(ot.size)] used_temps.extend(target) code = '' if expression.op","= generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for ttt in tt1: used_temps.remove(ttt) for","range(ot.size)] used_temps.extend(target) code = '' if expression.op in ['&&', '||']: if expression.op ==","f'execute if score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' else: if ot","Int from casting import cast from globals_consts import NAMESPACE from temps import used_temps,","+= t1.cast(ot, tt1, target) code += c2 code += ot.binary(expression.op, target, tt2, target)","import cast from globals_consts import NAMESPACE from temps import used_temps, get_temp, get_temp_func def","'&&']: rt = Int() if target is None or target == []: target","run function {NAMESPACE}:{f2}\\n' else: if ot == t1: code += c1 code +=","f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code","target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close()","= [get_temp() for _ in range(ot.size)] used_temps.extend(target) code = '' if expression.op in","tt2, target)) f2h.close() code += f'execute if score {target[0]} {NAMESPACE} matches 0 run","else: code += c1 code += t1.cast(ot, tt1, target) code += c2 code","code += t1.cast(ot, tt1, target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2)","t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1, target, target) else: code += c1","+= ot.binary(expression.op, tt1, target, target) else: code += c1 code += t1.cast(ot, tt1,","expression.op == '||': code += c1 code += t1.cast(ot, tt1, target) f2 =","== '&&': code += c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func()","== []: target = [get_temp() for _ in range(ot.size)] used_temps.extend(target) code = ''","+= f'execute if score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' else: if","'==', '!=', '&&']: rt = Int() if target is None or target ==","tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2, t2, tt2 = generate_expression(None,","import generate_expression c1, t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2,","{target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' elif expression.op == '||': code +=","import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression import","None or target == []: target = [get_temp() for _ in range(ot.size)] used_temps.extend(target)","target == []: target = [get_temp() for _ in range(ot.size)] used_temps.extend(target) code =","NAMESPACE from temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes):","tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt =","expression.op in ['<', '>', '<=', '>=', '==', '!=', '&&']: rt = Int() if","c1, t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2, t2, tt2","['&&', '||']: if expression.op == '&&': code += c1 code += t1.cast(ot, tt1,","'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute unless score {target[0]} {NAMESPACE}","code += c1 code += t1.cast(ot, tt1, target) code += c2 code +=","t2) rt = ot if expression.op in ['<', '>', '<=', '>=', '==', '!=',","if target is None or target == []: target = [get_temp() for _","tt1, target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target))","{NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' elif expression.op == '||': code += c1","'!=', '&&']: rt = Int() if target is None or target == []:","code += c1 code += c2 code += t2.cast(ot, tt2, target) code +=","used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression import generate_expression","def binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression import generate_expression c1, t1, tt1","for ttt in tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot = cast(t1,","if expression.op in ['<', '>', '<=', '>=', '==', '!=', '&&']: rt = Int()","target is None or target == []: target = [get_temp() for _ in","tt1, target, target) else: code += c1 code += t1.cast(ot, tt1, target) code","function {NAMESPACE}:{f2}\\n' else: if ot == t1: code += c1 code += c2","f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute if score {target[0]} {NAMESPACE} matches","= open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute unless score","{NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' else: if ot == t1: code +=","if ot == t1: code += c1 code += c2 code += t2.cast(ot,","+= t1.cast(ot, tt1, target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot,","unless score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' elif expression.op == '||':","t1: code += c1 code += c2 code += t2.cast(ot, tt2, target) code","[]: target = [get_temp() for _ in range(ot.size)] used_temps.extend(target) code = '' if","generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2, t2, tt2 = generate_expression(None, expression.right, vtypes,","temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression","[get_temp() for _ in range(ot.size)] used_temps.extend(target) code = '' if expression.op in ['&&',","['<', '>', '<=', '>=', '==', '!=', '&&']: rt = Int() if target is","_ in range(ot.size)] used_temps.extend(target) code = '' if expression.op in ['&&', '||']: if","'&&': code += c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func() f2h","f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute if","expression.left, vtypes, variables_name, copy_strings, False) c2, t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name,","code += f'execute unless score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' elif","+= c1 code += c2 code += t2.cast(ot, tt2, target) code += ot.binary(expression.op,","c2, t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for ttt in","cast from globals_consts import NAMESPACE from temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings,","False) c2, t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for ttt","tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for ttt in tt1: used_temps.remove(ttt)","= '' if expression.op in ['&&', '||']: if expression.op == '&&': code +=","expression.right, vtypes, variables_name, copy_strings, False) for ttt in tt1: used_temps.remove(ttt) for ttt in","for _ in range(ot.size)] used_temps.extend(target) code = '' if expression.op in ['&&', '||']:","expression.op in ['&&', '||']: if expression.op == '&&': code += c1 code +=","= get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code +=","tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt = ot if expression.op in ['<',","in tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt","f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute unless score {target[0]} {NAMESPACE} matches","cast(t1, t2) rt = ot if expression.op in ['<', '>', '<=', '>=', '==',","t1.cast(ot, tt1, target) code += c2 code += ot.binary(expression.op, target, tt2, target) return","'>=', '==', '!=', '&&']: rt = Int() if target is None or target","score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' elif expression.op == '||': code","c1 code += t1.cast(ot, tt1, target) code += c2 code += ot.binary(expression.op, target,","target, variables_name, vtypes): from expression import generate_expression c1, t1, tt1 = generate_expression(None, expression.left,","target) code += ot.binary(expression.op, tt1, target, target) else: code += c1 code +=","variables_name, vtypes): from expression import generate_expression c1, t1, tt1 = generate_expression(None, expression.left, vtypes,","0 run function {NAMESPACE}:{f2}\\n' elif expression.op == '||': code += c1 code +=","t1.cast(ot, tt1, target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2,","code += t1.cast(ot, tt1, target) code += c2 code += ot.binary(expression.op, target, tt2,","from c_int import Int from casting import cast from globals_consts import NAMESPACE from","t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for ttt in tt1:","generate_expression c1, t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2, t2,","ot == t1: code += c1 code += c2 code += t2.cast(ot, tt2,","function {NAMESPACE}:{f2}\\n' elif expression.op == '||': code += c1 code += t1.cast(ot, tt1,","f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute unless score {target[0]} {NAMESPACE} matches 0","get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute","code += c2 code += t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1, target,","f2h.close() code += f'execute unless score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n'","vtypes, variables_name, copy_strings, False) for ttt in tt1: used_temps.remove(ttt) for ttt in tt2:","'||': code += c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func() f2h","'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute if score {target[0]} {NAMESPACE}","target)) f2h.close() code += f'execute if score {target[0]} {NAMESPACE} matches 0 run function","get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression import generate_expression c1, t1,","ttt in tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt = ot if expression.op","vtypes, variables_name, copy_strings, False) c2, t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings,","= ot if expression.op in ['<', '>', '<=', '>=', '==', '!=', '&&']: rt","matches 0 run function {NAMESPACE}:{f2}\\n' else: if ot == t1: code += c1","ot if expression.op in ['<', '>', '<=', '>=', '==', '!=', '&&']: rt =","'<=', '>=', '==', '!=', '&&']: rt = Int() if target is None or","if expression.op == '&&': code += c1 code += t1.cast(ot, tt1, target) f2","== '||': code += c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func()","generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for ttt in tt1: used_temps.remove(ttt) for ttt","0 run function {NAMESPACE}:{f2}\\n' else: if ot == t1: code += c1 code","used_temps.extend(target) code = '' if expression.op in ['&&', '||']: if expression.op == '&&':","ot.binary(expression.op, tt1, target, target) else: code += c1 code += t1.cast(ot, tt1, target)","t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2, t2, tt2 =","tt2, target)) f2h.close() code += f'execute unless score {target[0]} {NAMESPACE} matches 0 run","from temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes): from","'>', '<=', '>=', '==', '!=', '&&']: rt = Int() if target is None","variables_name, copy_strings, False) c2, t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False)","c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w')","code += t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1, target, target) else: code","from globals_consts import NAMESPACE from temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression,","target)) f2h.close() code += f'execute unless score {target[0]} {NAMESPACE} matches 0 run function","is None or target == []: target = [get_temp() for _ in range(ot.size)]","f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute unless","c_int import Int from casting import cast from globals_consts import NAMESPACE from temps","or target == []: target = [get_temp() for _ in range(ot.size)] used_temps.extend(target) code","'' if expression.op in ['&&', '||']: if expression.op == '&&': code += c1","= cast(t1, t2) rt = ot if expression.op in ['<', '>', '<=', '>=',","expression.op == '&&': code += c1 code += t1.cast(ot, tt1, target) f2 =","score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' else: if ot == t1:","for ttt in tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt = ot if","from casting import cast from globals_consts import NAMESPACE from temps import used_temps, get_temp,","binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression import generate_expression c1, t1, tt1 =","+= c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction',","elif expression.op == '||': code += c1 code += t1.cast(ot, tt1, target) f2","target, target) else: code += c1 code += t1.cast(ot, tt1, target) code +=","variables_name, copy_strings, False) for ttt in tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt)","in ['&&', '||']: if expression.op == '&&': code += c1 code += t1.cast(ot,","f2h.close() code += f'execute if score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n'","Int() if target is None or target == []: target = [get_temp() for","vtypes): from expression import generate_expression c1, t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name,","False) for ttt in tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot ="],"string":"[\n \"code += f'execute if score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' else:\",\n \"+= c1 code += t1.cast(ot, tt1, target) code += c2 code += ot.binary(expression.op,\",\n \"= Int() if target is None or target == []: target = [get_temp()\",\n \"ttt in tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot = cast(t1, t2)\",\n \"in range(ot.size)] used_temps.extend(target) code = '' if expression.op in ['&&', '||']: if expression.op\",\n \"f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute if score {target[0]} {NAMESPACE} matches 0\",\n \"== t1: code += c1 code += c2 code += t2.cast(ot, tt2, target)\",\n \"if expression.op in ['&&', '||']: if expression.op == '&&': code += c1 code\",\n \"casting import cast from globals_consts import NAMESPACE from temps import used_temps, get_temp, get_temp_func\",\n \"{NAMESPACE}:{f2}\\\\n' else: if ot == t1: code += c1 code += c2 code\",\n \"+= c2 code += t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1, target, target)\",\n \"import Int from casting import cast from globals_consts import NAMESPACE from temps import\",\n \"ot = cast(t1, t2) rt = ot if expression.op in ['<', '>', '<=',\",\n \"in tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt = ot if expression.op in\",\n \"run function {NAMESPACE}:{f2}\\\\n' elif expression.op == '||': code += c1 code += t1.cast(ot,\",\n \"tt2, target) code += ot.binary(expression.op, tt1, target, target) else: code += c1 code\",\n \"else: if ot == t1: code += c1 code += c2 code +=\",\n \"code += ot.binary(expression.op, tt1, target, target) else: code += c1 code += t1.cast(ot,\",\n \"expression import generate_expression c1, t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False)\",\n \"code += c2 code += ot.binary(expression.op, target, tt2, target) return code, rt, target\",\n \"'||']: if expression.op == '&&': code += c1 code += t1.cast(ot, tt1, target)\",\n \"in ['<', '>', '<=', '>=', '==', '!=', '&&']: rt = Int() if target\",\n \"c2 code += t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1, target, target) else:\",\n \"copy_strings, False) for ttt in tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot\",\n \"rt = ot if expression.op in ['<', '>', '<=', '>=', '==', '!=', '&&']:\",\n \"f'execute unless score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' elif expression.op ==\",\n \"code += c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func() f2h =\",\n \"open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute unless score {target[0]}\",\n \"used_temps.remove(ttt) ot = cast(t1, t2) rt = ot if expression.op in ['<', '>',\",\n \"{NAMESPACE}:{f2}\\\\n' elif expression.op == '||': code += c1 code += t1.cast(ot, tt1, target)\",\n \"target) code += c2 code += ot.binary(expression.op, target, tt2, target) return code, rt,\",\n \"= open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute if score\",\n \"target) else: code += c1 code += t1.cast(ot, tt1, target) code += c2\",\n \"{target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' else: if ot == t1: code\",\n \"tt1, target) code += c2 code += ot.binary(expression.op, target, tt2, target) return code,\",\n \"+= t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1, target, target) else: code +=\",\n \"rt = Int() if target is None or target == []: target =\",\n \"if score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' else: if ot ==\",\n \"get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression import generate_expression c1,\",\n \"= generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2, t2, tt2 = generate_expression(None, expression.right,\",\n \"matches 0 run function {NAMESPACE}:{f2}\\\\n' elif expression.op == '||': code += c1 code\",\n \"expression, target, variables_name, vtypes): from expression import generate_expression c1, t1, tt1 = generate_expression(None,\",\n \"+= f'execute unless score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' elif expression.op\",\n \"copy_strings, False) c2, t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for\",\n \"open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute if score {target[0]}\",\n \"used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt = ot\",\n \"import NAMESPACE from temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name,\",\n \"code = '' if expression.op in ['&&', '||']: if expression.op == '&&': code\",\n \"c1 code += c2 code += t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1,\",\n \"from expression import generate_expression c1, t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings,\",\n \"globals_consts import NAMESPACE from temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target,\",\n \"target = [get_temp() for _ in range(ot.size)] used_temps.extend(target) code = '' if expression.op\",\n \"= generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for ttt in tt1: used_temps.remove(ttt) for\",\n \"range(ot.size)] used_temps.extend(target) code = '' if expression.op in ['&&', '||']: if expression.op ==\",\n \"f'execute if score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' else: if ot\",\n \"Int from casting import cast from globals_consts import NAMESPACE from temps import used_temps,\",\n \"+= t1.cast(ot, tt1, target) code += c2 code += ot.binary(expression.op, target, tt2, target)\",\n \"import cast from globals_consts import NAMESPACE from temps import used_temps, get_temp, get_temp_func def\",\n \"'&&']: rt = Int() if target is None or target == []: target\",\n \"run function {NAMESPACE}:{f2}\\\\n' else: if ot == t1: code += c1 code +=\",\n \"f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code\",\n \"target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close()\",\n \"= [get_temp() for _ in range(ot.size)] used_temps.extend(target) code = '' if expression.op in\",\n \"tt2, target)) f2h.close() code += f'execute if score {target[0]} {NAMESPACE} matches 0 run\",\n \"else: code += c1 code += t1.cast(ot, tt1, target) code += c2 code\",\n \"code += t1.cast(ot, tt1, target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2)\",\n \"t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1, target, target) else: code += c1\",\n \"+= ot.binary(expression.op, tt1, target, target) else: code += c1 code += t1.cast(ot, tt1,\",\n \"expression.op == '||': code += c1 code += t1.cast(ot, tt1, target) f2 =\",\n \"== '&&': code += c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func()\",\n \"== []: target = [get_temp() for _ in range(ot.size)] used_temps.extend(target) code = ''\",\n \"+= f'execute if score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' else: if\",\n \"'==', '!=', '&&']: rt = Int() if target is None or target ==\",\n \"tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2, t2, tt2 = generate_expression(None,\",\n \"import generate_expression c1, t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2,\",\n \"{target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' elif expression.op == '||': code +=\",\n \"import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression import\",\n \"None or target == []: target = [get_temp() for _ in range(ot.size)] used_temps.extend(target)\",\n \"target == []: target = [get_temp() for _ in range(ot.size)] used_temps.extend(target) code =\",\n \"NAMESPACE from temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes):\",\n \"tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt =\",\n \"expression.op in ['<', '>', '<=', '>=', '==', '!=', '&&']: rt = Int() if\",\n \"c1, t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2, t2, tt2\",\n \"['&&', '||']: if expression.op == '&&': code += c1 code += t1.cast(ot, tt1,\",\n \"'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute unless score {target[0]} {NAMESPACE}\",\n \"code += c1 code += t1.cast(ot, tt1, target) code += c2 code +=\",\n \"t2) rt = ot if expression.op in ['<', '>', '<=', '>=', '==', '!=',\",\n \"if target is None or target == []: target = [get_temp() for _\",\n \"tt1, target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target))\",\n \"{NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' elif expression.op == '||': code += c1\",\n \"'!=', '&&']: rt = Int() if target is None or target == []:\",\n \"code += c1 code += c2 code += t2.cast(ot, tt2, target) code +=\",\n \"used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression import generate_expression\",\n \"def binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression import generate_expression c1, t1, tt1\",\n \"for ttt in tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot = cast(t1,\",\n \"if expression.op in ['<', '>', '<=', '>=', '==', '!=', '&&']: rt = Int()\",\n \"target is None or target == []: target = [get_temp() for _ in\",\n \"tt1, target, target) else: code += c1 code += t1.cast(ot, tt1, target) code\",\n \"function {NAMESPACE}:{f2}\\\\n' else: if ot == t1: code += c1 code += c2\",\n \"f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute if score {target[0]} {NAMESPACE} matches\",\n \"= open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute unless score\",\n \"{NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' else: if ot == t1: code +=\",\n \"if ot == t1: code += c1 code += c2 code += t2.cast(ot,\",\n \"+= t1.cast(ot, tt1, target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot,\",\n \"unless score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' elif expression.op == '||':\",\n \"t1: code += c1 code += c2 code += t2.cast(ot, tt2, target) code\",\n \"[]: target = [get_temp() for _ in range(ot.size)] used_temps.extend(target) code = '' if\",\n \"generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2, t2, tt2 = generate_expression(None, expression.right, vtypes,\",\n \"temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression\",\n \"[get_temp() for _ in range(ot.size)] used_temps.extend(target) code = '' if expression.op in ['&&',\",\n \"['<', '>', '<=', '>=', '==', '!=', '&&']: rt = Int() if target is\",\n \"_ in range(ot.size)] used_temps.extend(target) code = '' if expression.op in ['&&', '||']: if\",\n \"'&&': code += c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func() f2h\",\n \"f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute if\",\n \"expression.left, vtypes, variables_name, copy_strings, False) c2, t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name,\",\n \"code += f'execute unless score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' elif\",\n \"+= c1 code += c2 code += t2.cast(ot, tt2, target) code += ot.binary(expression.op,\",\n \"c2, t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for ttt in\",\n \"cast from globals_consts import NAMESPACE from temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings,\",\n \"False) c2, t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for ttt\",\n \"tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for ttt in tt1: used_temps.remove(ttt)\",\n \"= '' if expression.op in ['&&', '||']: if expression.op == '&&': code +=\",\n \"expression.right, vtypes, variables_name, copy_strings, False) for ttt in tt1: used_temps.remove(ttt) for ttt in\",\n \"for _ in range(ot.size)] used_temps.extend(target) code = '' if expression.op in ['&&', '||']:\",\n \"expression.op in ['&&', '||']: if expression.op == '&&': code += c1 code +=\",\n \"= get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code +=\",\n \"tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt = ot if expression.op in ['<',\",\n \"in tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt\",\n \"f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute unless score {target[0]} {NAMESPACE} matches\",\n \"cast(t1, t2) rt = ot if expression.op in ['<', '>', '<=', '>=', '==',\",\n \"t1.cast(ot, tt1, target) code += c2 code += ot.binary(expression.op, target, tt2, target) return\",\n \"'>=', '==', '!=', '&&']: rt = Int() if target is None or target\",\n \"score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' elif expression.op == '||': code\",\n \"c1 code += t1.cast(ot, tt1, target) code += c2 code += ot.binary(expression.op, target,\",\n \"target, variables_name, vtypes): from expression import generate_expression c1, t1, tt1 = generate_expression(None, expression.left,\",\n \"target) code += ot.binary(expression.op, tt1, target, target) else: code += c1 code +=\",\n \"variables_name, vtypes): from expression import generate_expression c1, t1, tt1 = generate_expression(None, expression.left, vtypes,\",\n \"0 run function {NAMESPACE}:{f2}\\\\n' elif expression.op == '||': code += c1 code +=\",\n \"t1.cast(ot, tt1, target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2,\",\n \"code += t1.cast(ot, tt1, target) code += c2 code += ot.binary(expression.op, target, tt2,\",\n \"from c_int import Int from casting import cast from globals_consts import NAMESPACE from\",\n \"t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for ttt in tt1:\",\n \"generate_expression c1, t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2, t2,\",\n \"ot == t1: code += c1 code += c2 code += t2.cast(ot, tt2,\",\n \"function {NAMESPACE}:{f2}\\\\n' elif expression.op == '||': code += c1 code += t1.cast(ot, tt1,\",\n \"f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute unless score {target[0]} {NAMESPACE} matches 0\",\n \"get_temp_func() f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute\",\n \"code += c2 code += t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1, target,\",\n \"f2h.close() code += f'execute unless score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n'\",\n \"vtypes, variables_name, copy_strings, False) for ttt in tt1: used_temps.remove(ttt) for ttt in tt2:\",\n \"'||': code += c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func() f2h\",\n \"'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute if score {target[0]} {NAMESPACE}\",\n \"target)) f2h.close() code += f'execute if score {target[0]} {NAMESPACE} matches 0 run function\",\n \"get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression import generate_expression c1, t1,\",\n \"ttt in tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt = ot if expression.op\",\n \"vtypes, variables_name, copy_strings, False) c2, t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings,\",\n \"= ot if expression.op in ['<', '>', '<=', '>=', '==', '!=', '&&']: rt\",\n \"matches 0 run function {NAMESPACE}:{f2}\\\\n' else: if ot == t1: code += c1\",\n \"ot if expression.op in ['<', '>', '<=', '>=', '==', '!=', '&&']: rt =\",\n \"'<=', '>=', '==', '!=', '&&']: rt = Int() if target is None or\",\n \"if expression.op == '&&': code += c1 code += t1.cast(ot, tt1, target) f2\",\n \"== '||': code += c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func()\",\n \"generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False) for ttt in tt1: used_temps.remove(ttt) for ttt\",\n \"0 run function {NAMESPACE}:{f2}\\\\n' else: if ot == t1: code += c1 code\",\n \"used_temps.extend(target) code = '' if expression.op in ['&&', '||']: if expression.op == '&&':\",\n \"ot.binary(expression.op, tt1, target, target) else: code += c1 code += t1.cast(ot, tt1, target)\",\n \"t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name, copy_strings, False) c2, t2, tt2 =\",\n \"tt2, target)) f2h.close() code += f'execute unless score {target[0]} {NAMESPACE} matches 0 run\",\n \"from temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression, target, variables_name, vtypes): from\",\n \"'>', '<=', '>=', '==', '!=', '&&']: rt = Int() if target is None\",\n \"variables_name, copy_strings, False) c2, t2, tt2 = generate_expression(None, expression.right, vtypes, variables_name, copy_strings, False)\",\n \"c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction', 'w')\",\n \"code += t2.cast(ot, tt2, target) code += ot.binary(expression.op, tt1, target, target) else: code\",\n \"from globals_consts import NAMESPACE from temps import used_temps, get_temp, get_temp_func def binary_expression(copy_strings, expression,\",\n \"target)) f2h.close() code += f'execute unless score {target[0]} {NAMESPACE} matches 0 run function\",\n \"is None or target == []: target = [get_temp() for _ in range(ot.size)]\",\n \"f2h = open(f'{f2}.mcfunction', 'w') f2h.write(c2) f2h.write(t2.cast(ot, tt2, target)) f2h.close() code += f'execute unless\",\n \"c_int import Int from casting import cast from globals_consts import NAMESPACE from temps\",\n \"or target == []: target = [get_temp() for _ in range(ot.size)] used_temps.extend(target) code\",\n \"'' if expression.op in ['&&', '||']: if expression.op == '&&': code += c1\",\n \"= cast(t1, t2) rt = ot if expression.op in ['<', '>', '<=', '>=',\",\n \"expression.op == '&&': code += c1 code += t1.cast(ot, tt1, target) f2 =\",\n \"score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n' else: if ot == t1:\",\n \"for ttt in tt2: used_temps.remove(ttt) ot = cast(t1, t2) rt = ot if\",\n \"from casting import cast from globals_consts import NAMESPACE from temps import used_temps, get_temp,\",\n \"binary_expression(copy_strings, expression, target, variables_name, vtypes): from expression import generate_expression c1, t1, tt1 =\",\n \"+= c1 code += t1.cast(ot, tt1, target) f2 = get_temp_func() f2h = open(f'{f2}.mcfunction',\",\n \"elif expression.op == '||': code += c1 code += t1.cast(ot, tt1, target) f2\",\n \"target, target) else: code += c1 code += t1.cast(ot, tt1, target) code +=\",\n \"variables_name, copy_strings, False) for ttt in tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt)\",\n \"in ['&&', '||']: if expression.op == '&&': code += c1 code += t1.cast(ot,\",\n \"f2h.close() code += f'execute if score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\\\n'\",\n \"Int() if target is None or target == []: target = [get_temp() for\",\n \"vtypes): from expression import generate_expression c1, t1, tt1 = generate_expression(None, expression.left, vtypes, variables_name,\",\n \"False) for ttt in tt1: used_temps.remove(ttt) for ttt in tt2: used_temps.remove(ttt) ot =\"\n]","isConversational":false}}},{"rowIdx":2992,"cells":{"ngram":{"kind":"list like","value":["if len(args) > 1: error(\"too many arguments\") in_fname = args[0] try: out_fname except","--help show this help message and exit\" print >> sys.stderr, \" -o FILENAME,","<stdint.h>\\n\\n\") h_file.write(\"#include <stdint.h>\\n\\n\") addr = 0 i = 0 for line in in_file:","= int(m[0][0], 16) name = m[0][1] sym[addr] = name else: break for line","help message and exit\" print >> sys.stderr, \" -o FILENAME, --addr=FILENAME\" print >>","-o FILENAME, --addr=FILENAME\" print >> sys.stderr, \"\" def error(msg): print >> sys.stderr, \"\"","if o in (\"-h\", \"--help\"): usage() sys.exit() elif o in (\"-o\", \"--output\"): out_fname","can't open file: '%s'\" % in_fname sys.exit(1) try: c_file = open(out_fname, mode='w') except:","can't create file: %s\" % hdr_fname sys.exit(1) i = 0 for line in","sym = {} for line in in_file: m = s_pat.findall(line) if m: addr","in_file: if re.match(\"SYMBOL TABLE:\", line): break s_pat = re.compile(\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\") sym =","a in opts: if o in (\"-h\", \"--help\"): usage() sys.exit() elif o in","mode='w') except: print >> sys.stderr, \"#error: can't create file: %s\" % hdr_fname sys.exit(1)","line): break token_pat = re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\") c_file.write(\"#include <stdint.h>\\n\\n\") h_file.write(\"#include <stdint.h>\\n\\n\") addr = 0 i","* from getopt import * import sys import os import re def usage():","int(m[0][0], 16) name = m[0][1] sym[addr] = name else: break for line in","line): break s_pat = re.compile(\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\") sym = {} for line in","break for line in in_file: if re.match(\"Contents of section .text:\", line): break token_pat","in in_file: if re.match(\"Contents of section .text:\", line): break token_pat = re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\") c_file.write(\"#include","err: error(str(err)) for o, a in opts: if o in (\"-h\", \"--help\"): usage()","in_file = open(in_fname, mode='r') except: print >> sys.stderr, \"#error: can't open file: '%s'\"","try: h_file = open(hdr_fname, mode='w') except: print >> sys.stderr, \"#error: can't create file:","mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname, mode='r') except: print >> sys.stderr, \"#error:","mode='w') except: print >> sys.stderr, \"#error: can't create file: %s\" % out_fname sys.exit(1)","for line in in_file: for a, b, c, d in token_pat.findall(line): try: sym[addr]","in opts: if o in (\"-h\", \"--help\"): usage() sys.exit() elif o in (\"-o\",","out_fname = basename + '.' + 'c' dirname, fname = os.path.split(out_fname) basename, extension","args[0] try: out_fname except NameError: dirname, fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname)","getopt(sys.argv[1:], \"ho:\", \\ [\"help\", \"output=\"]) except GetoptError, err: error(str(err)) for o, a in","m[0][1] sym[addr] = name else: break for line in in_file: if re.match(\"Contents of","= os.path.splitext(fname) out_fname = basename + '.' + 'c' dirname, fname = os.path.split(out_fname)","addr + 4 c_file.write(\"\\n};\\n\") in_file.close() c_file.close() h_file.close() return def main(): global progname progname",">> sys.stderr, \"\" def error(msg): print >> sys.stderr, \"\" print >> sys.stderr, \"#error:\",","<gh_stars>1-10 #!/usr/bin/python from struct import * from getopt import * import sys import","import * from getopt import * import sys import os import re def","fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname = basename + '.' +","sys.stderr, \"\" print >> sys.stderr, \"Options\" print >> sys.stderr, \" -h, --help show","usage() sys.exit() elif o in (\"-o\", \"--output\"): out_fname = a else: assert False,","0 i = 0 for line in in_file: for a, b, c, d","else: break for line in in_file: if re.match(\"Contents of section .text:\", line): break","+ d + c + b + a ) i = i +","\"Options\" print >> sys.stderr, \" -h, --help show this help message and exit\"","sym[addr]) i = 0 except KeyError: pass if ((i % 4) == 0):","= a else: assert False, \"unhandled option\" if len(args) == 0: error(\"missing fname\")","print >> sys.stderr, \" -o FILENAME, --addr=FILENAME\" print >> sys.stderr, \"\" def error(msg):",">> sys.stderr, \" -o FILENAME, --addr=FILENAME\" print >> sys.stderr, \"\" def error(msg): print","0: error(\"missing fname\") if len(args) > 1: error(\"too many arguments\") in_fname = args[0]","([.A-Za-z_][A-Za-z_0-9]*)\") sym = {} for line in in_file: m = s_pat.findall(line) if m:","= sys.argv[0] try: opts, args = getopt(sys.argv[1:], \"ho:\", \\ [\"help\", \"output=\"]) except GetoptError,","for line in in_file: if re.match(\"SYMBOL TABLE:\", line): break s_pat = re.compile(\"([0-9a-f]{8}) ..*[0-9a-f]{8}","0): c_file.write(\"\\n};\\n\\n\") c_file.write(\"const uint32_t %s[] = {\" % sym[addr]) h_file.write(\"extern const uint32_t %s[];\\n\\n\"","error(\"too many arguments\") in_fname = args[0] try: out_fname except NameError: dirname, fname =","\"[options] fname\" print >> sys.stderr, \"\" print >> sys.stderr, \"Options\" print >> sys.stderr,","os.path.splitext(fname) hdr_fname = basename + '.' + 'h' mk_codelet(in_fname, out_fname, hdr_fname) if __name__","name else: break for line in in_file: if re.match(\"Contents of section .text:\", line):","fname\") if len(args) > 1: error(\"too many arguments\") in_fname = args[0] try: out_fname","+ '.' + 'c' dirname, fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname","FILENAME, --addr=FILENAME\" print >> sys.stderr, \"\" def error(msg): print >> sys.stderr, \"\" print","extension = os.path.splitext(fname) hdr_fname = basename + '.' + 'h' mk_codelet(in_fname, out_fname, hdr_fname)","open(hdr_fname, mode='w') except: print >> sys.stderr, \"#error: can't create file: %s\" % hdr_fname","% sym[addr]) h_file.write(\"extern const uint32_t %s[];\\n\\n\" % sym[addr]) i = 0 except KeyError:","= {\" % sym[addr]) h_file.write(\"extern const uint32_t %s[];\\n\\n\" % sym[addr]) i = 0","0x\" + d + c + b + a ) i = i","basename + '.' + 'c' dirname, fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname)","\"#error: can't open file: '%s'\" % in_fname sys.exit(1) try: c_file = open(out_fname, mode='w')","sym[addr]) h_file.write(\"extern const uint32_t %s[];\\n\\n\" % sym[addr]) i = 0 except KeyError: pass",">> sys.stderr, \"\" print >> sys.stderr, \"Options\" print >> sys.stderr, \" -h, --help","%s\" % hdr_fname sys.exit(1) i = 0 for line in in_file: if re.match(\"SYMBOL","except: print >> sys.stderr, \"#error: can't create file: %s\" % hdr_fname sys.exit(1) i","= 0 for line in in_file: if re.match(\"SYMBOL TABLE:\", line): break s_pat =","= args[0] try: out_fname except NameError: dirname, fname = os.path.split(in_fname) basename, extension =","in_file: m = s_pat.findall(line) if m: addr = int(m[0][0], 16) name = m[0][1]","const uint32_t %s[];\\n\\n\" % sym[addr]) i = 0 except KeyError: pass if ((i","\"\" print >> sys.stderr, \" Usage:\", progname, \"[options] fname\" print >> sys.stderr, \"\"","out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w') except: print >> sys.stderr, \"#error: can't","{} for line in in_file: m = s_pat.findall(line) if m: addr = int(m[0][0],","in (\"-o\", \"--output\"): out_fname = a else: assert False, \"unhandled option\" if len(args)","many arguments\") in_fname = args[0] try: out_fname except NameError: dirname, fname = os.path.split(in_fname)","\"#error: can't create file: %s\" % out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w')","print >> sys.stderr, \"#error: can't open file: '%s'\" % in_fname sys.exit(1) try: c_file",">> sys.stderr, \"#error: can't create file: %s\" % out_fname sys.exit(1) try: h_file =","for line in in_file: if re.match(\"Contents of section .text:\", line): break token_pat =","addr = int(m[0][0], 16) name = m[0][1] sym[addr] = name else: break for","usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname, mode='r') except: print","+ c + b + a) else: c_file.write(\", 0x\" + d + c","uint32_t %s[] = {\" % sym[addr]) h_file.write(\"extern const uint32_t %s[];\\n\\n\" % sym[addr]) i",">> sys.stderr, \" Usage:\", progname, \"[options] fname\" print >> sys.stderr, \"\" print >>","\" -o FILENAME, --addr=FILENAME\" print >> sys.stderr, \"\" def error(msg): print >> sys.stderr,","s_pat = re.compile(\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\") sym = {} for line in in_file: m","NameError: dirname, fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname = basename +","for a, b, c, d in token_pat.findall(line): try: sym[addr] if (i > 0):","\"\" def error(msg): print >> sys.stderr, \"\" print >> sys.stderr, \"#error:\", msg usage()","'%s'\" % in_fname sys.exit(1) try: c_file = open(out_fname, mode='w') except: print >> sys.stderr,","os.path.splitext(fname) out_fname = basename + '.' + 'c' dirname, fname = os.path.split(out_fname) basename,","\"\" print >> sys.stderr, \"#error:\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try:","\"ho:\", \\ [\"help\", \"output=\"]) except GetoptError, err: error(str(err)) for o, a in opts:","error(\"missing fname\") if len(args) > 1: error(\"too many arguments\") in_fname = args[0] try:","out_fname, hdr_fname): try: in_file = open(in_fname, mode='r') except: print >> sys.stderr, \"#error: can't","sys.stderr, \"#error:\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname,","c_file.write(\",\") c_file.write(\"\\n\\t0x\" + d + c + b + a) else: c_file.write(\", 0x\"","in_file.close() c_file.close() h_file.close() return def main(): global progname progname = sys.argv[0] try: opts,","c_file.write(\"const uint32_t %s[] = {\" % sym[addr]) h_file.write(\"extern const uint32_t %s[];\\n\\n\" % sym[addr])","0): if (i > 0): c_file.write(\",\") c_file.write(\"\\n\\t0x\" + d + c + b","re.match(\"SYMBOL TABLE:\", line): break s_pat = re.compile(\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\") sym = {} for","exit\" print >> sys.stderr, \" -o FILENAME, --addr=FILENAME\" print >> sys.stderr, \"\" def","except GetoptError, err: error(str(err)) for o, a in opts: if o in (\"-h\",","sys.stderr, \"#error: can't open file: '%s'\" % in_fname sys.exit(1) try: c_file = open(out_fname,","d in token_pat.findall(line): try: sym[addr] if (i > 0): c_file.write(\"\\n};\\n\\n\") c_file.write(\"const uint32_t %s[]","c_file.write(\"\\n\\t0x\" + d + c + b + a) else: c_file.write(\", 0x\" +","0 except KeyError: pass if ((i % 4) == 0): if (i >","error(msg): print >> sys.stderr, \"\" print >> sys.stderr, \"#error:\", msg usage() sys.exit(2) def","print >> sys.stderr, \"#error: can't create file: %s\" % hdr_fname sys.exit(1) i =","def usage(): global progname print >> sys.stderr, \"\" print >> sys.stderr, \" Usage:\",","print >> sys.stderr, \"\" print >> sys.stderr, \"#error:\", msg usage() sys.exit(2) def mk_codelet(in_fname,","+ b + a ) i = i + 1; addr = addr","if re.match(\"Contents of section .text:\", line): break token_pat = re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\") c_file.write(\"#include <stdint.h>\\n\\n\") h_file.write(\"#include","arguments\") in_fname = args[0] try: out_fname except NameError: dirname, fname = os.path.split(in_fname) basename,","dirname, fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname = basename + '.'","c_file = open(out_fname, mode='w') except: print >> sys.stderr, \"#error: can't create file: %s\"","1; addr = addr + 4 c_file.write(\"\\n};\\n\") in_file.close() c_file.close() h_file.close() return def main():","def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname, mode='r') except: print >> sys.stderr,","c_file.write(\"\\n};\\n\\n\") c_file.write(\"const uint32_t %s[] = {\" % sym[addr]) h_file.write(\"extern const uint32_t %s[];\\n\\n\" %","= os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname = basename + '.' + 'h'","for line in in_file: m = s_pat.findall(line) if m: addr = int(m[0][0], 16)","print >> sys.stderr, \"#error: can't create file: %s\" % out_fname sys.exit(1) try: h_file","else: assert False, \"unhandled option\" if len(args) == 0: error(\"missing fname\") if len(args)","\"--output\"): out_fname = a else: assert False, \"unhandled option\" if len(args) == 0:","try: out_fname except NameError: dirname, fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname","False, \"unhandled option\" if len(args) == 0: error(\"missing fname\") if len(args) > 1:","hdr_fname): try: in_file = open(in_fname, mode='r') except: print >> sys.stderr, \"#error: can't open","= 0 for line in in_file: for a, b, c, d in token_pat.findall(line):","((i % 4) == 0): if (i > 0): c_file.write(\",\") c_file.write(\"\\n\\t0x\" + d","#!/usr/bin/python from struct import * from getopt import * import sys import os","struct import * from getopt import * import sys import os import re","msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname, mode='r') except:","[\"help\", \"output=\"]) except GetoptError, err: error(str(err)) for o, a in opts: if o","(i > 0): c_file.write(\",\") c_file.write(\"\\n\\t0x\" + d + c + b + a)","= 0 i = 0 for line in in_file: for a, b, c,","sys.stderr, \"\" def error(msg): print >> sys.stderr, \"\" print >> sys.stderr, \"#error:\", msg","re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\") c_file.write(\"#include <stdint.h>\\n\\n\") h_file.write(\"#include <stdint.h>\\n\\n\") addr = 0 i = 0 for line","hdr_fname = basename + '.' + 'h' mk_codelet(in_fname, out_fname, hdr_fname) if __name__ ==","print >> sys.stderr, \"\" print >> sys.stderr, \" Usage:\", progname, \"[options] fname\" print","print >> sys.stderr, \" -h, --help show this help message and exit\" print","h_file = open(hdr_fname, mode='w') except: print >> sys.stderr, \"#error: can't create file: %s\"","if len(args) == 0: error(\"missing fname\") if len(args) > 1: error(\"too many arguments\")","\"\" print >> sys.stderr, \"Options\" print >> sys.stderr, \" -h, --help show this","open file: '%s'\" % in_fname sys.exit(1) try: c_file = open(out_fname, mode='w') except: print","dirname, fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname = basename + '.'","i = 0 except KeyError: pass if ((i % 4) == 0): if","= addr + 4 c_file.write(\"\\n};\\n\") in_file.close() c_file.close() h_file.close() return def main(): global progname","%s[] = {\" % sym[addr]) h_file.write(\"extern const uint32_t %s[];\\n\\n\" % sym[addr]) i =","c_file.write(\", 0x\" + d + c + b + a ) i =","addr = addr + 4 c_file.write(\"\\n};\\n\") in_file.close() c_file.close() h_file.close() return def main(): global","= re.compile(\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\") sym = {} for line in in_file: m =","% sym[addr]) i = 0 except KeyError: pass if ((i % 4) ==","% hdr_fname sys.exit(1) i = 0 for line in in_file: if re.match(\"SYMBOL TABLE:\",","h_file.close() return def main(): global progname progname = sys.argv[0] try: opts, args =","a ) i = i + 1; addr = addr + 4 c_file.write(\"\\n};\\n\")",">> sys.stderr, \"\" print >> sys.stderr, \" Usage:\", progname, \"[options] fname\" print >>","m = s_pat.findall(line) if m: addr = int(m[0][0], 16) name = m[0][1] sym[addr]","and exit\" print >> sys.stderr, \" -o FILENAME, --addr=FILENAME\" print >> sys.stderr, \"\"","= i + 1; addr = addr + 4 c_file.write(\"\\n};\\n\") in_file.close() c_file.close() h_file.close()","sym[addr] = name else: break for line in in_file: if re.match(\"Contents of section","16) name = m[0][1] sym[addr] = name else: break for line in in_file:","0): c_file.write(\",\") c_file.write(\"\\n\\t0x\" + d + c + b + a) else: c_file.write(\",",") i = i + 1; addr = addr + 4 c_file.write(\"\\n};\\n\") in_file.close()","> 0): c_file.write(\"\\n};\\n\\n\") c_file.write(\"const uint32_t %s[] = {\" % sym[addr]) h_file.write(\"extern const uint32_t","\"--help\"): usage() sys.exit() elif o in (\"-o\", \"--output\"): out_fname = a else: assert","c + b + a ) i = i + 1; addr =","sys.argv[0] try: opts, args = getopt(sys.argv[1:], \"ho:\", \\ [\"help\", \"output=\"]) except GetoptError, err:",">> sys.stderr, \"\" print >> sys.stderr, \"#error:\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname,","= getopt(sys.argv[1:], \"ho:\", \\ [\"help\", \"output=\"]) except GetoptError, err: error(str(err)) for o, a","c + b + a) else: c_file.write(\", 0x\" + d + c +","--addr=FILENAME\" print >> sys.stderr, \"\" def error(msg): print >> sys.stderr, \"\" print >>","= s_pat.findall(line) if m: addr = int(m[0][0], 16) name = m[0][1] sym[addr] =","== 0: error(\"missing fname\") if len(args) > 1: error(\"too many arguments\") in_fname =","can't create file: %s\" % out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w') except:","error(str(err)) for o, a in opts: if o in (\"-h\", \"--help\"): usage() sys.exit()","break token_pat = re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\") c_file.write(\"#include <stdint.h>\\n\\n\") h_file.write(\"#include <stdint.h>\\n\\n\") addr = 0 i =","import sys import os import re def usage(): global progname print >> sys.stderr,","0 for line in in_file: for a, b, c, d in token_pat.findall(line): try:","progname = sys.argv[0] try: opts, args = getopt(sys.argv[1:], \"ho:\", \\ [\"help\", \"output=\"]) except","basename + '.' + 'h' mk_codelet(in_fname, out_fname, hdr_fname) if __name__ == \"__main__\": main()","try: sym[addr] if (i > 0): c_file.write(\"\\n};\\n\\n\") c_file.write(\"const uint32_t %s[] = {\" %","b + a ) i = i + 1; addr = addr +","in in_file: if re.match(\"SYMBOL TABLE:\", line): break s_pat = re.compile(\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\") sym","create file: %s\" % hdr_fname sys.exit(1) i = 0 for line in in_file:","assert False, \"unhandled option\" if len(args) == 0: error(\"missing fname\") if len(args) >","= os.path.splitext(fname) hdr_fname = basename + '.' + 'h' mk_codelet(in_fname, out_fname, hdr_fname) if","= open(in_fname, mode='r') except: print >> sys.stderr, \"#error: can't open file: '%s'\" %","(\"-h\", \"--help\"): usage() sys.exit() elif o in (\"-o\", \"--output\"): out_fname = a else:","except: print >> sys.stderr, \"#error: can't open file: '%s'\" % in_fname sys.exit(1) try:","sys.exit(1) try: h_file = open(hdr_fname, mode='w') except: print >> sys.stderr, \"#error: can't create","import os import re def usage(): global progname print >> sys.stderr, \"\" print","sys.stderr, \"#error: can't create file: %s\" % out_fname sys.exit(1) try: h_file = open(hdr_fname,","\\ [\"help\", \"output=\"]) except GetoptError, err: error(str(err)) for o, a in opts: if","o in (\"-h\", \"--help\"): usage() sys.exit() elif o in (\"-o\", \"--output\"): out_fname =","sys.exit(1) try: c_file = open(out_fname, mode='w') except: print >> sys.stderr, \"#error: can't create","from getopt import * import sys import os import re def usage(): global","mode='r') except: print >> sys.stderr, \"#error: can't open file: '%s'\" % in_fname sys.exit(1)","\"unhandled option\" if len(args) == 0: error(\"missing fname\") if len(args) > 1: error(\"too","GetoptError, err: error(str(err)) for o, a in opts: if o in (\"-h\", \"--help\"):","re def usage(): global progname print >> sys.stderr, \"\" print >> sys.stderr, \"","extension = os.path.splitext(fname) out_fname = basename + '.' + 'c' dirname, fname =","% 4) == 0): if (i > 0): c_file.write(\",\") c_file.write(\"\\n\\t0x\" + d +",">> sys.stderr, \"#error:\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file =","\"#error: can't create file: %s\" % hdr_fname sys.exit(1) i = 0 for line","opts: if o in (\"-h\", \"--help\"): usage() sys.exit() elif o in (\"-o\", \"--output\"):","elif o in (\"-o\", \"--output\"): out_fname = a else: assert False, \"unhandled option\"","out_fname except NameError: dirname, fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname =","+ 'c' dirname, fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname = basename","'c' dirname, fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname = basename +","> 1: error(\"too many arguments\") in_fname = args[0] try: out_fname except NameError: dirname,","except NameError: dirname, fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname = basename",">> sys.stderr, \" -h, --help show this help message and exit\" print >>","sys.stderr, \" -o FILENAME, --addr=FILENAME\" print >> sys.stderr, \"\" def error(msg): print >>","print >> sys.stderr, \"\" print >> sys.stderr, \"Options\" print >> sys.stderr, \" -h,","%s\" % out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w') except: print >> sys.stderr,","sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname, mode='r') except: print >>","of section .text:\", line): break token_pat = re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\") c_file.write(\"#include <stdint.h>\\n\\n\") h_file.write(\"#include <stdint.h>\\n\\n\") addr","= 0 except KeyError: pass if ((i % 4) == 0): if (i","global progname progname = sys.argv[0] try: opts, args = getopt(sys.argv[1:], \"ho:\", \\ [\"help\",","from struct import * from getopt import * import sys import os import","progname, \"[options] fname\" print >> sys.stderr, \"\" print >> sys.stderr, \"Options\" print >>","%s[];\\n\\n\" % sym[addr]) i = 0 except KeyError: pass if ((i % 4)","b, c, d in token_pat.findall(line): try: sym[addr] if (i > 0): c_file.write(\"\\n};\\n\\n\") c_file.write(\"const","except: print >> sys.stderr, \"#error: can't create file: %s\" % out_fname sys.exit(1) try:","a, b, c, d in token_pat.findall(line): try: sym[addr] if (i > 0): c_file.write(\"\\n};\\n\\n\")","token_pat.findall(line): try: sym[addr] if (i > 0): c_file.write(\"\\n};\\n\\n\") c_file.write(\"const uint32_t %s[] = {\"","1: error(\"too many arguments\") in_fname = args[0] try: out_fname except NameError: dirname, fname","getopt import * import sys import os import re def usage(): global progname","args = getopt(sys.argv[1:], \"ho:\", \\ [\"help\", \"output=\"]) except GetoptError, err: error(str(err)) for o,","option\" if len(args) == 0: error(\"missing fname\") if len(args) > 1: error(\"too many","4 c_file.write(\"\\n};\\n\") in_file.close() c_file.close() h_file.close() return def main(): global progname progname = sys.argv[0]","len(args) == 0: error(\"missing fname\") if len(args) > 1: error(\"too many arguments\") in_fname","if (i > 0): c_file.write(\",\") c_file.write(\"\\n\\t0x\" + d + c + b +","def main(): global progname progname = sys.argv[0] try: opts, args = getopt(sys.argv[1:], \"ho:\",","= basename + '.' + 'c' dirname, fname = os.path.split(out_fname) basename, extension =","open(out_fname, mode='w') except: print >> sys.stderr, \"#error: can't create file: %s\" % out_fname","except KeyError: pass if ((i % 4) == 0): if (i > 0):","sys.stderr, \" -h, --help show this help message and exit\" print >> sys.stderr,","create file: %s\" % out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w') except: print","sys.stderr, \"\" print >> sys.stderr, \" Usage:\", progname, \"[options] fname\" print >> sys.stderr,","b + a) else: c_file.write(\", 0x\" + d + c + b +","4) == 0): if (i > 0): c_file.write(\",\") c_file.write(\"\\n\\t0x\" + d + c","progname progname = sys.argv[0] try: opts, args = getopt(sys.argv[1:], \"ho:\", \\ [\"help\", \"output=\"])","out_fname = a else: assert False, \"unhandled option\" if len(args) == 0: error(\"missing","progname print >> sys.stderr, \"\" print >> sys.stderr, \" Usage:\", progname, \"[options] fname\"","(\"-o\", \"--output\"): out_fname = a else: assert False, \"unhandled option\" if len(args) ==","sys import os import re def usage(): global progname print >> sys.stderr, \"\"","i + 1; addr = addr + 4 c_file.write(\"\\n};\\n\") in_file.close() c_file.close() h_file.close() return","sys.stderr, \"\" print >> sys.stderr, \"#error:\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname):","sys.exit() elif o in (\"-o\", \"--output\"): out_fname = a else: assert False, \"unhandled","% out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w') except: print >> sys.stderr, \"#error:","i = 0 for line in in_file: for a, b, c, d in","c_file.write(\"#include <stdint.h>\\n\\n\") h_file.write(\"#include <stdint.h>\\n\\n\") addr = 0 i = 0 for line in","print >> sys.stderr, \"\" def error(msg): print >> sys.stderr, \"\" print >> sys.stderr,","c_file.close() h_file.close() return def main(): global progname progname = sys.argv[0] try: opts, args","\" -h, --help show this help message and exit\" print >> sys.stderr, \"","sys.stderr, \" Usage:\", progname, \"[options] fname\" print >> sys.stderr, \"\" print >> sys.stderr,","in in_file: m = s_pat.findall(line) if m: addr = int(m[0][0], 16) name =","for o, a in opts: if o in (\"-h\", \"--help\"): usage() sys.exit() elif","os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname = basename + '.' + 'c' dirname,","break s_pat = re.compile(\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\") sym = {} for line in in_file:","d + c + b + a ) i = i + 1;","pass if ((i % 4) == 0): if (i > 0): c_file.write(\",\") c_file.write(\"\\n\\t0x\"","= name else: break for line in in_file: if re.match(\"Contents of section .text:\",","== 0): if (i > 0): c_file.write(\",\") c_file.write(\"\\n\\t0x\" + d + c +","line in in_file: m = s_pat.findall(line) if m: addr = int(m[0][0], 16) name","fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname = basename + '.' +","in token_pat.findall(line): try: sym[addr] if (i > 0): c_file.write(\"\\n};\\n\\n\") c_file.write(\"const uint32_t %s[] =","o, a in opts: if o in (\"-h\", \"--help\"): usage() sys.exit() elif o","\"#error:\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname, mode='r')","..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\") sym = {} for line in in_file: m = s_pat.findall(line) if","'.' + 'c' dirname, fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname =","= re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\") c_file.write(\"#include <stdint.h>\\n\\n\") h_file.write(\"#include <stdint.h>\\n\\n\") addr = 0 i = 0 for","h_file.write(\"extern const uint32_t %s[];\\n\\n\" % sym[addr]) i = 0 except KeyError: pass if","d + c + b + a) else: c_file.write(\", 0x\" + d +","\"output=\"]) except GetoptError, err: error(str(err)) for o, a in opts: if o in","line in in_file: if re.match(\"SYMBOL TABLE:\", line): break s_pat = re.compile(\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\")","{\" % sym[addr]) h_file.write(\"extern const uint32_t %s[];\\n\\n\" % sym[addr]) i = 0 except","usage(): global progname print >> sys.stderr, \"\" print >> sys.stderr, \" Usage:\", progname,","basename, extension = os.path.splitext(fname) hdr_fname = basename + '.' + 'h' mk_codelet(in_fname, out_fname,","+ 4 c_file.write(\"\\n};\\n\") in_file.close() c_file.close() h_file.close() return def main(): global progname progname =","basename, extension = os.path.splitext(fname) out_fname = basename + '.' + 'c' dirname, fname","hdr_fname sys.exit(1) i = 0 for line in in_file: if re.match(\"SYMBOL TABLE:\", line):","-h, --help show this help message and exit\" print >> sys.stderr, \" -o","message and exit\" print >> sys.stderr, \" -o FILENAME, --addr=FILENAME\" print >> sys.stderr,","h_file.write(\"#include <stdint.h>\\n\\n\") addr = 0 i = 0 for line in in_file: for","section .text:\", line): break token_pat = re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\") c_file.write(\"#include <stdint.h>\\n\\n\") h_file.write(\"#include <stdint.h>\\n\\n\") addr =","import re def usage(): global progname print >> sys.stderr, \"\" print >> sys.stderr,","sys.stderr, \"#error: can't create file: %s\" % hdr_fname sys.exit(1) i = 0 for","c, d in token_pat.findall(line): try: sym[addr] if (i > 0): c_file.write(\"\\n};\\n\\n\") c_file.write(\"const uint32_t","file: %s\" % out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w') except: print >>","file: '%s'\" % in_fname sys.exit(1) try: c_file = open(out_fname, mode='w') except: print >>","addr = 0 i = 0 for line in in_file: for a, b,",".text:\", line): break token_pat = re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\") c_file.write(\"#include <stdint.h>\\n\\n\") h_file.write(\"#include <stdint.h>\\n\\n\") addr = 0","sys.exit(1) i = 0 for line in in_file: if re.match(\"SYMBOL TABLE:\", line): break","import * import sys import os import re def usage(): global progname print","in_file: for a, b, c, d in token_pat.findall(line): try: sym[addr] if (i >","% in_fname sys.exit(1) try: c_file = open(out_fname, mode='w') except: print >> sys.stderr, \"#error:","= m[0][1] sym[addr] = name else: break for line in in_file: if re.match(\"Contents","i = i + 1; addr = addr + 4 c_file.write(\"\\n};\\n\") in_file.close() c_file.close()","this help message and exit\" print >> sys.stderr, \" -o FILENAME, --addr=FILENAME\" print","line in in_file: if re.match(\"Contents of section .text:\", line): break token_pat = re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\")","+ b + a) else: c_file.write(\", 0x\" + d + c + b","+ c + b + a ) i = i + 1; addr","try: c_file = open(out_fname, mode='w') except: print >> sys.stderr, \"#error: can't create file:",">> sys.stderr, \"Options\" print >> sys.stderr, \" -h, --help show this help message","= open(hdr_fname, mode='w') except: print >> sys.stderr, \"#error: can't create file: %s\" %",">> sys.stderr, \"#error: can't create file: %s\" % hdr_fname sys.exit(1) i = 0","a else: assert False, \"unhandled option\" if len(args) == 0: error(\"missing fname\") if","if (i > 0): c_file.write(\"\\n};\\n\\n\") c_file.write(\"const uint32_t %s[] = {\" % sym[addr]) h_file.write(\"extern","+ 1; addr = addr + 4 c_file.write(\"\\n};\\n\") in_file.close() c_file.close() h_file.close() return def","TABLE:\", line): break s_pat = re.compile(\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\") sym = {} for line","= os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname = basename + '.' + 'c'","+ a) else: c_file.write(\", 0x\" + d + c + b + a","m: addr = int(m[0][0], 16) name = m[0][1] sym[addr] = name else: break","else: c_file.write(\", 0x\" + d + c + b + a ) i","print >> sys.stderr, \" Usage:\", progname, \"[options] fname\" print >> sys.stderr, \"\" print","show this help message and exit\" print >> sys.stderr, \" -o FILENAME, --addr=FILENAME\"","print >> sys.stderr, \"#error:\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file","in in_file: for a, b, c, d in token_pat.findall(line): try: sym[addr] if (i","in_fname = args[0] try: out_fname except NameError: dirname, fname = os.path.split(in_fname) basename, extension","try: opts, args = getopt(sys.argv[1:], \"ho:\", \\ [\"help\", \"output=\"]) except GetoptError, err: error(str(err))","in_file: if re.match(\"Contents of section .text:\", line): break token_pat = re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\") c_file.write(\"#include <stdint.h>\\n\\n\")","KeyError: pass if ((i % 4) == 0): if (i > 0): c_file.write(\",\")","* import sys import os import re def usage(): global progname print >>","in_fname sys.exit(1) try: c_file = open(out_fname, mode='w') except: print >> sys.stderr, \"#error: can't","o in (\"-o\", \"--output\"): out_fname = a else: assert False, \"unhandled option\" if","= open(out_fname, mode='w') except: print >> sys.stderr, \"#error: can't create file: %s\" %","Usage:\", progname, \"[options] fname\" print >> sys.stderr, \"\" print >> sys.stderr, \"Options\" print","os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname = basename + '.' + 'h' mk_codelet(in_fname,","return def main(): global progname progname = sys.argv[0] try: opts, args = getopt(sys.argv[1:],","line in in_file: for a, b, c, d in token_pat.findall(line): try: sym[addr] if","fname\" print >> sys.stderr, \"\" print >> sys.stderr, \"Options\" print >> sys.stderr, \"","+ d + c + b + a) else: c_file.write(\", 0x\" + d","(i > 0): c_file.write(\"\\n};\\n\\n\") c_file.write(\"const uint32_t %s[] = {\" % sym[addr]) h_file.write(\"extern const","def error(msg): print >> sys.stderr, \"\" print >> sys.stderr, \"#error:\", msg usage() sys.exit(2)","file: %s\" % hdr_fname sys.exit(1) i = 0 for line in in_file: if","s_pat.findall(line) if m: addr = int(m[0][0], 16) name = m[0][1] sym[addr] = name","global progname print >> sys.stderr, \"\" print >> sys.stderr, \" Usage:\", progname, \"[options]","main(): global progname progname = sys.argv[0] try: opts, args = getopt(sys.argv[1:], \"ho:\", \\","name = m[0][1] sym[addr] = name else: break for line in in_file: if","if m: addr = int(m[0][0], 16) name = m[0][1] sym[addr] = name else:","> 0): c_file.write(\",\") c_file.write(\"\\n\\t0x\" + d + c + b + a) else:",">> sys.stderr, \"#error: can't open file: '%s'\" % in_fname sys.exit(1) try: c_file =","uint32_t %s[];\\n\\n\" % sym[addr]) i = 0 except KeyError: pass if ((i %","in (\"-h\", \"--help\"): usage() sys.exit() elif o in (\"-o\", \"--output\"): out_fname = a","a) else: c_file.write(\", 0x\" + d + c + b + a )","0 for line in in_file: if re.match(\"SYMBOL TABLE:\", line): break s_pat = re.compile(\"([0-9a-f]{8})","token_pat = re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\") c_file.write(\"#include <stdint.h>\\n\\n\") h_file.write(\"#include <stdint.h>\\n\\n\") addr = 0 i = 0","if re.match(\"SYMBOL TABLE:\", line): break s_pat = re.compile(\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\") sym = {}","if ((i % 4) == 0): if (i > 0): c_file.write(\",\") c_file.write(\"\\n\\t0x\" +","len(args) > 1: error(\"too many arguments\") in_fname = args[0] try: out_fname except NameError:","= {} for line in in_file: m = s_pat.findall(line) if m: addr =","os import re def usage(): global progname print >> sys.stderr, \"\" print >>","\" Usage:\", progname, \"[options] fname\" print >> sys.stderr, \"\" print >> sys.stderr, \"Options\"","+ a ) i = i + 1; addr = addr + 4","opts, args = getopt(sys.argv[1:], \"ho:\", \\ [\"help\", \"output=\"]) except GetoptError, err: error(str(err)) for","try: in_file = open(in_fname, mode='r') except: print >> sys.stderr, \"#error: can't open file:","c_file.write(\"\\n};\\n\") in_file.close() c_file.close() h_file.close() return def main(): global progname progname = sys.argv[0] try:","print >> sys.stderr, \"Options\" print >> sys.stderr, \" -h, --help show this help","re.compile(\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\") sym = {} for line in in_file: m = s_pat.findall(line)","sym[addr] if (i > 0): c_file.write(\"\\n};\\n\\n\") c_file.write(\"const uint32_t %s[] = {\" % sym[addr])","i = 0 for line in in_file: if re.match(\"SYMBOL TABLE:\", line): break s_pat","re.match(\"Contents of section .text:\", line): break token_pat = re.compile(\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\") c_file.write(\"#include <stdint.h>\\n\\n\") h_file.write(\"#include <stdint.h>\\n\\n\")","open(in_fname, mode='r') except: print >> sys.stderr, \"#error: can't open file: '%s'\" % in_fname","<stdint.h>\\n\\n\") addr = 0 i = 0 for line in in_file: for a,","= basename + '.' + 'h' mk_codelet(in_fname, out_fname, hdr_fname) if __name__ == \"__main__\":","sys.stderr, \"Options\" print >> sys.stderr, \" -h, --help show this help message and"],"string":"[\n \"if len(args) > 1: error(\\\"too many arguments\\\") in_fname = args[0] try: out_fname except\",\n \"--help show this help message and exit\\\" print >> sys.stderr, \\\" -o FILENAME,\",\n \"<stdint.h>\\\\n\\\\n\\\") h_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") addr = 0 i = 0 for line in in_file:\",\n \"= int(m[0][0], 16) name = m[0][1] sym[addr] = name else: break for line\",\n \"help message and exit\\\" print >> sys.stderr, \\\" -o FILENAME, --addr=FILENAME\\\" print >>\",\n \"-o FILENAME, --addr=FILENAME\\\" print >> sys.stderr, \\\"\\\" def error(msg): print >> sys.stderr, \\\"\\\"\",\n \"if o in (\\\"-h\\\", \\\"--help\\\"): usage() sys.exit() elif o in (\\\"-o\\\", \\\"--output\\\"): out_fname\",\n \"can't open file: '%s'\\\" % in_fname sys.exit(1) try: c_file = open(out_fname, mode='w') except:\",\n \"can't create file: %s\\\" % hdr_fname sys.exit(1) i = 0 for line in\",\n \"sym = {} for line in in_file: m = s_pat.findall(line) if m: addr\",\n \"in_file: if re.match(\\\"SYMBOL TABLE:\\\", line): break s_pat = re.compile(\\\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\\\") sym =\",\n \"a in opts: if o in (\\\"-h\\\", \\\"--help\\\"): usage() sys.exit() elif o in\",\n \"mode='w') except: print >> sys.stderr, \\\"#error: can't create file: %s\\\" % hdr_fname sys.exit(1)\",\n \"line): break token_pat = re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\") c_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") h_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") addr = 0 i\",\n \"* from getopt import * import sys import os import re def usage():\",\n \"int(m[0][0], 16) name = m[0][1] sym[addr] = name else: break for line in\",\n \"line): break s_pat = re.compile(\\\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\\\") sym = {} for line in\",\n \"break for line in in_file: if re.match(\\\"Contents of section .text:\\\", line): break token_pat\",\n \"in in_file: if re.match(\\\"Contents of section .text:\\\", line): break token_pat = re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\") c_file.write(\\\"#include\",\n \"err: error(str(err)) for o, a in opts: if o in (\\\"-h\\\", \\\"--help\\\"): usage()\",\n \"in_file = open(in_fname, mode='r') except: print >> sys.stderr, \\\"#error: can't open file: '%s'\\\"\",\n \"try: h_file = open(hdr_fname, mode='w') except: print >> sys.stderr, \\\"#error: can't create file:\",\n \"mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname, mode='r') except: print >> sys.stderr, \\\"#error:\",\n \"mode='w') except: print >> sys.stderr, \\\"#error: can't create file: %s\\\" % out_fname sys.exit(1)\",\n \"for line in in_file: for a, b, c, d in token_pat.findall(line): try: sym[addr]\",\n \"in opts: if o in (\\\"-h\\\", \\\"--help\\\"): usage() sys.exit() elif o in (\\\"-o\\\",\",\n \"out_fname = basename + '.' + 'c' dirname, fname = os.path.split(out_fname) basename, extension\",\n \"args[0] try: out_fname except NameError: dirname, fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname)\",\n \"getopt(sys.argv[1:], \\\"ho:\\\", \\\\ [\\\"help\\\", \\\"output=\\\"]) except GetoptError, err: error(str(err)) for o, a in\",\n \"m[0][1] sym[addr] = name else: break for line in in_file: if re.match(\\\"Contents of\",\n \"= os.path.splitext(fname) out_fname = basename + '.' + 'c' dirname, fname = os.path.split(out_fname)\",\n \"addr + 4 c_file.write(\\\"\\\\n};\\\\n\\\") in_file.close() c_file.close() h_file.close() return def main(): global progname progname\",\n \">> sys.stderr, \\\"\\\" def error(msg): print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"#error:\\\",\",\n \"<gh_stars>1-10 #!/usr/bin/python from struct import * from getopt import * import sys import\",\n \"import * from getopt import * import sys import os import re def\",\n \"fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname = basename + '.' +\",\n \"sys.stderr, \\\"\\\" print >> sys.stderr, \\\"Options\\\" print >> sys.stderr, \\\" -h, --help show\",\n \"usage() sys.exit() elif o in (\\\"-o\\\", \\\"--output\\\"): out_fname = a else: assert False,\",\n \"0 i = 0 for line in in_file: for a, b, c, d\",\n \"else: break for line in in_file: if re.match(\\\"Contents of section .text:\\\", line): break\",\n \"+ d + c + b + a ) i = i +\",\n \"\\\"Options\\\" print >> sys.stderr, \\\" -h, --help show this help message and exit\\\"\",\n \"sym[addr]) i = 0 except KeyError: pass if ((i % 4) == 0):\",\n \"= a else: assert False, \\\"unhandled option\\\" if len(args) == 0: error(\\\"missing fname\\\")\",\n \"print >> sys.stderr, \\\" -o FILENAME, --addr=FILENAME\\\" print >> sys.stderr, \\\"\\\" def error(msg):\",\n \">> sys.stderr, \\\" -o FILENAME, --addr=FILENAME\\\" print >> sys.stderr, \\\"\\\" def error(msg): print\",\n \"0: error(\\\"missing fname\\\") if len(args) > 1: error(\\\"too many arguments\\\") in_fname = args[0]\",\n \"([.A-Za-z_][A-Za-z_0-9]*)\\\") sym = {} for line in in_file: m = s_pat.findall(line) if m:\",\n \"= sys.argv[0] try: opts, args = getopt(sys.argv[1:], \\\"ho:\\\", \\\\ [\\\"help\\\", \\\"output=\\\"]) except GetoptError,\",\n \"for line in in_file: if re.match(\\\"SYMBOL TABLE:\\\", line): break s_pat = re.compile(\\\"([0-9a-f]{8}) ..*[0-9a-f]{8}\",\n \"0): c_file.write(\\\"\\\\n};\\\\n\\\\n\\\") c_file.write(\\\"const uint32_t %s[] = {\\\" % sym[addr]) h_file.write(\\\"extern const uint32_t %s[];\\\\n\\\\n\\\"\",\n \"error(\\\"too many arguments\\\") in_fname = args[0] try: out_fname except NameError: dirname, fname =\",\n \"\\\"[options] fname\\\" print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"Options\\\" print >> sys.stderr,\",\n \"os.path.splitext(fname) hdr_fname = basename + '.' + 'h' mk_codelet(in_fname, out_fname, hdr_fname) if __name__\",\n \"name else: break for line in in_file: if re.match(\\\"Contents of section .text:\\\", line):\",\n \"fname\\\") if len(args) > 1: error(\\\"too many arguments\\\") in_fname = args[0] try: out_fname\",\n \"+ '.' + 'c' dirname, fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname\",\n \"FILENAME, --addr=FILENAME\\\" print >> sys.stderr, \\\"\\\" def error(msg): print >> sys.stderr, \\\"\\\" print\",\n \"extension = os.path.splitext(fname) hdr_fname = basename + '.' + 'h' mk_codelet(in_fname, out_fname, hdr_fname)\",\n \"open(hdr_fname, mode='w') except: print >> sys.stderr, \\\"#error: can't create file: %s\\\" % hdr_fname\",\n \"% sym[addr]) h_file.write(\\\"extern const uint32_t %s[];\\\\n\\\\n\\\" % sym[addr]) i = 0 except KeyError:\",\n \"= {\\\" % sym[addr]) h_file.write(\\\"extern const uint32_t %s[];\\\\n\\\\n\\\" % sym[addr]) i = 0\",\n \"0x\\\" + d + c + b + a ) i = i\",\n \"basename + '.' + 'c' dirname, fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname)\",\n \"\\\"#error: can't open file: '%s'\\\" % in_fname sys.exit(1) try: c_file = open(out_fname, mode='w')\",\n \"sym[addr]) h_file.write(\\\"extern const uint32_t %s[];\\\\n\\\\n\\\" % sym[addr]) i = 0 except KeyError: pass\",\n \">> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"Options\\\" print >> sys.stderr, \\\" -h, --help\",\n \"%s\\\" % hdr_fname sys.exit(1) i = 0 for line in in_file: if re.match(\\\"SYMBOL\",\n \"except: print >> sys.stderr, \\\"#error: can't create file: %s\\\" % hdr_fname sys.exit(1) i\",\n \"= 0 for line in in_file: if re.match(\\\"SYMBOL TABLE:\\\", line): break s_pat =\",\n \"= args[0] try: out_fname except NameError: dirname, fname = os.path.split(in_fname) basename, extension =\",\n \"in_file: m = s_pat.findall(line) if m: addr = int(m[0][0], 16) name = m[0][1]\",\n \"const uint32_t %s[];\\\\n\\\\n\\\" % sym[addr]) i = 0 except KeyError: pass if ((i\",\n \"\\\"\\\" print >> sys.stderr, \\\" Usage:\\\", progname, \\\"[options] fname\\\" print >> sys.stderr, \\\"\\\"\",\n \"out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w') except: print >> sys.stderr, \\\"#error: can't\",\n \"{} for line in in_file: m = s_pat.findall(line) if m: addr = int(m[0][0],\",\n \"in (\\\"-o\\\", \\\"--output\\\"): out_fname = a else: assert False, \\\"unhandled option\\\" if len(args)\",\n \"many arguments\\\") in_fname = args[0] try: out_fname except NameError: dirname, fname = os.path.split(in_fname)\",\n \"\\\"#error: can't create file: %s\\\" % out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w')\",\n \"print >> sys.stderr, \\\"#error: can't open file: '%s'\\\" % in_fname sys.exit(1) try: c_file\",\n \">> sys.stderr, \\\"#error: can't create file: %s\\\" % out_fname sys.exit(1) try: h_file =\",\n \"for line in in_file: if re.match(\\\"Contents of section .text:\\\", line): break token_pat =\",\n \"addr = int(m[0][0], 16) name = m[0][1] sym[addr] = name else: break for\",\n \"usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname, mode='r') except: print\",\n \"+ c + b + a) else: c_file.write(\\\", 0x\\\" + d + c\",\n \"uint32_t %s[] = {\\\" % sym[addr]) h_file.write(\\\"extern const uint32_t %s[];\\\\n\\\\n\\\" % sym[addr]) i\",\n \">> sys.stderr, \\\" Usage:\\\", progname, \\\"[options] fname\\\" print >> sys.stderr, \\\"\\\" print >>\",\n \"\\\" -o FILENAME, --addr=FILENAME\\\" print >> sys.stderr, \\\"\\\" def error(msg): print >> sys.stderr,\",\n \"s_pat = re.compile(\\\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\\\") sym = {} for line in in_file: m\",\n \"NameError: dirname, fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname = basename +\",\n \"for a, b, c, d in token_pat.findall(line): try: sym[addr] if (i > 0):\",\n \"\\\"\\\" def error(msg): print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"#error:\\\", msg usage()\",\n \"'%s'\\\" % in_fname sys.exit(1) try: c_file = open(out_fname, mode='w') except: print >> sys.stderr,\",\n \"os.path.splitext(fname) out_fname = basename + '.' + 'c' dirname, fname = os.path.split(out_fname) basename,\",\n \"\\\"\\\" print >> sys.stderr, \\\"#error:\\\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try:\",\n \"\\\"ho:\\\", \\\\ [\\\"help\\\", \\\"output=\\\"]) except GetoptError, err: error(str(err)) for o, a in opts:\",\n \"error(\\\"missing fname\\\") if len(args) > 1: error(\\\"too many arguments\\\") in_fname = args[0] try:\",\n \"out_fname, hdr_fname): try: in_file = open(in_fname, mode='r') except: print >> sys.stderr, \\\"#error: can't\",\n \"sys.stderr, \\\"#error:\\\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname,\",\n \"c_file.write(\\\",\\\") c_file.write(\\\"\\\\n\\\\t0x\\\" + d + c + b + a) else: c_file.write(\\\", 0x\\\"\",\n \"in_file.close() c_file.close() h_file.close() return def main(): global progname progname = sys.argv[0] try: opts,\",\n \"c_file.write(\\\"const uint32_t %s[] = {\\\" % sym[addr]) h_file.write(\\\"extern const uint32_t %s[];\\\\n\\\\n\\\" % sym[addr])\",\n \"0): if (i > 0): c_file.write(\\\",\\\") c_file.write(\\\"\\\\n\\\\t0x\\\" + d + c + b\",\n \"re.match(\\\"SYMBOL TABLE:\\\", line): break s_pat = re.compile(\\\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\\\") sym = {} for\",\n \"exit\\\" print >> sys.stderr, \\\" -o FILENAME, --addr=FILENAME\\\" print >> sys.stderr, \\\"\\\" def\",\n \"except GetoptError, err: error(str(err)) for o, a in opts: if o in (\\\"-h\\\",\",\n \"sys.stderr, \\\"#error: can't open file: '%s'\\\" % in_fname sys.exit(1) try: c_file = open(out_fname,\",\n \"d in token_pat.findall(line): try: sym[addr] if (i > 0): c_file.write(\\\"\\\\n};\\\\n\\\\n\\\") c_file.write(\\\"const uint32_t %s[]\",\n \"c_file.write(\\\"\\\\n\\\\t0x\\\" + d + c + b + a) else: c_file.write(\\\", 0x\\\" +\",\n \"0 except KeyError: pass if ((i % 4) == 0): if (i >\",\n \"error(msg): print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"#error:\\\", msg usage() sys.exit(2) def\",\n \"print >> sys.stderr, \\\"#error: can't create file: %s\\\" % hdr_fname sys.exit(1) i =\",\n \"def usage(): global progname print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\" Usage:\\\",\",\n \"print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"#error:\\\", msg usage() sys.exit(2) def mk_codelet(in_fname,\",\n \"+ b + a ) i = i + 1; addr = addr\",\n \"if re.match(\\\"Contents of section .text:\\\", line): break token_pat = re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\") c_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") h_file.write(\\\"#include\",\n \"arguments\\\") in_fname = args[0] try: out_fname except NameError: dirname, fname = os.path.split(in_fname) basename,\",\n \"dirname, fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname = basename + '.'\",\n \"c_file = open(out_fname, mode='w') except: print >> sys.stderr, \\\"#error: can't create file: %s\\\"\",\n \"1; addr = addr + 4 c_file.write(\\\"\\\\n};\\\\n\\\") in_file.close() c_file.close() h_file.close() return def main():\",\n \"def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname, mode='r') except: print >> sys.stderr,\",\n \"c_file.write(\\\"\\\\n};\\\\n\\\\n\\\") c_file.write(\\\"const uint32_t %s[] = {\\\" % sym[addr]) h_file.write(\\\"extern const uint32_t %s[];\\\\n\\\\n\\\" %\",\n \"= os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname = basename + '.' + 'h'\",\n \"for line in in_file: m = s_pat.findall(line) if m: addr = int(m[0][0], 16)\",\n \"print >> sys.stderr, \\\"#error: can't create file: %s\\\" % out_fname sys.exit(1) try: h_file\",\n \"else: assert False, \\\"unhandled option\\\" if len(args) == 0: error(\\\"missing fname\\\") if len(args)\",\n \"\\\"--output\\\"): out_fname = a else: assert False, \\\"unhandled option\\\" if len(args) == 0:\",\n \"try: out_fname except NameError: dirname, fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname\",\n \"False, \\\"unhandled option\\\" if len(args) == 0: error(\\\"missing fname\\\") if len(args) > 1:\",\n \"hdr_fname): try: in_file = open(in_fname, mode='r') except: print >> sys.stderr, \\\"#error: can't open\",\n \"= 0 for line in in_file: for a, b, c, d in token_pat.findall(line):\",\n \"((i % 4) == 0): if (i > 0): c_file.write(\\\",\\\") c_file.write(\\\"\\\\n\\\\t0x\\\" + d\",\n \"#!/usr/bin/python from struct import * from getopt import * import sys import os\",\n \"struct import * from getopt import * import sys import os import re\",\n \"msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname, mode='r') except:\",\n \"[\\\"help\\\", \\\"output=\\\"]) except GetoptError, err: error(str(err)) for o, a in opts: if o\",\n \"(i > 0): c_file.write(\\\",\\\") c_file.write(\\\"\\\\n\\\\t0x\\\" + d + c + b + a)\",\n \"= 0 i = 0 for line in in_file: for a, b, c,\",\n \"sys.stderr, \\\"\\\" def error(msg): print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"#error:\\\", msg\",\n \"re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\") c_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") h_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") addr = 0 i = 0 for line\",\n \"hdr_fname = basename + '.' + 'h' mk_codelet(in_fname, out_fname, hdr_fname) if __name__ ==\",\n \"print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\" Usage:\\\", progname, \\\"[options] fname\\\" print\",\n \"print >> sys.stderr, \\\" -h, --help show this help message and exit\\\" print\",\n \"h_file = open(hdr_fname, mode='w') except: print >> sys.stderr, \\\"#error: can't create file: %s\\\"\",\n \"if len(args) == 0: error(\\\"missing fname\\\") if len(args) > 1: error(\\\"too many arguments\\\")\",\n \"\\\"\\\" print >> sys.stderr, \\\"Options\\\" print >> sys.stderr, \\\" -h, --help show this\",\n \"open file: '%s'\\\" % in_fname sys.exit(1) try: c_file = open(out_fname, mode='w') except: print\",\n \"dirname, fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname = basename + '.'\",\n \"i = 0 except KeyError: pass if ((i % 4) == 0): if\",\n \"= addr + 4 c_file.write(\\\"\\\\n};\\\\n\\\") in_file.close() c_file.close() h_file.close() return def main(): global progname\",\n \"%s[] = {\\\" % sym[addr]) h_file.write(\\\"extern const uint32_t %s[];\\\\n\\\\n\\\" % sym[addr]) i =\",\n \"c_file.write(\\\", 0x\\\" + d + c + b + a ) i =\",\n \"addr = addr + 4 c_file.write(\\\"\\\\n};\\\\n\\\") in_file.close() c_file.close() h_file.close() return def main(): global\",\n \"= re.compile(\\\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\\\") sym = {} for line in in_file: m =\",\n \"% sym[addr]) i = 0 except KeyError: pass if ((i % 4) ==\",\n \"% hdr_fname sys.exit(1) i = 0 for line in in_file: if re.match(\\\"SYMBOL TABLE:\\\",\",\n \"h_file.close() return def main(): global progname progname = sys.argv[0] try: opts, args =\",\n \"a ) i = i + 1; addr = addr + 4 c_file.write(\\\"\\\\n};\\\\n\\\")\",\n \">> sys.stderr, \\\"\\\" print >> sys.stderr, \\\" Usage:\\\", progname, \\\"[options] fname\\\" print >>\",\n \"m = s_pat.findall(line) if m: addr = int(m[0][0], 16) name = m[0][1] sym[addr]\",\n \"and exit\\\" print >> sys.stderr, \\\" -o FILENAME, --addr=FILENAME\\\" print >> sys.stderr, \\\"\\\"\",\n \"= i + 1; addr = addr + 4 c_file.write(\\\"\\\\n};\\\\n\\\") in_file.close() c_file.close() h_file.close()\",\n \"sym[addr] = name else: break for line in in_file: if re.match(\\\"Contents of section\",\n \"16) name = m[0][1] sym[addr] = name else: break for line in in_file:\",\n \"0): c_file.write(\\\",\\\") c_file.write(\\\"\\\\n\\\\t0x\\\" + d + c + b + a) else: c_file.write(\\\",\",\n \") i = i + 1; addr = addr + 4 c_file.write(\\\"\\\\n};\\\\n\\\") in_file.close()\",\n \"> 0): c_file.write(\\\"\\\\n};\\\\n\\\\n\\\") c_file.write(\\\"const uint32_t %s[] = {\\\" % sym[addr]) h_file.write(\\\"extern const uint32_t\",\n \"\\\"--help\\\"): usage() sys.exit() elif o in (\\\"-o\\\", \\\"--output\\\"): out_fname = a else: assert\",\n \"c + b + a ) i = i + 1; addr =\",\n \"sys.argv[0] try: opts, args = getopt(sys.argv[1:], \\\"ho:\\\", \\\\ [\\\"help\\\", \\\"output=\\\"]) except GetoptError, err:\",\n \">> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"#error:\\\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname,\",\n \"= getopt(sys.argv[1:], \\\"ho:\\\", \\\\ [\\\"help\\\", \\\"output=\\\"]) except GetoptError, err: error(str(err)) for o, a\",\n \"c + b + a) else: c_file.write(\\\", 0x\\\" + d + c +\",\n \"--addr=FILENAME\\\" print >> sys.stderr, \\\"\\\" def error(msg): print >> sys.stderr, \\\"\\\" print >>\",\n \"= s_pat.findall(line) if m: addr = int(m[0][0], 16) name = m[0][1] sym[addr] =\",\n \"== 0: error(\\\"missing fname\\\") if len(args) > 1: error(\\\"too many arguments\\\") in_fname =\",\n \"can't create file: %s\\\" % out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w') except:\",\n \"error(str(err)) for o, a in opts: if o in (\\\"-h\\\", \\\"--help\\\"): usage() sys.exit()\",\n \"break token_pat = re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\") c_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") h_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") addr = 0 i =\",\n \"import sys import os import re def usage(): global progname print >> sys.stderr,\",\n \"0 for line in in_file: for a, b, c, d in token_pat.findall(line): try:\",\n \"progname = sys.argv[0] try: opts, args = getopt(sys.argv[1:], \\\"ho:\\\", \\\\ [\\\"help\\\", \\\"output=\\\"]) except\",\n \"basename + '.' + 'h' mk_codelet(in_fname, out_fname, hdr_fname) if __name__ == \\\"__main__\\\": main()\",\n \"try: sym[addr] if (i > 0): c_file.write(\\\"\\\\n};\\\\n\\\\n\\\") c_file.write(\\\"const uint32_t %s[] = {\\\" %\",\n \"b + a ) i = i + 1; addr = addr +\",\n \"in in_file: if re.match(\\\"SYMBOL TABLE:\\\", line): break s_pat = re.compile(\\\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\\\") sym\",\n \"create file: %s\\\" % hdr_fname sys.exit(1) i = 0 for line in in_file:\",\n \"assert False, \\\"unhandled option\\\" if len(args) == 0: error(\\\"missing fname\\\") if len(args) >\",\n \"= os.path.splitext(fname) hdr_fname = basename + '.' + 'h' mk_codelet(in_fname, out_fname, hdr_fname) if\",\n \"= open(in_fname, mode='r') except: print >> sys.stderr, \\\"#error: can't open file: '%s'\\\" %\",\n \"(\\\"-h\\\", \\\"--help\\\"): usage() sys.exit() elif o in (\\\"-o\\\", \\\"--output\\\"): out_fname = a else:\",\n \"except: print >> sys.stderr, \\\"#error: can't open file: '%s'\\\" % in_fname sys.exit(1) try:\",\n \"sys.exit(1) try: h_file = open(hdr_fname, mode='w') except: print >> sys.stderr, \\\"#error: can't create\",\n \"import os import re def usage(): global progname print >> sys.stderr, \\\"\\\" print\",\n \"sys.stderr, \\\"#error: can't create file: %s\\\" % out_fname sys.exit(1) try: h_file = open(hdr_fname,\",\n \"\\\\ [\\\"help\\\", \\\"output=\\\"]) except GetoptError, err: error(str(err)) for o, a in opts: if\",\n \"o in (\\\"-h\\\", \\\"--help\\\"): usage() sys.exit() elif o in (\\\"-o\\\", \\\"--output\\\"): out_fname =\",\n \"sys.exit(1) try: c_file = open(out_fname, mode='w') except: print >> sys.stderr, \\\"#error: can't create\",\n \"from getopt import * import sys import os import re def usage(): global\",\n \"mode='r') except: print >> sys.stderr, \\\"#error: can't open file: '%s'\\\" % in_fname sys.exit(1)\",\n \"\\\"unhandled option\\\" if len(args) == 0: error(\\\"missing fname\\\") if len(args) > 1: error(\\\"too\",\n \"GetoptError, err: error(str(err)) for o, a in opts: if o in (\\\"-h\\\", \\\"--help\\\"):\",\n \"re def usage(): global progname print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"\",\n \"extension = os.path.splitext(fname) out_fname = basename + '.' + 'c' dirname, fname =\",\n \"% 4) == 0): if (i > 0): c_file.write(\\\",\\\") c_file.write(\\\"\\\\n\\\\t0x\\\" + d +\",\n \">> sys.stderr, \\\"#error:\\\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file =\",\n \"\\\"#error: can't create file: %s\\\" % hdr_fname sys.exit(1) i = 0 for line\",\n \"opts: if o in (\\\"-h\\\", \\\"--help\\\"): usage() sys.exit() elif o in (\\\"-o\\\", \\\"--output\\\"):\",\n \"elif o in (\\\"-o\\\", \\\"--output\\\"): out_fname = a else: assert False, \\\"unhandled option\\\"\",\n \"out_fname except NameError: dirname, fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname =\",\n \"+ 'c' dirname, fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname = basename\",\n \"'c' dirname, fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname = basename +\",\n \"> 1: error(\\\"too many arguments\\\") in_fname = args[0] try: out_fname except NameError: dirname,\",\n \"except NameError: dirname, fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname = basename\",\n \">> sys.stderr, \\\" -h, --help show this help message and exit\\\" print >>\",\n \"sys.stderr, \\\" -o FILENAME, --addr=FILENAME\\\" print >> sys.stderr, \\\"\\\" def error(msg): print >>\",\n \"print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"Options\\\" print >> sys.stderr, \\\" -h,\",\n \"%s\\\" % out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w') except: print >> sys.stderr,\",\n \"sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname, mode='r') except: print >>\",\n \"of section .text:\\\", line): break token_pat = re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\") c_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") h_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") addr\",\n \"= 0 except KeyError: pass if ((i % 4) == 0): if (i\",\n \"global progname progname = sys.argv[0] try: opts, args = getopt(sys.argv[1:], \\\"ho:\\\", \\\\ [\\\"help\\\",\",\n \"from struct import * from getopt import * import sys import os import\",\n \"progname, \\\"[options] fname\\\" print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"Options\\\" print >>\",\n \"%s[];\\\\n\\\\n\\\" % sym[addr]) i = 0 except KeyError: pass if ((i % 4)\",\n \"b, c, d in token_pat.findall(line): try: sym[addr] if (i > 0): c_file.write(\\\"\\\\n};\\\\n\\\\n\\\") c_file.write(\\\"const\",\n \"except: print >> sys.stderr, \\\"#error: can't create file: %s\\\" % out_fname sys.exit(1) try:\",\n \"a, b, c, d in token_pat.findall(line): try: sym[addr] if (i > 0): c_file.write(\\\"\\\\n};\\\\n\\\\n\\\")\",\n \"token_pat.findall(line): try: sym[addr] if (i > 0): c_file.write(\\\"\\\\n};\\\\n\\\\n\\\") c_file.write(\\\"const uint32_t %s[] = {\\\"\",\n \"1: error(\\\"too many arguments\\\") in_fname = args[0] try: out_fname except NameError: dirname, fname\",\n \"getopt import * import sys import os import re def usage(): global progname\",\n \"args = getopt(sys.argv[1:], \\\"ho:\\\", \\\\ [\\\"help\\\", \\\"output=\\\"]) except GetoptError, err: error(str(err)) for o,\",\n \"option\\\" if len(args) == 0: error(\\\"missing fname\\\") if len(args) > 1: error(\\\"too many\",\n \"4 c_file.write(\\\"\\\\n};\\\\n\\\") in_file.close() c_file.close() h_file.close() return def main(): global progname progname = sys.argv[0]\",\n \"len(args) == 0: error(\\\"missing fname\\\") if len(args) > 1: error(\\\"too many arguments\\\") in_fname\",\n \"if (i > 0): c_file.write(\\\",\\\") c_file.write(\\\"\\\\n\\\\t0x\\\" + d + c + b +\",\n \"def main(): global progname progname = sys.argv[0] try: opts, args = getopt(sys.argv[1:], \\\"ho:\\\",\",\n \"= basename + '.' + 'c' dirname, fname = os.path.split(out_fname) basename, extension =\",\n \"open(out_fname, mode='w') except: print >> sys.stderr, \\\"#error: can't create file: %s\\\" % out_fname\",\n \"except KeyError: pass if ((i % 4) == 0): if (i > 0):\",\n \"sys.stderr, \\\" -h, --help show this help message and exit\\\" print >> sys.stderr,\",\n \"create file: %s\\\" % out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w') except: print\",\n \"sys.stderr, \\\"\\\" print >> sys.stderr, \\\" Usage:\\\", progname, \\\"[options] fname\\\" print >> sys.stderr,\",\n \"b + a) else: c_file.write(\\\", 0x\\\" + d + c + b +\",\n \"4) == 0): if (i > 0): c_file.write(\\\",\\\") c_file.write(\\\"\\\\n\\\\t0x\\\" + d + c\",\n \"progname progname = sys.argv[0] try: opts, args = getopt(sys.argv[1:], \\\"ho:\\\", \\\\ [\\\"help\\\", \\\"output=\\\"])\",\n \"out_fname = a else: assert False, \\\"unhandled option\\\" if len(args) == 0: error(\\\"missing\",\n \"progname print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\" Usage:\\\", progname, \\\"[options] fname\\\"\",\n \"(\\\"-o\\\", \\\"--output\\\"): out_fname = a else: assert False, \\\"unhandled option\\\" if len(args) ==\",\n \"sys import os import re def usage(): global progname print >> sys.stderr, \\\"\\\"\",\n \"i + 1; addr = addr + 4 c_file.write(\\\"\\\\n};\\\\n\\\") in_file.close() c_file.close() h_file.close() return\",\n \"sys.stderr, \\\"\\\" print >> sys.stderr, \\\"#error:\\\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname):\",\n \"sys.exit() elif o in (\\\"-o\\\", \\\"--output\\\"): out_fname = a else: assert False, \\\"unhandled\",\n \"% out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w') except: print >> sys.stderr, \\\"#error:\",\n \"i = 0 for line in in_file: for a, b, c, d in\",\n \"c_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") h_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") addr = 0 i = 0 for line in\",\n \"print >> sys.stderr, \\\"\\\" def error(msg): print >> sys.stderr, \\\"\\\" print >> sys.stderr,\",\n \"c_file.close() h_file.close() return def main(): global progname progname = sys.argv[0] try: opts, args\",\n \"\\\" -h, --help show this help message and exit\\\" print >> sys.stderr, \\\"\",\n \"sys.stderr, \\\" Usage:\\\", progname, \\\"[options] fname\\\" print >> sys.stderr, \\\"\\\" print >> sys.stderr,\",\n \"in in_file: m = s_pat.findall(line) if m: addr = int(m[0][0], 16) name =\",\n \"for o, a in opts: if o in (\\\"-h\\\", \\\"--help\\\"): usage() sys.exit() elif\",\n \"os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname = basename + '.' + 'c' dirname,\",\n \"break s_pat = re.compile(\\\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\\\") sym = {} for line in in_file:\",\n \"d + c + b + a ) i = i + 1;\",\n \"pass if ((i % 4) == 0): if (i > 0): c_file.write(\\\",\\\") c_file.write(\\\"\\\\n\\\\t0x\\\"\",\n \"= name else: break for line in in_file: if re.match(\\\"Contents of section .text:\\\",\",\n \"== 0): if (i > 0): c_file.write(\\\",\\\") c_file.write(\\\"\\\\n\\\\t0x\\\" + d + c +\",\n \"line in in_file: m = s_pat.findall(line) if m: addr = int(m[0][0], 16) name\",\n \"fname = os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname = basename + '.' +\",\n \"in token_pat.findall(line): try: sym[addr] if (i > 0): c_file.write(\\\"\\\\n};\\\\n\\\\n\\\") c_file.write(\\\"const uint32_t %s[] =\",\n \"o, a in opts: if o in (\\\"-h\\\", \\\"--help\\\"): usage() sys.exit() elif o\",\n \"\\\"#error:\\\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file = open(in_fname, mode='r')\",\n \"..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\\\") sym = {} for line in in_file: m = s_pat.findall(line) if\",\n \"'.' + 'c' dirname, fname = os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname =\",\n \"= re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\") c_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") h_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") addr = 0 i = 0 for\",\n \"h_file.write(\\\"extern const uint32_t %s[];\\\\n\\\\n\\\" % sym[addr]) i = 0 except KeyError: pass if\",\n \"d + c + b + a) else: c_file.write(\\\", 0x\\\" + d +\",\n \"\\\"output=\\\"]) except GetoptError, err: error(str(err)) for o, a in opts: if o in\",\n \"line in in_file: if re.match(\\\"SYMBOL TABLE:\\\", line): break s_pat = re.compile(\\\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\\\")\",\n \"{\\\" % sym[addr]) h_file.write(\\\"extern const uint32_t %s[];\\\\n\\\\n\\\" % sym[addr]) i = 0 except\",\n \"usage(): global progname print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\" Usage:\\\", progname,\",\n \"basename, extension = os.path.splitext(fname) hdr_fname = basename + '.' + 'h' mk_codelet(in_fname, out_fname,\",\n \"+ 4 c_file.write(\\\"\\\\n};\\\\n\\\") in_file.close() c_file.close() h_file.close() return def main(): global progname progname =\",\n \"basename, extension = os.path.splitext(fname) out_fname = basename + '.' + 'c' dirname, fname\",\n \"hdr_fname sys.exit(1) i = 0 for line in in_file: if re.match(\\\"SYMBOL TABLE:\\\", line):\",\n \"-h, --help show this help message and exit\\\" print >> sys.stderr, \\\" -o\",\n \"message and exit\\\" print >> sys.stderr, \\\" -o FILENAME, --addr=FILENAME\\\" print >> sys.stderr,\",\n \"h_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") addr = 0 i = 0 for line in in_file: for\",\n \"section .text:\\\", line): break token_pat = re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\") c_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") h_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") addr =\",\n \"import re def usage(): global progname print >> sys.stderr, \\\"\\\" print >> sys.stderr,\",\n \"sys.stderr, \\\"#error: can't create file: %s\\\" % hdr_fname sys.exit(1) i = 0 for\",\n \"c, d in token_pat.findall(line): try: sym[addr] if (i > 0): c_file.write(\\\"\\\\n};\\\\n\\\\n\\\") c_file.write(\\\"const uint32_t\",\n \"file: %s\\\" % out_fname sys.exit(1) try: h_file = open(hdr_fname, mode='w') except: print >>\",\n \"file: '%s'\\\" % in_fname sys.exit(1) try: c_file = open(out_fname, mode='w') except: print >>\",\n \"addr = 0 i = 0 for line in in_file: for a, b,\",\n \".text:\\\", line): break token_pat = re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\") c_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") h_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") addr = 0\",\n \"sys.exit(1) i = 0 for line in in_file: if re.match(\\\"SYMBOL TABLE:\\\", line): break\",\n \"import * import sys import os import re def usage(): global progname print\",\n \"in_file: for a, b, c, d in token_pat.findall(line): try: sym[addr] if (i >\",\n \"% in_fname sys.exit(1) try: c_file = open(out_fname, mode='w') except: print >> sys.stderr, \\\"#error:\",\n \"= m[0][1] sym[addr] = name else: break for line in in_file: if re.match(\\\"Contents\",\n \"i = i + 1; addr = addr + 4 c_file.write(\\\"\\\\n};\\\\n\\\") in_file.close() c_file.close()\",\n \"this help message and exit\\\" print >> sys.stderr, \\\" -o FILENAME, --addr=FILENAME\\\" print\",\n \"line in in_file: if re.match(\\\"Contents of section .text:\\\", line): break token_pat = re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\")\",\n \"+ b + a) else: c_file.write(\\\", 0x\\\" + d + c + b\",\n \"+ c + b + a ) i = i + 1; addr\",\n \"try: c_file = open(out_fname, mode='w') except: print >> sys.stderr, \\\"#error: can't create file:\",\n \">> sys.stderr, \\\"Options\\\" print >> sys.stderr, \\\" -h, --help show this help message\",\n \"= open(hdr_fname, mode='w') except: print >> sys.stderr, \\\"#error: can't create file: %s\\\" %\",\n \">> sys.stderr, \\\"#error: can't create file: %s\\\" % hdr_fname sys.exit(1) i = 0\",\n \"a else: assert False, \\\"unhandled option\\\" if len(args) == 0: error(\\\"missing fname\\\") if\",\n \"if (i > 0): c_file.write(\\\"\\\\n};\\\\n\\\\n\\\") c_file.write(\\\"const uint32_t %s[] = {\\\" % sym[addr]) h_file.write(\\\"extern\",\n \"+ 1; addr = addr + 4 c_file.write(\\\"\\\\n};\\\\n\\\") in_file.close() c_file.close() h_file.close() return def\",\n \"TABLE:\\\", line): break s_pat = re.compile(\\\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\\\") sym = {} for line\",\n \"= os.path.split(in_fname) basename, extension = os.path.splitext(fname) out_fname = basename + '.' + 'c'\",\n \"+ a) else: c_file.write(\\\", 0x\\\" + d + c + b + a\",\n \"m: addr = int(m[0][0], 16) name = m[0][1] sym[addr] = name else: break\",\n \"else: c_file.write(\\\", 0x\\\" + d + c + b + a ) i\",\n \"print >> sys.stderr, \\\" Usage:\\\", progname, \\\"[options] fname\\\" print >> sys.stderr, \\\"\\\" print\",\n \"show this help message and exit\\\" print >> sys.stderr, \\\" -o FILENAME, --addr=FILENAME\\\"\",\n \"print >> sys.stderr, \\\"#error:\\\", msg usage() sys.exit(2) def mk_codelet(in_fname, out_fname, hdr_fname): try: in_file\",\n \"in in_file: for a, b, c, d in token_pat.findall(line): try: sym[addr] if (i\",\n \"in_fname = args[0] try: out_fname except NameError: dirname, fname = os.path.split(in_fname) basename, extension\",\n \"try: opts, args = getopt(sys.argv[1:], \\\"ho:\\\", \\\\ [\\\"help\\\", \\\"output=\\\"]) except GetoptError, err: error(str(err))\",\n \"in_file: if re.match(\\\"Contents of section .text:\\\", line): break token_pat = re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\") c_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\")\",\n \"KeyError: pass if ((i % 4) == 0): if (i > 0): c_file.write(\\\",\\\")\",\n \"* import sys import os import re def usage(): global progname print >>\",\n \"in_fname sys.exit(1) try: c_file = open(out_fname, mode='w') except: print >> sys.stderr, \\\"#error: can't\",\n \"o in (\\\"-o\\\", \\\"--output\\\"): out_fname = a else: assert False, \\\"unhandled option\\\" if\",\n \"= open(out_fname, mode='w') except: print >> sys.stderr, \\\"#error: can't create file: %s\\\" %\",\n \"Usage:\\\", progname, \\\"[options] fname\\\" print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"Options\\\" print\",\n \"os.path.split(out_fname) basename, extension = os.path.splitext(fname) hdr_fname = basename + '.' + 'h' mk_codelet(in_fname,\",\n \"return def main(): global progname progname = sys.argv[0] try: opts, args = getopt(sys.argv[1:],\",\n \"line in in_file: for a, b, c, d in token_pat.findall(line): try: sym[addr] if\",\n \"fname\\\" print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"Options\\\" print >> sys.stderr, \\\"\",\n \"+ d + c + b + a) else: c_file.write(\\\", 0x\\\" + d\",\n \"(i > 0): c_file.write(\\\"\\\\n};\\\\n\\\\n\\\") c_file.write(\\\"const uint32_t %s[] = {\\\" % sym[addr]) h_file.write(\\\"extern const\",\n \"def error(msg): print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"#error:\\\", msg usage() sys.exit(2)\",\n \"file: %s\\\" % hdr_fname sys.exit(1) i = 0 for line in in_file: if\",\n \"s_pat.findall(line) if m: addr = int(m[0][0], 16) name = m[0][1] sym[addr] = name\",\n \"global progname print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\" Usage:\\\", progname, \\\"[options]\",\n \"main(): global progname progname = sys.argv[0] try: opts, args = getopt(sys.argv[1:], \\\"ho:\\\", \\\\\",\n \"name = m[0][1] sym[addr] = name else: break for line in in_file: if\",\n \"if m: addr = int(m[0][0], 16) name = m[0][1] sym[addr] = name else:\",\n \"> 0): c_file.write(\\\",\\\") c_file.write(\\\"\\\\n\\\\t0x\\\" + d + c + b + a) else:\",\n \">> sys.stderr, \\\"#error: can't open file: '%s'\\\" % in_fname sys.exit(1) try: c_file =\",\n \"uint32_t %s[];\\\\n\\\\n\\\" % sym[addr]) i = 0 except KeyError: pass if ((i %\",\n \"in (\\\"-h\\\", \\\"--help\\\"): usage() sys.exit() elif o in (\\\"-o\\\", \\\"--output\\\"): out_fname = a\",\n \"a) else: c_file.write(\\\", 0x\\\" + d + c + b + a )\",\n \"0 for line in in_file: if re.match(\\\"SYMBOL TABLE:\\\", line): break s_pat = re.compile(\\\"([0-9a-f]{8})\",\n \"token_pat = re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\") c_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") h_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") addr = 0 i = 0\",\n \"if re.match(\\\"SYMBOL TABLE:\\\", line): break s_pat = re.compile(\\\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\\\") sym = {}\",\n \"if ((i % 4) == 0): if (i > 0): c_file.write(\\\",\\\") c_file.write(\\\"\\\\n\\\\t0x\\\" +\",\n \"len(args) > 1: error(\\\"too many arguments\\\") in_fname = args[0] try: out_fname except NameError:\",\n \"= {} for line in in_file: m = s_pat.findall(line) if m: addr =\",\n \"os import re def usage(): global progname print >> sys.stderr, \\\"\\\" print >>\",\n \"\\\" Usage:\\\", progname, \\\"[options] fname\\\" print >> sys.stderr, \\\"\\\" print >> sys.stderr, \\\"Options\\\"\",\n \"+ a ) i = i + 1; addr = addr + 4\",\n \"opts, args = getopt(sys.argv[1:], \\\"ho:\\\", \\\\ [\\\"help\\\", \\\"output=\\\"]) except GetoptError, err: error(str(err)) for\",\n \"try: in_file = open(in_fname, mode='r') except: print >> sys.stderr, \\\"#error: can't open file:\",\n \"c_file.write(\\\"\\\\n};\\\\n\\\") in_file.close() c_file.close() h_file.close() return def main(): global progname progname = sys.argv[0] try:\",\n \"print >> sys.stderr, \\\"Options\\\" print >> sys.stderr, \\\" -h, --help show this help\",\n \"re.compile(\\\"([0-9a-f]{8}) ..*[0-9a-f]{8} ([.A-Za-z_][A-Za-z_0-9]*)\\\") sym = {} for line in in_file: m = s_pat.findall(line)\",\n \"sym[addr] if (i > 0): c_file.write(\\\"\\\\n};\\\\n\\\\n\\\") c_file.write(\\\"const uint32_t %s[] = {\\\" % sym[addr])\",\n \"i = 0 for line in in_file: if re.match(\\\"SYMBOL TABLE:\\\", line): break s_pat\",\n \"re.match(\\\"Contents of section .text:\\\", line): break token_pat = re.compile(\\\"([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})\\\") c_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\") h_file.write(\\\"#include <stdint.h>\\\\n\\\\n\\\")\",\n \"open(in_fname, mode='r') except: print >> sys.stderr, \\\"#error: can't open file: '%s'\\\" % in_fname\",\n \"<stdint.h>\\\\n\\\\n\\\") addr = 0 i = 0 for line in in_file: for a,\",\n \"= basename + '.' + 'h' mk_codelet(in_fname, out_fname, hdr_fname) if __name__ == \\\"__main__\\\":\",\n \"sys.stderr, \\\"Options\\\" print >> sys.stderr, \\\" -h, --help show this help message and\"\n]","isConversational":false}}},{"rowIdx":2993,"cells":{"ngram":{"kind":"list like","value":["os.path.join(os.getcwd(), \"nonexistent\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \"%s\" does not exist' % path) logger.warn.assert_called_with(","== os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\"robot.api.logger.error\") mocker.patch(\"robot.api.logger.warn\") path = os.path.join(os.getcwd(), \"nonexistent\")","test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture,","under_test: x3270): mocker.patch(\"robot.api.logger.error\") mocker.patch(\"robot.api.logger.warn\") path = os.path.join(os.getcwd(), \"nonexistent\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \"%s\"","will be saved in \"%s\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270):","test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\"robot.api.logger.error\") mocker.patch(\"robot.api.logger.warn\") path = os.path.join(os.getcwd(), \"nonexistent\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots","os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\"robot.api.logger.error\") mocker.patch(\"robot.api.logger.warn\")","under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\"robot.api.logger.error\") mocker.patch(\"robot.api.logger.warn\") path","= os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\"robot.api.logger.error\")","saved in \"%s\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\") mocker.patch(\"robot.api.logger.write\")","% path) logger.warn.assert_called_with( 'Screenshots will be saved in \"%s\"' % under_test.imgfolder ) def","def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\") mocker.patch(\"robot.api.logger.write\") mocker.patch(\"time.time\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe","\"%s\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\") mocker.patch(\"robot.api.logger.write\") mocker.patch(\"time.time\", return_value=1.0)","<reponame>MichaelSeeburger/Robot-Framework-Mainframe-3270-Library<gh_stars>1-10 import os from pytest_mock import MockerFixture from robot.api import logger from Mainframe3270.x3270","import x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd()","\"%s\" does not exist' % path) logger.warn.assert_called_with( 'Screenshots will be saved in \"%s\"'","% under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\") mocker.patch(\"robot.api.logger.write\") mocker.patch(\"time.time\", return_value=1.0) under_test.take_screenshot(500,","under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \"%s\" does not exist' % path) logger.warn.assert_called_with( 'Screenshots will","mocker.patch(\"robot.api.logger.write\") mocker.patch(\"time.time\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe src=\"./screenshot_1000.html\" height=\"500\" width=\"500\"></iframe>', level=\"INFO\", html=True, )","from robot.api import logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path =","under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\") mocker.patch(\"robot.api.logger.write\") mocker.patch(\"time.time\", return_value=1.0) under_test.take_screenshot(500, 500)","MockerFixture, under_test: x3270): mocker.patch(\"robot.api.logger.error\") mocker.patch(\"robot.api.logger.warn\") path = os.path.join(os.getcwd(), \"nonexistent\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path","os from pytest_mock import MockerFixture from robot.api import logger from Mainframe3270.x3270 import x3270","does not exist' % path) logger.warn.assert_called_with( 'Screenshots will be saved in \"%s\"' %","mocker.patch(\"robot.api.logger.warn\") path = os.path.join(os.getcwd(), \"nonexistent\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \"%s\" does not exist'","screenshots path \"%s\" does not exist' % path) logger.warn.assert_called_with( 'Screenshots will be saved","in \"%s\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\") mocker.patch(\"robot.api.logger.write\") mocker.patch(\"time.time\",","under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\"robot.api.logger.error\") mocker.patch(\"robot.api.logger.warn\") path = os.path.join(os.getcwd(),","def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\"robot.api.logger.error\") mocker.patch(\"robot.api.logger.warn\") path = os.path.join(os.getcwd(), \"nonexistent\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given","import os from pytest_mock import MockerFixture from robot.api import logger from Mainframe3270.x3270 import","robot.api import logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd()","x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test:","path) logger.warn.assert_called_with( 'Screenshots will be saved in \"%s\"' % under_test.imgfolder ) def test_take_screenshot(mocker:","path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270):","under_test: x3270): mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\") mocker.patch(\"robot.api.logger.write\") mocker.patch(\"time.time\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe src=\"./screenshot_1000.html\" height=\"500\" width=\"500\"></iframe>',","import MockerFixture from robot.api import logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270):","import logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path)","'Screenshots will be saved in \"%s\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test:","MockerFixture from robot.api import logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path","path = os.path.join(os.getcwd(), \"nonexistent\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \"%s\" does not exist' %","logger.error.assert_called_with('Given screenshots path \"%s\" does not exist' % path) logger.warn.assert_called_with( 'Screenshots will be","logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert","pytest_mock import MockerFixture from robot.api import logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test:","assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\"robot.api.logger.error\") mocker.patch(\"robot.api.logger.warn\") path =",") def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\") mocker.patch(\"robot.api.logger.write\") mocker.patch(\"time.time\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with(","mocker.patch(\"robot.api.logger.error\") mocker.patch(\"robot.api.logger.warn\") path = os.path.join(os.getcwd(), \"nonexistent\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \"%s\" does not","path \"%s\" does not exist' % path) logger.warn.assert_called_with( 'Screenshots will be saved in","from pytest_mock import MockerFixture from robot.api import logger from Mainframe3270.x3270 import x3270 def","logger.warn.assert_called_with( 'Screenshots will be saved in \"%s\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture,","test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\") mocker.patch(\"robot.api.logger.write\") mocker.patch(\"time.time\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe src=\"./screenshot_1000.html\"","def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker:","\"nonexistent\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \"%s\" does not exist' % path) logger.warn.assert_called_with( 'Screenshots","MockerFixture, under_test: x3270): mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\") mocker.patch(\"robot.api.logger.write\") mocker.patch(\"time.time\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe src=\"./screenshot_1000.html\" height=\"500\"","x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def","be saved in \"%s\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\")","x3270): mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\") mocker.patch(\"robot.api.logger.write\") mocker.patch(\"time.time\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe src=\"./screenshot_1000.html\" height=\"500\" width=\"500\"></iframe>', level=\"INFO\",","x3270): mocker.patch(\"robot.api.logger.error\") mocker.patch(\"robot.api.logger.warn\") path = os.path.join(os.getcwd(), \"nonexistent\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \"%s\" does","from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder","os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\"robot.api.logger.error\") mocker.patch(\"robot.api.logger.warn\") path = os.path.join(os.getcwd(), \"nonexistent\") under_test.set_screenshot_folder(path)","exist' % path) logger.warn.assert_called_with( 'Screenshots will be saved in \"%s\"' % under_test.imgfolder )","mocker.patch(\"Mainframe3270.py3270.Emulator.save_screen\") mocker.patch(\"robot.api.logger.write\") mocker.patch(\"time.time\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe src=\"./screenshot_1000.html\" height=\"500\" width=\"500\"></iframe>', level=\"INFO\", html=True,","not exist' % path) logger.warn.assert_called_with( 'Screenshots will be saved in \"%s\"' % under_test.imgfolder","= os.path.join(os.getcwd(), \"nonexistent\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \"%s\" does not exist' % path)","Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder =="],"string":"[\n \"os.path.join(os.getcwd(), \\\"nonexistent\\\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \\\"%s\\\" does not exist' % path) logger.warn.assert_called_with(\",\n \"== os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"robot.api.logger.error\\\") mocker.patch(\\\"robot.api.logger.warn\\\") path = os.path.join(os.getcwd(), \\\"nonexistent\\\")\",\n \"test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture,\",\n \"under_test: x3270): mocker.patch(\\\"robot.api.logger.error\\\") mocker.patch(\\\"robot.api.logger.warn\\\") path = os.path.join(os.getcwd(), \\\"nonexistent\\\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \\\"%s\\\"\",\n \"will be saved in \\\"%s\\\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270):\",\n \"test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"robot.api.logger.error\\\") mocker.patch(\\\"robot.api.logger.warn\\\") path = os.path.join(os.getcwd(), \\\"nonexistent\\\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots\",\n \"os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"robot.api.logger.error\\\") mocker.patch(\\\"robot.api.logger.warn\\\")\",\n \"under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"robot.api.logger.error\\\") mocker.patch(\\\"robot.api.logger.warn\\\") path\",\n \"= os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"robot.api.logger.error\\\")\",\n \"saved in \\\"%s\\\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\") mocker.patch(\\\"robot.api.logger.write\\\")\",\n \"% path) logger.warn.assert_called_with( 'Screenshots will be saved in \\\"%s\\\"' % under_test.imgfolder ) def\",\n \"def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\") mocker.patch(\\\"robot.api.logger.write\\\") mocker.patch(\\\"time.time\\\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe\",\n \"\\\"%s\\\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\") mocker.patch(\\\"robot.api.logger.write\\\") mocker.patch(\\\"time.time\\\", return_value=1.0)\",\n \"<reponame>MichaelSeeburger/Robot-Framework-Mainframe-3270-Library<gh_stars>1-10 import os from pytest_mock import MockerFixture from robot.api import logger from Mainframe3270.x3270\",\n \"import x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd()\",\n \"\\\"%s\\\" does not exist' % path) logger.warn.assert_called_with( 'Screenshots will be saved in \\\"%s\\\"'\",\n \"% under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\") mocker.patch(\\\"robot.api.logger.write\\\") mocker.patch(\\\"time.time\\\", return_value=1.0) under_test.take_screenshot(500,\",\n \"under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \\\"%s\\\" does not exist' % path) logger.warn.assert_called_with( 'Screenshots will\",\n \"mocker.patch(\\\"robot.api.logger.write\\\") mocker.patch(\\\"time.time\\\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe src=\\\"./screenshot_1000.html\\\" height=\\\"500\\\" width=\\\"500\\\"></iframe>', level=\\\"INFO\\\", html=True, )\",\n \"from robot.api import logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path =\",\n \"under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\") mocker.patch(\\\"robot.api.logger.write\\\") mocker.patch(\\\"time.time\\\", return_value=1.0) under_test.take_screenshot(500, 500)\",\n \"MockerFixture, under_test: x3270): mocker.patch(\\\"robot.api.logger.error\\\") mocker.patch(\\\"robot.api.logger.warn\\\") path = os.path.join(os.getcwd(), \\\"nonexistent\\\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path\",\n \"os from pytest_mock import MockerFixture from robot.api import logger from Mainframe3270.x3270 import x3270\",\n \"does not exist' % path) logger.warn.assert_called_with( 'Screenshots will be saved in \\\"%s\\\"' %\",\n \"mocker.patch(\\\"robot.api.logger.warn\\\") path = os.path.join(os.getcwd(), \\\"nonexistent\\\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \\\"%s\\\" does not exist'\",\n \"screenshots path \\\"%s\\\" does not exist' % path) logger.warn.assert_called_with( 'Screenshots will be saved\",\n \"in \\\"%s\\\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\") mocker.patch(\\\"robot.api.logger.write\\\") mocker.patch(\\\"time.time\\\",\",\n \"under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"robot.api.logger.error\\\") mocker.patch(\\\"robot.api.logger.warn\\\") path = os.path.join(os.getcwd(),\",\n \"def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"robot.api.logger.error\\\") mocker.patch(\\\"robot.api.logger.warn\\\") path = os.path.join(os.getcwd(), \\\"nonexistent\\\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given\",\n \"import os from pytest_mock import MockerFixture from robot.api import logger from Mainframe3270.x3270 import\",\n \"robot.api import logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd()\",\n \"x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test:\",\n \"path) logger.warn.assert_called_with( 'Screenshots will be saved in \\\"%s\\\"' % under_test.imgfolder ) def test_take_screenshot(mocker:\",\n \"path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270):\",\n \"under_test: x3270): mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\") mocker.patch(\\\"robot.api.logger.write\\\") mocker.patch(\\\"time.time\\\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe src=\\\"./screenshot_1000.html\\\" height=\\\"500\\\" width=\\\"500\\\"></iframe>',\",\n \"import MockerFixture from robot.api import logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270):\",\n \"import logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path)\",\n \"'Screenshots will be saved in \\\"%s\\\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test:\",\n \"MockerFixture from robot.api import logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path\",\n \"path = os.path.join(os.getcwd(), \\\"nonexistent\\\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \\\"%s\\\" does not exist' %\",\n \"logger.error.assert_called_with('Given screenshots path \\\"%s\\\" does not exist' % path) logger.warn.assert_called_with( 'Screenshots will be\",\n \"logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert\",\n \"pytest_mock import MockerFixture from robot.api import logger from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test:\",\n \"assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"robot.api.logger.error\\\") mocker.patch(\\\"robot.api.logger.warn\\\") path =\",\n \") def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\") mocker.patch(\\\"robot.api.logger.write\\\") mocker.patch(\\\"time.time\\\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with(\",\n \"mocker.patch(\\\"robot.api.logger.error\\\") mocker.patch(\\\"robot.api.logger.warn\\\") path = os.path.join(os.getcwd(), \\\"nonexistent\\\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \\\"%s\\\" does not\",\n \"path \\\"%s\\\" does not exist' % path) logger.warn.assert_called_with( 'Screenshots will be saved in\",\n \"from pytest_mock import MockerFixture from robot.api import logger from Mainframe3270.x3270 import x3270 def\",\n \"logger.warn.assert_called_with( 'Screenshots will be saved in \\\"%s\\\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture,\",\n \"test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\") mocker.patch(\\\"robot.api.logger.write\\\") mocker.patch(\\\"time.time\\\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe src=\\\"./screenshot_1000.html\\\"\",\n \"def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def test_set_screenshot_folder_nonexistent(mocker:\",\n \"\\\"nonexistent\\\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \\\"%s\\\" does not exist' % path) logger.warn.assert_called_with( 'Screenshots\",\n \"MockerFixture, under_test: x3270): mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\") mocker.patch(\\\"robot.api.logger.write\\\") mocker.patch(\\\"time.time\\\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe src=\\\"./screenshot_1000.html\\\" height=\\\"500\\\"\",\n \"x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder == os.getcwd() def\",\n \"be saved in \\\"%s\\\"' % under_test.imgfolder ) def test_take_screenshot(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\")\",\n \"x3270): mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\") mocker.patch(\\\"robot.api.logger.write\\\") mocker.patch(\\\"time.time\\\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe src=\\\"./screenshot_1000.html\\\" height=\\\"500\\\" width=\\\"500\\\"></iframe>', level=\\\"INFO\\\",\",\n \"x3270): mocker.patch(\\\"robot.api.logger.error\\\") mocker.patch(\\\"robot.api.logger.warn\\\") path = os.path.join(os.getcwd(), \\\"nonexistent\\\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \\\"%s\\\" does\",\n \"from Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder\",\n \"os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.patch(\\\"robot.api.logger.error\\\") mocker.patch(\\\"robot.api.logger.warn\\\") path = os.path.join(os.getcwd(), \\\"nonexistent\\\") under_test.set_screenshot_folder(path)\",\n \"exist' % path) logger.warn.assert_called_with( 'Screenshots will be saved in \\\"%s\\\"' % under_test.imgfolder )\",\n \"mocker.patch(\\\"Mainframe3270.py3270.Emulator.save_screen\\\") mocker.patch(\\\"robot.api.logger.write\\\") mocker.patch(\\\"time.time\\\", return_value=1.0) under_test.take_screenshot(500, 500) logger.write.assert_called_with( '<iframe src=\\\"./screenshot_1000.html\\\" height=\\\"500\\\" width=\\\"500\\\"></iframe>', level=\\\"INFO\\\", html=True,\",\n \"not exist' % path) logger.warn.assert_called_with( 'Screenshots will be saved in \\\"%s\\\"' % under_test.imgfolder\",\n \"= os.path.join(os.getcwd(), \\\"nonexistent\\\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \\\"%s\\\" does not exist' % path)\",\n \"Mainframe3270.x3270 import x3270 def test_set_screenshot_folder(under_test: x3270): path = os.getcwd() under_test.set_screenshot_folder(path) assert under_test.imgfolder ==\"\n]","isConversational":false}}},{"rowIdx":2994,"cells":{"ngram":{"kind":"list like","value":["'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and","not isinstance(ftrans,numpy.ndarray): raise ValueError('\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)]","for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit =","else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b","system (optional, default = filter preference) :param units: units for energy as an","(optional, default = filter preference) :param units: units for energy as an astropy.units","magnitudes using one of several statistics ''' chi = 0. dm,em = [],[]","of fluxes ''' # check filter agreement if self.filter != other.filter: raise ValueError('magnitudes","physical constants in SI units import matplotlib.patches as patches import matplotlib.pyplot as plt","# except: # print('\\nWarning: unit {} is not an energy flux unit'.format(return_unit)) try:","em.append(unc[f]) # find best scale factor dm = numpy.array(dm) em = numpy.array(em) offset","ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) #","= kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples","given a correct physical quantity (vega, ab, energy, photons) to compute photometry\\n\\n') #","{} is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter","ValueError('Could not parse input {}'.format(filt)) # add a comparison spectrum sp = kwargs.get('spectrum',None)","included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did not recognize","numpy.nan, numpy.nan filt = f0 # reset filter calculation methods based on filter","except: # print('\\nWarning: unit {} is not an energy flux unit'.format(return_unit)) try: val.to(base_unit)","not given a correct physical quantity (vega, ab, energy, photons) to compute photometry\\n\\n')","nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that requested filter is in","convert to desired energy units # try: val.to(return_unit) err.to(return_unit) # except: # print('\\nWarning:","in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega rsr = FILTERS[filt]['rsr'] #","is not an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\nWarning: cannot convert","# list of filter names if isinstance(filt[0],str): for f in filt: fc =","= numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this","= False if photons: energy = False if energy: photons = False #","def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile or set of filter profiles,","(ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact =","filter name :Optional Parameters: **custom** = None: A 2 x N vector array","##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): ''' :Description: This is a","and choose the closest match based on various statistics ''' pass def SEDFitMCMC(verbose=True):","fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val","rename if necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if fflag == False:","the current list of filters in the SPLAT reference library. ''' verbose =","import * ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this function has been obseleted","raise ValueError('magnitudes filters {} and {} are not the same'.format(self.filter,other.filter)) # make a","Plots a filter profile or set of filter profiles, optionally on top of","= kwargs.get('flux',energy) if (photons or energy): vega = False ab = False if","return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True:","= (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))]","0. and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of","the closest match based on various statistics ''' pass def SEDFitMCMC(verbose=True): ''' this","Jy Pivot point: = 1.252 micron FWHM = 0.323 micron Wavelength range =","isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave","notch[0],fwave <= notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] #","lam x F_lam), with option of also comparing to SED spectrum ''' pass","None: A 2 element array that specifies the lower and upper wavelengths for","2MASS X not among the available filters: 2MASS H: 2MASS H-band 2MASS J:","THIS CODE IS ONLY PARTIALLY COMPLETE ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER)","= True: compute Vega magnitudes (may be set by filter) **ab** = False:","sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.)","(a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit","requested filter is in list if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if","elif (photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for","= 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples):","ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] =","within, 0% transmission without) **vega** = True: compute Vega magnitudes (may be set","= SPLAT_URL+FILTER_FOLDER # check that requested filter is in list f0 = checkFilterName(filt,","= [] if (vega): # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#',","on the AB system (optional, default = filter preference) :param vega: magnitude is","if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = []","function has been obseleted def checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt) f","print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot","# magnitude -> energy if kwargs.get('reverse',False) == False: if vega == True: #","f in list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best","import print_function, division \"\"\" .. note:: These are the spectrophotometry functions for SPLAT","sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.)","# reset filter calculation methods based on filter design if 'ab' in FILTERS[filt]['method']:","kwargs.get('verbose',True) if len(args) > 0: fname = list(args) elif kwargs.get('filter',False) != False: fname","custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave =","equivalent flux density of a magnitude :Output: astropy quantity in flux density units","'{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction for Magnitude","within ``filterFolder`` **nsamples** = 100: number of samples to use in Monte Carlo","a standard filter; some functions may not work'.format(filt)) self.knownfilter = False else: filt","''' verbose = kwargs.get('verbose',True) if len(args) > 0: fname = list(args) elif kwargs.get('filter',False)","# standard units from astropy import constants as const # physical constants in","==True: print(' Filter {} not in SPLAT filter list'.format(k)) kys = list(output.keys()) if","k in fname: f = checkFilterName(k) if f != False: output[f] = {}","other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty","from __future__ import print_function, division \"\"\" .. note:: These are the spectrophotometry functions","flux **filterFolder** = splat.FILTER_FOLDER: folder containing the filter transmission files **vegaFile** = 'vega_kurucz.txt':","kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig","magnitude on whatever system is defined for the filter or provided (required) :param","legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs)","vega = kwargs.get('vega',True) ab = not vega rsr = FILTERS[filt]['rsr'] # other possibilities","* ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this function has been obseleted def","\\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2","d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for","J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER)","magnitude object equal to the equivalent sum of fluxes ''' # check filter","to the diffence of values ''' # make a copy and fill in","if isinstance(filt,str): filt = [filt] if isinstance(filt,list): # list of filter names if","= filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans","magnitudes (may be set by filter) **energy** = False: compute energy flux **photon**","Filter 2MASS J: 2MASS J-band Zeropoint = 1594.0 Jy Pivot point: = 1.252","SPLAT filter list'.format(k)) kys = list(output.keys()) if len(kys) == 1: return output[kys[0]] else:","TO BE COMPLETED else: print('passed') pass # check that input is an energy","custom filter else: fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True: fwave","ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean']","dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <=","(14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder","splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J')","> numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: no overlap between spectrum for {} and filter","= kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.)","len(args) > 0: fname = list(args) elif kwargs.get('filter',False) != False: fname = kwargs['filter']","H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER","FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw","= fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05","a set of magnitudes to model magnitudes using an Amoeba (Nelder-Mead) wrapper, and","ab, energy, photons) to compute photometry\\n\\n') # val = numpy.nanmean(result)*outunit err = numpy.nanstd(result)","filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did not recognize any of the input","compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing the filter transmission files **vegaFile**","filter) **energy** = False: compute energy flux **photon** = False: compute photon flux","for {} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or","* from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this function","report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min']","A 2 element array that specifies the lower and upper wavelengths for a","filter. :param filter: name of filter, must be one of the specifed filters","or ab method specified') # convert to desired energy units # try: val.to(return_unit)","e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity):","isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave","not in SPLAT filter list'.format(k)) kys = list(output.keys()) if len(kys) == 1: return","vega if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter if","containing the filter transmission files **vegaFile** = 'vega_kurucz.txt': name of file containing Vega","m1+m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter","and filter transmission curve. :param filter: String giving the name of one of","the ``filter`` input. By default this filter is also convolved with a model","into magnitude instead (optional, default = False) :param ab: magnitude is on the","contact '+EMAIL+'\\n') filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile","= kwargs.get('output',file) if file != '': plt.savefig(file) return fig ######################################### ######## SED FITTING","e_mag.to(base_unit) # except: # raise ValueError('\\nInput quantity unit {} is not a flux","= (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave))","numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err = 0. return val*outunit,err*outunit","Magnitude object equal to the diffence of values ''' # make a copy","return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code will compare a set of","vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False)","Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose:","keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check","name of file containing Vega flux file, must be within ``filterFolder`` **nsamples** =","and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt","out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises if","Visualizes magnitudes on SED scale (flux = lam x F_lam), with option of","('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray)",":param ab: magnitude is on the AB system (optional, default = filter preference)","= numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+':","{}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation","Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot']))","= fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err = 0. # magnitude","J') Filter 2MASS J: 2MASS J-band Zeropoint = 1594.0 Jy Pivot point: =","result = [] if (vega): # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile),","interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if verbose==True: print('\\nWarning: data values in range","Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter","def __repr__(self): ''' :Purpose: A simple representation of the Spectrum object ''' if","ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False)","= [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if","report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into an energy, and vice","cannot convert result to an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val,","flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux density of a magnitude :Output: astropy","trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw)","# plan: def modelMagnitudes(verbose=True): ''' this will be a code that calculates a","combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine","Pivot point: = 1.252 micron FWHM = 0.323 micron Wavelength range = 1.066","``filterFolder`` **nsamples** = 100: number of samples to use in Monte Carlo error","in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif","= kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter","in splat.FILTERS.keys() (required) :param filterFolder: folder containing the filter transmission files (optional, default","numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: no overlap between spectrum for {} and","############### SPECTROPHOTOMETRY ############### ##################################################### # this function has been obseleted def checkFilter(filt,verbose=True): output","of the predefined filters listed in splat.FILTERS.keys() (required) :param filterFolder: folder containing the","not among the available filters: 2MASS H: 2MASS H-band 2MASS J: 2MASS J-band","Vega system (optional, default = filter preference) :param units: units for energy as","s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version def copy(self): ''' :Purpose:","addition for magnitudes (addition of fluxes) :Output: A new magnitude object equal to","(optional, default = False) :param ab: magnitude is on the AB system (optional,","in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\nfilterMag not given a correct physical quantity (vega,","full filter profile for {}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength function wgood","filter {} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result =","raise ValueError('\\nInput quantity unit {} is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): '''","ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend =","wrapper, and choose the closest match ''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes","a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version","except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans >","(1./em**2) dmo = numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True):","be within ``filterFolder`` **nsamples** = 100: number of samples to use in Monte","of model magnitudes and choose the closest match based on various statistics '''","SPLAT filter. Returns two arrays: the filter wavelength and filter transmission curve. :param","splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters","s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\nfilterMag not","the filter or provided (required) :param filter: name of filter, must be one","lower and upper wavelengths for a notch filter (100% transmission within, 0% transmission","# for numerical integration from scipy.interpolate import interp1d # splat functions and constants","self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and other.uncertainty","= [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse input","= m1-m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter != other.filter:","0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True:","IS ONLY PARTIALLY COMPLETE ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not","= False) WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE ''' # keyword parameters","scale factor dm = numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo","= trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value >","with a model of Vega to extract Vega magnitudes, but the user can","plt.legend(legend) # save if desired file = kwargs.get('file','') file = kwargs.get('filename',file) file =","model magnitudes using an MCMC wrapper, and choose best/average/distribution of parameters ''' pass","I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H long FOURSTAR H","= ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s","object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version def copy(self):","list of notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission","a code that calculates a set of magnitudes for a model set's SED","else: fwave = fwave*u.micron # check that spectrum and filter cover the same","and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >=","= FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans =","rsr = FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy","names available **verbose** = True: List the predefined filter names available :Example: >>>","= trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b))","'+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean']))","default = splat.FILTER_FOLDER) :Example: >>> import splat >>> import splat.photometry as spphot >>>","from .initialize import * from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY ############### #####################################################","FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False)","(optional, default = filter preference) :param vega: magnitude is on the Vega system","def SEDFitMCMC(verbose=True): ''' this code will conduct a comparison of a set of","(Nelder-Mead) wrapper, and choose the closest match ''' pass def SEDVisualize(verbose=True): ''' Visualizes","filters: 2MASS H: 2MASS H-band 2MASS J: 2MASS J-band 2MASS KS: 2MASS Ks-band","Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version def","combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine","notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans","vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave","model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set of","MCMC wrapper, and choose best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this","out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0","fwave = fwave.to(u.micron) else: fwave = fwave*u.micron # check that spectrum and filter","splat.FILTERS.keys() (required) :param filterFolder: folder containing the filter transmission files (optional, default =",":param filter: name of filter, must be one of the specifed filters given","String giving name of filter, which can either be one of the predefined","== 0: err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested","vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr:","= (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) #","dictionary containing key parameters for a particular filter. :param filter: name of filter,","yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in","containing key parameters for a particular filter. :param filter: name of filter, must","file = kwargs.get('output',file) if file != '': plt.savefig(file) return fig ######################################### ######## SED","if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)]","print out information about filter to screen (optional, default = False) WARNING: THIS","== 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if","[numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve')","verbose = kwargs.get('verbose',False) # check that requested filter is in list if isinstance(custom,bool)","must be within ``filterFolder`` **nsamples** = 100: number of samples to use in","not ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega","False if photons: energy = False if energy: photons = False # Read","import copy import os # imports - external import numpy from astropy import",">>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) '''","closest match based on various statistics ''' pass def SEDFitMCMC(verbose=True): ''' this code","object equal to the diffence of values ''' # make a copy and","# check that requested filter is in list filt = checkFilterName(filt) if filt","''' # make a copy and fill in combined magnitude out = copy.deepcopy(self)","splat.FILTERS.keys() :type filter: required :param verbose: print out information about filter to screen","up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired file = kwargs.get('file','')","= [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True:","= None: A 2 x N vector array specifying the wavelengths and transmissions","''' :Purpose: Plots a filter profile or set of filter profiles, optionally on","= numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray)","{} not in SPLAT filter list'.format(k)) kys = list(output.keys()) if len(kys) == 1:","can also specify AB magnitudes, photon flux or energy flux. :Required Parameters: **sp**:","fflag self.filter = filt # some things that are based on presets if","# check filter agreement if self.filter != other.filter: raise ValueError('magnitudes filters {} and","model of Vega to extract Vega magnitudes, but the user can also specify","0.027596454828421831) ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder =","f = checkFilterName(k) if f != False: output[f] = {} output[f]['description'] = FILTERS[f]['description']","convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else:","does not span full filter profile for {}'.format(sp.name,filt)) # interpolate spectrum onto filter","= vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave","report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron)","return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation of addition","dmo = numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): '''","WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE ''' # keyword parameters filterFolder =","elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave))","pass ##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): ''' :Description: This is","using an Amoeba (Nelder-Mead) wrapper, and choose the closest match ''' pass def","use in Monte Carlo error estimation **info** = False: List the predefined filter","const # physical constants in SI units import matplotlib.patches as patches import matplotlib.pyplot","convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst",":type verbose: optional, default = True :Example: >>> import splat >>> data =","ValueError('Did not recognize any of the input filters {}'.format(filters)) # prep parameters fwave,ftrans","a copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude","of the predefined filters listed in splat.FILTERS.keys() or a custom filter name :Optional","interpolate spectrum onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0:","filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron))","trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for i in numpy.arange(nsamples):","d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if verbose==True:","magnitude is on the Vega system (optional, default = filter preference) :param rsr:","conversion does not work, the conversion is ignored (optional, default = erg/cm2/s) :param","= kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit =","kwargs.get('filename',file) file = kwargs.get('output',file) if file != '': plt.savefig(file) return fig ######################################### ########","''' pass def SEDFitMCMC(verbose=True): ''' this code will conduct a comparison of a","vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu =","fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname = [fname] output = {}","if verbose ==True: print(' Filter {} not in SPLAT filter list'.format(k)) kys =","result = [] err = 0. # magnitude -> energy if kwargs.get('reverse',False) ==","information about filter to screen (optional, default = False) WARNING: THIS CODE IS","= False ab = False if photons: energy = False if energy: photons","'{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): '''","= kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if","numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron","ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: no","* u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) elif ab == True: fconst","if output == False and verbose == True: print('\\nFilter '+filt+' not currently available","output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max']","0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr']","energy flux # try: # mag.to(base_unit) # e_mag.to(base_unit) # except: # raise ValueError('\\nInput","= trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate","self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if fflag == False: if verbose== True:","be one of the specifed filters given by splat.FILTERS.keys() :type filter: required :param","file that could be uploaded pre-save some model magnitudes ''' pass def interpolateMagnitudes(verbose=True):","and constants from .initialize import * from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY","self.magnitude = magnitude self.uncertainty = uncertainty self.type = magtype # check filter and","in list filt = checkFilterName(filt) if filt == False: return None report =","energy if kwargs.get('reverse',False) == False: if vega == True: # Read in Vega","fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1.","print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra)","grid of model magnitudes and choose the closest match based on various statistics","copy import os # imports - external import numpy from astropy import units","''' Visualizes magnitudes on SED scale (flux = lam x F_lam), with option","copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty !=","import splat >>> data = splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band Zeropoint","{:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(report['lambda_min'],report['lambda_max'])) return report def","filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile for a SPLAT filter. Returns two","0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err","''' :Purpose: Make a copy of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty)","kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str): filt = [filt] if isinstance(filt,list): #","FOURSTAR H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder =","and rename if necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if fflag ==","magnitude # THIS NEEDS TO BE COMPLETED else: print('passed') pass # check that","else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)))","mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter is in list","in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega if 'rsr' in FILTERS[filt]['method']:","filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans =","splat.FILTERS.keys() (required) :param reverse: convert energy into magnitude instead (optional, default = False)","BE COMPLETED else: print('passed') pass # check that input is an energy flux","(a/b * c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif","= numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] =","SED models saves to file that could be uploaded pre-save some model magnitudes","if f in list(mags2.keys()) and f in list(unc.keys()) and f not in ignore:","A 2 x N vector array specifying the wavelengths and transmissions for a","no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega):","''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else:","interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2",":Purpose: A representation of addition for Magnitude classes that takes into account uncertainties","kwargs.get('ab',True) vega = not ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab","vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] #","in filt: fc = checkFilterName(f) filt.remove(f) if fc == False: if verbose==True: print('Removed","= f0 # read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values =","filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans)","the filter transmission files (optional, default = splat.FILTER_FOLDER) :Example: >>> import splat >>>","if isinstance(fname,list) == False: fname = [fname] output = {} for k in","print('filter {} is not a standard filter; some functions may not work'.format(filt)) self.knownfilter","= copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str): filt =","wavelength and filter transmission curve. :param filter: String giving the name of one","ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else:","user can also specify AB magnitudes, photon flux or energy flux. :Required Parameters:","fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err = 0. #","specify AB magnitudes, photon flux or energy flux. :Required Parameters: **sp**: Spectrum class","= ftrans # report values out if kwargs.get('verbose',False): print('\\nFilter '+filt+': '+report['description']) print('Zeropoint =","elif kwargs.get('filter',False) != False: fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list)","set of magnitudes using one of several statistics ''' chi = 0. dm,em","A representation of addition for magnitudes (addition of fluxes) :Output: A new magnitude","= [filt] if isinstance(filt,list): # list of filter names if isinstance(filt[0],str): for f","= kwargs.get('verbose',True) if len(args) > 0: fname = list(args) elif kwargs.get('filter',False) != False:","= interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out","magnitudes for a model set's SED models saves to file that could be","**ab** = False: compute AB magnitudes (may be set by filter) **energy** =","pass def SEDFitMCMC(verbose=True): ''' this code will conduct a comparison of a set","if filt == False: return numpy.nan, numpy.nan # reset filter calculation methods based","vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega","to extract Vega magnitudes, but the user can also specify AB magnitudes, photon","<= notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity)","except: # raise ValueError('\\nInput quantity unit {} is not a flux unit'.format(mag.unit)) def","a comparison spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True:","interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): # Read in Vega","preference) :param rsr: magnitude is on the Vega system (optional, default = filter","False) :param ab: magnitude is on the AB system (optional, default = filter","notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag =","verbose==True: print('\\nWarning: spectrum for {} does not span full filter profile for {}'.format(sp.name,filt))","e_mag=e_mag*mag.unit # check that requested filter is in list filt = checkFilterName(filt) if","if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan,","provided (required) :param filter: name of filter, must be one of the specifed","plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0:","plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave","c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val =","sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' #","{:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose:","but the user can also specify AB magnitudes, photon flux or energy flux.","specifed filters given by splat.FILTERS.keys() :type filter: required :param verbose: print out information","= vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate Vega onto","err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err = 0. return val*outunit,err*outunit def","kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2","fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact","trapz # for numerical integration from scipy.interpolate import interp1d # splat functions and","''' this code compares a set of magnitudes using one of several statistics","SED spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): '''","def checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt) f = f.replace(' ','_').upper() for","1594.0 Jy Pivot point: = 1.252 micron FWHM = 0.323 micron Wavelength range","if fc == False: if verbose==True: print('Removed filter {}: not included in SPLAT'.format(f))","be uploaded pre-save some model magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces an","= m1+m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter != other.filter:","report['transmission'] = ftrans # report values out if kwargs.get('verbose',False): print('\\nFilter '+filt+': '+report['description']) print('Zeropoint","numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: no overlap between spectrum for {} and filter {}'.format(sp.name,filt))","filter design if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not ab","= interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): # Read in Vega spectrum vwave,vflux","fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra =","key parameters for a particular filter. :param filter: name of filter, must be","I: Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H long","= 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave))","verbose ==True: print(' Filter {} not in SPLAT filter list'.format(k)) kys = list(output.keys())","wrapper, and choose best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this code","filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans >","micron FWHM = 0.323 micron Wavelength range = 1.066 to 1.442 micron >>>","'+EMAIL+'\\n') filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile for","3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value)","filter transmission files (optional, default = splat.FILTER_FOLDER) :Example: >>> import splat >>> import","{} for k in fname: f = checkFilterName(k) if f != False: output[f]","if len(kys) == 1: return output[kys[0]] else: return output def filterProperties(filt,**kwargs): ''' :Purpose:","matplotlib.pyplot as plt from scipy.integrate import trapz # for numerical integration from scipy.interpolate","splat >>> data = splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band Zeropoint =","a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): '''","flux density of a magnitude :Output: astropy quantity in flux density units (default","== True: print('\\nFilter '+filt+' not currently available for SPLAT; contact '+EMAIL+'\\n') filterInfo() return","numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return","NameError('\\nfilterMag not given a correct physical quantity (vega, ab, energy, photons) to compute","{} and {} are not the same'.format(self.filter,other.filter)) # make a copy and fill","dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <=","be set by filter) **ab** = False: compute AB magnitudes (may be set","else: raise NameError('\\nfilterMag not given a correct physical quantity (vega, ab, energy, photons)","fname: f = checkFilterName(k) if f != False: output[f] = {} output[f]['description'] =","''' this code will compare a set of magnitudes to a grid of","account uncertainties :Output: A new Magnitude object equal to the sum of values","vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate Vega","== False: fname = [fname] output = {} for k in fname: f","values in range of filter {} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n","predefined filter names available **verbose** = True: List the predefined filter names available","ValueError('\\nInput quantity unit {} is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose:","= {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if","legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) #","''' pass ##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): ''' :Description: This","FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H long FOURSTAR H SHORT:","= fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw))","print('\\nWarning: no overlap between spectrum for {} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan","optionally on top of a spectrum WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT,","on top of a spectrum WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS","= trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value >","about filter to screen :type verbose: optional, default = True :Example: >>> import","err # energy -> magnitude # THIS NEEDS TO BE COMPLETED else: print('passed')","''' pass def SEDFitAmoeba(verbose=True): ''' this code will conduct a comparison of a","# try: # mag.to(base_unit) # e_mag.to(base_unit) # except: # raise ValueError('\\nInput quantity unit","= kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons","agreement if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): '''","== 1: return output[kys[0]] else: return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a","be a code that calculates a set of magnitudes for a model set's","Returns a dictionary containing key parameters for a particular filter. :param filter: name","compute Vega magnitudes (may be set by filter) **ab** = False: compute AB","of a set of magnitudes to model magnitudes using an MCMC wrapper, and","filter profiles, optionally on top of a spectrum WARNING: THIS CODE IS CURRENTLY","isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\nProblem reading","e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s","unit {} is not an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\nWarning:","filter profile for a SPLAT filter. Returns two arrays: the filter wavelength and","> 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central']","if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab","filter profile or set of filter profiles, optionally on top of a spectrum","outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in","yl = kwargs.get('ylabel','Transmission Curve') # list of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float):","set of magnitudes for a model set's SED models saves to file that","that input is an energy flux # try: # mag.to(base_unit) # e_mag.to(base_unit) #","this conversion does not work, the conversion is ignored (optional, default = erg/cm2/s)","fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result","SEDFitMCMC(verbose=True): ''' this code will conduct a comparison of a set of magnitudes","magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises","predefined filter names available :Example: >>> import splat >>> import splat.photometry as spphot","vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value)","object equal to the equivalent sum of fluxes ''' # check filter agreement","in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan))","set of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a","f = copy.deepcopy(filt) f = f.replace(' ','_').upper() for k in list(FILTERS.keys()): if f==k.upper()","if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result =","report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans #","f0 # reset filter calculation methods based on filter design if 'ab' in","files **vegaFile** = 'vega_kurucz.txt': name of file containing Vega flux file, must be","point: = 1.252 micron FWHM = 0.323 micron Wavelength range = 1.066 to","= kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5))","ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude of a","= numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. # custom filter else:","(may be set by filter) **ab** = False: compute AB magnitudes (may be","self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def __copy__(self):","not vega if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter","0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if","val, err # energy -> magnitude # THIS NEEDS TO BE COMPLETED else:","other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent","on SED scale (flux = lam x F_lam), with option of also comparing","recognize any of the input filters {}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs)","custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else: fwave =","= checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt = f0 rsr","magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises","{}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose:","# print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] =","in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and isinstance(notch,bool):","False and verbose == True: print('\\nFilter '+filt+' not currently available for SPLAT; contact","range = {:.3f} to {:.3f}\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a","in range of filter {} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n =","xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise","out def __sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction for Magnitude classes that","a model set's SED models saves to file that could be uploaded pre-save","# interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact","object equal to the sum of values ''' # make a copy and","using an MCMC wrapper, and choose best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True):","same'.format(self.filter,other.filter)) # make a copy and fill in combined magnitude out = copy.deepcopy(self)","SPLAT_URL+FILTER_FOLDER # check that requested filter is in list f0 = checkFilterName(filt, verbose=True)","H long FOURSTAR H SHORT: FOURSTAR H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER)","= vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b =",":Output: A new Magnitude object equal to the sum of values ''' #","reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs):","ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega if",":param verbose: print out information about filter to screen (optional, default = False)","# custom filter else: fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True:","u.s) else: raise ValueError('\\nmagToFlux needs vega or ab method specified') # convert to","kwargs.get('ylabel','Transmission Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans","for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy):","result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\nfilterMag not given a correct physical quantity (vega, ab, energy,","<= notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] # units","fwave.to(u.micron) else: fwave = fwave*u.micron # check that spectrum and filter cover the","> 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] =","vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.)","== True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if","the Spectrum object ''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{} magnitude","vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is in list if isinstance(custom,bool) and isinstance(notch,bool):","* u.s) # calculate uncertainty if e_mag.value > 0.: for i in numpy.arange(nsamples):","from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this function has","keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile =","FOURSTAR H SHORT: FOURSTAR H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not","if f != False: output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint']","does not work, the conversion is ignored (optional, default = erg/cm2/s) :param verbose:","list(mags2.keys()) and f in list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) #","== True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err","FOURSTAR H long FOURSTAR H SHORT: FOURSTAR H short ... ''' filterFolder =","err = 0. # magnitude -> energy if kwargs.get('reverse',False) == False: if vega","Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'),","not currently available for SPLAT; contact '+EMAIL+'\\n') filterInfo() return output def filterProfile(filt,**kwargs): '''","onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else:","if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return","out if kwargs.get('verbose',False): print('\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: =","for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave =","(ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral())","energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err # energy -> magnitude","magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into an energy, and vice versa. :param","(required) :param filterFolder: folder containing the filter transmission files (optional, default = splat.FILTER_FOLDER)","= False else: filt = fflag self.filter = filt # some things that","False: fname = [fname] output = {} for k in fname: f =","without) **vega** = True: compute Vega magnitudes (may be set by filter) **ab**","val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.: for","UNDER DEVELOPMENT, BUGS MAY BE COMMON ''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT)","0% transmission without) **vega** = True: compute Vega magnitudes (may be set by","filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile for a","kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True)","specified by the ``filter`` input. By default this filter is also convolved with","wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM =","= splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band Zeropoint = 1594.0 Jy Pivot","= custom[0],custom[1] # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\","equal to the equivalent sum of fluxes ''' # check filter agreement if","# trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave","trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw)","True fflag = checkFilterName(filt,verbose=verbose) if fflag == False: if verbose== True: print('filter {}","==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean","for k in list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output = k","interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a =","coding: utf-8 -*- from __future__ import print_function, division \"\"\" .. note:: These are","units as u # standard units from astropy import constants as const #","fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1.","rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s')","= kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit)","as const # physical constants in SI units import matplotlib.patches as patches import","kwargs.get('vega',True) ab = not vega if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) #","numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra","in flux density units (default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose:","= vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu","i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2","takes into account uncertainties :Output: A new Magnitude object equal to the sum","A simple representation of the Spectrum object ''' if self.uncertainty != 0. and","pass # check that input is an energy flux # try: # mag.to(base_unit)","Magnitude classes that takes into account uncertainties :Output: A new Magnitude object equal","Prints out the current list of filters in the SPLAT reference library. '''","= const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise","if verbose==True: print('Removed filter {}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt)","print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM","filt = checkFilterName(filt) if filt == False: return None report = {} report['name']","# imports - external import numpy from astropy import units as u #","# make a copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude","fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) #","isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if","* u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value)","######### ### WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True): '''","screen (optional, default = False) WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE '''","if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter is in list filt","isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans","for f in list(mags1.keys()): if f in list(mags2.keys()) and f in list(unc.keys()) and","FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega rsr = FILTERS[filt]['rsr'] # other","else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) ==","trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit","1.442 micron >>> data = splat.filterProperties('2MASS X') Filter 2MASS X not among the","numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter","= 0. # magnitude -> energy if kwargs.get('reverse',False) == False: if vega ==","= kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str): filt = [filt] if isinstance(filt,list):","= kwargs.get('verbose',False) # check that requested filter is in list if isinstance(custom,bool) and","os # imports - external import numpy from astropy import units as u","!= 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty =","isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn =","1.252 micron FWHM = 0.323 micron Wavelength range = 1.066 to 1.442 micron","= fflag self.filter = filt # some things that are based on presets","== True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up","!= other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the","None: A 2 x N vector array specifying the wavelengths and transmissions for","{} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: =","currently available for SPLAT; contact '+EMAIL+'\\n') filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose:","been obseleted def checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt) f = f.replace('","fwave*u.micron # check that spectrum and filter cover the same wavelength ranges if","**photon** = False: compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing the filter","= {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print('","= True :Example: >>> import splat >>> data = splat.filterProperties('2MASS J') Filter 2MASS","checkFilterName(filt,verbose=verbose) if fflag == False: if verbose== True: print('filter {} is not a","CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON ''' filt = copy.deepcopy(filters)","Spectrum object ''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{} magnitude of","= interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a","a copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude)))","and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty =","= False: compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing the filter transmission","list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor","dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code will compare a set","Parameters: **sp**: Spectrum class object, which should contain wave, flux and noise array","raise ValueError('\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans","1.066 to 1.442 micron >>> data = splat.filterProperties('2MASS X') Filter 2MASS X not","self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and other.uncertainty != 0: m1","err = 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\nmagToFlux needs vega or ab method","filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty self.type = magtype","any of the input filters {}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs) if","system (optional, default = filter preference) :param rsr: magnitude is on the Vega","filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val","based on various statistics ''' pass def SEDFitMCMC(verbose=True): ''' this code will conduct","numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter","sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8))","this code will compare a set of magnitudes to a grid of model","These are the spectrophotometry functions for SPLAT \"\"\" # imports - internal import","ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude","input filters {}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit =","[] err = 0. # magnitude -> energy if kwargs.get('reverse',False) == False: if","filter agreement if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000):","i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra =","in list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output = k if output","spectrum. Spectral fluxes are convolved with the filter profile specified by the ``filter``","a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile or set","','_').upper() for k in list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output =","various statistics ''' pass def SEDFitMCMC(verbose=True): ''' this code will conduct a comparison","filter: name of filter, must be one of the specifed filters given by","unit {} is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a","Filter {} not in SPLAT filter list'.format(k)) kys = list(output.keys()) if len(kys) ==","filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info","m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val) # check filter agreement","val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is in list if isinstance(custom,bool)","False: raise ValueError filt = f0 # read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']),","yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse input {}'.format(filt)) #","if vega == True: # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#',",":Example: >>> import splat >>> import splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0]","energy flux. :Required Parameters: **sp**: Spectrum class object, which should contain wave, flux","* u.s) elif ab == True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 *","specifies the lower and upper wavelengths for a notch filter (100% transmission within,","splat.FILTERS.keys() or a custom filter name :Optional Parameters: **custom** = None: A 2","False: compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing the filter transmission files","is an energy flux # try: # mag.to(base_unit) # e_mag.to(base_unit) # except: #","u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val","print('Wavelength range = {:.3f} to {:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter {}","finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired file =","# keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER #","if len(filt) == 0: raise ValueError('Did not recognize any of the input filters","= 100: number of samples to use in Monte Carlo error estimation **info**","the filter wavelength and filter transmission curve. :param filter: String giving the name","kwargs.get('verbose',False) # check that requested filter is in list if isinstance(custom,bool) and isinstance(notch,bool):","methods based on filter design if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega","value for a grid set of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): '''","else: if verbose ==True: print(' Filter {} not in SPLAT filter list'.format(k)) kys","spectrum and filter cover the same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or","if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)]","def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is in list if isinstance(custom,bool) and","0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw))","energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or energy): vega = False","data values in range of filter {} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.)","numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron","True: fwave = fwave.to(u.micron) else: fwave = fwave*u.micron # check that spectrum and","match ''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale (flux =","numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or","u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val","specifed filters given by splat.FILTERS.keys() (required) :param reverse: convert energy into magnitude instead","elif ab == True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s *","# imports - internal import copy import os # imports - external import","= self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and","def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set of magnitudes using one of","into account uncertainties :Output: A new Magnitude object equal to the sum of","the AB system (optional, default = filter preference) :param vega: magnitude is on","filter transmission curve. :param filter: String giving the name of one of the","f0 == False: return numpy.nan, numpy.nan filt = f0 rsr = FILTERS[filt]['rsr'] #","= numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans # report values out if","= not ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not","= kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) #","vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False)","pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated value for a grid set of","calculation methods based on filter design if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True)","and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >=","Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value)","__repr__(self): ''' :Purpose: A simple representation of the Spectrum object ''' if self.uncertainty","= interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): # Read in","d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints","wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact =","print('\\nFilter '+filt+' not currently available for SPLAT; contact '+EMAIL+'\\n') filterInfo() return output def","check filter agreement if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def","names available :Example: >>> import splat >>> import splat.photometry as spphot >>> sp","self.uncertainty = uncertainty self.type = magtype # check filter and rename if necessary","giving the name of one of the predefined filters listed in splat.FILTERS.keys() (required)","Wavelength range = 1.066 to 1.442 micron >>> data = splat.filterProperties('2MASS X') Filter","Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))","== False: return numpy.nan, numpy.nan filt = f0 rsr = FILTERS[filt]['rsr'] # Read","Vega magnitudes (may be set by filter) **ab** = False: compute AB magnitudes","raise NameError('\\nfilterMag not given a correct physical quantity (vega, ab, energy, photons) to","info = filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make","s # backup version def copy(self): ''' :Purpose: Make a copy of a","'{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation of addition for","numpy.nan filt = f0 rsr = FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool)","**info** = False: List the predefined filter names available **verbose** = True: List","notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False)","**vega** = True: compute Vega magnitudes (may be set by filter) **ab** =","ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err = 0. # magnitude -> energy","function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n =","if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] #","raise ValueError('Could not parse input {}'.format(filt)) # add a comparison spectrum sp =","{} report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method']","= (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not","err = 0.*u.erg/(u.cm**2 * u.s) elif ab == True: fconst = 3631*u.jansky ftrans","nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst","2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR","output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron)","kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name)","= 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose","constants as const # physical constants in SI units import matplotlib.patches as patches",":param verbose: print out information about filter to screen :type verbose: optional, default","vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate Vega onto filter wavelength function v","# print('\\nWarning: unit {} is not an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit)","in SPLAT filter list'.format(k)) kys = list(output.keys()) if len(kys) == 1: return output[kys[0]]","= interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val =","N vector array specifying the wavelengths and transmissions for a custom filter **notch**","filter to screen :type verbose: optional, default = True :Example: >>> import splat","= [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission","of addition for Magnitude classes that takes into account uncertainties :Output: A new","agreement if self.filter != other.filter: raise ValueError('magnitudes filters {} and {} are not","# units if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else: fwave = fwave*u.micron","report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try:","interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): # Read in Vega spectrum vwave,vflux =","addition for Magnitude classes that takes into account uncertainties :Output: A new Magnitude","models else: if verbose==True: print('\\nWarning: data values in range of filter {} have","a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b *","False: compute energy flux **photon** = False: compute photon flux **filterFolder** = splat.FILTER_FOLDER:","not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is in list","print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True:","for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra","... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check","make a copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude =","rsr: magnitude is on the Vega system (optional, default = filter preference) :param","def modelMagnitudes(verbose=True): ''' this will be a code that calculates a set of","# check filter and rename if necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose)","== 0: raise ValueError('Did not recognize any of the input filters {}'.format(filters)) #","result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu","0. dm,em = [],[] for f in list(mags1.keys()): if f in list(mags2.keys()) and","numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: spectrum","isinstance(filt,str): filt = [filt] if isinstance(filt,list): # list of filter names if isinstance(filt[0],str):","flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err # energy -> magnitude #","of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set","= {:.3f} to {:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter {} not in","if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] # list of notch ranges if","True: List the predefined filter names available :Example: >>> import splat >>> import","0. # magnitude -> energy if kwargs.get('reverse',False) == False: if vega == True:","''' :Purpose: Retrieve the filter profile for a SPLAT filter. Returns two arrays:","result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) elif","m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val) #","= (a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c =","filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega","magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose:","fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw =","energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\nWarning: cannot convert result to an","(photons or energy): vega = False ab = False if photons: energy =","= kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s)","# check that spectrum and filter cover the same wavelength ranges if numpy.nanmax(fwave)","b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value)","noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n","{:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength","that requested filter is in list f0 = checkFilterName(filt, verbose=True) if f0 ==","and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-2.5*numpy.log10(1.+10.**(-0.4*(m2-m1)))","trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty","spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra)","with option of also comparing to SED spectrum ''' pass ##################################################### ############### MAGNITUDE","vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu","other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose: A representation of","numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans # report values out if kwargs.get('verbose',False):","for SPLAT \"\"\" # imports - internal import copy import os # imports","trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave =","elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu =","fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] =","out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0","* u.s * u.micron)) # interpolate Vega onto filter wavelength function v =","numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return","if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve') # list of notch ranges","2 x N vector array specifying the wavelengths and transmissions for a custom","upper wavelengths for a notch filter (100% transmission within, 0% transmission without) **vega**","**verbose** = True: List the predefined filter names available :Example: >>> import splat","into an energy, and vice versa. :param mag: magnitude on whatever system is","vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral())","the available filters: 2MASS H: 2MASS H-band 2MASS J: 2MASS J-band 2MASS KS:","# single filter name if isinstance(filt,str): filt = [filt] if isinstance(filt,list): # list","k in list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output = k if","vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave","ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra =","- external import numpy from astropy import units as u # standard units","of subtraction for Magnitude classes that takes into account uncertainties :Output: A new","representation of subtraction for Magnitude classes that takes into account uncertainties :Output: A","from astropy import units as u # standard units from astropy import constants","if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not ab if 'vega'","val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\nfilterMag not given","kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose =","= copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty","has been obseleted def checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt) f =","**filterFolder** = splat.FILTER_FOLDER: folder containing the filter transmission files **vegaFile** = 'vega_kurucz.txt': name","out information about filter to screen :type verbose: optional, default = True :Example:","* u.s) else: raise ValueError('\\nmagToFlux needs vega or ab method specified') # convert","kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for","can either be one of the predefined filters listed in splat.FILTERS.keys() or a","a custom filter name :Optional Parameters: **custom** = None: A 2 x N","# THIS NEEDS TO BE COMPLETED else: print('passed') pass # check that input","H: 2MASS H-band 2MASS J: 2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL I:","comparing to SED spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class","SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did not recognize any of","output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing key parameters for a","file = kwargs.get('filename',file) file = kwargs.get('output',file) if file != '': plt.savefig(file) return fig","kwargs.get('flux',energy) if (photons or energy): vega = False ab = False if photons:","len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter","source based on its spectrum. Spectral fluxes are convolved with the filter profile",":Optional Parameters: **custom** = None: A 2 x N vector array specifying the","rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b","that calculates a set of magnitudes for a model set's SED models saves","# result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu","else: filt = fflag self.filter = filt # some things that are based","a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit = u.erg/u.s/u.cm**2 if","if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for k in","isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan","def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile for a SPLAT filter. Returns","an energy flux # try: # mag.to(base_unit) # e_mag.to(base_unit) # except: # raise","numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code","# catch for models else: if verbose==True: print('\\nWarning: data values in range of","if (vega): # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\","system is defined for the filter or provided (required) :param filter: name of","physical quantity (vega, ab, energy, photons) to compute photometry\\n\\n') # val = numpy.nanmean(result)*outunit","verbose: print out information about filter to screen (optional, default = False) WARNING:","(10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.: for i in","else: return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing key parameters","= erg/cm2/s) :param verbose: print out information about filter to screen (optional, default","necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if fflag == False: if verbose==","if this conversion does not work, the conversion is ignored (optional, default =","= kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab =","f.replace(' ','_').upper() for k in list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output","transmission within, 0% transmission without) **vega** = True: compute Vega magnitudes (may be","array elements. **filter**: String giving name of filter, which can either be one","== True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k])","rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s)","also comparing to SED spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS ############### #####################################################","{} is not a standard filter; some functions may not work'.format(filt)) self.knownfilter =","numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: no overlap between","division \"\"\" .. note:: These are the spectrophotometry functions for SPLAT \"\"\" #","for numerical integration from scipy.interpolate import interp1d # splat functions and constants from","= (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.: for i","CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON ''' filt = copy.deepcopy(filters) wave_unit =","# print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise","{}'.format(i+1)) else: raise ValueError('Could not parse input {}'.format(filt)) # add a comparison spectrum","(energy): outunit = u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c","Zeropoint = 1594.0 Jy Pivot point: = 1.252 micron FWHM = 0.323 micron","patches import matplotlib.pyplot as plt from scipy.integrate import trapz # for numerical integration","is in list filt = checkFilterName(filt) if filt == False: return None report","for Magnitude classes that takes into account uncertainties :Output: A new Magnitude object","default = True :Example: >>> import splat >>> data = splat.filterProperties('2MASS J') Filter","magnitude instead (optional, default = False) :param ab: magnitude is on the AB","return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is in list if",":Example: >>> import splat >>> data = splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS","specifying the wavelengths and transmissions for a custom filter **notch** = None: A","checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt = f0 # reset","fluxes ''' # check filter agreement if self.filter != other.filter: raise ValueError('magnitudes filters","= numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans # report","m1-m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter","# mag.to(base_unit) # e_mag.to(base_unit) # except: # raise ValueError('\\nInput quantity unit {} is","__copy__(self): ''' :Purpose: Make a copy of a Magnitude object ''' s =","of addition for magnitudes (addition of fluxes) :Output: A new magnitude object equal","('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s *","copy(self): ''' :Purpose: Make a copy of a Magnitude object ''' s =","kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples =","if kwargs.get('verbose',False): print('\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot']))","kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1]","(photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i","f.lower() in FILTERS[k]['altnames']: output = k if output == False and verbose ==","u # standard units from astropy import constants as const # physical constants","xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl =","as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123,","comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not","not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom =","wavelengths for a notch filter (100% transmission within, 0% transmission without) **vega** =","(optional, default = splat.FILTER_FOLDER) :Example: >>> import splat >>> import splat.photometry as spphot","= checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt = f0 #","False: if verbose==True: print('Removed filter {}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if","= kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4]))","input. By default this filter is also convolved with a model of Vega","\"\"\" # imports - internal import copy import os # imports - external","other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty","raise ValueError filt = f0 # read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#',","* u.s * u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)]","##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this function has been obseleted def checkFilter(filt,verbose=True):","val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.: for","that requested filter is in list if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True)",":Output: A new Magnitude object equal to the diffence of values ''' #","pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition for magnitudes (addition of","magnitudes to a grid of model magnitudes and choose the closest match based","Spectrum class object, which should contain wave, flux and noise array elements. **filter**:","giving name of filter, which can either be one of the predefined filters","0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else:","file != '': plt.savefig(file) return fig ######################################### ######## SED FITTING TOOLS ######### ###",":param filterFolder: folder containing the filter transmission files (optional, default = splat.FILTER_FOLDER) :Example:","= interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if verbose==True: print('\\nWarning:","= vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength","values ''' # make a copy and fill in combined magnitude out =","filt: fc = checkFilterName(f) filt.remove(f) if fc == False: if verbose==True: print('Removed filter","# add a comparison spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum)","else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname = [fname] output =","in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)]","filt.remove(f) if fc == False: if verbose==True: print('Removed filter {}: not included in","if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else: fwave = fwave*u.micron # check","> 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] =","= FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except:","else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if","the conversion is ignored (optional, default = erg/cm2/s) :param verbose: print out information","magnitudes and choose the closest match based on various statistics ''' pass def","for {}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if","f0 rsr = FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans","def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty","f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor dm =","astropy quantity in flux density units (default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000):","s def __repr__(self): ''' :Purpose: A simple representation of the Spectrum object '''","u.s * u.micron)) # interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.)","outunit = u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c =","False) WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE ''' # keyword parameters filterFolder","fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl","[] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave):","of magnitudes to model magnitudes using an Amoeba (Nelder-Mead) wrapper, and choose the","if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave =","vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter","copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty !=","######## SED FITTING TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### #","and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude)","= numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission'] =","print('\\nWarning: data values in range of filter {} have no uncertainties'.format(filt)) d =","and transmissions for a custom filter **notch** = None: A 2 element array","err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) else: raise","in list(mags2.keys()) and f in list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f])","filt = f0 # reset filter calculation methods based on filter design if","import * from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this","= numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for","wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val =","filters {}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit)","profile or set of filter profiles, optionally on top of a spectrum WARNING:","if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for","== False and verbose == True: print('\\nFilter '+filt+' not currently available for SPLAT;","return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into an energy, and","# combine noises if self.uncertainty != 0 and other.uncertainty != 0: m1 =","spectrum onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d","if (photons or energy): vega = False ab = False if photons: energy","**vegaFile** = 'vega_kurucz.txt': name of file containing Vega flux file, must be within","**notch** = None: A 2 element array that specifies the lower and upper","if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: spectrum for","an interpolated value for a grid set of model magnitudes ''' pass def","element array that specifies the lower and upper wavelengths for a notch filter","= not vega if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in","= None: A 2 element array that specifies the lower and upper wavelengths","''' # check filter agreement if self.filter != other.filter: raise ValueError('magnitudes filters {}","-> energy if kwargs.get('reverse',False) == False: if vega == True: # Read in","val.to(return_unit) err.to(return_unit) # except: # print('\\nWarning: unit {} is not an energy flux","grid set of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares","u.s * u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux","fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft","COMMON ''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if","= [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could","KS: 2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H","magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty self.type = magtype # check","plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired file = kwargs.get('file','') file = kwargs.get('filename',file)","= filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] =","to {:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter {} not in SPLAT filter","= sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d =","vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim spectrum","= numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val) # check","energy = False if energy: photons = False # Read in filter if","# check that requested filter is in list if isinstance(custom,bool) and isinstance(notch,bool): f0","numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. # custom filter else: fwave,ftrans","custom filter else: fwave,ftrans = custom[0],custom[1] # Read in Vega spectrum vwave,vflux =","== False: if verbose==True: print('Removed filter {}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc)","''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str):","pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set of magnitudes using one","val = m1+m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter !=","transmission files (optional, default = splat.FILTER_FOLDER) :Example: >>> import splat >>> import splat.photometry","is on the Vega system (optional, default = filter preference) :param units: units","= numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave =","ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw)","else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave))","filter (100% transmission within, 0% transmission without) **vega** = True: compute Vega magnitudes","filter is in list f0 = checkFilterName(filt, verbose=True) if f0 == False: raise","filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return","wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range =","base_unit = u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag =","filt = [filt] # list of notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength","isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish","= numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val) # check","sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra","as plt from scipy.integrate import trapz # for numerical integration from scipy.interpolate import","numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch","vega = False ab = False if photons: energy = False if energy:","kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag","by filter) **ab** = False: compute AB magnitudes (may be set by filter)","isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter is in list filt = checkFilterName(filt)","return numpy.nan, numpy.nan filt = f0 rsr = FILTERS[filt]['rsr'] # Read in filter","[] if (vega): # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True,","on various statistics ''' pass def SEDFitMCMC(verbose=True): ''' this code will conduct a","energy units # try: val.to(return_unit) err.to(return_unit) # except: # print('\\nWarning: unit {} is","no overlap between spectrum for {} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if","magnitudes using an MCMC wrapper, and choose best/average/distribution of parameters ''' pass def","magtype # check filter and rename if necessary self.knownfilter = True fflag =","constants from .initialize import * from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY ###############",":param mag: magnitude on whatever system is defined for the filter or provided","by splat.FILTERS.keys() :type filter: required :param verbose: print out information about filter to","system (optional, default = filter preference) :param vega: magnitude is on the Vega","-> magnitude # THIS NEEDS TO BE COMPLETED else: print('passed') pass # check","and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) >",">>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder","val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err = 0.","report values out if kwargs.get('verbose',False): print('\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot","c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b","return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux density of a","(required) :param reverse: convert energy into magnitude instead (optional, default = False) :param","from astropy import constants as const # physical constants in SI units import","fwave = fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))]","magnitude -> energy if kwargs.get('reverse',False) == False: if vega == True: # Read","False: output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs)","this filter is also convolved with a model of Vega to extract Vega","### ######################################### # plan: def modelMagnitudes(verbose=True): ''' this will be a code that","len(sp.wave[wgood]) == 0: err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that","self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose: A","an MCMC wrapper, and choose best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True): '''","isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans","if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: no overlap","a filter profile or set of filter profiles, optionally on top of a","flux and noise array elements. **filter**: String giving name of filter, which can","not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile or","filt = f0 # read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values","Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to","import units as u # standard units from astropy import constants as const","b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for i","= 1.066 to 1.442 micron >>> data = splat.filterProperties('2MASS X') Filter 2MASS X","code that calculates a set of magnitudes for a model set's SED models","vector array specifying the wavelengths and transmissions for a custom filter **notch** =","as an astropy.units variable; if this conversion does not work, the conversion is","# some things that are based on presets if self.knownfilter == True: self.wave,self.transmission","default = filter preference) :param rsr: magnitude is on the Vega system (optional,","imports - external import numpy from astropy import units as u # standard","SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr =","vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate Vega onto filter wavelength","photometry\\n\\n') # val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err","filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan)) #","kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check","= vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu","for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\nfilterMag not given a correct physical","numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw)","isinstance(filt,list): # list of filter names if isinstance(filt[0],str): for f in filt: fc","0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central']","(numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate","False ab = False if photons: energy = False if energy: photons =","splat.filterProperties('2MASS X') Filter 2MASS X not among the available filters: 2MASS H: 2MASS","a set of magnitudes to a grid of model magnitudes and choose the","closest match ''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale (flux","outunit = 1. elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit","wave, flux and noise array elements. **filter**: String giving name of filter, which","compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set of magnitudes using one of several","of samples to use in Monte Carlo error estimation **info** = False: List","parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE)","print_function, division \"\"\" .. note:: These are the spectrophotometry functions for SPLAT \"\"\"","< numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: spectrum for {} does","SHORT: FOURSTAR H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder","that requested filter is in list filt = checkFilterName(filt) if filt == False:","filter names available **verbose** = True: List the predefined filter names available :Example:","takes into account uncertainties :Output: A new Magnitude object equal to the diffence","= trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] =","output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min']","EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True): ''' this will be a code","output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile for a SPLAT filter.","filter profile specified by the ``filter`` input. By default this filter is also","String giving the name of one of the predefined filters listed in splat.FILTERS.keys()","or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: spectrum for {} does not span","print('\\nWarning: spectrum for {} does not span full filter profile for {}'.format(sp.name,filt)) #","{:.3f} to {:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter {} not in SPLAT","fname = [fname] output = {} for k in fname: f = checkFilterName(k)","is in list filt = checkFilterName(filt) if filt == False: return numpy.nan, numpy.nan","return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000):","else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True:","fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra)","**custom** = None: A 2 x N vector array specifying the wavelengths and",":Purpose: A representation of subtraction for Magnitude classes that takes into account uncertainties","wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n","''' :Purpose: A representation of addition for magnitudes (addition of fluxes) :Output: A","for k in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a copy of","fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky","out the current list of filters in the SPLAT reference library. ''' verbose","filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is in list filt =","fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty","''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set of magnitudes using","A representation of addition for Magnitude classes that takes into account uncertainties :Output:","in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty #","= numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if","available **verbose** = True: List the predefined filter names available :Example: >>> import","for a grid set of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this","SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale (flux = lam x F_lam), with","= fwave*u.micron # check that spectrum and filter cover the same wavelength ranges","the input filters {}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit","SPLAT reference library. ''' verbose = kwargs.get('verbose',True) if len(args) > 0: fname =","output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if","[fname] output = {} for k in fname: f = checkFilterName(k) if f","will be a code that calculates a set of magnitudes for a model","to model magnitudes using an Amoeba (Nelder-Mead) wrapper, and choose the closest match","report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr']","vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate Vega onto filter","a class data structure for a magnitude value ''' def __init__(self, magnitude, filt,","H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H long FOURSTAR H SHORT: FOURSTAR","{} is not an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\nWarning: cannot","0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-2.5*numpy.log10(1.+10.**(-0.4*(m2-m1))) out.uncertainty = numpy.nanstd(val)","filter to screen (optional, default = False) WARNING: THIS CODE IS ONLY PARTIALLY","interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact =","0.323 micron Wavelength range = 1.066 to 1.442 micron >>> data = splat.filterProperties('2MASS","functions and constants from .initialize import * from .utilities import * ##################################################### ###############","variable; if this conversion does not work, the conversion is ignored (optional, default","f0 == False: raise ValueError filt = f0 # read in filter fwave,ftrans","FWHM = 0.323 micron Wavelength range = 1.066 to 1.442 micron >>> data","uploaded pre-save some model magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated","vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu =","magnitude into an energy, and vice versa. :param mag: magnitude on whatever system","# save if desired file = kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file)","utf-8 -*- from __future__ import print_function, division \"\"\" .. note:: These are the","verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty self.type = magtype # check filter","def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing key parameters for a particular","return output[kys[0]] else: return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing","= [],[] for f in list(mags1.keys()): if f in list(mags2.keys()) and f in","photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing the filter transmission files **vegaFile** =","or energy flux. :Required Parameters: **sp**: Spectrum class object, which should contain wave,","return numpy.nan, numpy.nan filt = f0 # reset filter calculation methods based on","default this filter is also convolved with a model of Vega to extract","# keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile","= kwargs.get('vega',True) ab = not vega if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True)","for energy as an astropy.units variable; if this conversion does not work, the","= numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter)","= FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs)","Filter 2MASS X not among the available filters: 2MASS H: 2MASS H-band 2MASS","desired energy units # try: val.to(return_unit) err.to(return_unit) # except: # print('\\nWarning: unit {}","set's SED models saves to file that could be uploaded pre-save some model","if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg","FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans","[filt] if isinstance(filt,list): # list of filter names if isinstance(filt[0],str): for f in","numerical integration from scipy.interpolate import interp1d # splat functions and constants from .initialize","= FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron","its spectrum. Spectral fluxes are convolved with the filter profile specified by the","[yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse input {}'.format(filt)) # add a","photons: energy = False if energy: photons = False # Read in filter","photons = False # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans =","spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)]","= (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\nmagToFlux","if energy: photons = False # Read in filter if isinstance(custom,bool) and isinstance(notch,bool):","magnitude self.uncertainty = uncertainty self.type = magtype # check filter and rename if","n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples):","model magnitudes and choose the closest match based on various statistics ''' pass","files (optional, default = splat.FILTER_FOLDER) :Example: >>> import splat >>> import splat.photometry as","numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit = u.erg/u.s/u.cm**2","transmission curve. :param filter: String giving the name of one of the predefined","interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the","''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale (flux = lam","about filter to screen (optional, default = False) WARNING: THIS CODE IS ONLY","= numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) #","notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] # Read in","# e_mag.to(base_unit) # except: # raise ValueError('\\nInput quantity unit {} is not a","choose the closest match based on various statistics ''' pass def SEDFitMCMC(verbose=True): '''","ftrans[~numpy.isnan(ftrans)] result = [] err = 0. # magnitude -> energy if kwargs.get('reverse',False)","f in list(mags2.keys()) and f in list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f])","in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty #","self.filter != other.filter: raise ValueError('magnitudes filters {} and {} are not the same'.format(self.filter,other.filter))","isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig","filt == False: return None report = {} report['name'] = filt report['description'] =","# Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\ missing_values =","catch for models else: if verbose==True: print('\\nWarning: data values in range of filter","{}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation of addition for Magnitude classes that","by the ``filter`` input. By default this filter is also convolved with a","# convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral())","else: err = 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\nmagToFlux needs vega or ab","flux # try: # mag.to(base_unit) # e_mag.to(base_unit) # except: # raise ValueError('\\nInput quantity","check that spectrum and filter cover the same wavelength ranges if numpy.nanmax(fwave) <","filter list'.format(k)) kys = list(output.keys()) if len(kys) == 1: return output[kys[0]] else: return","choose the closest match ''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED","not work'.format(filt)) self.knownfilter = False else: filt = fflag self.filter = filt #","{} does not span full filter profile for {}'.format(sp.name,filt)) # interpolate spectrum onto","overlap between spectrum for {} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave)","for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit =","and upper wavelengths for a notch filter (100% transmission within, 0% transmission without)","folder containing the filter transmission files (optional, default = splat.FILTER_FOLDER) :Example: >>> import","rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit","(numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\nmagToFlux needs","# try: val.to(return_unit) err.to(return_unit) # except: # print('\\nWarning: unit {} is not an","WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True): ''' this will","legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve') # list of notch","notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] # list of notch","result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value","a set of magnitudes for a model set's SED models saves to file","fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value","prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength","among the available filters: 2MASS H: 2MASS H-band 2MASS J: 2MASS J-band 2MASS","combine noises if self.uncertainty != 0 and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp)","# list of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] #","Amoeba (Nelder-Mead) wrapper, and choose the closest match ''' pass def SEDVisualize(verbose=True): '''","trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.:","out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0","interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in","List the predefined filter names available **verbose** = True: List the predefined filter","convert result to an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err","fc = checkFilterName(f) filt.remove(f) if fc == False: if verbose==True: print('Removed filter {}:","= kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr","filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is in list f0 =","in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not ab if 'vega' in FILTERS[filt]['method']:","= u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit","unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\nWarning: cannot convert result to an energy flux","SI units import matplotlib.patches as patches import matplotlib.pyplot as plt from scipy.integrate import","set by filter) **energy** = False: compute energy flux **photon** = False: compute","is in list f0 = checkFilterName(filt, verbose=True) if f0 == False: raise ValueError","photometric magnitude of a source based on its spectrum. Spectral fluxes are convolved","filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.)","# check that requested filter is in list f0 = checkFilterName(filt, verbose=True) if","of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation of addition for Magnitude classes","= numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if",":param units: units for energy as an astropy.units variable; if this conversion does","k if output == False and verbose == True: print('\\nFilter '+filt+' not currently","for m in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code will","BUGS MAY BE COMMON ''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single","the spectrophotometry functions for SPLAT \"\"\" # imports - internal import copy import","import interp1d # splat functions and constants from .initialize import * from .utilities","try: val.to(return_unit) err.to(return_unit) # except: # print('\\nWarning: unit {} is not an energy","THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON ''' filt =","account uncertainties :Output: A new Magnitude object equal to the diffence of values","return s def __repr__(self): ''' :Purpose: A simple representation of the Spectrum object","= splat.filterProperties('2MASS X') Filter 2MASS X not among the available filters: 2MASS H:","agreement if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): '''","= numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2), offset","trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.:","magnitudes to model magnitudes using an MCMC wrapper, and choose best/average/distribution of parameters","filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current list of filters in the SPLAT","u.micron)) # interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr:","and vice versa. :param mag: magnitude on whatever system is defined for the","in list(mags1.keys()): if f in list(mags2.keys()) and f in list(unc.keys()) and f not","things that are based on presets if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter)","either be one of the predefined filters listed in splat.FILTERS.keys() or a custom","(numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim","''' chi = 0. dm,em = [],[] for f in list(mags1.keys()): if f","a magnitude value ''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude =","are based on presets if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info =","ValueError('\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def","method specified') # convert to desired energy units # try: val.to(return_unit) err.to(return_unit) #","s.__dict__.update(self.__dict__) return s # backup version def copy(self): ''' :Purpose: Make a copy","= False: List the predefined filter names available **verbose** = True: List the","verbose=True) if f0 == False: raise ValueError filt = f0 # read in","numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw)","''' :Purpose: A representation of addition for Magnitude classes that takes into account","elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave))","to use in Monte Carlo error estimation **info** = False: List the predefined","extract Vega magnitudes, but the user can also specify AB magnitudes, photon flux","transmission without) **vega** = True: compute Vega magnitudes (may be set by filter)","if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])]","of magnitudes to model magnitudes using an MCMC wrapper, and choose best/average/distribution of","''' :Purpose: Returns a dictionary containing key parameters for a particular filter. :param","new Magnitude object equal to the sum of values ''' # make a","or provided (required) :param filter: name of filter, must be one of the","kwargs.get('ylabel','Transmission Curve') # list of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt =","photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or energy):","kwargs.get('output',file) if file != '': plt.savefig(file) return fig ######################################### ######## SED FITTING TOOLS","!= 0 and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val","in FILTERS[k]['altnames']: output = k if output == False and verbose == True:","a source based on its spectrum. Spectral fluxes are convolved with the filter","2MASS J: 2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR","True: compute Vega magnitudes (may be set by filter) **ab** = False: compute","os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is in list f0","data = splat.filterProperties('2MASS X') Filter 2MASS X not among the available filters: 2MASS","magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation of addition for Magnitude","not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron","### WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True): ''' this","kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or","parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt')","plt from scipy.integrate import trapz # for numerical integration from scipy.interpolate import interp1d","H-band FOURSTAR H LONG: FOURSTAR H long FOURSTAR H SHORT: FOURSTAR H short","(notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] =","diffence of values ''' # make a copy and fill in combined magnitude","are not the same'.format(self.filter,other.filter)) # make a copy and fill in combined magnitude","of one of the predefined filters listed in splat.FILTERS.keys() (required) :param filterFolder: folder","file containing Vega flux file, must be within ``filterFolder`` **nsamples** = 100: number","f in list(mags1.keys()): if f in list(mags2.keys()) and f in list(unc.keys()) and f","kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False)",":Output: astropy quantity in flux density units (default = erg/cm2/s/micron) ''' pass def","new Magnitude object equal to the diffence of values ''' # make a","def __add__(self,other,samp=1000): ''' :Purpose: A representation of addition for Magnitude classes that takes","k in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a copy of a","result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) else:","= splat.FILTER_FOLDER: folder containing the filter transmission files **vegaFile** = 'vega_kurucz.txt': name of","xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] ==",":Purpose: Determine the photometric magnitude of a source based on its spectrum. Spectral","ab method specified') # convert to desired energy units # try: val.to(return_unit) err.to(return_unit)","filt = checkFilterName(filt) if filt == False: return numpy.nan, numpy.nan # reset filter","numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. # custom","<= notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] # Read","filter is in list filt = checkFilterName(filt) if filt == False: return numpy.nan,","else: print('passed') pass # check that input is an energy flux # try:","def __copy__(self): ''' :Purpose: Make a copy of a Magnitude object ''' s","filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b","= True: List the predefined filter names available :Example: >>> import splat >>>","ValueError filt = f0 # read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True,","{} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave)","True: print('filter {} is not a standard filter; some functions may not work'.format(filt))","filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave):","unpack=True, \\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux =","else: fwave,ftrans = custom[0],custom[1] # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#',","else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val =","filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission","Returns two arrays: the filter wavelength and filter transmission curve. :param filter: String","filter is also convolved with a model of Vega to extract Vega magnitudes,","noises if self.uncertainty != 0 and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2","* u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux =","if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans)","of filter profiles, optionally on top of a spectrum WARNING: THIS CODE IS","trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else:","custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega)","filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr']","<filename>splat/photometry.py # -*- coding: utf-8 -*- from __future__ import print_function, division \"\"\" ..","f0 == False: return numpy.nan, numpy.nan filt = f0 # reset filter calculation","function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))","'vega_kurucz.txt': name of file containing Vega flux file, must be within ``filterFolder`` **nsamples**","compare a set of magnitudes to a grid of model magnitudes and choose","requested filter is in list filt = checkFilterName(filt) if filt == False: return","magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises","CODE IS ONLY PARTIALLY COMPLETE ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if","J-band Zeropoint = 1594.0 Jy Pivot point: = 1.252 micron FWHM = 0.323","magnitudes (may be set by filter) **ab** = False: compute AB magnitudes (may","not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file']))","not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor dm = numpy.array(dm)","fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value","plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired file = kwargs.get('file','') file","val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave)))","if len(args) > 0: fname = list(args) elif kwargs.get('filter',False) != False: fname =","= FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave","m in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code will compare","and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn","filter else: fwave,ftrans = custom[0],custom[1] # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile),","versa. :param mag: magnitude on whatever system is defined for the filter or","filters listed in splat.FILTERS.keys() or a custom filter name :Optional Parameters: **custom** =","= {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): '''","or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans =","FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave =","for a custom filter **notch** = None: A 2 element array that specifies","= sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst =","THIS NEEDS TO BE COMPLETED else: print('passed') pass # check that input is","is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile","raise ValueError('Did not recognize any of the input filters {}'.format(filters)) # prep parameters","vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave =","fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))]","(notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] =","if file != '': plt.savefig(file) return fig ######################################### ######## SED FITTING TOOLS #########","''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version def copy(self): '''","magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated value for a grid","!= False: fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False:","kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True)","kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch =","list if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 == False: return","= sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname = [fname] output = {} for","in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values","= SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch =","of Vega to extract Vega magnitudes, but the user can also specify AB","''' :Purpose: Converts a magnitude into an energy, and vice versa. :param mag:","= kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that requested filter","# check filter agreement if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out","if self.filter != other.filter: raise ValueError('magnitudes filters {} and {} are not the","Vega system (optional, default = filter preference) :param rsr: magnitude is on the","may not work'.format(filt)) self.knownfilter = False else: filt = fflag self.filter = filt","= filter preference) :param vega: magnitude is on the Vega system (optional, default","of magnitudes for a model set's SED models saves to file that could","is on the AB system (optional, default = filter preference) :param vega: magnitude","= [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans)",":param vega: magnitude is on the Vega system (optional, default = filter preference)","to an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err # energy","''' :Purpose: Determine the photometric magnitude of a source based on its spectrum.","visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile or set of filter profiles, optionally","option of also comparing to SED spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS","-2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif","presets if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for k","this will be a code that calculates a set of magnitudes for a","numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: spectrum for {}","information about filter to screen :type verbose: optional, default = True :Example: >>>","kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that requested filter is","This is a class data structure for a magnitude value ''' def __init__(self,","(optional, default = erg/cm2/s) :param verbose: print out information about filter to screen","of notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve')","= 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact =","a copy of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s","predefined filters listed in splat.FILTERS.keys() or a custom filter name :Optional Parameters: **custom**","-2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current list of filters in","or set of filter profiles, optionally on top of a spectrum WARNING: THIS","[yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve') # list of","numpy.nan filt = f0 # reset filter calculation methods based on filter design","= -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1.","internal import copy import os # imports - external import numpy from astropy","# Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\ missing_values =","= 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is in","cover the same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave):","if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 *","-*- coding: utf-8 -*- from __future__ import print_function, division \"\"\" .. note:: These","print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range","Magnitude object equal to the sum of values ''' # make a copy","filter: required :param verbose: print out information about filter to screen :type verbose:","from scipy.integrate import trapz # for numerical integration from scipy.interpolate import interp1d #","result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val","m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val) # check filter agreement","fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine","numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans # report values","n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): # Read in Vega spectrum","* u.micron)) # interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if","= f0 rsr = FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool) and isinstance(notch,bool):","u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2","and choose the closest match ''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on","to the equivalent sum of fluxes ''' # check filter agreement if self.filter","magnitudes to model magnitudes using an Amoeba (Nelder-Mead) wrapper, and choose the closest","= numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint =","default = erg/cm2/s) :param verbose: print out information about filter to screen (optional,","u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag","an energy, and vice versa. :param mag: magnitude on whatever system is defined","u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)]","is on the Vega system (optional, default = filter preference) :param rsr: magnitude","ab = not vega if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read","if verbose==True: print('\\nWarning: no overlap between spectrum for {} and filter {}'.format(sp.name,filt)) return","= filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave","vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate","== False: raise ValueError filt = f0 # read in filter fwave,ftrans =","for a notch filter (100% transmission within, 0% transmission without) **vega** = True:","for a magnitude value ''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude","= FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy =","in the SPLAT reference library. ''' verbose = kwargs.get('verbose',True) if len(args) > 0:","m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val) #","= checkFilterName(filt) if filt == False: return None report = {} report['name'] =","to 1.442 micron >>> data = splat.filterProperties('2MASS X') Filter 2MASS X not among","Monte Carlo error estimation **info** = False: List the predefined filter names available","set by filter) **ab** = False: compute AB magnitudes (may be set by","add a comparison spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) ==","check filter and rename if necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if","kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is","info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a copy of a Magnitude object","set of magnitudes to model magnitudes using an Amoeba (Nelder-Mead) wrapper, and choose","numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m","a set of magnitudes to model magnitudes using an MCMC wrapper, and choose","(numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) elif ab == True:","astropy import constants as const # physical constants in SI units import matplotlib.patches","type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version def copy(self): ''' :Purpose: Make a","convert energy into magnitude instead (optional, default = False) :param ab: magnitude is","None report = {} report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint']","splat >>> import splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5)","(numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) #","filter cover the same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) >","{} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = []","ignored (optional, default = erg/cm2/s) :param verbose: print out information about filter to","Converts a magnitude into an energy, and vice versa. :param mag: magnitude on","SED FITTING TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### # plan:","os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False)","object ''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty)","0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] =","except: print('\\nWarning: cannot convert result to an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan","classes that takes into account uncertainties :Output: A new Magnitude object equal to","self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and other.uncertainty","from scipy.interpolate import interp1d # splat functions and constants from .initialize import *","= fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b =","wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning:","''' :Purpose: Prints out the current list of filters in the SPLAT reference","representation of addition for Magnitude classes that takes into account uncertainties :Output: A","filt == False: return numpy.nan, numpy.nan # reset filter calculation methods based on","result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr: a =","= (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) elif ab ==","saves to file that could be uploaded pre-save some model magnitudes ''' pass","convolved with the filter profile specified by the ``filter`` input. By default this","numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: spectrum for {} does not span full","> numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: spectrum for {} does not span full filter","filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega)","len(filt) == 0: raise ValueError('Did not recognize any of the input filters {}'.format(filters))","setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a copy of a Magnitude object '''","= plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) ==","filter transmission files **vegaFile** = 'vega_kurucz.txt': name of file containing Vega flux file,","if isinstance(filt[0],str): for f in filt: fc = checkFilterName(f) filt.remove(f) if fc ==","vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that","= erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition for","# report values out if kwargs.get('verbose',False): print('\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint']))","s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose: A simple representation of the Spectrum","in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif","J: 2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR H:","xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse","comparison spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra)","array specifying the wavelengths and transmissions for a custom filter **notch** = None:","in SI units import matplotlib.patches as patches import matplotlib.pyplot as plt from scipy.integrate","is ignored (optional, default = erg/cm2/s) :param verbose: print out information about filter","-2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr:","or a custom filter name :Optional Parameters: **custom** = None: A 2 x","available filters: 2MASS H: 2MASS H-band 2MASS J: 2MASS J-band 2MASS KS: 2MASS","for k in fname: f = checkFilterName(k) if f != False: output[f] =","of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self):","kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not","= {} report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] =","fwave = fwave*u.micron # check that spectrum and filter cover the same wavelength","filter {}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0: raise","units import matplotlib.patches as patches import matplotlib.pyplot as plt from scipy.integrate import trapz",">>> data = splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band Zeropoint = 1594.0","function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value)","interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val =","set of magnitudes to model magnitudes using an MCMC wrapper, and choose best/average/distribution","''' :Purpose: A simple representation of the Spectrum object ''' if self.uncertainty !=","!= 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-2.5*numpy.log10(1.+10.**(-0.4*(m2-m1))) out.uncertainty =","one of the predefined filters listed in splat.FILTERS.keys() (required) :param filterFolder: folder containing","f in filt: fc = checkFilterName(f) filt.remove(f) if fc == False: if verbose==True:","numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {}","output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave =","0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw)","vega = kwargs.get('vega',True) ab = not vega if 'rsr' in FILTERS[filt]['method']: rsr =","fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra)","THESE ARE EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True): ''' this will be","isinstance(ftrans,numpy.ndarray): raise ValueError('\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return","# read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values","which can either be one of the predefined filters listed in splat.FILTERS.keys() or","WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON ''' filt","checkFilterName(f) filt.remove(f) if fc == False: if verbose==True: print('Removed filter {}: not included","u.s) elif ab == True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s","= kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch","verbose== True: print('filter {} is not a standard filter; some functions may not","J-band 2MASS KS: 2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR H-band","import matplotlib.pyplot as plt from scipy.integrate import trapz # for numerical integration from","fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans","for magnitudes (addition of fluxes) :Output: A new magnitude object equal to the","numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: no overlap between spectrum for","return None report = {} report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] =","top of a spectrum WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY","based on filter design if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega =","custom[0],custom[1] # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\ missing_values","vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave))","be one of the specifed filters given by splat.FILTERS.keys() (required) :param reverse: convert","f==k.upper() or f.lower() in FILTERS[k]['altnames']: output = k if output == False and","magnitude is on the AB system (optional, default = filter preference) :param vega:","an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err # energy ->","self.type = magtype # check filter and rename if necessary self.knownfilter = True","which should contain wave, flux and noise array elements. **filter**: String giving name","available :Example: >>> import splat >>> import splat.photometry as spphot >>> sp =","isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt =","= kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples","False: return None report = {} report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint']","and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2","other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-2.5*numpy.log10(1.+10.**(-0.4*(m2-m1))) out.uncertainty","filter and rename if necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if fflag","SPECTROPHOTOMETRY ############### ##################################################### # this function has been obseleted def checkFilter(filt,verbose=True): output =","a magnitude into an energy, and vice versa. :param mag: magnitude on whatever","one of the predefined filters listed in splat.FILTERS.keys() or a custom filter name","list f0 = checkFilterName(filt, verbose=True) if f0 == False: raise ValueError filt =","vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength function","whatever system is defined for the filter or provided (required) :param filter: name","f0 = checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt = f0","vice versa. :param mag: magnitude on whatever system is defined for the filter","matplotlib.patches as patches import matplotlib.pyplot as plt from scipy.integrate import trapz # for","other possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy =","and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor dm","an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\nWarning: cannot convert result to","some things that are based on presets if self.knownfilter == True: self.wave,self.transmission =","> 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] #","FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw","numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans","filters given by splat.FILTERS.keys() (required) :param reverse: convert energy into magnitude instead (optional,","def SEDFitAmoeba(verbose=True): ''' this code will conduct a comparison of a set of","u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if","if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose:","filt = [filt] if isinstance(filt,list): # list of filter names if isinstance(filt[0],str): for","numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s)","check that requested filter is in list if isinstance(custom,bool) and isinstance(notch,bool): f0 =","name of one of the predefined filters listed in splat.FILTERS.keys() (required) :param filterFolder:",".utilities import * ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this function has been","NEEDS TO BE COMPLETED else: print('passed') pass # check that input is an","= kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra =",":Output: A new magnitude object equal to the equivalent sum of fluxes '''","= False f = copy.deepcopy(filt) f = f.replace(' ','_').upper() for k in list(FILTERS.keys()):","default = False) :param ab: magnitude is on the AB system (optional, default","short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER #","if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples):","isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] # list of notch ranges if isinstance(filt[0],list):","= ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])]","= vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega","numpy.nan, numpy.nan filt = f0 rsr = FILTERS[filt]['rsr'] # Read in filter if","return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing key parameters for","######################################### # plan: def modelMagnitudes(verbose=True): ''' this will be a code that calculates","fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def","kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname = [fname] output","filter, must be one of the specifed filters given by splat.FILTERS.keys() :type filter:","on the Vega system (optional, default = filter preference) :param rsr: magnitude is","ONLY PARTIALLY COMPLETE ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder):","False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)]","erg/cm2/s) :param verbose: print out information about filter to screen (optional, default =","or f.lower() in FILTERS[k]['altnames']: output = k if output == False and verbose","len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description'])","current list of filters in the SPLAT reference library. ''' verbose = kwargs.get('verbose',True)","-2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit","comparison of a set of magnitudes to model magnitudes using an MCMC wrapper,","the predefined filter names available :Example: >>> import splat >>> import splat.photometry as","representation of addition for magnitudes (addition of fluxes) :Output: A new magnitude object","of filter, must be one of the specifed filters given by splat.FILTERS.keys() :type","# convert to desired energy units # try: val.to(return_unit) err.to(return_unit) # except: #","fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra =","to screen :type verbose: optional, default = True :Example: >>> import splat >>>","elements. **filter**: String giving name of filter, which can either be one of","FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) #","if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom","__add__(self,other,samp=1000): ''' :Purpose: A representation of addition for Magnitude classes that takes into","os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is in list filt","return numpy.nan, numpy.nan # reset filter calculation methods based on filter design if","print('\\nWarning: unit {} is not an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except:","numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: spectrum for {} does not","numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): #","def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current list of filters in the","= {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f}","2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG:","uncertainties :Output: A new Magnitude object equal to the diffence of values '''","that are based on presets if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info","AB magnitudes (may be set by filter) **energy** = False: compute energy flux","return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current list of filters","for a particular filter. :param filter: name of filter, must be one of","filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans >","v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val","A new Magnitude object equal to the sum of values ''' # make","custom filter **notch** = None: A 2 element array that specifies the lower","A new Magnitude object equal to the diffence of values ''' # make","reference library. ''' verbose = kwargs.get('verbose',True) if len(args) > 0: fname = list(args)","= ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw))","False: if verbose== True: print('filter {} is not a standard filter; some functions","one of the specifed filters given by splat.FILTERS.keys() :type filter: required :param verbose:",">= notch[0],fwave <= notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1]","numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code will compare a set of magnitudes","imports - internal import copy import os # imports - external import numpy","listed in splat.FILTERS.keys() (required) :param filterFolder: folder containing the filter transmission files (optional,","def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux density of a magnitude :Output:","filter is in list filt = checkFilterName(filt) if filt == False: return None","True: print('\\nFilter '+filt+' not currently available for SPLAT; contact '+EMAIL+'\\n') filterInfo() return output","= 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val =","= f0 # reset filter calculation methods based on filter design if 'ab'","< numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: no overlap between spectrum","BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H","is in list if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 ==","= type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose: A simple representation of","filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans =","self.uncertainty != 0 and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp)",">>> import splat >>> import splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>>","def copy(self): ''' :Purpose: Make a copy of a Magnitude object ''' s","filter preference) :param vega: magnitude is on the Vega system (optional, default =","def __sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction for Magnitude classes that takes","ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. # custom filter","DEVELOPMENT, BUGS MAY BE COMMON ''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) #","file = kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file) if file != '':","version def copy(self): ''' :Purpose: Make a copy of a Magnitude object '''","verbose = kwargs.get('verbose',True) if len(args) > 0: fname = list(args) elif kwargs.get('filter',False) !=","external import numpy from astropy import units as u # standard units from","try: val.to(base_unit) err.to(base_unit) except: print('\\nWarning: cannot convert result to an energy flux unit'.format(base_unit))","MAY BE COMMON ''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter","numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2), offset def","Vega to extract Vega magnitudes, but the user can also specify AB magnitudes,","= kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = []","in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): '''","[] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if","dm,em = [],[] for f in list(mags1.keys()): if f in list(mags2.keys()) and f","= interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i","self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report","not vega rsr = FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False) photons =","check that input is an energy flux # try: # mag.to(base_unit) # e_mag.to(base_unit)","0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is in list","filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested","splat.FILTER_FOLDER: folder containing the filter transmission files **vegaFile** = 'vega_kurucz.txt': name of file","filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude of a source based on its","range = {:.3f} to {:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter {} not","is a class data structure for a magnitude value ''' def __init__(self, magnitude,","checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt = f0 rsr =","a magnitude :Output: astropy quantity in flux density units (default = erg/cm2/s/micron) '''","point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else:","that spectrum and filter cover the same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave)","if kwargs.get('reverse',False) == False: if vega == True: # Read in Vega spectrum","Vega magnitudes, but the user can also specify AB magnitudes, photon flux or","onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else:","print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\nProblem","Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans =","0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val)","= k if output == False and verbose == True: print('\\nFilter '+filt+' not","the predefined filter names available **verbose** = True: List the predefined filter names",":param rsr: magnitude is on the Vega system (optional, default = filter preference)","Vega flux file, must be within ``filterFolder`` **nsamples** = 100: number of samples","= kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that","models saves to file that could be uploaded pre-save some model magnitudes '''","statistics ''' pass def SEDFitMCMC(verbose=True): ''' this code will conduct a comparison of","for a SPLAT filter. Returns two arrays: the filter wavelength and filter transmission","- internal import copy import os # imports - external import numpy from","def interpolateMagnitudes(verbose=True): ''' produces an interpolated value for a grid set of model","SEDFitAmoeba(verbose=True): ''' this code will conduct a comparison of a set of magnitudes","notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend","self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and other.uncertainty","* c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val","splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band Zeropoint = 1594.0 Jy Pivot point:","an Amoeba (Nelder-Mead) wrapper, and choose the closest match ''' pass def SEDVisualize(verbose=True):","!= other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose: A representation","fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value","custom filter name :Optional Parameters: **custom** = None: A 2 x N vector","with the filter profile specified by the ``filter`` input. By default this filter","fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value)","Ks-band BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR","######################################### ######## SED FITTING TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!! ### #########################################","are convolved with the filter profile specified by the ``filter`` input. By default","kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that requested filter is in list if","in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 *","output == False and verbose == True: print('\\nFilter '+filt+' not currently available for","energy): vega = False ab = False if photons: energy = False if","# except: # raise ValueError('\\nInput quantity unit {} is not a flux unit'.format(mag.unit))","is defined for the filter or provided (required) :param filter: name of filter,","0: err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter","= filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a","############### ##################################################### class Magnitude(object): ''' :Description: This is a class data structure for","filter. Returns two arrays: the filter wavelength and filter transmission curve. :param filter:","flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile or set of","i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1.","kwargs.get('filter',False) != False: fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) ==","mag.to(base_unit) # e_mag.to(base_unit) # except: # raise ValueError('\\nInput quantity unit {} is not","i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit","= 0. dm,em = [],[] for f in list(mags1.keys()): if f in list(mags2.keys())","= fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans >","else: filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did not recognize any of the","= [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse input {}'.format(filt)) # add","uncertainty self.type = magtype # check filter and rename if necessary self.knownfilter =","if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega rsr =","SPLAT; contact '+EMAIL+'\\n') filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter","= trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for i in","equivalent sum of fluxes ''' # check filter agreement if self.filter != other.filter:","work'.format(filt)) self.knownfilter = False else: filt = fflag self.filter = filt # some","defined for the filter or provided (required) :param filter: name of filter, must","2MASS H: 2MASS H-band 2MASS J: 2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL","plt.ylabel(yl) plt.legend(legend) # save if desired file = kwargs.get('file','') file = kwargs.get('filename',file) file","filterProfile(self.filter) info = filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose:","= f.replace(' ','_').upper() for k in list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']:","print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05)",":Description: This is a class data structure for a magnitude value ''' def","by splat.FILTERS.keys() (required) :param reverse: convert energy into magnitude instead (optional, default =","ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False)","magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty self.type =","False else: filt = fflag self.filter = filt # some things that are","class Magnitude(object): ''' :Description: This is a class data structure for a magnitude","class object, which should contain wave, flux and noise array elements. **filter**: String","= not vega rsr = FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False) photons","code will compare a set of magnitudes to a grid of model magnitudes","FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H long FOURSTAR H SHORT: FOURSTAR H","kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra","result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu =","= checkFilterName(filt,verbose=verbose) if fflag == False: if verbose== True: print('filter {} is not","of filter, which can either be one of the predefined filters listed in","# calculate uncertainty if e_mag.value > 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err","Determine the photometric magnitude of a source based on its spectrum. Spectral fluxes","folder containing the filter transmission files **vegaFile** = 'vega_kurucz.txt': name of file containing","By default this filter is also convolved with a model of Vega to","profile for a SPLAT filter. Returns two arrays: the filter wavelength and filter","# Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch","print('Removed filter {}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0:","fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d","micron Wavelength range = 1.066 to 1.442 micron >>> data = splat.filterProperties('2MASS X')","= kwargs.get('ylabel','Transmission Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt):","combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine","FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs)","AB system (optional, default = filter preference) :param vega: magnitude is on the","# physical constants in SI units import matplotlib.patches as patches import matplotlib.pyplot as","the lower and upper wavelengths for a notch filter (100% transmission within, 0%","= numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter)","and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty =","e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check","############### ##################################################### # this function has been obseleted def checkFilter(filt,verbose=True): output = False","err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) elif ab","in list f0 = checkFilterName(filt, verbose=True) if f0 == False: raise ValueError filt","{:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter","density of a magnitude :Output: astropy quantity in flux density units (default =","False: return numpy.nan, numpy.nan filt = f0 rsr = FILTERS[filt]['rsr'] # Read in","= trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] =","1. # custom filter else: fwave,ftrans = custom[0],custom[1] # Read in Vega spectrum","values out if kwargs.get('verbose',False): print('\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point:","em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m in","not a standard filter; some functions may not work'.format(filt)) self.knownfilter = False else:","''' this will be a code that calculates a set of magnitudes for","# backup version def copy(self): ''' :Purpose: Make a copy of a Magnitude","val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit =","def addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition for magnitudes (addition of fluxes)","spectrum WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON '''","ftrans # report values out if kwargs.get('verbose',False): print('\\nFilter '+filt+': '+report['description']) print('Zeropoint = {}","= vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu","''' pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated value for a grid set","10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.: for i in","c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit","predefined filters listed in splat.FILTERS.keys() (required) :param filterFolder: folder containing the filter transmission","energy as an astropy.units variable; if this conversion does not work, the conversion","kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag","long FOURSTAR H SHORT: FOURSTAR H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if","flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\nWarning: cannot convert result to an energy","verbose==True: print('Removed filter {}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) ==","the Vega system (optional, default = filter preference) :param units: units for energy","{} are not the same'.format(self.filter,other.filter)) # make a copy and fill in combined","legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse input {}'.format(filt)) # add a comparison","the photometric magnitude of a source based on its spectrum. Spectral fluxes are","the sum of values ''' # make a copy and fill in combined","or energy): vega = False ab = False if photons: energy = False","output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try:","= fwave.to(u.micron) else: fwave = fwave*u.micron # check that spectrum and filter cover","report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave']","= vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert","m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-2.5*numpy.log10(1.+10.**(-0.4*(m2-m1))) out.uncertainty = numpy.nanstd(val) return","vega or ab method specified') # convert to desired energy units # try:","other.filter: raise ValueError('magnitudes filters {} and {} are not the same'.format(self.filter,other.filter)) # make","const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\nfilterMag","note:: These are the spectrophotometry functions for SPLAT \"\"\" # imports - internal","FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not ab if 'vega' in FILTERS[filt]['method']: vega","print('\\nWarning: cannot convert result to an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return","offset def SEDFitGrid(verbose=True): ''' this code will compare a set of magnitudes to","copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty !=","sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder =","interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if verbose==True: print('\\nWarning: data","for the filter or provided (required) :param filter: name of filter, must be","names if isinstance(filt[0],str): for f in filt: fc = checkFilterName(f) filt.remove(f) if fc","= (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact","# custom filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if","1. elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b =","= SPLAT_URL+FILTER_FOLDER # check that requested filter is in list filt = checkFilterName(filt)","interpolated value for a grid set of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True):","new magnitude object equal to the equivalent sum of fluxes ''' # check","out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if",">>> import splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>>","containing the filter transmission files (optional, default = splat.FILTER_FOLDER) :Example: >>> import splat","not an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\nWarning: cannot convert result","= kwargs.get('ylabel','Transmission Curve') # list of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt","to screen (optional, default = False) WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE","= 1594.0 Jy Pivot point: = 1.252 micron FWHM = 0.323 micron Wavelength","data = splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band Zeropoint = 1594.0 Jy","``filter`` input. By default this filter is also convolved with a model of","'+filt+' not currently available for SPLAT; contact '+EMAIL+'\\n') filterInfo() return output def filterProfile(filt,**kwargs):",":Purpose: Report the equivalent flux density of a magnitude :Output: astropy quantity in","2 element array that specifies the lower and upper wavelengths for a notch","**energy** = False: compute energy flux **photon** = False: compute photon flux **filterFolder**","= lam x F_lam), with option of also comparing to SED spectrum '''","print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range","# prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl =","{:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose","class data structure for a magnitude value ''' def __init__(self, magnitude, filt, uncertainty=0.,","filter) **ab** = False: compute AB magnitudes (may be set by filter) **energy**","2MASS KS: 2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR","= list(output.keys()) if len(kys) == 1: return output[kys[0]] else: return output def filterProperties(filt,**kwargs):","magnitudes, photon flux or energy flux. :Required Parameters: **sp**: Spectrum class object, which","1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu","(trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\nfilterMag not given a correct","else: if verbose==True: print('\\nWarning: data values in range of filter {} have no","nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d","notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) ==","isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else: fwave = fwave*u.micron # check that","(addition of fluxes) :Output: A new magnitude object equal to the equivalent sum","isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = []","= magtype # check filter and rename if necessary self.knownfilter = True fflag","2MASS J: 2MASS J-band Zeropoint = 1594.0 Jy Pivot point: = 1.252 micron","in splat.FILTERS.keys() or a custom filter name :Optional Parameters: **custom** = None: A","modelMagnitudes(verbose=True): ''' this will be a code that calculates a set of magnitudes","this code will conduct a comparison of a set of magnitudes to model","the diffence of values ''' # make a copy and fill in combined","the same'.format(self.filter,other.filter)) # make a copy and fill in combined magnitude out =","in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit =","numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val) # check filter","a notch filter (100% transmission within, 0% transmission without) **vega** = True: compute","'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not ab if 'vega' in","!= 0. and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude","spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values = (numpy.nan))","filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool)","f0 = checkFilterName(filt, verbose=True) if f0 == False: raise ValueError filt = f0","list(args) elif kwargs.get('filter',False) != False: fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if","List the predefined filter names available :Example: >>> import splat >>> import splat.photometry","False if energy: photons = False # Read in filter if isinstance(custom,bool) and","number of samples to use in Monte Carlo error estimation **info** = False:","print(' Filter {} not in SPLAT filter list'.format(k)) kys = list(output.keys()) if len(kys)","filters in the SPLAT reference library. ''' verbose = kwargs.get('verbose',True) if len(args) >","import trapz # for numerical integration from scipy.interpolate import interp1d # splat functions","u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\nmagToFlux needs vega or","fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0:","# find best scale factor dm = numpy.array(dm) em = numpy.array(em) offset =","try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))]","a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b *","numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab):","scipy.interpolate import interp1d # splat functions and constants from .initialize import * from","an astropy.units variable; if this conversion does not work, the conversion is ignored","contain wave, flux and noise array elements. **filter**: String giving name of filter,","numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning:","uncertainties :Output: A new Magnitude object equal to the sum of values '''","filter, which can either be one of the predefined filters listed in splat.FILTERS.keys()","= kwargs.get('filename',file) file = kwargs.get('output',file) if file != '': plt.savefig(file) return fig #########################################","density units (default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation","photon flux or energy flux. :Required Parameters: **sp**: Spectrum class object, which should","output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint","list(output.keys()) if len(kys) == 1: return output[kys[0]] else: return output def filterProperties(filt,**kwargs): '''","this code compares a set of magnitudes using one of several statistics '''","of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A","a particular filter. :param filter: name of filter, must be one of the","try: # mag.to(base_unit) # e_mag.to(base_unit) # except: # raise ValueError('\\nInput quantity unit {}","in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert","(may be set by filter) **energy** = False: compute energy flux **photon** =","= [fname] output = {} for k in fname: f = checkFilterName(k) if","return numpy.nan, numpy.nan return val, err # energy -> magnitude # THIS NEEDS","and f in list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find","return val, err # energy -> magnitude # THIS NEEDS TO BE COMPLETED","if filt == False: return None report = {} report['name'] = filt report['description']","output = False f = copy.deepcopy(filt) f = f.replace(' ','_').upper() for k in","unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray))","output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint']))","Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H long FOURSTAR","True :Example: >>> import splat >>> data = splat.filterProperties('2MASS J') Filter 2MASS J:","raise ValueError('\\nmagToFlux needs vega or ab method specified') # convert to desired energy","Magnitude(object): ''' :Description: This is a class data structure for a magnitude value","A new magnitude object equal to the equivalent sum of fluxes ''' #","= {} for k in fname: f = checkFilterName(k) if f != False:","match based on various statistics ''' pass def SEDFitMCMC(verbose=True): ''' this code will","= kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file) if file != '': plt.savefig(file)","f = f.replace(' ','_').upper() for k in list(FILTERS.keys()): if f==k.upper() or f.lower() in","ValueError('magnitudes filters {} and {} are not the same'.format(self.filter,other.filter)) # make a copy","print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength:","else: raise ValueError('Could not parse input {}'.format(filt)) # add a comparison spectrum sp","= vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d =","= 1.252 micron FWHM = 0.323 micron Wavelength range = 1.066 to 1.442","* u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength function v","error estimation **info** = False: List the predefined filter names available **verbose** =","= False: compute energy flux **photon** = False: compute photon flux **filterFolder** =","compares a set of magnitudes using one of several statistics ''' chi =","to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst =","True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra)","2MASS J-band Zeropoint = 1594.0 Jy Pivot point: = 1.252 micron FWHM =","for SPLAT; contact '+EMAIL+'\\n') filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the","= numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err = 0. return","= 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.: for i","else: raise ValueError('\\nmagToFlux needs vega or ab method specified') # convert to desired","f0 # read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'),","Curve') # list of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt]","= interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if verbose==True: print('\\nWarning: data values in","print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max']))","quantity in flux density units (default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): '''","and verbose == True: print('\\nFilter '+filt+' not currently available for SPLAT; contact '+EMAIL+'\\n')","will conduct a comparison of a set of magnitudes to model magnitudes using","trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for","= type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version def copy(self): ''' :Purpose: Make","= FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except:","kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit #","wavelengths and transmissions for a custom filter **notch** = None: A 2 element","numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. # custom","= vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate Vega onto filter wavelength function","return out def __sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction for Magnitude classes","missing_values = ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if","and noise array elements. **filter**: String giving name of filter, which can either","integration from scipy.interpolate import interp1d # splat functions and constants from .initialize import","u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag)","fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw))","filter name if isinstance(filt,str): filt = [filt] if isinstance(filt,list): # list of filter","kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples =","yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve') #","offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2),","FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave","= checkFilterName(filt) if filt == False: return numpy.nan, numpy.nan # reset filter calculation","[numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse input {}'.format(filt))","= '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux density","= filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans =","= vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim spectrum vflux","two arrays: the filter wavelength and filter transmission curve. :param filter: String giving","Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values =","= ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot']","== False: return numpy.nan, numpy.nan # reset filter calculation methods based on filter","ab = False if photons: energy = False if energy: photons = False","numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: spectrum for {} does not span full filter profile","n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if verbose==True: print('\\nWarning: data values","numpy.nan, numpy.nan return val, err # energy -> magnitude # THIS NEEDS TO","ab = kwargs.get('ab',True) vega = not ab if 'vega' in FILTERS[filt]['method']: vega =","= trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b =","energy = kwargs.get('flux',energy) if (photons or energy): vega = False ab = False","if e_mag.value > 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 *","enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False):","= kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or energy): vega = False ab","filter, must be one of the specifed filters given by splat.FILTERS.keys() (required) :param",".initialize import * from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### #","flux or energy flux. :Required Parameters: **sp**: Spectrum class object, which should contain","default = filter preference) :param units: units for energy as an astropy.units variable;","f != False: output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans",":Required Parameters: **sp**: Spectrum class object, which should contain wave, flux and noise","spectrum for {} does not span full filter profile for {}'.format(sp.name,filt)) # interpolate","= -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)))","to SED spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object):","dm = numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset","optional, default = True :Example: >>> import splat >>> data = splat.filterProperties('2MASS J')","= trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value","sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky","'{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux density of","addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition for magnitudes (addition of fluxes) :Output:","dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor dm = numpy.array(dm) em = numpy.array(em)",":Purpose: A simple representation of the Spectrum object ''' if self.uncertainty != 0.","COMPLETE ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder =","fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans","checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt) f = f.replace(' ','_').upper() for k","filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing key parameters for a particular filter.","0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] =","the filter profile specified by the ``filter`` input. By default this filter is","kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested","convolved with a model of Vega to extract Vega magnitudes, but the user","best scale factor dm = numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2)","out information about filter to screen (optional, default = False) WARNING: THIS CODE","X not among the available filters: 2MASS H: 2MASS H-band 2MASS J: 2MASS","model set's SED models saves to file that could be uploaded pre-save some","in fname: f = checkFilterName(k) if f != False: output[f] = {} output[f]['description']","COMPLETED else: print('passed') pass # check that input is an energy flux #","numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert =","also specify AB magnitudes, photon flux or energy flux. :Required Parameters: **sp**: Spectrum","''' :Purpose: A representation of subtraction for Magnitude classes that takes into account","instead (optional, default = False) :param ab: magnitude is on the AB system","have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if","of filter, must be one of the specifed filters given by splat.FILTERS.keys() (required)","a copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude","splat.FILTER_FOLDER) :Example: >>> import splat >>> import splat.photometry as spphot >>> sp =","or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\nWarning: no overlap between spectrum for {}","default = filter preference) :param vega: magnitude is on the Vega system (optional,","sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl)","ValueError('\\nmagToFlux needs vega or ab method specified') # convert to desired energy units","!= 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty =","find best scale factor dm = numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum","if f0 == False: return numpy.nan, numpy.nan filt = f0 rsr = FILTERS[filt]['rsr']","the SPLAT reference library. ''' verbose = kwargs.get('verbose',True) if len(args) > 0: fname","filter; some functions may not work'.format(filt)) self.knownfilter = False else: filt = fflag","kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr =","True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err =","kwargs.get('verbose',False): print('\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM","False: List the predefined filter names available **verbose** = True: List the predefined","')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: =","astropy.units variable; if this conversion does not work, the conversion is ignored (optional,","needs vega or ab method specified') # convert to desired energy units #","several statistics ''' chi = 0. dm,em = [],[] for f in list(mags1.keys()):","transmissions for a custom filter **notch** = None: A 2 element array that","check that requested filter is in list filt = checkFilterName(filt) if filt ==","output = k if output == False and verbose == True: print('\\nFilter '+filt+'","= filterProfile(self.filter) info = filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def __copy__(self): '''","''' :Purpose: Report the equivalent flux density of a magnitude :Output: astropy quantity","kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file) if file != '': plt.savefig(file) return","using one of several statistics ''' chi = 0. dm,em = [],[] for","= FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron","to the sum of values ''' # make a copy and fill in","spectrum for {} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave)","magnitudes using an Amoeba (Nelder-Mead) wrapper, and choose the closest match ''' pass","fwave,ftrans = custom[0],custom[1] # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True,","of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] # list of","default = False) WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE ''' # keyword","AB magnitudes, photon flux or energy flux. :Required Parameters: **sp**: Spectrum class object,","if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter","filterFolder: folder containing the filter transmission files (optional, default = splat.FILTER_FOLDER) :Example: >>>","read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values =","if self.uncertainty != 0 and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 =","0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave","= filter preference) :param units: units for energy as an astropy.units variable; if","magnitude is on the Vega system (optional, default = filter preference) :param units:","energy flux **photon** = False: compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing","fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm']","fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude of a source based","comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux","**sp**: Spectrum class object, which should contain wave, flux and noise array elements.","SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False)","output = {} for k in fname: f = checkFilterName(k) if f !=","in list if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 == False:","J: 2MASS J-band Zeropoint = 1594.0 Jy Pivot point: = 1.252 micron FWHM","spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not","copy.deepcopy(filt) f = f.replace(' ','_').upper() for k in list(FILTERS.keys()): if f==k.upper() or f.lower()","factor dm = numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo =","return fig ######################################### ######## SED FITTING TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!!","if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and","filters {} and {} are not the same'.format(self.filter,other.filter)) # make a copy and","that specifies the lower and upper wavelengths for a notch filter (100% transmission","and {} are not the same'.format(self.filter,other.filter)) # make a copy and fill in","value ''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty","correct physical quantity (vega, ab, energy, photons) to compute photometry\\n\\n') # val =","a comparison of a set of magnitudes to model magnitudes using an MCMC","Parameters: **custom** = None: A 2 x N vector array specifying the wavelengths","rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): #","of fluxes) :Output: A new magnitude object equal to the equivalent sum of","parameters for a particular filter. :param filter: name of filter, must be one","filter preference) :param units: units for energy as an astropy.units variable; if this","import matplotlib.patches as patches import matplotlib.pyplot as plt from scipy.integrate import trapz #","0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val)","print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm']","= 0.323 micron Wavelength range = 1.066 to 1.442 micron >>> data =","filter wavelength and filter transmission curve. :param filter: String giving the name of","= [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if","enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra","self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for k in info.keys():","rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s)","= kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega =","BE COMMON ''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name","curve. :param filter: String giving the name of one of the predefined filters","Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'),","= magnitude self.uncertainty = uncertainty self.type = magtype # check filter and rename","= u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag)","magnitude :Output: astropy quantity in flux density units (default = erg/cm2/s/micron) ''' pass","profile for {}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise))","= (a/b * c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value)","= {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range =","numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint'])","must be one of the specifed filters given by splat.FILTERS.keys() (required) :param reverse:","quantity (vega, ab, energy, photons) to compute photometry\\n\\n') # val = numpy.nanmean(result)*outunit err","if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 *","kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) #","of magnitudes using one of several statistics ''' chi = 0. dm,em =","of several statistics ''' chi = 0. dm,em = [],[] for f in","statistics ''' chi = 0. dm,em = [],[] for f in list(mags1.keys()): if","energy: photons = False # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans","copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty","print('passed') pass # check that input is an energy flux # try: #","astropy import units as u # standard units from astropy import constants as","fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not",":param reverse: convert energy into magnitude instead (optional, default = False) :param ab:","unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err # energy -> magnitude # THIS","sum of fluxes ''' # check filter agreement if self.filter != other.filter: raise","list of filters in the SPLAT reference library. ''' verbose = kwargs.get('verbose',True) if","FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega if 'rsr' in FILTERS[filt]['method']: rsr","False # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) #","fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname =","numpy.nan, numpy.nan # reset filter calculation methods based on filter design if 'ab'","self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{}","span full filter profile for {}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength function","__future__ import print_function, division \"\"\" .. note:: These are the spectrophotometry functions for","vega rsr = FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons)","!= False: output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans =","given by splat.FILTERS.keys() :type filter: required :param verbose: print out information about filter","* u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val =","return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile for a SPLAT","compute photometry\\n\\n') # val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0:","True: # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\ missing_values","= numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m in dm])","quantity unit {} is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots","in list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale","# energy -> magnitude # THIS NEEDS TO BE COMPLETED else: print('passed') pass","subtraction for Magnitude classes that takes into account uncertainties :Output: A new Magnitude","the equivalent sum of fluxes ''' # check filter agreement if self.filter !=","verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central']))","reverse: convert energy into magnitude instead (optional, default = False) :param ab: magnitude","(numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray):","pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale (flux = lam x","kwargs.get('vega',True) ab = not vega rsr = FILTERS[filt]['rsr'] # other possibilities photons =","list(mags1.keys()): if f in list(mags2.keys()) and f in list(unc.keys()) and f not in",">>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if","to compute photometry\\n\\n') # val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) ==","__sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction for Magnitude classes that takes into","> 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else:","ARE EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True): ''' this will be a","produces an interpolated value for a grid set of model magnitudes ''' pass","the closest match ''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale","into account uncertainties :Output: A new Magnitude object equal to the diffence of","== True: fwave = fwave.to(u.micron) else: fwave = fwave*u.micron # check that spectrum","= trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else: a =","numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val) # check filter","list filt = checkFilterName(filt) if filt == False: return numpy.nan, numpy.nan # reset","spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values = (numpy.nan))","PARTIALLY COMPLETE ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder","({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f","or isinstance(filt[0],float): filt = [filt] # list of notch ranges if isinstance(filt[0],list): xl","# check that input is an energy flux # try: # mag.to(base_unit) #","else: fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron)","FITTING TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### # plan: def","parse input {}'.format(filt)) # add a comparison spectrum sp = kwargs.get('spectrum',None) sp =","set of magnitudes to a grid of model magnitudes and choose the closest","filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact","numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength:","if len(sp.wave[wgood]) == 0: err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check","(default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition",":Purpose: Converts a magnitude into an energy, and vice versa. :param mag: magnitude","requested filter is in list f0 = checkFilterName(filt, verbose=True) if f0 == False:","u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) elif ab == True: fconst =","False: if vega == True: # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile),","on presets if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for",":Purpose: Returns a dictionary containing key parameters for a particular filter. :param filter:","the predefined filters listed in splat.FILTERS.keys() (required) :param filterFolder: folder containing the filter","chi = 0. dm,em = [],[] for f in list(mags1.keys()): if f in","code will conduct a comparison of a set of magnitudes to model magnitudes","> 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else:","magnitudes (addition of fluxes) :Output: A new magnitude object equal to the equivalent","fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans","!= other.filter: raise ValueError('magnitudes filters {} and {} are not the same'.format(self.filter,other.filter)) #","notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans","of filter names if isinstance(filt[0],str): for f in filt: fc = checkFilterName(f) filt.remove(f)","vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to","vega = not ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab =","is not a standard filter; some functions may not work'.format(filt)) self.knownfilter = False","##################################################### class Magnitude(object): ''' :Description: This is a class data structure for a","TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True):","a SPLAT filter. Returns two arrays: the filter wavelength and filter transmission curve.","= filter preference) :param rsr: magnitude is on the Vega system (optional, default","Spectral fluxes are convolved with the filter profile specified by the ``filter`` input.","structure for a magnitude value ''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs):","for models else: if verbose==True: print('\\nWarning: data values in range of filter {}","= [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve') # list","calculate uncertainty if e_mag.value > 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err =","100: number of samples to use in Monte Carlo error estimation **info** =","rsr = kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans =","numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def","a dictionary containing key parameters for a particular filter. :param filter: name of","= kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega =","filters listed in splat.FILTERS.keys() (required) :param filterFolder: folder containing the filter transmission files","design if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not ab if","print('Wavelength range = {:.3f} to {:.3f}\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts","0 and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val =","trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr: a","= sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n =","False f = copy.deepcopy(filt) f = f.replace(' ','_').upper() for k in list(FILTERS.keys()): if","same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True:","A representation of subtraction for Magnitude classes that takes into account uncertainties :Output:","functions for SPLAT \"\"\" # imports - internal import copy import os #","energy, and vice versa. :param mag: magnitude on whatever system is defined for","= [filt] # list of notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit))","= True fflag = checkFilterName(filt,verbose=verbose) if fflag == False: if verbose== True: print('filter","of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup","verbose==True: print('\\nWarning: data values in range of filter {} have no uncertainties'.format(filt)) d","that takes into account uncertainties :Output: A new Magnitude object equal to the","if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output = k if output == False","os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not","constants in SI units import matplotlib.patches as patches import matplotlib.pyplot as plt from","[filt] # list of notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl","False: return numpy.nan, numpy.nan # reset filter calculation methods based on filter design","[numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl","i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2","v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for","= trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value","x F_lam), with option of also comparing to SED spectrum ''' pass #####################################################",":Purpose: Make a copy of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__)","be set by filter) **energy** = False: compute energy flux **photon** = False:","set of filter profiles, optionally on top of a spectrum WARNING: THIS CODE","noise array elements. **filter**: String giving name of filter, which can either be","''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition for magnitudes (addition","# raise ValueError('\\nInput quantity unit {} is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs):","1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value)","# print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw))","vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim spectrum vflux =","\"\"\" .. note:: These are the spectrophotometry functions for SPLAT \"\"\" # imports","of a spectrum WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE","== True: yl = kwargs.get('ylabel','Transmission Curve') # list of notch ranges if isinstance(filt[0],int)","1. # custom filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron","numpy.nan # reset filter calculation methods based on filter design if 'ab' in","True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def","report['wave'] = fwave report['transmission'] = ftrans # report values out if kwargs.get('verbose',False): print('\\nFilter","== False: if vega == True: # Read in Vega spectrum vwave,vflux =","rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu","filt = f0 rsr = FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool) and","# check filter agreement if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out","kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit","fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err = 0. # magnitude ->","not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab =","name of filter, which can either be one of the predefined filters listed","filter names available :Example: >>> import splat >>> import splat.photometry as spphot >>>","in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if","filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or","given by splat.FILTERS.keys() (required) :param reverse: convert energy into magnitude instead (optional, default","u.s) # calculate uncertainty if e_mag.value > 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact)","return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if","standard filter; some functions may not work'.format(filt)) self.knownfilter = False else: filt =","verbose==True: print('\\nWarning: no overlap between spectrum for {} and filter {}'.format(sp.name,filt)) return numpy.nan,","= numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. #","= (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))]","def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude of a source based on","should contain wave, flux and noise array elements. **filter**: String giving name of","uncertainty if e_mag.value > 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2","legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000)","filt = fflag self.filter = filt # some things that are based on","if isinstance(filt,list): # list of filter names if isinstance(filt[0],str): for f in filt:","{} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f}","comparison of a set of magnitudes to model magnitudes using an Amoeba (Nelder-Mead)","= copy.deepcopy(filt) f = f.replace(' ','_').upper() for k in list(FILTERS.keys()): if f==k.upper() or","err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is","3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose:","numpy.nan return val, err # energy -> magnitude # THIS NEEDS TO BE",":type filter: required :param verbose: print out information about filter to screen :type","energy into magnitude instead (optional, default = False) :param ab: magnitude is on","len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models","F_lam), with option of also comparing to SED spectrum ''' pass ##################################################### ###############","FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve') # list of notch ranges if","nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 *","filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a copy","work, the conversion is ignored (optional, default = erg/cm2/s) :param verbose: print out","plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired file = kwargs.get('file','') file =","output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_','","based on presets if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter)","if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose:","= numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])]","to desired energy units # try: val.to(return_unit) err.to(return_unit) # except: # print('\\nWarning: unit","filter else: fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True: fwave =","input is an energy flux # try: # mag.to(base_unit) # e_mag.to(base_unit) # except:","SPLAT_URL+FILTER_FOLDER # check that requested filter is in list filt = checkFilterName(filt) if","ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err = 0.","(vega): # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\ missing_values","= 1. # custom filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False:","{}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood])","a spectrum WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON","single filter name if isinstance(filt,str): filt = [filt] if isinstance(filt,list): # list of","fwave report['transmission'] = ftrans # report values out if kwargs.get('verbose',False): print('\\nFilter '+filt+': '+report['description'])","sum of values ''' # make a copy and fill in combined magnitude","fflag == False: if verbose== True: print('filter {} is not a standard filter;","photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if","not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did not","the predefined filters listed in splat.FILTERS.keys() or a custom filter name :Optional Parameters:","ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra","parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that","or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\nProblem reading in","output[kys[0]] else: return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing key","pass def SEDFitAmoeba(verbose=True): ''' this code will conduct a comparison of a set","and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2","H SHORT: FOURSTAR H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder):","for a model set's SED models saves to file that could be uploaded","ab = not vega rsr = FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False)","trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty","3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b)","= kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs)","= fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the","of filter {} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result","sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl)","-*- from __future__ import print_function, division \"\"\" .. note:: These are the spectrophotometry","import splat >>> import splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS","= kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom","containing Vega flux file, must be within ``filterFolder`` **nsamples** = 100: number of","splat functions and constants from .initialize import * from .utilities import * #####################################################","= splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword","[],[] for f in list(mags1.keys()): if f in list(mags2.keys()) and f in list(unc.keys())","notch[0],fwave <= notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] if","[numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not","not the same'.format(self.filter,other.filter)) # make a copy and fill in combined magnitude out","wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0)","parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit))","and choose best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this code will","magnitudes, but the user can also specify AB magnitudes, photon flux or energy","import os # imports - external import numpy from astropy import units as","out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if","if verbose==True: print('\\nWarning: spectrum for {} does not span full filter profile for","= False) :param ab: magnitude is on the AB system (optional, default =",">>> data = splat.filterProperties('2MASS X') Filter 2MASS X not among the available filters:","verbose: print out information about filter to screen :type verbose: optional, default =","= {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave","copy of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s #","= numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. #","range = 1.066 to 1.442 micron >>> data = splat.filterProperties('2MASS X') Filter 2MASS","that could be uploaded pre-save some model magnitudes ''' pass def interpolateMagnitudes(verbose=True): '''","* c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons):","vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100)","= kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit","import constants as const # physical constants in SI units import matplotlib.patches as",".. note:: These are the spectrophotometry functions for SPLAT \"\"\" # imports -","possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy)","= splat.FILTER_FOLDER) :Example: >>> import splat >>> import splat.photometry as spphot >>> sp","result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu =","wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend =","also convolved with a model of Vega to extract Vega magnitudes, but the","= kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname = [fname]","= -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if","{:.3f} to {:.3f}\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into","= kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or energy): vega","self.filter = filt # some things that are based on presets if self.knownfilter","{}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl","fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else:","out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux density of a magnitude","u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength function v =","len(kys) == 1: return output[kys[0]] else: return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns","0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05)","= 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] =","rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that requested","uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): #","== False: return numpy.nan, numpy.nan filt = f0 # reset filter calculation methods","if necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if fflag == False: if","on its spectrum. Spectral fluxes are convolved with the filter profile specified by","of a source based on its spectrum. Spectral fluxes are convolved with the","list of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] # list","SPLAT \"\"\" # imports - internal import copy import os # imports -","if desired file = kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file) if file","= 1. elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b","> 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw))","filt # some things that are based on presets if self.knownfilter == True:","# this function has been obseleted def checkFilter(filt,verbose=True): output = False f =","of parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this code will conduct a comparison","out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux","report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans","else: err = 0.*u.erg/(u.cm**2 * u.s) elif ab == True: fconst = 3631*u.jansky","yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save","= False: compute AB magnitudes (may be set by filter) **energy** = False:","of the specifed filters given by splat.FILTERS.keys() :type filter: required :param verbose: print","''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that","the filter transmission files **vegaFile** = 'vega_kurucz.txt': name of file containing Vega flux","X') Filter 2MASS X not among the available filters: 2MASS H: 2MASS H-band","to {:.3f}\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into an","compute AB magnitudes (may be set by filter) **energy** = False: compute energy","checkFilterName(filt) if filt == False: return numpy.nan, numpy.nan # reset filter calculation methods","if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val =","filter **notch** = None: A 2 element array that specifies the lower and","not recognize any of the input filters {}'.format(filters)) # prep parameters fwave,ftrans =","filter preference) :param rsr: magnitude is on the Vega system (optional, default =","based on its spectrum. Spectral fluxes are convolved with the filter profile specified","choose best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this code will conduct","preference) :param units: units for energy as an astropy.units variable; if this conversion","print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(report['lambda_min'],report['lambda_max']))","# custom filter else: fwave,ftrans = custom[0],custom[1] # Read in Vega spectrum vwave,vflux","H-band 2MASS J: 2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL I: Bessel I-band","reset filter calculation methods based on filter design if 'ab' in FILTERS[filt]['method']: ab","input {}'.format(filt)) # add a comparison spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp)","preference) :param vega: magnitude is on the Vega system (optional, default = filter","fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty","fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) #","to file that could be uploaded pre-save some model magnitudes ''' pass def","kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not","filters given by splat.FILTERS.keys() :type filter: required :param verbose: print out information about","(optional, default = False) WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE ''' #","= fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra","of filters in the SPLAT reference library. ''' verbose = kwargs.get('verbose',True) if len(args)","model magnitudes using an Amoeba (Nelder-Mead) wrapper, and choose the closest match '''","= ftrans[~numpy.isnan(ftrans)] result = [] err = 0. # magnitude -> energy if","val = m1-m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter !=","this function has been obseleted def checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt)","= False # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs)","units # try: val.to(return_unit) err.to(return_unit) # except: # print('\\nWarning: unit {} is not","interpolateMagnitudes(verbose=True): ''' produces an interpolated value for a grid set of model magnitudes","if f0 == False: raise ValueError filt = f0 # read in filter","report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max']","{} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave =","# list of notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl =","copy of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def","ab: magnitude is on the AB system (optional, default = filter preference) :param","parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this code will conduct a comparison of","CLASS ############### ##################################################### class Magnitude(object): ''' :Description: This is a class data structure","not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter is in list filt =","= list(args) elif kwargs.get('filter',False) != False: fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys()))","1. # custom filter else: fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) ==","b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current","profiles, optionally on top of a spectrum WARNING: THIS CODE IS CURRENTLY UNDER","in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty #","Carlo error estimation **info** = False: List the predefined filter names available **verbose**","units for energy as an astropy.units variable; if this conversion does not work,","checkFilterName(k) if f != False: output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] =","report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave =","filter profile for {}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength function wgood =","= 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): '''","* u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\nmagToFlux needs vega","copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str): filt = [filt]","= trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr:","a grid set of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code","must be one of the specifed filters given by splat.FILTERS.keys() :type filter: required","{}'.format(filt)) # add a comparison spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if","uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty self.type = magtype #","units (default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation of","arrays: the filter wavelength and filter transmission curve. :param filter: String giving the","def SEDFitGrid(verbose=True): ''' this code will compare a set of magnitudes to a","print out information about filter to screen :type verbose: optional, default = True","isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend","data structure for a magnitude value ''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent',","plan: def modelMagnitudes(verbose=True): ''' this will be a code that calculates a set","vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu =","# other possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy","sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname = [fname] output = {} for k","== False: return None report = {} report['name'] = filt report['description'] = FILTERS[filt]['description']","units: units for energy as an astropy.units variable; if this conversion does not","= checkFilterName(f) filt.remove(f) if fc == False: if verbose==True: print('Removed filter {}: not","== False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans =","# notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron","return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude of a source","in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor dm = numpy.array(dm) em","= kwargs.get('ab',True) vega = not ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True)","custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit =","= checkFilterName(k) if f != False: output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint']","SED scale (flux = lam x F_lam), with option of also comparing to","**nsamples** = 100: number of samples to use in Monte Carlo error estimation","kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom =",":Purpose: Retrieve the filter profile for a SPLAT filter. Returns two arrays: the","in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did not recognize any","# interpolate spectrum onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) >","err.to(return_unit) # except: # print('\\nWarning: unit {} is not an energy flux unit'.format(return_unit))","'': plt.savefig(file) return fig ######################################### ######## SED FITTING TOOLS ######### ### WARNING: THESE","a grid of model magnitudes and choose the closest match based on various","in list filt = checkFilterName(filt) if filt == False: return numpy.nan, numpy.nan #","functions may not work'.format(filt)) self.knownfilter = False else: filt = fflag self.filter =","compute energy flux **photon** = False: compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder","for f in filt: fc = checkFilterName(f) filt.remove(f) if fc == False: if","available for SPLAT; contact '+EMAIL+'\\n') filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve","in Monte Carlo error estimation **info** = False: List the predefined filter names","representation of the Spectrum object ''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return","unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile or set of filter","fluxes) :Output: A new magnitude object equal to the equivalent sum of fluxes","magnitude of a source based on its spectrum. Spectral fluxes are convolved with","= kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info =","filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave =","name if isinstance(filt,str): filt = [filt] if isinstance(filt,list): # list of filter names","def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into an energy, and vice versa.","err.to(base_unit) except: print('\\nWarning: cannot convert result to an energy flux unit'.format(base_unit)) return numpy.nan,","############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): ''' :Description: This is a class","# -*- coding: utf-8 -*- from __future__ import print_function, division \"\"\" .. note::","fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000","# notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)","the specifed filters given by splat.FILTERS.keys() :type filter: required :param verbose: print out","rsr = FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans =","= fwave report['transmission'] = ftrans # report values out if kwargs.get('verbose',False): print('\\nFilter '+filt+':","a custom filter **notch** = None: A 2 element array that specifies the","of also comparing to SED spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS ###############","= numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] =","{:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to","= SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr","will compare a set of magnitudes to a grid of model magnitudes and",":param filter: String giving the name of one of the predefined filters listed","the user can also specify AB magnitudes, photon flux or energy flux. :Required","= trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr:","energy, photons) to compute photometry\\n\\n') # val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if","= 1. # custom filter else: fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity)","if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega if 'rsr'","''' :Description: This is a class data structure for a magnitude value '''","copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty","val = (a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c","on whatever system is defined for the filter or provided (required) :param filter:","True: yl = kwargs.get('ylabel','Transmission Curve') # list of notch ranges if isinstance(filt[0],int) or","scipy.integrate import trapz # for numerical integration from scipy.interpolate import interp1d # splat","best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this code will conduct a","== 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1))","conduct a comparison of a set of magnitudes to model magnitudes using an","kwargs.get('reverse',False) == False: if vega == True: # Read in Vega spectrum vwave,vflux","''' this code will conduct a comparison of a set of magnitudes to","if verbose== True: print('filter {} is not a standard filter; some functions may","the Vega system (optional, default = filter preference) :param rsr: magnitude is on","some model magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated value for","LONG: FOURSTAR H long FOURSTAR H SHORT: FOURSTAR H short ... ''' filterFolder","def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale (flux = lam x F_lam),","ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] # list of notch ranges","= self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and","if verbose==True: print('\\nWarning: data values in range of filter {} have no uncertainties'.format(filt))","trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current list of","SEDFitGrid(verbose=True): ''' this code will compare a set of magnitudes to a grid","could be uploaded pre-save some model magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces","filter agreement if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000):","Retrieve the filter profile for a SPLAT filter. Returns two arrays: the filter","== True: # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\","val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else:","# val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err =","1: return output[kys[0]] else: return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary","= 1. # custom filter else: fwave,ftrans = custom[0],custom[1] # Read in Vega","if photons: energy = False if energy: photons = False # Read in","= filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans","if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool)","Make a copy of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return","'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega if 'rsr' in","= fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] =","kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or energy): vega = False ab =","isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and isinstance(notch,list):","numpy from astropy import units as u # standard units from astropy import","fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs):","IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON ''' filt = copy.deepcopy(filters) wave_unit","energy -> magnitude # THIS NEEDS TO BE COMPLETED else: print('passed') pass #","units from astropy import constants as const # physical constants in SI units","samples to use in Monte Carlo error estimation **info** = False: List the","type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose: A simple representation of the","fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw =","interp1d # splat functions and constants from .initialize import * from .utilities import","x N vector array specifying the wavelengths and transmissions for a custom filter","= numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-2.5*numpy.log10(1.+10.**(-0.4*(m2-m1))) out.uncertainty = numpy.nanstd(val) return out","(required) :param filter: name of filter, must be one of the specifed filters","some functions may not work'.format(filt)) self.knownfilter = False else: filt = fflag self.filter","= ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude of","vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto","filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch","(vega, ab, energy, photons) to compute photometry\\n\\n') # val = numpy.nanmean(result)*outunit err =","[yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired","vega: magnitude is on the Vega system (optional, default = filter preference) :param","= filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl =","fflag = checkFilterName(filt,verbose=verbose) if fflag == False: if verbose== True: print('filter {} is","pre-save some model magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated value","fluxes are convolved with the filter profile specified by the ``filter`` input. By","isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave","list'.format(k)) kys = list(output.keys()) if len(kys) == 1: return output[kys[0]] else: return output","= ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not","is also convolved with a model of Vega to extract Vega magnitudes, but","2MASS H-band 2MASS J: 2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL I: Bessel","kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity):","a set of magnitudes using one of several statistics ''' chi = 0.","object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose: A","out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter =","= self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and other.uncertainty != 0:","filter: String giving the name of one of the predefined filters listed in","missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 *","= fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b)","kys = list(output.keys()) if len(kys) == 1: return output[kys[0]] else: return output def","scale (flux = lam x F_lam), with option of also comparing to SED","= numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs):","* u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.)","between spectrum for {} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) <","fc == False: if verbose==True: print('Removed filter {}: not included in SPLAT'.format(f)) else:","save if desired file = kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file) if","be one of the predefined filters listed in splat.FILTERS.keys() or a custom filter","= kwargs.get('vega',True) ab = not vega rsr = FILTERS[filt]['rsr'] # other possibilities photons","fname = list(args) elif kwargs.get('filter',False) != False: fname = kwargs['filter'] else: fname =","True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr:","plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend)","val.to(base_unit) err.to(base_unit) except: print('\\nWarning: cannot convert result to an energy flux unit'.format(base_unit)) return","of the input filters {}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave):","the name of one of the predefined filters listed in splat.FILTERS.keys() (required) :param","of values ''' # make a copy and fill in combined magnitude out","name :Optional Parameters: **custom** = None: A 2 x N vector array specifying","of magnitudes to a grid of model magnitudes and choose the closest match","H LONG: FOURSTAR H long FOURSTAR H SHORT: FOURSTAR H short ... '''","''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty =","trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit","spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831)","= fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft =","calculates a set of magnitudes for a model set's SED models saves to","one of the specifed filters given by splat.FILTERS.keys() (required) :param reverse: convert energy","check filter agreement if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def","= copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty","= self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and","numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m in dm]) return","filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) ==","custom filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity)","if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral())","# splat functions and constants from .initialize import * from .utilities import *","list of filter names if isinstance(filt[0],str): for f in filt: fc = checkFilterName(f)",":Purpose: Prints out the current list of filters in the SPLAT reference library.","library. ''' verbose = kwargs.get('verbose',True) if len(args) > 0: fname = list(args) elif","False: fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname","filter is in list if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0","not parse input {}'.format(filt)) # add a comparison spectrum sp = kwargs.get('spectrum',None) sp","print('\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM =","= {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point:","0.*u.erg/(u.cm**2 * u.s) elif ab == True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2",":Purpose: Plots a filter profile or set of filter profiles, optionally on top","= trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current list","= 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\nmagToFlux needs vega or ab method specified')","to model magnitudes using an MCMC wrapper, and choose best/average/distribution of parameters '''","= False if energy: photons = False # Read in filter if isinstance(custom,bool)","desired file = kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file) if file !=","screen :type verbose: optional, default = True :Example: >>> import splat >>> data","flux density units (default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A","numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\nfilterMag not given a correct physical quantity (vega, ab,","isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave","equal to the diffence of values ''' # make a copy and fill","= kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that requested filter is in list","= 0.*u.erg/(u.cm**2 * u.s) elif ab == True: fconst = 3631*u.jansky ftrans =","= checkFilterName(filt, verbose=True) if f0 == False: raise ValueError filt = f0 #","wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str): filt = [filt] if","notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] # units if","FILTERS[k]['altnames']: output = k if output == False and verbose == True: print('\\nFilter","0: fname = list(args) elif kwargs.get('filter',False) != False: fname = kwargs['filter'] else: fname","b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else: a","''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose: A simple","model magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated value for a","report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission']","specified') # convert to desired energy units # try: val.to(return_unit) err.to(return_unit) # except:","i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\nfilterMag not given a correct physical quantity","== False: if verbose== True: print('filter {} is not a standard filter; some","as u # standard units from astropy import constants as const # physical","**filter**: String giving name of filter, which can either be one of the","= uncertainty self.type = magtype # check filter and rename if necessary self.knownfilter","verbose == True: print('\\nFilter '+filt+' not currently available for SPLAT; contact '+EMAIL+'\\n') filterInfo()","the filter profile for a SPLAT filter. Returns two arrays: the filter wavelength","micron >>> data = splat.filterProperties('2MASS X') Filter 2MASS X not among the available","units if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else: fwave = fwave*u.micron #","vega == True: # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True,","in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values","flux. :Required Parameters: **sp**: Spectrum class object, which should contain wave, flux and","listed in splat.FILTERS.keys() or a custom filter name :Optional Parameters: **custom** = None:","isinstance(filt[0],str): for f in filt: fc = checkFilterName(f) filt.remove(f) if fc == False:","if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is in","Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values =","out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction","kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or energy): vega =","''' produces an interpolated value for a grid set of model magnitudes '''","= numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central","= interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i","point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(report['lambda_min'],report['lambda_max'])) return","range of filter {} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.)","fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit)","= kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not","magnitude value ''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude","the equivalent flux density of a magnitude :Output: astropy quantity in flux density","'+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm']))","magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set of magnitudes","i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit","the same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if","= plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else:","'+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength","notch filter (100% transmission within, 0% transmission without) **vega** = True: compute Vega","= '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction for","print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs):","# interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val","check that requested filter is in list f0 = checkFilterName(filt, verbose=True) if f0","0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\nmagToFlux needs vega or ab method specified') #","c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for i in numpy.arange(nsamples): if","for {} does not span full filter profile for {}'.format(sp.name,filt)) # interpolate spectrum","{:.3f}\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter {} not in SPLAT filter list'.format(k))","the wavelengths and transmissions for a custom filter **notch** = None: A 2","(optional, default = filter preference) :param rsr: magnitude is on the Vega system","isinstance(fname,list) == False: fname = [fname] output = {} for k in fname:","##################################################### # this function has been obseleted def checkFilter(filt,verbose=True): output = False f","result to an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err #","ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega rsr","a model of Vega to extract Vega magnitudes, but the user can also","return s # backup version def copy(self): ''' :Purpose: Make a copy of","val = (a/b * c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else:","array that specifies the lower and upper wavelengths for a notch filter (100%","of a set of magnitudes to model magnitudes using an Amoeba (Nelder-Mead) wrapper,","ab == True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave))",":Purpose: A representation of addition for magnitudes (addition of fluxes) :Output: A new","on filter design if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not","filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str): filt","= kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose","if f0 == False: return numpy.nan, numpy.nan filt = f0 # reset filter","trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit","transmission files **vegaFile** = 'vega_kurucz.txt': name of file containing Vega flux file, must","on the Vega system (optional, default = filter preference) :param units: units for","object, which should contain wave, flux and noise array elements. **filter**: String giving","fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b","backup version def copy(self): ''' :Purpose: Make a copy of a Magnitude object","for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err =","filter calculation methods based on filter design if 'ab' in FILTERS[filt]['method']: ab =","in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code will compare a","fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 =","of the Spectrum object ''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{}","> 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean']","erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition for magnitudes","!= '': plt.savefig(file) return fig ######################################### ######## SED FITTING TOOLS ######### ### WARNING:","profile specified by the ``filter`` input. By default this filter is also convolved","s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose: A simple representation","file, must be within ``filterFolder`` **nsamples** = 100: number of samples to use","__init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty self.type","standard units from astropy import constants as const # physical constants in SI","d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): # Read","ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. # custom filter else: fwave,ftrans =","* u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag =","by filter) **energy** = False: compute energy flux **photon** = False: compute photon","checkFilterName(filt, verbose=True) if f0 == False: raise ValueError filt = f0 # read","conversion is ignored (optional, default = erg/cm2/s) :param verbose: print out information about","{:.3f}\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into an energy,","of a magnitude :Output: astropy quantity in flux density units (default = erg/cm2/s/micron)","check filter agreement if self.filter != other.filter: raise ValueError('magnitudes filters {} and {}","not span full filter profile for {}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength","xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig =","copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty","J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder):","required :param verbose: print out information about filter to screen :type verbose: optional,","of the specifed filters given by splat.FILTERS.keys() (required) :param reverse: convert energy into","if fflag == False: if verbose== True: print('filter {} is not a standard","photons) to compute photometry\\n\\n') # val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood])","list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output = k if output ==","a comparison of a set of magnitudes to model magnitudes using an Amoeba","interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in","isinstance(filt[0],float): filt = [filt] # list of notch ranges if isinstance(filt[0],list): xl =",">>> import splat >>> data = splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band","= {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm']))","import splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO","equal to the sum of values ''' # make a copy and fill","onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d =","outunit = 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu =","mag: magnitude on whatever system is defined for the filter or provided (required)","isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter is in","= [] err = 0. # magnitude -> energy if kwargs.get('reverse',False) == False:","{}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did","if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve')","code compares a set of magnitudes using one of several statistics ''' chi","ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor dm = numpy.array(dm) em =","spectrophotometry functions for SPLAT \"\"\" # imports - internal import copy import os","Report the equivalent flux density of a magnitude :Output: astropy quantity in flux","False: return numpy.nan, numpy.nan filt = f0 # reset filter calculation methods based","= 'vega_kurucz.txt': name of file containing Vega flux file, must be within ``filterFolder``","else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if","info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab","vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)]","'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega rsr = FILTERS[filt]['rsr']","= {:.3f} to {:.3f}\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude","spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): ''' :Description:","> 0: fname = list(args) elif kwargs.get('filter',False) != False: fname = kwargs['filter'] else:","= numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave =","filter or provided (required) :param filter: name of filter, must be one of","fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty","one of several statistics ''' chi = 0. dm,em = [],[] for f","''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER","name of filter, must be one of the specifed filters given by splat.FILTERS.keys()","as patches import matplotlib.pyplot as plt from scipy.integrate import trapz # for numerical","(flux = lam x F_lam), with option of also comparing to SED spectrum","MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): ''' :Description: This is a class data","report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans # report values out","particular filter. :param filter: name of filter, must be one of the specifed","plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra =","magnitudes on SED scale (flux = lam x F_lam), with option of also","fig ######################################### ######## SED FITTING TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!! ###","verbose: optional, default = True :Example: >>> import splat >>> data = splat.filterProperties('2MASS","0: raise ValueError('Did not recognize any of the input filters {}'.format(filters)) # prep","filter names if isinstance(filt[0],str): for f in filt: fc = checkFilterName(f) filt.remove(f) if","(100% transmission within, 0% transmission without) **vega** = True: compute Vega magnitudes (may","self.knownfilter = False else: filt = fflag self.filter = filt # some things","the specifed filters given by splat.FILTERS.keys() (required) :param reverse: convert energy into magnitude","simple representation of the Spectrum object ''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty):","= custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave","= custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else: fwave","a correct physical quantity (vega, ab, energy, photons) to compute photometry\\n\\n') # val","obseleted def checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt) f = f.replace(' ','_').upper()","flux file, must be within ``filterFolder`` **nsamples** = 100: number of samples to","= trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate","flux **photon** = False: compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing the","plt.savefig(file) return fig ######################################### ######## SED FITTING TOOLS ######### ### WARNING: THESE ARE","not work, the conversion is ignored (optional, default = erg/cm2/s) :param verbose: print","are the spectrophotometry functions for SPLAT \"\"\" # imports - internal import copy","= filt # some things that are based on presets if self.knownfilter ==","of file containing Vega flux file, must be within ``filterFolder`` **nsamples** = 100:","= copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty","{}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if","fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot']","filter agreement if self.filter != other.filter: raise ValueError('magnitudes filters {} and {} are","not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter is","import numpy from astropy import units as u # standard units from astropy","# finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired file","= {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\n'.format(report['lambda_min'],report['lambda_max'])) return report","estimation **info** = False: List the predefined filter names available **verbose** = True:","isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave","fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric","list filt = checkFilterName(filt) if filt == False: return None report = {}","out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and other.uncertainty !=","in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a copy of a Magnitude","elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit","= (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\nfilterMag not given a","if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: =","else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation of","False: compute AB magnitudes (may be set by filter) **energy** = False: compute","to a grid of model magnitudes and choose the closest match based on","FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave","and filter cover the same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave)","report = {} report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method']","trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value)","e_mag.value > 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s)","checkFilterName(filt) if filt == False: return None report = {} report['name'] = filt","FOURSTAR H LONG: FOURSTAR H long FOURSTAR H SHORT: FOURSTAR H short ...","and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty ="],"string":"[\n \"'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and\",\n \"not isinstance(ftrans,numpy.ndarray): raise ValueError('\\\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)]\",\n \"for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit =\",\n \"else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b\",\n \"system (optional, default = filter preference) :param units: units for energy as an\",\n \"(optional, default = filter preference) :param units: units for energy as an astropy.units\",\n \"magnitudes using one of several statistics ''' chi = 0. dm,em = [],[]\",\n \"of fluxes ''' # check filter agreement if self.filter != other.filter: raise ValueError('magnitudes\",\n \"physical constants in SI units import matplotlib.patches as patches import matplotlib.pyplot as plt\",\n \"# except: # print('\\\\nWarning: unit {} is not an energy flux unit'.format(return_unit)) try:\",\n \"em.append(unc[f]) # find best scale factor dm = numpy.array(dm) em = numpy.array(em) offset\",\n \"ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) #\",\n \"= kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples\",\n \"given a correct physical quantity (vega, ab, energy, photons) to compute photometry\\\\n\\\\n') #\",\n \"{} is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter\",\n \"ValueError('Could not parse input {}'.format(filt)) # add a comparison spectrum sp = kwargs.get('spectrum',None)\",\n \"included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did not recognize\",\n \"numpy.nan, numpy.nan filt = f0 # reset filter calculation methods based on filter\",\n \"except: # print('\\\\nWarning: unit {} is not an energy flux unit'.format(return_unit)) try: val.to(base_unit)\",\n \"not given a correct physical quantity (vega, ab, energy, photons) to compute photometry\\\\n\\\\n')\",\n \"nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that requested filter is in\",\n \"convert to desired energy units # try: val.to(return_unit) err.to(return_unit) # except: # print('\\\\nWarning:\",\n \"in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega rsr = FILTERS[filt]['rsr'] #\",\n \"is not an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\\\nWarning: cannot convert\",\n \"# list of filter names if isinstance(filt[0],str): for f in filt: fc =\",\n \"= numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this\",\n \"= False if photons: energy = False if energy: photons = False #\",\n \"def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile or set of filter profiles,\",\n \"(ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact =\",\n \"filter name :Optional Parameters: **custom** = None: A 2 x N vector array\",\n \"##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): ''' :Description: This is a\",\n \"and choose the closest match based on various statistics ''' pass def SEDFitMCMC(verbose=True):\",\n \"fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val\",\n \"rename if necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if fflag == False:\",\n \"the current list of filters in the SPLAT reference library. ''' verbose =\",\n \"import * ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this function has been obseleted\",\n \"raise ValueError('magnitudes filters {} and {} are not the same'.format(self.filter,other.filter)) # make a\",\n \"Plots a filter profile or set of filter profiles, optionally on top of\",\n \"= kwargs.get('flux',energy) if (photons or energy): vega = False ab = False if\",\n \"return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True:\",\n \"= (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))]\",\n \"0. and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of\",\n \"the closest match based on various statistics ''' pass def SEDFitMCMC(verbose=True): ''' this\",\n \"Jy Pivot point: = 1.252 micron FWHM = 0.323 micron Wavelength range =\",\n \"isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave\",\n \"notch[0],fwave <= notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] #\",\n \"lam x F_lam), with option of also comparing to SED spectrum ''' pass\",\n \"None: A 2 element array that specifies the lower and upper wavelengths for\",\n \"2MASS X not among the available filters: 2MASS H: 2MASS H-band 2MASS J:\",\n \"THIS CODE IS ONLY PARTIALLY COMPLETE ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER)\",\n \"= True: compute Vega magnitudes (may be set by filter) **ab** = False:\",\n \"sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.)\",\n \"(a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit\",\n \"requested filter is in list if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if\",\n \"elif (photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for\",\n \"= 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples):\",\n \"ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] =\",\n \"within, 0% transmission without) **vega** = True: compute Vega magnitudes (may be set\",\n \"= SPLAT_URL+FILTER_FOLDER # check that requested filter is in list f0 = checkFilterName(filt,\",\n \"= [] if (vega): # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#',\",\n \"on the AB system (optional, default = filter preference) :param vega: magnitude is\",\n \"if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = []\",\n \"function has been obseleted def checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt) f\",\n \"print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot\",\n \"# magnitude -> energy if kwargs.get('reverse',False) == False: if vega == True: #\",\n \"f in list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best\",\n \"import print_function, division \\\"\\\"\\\" .. note:: These are the spectrophotometry functions for SPLAT\",\n \"sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.)\",\n \"# reset filter calculation methods based on filter design if 'ab' in FILTERS[filt]['method']:\",\n \"kwargs.get('verbose',True) if len(args) > 0: fname = list(args) elif kwargs.get('filter',False) != False: fname\",\n \"custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave =\",\n \"equivalent flux density of a magnitude :Output: astropy quantity in flux density units\",\n \"'{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction for Magnitude\",\n \"within ``filterFolder`` **nsamples** = 100: number of samples to use in Monte Carlo\",\n \"a standard filter; some functions may not work'.format(filt)) self.knownfilter = False else: filt\",\n \"''' verbose = kwargs.get('verbose',True) if len(args) > 0: fname = list(args) elif kwargs.get('filter',False)\",\n \"# standard units from astropy import constants as const # physical constants in\",\n \"==True: print(' Filter {} not in SPLAT filter list'.format(k)) kys = list(output.keys()) if\",\n \"k in fname: f = checkFilterName(k) if f != False: output[f] = {}\",\n \"other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty\",\n \"from __future__ import print_function, division \\\"\\\"\\\" .. note:: These are the spectrophotometry functions\",\n \"flux **filterFolder** = splat.FILTER_FOLDER: folder containing the filter transmission files **vegaFile** = 'vega_kurucz.txt':\",\n \"kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig\",\n \"magnitude on whatever system is defined for the filter or provided (required) :param\",\n \"legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs)\",\n \"vega = kwargs.get('vega',True) ab = not vega rsr = FILTERS[filt]['rsr'] # other possibilities\",\n \"* ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this function has been obseleted def\",\n \"\\\\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2\",\n \"d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for\",\n \"J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER)\",\n \"magnitude object equal to the equivalent sum of fluxes ''' # check filter\",\n \"to the diffence of values ''' # make a copy and fill in\",\n \"if isinstance(filt,str): filt = [filt] if isinstance(filt,list): # list of filter names if\",\n \"= filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans\",\n \"magnitudes (may be set by filter) **energy** = False: compute energy flux **photon**\",\n \"Filter 2MASS J: 2MASS J-band Zeropoint = 1594.0 Jy Pivot point: = 1.252\",\n \"SPLAT filter list'.format(k)) kys = list(output.keys()) if len(kys) == 1: return output[kys[0]] else:\",\n \"TO BE COMPLETED else: print('passed') pass # check that input is an energy\",\n \"custom filter else: fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True: fwave\",\n \"ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean']\",\n \"dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <=\",\n \"(14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder\",\n \"splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J')\",\n \"> numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: no overlap between spectrum for {} and filter\",\n \"= kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.)\",\n \"len(args) > 0: fname = list(args) elif kwargs.get('filter',False) != False: fname = kwargs['filter']\",\n \"H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER\",\n \"FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw\",\n \"= fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05\",\n \"a set of magnitudes to model magnitudes using an Amoeba (Nelder-Mead) wrapper, and\",\n \"ab, energy, photons) to compute photometry\\\\n\\\\n') # val = numpy.nanmean(result)*outunit err = numpy.nanstd(result)\",\n \"filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did not recognize any of the input\",\n \"compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing the filter transmission files **vegaFile**\",\n \"filter) **energy** = False: compute energy flux **photon** = False: compute photon flux\",\n \"for {} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or\",\n \"* from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this function\",\n \"report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min']\",\n \"A 2 element array that specifies the lower and upper wavelengths for a\",\n \"filter. :param filter: name of filter, must be one of the specifed filters\",\n \"or ab method specified') # convert to desired energy units # try: val.to(return_unit)\",\n \"e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity):\",\n \"isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave\",\n \"not in SPLAT filter list'.format(k)) kys = list(output.keys()) if len(kys) == 1: return\",\n \"vega if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter if\",\n \"containing the filter transmission files **vegaFile** = 'vega_kurucz.txt': name of file containing Vega\",\n \"m1+m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter\",\n \"and filter transmission curve. :param filter: String giving the name of one of\",\n \"the ``filter`` input. By default this filter is also convolved with a model\",\n \"into magnitude instead (optional, default = False) :param ab: magnitude is on the\",\n \"contact '+EMAIL+'\\\\n') filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile\",\n \"= kwargs.get('output',file) if file != '': plt.savefig(file) return fig ######################################### ######## SED FITTING\",\n \"e_mag.to(base_unit) # except: # raise ValueError('\\\\nInput quantity unit {} is not a flux\",\n \"= (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave))\",\n \"numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err = 0. return val*outunit,err*outunit\",\n \"Magnitude object equal to the diffence of values ''' # make a copy\",\n \"return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code will compare a set of\",\n \"vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False)\",\n \"Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose:\",\n \"keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check\",\n \"name of file containing Vega flux file, must be within ``filterFolder`` **nsamples** =\",\n \"and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt\",\n \"out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises if\",\n \"Visualizes magnitudes on SED scale (flux = lam x F_lam), with option of\",\n \"('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray)\",\n \":param ab: magnitude is on the AB system (optional, default = filter preference)\",\n \"= numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+':\",\n \"{}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation\",\n \"Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot']))\",\n \"= fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err = 0. # magnitude\",\n \"J') Filter 2MASS J: 2MASS J-band Zeropoint = 1594.0 Jy Pivot point: =\",\n \"result = [] if (vega): # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile),\",\n \"interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if verbose==True: print('\\\\nWarning: data values in range\",\n \"Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter\",\n \"def __repr__(self): ''' :Purpose: A simple representation of the Spectrum object ''' if\",\n \"ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False)\",\n \"= [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if\",\n \"report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into an energy, and vice\",\n \"cannot convert result to an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val,\",\n \"flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux density of a magnitude :Output: astropy\",\n \"trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw)\",\n \"# plan: def modelMagnitudes(verbose=True): ''' this will be a code that calculates a\",\n \"combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine\",\n \"Pivot point: = 1.252 micron FWHM = 0.323 micron Wavelength range = 1.066\",\n \"``filterFolder`` **nsamples** = 100: number of samples to use in Monte Carlo error\",\n \"in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif\",\n \"= kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter\",\n \"in splat.FILTERS.keys() (required) :param filterFolder: folder containing the filter transmission files (optional, default\",\n \"numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: no overlap between spectrum for {} and\",\n \"############### SPECTROPHOTOMETRY ############### ##################################################### # this function has been obseleted def checkFilter(filt,verbose=True): output\",\n \"of the predefined filters listed in splat.FILTERS.keys() (required) :param filterFolder: folder containing the\",\n \"not among the available filters: 2MASS H: 2MASS H-band 2MASS J: 2MASS J-band\",\n \"Vega system (optional, default = filter preference) :param units: units for energy as\",\n \"s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version def copy(self): ''' :Purpose:\",\n \"addition for magnitudes (addition of fluxes) :Output: A new magnitude object equal to\",\n \"(optional, default = False) :param ab: magnitude is on the AB system (optional,\",\n \"in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\\\nfilterMag not given a correct physical quantity (vega,\",\n \"full filter profile for {}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength function wgood\",\n \"filter {} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result =\",\n \"raise ValueError('\\\\nInput quantity unit {} is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): '''\",\n \"ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend =\",\n \"wrapper, and choose the closest match ''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes\",\n \"a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version\",\n \"except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans >\",\n \"(1./em**2) dmo = numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True):\",\n \"be within ``filterFolder`` **nsamples** = 100: number of samples to use in Monte\",\n \"of model magnitudes and choose the closest match based on various statistics '''\",\n \"SPLAT filter. Returns two arrays: the filter wavelength and filter transmission curve. :param\",\n \"splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters\",\n \"s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\\\nfilterMag not\",\n \"the filter or provided (required) :param filter: name of filter, must be one\",\n \"lower and upper wavelengths for a notch filter (100% transmission within, 0% transmission\",\n \"# for numerical integration from scipy.interpolate import interp1d # splat functions and constants\",\n \"self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and other.uncertainty\",\n \"= [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse input\",\n \"= m1-m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter != other.filter:\",\n \"0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True:\",\n \"IS ONLY PARTIALLY COMPLETE ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not\",\n \"= False) WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE ''' # keyword parameters\",\n \"scale factor dm = numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo\",\n \"= trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value >\",\n \"with a model of Vega to extract Vega magnitudes, but the user can\",\n \"plt.legend(legend) # save if desired file = kwargs.get('file','') file = kwargs.get('filename',file) file =\",\n \"model magnitudes using an MCMC wrapper, and choose best/average/distribution of parameters ''' pass\",\n \"I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H long FOURSTAR H\",\n \"= ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s\",\n \"object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version def copy(self):\",\n \"list of notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission\",\n \"a code that calculates a set of magnitudes for a model set's SED\",\n \"else: fwave = fwave*u.micron # check that spectrum and filter cover the same\",\n \"and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >=\",\n \"= FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans =\",\n \"rsr = FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy\",\n \"names available **verbose** = True: List the predefined filter names available :Example: >>>\",\n \"= trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b))\",\n \"'+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean']))\",\n \"default = splat.FILTER_FOLDER) :Example: >>> import splat >>> import splat.photometry as spphot >>>\",\n \"from .initialize import * from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY ############### #####################################################\",\n \"FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False)\",\n \"(optional, default = filter preference) :param vega: magnitude is on the Vega system\",\n \"def SEDFitMCMC(verbose=True): ''' this code will conduct a comparison of a set of\",\n \"(Nelder-Mead) wrapper, and choose the closest match ''' pass def SEDVisualize(verbose=True): ''' Visualizes\",\n \"filters: 2MASS H: 2MASS H-band 2MASS J: 2MASS J-band 2MASS KS: 2MASS Ks-band\",\n \"Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version def\",\n \"combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine\",\n \"notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans\",\n \"vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave\",\n \"model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set of\",\n \"MCMC wrapper, and choose best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this\",\n \"out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0\",\n \"fwave = fwave.to(u.micron) else: fwave = fwave*u.micron # check that spectrum and filter\",\n \"splat.FILTERS.keys() (required) :param filterFolder: folder containing the filter transmission files (optional, default =\",\n \":param filter: name of filter, must be one of the specifed filters given\",\n \"String giving name of filter, which can either be one of the predefined\",\n \"== 0: err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested\",\n \"vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr:\",\n \"= (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) #\",\n \"dictionary containing key parameters for a particular filter. :param filter: name of filter,\",\n \"yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in\",\n \"containing key parameters for a particular filter. :param filter: name of filter, must\",\n \"file = kwargs.get('output',file) if file != '': plt.savefig(file) return fig ######################################### ######## SED\",\n \"if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)]\",\n \"print out information about filter to screen (optional, default = False) WARNING: THIS\",\n \"== 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if\",\n \"[numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve')\",\n \"verbose = kwargs.get('verbose',False) # check that requested filter is in list if isinstance(custom,bool)\",\n \"must be within ``filterFolder`` **nsamples** = 100: number of samples to use in\",\n \"not ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega\",\n \"False if photons: energy = False if energy: photons = False # Read\",\n \"import copy import os # imports - external import numpy from astropy import\",\n \">>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) '''\",\n \"closest match based on various statistics ''' pass def SEDFitMCMC(verbose=True): ''' this code\",\n \"object equal to the diffence of values ''' # make a copy and\",\n \"# check that requested filter is in list filt = checkFilterName(filt) if filt\",\n \"''' # make a copy and fill in combined magnitude out = copy.deepcopy(self)\",\n \"splat.FILTERS.keys() :type filter: required :param verbose: print out information about filter to screen\",\n \"up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired file = kwargs.get('file','')\",\n \"= [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True:\",\n \"= None: A 2 x N vector array specifying the wavelengths and transmissions\",\n \"''' :Purpose: Plots a filter profile or set of filter profiles, optionally on\",\n \"= numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray)\",\n \"{} not in SPLAT filter list'.format(k)) kys = list(output.keys()) if len(kys) == 1:\",\n \"can also specify AB magnitudes, photon flux or energy flux. :Required Parameters: **sp**:\",\n \"fflag self.filter = filt # some things that are based on presets if\",\n \"# check filter agreement if self.filter != other.filter: raise ValueError('magnitudes filters {} and\",\n \"model of Vega to extract Vega magnitudes, but the user can also specify\",\n \"0.027596454828421831) ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder =\",\n \"f = checkFilterName(k) if f != False: output[f] = {} output[f]['description'] = FILTERS[f]['description']\",\n \"convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else:\",\n \"does not span full filter profile for {}'.format(sp.name,filt)) # interpolate spectrum onto filter\",\n \"= vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave\",\n \"report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron)\",\n \"return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation of addition\",\n \"dmo = numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): '''\",\n \"WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE ''' # keyword parameters filterFolder =\",\n \"elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave))\",\n \"pass ##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): ''' :Description: This is\",\n \"using an Amoeba (Nelder-Mead) wrapper, and choose the closest match ''' pass def\",\n \"use in Monte Carlo error estimation **info** = False: List the predefined filter\",\n \"const # physical constants in SI units import matplotlib.patches as patches import matplotlib.pyplot\",\n \"convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst\",\n \":type verbose: optional, default = True :Example: >>> import splat >>> data =\",\n \"ValueError('Did not recognize any of the input filters {}'.format(filters)) # prep parameters fwave,ftrans\",\n \"a copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude\",\n \"of the predefined filters listed in splat.FILTERS.keys() or a custom filter name :Optional\",\n \"interpolate spectrum onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0:\",\n \"filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron))\",\n \"trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for i in numpy.arange(nsamples):\",\n \"d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if verbose==True:\",\n \"magnitude is on the Vega system (optional, default = filter preference) :param rsr:\",\n \"conversion does not work, the conversion is ignored (optional, default = erg/cm2/s) :param\",\n \"= kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit =\",\n \"kwargs.get('filename',file) file = kwargs.get('output',file) if file != '': plt.savefig(file) return fig ######################################### ########\",\n \"''' pass def SEDFitMCMC(verbose=True): ''' this code will conduct a comparison of a\",\n \"vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu =\",\n \"fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname = [fname] output = {}\",\n \"if verbose ==True: print(' Filter {} not in SPLAT filter list'.format(k)) kys =\",\n \"result = [] err = 0. # magnitude -> energy if kwargs.get('reverse',False) ==\",\n \"information about filter to screen (optional, default = False) WARNING: THIS CODE IS\",\n \"= False ab = False if photons: energy = False if energy: photons\",\n \"'{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): '''\",\n \"= kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if\",\n \"numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron\",\n \"ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: no\",\n \"* u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) elif ab == True: fconst\",\n \"if output == False and verbose == True: print('\\\\nFilter '+filt+' not currently available\",\n \"output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max']\",\n \"0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr']\",\n \"energy flux # try: # mag.to(base_unit) # e_mag.to(base_unit) # except: # raise ValueError('\\\\nInput\",\n \"= trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate\",\n \"self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if fflag == False: if verbose== True:\",\n \"be one of the specifed filters given by splat.FILTERS.keys() :type filter: required :param\",\n \"file that could be uploaded pre-save some model magnitudes ''' pass def interpolateMagnitudes(verbose=True):\",\n \"and constants from .initialize import * from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY\",\n \"self.magnitude = magnitude self.uncertainty = uncertainty self.type = magtype # check filter and\",\n \"in list filt = checkFilterName(filt) if filt == False: return None report =\",\n \"energy if kwargs.get('reverse',False) == False: if vega == True: # Read in Vega\",\n \"fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1.\",\n \"print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra)\",\n \"grid of model magnitudes and choose the closest match based on various statistics\",\n \"copy import os # imports - external import numpy from astropy import units\",\n \"''' Visualizes magnitudes on SED scale (flux = lam x F_lam), with option\",\n \"copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty !=\",\n \"import splat >>> data = splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band Zeropoint\",\n \"{:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max'])) return report def\",\n \"filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile for a SPLAT filter. Returns two\",\n \"0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err\",\n \"''' :Purpose: Make a copy of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty)\",\n \"kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str): filt = [filt] if isinstance(filt,list): #\",\n \"FOURSTAR H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder =\",\n \"and rename if necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if fflag ==\",\n \"magnitude # THIS NEEDS TO BE COMPLETED else: print('passed') pass # check that\",\n \"else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)))\",\n \"mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter is in list\",\n \"in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega if 'rsr' in FILTERS[filt]['method']:\",\n \"filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans =\",\n \"splat.FILTERS.keys() (required) :param reverse: convert energy into magnitude instead (optional, default = False)\",\n \"BE COMPLETED else: print('passed') pass # check that input is an energy flux\",\n \"(a/b * c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif\",\n \"= numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] =\",\n \"SED models saves to file that could be uploaded pre-save some model magnitudes\",\n \"if f in list(mags2.keys()) and f in list(unc.keys()) and f not in ignore:\",\n \"A 2 x N vector array specifying the wavelengths and transmissions for a\",\n \"no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega):\",\n \"''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else:\",\n \"interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2\",\n \":Purpose: A representation of addition for Magnitude classes that takes into account uncertainties\",\n \"kwargs.get('ab',True) vega = not ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab\",\n \"vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] #\",\n \"in filt: fc = checkFilterName(f) filt.remove(f) if fc == False: if verbose==True: print('Removed\",\n \"= f0 # read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values =\",\n \"filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans)\",\n \"the filter transmission files (optional, default = splat.FILTER_FOLDER) :Example: >>> import splat >>>\",\n \"if isinstance(fname,list) == False: fname = [fname] output = {} for k in\",\n \"print('filter {} is not a standard filter; some functions may not work'.format(filt)) self.knownfilter\",\n \"= copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str): filt =\",\n \"wavelength and filter transmission curve. :param filter: String giving the name of one\",\n \"ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else:\",\n \"user can also specify AB magnitudes, photon flux or energy flux. :Required Parameters:\",\n \"fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err = 0. #\",\n \"specify AB magnitudes, photon flux or energy flux. :Required Parameters: **sp**: Spectrum class\",\n \"= ftrans # report values out if kwargs.get('verbose',False): print('\\\\nFilter '+filt+': '+report['description']) print('Zeropoint =\",\n \"elif kwargs.get('filter',False) != False: fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list)\",\n \"set of magnitudes using one of several statistics ''' chi = 0. dm,em\",\n \"A representation of addition for magnitudes (addition of fluxes) :Output: A new magnitude\",\n \"= [filt] if isinstance(filt,list): # list of filter names if isinstance(filt[0],str): for f\",\n \"= kwargs.get('verbose',True) if len(args) > 0: fname = list(args) elif kwargs.get('filter',False) != False:\",\n \"= interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out\",\n \"magnitudes for a model set's SED models saves to file that could be\",\n \"**ab** = False: compute AB magnitudes (may be set by filter) **energy** =\",\n \"pass def SEDFitMCMC(verbose=True): ''' this code will conduct a comparison of a set\",\n \"if filt == False: return numpy.nan, numpy.nan # reset filter calculation methods based\",\n \"vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega\",\n \"to extract Vega magnitudes, but the user can also specify AB magnitudes, photon\",\n \"<= notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity)\",\n \"except: # raise ValueError('\\\\nInput quantity unit {} is not a flux unit'.format(mag.unit)) def\",\n \"a comparison spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True:\",\n \"interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): # Read in Vega\",\n \"preference) :param rsr: magnitude is on the Vega system (optional, default = filter\",\n \"False) :param ab: magnitude is on the AB system (optional, default = filter\",\n \"notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag =\",\n \"verbose==True: print('\\\\nWarning: spectrum for {} does not span full filter profile for {}'.format(sp.name,filt))\",\n \"e_mag=e_mag*mag.unit # check that requested filter is in list filt = checkFilterName(filt) if\",\n \"if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan,\",\n \"provided (required) :param filter: name of filter, must be one of the specifed\",\n \"plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0:\",\n \"plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave\",\n \"c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val =\",\n \"sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' #\",\n \"{:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose:\",\n \"but the user can also specify AB magnitudes, photon flux or energy flux.\",\n \"specifed filters given by splat.FILTERS.keys() :type filter: required :param verbose: print out information\",\n \"= vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate Vega onto\",\n \"err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err = 0. return val*outunit,err*outunit def\",\n \"kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2\",\n \"fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact\",\n \"trapz # for numerical integration from scipy.interpolate import interp1d # splat functions and\",\n \"''' this code compares a set of magnitudes using one of several statistics\",\n \"SED spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): '''\",\n \"def checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt) f = f.replace(' ','_').upper() for\",\n \"1594.0 Jy Pivot point: = 1.252 micron FWHM = 0.323 micron Wavelength range\",\n \"if fc == False: if verbose==True: print('Removed filter {}: not included in SPLAT'.format(f))\",\n \"be uploaded pre-save some model magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces an\",\n \"= m1+m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter != other.filter:\",\n \"report['transmission'] = ftrans # report values out if kwargs.get('verbose',False): print('\\\\nFilter '+filt+': '+report['description']) print('Zeropoint\",\n \"numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: no overlap between spectrum for {} and filter {}'.format(sp.name,filt))\",\n \"filter design if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not ab\",\n \"= interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): # Read in Vega spectrum vwave,vflux\",\n \"fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra =\",\n \"key parameters for a particular filter. :param filter: name of filter, must be\",\n \"I: Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H long\",\n \"= 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave))\",\n \"verbose ==True: print(' Filter {} not in SPLAT filter list'.format(k)) kys = list(output.keys())\",\n \"wrapper, and choose best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this code\",\n \"filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans >\",\n \"micron FWHM = 0.323 micron Wavelength range = 1.066 to 1.442 micron >>>\",\n \"'+EMAIL+'\\\\n') filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile for\",\n \"3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value)\",\n \"filter transmission files (optional, default = splat.FILTER_FOLDER) :Example: >>> import splat >>> import\",\n \"{} for k in fname: f = checkFilterName(k) if f != False: output[f]\",\n \"if len(kys) == 1: return output[kys[0]] else: return output def filterProperties(filt,**kwargs): ''' :Purpose:\",\n \"matplotlib.pyplot as plt from scipy.integrate import trapz # for numerical integration from scipy.interpolate\",\n \"splat >>> data = splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band Zeropoint =\",\n \"a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): '''\",\n \"flux density of a magnitude :Output: astropy quantity in flux density units (default\",\n \"== True: print('\\\\nFilter '+filt+' not currently available for SPLAT; contact '+EMAIL+'\\\\n') filterInfo() return\",\n \"numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return\",\n \"NameError('\\\\nfilterMag not given a correct physical quantity (vega, ab, energy, photons) to compute\",\n \"{} and {} are not the same'.format(self.filter,other.filter)) # make a copy and fill\",\n \"dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <=\",\n \"be set by filter) **ab** = False: compute AB magnitudes (may be set\",\n \"else: raise NameError('\\\\nfilterMag not given a correct physical quantity (vega, ab, energy, photons)\",\n \"fname: f = checkFilterName(k) if f != False: output[f] = {} output[f]['description'] =\",\n \"''' this code will compare a set of magnitudes to a grid of\",\n \"account uncertainties :Output: A new Magnitude object equal to the sum of values\",\n \"vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate Vega\",\n \"== False: fname = [fname] output = {} for k in fname: f\",\n \"values in range of filter {} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n\",\n \"predefined filter names available **verbose** = True: List the predefined filter names available\",\n \"ValueError('\\\\nInput quantity unit {} is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose:\",\n \"= {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if\",\n \"legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) #\",\n \"''' pass ##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): ''' :Description: This\",\n \"FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H long FOURSTAR H SHORT:\",\n \"= fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw))\",\n \"print('\\\\nWarning: no overlap between spectrum for {} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan\",\n \"optionally on top of a spectrum WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT,\",\n \"on top of a spectrum WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS\",\n \"= trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value >\",\n \"about filter to screen :type verbose: optional, default = True :Example: >>> import\",\n \"err # energy -> magnitude # THIS NEEDS TO BE COMPLETED else: print('passed')\",\n \"''' pass def SEDFitAmoeba(verbose=True): ''' this code will conduct a comparison of a\",\n \"# try: # mag.to(base_unit) # e_mag.to(base_unit) # except: # raise ValueError('\\\\nInput quantity unit\",\n \"= kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons\",\n \"agreement if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): '''\",\n \"== 1: return output[kys[0]] else: return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a\",\n \"be a code that calculates a set of magnitudes for a model set's\",\n \"Returns a dictionary containing key parameters for a particular filter. :param filter: name\",\n \"compute Vega magnitudes (may be set by filter) **ab** = False: compute AB\",\n \"of a set of magnitudes to model magnitudes using an MCMC wrapper, and\",\n \"filter profiles, optionally on top of a spectrum WARNING: THIS CODE IS CURRENTLY\",\n \"isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\\\nProblem reading\",\n \"e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s\",\n \"unit {} is not an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\\\nWarning:\",\n \"filter profile for a SPLAT filter. Returns two arrays: the filter wavelength and\",\n \"> 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central']\",\n \"if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab\",\n \"filter profile or set of filter profiles, optionally on top of a spectrum\",\n \"outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in\",\n \"yl = kwargs.get('ylabel','Transmission Curve') # list of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float):\",\n \"set of magnitudes for a model set's SED models saves to file that\",\n \"that input is an energy flux # try: # mag.to(base_unit) # e_mag.to(base_unit) #\",\n \"this conversion does not work, the conversion is ignored (optional, default = erg/cm2/s)\",\n \"fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result\",\n \"SEDFitMCMC(verbose=True): ''' this code will conduct a comparison of a set of magnitudes\",\n \"magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises\",\n \"predefined filter names available :Example: >>> import splat >>> import splat.photometry as spphot\",\n \"vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value)\",\n \"object equal to the equivalent sum of fluxes ''' # check filter agreement\",\n \"in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan))\",\n \"set of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a\",\n \"f = copy.deepcopy(filt) f = f.replace(' ','_').upper() for k in list(FILTERS.keys()): if f==k.upper()\",\n \"if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result =\",\n \"report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans #\",\n \"f0 # reset filter calculation methods based on filter design if 'ab' in\",\n \"files **vegaFile** = 'vega_kurucz.txt': name of file containing Vega flux file, must be\",\n \"point: = 1.252 micron FWHM = 0.323 micron Wavelength range = 1.066 to\",\n \"= kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5))\",\n \"ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude of a\",\n \"= numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. # custom filter else:\",\n \"(may be set by filter) **ab** = False: compute AB magnitudes (may be\",\n \"self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def __copy__(self):\",\n \"not vega if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter\",\n \"0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if\",\n \"val, err # energy -> magnitude # THIS NEEDS TO BE COMPLETED else:\",\n \"other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent\",\n \"on SED scale (flux = lam x F_lam), with option of also comparing\",\n \"recognize any of the input filters {}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs)\",\n \"custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else: fwave =\",\n \"= checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt = f0 rsr\",\n \"magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises\",\n \"{}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose:\",\n \"# print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] =\",\n \"in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and isinstance(notch,bool):\",\n \"False and verbose == True: print('\\\\nFilter '+filt+' not currently available for SPLAT; contact\",\n \"range = {:.3f} to {:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a\",\n \"in range of filter {} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n =\",\n \"xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise\",\n \"out def __sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction for Magnitude classes that\",\n \"a model set's SED models saves to file that could be uploaded pre-save\",\n \"# interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact\",\n \"object equal to the sum of values ''' # make a copy and\",\n \"using an MCMC wrapper, and choose best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True):\",\n \"same'.format(self.filter,other.filter)) # make a copy and fill in combined magnitude out = copy.deepcopy(self)\",\n \"SPLAT_URL+FILTER_FOLDER # check that requested filter is in list f0 = checkFilterName(filt, verbose=True)\",\n \"H long FOURSTAR H SHORT: FOURSTAR H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER)\",\n \"= vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b =\",\n \":Output: A new Magnitude object equal to the sum of values ''' #\",\n \"reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs):\",\n \"ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega if\",\n \":param verbose: print out information about filter to screen (optional, default = False)\",\n \"# custom filter else: fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True:\",\n \"u.s) else: raise ValueError('\\\\nmagToFlux needs vega or ab method specified') # convert to\",\n \"kwargs.get('ylabel','Transmission Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans\",\n \"for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy):\",\n \"result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\\\nfilterMag not given a correct physical quantity (vega, ab, energy,\",\n \"<= notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] # units\",\n \"fwave.to(u.micron) else: fwave = fwave*u.micron # check that spectrum and filter cover the\",\n \"> 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] =\",\n \"vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.)\",\n \"== True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if\",\n \"the Spectrum object ''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{} magnitude\",\n \"vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is in list if isinstance(custom,bool) and isinstance(notch,bool):\",\n \"* u.s) # calculate uncertainty if e_mag.value > 0.: for i in numpy.arange(nsamples):\",\n \"from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this function has\",\n \"keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile =\",\n \"FOURSTAR H SHORT: FOURSTAR H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not\",\n \"if f != False: output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint']\",\n \"does not work, the conversion is ignored (optional, default = erg/cm2/s) :param verbose:\",\n \"list(mags2.keys()) and f in list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) #\",\n \"== True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err\",\n \"FOURSTAR H long FOURSTAR H SHORT: FOURSTAR H short ... ''' filterFolder =\",\n \"err = 0. # magnitude -> energy if kwargs.get('reverse',False) == False: if vega\",\n \"Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'),\",\n \"not currently available for SPLAT; contact '+EMAIL+'\\\\n') filterInfo() return output def filterProfile(filt,**kwargs): '''\",\n \"onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else:\",\n \"if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return\",\n \"out if kwargs.get('verbose',False): print('\\\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: =\",\n \"for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave =\",\n \"(ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral())\",\n \"energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err # energy -> magnitude\",\n \"magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into an energy, and vice versa. :param\",\n \"(required) :param filterFolder: folder containing the filter transmission files (optional, default = splat.FILTER_FOLDER)\",\n \"= False else: filt = fflag self.filter = filt # some things that\",\n \"False: fname = [fname] output = {} for k in fname: f =\",\n \"without) **vega** = True: compute Vega magnitudes (may be set by filter) **ab**\",\n \"val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.: for\",\n \"UNDER DEVELOPMENT, BUGS MAY BE COMMON ''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT)\",\n \"0% transmission without) **vega** = True: compute Vega magnitudes (may be set by\",\n \"filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile for a\",\n \"kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True)\",\n \"specified by the ``filter`` input. By default this filter is also convolved with\",\n \"wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM =\",\n \"= splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band Zeropoint = 1594.0 Jy Pivot\",\n \"= custom[0],custom[1] # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\\\\",\n \"equal to the equivalent sum of fluxes ''' # check filter agreement if\",\n \"# trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave\",\n \"trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw)\",\n \"True fflag = checkFilterName(filt,verbose=verbose) if fflag == False: if verbose== True: print('filter {}\",\n \"==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean\",\n \"for k in list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output = k\",\n \"interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a =\",\n \"coding: utf-8 -*- from __future__ import print_function, division \\\"\\\"\\\" .. note:: These are\",\n \"units as u # standard units from astropy import constants as const #\",\n \"fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1.\",\n \"rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s')\",\n \"= kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit)\",\n \"as const # physical constants in SI units import matplotlib.patches as patches import\",\n \"kwargs.get('vega',True) ab = not vega if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) #\",\n \"numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra\",\n \"in flux density units (default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose:\",\n \"= vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu\",\n \"i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2\",\n \"takes into account uncertainties :Output: A new Magnitude object equal to the sum\",\n \"A simple representation of the Spectrum object ''' if self.uncertainty != 0. and\",\n \"pass # check that input is an energy flux # try: # mag.to(base_unit)\",\n \"Magnitude classes that takes into account uncertainties :Output: A new Magnitude object equal\",\n \"Prints out the current list of filters in the SPLAT reference library. '''\",\n \"= const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise\",\n \"if verbose==True: print('Removed filter {}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt)\",\n \"print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM\",\n \"filt = checkFilterName(filt) if filt == False: return None report = {} report['name']\",\n \"# imports - external import numpy from astropy import units as u #\",\n \"# make a copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude\",\n \"fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) #\",\n \"isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if\",\n \"* u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value)\",\n \"######### ### WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True): '''\",\n \"screen (optional, default = False) WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE '''\",\n \"if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter is in list filt\",\n \"isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans\",\n \"for f in list(mags1.keys()): if f in list(mags2.keys()) and f in list(unc.keys()) and\",\n \"FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega rsr = FILTERS[filt]['rsr'] # other\",\n \"else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) ==\",\n \"trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit\",\n \"1.442 micron >>> data = splat.filterProperties('2MASS X') Filter 2MASS X not among the\",\n \"numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter\",\n \"= 0. # magnitude -> energy if kwargs.get('reverse',False) == False: if vega ==\",\n \"= kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str): filt = [filt] if isinstance(filt,list):\",\n \"= kwargs.get('verbose',False) # check that requested filter is in list if isinstance(custom,bool) and\",\n \"os # imports - external import numpy from astropy import units as u\",\n \"!= 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty =\",\n \"isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn =\",\n \"1.252 micron FWHM = 0.323 micron Wavelength range = 1.066 to 1.442 micron\",\n \"= fflag self.filter = filt # some things that are based on presets\",\n \"== True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up\",\n \"!= other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the\",\n \"None: A 2 x N vector array specifying the wavelengths and transmissions for\",\n \"{} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: =\",\n \"currently available for SPLAT; contact '+EMAIL+'\\\\n') filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose:\",\n \"been obseleted def checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt) f = f.replace('\",\n \"fwave*u.micron # check that spectrum and filter cover the same wavelength ranges if\",\n \"**photon** = False: compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing the filter\",\n \"= {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print('\",\n \"= True :Example: >>> import splat >>> data = splat.filterProperties('2MASS J') Filter 2MASS\",\n \"checkFilterName(filt,verbose=verbose) if fflag == False: if verbose== True: print('filter {} is not a\",\n \"CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON ''' filt = copy.deepcopy(filters)\",\n \"Spectrum object ''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{} magnitude of\",\n \"= interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a\",\n \"a copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude)))\",\n \"and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty =\",\n \"= False: compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing the filter transmission\",\n \"list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor\",\n \"dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code will compare a set\",\n \"Parameters: **sp**: Spectrum class object, which should contain wave, flux and noise array\",\n \"raise ValueError('\\\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans\",\n \"1.066 to 1.442 micron >>> data = splat.filterProperties('2MASS X') Filter 2MASS X not\",\n \"self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and other.uncertainty != 0: m1\",\n \"err = 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\\\nmagToFlux needs vega or ab method\",\n \"filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty self.type = magtype\",\n \"any of the input filters {}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs) if\",\n \"system (optional, default = filter preference) :param rsr: magnitude is on the Vega\",\n \"filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val\",\n \"based on various statistics ''' pass def SEDFitMCMC(verbose=True): ''' this code will conduct\",\n \"numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter\",\n \"sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8))\",\n \"this code will compare a set of magnitudes to a grid of model\",\n \"These are the spectrophotometry functions for SPLAT \\\"\\\"\\\" # imports - internal import\",\n \"ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude\",\n \"input filters {}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit =\",\n \"[] err = 0. # magnitude -> energy if kwargs.get('reverse',False) == False: if\",\n \"filter agreement if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000):\",\n \"i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra =\",\n \"in list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output = k if output\",\n \"spectrum. Spectral fluxes are convolved with the filter profile specified by the ``filter``\",\n \"a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile or set\",\n \"','_').upper() for k in list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output =\",\n \"various statistics ''' pass def SEDFitMCMC(verbose=True): ''' this code will conduct a comparison\",\n \"filter: name of filter, must be one of the specifed filters given by\",\n \"unit {} is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a\",\n \"Filter {} not in SPLAT filter list'.format(k)) kys = list(output.keys()) if len(kys) ==\",\n \"filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info\",\n \"m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val) # check filter agreement\",\n \"val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is in list if isinstance(custom,bool)\",\n \"False: raise ValueError filt = f0 # read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']),\",\n \"yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse input {}'.format(filt)) #\",\n \"if vega == True: # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#',\",\n \":Example: >>> import splat >>> import splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0]\",\n \"energy flux. :Required Parameters: **sp**: Spectrum class object, which should contain wave, flux\",\n \"* u.s) elif ab == True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 *\",\n \"specifies the lower and upper wavelengths for a notch filter (100% transmission within,\",\n \"splat.FILTERS.keys() or a custom filter name :Optional Parameters: **custom** = None: A 2\",\n \"False: compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing the filter transmission files\",\n \"is an energy flux # try: # mag.to(base_unit) # e_mag.to(base_unit) # except: #\",\n \"u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val\",\n \"print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter {}\",\n \"finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired file =\",\n \"# keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER #\",\n \"if len(filt) == 0: raise ValueError('Did not recognize any of the input filters\",\n \"= 100: number of samples to use in Monte Carlo error estimation **info**\",\n \"the filter wavelength and filter transmission curve. :param filter: String giving the name\",\n \"kwargs.get('verbose',False) # check that requested filter is in list if isinstance(custom,bool) and isinstance(notch,bool):\",\n \"methods based on filter design if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega\",\n \"value for a grid set of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): '''\",\n \"else: if verbose ==True: print(' Filter {} not in SPLAT filter list'.format(k)) kys\",\n \"spectrum and filter cover the same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or\",\n \"if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)]\",\n \"def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is in list if isinstance(custom,bool) and\",\n \"0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw))\",\n \"energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or energy): vega = False\",\n \"data values in range of filter {} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.)\",\n \"numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron\",\n \"True: fwave = fwave.to(u.micron) else: fwave = fwave*u.micron # check that spectrum and\",\n \"match ''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale (flux =\",\n \"numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or\",\n \"u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val\",\n \"specifed filters given by splat.FILTERS.keys() (required) :param reverse: convert energy into magnitude instead\",\n \"elif ab == True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s *\",\n \"# imports - internal import copy import os # imports - external import\",\n \"= self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and\",\n \"def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set of magnitudes using one of\",\n \"into account uncertainties :Output: A new Magnitude object equal to the sum of\",\n \"the AB system (optional, default = filter preference) :param vega: magnitude is on\",\n \"filter transmission curve. :param filter: String giving the name of one of the\",\n \"f0 == False: return numpy.nan, numpy.nan filt = f0 rsr = FILTERS[filt]['rsr'] #\",\n \"= numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans # report values out if\",\n \"= not ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not\",\n \"= kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) #\",\n \"vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False)\",\n \"pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated value for a grid set of\",\n \"calculation methods based on filter design if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True)\",\n \"and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >=\",\n \"Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value)\",\n \"__repr__(self): ''' :Purpose: A simple representation of the Spectrum object ''' if self.uncertainty\",\n \"= interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): # Read in\",\n \"d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints\",\n \"wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact =\",\n \"print('\\\\nFilter '+filt+' not currently available for SPLAT; contact '+EMAIL+'\\\\n') filterInfo() return output def\",\n \"check filter agreement if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def\",\n \"names available :Example: >>> import splat >>> import splat.photometry as spphot >>> sp\",\n \"self.uncertainty = uncertainty self.type = magtype # check filter and rename if necessary\",\n \"giving the name of one of the predefined filters listed in splat.FILTERS.keys() (required)\",\n \"Wavelength range = 1.066 to 1.442 micron >>> data = splat.filterProperties('2MASS X') Filter\",\n \"Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))\",\n \"== False: return numpy.nan, numpy.nan filt = f0 rsr = FILTERS[filt]['rsr'] # Read\",\n \"Vega magnitudes (may be set by filter) **ab** = False: compute AB magnitudes\",\n \"raise NameError('\\\\nfilterMag not given a correct physical quantity (vega, ab, energy, photons) to\",\n \"info = filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make\",\n \"s # backup version def copy(self): ''' :Purpose: Make a copy of a\",\n \"'{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation of addition for\",\n \"numpy.nan filt = f0 rsr = FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool)\",\n \"**info** = False: List the predefined filter names available **verbose** = True: List\",\n \"notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False)\",\n \"**vega** = True: compute Vega magnitudes (may be set by filter) **ab** =\",\n \"ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err = 0. # magnitude -> energy\",\n \"function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n =\",\n \"if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] #\",\n \"raise ValueError('Could not parse input {}'.format(filt)) # add a comparison spectrum sp =\",\n \"{} report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method']\",\n \"= (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not\",\n \"err = 0.*u.erg/(u.cm**2 * u.s) elif ab == True: fconst = 3631*u.jansky ftrans\",\n \"nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst\",\n \"2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR\",\n \"output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron)\",\n \"kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name)\",\n \"= 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose\",\n \"constants as const # physical constants in SI units import matplotlib.patches as patches\",\n \":param verbose: print out information about filter to screen :type verbose: optional, default\",\n \"vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate Vega onto filter wavelength function v\",\n \"# print('\\\\nWarning: unit {} is not an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit)\",\n \"in SPLAT filter list'.format(k)) kys = list(output.keys()) if len(kys) == 1: return output[kys[0]]\",\n \"= interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val =\",\n \"N vector array specifying the wavelengths and transmissions for a custom filter **notch**\",\n \"filter to screen :type verbose: optional, default = True :Example: >>> import splat\",\n \"= [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission\",\n \"of addition for Magnitude classes that takes into account uncertainties :Output: A new\",\n \"agreement if self.filter != other.filter: raise ValueError('magnitudes filters {} and {} are not\",\n \"# units if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else: fwave = fwave*u.micron\",\n \"report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try:\",\n \"interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): # Read in Vega spectrum vwave,vflux =\",\n \"addition for Magnitude classes that takes into account uncertainties :Output: A new Magnitude\",\n \"models else: if verbose==True: print('\\\\nWarning: data values in range of filter {} have\",\n \"a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b *\",\n \"False: compute energy flux **photon** = False: compute photon flux **filterFolder** = splat.FILTER_FOLDER:\",\n \"not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is in list\",\n \"print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True:\",\n \"for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra\",\n \"... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check\",\n \"make a copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude =\",\n \"rsr: magnitude is on the Vega system (optional, default = filter preference) :param\",\n \"def modelMagnitudes(verbose=True): ''' this will be a code that calculates a set of\",\n \"# check filter and rename if necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose)\",\n \"== 0: raise ValueError('Did not recognize any of the input filters {}'.format(filters)) #\",\n \"result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu\",\n \"0. dm,em = [],[] for f in list(mags1.keys()): if f in list(mags2.keys()) and\",\n \"numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: spectrum\",\n \"isinstance(filt,str): filt = [filt] if isinstance(filt,list): # list of filter names if isinstance(filt[0],str):\",\n \"flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err # energy -> magnitude #\",\n \"of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set\",\n \"= {:.3f} to {:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter {} not in\",\n \"if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] # list of notch ranges if\",\n \"True: List the predefined filter names available :Example: >>> import splat >>> import\",\n \"0. # magnitude -> energy if kwargs.get('reverse',False) == False: if vega == True:\",\n \"''' :Purpose: Retrieve the filter profile for a SPLAT filter. Returns two arrays:\",\n \"result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) elif\",\n \"m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val) #\",\n \"= (a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c =\",\n \"filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega\",\n \"magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose:\",\n \"fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw =\",\n \"energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\\\nWarning: cannot convert result to an\",\n \"(photons or energy): vega = False ab = False if photons: energy =\",\n \"= kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s)\",\n \"# check that spectrum and filter cover the same wavelength ranges if numpy.nanmax(fwave)\",\n \"b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value)\",\n \"noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n\",\n \"{:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength\",\n \"that requested filter is in list f0 = checkFilterName(filt, verbose=True) if f0 ==\",\n \"and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-2.5*numpy.log10(1.+10.**(-0.4*(m2-m1)))\",\n \"trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty\",\n \"spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra)\",\n \"with option of also comparing to SED spectrum ''' pass ##################################################### ############### MAGNITUDE\",\n \"vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu\",\n \"other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose: A representation of\",\n \"numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans # report values out if kwargs.get('verbose',False):\",\n \"for SPLAT \\\"\\\"\\\" # imports - internal import copy import os # imports\",\n \"trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave =\",\n \"elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu =\",\n \"fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] =\",\n \"out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0\",\n \"* u.s * u.micron)) # interpolate Vega onto filter wavelength function v =\",\n \"numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return\",\n \"if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve') # list of notch ranges\",\n \"2 x N vector array specifying the wavelengths and transmissions for a custom\",\n \"upper wavelengths for a notch filter (100% transmission within, 0% transmission without) **vega**\",\n \"**verbose** = True: List the predefined filter names available :Example: >>> import splat\",\n \"into an energy, and vice versa. :param mag: magnitude on whatever system is\",\n \"vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral())\",\n \"the available filters: 2MASS H: 2MASS H-band 2MASS J: 2MASS J-band 2MASS KS:\",\n \"# single filter name if isinstance(filt,str): filt = [filt] if isinstance(filt,list): # list\",\n \"k in list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output = k if\",\n \"vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave\",\n \"ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra =\",\n \"- external import numpy from astropy import units as u # standard units\",\n \"of subtraction for Magnitude classes that takes into account uncertainties :Output: A new\",\n \"representation of subtraction for Magnitude classes that takes into account uncertainties :Output: A\",\n \"from astropy import units as u # standard units from astropy import constants\",\n \"if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not ab if 'vega'\",\n \"val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\\\nfilterMag not given\",\n \"kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose =\",\n \"= copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty\",\n \"has been obseleted def checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt) f =\",\n \"**filterFolder** = splat.FILTER_FOLDER: folder containing the filter transmission files **vegaFile** = 'vega_kurucz.txt': name\",\n \"out information about filter to screen :type verbose: optional, default = True :Example:\",\n \"* u.s) else: raise ValueError('\\\\nmagToFlux needs vega or ab method specified') # convert\",\n \"kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for\",\n \"can either be one of the predefined filters listed in splat.FILTERS.keys() or a\",\n \"a custom filter name :Optional Parameters: **custom** = None: A 2 x N\",\n \"# THIS NEEDS TO BE COMPLETED else: print('passed') pass # check that input\",\n \"H: 2MASS H-band 2MASS J: 2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL I:\",\n \"comparing to SED spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class\",\n \"SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did not recognize any of\",\n \"output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing key parameters for a\",\n \"file = kwargs.get('filename',file) file = kwargs.get('output',file) if file != '': plt.savefig(file) return fig\",\n \"kwargs.get('flux',energy) if (photons or energy): vega = False ab = False if photons:\",\n \"len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter\",\n \"source based on its spectrum. Spectral fluxes are convolved with the filter profile\",\n \":Optional Parameters: **custom** = None: A 2 x N vector array specifying the\",\n \"rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b\",\n \"that calculates a set of magnitudes for a model set's SED models saves\",\n \"# result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu\",\n \"else: filt = fflag self.filter = filt # some things that are based\",\n \"a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit = u.erg/u.s/u.cm**2 if\",\n \"if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for k in\",\n \"isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan\",\n \"def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile for a SPLAT filter. Returns\",\n \"an energy flux # try: # mag.to(base_unit) # e_mag.to(base_unit) # except: # raise\",\n \"numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code\",\n \"# catch for models else: if verbose==True: print('\\\\nWarning: data values in range of\",\n \"if (vega): # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\\\\",\n \"system is defined for the filter or provided (required) :param filter: name of\",\n \"physical quantity (vega, ab, energy, photons) to compute photometry\\\\n\\\\n') # val = numpy.nanmean(result)*outunit\",\n \"verbose: print out information about filter to screen (optional, default = False) WARNING:\",\n \"(10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.: for i in\",\n \"else: return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing key parameters\",\n \"= erg/cm2/s) :param verbose: print out information about filter to screen (optional, default\",\n \"necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if fflag == False: if verbose==\",\n \"if this conversion does not work, the conversion is ignored (optional, default =\",\n \"= kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab =\",\n \"f.replace(' ','_').upper() for k in list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output\",\n \"transmission within, 0% transmission without) **vega** = True: compute Vega magnitudes (may be\",\n \"array elements. **filter**: String giving name of filter, which can either be one\",\n \"== True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k])\",\n \"rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s)\",\n \"also comparing to SED spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS ############### #####################################################\",\n \"{} is not a standard filter; some functions may not work'.format(filt)) self.knownfilter =\",\n \"numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: no overlap between\",\n \"division \\\"\\\"\\\" .. note:: These are the spectrophotometry functions for SPLAT \\\"\\\"\\\" #\",\n \"for numerical integration from scipy.interpolate import interp1d # splat functions and constants from\",\n \"= (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.: for i\",\n \"CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON ''' filt = copy.deepcopy(filters) wave_unit =\",\n \"# print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise\",\n \"{}'.format(i+1)) else: raise ValueError('Could not parse input {}'.format(filt)) # add a comparison spectrum\",\n \"(energy): outunit = u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c\",\n \"Zeropoint = 1594.0 Jy Pivot point: = 1.252 micron FWHM = 0.323 micron\",\n \"patches import matplotlib.pyplot as plt from scipy.integrate import trapz # for numerical integration\",\n \"is in list filt = checkFilterName(filt) if filt == False: return None report\",\n \"for Magnitude classes that takes into account uncertainties :Output: A new Magnitude object\",\n \"default = True :Example: >>> import splat >>> data = splat.filterProperties('2MASS J') Filter\",\n \"magnitude instead (optional, default = False) :param ab: magnitude is on the AB\",\n \"return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is in list if\",\n \":Example: >>> import splat >>> data = splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS\",\n \"specifying the wavelengths and transmissions for a custom filter **notch** = None: A\",\n \"checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt = f0 # reset\",\n \"fluxes ''' # check filter agreement if self.filter != other.filter: raise ValueError('magnitudes filters\",\n \"= numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans # report\",\n \"m1-m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter\",\n \"# mag.to(base_unit) # e_mag.to(base_unit) # except: # raise ValueError('\\\\nInput quantity unit {} is\",\n \"__copy__(self): ''' :Purpose: Make a copy of a Magnitude object ''' s =\",\n \"of addition for magnitudes (addition of fluxes) :Output: A new magnitude object equal\",\n \"('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s *\",\n \"copy(self): ''' :Purpose: Make a copy of a Magnitude object ''' s =\",\n \"kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples =\",\n \"if kwargs.get('verbose',False): print('\\\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot']))\",\n \"kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1]\",\n \"(photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i\",\n \"f.lower() in FILTERS[k]['altnames']: output = k if output == False and verbose ==\",\n \"u # standard units from astropy import constants as const # physical constants\",\n \"xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl =\",\n \"as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123,\",\n \"comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not\",\n \"not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom =\",\n \"wavelengths for a notch filter (100% transmission within, 0% transmission without) **vega** =\",\n \"(optional, default = splat.FILTER_FOLDER) :Example: >>> import splat >>> import splat.photometry as spphot\",\n \"= checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt = f0 #\",\n \"False: if verbose==True: print('Removed filter {}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if\",\n \"= kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4]))\",\n \"input. By default this filter is also convolved with a model of Vega\",\n \"\\\"\\\"\\\" # imports - internal import copy import os # imports - external\",\n \"other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty\",\n \"raise ValueError filt = f0 # read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#',\",\n \"* u.s * u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)]\",\n \"##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this function has been obseleted def checkFilter(filt,verbose=True):\",\n \"val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.: for\",\n \"that requested filter is in list if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True)\",\n \":Output: A new Magnitude object equal to the diffence of values ''' #\",\n \"pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition for magnitudes (addition of\",\n \"magnitudes to a grid of model magnitudes and choose the closest match based\",\n \"Spectrum class object, which should contain wave, flux and noise array elements. **filter**:\",\n \"giving name of filter, which can either be one of the predefined filters\",\n \"0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else:\",\n \"file != '': plt.savefig(file) return fig ######################################### ######## SED FITTING TOOLS ######### ###\",\n \":param filterFolder: folder containing the filter transmission files (optional, default = splat.FILTER_FOLDER) :Example:\",\n \"= interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if verbose==True: print('\\\\nWarning:\",\n \"= vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength\",\n \"values ''' # make a copy and fill in combined magnitude out =\",\n \"filt: fc = checkFilterName(f) filt.remove(f) if fc == False: if verbose==True: print('Removed filter\",\n \"# add a comparison spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum)\",\n \"else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname = [fname] output =\",\n \"in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)]\",\n \"filt.remove(f) if fc == False: if verbose==True: print('Removed filter {}: not included in\",\n \"if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else: fwave = fwave*u.micron # check\",\n \"> 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] =\",\n \"= FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except:\",\n \"else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if\",\n \"the conversion is ignored (optional, default = erg/cm2/s) :param verbose: print out information\",\n \"magnitudes and choose the closest match based on various statistics ''' pass def\",\n \"for {}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if\",\n \"f0 rsr = FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans\",\n \"def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty\",\n \"f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor dm =\",\n \"astropy quantity in flux density units (default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000):\",\n \"s def __repr__(self): ''' :Purpose: A simple representation of the Spectrum object '''\",\n \"u.s * u.micron)) # interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.)\",\n \"outunit = u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c =\",\n \"False) WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE ''' # keyword parameters filterFolder\",\n \"fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl\",\n \"[] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave):\",\n \"of magnitudes to model magnitudes using an Amoeba (Nelder-Mead) wrapper, and choose the\",\n \"if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave =\",\n \"vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter\",\n \"copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty !=\",\n \"######## SED FITTING TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### #\",\n \"and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude)\",\n \"= numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission'] =\",\n \"print('\\\\nWarning: data values in range of filter {} have no uncertainties'.format(filt)) d =\",\n \"and transmissions for a custom filter **notch** = None: A 2 element array\",\n \"err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) else: raise\",\n \"in list(mags2.keys()) and f in list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f])\",\n \"filt = f0 # reset filter calculation methods based on filter design if\",\n \"import * from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this\",\n \"= numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for\",\n \"wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val =\",\n \"filters {}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit)\",\n \"profile or set of filter profiles, optionally on top of a spectrum WARNING:\",\n \"if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for\",\n \"== False and verbose == True: print('\\\\nFilter '+filt+' not currently available for SPLAT;\",\n \"return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into an energy, and\",\n \"# combine noises if self.uncertainty != 0 and other.uncertainty != 0: m1 =\",\n \"spectrum onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d\",\n \"if (photons or energy): vega = False ab = False if photons: energy\",\n \"**vegaFile** = 'vega_kurucz.txt': name of file containing Vega flux file, must be within\",\n \"**notch** = None: A 2 element array that specifies the lower and upper\",\n \"if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: spectrum for\",\n \"an interpolated value for a grid set of model magnitudes ''' pass def\",\n \"element array that specifies the lower and upper wavelengths for a notch filter\",\n \"= not vega if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in\",\n \"= None: A 2 element array that specifies the lower and upper wavelengths\",\n \"''' # check filter agreement if self.filter != other.filter: raise ValueError('magnitudes filters {}\",\n \"-> energy if kwargs.get('reverse',False) == False: if vega == True: # Read in\",\n \"val.to(return_unit) err.to(return_unit) # except: # print('\\\\nWarning: unit {} is not an energy flux\",\n \"grid set of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares\",\n \"u.s * u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux\",\n \"fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft\",\n \"COMMON ''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if\",\n \"= [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could\",\n \"KS: 2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H\",\n \"magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty self.type = magtype # check\",\n \"plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired file = kwargs.get('file','') file = kwargs.get('filename',file)\",\n \"= filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] =\",\n \"to {:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter {} not in SPLAT filter\",\n \"= sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d =\",\n \"vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim spectrum\",\n \"= numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val) # check\",\n \"energy = False if energy: photons = False # Read in filter if\",\n \"# check that requested filter is in list if isinstance(custom,bool) and isinstance(notch,bool): f0\",\n \"numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. # custom filter else: fwave,ftrans\",\n \"custom filter else: fwave,ftrans = custom[0],custom[1] # Read in Vega spectrum vwave,vflux =\",\n \"== False: if verbose==True: print('Removed filter {}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc)\",\n \"''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str):\",\n \"pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set of magnitudes using one\",\n \"val = m1+m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter !=\",\n \"transmission files (optional, default = splat.FILTER_FOLDER) :Example: >>> import splat >>> import splat.photometry\",\n \"is on the Vega system (optional, default = filter preference) :param units: units\",\n \"= numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave =\",\n \"ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw)\",\n \"else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave))\",\n \"filter (100% transmission within, 0% transmission without) **vega** = True: compute Vega magnitudes\",\n \"filter is in list f0 = checkFilterName(filt, verbose=True) if f0 == False: raise\",\n \"filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return\",\n \"wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range =\",\n \"base_unit = u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag =\",\n \"filt = [filt] # list of notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength\",\n \"isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish\",\n \"= numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val) # check\",\n \"sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra\",\n \"as plt from scipy.integrate import trapz # for numerical integration from scipy.interpolate import\",\n \"numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch\",\n \"vega = False ab = False if photons: energy = False if energy:\",\n \"kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag\",\n \"by filter) **ab** = False: compute AB magnitudes (may be set by filter)\",\n \"isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter is in list filt = checkFilterName(filt)\",\n \"return numpy.nan, numpy.nan filt = f0 rsr = FILTERS[filt]['rsr'] # Read in filter\",\n \"[] if (vega): # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True,\",\n \"on various statistics ''' pass def SEDFitMCMC(verbose=True): ''' this code will conduct a\",\n \"energy units # try: val.to(return_unit) err.to(return_unit) # except: # print('\\\\nWarning: unit {} is\",\n \"no overlap between spectrum for {} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if\",\n \"magnitudes using an MCMC wrapper, and choose best/average/distribution of parameters ''' pass def\",\n \"magtype # check filter and rename if necessary self.knownfilter = True fflag =\",\n \"constants from .initialize import * from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY ###############\",\n \":param mag: magnitude on whatever system is defined for the filter or provided\",\n \"by splat.FILTERS.keys() :type filter: required :param verbose: print out information about filter to\",\n \"system (optional, default = filter preference) :param vega: magnitude is on the Vega\",\n \"-> magnitude # THIS NEEDS TO BE COMPLETED else: print('passed') pass # check\",\n \"and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) >\",\n \">>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder\",\n \"val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err = 0.\",\n \"report values out if kwargs.get('verbose',False): print('\\\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot\",\n \"c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b\",\n \"return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux density of a\",\n \"(required) :param reverse: convert energy into magnitude instead (optional, default = False) :param\",\n \"from astropy import constants as const # physical constants in SI units import\",\n \"fwave = fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))]\",\n \"magnitude -> energy if kwargs.get('reverse',False) == False: if vega == True: # Read\",\n \"False: output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs)\",\n \"this filter is also convolved with a model of Vega to extract Vega\",\n \"### ######################################### # plan: def modelMagnitudes(verbose=True): ''' this will be a code that\",\n \"len(sp.wave[wgood]) == 0: err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that\",\n \"self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose: A\",\n \"an MCMC wrapper, and choose best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True): '''\",\n \"isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans\",\n \"if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: no overlap\",\n \"a filter profile or set of filter profiles, optionally on top of a\",\n \"flux and noise array elements. **filter**: String giving name of filter, which can\",\n \"not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile or\",\n \"filt = f0 # read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values\",\n \"Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to\",\n \"import units as u # standard units from astropy import constants as const\",\n \"b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for i\",\n \"= 1.066 to 1.442 micron >>> data = splat.filterProperties('2MASS X') Filter 2MASS X\",\n \"code that calculates a set of magnitudes for a model set's SED models\",\n \"vector array specifying the wavelengths and transmissions for a custom filter **notch** =\",\n \"as an astropy.units variable; if this conversion does not work, the conversion is\",\n \"# some things that are based on presets if self.knownfilter == True: self.wave,self.transmission\",\n \"default = filter preference) :param rsr: magnitude is on the Vega system (optional,\",\n \"imports - external import numpy from astropy import units as u # standard\",\n \"SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr =\",\n \"vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate Vega onto filter wavelength\",\n \"photometry\\\\n\\\\n') # val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err\",\n \"filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan)) #\",\n \"kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check\",\n \"= vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu\",\n \"for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\\\nfilterMag not given a correct physical\",\n \"numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw)\",\n \"isinstance(filt,list): # list of filter names if isinstance(filt[0],str): for f in filt: fc\",\n \"0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central']\",\n \"(numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate\",\n \"False ab = False if photons: energy = False if energy: photons =\",\n \"splat.filterProperties('2MASS X') Filter 2MASS X not among the available filters: 2MASS H: 2MASS\",\n \"a set of magnitudes to a grid of model magnitudes and choose the\",\n \"closest match ''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale (flux\",\n \"outunit = 1. elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit\",\n \"wave, flux and noise array elements. **filter**: String giving name of filter, which\",\n \"compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set of magnitudes using one of several\",\n \"of samples to use in Monte Carlo error estimation **info** = False: List\",\n \"parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE)\",\n \"print_function, division \\\"\\\"\\\" .. note:: These are the spectrophotometry functions for SPLAT \\\"\\\"\\\"\",\n \"< numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: spectrum for {} does\",\n \"SHORT: FOURSTAR H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder\",\n \"that requested filter is in list filt = checkFilterName(filt) if filt == False:\",\n \"filter names available **verbose** = True: List the predefined filter names available :Example:\",\n \"takes into account uncertainties :Output: A new Magnitude object equal to the diffence\",\n \"= trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] =\",\n \"output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min']\",\n \"EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True): ''' this will be a code\",\n \"output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile for a SPLAT filter.\",\n \"filter profile specified by the ``filter`` input. By default this filter is also\",\n \"String giving the name of one of the predefined filters listed in splat.FILTERS.keys()\",\n \"or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: spectrum for {} does not span\",\n \"print('\\\\nWarning: spectrum for {} does not span full filter profile for {}'.format(sp.name,filt)) #\",\n \"{:.3f} to {:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter {} not in SPLAT\",\n \"fname = [fname] output = {} for k in fname: f = checkFilterName(k)\",\n \"is in list filt = checkFilterName(filt) if filt == False: return numpy.nan, numpy.nan\",\n \"return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000):\",\n \"else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True:\",\n \"fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra)\",\n \"**custom** = None: A 2 x N vector array specifying the wavelengths and\",\n \":Purpose: A representation of subtraction for Magnitude classes that takes into account uncertainties\",\n \"wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n\",\n \"''' :Purpose: A representation of addition for magnitudes (addition of fluxes) :Output: A\",\n \"for k in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a copy of\",\n \"fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky\",\n \"out the current list of filters in the SPLAT reference library. ''' verbose\",\n \"filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is in list filt =\",\n \"fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty\",\n \"''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set of magnitudes using\",\n \"A representation of addition for Magnitude classes that takes into account uncertainties :Output:\",\n \"in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty #\",\n \"= numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if\",\n \"available **verbose** = True: List the predefined filter names available :Example: >>> import\",\n \"for a grid set of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this\",\n \"SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale (flux = lam x F_lam), with\",\n \"= fwave*u.micron # check that spectrum and filter cover the same wavelength ranges\",\n \"the input filters {}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit\",\n \"SPLAT reference library. ''' verbose = kwargs.get('verbose',True) if len(args) > 0: fname =\",\n \"output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if\",\n \"[fname] output = {} for k in fname: f = checkFilterName(k) if f\",\n \"will be a code that calculates a set of magnitudes for a model\",\n \"to model magnitudes using an Amoeba (Nelder-Mead) wrapper, and choose the closest match\",\n \"report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr']\",\n \"vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate Vega onto filter\",\n \"a class data structure for a magnitude value ''' def __init__(self, magnitude, filt,\",\n \"H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H long FOURSTAR H SHORT: FOURSTAR\",\n \"{} is not an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\\\nWarning: cannot\",\n \"0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-2.5*numpy.log10(1.+10.**(-0.4*(m2-m1))) out.uncertainty = numpy.nanstd(val)\",\n \"filter to screen (optional, default = False) WARNING: THIS CODE IS ONLY PARTIALLY\",\n \"interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact =\",\n \"0.323 micron Wavelength range = 1.066 to 1.442 micron >>> data = splat.filterProperties('2MASS\",\n \"functions and constants from .initialize import * from .utilities import * ##################################################### ###############\",\n \"variable; if this conversion does not work, the conversion is ignored (optional, default\",\n \"f0 == False: raise ValueError filt = f0 # read in filter fwave,ftrans\",\n \"FWHM = 0.323 micron Wavelength range = 1.066 to 1.442 micron >>> data\",\n \"uploaded pre-save some model magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated\",\n \"vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu =\",\n \"magnitude into an energy, and vice versa. :param mag: magnitude on whatever system\",\n \"# save if desired file = kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file)\",\n \"utf-8 -*- from __future__ import print_function, division \\\"\\\"\\\" .. note:: These are the\",\n \"verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty self.type = magtype # check filter\",\n \"def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing key parameters for a particular\",\n \"return output[kys[0]] else: return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing\",\n \"= [],[] for f in list(mags1.keys()): if f in list(mags2.keys()) and f in\",\n \"photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing the filter transmission files **vegaFile** =\",\n \"or energy flux. :Required Parameters: **sp**: Spectrum class object, which should contain wave,\",\n \"return numpy.nan, numpy.nan filt = f0 # reset filter calculation methods based on\",\n \"default this filter is also convolved with a model of Vega to extract\",\n \"# keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile\",\n \"= kwargs.get('vega',True) ab = not vega if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True)\",\n \"for energy as an astropy.units variable; if this conversion does not work, the\",\n \"= numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter)\",\n \"= FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs)\",\n \"Filter 2MASS X not among the available filters: 2MASS H: 2MASS H-band 2MASS\",\n \"desired energy units # try: val.to(return_unit) err.to(return_unit) # except: # print('\\\\nWarning: unit {}\",\n \"set's SED models saves to file that could be uploaded pre-save some model\",\n \"if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg\",\n \"FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans\",\n \"[filt] if isinstance(filt,list): # list of filter names if isinstance(filt[0],str): for f in\",\n \"numerical integration from scipy.interpolate import interp1d # splat functions and constants from .initialize\",\n \"= FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron\",\n \"its spectrum. Spectral fluxes are convolved with the filter profile specified by the\",\n \"[yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse input {}'.format(filt)) # add a\",\n \"photons: energy = False if energy: photons = False # Read in filter\",\n \"photons = False # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans =\",\n \"spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)]\",\n \"= (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\\\nmagToFlux\",\n \"if energy: photons = False # Read in filter if isinstance(custom,bool) and isinstance(notch,bool):\",\n \"magnitude self.uncertainty = uncertainty self.type = magtype # check filter and rename if\",\n \"n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples):\",\n \"model magnitudes and choose the closest match based on various statistics ''' pass\",\n \"files (optional, default = splat.FILTER_FOLDER) :Example: >>> import splat >>> import splat.photometry as\",\n \"numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit = u.erg/u.s/u.cm**2\",\n \"transmission curve. :param filter: String giving the name of one of the predefined\",\n \"interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the\",\n \"''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale (flux = lam\",\n \"about filter to screen (optional, default = False) WARNING: THIS CODE IS ONLY\",\n \"= numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) #\",\n \"notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] # Read in\",\n \"# e_mag.to(base_unit) # except: # raise ValueError('\\\\nInput quantity unit {} is not a\",\n \"choose the closest match based on various statistics ''' pass def SEDFitMCMC(verbose=True): '''\",\n \"ftrans[~numpy.isnan(ftrans)] result = [] err = 0. # magnitude -> energy if kwargs.get('reverse',False)\",\n \"f in list(mags2.keys()) and f in list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f])\",\n \"in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty #\",\n \"self.filter != other.filter: raise ValueError('magnitudes filters {} and {} are not the same'.format(self.filter,other.filter))\",\n \"isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig\",\n \"filt == False: return None report = {} report['name'] = filt report['description'] =\",\n \"# Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\\\ missing_values =\",\n \"catch for models else: if verbose==True: print('\\\\nWarning: data values in range of filter\",\n \"{}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation of addition for Magnitude classes that\",\n \"by the ``filter`` input. By default this filter is also convolved with a\",\n \"# convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral())\",\n \"else: err = 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\\\nmagToFlux needs vega or ab\",\n \"flux # try: # mag.to(base_unit) # e_mag.to(base_unit) # except: # raise ValueError('\\\\nInput quantity\",\n \"check that spectrum and filter cover the same wavelength ranges if numpy.nanmax(fwave) <\",\n \"filter list'.format(k)) kys = list(output.keys()) if len(kys) == 1: return output[kys[0]] else: return\",\n \"choose the closest match ''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED\",\n \"not work'.format(filt)) self.knownfilter = False else: filt = fflag self.filter = filt #\",\n \"{} does not span full filter profile for {}'.format(sp.name,filt)) # interpolate spectrum onto\",\n \"overlap between spectrum for {} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave)\",\n \"for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit =\",\n \"and upper wavelengths for a notch filter (100% transmission within, 0% transmission without)\",\n \"folder containing the filter transmission files (optional, default = splat.FILTER_FOLDER) :Example: >>> import\",\n \"rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit\",\n \"(numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\\\nmagToFlux needs\",\n \"# try: val.to(return_unit) err.to(return_unit) # except: # print('\\\\nWarning: unit {} is not an\",\n \"WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True): ''' this will\",\n \"legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve') # list of notch\",\n \"notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] # list of notch\",\n \"result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value\",\n \"a set of magnitudes for a model set's SED models saves to file\",\n \"fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value\",\n \"prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength\",\n \"among the available filters: 2MASS H: 2MASS H-band 2MASS J: 2MASS J-band 2MASS\",\n \"combine noises if self.uncertainty != 0 and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp)\",\n \"# list of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] #\",\n \"Amoeba (Nelder-Mead) wrapper, and choose the closest match ''' pass def SEDVisualize(verbose=True): '''\",\n \"trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.:\",\n \"out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0\",\n \"interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in\",\n \"List the predefined filter names available **verbose** = True: List the predefined filter\",\n \"convert result to an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err\",\n \"fc = checkFilterName(f) filt.remove(f) if fc == False: if verbose==True: print('Removed filter {}:\",\n \"= kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr\",\n \"filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is in list f0 =\",\n \"in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not ab if 'vega' in FILTERS[filt]['method']:\",\n \"= u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit\",\n \"unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\\\nWarning: cannot convert result to an energy flux\",\n \"SI units import matplotlib.patches as patches import matplotlib.pyplot as plt from scipy.integrate import\",\n \"set by filter) **energy** = False: compute energy flux **photon** = False: compute\",\n \"is in list f0 = checkFilterName(filt, verbose=True) if f0 == False: raise ValueError\",\n \"photometric magnitude of a source based on its spectrum. Spectral fluxes are convolved\",\n \"filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.)\",\n \"# check that requested filter is in list f0 = checkFilterName(filt, verbose=True) if\",\n \"of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation of addition for Magnitude classes\",\n \"= numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if\",\n \":param units: units for energy as an astropy.units variable; if this conversion does\",\n \"k if output == False and verbose == True: print('\\\\nFilter '+filt+' not currently\",\n \"for m in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code will\",\n \"BUGS MAY BE COMMON ''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single\",\n \"the spectrophotometry functions for SPLAT \\\"\\\"\\\" # imports - internal import copy import\",\n \"import interp1d # splat functions and constants from .initialize import * from .utilities\",\n \"try: val.to(return_unit) err.to(return_unit) # except: # print('\\\\nWarning: unit {} is not an energy\",\n \"THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON ''' filt =\",\n \"account uncertainties :Output: A new Magnitude object equal to the diffence of values\",\n \"return s def __repr__(self): ''' :Purpose: A simple representation of the Spectrum object\",\n \"= splat.filterProperties('2MASS X') Filter 2MASS X not among the available filters: 2MASS H:\",\n \"agreement if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): '''\",\n \"= numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2), offset\",\n \"trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.:\",\n \"magnitudes to model magnitudes using an MCMC wrapper, and choose best/average/distribution of parameters\",\n \"filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current list of filters in the SPLAT\",\n \"u.micron)) # interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr:\",\n \"and vice versa. :param mag: magnitude on whatever system is defined for the\",\n \"in list(mags1.keys()): if f in list(mags2.keys()) and f in list(unc.keys()) and f not\",\n \"things that are based on presets if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter)\",\n \"either be one of the predefined filters listed in splat.FILTERS.keys() or a custom\",\n \"(numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim\",\n \"''' chi = 0. dm,em = [],[] for f in list(mags1.keys()): if f\",\n \"a magnitude value ''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude =\",\n \"are based on presets if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info =\",\n \"ValueError('\\\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def\",\n \"method specified') # convert to desired energy units # try: val.to(return_unit) err.to(return_unit) #\",\n \"s.__dict__.update(self.__dict__) return s # backup version def copy(self): ''' :Purpose: Make a copy\",\n \"= False: List the predefined filter names available **verbose** = True: List the\",\n \"verbose=True) if f0 == False: raise ValueError filt = f0 # read in\",\n \"numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw)\",\n \"''' :Purpose: A representation of addition for Magnitude classes that takes into account\",\n \"elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave))\",\n \"to use in Monte Carlo error estimation **info** = False: List the predefined\",\n \"extract Vega magnitudes, but the user can also specify AB magnitudes, photon flux\",\n \"transmission without) **vega** = True: compute Vega magnitudes (may be set by filter)\",\n \"if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])]\",\n \"of magnitudes to model magnitudes using an MCMC wrapper, and choose best/average/distribution of\",\n \"''' :Purpose: Returns a dictionary containing key parameters for a particular filter. :param\",\n \"new Magnitude object equal to the sum of values ''' # make a\",\n \"or provided (required) :param filter: name of filter, must be one of the\",\n \"kwargs.get('ylabel','Transmission Curve') # list of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt =\",\n \"photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or energy):\",\n \"kwargs.get('output',file) if file != '': plt.savefig(file) return fig ######################################### ######## SED FITTING TOOLS\",\n \"!= 0 and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val\",\n \"in FILTERS[k]['altnames']: output = k if output == False and verbose == True:\",\n \"a source based on its spectrum. Spectral fluxes are convolved with the filter\",\n \"2MASS J: 2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR\",\n \"True: compute Vega magnitudes (may be set by filter) **ab** = False: compute\",\n \"os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is in list f0\",\n \"data = splat.filterProperties('2MASS X') Filter 2MASS X not among the available filters: 2MASS\",\n \"magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation of addition for Magnitude\",\n \"not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron\",\n \"### WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True): ''' this\",\n \"kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or\",\n \"parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt')\",\n \"plt from scipy.integrate import trapz # for numerical integration from scipy.interpolate import interp1d\",\n \"H-band FOURSTAR H LONG: FOURSTAR H long FOURSTAR H SHORT: FOURSTAR H short\",\n \"(notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] =\",\n \"diffence of values ''' # make a copy and fill in combined magnitude\",\n \"are not the same'.format(self.filter,other.filter)) # make a copy and fill in combined magnitude\",\n \"of one of the predefined filters listed in splat.FILTERS.keys() (required) :param filterFolder: folder\",\n \"file containing Vega flux file, must be within ``filterFolder`` **nsamples** = 100: number\",\n \"f in list(mags1.keys()): if f in list(mags2.keys()) and f in list(unc.keys()) and f\",\n \"kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False)\",\n \":Output: astropy quantity in flux density units (default = erg/cm2/s/micron) ''' pass def\",\n \"new Magnitude object equal to the diffence of values ''' # make a\",\n \"def __add__(self,other,samp=1000): ''' :Purpose: A representation of addition for Magnitude classes that takes\",\n \"k in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a copy of a\",\n \"result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) else:\",\n \"= splat.FILTER_FOLDER: folder containing the filter transmission files **vegaFile** = 'vega_kurucz.txt': name of\",\n \"xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] ==\",\n \":Purpose: Determine the photometric magnitude of a source based on its spectrum. Spectral\",\n \"ab method specified') # convert to desired energy units # try: val.to(return_unit) err.to(return_unit)\",\n \"filt = checkFilterName(filt) if filt == False: return numpy.nan, numpy.nan # reset filter\",\n \"numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. # custom\",\n \"<= notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] # Read\",\n \"filter is in list filt = checkFilterName(filt) if filt == False: return numpy.nan,\",\n \"else: print('passed') pass # check that input is an energy flux # try:\",\n \"def __copy__(self): ''' :Purpose: Make a copy of a Magnitude object ''' s\",\n \"filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b\",\n \"= True: List the predefined filter names available :Example: >>> import splat >>>\",\n \"ValueError filt = f0 # read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True,\",\n \"{} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave)\",\n \"True: print('filter {} is not a standard filter; some functions may not work'.format(filt))\",\n \"filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave):\",\n \"unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux =\",\n \"else: fwave,ftrans = custom[0],custom[1] # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#',\",\n \"else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val =\",\n \"filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission\",\n \"Returns two arrays: the filter wavelength and filter transmission curve. :param filter: String\",\n \"filter is also convolved with a model of Vega to extract Vega magnitudes,\",\n \"noises if self.uncertainty != 0 and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2\",\n \"* u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux =\",\n \"if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans)\",\n \"of filter profiles, optionally on top of a spectrum WARNING: THIS CODE IS\",\n \"trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else:\",\n \"custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega)\",\n \"filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr']\",\n \"<filename>splat/photometry.py # -*- coding: utf-8 -*- from __future__ import print_function, division \\\"\\\"\\\" ..\",\n \"f0 == False: return numpy.nan, numpy.nan filt = f0 # reset filter calculation\",\n \"function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))\",\n \"'vega_kurucz.txt': name of file containing Vega flux file, must be within ``filterFolder`` **nsamples**\",\n \"compare a set of magnitudes to a grid of model magnitudes and choose\",\n \"requested filter is in list filt = checkFilterName(filt) if filt == False: return\",\n \"magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises\",\n \"CODE IS ONLY PARTIALLY COMPLETE ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if\",\n \"J-band Zeropoint = 1594.0 Jy Pivot point: = 1.252 micron FWHM = 0.323\",\n \"magnitudes (may be set by filter) **ab** = False: compute AB magnitudes (may\",\n \"not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file']))\",\n \"not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor dm = numpy.array(dm)\",\n \"fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value\",\n \"plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired file = kwargs.get('file','') file\",\n \"val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave)))\",\n \"if len(args) > 0: fname = list(args) elif kwargs.get('filter',False) != False: fname =\",\n \"= FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave\",\n \"m in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code will compare\",\n \"and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn\",\n \"filter else: fwave,ftrans = custom[0],custom[1] # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile),\",\n \"versa. :param mag: magnitude on whatever system is defined for the filter or\",\n \"filters listed in splat.FILTERS.keys() or a custom filter name :Optional Parameters: **custom** =\",\n \"= {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): '''\",\n \"or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans =\",\n \"FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave =\",\n \"for a custom filter **notch** = None: A 2 element array that specifies\",\n \"= sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst =\",\n \"THIS NEEDS TO BE COMPLETED else: print('passed') pass # check that input is\",\n \"is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile\",\n \"raise ValueError('Did not recognize any of the input filters {}'.format(filters)) # prep parameters\",\n \"vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave =\",\n \"fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))]\",\n \"(notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] =\",\n \"if file != '': plt.savefig(file) return fig ######################################### ######## SED FITTING TOOLS #########\",\n \"''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version def copy(self): '''\",\n \"magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated value for a grid\",\n \"!= False: fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False:\",\n \"kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True)\",\n \"kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch =\",\n \"list if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 == False: return\",\n \"= sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname = [fname] output = {} for\",\n \"in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values\",\n \"= SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch =\",\n \"of Vega to extract Vega magnitudes, but the user can also specify AB\",\n \"''' :Purpose: Converts a magnitude into an energy, and vice versa. :param mag:\",\n \"= kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that requested filter\",\n \"# check filter agreement if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out\",\n \"if self.filter != other.filter: raise ValueError('magnitudes filters {} and {} are not the\",\n \"Vega system (optional, default = filter preference) :param rsr: magnitude is on the\",\n \"may not work'.format(filt)) self.knownfilter = False else: filt = fflag self.filter = filt\",\n \"= filter preference) :param vega: magnitude is on the Vega system (optional, default\",\n \"of magnitudes for a model set's SED models saves to file that could\",\n \"is on the AB system (optional, default = filter preference) :param vega: magnitude\",\n \"= [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans)\",\n \":param vega: magnitude is on the Vega system (optional, default = filter preference)\",\n \"to an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err # energy\",\n \"''' :Purpose: Determine the photometric magnitude of a source based on its spectrum.\",\n \"visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile or set of filter profiles, optionally\",\n \"option of also comparing to SED spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS\",\n \"-2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif\",\n \"presets if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for k\",\n \"this will be a code that calculates a set of magnitudes for a\",\n \"numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: spectrum for {}\",\n \"information about filter to screen :type verbose: optional, default = True :Example: >>>\",\n \"kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that requested filter is\",\n \"This is a class data structure for a magnitude value ''' def __init__(self,\",\n \"(optional, default = erg/cm2/s) :param verbose: print out information about filter to screen\",\n \"of notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve')\",\n \"= 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact =\",\n \"a copy of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s\",\n \"predefined filters listed in splat.FILTERS.keys() or a custom filter name :Optional Parameters: **custom**\",\n \"-2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current list of filters in\",\n \"or set of filter profiles, optionally on top of a spectrum WARNING: THIS\",\n \"[yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve') # list of\",\n \"numpy.nan filt = f0 # reset filter calculation methods based on filter design\",\n \"= -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1.\",\n \"internal import copy import os # imports - external import numpy from astropy\",\n \"# Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\\\ missing_values =\",\n \"= 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is in\",\n \"cover the same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave):\",\n \"if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 *\",\n \"-*- coding: utf-8 -*- from __future__ import print_function, division \\\"\\\"\\\" .. note:: These\",\n \"print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range\",\n \"Magnitude object equal to the sum of values ''' # make a copy\",\n \"filter: required :param verbose: print out information about filter to screen :type verbose:\",\n \"from scipy.integrate import trapz # for numerical integration from scipy.interpolate import interp1d #\",\n \"result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val\",\n \"m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val) # check filter agreement\",\n \"fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine\",\n \"numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans # report values\",\n \"n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): # Read in Vega spectrum\",\n \"* u.micron)) # interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if\",\n \"= f0 rsr = FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool) and isinstance(notch,bool):\",\n \"u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2\",\n \"and choose the closest match ''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on\",\n \"to the equivalent sum of fluxes ''' # check filter agreement if self.filter\",\n \"magnitudes to model magnitudes using an Amoeba (Nelder-Mead) wrapper, and choose the closest\",\n \"= numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint =\",\n \"default = erg/cm2/s) :param verbose: print out information about filter to screen (optional,\",\n \"u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag\",\n \"an energy, and vice versa. :param mag: magnitude on whatever system is defined\",\n \"u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)]\",\n \"is on the Vega system (optional, default = filter preference) :param rsr: magnitude\",\n \"ab = not vega if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read\",\n \"if verbose==True: print('\\\\nWarning: no overlap between spectrum for {} and filter {}'.format(sp.name,filt)) return\",\n \"= filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave\",\n \"vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate\",\n \"== False: raise ValueError filt = f0 # read in filter fwave,ftrans =\",\n \"for a notch filter (100% transmission within, 0% transmission without) **vega** = True:\",\n \"for a magnitude value ''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude\",\n \"= FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy =\",\n \"in the SPLAT reference library. ''' verbose = kwargs.get('verbose',True) if len(args) > 0:\",\n \"m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val) #\",\n \"= checkFilterName(filt) if filt == False: return None report = {} report['name'] =\",\n \"to 1.442 micron >>> data = splat.filterProperties('2MASS X') Filter 2MASS X not among\",\n \"Monte Carlo error estimation **info** = False: List the predefined filter names available\",\n \"set by filter) **ab** = False: compute AB magnitudes (may be set by\",\n \"add a comparison spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) ==\",\n \"check filter and rename if necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if\",\n \"kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is\",\n \"info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a copy of a Magnitude object\",\n \"set of magnitudes to model magnitudes using an Amoeba (Nelder-Mead) wrapper, and choose\",\n \"numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m\",\n \"a set of magnitudes to model magnitudes using an MCMC wrapper, and choose\",\n \"(numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) elif ab == True:\",\n \"astropy import constants as const # physical constants in SI units import matplotlib.patches\",\n \"type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version def copy(self): ''' :Purpose: Make a\",\n \"convert energy into magnitude instead (optional, default = False) :param ab: magnitude is\",\n \"None report = {} report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint']\",\n \"splat >>> import splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5)\",\n \"(numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) #\",\n \"filter cover the same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) >\",\n \"{} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = []\",\n \"ignored (optional, default = erg/cm2/s) :param verbose: print out information about filter to\",\n \"Converts a magnitude into an energy, and vice versa. :param mag: magnitude on\",\n \"SED FITTING TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### # plan:\",\n \"os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False)\",\n \"object ''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty)\",\n \"0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] =\",\n \"except: print('\\\\nWarning: cannot convert result to an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan\",\n \"classes that takes into account uncertainties :Output: A new Magnitude object equal to\",\n \"self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and other.uncertainty\",\n \"from scipy.interpolate import interp1d # splat functions and constants from .initialize import *\",\n \"= fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b =\",\n \"wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning:\",\n \"''' :Purpose: Prints out the current list of filters in the SPLAT reference\",\n \"representation of addition for Magnitude classes that takes into account uncertainties :Output: A\",\n \"filt == False: return numpy.nan, numpy.nan # reset filter calculation methods based on\",\n \"result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr: a =\",\n \"= (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) elif ab ==\",\n \"saves to file that could be uploaded pre-save some model magnitudes ''' pass\",\n \"convolved with the filter profile specified by the ``filter`` input. By default this\",\n \"numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: spectrum for {} does not span full\",\n \"> numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: spectrum for {} does not span full filter\",\n \"filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega)\",\n \"len(filt) == 0: raise ValueError('Did not recognize any of the input filters {}'.format(filters))\",\n \"setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a copy of a Magnitude object '''\",\n \"= plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) ==\",\n \"filter transmission files **vegaFile** = 'vega_kurucz.txt': name of file containing Vega flux file,\",\n \"if isinstance(filt[0],str): for f in filt: fc = checkFilterName(f) filt.remove(f) if fc ==\",\n \"vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that\",\n \"= erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition for\",\n \"# report values out if kwargs.get('verbose',False): print('\\\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint']))\",\n \"s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose: A simple representation of the Spectrum\",\n \"in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif\",\n \"J: 2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR H:\",\n \"xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse\",\n \"comparison spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra)\",\n \"array specifying the wavelengths and transmissions for a custom filter **notch** = None:\",\n \"in SI units import matplotlib.patches as patches import matplotlib.pyplot as plt from scipy.integrate\",\n \"is ignored (optional, default = erg/cm2/s) :param verbose: print out information about filter\",\n \"-2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr:\",\n \"or a custom filter name :Optional Parameters: **custom** = None: A 2 x\",\n \"available filters: 2MASS H: 2MASS H-band 2MASS J: 2MASS J-band 2MASS KS: 2MASS\",\n \"for k in fname: f = checkFilterName(k) if f != False: output[f] =\",\n \"of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self):\",\n \"kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not\",\n \"= {} report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] =\",\n \"fwave = fwave*u.micron # check that spectrum and filter cover the same wavelength\",\n \"filter {}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0: raise\",\n \"units import matplotlib.patches as patches import matplotlib.pyplot as plt from scipy.integrate import trapz\",\n \">>> data = splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band Zeropoint = 1594.0\",\n \"function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value)\",\n \"interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val =\",\n \"set of magnitudes to model magnitudes using an MCMC wrapper, and choose best/average/distribution\",\n \"''' :Purpose: A simple representation of the Spectrum object ''' if self.uncertainty !=\",\n \"!= 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-2.5*numpy.log10(1.+10.**(-0.4*(m2-m1))) out.uncertainty =\",\n \"one of the predefined filters listed in splat.FILTERS.keys() (required) :param filterFolder: folder containing\",\n \"f in filt: fc = checkFilterName(f) filt.remove(f) if fc == False: if verbose==True:\",\n \"numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {}\",\n \"output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave =\",\n \"0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw)\",\n \"vega = kwargs.get('vega',True) ab = not vega if 'rsr' in FILTERS[filt]['method']: rsr =\",\n \"fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra)\",\n \"THESE ARE EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True): ''' this will be\",\n \"isinstance(ftrans,numpy.ndarray): raise ValueError('\\\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return\",\n \"# read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values\",\n \"which can either be one of the predefined filters listed in splat.FILTERS.keys() or\",\n \"WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON ''' filt\",\n \"checkFilterName(f) filt.remove(f) if fc == False: if verbose==True: print('Removed filter {}: not included\",\n \"u.s) elif ab == True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s\",\n \"= kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch\",\n \"verbose== True: print('filter {} is not a standard filter; some functions may not\",\n \"J-band 2MASS KS: 2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR H-band\",\n \"import matplotlib.pyplot as plt from scipy.integrate import trapz # for numerical integration from\",\n \"fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans\",\n \"for magnitudes (addition of fluxes) :Output: A new magnitude object equal to the\",\n \"numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: no overlap between spectrum for\",\n \"return None report = {} report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] =\",\n \"top of a spectrum WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY\",\n \"based on filter design if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega =\",\n \"custom[0],custom[1] # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\\\ missing_values\",\n \"vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave))\",\n \"be one of the specifed filters given by splat.FILTERS.keys() (required) :param reverse: convert\",\n \"f==k.upper() or f.lower() in FILTERS[k]['altnames']: output = k if output == False and\",\n \"magnitude is on the AB system (optional, default = filter preference) :param vega:\",\n \"an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err # energy ->\",\n \"self.type = magtype # check filter and rename if necessary self.knownfilter = True\",\n \"which should contain wave, flux and noise array elements. **filter**: String giving name\",\n \"available :Example: >>> import splat >>> import splat.photometry as spphot >>> sp =\",\n \"isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt =\",\n \"= kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples\",\n \"False: return None report = {} report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint']\",\n \"and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2\",\n \"other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-2.5*numpy.log10(1.+10.**(-0.4*(m2-m1))) out.uncertainty\",\n \"filter and rename if necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if fflag\",\n \"SPECTROPHOTOMETRY ############### ##################################################### # this function has been obseleted def checkFilter(filt,verbose=True): output =\",\n \"a magnitude into an energy, and vice versa. :param mag: magnitude on whatever\",\n \"one of the predefined filters listed in splat.FILTERS.keys() or a custom filter name\",\n \"list f0 = checkFilterName(filt, verbose=True) if f0 == False: raise ValueError filt =\",\n \"vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength function\",\n \"whatever system is defined for the filter or provided (required) :param filter: name\",\n \"f0 = checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt = f0\",\n \"vice versa. :param mag: magnitude on whatever system is defined for the filter\",\n \"matplotlib.patches as patches import matplotlib.pyplot as plt from scipy.integrate import trapz # for\",\n \"other possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy =\",\n \"and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor dm\",\n \"an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\\\nWarning: cannot convert result to\",\n \"some things that are based on presets if self.knownfilter == True: self.wave,self.transmission =\",\n \"> 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] #\",\n \"FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw\",\n \"numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans\",\n \"filters given by splat.FILTERS.keys() (required) :param reverse: convert energy into magnitude instead (optional,\",\n \"def SEDFitAmoeba(verbose=True): ''' this code will conduct a comparison of a set of\",\n \"u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if\",\n \"if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose:\",\n \"filt = [filt] if isinstance(filt,list): # list of filter names if isinstance(filt[0],str): for\",\n \"numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s)\",\n \"check that requested filter is in list if isinstance(custom,bool) and isinstance(notch,bool): f0 =\",\n \"name of one of the predefined filters listed in splat.FILTERS.keys() (required) :param filterFolder:\",\n \".utilities import * ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### # this function has been\",\n \"NEEDS TO BE COMPLETED else: print('passed') pass # check that input is an\",\n \"= kwargs.get('comparison',sp) if isinstance(sp,splat.core.Spectrum) == True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra =\",\n \":Output: A new magnitude object equal to the equivalent sum of fluxes '''\",\n \"= False f = copy.deepcopy(filt) f = f.replace(' ','_').upper() for k in list(FILTERS.keys()):\",\n \"default = False) :param ab: magnitude is on the AB system (optional, default\",\n \"short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER #\",\n \"if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples):\",\n \"isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] # list of notch ranges if isinstance(filt[0],list):\",\n \"= ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])]\",\n \"= vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega\",\n \"numpy.nan, numpy.nan filt = f0 rsr = FILTERS[filt]['rsr'] # Read in filter if\",\n \"return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing key parameters for\",\n \"######################################### # plan: def modelMagnitudes(verbose=True): ''' this will be a code that calculates\",\n \"fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def\",\n \"kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname = [fname] output\",\n \"filter, must be one of the specifed filters given by splat.FILTERS.keys() :type filter:\",\n \"on the Vega system (optional, default = filter preference) :param rsr: magnitude is\",\n \"ONLY PARTIALLY COMPLETE ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder):\",\n \"False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)]\",\n \"erg/cm2/s) :param verbose: print out information about filter to screen (optional, default =\",\n \"or f.lower() in FILTERS[k]['altnames']: output = k if output == False and verbose\",\n \"len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description'])\",\n \"current list of filters in the SPLAT reference library. ''' verbose = kwargs.get('verbose',True)\",\n \"-2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit\",\n \"comparison of a set of magnitudes to model magnitudes using an MCMC wrapper,\",\n \"the predefined filter names available :Example: >>> import splat >>> import splat.photometry as\",\n \"representation of addition for magnitudes (addition of fluxes) :Output: A new magnitude object\",\n \"of filter, must be one of the specifed filters given by splat.FILTERS.keys() :type\",\n \"# convert to desired energy units # try: val.to(return_unit) err.to(return_unit) # except: #\",\n \"fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra =\",\n \"to screen :type verbose: optional, default = True :Example: >>> import splat >>>\",\n \"elements. **filter**: String giving name of filter, which can either be one of\",\n \"FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) #\",\n \"if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom\",\n \"__add__(self,other,samp=1000): ''' :Purpose: A representation of addition for Magnitude classes that takes into\",\n \"os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is in list filt\",\n \"return numpy.nan, numpy.nan # reset filter calculation methods based on filter design if\",\n \"print('\\\\nWarning: unit {} is not an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except:\",\n \"numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: spectrum for {} does not\",\n \"numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): #\",\n \"def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current list of filters in the\",\n \"= {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f}\",\n \"2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG:\",\n \"uncertainties :Output: A new Magnitude object equal to the diffence of values '''\",\n \"that are based on presets if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info\",\n \"AB magnitudes (may be set by filter) **energy** = False: compute energy flux\",\n \"return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current list of filters\",\n \"for a particular filter. :param filter: name of filter, must be one of\",\n \"filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans >\",\n \"v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val\",\n \"A new Magnitude object equal to the sum of values ''' # make\",\n \"custom filter **notch** = None: A 2 element array that specifies the lower\",\n \"A new Magnitude object equal to the diffence of values ''' # make\",\n \"reference library. ''' verbose = kwargs.get('verbose',True) if len(args) > 0: fname = list(args)\",\n \"= ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw))\",\n \"False: if verbose== True: print('filter {} is not a standard filter; some functions\",\n \"one of the specifed filters given by splat.FILTERS.keys() :type filter: required :param verbose:\",\n \">= notch[0],fwave <= notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1]\",\n \"numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code will compare a set of magnitudes\",\n \"imports - internal import copy import os # imports - external import numpy\",\n \"listed in splat.FILTERS.keys() (required) :param filterFolder: folder containing the filter transmission files (optional,\",\n \"def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux density of a magnitude :Output:\",\n \"filter is in list filt = checkFilterName(filt) if filt == False: return None\",\n \"True: print('\\\\nFilter '+filt+' not currently available for SPLAT; contact '+EMAIL+'\\\\n') filterInfo() return output\",\n \"= 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val =\",\n \"= f0 # reset filter calculation methods based on filter design if 'ab'\",\n \"< numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: no overlap between spectrum\",\n \"BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H\",\n \"is in list if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 ==\",\n \"= type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose: A simple representation of\",\n \"filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans =\",\n \"self.uncertainty != 0 and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp)\",\n \">>> import splat >>> import splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>>\",\n \"def copy(self): ''' :Purpose: Make a copy of a Magnitude object ''' s\",\n \"filter preference) :param vega: magnitude is on the Vega system (optional, default =\",\n \"def __sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction for Magnitude classes that takes\",\n \"ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. # custom filter\",\n \"DEVELOPMENT, BUGS MAY BE COMMON ''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) #\",\n \"file = kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file) if file != '':\",\n \"version def copy(self): ''' :Purpose: Make a copy of a Magnitude object '''\",\n \"verbose = kwargs.get('verbose',True) if len(args) > 0: fname = list(args) elif kwargs.get('filter',False) !=\",\n \"external import numpy from astropy import units as u # standard units from\",\n \"try: val.to(base_unit) err.to(base_unit) except: print('\\\\nWarning: cannot convert result to an energy flux unit'.format(base_unit))\",\n \"MAY BE COMMON ''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter\",\n \"numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2), offset def\",\n \"Vega to extract Vega magnitudes, but the user can also specify AB magnitudes,\",\n \"= kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = []\",\n \"in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): '''\",\n \"[] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if\",\n \"dm,em = [],[] for f in list(mags1.keys()): if f in list(mags2.keys()) and f\",\n \"= interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i\",\n \"self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report\",\n \"not vega rsr = FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False) photons =\",\n \"check that input is an energy flux # try: # mag.to(base_unit) # e_mag.to(base_unit)\",\n \"0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is in list\",\n \"filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested\",\n \"splat.FILTER_FOLDER: folder containing the filter transmission files **vegaFile** = 'vega_kurucz.txt': name of file\",\n \"filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude of a source based on its\",\n \"range = {:.3f} to {:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter {} not\",\n \"is a class data structure for a magnitude value ''' def __init__(self, magnitude,\",\n \"checkFilterName(filt,verbose=True) if f0 == False: return numpy.nan, numpy.nan filt = f0 rsr =\",\n \"a magnitude :Output: astropy quantity in flux density units (default = erg/cm2/s/micron) '''\",\n \"point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else:\",\n \"that spectrum and filter cover the same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave)\",\n \"if kwargs.get('reverse',False) == False: if vega == True: # Read in Vega spectrum\",\n \"Vega magnitudes, but the user can also specify AB magnitudes, photon flux or\",\n \"onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else:\",\n \"print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\\\nProblem\",\n \"Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans =\",\n \"0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val)\",\n \"= k if output == False and verbose == True: print('\\\\nFilter '+filt+' not\",\n \"the predefined filter names available **verbose** = True: List the predefined filter names\",\n \":param rsr: magnitude is on the Vega system (optional, default = filter preference)\",\n \"Vega flux file, must be within ``filterFolder`` **nsamples** = 100: number of samples\",\n \"= kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that\",\n \"models saves to file that could be uploaded pre-save some model magnitudes '''\",\n \"statistics ''' pass def SEDFitMCMC(verbose=True): ''' this code will conduct a comparison of\",\n \"for a SPLAT filter. Returns two arrays: the filter wavelength and filter transmission\",\n \"- internal import copy import os # imports - external import numpy from\",\n \"def interpolateMagnitudes(verbose=True): ''' produces an interpolated value for a grid set of model\",\n \"SEDFitAmoeba(verbose=True): ''' this code will conduct a comparison of a set of magnitudes\",\n \"notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend\",\n \"self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and other.uncertainty\",\n \"* c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val\",\n \"splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band Zeropoint = 1594.0 Jy Pivot point:\",\n \"an Amoeba (Nelder-Mead) wrapper, and choose the closest match ''' pass def SEDVisualize(verbose=True):\",\n \"!= other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose: A representation\",\n \"fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value\",\n \"custom filter name :Optional Parameters: **custom** = None: A 2 x N vector\",\n \"with the filter profile specified by the ``filter`` input. By default this filter\",\n \"fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value)\",\n \"Ks-band BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR\",\n \"######################################### ######## SED FITTING TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!! ### #########################################\",\n \"are convolved with the filter profile specified by the ``filter`` input. By default\",\n \"kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that requested filter is in list if\",\n \"in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 *\",\n \"output == False and verbose == True: print('\\\\nFilter '+filt+' not currently available for\",\n \"energy): vega = False ab = False if photons: energy = False if\",\n \"# except: # raise ValueError('\\\\nInput quantity unit {} is not a flux unit'.format(mag.unit))\",\n \"is defined for the filter or provided (required) :param filter: name of filter,\",\n \"0: err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter\",\n \"= filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a\",\n \"############### ##################################################### class Magnitude(object): ''' :Description: This is a class data structure for\",\n \"filter. Returns two arrays: the filter wavelength and filter transmission curve. :param filter:\",\n \"flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile or set of\",\n \"i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1.\",\n \"kwargs.get('filter',False) != False: fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) ==\",\n \"mag.to(base_unit) # e_mag.to(base_unit) # except: # raise ValueError('\\\\nInput quantity unit {} is not\",\n \"i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit\",\n \"= 0. dm,em = [],[] for f in list(mags1.keys()): if f in list(mags2.keys())\",\n \"= fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans >\",\n \"else: filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did not recognize any of the\",\n \"= [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse input {}'.format(filt)) # add\",\n \"uncertainty self.type = magtype # check filter and rename if necessary self.knownfilter =\",\n \"if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega rsr =\",\n \"SPLAT; contact '+EMAIL+'\\\\n') filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter\",\n \"= trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for i in\",\n \"equivalent sum of fluxes ''' # check filter agreement if self.filter != other.filter:\",\n \"work'.format(filt)) self.knownfilter = False else: filt = fflag self.filter = filt # some\",\n \"defined for the filter or provided (required) :param filter: name of filter, must\",\n \"2MASS H: 2MASS H-band 2MASS J: 2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL\",\n \"plt.ylabel(yl) plt.legend(legend) # save if desired file = kwargs.get('file','') file = kwargs.get('filename',file) file\",\n \"filterProfile(self.filter) info = filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose:\",\n \"= f.replace(' ','_').upper() for k in list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']:\",\n \"print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05)\",\n \":Description: This is a class data structure for a magnitude value ''' def\",\n \"by splat.FILTERS.keys() (required) :param reverse: convert energy into magnitude instead (optional, default =\",\n \"ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False)\",\n \"magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty self.type =\",\n \"False else: filt = fflag self.filter = filt # some things that are\",\n \"class Magnitude(object): ''' :Description: This is a class data structure for a magnitude\",\n \"class object, which should contain wave, flux and noise array elements. **filter**: String\",\n \"= not vega rsr = FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False) photons\",\n \"code will compare a set of magnitudes to a grid of model magnitudes\",\n \"FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H long FOURSTAR H SHORT: FOURSTAR H\",\n \"kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra\",\n \"result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu =\",\n \"= checkFilterName(filt,verbose=verbose) if fflag == False: if verbose== True: print('filter {} is not\",\n \"of filter, which can either be one of the predefined filters listed in\",\n \"# calculate uncertainty if e_mag.value > 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err\",\n \"Determine the photometric magnitude of a source based on its spectrum. Spectral fluxes\",\n \"folder containing the filter transmission files **vegaFile** = 'vega_kurucz.txt': name of file containing\",\n \"By default this filter is also convolved with a model of Vega to\",\n \"profile for a SPLAT filter. Returns two arrays: the filter wavelength and filter\",\n \"# Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch\",\n \"print('Removed filter {}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0:\",\n \"fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d\",\n \"micron Wavelength range = 1.066 to 1.442 micron >>> data = splat.filterProperties('2MASS X')\",\n \"= kwargs.get('ylabel','Transmission Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt):\",\n \"combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine\",\n \"FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs)\",\n \"AB system (optional, default = filter preference) :param vega: magnitude is on the\",\n \"# physical constants in SI units import matplotlib.patches as patches import matplotlib.pyplot as\",\n \"the lower and upper wavelengths for a notch filter (100% transmission within, 0%\",\n \"= numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter)\",\n \"and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty =\",\n \"e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check\",\n \"############### ##################################################### # this function has been obseleted def checkFilter(filt,verbose=True): output = False\",\n \"err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) elif ab\",\n \"in list f0 = checkFilterName(filt, verbose=True) if f0 == False: raise ValueError filt\",\n \"{:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter\",\n \"density of a magnitude :Output: astropy quantity in flux density units (default =\",\n \"False: return numpy.nan, numpy.nan filt = f0 rsr = FILTERS[filt]['rsr'] # Read in\",\n \"= trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] =\",\n \"1. # custom filter else: fwave,ftrans = custom[0],custom[1] # Read in Vega spectrum\",\n \"values out if kwargs.get('verbose',False): print('\\\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point:\",\n \"em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m in\",\n \"not a standard filter; some functions may not work'.format(filt)) self.knownfilter = False else:\",\n \"''' this will be a code that calculates a set of magnitudes for\",\n \"# backup version def copy(self): ''' :Purpose: Make a copy of a Magnitude\",\n \"val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit =\",\n \"def addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition for magnitudes (addition of fluxes)\",\n \"spectrum WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON '''\",\n \"ftrans # report values out if kwargs.get('verbose',False): print('\\\\nFilter '+filt+': '+report['description']) print('Zeropoint = {}\",\n \"= vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu\",\n \"''' pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated value for a grid set\",\n \"10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.: for i in\",\n \"c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit\",\n \"predefined filters listed in splat.FILTERS.keys() (required) :param filterFolder: folder containing the filter transmission\",\n \"energy as an astropy.units variable; if this conversion does not work, the conversion\",\n \"kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag\",\n \"long FOURSTAR H SHORT: FOURSTAR H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if\",\n \"flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\\\nWarning: cannot convert result to an energy\",\n \"verbose==True: print('Removed filter {}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) ==\",\n \"the Vega system (optional, default = filter preference) :param units: units for energy\",\n \"{} are not the same'.format(self.filter,other.filter)) # make a copy and fill in combined\",\n \"legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse input {}'.format(filt)) # add a comparison\",\n \"the photometric magnitude of a source based on its spectrum. Spectral fluxes are\",\n \"the sum of values ''' # make a copy and fill in combined\",\n \"or energy): vega = False ab = False if photons: energy = False\",\n \"output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try:\",\n \"= fwave.to(u.micron) else: fwave = fwave*u.micron # check that spectrum and filter cover\",\n \"report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave']\",\n \"= vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert\",\n \"m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-2.5*numpy.log10(1.+10.**(-0.4*(m2-m1))) out.uncertainty = numpy.nanstd(val) return\",\n \"vega or ab method specified') # convert to desired energy units # try:\",\n \"other.filter: raise ValueError('magnitudes filters {} and {} are not the same'.format(self.filter,other.filter)) # make\",\n \"const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\\\nfilterMag\",\n \"note:: These are the spectrophotometry functions for SPLAT \\\"\\\"\\\" # imports - internal\",\n \"FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not ab if 'vega' in FILTERS[filt]['method']: vega\",\n \"print('\\\\nWarning: cannot convert result to an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return\",\n \"offset def SEDFitGrid(verbose=True): ''' this code will compare a set of magnitudes to\",\n \"copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty !=\",\n \"sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder =\",\n \"interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if verbose==True: print('\\\\nWarning: data\",\n \"for the filter or provided (required) :param filter: name of filter, must be\",\n \"names if isinstance(filt[0],str): for f in filt: fc = checkFilterName(f) filt.remove(f) if fc\",\n \"= (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact\",\n \"# custom filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if\",\n \"1. elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b =\",\n \"= SPLAT_URL+FILTER_FOLDER # check that requested filter is in list filt = checkFilterName(filt)\",\n \"interpolated value for a grid set of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True):\",\n \"new magnitude object equal to the equivalent sum of fluxes ''' # check\",\n \"out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if\",\n \">>> import splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>>\",\n \"containing the filter transmission files (optional, default = splat.FILTER_FOLDER) :Example: >>> import splat\",\n \"not an energy flux unit'.format(return_unit)) try: val.to(base_unit) err.to(base_unit) except: print('\\\\nWarning: cannot convert result\",\n \"= kwargs.get('ylabel','Transmission Curve') # list of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt\",\n \"to screen (optional, default = False) WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE\",\n \"= 1594.0 Jy Pivot point: = 1.252 micron FWHM = 0.323 micron Wavelength\",\n \"data = splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band Zeropoint = 1594.0 Jy\",\n \"``filter`` input. By default this filter is also convolved with a model of\",\n \"'+filt+' not currently available for SPLAT; contact '+EMAIL+'\\\\n') filterInfo() return output def filterProfile(filt,**kwargs):\",\n \":Purpose: Report the equivalent flux density of a magnitude :Output: astropy quantity in\",\n \"2 element array that specifies the lower and upper wavelengths for a notch\",\n \"**energy** = False: compute energy flux **photon** = False: compute photon flux **filterFolder**\",\n \"= lam x F_lam), with option of also comparing to SED spectrum '''\",\n \"print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range\",\n \"# prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl =\",\n \"{:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose\",\n \"class data structure for a magnitude value ''' def __init__(self, magnitude, filt, uncertainty=0.,\",\n \"filter) **ab** = False: compute AB magnitudes (may be set by filter) **energy**\",\n \"2MASS KS: 2MASS Ks-band BESSEL I: Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR\",\n \"= list(output.keys()) if len(kys) == 1: return output[kys[0]] else: return output def filterProperties(filt,**kwargs):\",\n \"magnitudes, photon flux or energy flux. :Required Parameters: **sp**: Spectrum class object, which\",\n \"1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu\",\n \"(trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\\\nfilterMag not given a correct\",\n \"else: if verbose==True: print('\\\\nWarning: data values in range of filter {} have no\",\n \"nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d\",\n \"notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) ==\",\n \"isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else: fwave = fwave*u.micron # check that\",\n \"(addition of fluxes) :Output: A new magnitude object equal to the equivalent sum\",\n \"isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = []\",\n \"= magtype # check filter and rename if necessary self.knownfilter = True fflag\",\n \"2MASS J: 2MASS J-band Zeropoint = 1594.0 Jy Pivot point: = 1.252 micron\",\n \"in splat.FILTERS.keys() or a custom filter name :Optional Parameters: **custom** = None: A\",\n \"modelMagnitudes(verbose=True): ''' this will be a code that calculates a set of magnitudes\",\n \"this code will conduct a comparison of a set of magnitudes to model\",\n \"the diffence of values ''' # make a copy and fill in combined\",\n \"the same'.format(self.filter,other.filter)) # make a copy and fill in combined magnitude out =\",\n \"in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit =\",\n \"numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty = numpy.nanstd(val) # check filter\",\n \"a notch filter (100% transmission within, 0% transmission without) **vega** = True: compute\",\n \"'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not ab if 'vega' in\",\n \"!= 0. and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude\",\n \"spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values = (numpy.nan))\",\n \"filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool)\",\n \"f0 = checkFilterName(filt, verbose=True) if f0 == False: raise ValueError filt = f0\",\n \"list(args) elif kwargs.get('filter',False) != False: fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if\",\n \"List the predefined filter names available :Example: >>> import splat >>> import splat.photometry\",\n \"False if energy: photons = False # Read in filter if isinstance(custom,bool) and\",\n \"number of samples to use in Monte Carlo error estimation **info** = False:\",\n \"print(' Filter {} not in SPLAT filter list'.format(k)) kys = list(output.keys()) if len(kys)\",\n \"filters in the SPLAT reference library. ''' verbose = kwargs.get('verbose',True) if len(args) >\",\n \"import trapz # for numerical integration from scipy.interpolate import interp1d # splat functions\",\n \"u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\\\nmagToFlux needs vega or\",\n \"fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0:\",\n \"# find best scale factor dm = numpy.array(dm) em = numpy.array(em) offset =\",\n \"try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))]\",\n \"a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b *\",\n \"numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab):\",\n \"scipy.interpolate import interp1d # splat functions and constants from .initialize import * from\",\n \"an astropy.units variable; if this conversion does not work, the conversion is ignored\",\n \"contain wave, flux and noise array elements. **filter**: String giving name of filter,\",\n \"numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning:\",\n \"uncertainties :Output: A new Magnitude object equal to the sum of values '''\",\n \"filter, which can either be one of the predefined filters listed in splat.FILTERS.keys()\",\n \"= kwargs.get('filename',file) file = kwargs.get('output',file) if file != '': plt.savefig(file) return fig #########################################\",\n \"density units (default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation\",\n \"photon flux or energy flux. :Required Parameters: **sp**: Spectrum class object, which should\",\n \"output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint\",\n \"list(output.keys()) if len(kys) == 1: return output[kys[0]] else: return output def filterProperties(filt,**kwargs): '''\",\n \"this code compares a set of magnitudes using one of several statistics '''\",\n \"of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A\",\n \"a particular filter. :param filter: name of filter, must be one of the\",\n \"try: # mag.to(base_unit) # e_mag.to(base_unit) # except: # raise ValueError('\\\\nInput quantity unit {}\",\n \"in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert\",\n \"(may be set by filter) **energy** = False: compute energy flux **photon** =\",\n \"= [fname] output = {} for k in fname: f = checkFilterName(k) if\",\n \"return numpy.nan, numpy.nan return val, err # energy -> magnitude # THIS NEEDS\",\n \"and f in list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find\",\n \"return val, err # energy -> magnitude # THIS NEEDS TO BE COMPLETED\",\n \"if filt == False: return None report = {} report['name'] = filt report['description']\",\n \"output = False f = copy.deepcopy(filt) f = f.replace(' ','_').upper() for k in\",\n \"unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray))\",\n \"output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint']))\",\n \"Bessel I-band FOURSTAR H: FOURSTAR H-band FOURSTAR H LONG: FOURSTAR H long FOURSTAR\",\n \"True :Example: >>> import splat >>> data = splat.filterProperties('2MASS J') Filter 2MASS J:\",\n \"raise ValueError('\\\\nmagToFlux needs vega or ab method specified') # convert to desired energy\",\n \"Magnitude(object): ''' :Description: This is a class data structure for a magnitude value\",\n \"A new magnitude object equal to the equivalent sum of fluxes ''' #\",\n \"= {} for k in fname: f = checkFilterName(k) if f != False:\",\n \"match based on various statistics ''' pass def SEDFitMCMC(verbose=True): ''' this code will\",\n \"= kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file) if file != '': plt.savefig(file)\",\n \"f = f.replace(' ','_').upper() for k in list(FILTERS.keys()): if f==k.upper() or f.lower() in\",\n \"ValueError('magnitudes filters {} and {} are not the same'.format(self.filter,other.filter)) # make a copy\",\n \"print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength:\",\n \"else: raise ValueError('Could not parse input {}'.format(filt)) # add a comparison spectrum sp\",\n \"= vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d =\",\n \"= 1.252 micron FWHM = 0.323 micron Wavelength range = 1.066 to 1.442\",\n \"* u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength function v\",\n \"error estimation **info** = False: List the predefined filter names available **verbose** =\",\n \"= False: compute energy flux **photon** = False: compute photon flux **filterFolder** =\",\n \"compares a set of magnitudes using one of several statistics ''' chi =\",\n \"to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst =\",\n \"True: print(xra) sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra)\",\n \"2MASS J-band Zeropoint = 1594.0 Jy Pivot point: = 1.252 micron FWHM =\",\n \"for SPLAT; contact '+EMAIL+'\\\\n') filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the\",\n \"= numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err = 0. return\",\n \"= 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty if e_mag.value > 0.: for i\",\n \"else: raise ValueError('\\\\nmagToFlux needs vega or ab method specified') # convert to desired\",\n \"f0 # read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'),\",\n \"Curve') # list of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt]\",\n \"= interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if verbose==True: print('\\\\nWarning: data values in\",\n \"print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max']))\",\n \"quantity in flux density units (default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): '''\",\n \"and verbose == True: print('\\\\nFilter '+filt+' not currently available for SPLAT; contact '+EMAIL+'\\\\n')\",\n \"will conduct a comparison of a set of magnitudes to model magnitudes using\",\n \"trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for\",\n \"= type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup version def copy(self): ''' :Purpose: Make\",\n \"= FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except:\",\n \"kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit #\",\n \"wavelengths and transmissions for a custom filter **notch** = None: A 2 element\",\n \"numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. # custom\",\n \"= vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # interpolate Vega onto filter wavelength function\",\n \"return out def __sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction for Magnitude classes\",\n \"missing_values = ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if\",\n \"and noise array elements. **filter**: String giving name of filter, which can either\",\n \"integration from scipy.interpolate import interp1d # splat functions and constants from .initialize import\",\n \"u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag)\",\n \"fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw))\",\n \"filter name if isinstance(filt,str): filt = [filt] if isinstance(filt,list): # list of filter\",\n \"kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples =\",\n \"yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve') #\",\n \"offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m in dm]) return numpy.sum((dmo/em)**2),\",\n \"FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave\",\n \"= checkFilterName(filt) if filt == False: return numpy.nan, numpy.nan # reset filter calculation\",\n \"[numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not parse input {}'.format(filt))\",\n \"= '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux density\",\n \"= filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans =\",\n \"= vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim spectrum vflux\",\n \"two arrays: the filter wavelength and filter transmission curve. :param filter: String giving\",\n \"Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values =\",\n \"= ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot']\",\n \"== False: return numpy.nan, numpy.nan # reset filter calculation methods based on filter\",\n \"ab = False if photons: energy = False if energy: photons = False\",\n \"numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: spectrum for {} does not span full filter profile\",\n \"n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else: if verbose==True: print('\\\\nWarning: data values\",\n \"numpy.nan, numpy.nan return val, err # energy -> magnitude # THIS NEEDS TO\",\n \"ab = kwargs.get('ab',True) vega = not ab if 'vega' in FILTERS[filt]['method']: vega =\",\n \"= trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b =\",\n \"energy = kwargs.get('flux',energy) if (photons or energy): vega = False ab = False\",\n \"if e_mag.value > 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 *\",\n \"enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False):\",\n \"= kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or energy): vega = False ab\",\n \"filter, must be one of the specifed filters given by splat.FILTERS.keys() (required) :param\",\n \".initialize import * from .utilities import * ##################################################### ############### SPECTROPHOTOMETRY ############### ##################################################### #\",\n \"flux or energy flux. :Required Parameters: **sp**: Spectrum class object, which should contain\",\n \"default = filter preference) :param units: units for energy as an astropy.units variable;\",\n \"f != False: output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans\",\n \":Required Parameters: **sp**: Spectrum class object, which should contain wave, flux and noise\",\n \"spectrum for {} does not span full filter profile for {}'.format(sp.name,filt)) # interpolate\",\n \"= -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)))\",\n \"to SED spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object):\",\n \"dm = numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset\",\n \"optional, default = True :Example: >>> import splat >>> data = splat.filterProperties('2MASS J')\",\n \"= trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value\",\n \"sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu = sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky\",\n \"'{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux density of\",\n \"addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition for magnitudes (addition of fluxes) :Output:\",\n \"dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor dm = numpy.array(dm) em = numpy.array(em)\",\n \":Purpose: A simple representation of the Spectrum object ''' if self.uncertainty != 0.\",\n \"COMPLETE ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder =\",\n \"fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if kwargs.get('normalize',False): ftrans\",\n \"checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt) f = f.replace(' ','_').upper() for k\",\n \"filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing key parameters for a particular filter.\",\n \"0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] =\",\n \"the filter profile specified by the ``filter`` input. By default this filter is\",\n \"kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested\",\n \"convolved with a model of Vega to extract Vega magnitudes, but the user\",\n \"best scale factor dm = numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2)\",\n \"out information about filter to screen (optional, default = False) WARNING: THIS CODE\",\n \"X not among the available filters: 2MASS H: 2MASS H-band 2MASS J: 2MASS\",\n \"model set's SED models saves to file that could be uploaded pre-save some\",\n \"in fname: f = checkFilterName(k) if f != False: output[f] = {} output[f]['description']\",\n \"COMPLETED else: print('passed') pass # check that input is an energy flux #\",\n \"numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons): outunit = 1./u.s/u.cm**2 convert =\",\n \"also specify AB magnitudes, photon flux or energy flux. :Required Parameters: **sp**: Spectrum\",\n \"''' :Purpose: A representation of subtraction for Magnitude classes that takes into account\",\n \"instead (optional, default = False) :param ab: magnitude is on the AB system\",\n \"have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if\",\n \"of filter, must be one of the specifed filters given by splat.FILTERS.keys() (required)\",\n \"a copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude\",\n \"splat.FILTER_FOLDER) :Example: >>> import splat >>> import splat.photometry as spphot >>> sp =\",\n \"or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True: print('\\\\nWarning: no overlap between spectrum for {}\",\n \"default = filter preference) :param vega: magnitude is on the Vega system (optional,\",\n \"sp.normalize(xra) sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl)\",\n \"ValueError('\\\\nmagToFlux needs vega or ab method specified') # convert to desired energy units\",\n \"!= 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-m2 out.uncertainty =\",\n \"find best scale factor dm = numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum\",\n \"if f0 == False: return numpy.nan, numpy.nan filt = f0 rsr = FILTERS[filt]['rsr']\",\n \"the SPLAT reference library. ''' verbose = kwargs.get('verbose',True) if len(args) > 0: fname\",\n \"filter; some functions may not work'.format(filt)) self.knownfilter = False else: filt = fflag\",\n \"kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr =\",\n \"True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err =\",\n \"kwargs.get('verbose',False): print('\\\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM\",\n \"False: List the predefined filter names available **verbose** = True: List the predefined\",\n \"')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: =\",\n \"astropy.units variable; if this conversion does not work, the conversion is ignored (optional,\",\n \"needs vega or ab method specified') # convert to desired energy units #\",\n \"several statistics ''' chi = 0. dm,em = [],[] for f in list(mags1.keys()):\",\n \"transmissions for a custom filter **notch** = None: A 2 element array that\",\n \"check that requested filter is in list filt = checkFilterName(filt) if filt ==\",\n \"output = k if output == False and verbose == True: print('\\\\nFilter '+filt+'\",\n \"= filterProfile(self.filter) info = filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def __copy__(self): '''\",\n \"''' :Purpose: Report the equivalent flux density of a magnitude :Output: astropy quantity\",\n \"kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file) if file != '': plt.savefig(file) return\",\n \"using one of several statistics ''' chi = 0. dm,em = [],[] for\",\n \"= FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron\",\n \"to the sum of values ''' # make a copy and fill in\",\n \"spectrum for {} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave)\",\n \"magnitudes using an Amoeba (Nelder-Mead) wrapper, and choose the closest match ''' pass\",\n \"fwave,ftrans = custom[0],custom[1] # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True,\",\n \"of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] # list of\",\n \"default = False) WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE ''' # keyword\",\n \"AB magnitudes, photon flux or energy flux. :Required Parameters: **sp**: Spectrum class object,\",\n \"if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter\",\n \"filterFolder: folder containing the filter transmission files (optional, default = splat.FILTER_FOLDER) :Example: >>>\",\n \"read in filter fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values =\",\n \"if self.uncertainty != 0 and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 =\",\n \"0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave\",\n \"= filter preference) :param units: units for energy as an astropy.units variable; if\",\n \"magnitude is on the Vega system (optional, default = filter preference) :param units:\",\n \"energy flux **photon** = False: compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing\",\n \"fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm']\",\n \"fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude of a source based\",\n \"comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux\",\n \"**sp**: Spectrum class object, which should contain wave, flux and noise array elements.\",\n \"SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False)\",\n \"output = {} for k in fname: f = checkFilterName(k) if f !=\",\n \"in list if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0 == False:\",\n \"J: 2MASS J-band Zeropoint = 1594.0 Jy Pivot point: = 1.252 micron FWHM\",\n \"spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not\",\n \"copy.deepcopy(filt) f = f.replace(' ','_').upper() for k in list(FILTERS.keys()): if f==k.upper() or f.lower()\",\n \"factor dm = numpy.array(dm) em = numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo =\",\n \"return fig ######################################### ######## SED FITTING TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!!\",\n \"if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and\",\n \"filters {} and {} are not the same'.format(self.filter,other.filter)) # make a copy and\",\n \"that specifies the lower and upper wavelengths for a notch filter (100% transmission\",\n \"and {} are not the same'.format(self.filter,other.filter)) # make a copy and fill in\",\n \"value ''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty\",\n \"correct physical quantity (vega, ab, energy, photons) to compute photometry\\\\n\\\\n') # val =\",\n \"a comparison of a set of magnitudes to model magnitudes using an MCMC\",\n \"Parameters: **custom** = None: A 2 x N vector array specifying the wavelengths\",\n \"rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): #\",\n \"of fluxes) :Output: A new magnitude object equal to the equivalent sum of\",\n \"parameters for a particular filter. :param filter: name of filter, must be one\",\n \"filter preference) :param units: units for energy as an astropy.units variable; if this\",\n \"import matplotlib.patches as patches import matplotlib.pyplot as plt from scipy.integrate import trapz #\",\n \"0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val)\",\n \"print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm']\",\n \"= 0.323 micron Wavelength range = 1.066 to 1.442 micron >>> data =\",\n \"filter wavelength and filter transmission curve. :param filter: String giving the name of\",\n \"= [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if\",\n \"enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra\",\n \"self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for k in info.keys():\",\n \"rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s)\",\n \"= kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega =\",\n \"BE COMMON ''' filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name\",\n \"curve. :param filter: String giving the name of one of the predefined filters\",\n \"Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'),\",\n \"= magnitude self.uncertainty = uncertainty self.type = magtype # check filter and rename\",\n \"= u.erg/(u.cm**2 * u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag)\",\n \"magnitude :Output: astropy quantity in flux density units (default = erg/cm2/s/micron) ''' pass\",\n \"profile for {}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise))\",\n \"= (a/b * c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value)\",\n \"= {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range =\",\n \"numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint'])\",\n \"must be one of the specifed filters given by splat.FILTERS.keys() (required) :param reverse:\",\n \"quantity (vega, ab, energy, photons) to compute photometry\\\\n\\\\n') # val = numpy.nanmean(result)*outunit err\",\n \"if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 *\",\n \"kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) #\",\n \"of magnitudes using one of several statistics ''' chi = 0. dm,em =\",\n \"of several statistics ''' chi = 0. dm,em = [],[] for f in\",\n \"statistics ''' chi = 0. dm,em = [],[] for f in list(mags1.keys()): if\",\n \"energy: photons = False # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans\",\n \"copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty\",\n \"print('passed') pass # check that input is an energy flux # try: #\",\n \"astropy import units as u # standard units from astropy import constants as\",\n \"fwave,ftrans = numpy.genfromtxt(os.path.normpath(filterFolder+FILTERS[filt]['file']), comments='#', unpack=True, missing_values = ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not\",\n \":param reverse: convert energy into magnitude instead (optional, default = False) :param ab:\",\n \"unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err # energy -> magnitude # THIS\",\n \"sum of fluxes ''' # check filter agreement if self.filter != other.filter: raise\",\n \"list of filters in the SPLAT reference library. ''' verbose = kwargs.get('verbose',True) if\",\n \"FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega if 'rsr' in FILTERS[filt]['method']: rsr\",\n \"False # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) #\",\n \"fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname =\",\n \"numpy.nan, numpy.nan # reset filter calculation methods based on filter design if 'ab'\",\n \"self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{}\",\n \"span full filter profile for {}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength function\",\n \"__future__ import print_function, division \\\"\\\"\\\" .. note:: These are the spectrophotometry functions for\",\n \"vega rsr = FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons)\",\n \"!= False: output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans =\",\n \"given by splat.FILTERS.keys() :type filter: required :param verbose: print out information about filter\",\n \"* u.micron),equivalencies=u.spectral_density(fwave)) if rsr: fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val =\",\n \"return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve the filter profile for a SPLAT\",\n \"compute photometry\\\\n\\\\n') # val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0:\",\n \"True: # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\\\ missing_values\",\n \"= numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m in dm])\",\n \"quantity unit {} is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots\",\n \"in list(unc.keys()) and f not in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale\",\n \"# energy -> magnitude # THIS NEEDS TO BE COMPLETED else: print('passed') pass\",\n \"subtraction for Magnitude classes that takes into account uncertainties :Output: A new Magnitude\",\n \"the equivalent sum of fluxes ''' # check filter agreement if self.filter !=\",\n \"verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central']))\",\n \"reverse: convert energy into magnitude instead (optional, default = False) :param ab: magnitude\",\n \"(numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray):\",\n \"pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale (flux = lam x\",\n \"kwargs.get('vega',True) ab = not vega rsr = FILTERS[filt]['rsr'] # other possibilities photons =\",\n \"list(mags1.keys()): if f in list(mags2.keys()) and f in list(unc.keys()) and f not in\",\n \">>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if\",\n \"to compute photometry\\\\n\\\\n') # val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) ==\",\n \"__sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction for Magnitude classes that takes into\",\n \"> 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else:\",\n \"ARE EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True): ''' this will be a\",\n \"produces an interpolated value for a grid set of model magnitudes ''' pass\",\n \"the closest match ''' pass def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale\",\n \"into account uncertainties :Output: A new Magnitude object equal to the diffence of\",\n \"== True: fwave = fwave.to(u.micron) else: fwave = fwave*u.micron # check that spectrum\",\n \"= trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else: a =\",\n \"numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2 out.uncertainty = numpy.nanstd(val) # check filter\",\n \"list filt = checkFilterName(filt) if filt == False: return numpy.nan, numpy.nan # reset\",\n \"spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values = (numpy.nan))\",\n \"PARTIALLY COMPLETE ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder\",\n \"({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f\",\n \"or isinstance(filt[0],float): filt = [filt] # list of notch ranges if isinstance(filt[0],list): xl\",\n \"# check that input is an energy flux # try: # mag.to(base_unit) #\",\n \"else: fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron)\",\n \"FITTING TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### # plan: def\",\n \"parse input {}'.format(filt)) # add a comparison spectrum sp = kwargs.get('spectrum',None) sp =\",\n \"set of magnitudes to a grid of model magnitudes and choose the closest\",\n \"filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: fact = trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact\",\n \"numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength:\",\n \"if len(sp.wave[wgood]) == 0: err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check\",\n \"(default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition\",\n \":Purpose: Converts a magnitude into an energy, and vice versa. :param mag: magnitude\",\n \"requested filter is in list f0 = checkFilterName(filt, verbose=True) if f0 == False:\",\n \"u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) elif ab == True: fconst =\",\n \"False: if vega == True: # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile),\",\n \"on presets if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for\",\n \":Purpose: Returns a dictionary containing key parameters for a particular filter. :param filter:\",\n \"the predefined filters listed in splat.FILTERS.keys() (required) :param filterFolder: folder containing the filter\",\n \"chi = 0. dm,em = [],[] for f in list(mags1.keys()): if f in\",\n \"code will conduct a comparison of a set of magnitudes to model magnitudes\",\n \"> 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models else:\",\n \"magnitudes (addition of fluxes) :Output: A new magnitude object equal to the equivalent\",\n \"fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans\",\n \"!= other.filter: raise ValueError('magnitudes filters {} and {} are not the same'.format(self.filter,other.filter)) #\",\n \"notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans\",\n \"of filter names if isinstance(filt[0],str): for f in filt: fc = checkFilterName(f) filt.remove(f)\",\n \"vflux[vwave>=numpy.nanmin(fwave)] vwave = vwave[vwave>=numpy.nanmin(fwave)] vflux = vflux[vwave<=numpy.nanmax(fwave)] vwave = vwave[vwave<=numpy.nanmax(fwave)] # convert to\",\n \"vega = not ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab =\",\n \"is not a standard filter; some functions may not work'.format(filt)) self.knownfilter = False\",\n \"##################################################### class Magnitude(object): ''' :Description: This is a class data structure for a\",\n \"TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!! ### ######################################### # plan: def modelMagnitudes(verbose=True):\",\n \"a SPLAT filter. Returns two arrays: the filter wavelength and filter transmission curve.\",\n \"= filter preference) :param rsr: magnitude is on the Vega system (optional, default\",\n \"Spectral fluxes are convolved with the filter profile specified by the ``filter`` input.\",\n \"structure for a magnitude value ''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs):\",\n \"for models else: if verbose==True: print('\\\\nWarning: data values in range of filter {}\",\n \"= [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve') # list\",\n \"calculate uncertainty if e_mag.value > 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err =\",\n \"100: number of samples to use in Monte Carlo error estimation **info** =\",\n \"rsr = kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans =\",\n \"numpy.isfinite(self.uncertainty): return '{} magnitude of {}+/-{}'.format(self.filter,self.magnitude,self.uncertainty) else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def\",\n \"a dictionary containing key parameters for a particular filter. :param filter: name of\",\n \"= kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega =\",\n \"filters listed in splat.FILTERS.keys() (required) :param filterFolder: folder containing the filter transmission files\",\n \"design if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not ab if\",\n \"print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts\",\n \"0 and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val =\",\n \"trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr: a\",\n \"= sp.noise.to('Jy',equivalencies=u.spectral_density(sp.wave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n =\",\n \"False f = copy.deepcopy(filt) f = f.replace(' ','_').upper() for k in list(FILTERS.keys()): if\",\n \"same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if verbose==True:\",\n \"A representation of subtraction for Magnitude classes that takes into account uncertainties :Output:\",\n \"functions for SPLAT \\\"\\\"\\\" # imports - internal import copy import os #\",\n \"energy, and vice versa. :param mag: magnitude on whatever system is defined for\",\n \"= [filt] # list of notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit))\",\n \"= True fflag = checkFilterName(filt,verbose=verbose) if fflag == False: if verbose== True: print('filter\",\n \"of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s # backup\",\n \"verbose==True: print('\\\\nWarning: data values in range of filter {} have no uncertainties'.format(filt)) d\",\n \"that takes into account uncertainties :Output: A new Magnitude object equal to the\",\n \"if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output = k if output == False\",\n \"os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not\",\n \"constants in SI units import matplotlib.patches as patches import matplotlib.pyplot as plt from\",\n \"[filt] # list of notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl\",\n \"False: return numpy.nan, numpy.nan # reset filter calculation methods based on filter design\",\n \"[numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append(FILTERS[f]['description']) if FILTERS[f]['rsr'] == True: yl\",\n \"i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err = 0.*u.erg/(u.cm**2\",\n \"v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val = -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for\",\n \"= trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value\",\n \"x F_lam), with option of also comparing to SED spectrum ''' pass #####################################################\",\n \":Purpose: Make a copy of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__)\",\n \"be set by filter) **energy** = False: compute energy flux **photon** = False:\",\n \"set of filter profiles, optionally on top of a spectrum WARNING: THIS CODE\",\n \"noise array elements. **filter**: String giving name of filter, which can either be\",\n \"''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition for magnitudes (addition\",\n \"# raise ValueError('\\\\nInput quantity unit {} is not a flux unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs):\",\n \"1./u.s/u.cm**2 convert = const.h.to('erg s')*const.c.to('micron/s') val = (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value)\",\n \"# print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw))\",\n \"vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim spectrum vflux =\",\n \"\\\"\\\"\\\" .. note:: These are the spectrophotometry functions for SPLAT \\\"\\\"\\\" # imports\",\n \"of a spectrum WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE\",\n \"== True: yl = kwargs.get('ylabel','Transmission Curve') # list of notch ranges if isinstance(filt[0],int)\",\n \"1. # custom filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron\",\n \"numpy.nan # reset filter calculation methods based on filter design if 'ab' in\",\n \"True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def\",\n \"report['wave'] = fwave report['transmission'] = ftrans # report values out if kwargs.get('verbose',False): print('\\\\nFilter\",\n \"== False: if vega == True: # Read in Vega spectrum vwave,vflux =\",\n \"rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu\",\n \"filt = f0 rsr = FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool) and\",\n \"# check filter agreement if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out\",\n \"kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit\",\n \"fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err = 0. # magnitude ->\",\n \"not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab =\",\n \"name of filter, which can either be one of the predefined filters listed\",\n \"filter names available :Example: >>> import splat >>> import splat.photometry as spphot >>>\",\n \"in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else: fwave = fwave*wave_unit if\",\n \"filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or\",\n \"given by splat.FILTERS.keys() (required) :param reverse: convert energy into magnitude instead (optional, default\",\n \"u.s) # calculate uncertainty if e_mag.value > 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact)\",\n \"return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if\",\n \"standard filter; some functions may not work'.format(filt)) self.knownfilter = False else: filt =\",\n \"verbose==True: print('\\\\nWarning: no overlap between spectrum for {} and filter {}'.format(sp.name,filt)) return numpy.nan,\",\n \"= numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. #\",\n \"= (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))]\",\n \"def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude of a source based on\",\n \"should contain wave, flux and noise array elements. **filter**: String giving name of\",\n \"uncertainty if e_mag.value > 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2\",\n \"legend = [] fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000)\",\n \"filt = fflag self.filter = filt # some things that are based on\",\n \"if isinstance(filt,list): # list of filter names if isinstance(filt[0],str): for f in filt:\",\n \"{} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f}\",\n \"comparison of a set of magnitudes to model magnitudes using an Amoeba (Nelder-Mead)\",\n \"= copy.deepcopy(filt) f = f.replace(' ','_').upper() for k in list(FILTERS.keys()): if f==k.upper() or\",\n \"err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs): # check that requested filter is\",\n \"3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose:\",\n \"numpy.nan return val, err # energy -> magnitude # THIS NEEDS TO BE\",\n \":type filter: required :param verbose: print out information about filter to screen :type\",\n \"energy into magnitude instead (optional, default = False) :param ab: magnitude is on\",\n \"len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0) # catch for models\",\n \"F_lam), with option of also comparing to SED spectrum ''' pass ##################################################### ###############\",\n \"FILTERS[f]['rsr'] == True: yl = kwargs.get('ylabel','Transmission Curve') # list of notch ranges if\",\n \"nsamples = kwargs.get('nsamples',100) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 *\",\n \"filterProperties(self.filter) for k in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a copy\",\n \"work, the conversion is ignored (optional, default = erg/cm2/s) :param verbose: print out\",\n \"plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired file = kwargs.get('file','') file =\",\n \"output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] = numpy.max(fw) if verbose ==True: print(f.replace('_','\",\n \"based on presets if self.knownfilter == True: self.wave,self.transmission = filterProfile(self.filter) info = filterProperties(self.filter)\",\n \"if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose:\",\n \"= numpy.linspace(f[0],f[1],1000)*wave_unit,numpy.ones(1000) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])]\",\n \"to desired energy units # try: val.to(return_unit) err.to(return_unit) # except: # print('\\\\nWarning: unit\",\n \"filter else: fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True: fwave =\",\n \"input is an energy flux # try: # mag.to(base_unit) # e_mag.to(base_unit) # except:\",\n \"SPLAT_URL+FILTER_FOLDER # check that requested filter is in list filt = checkFilterName(filt) if\",\n \"ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans = ftrans[~numpy.isnan(ftrans)] result = [] err = 0.\",\n \"(vega): # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\\\ missing_values\",\n \"= 1. # custom filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False:\",\n \"{}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood])\",\n \"a spectrum WARNING: THIS CODE IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON\",\n \"single filter name if isinstance(filt,str): filt = [filt] if isinstance(filt,list): # list of\",\n \"fwave report['transmission'] = ftrans # report values out if kwargs.get('verbose',False): print('\\\\nFilter '+filt+': '+report['description'])\",\n \"sum of values ''' # make a copy and fill in combined magnitude\",\n \"fflag == False: if verbose== True: print('filter {} is not a standard filter;\",\n \"photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if\",\n \"not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did not\",\n \"the predefined filters listed in splat.FILTERS.keys() or a custom filter name :Optional Parameters:\",\n \"ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra\",\n \"parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that\",\n \"or not isinstance(ftrans,numpy.ndarray)) if not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray): raise ValueError('\\\\nProblem reading in\",\n \"output[kys[0]] else: return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary containing key\",\n \"pass def SEDFitAmoeba(verbose=True): ''' this code will conduct a comparison of a set\",\n \"and other.uncertainty != 0: m1 = numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1+m2\",\n \"H SHORT: FOURSTAR H short ... ''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder):\",\n \"for a model set's SED models saves to file that could be uploaded\",\n \"ab = not vega rsr = FILTERS[filt]['rsr'] # other possibilities photons = kwargs.get('photons',False)\",\n \"trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate uncertainty\",\n \"3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b)\",\n \"= kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs)\",\n \"= fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the\",\n \"of filter {} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result\",\n \"sp.scale(numpy.nanmax(ftrans)*kwargs.get('comparison_scale',0.8)) plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl)\",\n \"-*- from __future__ import print_function, division \\\"\\\"\\\" .. note:: These are the spectrophotometry\",\n \"import splat >>> import splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS\",\n \"= kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom\",\n \"containing Vega flux file, must be within ``filterFolder`` **nsamples** = 100: number of\",\n \"splat functions and constants from .initialize import * from .utilities import * #####################################################\",\n \"= splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831) ''' # keyword\",\n \"[],[] for f in list(mags1.keys()): if f in list(mags2.keys()) and f in list(unc.keys())\",\n \"notch[0],fwave <= notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] if\",\n \"[numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1)) else: raise ValueError('Could not\",\n \"not the same'.format(self.filter,other.filter)) # make a copy and fill in combined magnitude out\",\n \"wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d = interp1d(sp.wave[wgood].value,sp.flux[wgood].value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave[wgood].value,sp.noise[wgood].value,bounds_error=False,fill_value=0)\",\n \"parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit))\",\n \"and choose best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this code will\",\n \"magnitudes, but the user can also specify AB magnitudes, photon flux or energy\",\n \"import os # imports - external import numpy from astropy import units as\",\n \"out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if\",\n \"if verbose==True: print('\\\\nWarning: spectrum for {} does not span full filter profile for\",\n \"= False) :param ab: magnitude is on the AB system (optional, default =\",\n \">>> data = splat.filterProperties('2MASS X') Filter 2MASS X not among the available filters:\",\n \"verbose: print out information about filter to screen :type verbose: optional, default =\",\n \"= {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave\",\n \"copy of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s #\",\n \"= numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. #\",\n \"range = 1.066 to 1.442 micron >>> data = splat.filterProperties('2MASS X') Filter 2MASS\",\n \"that could be uploaded pre-save some model magnitudes ''' pass def interpolateMagnitudes(verbose=True): '''\",\n \"* c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else: result.append((trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) elif (photons):\",\n \"vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100)\",\n \"= kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit\",\n \"import constants as const # physical constants in SI units import matplotlib.patches as\",\n \".. note:: These are the spectrophotometry functions for SPLAT \\\"\\\"\\\" # imports -\",\n \"possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy)\",\n \"= splat.FILTER_FOLDER) :Example: >>> import splat >>> import splat.photometry as spphot >>> sp\",\n \"result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu =\",\n \"wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend =\",\n \"also convolved with a model of Vega to extract Vega magnitudes, but the\",\n \"= kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname = [fname]\",\n \"= -2.5*numpy.log10(trapz(ftrans*fwave.value*d(fwave.value),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value)) else: val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if\",\n \"{:.3f} to {:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into\",\n \"= kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or energy): vega\",\n \"self.filter = filt # some things that are based on presets if self.knownfilter\",\n \"{}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl\",\n \"fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else:\",\n \"out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux density of a magnitude\",\n \"u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength function v =\",\n \"len(kys) == 1: return output[kys[0]] else: return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns\",\n \"0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05)\",\n \"= 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] =\",\n \"rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that requested\",\n \"uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): #\",\n \"== False: return numpy.nan, numpy.nan filt = f0 # reset filter calculation methods\",\n \"if necessary self.knownfilter = True fflag = checkFilterName(filt,verbose=verbose) if fflag == False: if\",\n \"on its spectrum. Spectral fluxes are convolved with the filter profile specified by\",\n \"of a source based on its spectrum. Spectral fluxes are convolved with the\",\n \"list of notch ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] # list\",\n \"SPLAT \\\"\\\"\\\" # imports - internal import copy import os # imports -\",\n \"if desired file = kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file) if file\",\n \"= 1. elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b\",\n \"> 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) output[f]['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw))\",\n \"filt # some things that are based on presets if self.knownfilter == True:\",\n \"# this function has been obseleted def checkFilter(filt,verbose=True): output = False f =\",\n \"of parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this code will conduct a comparison\",\n \"out.filter = '{}-{}'.format(self.filter,other.filter) return out def flux(self,type='fnu',samp=1000): ''' :Purpose: Report the equivalent flux\",\n \"report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method'] = FILTERS[filt]['method'] report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans\",\n \"else: err = 0.*u.erg/(u.cm**2 * u.s) elif ab == True: fconst = 3631*u.jansky\",\n \"yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save\",\n \"= False: compute AB magnitudes (may be set by filter) **energy** = False:\",\n \"of the specifed filters given by splat.FILTERS.keys() :type filter: required :param verbose: print\",\n \"''' filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that\",\n \"the filter transmission files **vegaFile** = 'vega_kurucz.txt': name of file containing Vega flux\",\n \"X') Filter 2MASS X not among the available filters: 2MASS H: 2MASS H-band\",\n \"to {:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into an\",\n \"compute AB magnitudes (may be set by filter) **energy** = False: compute energy\",\n \"checkFilterName(filt) if filt == False: return numpy.nan, numpy.nan # reset filter calculation methods\",\n \"if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val =\",\n \"filter **notch** = None: A 2 element array that specifies the lower and\",\n \"not recognize any of the input filters {}'.format(filters)) # prep parameters fwave,ftrans =\",\n \"filter preference) :param rsr: magnitude is on the Vega system (optional, default =\",\n \"based on its spectrum. Spectral fluxes are convolved with the filter profile specified\",\n \"choose best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this code will conduct\",\n \"preference) :param units: units for energy as an astropy.units variable; if this conversion\",\n \"print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max']))\",\n \"# custom filter else: fwave,ftrans = custom[0],custom[1] # Read in Vega spectrum vwave,vflux\",\n \"H-band 2MASS J: 2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL I: Bessel I-band\",\n \"reset filter calculation methods based on filter design if 'ab' in FILTERS[filt]['method']: ab\",\n \"input {}'.format(filt)) # add a comparison spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp)\",\n \"preference) :param vega: magnitude is on the Vega system (optional, default = filter\",\n \"fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty\",\n \"fact = trapz(ftrans.value*fwave.value,fwave.value) else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) #\",\n \"to file that could be uploaded pre-save some model magnitudes ''' pass def\",\n \"kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not\",\n \"filters given by splat.FILTERS.keys() :type filter: required :param verbose: print out information about\",\n \"(optional, default = False) WARNING: THIS CODE IS ONLY PARTIALLY COMPLETE ''' #\",\n \"= fwave*wave_unit if kwargs.get('normalize',False): ftrans = ftrans/numpy.nanmax(ftrans) plt.plot(fwave,ftrans) if len(xra) == 0: xra\",\n \"of filters in the SPLAT reference library. ''' verbose = kwargs.get('verbose',True) if len(args)\",\n \"model magnitudes using an Amoeba (Nelder-Mead) wrapper, and choose the closest match '''\",\n \"= ftrans[~numpy.isnan(ftrans)] result = [] err = 0. # magnitude -> energy if\",\n \"val = m1-m2 out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter !=\",\n \"this function has been obseleted def checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt)\",\n \"= False # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs)\",\n \"units # try: val.to(return_unit) err.to(return_unit) # except: # print('\\\\nWarning: unit {} is not\",\n \"interpolateMagnitudes(verbose=True): ''' produces an interpolated value for a grid set of model magnitudes\",\n \"if f0 == False: raise ValueError filt = f0 # read in filter\",\n \"report['lambda_pivot'] = numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) report['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max']\",\n \"{} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave =\",\n \"# list of notch ranges if isinstance(filt[0],list): xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl =\",\n \"copy of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def\",\n \"ab: magnitude is on the AB system (optional, default = filter preference) :param\",\n \"parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this code will conduct a comparison of\",\n \"CLASS ############### ##################################################### class Magnitude(object): ''' :Description: This is a class data structure\",\n \"not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter is in list filt =\",\n \"= list(args) elif kwargs.get('filter',False) != False: fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys()))\",\n \"1. # custom filter else: fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) ==\",\n \"b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current\",\n \"profiles, optionally on top of a spectrum WARNING: THIS CODE IS CURRENTLY UNDER\",\n \"in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty #\",\n \"Carlo error estimation **info** = False: List the predefined filter names available **verbose**\",\n \"units for energy as an astropy.units variable; if this conversion does not work,\",\n \"checkFilterName(k) if f != False: output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint'] =\",\n \"report['rsr'] = FILTERS[filt]['rsr'] fwave,ftrans = filterProfile(filt,**kwargs) try: fwave = fwave.to(u.micron) except: fwave =\",\n \"filter profile for {}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength function wgood =\",\n \"= 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): '''\",\n \"* u.s) else: err = 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\\\nmagToFlux needs vega\",\n \"copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str): filt = [filt]\",\n \"= trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr:\",\n \"a grid set of model magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code\",\n \"must be one of the specifed filters given by splat.FILTERS.keys() :type filter: required\",\n \"{}'.format(filt)) # add a comparison spectrum sp = kwargs.get('spectrum',None) sp = kwargs.get('comparison',sp) if\",\n \"uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty self.type = magtype #\",\n \"units (default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation of\",\n \"arrays: the filter wavelength and filter transmission curve. :param filter: String giving the\",\n \"def SEDFitGrid(verbose=True): ''' this code will compare a set of magnitudes to a\",\n \"print out information about filter to screen :type verbose: optional, default = True\",\n \"isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend\",\n \"data structure for a magnitude value ''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent',\",\n \"plan: def modelMagnitudes(verbose=True): ''' this will be a code that calculates a set\",\n \"vwave[vwave<=numpy.nanmax(fwave)] # convert to fnu nu = vwave.to('Hz',equivalencies=u.spectral()) fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu =\",\n \"# other possibilities photons = kwargs.get('photons',False) photons = kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy\",\n \"sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname = [fname] output = {} for k\",\n \"== False: return None report = {} report['name'] = filt report['description'] = FILTERS[filt]['description']\",\n \"units: units for energy as an astropy.units variable; if this conversion does not\",\n \"= checkFilterName(f) filt.remove(f) if fc == False: if verbose==True: print('Removed filter {}: not\",\n \"== False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave = fwave[~numpy.isnan(ftrans)] ftrans =\",\n \"# notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron\",\n \"return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude of a source\",\n \"in ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor dm = numpy.array(dm) em\",\n \"= kwargs.get('ab',True) vega = not ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True)\",\n \"custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) base_unit = u.erg/(u.cm**2 * u.s) return_unit =\",\n \"= checkFilterName(k) if f != False: output[f] = {} output[f]['description'] = FILTERS[f]['description'] output[f]['zeropoint']\",\n \"SED scale (flux = lam x F_lam), with option of also comparing to\",\n \"**nsamples** = 100: number of samples to use in Monte Carlo error estimation\",\n \"kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) custom =\",\n \":Purpose: Retrieve the filter profile for a SPLAT filter. Returns two arrays: the\",\n \"in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did not recognize any\",\n \"# interpolate spectrum onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) >\",\n \"err.to(return_unit) # except: # print('\\\\nWarning: unit {} is not an energy flux unit'.format(return_unit))\",\n \"'': plt.savefig(file) return fig ######################################### ######## SED FITTING TOOLS ######### ### WARNING: THESE\",\n \"a grid of model magnitudes and choose the closest match based on various\",\n \"in list filt = checkFilterName(filt) if filt == False: return numpy.nan, numpy.nan #\",\n \"functions may not work'.format(filt)) self.knownfilter = False else: filt = fflag self.filter =\",\n \"compute energy flux **photon** = False: compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder\",\n \"for f in filt: fc = checkFilterName(f) filt.remove(f) if fc == False: if\",\n \"available for SPLAT; contact '+EMAIL+'\\\\n') filterInfo() return output def filterProfile(filt,**kwargs): ''' :Purpose: Retrieve\",\n \"in Monte Carlo error estimation **info** = False: List the predefined filter names\",\n \"representation of the Spectrum object ''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return\",\n \"unit'.format(mag.unit)) def visualizeFilter(filters,verbose=True,xra=[],yra=[0,1.2],**kwargs): ''' :Purpose: Plots a filter profile or set of filter\",\n \"fluxes) :Output: A new magnitude object equal to the equivalent sum of fluxes\",\n \"magnitude of a source based on its spectrum. Spectral fluxes are convolved with\",\n \"= kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info =\",\n \"filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave =\",\n \"name if isinstance(filt,str): filt = [filt] if isinstance(filt,list): # list of filter names\",\n \"def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into an energy, and vice versa.\",\n \"err.to(base_unit) except: print('\\\\nWarning: cannot convert result to an energy flux unit'.format(base_unit)) return numpy.nan,\",\n \"############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): ''' :Description: This is a class\",\n \"# -*- coding: utf-8 -*- from __future__ import print_function, division \\\"\\\"\\\" .. note::\",\n \"fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000\",\n \"# notch filter elif isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)\",\n \"the specifed filters given by splat.FILTERS.keys() :type filter: required :param verbose: print out\",\n \"rsr = FILTERS[filt]['rsr'] # Read in filter if isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans =\",\n \"= fwave report['transmission'] = ftrans # report values out if kwargs.get('verbose',False): print('\\\\nFilter '+filt+':\",\n \"a custom filter **notch** = None: A 2 element array that specifies the\",\n \"of also comparing to SED spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS ###############\",\n \"= numpy.sqrt(trapz(fw*ft,fw)/trapz(ft/fw,fw)) output[f]['lambda_central'] = 0.5*(numpy.max(fw)+numpy.min(fw)) output[f]['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) output[f]['lambda_min'] = numpy.min(fw) output[f]['lambda_max'] =\",\n \"{:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm'])) print('Wavelength range = {:.3f} to\",\n \"= SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile','vega_kurucz.txt') vega = kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr\",\n \"will compare a set of magnitudes to a grid of model magnitudes and\",\n \":param filter: String giving the name of one of the predefined filters listed\",\n \"the user can also specify AB magnitudes, photon flux or energy flux. :Required\",\n \"= trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr:\",\n \"energy, photons) to compute photometry\\\\n\\\\n') # val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if\",\n \"= 1. # custom filter else: fwave,ftrans = custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity)\",\n \"if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega if 'rsr'\",\n \"''' :Description: This is a class data structure for a magnitude value '''\",\n \"copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty\",\n \"val = (a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit c\",\n \"on whatever system is defined for the filter or provided (required) :param filter:\",\n \"True: yl = kwargs.get('ylabel','Transmission Curve') # list of notch ranges if isinstance(filt[0],int) or\",\n \"scipy.integrate import trapz # for numerical integration from scipy.interpolate import interp1d # splat\",\n \"best/average/distribution of parameters ''' pass def SEDFitAmoeba(verbose=True): ''' this code will conduct a\",\n \"== 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra = [yra[0],numpy.nanmax([yra[1],numpy.nanmax(ftrans)])] legend.append('Filter {}'.format(i+1))\",\n \"conduct a comparison of a set of magnitudes to model magnitudes using an\",\n \"kwargs.get('reverse',False) == False: if vega == True: # Read in Vega spectrum vwave,vflux\",\n \"''' this code will conduct a comparison of a set of magnitudes to\",\n \"if verbose== True: print('filter {} is not a standard filter; some functions may\",\n \"the Vega system (optional, default = filter preference) :param rsr: magnitude is on\",\n \"some model magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated value for\",\n \"LONG: FOURSTAR H long FOURSTAR H SHORT: FOURSTAR H short ... ''' filterFolder\",\n \"def SEDVisualize(verbose=True): ''' Visualizes magnitudes on SED scale (flux = lam x F_lam),\",\n \"ranges if isinstance(filt[0],int) or isinstance(filt[0],float): filt = [filt] # list of notch ranges\",\n \"= self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and\",\n \"if verbose==True: print('\\\\nWarning: data values in range of filter {} have no uncertainties'.format(filt))\",\n \"trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current list of\",\n \"SEDFitGrid(verbose=True): ''' this code will compare a set of magnitudes to a grid\",\n \"could be uploaded pre-save some model magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces\",\n \"filter agreement if self.filter != other.filter: out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000):\",\n \"Retrieve the filter profile for a SPLAT filter. Returns two arrays: the filter\",\n \"== True: # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True, \\\\\",\n \"val = -2.5*numpy.log10(trapz(ftrans*d(fwave.value),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value)) for i in numpy.arange(nsamples): # result.append(-2.5*numpy.log10(trapz(ftrans*numpy.random.normal(d(fwave),n(fwave))*sp.flux_unit,fwave)/trapz(ftrans*v(fwave)*sp.flux_unit,fwave))) if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else:\",\n \"# val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err =\",\n \"1: return output[kys[0]] else: return output def filterProperties(filt,**kwargs): ''' :Purpose: Returns a dictionary\",\n \"= 1. # custom filter else: fwave,ftrans = custom[0],custom[1] # Read in Vega\",\n \"if photons: energy = False if energy: photons = False # Read in\",\n \"= filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans\",\n \"if 'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool)\",\n \"Make a copy of a Magnitude object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return\",\n \"'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega if 'rsr' in\",\n \"= fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot'] =\",\n \"kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or energy): vega = False ab =\",\n \"isinstance(custom,bool) and isinstance(notch,bool): fwave,ftrans = filterProfile(filt,**kwargs) # notch filter elif isinstance(custom,bool) and isinstance(notch,list):\",\n \"numpy from astropy import units as u # standard units from astropy import\",\n \"fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs):\",\n \"IS CURRENTLY UNDER DEVELOPMENT, BUGS MAY BE COMMON ''' filt = copy.deepcopy(filters) wave_unit\",\n \"energy -> magnitude # THIS NEEDS TO BE COMPLETED else: print('passed') pass #\",\n \"units from astropy import constants as const # physical constants in SI units\",\n \"samples to use in Monte Carlo error estimation **info** = False: List the\",\n \"type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose: A simple representation of the\",\n \"fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave = fwave*u.micron fw =\",\n \"interp1d # splat functions and constants from .initialize import * from .utilities import\",\n \"x N vector array specifying the wavelengths and transmissions for a custom filter\",\n \"= numpy.random.normal(self.magnitude,self.uncertainty,samp) m2 = numpy.random.normal(other.magnitude,other.uncertainty,samp) val = m1-2.5*numpy.log10(1.+10.**(-0.4*(m2-m1))) out.uncertainty = numpy.nanstd(val) return out\",\n \"(required) :param filter: name of filter, must be one of the specifed filters\",\n \"some functions may not work'.format(filt)) self.knownfilter = False else: filt = fflag self.filter\",\n \"= ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric magnitude of\",\n \"vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto\",\n \"filterFolder = SPLAT_URL+FILTER_FOLDER vegaFile = kwargs.get('vegaFile',VEGAFILE) info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch\",\n \"(vega, ab, energy, photons) to compute photometry\\\\n\\\\n') # val = numpy.nanmean(result)*outunit err =\",\n \"[yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired\",\n \"vega: magnitude is on the Vega system (optional, default = filter preference) :param\",\n \"= filterProfile(f,**kwargs) if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl =\",\n \"fflag = checkFilterName(filt,verbose=verbose) if fflag == False: if verbose== True: print('filter {} is\",\n \"pre-save some model magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated value\",\n \"fluxes are convolved with the filter profile specified by the ``filter`` input. By\",\n \"isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn) ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave\",\n \"list'.format(k)) kys = list(output.keys()) if len(kys) == 1: return output[kys[0]] else: return output\",\n \"= ('NaN','nan'), filling_values = (numpy.nan)) # print(type(fwave),type(ftrans),isinstance(fwave,numpy.ndarray),isinstance(ftrans,numpy.ndarray),not isinstance(fwave,numpy.ndarray) or not isinstance(ftrans,numpy.ndarray)) if not\",\n \"is also convolved with a model of Vega to extract Vega magnitudes, but\",\n \"2MASS H-band 2MASS J: 2MASS J-band 2MASS KS: 2MASS Ks-band BESSEL I: Bessel\",\n \"kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not isinstance(mag,u.quantity.Quantity):\",\n \"a set of magnitudes using one of several statistics ''' chi = 0.\",\n \"object ''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose: A\",\n \"out.uncertainty = numpy.nanstd(val) # check filter agreement if self.filter != other.filter: out.filter =\",\n \"= self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and other.uncertainty != 0:\",\n \"filter: String giving the name of one of the predefined filters listed in\",\n \"missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave = vwave[~numpy.isnan(vflux)]*u.micron vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 *\",\n \"= fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b)\",\n \"kys = list(output.keys()) if len(kys) == 1: return output[kys[0]] else: return output def\",\n \"scale (flux = lam x F_lam), with option of also comparing to SED\",\n \"= numpy.nanstd(result) if len(sp.wave[wgood]) == 0: err = 0. return val*outunit,err*outunit def vegaToAB(filt,vegafile=VEGAFILE,filterfolder=SPLAT_PATH+FILTER_FOLDER,custom=False,notch=False,rsr=False,**kwargs):\",\n \"* u.micron)) vflux.to(sp.flux_unit,equivalencies=u.spectral_density(vwave)) # interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.)\",\n \"between spectrum for {} and filter {}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) <\",\n \"fc == False: if verbose==True: print('Removed filter {}: not included in SPLAT'.format(f)) else:\",\n \"save if desired file = kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file) if\",\n \"be one of the predefined filters listed in splat.FILTERS.keys() or a custom filter\",\n \"= kwargs.get('vega',True) ab = not vega rsr = FILTERS[filt]['rsr'] # other possibilities photons\",\n \"fname = list(args) elif kwargs.get('filter',False) != False: fname = kwargs['filter'] else: fname =\",\n \"True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave)) if rsr:\",\n \"plt.plot(sp.wave,sp.flux,color=kwargs.get('comparison_color','k'),alpha=kwargs.get('comparison_alpha',0.5)) legend.append(sp.name) yra = [yra[0],yra[1]*1.1] # finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend)\",\n \"val.to(base_unit) err.to(base_unit) except: print('\\\\nWarning: cannot convert result to an energy flux unit'.format(base_unit)) return\",\n \"of the input filters {}'.format(filters)) # prep parameters fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave):\",\n \"the name of one of the predefined filters listed in splat.FILTERS.keys() (required) :param\",\n \"of values ''' # make a copy and fill in combined magnitude out\",\n \"name :Optional Parameters: **custom** = None: A 2 x N vector array specifying\",\n \"of magnitudes to a grid of model magnitudes and choose the closest match\",\n \"H LONG: FOURSTAR H long FOURSTAR H SHORT: FOURSTAR H short ... '''\",\n \"''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty =\",\n \"trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else: a = trapz(ftrans*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans,fwave.value)*sp.wave.unit\",\n \"spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO J') (14.345894376898123, 0.027596454828421831)\",\n \"= fwave.to(u.micron) except: fwave = fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft =\",\n \"calculates a set of magnitudes for a model set's SED models saves to\",\n \"one of the specifed filters given by splat.FILTERS.keys() (required) :param reverse: convert energy\",\n \"check filter agreement if self.filter != other.filter: out.filter = '{}-{}'.format(self.filter,other.filter) return out def\",\n \"= copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty\",\n \"= self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and\",\n \"numpy.array(em) offset = numpy.sum(dm/em**2)/numpy.sum (1./em**2) dmo = numpy.array([m-offset for m in dm]) return\",\n \"filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) ==\",\n \"custom filter else: fwave,ftrans = custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity)\",\n \"if rsr: result.append(-2.5*numpy.log10(trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*fwave.value*v(fwave.value),fwave.value))) else: result.append(-2.5*numpy.log10(trapz(ftrans*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)/trapz(ftrans*v(fwave.value),fwave.value))) outunit = 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral())\",\n \"# splat functions and constants from .initialize import * from .utilities import *\",\n \"list of filter names if isinstance(filt[0],str): for f in filt: fc = checkFilterName(f)\",\n \":Purpose: Prints out the current list of filters in the SPLAT reference library.\",\n \"library. ''' verbose = kwargs.get('verbose',True) if len(args) > 0: fname = list(args) elif\",\n \"False: fname = kwargs['filter'] else: fname = sorted(list(FILTERS.keys())) if isinstance(fname,list) == False: fname\",\n \"filter is in list if isinstance(custom,bool) and isinstance(notch,bool): f0 = checkFilterName(filt,verbose=True) if f0\",\n \"not parse input {}'.format(filt)) # add a comparison spectrum sp = kwargs.get('spectrum',None) sp\",\n \"print('\\\\nFilter '+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM =\",\n \"= {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: = {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point:\",\n \"0.*u.erg/(u.cm**2 * u.s) elif ab == True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2\",\n \":Purpose: Plots a filter profile or set of filter profiles, optionally on top\",\n \"= trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) return -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) def filterInfo(*args,**kwargs): ''' :Purpose: Prints out the current list\",\n \"= 0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\\\nmagToFlux needs vega or ab method specified')\",\n \"to model magnitudes using an MCMC wrapper, and choose best/average/distribution of parameters '''\",\n \"= False if energy: photons = False # Read in filter if isinstance(custom,bool)\",\n \"desired file = kwargs.get('file','') file = kwargs.get('filename',file) file = kwargs.get('output',file) if file !=\",\n \"screen :type verbose: optional, default = True :Example: >>> import splat >>> data\",\n \"flux density units (default = erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A\",\n \"numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\\\nfilterMag not given a correct physical quantity (vega, ab,\",\n \"isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave\",\n \"equal to the diffence of values ''' # make a copy and fill\",\n \"= kwargs.get('nsamples',100) verbose = kwargs.get('verbose',False) # check that requested filter is in list\",\n \"= 0.*u.erg/(u.cm**2 * u.s) elif ab == True: fconst = 3631*u.jansky ftrans =\",\n \"= checkFilterName(filt, verbose=True) if f0 == False: raise ValueError filt = f0 #\",\n \"wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str): filt = [filt] if\",\n \"notch[1]))] = 1. # custom filter else: fwave,ftrans = custom[0],custom[1] # units if\",\n \"FILTERS[k]['altnames']: output = k if output == False and verbose == True: print('\\\\nFilter\",\n \"0: fname = list(args) elif kwargs.get('filter',False) != False: fname = kwargs['filter'] else: fname\",\n \"b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit c = trapz(ftrans*fwave.value*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value else: a\",\n \"''' s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose: A simple\",\n \"model magnitudes ''' pass def interpolateMagnitudes(verbose=True): ''' produces an interpolated value for a\",\n \"report['lambda_fwhm'] = numpy.max(fw05)-numpy.min(fw05) report['lambda_min'] = numpy.min(fw) report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission']\",\n \"specified') # convert to desired energy units # try: val.to(return_unit) err.to(return_unit) # except:\",\n \"i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\\\nfilterMag not given a correct physical quantity\",\n \"== False: if verbose== True: print('filter {} is not a standard filter; some\",\n \"as u # standard units from astropy import constants as const # physical\",\n \"**filter**: String giving name of filter, which can either be one of the\",\n \"= uncertainty self.type = magtype # check filter and rename if necessary self.knownfilter\",\n \"verbose == True: print('\\\\nFilter '+filt+' not currently available for SPLAT; contact '+EMAIL+'\\\\n') filterInfo()\",\n \"the filter profile for a SPLAT filter. Returns two arrays: the filter wavelength\",\n \"micron >>> data = splat.filterProperties('2MASS X') Filter 2MASS X not among the available\",\n \"units if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else: fwave = fwave*u.micron #\",\n \"vega == True: # Read in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterFolder+vegaFile), comments='#', unpack=True,\",\n \"in Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values\",\n \"flux. :Required Parameters: **sp**: Spectrum class object, which should contain wave, flux and\",\n \"listed in splat.FILTERS.keys() or a custom filter name :Optional Parameters: **custom** = None:\",\n \"isinstance(filt[0],str): for f in filt: fc = checkFilterName(f) filt.remove(f) if fc == False:\",\n \"if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER # check that requested filter is in\",\n \"Vega spectrum vwave,vflux = numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values =\",\n \"out.filter = '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction\",\n \"kwargs.get('photon',photons) energy = kwargs.get('energy',False) energy = kwargs.get('flux',energy) if (photons or energy): vega =\",\n \"''' produces an interpolated value for a grid set of model magnitudes '''\",\n \"= numpy.max(fw) if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central\",\n \"= interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i\",\n \"point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max'])) return\",\n \"range of filter {} have no uncertainties'.format(filt)) d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.)\",\n \"fig = plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit)\",\n \"= kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag = kwargs.get('e_mag',e_mag) if not\",\n \"magnitude value ''' def __init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude\",\n \"the equivalent flux density of a magnitude :Output: astropy quantity in flux density\",\n \"'+filt+': '+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm']))\",\n \"magnitudes ''' pass def compareMagnitudes(mags1,mags2,unc=None,unc2=None,ignore=[],verbose=True): ''' this code compares a set of magnitudes\",\n \"i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit = 1. elif (energy): outunit\",\n \"the same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave) > numpy.nanmax(sp.wave): if\",\n \"= plt.figure(figsize=kwargs.get('figsize',[5,4])) for i,f in enumerate(filt): fwave,ftrans = filterProfile(f,**kwargs) if isUnit(fwave): fwave.to(wave_unit) else:\",\n \"'+report['description']) print('Zeropoint = {} Jy'.format(report['zeropoint'])) print('Pivot point: = {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength\",\n \"notch filter (100% transmission within, 0% transmission without) **vega** = True: compute Vega\",\n \"= '{}+{}'.format(self.filter,other.filter) return out def __sub__(self,other,samp=1000): ''' :Purpose: A representation of subtraction for\",\n \"print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs):\",\n \"# interpolate Vega onto filter wavelength function v = interp1d(vwave.value,vflux.value,bounds_error=False,fill_value=0.) if rsr: val\",\n \"check that requested filter is in list f0 = checkFilterName(filt, verbose=True) if f0\",\n \"0.*u.erg/(u.cm**2 * u.s) else: raise ValueError('\\\\nmagToFlux needs vega or ab method specified') #\",\n \"c = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for i in numpy.arange(nsamples): if\",\n \"for {} does not span full filter profile for {}'.format(sp.name,filt)) # interpolate spectrum\",\n \"{:.3f}\\\\n'.format(output[f]['lambda_min'],output[f]['lambda_max'])) else: if verbose ==True: print(' Filter {} not in SPLAT filter list'.format(k))\",\n \"the wavelengths and transmissions for a custom filter **notch** = None: A 2\",\n \"(optional, default = filter preference) :param rsr: magnitude is on the Vega system\",\n \"isinstance(fname,list) == False: fname = [fname] output = {} for k in fname:\",\n \"##################################################### # this function has been obseleted def checkFilter(filt,verbose=True): output = False f\",\n \"result to an energy flux unit'.format(base_unit)) return numpy.nan, numpy.nan return val, err #\",\n \"ab if 'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega rsr\",\n \"a model of Vega to extract Vega magnitudes, but the user can also\",\n \"return s # backup version def copy(self): ''' :Purpose: Make a copy of\",\n \"val = (a/b * c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value) else:\",\n \"array that specifies the lower and upper wavelengths for a notch filter (100%\",\n \"of a set of magnitudes to model magnitudes using an Amoeba (Nelder-Mead) wrapper,\",\n \"ab == True: fconst = 3631*u.jansky ftrans = (ftrans*fconst).to(u.erg/(u.cm**2 * u.s * u.micron),equivalencies=u.spectral_density(fwave))\",\n \":Purpose: A representation of addition for magnitudes (addition of fluxes) :Output: A new\",\n \"on filter design if 'ab' in FILTERS[filt]['method']: ab = kwargs.get('ab',True) vega = not\",\n \"filt = copy.deepcopy(filters) wave_unit = kwargs.get('wave_unit',DEFAULT_WAVE_UNIT) # single filter name if isinstance(filt,str): filt\",\n \"= kwargs.get('vega',True) ab = kwargs.get('ab',not vega) rsr = kwargs.get('rsr',False) nsamples = kwargs.get('nsamples',100) verbose\",\n \"if f0 == False: return numpy.nan, numpy.nan filt = f0 # reset filter\",\n \"trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in numpy.arange(nsamples): a = trapz(ftrans*(d(filtnu.value)+numpy.random.normal(0,1)*n(filtnu.value))/filtnu.value,filtnu.value) result.append(-2.5*numpy.log10(a/b)) outunit\",\n \"transmission files **vegaFile** = 'vega_kurucz.txt': name of file containing Vega flux file, must\",\n \"on the Vega system (optional, default = filter preference) :param units: units for\",\n \"object, which should contain wave, flux and noise array elements. **filter**: String giving\",\n \"fnu = vflux.to('Jy',equivalencies=u.spectral_density(vwave)) filtnu = fwave.to('Hz',equivalencies=u.spectral()) fconst = 3631*u.jansky d = interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) b\",\n \"backup version def copy(self): ''' :Purpose: Make a copy of a Magnitude object\",\n \"for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s) else: err =\",\n \"filter calculation methods based on filter design if 'ab' in FILTERS[filt]['method']: ab =\",\n \"in dm]) return numpy.sum((dmo/em)**2), offset def SEDFitGrid(verbose=True): ''' this code will compare a\",\n \"fwave*u.micron fw = fwave[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 =\",\n \"of the Spectrum object ''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty): return '{}\",\n \"> 0.01*numpy.nanmax(ftrans))] ft = ftrans[numpy.where(ftrans > 0.01*numpy.nanmax(ftrans))] fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] output[f]['lambda_mean']\",\n \"erg/cm2/s/micron) ''' pass def addFlux(self,other,samp=1000): ''' :Purpose: A representation of addition for magnitudes\",\n \"!= '': plt.savefig(file) return fig ######################################### ######## SED FITTING TOOLS ######### ### WARNING:\",\n \"profile specified by the ``filter`` input. By default this filter is also convolved\",\n \"s = type(self)(self.magnitude,self.filter,uncertainty=self.uncertainty) s.__dict__.update(self.__dict__) return s def __repr__(self): ''' :Purpose: A simple representation\",\n \"file, must be within ``filterFolder`` **nsamples** = 100: number of samples to use\",\n \"__init__(self, magnitude, filt, uncertainty=0., magtype='apparent', verbose=False,**kwargs): self.magnitude = magnitude self.uncertainty = uncertainty self.type\",\n \"standard units from astropy import constants as const # physical constants in SI\",\n \"d = interp1d(sp.wave.value,sp.flux.value,bounds_error=False,fill_value=0.) n = interp1d(sp.wave.value,sp.flux.value*1.e-9,bounds_error=False,fill_value=0.) result = [] if (vega): # Read\",\n \"ftrans[numpy.where(numpy.logical_and(fwave >= notch[0],fwave <= notch[1]))] = 1. # custom filter else: fwave,ftrans =\",\n \"* u.s) return_unit = kwargs.get('unit',base_unit) e_mag = kwargs.get('uncertainty',0.) e_mag = kwargs.get('unc',e_mag) e_mag =\",\n \"by filter) **energy** = False: compute energy flux **photon** = False: compute photon\",\n \"checkFilterName(filt, verbose=True) if f0 == False: raise ValueError filt = f0 # read\",\n \"conversion is ignored (optional, default = erg/cm2/s) :param verbose: print out information about\",\n \"{:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude into an energy,\",\n \"of a magnitude :Output: astropy quantity in flux density units (default = erg/cm2/s/micron)\",\n \"check filter agreement if self.filter != other.filter: raise ValueError('magnitudes filters {} and {}\",\n \"not span full filter profile for {}'.format(sp.name,filt)) # interpolate spectrum onto filter wavelength\",\n \"xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve') legend = [] fig =\",\n \"copy and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude-2.5*numpy.log10(1.+10.**(-0.4*(other.magnitude-self.magnitude))) out.uncertainty\",\n \"J') (14.345894376898123, 0.027596454828421831) ''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder):\",\n \"required :param verbose: print out information about filter to screen :type verbose: optional,\",\n \"of the specifed filters given by splat.FILTERS.keys() (required) :param reverse: convert energy into\",\n \"if fflag == False: if verbose== True: print('filter {} is not a standard\",\n \"photons) to compute photometry\\\\n\\\\n') # val = numpy.nanmean(result)*outunit err = numpy.nanstd(result) if len(sp.wave[wgood])\",\n \"list(FILTERS.keys()): if f==k.upper() or f.lower() in FILTERS[k]['altnames']: output = k if output ==\",\n \"a comparison of a set of magnitudes to model magnitudes using an Amoeba\",\n \"interp1d(nu.value,fnu.value,bounds_error=False,fill_value=0.) n = interp1d(nu.value,noisenu.value,bounds_error=False,fill_value=0.) b = trapz((ftrans/filtnu.value)*fconst.value,filtnu.value) val = -2.5*numpy.log10(trapz(ftrans*d(filtnu.value)/filtnu.value,filtnu.value)/b) for i in\",\n \"isinstance(filt[0],float): filt = [filt] # list of notch ranges if isinstance(filt[0],list): xl =\",\n \">>> import splat >>> data = splat.filterProperties('2MASS J') Filter 2MASS J: 2MASS J-band\",\n \"= {:.3f}'.format(output[f]['lambda_central'])) print('Mean wavelength: = {:.3f}'.format(output[f]['lambda_mean'])) print('Pivot point: = {:.3f}'.format(output[f]['lambda_pivot'])) print('FWHM = {:.3f}'.format(output[f]['lambda_fwhm']))\",\n \"import splat.photometry as spphot >>> sp = splat.getSpectrum(shortname='1507-1627')[0] >>> sp.fluxCalibrate('2MASS J',14.5) >>> spphot.filterMag(sp,'MKO\",\n \"equal to the sum of values ''' # make a copy and fill\",\n \"onto filter wavelength function wgood = numpy.where(~numpy.isnan(sp.noise)) if len(sp.wave[wgood]) > 0: d =\",\n \"outunit = 1. elif (ab): nu = sp.wave.to('Hz',equivalencies=u.spectral()) fnu = sp.flux.to('Jy',equivalencies=u.spectral_density(sp.wave)) noisenu =\",\n \"mag: magnitude on whatever system is defined for the filter or provided (required)\",\n \"isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter is in\",\n \"= [] err = 0. # magnitude -> energy if kwargs.get('reverse',False) == False:\",\n \"{}: not included in SPLAT'.format(f)) else: filt.insert(len(filt),fc) if len(filt) == 0: raise ValueError('Did\",\n \"if isUnit(fwave): wave_unit = kwargs.get('wave_unit',fwave.unit) xl = kwargs.get('xlabel','Wavelength ({})'.format(wave_unit)) yl = kwargs.get('ylabel','Transmission Curve')\",\n \"code compares a set of magnitudes using one of several statistics ''' chi\",\n \"ignore: dm.append(mags1[f]-mags2[f]) em.append(unc[f]) # find best scale factor dm = numpy.array(dm) em =\",\n \"spectrophotometry functions for SPLAT \\\"\\\"\\\" # imports - internal import copy import os\",\n \"Report the equivalent flux density of a magnitude :Output: astropy quantity in flux\",\n \"False: return numpy.nan, numpy.nan filt = f0 # reset filter calculation methods based\",\n \"= 'vega_kurucz.txt': name of file containing Vega flux file, must be within ``filterFolder``\",\n \"else: fact = trapz(ftrans.value,fwave.value) val = (10.**(-0.4*mag.value)*fact)*u.erg/(u.cm**2 * u.s) # calculate uncertainty if\",\n \"info = kwargs.get('info',False) custom = kwargs.get('custom',False) notch = kwargs.get('notch',False) vega = kwargs.get('vega',True) ab\",\n \"vflux = vflux[~numpy.isnan(vflux)]*(u.erg/(u.cm**2 * u.s * u.micron)) # trim spectrum vflux = vflux[vwave>=numpy.nanmin(fwave)]\",\n \"'vega' in FILTERS[filt]['method']: vega = kwargs.get('vega',True) ab = not vega rsr = FILTERS[filt]['rsr']\",\n \"= {:.3f} to {:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max'])) return report def magToFlux(mag,filt,**kwargs): ''' :Purpose: Converts a magnitude\",\n \"spectrum ''' pass ##################################################### ############### MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): ''' :Description:\",\n \"> 0: fname = list(args) elif kwargs.get('filter',False) != False: fname = kwargs['filter'] else:\",\n \"= numpy.genfromtxt(os.path.normpath(filterfolder+vegafile), comments='#', unpack=True, \\\\ missing_values = ('NaN','nan'), filling_values = (numpy.nan)) vwave =\",\n \"filter or provided (required) :param filter: name of filter, must be one of\",\n \"fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty = self.uncertainty+other.uncertainty\",\n \"one of several statistics ''' chi = 0. dm,em = [],[] for f\",\n \"''' # keyword parameters filterFolder = kwargs.get('filterFolder',SPLAT_PATH+FILTER_FOLDER) if not os.path.exists(filterFolder): filterFolder = SPLAT_URL+FILTER_FOLDER\",\n \"name of filter, must be one of the specifed filters given by splat.FILTERS.keys()\",\n \"as patches import matplotlib.pyplot as plt from scipy.integrate import trapz # for numerical\",\n \"(flux = lam x F_lam), with option of also comparing to SED spectrum\",\n \"MAGNITUDE CLASS ############### ##################################################### class Magnitude(object): ''' :Description: This is a class data\",\n \"report['lambda_max'] = numpy.max(fw) report['wave'] = fwave report['transmission'] = ftrans # report values out\",\n \"particular filter. :param filter: name of filter, must be one of the specifed\",\n \"plt.plot(fwave,ftrans) if len(xra) == 0: xra = [numpy.nanmin(fwave.value),numpy.nanmax(fwave.value)] xra = [numpy.nanmin([xra[0],numpy.nanmin(fwave.value)]),numpy.nanmax([xra[1],numpy.nanmax(fwave.value)])] yra =\",\n \"magnitudes on SED scale (flux = lam x F_lam), with option of also\",\n \"fig ######################################### ######## SED FITTING TOOLS ######### ### WARNING: THESE ARE EXPERIMENTAL!! ###\",\n \"verbose: optional, default = True :Example: >>> import splat >>> data = splat.filterProperties('2MASS\",\n \"0: raise ValueError('Did not recognize any of the input filters {}'.format(filters)) # prep\",\n \"filter names if isinstance(filt[0],str): for f in filt: fc = checkFilterName(f) filt.remove(f) if\",\n \"(100% transmission within, 0% transmission without) **vega** = True: compute Vega magnitudes (may\",\n \"self.knownfilter = False else: filt = fflag self.filter = filt # some things\",\n \"the specifed filters given by splat.FILTERS.keys() (required) :param reverse: convert energy into magnitude\",\n \"simple representation of the Spectrum object ''' if self.uncertainty != 0. and numpy.isfinite(self.uncertainty):\",\n \"= custom[0],custom[1] if isinstance(fwave,u.quantity.Quantity) == False: fwave=fwave*u.micron if isinstance(ftrans,u.quantity.Quantity) == True: ftrans=ftrans.value fwave\",\n \"= custom[0],custom[1] # units if isinstance(fwave,u.quantity.Quantity) == True: fwave = fwave.to(u.micron) else: fwave\",\n \"a correct physical quantity (vega, ab, energy, photons) to compute photometry\\\\n\\\\n') # val\",\n \"obseleted def checkFilter(filt,verbose=True): output = False f = copy.deepcopy(filt) f = f.replace(' ','_').upper()\",\n \"flux file, must be within ``filterFolder`` **nsamples** = 100: number of samples to\",\n \"= trapz(ftrans*fwave.value*v(fwave.value),fwave.value) else: fact = trapz(ftrans*v(fwave.value),fwave.value) val = 10.**(-0.4*mag.value)*fact*u.erg/(u.cm**2 * u.s) # calculate\",\n \"flux **photon** = False: compute photon flux **filterFolder** = splat.FILTER_FOLDER: folder containing the\",\n \"plt.savefig(file) return fig ######################################### ######## SED FITTING TOOLS ######### ### WARNING: THESE ARE\",\n \"not work, the conversion is ignored (optional, default = erg/cm2/s) :param verbose: print\",\n \"are the spectrophotometry functions for SPLAT \\\"\\\"\\\" # imports - internal import copy\",\n \"= filt # some things that are based on presets if self.knownfilter ==\",\n \"of file containing Vega flux file, must be within ``filterFolder`` **nsamples** = 100:\",\n \"= copy.deepcopy(self) out.magnitude = self.magnitude-other.magnitude out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty\",\n \"{}'.format(sp.name,filt)) return numpy.nan, numpy.nan if numpy.nanmin(fwave) < numpy.nanmin(sp.wave) or numpy.nanmax(fwave) > numpy.nanmax(sp.wave): if\",\n \"fw05 = fwave[numpy.where(ftrans > 0.5*numpy.nanmax(ftrans))] # print(trapz(ft,fw)) # print(trapz(fw*ft,fw)) report['lambda_mean'] = trapz(ft*fw,fw)/trapz(ft,fw) report['lambda_pivot']\",\n \"filter agreement if self.filter != other.filter: raise ValueError('magnitudes filters {} and {} are\",\n \"not isinstance(mag,u.quantity.Quantity): mag=mag*u.s/u.s if not isinstance(e_mag,u.quantity.Quantity): e_mag=e_mag*mag.unit # check that requested filter is\",\n \"import numpy from astropy import units as u # standard units from astropy\",\n \"# finish up plt.xlim(xra) plt.ylim(yra) plt.xlabel(xl) plt.ylabel(yl) plt.legend(legend) # save if desired file\",\n \"= {:.3f}'.format(report['lambda_pivot'])) print('FWHM = {:.3f}'.format(report['lambda_fwhm'])) print('Wavelength range = {:.3f} to {:.3f}\\\\n'.format(report['lambda_min'],report['lambda_max'])) return report\",\n \"estimation **info** = False: List the predefined filter names available **verbose** = True:\",\n \"isinstance(custom,bool) and isinstance(notch,list): dn = (notch[1]-notch[0])/1000 fwave = numpy.arange(notch[0]-5.*dn,notch[1]+5.*dn,dn)*u.micron ftrans = numpy.zeros(len(fwave)) ftrans[numpy.where(numpy.logical_and(fwave\",\n \"fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan(ftrans)] return fwave,ftrans def filterMag(sp,filt,*args,**kwargs): ''' :Purpose: Determine the photometric\",\n \"list filt = checkFilterName(filt) if filt == False: return None report = {}\",\n \"out.uncertainty = self.uncertainty+other.uncertainty # combine noises if self.uncertainty != 0 and other.uncertainty !=\",\n \"in info.keys(): setattr(self,k,info[k]) def __copy__(self): ''' :Purpose: Make a copy of a Magnitude\",\n \"elif (energy): outunit = u.erg/u.s/u.cm**2 if rsr: a = trapz(ftrans*fwave.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit b = trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit\",\n \"= (trapz(ftrans*fwave.value*convert.value*d(fwave.value),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value for i in numpy.arange(nsamples): result.append((trapz(ftrans*fwave.value*convert.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit*convert.unit).to(outunit).value) else: raise NameError('\\\\nfilterMag not given a\",\n \"if verbose ==True: print(f.replace('_',' ')+': '+output[f]['zeropoint']) print('Zeropoint = {} Jy'.format(output[f]['zeropoint'])) print('Central wavelength: =\",\n \"else: return '{} magnitude of {}'.format(self.filter,self.magnitude) def __add__(self,other,samp=1000): ''' :Purpose: A representation of\",\n \"False: compute AB magnitudes (may be set by filter) **energy** = False: compute\",\n \"to a grid of model magnitudes and choose the closest match based on\",\n \"FILTERS[f]['description'] output[f]['zeropoint'] = FILTERS[f]['zeropoint'] fwave,ftrans = filterProfile(f,**kwargs) try: fwave = fwave.to(u.micron) except: fwave\",\n \"and filter cover the same wavelength ranges if numpy.nanmax(fwave) < numpy.nanmin(sp.wave) or numpy.nanmin(fwave)\",\n \"report = {} report['name'] = filt report['description'] = FILTERS[filt]['description'] report['zeropoint'] = FILTERS[filt]['zeropoint'] report['method']\",\n \"trapz(ftrans*fwave.value,fwave.value)*sp.wave.unit*sp.wave.unit val = (a/b * c/b).to(outunit).value for i in numpy.arange(nsamples): if rsr: result.append((trapz(ftrans*fwave.value*(d(fwave.value)+numpy.random.normal(0,1.)*n(fwave.value)),fwave.value)*sp.wave.unit*sp.flux.unit).to(outunit).value)\",\n \"e_mag.value > 0.: for i in numpy.arange(nsamples): result.append(10.**(-0.4*(mag.value+numpy.random.normal(0,1.)*e_mag.value))*fact) err = (numpy.nanstd(result))*u.erg/(u.cm**2 * u.s)\",\n \"checkFilterName(filt) if filt == False: return None report = {} report['name'] = filt\",\n \"FOURSTAR H LONG: FOURSTAR H long FOURSTAR H SHORT: FOURSTAR H short ...\",\n \"and fill in combined magnitude out = copy.deepcopy(self) out.magnitude = self.magnitude+other.magnitude out.uncertainty =\"\n]","isConversational":false}}},{"rowIdx":2995,"cells":{"ngram":{"kind":"list like","value":["def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration):","int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return duration / ONE_DAY","def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return duration / ONE_DAY def","int(days * 86400.0) def hours(hours): return int(hours * 3600.0) def minutes(minutes): return int(minutes","datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%SZ\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def","60 ONE_HOUR = 3600 ONE_DAY = 24 * ONE_HOUR ONE_YEAR = 1 *","minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%SZ\") def to_timestamp(date): print(date.timestamp()) return","return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return duration /","* 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%SZ\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration):","def hours(hours): return int(hours * 3600.0) def minutes(minutes): return int(minutes * 60.0) def","to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return","def days(days): return int(days * 86400.0) def hours(hours): return int(hours * 3600.0) def","to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return duration / ONE_DAY def to_hours(duration):","ONE_MINUTE def to_days(duration): return duration / ONE_DAY def to_hours(duration): return duration / ONE_HOUR","= 60 ONE_HOUR = 3600 ONE_DAY = 24 * ONE_HOUR ONE_YEAR = 1","365 * ONE_DAY def days(days): return int(days * 86400.0) def hours(hours): return int(hours","return duration / ONE_MINUTE def to_days(duration): return duration / 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60.0)","* 3600.0) def minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%SZ\") def","ONE_DAY = 24 * ONE_HOUR ONE_YEAR = 1 * 365 * ONE_DAY def","return datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%SZ\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE","1 * 365 * ONE_DAY def days(days): return int(days * 86400.0) def hours(hours):","= 24 * ONE_HOUR ONE_YEAR = 1 * 365 * ONE_DAY def days(days):","<reponame>mandalorian-101/badger-system import datetime ONE_MINUTE = 60 ONE_HOUR = 3600 ONE_DAY = 24 *","int(hours * 3600.0) def minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%SZ\")","datetime ONE_MINUTE = 60 ONE_HOUR = 3600 ONE_DAY = 24 * ONE_HOUR ONE_YEAR","ONE_DAY def days(days): return int(days * 86400.0) def hours(hours): return int(hours * 3600.0)","to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%SZ\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration /","duration / ONE_MINUTE def to_days(duration): return duration / ONE_DAY def to_hours(duration): return duration","import datetime ONE_MINUTE = 60 ONE_HOUR = 3600 ONE_DAY = 24 * ONE_HOUR","days(days): return int(days * 86400.0) def hours(hours): return int(hours * 3600.0) def minutes(minutes):","3600.0) def minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%SZ\") def to_timestamp(date):","ONE_YEAR = 1 * 365 * ONE_DAY def days(days): return int(days * 86400.0)","return int(days * 86400.0) def hours(hours): return int(hours * 3600.0) def minutes(minutes): return","def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%SZ\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration","= 3600 ONE_DAY = 24 * ONE_HOUR ONE_YEAR = 1 * 365 *","int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%SZ\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def","return int(hours * 3600.0) def minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp): return","def minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%SZ\") def to_timestamp(date): print(date.timestamp())","ONE_HOUR = 3600 ONE_DAY = 24 * ONE_HOUR ONE_YEAR = 1 * 365","* 365 * ONE_DAY def days(days): return int(days * 86400.0) def hours(hours): return","* ONE_DAY def days(days): return int(days * 86400.0) def hours(hours): return int(hours *","print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return duration","60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%SZ\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return","/ ONE_MINUTE def to_days(duration): return duration / ONE_DAY def to_hours(duration): return duration /"],"string":"[\n \"def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration):\",\n \"int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return duration / ONE_DAY\",\n \"def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return duration / ONE_DAY def\",\n \"int(days * 86400.0) def hours(hours): return int(hours * 3600.0) def minutes(minutes): return int(minutes\",\n \"datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def\",\n \"60 ONE_HOUR = 3600 ONE_DAY = 24 * ONE_HOUR ONE_YEAR = 1 *\",\n \"minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") def to_timestamp(date): print(date.timestamp()) return\",\n \"return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return duration /\",\n \"* 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration):\",\n \"def hours(hours): return int(hours * 3600.0) def minutes(minutes): return int(minutes * 60.0) def\",\n \"to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return\",\n \"def days(days): return int(days * 86400.0) def hours(hours): return int(hours * 3600.0) def\",\n \"to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return duration / ONE_DAY def to_hours(duration):\",\n \"ONE_MINUTE def to_days(duration): return duration / ONE_DAY def to_hours(duration): return duration / ONE_HOUR\",\n \"= 60 ONE_HOUR = 3600 ONE_DAY = 24 * ONE_HOUR ONE_YEAR = 1\",\n \"365 * ONE_DAY def days(days): return int(days * 86400.0) def hours(hours): return int(hours\",\n \"return duration / ONE_MINUTE def to_days(duration): return duration / ONE_DAY def to_hours(duration): return\",\n \"ONE_MINUTE = 60 ONE_HOUR = 3600 ONE_DAY = 24 * ONE_HOUR ONE_YEAR =\",\n \"24 * ONE_HOUR ONE_YEAR = 1 * 365 * ONE_DAY def days(days): return\",\n \"* ONE_HOUR ONE_YEAR = 1 * 365 * ONE_DAY def days(days): return int(days\",\n \"hours(hours): return int(hours * 3600.0) def minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp):\",\n \"return int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp())\",\n \"= 1 * 365 * ONE_DAY def days(days): return int(days * 86400.0) def\",\n \"* 86400.0) def hours(hours): return int(hours * 3600.0) def minutes(minutes): return int(minutes *\",\n \"3600 ONE_DAY = 24 * ONE_HOUR ONE_YEAR = 1 * 365 * ONE_DAY\",\n \"ONE_HOUR ONE_YEAR = 1 * 365 * ONE_DAY def days(days): return int(days *\",\n \"86400.0) def hours(hours): return int(hours * 3600.0) def minutes(minutes): return int(minutes * 60.0)\",\n \"* 3600.0) def minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") def\",\n \"ONE_DAY = 24 * ONE_HOUR ONE_YEAR = 1 * 365 * ONE_DAY def\",\n \"return datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE\",\n \"1 * 365 * ONE_DAY def days(days): return int(days * 86400.0) def hours(hours):\",\n \"= 24 * ONE_HOUR ONE_YEAR = 1 * 365 * ONE_DAY def days(days):\",\n \"<reponame>mandalorian-101/badger-system import datetime ONE_MINUTE = 60 ONE_HOUR = 3600 ONE_DAY = 24 *\",\n \"int(hours * 3600.0) def minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\")\",\n \"datetime ONE_MINUTE = 60 ONE_HOUR = 3600 ONE_DAY = 24 * ONE_HOUR ONE_YEAR\",\n \"ONE_DAY def days(days): return int(days * 86400.0) def hours(hours): return int(hours * 3600.0)\",\n \"to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration /\",\n \"duration / ONE_MINUTE def to_days(duration): return duration / ONE_DAY def to_hours(duration): return duration\",\n \"import datetime ONE_MINUTE = 60 ONE_HOUR = 3600 ONE_DAY = 24 * ONE_HOUR\",\n \"days(days): return int(days * 86400.0) def hours(hours): return int(hours * 3600.0) def minutes(minutes):\",\n \"3600.0) def minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") def to_timestamp(date):\",\n \"ONE_YEAR = 1 * 365 * ONE_DAY def days(days): return int(days * 86400.0)\",\n \"return int(days * 86400.0) def hours(hours): return int(hours * 3600.0) def minutes(minutes): return\",\n \"def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration\",\n \"= 3600 ONE_DAY = 24 * ONE_HOUR ONE_YEAR = 1 * 365 *\",\n \"int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def\",\n \"return int(hours * 3600.0) def minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp): return\",\n \"def minutes(minutes): return int(minutes * 60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") def to_timestamp(date): print(date.timestamp())\",\n \"ONE_HOUR = 3600 ONE_DAY = 24 * ONE_HOUR ONE_YEAR = 1 * 365\",\n \"* 365 * ONE_DAY def days(days): return int(days * 86400.0) def hours(hours): return\",\n \"* ONE_DAY def days(days): return int(days * 86400.0) def hours(hours): return int(hours *\",\n \"print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return duration\",\n \"60.0) def to_utc_date(timestamp): return datetime.datetime.utcfromtimestamp(timestamp).strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\") def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return\",\n \"/ ONE_MINUTE def to_days(duration): return duration / ONE_DAY def to_hours(duration): return duration /\"\n]","isConversational":false}}},{"rowIdx":2996,"cells":{"ngram":{"kind":"list like","value":["profile object with no z_dim (vertical dimension). Provide an array of netcdf files","range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season if required if plot_seasons:","fn_out = \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%% General Plot Settings var_name = \"abs_diff_temperature\" # Variable name","use for labelling plots subtitle_fontsize = 11 # Fontsize for dataset subtitles subtitle_fontweight","etc. save_plot = False # Masking out grid cells that don't contain many","# Create plot and flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r,","many rows and columns the plot will have. This script will plot the","be placed right of the figure. \"\"\" import xarray as xr import matplotlib.pyplot","between plots fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top,","an array of netcdf files and mess with the options to get a","[xr.open_dataset(dd) for dd in fn_list] n_ds = len(ds_list) n_ax = n_r * n_c","season_suffixes will be added to var_name # for each subplot panel. plot_seasons =","# Names to use for labelling plots subtitle_fontsize = 11 # Fontsize for","Latitude bounds subplot_padding = 0.5 # Amount of vertical and horizontal padding between","the colormap or not extend_cbar = [] # Loop over dataset for ii","# Handle colorbar -- will we extend it? if \"both\" in extend_cbar: extend","vmin=clim[0], vmax=clim[1], ) # Will we extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data,","= f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax,","dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set","# on each axis of the plot fn_list = [ \"~/transfer/test_grid.nc\", \"~/transfer/test_grid.nc\", ]","\"\"\" import xarray as xr import matplotlib.pyplot as plt import numpy as np","will we extend it? if \"both\" in extend_cbar: extend = \"both\" elif \"max\"","# Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1","= \"{0}_{1}\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\"bold\") else: ds = ds_list[ii] var_ii","with the options to get a figure you like. You can define how","will have. This script will plot the provided list of netcdf datasets from","latbounds = [45, 64] # Latitude bounds subplot_padding = 0.5 # Amount of","subplot_padding = 0.5 # Amount of vertical and horizontal padding between plots fig_pad","from left to right and top to bottom. A colorbar will be placed","(0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom) # Leave","fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values count_var = ds[N_var] data[count_var <","clim[0], clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure","Select season if required if plot_seasons: ds = ds_list[0] var_ii = var_name +","into list ds_list = [xr.open_dataset(dd) for dd in fn_list] n_ds = len(ds_list) n_ax","for dataset subtitles subtitle_fontweight = \"normal\" # Fontweight for dataset subtitles # PLOT","\"min\" in extend_cbar: extend = \"min\" else: extend = \"neither\" cbar_ax = f.add_axes([(1","fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) # Save plot maybe","# List of analysis output files. Profiles from each will be plotted #","Plot Settings var_name = \"abs_diff_temperature\" # Variable name in analysis file to plot","import xarray as xr import matplotlib.pyplot as plt import numpy as np import","= \"abs_diff_temperature\" # Variable name in analysis file to plot # If you","size cmap = \"bwr\" # Colormap for normal points clim = (-1, 1)","Profiles from each will be plotted # on each axis of the plot","Figure size lonbounds = [-15, 9.5] # Longitude bounds latbounds = [45, 64]","cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we extend the colorbar for this dataset?","it? if \"both\" in extend_cbar: extend = \"both\" elif \"max\" in extend_cbar and","the output fn_out = \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%% General Plot Settings var_name = \"abs_diff_temperature\" #","on right for colorbar # Scatter opts marker_size = 3 # Marker size","in extend_cbar: extend = \"both\" elif \"max\" in extend_cbar and \"min\" in extend_cbar:","files. Profiles from each will be plotted # on each axis of the","list of netcdf datasets from left to right and top to bottom. A","right of the figure. \"\"\" import xarray as xr import matplotlib.pyplot as plt","subplot. The season_suffixes will be added to var_name # for each subplot panel.","don't contain many points min_points_in_average = 5 name_of_count_variable = \"grid_N\" # Subplot axes","extend_cbar: extend = \"both\" elif \"max\" in extend_cbar and \"min\" in extend_cbar: extend","mess with the options to get a figure you like. You can define","colorbar will be placed right of the figure. \"\"\" import xarray as xr","many points min_points_in_average = 5 name_of_count_variable = \"grid_N\" # Subplot axes settings n_r","dd in fn_list] n_ds = len(ds_list) n_ax = n_r * n_c # Create","14 # Labels and Titles fig_title = \"SST Errors\" # Whole figure title","padding between plots fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left,","# Colormap for normal points clim = (-1, 1) # Color limits for","= var_name + \"_{0}\".format(season_suffixes[ii]) N_var = \"{0}_{1}\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\"bold\")","title title_fontsize = 13 # Fontsize of title title_fontweight = \"bold\" # Fontweight","like. You can define how many rows and columns the plot will have.","be added to var_name # for each subplot panel. plot_seasons = True season_suffixes","ds_list[0] var_ii = var_name + \"_{0}\".format(season_suffixes[ii]) N_var = \"{0}_{1}\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii],","np.nan # Scatter and set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap,","2 # Number of subplot columns figsize = (10, 5) # Figure size","Read and plotdata # Read all datasets into list ds_list = [xr.open_dataset(dd) for","= var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values count_var =","for normal points discrete_cmap = True # Discretize colormap cmap_levels = 14 #","added to var_name # for each subplot panel. plot_seasons = True season_suffixes =","used var modified to make gridded data # then this is where to","= 2 # Number of subplot rows n_c = 2 # Number of","and plotdata # Read all datasets into list ds_list = [xr.open_dataset(dd) for dd","columns the plot will have. This script will plot the provided list of","as xr import matplotlib.pyplot as plt import numpy as np import sys sys.path.append(\"/Users/dbyrne/code/COAsT\")","plotted # on each axis of the plot fn_list = [ \"~/transfer/test_grid.nc\", \"~/transfer/test_grid.nc\",","to use for labelling plots subtitle_fontsize = 11 # Fontsize for dataset subtitles","= 2 # If this option is true, only the first dataset will","for dd in fn_list] n_ds = len(ds_list) n_ax = n_r * n_c #","Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 -","Marker size cmap = \"bwr\" # Colormap for normal points clim = (-1,","clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout","Make sure n_r = 2 and n_c = 2 # If this option","this is where to select season etc. save_plot = False # Masking out","Figure padding (left, top, right, bottom) # Leave some space on right for","and horizontal padding between plots fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure","be plotted # on each axis of the plot fn_list = [ \"~/transfer/test_grid.nc\",","# for each subplot panel. plot_seasons = True season_suffixes = [\"DJF\", \"MAM\", \"JJA\",","xarray as xr import matplotlib.pyplot as plt import numpy as np import sys","right for colorbar # Scatter opts marker_size = 3 # Marker size cmap","coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten() # Dicretize colormap maybe if","to select season etc. save_plot = False # Masking out grid cells that","Names to use for labelling plots subtitle_fontsize = 11 # Fontsize for dataset","no z_dim (vertical dimension). Provide an array of netcdf files and mess with","\"min\" in extend_cbar: extend = \"both\" elif \"max\" in extend_cbar: extend = \"max\"","f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1","import numpy as np import sys sys.path.append(\"/Users/dbyrne/code/COAsT\") import coast import pandas as pd","If you used var modified to make gridded data # then this is","object with no z_dim (vertical dimension). Provide an array of netcdf files and","n_ax = n_r * n_c # Create plot and flatten axis array f,","ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we extend the colorbar for","= n_r * n_c # Create plot and flatten axis array f, a","right, bottom) # Leave some space on right for colorbar # Scatter opts","\"max\" in extend_cbar: extend = \"max\" elif \"min\" in extend_cbar: extend = \"min\"","subtitle_fontsize = 11 # Fontsize for dataset subtitles subtitle_fontweight = \"normal\" # Fontweight","var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values count_var","bounds latbounds = [45, 64] # Latitude bounds subplot_padding = 0.5 # Amount","fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom)","of subplot columns figsize = (10, 5) # Figure size lonbounds = [-15,","will extend the colormap or not extend_cbar = [] # Loop over dataset","Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding)","and mess with the options to get a figure you like. You can","# Number of subplot columns figsize = (10, 5) # Figure size lonbounds","padding (left, top, right, bottom) # Leave some space on right for colorbar","var_ii = var_name + \"_{0}\".format(season_suffixes[ii]) N_var = \"{0}_{1}\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes,","#%% File settings run_name = \"test\" # List of analysis output files. Profiles","\"both\" elif \"max\" in extend_cbar: extend = \"max\" elif \"min\" in extend_cbar: extend","and flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat","= [] # Loop over dataset for ii in range(n_ax): ur_index = np.unravel_index(ii,","= \"bold\" # Fontweight to use for title dataset_names = [\"CO9p0\", \"CO9p0\", \"CO9p0\"]","\"CO9p0\"] # Names to use for labelling plots subtitle_fontsize = 11 # Fontsize","bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar -- will we","season if required if plot_seasons: ds = ds_list[0] var_ii = var_name + \"_{0}\".format(season_suffixes[ii])","plot the provided list of netcdf datasets from left to right and top","# Scatter and set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0],","extend_cbar and \"min\" in extend_cbar: extend = \"both\" elif \"max\" in extend_cbar: extend","#%% General Plot Settings var_name = \"abs_diff_temperature\" # Variable name in analysis file","file to plot # If you used var modified to make gridded data","0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) # Save plot maybe if save_plot:","save_plot = False # Masking out grid cells that don't contain many points","labelling plots subtitle_fontsize = 11 # Fontsize for dataset subtitles subtitle_fontweight = \"normal\"","1) # Color limits for normal points discrete_cmap = True # Discretize colormap","# Read all datasets into list ds_list = [xr.open_dataset(dd) for dd in fn_list]","\"abs_diff_temperature\" # Variable name in analysis file to plot # If you used","and n_c = 2 # If this option is true, only the first","each will be plotted # on each axis of the plot fn_list =","Scatter opts marker_size = 3 # Marker size cmap = \"bwr\" # Colormap","the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title f.suptitle(fig_title,","# Subplot axes settings n_r = 2 # Number of subplot rows n_c","subplot columns figsize = (10, 5) # Figure size lonbounds = [-15, 9.5]","fontweight=\"bold\") else: ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var =","(-1, 1) # Color limits for normal points discrete_cmap = True # Discretize","(vertical dimension). Provide an array of netcdf files and mess with the options","points clim = (-1, 1) # Color limits for normal points discrete_cmap =","np.unravel_index(ii, (n_r, n_c)) # Select season if required if plot_seasons: ds = ds_list[0]","= True season_suffixes = [\"DJF\", \"MAM\", \"JJA\", \"SON\"] #%% Read and plotdata #","the plot will have. This script will plot the provided list of netcdf","modified to make gridded data # then this is where to select season","File settings run_name = \"test\" # List of analysis output files. Profiles from","the options to get a figure you like. You can define how many","n_c)) # Select season if required if plot_seasons: ds = ds_list[0] var_ii =","figure you like. You can define how many rows and columns the plot","False # Masking out grid cells that don't contain many points min_points_in_average =","= ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average] = np.nan # Scatter and","f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar -- will","a_flat = a.flatten() # Dicretize colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels)","as pd #%% File settings run_name = \"test\" # List of analysis output","Fontsize of title title_fontweight = \"bold\" # Fontweight to use for title dataset_names","import pandas as pd #%% File settings run_name = \"test\" # List of","settings n_r = 2 # Number of subplot rows n_c = 2 #","any fixed level) errors from a profile object with no z_dim (vertical dimension).","settings run_name = \"test\" # List of analysis output files. Profiles from each","= a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we extend","will be placed right of the figure. \"\"\" import xarray as xr import","(left, top, right, bottom) # Leave some space on right for colorbar #","for each subplot panel. plot_seasons = True season_suffixes = [\"DJF\", \"MAM\", \"JJA\", \"SON\"]","var modified to make gridded data # then this is where to select","cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will extend the colormap or","= ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data =","Variable name in analysis file to plot # If you used var modified","subplot rows n_c = 2 # Number of subplot columns figsize = (10,","on each subplot. The season_suffixes will be added to var_name # for each","elif \"max\" in extend_cbar: extend = \"max\" elif \"min\" in extend_cbar: extend =","# Figure size lonbounds = [-15, 9.5] # Longitude bounds latbounds = [45,","11 # Fontsize for dataset subtitles subtitle_fontweight = \"normal\" # Fontweight for dataset","a figure you like. You can define how many rows and columns the","axes settings n_r = 2 # Number of subplot rows n_c = 2","# Number of subplot rows n_c = 2 # Number of subplot columns","xr import matplotlib.pyplot as plt import numpy as np import sys sys.path.append(\"/Users/dbyrne/code/COAsT\") import","marker_size = 3 # Marker size cmap = \"bwr\" # Colormap for normal","dataset will be plotted, with seasonal # variables on each subplot. The season_suffixes","= 14 # Labels and Titles fig_title = \"SST Errors\" # Whole figure","netcdf files and mess with the options to get a figure you like.","Leave some space on right for colorbar # Scatter opts marker_size = 3","first dataset will be plotted, with seasonal # variables on each subplot. The","True season_suffixes = [\"DJF\", \"MAM\", \"JJA\", \"SON\"] #%% Read and plotdata # Read","each axis of the plot fn_list = [ \"~/transfer/test_grid.nc\", \"~/transfer/test_grid.nc\", ] # Filename","Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]),","title_fontsize = 13 # Fontsize of title title_fontweight = \"bold\" # Fontweight to","colormap cmap_levels = 14 # Labels and Titles fig_title = \"SST Errors\" #","[45, 64] # Latitude bounds subplot_padding = 0.5 # Amount of vertical and","= [45, 64] # Latitude bounds subplot_padding = 0.5 # Amount of vertical","# Scatter opts marker_size = 3 # Marker size cmap = \"bwr\" #","fn_list] n_ds = len(ds_list) n_ax = n_r * n_c # Create plot and","provided list of netcdf datasets from left to right and top to bottom.","of netcdf files and mess with the options to get a figure you","and columns the plot will have. This script will plot the provided list","fn_list = [ \"~/transfer/test_grid.nc\", \"~/transfer/test_grid.nc\", ] # Filename for the output fn_out =","data[count_var < min_points_in_average] = np.nan # Scatter and set title pc = a_flat[ii].pcolormesh(","output fn_out = \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%% General Plot Settings var_name = \"abs_diff_temperature\" # Variable","name_of_count_variable = \"grid_N\" # Subplot axes settings n_r = 2 # Number of","\"_{0}\".format(season_suffixes[ii]) N_var = \"{0}_{1}\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\"bold\") else: ds =","array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten() #","cmap_levels) # Determine if we will extend the colormap or not extend_cbar =","var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values count_var = ds[N_var]","# Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding,","(10, 5) # Figure size lonbounds = [-15, 9.5] # Longitude bounds latbounds","plotdata # Read all datasets into list ds_list = [xr.open_dataset(dd) for dd in","\"JJA\", \"SON\"] #%% Read and plotdata # Read all datasets into list ds_list","- fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar -- will we extend it?","bounds subplot_padding = 0.5 # Amount of vertical and horizontal padding between plots","plot and flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize)","n_c, figsize=figsize) a_flat = a.flatten() # Dicretize colormap maybe if discrete_cmap: cmap =","out grid cells that don't contain many points min_points_in_average = 5 name_of_count_variable =","#%% Read and plotdata # Read all datasets into list ds_list = [xr.open_dataset(dd)","bottom) # Leave some space on right for colorbar # Scatter opts marker_size","else: ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable","surface or bottom (or any fixed level) errors from a profile object with","= 0.5 # Amount of vertical and horizontal padding between plots fig_pad =","season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\"bold\") else: ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight)","title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]),","will be plotted, with seasonal # variables on each subplot. The season_suffixes will","\"bwr\" # Colormap for normal points clim = (-1, 1) # Color limits","fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]),","seasonal # variables on each subplot. The season_suffixes will be added to var_name","ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season if required if plot_seasons: ds","a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\"bold\") else: ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii],","have. This script will plot the provided list of netcdf datasets from left","= 5 name_of_count_variable = \"grid_N\" # Subplot axes settings n_r = 2 #","true, only the first dataset will be plotted, with seasonal # variables on","figsize = (10, 5) # Figure size lonbounds = [-15, 9.5] # Longitude","a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten() # Dicretize colormap","placed right of the figure. \"\"\" import xarray as xr import matplotlib.pyplot as","# Select season if required if plot_seasons: ds = ds_list[0] var_ii = var_name","axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten()","import sys sys.path.append(\"/Users/dbyrne/code/COAsT\") import coast import pandas as pd #%% File settings run_name","extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight","= \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%% General Plot Settings var_name = \"abs_diff_temperature\" # Variable name in","subtitles # PLOT SEASONS. Make sure n_r = 2 and n_c = 2","extend = \"neither\" cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15,","and set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], )","n_c # Create plot and flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds,","\"{0}_{1}\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\"bold\") else: ds = ds_list[ii] var_ii =","options to get a figure you like. You can define how many rows","a.flatten() # Dicretize colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine","the figure. \"\"\" import xarray as xr import matplotlib.pyplot as plt import numpy","n_r = 2 and n_c = 2 # If this option is true,","for colorbar # Scatter opts marker_size = 3 # Marker size cmap =","\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%% General Plot Settings var_name = \"abs_diff_temperature\" # Variable name in analysis","netcdf datasets from left to right and top to bottom. A colorbar will","to make gridded data # then this is where to select season etc.","cells that don't contain many points min_points_in_average = 5 name_of_count_variable = \"grid_N\" #","to right and top to bottom. A colorbar will be placed right of","fig_pad[3])) # Handle colorbar -- will we extend it? if \"both\" in extend_cbar:","option is true, only the first dataset will be plotted, with seasonal #","Subplot axes settings n_r = 2 # Number of subplot rows n_c =","\"neither\" cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7])","maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will extend","use for title dataset_names = [\"CO9p0\", \"CO9p0\", \"CO9p0\"] # Names to use for","as np import sys sys.path.append(\"/Users/dbyrne/code/COAsT\") import coast import pandas as pd #%% File","= [\"CO9p0\", \"CO9p0\", \"CO9p0\"] # Names to use for labelling plots subtitle_fontsize =","variables on each subplot. The season_suffixes will be added to var_name # for","np import sys sys.path.append(\"/Users/dbyrne/code/COAsT\") import coast import pandas as pd #%% File settings","\"both\" elif \"max\" in extend_cbar and \"min\" in extend_cbar: extend = \"both\" elif","# Fontweight to use for title dataset_names = [\"CO9p0\", \"CO9p0\", \"CO9p0\"] # Names","= [xr.open_dataset(dd) for dd in fn_list] n_ds = len(ds_list) n_ax = n_r *","in fn_list] n_ds = len(ds_list) n_ax = n_r * n_c # Create plot","ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average] = np.nan # Scatter and set","of the figure. \"\"\" import xarray as xr import matplotlib.pyplot as plt import","+ \"_{0}\".format(season_suffixes[ii]) N_var = \"{0}_{1}\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\"bold\") else: ds","if required if plot_seasons: ds = ds_list[0] var_ii = var_name + \"_{0}\".format(season_suffixes[ii]) N_var","You can define how many rows and columns the plot will have. This","- fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) #","f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten() # Dicretize","fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average]","ds[N_var] data[count_var < min_points_in_average] = np.nan # Scatter and set title pc =","Errors\" # Whole figure title title_fontsize = 13 # Fontsize of title title_fontweight","\"~/transfer/test_grid.nc\", ] # Filename for the output fn_out = \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%% General Plot","define how many rows and columns the plot will have. This script will","pd #%% File settings run_name = \"test\" # List of analysis output files.","5 name_of_count_variable = \"grid_N\" # Subplot axes settings n_r = 2 # Number","you like. You can define how many rows and columns the plot will","\"normal\" # Fontweight for dataset subtitles # PLOT SEASONS. Make sure n_r =","fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) # Save","# Dicretize colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if","= ds_list[0] var_ii = var_name + \"_{0}\".format(season_suffixes[ii]) N_var = \"{0}_{1}\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02,","(n_r, n_c)) # Select season if required if plot_seasons: ds = ds_list[0] var_ii","# variables on each subplot. The season_suffixes will be added to var_name #","2 # If this option is true, only the first dataset will be","= \"bwr\" # Colormap for normal points clim = (-1, 1) # Color","errors from a profile object with no z_dim (vertical dimension). Provide an array","or bottom (or any fixed level) errors from a profile object with no","64] # Latitude bounds subplot_padding = 0.5 # Amount of vertical and horizontal","sys.path.append(\"/Users/dbyrne/code/COAsT\") import coast import pandas as pd #%% File settings run_name = \"test\"","normal points discrete_cmap = True # Discretize colormap cmap_levels = 14 # Labels","will be plotted # on each axis of the plot fn_list = [","be plotted, with seasonal # variables on each subplot. The season_suffixes will be","3 # Marker size cmap = \"bwr\" # Colormap for normal points clim","plot_seasons = True season_suffixes = [\"DJF\", \"MAM\", \"JJA\", \"SON\"] #%% Read and plotdata","ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data","\"MAM\", \"JJA\", \"SON\"] #%% Read and plotdata # Read all datasets into list","# Leave some space on right for colorbar # Scatter opts marker_size =","in extend_cbar: extend = \"min\" else: extend = \"neither\" cbar_ax = f.add_axes([(1 -","to bottom. A colorbar will be placed right of the figure. \"\"\" import","# Amount of vertical and horizontal padding between plots fig_pad = (0.075, 0.075,","# Color limits for normal points discrete_cmap = True # Discretize colormap cmap_levels","Masking out grid cells that don't contain many points min_points_in_average = 5 name_of_count_variable","figsize=figsize) a_flat = a.flatten() # Dicretize colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap,","= [-15, 9.5] # Longitude bounds latbounds = [45, 64] # Latitude bounds","= \"both\" elif \"max\" in extend_cbar: extend = \"max\" elif \"min\" in extend_cbar:","discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will extend the colormap","to use for title dataset_names = [\"CO9p0\", \"CO9p0\", \"CO9p0\"] # Names to use","discrete_cmap = True # Discretize colormap cmap_levels = 14 # Labels and Titles","opts marker_size = 3 # Marker size cmap = \"bwr\" # Colormap for","all datasets into list ds_list = [xr.open_dataset(dd) for dd in fn_list] n_ds =","not extend_cbar = [] # Loop over dataset for ii in range(n_ax): ur_index","extend it? if \"both\" in extend_cbar: extend = \"both\" elif \"max\" in extend_cbar","matplotlib.pyplot as plt import numpy as np import sys sys.path.append(\"/Users/dbyrne/code/COAsT\") import coast import","dataset_names = [\"CO9p0\", \"CO9p0\", \"CO9p0\"] # Names to use for labelling plots subtitle_fontsize","# Latitude bounds subplot_padding = 0.5 # Amount of vertical and horizontal padding","If this option is true, only the first dataset will be plotted, with","where to select season etc. save_plot = False # Masking out grid cells","a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we extend the","This script will plot the provided list of netcdf datasets from left to","h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar --","fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar -- will we extend it? if","Number of subplot rows n_c = 2 # Number of subplot columns figsize","colorbar # Scatter opts marker_size = 3 # Marker size cmap = \"bwr\"","\"SON\"] #%% Read and plotdata # Read all datasets into list ds_list =","data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we extend the colorbar for this","= np.unravel_index(ii, (n_r, n_c)) # Select season if required if plot_seasons: ds =","plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will extend the colormap or not extend_cbar","transform=a_flat[ii].transAxes, fontweight=\"bold\") else: ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var","< min_points_in_average] = np.nan # Scatter and set title pc = a_flat[ii].pcolormesh( ds.longitude,","5) # Figure size lonbounds = [-15, 9.5] # Longitude bounds latbounds =","rows and columns the plot will have. This script will plot the provided","clim = (-1, 1) # Color limits for normal points discrete_cmap = True","left to right and top to bottom. A colorbar will be placed right","can define how many rows and columns the plot will have. This script","select season etc. save_plot = False # Masking out grid cells that don't","Dicretize colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if we","Titles fig_title = \"SST Errors\" # Whole figure title title_fontsize = 13 #","cmap_levels = 14 # Labels and Titles fig_title = \"SST Errors\" # Whole","\"SST Errors\" # Whole figure title title_fontsize = 13 # Fontsize of title","of vertical and horizontal padding between plots fig_pad = (0.075, 0.075, 0.1, 0.1)","] # Filename for the output fn_out = \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%% General Plot Settings","Discretize colormap cmap_levels = 14 # Labels and Titles fig_title = \"SST Errors\"","# Filename for the output fn_out = \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%% General Plot Settings var_name","= 13 # Fontsize of title title_fontweight = \"bold\" # Fontweight to use","colorbar -- will we extend it? if \"both\" in extend_cbar: extend = \"both\"","dataset subtitles subtitle_fontweight = \"normal\" # Fontweight for dataset subtitles # PLOT SEASONS.","bottom (or any fixed level) errors from a profile object with no z_dim","name in analysis file to plot # If you used var modified to","array of netcdf files and mess with the options to get a figure","n_ds = len(ds_list) n_ax = n_r * n_c # Create plot and flatten","colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize,","points discrete_cmap = True # Discretize colormap cmap_levels = 14 # Labels and","= \"both\" elif \"max\" in extend_cbar and \"min\" in extend_cbar: extend = \"both\"","for the output fn_out = \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%% General Plot Settings var_name = \"abs_diff_temperature\"","will be added to var_name # for each subplot panel. plot_seasons = True","tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3]))","\"max\" in extend_cbar and \"min\" in extend_cbar: extend = \"both\" elif \"max\" in","and top to bottom. A colorbar will be placed right of the figure.","* 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) # Save plot maybe if","\"CO9p0\", \"CO9p0\"] # Names to use for labelling plots subtitle_fontsize = 11 #","required if plot_seasons: ds = ds_list[0] var_ii = var_name + \"_{0}\".format(season_suffixes[ii]) N_var =","# Fontsize of title title_fontweight = \"bold\" # Fontweight to use for title","Settings var_name = \"abs_diff_temperature\" # Variable name in analysis file to plot #","= 2 # Number of subplot columns figsize = (10, 5) # Figure","extend_cbar: extend = \"both\" elif \"max\" in extend_cbar: extend = \"max\" elif \"min\"","extend_cbar: extend = \"max\" elif \"min\" in extend_cbar: extend = \"min\" else: extend","to plot # If you used var modified to make gridded data #","script will plot the provided list of netcdf datasets from left to right","for ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season if","count_var = ds[N_var] data[count_var < min_points_in_average] = np.nan # Scatter and set title","space on right for colorbar # Scatter opts marker_size = 3 # Marker","cmap = \"bwr\" # Colormap for normal points clim = (-1, 1) #","make gridded data # then this is where to select season etc. save_plot","we extend it? if \"both\" in extend_cbar: extend = \"both\" elif \"max\" in","f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar","right and top to bottom. A colorbar will be placed right of the","this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) #","fig_title = \"SST Errors\" # Whole figure title title_fontsize = 13 # Fontsize","in extend_cbar and \"min\" in extend_cbar: extend = \"both\" elif \"max\" in extend_cbar:","# Loop over dataset for ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c))","top, right, bottom) # Leave some space on right for colorbar # Scatter","the provided list of netcdf datasets from left to right and top to","# Determine if we will extend the colormap or not extend_cbar = []","in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season if required if","# Figure padding (left, top, right, bottom) # Leave some space on right","plot_seasons: ds = ds_list[0] var_ii = var_name + \"_{0}\".format(season_suffixes[ii]) N_var = \"{0}_{1}\".format(name_of_count_variable, season_suffixes[ii])","= \"SST Errors\" # Whole figure title title_fontsize = 13 # Fontsize of","top to bottom. A colorbar will be placed right of the figure. \"\"\"","Handle colorbar -- will we extend it? if \"both\" in extend_cbar: extend =","[-15, 9.5] # Longitude bounds latbounds = [45, 64] # Latitude bounds subplot_padding","sys sys.path.append(\"/Users/dbyrne/code/COAsT\") import coast import pandas as pd #%% File settings run_name =","* n_c # Create plot and flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds,","for title dataset_names = [\"CO9p0\", \"CO9p0\", \"CO9p0\"] # Names to use for labelling","Amount of vertical and horizontal padding between plots fig_pad = (0.075, 0.075, 0.1,","var_name + \"_{0}\".format(season_suffixes[ii]) N_var = \"{0}_{1}\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\"bold\") else:","= True # Discretize colormap cmap_levels = 14 # Labels and Titles fig_title","fixed level) errors from a profile object with no z_dim (vertical dimension). Provide","of analysis output files. Profiles from each will be plotted # on each","[\"DJF\", \"MAM\", \"JJA\", \"SON\"] #%% Read and plotdata # Read all datasets into","Provide an array of netcdf files and mess with the options to get","= (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom) #","extend = \"both\" elif \"max\" in extend_cbar: extend = \"max\" elif \"min\" in","extend_cbar: extend = \"min\" else: extend = \"neither\" cbar_ax = f.add_axes([(1 - fig_pad[2]","= \"min\" else: extend = \"neither\" cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2]","extend the colormap or not extend_cbar = [] # Loop over dataset for","sure n_r = 2 and n_c = 2 # If this option is","some space on right for colorbar # Scatter opts marker_size = 3 #","A colorbar will be placed right of the figure. \"\"\" import xarray as","figure. \"\"\" import xarray as xr import matplotlib.pyplot as plt import numpy as","the plot fn_list = [ \"~/transfer/test_grid.nc\", \"~/transfer/test_grid.nc\", ] # Filename for the output",") # Will we extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1]))","layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) # Handle","= a.flatten() # Dicretize colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) #","panel. plot_seasons = True season_suffixes = [\"DJF\", \"MAM\", \"JJA\", \"SON\"] #%% Read and","else: extend = \"neither\" cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15),","[] # Loop over dataset for ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r,","limits for normal points discrete_cmap = True # Discretize colormap cmap_levels = 14","\"max\" elif \"min\" in extend_cbar: extend = \"min\" else: extend = \"neither\" cbar_ax","elif \"max\" in extend_cbar and \"min\" in extend_cbar: extend = \"both\" elif \"max\"","in extend_cbar: extend = \"max\" elif \"min\" in extend_cbar: extend = \"min\" else:","\"test\" # List of analysis output files. Profiles from each will be plotted","# Whole figure title title_fontsize = 13 # Fontsize of title title_fontweight =","level) errors from a profile object with no z_dim (vertical dimension). Provide an","for normal points clim = (-1, 1) # Color limits for normal points","f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend)","0.5 # Amount of vertical and horizontal padding between plots fig_pad = (0.075,","= len(ds_list) n_ax = n_r * n_c # Create plot and flatten axis","we extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure","n_c = 2 # If this option is true, only the first dataset","plot will have. This script will plot the provided list of netcdf datasets","Loop over dataset for ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) #","figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) #","latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten() # Dicretize colormap maybe if discrete_cmap:","title_fontweight = \"bold\" # Fontweight to use for title dataset_names = [\"CO9p0\", \"CO9p0\",","extend = \"max\" elif \"min\" in extend_cbar: extend = \"min\" else: extend =","vmax=clim[1], ) # Will we extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0],","= plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will extend the colormap or not","dataset subtitles # PLOT SEASONS. Make sure n_r = 2 and n_c =","0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) # Save plot maybe if save_plot: f.savefig(fn_out)","will plot the provided list of netcdf datasets from left to right and","0.1) # Figure padding (left, top, right, bottom) # Leave some space on","season_suffixes = [\"DJF\", \"MAM\", \"JJA\", \"SON\"] #%% Read and plotdata # Read all","N_var = name_of_count_variable data = ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average] =","if plot_seasons: ds = ds_list[0] var_ii = var_name + \"_{0}\".format(season_suffixes[ii]) N_var = \"{0}_{1}\".format(name_of_count_variable,","on each axis of the plot fn_list = [ \"~/transfer/test_grid.nc\", \"~/transfer/test_grid.nc\", ] #","flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat =","title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will","# Marker size cmap = \"bwr\" # Colormap for normal points clim =","Determine if we will extend the colormap or not extend_cbar = [] #","get a figure you like. You can define how many rows and columns","from a profile object with no z_dim (vertical dimension). Provide an array of","ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season if required","extend = \"both\" elif \"max\" in extend_cbar and \"min\" in extend_cbar: extend =","= 3 # Marker size cmap = \"bwr\" # Colormap for normal points","(or any fixed level) errors from a profile object with no z_dim (vertical","= (-1, 1) # Color limits for normal points discrete_cmap = True #","- fig_pad[3])) # Handle colorbar -- will we extend it? if \"both\" in","extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title","var_name = \"abs_diff_temperature\" # Variable name in analysis file to plot # If","name_of_count_variable data = ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average] = np.nan #","colormap or not extend_cbar = [] # Loop over dataset for ii in","0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom) # Leave some","list ds_list = [xr.open_dataset(dd) for dd in fn_list] n_ds = len(ds_list) n_ax =","# Longitude bounds latbounds = [45, 64] # Latitude bounds subplot_padding = 0.5","then this is where to select season etc. save_plot = False # Masking","for dataset subtitles # PLOT SEASONS. Make sure n_r = 2 and n_c","season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\"bold\") else: ds = ds_list[ii] var_ii = var_name","import coast import pandas as pd #%% File settings run_name = \"test\" #","output files. Profiles from each will be plotted # on each axis of","normal points clim = (-1, 1) # Color limits for normal points discrete_cmap","analysis file to plot # If you used var modified to make gridded","= np.nan # Scatter and set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data,","rows n_c = 2 # Number of subplot columns figsize = (10, 5)","of the plot fn_list = [ \"~/transfer/test_grid.nc\", \"~/transfer/test_grid.nc\", ] # Filename for the","min_points_in_average = 5 name_of_count_variable = \"grid_N\" # Subplot axes settings n_r = 2","for labelling plots subtitle_fontsize = 11 # Fontsize for dataset subtitles subtitle_fontweight =","up surface or bottom (or any fixed level) errors from a profile object","grid cells that don't contain many points min_points_in_average = 5 name_of_count_variable = \"grid_N\"","import matplotlib.pyplot as plt import numpy as np import sys sys.path.append(\"/Users/dbyrne/code/COAsT\") import coast","of title title_fontweight = \"bold\" # Fontweight to use for title dataset_names =","a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values count_var = ds[N_var] data[count_var","and \"min\" in extend_cbar: extend = \"both\" elif \"max\" in extend_cbar: extend =","Longitude bounds latbounds = [45, 64] # Latitude bounds subplot_padding = 0.5 #","cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc,","= \"test\" # List of analysis output files. Profiles from each will be","2 # Number of subplot rows n_c = 2 # Number of subplot","var_name # for each subplot panel. plot_seasons = True season_suffixes = [\"DJF\", \"MAM\",","= 11 # Fontsize for dataset subtitles subtitle_fontweight = \"normal\" # Fontweight for","datasets into list ds_list = [xr.open_dataset(dd) for dd in fn_list] n_ds = len(ds_list)","True # Discretize colormap cmap_levels = 14 # Labels and Titles fig_title =","Labels and Titles fig_title = \"SST Errors\" # Whole figure title title_fontsize =","if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will extend the","is true, only the first dataset will be plotted, with seasonal # variables","top=(1 - fig_pad[3])) # Handle colorbar -- will we extend it? if \"both\"","General Plot Settings var_name = \"abs_diff_temperature\" # Variable name in analysis file to","we will extend the colormap or not extend_cbar = [] # Loop over","\"grid_N\" # Subplot axes settings n_r = 2 # Number of subplot rows","ds = ds_list[0] var_ii = var_name + \"_{0}\".format(season_suffixes[ii]) N_var = \"{0}_{1}\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05,","Fontweight to use for title dataset_names = [\"CO9p0\", \"CO9p0\", \"CO9p0\"] # Names to","2 and n_c = 2 # If this option is true, only the","datasets from left to right and top to bottom. A colorbar will be","= name_of_count_variable data = ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average] = np.nan","Colormap for normal points clim = (-1, 1) # Color limits for normal","Filename for the output fn_out = \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%% General Plot Settings var_name =","points min_points_in_average = 5 name_of_count_variable = \"grid_N\" # Subplot axes settings n_r =","contain many points min_points_in_average = 5 name_of_count_variable = \"grid_N\" # Subplot axes settings","plots fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right,","N_var = \"{0}_{1}\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\"bold\") else: ds = ds_list[ii]","ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we extend the colorbar","= \"grid_N\" # Subplot axes settings n_r = 2 # Number of subplot","only the first dataset will be plotted, with seasonal # variables on each","title dataset_names = [\"CO9p0\", \"CO9p0\", \"CO9p0\"] # Names to use for labelling plots","from each will be plotted # on each axis of the plot fn_list","The season_suffixes will be added to var_name # for each subplot panel. plot_seasons","coast import pandas as pd #%% File settings run_name = \"test\" # List","1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\"bold\") else: ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize,","a profile object with no z_dim (vertical dimension). Provide an array of netcdf","that don't contain many points min_points_in_average = 5 name_of_count_variable = \"grid_N\" # Subplot","0.1, 0.1) # Figure padding (left, top, right, bottom) # Leave some space","Plot up surface or bottom (or any fixed level) errors from a profile","of netcdf datasets from left to right and top to bottom. A colorbar","= False # Masking out grid cells that don't contain many points min_points_in_average","Read all datasets into list ds_list = [xr.open_dataset(dd) for dd in fn_list] n_ds","pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we","plotted, with seasonal # variables on each subplot. The season_suffixes will be added","numpy as np import sys sys.path.append(\"/Users/dbyrne/code/COAsT\") import coast import pandas as pd #%%","files and mess with the options to get a figure you like. You","List of analysis output files. Profiles from each will be plotted # on","each subplot panel. plot_seasons = True season_suffixes = [\"DJF\", \"MAM\", \"JJA\", \"SON\"] #%%","as plt import numpy as np import sys sys.path.append(\"/Users/dbyrne/code/COAsT\") import coast import pandas","data = ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average] = np.nan # Scatter","Will we extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set","each subplot. The season_suffixes will be added to var_name # for each subplot","= \"neither\" cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025,","the first dataset will be plotted, with seasonal # variables on each subplot.","lonbounds = [-15, 9.5] # Longitude bounds latbounds = [45, 64] # Latitude","size lonbounds = [-15, 9.5] # Longitude bounds latbounds = [45, 64] #","# Labels and Titles fig_title = \"SST Errors\" # Whole figure title title_fontsize","= \"normal\" # Fontweight for dataset subtitles # PLOT SEASONS. Make sure n_r","# Variable name in analysis file to plot # If you used var","elif \"min\" in extend_cbar: extend = \"min\" else: extend = \"neither\" cbar_ax =","-- will we extend it? if \"both\" in extend_cbar: extend = \"both\" elif","plot # If you used var modified to make gridded data # then","= 2 and n_c = 2 # If this option is true, only","subtitles subtitle_fontweight = \"normal\" # Fontweight for dataset subtitles # PLOT SEASONS. Make","with seasonal # variables on each subplot. The season_suffixes will be added to","columns figsize = (10, 5) # Figure size lonbounds = [-15, 9.5] #","= ds[N_var] data[count_var < min_points_in_average] = np.nan # Scatter and set title pc","you used var modified to make gridded data # then this is where","plt import numpy as np import sys sys.path.append(\"/Users/dbyrne/code/COAsT\") import coast import pandas as","Fontsize for dataset subtitles subtitle_fontweight = \"normal\" # Fontweight for dataset subtitles #","title title_fontweight = \"bold\" # Fontweight to use for title dataset_names = [\"CO9p0\",","n_r * n_c # Create plot and flatten axis array f, a =","extend = \"min\" else: extend = \"neither\" cbar_ax = f.add_axes([(1 - fig_pad[2] +","Scatter and set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1],","right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar -- will we extend","run_name = \"test\" # List of analysis output files. Profiles from each will","to get a figure you like. You can define how many rows and","subplot panel. plot_seasons = True season_suffixes = [\"DJF\", \"MAM\", \"JJA\", \"SON\"] #%% Read","# PLOT SEASONS. Make sure n_r = 2 and n_c = 2 #","= [\"DJF\", \"MAM\", \"JJA\", \"SON\"] #%% Read and plotdata # Read all datasets","[\"CO9p0\", \"CO9p0\", \"CO9p0\"] # Names to use for labelling plots subtitle_fontsize = 11","PLOT SEASONS. Make sure n_r = 2 and n_c = 2 # If","if \"both\" in extend_cbar: extend = \"both\" elif \"max\" in extend_cbar and \"min\"","# Discretize colormap cmap_levels = 14 # Labels and Titles fig_title = \"SST","SEASONS. Make sure n_r = 2 and n_c = 2 # If this","min_points_in_average] = np.nan # Scatter and set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude,","n_r, n_c, figsize=figsize) a_flat = a.flatten() # Dicretize colormap maybe if discrete_cmap: cmap","how many rows and columns the plot will have. This script will plot","fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 -","season etc. save_plot = False # Masking out grid cells that don't contain","ds_list = [xr.open_dataset(dd) for dd in fn_list] n_ds = len(ds_list) n_ax = n_r","Number of subplot columns figsize = (10, 5) # Figure size lonbounds =","# If you used var modified to make gridded data # then this","if we will extend the colormap or not extend_cbar = [] # Loop","over dataset for ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select","\"~/transfer/test_grid.nc\", \"~/transfer/test_grid.nc\", ] # Filename for the output fn_out = \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%% General","= (10, 5) # Figure size lonbounds = [-15, 9.5] # Longitude bounds","for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight)","[ \"~/transfer/test_grid.nc\", \"~/transfer/test_grid.nc\", ] # Filename for the output fn_out = \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%%","dataset for ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season","dimension). Provide an array of netcdf files and mess with the options to","# Fontsize for dataset subtitles subtitle_fontweight = \"normal\" # Fontweight for dataset subtitles","axis of the plot fn_list = [ \"~/transfer/test_grid.nc\", \"~/transfer/test_grid.nc\", ] # Filename for","z_dim (vertical dimension). Provide an array of netcdf files and mess with the","# Masking out grid cells that don't contain many points min_points_in_average = 5","with no z_dim (vertical dimension). Provide an array of netcdf files and mess","is where to select season etc. save_plot = False # Masking out grid","= \"max\" elif \"min\" in extend_cbar: extend = \"min\" else: extend = \"neither\"","\"both\" in extend_cbar: extend = \"both\" elif \"max\" in extend_cbar and \"min\" in","len(ds_list) n_ax = n_r * n_c # Create plot and flatten axis array","13 # Fontsize of title title_fontweight = \"bold\" # Fontweight to use for","subtitle_fontweight = \"normal\" # Fontweight for dataset subtitles # PLOT SEASONS. Make sure","\"bold\" # Fontweight to use for title dataset_names = [\"CO9p0\", \"CO9p0\", \"CO9p0\"] #","this option is true, only the first dataset will be plotted, with seasonal","of subplot rows n_c = 2 # Number of subplot columns figsize =","horizontal padding between plots fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding","ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values","Color limits for normal points discrete_cmap = True # Discretize colormap cmap_levels =","in extend_cbar: extend = \"both\" elif \"max\" in extend_cbar: extend = \"max\" elif","\"\"\" Plot up surface or bottom (or any fixed level) errors from a","pandas as pd #%% File settings run_name = \"test\" # List of analysis","and Titles fig_title = \"SST Errors\" # Whole figure title title_fontsize = 13","set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) #","= [ \"~/transfer/test_grid.nc\", \"~/transfer/test_grid.nc\", ] # Filename for the output fn_out = \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name)","Fontweight for dataset subtitles # PLOT SEASONS. Make sure n_r = 2 and","analysis output files. Profiles from each will be plotted # on each axis","# Fontweight for dataset subtitles # PLOT SEASONS. Make sure n_r = 2","# then this is where to select season etc. save_plot = False #","Whole figure title title_fontsize = 13 # Fontsize of title title_fontweight = \"bold\"","# If this option is true, only the first dataset will be plotted,","Create plot and flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c,","to var_name # for each subplot panel. plot_seasons = True season_suffixes = [\"DJF\",","= coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten() # Dicretize colormap maybe","colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will","gridded data # then this is where to select season etc. save_plot =","+ fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) # Save plot","n_r = 2 # Number of subplot rows n_c = 2 # Number","plot fn_list = [ \"~/transfer/test_grid.nc\", \"~/transfer/test_grid.nc\", ] # Filename for the output fn_out","bottom. A colorbar will be placed right of the figure. \"\"\" import xarray","in analysis file to plot # If you used var modified to make","data # then this is where to select season etc. save_plot = False","\"min\" else: extend = \"neither\" cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] *","extend_cbar = [] # Loop over dataset for ii in range(n_ax): ur_index =","n_c = 2 # Number of subplot columns figsize = (10, 5) #","or not extend_cbar = [] # Loop over dataset for ii in range(n_ax):","# Will we extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) #","vertical and horizontal padding between plots fig_pad = (0.075, 0.075, 0.1, 0.1) #","plots subtitle_fontsize = 11 # Fontsize for dataset subtitles subtitle_fontweight = \"normal\" #","9.5] # Longitude bounds latbounds = [45, 64] # Latitude bounds subplot_padding =","figure title title_fontsize = 13 # Fontsize of title title_fontweight = \"bold\" #"],"string":"[\n \"profile object with no z_dim (vertical dimension). Provide an array of netcdf files\",\n \"range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season if required if plot_seasons:\",\n \"fn_out = \\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name) #%% General Plot Settings var_name = \\\"abs_diff_temperature\\\" # Variable name\",\n \"use for labelling plots subtitle_fontsize = 11 # Fontsize for dataset subtitles subtitle_fontweight\",\n \"etc. save_plot = False # Masking out grid cells that don't contain many\",\n \"# Create plot and flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r,\",\n \"many rows and columns the plot will have. This script will plot the\",\n \"be placed right of the figure. \\\"\\\"\\\" import xarray as xr import matplotlib.pyplot\",\n \"between plots fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top,\",\n \"an array of netcdf files and mess with the options to get a\",\n \"[xr.open_dataset(dd) for dd in fn_list] n_ds = len(ds_list) n_ax = n_r * n_c\",\n \"season_suffixes will be added to var_name # for each subplot panel. plot_seasons =\",\n \"# Names to use for labelling plots subtitle_fontsize = 11 # Fontsize for\",\n \"Latitude bounds subplot_padding = 0.5 # Amount of vertical and horizontal padding between\",\n \"the colormap or not extend_cbar = [] # Loop over dataset for ii\",\n \"# Handle colorbar -- will we extend it? if \\\"both\\\" in extend_cbar: extend\",\n \"vmin=clim[0], vmax=clim[1], ) # Will we extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data,\",\n \"= f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax,\",\n \"dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set\",\n \"# on each axis of the plot fn_list = [ \\\"~/transfer/test_grid.nc\\\", \\\"~/transfer/test_grid.nc\\\", ]\",\n \"\\\"\\\"\\\" import xarray as xr import matplotlib.pyplot as plt import numpy as np\",\n \"will we extend it? if \\\"both\\\" in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\"\",\n \"# Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1\",\n \"= \\\"{0}_{1}\\\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\\\"bold\\\") else: ds = ds_list[ii] var_ii\",\n \"with the options to get a figure you like. You can define how\",\n \"will have. This script will plot the provided list of netcdf datasets from\",\n \"latbounds = [45, 64] # Latitude bounds subplot_padding = 0.5 # Amount of\",\n \"subplot_padding = 0.5 # Amount of vertical and horizontal padding between plots fig_pad\",\n \"from left to right and top to bottom. A colorbar will be placed\",\n \"(0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom) # Leave\",\n \"fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values count_var = ds[N_var] data[count_var <\",\n \"clim[0], clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure\",\n \"Select season if required if plot_seasons: ds = ds_list[0] var_ii = var_name +\",\n \"into list ds_list = [xr.open_dataset(dd) for dd in fn_list] n_ds = len(ds_list) n_ax\",\n \"for dataset subtitles subtitle_fontweight = \\\"normal\\\" # Fontweight for dataset subtitles # PLOT\",\n \"\\\"min\\\" in extend_cbar: extend = \\\"min\\\" else: extend = \\\"neither\\\" cbar_ax = f.add_axes([(1\",\n \"fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) # Save plot maybe\",\n \"# List of analysis output files. Profiles from each will be plotted #\",\n \"Plot Settings var_name = \\\"abs_diff_temperature\\\" # Variable name in analysis file to plot\",\n \"import xarray as xr import matplotlib.pyplot as plt import numpy as np import\",\n \"= \\\"abs_diff_temperature\\\" # Variable name in analysis file to plot # If you\",\n \"size cmap = \\\"bwr\\\" # Colormap for normal points clim = (-1, 1)\",\n \"Profiles from each will be plotted # on each axis of the plot\",\n \"Figure size lonbounds = [-15, 9.5] # Longitude bounds latbounds = [45, 64]\",\n \"cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we extend the colorbar for this dataset?\",\n \"it? if \\\"both\\\" in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar and\",\n \"the output fn_out = \\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name) #%% General Plot Settings var_name = \\\"abs_diff_temperature\\\" #\",\n \"on right for colorbar # Scatter opts marker_size = 3 # Marker size\",\n \"in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar and \\\"min\\\" in extend_cbar:\",\n \"files. Profiles from each will be plotted # on each axis of the\",\n \"list of netcdf datasets from left to right and top to bottom. A\",\n \"right of the figure. \\\"\\\"\\\" import xarray as xr import matplotlib.pyplot as plt\",\n \"subplot. The season_suffixes will be added to var_name # for each subplot panel.\",\n \"don't contain many points min_points_in_average = 5 name_of_count_variable = \\\"grid_N\\\" # Subplot axes\",\n \"extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar and \\\"min\\\" in extend_cbar: extend\",\n \"mess with the options to get a figure you like. You can define\",\n \"colorbar will be placed right of the figure. \\\"\\\"\\\" import xarray as xr\",\n \"many points min_points_in_average = 5 name_of_count_variable = \\\"grid_N\\\" # Subplot axes settings n_r\",\n \"dd in fn_list] n_ds = len(ds_list) n_ax = n_r * n_c # Create\",\n \"14 # Labels and Titles fig_title = \\\"SST Errors\\\" # Whole figure title\",\n \"padding between plots fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left,\",\n \"# Colormap for normal points clim = (-1, 1) # Color limits for\",\n \"= var_name + \\\"_{0}\\\".format(season_suffixes[ii]) N_var = \\\"{0}_{1}\\\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\\\"bold\\\")\",\n \"title title_fontsize = 13 # Fontsize of title title_fontweight = \\\"bold\\\" # Fontweight\",\n \"like. You can define how many rows and columns the plot will have.\",\n \"be added to var_name # for each subplot panel. plot_seasons = True season_suffixes\",\n \"ds_list[0] var_ii = var_name + \\\"_{0}\\\".format(season_suffixes[ii]) N_var = \\\"{0}_{1}\\\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii],\",\n \"np.nan # Scatter and set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap,\",\n \"2 # Number of subplot columns figsize = (10, 5) # Figure size\",\n \"Read and plotdata # Read all datasets into list ds_list = [xr.open_dataset(dd) for\",\n \"= var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values count_var =\",\n \"for normal points discrete_cmap = True # Discretize colormap cmap_levels = 14 #\",\n \"added to var_name # for each subplot panel. plot_seasons = True season_suffixes =\",\n \"used var modified to make gridded data # then this is where to\",\n \"= 2 # Number of subplot rows n_c = 2 # Number of\",\n \"and plotdata # Read all datasets into list ds_list = [xr.open_dataset(dd) for dd\",\n \"columns the plot will have. This script will plot the provided list of\",\n \"as xr import matplotlib.pyplot as plt import numpy as np import sys sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\")\",\n \"plotted # on each axis of the plot fn_list = [ \\\"~/transfer/test_grid.nc\\\", \\\"~/transfer/test_grid.nc\\\",\",\n \"to use for labelling plots subtitle_fontsize = 11 # Fontsize for dataset subtitles\",\n \"= 2 # If this option is true, only the first dataset will\",\n \"for dd in fn_list] n_ds = len(ds_list) n_ax = n_r * n_c #\",\n \"Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 -\",\n \"Marker size cmap = \\\"bwr\\\" # Colormap for normal points clim = (-1,\",\n \"clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout\",\n \"Make sure n_r = 2 and n_c = 2 # If this option\",\n \"this is where to select season etc. save_plot = False # Masking out\",\n \"Figure padding (left, top, right, bottom) # Leave some space on right for\",\n \"and horizontal padding between plots fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure\",\n \"be plotted # on each axis of the plot fn_list = [ \\\"~/transfer/test_grid.nc\\\",\",\n \"# for each subplot panel. plot_seasons = True season_suffixes = [\\\"DJF\\\", \\\"MAM\\\", \\\"JJA\\\",\",\n \"xarray as xr import matplotlib.pyplot as plt import numpy as np import sys\",\n \"right for colorbar # Scatter opts marker_size = 3 # Marker size cmap\",\n \"coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten() # Dicretize colormap maybe if\",\n \"to select season etc. save_plot = False # Masking out grid cells that\",\n \"Names to use for labelling plots subtitle_fontsize = 11 # Fontsize for dataset\",\n \"no z_dim (vertical dimension). Provide an array of netcdf files and mess with\",\n \"\\\"min\\\" in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar: extend = \\\"max\\\"\",\n \"f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1\",\n \"import numpy as np import sys sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\") import coast import pandas as pd\",\n \"If you used var modified to make gridded data # then this is\",\n \"object with no z_dim (vertical dimension). Provide an array of netcdf files and\",\n \"n_ax = n_r * n_c # Create plot and flatten axis array f,\",\n \"ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we extend the colorbar for\",\n \"= n_r * n_c # Create plot and flatten axis array f, a\",\n \"right, bottom) # Leave some space on right for colorbar # Scatter opts\",\n \"\\\"max\\\" in extend_cbar: extend = \\\"max\\\" elif \\\"min\\\" in extend_cbar: extend = \\\"min\\\"\",\n \"subtitle_fontsize = 11 # Fontsize for dataset subtitles subtitle_fontweight = \\\"normal\\\" # Fontweight\",\n \"var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values count_var\",\n \"bounds latbounds = [45, 64] # Latitude bounds subplot_padding = 0.5 # Amount\",\n \"fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom)\",\n \"of subplot columns figsize = (10, 5) # Figure size lonbounds = [-15,\",\n \"will extend the colormap or not extend_cbar = [] # Loop over dataset\",\n \"Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding)\",\n \"and mess with the options to get a figure you like. You can\",\n \"# Number of subplot columns figsize = (10, 5) # Figure size lonbounds\",\n \"padding (left, top, right, bottom) # Leave some space on right for colorbar\",\n \"var_ii = var_name + \\\"_{0}\\\".format(season_suffixes[ii]) N_var = \\\"{0}_{1}\\\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes,\",\n \"#%% File settings run_name = \\\"test\\\" # List of analysis output files. Profiles\",\n \"\\\"both\\\" elif \\\"max\\\" in extend_cbar: extend = \\\"max\\\" elif \\\"min\\\" in extend_cbar: extend\",\n \"and flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat\",\n \"= [] # Loop over dataset for ii in range(n_ax): ur_index = np.unravel_index(ii,\",\n \"= \\\"bold\\\" # Fontweight to use for title dataset_names = [\\\"CO9p0\\\", \\\"CO9p0\\\", \\\"CO9p0\\\"]\",\n \"\\\"CO9p0\\\"] # Names to use for labelling plots subtitle_fontsize = 11 # Fontsize\",\n \"bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar -- will we\",\n \"season if required if plot_seasons: ds = ds_list[0] var_ii = var_name + \\\"_{0}\\\".format(season_suffixes[ii])\",\n \"plot the provided list of netcdf datasets from left to right and top\",\n \"# Scatter and set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0],\",\n \"extend_cbar and \\\"min\\\" in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar: extend\",\n \"#%% General Plot Settings var_name = \\\"abs_diff_temperature\\\" # Variable name in analysis file\",\n \"file to plot # If you used var modified to make gridded data\",\n \"0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) # Save plot maybe if save_plot:\",\n \"save_plot = False # Masking out grid cells that don't contain many points\",\n \"labelling plots subtitle_fontsize = 11 # Fontsize for dataset subtitles subtitle_fontweight = \\\"normal\\\"\",\n \"1) # Color limits for normal points discrete_cmap = True # Discretize colormap\",\n \"# Read all datasets into list ds_list = [xr.open_dataset(dd) for dd in fn_list]\",\n \"\\\"abs_diff_temperature\\\" # Variable name in analysis file to plot # If you used\",\n \"and n_c = 2 # If this option is true, only the first\",\n \"each will be plotted # on each axis of the plot fn_list =\",\n \"Scatter opts marker_size = 3 # Marker size cmap = \\\"bwr\\\" # Colormap\",\n \"the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title f.suptitle(fig_title,\",\n \"# Subplot axes settings n_r = 2 # Number of subplot rows n_c\",\n \"subplot columns figsize = (10, 5) # Figure size lonbounds = [-15, 9.5]\",\n \"fontweight=\\\"bold\\\") else: ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var =\",\n \"(-1, 1) # Color limits for normal points discrete_cmap = True # Discretize\",\n \"(vertical dimension). Provide an array of netcdf files and mess with the options\",\n \"points clim = (-1, 1) # Color limits for normal points discrete_cmap =\",\n \"np.unravel_index(ii, (n_r, n_c)) # Select season if required if plot_seasons: ds = ds_list[0]\",\n \"= True season_suffixes = [\\\"DJF\\\", \\\"MAM\\\", \\\"JJA\\\", \\\"SON\\\"] #%% Read and plotdata #\",\n \"the plot will have. This script will plot the provided list of netcdf\",\n \"modified to make gridded data # then this is where to select season\",\n \"File settings run_name = \\\"test\\\" # List of analysis output files. Profiles from\",\n \"the options to get a figure you like. You can define how many\",\n \"n_c)) # Select season if required if plot_seasons: ds = ds_list[0] var_ii =\",\n \"figure you like. You can define how many rows and columns the plot\",\n \"False # Masking out grid cells that don't contain many points min_points_in_average =\",\n \"= ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average] = np.nan # Scatter and\",\n \"f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar -- will\",\n \"a_flat = a.flatten() # Dicretize colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels)\",\n \"as pd #%% File settings run_name = \\\"test\\\" # List of analysis output\",\n \"Fontsize of title title_fontweight = \\\"bold\\\" # Fontweight to use for title dataset_names\",\n \"import pandas as pd #%% File settings run_name = \\\"test\\\" # List of\",\n \"settings n_r = 2 # Number of subplot rows n_c = 2 #\",\n \"any fixed level) errors from a profile object with no z_dim (vertical dimension).\",\n \"settings run_name = \\\"test\\\" # List of analysis output files. Profiles from each\",\n \"= a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we extend\",\n \"will be placed right of the figure. \\\"\\\"\\\" import xarray as xr import\",\n \"(left, top, right, bottom) # Leave some space on right for colorbar #\",\n \"for each subplot panel. plot_seasons = True season_suffixes = [\\\"DJF\\\", \\\"MAM\\\", \\\"JJA\\\", \\\"SON\\\"]\",\n \"var modified to make gridded data # then this is where to select\",\n \"cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will extend the colormap or\",\n \"= ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data =\",\n \"Variable name in analysis file to plot # If you used var modified\",\n \"subplot rows n_c = 2 # Number of subplot columns figsize = (10,\",\n \"on each subplot. The season_suffixes will be added to var_name # for each\",\n \"elif \\\"max\\\" in extend_cbar: extend = \\\"max\\\" elif \\\"min\\\" in extend_cbar: extend =\",\n \"# Figure size lonbounds = [-15, 9.5] # Longitude bounds latbounds = [45,\",\n \"11 # Fontsize for dataset subtitles subtitle_fontweight = \\\"normal\\\" # Fontweight for dataset\",\n \"a figure you like. You can define how many rows and columns the\",\n \"axes settings n_r = 2 # Number of subplot rows n_c = 2\",\n \"# Number of subplot rows n_c = 2 # Number of subplot columns\",\n \"xr import matplotlib.pyplot as plt import numpy as np import sys sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\") import\",\n \"marker_size = 3 # Marker size cmap = \\\"bwr\\\" # Colormap for normal\",\n \"dataset will be plotted, with seasonal # variables on each subplot. The season_suffixes\",\n \"= 14 # Labels and Titles fig_title = \\\"SST Errors\\\" # Whole figure\",\n \"netcdf files and mess with the options to get a figure you like.\",\n \"Leave some space on right for colorbar # Scatter opts marker_size = 3\",\n \"first dataset will be plotted, with seasonal # variables on each subplot. The\",\n \"True season_suffixes = [\\\"DJF\\\", \\\"MAM\\\", \\\"JJA\\\", \\\"SON\\\"] #%% Read and plotdata # Read\",\n \"each axis of the plot fn_list = [ \\\"~/transfer/test_grid.nc\\\", \\\"~/transfer/test_grid.nc\\\", ] # Filename\",\n \"Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]),\",\n \"title_fontsize = 13 # Fontsize of title title_fontweight = \\\"bold\\\" # Fontweight to\",\n \"colormap cmap_levels = 14 # Labels and Titles fig_title = \\\"SST Errors\\\" #\",\n \"[45, 64] # Latitude bounds subplot_padding = 0.5 # Amount of vertical and\",\n \"= [45, 64] # Latitude bounds subplot_padding = 0.5 # Amount of vertical\",\n \"# Scatter opts marker_size = 3 # Marker size cmap = \\\"bwr\\\" #\",\n \"fn_list] n_ds = len(ds_list) n_ax = n_r * n_c # Create plot and\",\n \"provided list of netcdf datasets from left to right and top to bottom.\",\n \"of netcdf files and mess with the options to get a figure you\",\n \"and columns the plot will have. This script will plot the provided list\",\n \"fn_list = [ \\\"~/transfer/test_grid.nc\\\", \\\"~/transfer/test_grid.nc\\\", ] # Filename for the output fn_out =\",\n \"data[count_var < min_points_in_average] = np.nan # Scatter and set title pc = a_flat[ii].pcolormesh(\",\n \"output fn_out = \\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name) #%% General Plot Settings var_name = \\\"abs_diff_temperature\\\" # Variable\",\n \"name_of_count_variable = \\\"grid_N\\\" # Subplot axes settings n_r = 2 # Number of\",\n \"\\\"_{0}\\\".format(season_suffixes[ii]) N_var = \\\"{0}_{1}\\\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\\\"bold\\\") else: ds =\",\n \"array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten() #\",\n \"cmap_levels) # Determine if we will extend the colormap or not extend_cbar =\",\n \"var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values count_var = ds[N_var]\",\n \"# Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding,\",\n \"(10, 5) # Figure size lonbounds = [-15, 9.5] # Longitude bounds latbounds\",\n \"plotdata # Read all datasets into list ds_list = [xr.open_dataset(dd) for dd in\",\n \"\\\"JJA\\\", \\\"SON\\\"] #%% Read and plotdata # Read all datasets into list ds_list\",\n \"- fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar -- will we extend it?\",\n \"bounds subplot_padding = 0.5 # Amount of vertical and horizontal padding between plots\",\n \"plot and flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize)\",\n \"n_c, figsize=figsize) a_flat = a.flatten() # Dicretize colormap maybe if discrete_cmap: cmap =\",\n \"out grid cells that don't contain many points min_points_in_average = 5 name_of_count_variable =\",\n \"#%% Read and plotdata # Read all datasets into list ds_list = [xr.open_dataset(dd)\",\n \"bottom) # Leave some space on right for colorbar # Scatter opts marker_size\",\n \"else: ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable\",\n \"surface or bottom (or any fixed level) errors from a profile object with\",\n \"= 0.5 # Amount of vertical and horizontal padding between plots fig_pad =\",\n \"season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\\\"bold\\\") else: ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight)\",\n \"title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]),\",\n \"will be plotted, with seasonal # variables on each subplot. The season_suffixes will\",\n \"\\\"bwr\\\" # Colormap for normal points clim = (-1, 1) # Color limits\",\n \"fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]),\",\n \"seasonal # variables on each subplot. The season_suffixes will be added to var_name\",\n \"ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season if required if plot_seasons: ds\",\n \"a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\\\"bold\\\") else: ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii],\",\n \"have. This script will plot the provided list of netcdf datasets from left\",\n \"= 5 name_of_count_variable = \\\"grid_N\\\" # Subplot axes settings n_r = 2 #\",\n \"true, only the first dataset will be plotted, with seasonal # variables on\",\n \"figsize = (10, 5) # Figure size lonbounds = [-15, 9.5] # Longitude\",\n \"a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten() # Dicretize colormap\",\n \"placed right of the figure. \\\"\\\"\\\" import xarray as xr import matplotlib.pyplot as\",\n \"# Select season if required if plot_seasons: ds = ds_list[0] var_ii = var_name\",\n \"axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten()\",\n \"import sys sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\") import coast import pandas as pd #%% File settings run_name\",\n \"extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) # Set tight\",\n \"= \\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name) #%% General Plot Settings var_name = \\\"abs_diff_temperature\\\" # Variable name in\",\n \"subtitles # PLOT SEASONS. Make sure n_r = 2 and n_c = 2\",\n \"extend = \\\"neither\\\" cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15,\",\n \"and set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], )\",\n \"n_c # Create plot and flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds,\",\n \"\\\"{0}_{1}\\\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\\\"bold\\\") else: ds = ds_list[ii] var_ii =\",\n \"options to get a figure you like. You can define how many rows\",\n \"a.flatten() # Dicretize colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine\",\n \"the figure. \\\"\\\"\\\" import xarray as xr import matplotlib.pyplot as plt import numpy\",\n \"n_r = 2 and n_c = 2 # If this option is true,\",\n \"for colorbar # Scatter opts marker_size = 3 # Marker size cmap =\",\n \"\\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name) #%% General Plot Settings var_name = \\\"abs_diff_temperature\\\" # Variable name in analysis\",\n \"netcdf datasets from left to right and top to bottom. A colorbar will\",\n \"to make gridded data # then this is where to select season etc.\",\n \"cells that don't contain many points min_points_in_average = 5 name_of_count_variable = \\\"grid_N\\\" #\",\n \"to right and top to bottom. A colorbar will be placed right of\",\n \"fig_pad[3])) # Handle colorbar -- will we extend it? if \\\"both\\\" in extend_cbar:\",\n \"option is true, only the first dataset will be plotted, with seasonal #\",\n \"Subplot axes settings n_r = 2 # Number of subplot rows n_c =\",\n \"\\\"neither\\\" cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7])\",\n \"maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will extend\",\n \"use for title dataset_names = [\\\"CO9p0\\\", \\\"CO9p0\\\", \\\"CO9p0\\\"] # Names to use for\",\n \"as np import sys sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\") import coast import pandas as pd #%% File\",\n \"= [\\\"CO9p0\\\", \\\"CO9p0\\\", \\\"CO9p0\\\"] # Names to use for labelling plots subtitle_fontsize =\",\n \"variables on each subplot. The season_suffixes will be added to var_name # for\",\n \"np import sys sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\") import coast import pandas as pd #%% File settings\",\n \"\\\"both\\\" elif \\\"max\\\" in extend_cbar and \\\"min\\\" in extend_cbar: extend = \\\"both\\\" elif\",\n \"# Fontweight to use for title dataset_names = [\\\"CO9p0\\\", \\\"CO9p0\\\", \\\"CO9p0\\\"] # Names\",\n \"= [xr.open_dataset(dd) for dd in fn_list] n_ds = len(ds_list) n_ax = n_r *\",\n \"in fn_list] n_ds = len(ds_list) n_ax = n_r * n_c # Create plot\",\n \"ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average] = np.nan # Scatter and set\",\n \"of the figure. \\\"\\\"\\\" import xarray as xr import matplotlib.pyplot as plt import\",\n \"+ \\\"_{0}\\\".format(season_suffixes[ii]) N_var = \\\"{0}_{1}\\\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\\\"bold\\\") else: ds\",\n \"if required if plot_seasons: ds = ds_list[0] var_ii = var_name + \\\"_{0}\\\".format(season_suffixes[ii]) N_var\",\n \"You can define how many rows and columns the plot will have. This\",\n \"- fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) #\",\n \"f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten() # Dicretize\",\n \"fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average]\",\n \"ds[N_var] data[count_var < min_points_in_average] = np.nan # Scatter and set title pc =\",\n \"Errors\\\" # Whole figure title title_fontsize = 13 # Fontsize of title title_fontweight\",\n \"\\\"~/transfer/test_grid.nc\\\", ] # Filename for the output fn_out = \\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name) #%% General Plot\",\n \"define how many rows and columns the plot will have. This script will\",\n \"pd #%% File settings run_name = \\\"test\\\" # List of analysis output files.\",\n \"5 name_of_count_variable = \\\"grid_N\\\" # Subplot axes settings n_r = 2 # Number\",\n \"you like. You can define how many rows and columns the plot will\",\n \"\\\"normal\\\" # Fontweight for dataset subtitles # PLOT SEASONS. Make sure n_r =\",\n \"fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) # Save\",\n \"# Dicretize colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if\",\n \"= ds_list[0] var_ii = var_name + \\\"_{0}\\\".format(season_suffixes[ii]) N_var = \\\"{0}_{1}\\\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02,\",\n \"(n_r, n_c)) # Select season if required if plot_seasons: ds = ds_list[0] var_ii\",\n \"# variables on each subplot. The season_suffixes will be added to var_name #\",\n \"2 # If this option is true, only the first dataset will be\",\n \"= \\\"bwr\\\" # Colormap for normal points clim = (-1, 1) # Color\",\n \"errors from a profile object with no z_dim (vertical dimension). Provide an array\",\n \"or bottom (or any fixed level) errors from a profile object with no\",\n \"64] # Latitude bounds subplot_padding = 0.5 # Amount of vertical and horizontal\",\n \"sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\") import coast import pandas as pd #%% File settings run_name = \\\"test\\\"\",\n \"normal points discrete_cmap = True # Discretize colormap cmap_levels = 14 # Labels\",\n \"will be plotted # on each axis of the plot fn_list = [\",\n \"be plotted, with seasonal # variables on each subplot. The season_suffixes will be\",\n \"3 # Marker size cmap = \\\"bwr\\\" # Colormap for normal points clim\",\n \"plot_seasons = True season_suffixes = [\\\"DJF\\\", \\\"MAM\\\", \\\"JJA\\\", \\\"SON\\\"] #%% Read and plotdata\",\n \"ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data\",\n \"\\\"MAM\\\", \\\"JJA\\\", \\\"SON\\\"] #%% Read and plotdata # Read all datasets into list\",\n \"# Leave some space on right for colorbar # Scatter opts marker_size =\",\n \"in extend_cbar: extend = \\\"min\\\" else: extend = \\\"neither\\\" cbar_ax = f.add_axes([(1 -\",\n \"to bottom. A colorbar will be placed right of the figure. \\\"\\\"\\\" import\",\n \"# Amount of vertical and horizontal padding between plots fig_pad = (0.075, 0.075,\",\n \"# Color limits for normal points discrete_cmap = True # Discretize colormap cmap_levels\",\n \"Masking out grid cells that don't contain many points min_points_in_average = 5 name_of_count_variable\",\n \"figsize=figsize) a_flat = a.flatten() # Dicretize colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap,\",\n \"= [-15, 9.5] # Longitude bounds latbounds = [45, 64] # Latitude bounds\",\n \"= \\\"both\\\" elif \\\"max\\\" in extend_cbar: extend = \\\"max\\\" elif \\\"min\\\" in extend_cbar:\",\n \"discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will extend the colormap\",\n \"to use for title dataset_names = [\\\"CO9p0\\\", \\\"CO9p0\\\", \\\"CO9p0\\\"] # Names to use\",\n \"discrete_cmap = True # Discretize colormap cmap_levels = 14 # Labels and Titles\",\n \"opts marker_size = 3 # Marker size cmap = \\\"bwr\\\" # Colormap for\",\n \"all datasets into list ds_list = [xr.open_dataset(dd) for dd in fn_list] n_ds =\",\n \"not extend_cbar = [] # Loop over dataset for ii in range(n_ax): ur_index\",\n \"extend it? if \\\"both\\\" in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar\",\n \"matplotlib.pyplot as plt import numpy as np import sys sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\") import coast import\",\n \"dataset_names = [\\\"CO9p0\\\", \\\"CO9p0\\\", \\\"CO9p0\\\"] # Names to use for labelling plots subtitle_fontsize\",\n \"# Latitude bounds subplot_padding = 0.5 # Amount of vertical and horizontal padding\",\n \"If this option is true, only the first dataset will be plotted, with\",\n \"where to select season etc. save_plot = False # Masking out grid cells\",\n \"a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we extend the\",\n \"This script will plot the provided list of netcdf datasets from left to\",\n \"h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar --\",\n \"fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar -- will we extend it? if\",\n \"Number of subplot rows n_c = 2 # Number of subplot columns figsize\",\n \"colorbar # Scatter opts marker_size = 3 # Marker size cmap = \\\"bwr\\\"\",\n \"\\\"SON\\\"] #%% Read and plotdata # Read all datasets into list ds_list =\",\n \"data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we extend the colorbar for this\",\n \"= np.unravel_index(ii, (n_r, n_c)) # Select season if required if plot_seasons: ds =\",\n \"plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will extend the colormap or not extend_cbar\",\n \"transform=a_flat[ii].transAxes, fontweight=\\\"bold\\\") else: ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var\",\n \"< min_points_in_average] = np.nan # Scatter and set title pc = a_flat[ii].pcolormesh( ds.longitude,\",\n \"5) # Figure size lonbounds = [-15, 9.5] # Longitude bounds latbounds =\",\n \"rows and columns the plot will have. This script will plot the provided\",\n \"clim = (-1, 1) # Color limits for normal points discrete_cmap = True\",\n \"left to right and top to bottom. A colorbar will be placed right\",\n \"can define how many rows and columns the plot will have. This script\",\n \"select season etc. save_plot = False # Masking out grid cells that don't\",\n \"Dicretize colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if we\",\n \"Titles fig_title = \\\"SST Errors\\\" # Whole figure title title_fontsize = 13 #\",\n \"cmap_levels = 14 # Labels and Titles fig_title = \\\"SST Errors\\\" # Whole\",\n \"\\\"SST Errors\\\" # Whole figure title title_fontsize = 13 # Fontsize of title\",\n \"of vertical and horizontal padding between plots fig_pad = (0.075, 0.075, 0.1, 0.1)\",\n \"] # Filename for the output fn_out = \\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name) #%% General Plot Settings\",\n \"Discretize colormap cmap_levels = 14 # Labels and Titles fig_title = \\\"SST Errors\\\"\",\n \"# Filename for the output fn_out = \\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name) #%% General Plot Settings var_name\",\n \"= 13 # Fontsize of title title_fontweight = \\\"bold\\\" # Fontweight to use\",\n \"colorbar -- will we extend it? if \\\"both\\\" in extend_cbar: extend = \\\"both\\\"\",\n \"dataset subtitles subtitle_fontweight = \\\"normal\\\" # Fontweight for dataset subtitles # PLOT SEASONS.\",\n \"bottom (or any fixed level) errors from a profile object with no z_dim\",\n \"name in analysis file to plot # If you used var modified to\",\n \"array of netcdf files and mess with the options to get a figure\",\n \"n_ds = len(ds_list) n_ax = n_r * n_c # Create plot and flatten\",\n \"colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize,\",\n \"points discrete_cmap = True # Discretize colormap cmap_levels = 14 # Labels and\",\n \"= \\\"both\\\" elif \\\"max\\\" in extend_cbar and \\\"min\\\" in extend_cbar: extend = \\\"both\\\"\",\n \"for the output fn_out = \\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name) #%% General Plot Settings var_name = \\\"abs_diff_temperature\\\"\",\n \"will be added to var_name # for each subplot panel. plot_seasons = True\",\n \"tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3]))\",\n \"\\\"max\\\" in extend_cbar and \\\"min\\\" in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in\",\n \"and top to bottom. A colorbar will be placed right of the figure.\",\n \"* 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) # Save plot maybe if\",\n \"\\\"CO9p0\\\", \\\"CO9p0\\\"] # Names to use for labelling plots subtitle_fontsize = 11 #\",\n \"required if plot_seasons: ds = ds_list[0] var_ii = var_name + \\\"_{0}\\\".format(season_suffixes[ii]) N_var =\",\n \"# Fontsize of title title_fontweight = \\\"bold\\\" # Fontweight to use for title\",\n \"Settings var_name = \\\"abs_diff_temperature\\\" # Variable name in analysis file to plot #\",\n \"= 2 # Number of subplot columns figsize = (10, 5) # Figure\",\n \"extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar: extend = \\\"max\\\" elif \\\"min\\\"\",\n \"extend_cbar: extend = \\\"max\\\" elif \\\"min\\\" in extend_cbar: extend = \\\"min\\\" else: extend\",\n \"to plot # If you used var modified to make gridded data #\",\n \"script will plot the provided list of netcdf datasets from left to right\",\n \"for ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season if\",\n \"count_var = ds[N_var] data[count_var < min_points_in_average] = np.nan # Scatter and set title\",\n \"space on right for colorbar # Scatter opts marker_size = 3 # Marker\",\n \"cmap = \\\"bwr\\\" # Colormap for normal points clim = (-1, 1) #\",\n \"make gridded data # then this is where to select season etc. save_plot\",\n \"we extend it? if \\\"both\\\" in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in\",\n \"f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar\",\n \"right and top to bottom. A colorbar will be placed right of the\",\n \"this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) #\",\n \"fig_title = \\\"SST Errors\\\" # Whole figure title title_fontsize = 13 # Fontsize\",\n \"in extend_cbar and \\\"min\\\" in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar:\",\n \"# Loop over dataset for ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c))\",\n \"top, right, bottom) # Leave some space on right for colorbar # Scatter\",\n \"the provided list of netcdf datasets from left to right and top to\",\n \"# Determine if we will extend the colormap or not extend_cbar = []\",\n \"in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season if required if\",\n \"# Figure padding (left, top, right, bottom) # Leave some space on right\",\n \"plot_seasons: ds = ds_list[0] var_ii = var_name + \\\"_{0}\\\".format(season_suffixes[ii]) N_var = \\\"{0}_{1}\\\".format(name_of_count_variable, season_suffixes[ii])\",\n \"= \\\"SST Errors\\\" # Whole figure title title_fontsize = 13 # Fontsize of\",\n \"top to bottom. A colorbar will be placed right of the figure. \\\"\\\"\\\"\",\n \"Handle colorbar -- will we extend it? if \\\"both\\\" in extend_cbar: extend =\",\n \"[-15, 9.5] # Longitude bounds latbounds = [45, 64] # Latitude bounds subplot_padding\",\n \"sys sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\") import coast import pandas as pd #%% File settings run_name =\",\n \"* n_c # Create plot and flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds,\",\n \"for title dataset_names = [\\\"CO9p0\\\", \\\"CO9p0\\\", \\\"CO9p0\\\"] # Names to use for labelling\",\n \"Amount of vertical and horizontal padding between plots fig_pad = (0.075, 0.075, 0.1,\",\n \"var_name + \\\"_{0}\\\".format(season_suffixes[ii]) N_var = \\\"{0}_{1}\\\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\\\"bold\\\") else:\",\n \"= True # Discretize colormap cmap_levels = 14 # Labels and Titles fig_title\",\n \"fixed level) errors from a profile object with no z_dim (vertical dimension). Provide\",\n \"of analysis output files. Profiles from each will be plotted # on each\",\n \"[\\\"DJF\\\", \\\"MAM\\\", \\\"JJA\\\", \\\"SON\\\"] #%% Read and plotdata # Read all datasets into\",\n \"Provide an array of netcdf files and mess with the options to get\",\n \"= (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom) #\",\n \"extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar: extend = \\\"max\\\" elif \\\"min\\\" in\",\n \"extend_cbar: extend = \\\"min\\\" else: extend = \\\"neither\\\" cbar_ax = f.add_axes([(1 - fig_pad[2]\",\n \"= \\\"min\\\" else: extend = \\\"neither\\\" cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2]\",\n \"extend the colormap or not extend_cbar = [] # Loop over dataset for\",\n \"sure n_r = 2 and n_c = 2 # If this option is\",\n \"some space on right for colorbar # Scatter opts marker_size = 3 #\",\n \"A colorbar will be placed right of the figure. \\\"\\\"\\\" import xarray as\",\n \"figure. \\\"\\\"\\\" import xarray as xr import matplotlib.pyplot as plt import numpy as\",\n \"the plot fn_list = [ \\\"~/transfer/test_grid.nc\\\", \\\"~/transfer/test_grid.nc\\\", ] # Filename for the output\",\n \") # Will we extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1]))\",\n \"layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) # Handle\",\n \"= a.flatten() # Dicretize colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) #\",\n \"panel. plot_seasons = True season_suffixes = [\\\"DJF\\\", \\\"MAM\\\", \\\"JJA\\\", \\\"SON\\\"] #%% Read and\",\n \"else: extend = \\\"neither\\\" cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15),\",\n \"[] # Loop over dataset for ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r,\",\n \"limits for normal points discrete_cmap = True # Discretize colormap cmap_levels = 14\",\n \"\\\"max\\\" elif \\\"min\\\" in extend_cbar: extend = \\\"min\\\" else: extend = \\\"neither\\\" cbar_ax\",\n \"elif \\\"max\\\" in extend_cbar and \\\"min\\\" in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\"\",\n \"in extend_cbar: extend = \\\"max\\\" elif \\\"min\\\" in extend_cbar: extend = \\\"min\\\" else:\",\n \"\\\"test\\\" # List of analysis output files. Profiles from each will be plotted\",\n \"# Whole figure title title_fontsize = 13 # Fontsize of title title_fontweight =\",\n \"level) errors from a profile object with no z_dim (vertical dimension). Provide an\",\n \"for normal points clim = (-1, 1) # Color limits for normal points\",\n \"f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend)\",\n \"0.5 # Amount of vertical and horizontal padding between plots fig_pad = (0.075,\",\n \"= len(ds_list) n_ax = n_r * n_c # Create plot and flatten axis\",\n \"we extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure\",\n \"n_c = 2 # If this option is true, only the first dataset\",\n \"plot will have. This script will plot the provided list of netcdf datasets\",\n \"Loop over dataset for ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) #\",\n \"figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) #\",\n \"latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten() # Dicretize colormap maybe if discrete_cmap:\",\n \"title_fontweight = \\\"bold\\\" # Fontweight to use for title dataset_names = [\\\"CO9p0\\\", \\\"CO9p0\\\",\",\n \"extend = \\\"max\\\" elif \\\"min\\\" in extend_cbar: extend = \\\"min\\\" else: extend =\",\n \"vmax=clim[1], ) # Will we extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0],\",\n \"= plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will extend the colormap or not\",\n \"dataset subtitles # PLOT SEASONS. Make sure n_r = 2 and n_c =\",\n \"0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) # Save plot maybe if save_plot: f.savefig(fn_out)\",\n \"will plot the provided list of netcdf datasets from left to right and\",\n \"0.1) # Figure padding (left, top, right, bottom) # Leave some space on\",\n \"season_suffixes = [\\\"DJF\\\", \\\"MAM\\\", \\\"JJA\\\", \\\"SON\\\"] #%% Read and plotdata # Read all\",\n \"N_var = name_of_count_variable data = ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average] =\",\n \"if plot_seasons: ds = ds_list[0] var_ii = var_name + \\\"_{0}\\\".format(season_suffixes[ii]) N_var = \\\"{0}_{1}\\\".format(name_of_count_variable,\",\n \"on each axis of the plot fn_list = [ \\\"~/transfer/test_grid.nc\\\", \\\"~/transfer/test_grid.nc\\\", ] #\",\n \"flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat =\",\n \"title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will\",\n \"# Marker size cmap = \\\"bwr\\\" # Colormap for normal points clim =\",\n \"Determine if we will extend the colormap or not extend_cbar = [] #\",\n \"get a figure you like. You can define how many rows and columns\",\n \"from a profile object with no z_dim (vertical dimension). Provide an array of\",\n \"ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season if required\",\n \"extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar and \\\"min\\\" in extend_cbar: extend =\",\n \"= 3 # Marker size cmap = \\\"bwr\\\" # Colormap for normal points\",\n \"(or any fixed level) errors from a profile object with no z_dim (vertical\",\n \"= (-1, 1) # Color limits for normal points discrete_cmap = True #\",\n \"- fig_pad[3])) # Handle colorbar -- will we extend it? if \\\"both\\\" in\",\n \"extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title\",\n \"var_name = \\\"abs_diff_temperature\\\" # Variable name in analysis file to plot # If\",\n \"name_of_count_variable data = ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average] = np.nan #\",\n \"colormap or not extend_cbar = [] # Loop over dataset for ii in\",\n \"0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom) # Leave some\",\n \"list ds_list = [xr.open_dataset(dd) for dd in fn_list] n_ds = len(ds_list) n_ax =\",\n \"# Longitude bounds latbounds = [45, 64] # Latitude bounds subplot_padding = 0.5\",\n \"then this is where to select season etc. save_plot = False # Masking\",\n \"for dataset subtitles # PLOT SEASONS. Make sure n_r = 2 and n_c\",\n \"season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\\\"bold\\\") else: ds = ds_list[ii] var_ii = var_name\",\n \"import coast import pandas as pd #%% File settings run_name = \\\"test\\\" #\",\n \"output files. Profiles from each will be plotted # on each axis of\",\n \"normal points clim = (-1, 1) # Color limits for normal points discrete_cmap\",\n \"analysis file to plot # If you used var modified to make gridded\",\n \"= np.nan # Scatter and set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data,\",\n \"rows n_c = 2 # Number of subplot columns figsize = (10, 5)\",\n \"of the plot fn_list = [ \\\"~/transfer/test_grid.nc\\\", \\\"~/transfer/test_grid.nc\\\", ] # Filename for the\",\n \"min_points_in_average = 5 name_of_count_variable = \\\"grid_N\\\" # Subplot axes settings n_r = 2\",\n \"for labelling plots subtitle_fontsize = 11 # Fontsize for dataset subtitles subtitle_fontweight =\",\n \"up surface or bottom (or any fixed level) errors from a profile object\",\n \"grid cells that don't contain many points min_points_in_average = 5 name_of_count_variable = \\\"grid_N\\\"\",\n \"import matplotlib.pyplot as plt import numpy as np import sys sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\") import coast\",\n \"of title title_fontweight = \\\"bold\\\" # Fontweight to use for title dataset_names =\",\n \"a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values count_var = ds[N_var] data[count_var\",\n \"and \\\"min\\\" in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar: extend =\",\n \"Longitude bounds latbounds = [45, 64] # Latitude bounds subplot_padding = 0.5 #\",\n \"cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc,\",\n \"= \\\"test\\\" # List of analysis output files. Profiles from each will be\",\n \"2 # Number of subplot rows n_c = 2 # Number of subplot\",\n \"var_name # for each subplot panel. plot_seasons = True season_suffixes = [\\\"DJF\\\", \\\"MAM\\\",\",\n \"= 11 # Fontsize for dataset subtitles subtitle_fontweight = \\\"normal\\\" # Fontweight for\",\n \"datasets into list ds_list = [xr.open_dataset(dd) for dd in fn_list] n_ds = len(ds_list)\",\n \"True # Discretize colormap cmap_levels = 14 # Labels and Titles fig_title =\",\n \"Labels and Titles fig_title = \\\"SST Errors\\\" # Whole figure title title_fontsize =\",\n \"if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will extend the\",\n \"is true, only the first dataset will be plotted, with seasonal # variables\",\n \"top=(1 - fig_pad[3])) # Handle colorbar -- will we extend it? if \\\"both\\\"\",\n \"General Plot Settings var_name = \\\"abs_diff_temperature\\\" # Variable name in analysis file to\",\n \"we will extend the colormap or not extend_cbar = [] # Loop over\",\n \"\\\"grid_N\\\" # Subplot axes settings n_r = 2 # Number of subplot rows\",\n \"ds = ds_list[0] var_ii = var_name + \\\"_{0}\\\".format(season_suffixes[ii]) N_var = \\\"{0}_{1}\\\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05,\",\n \"Fontweight to use for title dataset_names = [\\\"CO9p0\\\", \\\"CO9p0\\\", \\\"CO9p0\\\"] # Names to\",\n \"2 and n_c = 2 # If this option is true, only the\",\n \"datasets from left to right and top to bottom. A colorbar will be\",\n \"= name_of_count_variable data = ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average] = np.nan\",\n \"Colormap for normal points clim = (-1, 1) # Color limits for normal\",\n \"Filename for the output fn_out = \\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name) #%% General Plot Settings var_name =\",\n \"points min_points_in_average = 5 name_of_count_variable = \\\"grid_N\\\" # Subplot axes settings n_r =\",\n \"contain many points min_points_in_average = 5 name_of_count_variable = \\\"grid_N\\\" # Subplot axes settings\",\n \"plots fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right,\",\n \"N_var = \\\"{0}_{1}\\\".format(name_of_count_variable, season_suffixes[ii]) a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\\\"bold\\\") else: ds = ds_list[ii]\",\n \"ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we extend the colorbar\",\n \"= \\\"grid_N\\\" # Subplot axes settings n_r = 2 # Number of subplot\",\n \"only the first dataset will be plotted, with seasonal # variables on each\",\n \"title dataset_names = [\\\"CO9p0\\\", \\\"CO9p0\\\", \\\"CO9p0\\\"] # Names to use for labelling plots\",\n \"from each will be plotted # on each axis of the plot fn_list\",\n \"The season_suffixes will be added to var_name # for each subplot panel. plot_seasons\",\n \"coast import pandas as pd #%% File settings run_name = \\\"test\\\" # List\",\n \"1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight=\\\"bold\\\") else: ds = ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize,\",\n \"a profile object with no z_dim (vertical dimension). Provide an array of netcdf\",\n \"that don't contain many points min_points_in_average = 5 name_of_count_variable = \\\"grid_N\\\" # Subplot\",\n \"0.1, 0.1) # Figure padding (left, top, right, bottom) # Leave some space\",\n \"Plot up surface or bottom (or any fixed level) errors from a profile\",\n \"of netcdf datasets from left to right and top to bottom. A colorbar\",\n \"= False # Masking out grid cells that don't contain many points min_points_in_average\",\n \"Read all datasets into list ds_list = [xr.open_dataset(dd) for dd in fn_list] n_ds\",\n \"pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) # Will we\",\n \"plotted, with seasonal # variables on each subplot. The season_suffixes will be added\",\n \"numpy as np import sys sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\") import coast import pandas as pd #%%\",\n \"files and mess with the options to get a figure you like. You\",\n \"List of analysis output files. Profiles from each will be plotted # on\",\n \"each subplot panel. plot_seasons = True season_suffixes = [\\\"DJF\\\", \\\"MAM\\\", \\\"JJA\\\", \\\"SON\\\"] #%%\",\n \"as plt import numpy as np import sys sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\") import coast import pandas\",\n \"data = ds[var_ii].values count_var = ds[N_var] data[count_var < min_points_in_average] = np.nan # Scatter\",\n \"Will we extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set\",\n \"each subplot. The season_suffixes will be added to var_name # for each subplot\",\n \"= \\\"neither\\\" cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025,\",\n \"the first dataset will be plotted, with seasonal # variables on each subplot.\",\n \"lonbounds = [-15, 9.5] # Longitude bounds latbounds = [45, 64] # Latitude\",\n \"size lonbounds = [-15, 9.5] # Longitude bounds latbounds = [45, 64] #\",\n \"# Labels and Titles fig_title = \\\"SST Errors\\\" # Whole figure title title_fontsize\",\n \"= \\\"normal\\\" # Fontweight for dataset subtitles # PLOT SEASONS. Make sure n_r\",\n \"# Variable name in analysis file to plot # If you used var\",\n \"elif \\\"min\\\" in extend_cbar: extend = \\\"min\\\" else: extend = \\\"neither\\\" cbar_ax =\",\n \"-- will we extend it? if \\\"both\\\" in extend_cbar: extend = \\\"both\\\" elif\",\n \"plot # If you used var modified to make gridded data # then\",\n \"= 2 and n_c = 2 # If this option is true, only\",\n \"subtitles subtitle_fontweight = \\\"normal\\\" # Fontweight for dataset subtitles # PLOT SEASONS. Make\",\n \"with seasonal # variables on each subplot. The season_suffixes will be added to\",\n \"columns figsize = (10, 5) # Figure size lonbounds = [-15, 9.5] #\",\n \"= ds[N_var] data[count_var < min_points_in_average] = np.nan # Scatter and set title pc\",\n \"you used var modified to make gridded data # then this is where\",\n \"plt import numpy as np import sys sys.path.append(\\\"/Users/dbyrne/code/COAsT\\\") import coast import pandas as\",\n \"Fontsize for dataset subtitles subtitle_fontweight = \\\"normal\\\" # Fontweight for dataset subtitles #\",\n \"title title_fontweight = \\\"bold\\\" # Fontweight to use for title dataset_names = [\\\"CO9p0\\\",\",\n \"n_r * n_c # Create plot and flatten axis array f, a =\",\n \"extend = \\\"min\\\" else: extend = \\\"neither\\\" cbar_ax = f.add_axes([(1 - fig_pad[2] +\",\n \"Scatter and set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1],\",\n \"right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) # Handle colorbar -- will we extend\",\n \"run_name = \\\"test\\\" # List of analysis output files. Profiles from each will\",\n \"to get a figure you like. You can define how many rows and\",\n \"subplot panel. plot_seasons = True season_suffixes = [\\\"DJF\\\", \\\"MAM\\\", \\\"JJA\\\", \\\"SON\\\"] #%% Read\",\n \"# PLOT SEASONS. Make sure n_r = 2 and n_c = 2 #\",\n \"= [\\\"DJF\\\", \\\"MAM\\\", \\\"JJA\\\", \\\"SON\\\"] #%% Read and plotdata # Read all datasets\",\n \"[\\\"CO9p0\\\", \\\"CO9p0\\\", \\\"CO9p0\\\"] # Names to use for labelling plots subtitle_fontsize = 11\",\n \"PLOT SEASONS. Make sure n_r = 2 and n_c = 2 # If\",\n \"if \\\"both\\\" in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar and \\\"min\\\"\",\n \"# Discretize colormap cmap_levels = 14 # Labels and Titles fig_title = \\\"SST\",\n \"SEASONS. Make sure n_r = 2 and n_c = 2 # If this\",\n \"min_points_in_average] = np.nan # Scatter and set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude,\",\n \"n_r, n_c, figsize=figsize) a_flat = a.flatten() # Dicretize colormap maybe if discrete_cmap: cmap\",\n \"how many rows and columns the plot will have. This script will plot\",\n \"fontsize=title_fontsize, fontweight=title_fontweight) # Set tight figure layout f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 -\",\n \"season etc. save_plot = False # Masking out grid cells that don't contain\",\n \"ds_list = [xr.open_dataset(dd) for dd in fn_list] n_ds = len(ds_list) n_ax = n_r\",\n \"Number of subplot columns figsize = (10, 5) # Figure size lonbounds =\",\n \"# If you used var modified to make gridded data # then this\",\n \"if we will extend the colormap or not extend_cbar = [] # Loop\",\n \"over dataset for ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select\",\n \"\\\"~/transfer/test_grid.nc\\\", \\\"~/transfer/test_grid.nc\\\", ] # Filename for the output fn_out = \\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name) #%% General\",\n \"= (10, 5) # Figure size lonbounds = [-15, 9.5] # Longitude bounds\",\n \"for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) # Set Figure title f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight)\",\n \"[ \\\"~/transfer/test_grid.nc\\\", \\\"~/transfer/test_grid.nc\\\", ] # Filename for the output fn_out = \\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name) #%%\",\n \"dataset for ii in range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season\",\n \"dimension). Provide an array of netcdf files and mess with the options to\",\n \"# Fontsize for dataset subtitles subtitle_fontweight = \\\"normal\\\" # Fontweight for dataset subtitles\",\n \"axis of the plot fn_list = [ \\\"~/transfer/test_grid.nc\\\", \\\"~/transfer/test_grid.nc\\\", ] # Filename for\",\n \"z_dim (vertical dimension). Provide an array of netcdf files and mess with the\",\n \"# Masking out grid cells that don't contain many points min_points_in_average = 5\",\n \"with no z_dim (vertical dimension). Provide an array of netcdf files and mess\",\n \"is where to select season etc. save_plot = False # Masking out grid\",\n \"= \\\"max\\\" elif \\\"min\\\" in extend_cbar: extend = \\\"min\\\" else: extend = \\\"neither\\\"\",\n \"\\\"both\\\" in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar and \\\"min\\\" in\",\n \"len(ds_list) n_ax = n_r * n_c # Create plot and flatten axis array\",\n \"13 # Fontsize of title title_fontweight = \\\"bold\\\" # Fontweight to use for\",\n \"subtitle_fontweight = \\\"normal\\\" # Fontweight for dataset subtitles # PLOT SEASONS. Make sure\",\n \"\\\"bold\\\" # Fontweight to use for title dataset_names = [\\\"CO9p0\\\", \\\"CO9p0\\\", \\\"CO9p0\\\"] #\",\n \"this option is true, only the first dataset will be plotted, with seasonal\",\n \"of subplot rows n_c = 2 # Number of subplot columns figsize =\",\n \"horizontal padding between plots fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding\",\n \"ds_list[ii] var_ii = var_name a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) N_var = name_of_count_variable data = ds[var_ii].values\",\n \"Color limits for normal points discrete_cmap = True # Discretize colormap cmap_levels =\",\n \"in extend_cbar: extend = \\\"both\\\" elif \\\"max\\\" in extend_cbar: extend = \\\"max\\\" elif\",\n \"\\\"\\\"\\\" Plot up surface or bottom (or any fixed level) errors from a\",\n \"pandas as pd #%% File settings run_name = \\\"test\\\" # List of analysis\",\n \"and Titles fig_title = \\\"SST Errors\\\" # Whole figure title title_fontsize = 13\",\n \"set title pc = a_flat[ii].pcolormesh( ds.longitude, ds.latitude, data, cmap=cmap, vmin=clim[0], vmax=clim[1], ) #\",\n \"= [ \\\"~/transfer/test_grid.nc\\\", \\\"~/transfer/test_grid.nc\\\", ] # Filename for the output fn_out = \\\"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\\\".format(run_name)\",\n \"Fontweight for dataset subtitles # PLOT SEASONS. Make sure n_r = 2 and\",\n \"analysis output files. Profiles from each will be plotted # on each axis\",\n \"# Fontweight for dataset subtitles # PLOT SEASONS. Make sure n_r = 2\",\n \"# then this is where to select season etc. save_plot = False #\",\n \"Whole figure title title_fontsize = 13 # Fontsize of title title_fontweight = \\\"bold\\\"\",\n \"# If this option is true, only the first dataset will be plotted,\",\n \"Create plot and flatten axis array f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c,\",\n \"to var_name # for each subplot panel. plot_seasons = True season_suffixes = [\\\"DJF\\\",\",\n \"= coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) a_flat = a.flatten() # Dicretize colormap maybe\",\n \"colormap maybe if discrete_cmap: cmap = plt.cm.get_cmap(cmap, cmap_levels) # Determine if we will\",\n \"gridded data # then this is where to select season etc. save_plot =\",\n \"+ fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) f.colorbar(pc, cax=cbar_ax, extend=extend) # Save plot\",\n \"n_r = 2 # Number of subplot rows n_c = 2 # Number\",\n \"plot fn_list = [ \\\"~/transfer/test_grid.nc\\\", \\\"~/transfer/test_grid.nc\\\", ] # Filename for the output fn_out\",\n \"bottom. A colorbar will be placed right of the figure. \\\"\\\"\\\" import xarray\",\n \"in analysis file to plot # If you used var modified to make\",\n \"data # then this is where to select season etc. save_plot = False\",\n \"\\\"min\\\" else: extend = \\\"neither\\\" cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] *\",\n \"extend_cbar = [] # Loop over dataset for ii in range(n_ax): ur_index =\",\n \"n_c = 2 # Number of subplot columns figsize = (10, 5) #\",\n \"or not extend_cbar = [] # Loop over dataset for ii in range(n_ax):\",\n \"# Will we extend the colorbar for this dataset? extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) #\",\n \"vertical and horizontal padding between plots fig_pad = (0.075, 0.075, 0.1, 0.1) #\",\n \"plots subtitle_fontsize = 11 # Fontsize for dataset subtitles subtitle_fontweight = \\\"normal\\\" #\",\n \"9.5] # Longitude bounds latbounds = [45, 64] # Latitude bounds subplot_padding =\",\n \"figure title title_fontsize = 13 # Fontsize of title title_fontweight = \\\"bold\\\" #\"\n]","isConversational":false}}},{"rowIdx":2997,"cells":{"ngram":{"kind":"list like","value":["documents. :returns: tf-idf of corpus. \"\"\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus","- inverse document frequency (idf). :param corpus: List of documents. :returns: tf-idf of","if __name__ == '__main__': sample_corpus = [ 'This is sample document.', 'another random","'This is sample document.', 'another random document.', 'third sample document text' ] print(compute_tf_idf(sample_corpus))",":returns: tf-idf of corpus. \"\"\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus =","__name__ == '__main__': sample_corpus = [ 'This is sample document.', 'another random document.',","'__main__': sample_corpus = [ 'This is sample document.', 'another random document.', 'third sample","= [ 'This is sample document.', 'another random document.', 'third sample document text'","document frequency (idf). :param corpus: List of documents. :returns: tf-idf of corpus. \"\"\"","sklearn.feature_extraction.text import TfidfVectorizer def compute_tf_idf(corpus): \"\"\"Computing term frequency (tf) - inverse document frequency","inverse document frequency (idf). :param corpus: List of documents. :returns: tf-idf of corpus.","\"\"\"Computing term frequency (tf) - inverse document frequency (idf). :param corpus: List of","frequency (idf). :param corpus: List of documents. :returns: tf-idf of corpus. \"\"\" return",":param corpus: List of documents. :returns: tf-idf of corpus. \"\"\" return TfidfVectorizer().fit_transform(corpus) if","TfidfVectorizer def compute_tf_idf(corpus): \"\"\"Computing term frequency (tf) - inverse document frequency (idf). :param","tf-idf of corpus. \"\"\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus = [","TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus = [ 'This is sample document.', 'another","\"\"\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus = [ 'This is sample","from sklearn.feature_extraction.text import TfidfVectorizer def compute_tf_idf(corpus): \"\"\"Computing term frequency (tf) - inverse document","(idf). :param corpus: List of documents. :returns: tf-idf of corpus. \"\"\" return TfidfVectorizer().fit_transform(corpus)","def compute_tf_idf(corpus): \"\"\"Computing term frequency (tf) - inverse document frequency (idf). :param corpus:","[ 'This is sample document.', 'another random document.', 'third sample document text' ]","(tf) - inverse document frequency (idf). :param corpus: List of documents. :returns: tf-idf","== '__main__': sample_corpus = [ 'This is sample document.', 'another random document.', 'third","corpus: List of documents. :returns: tf-idf of corpus. \"\"\" return TfidfVectorizer().fit_transform(corpus) if __name__","return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus = [ 'This is sample document.',","List of documents. :returns: tf-idf of corpus. \"\"\" return TfidfVectorizer().fit_transform(corpus) if __name__ ==","term frequency (tf) - inverse document frequency (idf). :param corpus: List of documents.","compute_tf_idf(corpus): \"\"\"Computing term frequency (tf) - inverse document frequency (idf). :param corpus: List","frequency (tf) - inverse document frequency (idf). :param corpus: List of documents. :returns:","of corpus. \"\"\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus = [ 'This","sample_corpus = [ 'This is sample document.', 'another random document.', 'third sample document","corpus. \"\"\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus = [ 'This is","import TfidfVectorizer def compute_tf_idf(corpus): \"\"\"Computing term frequency (tf) - inverse document frequency (idf).","of documents. :returns: tf-idf of corpus. \"\"\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__':"],"string":"[\n \"documents. :returns: tf-idf of corpus. \\\"\\\"\\\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus\",\n \"- inverse document frequency (idf). :param corpus: List of documents. :returns: tf-idf of\",\n \"if __name__ == '__main__': sample_corpus = [ 'This is sample document.', 'another random\",\n \"'This is sample document.', 'another random document.', 'third sample document text' ] print(compute_tf_idf(sample_corpus))\",\n \":returns: tf-idf of corpus. \\\"\\\"\\\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus =\",\n \"__name__ == '__main__': sample_corpus = [ 'This is sample document.', 'another random document.',\",\n \"'__main__': sample_corpus = [ 'This is sample document.', 'another random document.', 'third sample\",\n \"= [ 'This is sample document.', 'another random document.', 'third sample document text'\",\n \"document frequency (idf). :param corpus: List of documents. :returns: tf-idf of corpus. \\\"\\\"\\\"\",\n \"sklearn.feature_extraction.text import TfidfVectorizer def compute_tf_idf(corpus): \\\"\\\"\\\"Computing term frequency (tf) - inverse document frequency\",\n \"inverse document frequency (idf). :param corpus: List of documents. :returns: tf-idf of corpus.\",\n \"\\\"\\\"\\\"Computing term frequency (tf) - inverse document frequency (idf). :param corpus: List of\",\n \"frequency (idf). :param corpus: List of documents. :returns: tf-idf of corpus. \\\"\\\"\\\" return\",\n \":param corpus: List of documents. :returns: tf-idf of corpus. \\\"\\\"\\\" return TfidfVectorizer().fit_transform(corpus) if\",\n \"TfidfVectorizer def compute_tf_idf(corpus): \\\"\\\"\\\"Computing term frequency (tf) - inverse document frequency (idf). :param\",\n \"tf-idf of corpus. \\\"\\\"\\\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus = [\",\n \"TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus = [ 'This is sample document.', 'another\",\n \"\\\"\\\"\\\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus = [ 'This is sample\",\n \"from sklearn.feature_extraction.text import TfidfVectorizer def compute_tf_idf(corpus): \\\"\\\"\\\"Computing term frequency (tf) - inverse document\",\n \"(idf). :param corpus: List of documents. :returns: tf-idf of corpus. \\\"\\\"\\\" return TfidfVectorizer().fit_transform(corpus)\",\n \"def compute_tf_idf(corpus): \\\"\\\"\\\"Computing term frequency (tf) - inverse document frequency (idf). :param corpus:\",\n \"[ 'This is sample document.', 'another random document.', 'third sample document text' ]\",\n \"(tf) - inverse document frequency (idf). :param corpus: List of documents. :returns: tf-idf\",\n \"== '__main__': sample_corpus = [ 'This is sample document.', 'another random document.', 'third\",\n \"corpus: List of documents. :returns: tf-idf of corpus. \\\"\\\"\\\" return TfidfVectorizer().fit_transform(corpus) if __name__\",\n \"return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus = [ 'This is sample document.',\",\n \"List of documents. :returns: tf-idf of corpus. \\\"\\\"\\\" return TfidfVectorizer().fit_transform(corpus) if __name__ ==\",\n \"term frequency (tf) - inverse document frequency (idf). :param corpus: List of documents.\",\n \"compute_tf_idf(corpus): \\\"\\\"\\\"Computing term frequency (tf) - inverse document frequency (idf). :param corpus: List\",\n \"frequency (tf) - inverse document frequency (idf). :param corpus: List of documents. :returns:\",\n \"of corpus. \\\"\\\"\\\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus = [ 'This\",\n \"sample_corpus = [ 'This is sample document.', 'another random document.', 'third sample document\",\n \"corpus. \\\"\\\"\\\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus = [ 'This is\",\n \"import TfidfVectorizer def compute_tf_idf(corpus): \\\"\\\"\\\"Computing term frequency (tf) - inverse document frequency (idf).\",\n \"of documents. :returns: tf-idf of corpus. \\\"\\\"\\\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__':\"\n]","isConversational":false}}},{"rowIdx":2998,"cells":{"ngram":{"kind":"list like","value":["writting to destinations. \"\"\" dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer):","== b\"Register\": LOCK.acquire() name = params[1] if b\"FSW\" in name: if FSW_clients: process_id","\"\"\" dest_list = [] # Process data here... head, dst = header.strip(b\" \").split(b\"","if chunk == b\"\": print(\"read data from socket is empty!\") return b\"\" msg","= params[1] + b\"_\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\"GUI\" in name: if","b\"List\": print(\"List of registered clients: \") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\"\\t\" +","TCP\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print","goes wrong report and shutdown server. \"\"\" dest_list = [] if header ==","self.recv(4) sizeb = self.recv(4) size = struct.unpack(\">I\", sizeb)[0] data = desc + sizeb","so read the data of the message here... data = self.readData(header, packet) #","not header: return # Got the header data so read the data of","# print \"Sending TCP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Packet","= [] FSW_ids = [] GUI_ids = [] def signal_handler(*_): print(\"Ctrl-C received, server","Once the entire header tstring is processed send it on queue. If something","gse.py applicaiton. TCP socket server for commands, log events, and telemetry data. Later","is a List. Once the entire header tstring is processed send it on","or \"A5A5 FSW \"), or just read the \"List\\n\" command. \"\"\" header =","size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + packet[4 : 4 + size]","Header # print \"Received Packet: %s %s...\\n\" % (head,dst) if data == b\"\":","import SocketServer as socketserver import time import os import signal import sys import","time import os import signal import sys import struct import errno from fprime.constants","socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on each client connect","default=\"127.0.0.1\", ) # process options (opts, args) = parser.parse_args(argv) HOST = opts.host PORT","client. Registration is done by sending the string \"Register <name>\". Sending a message","part of the message sent to either GUI or FSW. GUI receives telemetry.","the message sent to either GUI or FSW. GUI receives telemetry. FSW receives","return (header + header2, packet) def readData(self, header, packet): \"\"\" Read the data","the 9 byte header (e.g. \"A5A5 GUI \" or \"A5A5 FSW \"), or","server shutting down.\") shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \"\"\" Derived from","and data here. The command must always start with A5A5 except if it","TCP Socket server. Keep a dictionary of destination objects containing queues and socket","if data == \"\": print(\" Data is empty, returning.\") if b\"GUI\" in dst:","print(\"Ctrl-C received, server shutting down.\") shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \"\"\"","0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\"Quit processed!\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the header","signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0)","user_name (California Institute of Technology) \\ ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\"","threading try: import socketserver except ImportError: import SocketServer as socketserver import time import","request): \"\"\" Constructor \"\"\" self.name = name self.socket = request self.packet = b\"\"","either A5A5 or List header = self.readHeader() # If the received header is","header == b\"Quit\\n\": return b\"Quit\" elif header[:-1] == b\"A5A5\": header2 = self.recv(4) return","packet of the message here... self.processNewPkt(header, data) def readHeader(self, packet): \"\"\" Read the","listening on host addr %s, port %s\" % (HOST, PORT)) # Start a","def fileno(self): \"\"\" \"\"\" return self.socket def main(argv=None): global SERVER, LOCK program_name =","+ self.name.decode(DATA_ENCODING) + \" exiting.\" ) return header if header == b\"List\\n\": return","the data part of the message sent to either GUI or FSW. GUI","self.registered = True self.name = name self.id = process_id print(\"Registered client \" +","a thread with the server -- that thread will then start one #","that is invoked when a packet is received. This function listens for data","tlm_packet_size = self.recv(4) size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else:","0: print( \"Header information is empty, client \" + self.name.decode(DATA_ENCODING) + \" exiting.\"","\")[1].strip(b\" \") if dst == b\"FSW\": # Read variable length command data here...","Process the data into the cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue() if","n: try: chunk = self.request.recv(l - n) if chunk == b\"\": print(\"read data","def processNewPkt(self, header, data): \"\"\" Process a single command here header and data","FSW gse.py applicaiton. TCP socket server for commands, log events, and telemetry data.","== b\"Quit\": return b\"\" dst = header.split(b\" \")[1].strip(b\" \") if dst == b\"FSW\":","for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd): params =","repr(e) + \"\\n\") sys.stderr.write(indent + \" for help use --help\\n\") return 2 if","= threading.Event() FSW_clients = [] GUI_clients = [] FSW_ids = [] GUI_ids =","as err: if err.errno == errno.ECONNRESET: print( \"Socket error \" + str(err.errno) +","separated by a newline. For each packet, call processPkt. \"\"\" self.getNewMsg(self.request[0]) ################################################# #","%s %s...\\n\" % (head,dst) if data == b\"\": print(\" Data is empty, returning.\")","files and parameters. Handle is instanced in own thread for each client. Registration","dest_list = GUI_clients else: print(\"dest? %s\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire()","and send the packet of the message here... self.processNewPkt(header, data) def readHeader(self, packet):","print(\"TCP Socket Server listening on host addr %s, port %s\" % (HOST, PORT))","- n) if chunk == b\"\": print(\"read data from socket is empty!\") return","R&TD and adapted for use in new FSW gse.py applicaiton. TCP socket server","as e: indent = len(program_name) * \" \" sys.stderr.write(program_name + \": \" +","def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd):","data = tlm_packet_size + self.recv(size) else: raise RuntimeError(\"unrecognized client %s\" % dst.decode(DATA_ENCODING)) return","SERVER.dest_obj[self.name].put(struct.pack(\">I\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\"Quit processed!\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the","The function that is invoked when a packet is received. This function listens","if len(self.partial): commands[0] = self.partial + commands[0] self.partial = b\"\" if len(commands[-1]): self.partial","# print \"Received Packet: %s %s...\\n\" % (head,dst) if data == \"\": print(\"","optional - give further explanation about what the program does ) if argv","name: if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name = params[1] + b\"_\"","not data: print(\"Client exited.\") return else: # Process the data into the cmdQueue","from the socket either A5A5 or List header = self.readHeader() # If the","function listens for data on the socket. Packets for now are assumed to","\"List\" comment makes the server display all registered clients. \"\"\" socketserver.StreamRequestHandler.allow_reuse_address = True","__updated__ = \"2016-04-07\" # Universal server id global SERVER = None LOCK =","data) def readHeader(self, packet): \"\"\" Read the 9 byte header (e.g. \"A5A5 GUI","packet, call processPkt. \"\"\" self.getNewMsg(self.request[0]) ################################################# # New Routines to process the command","msg = b\"\" n = 0 while l > n: try: chunk =","immediately server.allow_reuse_address = True SERVER = server LOCK = server.lock_obj ip, port =","the message here... self.processNewPkt(header, data) def recv(self, l): \"\"\" Read l bytes from","= [] GUI_ids = [] def signal_handler(*_): print(\"Ctrl-C received, server shutting down.\") shutdown_event.set()","b\"\" def put(self, msg): \"\"\" Write out the message to the destination socket","GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered = True self.name = name self.id","destinations. \"\"\" dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \"\"\" UDP","(%s)\" % (program_version, program_build_date) program_longdesc = ( \"\"\"\"\"\" # optional - give further","reuse and server to restart immediately server.allow_reuse_address = True SERVER = server LOCK","processPkt. \"\"\" self.getNewMsg(self.request[0]) ################################################# # New Routines to process the command messages #################################################","the command messages ################################################# def getNewMsg(self): \"\"\" After registration wait for an incoming","b\"FSW\": # Read variable length command data here... desc = self.recv(4) sizeb =","if b\"FSW\" in name: if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name =","Sending a message to destination <name> is done as \"A5A5 <name> <data>\" Note","== b\"Quit\": LOCK.acquire() print(\"Quit received!\") SERVER.dest_obj[self.name].put(struct.pack(\">I\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\"Quit processed!\") SERVER.shutdown() SERVER.server_close()","= self.partial + commands[0] self.partial = b\"\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1])","dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \"\"\" UDP Socket server.","= GUI_clients else: print(\"dest? %s\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if","here... tlm_packet_size = self.recv(4) size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size)","events, and telemetry data. Later this will handle other things such as sequence","return b\"\" elif header == b\"Quit\": return b\"\" dst = header.split(b\" \")[1].strip(b\" \")","command. \"\"\" header = packet[:4] header2 = packet[4:9] packet = packet[9:] return (header","\" or a \"List \" \"\"\" # Loop while the connected client has","errno.ECONNRESET: print( \"Socket error \" + str(err.errno) + \" (Connection reset by peer)","the message here... self.processNewPkt(header, data) def readHeader(self, packet): \"\"\" Read the 9 byte","the socket data = self.recv(13) # Connection was closed by the client if","something goes wrong report and shutdown server. \"\"\" dest_list = [] # Process","print(\"Socket error \" + str(err.errno) + \" occurred on send().\") def fileno(self): \"\"\"","+ self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process the command messages ################################################# def","return b\"Quit\\n\" continue except socket.error as err: if err.errno == errno.ECONNRESET: print( \"Socket","the data of the message here... data = self.readData(header, packet) # Process and","self.socket = request self.packet = b\"\" def put(self, msg): \"\"\" Write out the","commands of various lengths. \"\"\" data = \"\" dst = header.split(b\" \")[1].strip(b\" \")","<name>\". Sending a message to destination <name> is done as \"A5A5 <name> <data>\"","class DestObj: \"\"\" Destination object for all clients registered. \"\"\" def __init__(self, name,","= b\"\" def put(self, msg): \"\"\" Write out the message to the destination","# print \"about to send data to \" + self.name self.socket.send(msg) except socket.error","\" + repr(e) + \"\\n\") sys.stderr.write(indent + \" for help use --help\\n\") return","b\"List\": return b\"\" elif header == b\"Quit\": return b\"\" dst = header.split(b\" \")[1].strip(b\"","single command here header and data here. The command must always start with","= 0 # print self.client_address, now() # show this client's address # Read","print(\" Data is empty, returning.\") if b\"GUI\" in dst: dest_list = GUI_clients elif","print(\"read data from socket is empty!\") return b\"\" msg = msg + chunk","send the packet of the message here... self.processNewPkt(header, data) def recv(self, l): \"\"\"","thread for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon","shutdown server. \"\"\" dest_list = [] if header == b\"List\": print(\"List of registered","invoked upon a new client. This function listens for data on the socket.","l = len(reg_client_str) reg_client_str = struct.pack(\"i%ds\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return","in dst: dest_list = GUI_clients elif b\"FSW\" in dst: dest_list = FSW_clients for","udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\"shutdown from main thread\")","name self.socket = request self.packet = b\"\" def put(self, msg): \"\"\" Write out","err: if err.errno == errno.ECONNRESET: print( \"Socket error \" + str(err.errno) + \"","to \" + self.name self.socket.send(msg) except socket.error as err: print(\"Socket error \" +","\"Header information is empty, client \" + self.name.decode(DATA_ENCODING) + \" exiting.\" ) return","tlm_packet_size = packet[:4] size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + packet[4 :","+ bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered = True self.name","print \"about to send data to \" + self.name self.socket.send(msg) except socket.error as","print(\"shutdown from main thread\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e:","data so read the data of the message here... data = self.readData(header) #","invoked when a packet is received. This function listens for data on the","the destination socket \"\"\" try: # print \"about to send data to \"","so read the data of the message here... data = self.readData(header) # Process","GUI or FSW. GUI receives telemetry. FSW receives commands of various lengths. \"\"\"","print \"Sending UDP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Telemetry missing","socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on each client connect \"\"\" The function","server.lock_obj ip, port = server.server_address print(\"TCP Socket Server listening on host addr %s,","as sequence files and parameters. Handle is instanced in own thread for each","LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print \"Sending","if argv is None: argv = sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc,","into the cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue() if self.registered: print(\"Registration complete","id global SERVER = None LOCK = None shutdown_event = threading.Event() FSW_clients =","self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self,","program_build_date = \"%s\" % __updated__ program_version_string = \"%%prog %s (%s)\" % (program_version, program_build_date)","def processRegistration(self, cmd): params = cmd.split() process_id = 0 if params[0] == b\"Register\":","clients: \") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\"\\t\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\"List","udp_server.server_close() time.sleep(1) except Exception as e: indent = len(program_name) * \" \" sys.stderr.write(program_name","+ \" occurred on recv().\") return msg def readHeader(self): \"\"\" Read the 9","List (header, packet) = self.readHeader(packet) # If the received header is an empty","are assumed to be separated by a newline. For each packet, call processPkt.","has packets to send/receive while not shutdown_event.is_set(): # Read the header data from","server.server_address print(\"TCP Socket Server listening on host addr %s, port %s\" % (HOST,","[] GUI_clients = [] FSW_ids = [] GUI_ids = [] def signal_handler(*_): print(\"Ctrl-C","message to the destination socket \"\"\" try: # print \"about to send data","= [] def processRegistration(self, cmd): params = cmd.split() process_id = 0 if params[0]","%s...\\n\" % (head,dst) if data == \"\": print(\" Data is empty, returning.\") if","\"\"\" Destination object for all clients registered. \"\"\" def __init__(self, name, request): \"\"\"","= packet[:4] header2 = packet[4:9] packet = packet[9:] return (header + header2, packet)","readData(self, header): \"\"\" Read the data part of the message sent to either","threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False","now() # show this client's address # Read the data from the socket","commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue =","l > n: try: chunk = self.request.recv(l - n) if chunk == b\"\":","is None: argv = sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, )","e: indent = len(program_name) * \" \" sys.stderr.write(program_name + \": \" + repr(e)","DATA_ENCODING from optparse import OptionParser __version__ = 0.1 __date__ = \"2015-04-03\" __updated__ =","clients. \"\"\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on each","data = self.readData(header) # Process and send the packet of the message here...","handle(self): # on each client connect \"\"\" The function that is invoked upon","print(\"Unable to register client.\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name)","receives commands of various lengths. \"\"\" data = b\"\" if header == b\"List\":","cmd.split() process_id = 0 if params[0] == b\"Register\": LOCK.acquire() name = params[1] if","dst == b\"GUI\": # Read telemetry data here... tlm_packet_size = self.recv(4) size =","information is empty, client \" + self.name.decode(DATA_ENCODING) + \" exiting.\" ) return header","= dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \"\"\" UDP Socket server. \"\"\"","during R&TD and adapted for use in new FSW gse.py applicaiton. TCP socket","= True def handle(self): # on each packet \"\"\" The function that is","be separated by a newline. For each packet, call processPkt. \"\"\" self.getNewMsg(self.request[0]) #################################################","header == b\"Quit\": LOCK.acquire() print(\"Quit received!\") SERVER.dest_obj[self.name].put(struct.pack(\">I\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\"Quit processed!\") SERVER.shutdown()","client \" + self.name.decode(DATA_ENCODING) + \" exiting.\" ) return header if header ==","wrong report and shutdown server. \"\"\" dest_list = [] if header == b\"List\":","while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\"shutdown from main thread\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close()","self.packet = b\"\" def put(self, msg): \"\"\" Write out the message to the","The first part must always be an \"A5A5 \" or a \"List \"","if not header: break elif header == b\"Quit\": LOCK.acquire() print(\"Quit received!\") SERVER.dest_obj[self.name].put(struct.pack(\">I\", 0xA5A5A5A5))",": 4 + size] return data def processNewPkt(self, header, data): \"\"\" Process a","threaded tcp socket server port [default: %default]\", default=50007, ) parser.add_option( \"-i\", \"--host\", dest=\"host\",","processPkt. \"\"\" self.partial = b\"\" self.cmdQueue = [] self.registered = False self.name =","for commands, log events, and telemetry data. Later this will handle other things","handle other things such as sequence files and parameters. Handle is instanced in","data here. The command must always start with A5A5 except if it is","ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will allow address reuse","data = b\"\" if header == b\"List\": return b\"\" elif header == b\"Quit\":","string \"Register <name>\". Sending a message to destination <name> is done as \"A5A5","SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Packet missing A5A5 header\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \"\"\" Derived from","\" or a \"List \" \"\"\" # Read the header data from the","self.partial + commands[0] self.partial = b\"\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else:","tcp socket server ip [default: %default]\", default=\"127.0.0.1\", ) # process options (opts, args)","print(\"Registration complete waiting for message.\") self.getNewMsg() else: print(\"Unable to register client.\") return LOCK.acquire()","\"Received Packet: %s %s...\\n\" % (head,dst) if data == b\"\": print(\" Data is","= header.split(b\" \")[1].strip(b\" \") # Read telemetry data here... tlm_packet_size = packet[:4] size","server. Keep a dictionary of destination objects containing queues and socket id's for","\"Sending UDP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Telemetry missing A5A5","A5A5 or List header = self.readHeader() # If the received header is an","if it is a List. Once the entire header tstring is processed send","# Read the data from the socket data = self.recv(13) # Connection was","# Hopefully this will allow address reuse and server to restart immediately server.allow_reuse_address","def getNewMsg(self, packet): \"\"\" After registration wait for an incoming message The first","header): \"\"\" Read the data part of the message sent to either GUI","show this client's address # Read the data from the socket data =","b\"Quit\\n\" continue except socket.error as err: if err.errno == errno.ECONNRESET: print( \"Socket error","the entire header tstring is processed send it on queue. If something goes","params[1] + b\"_\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered","# Process the data into the cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue()","just read the \"List\\n\" command. \"\"\" header = packet[:4] header2 = packet[4:9] packet","shutdown_event.is_set(): print(\"socket timed out and shutdown is requested\") return b\"Quit\\n\" continue except socket.error","self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd): params = cmd.split() process_id =","= OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \"-p\", \"--port\", dest=\"port\", action=\"store\", type=\"int\", help=\"Set","program_longdesc = ( \"\"\"\"\"\" # optional - give further explanation about what the","or FSW. GUI receives telemetry. FSW receives commands of various lengths. \"\"\" data","Data is empty, returning.\") if b\"GUI\" in dst: dest_list = GUI_clients elif b\"FSW\"","LOCK.release() else: raise RuntimeError(\"Packet missing A5A5 header\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \"\"\" Derived from original","string is processed send it on queue. If something goes wrong report and","demo during R&TD and adapted for use in new FSW gse.py applicaiton. TCP","send().\") def fileno(self): \"\"\" \"\"\" return self.socket def main(argv=None): global SERVER, LOCK program_name","always be an \"A5A5 \" or a \"List \" \"\"\" # Read the","RuntimeError(\"Packet missing A5A5 header\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \"\"\" Derived from original Stable demo during","send data to \" + self.name self.socket.send(msg) except socket.error as err: print(\"Socket error","Read telemetry data here... tlm_packet_size = self.recv(4) size = struct.unpack(\">I\", tlm_packet_size)[0] data =","GUI receives telemetry. FSW receives commands of various lengths. \"\"\" data = b\"\"","FSW_clients = [] GUI_clients = [] FSW_ids = [] GUI_ids = [] def","packet of the message here... self.processNewPkt(header, data) def recv(self, l): \"\"\" Read l","size = struct.unpack(\">I\", sizeb)[0] data = desc + sizeb + self.recv(size) elif dst","1 name = params[1] + b\"_\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name,","in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\"Closed %s","exit loop if not header: break elif header == b\"Quit\": LOCK.acquire() print(\"Quit received!\")","if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print \"Sending UDP","requested\") return b\"Quit\\n\" continue except socket.error as err: if err.errno == errno.ECONNRESET: print(","= \"2016-04-07\" # Universal server id global SERVER = None LOCK = None","= packet[:4] size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + packet[4 : 4","PORT), ThreadedUDPRequestHandler) # Hopefully this will allow address reuse and server to restart","b\"Quit\": LOCK.acquire() print(\"Quit received!\") SERVER.dest_obj[self.name].put(struct.pack(\">I\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\"Quit processed!\") SERVER.shutdown() SERVER.server_close() LOCK.release()","\" \" sys.stderr.write(program_name + \": \" + repr(e) + \"\\n\") sys.stderr.write(indent + \"","done by sending the string \"Register <name>\". Sending a message to destination <name>","udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start()","\"Copyright 2015 user_name (California Institute of Technology) \\ ALL RIGHTS RESERVED. U.S. Government","comment makes the server display all registered clients. \"\"\" socketserver.BaseRequestHandler.allow_reuse_address = True def","from the socket either A5A5 or List (header, packet) = self.readHeader(packet) # If","header is an empty string, connection closed, exit loop if not header: return","such as sequence files and parameters. Handle is instanced in own thread for","peer) occurred on recv().\" ) else: print(\"Socket error \" + str(err.errno) + \"","Later this will handle other things such as sequence files and parameters. Handle","message.\") self.getNewMsg() else: print(\"Unable to register client.\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name","\"\"\" \"\"\" return self.socket def main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license","elif header == b\"Quit\": LOCK.acquire() print(\"Quit received!\") SERVER.dest_obj[self.name].put(struct.pack(\">I\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\"Quit processed!\")","data = \"\" dst = header.split(b\" \")[1].strip(b\" \") # Read telemetry data here...","of various lengths. \"\"\" data = \"\" dst = header.split(b\" \")[1].strip(b\" \") #","% self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self, inputString, end_of_command=b\"\\n\"): \"\"\" Build a","server to restart immediately server.allow_reuse_address = True SERVER = server LOCK = server.lock_obj","= desc + sizeb + self.recv(size) elif dst == b\"GUI\": # Read telemetry","\" exiting.\" ) return header if header == b\"List\\n\": return b\"List\" elif header","# Read variable length command data here... desc = self.recv(4) sizeb = self.recv(4)","(HOST, PORT)) # Start a thread with the server -- that thread will","packet \"\"\" The function that is invoked when a packet is received. This","= struct.unpack(\">I\", sizeb)[0] data = desc + sizeb + self.recv(size) elif dst ==","here... desc = self.recv(4) sizeb = self.recv(4) size = struct.unpack(\">I\", sizeb)[0] data =","process_id = 0 if params[0] == b\"Register\": LOCK.acquire() name = params[1] if b\"FSW\"","a \"List\" comment makes the server display all registered clients. \"\"\" socketserver.BaseRequestHandler.allow_reuse_address =","connection.\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self, inputString, end_of_command=b\"\\n\"): \"\"\" Build","here... tlm_packet_size = packet[:4] size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + packet[4","when a packet is received. This function listens for data on the socket.","is invoked when a packet is received. This function listens for data on","by a newline. For each packet, call processPkt. \"\"\" self.getNewMsg(self.request[0]) ################################################# # New","l bytes from socket. \"\"\" chunk = b\"\" msg = b\"\" n =","__date__ = \"2015-04-03\" __updated__ = \"2016-04-07\" # Universal server id global SERVER =","def getNewMsg(self): \"\"\" After registration wait for an incoming message The first part","command. \"\"\" header = self.recv(5) if len(header) == 0: print( \"Header information is","= server LOCK = server.lock_obj ip, port = server.server_address print(\"TCP Socket Server listening","\"\"\" Constructor \"\"\" self.name = name self.socket = request self.packet = b\"\" def","a newline. For each packet, call processPkt. \"\"\" self.partial = b\"\" self.cmdQueue =","print \"Received Packet: %s %s...\\n\" % (head,dst) if data == b\"\": print(\" Data","= [] if header == b\"List\": print(\"List of registered clients: \") LOCK.acquire() for","################################################# def getNewMsg(self): \"\"\" After registration wait for an incoming message The first","telemetry data. Later this will handle other things such as sequence files and","SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered = True self.name = name self.id =","+ sizeb + self.recv(size) elif dst == b\"GUI\": # Read telemetry data here...","the socket either A5A5 or List header = self.readHeader() # If the received","msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Telemetry missing A5A5 header\") class","The command must always start with A5A5 except if it is a List.","except ImportError: import SocketServer as socketserver import time import os import signal import","chunk = b\"\" msg = b\"\" n = 0 while l > n:","is empty, client \" + self.name.decode(DATA_ENCODING) + \" exiting.\" ) return header if","is done as \"A5A5 <name> <data>\" Note only <data> is sent. Any client","- give further explanation about what the program does ) if argv is","\"List \" \"\"\" # Read the header data from the socket either A5A5","\" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\"i%ds\" % l, l, reg_client_str)","\" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process the command messages #################################################","New Routines to process the command messages ################################################# def getNewMsg(self, packet): \"\"\" After","down.\") shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \"\"\" Derived from original Stable","= sorted(GUI_ids)[-1] + 1 name = params[1] + b\"_\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id)",") return header if header == b\"List\\n\": return b\"List\" elif header == b\"Quit\\n\":","import os import signal import sys import struct import errno from fprime.constants import","a \"List\" comment makes the server display all registered clients. \"\"\" socketserver.StreamRequestHandler.allow_reuse_address =","message here... self.processNewPkt(header, data) def recv(self, l): \"\"\" Read l bytes from socket.","# Process the cmdQueue self.processQueue() if self.registered: print(\"Registration complete waiting for message.\") self.getNewMsg()","if len(header) == 0: print( \"Header information is empty, client \" + self.name.decode(DATA_ENCODING)","self.getNewMsg(self.request[0]) ################################################# # New Routines to process the command messages ################################################# def getNewMsg(self,","################################################# # New Routines to process the command messages ################################################# def getNewMsg(self): \"\"\"","+ \" occurred on send().\") def fileno(self): \"\"\" \"\"\" return self.socket def main(argv=None):","= len(msg) except socket.timeout: if shutdown_event.is_set(): print(\"socket timed out and shutdown is requested\")","for writting to destinations. \"\"\" dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn,","it on queue. If something goes wrong report and shutdown server. \"\"\" dest_list","inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial + commands[0] self.partial = b\"\" if len(commands[-1]):","thread\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e: indent = len(program_name)","SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Telemetry missing A5A5 header\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \"\"\" TCP","header = self.recv(5) if len(header) == 0: print( \"Header information is empty, client","empty string, connection closed, exit loop if not header: return # Got the","parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \"-p\", \"--port\", dest=\"port\", action=\"store\", type=\"int\",","receives telemetry. FSW receives commands of various lengths. \"\"\" data = b\"\" if","\"\"\" # Read the header data from the socket either A5A5 or List","bytes from socket. \"\"\" chunk = b\"\" msg = b\"\" n = 0","n = 0 while l > n: try: chunk = self.request.recv(l - n)","python3 from __future__ import print_function import socket import threading try: import socketserver except","acknowledged.\" program_version = \"v0.1\" program_build_date = \"%s\" % __updated__ program_version_string = \"%%prog %s","function that is invoked upon a new client. This function listens for data","object for all clients registered. \"\"\" def __init__(self, name, request): \"\"\" Constructor \"\"\"","self.name = name self.id = process_id print(\"Registered client \" + self.name.decode(DATA_ENCODING)) ################################################# #","params[1] if b\"FSW\" in name: if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name","a single command here header and data here. The command must always start","sent to either GUI or FSW. GUI receives telemetry. FSW receives commands of","A5A5 header\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \"\"\" Derived from original Stable demo during R&TD and","header: break elif header == b\"Quit\": LOCK.acquire() print(\"Quit received!\") SERVER.dest_obj[self.name].put(struct.pack(\">I\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1)","data part of the message sent to either GUI or FSW. GUI receives","= b\"\" self.id = 0 # print self.client_address, now() # show this client's","dest_list = [] if header == b\"List\": print(\"List of registered clients: \") LOCK.acquire()","header data so read the data of the message here... data = self.readData(header,","connection closed, exit loop if not header: return # Got the header data","error \" + str(err.errno) + \" occurred on send().\") def fileno(self): \"\"\" \"\"\"","fileno(self): \"\"\" \"\"\" return self.socket def main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0])","server. \"\"\" class DestObj: \"\"\" Destination object for all clients registered. \"\"\" def","self.recv(5) if len(header) == 0: print( \"Header information is empty, client \" +","threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not","else: print(\"Socket error \" + str(err.errno) + \" occurred on recv().\") return msg","tlm_packet_size + packet[4 : 4 + size] return data def processNewPkt(self, header, data):","= self.readData(header, packet) # Process and send the packet of the message here...",") parser.add_option( \"-i\", \"--host\", dest=\"host\", action=\"store\", type=\"string\", help=\"Set threaded tcp socket server ip","received!\") SERVER.dest_obj[self.name].put(struct.pack(\">I\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\"Quit processed!\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got","\"\"\" Derived from original Stable demo during R&TD and adapted for use in","\"A5A5 \" or a \"List \" \"\"\" # Read the header data from","for d in list(SERVER.dest_obj.keys()): print(\"\\t\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\"List \" + SERVER.dest_obj[d].name","here... head, dst = header.strip(b\" \").split(b\" \") if head == b\"A5A5\": # Packet","data from the socket either A5A5 or List header = self.readHeader() # If","tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else: raise RuntimeError(\"unrecognized client %s\" % dst.decode(DATA_ENCODING))","dest=\"host\", action=\"store\", type=\"string\", help=\"Set threaded tcp socket server ip [default: %default]\", default=\"127.0.0.1\", )","on each client connect \"\"\" The function that is invoked upon a new","dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print \"Sending UDP msg","server. \"\"\" dest_list = [] # Process data here... head, dst = header.strip(b\"","+ header2, packet) def readData(self, header, packet): \"\"\" Read the data part of","registered clients: \") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\"\\t\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str =","FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name = params[1] + b\"_\" + bytes(process_id)","size] return data def processNewPkt(self, header, data): \"\"\" Process a single command here","ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \"\"\" TCP Socket server. Keep a dictionary of destination objects containing","import signal import sys import struct import errno from fprime.constants import DATA_ENCODING from","to destinations. \"\"\" dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \"\"\"","[default: %default]\", default=\"127.0.0.1\", ) # process options (opts, args) = parser.parse_args(argv) HOST =","# on each client connect \"\"\" The function that is invoked upon a","fprime.constants import DATA_ENCODING from optparse import OptionParser __version__ = 0.1 __date__ = \"2015-04-03\"","sends a \"List\" comment makes the server display all registered clients. \"\"\" socketserver.StreamRequestHandler.allow_reuse_address","sequence files and parameters. Handle is instanced in own thread for each client.","data from the socket data = self.recv(13) # Connection was closed by the","argv = sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \"-p\",","packets to send/receive while not shutdown_event.is_set(): # Read the header data from the","if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in","command must always start with A5A5 except if it is a List. Once","sent. Any client that sends a \"List\" comment makes the server display all","ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \"\"\" Derived from original Stable demo during R&TD and adapted for use","GUI receives telemetry. FSW receives commands of various lengths. \"\"\" data = \"\"",") parser.add_option( \"-p\", \"--port\", dest=\"port\", action=\"store\", type=\"int\", help=\"Set threaded tcp socket server port","If something goes wrong report and shutdown server. \"\"\" dest_list = [] #","occurred on recv().\" ) else: print(\"Socket error \" + str(err.errno) + \" occurred","read the \"List\\n\" command. \"\"\" header = self.recv(5) if len(header) == 0: print(","Note only <data> is sent. Any client that sends a \"List\" comment makes","\") if dst == b\"FSW\": # Read variable length command data here... desc","# print \"Received Packet: %s %s...\\n\" % (head,dst) if data == b\"\": print(\"","\"Socket error \" + str(err.errno) + \" (Connection reset by peer) occurred on","header2 = packet[4:9] packet = packet[9:] return (header + header2, packet) def readData(self,","data from socket is empty!\") return b\"\" msg = msg + chunk n","GUI_clients else: print(\"dest? %s\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if dest_elem","a command from partial or full socket input \"\"\" commands = inputString.split(end_of_command) if","header if header == b\"List\\n\": return b\"List\" elif header == b\"Quit\\n\": return b\"Quit\"","elif header[:-1] == b\"A5A5\": header2 = self.recv(4) return header + header2 else: return","each packet, call processPkt. \"\"\" self.getNewMsg(self.request[0]) ################################################# # New Routines to process the","+ self.recv(size) else: raise RuntimeError(\"unrecognized client %s\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self,","Socket server. Keep a dictionary of destination objects containing queues and socket id's","in name: if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name = params[1] +","A5A5 except if it is a List. Once the entire header string is","the cmdQueue self.processQueue() if self.registered: print(\"Registration complete waiting for message.\") self.getNewMsg() else: print(\"Unable","= parser.parse_args(argv) HOST = opts.host PORT = opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler)","empty, returning.\") if b\"GUI\" in dst: dest_list = GUI_clients elif b\"FSW\" in dst:","lengths. \"\"\" data = b\"\" if header == b\"List\": return b\"\" elif header","# more thread for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT,","is sent. Any client that sends a \"List\" comment makes the server display","SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id)","msg def readHeader(self): \"\"\" Read the 9 byte header (e.g. \"A5A5 GUI \"","# Read the header data from the socket either A5A5 or List (header,","to either GUI or FSW. GUI receives telemetry. FSW receives commands of various","an empty string, connection closed, exit loop if not header: return # Got","################################################# # New Routines to process the command messages ################################################# def getNewMsg(self, packet):","= len(reg_client_str) reg_client_str = struct.pack(\"i%ds\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0","from partial or full socket input \"\"\" commands = inputString.split(end_of_command) if len(self.partial): commands[0]","a newline. For each packet, call processPkt. \"\"\" self.getNewMsg(self.request[0]) ################################################# # New Routines","process options (opts, args) = parser.parse_args(argv) HOST = opts.host PORT = opts.port server","print(\"List of registered clients: \") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\"\\t\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING))","else: print(\"dest? %s\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if dest_elem in","message here... self.processNewPkt(header, data) def readHeader(self, packet): \"\"\" Read the 9 byte header","# Process and send the packet of the message here... self.processNewPkt(header, data) def","self.registered = False self.request.close() def getCmds(self, inputString, end_of_command=b\"\\n\"): \"\"\" Build a command from","= 0 if params[0] == b\"Register\": LOCK.acquire() name = params[1] if b\"FSW\" in","RuntimeError(\"Telemetry missing A5A5 header\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \"\"\" TCP Socket server. Keep a","from socket is empty!\") return b\"\" msg = msg + chunk n =","program_license = \"Copyright 2015 user_name (California Institute of Technology) \\ ALL RIGHTS RESERVED.","str(err.errno) + \" (Connection reset by peer) occurred on recv().\" ) else: print(\"Socket","len(reg_client_str) reg_client_str = struct.pack(\"i%ds\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 #","== \"\": print(\" Data is empty, returning.\") if b\"GUI\" in dst: dest_list =","Packet: %s %s...\\n\" % (head,dst) if data == \"\": print(\" Data is empty,","packet[9:] return (header + header2, packet) def readData(self, header, packet): \"\"\" Read the","an \"A5A5 \" or a \"List \" \"\"\" # Read the header data","in list(SERVER.dest_obj.keys()): # Send the message here.... # print \"Sending UDP msg to","or a \"List \" \"\"\" # Read the header data from the socket","% (head,dst) if data == \"\": print(\" Data is empty, returning.\") if b\"GUI\"","if b\"GUI\" in dst: dest_list = GUI_clients elif b\"FSW\" in dst: dest_list =","def recv(self, l): \"\"\" Read l bytes from socket. \"\"\" chunk = b\"\"","global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license = \"Copyright 2015 user_name (California Institute","socket.timeout: if shutdown_event.is_set(): print(\"socket timed out and shutdown is requested\") return b\"Quit\\n\" continue","in name: if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name = params[1] +","\"\"\" try: # print \"about to send data to \" + self.name self.socket.send(msg)","= request self.packet = b\"\" def put(self, msg): \"\"\" Write out the message","+ self.name self.socket.send(msg) except socket.error as err: print(\"Socket error \" + str(err.errno) +","+ commands[0] self.partial = b\"\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1])","def main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license = \"Copyright 2015 user_name","other things such as sequence files and parameters. Handle is instanced in own","header == b\"Quit\": return b\"\" dst = header.split(b\" \")[1].strip(b\" \") if dst ==","call processPkt. \"\"\" self.partial = b\"\" self.cmdQueue = [] self.registered = False self.name","header\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \"\"\" TCP Socket server. Keep a dictionary of destination","= \"\" dst = header.split(b\" \")[1].strip(b\" \") # Read telemetry data here... tlm_packet_size","occurred on recv().\") return msg def readHeader(self): \"\"\" Read the 9 byte header","data): \"\"\" Process a single command here header and data here. The command","self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\"Closed %s connection.\" % self.name.decode(DATA_ENCODING)) self.registered =","self.recv(size) elif dst == b\"GUI\": # Read telemetry data here... tlm_packet_size = self.recv(4)","elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\"Closed %s connection.\" % self.name.decode(DATA_ENCODING)) self.registered","return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in","(program_version, program_build_date) program_longdesc = ( \"\"\"\"\"\" # optional - give further explanation about","port = server.server_address print(\"TCP Socket Server listening on host addr %s, port %s\"","Build a command from partial or full socket input \"\"\" commands = inputString.split(end_of_command)","header tstring is processed send it on queue. If something goes wrong report",") if argv is None: argv = sys.argv[1:] try: parser = OptionParser( version=program_version_string,","bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered = True self.name =","elif b\"FSW\" in dst: dest_list = FSW_clients for dest_elem in dest_list: # print","data on the socket. Packets for now are assumed to be separated by","= self.recv(4) size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else: raise","self.readHeader() # If the received header is an empty string, connection closed, exit","the cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue() if self.registered: print(\"Registration complete waiting","not shutdown_event.is_set(): # Read the header data from the socket either A5A5 or","Keep a dictionary of destination objects containing queues and socket id's for writting","# Read telemetry data here... tlm_packet_size = self.recv(4) size = struct.unpack(\">I\", tlm_packet_size)[0] data","__future__ import print_function import socket import threading try: import socketserver except ImportError: import","thread with the server -- that thread will then start one # more","self.request.recv(l - n) if chunk == b\"\": print(\"read data from socket is empty!\")","header data from the socket either A5A5 or List (header, packet) = self.readHeader(packet)","self.request.close() def getCmds(self, inputString, end_of_command=b\"\\n\"): \"\"\" Build a command from partial or full","import OptionParser __version__ = 0.1 __date__ = \"2015-04-03\" __updated__ = \"2016-04-07\" # Universal","GUI \" or \"A5A5 FSW \"), or just read the \"List\\n\" command. \"\"\"","= commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue","explanation about what the program does ) if argv is None: argv =","This function listens for data on the socket. Packets for now are assumed","further explanation about what the program does ) if argv is None: argv","= msg + chunk n = len(msg) except socket.timeout: if shutdown_event.is_set(): print(\"socket timed","returning.\") if b\"GUI\" in dst: dest_list = GUI_clients else: print(\"dest? %s\" % dst.decode(DATA_ENCODING))","len(self.partial): commands[0] = self.partial + commands[0] self.partial = b\"\" if len(commands[-1]): self.partial =","Data is empty, returning.\") if b\"GUI\" in dst: dest_list = GUI_clients else: print(\"dest?","= b\"List \" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\"i%ds\" % l,","or full socket input \"\"\" commands = inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial","FSW_ids.append(process_id) elif b\"GUI\" in name: if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name","processed!\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the header data so read the","port %s\" % (HOST, PORT)) # Start a thread with the server --","ThreadedUDPRequestHandler) # Hopefully this will allow address reuse and server to restart immediately","LOCK program_name = os.path.basename(sys.argv[0]) program_license = \"Copyright 2015 user_name (California Institute of Technology)","= sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \"-p\", \"--port\",","list(SERVER.dest_obj.keys()): # Send the message here.... # print \"Sending UDP msg to \",","self.name self.socket.send(msg) except socket.error as err: print(\"Socket error \" + str(err.errno) + \"","= \"%%prog %s (%s)\" % (program_version, program_build_date) program_longdesc = ( \"\"\"\"\"\" # optional","= packet[9:] return (header + header2, packet) def readData(self, header, packet): \"\"\" Read","in list(SERVER.dest_obj.keys()): print(\"\\t\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\"List \" + SERVER.dest_obj[d].name l =","shutdown_event.is_set(): # Read the header data from the socket either A5A5 or List","PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will allow address","= \"2015-04-03\" __updated__ = \"2016-04-07\" # Universal server id global SERVER = None","received header is an empty string, connection closed, exit loop if not header:","by the client if not data: print(\"Client exited.\") return else: # Process the","name: if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name = params[1] + b\"_\"","socket server for commands, log events, and telemetry data. Later this will handle","data of the message here... data = self.readData(header, packet) # Process and send","New Routines to process the command messages ################################################# def getNewMsg(self): \"\"\" After registration","[] # Process data here... head, dst = header.strip(b\" \").split(b\" \") if head","server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this","name = params[1] + b\"_\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\"GUI\" in name:","in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd): params = cmd.split() process_id","Any client that sends a \"List\" comment makes the server display all registered","size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else: raise RuntimeError(\"unrecognized client","on host addr %s, port %s\" % (HOST, PORT)) # Start a thread","return b\"\" dst = header.split(b\" \")[1].strip(b\" \") if dst == b\"FSW\": # Read","Routines to process the command messages ################################################# def getNewMsg(self): \"\"\" After registration wait","restart immediately server.allow_reuse_address = True SERVER = server LOCK = server.lock_obj ip, port","client.\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name","except if it is a List. Once the entire header string is processed","= False self.request.close() def getCmds(self, inputString, end_of_command=b\"\\n\"): \"\"\" Build a command from partial","Read the data part of the message sent to either GUI or FSW.","variable length command data here... desc = self.recv(4) sizeb = self.recv(4) size =","(header + header2, packet) def readData(self, header, packet): \"\"\" Read the data part","header\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \"\"\" Derived from original Stable demo during R&TD and adapted","else: raise RuntimeError(\"Telemetry missing A5A5 header\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \"\"\" TCP Socket server.","= opts.host PORT = opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST,","from socket. \"\"\" chunk = b\"\" msg = b\"\" n = 0 while","l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process data here... head, dst =","by a newline. For each packet, call processPkt. \"\"\" self.partial = b\"\" self.cmdQueue","header.split(b\" \")[1].strip(b\" \") # Read telemetry data here... tlm_packet_size = packet[:4] size =","0 while l > n: try: chunk = self.request.recv(l - n) if chunk","data from the socket either A5A5 or List (header, packet) = self.readHeader(packet) #","socket import threading try: import socketserver except ImportError: import SocketServer as socketserver import","host addr %s, port %s\" % (HOST, PORT)) # Start a thread with","except Exception as e: indent = len(program_name) * \" \" sys.stderr.write(program_name + \":","def handle(self): # on each packet \"\"\" The function that is invoked when","try: chunk = self.request.recv(l - n) if chunk == b\"\": print(\"read data from","self.request) LOCK.release() self.registered = True self.name = name self.id = process_id print(\"Registered client","= tlm_packet_size + packet[4 : 4 + size] return data def processNewPkt(self, header,","argv is None: argv = sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license,","ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will allow address reuse and server to","\"\"\" dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \"\"\" UDP Socket","in own thread for each client. Registration is done by sending the string","b\"\" if header == b\"List\": return b\"\" elif header == b\"Quit\": return b\"\"","server. \"\"\" dest_list = [] if header == b\"List\": print(\"List of registered clients:","= b\"\" msg = b\"\" n = 0 while l > n: try:","Process the cmdQueue self.processQueue() if self.registered: print(\"Registration complete waiting for message.\") self.getNewMsg() else:","% (HOST, PORT)) # Start a thread with the server -- that thread","message here... data = self.readData(header, packet) # Process and send the packet of","= tlm_packet_size + self.recv(size) else: raise RuntimeError(\"unrecognized client %s\" % dst.decode(DATA_ENCODING)) return data","\"about to send data to \" + self.name self.socket.send(msg) except socket.error as err:","Write out the message to the destination socket \"\"\" try: # print \"about","0.1 __date__ = \"2015-04-03\" __updated__ = \"2016-04-07\" # Universal server id global SERVER","os.path.basename(sys.argv[0]) program_license = \"Copyright 2015 user_name (California Institute of Technology) \\ ALL RIGHTS","applicaiton. TCP socket server for commands, log events, and telemetry data. Later this","return self.socket def main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license = \"Copyright","\"-p\", \"--port\", dest=\"port\", action=\"store\", type=\"int\", help=\"Set threaded tcp socket server port [default: %default]\",","\"List\" comment makes the server display all registered clients. \"\"\" socketserver.BaseRequestHandler.allow_reuse_address = True","header == b\"List\\n\": return b\"List\" elif header == b\"Quit\\n\": return b\"Quit\" elif header[:-1]","ip, port = server.server_address print(\"TCP Socket Server listening on host addr %s, port","data here... tlm_packet_size = self.recv(4) size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size +","each packet \"\"\" The function that is invoked when a packet is received.","socket input \"\"\" commands = inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial + commands[0]","socket either A5A5 or List (header, packet) = self.readHeader(packet) # If the received","of destination objects containing queues and socket id's for writting to destinations. \"\"\"","print(\"Quit received!\") SERVER.dest_obj[self.name].put(struct.pack(\">I\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\"Quit processed!\") SERVER.shutdown() SERVER.server_close() LOCK.release() break #","of Technology) \\ ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\" program_version = \"v0.1\"","as socketserver import time import os import signal import sys import struct import","command here header and data here. The command must always start with A5A5","LOCK = server.lock_obj ip, port = server.server_address print(\"TCP Socket Server listening on host","to process the command messages ################################################# def getNewMsg(self): \"\"\" After registration wait for","call processPkt. \"\"\" self.getNewMsg(self.request[0]) ################################################# # New Routines to process the command messages","packet): \"\"\" Read the data part of the message sent to either GUI","a new client. This function listens for data on the socket. Packets for","= self.request.recv(l - n) if chunk == b\"\": print(\"read data from socket is","\"%s\" % __updated__ program_version_string = \"%%prog %s (%s)\" % (program_version, program_build_date) program_longdesc =","and parameters. Handle is instanced in own thread for each client. Registration is","registered clients. \"\"\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on","msg = msg + chunk n = len(msg) except socket.timeout: if shutdown_event.is_set(): print(\"socket","on recv().\") return msg def readHeader(self): \"\"\" Read the 9 byte header (e.g.","b\"Register\": LOCK.acquire() name = params[1] if b\"FSW\" in name: if FSW_clients: process_id =","closed, exit loop if not header: return # Got the header data so","queue. If something goes wrong report and shutdown server. \"\"\" dest_list = []","dest_elem in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here....","params[0] == b\"Register\": LOCK.acquire() name = params[1] if b\"FSW\" in name: if FSW_clients:","\"A5A5 GUI \" or \"A5A5 FSW \"), or just read the \"List\\n\" command.","all registered clients. \"\"\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on each packet","program_build_date) program_longdesc = ( \"\"\"\"\"\" # optional - give further explanation about what","data = self.recv(13) # Connection was closed by the client if not data:","def signal_handler(*_): print(\"Ctrl-C received, server shutting down.\") shutdown_event.set() def now(): return time.ctime(time.time()) class","is empty!\") return b\"\" msg = msg + chunk n = len(msg) except","data == b\"\": print(\" Data is empty, returning.\") if b\"GUI\" in dst: dest_list","this client's address # Read the data from the socket data = self.recv(13)","SERVER = None LOCK = None shutdown_event = threading.Event() FSW_clients = [] GUI_clients","one # more thread for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever)","= b\"\" if header == b\"List\": return b\"\" elif header == b\"Quit\": return","self.recv(4) size = struct.unpack(\">I\", sizeb)[0] data = desc + sizeb + self.recv(size) elif","allow address reuse and server to restart immediately server.allow_reuse_address = True SERVER =","exited.\") return else: # Process the data into the cmdQueue self.getCmds(data) # Process","of the message sent to either GUI or FSW. GUI receives telemetry. FSW","= struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else: raise RuntimeError(\"unrecognized client %s\"","\"A5A5 FSW \"), or just read the \"List\\n\" command. \"\"\" header = packet[:4]","continue except socket.error as err: if err.errno == errno.ECONNRESET: print( \"Socket error \"","header == b\"List\": return b\"\" elif header == b\"Quit\": return b\"\" dst =","reg_client_str = b\"List \" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\"i%ds\" %","if header == b\"List\": return b\"\" elif header == b\"Quit\": return b\"\" dst","program_version = \"v0.1\" program_build_date = \"%s\" % __updated__ program_version_string = \"%%prog %s (%s)\"","import socketserver except ImportError: import SocketServer as socketserver import time import os import","b\"Quit\" elif header[:-1] == b\"A5A5\": header2 = self.recv(4) return header + header2 else:","tstring is processed send it on queue. If something goes wrong report and","for use in new FSW gse.py applicaiton. TCP socket server for commands, log","server LOCK = server.lock_obj ip, port = server.server_address print(\"TCP Socket Server listening on","Once the entire header string is processed send it on queue. If something","struct import errno from fprime.constants import DATA_ENCODING from optparse import OptionParser __version__ =","in new FSW gse.py applicaiton. TCP socket server for commands, log events, and","dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the","thread for each client. Registration is done by sending the string \"Register <name>\".","the server -- that thread will then start one # more thread for","FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\"Closed %s connection.\" % self.name.decode(DATA_ENCODING))","the data from the socket data = self.recv(13) # Connection was closed by","Handle is instanced in own thread for each client. Registration is done by","getNewMsg(self): \"\"\" After registration wait for an incoming message The first part must","closed, exit loop if not header: break elif header == b\"Quit\": LOCK.acquire() print(\"Quit","b\"List\" elif header == b\"Quit\\n\": return b\"Quit\" elif header[:-1] == b\"A5A5\": header2 =","None: argv = sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option(","makes the server display all registered clients. \"\"\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout =","try: import socketserver except ImportError: import SocketServer as socketserver import time import os","type=\"string\", help=\"Set threaded tcp socket server ip [default: %default]\", default=\"127.0.0.1\", ) # process","args) = parser.parse_args(argv) HOST = opts.host PORT = opts.port server = ThreadedTCPServer((HOST, PORT),","be separated by a newline. For each packet, call processPkt. \"\"\" self.partial =","Socket Server listening on host addr %s, port %s\" % (HOST, PORT)) #","newline. For each packet, call processPkt. \"\"\" self.getNewMsg(self.request[0]) ################################################# # New Routines to","# Process data here... head, dst = header.strip(b\" \").split(b\" \") if head ==","an incoming message The first part must always be an \"A5A5 \" or","or just read the \"List\\n\" command. \"\"\" header = packet[:4] header2 = packet[4:9]","= False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\"shutdown from main thread\") SERVER.shutdown()","tcp socket server port [default: %default]\", default=50007, ) parser.add_option( \"-i\", \"--host\", dest=\"host\", action=\"store\",","Send the message here.... # print \"Sending UDP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data)","# Got the header data so read the data of the message here...","if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print \"Sending TCP","break # Got the header data so read the data of the message","an \"A5A5 \" or a \"List \" \"\"\" # Loop while the connected","cmdQueue self.processQueue() if self.registered: print(\"Registration complete waiting for message.\") self.getNewMsg() else: print(\"Unable to","if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release()","= b\"\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for","destination socket \"\"\" try: # print \"about to send data to \" +","Government Sponsorship acknowledged.\" program_version = \"v0.1\" program_build_date = \"%s\" % __updated__ program_version_string =","error \" + str(err.errno) + \" occurred on recv().\") return msg def readHeader(self):","self.partial = b\"\" self.cmdQueue = [] self.registered = False self.name = b\"\" self.id","in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... #","instanced in own thread for each client. Registration is done by sending the","now are assumed to be separated by a newline. For each packet, call","\"Register <name>\". Sending a message to destination <name> is done as \"A5A5 <name>","Loop while the connected client has packets to send/receive while not shutdown_event.is_set(): #","\"-i\", \"--host\", dest=\"host\", action=\"store\", type=\"string\", help=\"Set threaded tcp socket server ip [default: %default]\",","True def handle(self): # on each packet \"\"\" The function that is invoked","\"\"\" After registration wait for an incoming message The first part must always","shutting down.\") shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \"\"\" Derived from original","self.cmdQueue = [] def processRegistration(self, cmd): params = cmd.split() process_id = 0 if","clients. \"\"\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on each packet \"\"\" The","version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \"-p\", \"--port\", dest=\"port\", action=\"store\", type=\"int\", help=\"Set threaded tcp","dst = header.split(b\" \")[1].strip(b\" \") if dst == b\"FSW\": # Read variable length","socketserver import time import os import signal import sys import struct import errno","display all registered clients. \"\"\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def handle(self):","\" + str(err.errno) + \" occurred on send().\") def fileno(self): \"\"\" \"\"\" return","self.processNewPkt(header, data) def recv(self, l): \"\"\" Read l bytes from socket. \"\"\" chunk","__init__(self, name, request): \"\"\" Constructor \"\"\" self.name = name self.socket = request self.packet","time.sleep(1) print(\"Quit processed!\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the header data so",") else: print(\"Socket error \" + str(err.errno) + \" occurred on recv().\") return","b\"List \" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\"i%ds\" % l, l,","list(SERVER.dest_obj.keys()): # Send the message here.... # print \"Sending TCP msg to \",","self.readData(header) # Process and send the packet of the message here... self.processNewPkt(header, data)","\"), or just read the \"List\\n\" command. \"\"\" header = packet[:4] header2 =","newline. For each packet, call processPkt. \"\"\" self.partial = b\"\" self.cmdQueue = []","\"\"\" def __init__(self, name, request): \"\"\" Constructor \"\"\" self.name = name self.socket =","the socket either A5A5 or List (header, packet) = self.readHeader(packet) # If the","start one # more thread for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread =","+ SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\"List \" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str =","program_name = os.path.basename(sys.argv[0]) program_license = \"Copyright 2015 user_name (California Institute of Technology) \\","message here... data = self.readData(header) # Process and send the packet of the","LOCK.release() self.registered = True self.name = name self.id = process_id print(\"Registered client \"","ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \"\"\" UDP Socket server. \"\"\" class DestObj: \"\"\" Destination object for","b\"FSW\" in name: if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name = params[1]","%s, port %s\" % (HOST, PORT)) # Start a thread with the server","server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0)","data = tlm_packet_size + packet[4 : 4 + size] return data def processNewPkt(self,","= struct.pack(\"i%ds\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process data","\"List \" \"\"\" # Loop while the connected client has packets to send/receive","part must always be an \"A5A5 \" or a \"List \" \"\"\" #","= self.recv(4) return header + header2 else: return def readData(self, header): \"\"\" Read","header + header2 else: return def readData(self, header): \"\"\" Read the data part","Read telemetry data here... tlm_packet_size = packet[:4] size = struct.unpack(\">I\", tlm_packet_size)[0] data =","report and shutdown server. \"\"\" dest_list = [] # Process data here... head,","self.id = 0 # print self.client_address, now() # show this client's address #","loop if not header: return # Got the header data so read the","addr %s, port %s\" % (HOST, PORT)) # Start a thread with the","threading.Event() FSW_clients = [] GUI_clients = [] FSW_ids = [] GUI_ids = []","waiting for message.\") self.getNewMsg() else: print(\"Unable to register client.\") return LOCK.acquire() del SERVER.dest_obj[self.name]","err.errno == errno.ECONNRESET: print( \"Socket error \" + str(err.errno) + \" (Connection reset","a packet is received. This function listens for data on the socket. Packets","def getCmds(self, inputString, end_of_command=b\"\\n\"): \"\"\" Build a command from partial or full socket","address reuse and server to restart immediately server.allow_reuse_address = True SERVER = server","self.recv(4) size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else: raise RuntimeError(\"unrecognized","if header == b\"List\": print(\"List of registered clients: \") LOCK.acquire() for d in","socket server port [default: %default]\", default=50007, ) parser.add_option( \"-i\", \"--host\", dest=\"host\", action=\"store\", type=\"string\",","readData(self, header, packet): \"\"\" Read the data part of the message sent to","= self.recv(4) size = struct.unpack(\">I\", sizeb)[0] data = desc + sizeb + self.recv(size)","HOST = opts.host PORT = opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server =","threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \"\"\" UDP Socket server. \"\"\" class DestObj: \"\"\" Destination","import socket import threading try: import socketserver except ImportError: import SocketServer as socketserver","header == b\"List\": print(\"List of registered clients: \") LOCK.acquire() for d in list(SERVER.dest_obj.keys()):","commands = inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial + commands[0] self.partial = b\"\"",") # process options (opts, args) = parser.parse_args(argv) HOST = opts.host PORT =","on queue. If something goes wrong report and shutdown server. \"\"\" dest_list =","all registered clients. \"\"\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def handle(self): #","original Stable demo during R&TD and adapted for use in new FSW gse.py","self.partial = b\"\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self):","all clients registered. \"\"\" def __init__(self, name, request): \"\"\" Constructor \"\"\" self.name =","the \"List\\n\" command. \"\"\" header = packet[:4] header2 = packet[4:9] packet = packet[9:]","commands of various lengths. \"\"\" data = b\"\" if header == b\"List\": return","b\"List\\n\": return b\"List\" elif header == b\"Quit\\n\": return b\"Quit\" elif header[:-1] == b\"A5A5\":","and shutdown server. \"\"\" dest_list = [] # Process data here... head, dst","except socket.error as err: print(\"Socket error \" + str(err.errno) + \" occurred on","as \"A5A5 <name> <data>\" Note only <data> is sent. Any client that sends","U.S. Government Sponsorship acknowledged.\" program_version = \"v0.1\" program_build_date = \"%s\" % __updated__ program_version_string","objects containing queues and socket id's for writting to destinations. \"\"\" dest_obj =","receives commands of various lengths. \"\"\" data = \"\" dst = header.split(b\" \")[1].strip(b\"","socketserver.UDPServer): \"\"\" UDP Socket server. \"\"\" class DestObj: \"\"\" Destination object for all","+ b\"_\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered =","(e.g. \"A5A5 GUI \" or \"A5A5 FSW \"), or just read the \"List\\n\"","self.processNewPkt(header, data) def readHeader(self, packet): \"\"\" Read the 9 byte header (e.g. \"A5A5","or List (header, packet) = self.readHeader(packet) # If the received header is an","udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e: indent = len(program_name) * \" \"","if self.registered: print(\"Registration complete waiting for message.\") self.getNewMsg() else: print(\"Unable to register client.\")","def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \"\"\" Derived from original Stable demo during","1 name = params[1] + b\"_\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\"GUI\" in","GUI_ids.remove(self.id) LOCK.release() print(\"Closed %s connection.\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self,","header = self.readHeader() # If the received header is an empty string, connection","class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \"\"\" UDP Socket server. \"\"\" class DestObj: \"\"\" Destination object","packet, call processPkt. \"\"\" self.partial = b\"\" self.cmdQueue = [] self.registered = False","# print self.client_address, now() # show this client's address # Read the data","bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\"GUI\" in name: if GUI_clients: process_id = sorted(GUI_ids)[-1] +","full socket input \"\"\" commands = inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial +","shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \"\"\" Derived from original Stable demo","( \"\"\"\"\"\" # optional - give further explanation about what the program does","== b\"\": print(\"read data from socket is empty!\") return b\"\" msg = msg","= self.recv(13) # Connection was closed by the client if not data: print(\"Client","empty, client \" + self.name.decode(DATA_ENCODING) + \" exiting.\" ) return header if header","len(program_name) * \" \" sys.stderr.write(program_name + \": \" + repr(e) + \"\\n\") sys.stderr.write(indent","sys.stderr.write(program_name + \": \" + repr(e) + \"\\n\") sys.stderr.write(indent + \" for help","lengths. \"\"\" data = \"\" dst = header.split(b\" \")[1].strip(b\" \") # Read telemetry","each client connect \"\"\" The function that is invoked upon a new client.","telemetry. FSW receives commands of various lengths. \"\"\" data = b\"\" if header","return 0 # Process data here... head, dst = header.strip(b\" \").split(b\" \") if","Constructor \"\"\" self.name = name self.socket = request self.packet = b\"\" def put(self,","this will allow address reuse and server to restart immediately server.allow_reuse_address = True","\"--port\", dest=\"port\", action=\"store\", type=\"int\", help=\"Set threaded tcp socket server port [default: %default]\", default=50007,","readHeader(self, packet): \"\"\" Read the 9 byte header (e.g. \"A5A5 GUI \" or","packet = packet[9:] return (header + header2, packet) def readData(self, header, packet): \"\"\"","else: # Process the data into the cmdQueue self.getCmds(data) # Process the cmdQueue","OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \"-p\", \"--port\", dest=\"port\", action=\"store\", type=\"int\", help=\"Set threaded","while not shutdown_event.is_set(): # Read the header data from the socket either A5A5","tlm_packet_size)[0] data = tlm_packet_size + packet[4 : 4 + size] return data def","more thread for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler)","class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \"\"\" Derived from original Stable demo during R&TD and adapted for","== b\"A5A5\": header2 = self.recv(4) return header + header2 else: return def readData(self,","if not header: return # Got the header data so read the data","= b\"\" n = 0 while l > n: try: chunk = self.request.recv(l","%s (%s)\" % (program_version, program_build_date) program_longdesc = ( \"\"\"\"\"\" # optional - give","= self.readHeader() # If the received header is an empty string, connection closed,","chunk = self.request.recv(l - n) if chunk == b\"\": print(\"read data from socket","header[:-1] == b\"A5A5\": header2 = self.recv(4) return header + header2 else: return def","self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd)","containing queues and socket id's for writting to destinations. \"\"\" dest_obj = dict()","= self.readHeader(packet) # If the received header is an empty string, connection closed,","FSW. GUI receives telemetry. FSW receives commands of various lengths. \"\"\" data =","parser.parse_args(argv) HOST = opts.host PORT = opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server","indent = len(program_name) * \" \" sys.stderr.write(program_name + \": \" + repr(e) +","destination <name> is done as \"A5A5 <name> <data>\" Note only <data> is sent.","each packet, call processPkt. \"\"\" self.partial = b\"\" self.cmdQueue = [] self.registered =","= params[1] if b\"FSW\" in name: if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1","on recv().\" ) else: print(\"Socket error \" + str(err.errno) + \" occurred on","b\"GUI\": # Read telemetry data here... tlm_packet_size = self.recv(4) size = struct.unpack(\">I\", tlm_packet_size)[0]","\" or \"A5A5 FSW \"), or just read the \"List\\n\" command. \"\"\" header","d in list(SERVER.dest_obj.keys()): print(\"\\t\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\"List \" + SERVER.dest_obj[d].name l","packet[4 : 4 + size] return data def processNewPkt(self, header, data): \"\"\" Process","> n: try: chunk = self.request.recv(l - n) if chunk == b\"\": print(\"read","the command messages ################################################# def getNewMsg(self, packet): \"\"\" After registration wait for an","returning.\") if b\"GUI\" in dst: dest_list = GUI_clients elif b\"FSW\" in dst: dest_list","= [] self.registered = False self.name = b\"\" self.id = 0 # print","== b\"List\\n\": return b\"List\" elif header == b\"Quit\\n\": return b\"Quit\" elif header[:-1] ==","2015 user_name (California Institute of Technology) \\ ALL RIGHTS RESERVED. U.S. Government Sponsorship","= ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will","process_id print(\"Registered client \" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process the","from optparse import OptionParser __version__ = 0.1 __date__ = \"2015-04-03\" __updated__ = \"2016-04-07\"","either A5A5 or List (header, packet) = self.readHeader(packet) # If the received header","(head,dst) if data == b\"\": print(\" Data is empty, returning.\") if b\"GUI\" in","\"List\\n\" command. \"\"\" header = packet[:4] header2 = packet[4:9] packet = packet[9:] return","name = params[1] + b\"_\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request)","\"\\n\") sys.stderr.write(indent + \" for help use --help\\n\") return 2 if __name__ ==","Derived from original Stable demo during R&TD and adapted for use in new","\"Locking TCP\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... #","just read the \"List\\n\" command. \"\"\" header = self.recv(5) if len(header) == 0:","\"\"\" UDP Socket server. \"\"\" class DestObj: \"\"\" Destination object for all clients","dest_list: # print \"Locking TCP\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the","l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process data here... head, dst","packet[4:9] packet = packet[9:] return (header + header2, packet) def readData(self, header, packet):","# print \"Sending UDP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Telemetry","queues and socket id's for writting to destinations. \"\"\" dest_obj = dict() lock_obj","id's for writting to destinations. \"\"\" dest_obj = dict() lock_obj = threading.Lock() class","if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name = params[1] + b\"_\" +","break elif header == b\"Quit\": LOCK.acquire() print(\"Quit received!\") SERVER.dest_obj[self.name].put(struct.pack(\">I\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\"Quit","recv().\") return msg def readHeader(self): \"\"\" Read the 9 byte header (e.g. \"A5A5","%s...\\n\" % (head,dst) if data == b\"\": print(\" Data is empty, returning.\") if","socketserver except ImportError: import SocketServer as socketserver import time import os import signal","print \"Sending TCP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Packet missing","data. Later this will handle other things such as sequence files and parameters.","string, connection closed, exit loop if not header: break elif header == b\"Quit\":","FSW_clients.append(name) FSW_ids.append(process_id) elif b\"GUI\" in name: if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1","Process data here... head, dst = header.strip(b\" \").split(b\" \") if head == b\"A5A5\":","dest_list = [] # Process data here... head, dst = header.strip(b\" \").split(b\" \")","try: # print \"about to send data to \" + self.name self.socket.send(msg) except","tlm_packet_size + self.recv(size) else: raise RuntimeError(\"unrecognized client %s\" % dst.decode(DATA_ENCODING)) return data def","= opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) #","== b\"GUI\": # Read telemetry data here... tlm_packet_size = self.recv(4) size = struct.unpack(\">I\",","elif header == b\"Quit\\n\": return b\"Quit\" elif header[:-1] == b\"A5A5\": header2 = self.recv(4)","packet[:4] size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + packet[4 : 4 +","here.... # print \"Sending TCP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise","from the socket data = self.recv(13) # Connection was closed by the client","complete waiting for message.\") self.getNewMsg() else: print(\"Unable to register client.\") return LOCK.acquire() del","exiting.\" ) return header if header == b\"List\\n\": return b\"List\" elif header ==","UDP Socket server. \"\"\" class DestObj: \"\"\" Destination object for all clients registered.","+ 1 name = params[1] + b\"_\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\"GUI\"","each client. Registration is done by sending the string \"Register <name>\". Sending a","import errno from fprime.constants import DATA_ENCODING from optparse import OptionParser __version__ = 0.1","if shutdown_event.is_set(): print(\"socket timed out and shutdown is requested\") return b\"Quit\\n\" continue except","\") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\"\\t\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\"List \"","struct.pack(\"i%ds\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process data here...","socket is empty!\") return b\"\" msg = msg + chunk n = len(msg)","self.readData(header, packet) # Process and send the packet of the message here... self.processNewPkt(header,","client's address # Read the data from the socket data = self.recv(13) #","<name> <data>\" Note only <data> is sent. Any client that sends a \"List\"","global SERVER = None LOCK = None shutdown_event = threading.Event() FSW_clients = []","sending the string \"Register <name>\". Sending a message to destination <name> is done","return else: # Process the data into the cmdQueue self.getCmds(data) # Process the","1 def handle(self): # on each client connect \"\"\" The function that is","header2, packet) def readData(self, header, packet): \"\"\" Read the data part of the","= 1 def handle(self): # on each client connect \"\"\" The function that","message The first part must always be an \"A5A5 \" or a \"List","the header data so read the data of the message here... data =","SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the header data so read the data","[] FSW_ids = [] GUI_ids = [] def signal_handler(*_): print(\"Ctrl-C received, server shutting","optparse import OptionParser __version__ = 0.1 __date__ = \"2015-04-03\" __updated__ = \"2016-04-07\" #","raise RuntimeError(\"unrecognized client %s\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data): \"\"\"","the header data from the socket either A5A5 or List header = self.readHeader()","#!/usr/bin/env python3 from __future__ import print_function import socket import threading try: import socketserver","\"\"\" Process a single command here header and data here. The command must","always start with A5A5 except if it is a List. Once the entire","shutdown_event = threading.Event() FSW_clients = [] GUI_clients = [] FSW_ids = [] GUI_ids","= DestObj(name, self.request) LOCK.release() self.registered = True self.name = name self.id = process_id","struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else: raise RuntimeError(\"unrecognized client %s\" %","\" \"\"\" # Loop while the connected client has packets to send/receive while","GUI_clients elif b\"FSW\" in dst: dest_list = FSW_clients for dest_elem in dest_list: #","A5A5 header\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \"\"\" TCP Socket server. Keep a dictionary of","read the \"List\\n\" command. \"\"\" header = packet[:4] header2 = packet[4:9] packet =","dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data): \"\"\" Process a single command here","== b\"List\": print(\"List of registered clients: \") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\"\\t\"","each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False","what the program does ) if argv is None: argv = sys.argv[1:] try:","client connect \"\"\" The function that is invoked upon a new client. This","b\"\" dst = header.split(b\" \")[1].strip(b\" \") if dst == b\"FSW\": # Read variable","self.name = b\"\" self.id = 0 # print self.client_address, now() # show this","\"\"\" header = self.recv(5) if len(header) == 0: print( \"Header information is empty,","commands[0] = self.partial + commands[0] self.partial = b\"\" if len(commands[-1]): self.partial = commands[-1]","import print_function import socket import threading try: import socketserver except ImportError: import SocketServer","else: print(\"Unable to register client.\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients:","%s\" % (HOST, PORT)) # Start a thread with the server -- that","in dst: dest_list = FSW_clients for dest_elem in dest_list: # print \"Locking TCP\"","== errno.ECONNRESET: print( \"Socket error \" + str(err.errno) + \" (Connection reset by","in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\"Closed %s connection.\" % self.name.decode(DATA_ENCODING)) self.registered = False","the message to the destination socket \"\"\" try: # print \"about to send","socket \"\"\" try: # print \"about to send data to \" + self.name","must always start with A5A5 except if it is a List. Once the","to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Packet missing A5A5 header\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler):","None shutdown_event = threading.Event() FSW_clients = [] GUI_clients = [] FSW_ids = []","# New Routines to process the command messages ################################################# def getNewMsg(self, packet): \"\"\"","time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \"\"\" Derived from original Stable demo during R&TD and adapted","Universal server id global SERVER = None LOCK = None shutdown_event = threading.Event()","n = len(msg) except socket.timeout: if shutdown_event.is_set(): print(\"socket timed out and shutdown is","for each client. Registration is done by sending the string \"Register <name>\". Sending","l): \"\"\" Read l bytes from socket. \"\"\" chunk = b\"\" msg =","opts.host PORT = opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT),","Header # print \"Received Packet: %s %s...\\n\" % (head,dst) if data == \"\":","LOCK.acquire() name = params[1] if b\"FSW\" in name: if FSW_clients: process_id = sorted(FSW_ids)[-1]","Read l bytes from socket. \"\"\" chunk = b\"\" msg = b\"\" n","if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name = params[1] + b\"_\" +","self.registered: print(\"Registration complete waiting for message.\") self.getNewMsg() else: print(\"Unable to register client.\") return","self.processQueue() if self.registered: print(\"Registration complete waiting for message.\") self.getNewMsg() else: print(\"Unable to register","<data> is sent. Any client that sends a \"List\" comment makes the server","After registration wait for an incoming message The first part must always be","\"\"\" Read the 9 byte header (e.g. \"A5A5 GUI \" or \"A5A5 FSW","params = cmd.split() process_id = 0 if params[0] == b\"Register\": LOCK.acquire() name =","length command data here... desc = self.recv(4) sizeb = self.recv(4) size = struct.unpack(\">I\",","\" (Connection reset by peer) occurred on recv().\" ) else: print(\"Socket error \"","header.split(b\" \")[1].strip(b\" \") if dst == b\"FSW\": # Read variable length command data","a List. Once the entire header string is processed send it on queue.","request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start()","RESERVED. U.S. Government Sponsorship acknowledged.\" program_version = \"v0.1\" program_build_date = \"%s\" % __updated__","header data from the socket either A5A5 or List header = self.readHeader() #","loop if not header: break elif header == b\"Quit\": LOCK.acquire() print(\"Quit received!\") SERVER.dest_obj[self.name].put(struct.pack(\">I\",","b\"Quit\\n\": return b\"Quit\" elif header[:-1] == b\"A5A5\": header2 = self.recv(4) return header +","len(msg) except socket.timeout: if shutdown_event.is_set(): print(\"socket timed out and shutdown is requested\") return","elif dst == b\"GUI\": # Read telemetry data here... tlm_packet_size = self.recv(4) size","the program does ) if argv is None: argv = sys.argv[1:] try: parser","for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon =","socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on each packet \"\"\" The function that","will then start one # more thread for each request server_thread = threading.Thread(target=server.serve_forever)","= self.readData(header) # Process and send the packet of the message here... self.processNewPkt(header,","= packet[4:9] packet = packet[9:] return (header + header2, packet) def readData(self, header,","\"\"\" data = b\"\" if header == b\"List\": return b\"\" elif header ==","\"Received Packet: %s %s...\\n\" % (head,dst) if data == \"\": print(\" Data is","self.name.decode(DATA_ENCODING) + \" exiting.\" ) return header if header == b\"List\\n\": return b\"List\"","received. This function listens for data on the socket. Packets for now are","be an \"A5A5 \" or a \"List \" \"\"\" # Loop while the","print \"Locking TCP\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here....","% l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process data here... head,","= self.recv(4) sizeb = self.recv(4) size = struct.unpack(\">I\", sizeb)[0] data = desc +","a \"List \" \"\"\" # Read the header data from the socket either","udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\"shutdown from main thread\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown()","\"\"\" The function that is invoked when a packet is received. This function","signal_handler(*_): print(\"Ctrl-C received, server shutting down.\") shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler):","now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \"\"\" Derived from original Stable demo during R&TD","socketserver.TCPServer): \"\"\" TCP Socket server. Keep a dictionary of destination objects containing queues","[] def processRegistration(self, cmd): params = cmd.split() process_id = 0 if params[0] ==","out and shutdown is requested\") return b\"Quit\\n\" continue except socket.error as err: if","self.socket.send(msg) except socket.error as err: print(\"Socket error \" + str(err.errno) + \" occurred","if not data: print(\"Client exited.\") return else: # Process the data into the","+ \" exiting.\" ) return header if header == b\"List\\n\": return b\"List\" elif","sorted(GUI_ids)[-1] + 1 name = params[1] + b\"_\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name]","Read the header data from the socket either A5A5 or List header =","register client.\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif","sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \"-p\", \"--port\", dest=\"port\",","does ) if argv is None: argv = sys.argv[1:] try: parser = OptionParser(","TCP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Packet missing A5A5 header\")","Socket server. \"\"\" class DestObj: \"\"\" Destination object for all clients registered. \"\"\"","if params[0] == b\"Register\": LOCK.acquire() name = params[1] if b\"FSW\" in name: if","partial or full socket input \"\"\" commands = inputString.split(end_of_command) if len(self.partial): commands[0] =","\"A5A5 FSW \"), or just read the \"List\\n\" command. \"\"\" header = self.recv(5)","[default: %default]\", default=50007, ) parser.add_option( \"-i\", \"--host\", dest=\"host\", action=\"store\", type=\"string\", help=\"Set threaded tcp","\"\"\" # Loop while the connected client has packets to send/receive while not","+ str(err.errno) + \" occurred on send().\") def fileno(self): \"\"\" \"\"\" return self.socket","dst: dest_list = GUI_clients else: print(\"dest? %s\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list:","FSW_ids = [] GUI_ids = [] def signal_handler(*_): print(\"Ctrl-C received, server shutting down.\")","<name> is done as \"A5A5 <name> <data>\" Note only <data> is sent. Any","= sorted(FSW_ids)[-1] + 1 name = params[1] + b\"_\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id)","\"\"\" data = \"\" dst = header.split(b\" \")[1].strip(b\" \") # Read telemetry data","# Universal server id global SERVER = None LOCK = None shutdown_event =","read the data of the message here... data = self.readData(header) # Process and","time.sleep(1) except Exception as e: indent = len(program_name) * \" \" sys.stderr.write(program_name +","+ self.recv(size) elif dst == b\"GUI\": # Read telemetry data here... tlm_packet_size =","of the message here... data = self.readData(header) # Process and send the packet","is instanced in own thread for each client. Registration is done by sending","\")[1].strip(b\" \") # Read telemetry data here... tlm_packet_size = packet[:4] size = struct.unpack(\">I\",","(Connection reset by peer) occurred on recv().\" ) else: print(\"Socket error \" +","server -- that thread will then start one # more thread for each","here... data = self.readData(header) # Process and send the packet of the message","= threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while","help=\"Set threaded tcp socket server port [default: %default]\", default=50007, ) parser.add_option( \"-i\", \"--host\",","of the message here... self.processNewPkt(header, data) def recv(self, l): \"\"\" Read l bytes","print \"Received Packet: %s %s...\\n\" % (head,dst) if data == \"\": print(\" Data","= \"Copyright 2015 user_name (California Institute of Technology) \\ ALL RIGHTS RESERVED. U.S.","inputString, end_of_command=b\"\\n\"): \"\"\" Build a command from partial or full socket input \"\"\"","params[1] + b\"_\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\"GUI\" in name: if GUI_clients:","FSW \"), or just read the \"List\\n\" command. \"\"\" header = packet[:4] header2","# Send the message here.... # print \"Sending UDP msg to \", dest_elem","assumed to be separated by a newline. For each packet, call processPkt. \"\"\"","the client if not data: print(\"Client exited.\") return else: # Process the data","== b\"FSW\": # Read variable length command data here... desc = self.recv(4) sizeb","\").split(b\" \") if head == b\"A5A5\": # Packet Header # print \"Received Packet:","that is invoked upon a new client. This function listens for data on","and telemetry data. Later this will handle other things such as sequence files","= name self.socket = request self.packet = b\"\" def put(self, msg): \"\"\" Write","= \"v0.1\" program_build_date = \"%s\" % __updated__ program_version_string = \"%%prog %s (%s)\" %","print(\"socket timed out and shutdown is requested\") return b\"Quit\\n\" continue except socket.error as","the entire header string is processed send it on queue. If something goes","msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Packet missing A5A5 header\") class","(California Institute of Technology) \\ ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\" program_version","[] def signal_handler(*_): print(\"Ctrl-C received, server shutting down.\") shutdown_event.set() def now(): return time.ctime(time.time())","byte header (e.g. \"A5A5 GUI \" or \"A5A5 FSW \"), or just read","socket data = self.recv(13) # Connection was closed by the client if not","readHeader(self): \"\"\" Read the 9 byte header (e.g. \"A5A5 GUI \" or \"A5A5","return def readData(self, header): \"\"\" Read the data part of the message sent","the message here.... # print \"Sending TCP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release()","print(\"Closed %s connection.\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self, inputString, end_of_command=b\"\\n\"):","% (program_version, program_build_date) program_longdesc = ( \"\"\"\"\"\" # optional - give further explanation","if it is a List. Once the entire header string is processed send","\"Sending TCP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Packet missing A5A5","self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = []","except if it is a List. Once the entire header tstring is processed","\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Telemetry missing A5A5 header\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer):","def readHeader(self, packet): \"\"\" Read the 9 byte header (e.g. \"A5A5 GUI \"","client \" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process the command messages","List. Once the entire header string is processed send it on queue. If","DestObj(name, self.request) LOCK.release() self.registered = True self.name = name self.id = process_id print(\"Registered","= 0.1 __date__ = \"2015-04-03\" __updated__ = \"2016-04-07\" # Universal server id global","command from partial or full socket input \"\"\" commands = inputString.split(end_of_command) if len(self.partial):","msg + chunk n = len(msg) except socket.timeout: if shutdown_event.is_set(): print(\"socket timed out","or a \"List \" \"\"\" # Loop while the connected client has packets","self.name = name self.socket = request self.packet = b\"\" def put(self, msg): \"\"\"","function that is invoked when a packet is received. This function listens for","the message here... data = self.readData(header, packet) # Process and send the packet","Packet Header # print \"Received Packet: %s %s...\\n\" % (head,dst) if data ==","% __updated__ program_version_string = \"%%prog %s (%s)\" % (program_version, program_build_date) program_longdesc = (","If the received header is an empty string, connection closed, exit loop if","data here... desc = self.recv(4) sizeb = self.recv(4) size = struct.unpack(\">I\", sizeb)[0] data","must always be an \"A5A5 \" or a \"List \" \"\"\" # Read","for dest_elem in dest_list: # print \"Locking TCP\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()):","if head == b\"A5A5\": # Packet Header # print \"Received Packet: %s %s...\\n\"","then start one # more thread for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread","# on each packet \"\"\" The function that is invoked when a packet","= ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will allow address reuse and server","shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\"shutdown from main thread\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except","head, dst = header.strip(b\" \").split(b\" \") if head == b\"A5A5\": # Packet Header","and shutdown server. \"\"\" dest_list = [] if header == b\"List\": print(\"List of","+ str(err.errno) + \" (Connection reset by peer) occurred on recv().\" ) else:","shutdown is requested\") return b\"Quit\\n\" continue except socket.error as err: if err.errno ==","= threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon =","dst == b\"FSW\": # Read variable length command data here... desc = self.recv(4)","message here.... # print \"Sending TCP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else:","by sending the string \"Register <name>\". Sending a message to destination <name> is","\" + self.name self.socket.send(msg) except socket.error as err: print(\"Socket error \" + str(err.errno)","or List header = self.readHeader() # If the received header is an empty","header, packet): \"\"\" Read the data part of the message sent to either","sorted(FSW_ids)[-1] + 1 name = params[1] + b\"_\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif","new FSW gse.py applicaiton. TCP socket server for commands, log events, and telemetry","True socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on each client connect \"\"\" The","ImportError: import SocketServer as socketserver import time import os import signal import sys","= name self.id = process_id print(\"Registered client \" + self.name.decode(DATA_ENCODING)) ################################################# # New","dst: dest_list = FSW_clients for dest_elem in dest_list: # print \"Locking TCP\" LOCK.acquire()","LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\"\\t\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\"List \" +","Read variable length command data here... desc = self.recv(4) sizeb = self.recv(4) size","ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \"\"\" Derived from original Stable demo during R&TD and adapted for use","b\"Quit\": return b\"\" dst = header.split(b\" \")[1].strip(b\" \") if dst == b\"FSW\": #","GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name = params[1] + b\"_\" + bytes(process_id)","closed by the client if not data: print(\"Client exited.\") return else: # Process","err: print(\"Socket error \" + str(err.errno) + \" occurred on send().\") def fileno(self):","n) if chunk == b\"\": print(\"read data from socket is empty!\") return b\"\"","= None shutdown_event = threading.Event() FSW_clients = [] GUI_clients = [] FSW_ids =","signal import sys import struct import errno from fprime.constants import DATA_ENCODING from optparse","print(\"Registered client \" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process the command","\" sys.stderr.write(program_name + \": \" + repr(e) + \"\\n\") sys.stderr.write(indent + \" for","main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license = \"Copyright 2015 user_name (California","FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\"Closed %s connection.\"","== 0: print( \"Header information is empty, client \" + self.name.decode(DATA_ENCODING) + \"","dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print","\"\"\" self.name = name self.socket = request self.packet = b\"\" def put(self, msg):","A5A5 except if it is a List. Once the entire header tstring is","def readData(self, header, packet): \"\"\" Read the data part of the message sent","0 if params[0] == b\"Register\": LOCK.acquire() name = params[1] if b\"FSW\" in name:","%s %s...\\n\" % (head,dst) if data == \"\": print(\" Data is empty, returning.\")","= header.strip(b\" \").split(b\" \") if head == b\"A5A5\": # Packet Header # print","registered clients. \"\"\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on each packet \"\"\"","print self.client_address, now() # show this client's address # Read the data from","socket.error as err: if err.errno == errno.ECONNRESET: print( \"Socket error \" + str(err.errno)","DestObj: \"\"\" Destination object for all clients registered. \"\"\" def __init__(self, name, request):","<data>\" Note only <data> is sent. Any client that sends a \"List\" comment","socket.error as err: print(\"Socket error \" + str(err.errno) + \" occurred on send().\")","return b\"Quit\" elif header[:-1] == b\"A5A5\": header2 = self.recv(4) return header + header2","shutdown_event.set() time.sleep(1) print(\"Quit processed!\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the header data","if b\"GUI\" in dst: dest_list = GUI_clients else: print(\"dest? %s\" % dst.decode(DATA_ENCODING)) for","for data on the socket. Packets for now are assumed to be separated","Send the message here.... # print \"Sending TCP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data)","port [default: %default]\", default=50007, ) parser.add_option( \"-i\", \"--host\", dest=\"host\", action=\"store\", type=\"string\", help=\"Set threaded","+ \": \" + repr(e) + \"\\n\") sys.stderr.write(indent + \" for help use","as err: print(\"Socket error \" + str(err.errno) + \" occurred on send().\") def","b\"\" n = 0 while l > n: try: chunk = self.request.recv(l -","lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \"\"\" UDP Socket server. \"\"\" class DestObj:","to process the command messages ################################################# def getNewMsg(self, packet): \"\"\" After registration wait","SERVER.server_close() LOCK.release() break # Got the header data so read the data of","def __init__(self, name, request): \"\"\" Constructor \"\"\" self.name = name self.socket = request","SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e: indent = len(program_name) * \"","parameters. Handle is instanced in own thread for each client. Registration is done","Packet: %s %s...\\n\" % (head,dst) if data == b\"\": print(\" Data is empty,","If something goes wrong report and shutdown server. \"\"\" dest_list = [] if","SocketServer as socketserver import time import os import signal import sys import struct","dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Telemetry missing A5A5 header\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \"\"\"","= cmd.split() process_id = 0 if params[0] == b\"Register\": LOCK.acquire() name = params[1]","raise RuntimeError(\"Telemetry missing A5A5 header\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \"\"\" TCP Socket server. Keep","the packet of the message here... self.processNewPkt(header, data) def readHeader(self, packet): \"\"\" Read","program does ) if argv is None: argv = sys.argv[1:] try: parser =","+ bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\"GUI\" in name: if GUI_clients: process_id = sorted(GUI_ids)[-1]","server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\"shutdown from main","various lengths. \"\"\" data = \"\" dst = header.split(b\" \")[1].strip(b\" \") # Read","things such as sequence files and parameters. Handle is instanced in own thread","def readHeader(self): \"\"\" Read the 9 byte header (e.g. \"A5A5 GUI \" or","will handle other things such as sequence files and parameters. Handle is instanced","process_id = sorted(FSW_ids)[-1] + 1 name = params[1] + b\"_\" + bytes(process_id) FSW_clients.append(name)","that sends a \"List\" comment makes the server display all registered clients. \"\"\"","clients registered. \"\"\" def __init__(self, name, request): \"\"\" Constructor \"\"\" self.name = name","\"\"\" self.getNewMsg(self.request[0]) ################################################# # New Routines to process the command messages ################################################# def","= True socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on each client connect \"\"\"","message here.... # print \"Sending UDP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else:","sys import struct import errno from fprime.constants import DATA_ENCODING from optparse import OptionParser","recv(self, l): \"\"\" Read l bytes from socket. \"\"\" chunk = b\"\" msg","SERVER = server LOCK = server.lock_obj ip, port = server.server_address print(\"TCP Socket Server","Sponsorship acknowledged.\" program_version = \"v0.1\" program_build_date = \"%s\" % __updated__ program_version_string = \"%%prog","+ 1 name = params[1] + b\"_\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] =","options (opts, args) = parser.parse_args(argv) HOST = opts.host PORT = opts.port server =","entire header tstring is processed send it on queue. If something goes wrong","here.... # print \"Sending UDP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise","del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name)","sends a \"List\" comment makes the server display all registered clients. \"\"\" socketserver.BaseRequestHandler.allow_reuse_address","data: print(\"Client exited.\") return else: # Process the data into the cmdQueue self.getCmds(data)","\"\"\" class DestObj: \"\"\" Destination object for all clients registered. \"\"\" def __init__(self,","if err.errno == errno.ECONNRESET: print( \"Socket error \" + str(err.errno) + \" (Connection","dest=\"port\", action=\"store\", type=\"int\", help=\"Set threaded tcp socket server port [default: %default]\", default=50007, )","that thread will then start one # more thread for each request server_thread","out the message to the destination socket \"\"\" try: # print \"about to","client has packets to send/receive while not shutdown_event.is_set(): # Read the header data","For each packet, call processPkt. \"\"\" self.getNewMsg(self.request[0]) ################################################# # New Routines to process","%default]\", default=50007, ) parser.add_option( \"-i\", \"--host\", dest=\"host\", action=\"store\", type=\"string\", help=\"Set threaded tcp socket","while l > n: try: chunk = self.request.recv(l - n) if chunk ==","= len(program_name) * \" \" sys.stderr.write(program_name + \": \" + repr(e) + \"\\n\")","Start a thread with the server -- that thread will then start one","For each packet, call processPkt. \"\"\" self.partial = b\"\" self.cmdQueue = [] self.registered","\"\"\" TCP Socket server. Keep a dictionary of destination objects containing queues and","\"%%prog %s (%s)\" % (program_version, program_build_date) program_longdesc = ( \"\"\"\"\"\" # optional -","to send data to \" + self.name self.socket.send(msg) except socket.error as err: print(\"Socket","GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered = True self.name = name","put(self, msg): \"\"\" Write out the message to the destination socket \"\"\" try:","about what the program does ) if argv is None: argv = sys.argv[1:]","messages ################################################# def getNewMsg(self): \"\"\" After registration wait for an incoming message The","the packet of the message here... self.processNewPkt(header, data) def recv(self, l): \"\"\" Read","str(err.errno) + \" occurred on recv().\") return msg def readHeader(self): \"\"\" Read the","wait for an incoming message The first part must always be an \"A5A5","SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\"i%ds\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release()","server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon","The function that is invoked upon a new client. This function listens for","cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd): params = cmd.split()","msg): \"\"\" Write out the message to the destination socket \"\"\" try: #","dest_list = FSW_clients for dest_elem in dest_list: # print \"Locking TCP\" LOCK.acquire() if","self.cmdQueue = [] self.registered = False self.name = b\"\" self.id = 0 #","Read the 9 byte header (e.g. \"A5A5 GUI \" or \"A5A5 FSW \"),","to register client.\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id)","receives telemetry. FSW receives commands of various lengths. \"\"\" data = \"\" dst","\"\"\" Read the data part of the message sent to either GUI or","missing A5A5 header\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \"\"\" TCP Socket server. Keep a dictionary","sizeb)[0] data = desc + sizeb + self.recv(size) elif dst == b\"GUI\": #","b\"GUI\" in dst: dest_list = GUI_clients elif b\"FSW\" in dst: dest_list = FSW_clients","connected client has packets to send/receive while not shutdown_event.is_set(): # Read the header","= self.recv(5) if len(header) == 0: print( \"Header information is empty, client \"","missing A5A5 header\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \"\"\" Derived from original Stable demo during R&TD","empty string, connection closed, exit loop if not header: break elif header ==","struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + packet[4 : 4 + size] return data","must always be an \"A5A5 \" or a \"List \" \"\"\" # Loop","header (e.g. \"A5A5 GUI \" or \"A5A5 FSW \"), or just read the","telemetry data here... tlm_packet_size = packet[:4] size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size","process the command messages ################################################# def getNewMsg(self, packet): \"\"\" After registration wait for","dst = header.strip(b\" \").split(b\" \") if head == b\"A5A5\": # Packet Header #","GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\"Closed %s connection.\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close()","\"), or just read the \"List\\n\" command. \"\"\" header = self.recv(5) if len(header)","the socket. Packets for now are assumed to be separated by a newline.","name = params[1] if b\"FSW\" in name: if FSW_clients: process_id = sorted(FSW_ids)[-1] +","else: return def readData(self, header): \"\"\" Read the data part of the message","header2 else: return def readData(self, header): \"\"\" Read the data part of the","packet): \"\"\" Read the 9 byte header (e.g. \"A5A5 GUI \" or \"A5A5","Registration is done by sending the string \"Register <name>\". Sending a message to","b\"\" msg = b\"\" n = 0 while l > n: try: chunk","# optional - give further explanation about what the program does ) if","not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\"shutdown from main thread\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1)","elif b\"GUI\" in name: if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name =","else: raise RuntimeError(\"Packet missing A5A5 header\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \"\"\" Derived from original Stable","listens for data on the socket. Packets for now are assumed to be","done as \"A5A5 <name> <data>\" Note only <data> is sent. Any client that","self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\"Closed","server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\"shutdown from main thread\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception","packet) def readData(self, header, packet): \"\"\" Read the data part of the message","\"\"\" return self.socket def main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license =","os import signal import sys import struct import errno from fprime.constants import DATA_ENCODING","LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients:","+ str(err.errno) + \" occurred on recv().\") return msg def readHeader(self): \"\"\" Read","b\"\" elif header == b\"Quit\": return b\"\" dst = header.split(b\" \")[1].strip(b\" \") if","data to \" + self.name self.socket.send(msg) except socket.error as err: print(\"Socket error \"","header is an empty string, connection closed, exit loop if not header: break","socket id's for writting to destinations. \"\"\" dest_obj = dict() lock_obj = threading.Lock()","= struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size + packet[4 : 4 + size] return","# Packet Header # print \"Received Packet: %s %s...\\n\" % (head,dst) if data","OptionParser __version__ = 0.1 __date__ = \"2015-04-03\" __updated__ = \"2016-04-07\" # Universal server","reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process data here... head, dst = header.strip(b\"","command data here... desc = self.recv(4) sizeb = self.recv(4) size = struct.unpack(\">I\", sizeb)[0]","with the server -- that thread will then start one # more thread","== b\"List\": return b\"\" elif header == b\"Quit\": return b\"\" dst = header.split(b\"","registration wait for an incoming message The first part must always be an","server display all registered clients. \"\"\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on","except socket.error as err: if err.errno == errno.ECONNRESET: print( \"Socket error \" +","header: return # Got the header data so read the data of the","\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Packet missing A5A5 header\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \"\"\"","to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Telemetry missing A5A5 header\") class ThreadedTCPServer(socketserver.ThreadingMixIn,","return b\"\" msg = msg + chunk n = len(msg) except socket.timeout: if","Got the header data so read the data of the message here... data","= True self.name = name self.id = process_id print(\"Registered client \" + self.name.decode(DATA_ENCODING))","ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\" program_version = \"v0.1\" program_build_date = \"%s\"","% dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send","<gh_stars>0 #!/usr/bin/env python3 from __future__ import print_function import socket import threading try: import","process_id = sorted(GUI_ids)[-1] + 1 name = params[1] + b\"_\" + bytes(process_id) GUI_clients.append(name)","it is a List. Once the entire header string is processed send it","+ packet[4 : 4 + size] return data def processNewPkt(self, header, data): \"\"\"","thread will then start one # more thread for each request server_thread =","string, connection closed, exit loop if not header: return # Got the header","for an incoming message The first part must always be an \"A5A5 \"","dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \"\"\" UDP Socket server. \"\"\" class","\"\"\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on each client","= b\"\" self.cmdQueue = [] self.registered = False self.name = b\"\" self.id =","return header if header == b\"List\\n\": return b\"List\" elif header == b\"Quit\\n\": return","sys.stderr.write(indent + \" for help use --help\\n\") return 2 if __name__ == \"__main__\":","read the data of the message here... data = self.readData(header, packet) # Process","entire header string is processed send it on queue. If something goes wrong","Server listening on host addr %s, port %s\" % (HOST, PORT)) # Start","\"A5A5 \" or a \"List \" \"\"\" # Loop while the connected client","[] self.registered = False self.name = b\"\" self.id = 0 # print self.client_address,","errno from fprime.constants import DATA_ENCODING from optparse import OptionParser __version__ = 0.1 __date__","help=\"Set threaded tcp socket server ip [default: %default]\", default=\"127.0.0.1\", ) # process options","new client. This function listens for data on the socket. Packets for now","this will handle other things such as sequence files and parameters. Handle is","b\"\" self.id = 0 # print self.client_address, now() # show this client's address","= [] GUI_clients = [] FSW_ids = [] GUI_ids = [] def signal_handler(*_):","empty!\") return b\"\" msg = msg + chunk n = len(msg) except socket.timeout:","b\"A5A5\": # Packet Header # print \"Received Packet: %s %s...\\n\" % (head,dst) if","destination objects containing queues and socket id's for writting to destinations. \"\"\" dest_obj","\"\": print(\" Data is empty, returning.\") if b\"GUI\" in dst: dest_list = GUI_clients","makes the server display all registered clients. \"\"\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self):","SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license = \"Copyright 2015 user_name (California Institute of","header data so read the data of the message here... data = self.readData(header)","server display all registered clients. \"\"\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def","False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\"shutdown from","+ header2 else: return def readData(self, header): \"\"\" Read the data part of","print( \"Header information is empty, client \" + self.name.decode(DATA_ENCODING) + \" exiting.\" )","received, server shutting down.\") shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \"\"\" Derived","9 byte header (e.g. \"A5A5 GUI \" or \"A5A5 FSW \"), or just","packet) # Process and send the packet of the message here... self.processNewPkt(header, data)","telemetry data here... tlm_packet_size = self.recv(4) size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size","recv().\" ) else: print(\"Socket error \" + str(err.errno) + \" occurred on recv().\")","Hopefully this will allow address reuse and server to restart immediately server.allow_reuse_address =","to be separated by a newline. For each packet, call processPkt. \"\"\" self.partial","client that sends a \"List\" comment makes the server display all registered clients.","and adapted for use in new FSW gse.py applicaiton. TCP socket server for","Connection was closed by the client if not data: print(\"Client exited.\") return else:","self.getCmds(data) # Process the cmdQueue self.processQueue() if self.registered: print(\"Registration complete waiting for message.\")","is done by sending the string \"Register <name>\". Sending a message to destination","+ \"\\n\") sys.stderr.write(indent + \" for help use --help\\n\") return 2 if __name__","header = packet[:4] header2 = packet[4:9] packet = packet[9:] return (header + header2,","print(\" Data is empty, returning.\") if b\"GUI\" in dst: dest_list = GUI_clients else:","and socket id's for writting to destinations. \"\"\" dest_obj = dict() lock_obj =","header, data): \"\"\" Process a single command here header and data here. The","is invoked upon a new client. This function listens for data on the","List header = self.readHeader() # If the received header is an empty string,","dst = header.split(b\" \")[1].strip(b\" \") # Read telemetry data here... tlm_packet_size = packet[:4]","data here... tlm_packet_size = packet[:4] size = struct.unpack(\">I\", tlm_packet_size)[0] data = tlm_packet_size +","the message here.... # print \"Sending UDP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release()","A5A5 or List (header, packet) = self.readHeader(packet) # If the received header is","on send().\") def fileno(self): \"\"\" \"\"\" return self.socket def main(argv=None): global SERVER, LOCK","own thread for each client. Registration is done by sending the string \"Register","goes wrong report and shutdown server. \"\"\" dest_list = [] # Process data","RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\" program_version = \"v0.1\" program_build_date = \"%s\" %","Institute of Technology) \\ ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\" program_version =","b\"\": print(\"read data from socket is empty!\") return b\"\" msg = msg +","report and shutdown server. \"\"\" dest_list = [] if header == b\"List\": print(\"List","description=program_license, ) parser.add_option( \"-p\", \"--port\", dest=\"port\", action=\"store\", type=\"int\", help=\"Set threaded tcp socket server","separated by a newline. For each packet, call processPkt. \"\"\" self.partial = b\"\"","List. Once the entire header tstring is processed send it on queue. If","on the socket. Packets for now are assumed to be separated by a","Read the data from the socket data = self.recv(13) # Connection was closed","def readData(self, header): \"\"\" Read the data part of the message sent to","start with A5A5 except if it is a List. Once the entire header","with A5A5 except if it is a List. Once the entire header tstring","+ chunk n = len(msg) except socket.timeout: if shutdown_event.is_set(): print(\"socket timed out and","= \"%s\" % __updated__ program_version_string = \"%%prog %s (%s)\" % (program_version, program_build_date) program_longdesc","on each packet \"\"\" The function that is invoked when a packet is","\"\" dst = header.split(b\" \")[1].strip(b\" \") # Read telemetry data here... tlm_packet_size =","__version__ = 0.1 __date__ = \"2015-04-03\" __updated__ = \"2016-04-07\" # Universal server id","a message to destination <name> is done as \"A5A5 <name> <data>\" Note only","of registered clients: \") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\"\\t\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str","(header, packet) = self.readHeader(packet) # If the received header is an empty string,","== b\"Quit\\n\": return b\"Quit\" elif header[:-1] == b\"A5A5\": header2 = self.recv(4) return header","it is a List. Once the entire header tstring is processed send it","raise RuntimeError(\"Packet missing A5A5 header\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \"\"\" Derived from original Stable demo","packet): \"\"\" After registration wait for an incoming message The first part must","getCmds(self, inputString, end_of_command=b\"\\n\"): \"\"\" Build a command from partial or full socket input","name, request): \"\"\" Constructor \"\"\" self.name = name self.socket = request self.packet =","action=\"store\", type=\"int\", help=\"Set threaded tcp socket server port [default: %default]\", default=50007, ) parser.add_option(","parser.add_option( \"-i\", \"--host\", dest=\"host\", action=\"store\", type=\"string\", help=\"Set threaded tcp socket server ip [default:","header.strip(b\" \").split(b\" \") if head == b\"A5A5\": # Packet Header # print \"Received","Exception as e: indent = len(program_name) * \" \" sys.stderr.write(program_name + \": \"","\"\"\" chunk = b\"\" msg = b\"\" n = 0 while l >","self.recv(size) else: raise RuntimeError(\"unrecognized client %s\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header,","print(\"\\t\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\"List \" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str","%default]\", default=\"127.0.0.1\", ) # process options (opts, args) = parser.parse_args(argv) HOST = opts.host","a \"List \" \"\"\" # Loop while the connected client has packets to","data so read the data of the message here... data = self.readData(header, packet)","comment makes the server display all registered clients. \"\"\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout","list(SERVER.dest_obj.keys()): print(\"\\t\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\"List \" + SERVER.dest_obj[d].name l = len(reg_client_str)","4 + size] return data def processNewPkt(self, header, data): \"\"\" Process a single","= server.lock_obj ip, port = server.server_address print(\"TCP Socket Server listening on host addr","dictionary of destination objects containing queues and socket id's for writting to destinations.","was closed by the client if not data: print(\"Client exited.\") return else: #","to be separated by a newline. For each packet, call processPkt. \"\"\" self.getNewMsg(self.request[0])","LOCK = None shutdown_event = threading.Event() FSW_clients = [] GUI_clients = [] FSW_ids","command messages ################################################# def getNewMsg(self): \"\"\" After registration wait for an incoming message","\" + str(err.errno) + \" occurred on recv().\") return msg def readHeader(self): \"\"\"","\" occurred on recv().\") return msg def readHeader(self): \"\"\" Read the 9 byte","of various lengths. \"\"\" data = b\"\" if header == b\"List\": return b\"\"","% (head,dst) if data == b\"\": print(\" Data is empty, returning.\") if b\"GUI\"","# Send the message here.... # print \"Sending TCP msg to \", dest_elem","(opts, args) = parser.parse_args(argv) HOST = opts.host PORT = opts.port server = ThreadedTCPServer((HOST,","= [] def signal_handler(*_): print(\"Ctrl-C received, server shutting down.\") shutdown_event.set() def now(): return","return b\"List\" elif header == b\"Quit\\n\": return b\"Quit\" elif header[:-1] == b\"A5A5\": header2","cmd): params = cmd.split() process_id = 0 if params[0] == b\"Register\": LOCK.acquire() name","send the packet of the message here... self.processNewPkt(header, data) def readHeader(self, packet): \"\"\"","for all clients registered. \"\"\" def __init__(self, name, request): \"\"\" Constructor \"\"\" self.name","to restart immediately server.allow_reuse_address = True SERVER = server LOCK = server.lock_obj ip,","return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \"\"\" Derived from original Stable demo during R&TD and","of the message here... self.processNewPkt(header, data) def readHeader(self, packet): \"\"\" Read the 9","incoming message The first part must always be an \"A5A5 \" or a","b\"\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd","\" + str(err.errno) + \" (Connection reset by peer) occurred on recv().\" )","client. This function listens for data on the socket. Packets for now are","self.client_address, now() # show this client's address # Read the data from the","display all registered clients. \"\"\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on each","\"\"\" dest_list = [] if header == b\"List\": print(\"List of registered clients: \")","= header.split(b\" \")[1].strip(b\" \") if dst == b\"FSW\": # Read variable length command","packet is received. This function listens for data on the socket. Packets for","return header + header2 else: return def readData(self, header): \"\"\" Read the data","%s connection.\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self, inputString, end_of_command=b\"\\n\"): \"\"\"","from main thread\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e: indent","end_of_command=b\"\\n\"): \"\"\" Build a command from partial or full socket input \"\"\" commands","Technology) \\ ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\" program_version = \"v0.1\" program_build_date","print_function import socket import threading try: import socketserver except ImportError: import SocketServer as","always be an \"A5A5 \" or a \"List \" \"\"\" # Loop while","Stable demo during R&TD and adapted for use in new FSW gse.py applicaiton.","from fprime.constants import DATA_ENCODING from optparse import OptionParser __version__ = 0.1 __date__ =","\"2015-04-03\" __updated__ = \"2016-04-07\" # Universal server id global SERVER = None LOCK","a dictionary of destination objects containing queues and socket id's for writting to","%s\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data): \"\"\" Process a single","is processed send it on queue. If something goes wrong report and shutdown","= [] # Process data here... head, dst = header.strip(b\" \").split(b\" \") if","# Loop while the connected client has packets to send/receive while not shutdown_event.is_set():","# Read the header data from the socket either A5A5 or List header","empty, returning.\") if b\"GUI\" in dst: dest_list = GUI_clients else: print(\"dest? %s\" %","in dest_list: # print \"Locking TCP\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send","in list(SERVER.dest_obj.keys()): # Send the message here.... # print \"Sending TCP msg to","reset by peer) occurred on recv().\" ) else: print(\"Socket error \" + str(err.errno)","SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\"List \" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\"i%ds\"","will allow address reuse and server to restart immediately server.allow_reuse_address = True SERVER","signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set():","else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def","not header: break elif header == b\"Quit\": LOCK.acquire() print(\"Quit received!\") SERVER.dest_obj[self.name].put(struct.pack(\">I\", 0xA5A5A5A5)) shutdown_event.set()","False self.request.close() def getCmds(self, inputString, end_of_command=b\"\\n\"): \"\"\" Build a command from partial or","= FSW_clients for dest_elem in dest_list: # print \"Locking TCP\" LOCK.acquire() if dest_elem","[] if header == b\"List\": print(\"List of registered clients: \") LOCK.acquire() for d","self.socket def main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license = \"Copyright 2015","= process_id print(\"Registered client \" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process","to send/receive while not shutdown_event.is_set(): # Read the header data from the socket","\"v0.1\" program_build_date = \"%s\" % __updated__ program_version_string = \"%%prog %s (%s)\" % (program_version,","parser.add_option( \"-p\", \"--port\", dest=\"port\", action=\"store\", type=\"int\", help=\"Set threaded tcp socket server port [default:","except socket.timeout: if shutdown_event.is_set(): print(\"socket timed out and shutdown is requested\") return b\"Quit\\n\"","be an \"A5A5 \" or a \"List \" \"\"\" # Read the header","address # Read the data from the socket data = self.recv(13) # Connection","the header data from the socket either A5A5 or List (header, packet) =","threaded tcp socket server ip [default: %default]\", default=\"127.0.0.1\", ) # process options (opts,","in dst: dest_list = GUI_clients else: print(\"dest? %s\" % dst.decode(DATA_ENCODING)) for dest_elem in","print(\"Socket error \" + str(err.errno) + \" occurred on recv().\") return msg def","import sys import struct import errno from fprime.constants import DATA_ENCODING from optparse import","message to destination <name> is done as \"A5A5 <name> <data>\" Note only <data>","occurred on send().\") def fileno(self): \"\"\" \"\"\" return self.socket def main(argv=None): global SERVER,","= False self.name = b\"\" self.id = 0 # print self.client_address, now() #","is received. This function listens for data on the socket. Packets for now","= os.path.basename(sys.argv[0]) program_license = \"Copyright 2015 user_name (California Institute of Technology) \\ ALL","# print \"Locking TCP\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message","udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will allow address reuse and","= GUI_clients elif b\"FSW\" in dst: dest_list = FSW_clients for dest_elem in dest_list:","False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\"shutdown from main thread\") SERVER.shutdown() SERVER.server_close()","print(\"Client exited.\") return else: # Process the data into the cmdQueue self.getCmds(data) #","def put(self, msg): \"\"\" Write out the message to the destination socket \"\"\"","-- that thread will then start one # more thread for each request","only <data> is sent. Any client that sends a \"List\" comment makes the","\" + self.name.decode(DATA_ENCODING) + \" exiting.\" ) return header if header == b\"List\\n\":","PORT)) # Start a thread with the server -- that thread will then","something goes wrong report and shutdown server. \"\"\" dest_list = [] if header","send/receive while not shutdown_event.is_set(): # Read the header data from the socket either","for dest_elem in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message","dest_elem in dest_list: # print \"Locking TCP\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): #","registered. \"\"\" def __init__(self, name, request): \"\"\" Constructor \"\"\" self.name = name self.socket","b\"\" self.cmdQueue = [] self.registered = False self.name = b\"\" self.id = 0","import DATA_ENCODING from optparse import OptionParser __version__ = 0.1 __date__ = \"2015-04-03\" __updated__","processed send it on queue. If something goes wrong report and shutdown server.","server id global SERVER = None LOCK = None shutdown_event = threading.Event() FSW_clients","= ( \"\"\"\"\"\" # optional - give further explanation about what the program","self.id = process_id print(\"Registered client \" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines to","False self.name = b\"\" self.id = 0 # print self.client_address, now() # show","\"\"\"\"\"\" # optional - give further explanation about what the program does )","len(header) == 0: print( \"Header information is empty, client \" + self.name.decode(DATA_ENCODING) +","= False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\"shutdown","processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd): params","import time import os import signal import sys import struct import errno from","first part must always be an \"A5A5 \" or a \"List \" \"\"\"","b\"GUI\" in dst: dest_list = GUI_clients else: print(\"dest? %s\" % dst.decode(DATA_ENCODING)) for dest_elem","here. The command must always start with A5A5 except if it is a","# Connection was closed by the client if not data: print(\"Client exited.\") return","\"\"\" The function that is invoked upon a new client. This function listens","= True SERVER = server LOCK = server.lock_obj ip, port = server.server_address print(\"TCP","import struct import errno from fprime.constants import DATA_ENCODING from optparse import OptionParser __version__","True self.name = name self.id = process_id print(\"Registered client \" + self.name.decode(DATA_ENCODING)) #################################################","to destination <name> is done as \"A5A5 <name> <data>\" Note only <data> is","return data def processNewPkt(self, header, data): \"\"\" Process a single command here header","Read the header data from the socket either A5A5 or List (header, packet)","command messages ################################################# def getNewMsg(self, packet): \"\"\" After registration wait for an incoming","here... self.processNewPkt(header, data) def recv(self, l): \"\"\" Read l bytes from socket. \"\"\"","= inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial + commands[0] self.partial = b\"\" if","if data == b\"\": print(\" Data is empty, returning.\") if b\"GUI\" in dst:","return # Got the header data so read the data of the message","LOCK.acquire() print(\"Quit received!\") SERVER.dest_obj[self.name].put(struct.pack(\">I\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\"Quit processed!\") SERVER.shutdown() SERVER.server_close() LOCK.release() break","# process options (opts, args) = parser.parse_args(argv) HOST = opts.host PORT = opts.port","the data of the message here... data = self.readData(header) # Process and send","\"\"\" header = packet[:4] header2 = packet[4:9] packet = packet[9:] return (header +","or just read the \"List\\n\" command. \"\"\" header = self.recv(5) if len(header) ==","the server display all registered clients. \"\"\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): #","Routines to process the command messages ################################################# def getNewMsg(self, packet): \"\"\" After registration","the data into the cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue() if self.registered:","message sent to either GUI or FSW. GUI receives telemetry. FSW receives commands","server.allow_reuse_address = True SERVER = server LOCK = server.lock_obj ip, port = server.server_address","reg_client_str = struct.pack(\"i%ds\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process","FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\"Closed %s connection.\" %","== b\"\": print(\" Data is empty, returning.\") if b\"GUI\" in dst: dest_list =","either GUI or FSW. GUI receives telemetry. FSW receives commands of various lengths.","an empty string, connection closed, exit loop if not header: break elif header","+ \" for help use --help\\n\") return 2 if __name__ == \"__main__\": sys.exit(main())","self.registered = False self.name = b\"\" self.id = 0 # print self.client_address, now()","wrong report and shutdown server. \"\"\" dest_list = [] # Process data here...","give further explanation about what the program does ) if argv is None:","adapted for use in new FSW gse.py applicaiton. TCP socket server for commands,","various lengths. \"\"\" data = b\"\" if header == b\"List\": return b\"\" elif","from __future__ import print_function import socket import threading try: import socketserver except ImportError:","while the connected client has packets to send/receive while not shutdown_event.is_set(): # Read","LOCK.release() print(\"Closed %s connection.\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self, inputString,","__updated__ program_version_string = \"%%prog %s (%s)\" % (program_version, program_build_date) program_longdesc = ( \"\"\"\"\"\"","\"2016-04-07\" # Universal server id global SERVER = None LOCK = None shutdown_event","process the command messages ################################################# def getNewMsg(self): \"\"\" After registration wait for an","LOCK.release() break # Got the header data so read the data of the","chunk n = len(msg) except socket.timeout: if shutdown_event.is_set(): print(\"socket timed out and shutdown","here... data = self.readData(header, packet) # Process and send the packet of the","packet) = self.readHeader(packet) # If the received header is an empty string, connection","default=50007, ) parser.add_option( \"-i\", \"--host\", dest=\"host\", action=\"store\", type=\"string\", help=\"Set threaded tcp socket server","header2 = self.recv(4) return header + header2 else: return def readData(self, header): \"\"\"","= threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \"\"\" UDP Socket server. \"\"\" class DestObj: \"\"\"","socket. \"\"\" chunk = b\"\" msg = b\"\" n = 0 while l","exit loop if not header: return # Got the header data so read","by peer) occurred on recv().\" ) else: print(\"Socket error \" + str(err.errno) +","GUI_clients = [] FSW_ids = [] GUI_ids = [] def signal_handler(*_): print(\"Ctrl-C received,","data of the message here... data = self.readData(header) # Process and send the","b\"\" msg = msg + chunk n = len(msg) except socket.timeout: if shutdown_event.is_set():","self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self, inputString, end_of_command=b\"\\n\"): \"\"\" Build a command","\") if head == b\"A5A5\": # Packet Header # print \"Received Packet: %s","(head,dst) if data == \"\": print(\" Data is empty, returning.\") if b\"GUI\" in","\") # Read telemetry data here... tlm_packet_size = packet[:4] size = struct.unpack(\">I\", tlm_packet_size)[0]","FSW \"), or just read the \"List\\n\" command. \"\"\" header = self.recv(5) if","* \" \" sys.stderr.write(program_name + \": \" + repr(e) + \"\\n\") sys.stderr.write(indent +","client %s\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data): \"\"\" Process a","\"\"\" Build a command from partial or full socket input \"\"\" commands =","Packets for now are assumed to be separated by a newline. For each","socket either A5A5 or List header = self.readHeader() # If the received header","+ size] return data def processNewPkt(self, header, data): \"\"\" Process a single command","\"\"\" Write out the message to the destination socket \"\"\" try: # print","client if not data: print(\"Client exited.\") return else: # Process the data into","the \"List\\n\" command. \"\"\" header = self.recv(5) if len(header) == 0: print( \"Header","self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process the command messages ################################################# def getNewMsg(self):","# Start a thread with the server -- that thread will then start","self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd): params = cmd.split() process_id = 0","+ SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\"i%ds\" % l, l, reg_client_str) self.request.send(reg_client_str)","################################################# def getNewMsg(self, packet): \"\"\" After registration wait for an incoming message The","class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \"\"\" Derived from original Stable demo during R&TD and adapted for","the server display all registered clients. \"\"\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1","desc + sizeb + self.recv(size) elif dst == b\"GUI\": # Read telemetry data","header and data here. The command must always start with A5A5 except if","data == \"\": print(\" Data is empty, returning.\") if b\"GUI\" in dst: dest_list","b\"A5A5\": header2 = self.recv(4) return header + header2 else: return def readData(self, header):","commands, log events, and telemetry data. Later this will handle other things such","data into the cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue() if self.registered: print(\"Registration","\"--host\", dest=\"host\", action=\"store\", type=\"string\", help=\"Set threaded tcp socket server ip [default: %default]\", default=\"127.0.0.1\",","None LOCK = None shutdown_event = threading.Event() FSW_clients = [] GUI_clients = []","data def processNewPkt(self, header, data): \"\"\" Process a single command here header and","0 # Process data here... head, dst = header.strip(b\" \").split(b\" \") if head","PORT = opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler)","here... self.processNewPkt(header, data) def readHeader(self, packet): \"\"\" Read the 9 byte header (e.g.","processRegistration(self, cmd): params = cmd.split() process_id = 0 if params[0] == b\"Register\": LOCK.acquire()","chunk == b\"\": print(\"read data from socket is empty!\") return b\"\" msg =","action=\"store\", type=\"string\", help=\"Set threaded tcp socket server ip [default: %default]\", default=\"127.0.0.1\", ) #","data) def recv(self, l): \"\"\" Read l bytes from socket. \"\"\" chunk =","\": \" + repr(e) + \"\\n\") sys.stderr.write(indent + \" for help use --help\\n\")","# New Routines to process the command messages ################################################# def getNewMsg(self): \"\"\" After","log events, and telemetry data. Later this will handle other things such as","if dst == b\"FSW\": # Read variable length command data here... desc =","server port [default: %default]\", default=50007, ) parser.add_option( \"-i\", \"--host\", dest=\"host\", action=\"store\", type=\"string\", help=\"Set","\"A5A5 <name> <data>\" Note only <data> is sent. Any client that sends a","data = desc + sizeb + self.recv(size) elif dst == b\"GUI\": # Read","processNewPkt(self, header, data): \"\"\" Process a single command here header and data here.","+ \" (Connection reset by peer) occurred on recv().\" ) else: print(\"Socket error","dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Packet missing A5A5 header\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \"\"\" Derived","try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \"-p\", \"--port\", dest=\"port\", action=\"store\",","return msg def readHeader(self): \"\"\" Read the 9 byte header (e.g. \"A5A5 GUI","data here... head, dst = header.strip(b\" \").split(b\" \") if head == b\"A5A5\": #","import threading try: import socketserver except ImportError: import SocketServer as socketserver import time","print( \"Socket error \" + str(err.errno) + \" (Connection reset by peer) occurred","head == b\"A5A5\": # Packet Header # print \"Received Packet: %s %s...\\n\" %","udp_server_thread.join(timeout=5.0) print(\"shutdown from main thread\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as","dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print \"Sending TCP msg","epilog=program_longdesc, description=program_license, ) parser.add_option( \"-p\", \"--port\", dest=\"port\", action=\"store\", type=\"int\", help=\"Set threaded tcp socket","0 # print self.client_address, now() # show this client's address # Read the","send it on queue. If something goes wrong report and shutdown server. \"\"\"","error \" + str(err.errno) + \" (Connection reset by peer) occurred on recv().\"","self.readHeader(packet) # If the received header is an empty string, connection closed, exit","GUI_ids = [] def signal_handler(*_): print(\"Ctrl-C received, server shutting down.\") shutdown_event.set() def now():","= params[1] + b\"_\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release()","socket. Packets for now are assumed to be separated by a newline. For","cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue() if self.registered: print(\"Registration complete waiting for","ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will allow","handle(self): # on each packet \"\"\" The function that is invoked when a","\" occurred on send().\") def fileno(self): \"\"\" \"\"\" return self.socket def main(argv=None): global","header string is processed send it on queue. If something goes wrong report","self.recv(4) return header + header2 else: return def readData(self, header): \"\"\" Read the","Process and send the packet of the message here... self.processNewPkt(header, data) def readHeader(self,","[] GUI_ids = [] def signal_handler(*_): print(\"Ctrl-C received, server shutting down.\") shutdown_event.set() def","desc = self.recv(4) sizeb = self.recv(4) size = struct.unpack(\">I\", sizeb)[0] data = desc","messages ################################################# def getNewMsg(self, packet): \"\"\" After registration wait for an incoming message","connection closed, exit loop if not header: break elif header == b\"Quit\": LOCK.acquire()","and shutdown is requested\") return b\"Quit\\n\" continue except socket.error as err: if err.errno","SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e: indent = len(program_name) *","upon a new client. This function listens for data on the socket. Packets","GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\"Closed %s connection.\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def","b\"FSW\" in dst: dest_list = FSW_clients for dest_elem in dest_list: # print \"Locking","elif header == b\"Quit\": return b\"\" dst = header.split(b\" \")[1].strip(b\" \") if dst","socket server ip [default: %default]\", default=\"127.0.0.1\", ) # process options (opts, args) =","to the destination socket \"\"\" try: # print \"about to send data to","from original Stable demo during R&TD and adapted for use in new FSW","# Read telemetry data here... tlm_packet_size = packet[:4] size = struct.unpack(\">I\", tlm_packet_size)[0] data","FSW receives commands of various lengths. \"\"\" data = b\"\" if header ==","of the message here... data = self.readData(header, packet) # Process and send the","+ repr(e) + \"\\n\") sys.stderr.write(indent + \" for help use --help\\n\") return 2","is an empty string, connection closed, exit loop if not header: break elif","program_version_string = \"%%prog %s (%s)\" % (program_version, program_build_date) program_longdesc = ( \"\"\"\"\"\" #","len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue:","\"\"\" Read l bytes from socket. \"\"\" chunk = b\"\" msg = b\"\"","b\"\": print(\" Data is empty, returning.\") if b\"GUI\" in dst: dest_list = GUI_clients","server ip [default: %default]\", default=\"127.0.0.1\", ) # process options (opts, args) = parser.parse_args(argv)","here header and data here. The command must always start with A5A5 except","data = self.readData(header, packet) # Process and send the packet of the message","is an empty string, connection closed, exit loop if not header: return #","= server.server_address print(\"TCP Socket Server listening on host addr %s, port %s\" %","Destination object for all clients registered. \"\"\" def __init__(self, name, request): \"\"\" Constructor","type=\"int\", help=\"Set threaded tcp socket server port [default: %default]\", default=50007, ) parser.add_option( \"-i\",","sizeb = self.recv(4) size = struct.unpack(\">I\", sizeb)[0] data = desc + sizeb +","\" \"\"\" # Read the header data from the socket either A5A5 or","ip [default: %default]\", default=\"127.0.0.1\", ) # process options (opts, args) = parser.parse_args(argv) HOST","if header == b\"List\\n\": return b\"List\" elif header == b\"Quit\\n\": return b\"Quit\" elif","input \"\"\" commands = inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial + commands[0] self.partial","is empty, returning.\") if b\"GUI\" in dst: dest_list = GUI_clients elif b\"FSW\" in","= 0 while l > n: try: chunk = self.request.recv(l - n) if","UDP msg to \", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\"Telemetry missing A5A5 header\")","name self.id = process_id print(\"Registered client \" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines","\"\"\" commands = inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial + commands[0] self.partial =","+ b\"_\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\"GUI\" in name: if GUI_clients: process_id","\"\"\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on each packet \"\"\" The function","# If the received header is an empty string, connection closed, exit loop","the connected client has packets to send/receive while not shutdown_event.is_set(): # Read the","sizeb + self.recv(size) elif dst == b\"GUI\": # Read telemetry data here... tlm_packet_size","print(\"dest? %s\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()):","the received header is an empty string, connection closed, exit loop if not","self.recv(13) # Connection was closed by the client if not data: print(\"Client exited.\")","and server to restart immediately server.allow_reuse_address = True SERVER = server LOCK =","FSW_clients for dest_elem in dest_list: # print \"Locking TCP\" LOCK.acquire() if dest_elem in","% dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data): \"\"\" Process a single command","use in new FSW gse.py applicaiton. TCP socket server for commands, log events,","the message here... data = self.readData(header) # Process and send the packet of","the string \"Register <name>\". Sending a message to destination <name> is done as","FSW receives commands of various lengths. \"\"\" data = \"\" dst = header.split(b\"","RuntimeError(\"unrecognized client %s\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data): \"\"\" Process","main thread\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e: indent =","Process and send the packet of the message here... self.processNewPkt(header, data) def recv(self,","a List. Once the entire header tstring is processed send it on queue.","class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \"\"\" TCP Socket server. Keep a dictionary of destination objects","is empty, returning.\") if b\"GUI\" in dst: dest_list = GUI_clients else: print(\"dest? %s\"","self.request.send(reg_client_str) LOCK.release() return 0 # Process data here... head, dst = header.strip(b\" \").split(b\"","# show this client's address # Read the data from the socket data","self.getNewMsg() else: print(\"Unable to register client.\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in","print(\"Quit processed!\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the header data so read","def handle(self): # on each client connect \"\"\" The function that is invoked","\"List\\n\" command. \"\"\" header = self.recv(5) if len(header) == 0: print( \"Header information","is a List. Once the entire header string is processed send it on","\\ ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\" program_version = \"v0.1\" program_build_date =","else: raise RuntimeError(\"unrecognized client %s\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data):","\"\"\" self.partial = b\"\" self.cmdQueue = [] self.registered = False self.name = b\"\"","telemetry. FSW receives commands of various lengths. \"\"\" data = \"\" dst =","opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully","b\"GUI\" in name: if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name = params[1]","with A5A5 except if it is a List. Once the entire header string","getNewMsg(self, packet): \"\"\" After registration wait for an incoming message The first part","Process a single command here header and data here. The command must always","LOCK.release() return 0 # Process data here... head, dst = header.strip(b\" \").split(b\" \")","== b\"A5A5\": # Packet Header # print \"Received Packet: %s %s...\\n\" % (head,dst)","LOCK.release() else: raise RuntimeError(\"Telemetry missing A5A5 header\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \"\"\" TCP Socket","for message.\") self.getNewMsg() else: print(\"Unable to register client.\") return LOCK.acquire() del SERVER.dest_obj[self.name] if","struct.unpack(\">I\", sizeb)[0] data = desc + sizeb + self.recv(size) elif dst == b\"GUI\":","commands[0] self.partial = b\"\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def","is requested\") return b\"Quit\\n\" continue except socket.error as err: if err.errno == errno.ECONNRESET:","timed out and shutdown is requested\") return b\"Quit\\n\" continue except socket.error as err:","request self.packet = b\"\" def put(self, msg): \"\"\" Write out the message to","True SERVER = server LOCK = server.lock_obj ip, port = server.server_address print(\"TCP Socket","for now are assumed to be separated by a newline. For each packet,","= None LOCK = None shutdown_event = threading.Event() FSW_clients = [] GUI_clients =","%s\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): #","packet[:4] header2 = packet[4:9] packet = packet[9:] return (header + header2, packet) def","b\"_\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\"GUI\" in name: if GUI_clients: process_id =","dest_list = GUI_clients elif b\"FSW\" in dst: dest_list = FSW_clients for dest_elem in","dst: dest_list = GUI_clients elif b\"FSW\" in dst: dest_list = FSW_clients for dest_elem","shutdown server. \"\"\" dest_list = [] # Process data here... head, dst =","server for commands, log events, and telemetry data. Later this will handle other","b\"_\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered = True","and send the packet of the message here... self.processNewPkt(header, data) def recv(self, l):","str(err.errno) + \" occurred on send().\") def fileno(self): \"\"\" \"\"\" return self.socket def","TCP socket server for commands, log events, and telemetry data. Later this will","connect \"\"\" The function that is invoked upon a new client. This function"],"string":"[\n \"writting to destinations. \\\"\\\"\\\" dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer):\",\n \"== b\\\"Register\\\": LOCK.acquire() name = params[1] if b\\\"FSW\\\" in name: if FSW_clients: process_id\",\n \"\\\"\\\"\\\" dest_list = [] # Process data here... head, dst = header.strip(b\\\" \\\").split(b\\\"\",\n \"if chunk == b\\\"\\\": print(\\\"read data from socket is empty!\\\") return b\\\"\\\" msg\",\n \"= params[1] + b\\\"_\\\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\\\"GUI\\\" in name: if\",\n \"b\\\"List\\\": print(\\\"List of registered clients: \\\") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\\\"\\\\t\\\" +\",\n \"TCP\\\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print\",\n \"goes wrong report and shutdown server. \\\"\\\"\\\" dest_list = [] if header ==\",\n \"self.recv(4) sizeb = self.recv(4) size = struct.unpack(\\\">I\\\", sizeb)[0] data = desc + sizeb\",\n \"so read the data of the message here... data = self.readData(header, packet) #\",\n \"not header: return # Got the header data so read the data of\",\n \"# print \\\"Sending TCP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Packet\",\n \"= [] FSW_ids = [] GUI_ids = [] def signal_handler(*_): print(\\\"Ctrl-C received, server\",\n \"Once the entire header tstring is processed send it on queue. If something\",\n \"gse.py applicaiton. TCP socket server for commands, log events, and telemetry data. Later\",\n \"is a List. Once the entire header tstring is processed send it on\",\n \"or \\\"A5A5 FSW \\\"), or just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header =\",\n \"size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + packet[4 : 4 + size]\",\n \"Header # print \\\"Received Packet: %s %s...\\\\n\\\" % (head,dst) if data == b\\\"\\\":\",\n \"import SocketServer as socketserver import time import os import signal import sys import\",\n \"time import os import signal import sys import struct import errno from fprime.constants\",\n \"socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on each client connect\",\n \"default=\\\"127.0.0.1\\\", ) # process options (opts, args) = parser.parse_args(argv) HOST = opts.host PORT\",\n \"client. Registration is done by sending the string \\\"Register <name>\\\". Sending a message\",\n \"part of the message sent to either GUI or FSW. GUI receives telemetry.\",\n \"the message sent to either GUI or FSW. GUI receives telemetry. FSW receives\",\n \"return (header + header2, packet) def readData(self, header, packet): \\\"\\\"\\\" Read the data\",\n \"the 9 byte header (e.g. \\\"A5A5 GUI \\\" or \\\"A5A5 FSW \\\"), or\",\n \"server shutting down.\\\") shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \\\"\\\"\\\" Derived from\",\n \"and data here. The command must always start with A5A5 except if it\",\n \"TCP Socket server. Keep a dictionary of destination objects containing queues and socket\",\n \"if data == \\\"\\\": print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\" in dst:\",\n \"print(\\\"Ctrl-C received, server shutting down.\\\") shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \\\"\\\"\\\"\",\n \"0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\\\"Quit processed!\\\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the header\",\n \"signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0)\",\n \"user_name (California Institute of Technology) \\\\ ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\\\"\",\n \"threading try: import socketserver except ImportError: import SocketServer as socketserver import time import\",\n \"request): \\\"\\\"\\\" Constructor \\\"\\\"\\\" self.name = name self.socket = request self.packet = b\\\"\\\"\",\n \"either A5A5 or List header = self.readHeader() # If the received header is\",\n \"header == b\\\"Quit\\\\n\\\": return b\\\"Quit\\\" elif header[:-1] == b\\\"A5A5\\\": header2 = self.recv(4) return\",\n \"packet of the message here... self.processNewPkt(header, data) def readHeader(self, packet): \\\"\\\"\\\" Read the\",\n \"listening on host addr %s, port %s\\\" % (HOST, PORT)) # Start a\",\n \"def fileno(self): \\\"\\\"\\\" \\\"\\\"\\\" return self.socket def main(argv=None): global SERVER, LOCK program_name =\",\n \"+ self.name.decode(DATA_ENCODING) + \\\" exiting.\\\" ) return header if header == b\\\"List\\\\n\\\": return\",\n \"the data part of the message sent to either GUI or FSW. GUI\",\n \"self.registered = True self.name = name self.id = process_id print(\\\"Registered client \\\" +\",\n \"a thread with the server -- that thread will then start one #\",\n \"that is invoked when a packet is received. This function listens for data\",\n \"tlm_packet_size = self.recv(4) size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else:\",\n \"0: print( \\\"Header information is empty, client \\\" + self.name.decode(DATA_ENCODING) + \\\" exiting.\\\"\",\n \"\\\")[1].strip(b\\\" \\\") if dst == b\\\"FSW\\\": # Read variable length command data here...\",\n \"Process the data into the cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue() if\",\n \"n: try: chunk = self.request.recv(l - n) if chunk == b\\\"\\\": print(\\\"read data\",\n \"def processNewPkt(self, header, data): \\\"\\\"\\\" Process a single command here header and data\",\n \"FSW gse.py applicaiton. TCP socket server for commands, log events, and telemetry data.\",\n \"== b\\\"Quit\\\": return b\\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") if dst == b\\\"FSW\\\":\",\n \"for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd): params =\",\n \"repr(e) + \\\"\\\\n\\\") sys.stderr.write(indent + \\\" for help use --help\\\\n\\\") return 2 if\",\n \"= threading.Event() FSW_clients = [] GUI_clients = [] FSW_ids = [] GUI_ids =\",\n \"as err: if err.errno == errno.ECONNRESET: print( \\\"Socket error \\\" + str(err.errno) +\",\n \"separated by a newline. For each packet, call processPkt. \\\"\\\"\\\" self.getNewMsg(self.request[0]) ################################################# #\",\n \"%s %s...\\\\n\\\" % (head,dst) if data == b\\\"\\\": print(\\\" Data is empty, returning.\\\")\",\n \"files and parameters. Handle is instanced in own thread for each client. Registration\",\n \"dest_list = GUI_clients else: print(\\\"dest? %s\\\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire()\",\n \"and send the packet of the message here... self.processNewPkt(header, data) def readHeader(self, packet):\",\n \"print(\\\"TCP Socket Server listening on host addr %s, port %s\\\" % (HOST, PORT))\",\n \"- n) if chunk == b\\\"\\\": print(\\\"read data from socket is empty!\\\") return\",\n \"R&TD and adapted for use in new FSW gse.py applicaiton. TCP socket server\",\n \"as e: indent = len(program_name) * \\\" \\\" sys.stderr.write(program_name + \\\": \\\" +\",\n \"def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd):\",\n \"data = tlm_packet_size + self.recv(size) else: raise RuntimeError(\\\"unrecognized client %s\\\" % dst.decode(DATA_ENCODING)) return\",\n \"SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\\\"Quit processed!\\\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the\",\n \"The function that is invoked when a packet is received. This function listens\",\n \"if len(self.partial): commands[0] = self.partial + commands[0] self.partial = b\\\"\\\" if len(commands[-1]): self.partial\",\n \"# print \\\"Received Packet: %s %s...\\\\n\\\" % (head,dst) if data == \\\"\\\": print(\\\"\",\n \"optional - give further explanation about what the program does ) if argv\",\n \"name: if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name = params[1] + b\\\"_\\\"\",\n \"not data: print(\\\"Client exited.\\\") return else: # Process the data into the cmdQueue\",\n \"from the socket either A5A5 or List header = self.readHeader() # If the\",\n \"function listens for data on the socket. Packets for now are assumed to\",\n \"\\\"List\\\" comment makes the server display all registered clients. \\\"\\\"\\\" socketserver.StreamRequestHandler.allow_reuse_address = True\",\n \"__updated__ = \\\"2016-04-07\\\" # Universal server id global SERVER = None LOCK =\",\n \"data) def readHeader(self, packet): \\\"\\\"\\\" Read the 9 byte header (e.g. \\\"A5A5 GUI\",\n \"packet, call processPkt. \\\"\\\"\\\" self.getNewMsg(self.request[0]) ################################################# # New Routines to process the command\",\n \"msg = b\\\"\\\" n = 0 while l > n: try: chunk =\",\n \"immediately server.allow_reuse_address = True SERVER = server LOCK = server.lock_obj ip, port =\",\n \"the message here... self.processNewPkt(header, data) def recv(self, l): \\\"\\\"\\\" Read l bytes from\",\n \"= [] GUI_ids = [] def signal_handler(*_): print(\\\"Ctrl-C received, server shutting down.\\\") shutdown_event.set()\",\n \"b\\\"\\\" def put(self, msg): \\\"\\\"\\\" Write out the message to the destination socket\",\n \"GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered = True self.name = name self.id\",\n \"destinations. \\\"\\\"\\\" dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \\\"\\\"\\\" UDP\",\n \"(%s)\\\" % (program_version, program_build_date) program_longdesc = ( \\\"\\\"\\\"\\\"\\\"\\\" # optional - give further\",\n \"reuse and server to restart immediately server.allow_reuse_address = True SERVER = server LOCK\",\n \"processPkt. \\\"\\\"\\\" self.getNewMsg(self.request[0]) ################################################# # New Routines to process the command messages #################################################\",\n \"the command messages ################################################# def getNewMsg(self): \\\"\\\"\\\" After registration wait for an incoming\",\n \"b\\\"FSW\\\": # Read variable length command data here... desc = self.recv(4) sizeb =\",\n \"if b\\\"FSW\\\" in name: if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name =\",\n \"Sending a message to destination <name> is done as \\\"A5A5 <name> <data>\\\" Note\",\n \"== b\\\"Quit\\\": LOCK.acquire() print(\\\"Quit received!\\\") SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\\\"Quit processed!\\\") SERVER.shutdown() SERVER.server_close()\",\n \"= self.partial + commands[0] self.partial = b\\\"\\\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1])\",\n \"dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \\\"\\\"\\\" UDP Socket server.\",\n \"= GUI_clients else: print(\\\"dest? %s\\\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if\",\n \"here... tlm_packet_size = self.recv(4) size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size)\",\n \"events, and telemetry data. Later this will handle other things such as sequence\",\n \"return b\\\"\\\" elif header == b\\\"Quit\\\": return b\\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\")\",\n \"command. \\\"\\\"\\\" header = packet[:4] header2 = packet[4:9] packet = packet[9:] return (header\",\n \"\\\" or a \\\"List \\\" \\\"\\\"\\\" # Loop while the connected client has\",\n \"errno.ECONNRESET: print( \\\"Socket error \\\" + str(err.errno) + \\\" (Connection reset by peer)\",\n \"the message here... self.processNewPkt(header, data) def readHeader(self, packet): \\\"\\\"\\\" Read the 9 byte\",\n \"the socket data = self.recv(13) # Connection was closed by the client if\",\n \"something goes wrong report and shutdown server. \\\"\\\"\\\" dest_list = [] # Process\",\n \"print(\\\"Socket error \\\" + str(err.errno) + \\\" occurred on send().\\\") def fileno(self): \\\"\\\"\\\"\",\n \"+ self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process the command messages ################################################# def\",\n \"return b\\\"Quit\\\\n\\\" continue except socket.error as err: if err.errno == errno.ECONNRESET: print( \\\"Socket\",\n \"the data of the message here... data = self.readData(header, packet) # Process and\",\n \"self.socket = request self.packet = b\\\"\\\" def put(self, msg): \\\"\\\"\\\" Write out the\",\n \"commands of various lengths. \\\"\\\"\\\" data = \\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\")\",\n \"<name>\\\". Sending a message to destination <name> is done as \\\"A5A5 <name> <data>\\\"\",\n \"class DestObj: \\\"\\\"\\\" Destination object for all clients registered. \\\"\\\"\\\" def __init__(self, name,\",\n \"= b\\\"\\\" def put(self, msg): \\\"\\\"\\\" Write out the message to the destination\",\n \"# print \\\"about to send data to \\\" + self.name self.socket.send(msg) except socket.error\",\n \"\\\" + repr(e) + \\\"\\\\n\\\") sys.stderr.write(indent + \\\" for help use --help\\\\n\\\") return\",\n \"b\\\"List\\\": return b\\\"\\\" elif header == b\\\"Quit\\\": return b\\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\"\",\n \"single command here header and data here. The command must always start with\",\n \"= 0 # print self.client_address, now() # show this client's address # Read\",\n \"print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list = GUI_clients elif\",\n \"print(\\\"read data from socket is empty!\\\") return b\\\"\\\" msg = msg + chunk\",\n \"send the packet of the message here... self.processNewPkt(header, data) def recv(self, l): \\\"\\\"\\\"\",\n \"thread for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon\",\n \"shutdown server. \\\"\\\"\\\" dest_list = [] if header == b\\\"List\\\": print(\\\"List of registered\",\n \"invoked upon a new client. This function listens for data on the socket.\",\n \"l = len(reg_client_str) reg_client_str = struct.pack(\\\"i%ds\\\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return\",\n \"in dst: dest_list = GUI_clients elif b\\\"FSW\\\" in dst: dest_list = FSW_clients for\",\n \"udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\\\"shutdown from main thread\\\")\",\n \"name self.socket = request self.packet = b\\\"\\\" def put(self, msg): \\\"\\\"\\\" Write out\",\n \"err: if err.errno == errno.ECONNRESET: print( \\\"Socket error \\\" + str(err.errno) + \\\"\",\n \"to \\\" + self.name self.socket.send(msg) except socket.error as err: print(\\\"Socket error \\\" +\",\n \"\\\"Header information is empty, client \\\" + self.name.decode(DATA_ENCODING) + \\\" exiting.\\\" ) return\",\n \"tlm_packet_size = packet[:4] size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + packet[4 :\",\n \"+ bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered = True self.name\",\n \"print \\\"about to send data to \\\" + self.name self.socket.send(msg) except socket.error as\",\n \"print(\\\"shutdown from main thread\\\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e:\",\n \"data so read the data of the message here... data = self.readData(header) #\",\n \"invoked when a packet is received. This function listens for data on the\",\n \"the destination socket \\\"\\\"\\\" try: # print \\\"about to send data to \\\"\",\n \"so read the data of the message here... data = self.readData(header) # Process\",\n \"GUI or FSW. GUI receives telemetry. FSW receives commands of various lengths. \\\"\\\"\\\"\",\n \"print \\\"Sending UDP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Telemetry missing\",\n \"socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on each client connect \\\"\\\"\\\" The function\",\n \"server.lock_obj ip, port = server.server_address print(\\\"TCP Socket Server listening on host addr %s,\",\n \"as sequence files and parameters. Handle is instanced in own thread for each\",\n \"LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print \\\"Sending\",\n \"if argv is None: argv = sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc,\",\n \"into the cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue() if self.registered: print(\\\"Registration complete\",\n \"id global SERVER = None LOCK = None shutdown_event = threading.Event() FSW_clients =\",\n \"self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self,\",\n \"program_build_date = \\\"%s\\\" % __updated__ program_version_string = \\\"%%prog %s (%s)\\\" % (program_version, program_build_date)\",\n \"def processRegistration(self, cmd): params = cmd.split() process_id = 0 if params[0] == b\\\"Register\\\":\",\n \"clients: \\\") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\\\"\\\\t\\\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\\\"List\",\n \"udp_server.server_close() time.sleep(1) except Exception as e: indent = len(program_name) * \\\" \\\" sys.stderr.write(program_name\",\n \"+ \\\" occurred on recv().\\\") return msg def readHeader(self): \\\"\\\"\\\" Read the 9\",\n \"List (header, packet) = self.readHeader(packet) # If the received header is an empty\",\n \"are assumed to be separated by a newline. For each packet, call processPkt.\",\n \"has packets to send/receive while not shutdown_event.is_set(): # Read the header data from\",\n \"server.server_address print(\\\"TCP Socket Server listening on host addr %s, port %s\\\" % (HOST,\",\n \"[] GUI_clients = [] FSW_ids = [] GUI_ids = [] def signal_handler(*_): print(\\\"Ctrl-C\",\n \"message to the destination socket \\\"\\\"\\\" try: # print \\\"about to send data\",\n \"= [] def processRegistration(self, cmd): params = cmd.split() process_id = 0 if params[0]\",\n \"%s...\\\\n\\\" % (head,dst) if data == \\\"\\\": print(\\\" Data is empty, returning.\\\") if\",\n \"\\\"\\\"\\\" Destination object for all clients registered. \\\"\\\"\\\" def __init__(self, name, request): \\\"\\\"\\\"\",\n \"= packet[:4] header2 = packet[4:9] packet = packet[9:] return (header + header2, packet)\",\n \"readData(self, header): \\\"\\\"\\\" Read the data part of the message sent to either\",\n \"threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False\",\n \"now() # show this client's address # Read the data from the socket\",\n \"commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue =\",\n \"l > n: try: chunk = self.request.recv(l - n) if chunk == b\\\"\\\":\",\n \"is None: argv = sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, )\",\n \"e: indent = len(program_name) * \\\" \\\" sys.stderr.write(program_name + \\\": \\\" + repr(e)\",\n \"DATA_ENCODING from optparse import OptionParser __version__ = 0.1 __date__ = \\\"2015-04-03\\\" __updated__ =\",\n \"clients. \\\"\\\"\\\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on each\",\n \"data = self.readData(header) # Process and send the packet of the message here...\",\n \"handle(self): # on each client connect \\\"\\\"\\\" The function that is invoked upon\",\n \"print(\\\"Unable to register client.\\\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name)\",\n \"receives commands of various lengths. \\\"\\\"\\\" data = b\\\"\\\" if header == b\\\"List\\\":\",\n \"cmd.split() process_id = 0 if params[0] == b\\\"Register\\\": LOCK.acquire() name = params[1] if\",\n \"dst == b\\\"GUI\\\": # Read telemetry data here... tlm_packet_size = self.recv(4) size =\",\n \"information is empty, client \\\" + self.name.decode(DATA_ENCODING) + \\\" exiting.\\\" ) return header\",\n \"= dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \\\"\\\"\\\" UDP Socket server. \\\"\\\"\\\"\",\n \"during R&TD and adapted for use in new FSW gse.py applicaiton. TCP socket\",\n \"= True def handle(self): # on each packet \\\"\\\"\\\" The function that is\",\n \"be separated by a newline. For each packet, call processPkt. \\\"\\\"\\\" self.getNewMsg(self.request[0]) #################################################\",\n \"header == b\\\"Quit\\\": LOCK.acquire() print(\\\"Quit received!\\\") SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\\\"Quit processed!\\\") SERVER.shutdown()\",\n \"client \\\" + self.name.decode(DATA_ENCODING) + \\\" exiting.\\\" ) return header if header ==\",\n \"wrong report and shutdown server. \\\"\\\"\\\" dest_list = [] if header == b\\\"List\\\":\",\n \"while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\\\"shutdown from main thread\\\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close()\",\n \"self.packet = b\\\"\\\" def put(self, msg): \\\"\\\"\\\" Write out the message to the\",\n \"The first part must always be an \\\"A5A5 \\\" or a \\\"List \\\"\",\n \"if not header: break elif header == b\\\"Quit\\\": LOCK.acquire() print(\\\"Quit received!\\\") SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\", 0xA5A5A5A5))\",\n \": 4 + size] return data def processNewPkt(self, header, data): \\\"\\\"\\\" Process a\",\n \"threaded tcp socket server port [default: %default]\\\", default=50007, ) parser.add_option( \\\"-i\\\", \\\"--host\\\", dest=\\\"host\\\",\",\n \"processPkt. \\\"\\\"\\\" self.partial = b\\\"\\\" self.cmdQueue = [] self.registered = False self.name =\",\n \"for commands, log events, and telemetry data. Later this will handle other things\",\n \"handle other things such as sequence files and parameters. Handle is instanced in\",\n \"data here. The command must always start with A5A5 except if it is\",\n \"ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will allow address reuse\",\n \"data = b\\\"\\\" if header == b\\\"List\\\": return b\\\"\\\" elif header == b\\\"Quit\\\":\",\n \"string \\\"Register <name>\\\". Sending a message to destination <name> is done as \\\"A5A5\",\n \"SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Packet missing A5A5 header\\\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \\\"\\\"\\\" Derived from\",\n \"\\\" or a \\\"List \\\" \\\"\\\"\\\" # Read the header data from the\",\n \"self.partial + commands[0] self.partial = b\\\"\\\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else:\",\n \"tcp socket server ip [default: %default]\\\", default=\\\"127.0.0.1\\\", ) # process options (opts, args)\",\n \"print(\\\"Registration complete waiting for message.\\\") self.getNewMsg() else: print(\\\"Unable to register client.\\\") return LOCK.acquire()\",\n \"\\\"Received Packet: %s %s...\\\\n\\\" % (head,dst) if data == b\\\"\\\": print(\\\" Data is\",\n \"= header.split(b\\\" \\\")[1].strip(b\\\" \\\") # Read telemetry data here... tlm_packet_size = packet[:4] size\",\n \"server. Keep a dictionary of destination objects containing queues and socket id's for\",\n \"\\\"Sending UDP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Telemetry missing A5A5\",\n \"A5A5 or List header = self.readHeader() # If the received header is an\",\n \"if it is a List. Once the entire header tstring is processed send\",\n \"# Read the data from the socket data = self.recv(13) # Connection was\",\n \"# Hopefully this will allow address reuse and server to restart immediately server.allow_reuse_address\",\n \"def getNewMsg(self, packet): \\\"\\\"\\\" After registration wait for an incoming message The first\",\n \"header): \\\"\\\"\\\" Read the data part of the message sent to either GUI\",\n \"show this client's address # Read the data from the socket data =\",\n \"b\\\"Quit\\\\n\\\" continue except socket.error as err: if err.errno == errno.ECONNRESET: print( \\\"Socket error\",\n \"the entire header tstring is processed send it on queue. If something goes\",\n \"params[1] + b\\\"_\\\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered\",\n \"# Process the data into the cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue()\",\n \"just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = packet[:4] header2 = packet[4:9] packet\",\n \"shutdown_event.is_set(): print(\\\"socket timed out and shutdown is requested\\\") return b\\\"Quit\\\\n\\\" continue except socket.error\",\n \"self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd): params = cmd.split() process_id =\",\n \"= OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \\\"-p\\\", \\\"--port\\\", dest=\\\"port\\\", action=\\\"store\\\", type=\\\"int\\\", help=\\\"Set\",\n \"program_longdesc = ( \\\"\\\"\\\"\\\"\\\"\\\" # optional - give further explanation about what the\",\n \"or FSW. GUI receives telemetry. FSW receives commands of various lengths. \\\"\\\"\\\" data\",\n \"Data is empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list = GUI_clients elif b\\\"FSW\\\"\",\n \"LOCK.release() else: raise RuntimeError(\\\"Packet missing A5A5 header\\\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \\\"\\\"\\\" Derived from original\",\n \"string is processed send it on queue. If something goes wrong report and\",\n \"demo during R&TD and adapted for use in new FSW gse.py applicaiton. TCP\",\n \"send().\\\") def fileno(self): \\\"\\\"\\\" \\\"\\\"\\\" return self.socket def main(argv=None): global SERVER, LOCK program_name\",\n \"always be an \\\"A5A5 \\\" or a \\\"List \\\" \\\"\\\"\\\" # Read the\",\n \"RuntimeError(\\\"Packet missing A5A5 header\\\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \\\"\\\"\\\" Derived from original Stable demo during\",\n \"send data to \\\" + self.name self.socket.send(msg) except socket.error as err: print(\\\"Socket error\",\n \"Read telemetry data here... tlm_packet_size = self.recv(4) size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data =\",\n \"GUI receives telemetry. FSW receives commands of various lengths. \\\"\\\"\\\" data = b\\\"\\\"\",\n \"FSW_clients = [] GUI_clients = [] FSW_ids = [] GUI_ids = [] def\",\n \"packet of the message here... self.processNewPkt(header, data) def recv(self, l): \\\"\\\"\\\" Read l\",\n \"size = struct.unpack(\\\">I\\\", sizeb)[0] data = desc + sizeb + self.recv(size) elif dst\",\n \"1 name = params[1] + b\\\"_\\\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name,\",\n \"in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\\\"Closed %s\",\n \"exit loop if not header: break elif header == b\\\"Quit\\\": LOCK.acquire() print(\\\"Quit received!\\\")\",\n \"if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print \\\"Sending UDP\",\n \"requested\\\") return b\\\"Quit\\\\n\\\" continue except socket.error as err: if err.errno == errno.ECONNRESET: print(\",\n \"= \\\"2016-04-07\\\" # Universal server id global SERVER = None LOCK = None\",\n \"= packet[:4] size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + packet[4 : 4\",\n \"PORT), ThreadedUDPRequestHandler) # Hopefully this will allow address reuse and server to restart\",\n \"b\\\"Quit\\\": LOCK.acquire() print(\\\"Quit received!\\\") SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\\\"Quit processed!\\\") SERVER.shutdown() SERVER.server_close() LOCK.release()\",\n \"\\\" \\\" sys.stderr.write(program_name + \\\": \\\" + repr(e) + \\\"\\\\n\\\") sys.stderr.write(indent + \\\"\",\n \"done by sending the string \\\"Register <name>\\\". Sending a message to destination <name>\",\n \"udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start()\",\n \"\\\"Copyright 2015 user_name (California Institute of Technology) \\\\ ALL RIGHTS RESERVED. U.S. Government\",\n \"comment makes the server display all registered clients. \\\"\\\"\\\" socketserver.BaseRequestHandler.allow_reuse_address = True def\",\n \"from the socket either A5A5 or List (header, packet) = self.readHeader(packet) # If\",\n \"header is an empty string, connection closed, exit loop if not header: return\",\n \"such as sequence files and parameters. Handle is instanced in own thread for\",\n \"peer) occurred on recv().\\\" ) else: print(\\\"Socket error \\\" + str(err.errno) + \\\"\",\n \"Later this will handle other things such as sequence files and parameters. Handle\",\n \"message.\\\") self.getNewMsg() else: print(\\\"Unable to register client.\\\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name\",\n \"\\\"\\\"\\\" \\\"\\\"\\\" return self.socket def main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license\",\n \"elif header == b\\\"Quit\\\": LOCK.acquire() print(\\\"Quit received!\\\") SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\\\"Quit processed!\\\")\",\n \"data = \\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") # Read telemetry data here...\",\n \"of various lengths. \\\"\\\"\\\" data = \\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") #\",\n \"% self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self, inputString, end_of_command=b\\\"\\\\n\\\"): \\\"\\\"\\\" Build a\",\n \"server to restart immediately server.allow_reuse_address = True SERVER = server LOCK = server.lock_obj\",\n \"= desc + sizeb + self.recv(size) elif dst == b\\\"GUI\\\": # Read telemetry\",\n \"\\\" exiting.\\\" ) return header if header == b\\\"List\\\\n\\\": return b\\\"List\\\" elif header\",\n \"# Read variable length command data here... desc = self.recv(4) sizeb = self.recv(4)\",\n \"(HOST, PORT)) # Start a thread with the server -- that thread will\",\n \"packet \\\"\\\"\\\" The function that is invoked when a packet is received. This\",\n \"= struct.unpack(\\\">I\\\", sizeb)[0] data = desc + sizeb + self.recv(size) elif dst ==\",\n \"here... desc = self.recv(4) sizeb = self.recv(4) size = struct.unpack(\\\">I\\\", sizeb)[0] data =\",\n \"process_id = 0 if params[0] == b\\\"Register\\\": LOCK.acquire() name = params[1] if b\\\"FSW\\\"\",\n \"a \\\"List\\\" comment makes the server display all registered clients. \\\"\\\"\\\" socketserver.BaseRequestHandler.allow_reuse_address =\",\n \"connection.\\\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self, inputString, end_of_command=b\\\"\\\\n\\\"): \\\"\\\"\\\" Build\",\n \"here... tlm_packet_size = packet[:4] size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + packet[4\",\n \"when a packet is received. This function listens for data on the socket.\",\n \"is invoked when a packet is received. This function listens for data on\",\n \"by a newline. For each packet, call processPkt. \\\"\\\"\\\" self.getNewMsg(self.request[0]) ################################################# # New\",\n \"l bytes from socket. \\\"\\\"\\\" chunk = b\\\"\\\" msg = b\\\"\\\" n =\",\n \"__date__ = \\\"2015-04-03\\\" __updated__ = \\\"2016-04-07\\\" # Universal server id global SERVER =\",\n \"def getNewMsg(self): \\\"\\\"\\\" After registration wait for an incoming message The first part\",\n \"command. \\\"\\\"\\\" header = self.recv(5) if len(header) == 0: print( \\\"Header information is\",\n \"= server LOCK = server.lock_obj ip, port = server.server_address print(\\\"TCP Socket Server listening\",\n \"\\\"\\\"\\\" Constructor \\\"\\\"\\\" self.name = name self.socket = request self.packet = b\\\"\\\" def\",\n \"a newline. For each packet, call processPkt. \\\"\\\"\\\" self.partial = b\\\"\\\" self.cmdQueue =\",\n \"print \\\"Received Packet: %s %s...\\\\n\\\" % (head,dst) if data == b\\\"\\\": print(\\\" Data\",\n \"= [] if header == b\\\"List\\\": print(\\\"List of registered clients: \\\") LOCK.acquire() for\",\n \"################################################# def getNewMsg(self): \\\"\\\"\\\" After registration wait for an incoming message The first\",\n \"telemetry data. Later this will handle other things such as sequence files and\",\n \"SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered = True self.name = name self.id =\",\n \"+ sizeb + self.recv(size) elif dst == b\\\"GUI\\\": # Read telemetry data here...\",\n \"the socket either A5A5 or List header = self.readHeader() # If the received\",\n \"msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Telemetry missing A5A5 header\\\") class\",\n \"The command must always start with A5A5 except if it is a List.\",\n \"except ImportError: import SocketServer as socketserver import time import os import signal import\",\n \"chunk = b\\\"\\\" msg = b\\\"\\\" n = 0 while l > n:\",\n \"is empty, client \\\" + self.name.decode(DATA_ENCODING) + \\\" exiting.\\\" ) return header if\",\n \"is done as \\\"A5A5 <name> <data>\\\" Note only <data> is sent. Any client\",\n \"- give further explanation about what the program does ) if argv is\",\n \"\\\"List \\\" \\\"\\\"\\\" # Read the header data from the socket either A5A5\",\n \"\\\" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\\\"i%ds\\\" % l, l, reg_client_str)\",\n \"\\\" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process the command messages #################################################\",\n \"New Routines to process the command messages ################################################# def getNewMsg(self, packet): \\\"\\\"\\\" After\",\n \"down.\\\") shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \\\"\\\"\\\" Derived from original Stable\",\n \"= sorted(GUI_ids)[-1] + 1 name = params[1] + b\\\"_\\\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id)\",\n \") return header if header == b\\\"List\\\\n\\\": return b\\\"List\\\" elif header == b\\\"Quit\\\\n\\\":\",\n \"import os import signal import sys import struct import errno from fprime.constants import\",\n \"a \\\"List\\\" comment makes the server display all registered clients. \\\"\\\"\\\" socketserver.StreamRequestHandler.allow_reuse_address =\",\n \"message here... self.processNewPkt(header, data) def recv(self, l): \\\"\\\"\\\" Read l bytes from socket.\",\n \"# Process the cmdQueue self.processQueue() if self.registered: print(\\\"Registration complete waiting for message.\\\") self.getNewMsg()\",\n \"if len(header) == 0: print( \\\"Header information is empty, client \\\" + self.name.decode(DATA_ENCODING)\",\n \"self.getNewMsg(self.request[0]) ################################################# # New Routines to process the command messages ################################################# def getNewMsg(self,\",\n \"################################################# # New Routines to process the command messages ################################################# def getNewMsg(self): \\\"\\\"\\\"\",\n \"+ \\\" occurred on send().\\\") def fileno(self): \\\"\\\"\\\" \\\"\\\"\\\" return self.socket def main(argv=None):\",\n \"= len(msg) except socket.timeout: if shutdown_event.is_set(): print(\\\"socket timed out and shutdown is requested\\\")\",\n \"for writting to destinations. \\\"\\\"\\\" dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn,\",\n \"it on queue. If something goes wrong report and shutdown server. \\\"\\\"\\\" dest_list\",\n \"inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial + commands[0] self.partial = b\\\"\\\" if len(commands[-1]):\",\n \"thread\\\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e: indent = len(program_name)\",\n \"SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Telemetry missing A5A5 header\\\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \\\"\\\"\\\" TCP\",\n \"header = self.recv(5) if len(header) == 0: print( \\\"Header information is empty, client\",\n \"empty string, connection closed, exit loop if not header: return # Got the\",\n \"parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \\\"-p\\\", \\\"--port\\\", dest=\\\"port\\\", action=\\\"store\\\", type=\\\"int\\\",\",\n \"receives telemetry. FSW receives commands of various lengths. \\\"\\\"\\\" data = b\\\"\\\" if\",\n \"\\\"\\\"\\\" # Read the header data from the socket either A5A5 or List\",\n \"bytes from socket. \\\"\\\"\\\" chunk = b\\\"\\\" msg = b\\\"\\\" n = 0\",\n \"n = 0 while l > n: try: chunk = self.request.recv(l - n)\",\n \"python3 from __future__ import print_function import socket import threading try: import socketserver except\",\n \"acknowledged.\\\" program_version = \\\"v0.1\\\" program_build_date = \\\"%s\\\" % __updated__ program_version_string = \\\"%%prog %s\",\n \"function that is invoked upon a new client. This function listens for data\",\n \"object for all clients registered. \\\"\\\"\\\" def __init__(self, name, request): \\\"\\\"\\\" Constructor \\\"\\\"\\\"\",\n \"self.name = name self.id = process_id print(\\\"Registered client \\\" + self.name.decode(DATA_ENCODING)) ################################################# #\",\n \"params[1] if b\\\"FSW\\\" in name: if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name\",\n \"a single command here header and data here. The command must always start\",\n \"sent to either GUI or FSW. GUI receives telemetry. FSW receives commands of\",\n \"A5A5 header\\\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \\\"\\\"\\\" Derived from original Stable demo during R&TD and\",\n \"header: break elif header == b\\\"Quit\\\": LOCK.acquire() print(\\\"Quit received!\\\") SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1)\",\n \"data part of the message sent to either GUI or FSW. GUI receives\",\n \"= b\\\"\\\" self.id = 0 # print self.client_address, now() # show this client's\",\n \"dest_list = [] if header == b\\\"List\\\": print(\\\"List of registered clients: \\\") LOCK.acquire()\",\n \"header data so read the data of the message here... data = self.readData(header,\",\n \"connection closed, exit loop if not header: return # Got the header data\",\n \"error \\\" + str(err.errno) + \\\" occurred on send().\\\") def fileno(self): \\\"\\\"\\\" \\\"\\\"\\\"\",\n \"fileno(self): \\\"\\\"\\\" \\\"\\\"\\\" return self.socket def main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0])\",\n \"server. \\\"\\\"\\\" class DestObj: \\\"\\\"\\\" Destination object for all clients registered. \\\"\\\"\\\" def\",\n \"self.recv(5) if len(header) == 0: print( \\\"Header information is empty, client \\\" +\",\n \"threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not\",\n \"else: print(\\\"Socket error \\\" + str(err.errno) + \\\" occurred on recv().\\\") return msg\",\n \"tlm_packet_size + packet[4 : 4 + size] return data def processNewPkt(self, header, data):\",\n \"= self.readData(header, packet) # Process and send the packet of the message here...\",\n \") parser.add_option( \\\"-i\\\", \\\"--host\\\", dest=\\\"host\\\", action=\\\"store\\\", type=\\\"string\\\", help=\\\"Set threaded tcp socket server ip\",\n \"received!\\\") SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\\\"Quit processed!\\\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got\",\n \"\\\"\\\"\\\" Derived from original Stable demo during R&TD and adapted for use in\",\n \"\\\"A5A5 \\\" or a \\\"List \\\" \\\"\\\"\\\" # Read the header data from\",\n \"for d in list(SERVER.dest_obj.keys()): print(\\\"\\\\t\\\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\\\"List \\\" + SERVER.dest_obj[d].name\",\n \"here... head, dst = header.strip(b\\\" \\\").split(b\\\" \\\") if head == b\\\"A5A5\\\": # Packet\",\n \"data from the socket either A5A5 or List header = self.readHeader() # If\",\n \"tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else: raise RuntimeError(\\\"unrecognized client %s\\\" % dst.decode(DATA_ENCODING))\",\n \"dest=\\\"host\\\", action=\\\"store\\\", type=\\\"string\\\", help=\\\"Set threaded tcp socket server ip [default: %default]\\\", default=\\\"127.0.0.1\\\", )\",\n \"on each client connect \\\"\\\"\\\" The function that is invoked upon a new\",\n \"dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print \\\"Sending UDP msg\",\n \"server. \\\"\\\"\\\" dest_list = [] # Process data here... head, dst = header.strip(b\\\"\",\n \"+ header2, packet) def readData(self, header, packet): \\\"\\\"\\\" Read the data part of\",\n \"registered clients: \\\") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\\\"\\\\t\\\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str =\",\n \"FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name = params[1] + b\\\"_\\\" + bytes(process_id)\",\n \"size] return data def processNewPkt(self, header, data): \\\"\\\"\\\" Process a single command here\",\n \"ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \\\"\\\"\\\" TCP Socket server. Keep a dictionary of destination objects containing\",\n \"import signal import sys import struct import errno from fprime.constants import DATA_ENCODING from\",\n \"to destinations. \\\"\\\"\\\" dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \\\"\\\"\\\"\",\n \"[default: %default]\\\", default=\\\"127.0.0.1\\\", ) # process options (opts, args) = parser.parse_args(argv) HOST =\",\n \"# on each client connect \\\"\\\"\\\" The function that is invoked upon a\",\n \"fprime.constants import DATA_ENCODING from optparse import OptionParser __version__ = 0.1 __date__ = \\\"2015-04-03\\\"\",\n \"sends a \\\"List\\\" comment makes the server display all registered clients. \\\"\\\"\\\" socketserver.StreamRequestHandler.allow_reuse_address\",\n \"sequence files and parameters. Handle is instanced in own thread for each client.\",\n \"data from the socket data = self.recv(13) # Connection was closed by the\",\n \"argv = sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \\\"-p\\\",\",\n \"packets to send/receive while not shutdown_event.is_set(): # Read the header data from the\",\n \"if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in\",\n \"command must always start with A5A5 except if it is a List. Once\",\n \"sent. Any client that sends a \\\"List\\\" comment makes the server display all\",\n \"ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \\\"\\\"\\\" Derived from original Stable demo during R&TD and adapted for use\",\n \"GUI receives telemetry. FSW receives commands of various lengths. \\\"\\\"\\\" data = \\\"\\\"\",\n \") parser.add_option( \\\"-p\\\", \\\"--port\\\", dest=\\\"port\\\", action=\\\"store\\\", type=\\\"int\\\", help=\\\"Set threaded tcp socket server port\",\n \"If something goes wrong report and shutdown server. \\\"\\\"\\\" dest_list = [] #\",\n \"occurred on recv().\\\" ) else: print(\\\"Socket error \\\" + str(err.errno) + \\\" occurred\",\n \"read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = self.recv(5) if len(header) == 0: print(\",\n \"Note only <data> is sent. Any client that sends a \\\"List\\\" comment makes\",\n \"\\\") if dst == b\\\"FSW\\\": # Read variable length command data here... desc\",\n \"# print \\\"Received Packet: %s %s...\\\\n\\\" % (head,dst) if data == b\\\"\\\": print(\\\"\",\n \"\\\"Socket error \\\" + str(err.errno) + \\\" (Connection reset by peer) occurred on\",\n \"header2 = packet[4:9] packet = packet[9:] return (header + header2, packet) def readData(self,\",\n \"data from socket is empty!\\\") return b\\\"\\\" msg = msg + chunk n\",\n \"GUI_clients else: print(\\\"dest? %s\\\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if dest_elem\",\n \"a command from partial or full socket input \\\"\\\"\\\" commands = inputString.split(end_of_command) if\",\n \"header if header == b\\\"List\\\\n\\\": return b\\\"List\\\" elif header == b\\\"Quit\\\\n\\\": return b\\\"Quit\\\"\",\n \"elif header[:-1] == b\\\"A5A5\\\": header2 = self.recv(4) return header + header2 else: return\",\n \"each packet, call processPkt. \\\"\\\"\\\" self.getNewMsg(self.request[0]) ################################################# # New Routines to process the\",\n \"+ self.recv(size) else: raise RuntimeError(\\\"unrecognized client %s\\\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self,\",\n \"Socket server. Keep a dictionary of destination objects containing queues and socket id's\",\n \"in name: if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name = params[1] +\",\n \"A5A5 except if it is a List. Once the entire header string is\",\n \"the cmdQueue self.processQueue() if self.registered: print(\\\"Registration complete waiting for message.\\\") self.getNewMsg() else: print(\\\"Unable\",\n \"= parser.parse_args(argv) HOST = opts.host PORT = opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler)\",\n \"empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list = GUI_clients elif b\\\"FSW\\\" in dst:\",\n \"lengths. \\\"\\\"\\\" data = b\\\"\\\" if header == b\\\"List\\\": return b\\\"\\\" elif header\",\n \"# more thread for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT,\",\n \"is sent. Any client that sends a \\\"List\\\" comment makes the server display\",\n \"SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id)\",\n \"msg def readHeader(self): \\\"\\\"\\\" Read the 9 byte header (e.g. \\\"A5A5 GUI \\\"\",\n \"# Read the header data from the socket either A5A5 or List (header,\",\n \"to either GUI or FSW. GUI receives telemetry. FSW receives commands of various\",\n \"an empty string, connection closed, exit loop if not header: return # Got\",\n \"################################################# # New Routines to process the command messages ################################################# def getNewMsg(self, packet):\",\n \"= len(reg_client_str) reg_client_str = struct.pack(\\\"i%ds\\\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0\",\n \"from partial or full socket input \\\"\\\"\\\" commands = inputString.split(end_of_command) if len(self.partial): commands[0]\",\n \"a newline. For each packet, call processPkt. \\\"\\\"\\\" self.getNewMsg(self.request[0]) ################################################# # New Routines\",\n \"process options (opts, args) = parser.parse_args(argv) HOST = opts.host PORT = opts.port server\",\n \"print(\\\"List of registered clients: \\\") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\\\"\\\\t\\\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING))\",\n \"else: print(\\\"dest? %s\\\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if dest_elem in\",\n \"message here... self.processNewPkt(header, data) def readHeader(self, packet): \\\"\\\"\\\" Read the 9 byte header\",\n \"# Process and send the packet of the message here... self.processNewPkt(header, data) def\",\n \"self.registered = False self.request.close() def getCmds(self, inputString, end_of_command=b\\\"\\\\n\\\"): \\\"\\\"\\\" Build a command from\",\n \"= 0 if params[0] == b\\\"Register\\\": LOCK.acquire() name = params[1] if b\\\"FSW\\\" in\",\n \"RuntimeError(\\\"Telemetry missing A5A5 header\\\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \\\"\\\"\\\" TCP Socket server. Keep a\",\n \"from socket is empty!\\\") return b\\\"\\\" msg = msg + chunk n =\",\n \"program_license = \\\"Copyright 2015 user_name (California Institute of Technology) \\\\ ALL RIGHTS RESERVED.\",\n \"str(err.errno) + \\\" (Connection reset by peer) occurred on recv().\\\" ) else: print(\\\"Socket\",\n \"len(reg_client_str) reg_client_str = struct.pack(\\\"i%ds\\\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 #\",\n \"== \\\"\\\": print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list =\",\n \"Packet: %s %s...\\\\n\\\" % (head,dst) if data == \\\"\\\": print(\\\" Data is empty,\",\n \"packet[9:] return (header + header2, packet) def readData(self, header, packet): \\\"\\\"\\\" Read the\",\n \"an \\\"A5A5 \\\" or a \\\"List \\\" \\\"\\\"\\\" # Read the header data\",\n \"in list(SERVER.dest_obj.keys()): # Send the message here.... # print \\\"Sending UDP msg to\",\n \"or a \\\"List \\\" \\\"\\\"\\\" # Read the header data from the socket\",\n \"% (head,dst) if data == \\\"\\\": print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\"\",\n \"if b\\\"GUI\\\" in dst: dest_list = GUI_clients elif b\\\"FSW\\\" in dst: dest_list =\",\n \"def recv(self, l): \\\"\\\"\\\" Read l bytes from socket. \\\"\\\"\\\" chunk = b\\\"\\\"\",\n \"global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license = \\\"Copyright 2015 user_name (California Institute\",\n \"socket.timeout: if shutdown_event.is_set(): print(\\\"socket timed out and shutdown is requested\\\") return b\\\"Quit\\\\n\\\" continue\",\n \"in name: if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name = params[1] +\",\n \"\\\"\\\"\\\" try: # print \\\"about to send data to \\\" + self.name self.socket.send(msg)\",\n \"= request self.packet = b\\\"\\\" def put(self, msg): \\\"\\\"\\\" Write out the message\",\n \"+ self.name self.socket.send(msg) except socket.error as err: print(\\\"Socket error \\\" + str(err.errno) +\",\n \"+ commands[0] self.partial = b\\\"\\\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1])\",\n \"def main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license = \\\"Copyright 2015 user_name\",\n \"other things such as sequence files and parameters. Handle is instanced in own\",\n \"header == b\\\"Quit\\\": return b\\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") if dst ==\",\n \"call processPkt. \\\"\\\"\\\" self.partial = b\\\"\\\" self.cmdQueue = [] self.registered = False self.name\",\n \"header\\\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \\\"\\\"\\\" TCP Socket server. Keep a dictionary of destination\",\n \"= \\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") # Read telemetry data here... tlm_packet_size\",\n \"occurred on recv().\\\") return msg def readHeader(self): \\\"\\\"\\\" Read the 9 byte header\",\n \"data): \\\"\\\"\\\" Process a single command here header and data here. The command\",\n \"self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\\\"Closed %s connection.\\\" % self.name.decode(DATA_ENCODING)) self.registered =\",\n \"self.recv(size) elif dst == b\\\"GUI\\\": # Read telemetry data here... tlm_packet_size = self.recv(4)\",\n \"elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\\\"Closed %s connection.\\\" % self.name.decode(DATA_ENCODING)) self.registered\",\n \"return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in\",\n \"(program_version, program_build_date) program_longdesc = ( \\\"\\\"\\\"\\\"\\\"\\\" # optional - give further explanation about\",\n \"port = server.server_address print(\\\"TCP Socket Server listening on host addr %s, port %s\\\"\",\n \"Build a command from partial or full socket input \\\"\\\"\\\" commands = inputString.split(end_of_command)\",\n \"header tstring is processed send it on queue. If something goes wrong report\",\n \") if argv is None: argv = sys.argv[1:] try: parser = OptionParser( version=program_version_string,\",\n \"bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered = True self.name =\",\n \"elif b\\\"FSW\\\" in dst: dest_list = FSW_clients for dest_elem in dest_list: # print\",\n \"data on the socket. Packets for now are assumed to be separated by\",\n \"= self.recv(4) size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else: raise\",\n \"self.readHeader() # If the received header is an empty string, connection closed, exit\",\n \"the cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue() if self.registered: print(\\\"Registration complete waiting\",\n \"not shutdown_event.is_set(): # Read the header data from the socket either A5A5 or\",\n \"Keep a dictionary of destination objects containing queues and socket id's for writting\",\n \"# Read telemetry data here... tlm_packet_size = self.recv(4) size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data\",\n \"__future__ import print_function import socket import threading try: import socketserver except ImportError: import\",\n \"thread with the server -- that thread will then start one # more\",\n \"self.request.recv(l - n) if chunk == b\\\"\\\": print(\\\"read data from socket is empty!\\\")\",\n \"header data from the socket either A5A5 or List (header, packet) = self.readHeader(packet)\",\n \"self.request.close() def getCmds(self, inputString, end_of_command=b\\\"\\\\n\\\"): \\\"\\\"\\\" Build a command from partial or full\",\n \"import OptionParser __version__ = 0.1 __date__ = \\\"2015-04-03\\\" __updated__ = \\\"2016-04-07\\\" # Universal\",\n \"GUI \\\" or \\\"A5A5 FSW \\\"), or just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\"\",\n \"= commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue\",\n \"explanation about what the program does ) if argv is None: argv =\",\n \"This function listens for data on the socket. Packets for now are assumed\",\n \"further explanation about what the program does ) if argv is None: argv\",\n \"= msg + chunk n = len(msg) except socket.timeout: if shutdown_event.is_set(): print(\\\"socket timed\",\n \"returning.\\\") if b\\\"GUI\\\" in dst: dest_list = GUI_clients else: print(\\\"dest? %s\\\" % dst.decode(DATA_ENCODING))\",\n \"len(self.partial): commands[0] = self.partial + commands[0] self.partial = b\\\"\\\" if len(commands[-1]): self.partial =\",\n \"Data is empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list = GUI_clients else: print(\\\"dest?\",\n \"= b\\\"List \\\" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\\\"i%ds\\\" % l,\",\n \"or full socket input \\\"\\\"\\\" commands = inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial\",\n \"FSW_ids.append(process_id) elif b\\\"GUI\\\" in name: if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name\",\n \"processed!\\\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the header data so read the\",\n \"port %s\\\" % (HOST, PORT)) # Start a thread with the server --\",\n \"ThreadedUDPRequestHandler) # Hopefully this will allow address reuse and server to restart immediately\",\n \"LOCK program_name = os.path.basename(sys.argv[0]) program_license = \\\"Copyright 2015 user_name (California Institute of Technology)\",\n \"= sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \\\"-p\\\", \\\"--port\\\",\",\n \"list(SERVER.dest_obj.keys()): # Send the message here.... # print \\\"Sending UDP msg to \\\",\",\n \"self.name self.socket.send(msg) except socket.error as err: print(\\\"Socket error \\\" + str(err.errno) + \\\"\",\n \"= \\\"%%prog %s (%s)\\\" % (program_version, program_build_date) program_longdesc = ( \\\"\\\"\\\"\\\"\\\"\\\" # optional\",\n \"= packet[9:] return (header + header2, packet) def readData(self, header, packet): \\\"\\\"\\\" Read\",\n \"in list(SERVER.dest_obj.keys()): print(\\\"\\\\t\\\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\\\"List \\\" + SERVER.dest_obj[d].name l =\",\n \"shutdown_event.is_set(): # Read the header data from the socket either A5A5 or List\",\n \"PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will allow address\",\n \"= \\\"2015-04-03\\\" __updated__ = \\\"2016-04-07\\\" # Universal server id global SERVER = None\",\n \"received header is an empty string, connection closed, exit loop if not header:\",\n \"by the client if not data: print(\\\"Client exited.\\\") return else: # Process the\",\n \"name: if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name = params[1] + b\\\"_\\\"\",\n \"socket server for commands, log events, and telemetry data. Later this will handle\",\n \"data of the message here... data = self.readData(header, packet) # Process and send\",\n \"New Routines to process the command messages ################################################# def getNewMsg(self): \\\"\\\"\\\" After registration\",\n \"[] # Process data here... head, dst = header.strip(b\\\" \\\").split(b\\\" \\\") if head\",\n \"server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this\",\n \"name = params[1] + b\\\"_\\\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\\\"GUI\\\" in name:\",\n \"in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd): params = cmd.split() process_id\",\n \"Any client that sends a \\\"List\\\" comment makes the server display all registered\",\n \"size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else: raise RuntimeError(\\\"unrecognized client\",\n \"on host addr %s, port %s\\\" % (HOST, PORT)) # Start a thread\",\n \"return b\\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") if dst == b\\\"FSW\\\": # Read\",\n \"Routines to process the command messages ################################################# def getNewMsg(self): \\\"\\\"\\\" After registration wait\",\n \"restart immediately server.allow_reuse_address = True SERVER = server LOCK = server.lock_obj ip, port\",\n \"client.\\\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name\",\n \"except if it is a List. Once the entire header string is processed\",\n \"= False self.request.close() def getCmds(self, inputString, end_of_command=b\\\"\\\\n\\\"): \\\"\\\"\\\" Build a command from partial\",\n \"Read the data part of the message sent to either GUI or FSW.\",\n \"variable length command data here... desc = self.recv(4) sizeb = self.recv(4) size =\",\n \"(header + header2, packet) def readData(self, header, packet): \\\"\\\"\\\" Read the data part\",\n \"header\\\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \\\"\\\"\\\" Derived from original Stable demo during R&TD and adapted\",\n \"else: raise RuntimeError(\\\"Telemetry missing A5A5 header\\\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \\\"\\\"\\\" TCP Socket server.\",\n \"= opts.host PORT = opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST,\",\n \"from socket. \\\"\\\"\\\" chunk = b\\\"\\\" msg = b\\\"\\\" n = 0 while\",\n \"l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process data here... head, dst =\",\n \"by a newline. For each packet, call processPkt. \\\"\\\"\\\" self.partial = b\\\"\\\" self.cmdQueue\",\n \"header.split(b\\\" \\\")[1].strip(b\\\" \\\") # Read telemetry data here... tlm_packet_size = packet[:4] size =\",\n \"0 while l > n: try: chunk = self.request.recv(l - n) if chunk\",\n \"data from the socket either A5A5 or List (header, packet) = self.readHeader(packet) #\",\n \"socket import threading try: import socketserver except ImportError: import SocketServer as socketserver import\",\n \"host addr %s, port %s\\\" % (HOST, PORT)) # Start a thread with\",\n \"except Exception as e: indent = len(program_name) * \\\" \\\" sys.stderr.write(program_name + \\\":\",\n \"def handle(self): # on each packet \\\"\\\"\\\" The function that is invoked when\",\n \"try: chunk = self.request.recv(l - n) if chunk == b\\\"\\\": print(\\\"read data from\",\n \"self.request) LOCK.release() self.registered = True self.name = name self.id = process_id print(\\\"Registered client\",\n \"= tlm_packet_size + packet[4 : 4 + size] return data def processNewPkt(self, header,\",\n \"argv is None: argv = sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license,\",\n \"ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will allow address reuse and server to\",\n \"\\\"\\\"\\\" dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \\\"\\\"\\\" UDP Socket\",\n \"in own thread for each client. Registration is done by sending the string\",\n \"b\\\"\\\" if header == b\\\"List\\\": return b\\\"\\\" elif header == b\\\"Quit\\\": return b\\\"\\\"\",\n \"server. \\\"\\\"\\\" dest_list = [] if header == b\\\"List\\\": print(\\\"List of registered clients:\",\n \"= b\\\"\\\" msg = b\\\"\\\" n = 0 while l > n: try:\",\n \"Process the cmdQueue self.processQueue() if self.registered: print(\\\"Registration complete waiting for message.\\\") self.getNewMsg() else:\",\n \"% (HOST, PORT)) # Start a thread with the server -- that thread\",\n \"message here... data = self.readData(header, packet) # Process and send the packet of\",\n \"= tlm_packet_size + self.recv(size) else: raise RuntimeError(\\\"unrecognized client %s\\\" % dst.decode(DATA_ENCODING)) return data\",\n \"\\\"about to send data to \\\" + self.name self.socket.send(msg) except socket.error as err:\",\n \"Write out the message to the destination socket \\\"\\\"\\\" try: # print \\\"about\",\n \"0.1 __date__ = \\\"2015-04-03\\\" __updated__ = \\\"2016-04-07\\\" # Universal server id global SERVER\",\n \"os.path.basename(sys.argv[0]) program_license = \\\"Copyright 2015 user_name (California Institute of Technology) \\\\ ALL RIGHTS\",\n \"applicaiton. TCP socket server for commands, log events, and telemetry data. Later this\",\n \"return self.socket def main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license = \\\"Copyright\",\n \"\\\"-p\\\", \\\"--port\\\", dest=\\\"port\\\", action=\\\"store\\\", type=\\\"int\\\", help=\\\"Set threaded tcp socket server port [default: %default]\\\",\",\n \"\\\"List\\\" comment makes the server display all registered clients. \\\"\\\"\\\" socketserver.BaseRequestHandler.allow_reuse_address = True\",\n \"header == b\\\"List\\\\n\\\": return b\\\"List\\\" elif header == b\\\"Quit\\\\n\\\": return b\\\"Quit\\\" elif header[:-1]\",\n \"ip, port = server.server_address print(\\\"TCP Socket Server listening on host addr %s, port\",\n \"data here... tlm_packet_size = self.recv(4) size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size +\",\n \"each packet \\\"\\\"\\\" The function that is invoked when a packet is received.\",\n \"socket input \\\"\\\"\\\" commands = inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial + commands[0]\",\n \"socket either A5A5 or List (header, packet) = self.readHeader(packet) # If the received\",\n \"of destination objects containing queues and socket id's for writting to destinations. \\\"\\\"\\\"\",\n \"print(\\\"Quit received!\\\") SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\\\"Quit processed!\\\") SERVER.shutdown() SERVER.server_close() LOCK.release() break #\",\n \"of Technology) \\\\ ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\\\" program_version = \\\"v0.1\\\"\",\n \"as socketserver import time import os import signal import sys import struct import\",\n \"command here header and data here. The command must always start with A5A5\",\n \"LOCK = server.lock_obj ip, port = server.server_address print(\\\"TCP Socket Server listening on host\",\n \"to process the command messages ################################################# def getNewMsg(self): \\\"\\\"\\\" After registration wait for\",\n \"call processPkt. \\\"\\\"\\\" self.getNewMsg(self.request[0]) ################################################# # New Routines to process the command messages\",\n \"packet): \\\"\\\"\\\" Read the data part of the message sent to either GUI\",\n \"a new client. This function listens for data on the socket. Packets for\",\n \"= self.request.recv(l - n) if chunk == b\\\"\\\": print(\\\"read data from socket is\",\n \"\\\"%s\\\" % __updated__ program_version_string = \\\"%%prog %s (%s)\\\" % (program_version, program_build_date) program_longdesc =\",\n \"and parameters. Handle is instanced in own thread for each client. Registration is\",\n \"registered clients. \\\"\\\"\\\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on\",\n \"msg = msg + chunk n = len(msg) except socket.timeout: if shutdown_event.is_set(): print(\\\"socket\",\n \"on recv().\\\") return msg def readHeader(self): \\\"\\\"\\\" Read the 9 byte header (e.g.\",\n \"b\\\"Register\\\": LOCK.acquire() name = params[1] if b\\\"FSW\\\" in name: if FSW_clients: process_id =\",\n \"closed, exit loop if not header: return # Got the header data so\",\n \"queue. If something goes wrong report and shutdown server. \\\"\\\"\\\" dest_list = []\",\n \"dest_elem in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here....\",\n \"params[0] == b\\\"Register\\\": LOCK.acquire() name = params[1] if b\\\"FSW\\\" in name: if FSW_clients:\",\n \"\\\"A5A5 GUI \\\" or \\\"A5A5 FSW \\\"), or just read the \\\"List\\\\n\\\" command.\",\n \"all registered clients. \\\"\\\"\\\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on each packet\",\n \"program_build_date) program_longdesc = ( \\\"\\\"\\\"\\\"\\\"\\\" # optional - give further explanation about what\",\n \"data = self.recv(13) # Connection was closed by the client if not data:\",\n \"def signal_handler(*_): print(\\\"Ctrl-C received, server shutting down.\\\") shutdown_event.set() def now(): return time.ctime(time.time()) class\",\n \"is empty!\\\") return b\\\"\\\" msg = msg + chunk n = len(msg) except\",\n \"data == b\\\"\\\": print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list\",\n \"this client's address # Read the data from the socket data = self.recv(13)\",\n \"SERVER = None LOCK = None shutdown_event = threading.Event() FSW_clients = [] GUI_clients\",\n \"one # more thread for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever)\",\n \"= b\\\"\\\" if header == b\\\"List\\\": return b\\\"\\\" elif header == b\\\"Quit\\\": return\",\n \"self.recv(4) size = struct.unpack(\\\">I\\\", sizeb)[0] data = desc + sizeb + self.recv(size) elif\",\n \"allow address reuse and server to restart immediately server.allow_reuse_address = True SERVER =\",\n \"exited.\\\") return else: # Process the data into the cmdQueue self.getCmds(data) # Process\",\n \"of the message sent to either GUI or FSW. GUI receives telemetry. FSW\",\n \"= struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else: raise RuntimeError(\\\"unrecognized client %s\\\"\",\n \"\\\"A5A5 FSW \\\"), or just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = packet[:4]\",\n \"continue except socket.error as err: if err.errno == errno.ECONNRESET: print( \\\"Socket error \\\"\",\n \"header == b\\\"List\\\": return b\\\"\\\" elif header == b\\\"Quit\\\": return b\\\"\\\" dst =\",\n \"reg_client_str = b\\\"List \\\" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\\\"i%ds\\\" %\",\n \"if header == b\\\"List\\\": return b\\\"\\\" elif header == b\\\"Quit\\\": return b\\\"\\\" dst\",\n \"program_version = \\\"v0.1\\\" program_build_date = \\\"%s\\\" % __updated__ program_version_string = \\\"%%prog %s (%s)\\\"\",\n \"import socketserver except ImportError: import SocketServer as socketserver import time import os import\",\n \"b\\\"Quit\\\" elif header[:-1] == b\\\"A5A5\\\": header2 = self.recv(4) return header + header2 else:\",\n \"tstring is processed send it on queue. If something goes wrong report and\",\n \"for use in new FSW gse.py applicaiton. TCP socket server for commands, log\",\n \"server LOCK = server.lock_obj ip, port = server.server_address print(\\\"TCP Socket Server listening on\",\n \"Once the entire header string is processed send it on queue. If something\",\n \"struct import errno from fprime.constants import DATA_ENCODING from optparse import OptionParser __version__ =\",\n \"in new FSW gse.py applicaiton. TCP socket server for commands, log events, and\",\n \"dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the\",\n \"thread for each client. Registration is done by sending the string \\\"Register <name>\\\".\",\n \"the server -- that thread will then start one # more thread for\",\n \"FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\\\"Closed %s connection.\\\" % self.name.decode(DATA_ENCODING))\",\n \"the data from the socket data = self.recv(13) # Connection was closed by\",\n \"Handle is instanced in own thread for each client. Registration is done by\",\n \"getNewMsg(self): \\\"\\\"\\\" After registration wait for an incoming message The first part must\",\n \"closed, exit loop if not header: break elif header == b\\\"Quit\\\": LOCK.acquire() print(\\\"Quit\",\n \"b\\\"List\\\" elif header == b\\\"Quit\\\\n\\\": return b\\\"Quit\\\" elif header[:-1] == b\\\"A5A5\\\": header2 =\",\n \"None: argv = sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option(\",\n \"makes the server display all registered clients. \\\"\\\"\\\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout =\",\n \"try: import socketserver except ImportError: import SocketServer as socketserver import time import os\",\n \"type=\\\"string\\\", help=\\\"Set threaded tcp socket server ip [default: %default]\\\", default=\\\"127.0.0.1\\\", ) # process\",\n \"args) = parser.parse_args(argv) HOST = opts.host PORT = opts.port server = ThreadedTCPServer((HOST, PORT),\",\n \"be separated by a newline. For each packet, call processPkt. \\\"\\\"\\\" self.partial =\",\n \"Socket Server listening on host addr %s, port %s\\\" % (HOST, PORT)) #\",\n \"newline. For each packet, call processPkt. \\\"\\\"\\\" self.getNewMsg(self.request[0]) ################################################# # New Routines to\",\n \"# Process data here... head, dst = header.strip(b\\\" \\\").split(b\\\" \\\") if head ==\",\n \"an incoming message The first part must always be an \\\"A5A5 \\\" or\",\n \"or just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = packet[:4] header2 = packet[4:9]\",\n \"= False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\\\"shutdown from main thread\\\") SERVER.shutdown()\",\n \"tcp socket server port [default: %default]\\\", default=50007, ) parser.add_option( \\\"-i\\\", \\\"--host\\\", dest=\\\"host\\\", action=\\\"store\\\",\",\n \"Send the message here.... # print \\\"Sending UDP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data)\",\n \"# Got the header data so read the data of the message here...\",\n \"if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print \\\"Sending TCP\",\n \"break # Got the header data so read the data of the message\",\n \"an \\\"A5A5 \\\" or a \\\"List \\\" \\\"\\\"\\\" # Loop while the connected\",\n \"cmdQueue self.processQueue() if self.registered: print(\\\"Registration complete waiting for message.\\\") self.getNewMsg() else: print(\\\"Unable to\",\n \"if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release()\",\n \"= b\\\"\\\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for\",\n \"destination socket \\\"\\\"\\\" try: # print \\\"about to send data to \\\" +\",\n \"Government Sponsorship acknowledged.\\\" program_version = \\\"v0.1\\\" program_build_date = \\\"%s\\\" % __updated__ program_version_string =\",\n \"error \\\" + str(err.errno) + \\\" occurred on recv().\\\") return msg def readHeader(self):\",\n \"self.partial = b\\\"\\\" self.cmdQueue = [] self.registered = False self.name = b\\\"\\\" self.id\",\n \"in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... #\",\n \"instanced in own thread for each client. Registration is done by sending the\",\n \"now are assumed to be separated by a newline. For each packet, call\",\n \"\\\"Register <name>\\\". Sending a message to destination <name> is done as \\\"A5A5 <name>\",\n \"Loop while the connected client has packets to send/receive while not shutdown_event.is_set(): #\",\n \"\\\"-i\\\", \\\"--host\\\", dest=\\\"host\\\", action=\\\"store\\\", type=\\\"string\\\", help=\\\"Set threaded tcp socket server ip [default: %default]\\\",\",\n \"True def handle(self): # on each packet \\\"\\\"\\\" The function that is invoked\",\n \"\\\"\\\"\\\" After registration wait for an incoming message The first part must always\",\n \"shutting down.\\\") shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \\\"\\\"\\\" Derived from original\",\n \"self.cmdQueue = [] def processRegistration(self, cmd): params = cmd.split() process_id = 0 if\",\n \"clients. \\\"\\\"\\\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on each packet \\\"\\\"\\\" The\",\n \"version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \\\"-p\\\", \\\"--port\\\", dest=\\\"port\\\", action=\\\"store\\\", type=\\\"int\\\", help=\\\"Set threaded tcp\",\n \"dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") if dst == b\\\"FSW\\\": # Read variable length\",\n \"socketserver import time import os import signal import sys import struct import errno\",\n \"display all registered clients. \\\"\\\"\\\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def handle(self):\",\n \"\\\" + str(err.errno) + \\\" occurred on send().\\\") def fileno(self): \\\"\\\"\\\" \\\"\\\"\\\" return\",\n \"self.processNewPkt(header, data) def recv(self, l): \\\"\\\"\\\" Read l bytes from socket. \\\"\\\"\\\" chunk\",\n \"__init__(self, name, request): \\\"\\\"\\\" Constructor \\\"\\\"\\\" self.name = name self.socket = request self.packet\",\n \"time.sleep(1) print(\\\"Quit processed!\\\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the header data so\",\n \") else: print(\\\"Socket error \\\" + str(err.errno) + \\\" occurred on recv().\\\") return\",\n \"b\\\"List \\\" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\\\"i%ds\\\" % l, l,\",\n \"list(SERVER.dest_obj.keys()): # Send the message here.... # print \\\"Sending TCP msg to \\\",\",\n \"self.readData(header) # Process and send the packet of the message here... self.processNewPkt(header, data)\",\n \"\\\"), or just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = packet[:4] header2 =\",\n \"newline. For each packet, call processPkt. \\\"\\\"\\\" self.partial = b\\\"\\\" self.cmdQueue = []\",\n \"\\\"\\\"\\\" def __init__(self, name, request): \\\"\\\"\\\" Constructor \\\"\\\"\\\" self.name = name self.socket =\",\n \"the socket either A5A5 or List (header, packet) = self.readHeader(packet) # If the\",\n \"start one # more thread for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread =\",\n \"+ SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\\\"List \\\" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str =\",\n \"program_name = os.path.basename(sys.argv[0]) program_license = \\\"Copyright 2015 user_name (California Institute of Technology) \\\\\",\n \"message here... data = self.readData(header) # Process and send the packet of the\",\n \"LOCK.release() self.registered = True self.name = name self.id = process_id print(\\\"Registered client \\\"\",\n \"ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \\\"\\\"\\\" UDP Socket server. \\\"\\\"\\\" class DestObj: \\\"\\\"\\\" Destination object for\",\n \"b\\\"FSW\\\" in name: if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name = params[1]\",\n \"%s, port %s\\\" % (HOST, PORT)) # Start a thread with the server\",\n \"server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0)\",\n \"data = tlm_packet_size + packet[4 : 4 + size] return data def processNewPkt(self,\",\n \"= struct.pack(\\\"i%ds\\\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process data\",\n \"\\\"List \\\" \\\"\\\"\\\" # Loop while the connected client has packets to send/receive\",\n \"part must always be an \\\"A5A5 \\\" or a \\\"List \\\" \\\"\\\"\\\" #\",\n \"= self.recv(4) return header + header2 else: return def readData(self, header): \\\"\\\"\\\" Read\",\n \"header + header2 else: return def readData(self, header): \\\"\\\"\\\" Read the data part\",\n \"Read telemetry data here... tlm_packet_size = packet[:4] size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data =\",\n \"report and shutdown server. \\\"\\\"\\\" dest_list = [] # Process data here... head,\",\n \"self.id = 0 # print self.client_address, now() # show this client's address #\",\n \"loop if not header: return # Got the header data so read the\",\n \"addr %s, port %s\\\" % (HOST, PORT)) # Start a thread with the\",\n \"threading.Event() FSW_clients = [] GUI_clients = [] FSW_ids = [] GUI_ids = []\",\n \"waiting for message.\\\") self.getNewMsg() else: print(\\\"Unable to register client.\\\") return LOCK.acquire() del SERVER.dest_obj[self.name]\",\n \"err.errno == errno.ECONNRESET: print( \\\"Socket error \\\" + str(err.errno) + \\\" (Connection reset\",\n \"a packet is received. This function listens for data on the socket. Packets\",\n \"def getCmds(self, inputString, end_of_command=b\\\"\\\\n\\\"): \\\"\\\"\\\" Build a command from partial or full socket\",\n \"address reuse and server to restart immediately server.allow_reuse_address = True SERVER = server\",\n \"self.recv(4) size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else: raise RuntimeError(\\\"unrecognized\",\n \"if header == b\\\"List\\\": print(\\\"List of registered clients: \\\") LOCK.acquire() for d in\",\n \"socket server port [default: %default]\\\", default=50007, ) parser.add_option( \\\"-i\\\", \\\"--host\\\", dest=\\\"host\\\", action=\\\"store\\\", type=\\\"string\\\",\",\n \"readData(self, header, packet): \\\"\\\"\\\" Read the data part of the message sent to\",\n \"= self.recv(4) size = struct.unpack(\\\">I\\\", sizeb)[0] data = desc + sizeb + self.recv(size)\",\n \"HOST = opts.host PORT = opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server =\",\n \"threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \\\"\\\"\\\" UDP Socket server. \\\"\\\"\\\" class DestObj: \\\"\\\"\\\" Destination\",\n \"import socket import threading try: import socketserver except ImportError: import SocketServer as socketserver\",\n \"header == b\\\"List\\\": print(\\\"List of registered clients: \\\") LOCK.acquire() for d in list(SERVER.dest_obj.keys()):\",\n \"commands = inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial + commands[0] self.partial = b\\\"\\\"\",\n \") # process options (opts, args) = parser.parse_args(argv) HOST = opts.host PORT =\",\n \"on queue. If something goes wrong report and shutdown server. \\\"\\\"\\\" dest_list =\",\n \"all registered clients. \\\"\\\"\\\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def handle(self): #\",\n \"original Stable demo during R&TD and adapted for use in new FSW gse.py\",\n \"self.partial = b\\\"\\\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self):\",\n \"all clients registered. \\\"\\\"\\\" def __init__(self, name, request): \\\"\\\"\\\" Constructor \\\"\\\"\\\" self.name =\",\n \"the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = packet[:4] header2 = packet[4:9] packet = packet[9:]\",\n \"commands of various lengths. \\\"\\\"\\\" data = b\\\"\\\" if header == b\\\"List\\\": return\",\n \"b\\\"List\\\\n\\\": return b\\\"List\\\" elif header == b\\\"Quit\\\\n\\\": return b\\\"Quit\\\" elif header[:-1] == b\\\"A5A5\\\":\",\n \"and shutdown server. \\\"\\\"\\\" dest_list = [] # Process data here... head, dst\",\n \"except socket.error as err: print(\\\"Socket error \\\" + str(err.errno) + \\\" occurred on\",\n \"as \\\"A5A5 <name> <data>\\\" Note only <data> is sent. Any client that sends\",\n \"U.S. Government Sponsorship acknowledged.\\\" program_version = \\\"v0.1\\\" program_build_date = \\\"%s\\\" % __updated__ program_version_string\",\n \"objects containing queues and socket id's for writting to destinations. \\\"\\\"\\\" dest_obj =\",\n \"receives commands of various lengths. \\\"\\\"\\\" data = \\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\"\",\n \"socketserver.UDPServer): \\\"\\\"\\\" UDP Socket server. \\\"\\\"\\\" class DestObj: \\\"\\\"\\\" Destination object for all\",\n \"+ b\\\"_\\\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered =\",\n \"(e.g. \\\"A5A5 GUI \\\" or \\\"A5A5 FSW \\\"), or just read the \\\"List\\\\n\\\"\",\n \"self.processNewPkt(header, data) def readHeader(self, packet): \\\"\\\"\\\" Read the 9 byte header (e.g. \\\"A5A5\",\n \"or List (header, packet) = self.readHeader(packet) # If the received header is an\",\n \"udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e: indent = len(program_name) * \\\" \\\"\",\n \"if self.registered: print(\\\"Registration complete waiting for message.\\\") self.getNewMsg() else: print(\\\"Unable to register client.\\\")\",\n \"def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \\\"\\\"\\\" Derived from original Stable demo during\",\n \"1 name = params[1] + b\\\"_\\\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\\\"GUI\\\" in\",\n \"GUI_ids.remove(self.id) LOCK.release() print(\\\"Closed %s connection.\\\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self,\",\n \"header = self.readHeader() # If the received header is an empty string, connection\",\n \"class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \\\"\\\"\\\" UDP Socket server. \\\"\\\"\\\" class DestObj: \\\"\\\"\\\" Destination object\",\n \"packet, call processPkt. \\\"\\\"\\\" self.partial = b\\\"\\\" self.cmdQueue = [] self.registered = False\",\n \"# print self.client_address, now() # show this client's address # Read the data\",\n \"bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\\\"GUI\\\" in name: if GUI_clients: process_id = sorted(GUI_ids)[-1] +\",\n \"full socket input \\\"\\\"\\\" commands = inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial +\",\n \"shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \\\"\\\"\\\" Derived from original Stable demo\",\n \"( \\\"\\\"\\\"\\\"\\\"\\\" # optional - give further explanation about what the program does\",\n \"== b\\\"\\\": print(\\\"read data from socket is empty!\\\") return b\\\"\\\" msg = msg\",\n \"= self.recv(13) # Connection was closed by the client if not data: print(\\\"Client\",\n \"empty, client \\\" + self.name.decode(DATA_ENCODING) + \\\" exiting.\\\" ) return header if header\",\n \"len(program_name) * \\\" \\\" sys.stderr.write(program_name + \\\": \\\" + repr(e) + \\\"\\\\n\\\") sys.stderr.write(indent\",\n \"sys.stderr.write(program_name + \\\": \\\" + repr(e) + \\\"\\\\n\\\") sys.stderr.write(indent + \\\" for help\",\n \"lengths. \\\"\\\"\\\" data = \\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") # Read telemetry\",\n \"each client connect \\\"\\\"\\\" The function that is invoked upon a new client.\",\n \"telemetry. FSW receives commands of various lengths. \\\"\\\"\\\" data = b\\\"\\\" if header\",\n \"return 0 # Process data here... head, dst = header.strip(b\\\" \\\").split(b\\\" \\\") if\",\n \"Constructor \\\"\\\"\\\" self.name = name self.socket = request self.packet = b\\\"\\\" def put(self,\",\n \"this will allow address reuse and server to restart immediately server.allow_reuse_address = True\",\n \"\\\"--port\\\", dest=\\\"port\\\", action=\\\"store\\\", type=\\\"int\\\", help=\\\"Set threaded tcp socket server port [default: %default]\\\", default=50007,\",\n \"readHeader(self, packet): \\\"\\\"\\\" Read the 9 byte header (e.g. \\\"A5A5 GUI \\\" or\",\n \"packet = packet[9:] return (header + header2, packet) def readData(self, header, packet): \\\"\\\"\\\"\",\n \"else: # Process the data into the cmdQueue self.getCmds(data) # Process the cmdQueue\",\n \"OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \\\"-p\\\", \\\"--port\\\", dest=\\\"port\\\", action=\\\"store\\\", type=\\\"int\\\", help=\\\"Set threaded\",\n \"while not shutdown_event.is_set(): # Read the header data from the socket either A5A5\",\n \"tlm_packet_size)[0] data = tlm_packet_size + packet[4 : 4 + size] return data def\",\n \"more thread for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler)\",\n \"class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \\\"\\\"\\\" Derived from original Stable demo during R&TD and adapted for\",\n \"== b\\\"A5A5\\\": header2 = self.recv(4) return header + header2 else: return def readData(self,\",\n \"if not header: return # Got the header data so read the data\",\n \"= b\\\"\\\" n = 0 while l > n: try: chunk = self.request.recv(l\",\n \"%s (%s)\\\" % (program_version, program_build_date) program_longdesc = ( \\\"\\\"\\\"\\\"\\\"\\\" # optional - give\",\n \"= self.readHeader() # If the received header is an empty string, connection closed,\",\n \"chunk = self.request.recv(l - n) if chunk == b\\\"\\\": print(\\\"read data from socket\",\n \"header[:-1] == b\\\"A5A5\\\": header2 = self.recv(4) return header + header2 else: return def\",\n \"self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd)\",\n \"containing queues and socket id's for writting to destinations. \\\"\\\"\\\" dest_obj = dict()\",\n \"= self.readHeader(packet) # If the received header is an empty string, connection closed,\",\n \"FSW. GUI receives telemetry. FSW receives commands of various lengths. \\\"\\\"\\\" data =\",\n \"parser.parse_args(argv) HOST = opts.host PORT = opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server\",\n \"indent = len(program_name) * \\\" \\\" sys.stderr.write(program_name + \\\": \\\" + repr(e) +\",\n \"destination <name> is done as \\\"A5A5 <name> <data>\\\" Note only <data> is sent.\",\n \"each packet, call processPkt. \\\"\\\"\\\" self.partial = b\\\"\\\" self.cmdQueue = [] self.registered =\",\n \"= params[1] if b\\\"FSW\\\" in name: if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1\",\n \"on recv().\\\" ) else: print(\\\"Socket error \\\" + str(err.errno) + \\\" occurred on\",\n \"b\\\"GUI\\\": # Read telemetry data here... tlm_packet_size = self.recv(4) size = struct.unpack(\\\">I\\\", tlm_packet_size)[0]\",\n \"\\\" or \\\"A5A5 FSW \\\"), or just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header\",\n \"d in list(SERVER.dest_obj.keys()): print(\\\"\\\\t\\\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\\\"List \\\" + SERVER.dest_obj[d].name l\",\n \"packet[4 : 4 + size] return data def processNewPkt(self, header, data): \\\"\\\"\\\" Process\",\n \"> n: try: chunk = self.request.recv(l - n) if chunk == b\\\"\\\": print(\\\"read\",\n \"the command messages ################################################# def getNewMsg(self, packet): \\\"\\\"\\\" After registration wait for an\",\n \"returning.\\\") if b\\\"GUI\\\" in dst: dest_list = GUI_clients elif b\\\"FSW\\\" in dst: dest_list\",\n \"= [] self.registered = False self.name = b\\\"\\\" self.id = 0 # print\",\n \"== b\\\"List\\\\n\\\": return b\\\"List\\\" elif header == b\\\"Quit\\\\n\\\": return b\\\"Quit\\\" elif header[:-1] ==\",\n \"2015 user_name (California Institute of Technology) \\\\ ALL RIGHTS RESERVED. U.S. Government Sponsorship\",\n \"= ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will\",\n \"process_id print(\\\"Registered client \\\" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process the\",\n \"from optparse import OptionParser __version__ = 0.1 __date__ = \\\"2015-04-03\\\" __updated__ = \\\"2016-04-07\\\"\",\n \"either A5A5 or List (header, packet) = self.readHeader(packet) # If the received header\",\n \"(head,dst) if data == b\\\"\\\": print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\" in\",\n \"\\\"List\\\\n\\\" command. \\\"\\\"\\\" header = packet[:4] header2 = packet[4:9] packet = packet[9:] return\",\n \"name = params[1] + b\\\"_\\\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request)\",\n \"\\\"\\\\n\\\") sys.stderr.write(indent + \\\" for help use --help\\\\n\\\") return 2 if __name__ ==\",\n \"Derived from original Stable demo during R&TD and adapted for use in new\",\n \"\\\"Locking TCP\\\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... #\",\n \"just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = self.recv(5) if len(header) == 0:\",\n \"\\\"\\\"\\\" UDP Socket server. \\\"\\\"\\\" class DestObj: \\\"\\\"\\\" Destination object for all clients\",\n \"dest_list: # print \\\"Locking TCP\\\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the\",\n \"l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process data here... head, dst\",\n \"packet[4:9] packet = packet[9:] return (header + header2, packet) def readData(self, header, packet):\",\n \"# print \\\"Sending UDP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Telemetry\",\n \"queues and socket id's for writting to destinations. \\\"\\\"\\\" dest_obj = dict() lock_obj\",\n \"id's for writting to destinations. \\\"\\\"\\\" dest_obj = dict() lock_obj = threading.Lock() class\",\n \"if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name = params[1] + b\\\"_\\\" +\",\n \"break elif header == b\\\"Quit\\\": LOCK.acquire() print(\\\"Quit received!\\\") SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\\\"Quit\",\n \"recv().\\\") return msg def readHeader(self): \\\"\\\"\\\" Read the 9 byte header (e.g. \\\"A5A5\",\n \"%s...\\\\n\\\" % (head,dst) if data == b\\\"\\\": print(\\\" Data is empty, returning.\\\") if\",\n \"socketserver except ImportError: import SocketServer as socketserver import time import os import signal\",\n \"print \\\"Sending TCP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Packet missing\",\n \"data. Later this will handle other things such as sequence files and parameters.\",\n \"string, connection closed, exit loop if not header: break elif header == b\\\"Quit\\\":\",\n \"FSW_clients.append(name) FSW_ids.append(process_id) elif b\\\"GUI\\\" in name: if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1\",\n \"Process data here... head, dst = header.strip(b\\\" \\\").split(b\\\" \\\") if head == b\\\"A5A5\\\":\",\n \"dest_list = [] # Process data here... head, dst = header.strip(b\\\" \\\").split(b\\\" \\\")\",\n \"try: # print \\\"about to send data to \\\" + self.name self.socket.send(msg) except\",\n \"tlm_packet_size + self.recv(size) else: raise RuntimeError(\\\"unrecognized client %s\\\" % dst.decode(DATA_ENCODING)) return data def\",\n \"= opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) #\",\n \"== b\\\"GUI\\\": # Read telemetry data here... tlm_packet_size = self.recv(4) size = struct.unpack(\\\">I\\\",\",\n \"elif header == b\\\"Quit\\\\n\\\": return b\\\"Quit\\\" elif header[:-1] == b\\\"A5A5\\\": header2 = self.recv(4)\",\n \"packet[:4] size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + packet[4 : 4 +\",\n \"here.... # print \\\"Sending TCP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise\",\n \"from the socket data = self.recv(13) # Connection was closed by the client\",\n \"complete waiting for message.\\\") self.getNewMsg() else: print(\\\"Unable to register client.\\\") return LOCK.acquire() del\",\n \"exiting.\\\" ) return header if header == b\\\"List\\\\n\\\": return b\\\"List\\\" elif header ==\",\n \"UDP Socket server. \\\"\\\"\\\" class DestObj: \\\"\\\"\\\" Destination object for all clients registered.\",\n \"+ 1 name = params[1] + b\\\"_\\\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\\\"GUI\\\"\",\n \"each client. Registration is done by sending the string \\\"Register <name>\\\". Sending a\",\n \"import errno from fprime.constants import DATA_ENCODING from optparse import OptionParser __version__ = 0.1\",\n \"if shutdown_event.is_set(): print(\\\"socket timed out and shutdown is requested\\\") return b\\\"Quit\\\\n\\\" continue except\",\n \"\\\") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\\\"\\\\t\\\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\\\"List \\\"\",\n \"struct.pack(\\\"i%ds\\\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process data here...\",\n \"socket is empty!\\\") return b\\\"\\\" msg = msg + chunk n = len(msg)\",\n \"self.readData(header, packet) # Process and send the packet of the message here... self.processNewPkt(header,\",\n \"client's address # Read the data from the socket data = self.recv(13) #\",\n \"<name> <data>\\\" Note only <data> is sent. Any client that sends a \\\"List\\\"\",\n \"global SERVER = None LOCK = None shutdown_event = threading.Event() FSW_clients = []\",\n \"sending the string \\\"Register <name>\\\". Sending a message to destination <name> is done\",\n \"return else: # Process the data into the cmdQueue self.getCmds(data) # Process the\",\n \"1 def handle(self): # on each client connect \\\"\\\"\\\" The function that is\",\n \"header2, packet) def readData(self, header, packet): \\\"\\\"\\\" Read the data part of the\",\n \"= 1 def handle(self): # on each client connect \\\"\\\"\\\" The function that\",\n \"message The first part must always be an \\\"A5A5 \\\" or a \\\"List\",\n \"the header data so read the data of the message here... data =\",\n \"SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the header data so read the data\",\n \"[] FSW_ids = [] GUI_ids = [] def signal_handler(*_): print(\\\"Ctrl-C received, server shutting\",\n \"optparse import OptionParser __version__ = 0.1 __date__ = \\\"2015-04-03\\\" __updated__ = \\\"2016-04-07\\\" #\",\n \"raise RuntimeError(\\\"unrecognized client %s\\\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data): \\\"\\\"\\\"\",\n \"the header data from the socket either A5A5 or List header = self.readHeader()\",\n \"#!/usr/bin/env python3 from __future__ import print_function import socket import threading try: import socketserver\",\n \"\\\"\\\"\\\" Process a single command here header and data here. The command must\",\n \"always start with A5A5 except if it is a List. Once the entire\",\n \"shutdown_event = threading.Event() FSW_clients = [] GUI_clients = [] FSW_ids = [] GUI_ids\",\n \"= DestObj(name, self.request) LOCK.release() self.registered = True self.name = name self.id = process_id\",\n \"struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + self.recv(size) else: raise RuntimeError(\\\"unrecognized client %s\\\" %\",\n \"\\\" \\\"\\\"\\\" # Loop while the connected client has packets to send/receive while\",\n \"GUI_clients elif b\\\"FSW\\\" in dst: dest_list = FSW_clients for dest_elem in dest_list: #\",\n \"A5A5 header\\\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \\\"\\\"\\\" TCP Socket server. Keep a dictionary of\",\n \"read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = packet[:4] header2 = packet[4:9] packet =\",\n \"dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data): \\\"\\\"\\\" Process a single command here\",\n \"== b\\\"List\\\": print(\\\"List of registered clients: \\\") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\\\"\\\\t\\\"\",\n \"each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False\",\n \"what the program does ) if argv is None: argv = sys.argv[1:] try:\",\n \"client connect \\\"\\\"\\\" The function that is invoked upon a new client. This\",\n \"b\\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") if dst == b\\\"FSW\\\": # Read variable\",\n \"self.name = b\\\"\\\" self.id = 0 # print self.client_address, now() # show this\",\n \"\\\"\\\"\\\" header = self.recv(5) if len(header) == 0: print( \\\"Header information is empty,\",\n \"commands[0] = self.partial + commands[0] self.partial = b\\\"\\\" if len(commands[-1]): self.partial = commands[-1]\",\n \"import print_function import socket import threading try: import socketserver except ImportError: import SocketServer\",\n \"else: print(\\\"Unable to register client.\\\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients:\",\n \"%s\\\" % (HOST, PORT)) # Start a thread with the server -- that\",\n \"in dst: dest_list = FSW_clients for dest_elem in dest_list: # print \\\"Locking TCP\\\"\",\n \"== errno.ECONNRESET: print( \\\"Socket error \\\" + str(err.errno) + \\\" (Connection reset by\",\n \"in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\\\"Closed %s connection.\\\" % self.name.decode(DATA_ENCODING)) self.registered = False\",\n \"the message to the destination socket \\\"\\\"\\\" try: # print \\\"about to send\",\n \"socket \\\"\\\"\\\" try: # print \\\"about to send data to \\\" + self.name\",\n \"must always start with A5A5 except if it is a List. Once the\",\n \"to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Packet missing A5A5 header\\\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler):\",\n \"None shutdown_event = threading.Event() FSW_clients = [] GUI_clients = [] FSW_ids = []\",\n \"# New Routines to process the command messages ################################################# def getNewMsg(self, packet): \\\"\\\"\\\"\",\n \"time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \\\"\\\"\\\" Derived from original Stable demo during R&TD and adapted\",\n \"Universal server id global SERVER = None LOCK = None shutdown_event = threading.Event()\",\n \"n = len(msg) except socket.timeout: if shutdown_event.is_set(): print(\\\"socket timed out and shutdown is\",\n \"for each client. Registration is done by sending the string \\\"Register <name>\\\". Sending\",\n \"l): \\\"\\\"\\\" Read l bytes from socket. \\\"\\\"\\\" chunk = b\\\"\\\" msg =\",\n \"opts.host PORT = opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT),\",\n \"Header # print \\\"Received Packet: %s %s...\\\\n\\\" % (head,dst) if data == \\\"\\\":\",\n \"LOCK.acquire() name = params[1] if b\\\"FSW\\\" in name: if FSW_clients: process_id = sorted(FSW_ids)[-1]\",\n \"Read l bytes from socket. \\\"\\\"\\\" chunk = b\\\"\\\" msg = b\\\"\\\" n\",\n \"if FSW_clients: process_id = sorted(FSW_ids)[-1] + 1 name = params[1] + b\\\"_\\\" +\",\n \"self.registered: print(\\\"Registration complete waiting for message.\\\") self.getNewMsg() else: print(\\\"Unable to register client.\\\") return\",\n \"self.processQueue() if self.registered: print(\\\"Registration complete waiting for message.\\\") self.getNewMsg() else: print(\\\"Unable to register\",\n \"<data> is sent. Any client that sends a \\\"List\\\" comment makes the server\",\n \"After registration wait for an incoming message The first part must always be\",\n \"\\\"\\\"\\\" Read the 9 byte header (e.g. \\\"A5A5 GUI \\\" or \\\"A5A5 FSW\",\n \"params = cmd.split() process_id = 0 if params[0] == b\\\"Register\\\": LOCK.acquire() name =\",\n \"length command data here... desc = self.recv(4) sizeb = self.recv(4) size = struct.unpack(\\\">I\\\",\",\n \"\\\" (Connection reset by peer) occurred on recv().\\\" ) else: print(\\\"Socket error \\\"\",\n \"header.split(b\\\" \\\")[1].strip(b\\\" \\\") if dst == b\\\"FSW\\\": # Read variable length command data\",\n \"a List. Once the entire header string is processed send it on queue.\",\n \"request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start()\",\n \"RESERVED. U.S. Government Sponsorship acknowledged.\\\" program_version = \\\"v0.1\\\" program_build_date = \\\"%s\\\" % __updated__\",\n \"header data from the socket either A5A5 or List header = self.readHeader() #\",\n \"loop if not header: break elif header == b\\\"Quit\\\": LOCK.acquire() print(\\\"Quit received!\\\") SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\",\",\n \"b\\\"Quit\\\\n\\\": return b\\\"Quit\\\" elif header[:-1] == b\\\"A5A5\\\": header2 = self.recv(4) return header +\",\n \"len(msg) except socket.timeout: if shutdown_event.is_set(): print(\\\"socket timed out and shutdown is requested\\\") return\",\n \"elif dst == b\\\"GUI\\\": # Read telemetry data here... tlm_packet_size = self.recv(4) size\",\n \"the program does ) if argv is None: argv = sys.argv[1:] try: parser\",\n \"for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon =\",\n \"socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on each packet \\\"\\\"\\\" The function that\",\n \"will then start one # more thread for each request server_thread = threading.Thread(target=server.serve_forever)\",\n \"= self.readData(header) # Process and send the packet of the message here... self.processNewPkt(header,\",\n \"= packet[4:9] packet = packet[9:] return (header + header2, packet) def readData(self, header,\",\n \"\\\"\\\"\\\" data = b\\\"\\\" if header == b\\\"List\\\": return b\\\"\\\" elif header ==\",\n \"\\\"Received Packet: %s %s...\\\\n\\\" % (head,dst) if data == \\\"\\\": print(\\\" Data is\",\n \"self.name.decode(DATA_ENCODING) + \\\" exiting.\\\" ) return header if header == b\\\"List\\\\n\\\": return b\\\"List\\\"\",\n \"received. This function listens for data on the socket. Packets for now are\",\n \"be an \\\"A5A5 \\\" or a \\\"List \\\" \\\"\\\"\\\" # Loop while the\",\n \"print \\\"Locking TCP\\\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here....\",\n \"% l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process data here... head,\",\n \"= self.recv(4) sizeb = self.recv(4) size = struct.unpack(\\\">I\\\", sizeb)[0] data = desc +\",\n \"a \\\"List \\\" \\\"\\\"\\\" # Read the header data from the socket either\",\n \"udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\\\"shutdown from main thread\\\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown()\",\n \"\\\"\\\"\\\" The function that is invoked when a packet is received. This function\",\n \"signal_handler(*_): print(\\\"Ctrl-C received, server shutting down.\\\") shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler):\",\n \"now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \\\"\\\"\\\" Derived from original Stable demo during R&TD\",\n \"socketserver.TCPServer): \\\"\\\"\\\" TCP Socket server. Keep a dictionary of destination objects containing queues\",\n \"[] def processRegistration(self, cmd): params = cmd.split() process_id = 0 if params[0] ==\",\n \"out and shutdown is requested\\\") return b\\\"Quit\\\\n\\\" continue except socket.error as err: if\",\n \"self.socket.send(msg) except socket.error as err: print(\\\"Socket error \\\" + str(err.errno) + \\\" occurred\",\n \"if not data: print(\\\"Client exited.\\\") return else: # Process the data into the\",\n \"+ \\\" exiting.\\\" ) return header if header == b\\\"List\\\\n\\\": return b\\\"List\\\" elif\",\n \"sorted(GUI_ids)[-1] + 1 name = params[1] + b\\\"_\\\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name]\",\n \"Read the header data from the socket either A5A5 or List header =\",\n \"register client.\\\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif\",\n \"sys.argv[1:] try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \\\"-p\\\", \\\"--port\\\", dest=\\\"port\\\",\",\n \"does ) if argv is None: argv = sys.argv[1:] try: parser = OptionParser(\",\n \"TCP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Packet missing A5A5 header\\\")\",\n \"Socket server. \\\"\\\"\\\" class DestObj: \\\"\\\"\\\" Destination object for all clients registered. \\\"\\\"\\\"\",\n \"if params[0] == b\\\"Register\\\": LOCK.acquire() name = params[1] if b\\\"FSW\\\" in name: if\",\n \"partial or full socket input \\\"\\\"\\\" commands = inputString.split(end_of_command) if len(self.partial): commands[0] =\",\n \"\\\"A5A5 FSW \\\"), or just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = self.recv(5)\",\n \"[default: %default]\\\", default=50007, ) parser.add_option( \\\"-i\\\", \\\"--host\\\", dest=\\\"host\\\", action=\\\"store\\\", type=\\\"string\\\", help=\\\"Set threaded tcp\",\n \"\\\"\\\"\\\" # Loop while the connected client has packets to send/receive while not\",\n \"+ str(err.errno) + \\\" occurred on send().\\\") def fileno(self): \\\"\\\"\\\" \\\"\\\"\\\" return self.socket\",\n \"dst: dest_list = GUI_clients else: print(\\\"dest? %s\\\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list:\",\n \"FSW_ids = [] GUI_ids = [] def signal_handler(*_): print(\\\"Ctrl-C received, server shutting down.\\\")\",\n \"<name> is done as \\\"A5A5 <name> <data>\\\" Note only <data> is sent. Any\",\n \"= sorted(FSW_ids)[-1] + 1 name = params[1] + b\\\"_\\\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id)\",\n \"\\\"\\\"\\\" data = \\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") # Read telemetry data\",\n \"# Universal server id global SERVER = None LOCK = None shutdown_event =\",\n \"read the data of the message here... data = self.readData(header) # Process and\",\n \"time.sleep(1) except Exception as e: indent = len(program_name) * \\\" \\\" sys.stderr.write(program_name +\",\n \"+ self.recv(size) elif dst == b\\\"GUI\\\": # Read telemetry data here... tlm_packet_size =\",\n \"of the message here... data = self.readData(header) # Process and send the packet\",\n \"is instanced in own thread for each client. Registration is done by sending\",\n \"\\\")[1].strip(b\\\" \\\") # Read telemetry data here... tlm_packet_size = packet[:4] size = struct.unpack(\\\">I\\\",\",\n \"(Connection reset by peer) occurred on recv().\\\" ) else: print(\\\"Socket error \\\" +\",\n \"server -- that thread will then start one # more thread for each\",\n \"here... data = self.readData(header) # Process and send the packet of the message\",\n \"= threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while\",\n \"help=\\\"Set threaded tcp socket server port [default: %default]\\\", default=50007, ) parser.add_option( \\\"-i\\\", \\\"--host\\\",\",\n \"of the message here... self.processNewPkt(header, data) def recv(self, l): \\\"\\\"\\\" Read l bytes\",\n \"print \\\"Received Packet: %s %s...\\\\n\\\" % (head,dst) if data == \\\"\\\": print(\\\" Data\",\n \"= \\\"Copyright 2015 user_name (California Institute of Technology) \\\\ ALL RIGHTS RESERVED. U.S.\",\n \"inputString, end_of_command=b\\\"\\\\n\\\"): \\\"\\\"\\\" Build a command from partial or full socket input \\\"\\\"\\\"\",\n \"params[1] + b\\\"_\\\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\\\"GUI\\\" in name: if GUI_clients:\",\n \"FSW \\\"), or just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = packet[:4] header2\",\n \"# Send the message here.... # print \\\"Sending UDP msg to \\\", dest_elem\",\n \"assumed to be separated by a newline. For each packet, call processPkt. \\\"\\\"\\\"\",\n \"the client if not data: print(\\\"Client exited.\\\") return else: # Process the data\",\n \"== b\\\"FSW\\\": # Read variable length command data here... desc = self.recv(4) sizeb\",\n \"\\\").split(b\\\" \\\") if head == b\\\"A5A5\\\": # Packet Header # print \\\"Received Packet:\",\n \"that is invoked upon a new client. This function listens for data on\",\n \"and telemetry data. Later this will handle other things such as sequence files\",\n \"= name self.socket = request self.packet = b\\\"\\\" def put(self, msg): \\\"\\\"\\\" Write\",\n \"= \\\"v0.1\\\" program_build_date = \\\"%s\\\" % __updated__ program_version_string = \\\"%%prog %s (%s)\\\" %\",\n \"print(\\\"socket timed out and shutdown is requested\\\") return b\\\"Quit\\\\n\\\" continue except socket.error as\",\n \"the entire header string is processed send it on queue. If something goes\",\n \"msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Packet missing A5A5 header\\\") class\",\n \"(California Institute of Technology) \\\\ ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\\\" program_version\",\n \"[] def signal_handler(*_): print(\\\"Ctrl-C received, server shutting down.\\\") shutdown_event.set() def now(): return time.ctime(time.time())\",\n \"byte header (e.g. \\\"A5A5 GUI \\\" or \\\"A5A5 FSW \\\"), or just read\",\n \"socket data = self.recv(13) # Connection was closed by the client if not\",\n \"readHeader(self): \\\"\\\"\\\" Read the 9 byte header (e.g. \\\"A5A5 GUI \\\" or \\\"A5A5\",\n \"return def readData(self, header): \\\"\\\"\\\" Read the data part of the message sent\",\n \"the message here.... # print \\\"Sending TCP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release()\",\n \"print(\\\"Closed %s connection.\\\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self, inputString, end_of_command=b\\\"\\\\n\\\"):\",\n \"% (program_version, program_build_date) program_longdesc = ( \\\"\\\"\\\"\\\"\\\"\\\" # optional - give further explanation\",\n \"if it is a List. Once the entire header string is processed send\",\n \"\\\"Sending TCP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Packet missing A5A5\",\n \"self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = []\",\n \"except if it is a List. Once the entire header tstring is processed\",\n \"\\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Telemetry missing A5A5 header\\\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer):\",\n \"def readHeader(self, packet): \\\"\\\"\\\" Read the 9 byte header (e.g. \\\"A5A5 GUI \\\"\",\n \"client \\\" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process the command messages\",\n \"List. Once the entire header string is processed send it on queue. If\",\n \"DestObj(name, self.request) LOCK.release() self.registered = True self.name = name self.id = process_id print(\\\"Registered\",\n \"= 0.1 __date__ = \\\"2015-04-03\\\" __updated__ = \\\"2016-04-07\\\" # Universal server id global\",\n \"command from partial or full socket input \\\"\\\"\\\" commands = inputString.split(end_of_command) if len(self.partial):\",\n \"msg + chunk n = len(msg) except socket.timeout: if shutdown_event.is_set(): print(\\\"socket timed out\",\n \"or a \\\"List \\\" \\\"\\\"\\\" # Loop while the connected client has packets\",\n \"self.name = name self.socket = request self.packet = b\\\"\\\" def put(self, msg): \\\"\\\"\\\"\",\n \"function that is invoked when a packet is received. This function listens for\",\n \"the message here... data = self.readData(header, packet) # Process and send the packet\",\n \"Packet Header # print \\\"Received Packet: %s %s...\\\\n\\\" % (head,dst) if data ==\",\n \"% __updated__ program_version_string = \\\"%%prog %s (%s)\\\" % (program_version, program_build_date) program_longdesc = (\",\n \"If the received header is an empty string, connection closed, exit loop if\",\n \"data here... desc = self.recv(4) sizeb = self.recv(4) size = struct.unpack(\\\">I\\\", sizeb)[0] data\",\n \"must always be an \\\"A5A5 \\\" or a \\\"List \\\" \\\"\\\"\\\" # Read\",\n \"for dest_elem in dest_list: # print \\\"Locking TCP\\\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()):\",\n \"if head == b\\\"A5A5\\\": # Packet Header # print \\\"Received Packet: %s %s...\\\\n\\\"\",\n \"then start one # more thread for each request server_thread = threading.Thread(target=server.serve_forever) udp_server_thread\",\n \"# on each packet \\\"\\\"\\\" The function that is invoked when a packet\",\n \"= ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will allow address reuse and server\",\n \"shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\\\"shutdown from main thread\\\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except\",\n \"head, dst = header.strip(b\\\" \\\").split(b\\\" \\\") if head == b\\\"A5A5\\\": # Packet Header\",\n \"and shutdown server. \\\"\\\"\\\" dest_list = [] if header == b\\\"List\\\": print(\\\"List of\",\n \"+ str(err.errno) + \\\" (Connection reset by peer) occurred on recv().\\\" ) else:\",\n \"shutdown is requested\\\") return b\\\"Quit\\\\n\\\" continue except socket.error as err: if err.errno ==\",\n \"= threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon =\",\n \"dst == b\\\"FSW\\\": # Read variable length command data here... desc = self.recv(4)\",\n \"message here.... # print \\\"Sending TCP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else:\",\n \"by sending the string \\\"Register <name>\\\". Sending a message to destination <name> is\",\n \"\\\" + self.name self.socket.send(msg) except socket.error as err: print(\\\"Socket error \\\" + str(err.errno)\",\n \"or List header = self.readHeader() # If the received header is an empty\",\n \"header, packet): \\\"\\\"\\\" Read the data part of the message sent to either\",\n \"sorted(FSW_ids)[-1] + 1 name = params[1] + b\\\"_\\\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif\",\n \"new FSW gse.py applicaiton. TCP socket server for commands, log events, and telemetry\",\n \"True socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on each client connect \\\"\\\"\\\" The\",\n \"ImportError: import SocketServer as socketserver import time import os import signal import sys\",\n \"= name self.id = process_id print(\\\"Registered client \\\" + self.name.decode(DATA_ENCODING)) ################################################# # New\",\n \"dst: dest_list = FSW_clients for dest_elem in dest_list: # print \\\"Locking TCP\\\" LOCK.acquire()\",\n \"LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\\\"\\\\t\\\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\\\"List \\\" +\",\n \"Read variable length command data here... desc = self.recv(4) sizeb = self.recv(4) size\",\n \"ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \\\"\\\"\\\" Derived from original Stable demo during R&TD and adapted for use\",\n \"b\\\"Quit\\\": return b\\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") if dst == b\\\"FSW\\\": #\",\n \"GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name = params[1] + b\\\"_\\\" + bytes(process_id)\",\n \"closed by the client if not data: print(\\\"Client exited.\\\") return else: # Process\",\n \"err: print(\\\"Socket error \\\" + str(err.errno) + \\\" occurred on send().\\\") def fileno(self):\",\n \"n) if chunk == b\\\"\\\": print(\\\"read data from socket is empty!\\\") return b\\\"\\\"\",\n \"= None shutdown_event = threading.Event() FSW_clients = [] GUI_clients = [] FSW_ids =\",\n \"signal import sys import struct import errno from fprime.constants import DATA_ENCODING from optparse\",\n \"print(\\\"Registered client \\\" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process the command\",\n \"\\\" sys.stderr.write(program_name + \\\": \\\" + repr(e) + \\\"\\\\n\\\") sys.stderr.write(indent + \\\" for\",\n \"main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license = \\\"Copyright 2015 user_name (California\",\n \"FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\\\"Closed %s connection.\\\"\",\n \"== 0: print( \\\"Header information is empty, client \\\" + self.name.decode(DATA_ENCODING) + \\\"\",\n \"dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print\",\n \"\\\"\\\"\\\" self.name = name self.socket = request self.packet = b\\\"\\\" def put(self, msg):\",\n \"A5A5 except if it is a List. Once the entire header tstring is\",\n \"def readData(self, header, packet): \\\"\\\"\\\" Read the data part of the message sent\",\n \"0 if params[0] == b\\\"Register\\\": LOCK.acquire() name = params[1] if b\\\"FSW\\\" in name:\",\n \"%s %s...\\\\n\\\" % (head,dst) if data == \\\"\\\": print(\\\" Data is empty, returning.\\\")\",\n \"= header.strip(b\\\" \\\").split(b\\\" \\\") if head == b\\\"A5A5\\\": # Packet Header # print\",\n \"registered clients. \\\"\\\"\\\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on each packet \\\"\\\"\\\"\",\n \"print self.client_address, now() # show this client's address # Read the data from\",\n \"socket.error as err: if err.errno == errno.ECONNRESET: print( \\\"Socket error \\\" + str(err.errno)\",\n \"DestObj: \\\"\\\"\\\" Destination object for all clients registered. \\\"\\\"\\\" def __init__(self, name, request):\",\n \"<data>\\\" Note only <data> is sent. Any client that sends a \\\"List\\\" comment\",\n \"socket.error as err: print(\\\"Socket error \\\" + str(err.errno) + \\\" occurred on send().\\\")\",\n \"return b\\\"Quit\\\" elif header[:-1] == b\\\"A5A5\\\": header2 = self.recv(4) return header + header2\",\n \"shutdown_event.set() time.sleep(1) print(\\\"Quit processed!\\\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the header data\",\n \"if b\\\"GUI\\\" in dst: dest_list = GUI_clients else: print(\\\"dest? %s\\\" % dst.decode(DATA_ENCODING)) for\",\n \"for data on the socket. Packets for now are assumed to be separated\",\n \"Send the message here.... # print \\\"Sending TCP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data)\",\n \"port [default: %default]\\\", default=50007, ) parser.add_option( \\\"-i\\\", \\\"--host\\\", dest=\\\"host\\\", action=\\\"store\\\", type=\\\"string\\\", help=\\\"Set threaded\",\n \"+ \\\": \\\" + repr(e) + \\\"\\\\n\\\") sys.stderr.write(indent + \\\" for help use\",\n \"as err: print(\\\"Socket error \\\" + str(err.errno) + \\\" occurred on send().\\\") def\",\n \"b\\\"\\\" n = 0 while l > n: try: chunk = self.request.recv(l -\",\n \"lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \\\"\\\"\\\" UDP Socket server. \\\"\\\"\\\" class DestObj:\",\n \"to process the command messages ################################################# def getNewMsg(self, packet): \\\"\\\"\\\" After registration wait\",\n \"SERVER.server_close() LOCK.release() break # Got the header data so read the data of\",\n \"def __init__(self, name, request): \\\"\\\"\\\" Constructor \\\"\\\"\\\" self.name = name self.socket = request\",\n \"SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e: indent = len(program_name) * \\\"\",\n \"parameters. Handle is instanced in own thread for each client. Registration is done\",\n \"Packet: %s %s...\\\\n\\\" % (head,dst) if data == b\\\"\\\": print(\\\" Data is empty,\",\n \"If something goes wrong report and shutdown server. \\\"\\\"\\\" dest_list = [] if\",\n \"SocketServer as socketserver import time import os import signal import sys import struct\",\n \"dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Telemetry missing A5A5 header\\\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \\\"\\\"\\\"\",\n \"= cmd.split() process_id = 0 if params[0] == b\\\"Register\\\": LOCK.acquire() name = params[1]\",\n \"raise RuntimeError(\\\"Telemetry missing A5A5 header\\\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \\\"\\\"\\\" TCP Socket server. Keep\",\n \"the packet of the message here... self.processNewPkt(header, data) def readHeader(self, packet): \\\"\\\"\\\" Read\",\n \"program does ) if argv is None: argv = sys.argv[1:] try: parser =\",\n \"+ bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\\\"GUI\\\" in name: if GUI_clients: process_id = sorted(GUI_ids)[-1]\",\n \"server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\\\"shutdown from main\",\n \"various lengths. \\\"\\\"\\\" data = \\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") # Read\",\n \"things such as sequence files and parameters. Handle is instanced in own thread\",\n \"def readHeader(self): \\\"\\\"\\\" Read the 9 byte header (e.g. \\\"A5A5 GUI \\\" or\",\n \"will handle other things such as sequence files and parameters. Handle is instanced\",\n \"process_id = sorted(FSW_ids)[-1] + 1 name = params[1] + b\\\"_\\\" + bytes(process_id) FSW_clients.append(name)\",\n \"that sends a \\\"List\\\" comment makes the server display all registered clients. \\\"\\\"\\\"\",\n \"clients registered. \\\"\\\"\\\" def __init__(self, name, request): \\\"\\\"\\\" Constructor \\\"\\\"\\\" self.name = name\",\n \"\\\"\\\"\\\" self.getNewMsg(self.request[0]) ################################################# # New Routines to process the command messages ################################################# def\",\n \"= True socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on each client connect \\\"\\\"\\\"\",\n \"message here.... # print \\\"Sending UDP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else:\",\n \"sys import struct import errno from fprime.constants import DATA_ENCODING from optparse import OptionParser\",\n \"recv(self, l): \\\"\\\"\\\" Read l bytes from socket. \\\"\\\"\\\" chunk = b\\\"\\\" msg\",\n \"SERVER = server LOCK = server.lock_obj ip, port = server.server_address print(\\\"TCP Socket Server\",\n \"Sponsorship acknowledged.\\\" program_version = \\\"v0.1\\\" program_build_date = \\\"%s\\\" % __updated__ program_version_string = \\\"%%prog\",\n \"+ 1 name = params[1] + b\\\"_\\\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] =\",\n \"options (opts, args) = parser.parse_args(argv) HOST = opts.host PORT = opts.port server =\",\n \"entire header tstring is processed send it on queue. If something goes wrong\",\n \"here.... # print \\\"Sending UDP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise\",\n \"del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name)\",\n \"sends a \\\"List\\\" comment makes the server display all registered clients. \\\"\\\"\\\" socketserver.BaseRequestHandler.allow_reuse_address\",\n \"data: print(\\\"Client exited.\\\") return else: # Process the data into the cmdQueue self.getCmds(data)\",\n \"\\\"\\\"\\\" class DestObj: \\\"\\\"\\\" Destination object for all clients registered. \\\"\\\"\\\" def __init__(self,\",\n \"if err.errno == errno.ECONNRESET: print( \\\"Socket error \\\" + str(err.errno) + \\\" (Connection\",\n \"dest=\\\"port\\\", action=\\\"store\\\", type=\\\"int\\\", help=\\\"Set threaded tcp socket server port [default: %default]\\\", default=50007, )\",\n \"that thread will then start one # more thread for each request server_thread\",\n \"out the message to the destination socket \\\"\\\"\\\" try: # print \\\"about to\",\n \"client has packets to send/receive while not shutdown_event.is_set(): # Read the header data\",\n \"For each packet, call processPkt. \\\"\\\"\\\" self.getNewMsg(self.request[0]) ################################################# # New Routines to process\",\n \"%default]\\\", default=50007, ) parser.add_option( \\\"-i\\\", \\\"--host\\\", dest=\\\"host\\\", action=\\\"store\\\", type=\\\"string\\\", help=\\\"Set threaded tcp socket\",\n \"while l > n: try: chunk = self.request.recv(l - n) if chunk ==\",\n \"= len(program_name) * \\\" \\\" sys.stderr.write(program_name + \\\": \\\" + repr(e) + \\\"\\\\n\\\")\",\n \"Start a thread with the server -- that thread will then start one\",\n \"For each packet, call processPkt. \\\"\\\"\\\" self.partial = b\\\"\\\" self.cmdQueue = [] self.registered\",\n \"\\\"\\\"\\\" TCP Socket server. Keep a dictionary of destination objects containing queues and\",\n \"\\\"%%prog %s (%s)\\\" % (program_version, program_build_date) program_longdesc = ( \\\"\\\"\\\"\\\"\\\"\\\" # optional -\",\n \"to send data to \\\" + self.name self.socket.send(msg) except socket.error as err: print(\\\"Socket\",\n \"GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered = True self.name = name\",\n \"put(self, msg): \\\"\\\"\\\" Write out the message to the destination socket \\\"\\\"\\\" try:\",\n \"about what the program does ) if argv is None: argv = sys.argv[1:]\",\n \"messages ################################################# def getNewMsg(self): \\\"\\\"\\\" After registration wait for an incoming message The\",\n \"the packet of the message here... self.processNewPkt(header, data) def recv(self, l): \\\"\\\"\\\" Read\",\n \"str(err.errno) + \\\" occurred on recv().\\\") return msg def readHeader(self): \\\"\\\"\\\" Read the\",\n \"wait for an incoming message The first part must always be an \\\"A5A5\",\n \"SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\\\"i%ds\\\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release()\",\n \"server_thread = threading.Thread(target=server.serve_forever) udp_server_thread = threading.Thread(target=udp_server.serve_forever) signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon\",\n \"The function that is invoked upon a new client. This function listens for\",\n \"cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd): params = cmd.split()\",\n \"msg): \\\"\\\"\\\" Write out the message to the destination socket \\\"\\\"\\\" try: #\",\n \"dest_list = FSW_clients for dest_elem in dest_list: # print \\\"Locking TCP\\\" LOCK.acquire() if\",\n \"self.cmdQueue = [] self.registered = False self.name = b\\\"\\\" self.id = 0 #\",\n \"Read the 9 byte header (e.g. \\\"A5A5 GUI \\\" or \\\"A5A5 FSW \\\"),\",\n \"to register client.\\\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id)\",\n \"receives telemetry. FSW receives commands of various lengths. \\\"\\\"\\\" data = \\\"\\\" dst\",\n \"\\\"\\\"\\\" Read the data part of the message sent to either GUI or\",\n \"missing A5A5 header\\\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \\\"\\\"\\\" TCP Socket server. Keep a dictionary\",\n \"sizeb)[0] data = desc + sizeb + self.recv(size) elif dst == b\\\"GUI\\\": #\",\n \"b\\\"GUI\\\" in dst: dest_list = GUI_clients elif b\\\"FSW\\\" in dst: dest_list = FSW_clients\",\n \"connected client has packets to send/receive while not shutdown_event.is_set(): # Read the header\",\n \"= self.recv(5) if len(header) == 0: print( \\\"Header information is empty, client \\\"\",\n \"missing A5A5 header\\\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \\\"\\\"\\\" Derived from original Stable demo during R&TD\",\n \"empty string, connection closed, exit loop if not header: break elif header ==\",\n \"struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + packet[4 : 4 + size] return data\",\n \"must always be an \\\"A5A5 \\\" or a \\\"List \\\" \\\"\\\"\\\" # Loop\",\n \"header (e.g. \\\"A5A5 GUI \\\" or \\\"A5A5 FSW \\\"), or just read the\",\n \"telemetry data here... tlm_packet_size = packet[:4] size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size\",\n \"process the command messages ################################################# def getNewMsg(self, packet): \\\"\\\"\\\" After registration wait for\",\n \"dst = header.strip(b\\\" \\\").split(b\\\" \\\") if head == b\\\"A5A5\\\": # Packet Header #\",\n \"GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\\\"Closed %s connection.\\\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close()\",\n \"\\\"), or just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = self.recv(5) if len(header)\",\n \"the socket. Packets for now are assumed to be separated by a newline.\",\n \"name = params[1] if b\\\"FSW\\\" in name: if FSW_clients: process_id = sorted(FSW_ids)[-1] +\",\n \"else: return def readData(self, header): \\\"\\\"\\\" Read the data part of the message\",\n \"header2 else: return def readData(self, header): \\\"\\\"\\\" Read the data part of the\",\n \"packet): \\\"\\\"\\\" Read the 9 byte header (e.g. \\\"A5A5 GUI \\\" or \\\"A5A5\",\n \"Registration is done by sending the string \\\"Register <name>\\\". Sending a message to\",\n \"b\\\"\\\" msg = b\\\"\\\" n = 0 while l > n: try: chunk\",\n \"# optional - give further explanation about what the program does ) if\",\n \"not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\\\"shutdown from main thread\\\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1)\",\n \"elif b\\\"GUI\\\" in name: if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name =\",\n \"else: raise RuntimeError(\\\"Packet missing A5A5 header\\\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \\\"\\\"\\\" Derived from original Stable\",\n \"listens for data on the socket. Packets for now are assumed to be\",\n \"done as \\\"A5A5 <name> <data>\\\" Note only <data> is sent. Any client that\",\n \"self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\\\"Closed\",\n \"server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\\\"shutdown from main thread\\\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception\",\n \"packet) def readData(self, header, packet): \\\"\\\"\\\" Read the data part of the message\",\n \"\\\"\\\"\\\" return self.socket def main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license =\",\n \"os import signal import sys import struct import errno from fprime.constants import DATA_ENCODING\",\n \"LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in FSW_clients: FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients:\",\n \"+ str(err.errno) + \\\" occurred on recv().\\\") return msg def readHeader(self): \\\"\\\"\\\" Read\",\n \"b\\\"\\\" elif header == b\\\"Quit\\\": return b\\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") if\",\n \"data to \\\" + self.name self.socket.send(msg) except socket.error as err: print(\\\"Socket error \\\"\",\n \"header is an empty string, connection closed, exit loop if not header: break\",\n \"socket id's for writting to destinations. \\\"\\\"\\\" dest_obj = dict() lock_obj = threading.Lock()\",\n \"= struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size + packet[4 : 4 + size] return\",\n \"# Packet Header # print \\\"Received Packet: %s %s...\\\\n\\\" % (head,dst) if data\",\n \"OptionParser __version__ = 0.1 __date__ = \\\"2015-04-03\\\" __updated__ = \\\"2016-04-07\\\" # Universal server\",\n \"reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process data here... head, dst = header.strip(b\\\"\",\n \"command data here... desc = self.recv(4) sizeb = self.recv(4) size = struct.unpack(\\\">I\\\", sizeb)[0]\",\n \"with the server -- that thread will then start one # more thread\",\n \"== b\\\"List\\\": return b\\\"\\\" elif header == b\\\"Quit\\\": return b\\\"\\\" dst = header.split(b\\\"\",\n \"registration wait for an incoming message The first part must always be an\",\n \"server display all registered clients. \\\"\\\"\\\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on\",\n \"except socket.error as err: if err.errno == errno.ECONNRESET: print( \\\"Socket error \\\" +\",\n \"header: return # Got the header data so read the data of the\",\n \"\\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Packet missing A5A5 header\\\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \\\"\\\"\\\"\",\n \"to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Telemetry missing A5A5 header\\\") class ThreadedTCPServer(socketserver.ThreadingMixIn,\",\n \"return b\\\"\\\" msg = msg + chunk n = len(msg) except socket.timeout: if\",\n \"Got the header data so read the data of the message here... data\",\n \"= True self.name = name self.id = process_id print(\\\"Registered client \\\" + self.name.decode(DATA_ENCODING))\",\n \"ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\\\" program_version = \\\"v0.1\\\" program_build_date = \\\"%s\\\"\",\n \"% dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send\",\n \"<gh_stars>0 #!/usr/bin/env python3 from __future__ import print_function import socket import threading try: import\",\n \"process_id = sorted(GUI_ids)[-1] + 1 name = params[1] + b\\\"_\\\" + bytes(process_id) GUI_clients.append(name)\",\n \"it is a List. Once the entire header string is processed send it\",\n \"+ packet[4 : 4 + size] return data def processNewPkt(self, header, data): \\\"\\\"\\\"\",\n \"thread will then start one # more thread for each request server_thread =\",\n \"string, connection closed, exit loop if not header: return # Got the header\",\n \"for an incoming message The first part must always be an \\\"A5A5 \\\"\",\n \"dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \\\"\\\"\\\" UDP Socket server. \\\"\\\"\\\" class\",\n \"\\\"\\\"\\\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def handle(self): # on each client\",\n \"= b\\\"\\\" self.cmdQueue = [] self.registered = False self.name = b\\\"\\\" self.id =\",\n \"return header if header == b\\\"List\\\\n\\\": return b\\\"List\\\" elif header == b\\\"Quit\\\\n\\\": return\",\n \"sys.stderr.write(indent + \\\" for help use --help\\\\n\\\") return 2 if __name__ == \\\"__main__\\\":\",\n \"read the data of the message here... data = self.readData(header, packet) # Process\",\n \"entire header string is processed send it on queue. If something goes wrong\",\n \"Server listening on host addr %s, port %s\\\" % (HOST, PORT)) # Start\",\n \"\\\"A5A5 \\\" or a \\\"List \\\" \\\"\\\"\\\" # Loop while the connected client\",\n \"[] self.registered = False self.name = b\\\"\\\" self.id = 0 # print self.client_address,\",\n \"errno from fprime.constants import DATA_ENCODING from optparse import OptionParser __version__ = 0.1 __date__\",\n \"help=\\\"Set threaded tcp socket server ip [default: %default]\\\", default=\\\"127.0.0.1\\\", ) # process options\",\n \"new client. This function listens for data on the socket. Packets for now\",\n \"this will handle other things such as sequence files and parameters. Handle is\",\n \"b\\\"\\\" self.id = 0 # print self.client_address, now() # show this client's address\",\n \"= [] GUI_clients = [] FSW_ids = [] GUI_ids = [] def signal_handler(*_):\",\n \"empty!\\\") return b\\\"\\\" msg = msg + chunk n = len(msg) except socket.timeout:\",\n \"b\\\"A5A5\\\": # Packet Header # print \\\"Received Packet: %s %s...\\\\n\\\" % (head,dst) if\",\n \"destination objects containing queues and socket id's for writting to destinations. \\\"\\\"\\\" dest_obj\",\n \"\\\"\\\": print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list = GUI_clients\",\n \"makes the server display all registered clients. \\\"\\\"\\\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self):\",\n \"SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license = \\\"Copyright 2015 user_name (California Institute of\",\n \"header data so read the data of the message here... data = self.readData(header)\",\n \"server display all registered clients. \\\"\\\"\\\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1 def\",\n \"False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\\\"shutdown from\",\n \"+ header2 else: return def readData(self, header): \\\"\\\"\\\" Read the data part of\",\n \"print( \\\"Header information is empty, client \\\" + self.name.decode(DATA_ENCODING) + \\\" exiting.\\\" )\",\n \"received, server shutting down.\\\") shutdown_event.set() def now(): return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \\\"\\\"\\\" Derived\",\n \"9 byte header (e.g. \\\"A5A5 GUI \\\" or \\\"A5A5 FSW \\\"), or just\",\n \"packet) # Process and send the packet of the message here... self.processNewPkt(header, data)\",\n \"telemetry data here... tlm_packet_size = self.recv(4) size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size\",\n \"recv().\\\" ) else: print(\\\"Socket error \\\" + str(err.errno) + \\\" occurred on recv().\\\")\",\n \"Hopefully this will allow address reuse and server to restart immediately server.allow_reuse_address =\",\n \"to be separated by a newline. For each packet, call processPkt. \\\"\\\"\\\" self.partial\",\n \"client that sends a \\\"List\\\" comment makes the server display all registered clients.\",\n \"and adapted for use in new FSW gse.py applicaiton. TCP socket server for\",\n \"Connection was closed by the client if not data: print(\\\"Client exited.\\\") return else:\",\n \"self.getCmds(data) # Process the cmdQueue self.processQueue() if self.registered: print(\\\"Registration complete waiting for message.\\\")\",\n \"is done by sending the string \\\"Register <name>\\\". Sending a message to destination\",\n \"+ \\\"\\\\n\\\") sys.stderr.write(indent + \\\" for help use --help\\\\n\\\") return 2 if __name__\",\n \"header = packet[:4] header2 = packet[4:9] packet = packet[9:] return (header + header2,\",\n \"print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list = GUI_clients else:\",\n \"and socket id's for writting to destinations. \\\"\\\"\\\" dest_obj = dict() lock_obj =\",\n \"header, data): \\\"\\\"\\\" Process a single command here header and data here. The\",\n \"is invoked upon a new client. This function listens for data on the\",\n \"List header = self.readHeader() # If the received header is an empty string,\",\n \"dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") # Read telemetry data here... tlm_packet_size = packet[:4]\",\n \"data here... tlm_packet_size = packet[:4] size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data = tlm_packet_size +\",\n \"the message here.... # print \\\"Sending UDP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release()\",\n \"A5A5 or List (header, packet) = self.readHeader(packet) # If the received header is\",\n \"on send().\\\") def fileno(self): \\\"\\\"\\\" \\\"\\\"\\\" return self.socket def main(argv=None): global SERVER, LOCK\",\n \"own thread for each client. Registration is done by sending the string \\\"Register\",\n \"goes wrong report and shutdown server. \\\"\\\"\\\" dest_list = [] # Process data\",\n \"RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\\\" program_version = \\\"v0.1\\\" program_build_date = \\\"%s\\\" %\",\n \"Institute of Technology) \\\\ ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\\\" program_version =\",\n \"b\\\"\\\": print(\\\"read data from socket is empty!\\\") return b\\\"\\\" msg = msg +\",\n \"report and shutdown server. \\\"\\\"\\\" dest_list = [] if header == b\\\"List\\\": print(\\\"List\",\n \"description=program_license, ) parser.add_option( \\\"-p\\\", \\\"--port\\\", dest=\\\"port\\\", action=\\\"store\\\", type=\\\"int\\\", help=\\\"Set threaded tcp socket server\",\n \"separated by a newline. For each packet, call processPkt. \\\"\\\"\\\" self.partial = b\\\"\\\"\",\n \"List. Once the entire header tstring is processed send it on queue. If\",\n \"on the socket. Packets for now are assumed to be separated by a\",\n \"Read the data from the socket data = self.recv(13) # Connection was closed\",\n \"def readData(self, header): \\\"\\\"\\\" Read the data part of the message sent to\",\n \"start with A5A5 except if it is a List. Once the entire header\",\n \"with A5A5 except if it is a List. Once the entire header tstring\",\n \"+ chunk n = len(msg) except socket.timeout: if shutdown_event.is_set(): print(\\\"socket timed out and\",\n \"= \\\"%s\\\" % __updated__ program_version_string = \\\"%%prog %s (%s)\\\" % (program_version, program_build_date) program_longdesc\",\n \"on each packet \\\"\\\"\\\" The function that is invoked when a packet is\",\n \"\\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") # Read telemetry data here... tlm_packet_size =\",\n \"__version__ = 0.1 __date__ = \\\"2015-04-03\\\" __updated__ = \\\"2016-04-07\\\" # Universal server id\",\n \"a message to destination <name> is done as \\\"A5A5 <name> <data>\\\" Note only\",\n \"of registered clients: \\\") LOCK.acquire() for d in list(SERVER.dest_obj.keys()): print(\\\"\\\\t\\\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str\",\n \"(header, packet) = self.readHeader(packet) # If the received header is an empty string,\",\n \"== b\\\"Quit\\\\n\\\": return b\\\"Quit\\\" elif header[:-1] == b\\\"A5A5\\\": header2 = self.recv(4) return header\",\n \"it is a List. Once the entire header tstring is processed send it\",\n \"raise RuntimeError(\\\"Packet missing A5A5 header\\\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \\\"\\\"\\\" Derived from original Stable demo\",\n \"packet): \\\"\\\"\\\" After registration wait for an incoming message The first part must\",\n \"getCmds(self, inputString, end_of_command=b\\\"\\\\n\\\"): \\\"\\\"\\\" Build a command from partial or full socket input\",\n \"name, request): \\\"\\\"\\\" Constructor \\\"\\\"\\\" self.name = name self.socket = request self.packet =\",\n \"action=\\\"store\\\", type=\\\"int\\\", help=\\\"Set threaded tcp socket server port [default: %default]\\\", default=50007, ) parser.add_option(\",\n \"parser.add_option( \\\"-i\\\", \\\"--host\\\", dest=\\\"host\\\", action=\\\"store\\\", type=\\\"string\\\", help=\\\"Set threaded tcp socket server ip [default:\",\n \"header.strip(b\\\" \\\").split(b\\\" \\\") if head == b\\\"A5A5\\\": # Packet Header # print \\\"Received\",\n \"Exception as e: indent = len(program_name) * \\\" \\\" sys.stderr.write(program_name + \\\": \\\"\",\n \"\\\"\\\"\\\" chunk = b\\\"\\\" msg = b\\\"\\\" n = 0 while l >\",\n \"self.recv(size) else: raise RuntimeError(\\\"unrecognized client %s\\\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header,\",\n \"print(\\\"\\\\t\\\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\\\"List \\\" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str\",\n \"%default]\\\", default=\\\"127.0.0.1\\\", ) # process options (opts, args) = parser.parse_args(argv) HOST = opts.host\",\n \"a \\\"List \\\" \\\"\\\"\\\" # Loop while the connected client has packets to\",\n \"data so read the data of the message here... data = self.readData(header, packet)\",\n \"comment makes the server display all registered clients. \\\"\\\"\\\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout\",\n \"list(SERVER.dest_obj.keys()): print(\\\"\\\\t\\\" + SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\\\"List \\\" + SERVER.dest_obj[d].name l = len(reg_client_str)\",\n \"4 + size] return data def processNewPkt(self, header, data): \\\"\\\"\\\" Process a single\",\n \"= server.lock_obj ip, port = server.server_address print(\\\"TCP Socket Server listening on host addr\",\n \"dictionary of destination objects containing queues and socket id's for writting to destinations.\",\n \"was closed by the client if not data: print(\\\"Client exited.\\\") return else: #\",\n \"to be separated by a newline. For each packet, call processPkt. \\\"\\\"\\\" self.getNewMsg(self.request[0])\",\n \"LOCK = None shutdown_event = threading.Event() FSW_clients = [] GUI_clients = [] FSW_ids\",\n \"command messages ################################################# def getNewMsg(self): \\\"\\\"\\\" After registration wait for an incoming message\",\n \"\\\" + str(err.errno) + \\\" occurred on recv().\\\") return msg def readHeader(self): \\\"\\\"\\\"\",\n \"\\\" occurred on recv().\\\") return msg def readHeader(self): \\\"\\\"\\\" Read the 9 byte\",\n \"of various lengths. \\\"\\\"\\\" data = b\\\"\\\" if header == b\\\"List\\\": return b\\\"\\\"\",\n \"% (head,dst) if data == b\\\"\\\": print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\"\",\n \"# Send the message here.... # print \\\"Sending TCP msg to \\\", dest_elem\",\n \"(opts, args) = parser.parse_args(argv) HOST = opts.host PORT = opts.port server = ThreadedTCPServer((HOST,\",\n \"= [] def signal_handler(*_): print(\\\"Ctrl-C received, server shutting down.\\\") shutdown_event.set() def now(): return\",\n \"return b\\\"List\\\" elif header == b\\\"Quit\\\\n\\\": return b\\\"Quit\\\" elif header[:-1] == b\\\"A5A5\\\": header2\",\n \"cmd): params = cmd.split() process_id = 0 if params[0] == b\\\"Register\\\": LOCK.acquire() name\",\n \"send the packet of the message here... self.processNewPkt(header, data) def readHeader(self, packet): \\\"\\\"\\\"\",\n \"for all clients registered. \\\"\\\"\\\" def __init__(self, name, request): \\\"\\\"\\\" Constructor \\\"\\\"\\\" self.name\",\n \"to restart immediately server.allow_reuse_address = True SERVER = server LOCK = server.lock_obj ip,\",\n \"return time.ctime(time.time()) class ThreadedTCPRequestHandler(socketserver.StreamRequestHandler): \\\"\\\"\\\" Derived from original Stable demo during R&TD and\",\n \"of the message here... self.processNewPkt(header, data) def readHeader(self, packet): \\\"\\\"\\\" Read the 9\",\n \"incoming message The first part must always be an \\\"A5A5 \\\" or a\",\n \"b\\\"\\\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd\",\n \"\\\" + str(err.errno) + \\\" (Connection reset by peer) occurred on recv().\\\" )\",\n \"client. This function listens for data on the socket. Packets for now are\",\n \"self.client_address, now() # show this client's address # Read the data from the\",\n \"display all registered clients. \\\"\\\"\\\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on each\",\n \"\\\"\\\"\\\" dest_list = [] if header == b\\\"List\\\": print(\\\"List of registered clients: \\\")\",\n \"= header.split(b\\\" \\\")[1].strip(b\\\" \\\") if dst == b\\\"FSW\\\": # Read variable length command\",\n \"packet is received. This function listens for data on the socket. Packets for\",\n \"return header + header2 else: return def readData(self, header): \\\"\\\"\\\" Read the data\",\n \"%s connection.\\\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self, inputString, end_of_command=b\\\"\\\\n\\\"): \\\"\\\"\\\"\",\n \"from main thread\\\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e: indent\",\n \"end_of_command=b\\\"\\\\n\\\"): \\\"\\\"\\\" Build a command from partial or full socket input \\\"\\\"\\\" commands\",\n \"Technology) \\\\ ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\\\" program_version = \\\"v0.1\\\" program_build_date\",\n \"print_function import socket import threading try: import socketserver except ImportError: import SocketServer as\",\n \"always be an \\\"A5A5 \\\" or a \\\"List \\\" \\\"\\\"\\\" # Loop while\",\n \"Stable demo during R&TD and adapted for use in new FSW gse.py applicaiton.\",\n \"from fprime.constants import DATA_ENCODING from optparse import OptionParser __version__ = 0.1 __date__ =\",\n \"\\\"2015-04-03\\\" __updated__ = \\\"2016-04-07\\\" # Universal server id global SERVER = None LOCK\",\n \"a dictionary of destination objects containing queues and socket id's for writting to\",\n \"%s\\\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data): \\\"\\\"\\\" Process a single\",\n \"is processed send it on queue. If something goes wrong report and shutdown\",\n \"= [] # Process data here... head, dst = header.strip(b\\\" \\\").split(b\\\" \\\") if\",\n \"# Loop while the connected client has packets to send/receive while not shutdown_event.is_set():\",\n \"# Read the header data from the socket either A5A5 or List header\",\n \"empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list = GUI_clients else: print(\\\"dest? %s\\\" %\",\n \"in dest_list: # print \\\"Locking TCP\\\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send\",\n \"in list(SERVER.dest_obj.keys()): # Send the message here.... # print \\\"Sending TCP msg to\",\n \"reset by peer) occurred on recv().\\\" ) else: print(\\\"Socket error \\\" + str(err.errno)\",\n \"SERVER.dest_obj[d].name.decode(DATA_ENCODING)) reg_client_str = b\\\"List \\\" + SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\\\"i%ds\\\"\",\n \"will allow address reuse and server to restart immediately server.allow_reuse_address = True SERVER\",\n \"signal.signal(signal.SIGINT, signal_handler) server_thread.daemon = False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set():\",\n \"else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def\",\n \"not header: break elif header == b\\\"Quit\\\": LOCK.acquire() print(\\\"Quit received!\\\") SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\", 0xA5A5A5A5)) shutdown_event.set()\",\n \"False self.request.close() def getCmds(self, inputString, end_of_command=b\\\"\\\\n\\\"): \\\"\\\"\\\" Build a command from partial or\",\n \"= FSW_clients for dest_elem in dest_list: # print \\\"Locking TCP\\\" LOCK.acquire() if dest_elem\",\n \"[] if header == b\\\"List\\\": print(\\\"List of registered clients: \\\") LOCK.acquire() for d\",\n \"self.socket def main(argv=None): global SERVER, LOCK program_name = os.path.basename(sys.argv[0]) program_license = \\\"Copyright 2015\",\n \"= process_id print(\\\"Registered client \\\" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process\",\n \"to send/receive while not shutdown_event.is_set(): # Read the header data from the socket\",\n \"\\\"v0.1\\\" program_build_date = \\\"%s\\\" % __updated__ program_version_string = \\\"%%prog %s (%s)\\\" % (program_version,\",\n \"parser.add_option( \\\"-p\\\", \\\"--port\\\", dest=\\\"port\\\", action=\\\"store\\\", type=\\\"int\\\", help=\\\"Set threaded tcp socket server port [default:\",\n \"except socket.timeout: if shutdown_event.is_set(): print(\\\"socket timed out and shutdown is requested\\\") return b\\\"Quit\\\\n\\\"\",\n \"be an \\\"A5A5 \\\" or a \\\"List \\\" \\\"\\\"\\\" # Read the header\",\n \"address # Read the data from the socket data = self.recv(13) # Connection\",\n \"the header data from the socket either A5A5 or List (header, packet) =\",\n \"threaded tcp socket server ip [default: %default]\\\", default=\\\"127.0.0.1\\\", ) # process options (opts,\",\n \"in dst: dest_list = GUI_clients else: print(\\\"dest? %s\\\" % dst.decode(DATA_ENCODING)) for dest_elem in\",\n \"print(\\\"Socket error \\\" + str(err.errno) + \\\" occurred on recv().\\\") return msg def\",\n \"import sys import struct import errno from fprime.constants import DATA_ENCODING from optparse import\",\n \"message to destination <name> is done as \\\"A5A5 <name> <data>\\\" Note only <data>\",\n \"occurred on send().\\\") def fileno(self): \\\"\\\"\\\" \\\"\\\"\\\" return self.socket def main(argv=None): global SERVER,\",\n \"= False self.name = b\\\"\\\" self.id = 0 # print self.client_address, now() #\",\n \"is received. This function listens for data on the socket. Packets for now\",\n \"= os.path.basename(sys.argv[0]) program_license = \\\"Copyright 2015 user_name (California Institute of Technology) \\\\ ALL\",\n \"# print \\\"Locking TCP\\\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message\",\n \"udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will allow address reuse and\",\n \"= GUI_clients elif b\\\"FSW\\\" in dst: dest_list = FSW_clients for dest_elem in dest_list:\",\n \"False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\\\"shutdown from main thread\\\") SERVER.shutdown() SERVER.server_close()\",\n \"print(\\\"Client exited.\\\") return else: # Process the data into the cmdQueue self.getCmds(data) #\",\n \"def put(self, msg): \\\"\\\"\\\" Write out the message to the destination socket \\\"\\\"\\\"\",\n \"-- that thread will then start one # more thread for each request\",\n \"only <data> is sent. Any client that sends a \\\"List\\\" comment makes the\",\n \"\\\" + self.name.decode(DATA_ENCODING) + \\\" exiting.\\\" ) return header if header == b\\\"List\\\\n\\\":\",\n \"PORT)) # Start a thread with the server -- that thread will then\",\n \"something goes wrong report and shutdown server. \\\"\\\"\\\" dest_list = [] if header\",\n \"send/receive while not shutdown_event.is_set(): # Read the header data from the socket either\",\n \"for dest_elem in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): # Send the message\",\n \"dest_elem in dest_list: # print \\\"Locking TCP\\\" LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): #\",\n \"registered. \\\"\\\"\\\" def __init__(self, name, request): \\\"\\\"\\\" Constructor \\\"\\\"\\\" self.name = name self.socket\",\n \"b\\\"\\\" self.cmdQueue = [] self.registered = False self.name = b\\\"\\\" self.id = 0\",\n \"import DATA_ENCODING from optparse import OptionParser __version__ = 0.1 __date__ = \\\"2015-04-03\\\" __updated__\",\n \"processed send it on queue. If something goes wrong report and shutdown server.\",\n \"server id global SERVER = None LOCK = None shutdown_event = threading.Event() FSW_clients\",\n \"= ( \\\"\\\"\\\"\\\"\\\"\\\" # optional - give further explanation about what the program\",\n \"self.id = process_id print(\\\"Registered client \\\" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines to\",\n \"False self.name = b\\\"\\\" self.id = 0 # print self.client_address, now() # show\",\n \"\\\"\\\"\\\"\\\"\\\"\\\" # optional - give further explanation about what the program does )\",\n \"len(header) == 0: print( \\\"Header information is empty, client \\\" + self.name.decode(DATA_ENCODING) +\",\n \"= False server_thread.start() udp_server_thread.daemon = False udp_server_thread.start() while not shutdown_event.is_set(): server_thread.join(timeout=5.0) udp_server_thread.join(timeout=5.0) print(\\\"shutdown\",\n \"processQueue(self): for cmd in self.cmdQueue: self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd): params\",\n \"import time import os import signal import sys import struct import errno from\",\n \"first part must always be an \\\"A5A5 \\\" or a \\\"List \\\" \\\"\\\"\\\"\",\n \"b\\\"GUI\\\" in dst: dest_list = GUI_clients else: print(\\\"dest? %s\\\" % dst.decode(DATA_ENCODING)) for dest_elem\",\n \"here. The command must always start with A5A5 except if it is a\",\n \"# Connection was closed by the client if not data: print(\\\"Client exited.\\\") return\",\n \"\\\"\\\"\\\" The function that is invoked upon a new client. This function listens\",\n \"= True SERVER = server LOCK = server.lock_obj ip, port = server.server_address print(\\\"TCP\",\n \"import struct import errno from fprime.constants import DATA_ENCODING from optparse import OptionParser __version__\",\n \"True self.name = name self.id = process_id print(\\\"Registered client \\\" + self.name.decode(DATA_ENCODING)) #################################################\",\n \"to destination <name> is done as \\\"A5A5 <name> <data>\\\" Note only <data> is\",\n \"return data def processNewPkt(self, header, data): \\\"\\\"\\\" Process a single command here header\",\n \"Read the header data from the socket either A5A5 or List (header, packet)\",\n \"command messages ################################################# def getNewMsg(self, packet): \\\"\\\"\\\" After registration wait for an incoming\",\n \"here... self.processNewPkt(header, data) def recv(self, l): \\\"\\\"\\\" Read l bytes from socket. \\\"\\\"\\\"\",\n \"= inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial + commands[0] self.partial = b\\\"\\\" if\",\n \"if data == b\\\"\\\": print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\" in dst:\",\n \"return # Got the header data so read the data of the message\",\n \"LOCK.acquire() print(\\\"Quit received!\\\") SERVER.dest_obj[self.name].put(struct.pack(\\\">I\\\", 0xA5A5A5A5)) shutdown_event.set() time.sleep(1) print(\\\"Quit processed!\\\") SERVER.shutdown() SERVER.server_close() LOCK.release() break\",\n \"# process options (opts, args) = parser.parse_args(argv) HOST = opts.host PORT = opts.port\",\n \"the data of the message here... data = self.readData(header) # Process and send\",\n \"\\\"\\\"\\\" header = packet[:4] header2 = packet[4:9] packet = packet[9:] return (header +\",\n \"or just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = self.recv(5) if len(header) ==\",\n \"the server display all registered clients. \\\"\\\"\\\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): #\",\n \"Routines to process the command messages ################################################# def getNewMsg(self, packet): \\\"\\\"\\\" After registration\",\n \"the data into the cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue() if self.registered:\",\n \"message sent to either GUI or FSW. GUI receives telemetry. FSW receives commands\",\n \"server.allow_reuse_address = True SERVER = server LOCK = server.lock_obj ip, port = server.server_address\",\n \"reg_client_str = struct.pack(\\\"i%ds\\\" % l, l, reg_client_str) self.request.send(reg_client_str) LOCK.release() return 0 # Process\",\n \"FSW_clients.remove(self.name) FSW_ids.remove(self.id) elif self.name in GUI_clients: GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\\\"Closed %s connection.\\\" %\",\n \"== b\\\"\\\": print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list =\",\n \"either GUI or FSW. GUI receives telemetry. FSW receives commands of various lengths.\",\n \"an empty string, connection closed, exit loop if not header: break elif header\",\n \"+ \\\" for help use --help\\\\n\\\") return 2 if __name__ == \\\"__main__\\\": sys.exit(main())\",\n \"self.registered = False self.name = b\\\"\\\" self.id = 0 # print self.client_address, now()\",\n \"wrong report and shutdown server. \\\"\\\"\\\" dest_list = [] # Process data here...\",\n \"give further explanation about what the program does ) if argv is None:\",\n \"adapted for use in new FSW gse.py applicaiton. TCP socket server for commands,\",\n \"various lengths. \\\"\\\"\\\" data = b\\\"\\\" if header == b\\\"List\\\": return b\\\"\\\" elif\",\n \"from __future__ import print_function import socket import threading try: import socketserver except ImportError:\",\n \"while the connected client has packets to send/receive while not shutdown_event.is_set(): # Read\",\n \"LOCK.release() print(\\\"Closed %s connection.\\\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self, inputString,\",\n \"__updated__ program_version_string = \\\"%%prog %s (%s)\\\" % (program_version, program_build_date) program_longdesc = ( \\\"\\\"\\\"\\\"\\\"\\\"\",\n \"\\\"2016-04-07\\\" # Universal server id global SERVER = None LOCK = None shutdown_event\",\n \"process the command messages ################################################# def getNewMsg(self): \\\"\\\"\\\" After registration wait for an\",\n \"LOCK.release() break # Got the header data so read the data of the\",\n \"chunk n = len(msg) except socket.timeout: if shutdown_event.is_set(): print(\\\"socket timed out and shutdown\",\n \"here... data = self.readData(header, packet) # Process and send the packet of the\",\n \"packet) = self.readHeader(packet) # If the received header is an empty string, connection\",\n \"default=50007, ) parser.add_option( \\\"-i\\\", \\\"--host\\\", dest=\\\"host\\\", action=\\\"store\\\", type=\\\"string\\\", help=\\\"Set threaded tcp socket server\",\n \"header2 = self.recv(4) return header + header2 else: return def readData(self, header): \\\"\\\"\\\"\",\n \"= threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer): \\\"\\\"\\\" UDP Socket server. \\\"\\\"\\\" class DestObj: \\\"\\\"\\\"\",\n \"socket. \\\"\\\"\\\" chunk = b\\\"\\\" msg = b\\\"\\\" n = 0 while l\",\n \"exit loop if not header: return # Got the header data so read\",\n \"by peer) occurred on recv().\\\" ) else: print(\\\"Socket error \\\" + str(err.errno) +\",\n \"GUI_clients = [] FSW_ids = [] GUI_ids = [] def signal_handler(*_): print(\\\"Ctrl-C received,\",\n \"data of the message here... data = self.readData(header) # Process and send the\",\n \"b\\\"\\\" msg = msg + chunk n = len(msg) except socket.timeout: if shutdown_event.is_set():\",\n \"self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def getCmds(self, inputString, end_of_command=b\\\"\\\\n\\\"): \\\"\\\"\\\" Build a command\",\n \"\\\") if head == b\\\"A5A5\\\": # Packet Header # print \\\"Received Packet: %s\",\n \"(head,dst) if data == \\\"\\\": print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\" in\",\n \"\\\") # Read telemetry data here... tlm_packet_size = packet[:4] size = struct.unpack(\\\">I\\\", tlm_packet_size)[0]\",\n \"FSW \\\"), or just read the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = self.recv(5) if\",\n \"* \\\" \\\" sys.stderr.write(program_name + \\\": \\\" + repr(e) + \\\"\\\\n\\\") sys.stderr.write(indent +\",\n \"client %s\\\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data): \\\"\\\"\\\" Process a\",\n \"\\\"\\\"\\\" Build a command from partial or full socket input \\\"\\\"\\\" commands =\",\n \"Packets for now are assumed to be separated by a newline. For each\",\n \"socket either A5A5 or List header = self.readHeader() # If the received header\",\n \"+ size] return data def processNewPkt(self, header, data): \\\"\\\"\\\" Process a single command\",\n \"\\\"\\\"\\\" Write out the message to the destination socket \\\"\\\"\\\" try: # print\",\n \"client if not data: print(\\\"Client exited.\\\") return else: # Process the data into\",\n \"the \\\"List\\\\n\\\" command. \\\"\\\"\\\" header = self.recv(5) if len(header) == 0: print( \\\"Header\",\n \"self.name.decode(DATA_ENCODING)) ################################################# # New Routines to process the command messages ################################################# def getNewMsg(self):\",\n \"# Start a thread with the server -- that thread will then start\",\n \"self.processRegistration(cmd) self.cmdQueue = [] def processRegistration(self, cmd): params = cmd.split() process_id = 0\",\n \"+ SERVER.dest_obj[d].name l = len(reg_client_str) reg_client_str = struct.pack(\\\"i%ds\\\" % l, l, reg_client_str) self.request.send(reg_client_str)\",\n \"################################################# def getNewMsg(self, packet): \\\"\\\"\\\" After registration wait for an incoming message The\",\n \"class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \\\"\\\"\\\" Derived from original Stable demo during R&TD and adapted for\",\n \"the server display all registered clients. \\\"\\\"\\\" socketserver.StreamRequestHandler.allow_reuse_address = True socketserver.StreamRequestHandler.timeout = 1\",\n \"desc + sizeb + self.recv(size) elif dst == b\\\"GUI\\\": # Read telemetry data\",\n \"header and data here. The command must always start with A5A5 except if\",\n \"data == \\\"\\\": print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list\",\n \"b\\\"A5A5\\\": header2 = self.recv(4) return header + header2 else: return def readData(self, header):\",\n \"commands, log events, and telemetry data. Later this will handle other things such\",\n \"data into the cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue() if self.registered: print(\\\"Registration\",\n \"\\\"--host\\\", dest=\\\"host\\\", action=\\\"store\\\", type=\\\"string\\\", help=\\\"Set threaded tcp socket server ip [default: %default]\\\", default=\\\"127.0.0.1\\\",\",\n \"None LOCK = None shutdown_event = threading.Event() FSW_clients = [] GUI_clients = []\",\n \"data def processNewPkt(self, header, data): \\\"\\\"\\\" Process a single command here header and\",\n \"0 # Process data here... head, dst = header.strip(b\\\" \\\").split(b\\\" \\\") if head\",\n \"PORT = opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler)\",\n \"here... self.processNewPkt(header, data) def readHeader(self, packet): \\\"\\\"\\\" Read the 9 byte header (e.g.\",\n \"processRegistration(self, cmd): params = cmd.split() process_id = 0 if params[0] == b\\\"Register\\\": LOCK.acquire()\",\n \"chunk == b\\\"\\\": print(\\\"read data from socket is empty!\\\") return b\\\"\\\" msg =\",\n \"action=\\\"store\\\", type=\\\"string\\\", help=\\\"Set threaded tcp socket server ip [default: %default]\\\", default=\\\"127.0.0.1\\\", ) #\",\n \"data) def recv(self, l): \\\"\\\"\\\" Read l bytes from socket. \\\"\\\"\\\" chunk =\",\n \"\\\": \\\" + repr(e) + \\\"\\\\n\\\") sys.stderr.write(indent + \\\" for help use --help\\\\n\\\")\",\n \"# New Routines to process the command messages ################################################# def getNewMsg(self): \\\"\\\"\\\" After\",\n \"log events, and telemetry data. Later this will handle other things such as\",\n \"if dst == b\\\"FSW\\\": # Read variable length command data here... desc =\",\n \"server port [default: %default]\\\", default=50007, ) parser.add_option( \\\"-i\\\", \\\"--host\\\", dest=\\\"host\\\", action=\\\"store\\\", type=\\\"string\\\", help=\\\"Set\",\n \"\\\"A5A5 <name> <data>\\\" Note only <data> is sent. Any client that sends a\",\n \"data = desc + sizeb + self.recv(size) elif dst == b\\\"GUI\\\": # Read\",\n \"processNewPkt(self, header, data): \\\"\\\"\\\" Process a single command here header and data here.\",\n \"+ \\\" (Connection reset by peer) occurred on recv().\\\" ) else: print(\\\"Socket error\",\n \"dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Packet missing A5A5 header\\\") class ThreadedUDPRequestHandler(socketserver.BaseRequestHandler): \\\"\\\"\\\" Derived\",\n \"try: parser = OptionParser( version=program_version_string, epilog=program_longdesc, description=program_license, ) parser.add_option( \\\"-p\\\", \\\"--port\\\", dest=\\\"port\\\", action=\\\"store\\\",\",\n \"return msg def readHeader(self): \\\"\\\"\\\" Read the 9 byte header (e.g. \\\"A5A5 GUI\",\n \"data here... head, dst = header.strip(b\\\" \\\").split(b\\\" \\\") if head == b\\\"A5A5\\\": #\",\n \"import threading try: import socketserver except ImportError: import SocketServer as socketserver import time\",\n \"print( \\\"Socket error \\\" + str(err.errno) + \\\" (Connection reset by peer) occurred\",\n \"head == b\\\"A5A5\\\": # Packet Header # print \\\"Received Packet: %s %s...\\\\n\\\" %\",\n \"udp_server_thread.join(timeout=5.0) print(\\\"shutdown from main thread\\\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as\",\n \"dest_elem in list(SERVER.dest_obj.keys()): # Send the message here.... # print \\\"Sending TCP msg\",\n \"epilog=program_longdesc, description=program_license, ) parser.add_option( \\\"-p\\\", \\\"--port\\\", dest=\\\"port\\\", action=\\\"store\\\", type=\\\"int\\\", help=\\\"Set threaded tcp socket\",\n \"0 # print self.client_address, now() # show this client's address # Read the\",\n \"send it on queue. If something goes wrong report and shutdown server. \\\"\\\"\\\"\",\n \"error \\\" + str(err.errno) + \\\" (Connection reset by peer) occurred on recv().\\\"\",\n \"self.readHeader(packet) # If the received header is an empty string, connection closed, exit\",\n \"GUI_ids = [] def signal_handler(*_): print(\\\"Ctrl-C received, server shutting down.\\\") shutdown_event.set() def now():\",\n \"= params[1] + b\\\"_\\\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release()\",\n \"socket. Packets for now are assumed to be separated by a newline. For\",\n \"cmdQueue self.getCmds(data) # Process the cmdQueue self.processQueue() if self.registered: print(\\\"Registration complete waiting for\",\n \"ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully this will allow\",\n \"handle(self): # on each packet \\\"\\\"\\\" The function that is invoked when a\",\n \"\\\" occurred on send().\\\") def fileno(self): \\\"\\\"\\\" \\\"\\\"\\\" return self.socket def main(argv=None): global\",\n \"header string is processed send it on queue. If something goes wrong report\",\n \"self.recv(4) return header + header2 else: return def readData(self, header): \\\"\\\"\\\" Read the\",\n \"Process and send the packet of the message here... self.processNewPkt(header, data) def readHeader(self,\",\n \"[] GUI_ids = [] def signal_handler(*_): print(\\\"Ctrl-C received, server shutting down.\\\") shutdown_event.set() def\",\n \"desc = self.recv(4) sizeb = self.recv(4) size = struct.unpack(\\\">I\\\", sizeb)[0] data = desc\",\n \"messages ################################################# def getNewMsg(self, packet): \\\"\\\"\\\" After registration wait for an incoming message\",\n \"connection closed, exit loop if not header: break elif header == b\\\"Quit\\\": LOCK.acquire()\",\n \"and shutdown is requested\\\") return b\\\"Quit\\\\n\\\" continue except socket.error as err: if err.errno\",\n \"SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e: indent = len(program_name) *\",\n \"upon a new client. This function listens for data on the socket. Packets\",\n \"GUI_clients.remove(self.name) GUI_ids.remove(self.id) LOCK.release() print(\\\"Closed %s connection.\\\" % self.name.decode(DATA_ENCODING)) self.registered = False self.request.close() def\",\n \"b\\\"FSW\\\" in dst: dest_list = FSW_clients for dest_elem in dest_list: # print \\\"Locking\",\n \"elif header == b\\\"Quit\\\": return b\\\"\\\" dst = header.split(b\\\" \\\")[1].strip(b\\\" \\\") if dst\",\n \"socket server ip [default: %default]\\\", default=\\\"127.0.0.1\\\", ) # process options (opts, args) =\",\n \"to the destination socket \\\"\\\"\\\" try: # print \\\"about to send data to\",\n \"from original Stable demo during R&TD and adapted for use in new FSW\",\n \"# Read telemetry data here... tlm_packet_size = packet[:4] size = struct.unpack(\\\">I\\\", tlm_packet_size)[0] data\",\n \"FSW receives commands of various lengths. \\\"\\\"\\\" data = b\\\"\\\" if header ==\",\n \"of the message here... data = self.readData(header, packet) # Process and send the\",\n \"+ repr(e) + \\\"\\\\n\\\") sys.stderr.write(indent + \\\" for help use --help\\\\n\\\") return 2\",\n \"is an empty string, connection closed, exit loop if not header: break elif\",\n \"program_version_string = \\\"%%prog %s (%s)\\\" % (program_version, program_build_date) program_longdesc = ( \\\"\\\"\\\"\\\"\\\"\\\" #\",\n \"len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def processQueue(self): for cmd in self.cmdQueue:\",\n \"\\\"\\\"\\\" Read l bytes from socket. \\\"\\\"\\\" chunk = b\\\"\\\" msg = b\\\"\\\"\",\n \"b\\\"\\\": print(\\\" Data is empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list = GUI_clients\",\n \"server ip [default: %default]\\\", default=\\\"127.0.0.1\\\", ) # process options (opts, args) = parser.parse_args(argv)\",\n \"here header and data here. The command must always start with A5A5 except\",\n \"data = self.readData(header, packet) # Process and send the packet of the message\",\n \"is an empty string, connection closed, exit loop if not header: return #\",\n \"= server.server_address print(\\\"TCP Socket Server listening on host addr %s, port %s\\\" %\",\n \"Destination object for all clients registered. \\\"\\\"\\\" def __init__(self, name, request): \\\"\\\"\\\" Constructor\",\n \"type=\\\"int\\\", help=\\\"Set threaded tcp socket server port [default: %default]\\\", default=50007, ) parser.add_option( \\\"-i\\\",\",\n \"sizeb = self.recv(4) size = struct.unpack(\\\">I\\\", sizeb)[0] data = desc + sizeb +\",\n \"\\\" \\\"\\\"\\\" # Read the header data from the socket either A5A5 or\",\n \"ip [default: %default]\\\", default=\\\"127.0.0.1\\\", ) # process options (opts, args) = parser.parse_args(argv) HOST\",\n \"if header == b\\\"List\\\\n\\\": return b\\\"List\\\" elif header == b\\\"Quit\\\\n\\\": return b\\\"Quit\\\" elif\",\n \"input \\\"\\\"\\\" commands = inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial + commands[0] self.partial\",\n \"is empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list = GUI_clients elif b\\\"FSW\\\" in\",\n \"= 0 while l > n: try: chunk = self.request.recv(l - n) if\",\n \"UDP msg to \\\", dest_elem SERVER.dest_obj[dest_elem].put(data) LOCK.release() else: raise RuntimeError(\\\"Telemetry missing A5A5 header\\\")\",\n \"name self.id = process_id print(\\\"Registered client \\\" + self.name.decode(DATA_ENCODING)) ################################################# # New Routines\",\n \"\\\"\\\"\\\" commands = inputString.split(end_of_command) if len(self.partial): commands[0] = self.partial + commands[0] self.partial =\",\n \"+ b\\\"_\\\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\\\"GUI\\\" in name: if GUI_clients: process_id\",\n \"\\\"\\\"\\\" socketserver.BaseRequestHandler.allow_reuse_address = True def handle(self): # on each packet \\\"\\\"\\\" The function\",\n \"# If the received header is an empty string, connection closed, exit loop\",\n \"the connected client has packets to send/receive while not shutdown_event.is_set(): # Read the\",\n \"sizeb + self.recv(size) elif dst == b\\\"GUI\\\": # Read telemetry data here... tlm_packet_size\",\n \"print(\\\"dest? %s\\\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()):\",\n \"the received header is an empty string, connection closed, exit loop if not\",\n \"self.recv(13) # Connection was closed by the client if not data: print(\\\"Client exited.\\\")\",\n \"and server to restart immediately server.allow_reuse_address = True SERVER = server LOCK =\",\n \"FSW_clients for dest_elem in dest_list: # print \\\"Locking TCP\\\" LOCK.acquire() if dest_elem in\",\n \"% dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data): \\\"\\\"\\\" Process a single command\",\n \"use in new FSW gse.py applicaiton. TCP socket server for commands, log events,\",\n \"the message here... data = self.readData(header) # Process and send the packet of\",\n \"the string \\\"Register <name>\\\". Sending a message to destination <name> is done as\",\n \"FSW receives commands of various lengths. \\\"\\\"\\\" data = \\\"\\\" dst = header.split(b\\\"\",\n \"RuntimeError(\\\"unrecognized client %s\\\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data): \\\"\\\"\\\" Process\",\n \"main thread\\\") SERVER.shutdown() SERVER.server_close() udp_server.shutdown() udp_server.server_close() time.sleep(1) except Exception as e: indent =\",\n \"Process and send the packet of the message here... self.processNewPkt(header, data) def recv(self,\",\n \"a List. Once the entire header tstring is processed send it on queue.\",\n \"class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \\\"\\\"\\\" TCP Socket server. Keep a dictionary of destination objects\",\n \"is empty, returning.\\\") if b\\\"GUI\\\" in dst: dest_list = GUI_clients else: print(\\\"dest? %s\\\"\",\n \"self.request.send(reg_client_str) LOCK.release() return 0 # Process data here... head, dst = header.strip(b\\\" \\\").split(b\\\"\",\n \"# show this client's address # Read the data from the socket data\",\n \"self.getNewMsg() else: print(\\\"Unable to register client.\\\") return LOCK.acquire() del SERVER.dest_obj[self.name] if self.name in\",\n \"print(\\\"Quit processed!\\\") SERVER.shutdown() SERVER.server_close() LOCK.release() break # Got the header data so read\",\n \"def handle(self): # on each client connect \\\"\\\"\\\" The function that is invoked\",\n \"\\\"List\\\\n\\\" command. \\\"\\\"\\\" header = self.recv(5) if len(header) == 0: print( \\\"Header information\",\n \"is a List. Once the entire header string is processed send it on\",\n \"\\\\ ALL RIGHTS RESERVED. U.S. Government Sponsorship acknowledged.\\\" program_version = \\\"v0.1\\\" program_build_date =\",\n \"else: raise RuntimeError(\\\"unrecognized client %s\\\" % dst.decode(DATA_ENCODING)) return data def processNewPkt(self, header, data):\",\n \"\\\"\\\"\\\" self.partial = b\\\"\\\" self.cmdQueue = [] self.registered = False self.name = b\\\"\\\"\",\n \"telemetry. FSW receives commands of various lengths. \\\"\\\"\\\" data = \\\"\\\" dst =\",\n \"opts.port server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler) udp_server = ThreadedUDPServer((HOST, PORT), ThreadedUDPRequestHandler) # Hopefully\",\n \"b\\\"GUI\\\" in name: if GUI_clients: process_id = sorted(GUI_ids)[-1] + 1 name = params[1]\",\n \"with A5A5 except if it is a List. Once the entire header string\",\n \"getNewMsg(self, packet): \\\"\\\"\\\" After registration wait for an incoming message The first part\",\n \"Process a single command here header and data here. The command must always\",\n \"LOCK.release() return 0 # Process data here... head, dst = header.strip(b\\\" \\\").split(b\\\" \\\")\",\n \"== b\\\"A5A5\\\": # Packet Header # print \\\"Received Packet: %s %s...\\\\n\\\" % (head,dst)\",\n \"LOCK.release() else: raise RuntimeError(\\\"Telemetry missing A5A5 header\\\") class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer): \\\"\\\"\\\" TCP Socket\",\n \"for message.\\\") self.getNewMsg() else: print(\\\"Unable to register client.\\\") return LOCK.acquire() del SERVER.dest_obj[self.name] if\",\n \"struct.unpack(\\\">I\\\", sizeb)[0] data = desc + sizeb + self.recv(size) elif dst == b\\\"GUI\\\":\",\n \"commands[0] self.partial = b\\\"\\\" if len(commands[-1]): self.partial = commands[-1] self.cmdQueue.extend(commands[:-1]) else: self.cmdQueue.extend(commands[:-1]) def\",\n \"is requested\\\") return b\\\"Quit\\\\n\\\" continue except socket.error as err: if err.errno == errno.ECONNRESET:\",\n \"timed out and shutdown is requested\\\") return b\\\"Quit\\\\n\\\" continue except socket.error as err:\",\n \"request self.packet = b\\\"\\\" def put(self, msg): \\\"\\\"\\\" Write out the message to\",\n \"True SERVER = server LOCK = server.lock_obj ip, port = server.server_address print(\\\"TCP Socket\",\n \"for now are assumed to be separated by a newline. For each packet,\",\n \"= None LOCK = None shutdown_event = threading.Event() FSW_clients = [] GUI_clients =\",\n \"%s\\\" % dst.decode(DATA_ENCODING)) for dest_elem in dest_list: LOCK.acquire() if dest_elem in list(SERVER.dest_obj.keys()): #\",\n \"packet[:4] header2 = packet[4:9] packet = packet[9:] return (header + header2, packet) def\",\n \"b\\\"_\\\" + bytes(process_id) FSW_clients.append(name) FSW_ids.append(process_id) elif b\\\"GUI\\\" in name: if GUI_clients: process_id =\",\n \"dest_list = GUI_clients elif b\\\"FSW\\\" in dst: dest_list = FSW_clients for dest_elem in\",\n \"dst: dest_list = GUI_clients elif b\\\"FSW\\\" in dst: dest_list = FSW_clients for dest_elem\",\n \"shutdown server. \\\"\\\"\\\" dest_list = [] # Process data here... head, dst =\",\n \"server for commands, log events, and telemetry data. Later this will handle other\",\n \"b\\\"_\\\" + bytes(process_id) GUI_clients.append(name) GUI_ids.append(process_id) SERVER.dest_obj[name] = DestObj(name, self.request) LOCK.release() self.registered = True\",\n \"and send the packet of the message here... self.processNewPkt(header, data) def recv(self, l):\",\n \"str(err.errno) + \\\" occurred on send().\\\") def fileno(self): \\\"\\\"\\\" \\\"\\\"\\\" return self.socket def\",\n \"TCP socket server for commands, log events, and telemetry data. Later this will\",\n \"connect \\\"\\\"\\\" The function that is invoked upon a new client. This function\"\n]","isConversational":false}}},{"rowIdx":2999,"cells":{"ngram":{"kind":"list like","value":["range(4) logging.basicConfig( format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\", level=logging.INFO ) logger =","DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING =","import time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) =","BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\"%(asctime)s - %(name)s - %(levelname)s -","SOLD, SELLING = range(4) logging.basicConfig( format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\", level=logging.INFO","( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT,","logging import sched import time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON,","- %(levelname)s - %(message)s\", level=logging.INFO ) logger = logging.getLogger(\"btb_manager_telegram_logger\") scheduler = sched.scheduler(time.time, time.sleep)",") = range(8) BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\"%(asctime)s - %(name)s","range(8) BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\"%(asctime)s - %(name)s - %(levelname)s","CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\"%(asctime)s -","= range(4) logging.basicConfig( format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\", level=logging.INFO ) logger","BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",","EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING","sched import time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, )","PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\"%(asctime)s","EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD,","import logging import sched import time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB,","%(name)s - %(levelname)s - %(message)s\", level=logging.INFO ) logger = logging.getLogger(\"btb_manager_telegram_logger\") scheduler = sched.scheduler(time.time,","- %(name)s - %(levelname)s - %(message)s\", level=logging.INFO ) logger = logging.getLogger(\"btb_manager_telegram_logger\") scheduler =","import sched import time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT,","time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8)","UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig(","SELLING = range(4) logging.basicConfig( format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\", level=logging.INFO )","format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\", level=logging.INFO ) logger = logging.getLogger(\"btb_manager_telegram_logger\") scheduler","= range(8) BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\"%(asctime)s - %(name)s -","UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING = range(4)","logging.basicConfig( format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\", level=logging.INFO ) logger = logging.getLogger(\"btb_manager_telegram_logger\")","MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING,"],"string":"[\n \"range(4) logging.basicConfig( format=\\\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\\\", level=logging.INFO ) logger =\",\n \"DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING =\",\n \"import time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) =\",\n \"BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\\\"%(asctime)s - %(name)s - %(levelname)s -\",\n \"SOLD, SELLING = range(4) logging.basicConfig( format=\\\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\\\", level=logging.INFO\",\n \"( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT,\",\n \"logging import sched import time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON,\",\n \"- %(levelname)s - %(message)s\\\", level=logging.INFO ) logger = logging.getLogger(\\\"btb_manager_telegram_logger\\\") scheduler = sched.scheduler(time.time, time.sleep)\",\n \") = range(8) BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\\\"%(asctime)s - %(name)s\",\n \"range(8) BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\\\"%(asctime)s - %(name)s - %(levelname)s\",\n \"CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\\\"%(asctime)s -\",\n \"= range(4) logging.basicConfig( format=\\\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\\\", level=logging.INFO ) logger\",\n \"BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\\\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\\\",\",\n \"EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING\",\n \"sched import time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, )\",\n \"PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\\\"%(asctime)s\",\n \"EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD,\",\n \"import logging import sched import time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB,\",\n \"%(name)s - %(levelname)s - %(message)s\\\", level=logging.INFO ) logger = logging.getLogger(\\\"btb_manager_telegram_logger\\\") scheduler = sched.scheduler(time.time,\",\n \"- %(name)s - %(levelname)s - %(message)s\\\", level=logging.INFO ) logger = logging.getLogger(\\\"btb_manager_telegram_logger\\\") scheduler =\",\n \"import sched import time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT,\",\n \"time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8)\",\n \"UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig(\",\n \"SELLING = range(4) logging.basicConfig( format=\\\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\\\", level=logging.INFO )\",\n \"format=\\\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\\\", level=logging.INFO ) logger = logging.getLogger(\\\"btb_manager_telegram_logger\\\") scheduler\",\n \"= range(8) BOUGHT, BUYING, SOLD, SELLING = range(4) logging.basicConfig( format=\\\"%(asctime)s - %(name)s -\",\n \"UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING = range(4)\",\n \"logging.basicConfig( format=\\\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\\\", level=logging.INFO ) logger = logging.getLogger(\\\"btb_manager_telegram_logger\\\")\",\n \"MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING,\"\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":29,"numItemsPerPage":100,"numTotalItems":12866649,"offset":2900,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6Im1sR1pFYktIVHRVSGRhX1RRdEczQ0N1S3k5ME9Qd25DNHY5elRjM3FVYzgifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4MzUwNzcxMCwianRpIjoiZTg4OGNiMTItNzBkOS00MzZhLTlmZGEtYzYxZTg1Y2Y5NjBhIiwic3ViIjoiL2RhdGFzZXRzL3RlcnJ5eXovc3RhcmNvZGVyZGF0YV9uZ3JhbV8xMyIsImV4cCI6MTc4MzUxMTMxMCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.5fe4GVCtk79JwlX54FLle5WVt4po-gEhMLpBIX_L36JFHXZiINwAPzm274aMTEnuLIx6JA9GPy--zDCEyOCuBQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":12866649,"numBytesParquet":149232926747}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
ngram listlengths 0 67.8k |
|---|
[
"NEVER be displayed outside the admin panel. \"\"\" anonymous_user = models.GenericIPAddressField(blank=True, null=True) def",
"Poll class BasePollTelemetry(models.Model): \"\"\" This Base class gives a hint that in the",
"UsersPollTelemetry(BasePollTelemetry): users = models.ManyToManyField(db_index=True, to=g... |
[
"BaseBEVBackbone from .decouple_bev_backbone import DecoupledBEVBackbone __all__ = { 'BaseBEVBackbone': BaseBEVBackbone, 'DecoupledBEVBackbone': DecoupledBEVBackbone, }",
"import BaseBEVBackbone from .decouple_bev_backbone import DecoupledBEVBackbone __all__ = { 'BaseBEVBackbone': BaseBEVBackbone, 'DecoupledBEVBa... |
[
"as exc_info: self.param.from_json(\"42\", self.trans, {\"input_bam\": model.HistoryDatasetAssociation()}) assert str(exc_info.value) == \"parameter 'my_name': requires a",
"overlap here with DataToolParameterTestCase, # refactor. def setUp(self): super().setUp() self.test_history = model.History() self.app.model... |
[
"#!/usr/bin/env python # -*- coding: utf-8 -*- \"\"\"recumpiler Recompile text to be semi-readable",
"python # -*- coding: utf-8 -*- \"\"\"recumpiler Recompile text to be semi-readable memey",
"Recompile text to be semi-readable memey garbage. \"\"\" __version__ = (0, 0, 0)",
"-*- coding: utf-8 -*- \"\"\"recu... |
[
"'http/http_pipelined_host_forced.h', 'http/http_pipelined_host_impl.cc', 'http/http_pipelined_host_impl.h', 'http/http_pipelined_host_pool.cc', 'http/http_pipelined_host_pool.h', 'http/http_pipelined_stream.cc', 'http/http_pipelined_stream.h', 'http/http_proxy_client_socket.cc', 'http/http_proxy_client_socket.h', ... |
[
"% (gpu_count)) self.__model = nn.DataParallel(self.model) self.__set_model2cuda() else: self.__set_model2cuda() def __set_optimizer(self): self.__optimizer = torch.optim.RMSprop(",
"__init__(self, lr=0.001): self.lr = lr self.__model = None self.__optimizer = None self.__criterion =",
"as nn from abc import AB... |
[
"'as {0} as', }) return ('Place #{p1} is {rating} place #{p2}').format( p1=self.place1, p2=self.place2,",
"this rating compares \"\"\" score = models.IntegerField() \"\"\" The rating score. 1 means",
"= models.CharField(max_length=32, choices=SOURCES) location_data = models.CharField(max_length=2048) \"\"\" The... |
[
"TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE",
"BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED",
"AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL",
"CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES,... |
[] |
[
"= None password = <PASSWORD> id = None account_number = None production =",
"None account_number = None production = None from estafeta.core.error import EstafetaWrongData, EstafetaEmptyField __url_label__",
"import EstafetaClient user = None password = <PASSWORD> id = None account_number =",
"<PASSWORD> id ... |
[
"yunionclient.common import base class FlavorManager(base.StandaloneManager): keyword = 'flavor' keyword_plural = 'flavors' _columns =",
"class FlavorManager(base.StandaloneManager): keyword = 'flavor' keyword_plural = 'flavors' _columns = ['ID', 'Name', 'VCPU_count',",
"keyword = 'flavor' keyword_plural = 'fla... |
[
"26 স 27 ষ 28 ই 29 আ 30 ছ 31 গ 32",
"{} index_map = {} for line in char_map_str.strip().split('\\n'): ch, index = line.split() char_map[ch]",
"র 17 ণ 18 ক 19 ড 20 হ 21 উ 22 প",
"text and integer sequences. \"\"\" char_map_str = \"\"\" ' 0 <SPACE> 1 ব",
"90 ঋ 91 i 92 z 93 m 94 e 95 ঊ 96",
"আ 30 ছ 31 গ 32 ু... |
[
"Api # from restful import Api from resources.Hello import CategoryResource api_bp = Blueprint('api',",
"import Api from resources.Hello import CategoryResource api_bp = Blueprint('api', __name__) api = Api(api_bp)",
"flask import Blueprint from flask_restful import Api # from restful import Api from",
"impor... |
[
"} else: message = {'unknown_signal': args } socketio.emit('message', json.dumps(message)) def dbus_monitor(): bus.subscribe(iface =",
"bus.subscribe(iface = 'org.ofono.Modem', signal_fired = cb_server_signal_emission) bus.subscribe(iface = 'org.ofono.NetworkRegistration', signal_fired = cb_server_signal_emission... |
[
"def search(env, r, *args): return r.execute_command('ft.search', *args) def testTagIndex(env): r = env env.expect('ft.create',",
"if not env.is_cluster(): forceInvokeGC(env, 'idx1') forceInvokeGC(env, 'idx2') forceInvokeGC(env, 'idx3') forceInvokeGC(env, 'idx4') forceInvokeGC(env, 'idx5')",
"idx3 SCHEMA t TAG ... |
[
"cant replace 1 with \"Target.None.value\" because \".None\" doesnt seem to be a valid",
"range(-distance, distance + 1, placement_step)] ) ] res = await self._client.query_building_placement(building, possible_positions) possible",
"and unit.distance_to(target) <= cast_range ): # cant replace 1 with \"Target.N... |
[
"str, ) -> JVMLockfileMetadataV1: metadata = _get_metadata(json_dict, lockfile_description, error_suffix) requirements = metadata( \"generated_with_requirements\",",
"typing import Any, Iterable, cast from pants.core.util_rules.lockfile_metadata import ( LockfileMetadata, LockfileMetadataValidation, LockfileScope... |
[
"filename) includes = include_pattern.findall(dbc_file_in) output_filename = filename.replace('.dbc', '_generated.dbc') output_file_location = os.path.join(output_path, output_filename) with",
"filename) core_dbc = include_pattern.sub('', dbc_file_in) dbc_file_out.write(core_dbc) def create_all(output_path): for ... |
[
"\"\"\" Breed ModelAdmin. \"\"\" search_fields = ['name'] class SubBreedAdmin(admin.ModelAdmin): \"\"\" SubBreed ModelAdmin. \"\"\"",
"\"\"\" search_fields = ['name', 'owner__name'] autocomplete_fields = ['owner', 'breed', 'sub_breed'] list_display = ['name',",
"Dog, Breed, SubBreed class OwnerAdmin(admin.Model... |
[
"__future__ import absolute_import from redis import Redis from rq.decorators import job from kaneda.utils",
"connection=Redis()) def report(name, metric, value, tags, id_): \"\"\" RQ job to report metrics",
"get_backend backend = get_backend() @job(queue='kaneda', connection=Redis()) def report(name, metric, v... |
[
"= f\"{self.ripped_certs_path}\\\\{file_name}.pdf\" with open(output_file_name, \"wb\") as out: pdf_writer.write(out) print( f\"\\x1b[95m[{process_index}]\\x1b[0m Ripped \\x1b[92m[{file_name}]\\x1b[0m from",
"pdf_writer = PdfFileWriter() pdf_writer.addPage(page) output_file_name = f\"{self.ripped_certs_path}\\\\{... |
[
"num8 = ibm_db.field_num(stmt, \"WEIGHT\") else: num1 = ibm_db.field_num(stmt, \"ID\") num2 = ibm_db.field_num(stmt, \"BREED\")",
"= ibm_db.field_num(stmt, \"Breed\") num6 = ibm_db.field_num(stmt, 8) num7 = ibm_db.field_num(stmt, 1) num8 =",
"if conn: ibm_db.autocommit(conn, ibm_db.SQL_AUTOCOMMIT_OFF) insert = ... |
[
"reverse, NoReverseMatch from django.utils.translation import ugettext_lazy as _ from .models import PageBannerExtension _banner_change_url",
"= (reverse(_banner_add_url) + '?extended_object=%s' % self.page.pk) except NoReverseMatch: # not in urls pass",
"not self.page: # Nothing to do return # check global per... |
[
"are ['LSTM', 'GRU', 'RNN_TANH' or 'RNN_RELU']\"\"\") self.rnn = nn.RNN(ninp, nhid, nlayers, nonlinearity=nonlinearity, dropout=dropout)",
"else: self.decoder = decoder def _generate_square_subsequent_mask(self, sz): mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0,",
"decoder def _generate_square_subse... |
[
"main(): a=input(\"The enter the first number :\") b=input(\"The enter the second number :\")",
"enter the second number :\") range=input(\"Please enter the range\") i=0;count=0; print a print",
"def main(): a=input(\"The enter the first number :\") b=input(\"The enter the second number",
"first number :\") b... |
[] |
[
"判定する数字 Returns: str: 答えのmax（答えになりうる値の最大）を変更する。デフォは50 1にするとマジ？と訊かれる。それだけ。 不可思議な数字は0である ''' global answer global max # 入力された値に応じて返答を構成、maxを変更、ニューゲーム",
"'''number 判定 Args: num (int): 判定する数字 Returns: str: num が answer より大きい: 'Too",
"if num > answer: return ' is too large. The answer is more",
"が answer より小さい: 'T... |
[
"funcv.bindings: func = funcb.data log.info(\"Trying %s with fake arguments\", func) if isinstance(func, abstract.INTERPRETER_FUNCTION_TYPES):",
"node2 = self.maybe_analyze_method(node1, val, cls) node2.ConnectTo(node0) return node0 def bind_method(self, node, name, methodvar,",
"proc = subprocess.Popen([\"/usr... |
[
"pooled_gff, demux_count_file, output_prefix, out_group_dict, in_fafq=None ): \"\"\" :param pooled_sam: SAM file :param demux_count_file:",
"group} GFFs If input fasta/fastq is given, optionally also output per-{barcode group} FASTA/FASTQ",
"eval(outgroup_dict) out_group_dict = dict([tmp]) if len(tmp) == 1 else... |
[
"skimage.util import random_noise maxSlope = 10 # restrict the maximum slope of generated",
"image to draw to cX -- x value of circle center cY --",
"of line parameters (intercept, slope) scores -- hypotheses scores, array shape (NxM), see",
"self.bg_clr = bg_clr def draw_circle(self, data, cX, cY, r, clr, al... |
[
"print('Converting Seprsco to WAV Audio') wav_tracks = [] for track in seprsco_tracks: wav",
"dropout = .1 maxnorm = None vae_b1 , vae_b2 = .02 , .1",
".1 maxnorm = None vae_b1 , vae_b2 = .02 , .1 print('Reinitiating VAE",
"than each of the four voices present in an NESM track. To do",
"load data training_f... |
[
"and self.syslog not in (False, 0): if isinstance(self.syslog, (list, tuple)): _addr = tuple(self.syslog)",
"fmt if fmt is not None else \"%(asctime)-15s %(name)s %(levelname)s %(message)s\" if 'LOGLEVEL'",
"_addr = \"/dev/log\" if os.path.exists(\"/dev/log\") else None if _addr is not None: handler",
"(list,... |
[
"#Author <NAME> print(\"Hello World\") hello_list = [\"Hello World\"] print(hello_list[0]) for i in hello_list:",
"<NAME> print(\"Hello World\") hello_list = [\"Hello World\"] print(hello_list[0]) for i in hello_list: print(i)"
] |
[
"namedtuple('_', ['qtype', 'kb_ident']) class FakeDoc: def __init__(self, text, qtype, kb_ident): self._ = _fake_ext(qtype,",
"def __init__(self, text, qtype, kb_ident): self._ = _fake_ext(qtype, kb_ident) self.text = text def",
"kb_ident): self._ = _fake_ext(qtype, kb_ident) self.text = text def __str__(self):... |
[
"import ABCMeta, abstractmethod from typing import Tuple, Callable class BaseTradeStrategy(object, metaclass=ABCMeta): @abstractmethod def",
"asset_precision: int, min_cost_limit: float, min_amount_limit: float): pass @abstractmethod def trade(self, action: int, n_discrete_actions:",
"abstractmethod from typing... |
[
"= Book(); print(b1.category) b2 = b1; print(b2.category) print(Book.category) Book.category = '수필' print(b2.category); print(b1.category)",
"b2 = b1; print(b2.category) print(Book.category) Book.category = '수필' print(b2.category); print(b1.category) ; print(Book.category) b2.category",
"Book04_1.py class Book:... |
[
"underlying C context.\"\"\" self._lib_vscf_kdf1.vscf_kdf1_delete(self.ctx) def set_hash(self, hash): self._lib_vscf_kdf1.vscf_kdf1_use_hash(self.ctx, hash.c_impl) def alg_id(self): \"\"\"Provide algorithm",
"context.\"\"\" self._lib_vscf_kdf1 = VscfKdf1() self._c_impl = None self._ctx = None self.ctx = self._lib... |
[
"dog = models.Dog(name='Snoopy') self.assertEqual(dog.says(), 'woof') def test_cat_says(self): \"\"\"test cat says meow of not",
"test_cat_says(self): \"\"\"test cat says meow of not \"\"\" cat = models.Cat(name='Garfield') self.assertEqual(cat.says(), 'meow')",
"models class AnimalTestCase(TestCase): \"\"\"Tes... |
[
"print('Digite seu nome completo: ') nome = input().strip().upper() print('Seu nome tem \"Silva\"?') print('SILVA'",
"seu nome completo: ') nome = input().strip().upper() print('Seu nome tem \"Silva\"?') print('SILVA' in",
"nome completo: ') nome = input().strip().upper() print('Seu nome tem \"Silva\"?') print(... |
[
"= 'data/datasets/stvqa/defaults/annotations/' vocab_path = 'data/datasets/stvqa/defaults/extras/vocabs/' train_datasets = ['train'] test_datasets = ['val'] reader_train_cfg =",
"+ 'ocr_en_frcn_features.lmdb', ), mix_annotations=dict( train=data_root + annotation_path + 'imdb_subtrain.npy', val=data_root + annota... |
[
"field=models.ManyToManyField(blank=True, to='rpocore.FormalStatement', verbose_name='Formal statements'), ), migrations.AlterField( model_name='statementpage', name='informal_statements', field=models.ManyToManyField(blank=True, to='rpocore.InformalStatement', verbose_name='Informal statements'), ),",
"to='rpoco... |
[
"gpiozero import LineSensor from time import sleep from signal import pause def lineDetected():",
"from gpiozero import LineSensor from time import sleep from signal import pause def",
"import pause def lineDetected(): print('line detected') def noLineDetected(): print('no line detected') sensor =",
"from tim... |
[
"71 72 96 95 94 93 -\"], [\"J2\", \"- 136 135 134 133",
"Pins(\"138 142 9 11 12 139 8 10\"), IOStandard(\"LVCMOS33\")), ] # Connectors ---------------------------------------------------------------------------------------",
"1, Pins(\"59\"), IOStandard(\"LVCMOS33\")), (\"user_btn\", 2, Pins(\"60\"), IOStandard... |
[
"= np.zeros((N, len(self.actions))) new_test = self.test.get_subset(chosed_actions) virtual_t \\ = self.predictor.get_predict_samples(self.training, new_test, N) for",
"= virtual_t[n, :] if virtual_train.Z is None: train.add(virtual_train.X, virtual_train.t) else: train.add(virtual_train.X, virtual_train.t, virtu... |
[
"def _disable_linecache(): import linecache def fake_getline(*args, **kwargs): return \"\" linecache.orig_getline = linecache.getline linecache.getline",
"_disable_linecache(): import linecache def fake_getline(*args, **kwargs): return \"\" linecache.orig_getline = linecache.getline linecache.getline =",
"impor... |
[
"#locate image_loc = html.findAll('img') #create folder for located imgs if not os.path.exists('imgs'): os.makedirs('imgs')",
"requests.get(url) html = bs(page.text, 'html.parser') #locate image_loc = html.findAll('img') #create folder for located",
"image in image_loc: try: url = image['src'] source = requests... |
[
"time from appium import webdriver import queue # 声明一个python队列 driver_queue = queue.Queue() class",
": %s' % command) subprocess.Popen(command, stdout=open(SYSTEMPATH, 'a+'), stderr=open(ERRORPATH, 'a+'), shell=True) def test_server(self): #",
"2、启动driver ''' from lib.tools import Tool import subprocess from li... |
[
"flag: ans.add((iR + 1, iC)) if (iR+1, iC + 1) in flag: ans.add((iR+1,",
"from collections import deque class Solution: def pondSizes(self, land: List[List[int]]) -> List[int]: def",
"提示： 0 < len(land) <= 1000 0 < len(land[i]) <= 1000 来源：力扣（LeetCode） 链接：https://leetcode-cn.com/problems/pond-sizes-lcci",
"0 < ... |
[
"\"\"\" Return a dict whose keys are instances of :class:`re.Match` and whose values",
"(latsec != '00' or longsec != '00'): precision = Decimal('0.000001') elif (latmin !=",
"longsec = '00' else: latsec = parts.get('latsec', '') or '00' longsec = parts.get('longsec',",
"longsec > 59: # Assume that 'seconds' ... |
[
"package_data={ 'pokedex': ['data/csv/*.csv'] }, install_requires=[ 'SQLAlchemy>=1.0,<2.0', 'whoosh>=2.5,<2.7', 'markdown==2.4.1', 'construct==2.5.3', 'six>=1.9.0', ], entry_points={ 'console_scripts':",
"'markdown==2.4.1', 'construct==2.5.3', 'six>=1.9.0', ], entry_points={ 'console_scripts': [ 'pokedex = pokede... |
[
"for i in range(p.num_lstm_layers): if i == 0: input_dim = self._first_lstm_input_dim else: input_dim",
"# cnn/batch-norm/relu -> # bidirectional conv-lstm -> # cnn/batch-norm/relu # bidirectional lstm ->",
"# requirement from FRNN). first_lstm_input_dim_unpadded = lstm_input_shape[2] * lstm_input_shape[3] if s... |
[
"백분율 텍스트 표시 else: ## 총합을 100으로 맞추기위해 마지막 백분율은 100에서 백분율 누적값을",
"누적값을 빼준다. ax.text(x, y, f'{100 - sum_pct:.2f}%', ha='center', va='center',size=22,color='white', weight='bold') # pie.rc('font', family=font_name)",
"matplotlib.pyplot as plt import numpy as np font_location = './wordcloud_file/malgun.ttf' # For Wi... |
[
"<reponame>kbilak/City-portal from django.shortcuts import render from django.views.generic import TemplateView def index(request): return render(request,",
"from django.shortcuts import render from django.views.generic import TemplateView def index(request): return render(request, 'index.html')"
] |
[
"return redirect('detail', myloc_id=myloc.id) else: activity_form = NewActivityForm() return render(request, 'new_activity.html', {\"form\": activity_form,'myloc':myloc}) @login_required(login_url=\"/accounts/login/\")",
"= current_user post.myloc=myloc post.save() return redirect('detail', myloc_id=myloc.id) els... |
[
"part2 if __name__ == \"__main__\": # trying out the new walrus[:=] oprtr in",
"part1, part2 if __name__ == \"__main__\": # trying out the new walrus[:=] oprtr",
"Day 1 Main Module \"\"\" from day01 import parse_input, part1, part2 if __name__",
"day01 import parse_input, part1, part2 if __name__ == \"__main_... |
[
"key used in production secret! SECRET_KEY = os.environ.get('SECRET_KEY') # SECURITY WARNING: don't run",
"http://developer.yahoo.com/performance/rules.html#expires 'Expires': 'Thu, 31 Dec 2099 20:00:00 GMT', 'Cache-Control': 'max-age=94608000', } AWS_STORAGE_BUCKET_NAME =",
"= [ { 'NAME': 'django.contrib.auth.... |
[
"where toppling occurs. ''' # size is either batch_size, or shorter if we're",
"def next_batch(self, random_window=True, focus_toppling=False): ''' Returns the next batch of data. if random_window=True",
"norm_info.density_offset self.density_max = norm_info.density_max self.mass_offset = norm_info.mass_offset ... |
[
"= np.diag(confusion_matrix) list_raw_sum = np.sum(confusion_matrix, axis=1) each_acc = np.nan_to_num(truediv(list_diag, list_raw_sum)) average_acc = np.mean(each_acc)",
"AA_andEachClassAccuracy(confusion_matrix): counter = confusion_matrix.shape[0] list_diag = np.diag(confusion_matrix) list_raw_sum = np.sum(conf... |
[
"fname_out == '': path_out = '' # output in user's current directory file_out",
"= arguments[\"-o\"] if \"-x\" in arguments: transform.interp = arguments[\"-x\"] if \"-r\" in arguments:",
"is a wrapper for sct_WarpImageMultiTransform # # --------------------------------------------------------------------------... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"KIND, either express or implied. # See the License for the specific language",
"# limitations under the License. \"\"\"Tests methods in plugin_bas... |
[
"Address(raw=value) obj.save() return obj # A dictionary of named address components. elif isinstance(value,",
"= Country.objects.create(name=country, code=country_code) else: country_obj = None # Handle the state. try: state_obj",
"txt = '%s' % self.raw return txt def clean(self): if not self.raw: raise",
"=... |
[
"can be run as tasks. \"\"\" from __future__ import (absolute_import, division, print_function, unicode_literals)",
"utf-8 -*- \"\"\" Modules for exposing functions that can be run as tasks.",
"# -*- coding: utf-8 -*- \"\"\" Modules for exposing functions that can be",
"that can be run as tasks. \"\"\" from _... |
[
"'name': line.name, 'partner_id': line._get_partner_id(credit_account=True), 'account_id': credit_account_id, 'journal_id': slip.journal_id.id, 'date': date, 'debit': amount <",
"raise UserError(_('The Expense Journal \"%s\" has not properly configured the Credit Account!') %",
"odoo.tools import float_compare,... |
[
"hspace=0.05) font = {\"family\": \"serif\", \"weight\": \"bold\"} plt.suptitle(title) j = 0 for i",
"else: ax1.imshow(x[idx, ...], cmap=cmap) plt.axis(\"off\") j += 1 plt.show() def plot_2D(x, y, title,",
"list(np.where(y == list(labels.keys())[i])[0][: grid_x - 1]) label = labels[list(labels.keys())[i]] for k... |
[
"logger.setLevel(logging.INFO) self.logger = logger def run(self): self.state.run(self) @property def state(self): return self._state @state.setter",
"'user3', 'user4', 'user5'] FACILITY = ['KERN', 'USER', 'MAIL', 'DAEMON', 'AUTH', 'SYSLOG', 'LPR', 'NEWS',",
"'hostip': HOSTIP[1], 'severity': SEVERITY[1], 'facil... |
[] |
[
"<reponame>utiasASRL/pysteam from typing import Optional import numpy as np from . import Evaluator",
"vector space state variable\"\"\" def __init__(self, meas: np.ndarray, state_vec: VectorSpaceStateVar) -> None: super().__init__()",
"import VectorSpaceStateVar class VectorSpaceErrorEval(Evaluator): \"\"\"Err... |
[] |
[
"import hashlib def is_address(ck: PyAddress, hrp, ver): \"\"\"check bech32 format and version\"\"\" try:",
"-> PyAddress: assert len(dummy_identifier) == 20 return PyAddress.from_param('dummy', 0, dummy_identifier) __all__ = [",
"\"\"\"check bech32 format and version\"\"\" try: if ck.hrp != hrp: return False i... |
[
"\", \".join(steps)) logger.info(\"Submitting with sbatch...\") jids: typing.Dict[str, str] = {} for step in",
"[jids[dep] for dep in common.DEPENDENCIES[step] if dep in jids] cmd = [\"sbatch\"] if",
"from logzero import logger from toposort import toposort from . import common from",
"stdout_raw = subprocess... |
[
"2D and 3D operators. \"\"\" grid = Grid(shape=shape) buffer = StringIO() temporary_handler =",
"numpy as np from devito import Grid, Function, TimeFunction, Eq, Operator, configuration, silencio",
"ImportError: # Python3 compatibility from io import StringIO import pytest from conftest import",
"to in [1, 2,... |
[
"parser = argparse.ArgumentParser() parser.add_argument('--img_dir', type=str) parser.add_argument('--gt_dir', type=str) parser.add_argument('--data_list', type=str) parser.add_argument('--out_dir', type=str) parser.add_argument('--out_name', type=str)",
"'segmentation': [word.bbox.val], 'category_id': word.label... |
[
"import run as running_run @app.command() def run( targets: Optional[List[str]] = Argument(None), handout: bool",
"import Paths from deckz.running import run as running_run @app.command() def run( targets: Optional[List[str]]",
"Path(\".\"), ) -> None: \"\"\"Compile main targets.\"\"\" paths = Paths.from_defaul... |
[
"django import template register = template.Library() @register.filter(name='isboolean') def isboolean(value): return isinstance(value, bool) @register.filter(name='vartypename')",
"register = template.Library() @register.filter(name='isboolean') def isboolean(value): return isinstance(value, bool) @register.filt... |
[
"app = FastAPI() app.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\") app.add_middleware(CustomHeaderMiddleware) app.include_router(apirest.router) app.include_router(websockets.router) @app.on_event(\"startup\") async def handle_startup()",
"encoding='utf-8', maxsize=20) cvar_redis.set(pool... |
[
"image file name', required=True) opts = parser.parse_args() if opts.action == 'encode': autoencoder.encode(opts.model, opts.input,",
"= parser.add_subparsers(dest=\"action\") encode_parser = subparsers.add_parser('encode') addTrainablesArg(encode_parser) encode_parser.add_argument('--input', dest='input', help='... |
[
"the client requested some type of Pluggable Transport. \"\"\" transport = None logging.debug(\"Parsing",
"had its filters generated via :meth:`~EmailBridgeRequest.generateFilters`. \"\"\" request = EmailBridgeRequest() skippedHeaders = False",
"bridge distribution system. # # :authors: <NAME> <<EMAIL>> # <NAME... |
[
"docstring \"\"\" def _impl(_ctx): \"\"\" Function docstring \"\"\" pass some_rule = rule( attrs",
"attrs = { \"attr1\": attr.int( default = 2, mandatory = False, ), \"attr2\":",
"= { \"attr1\": attr.int( default = 2, mandatory = False, ), \"attr2\": 5,",
"def _impl(_ctx): \"\"\" Function docstring \"\"\" pas... |
[
"connection import * from jinja2 import Environment, FileSystemLoader import webbrowser def print_report(id): env",
"import Environment, FileSystemLoader import webbrowser def print_report(id): env = Environment(loader=FileSystemLoader('.')) template = env.get_template(\"src/template.html\")",
"with open('print... |
[
"user may want {None} as the default # value; we need a way",
"my name yield renderer.trait(name=self.name, value='') # go through the items for item in",
"value, workload): \"\"\" Render {value} using {renderer} \"\"\" # get my schema schema",
"want {None} as the default # value; we need a way to know",
"c... |
[
"other energies are reported. energy_fermi = sec_scc.energy.fermi if energy_fermi is None: continue energy_fermi",
"def init_quantities(self): self._quantities = [] self._quantities.append( Quantity( 'fermi_energy', r'Fermi Energy:\\s*([\\d\\.]+)\\s*', unit=ureg.hartree, repeats=False)) def",
"float(v[1]) * ure... |
[
"required parameter `user_id` when calling `get_file_metadata`\") # noqa: E501 collection_formats = {} path_params",
"noqa: E501 return self.api_client.call_api( '/locations/{location_id}/files/{fileId}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_f... |
[
"2021-10-22 14:23 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('reservation_management', '0020_greenpass'),",
"# Generated by Django 3.2.7 on 2021-10-22 14:23 from django.db import migrations class",
"dependencies = [ ('reservation_management', '0020_greenpass'), ] o... |
[
"bw = 2*np.pi*f t = np.arange(0, dur, dt) np.random.seed(0) noise_power = None if",
"output_ext) output_count += 1 fig_title = 'Signal Decoded Using Leaky IAF Population Decoder'",
"a # display: import matplotlib matplotlib.use('AGG') from bionet.utils.misc import func_timer import bionet.utils.band_limited as"... |
[
"= function( x,y ): return x+y def main(): TestError( F(1,2) == 3 )",
"expr\"\"\" F = function( x,y ): return x+y def main(): TestError( F(1,2) ==",
"\"\"\"func expr\"\"\" F = function( x,y ): return x+y def main(): TestError( F(1,2)",
"F = function( x,y ): return x+y def main(): TestError( F(1,2) == 3"
] |
[
"return media # Block Views def block_top_toolbar(self, context, nodes): if self.sortable_fields: pass #",
"sortable_fields = ['sort'] # Media def get_media(self, media): if self.sortable_fields and self.request.GET.get(SORTBY_VAR): media",
"= self.request.GET.get(REFRESH_VAR) # context.update({ # 'has_refresh'... |
[
"@celery.task() def charge_cards(): lock = Lock(key=\"charge-cards-lock\") lock.acquire() log = Log() log.it(\"---Starting batch job...\")",
"pass class Lock(object): \"\"\" Claim an exclusive lock. Using Redis. \"\"\" def __init__(self,",
"today = datetime.now(tz=zone).strftime(\"%Y-%m-%d\") opportunities = Op... |
[
"# pylint: disable=unused-argument ): \"\"\"TODO for this test. Either: 1. Not use own",
"been called' addr2 = '0x78a087fCf440315b843632cFd6FDE6E5adcCc2C2' with etherscan_patch, ethtokens_max_chunks_patch, patch.object(blockchain, 'query_balances') as mock: #",
"not have been called' addr2 = '0x78a087fCf440315b... |
[
"(c) I am 2017 # Licence: <your licence> #------------------------------------------------------------------------------- def main(): pass if",
"Created: 02/11/2017 # Copyright: (c) I am 2017 # Licence: <your licence> #-------------------------------------------------------------------------------",
"02/11/2017... |
[
"SQLAlchemy(metadata=metadata) migrate = Migrate() login = LoginManager() login.login_view = \"auth.login\" moment = Moment()",
"# migrate.init_app(app, db) login.init_app(app) moment.init_app(app) md.init_app(app) bootstrap.init_app(app) from app.errors import bp as errors_bp",
"import bp as cli_bp app.registe... |
[
"sampled (s,pi,z)'s from the buffer # ~ 0.1s # TODO: if error, set",
"== config.period_token: new_array[i, :j + 1] = 1 break elif array[i, j] ==",
"np.concatenate(z_tmp, 0) # print('io time: ',time() - start_time) # decompresses sampled pi's #",
"(s,pi,z)'s from the buffer # ~ 0.1s # TODO: if error, set a",
... |
[
"code += f'execute if score {target[0]} {NAMESPACE} matches 0 run function {NAMESPACE}:{f2}\\n' else:",
"+= c1 code += t1.cast(ot, tt1, target) code += c2 code += ot.binary(expression.op,",
"= Int() if target is None or target == []: target = [get_temp()",
"ttt in tt1: used_temps.remove(ttt) for ttt in tt2: u... |
[
"if len(args) > 1: error(\"too many arguments\") in_fname = args[0] try: out_fname except",
"--help show this help message and exit\" print >> sys.stderr, \" -o FILENAME,",
"<stdint.h>\\n\\n\") h_file.write(\"#include <stdint.h>\\n\\n\") addr = 0 i = 0 for line in in_file:",
"= int(m[0][0], 16) name = m[0][1]... |
[
"os.path.join(os.getcwd(), \"nonexistent\") under_test.set_screenshot_folder(path) logger.error.assert_called_with('Given screenshots path \"%s\" does not exist' % path) logger.warn.assert_called_with(",
"== os.getcwd() def test_set_screenshot_folder_nonexistent(mocker: MockerFixture, under_test: x3270): mocker.p... |
[
"'rsr' in FILTERS[filt]['method']: rsr = kwargs.get('rsr',True) # Read in filter if isinstance(custom,bool) and",
"not isinstance(ftrans,numpy.ndarray): raise ValueError('\\nProblem reading in {}'.format(filterFolder+FILTERS[filt]['file'])) fwave = fwave[~numpy.isnan(ftrans)]*u.micron ftrans = ftrans[~numpy.isnan... |
[
"def to_timestamp(date): print(date.timestamp()) return int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration):",
"int(date.timestamp()) def to_minutes(duration): return duration / ONE_MINUTE def to_days(duration): return duration / ONE_DAY",
"def to_minutes(duration)... |
[
"profile object with no z_dim (vertical dimension). Provide an array of netcdf files",
"range(n_ax): ur_index = np.unravel_index(ii, (n_r, n_c)) # Select season if required if plot_seasons:",
"fn_out = \"/Users/dbyrne/transfer/surface_gridded_errors_{0}.png\".format(run_name) #%% General Plot Settings var_name ... |
[
"documents. :returns: tf-idf of corpus. \"\"\" return TfidfVectorizer().fit_transform(corpus) if __name__ == '__main__': sample_corpus",
"- inverse document frequency (idf). :param corpus: List of documents. :returns: tf-idf of",
"if __name__ == '__main__': sample_corpus = [ 'This is sample document.', 'another... |
[
"writting to destinations. \"\"\" dest_obj = dict() lock_obj = threading.Lock() class ThreadedUDPServer(socketserver.ThreadingMixIn, socketserver.UDPServer):",
"== b\"Register\": LOCK.acquire() name = params[1] if b\"FSW\" in name: if FSW_clients: process_id",
"\"\"\" dest_list = [] # Process data here... head,... |
[
"range(4) logging.basicConfig( format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\", level=logging.INFO ) logger =",
"DELETE_DB, UPDATE_TG, UPDATE_BTB, PANIC_BUTTON, CUSTOM_SCRIPT, ) = range(8) BOUGHT, BUYING, SOLD, SELLING =",
"import time ( MENU, EDIT_COIN_LIST, EDIT_USER_CONFIG, DELETE_DB, UPDATE_... |