def _create_astrotable(rows, column_names, column_dtypes, column_units):
results = Table(data = rows ,
names = column_names,
dtypes = column_dtypes)
for i in _np.arange(len(column_units)):
results.field(i).units = column_units[i]
return results
def _parse_result(data, output='astropy.table'):
"""
Only one output type at the moment
"""
if output == 'astropy.table':
# get each line (i.e. each molecule)
rows = data.split('\n')
# get the names of the columns
column_names = rows[0]
column_names = column_names.split(':')
for i in np.arange(len(column_names)):
column_names[i] = column_names[i].replace('<br>', ' ')
column_names[i] = column_names[i].replace('<sub>', '_')
column_names[i] = column_names[i].replace('<sup>', '^')
column_names[i] = column_names[i].replace('</sup>', '')
column_names[i] = column_names[i].replace('</sub>', '')
column_names[i] = column_names[i].replace('μ', 'mu')
column_names[i] = column_names[i].replace('sid[0] is null', '')
rows = rows[1:-1]
rows = [i.split(':') for i in rows]
rows = np.array(rows)
rows[rows == ''] = 'nan'
column_dtypes = [
'str', 'str', 'float', 'float', 'float', 'float', 'str', 'str',
'float', 'float', 'float', 'float', 'str', 'float', 'float',
'float', 'float', 'float', 'float', 'float', 'str'
]
column_units = [
None, None, 'GHz?', 'GHz?', 'GHz?', 'GHz?', None, None, '?',
'Debye^2', '?', 'log10(Aij)', '?', 'cm^-1', 'K', 'cm^-1', 'K',
None, None, None, None
]
results = Table(data=rows, names=column_names, dtypes=column_dtypes)
for i in np.arange(len(column_units)):
results.field(i).units = column_units[i]
return results
def _parse_result(data, output='astropy.table'):
"""
Only one output type at the moment
"""
if output == 'astropy.table':
# get each line (i.e. each molecule)
rows = data.split('\n')
# get the names of the columns
column_names = rows[0]
column_names = column_names.split(':')
for i in np.arange(len(column_names)):
column_names[i] = column_names[i].replace('<br>', ' ')
column_names[i] = column_names[i].replace('<sub>', '_')
column_names[i] = column_names[i].replace('<sup>', '^')
column_names[i] = column_names[i].replace('</sup>', '')
column_names[i] = column_names[i].replace('</sub>', '')
column_names[i] = column_names[i].replace('μ', 'mu')
column_names[i] = column_names[i].replace('sid[0] is null', '')
rows = rows[1:-1]
rows = [i.split(':') for i in rows]
rows = np.array(rows)
rows[rows == ''] = 'nan'
column_dtypes = ['str', 'str', 'float', 'float', 'float' , 'float' ,
'str', 'str', 'float',
'float', 'float', 'float', 'str', 'float', 'float',
'float', 'float', 'float', 'float','float','str']
column_units = [None, None, 'GHz?', 'GHz?', 'GHz?', 'GHz?', None,
None, '?', 'Debye^2', '?', 'log10(Aij)', '?',
'cm^-1', 'K', 'cm^-1', 'K', None, None, None, None]
results = Table(data = rows ,
names = column_names,
dtypes = column_dtypes)
for i in np.arange(len(column_units)):
results.field(i).units = column_units[i]
return results
class SED(list):
"""Class to plot GeV -- TeV SEDs
Internally the same units as in the Fermi catalog are used:
- Energies in MeV
- Flux densities in cm^-2 s^-2 MeV^-1
- Fluxes in cm^-2 s^-1
- Energy fluxes in erg cm^-2 s^-1"""
"""
def add_Fermi(self, name):
try:
self._fermi
self.append(self._fermi.sed_component(name))
except
component = catalog.sed_component(name)
self.append(component)
"""
def add(self, names, catalogs):
for name in names:
for catalog in catalogs:
try:
component = catalog.sed_component(name)
self.append(component)
log.info('%s found in %s', name, catalog.table.table_name)
except ValueError as e:
log.warning(e)
log.warning('%s not found in %s', name,
catalog.table.table_name)
pass
def plot(self, filename='sed.png', xlim=(8e-2, 2e5), ylim=(1e-14, 1e-8)):
import matplotlib.pyplot as plt
plt.figure()
plt.ylabel(r'E$^2$ dF/DE (erg cm$^{-2}$ s$^{-1}$)')
plt.xlabel('Energy (GeV)')
plt.loglog()
log.info('Plotting {0} components in SED'.format(len(self)))
for component in self:
component.plot()
plt.xlim(xlim)
plt.ylim(ylim)
plt.legend()
log.info('Writing {0}'.format(filename))
plt.savefig(filename)
def add_component(self,
catalog_format,
catalog_name,
object_name,
plot_pivot=False,
**ecpl_params):
""" Read necessary parameters from FITS file and plot butterfly
Parameters:
catalog_format = 'hess', 'fermi'
catalog_name = FITS file name
object_name = object name string in 'name' column
Note: Since every catalog has columns with different
names and units, a general SED plotting is not possible.
Instead for each catalog type a handler function that
deals converts to a standard format is called.
@todo: Possibly pass plotting parameters along here by
appending them to the ecpl_params dictionary
-> I don't think this works at the moment!!!"""
from atpy import Table
# Get the catalog from file and initialize some things
self.catalog_format = catalog_format
self.catalog_name = catalog_name
self.object_name = object_name
self.catalog = Table(catalog_name).data
# Build a dictionary of parameters needed for the plot
self.ecpl_params = ecpl_params
self.get_ecpl_params()
# Plot curve
self.plot_ecpl(plot_pivot=plot_pivot, **ecpl_params)
# Plot points if present
if self.plot_points is not None:
# Get the values needed for plotting
e = self.plot_points[0]
f = self.plot_points[1]
f_err = self.plot_points[2]
e_err = self.plot_points[3]
is_ul = self.plot_points[4]
for ii in range(e.size):
self.plot_point(e[ii],
f[ii],
f_err=f_err[ii],
e_err=[[e_err[0][ii]], [e_err[1][ii]]],
ul=is_ul[ii])
# Remove so that it doesn't get plotted again.
self.plot_points = None
def get_ecpl_params(self):
"""Build self.ecpl_params dictionary
by parsing one of the supported catalogs"""
if self.catalog_format == 'hess':
self.get_ecpl_params_hess_cat()
elif self.catalog_format == 'fermi':
self.get_ecpl_params_fermi_cat()
# Change numpy types to regular types
# and replace nan values with 0
for key, value in self.ecpl_params.items():
if isinstance(value, np.float32):
value = float(value)
if isinstance(value, np.int16):
value = int(value)
def get_ecpl_params_fermi_cat(self):
""" Build self.ecpl_params dictionary from Fermi catalog fields """
i = self.find_object_index('source_name')
# Set all plot parameters:
self.ecpl_params['e_pivot'] = self.catalog.field('Pivot_Energy')[i]
self.ecpl_params['e_min'] = 1e2
self.ecpl_params['e_max'] = 1e5
self.ecpl_params['e_cut'] = 0.0
self.ecpl_params['e_cut_err'] = 0.0
self.ecpl_params['e_scale'] = 1
self.ecpl_params['norm'] = self.catalog.field('Flux_Density')[i]
self.ecpl_params['norm_err'] = self.catalog.field(
'Unc_Flux_Density')[i]
self.ecpl_params['norm_scale'] = 1
self.ecpl_params['index'] = self.catalog.field('Spectral_Index')[i]
self.ecpl_params['index_err'] = self.catalog.field(
'Unc_Spectral_Index')[i]
self.ecpl_params['color'] = 'green'
self.ecpl_params['butterfly'] = True
# Set flux point data
self.plot_points = self.get_flux_points_fermi(i)
# Add text label
fmt = '%s\n%s, %s\n' + \
r'S = %3.1f, C = %3.1f, $\Gamma = %1.2f \pm %1.2f$'
values = (self.object_name, self.catalog.field('class1')[i],
self.catalog.field('assoc1')[i],
self.catalog.field('signif_avg')[i],
self.catalog.field('curvature_index')[i],
self.catalog.field('spectral_index')[i],
self.catalog.field('unc_spectral_index')[i])
self.ax.text(0.05,
0.95,
fmt % values,
horizontalalignment='left',
verticalalignment='top',
transform=self.ax.transAxes)
class SED(list):
"""Class to plot GeV -- TeV SEDs
Internally the same units as in the Fermi catalog are used:
- Energies in MeV
- Flux densities in cm^-2 s^-2 MeV^-1
- Fluxes in cm^-2 s^-1
- Energy fluxes in erg cm^-2 s^-1"""
"""
def add_Fermi(self, name):
try:
self._fermi
self.append(self._fermi.sed_component(name))
except
component = catalog.sed_component(name)
self.append(component)
"""
def add(self, names, catalogs):
for name in names:
for catalog in catalogs:
try:
component = catalog.sed_component(name)
self.append(component)
log.info('%s found in %s',
name, catalog.table.table_name)
except ValueError as e:
log.warning(e)
log.warning('%s not found in %s',
name, catalog.table.table_name)
pass
def plot(self, filename='sed.png', xlim=(8e-2, 2e5), ylim=(1e-14, 1e-8)):
import matplotlib.pyplot as plt
plt.figure()
plt.ylabel(r'E$^2$ dF/DE (erg cm$^{-2}$ s$^{-1}$)')
plt.xlabel('Energy (GeV)')
plt.loglog()
log.info('Plotting {0} components in SED'.format(len(self)))
for component in self:
component.plot()
plt.xlim(xlim)
plt.ylim(ylim)
plt.legend()
log.info('Writing {0}'.format(filename))
plt.savefig(filename)
def add_component(self, catalog_format, catalog_name,
object_name, plot_pivot=False, **ecpl_params):
""" Read necessary parameters from FITS file and plot butterfly
Parameters:
catalog_format = 'hess', 'fermi'
catalog_name = FITS file name
object_name = object name string in 'name' column
Note: Since every catalog has columns with different
names and units, a general SED plotting is not possible.
Instead for each catalog type a handler function that
deals converts to a standard format is called.
@todo: Possibly pass plotting parameters along here by
appending them to the ecpl_params dictionary
-> I don't think this works at the moment!!!"""
from atpy import Table
# Get the catalog from file and initialize some things
self.catalog_format = catalog_format
self.catalog_name = catalog_name
self.object_name = object_name
self.catalog = Table(catalog_name).data
# Build a dictionary of parameters needed for the plot
self.ecpl_params = ecpl_params
self.get_ecpl_params()
# Plot curve
self.plot_ecpl(plot_pivot=plot_pivot, **ecpl_params)
# Plot points if present
if self.plot_points is not None:
# Get the values needed for plotting
e = self.plot_points[0]
f = self.plot_points[1]
f_err = self.plot_points[2]
e_err = self.plot_points[3]
is_ul = self.plot_points[4]
for ii in range(e.size):
self.plot_point(e[ii], f[ii],
f_err=f_err[ii],
e_err=[[e_err[0][ii]], [e_err[1][ii]]],
ul=is_ul[ii])
# Remove so that it doesn't get plotted again.
self.plot_points = None
def get_ecpl_params(self):
"""Build self.ecpl_params dictionary
by parsing one of the supported catalogs"""
if self.catalog_format == 'hess':
self.get_ecpl_params_hess_cat()
elif self.catalog_format == 'fermi':
self.get_ecpl_params_fermi_cat()
# Change numpy types to regular types
# and replace nan values with 0
for key, value in self.ecpl_params.items():
if isinstance(value, np.float32):
value = float(value)
if isinstance(value, np.int16):
value = int(value)
def get_ecpl_params_fermi_cat(self):
""" Build self.ecpl_params dictionary from Fermi catalog fields """
i = self.find_object_index('source_name')
# Set all plot parameters:
self.ecpl_params['e_pivot'] = self.catalog.field('Pivot_Energy')[i]
self.ecpl_params['e_min'] = 1e2
self.ecpl_params['e_max'] = 1e5
self.ecpl_params['e_cut'] = 0.0
self.ecpl_params['e_cut_err'] = 0.0
self.ecpl_params['e_scale'] = 1
self.ecpl_params['norm'] = self.catalog.field('Flux_Density')[i]
self.ecpl_params['norm_err'] = self.catalog.field('Unc_Flux_Density')[i]
self.ecpl_params['norm_scale'] = 1
self.ecpl_params['index'] = self.catalog.field('Spectral_Index')[i]
self.ecpl_params['index_err'] = self.catalog.field('Unc_Spectral_Index')[i]
self.ecpl_params['color'] = 'green'
self.ecpl_params['butterfly'] = True
# Set flux point data
self.plot_points = self.get_flux_points_fermi(i)
# Add text label
fmt = '%s\n%s, %s\n' + \
r'S = %3.1f, C = %3.1f, $\Gamma = %1.2f \pm %1.2f$'
values = (self.object_name,
self.catalog.field('class1')[i],
self.catalog.field('assoc1')[i],
self.catalog.field('signif_avg')[i],
self.catalog.field('curvature_index')[i],
self.catalog.field('spectral_index')[i],
self.catalog.field('unc_spectral_index')[i]
)
self.ax.text(0.05, 0.95, fmt % values,
horizontalalignment='left',
verticalalignment='top',
transform=self.ax.transAxes)
def _parse_results(data, output='astropy.table'):
"""
Only one output type at the moment, the astropy.table table
"""
#TODO : what if results are empty
if output == 'astropy.table':
if not use_astropy:
#~ print('Astropy not installed, try other output format')
raise (
ImportError('Astropy not installed, try other output format'))
# get each line (i.e. each molecule)
rows = data.split('\n')
# get the names of the columns
column_names = rows[0]
column_names = column_names.split(':')
# clean them up a bit
for i in _np.arange(len(column_names)):
column_names[i] = column_names[i].replace('<br>', ' ')
column_names[i] = column_names[i].replace('<sub>', '_')
column_names[i] = column_names[i].replace('<sup>', '^')
column_names[i] = column_names[i].replace('</sup>', '')
column_names[i] = column_names[i].replace('</sub>', '')
column_names[i] = column_names[i].replace('μ', 'mu')
column_names[i] = column_names[i].replace('sid[0] is null', '')
column_names[i] = column_names[i].replace('sid[0] is null', '')
"""
Column Names should now be:
['Species',
'NRAO Recommended',
'Chemical Name',
'Freq-GHz',
'Freq Err',
'Meas Freq-GHz',
'Meas Freq Err',
'Resolved QNs',
'Unresolved Quantum Numbers',
'CDMS/JPL Intensity',
'S_ijmu^2 (D^2)',
'S_ij',
'Log_10 (A_ij)',
'Lovas/AST Intensity',
'E_L (cm^-1)',
'E_L (K)',
'E_U (cm^-1)',
'E_U (K)',
'HFS int',
'Upper State Degeneracy',
'Molecule Tag',
'Quantum Number Code',
'Linelist']
"""
rows = rows[1:-1]
rows = [i.split(':') for i in rows]
rows = _np.array(rows)
rows[rows == ''] = -999999
#~ print column_names
#~ return rows
column_dtypes = [
'str', # 'Species',
'str', # 'NRAO Recommended',
'str', # 'Chemical Name',
'float', # 'Freq-GHz',
'float', # 'Freq Err',
'float', # 'Meas Freq-GHz',
'float', # 'Meas Freq Err',
'str', # 'Resolved QNs',
'str', # 'Unresolved Quantum Numbers',
'float', # 'CDMS/JPL Intensity',
'float', # 'S_ijmu^2 (D^2)',
'float', # 'S_ij',
'float', # 'Log_10 (A_ij)',
'str', # 'Lovas/AST Intensity',
'float', # 'E_L (cm^-1)',
'float', # 'E_L (K)',
'float', # 'E_U (cm^-1)',
'float', # 'E_U (K)',
'float', # 'HFS int',
'float', # 'Upper State Degeneracy',
'int', # 'Molecule Tag',
'int', # 'Quantum Number Code',
'str'
] # 'Linelist']
funit = str(column_names[3][-3:])
column_units = [
None, # 'Species',
None, # 'NRAO Recommended',
None, # 'Chemical Name',
funit, # 'Freq-GHz',
funit, # 'Freq Err',
funit, # 'Meas Freq-GHz',
funit, # 'Meas Freq Err',
None, # 'Resolved QNs',
None, # 'Unresolved Quantum Numbers',
'?', # 'CDMS/JPL Intensity',
'Debye^2', # 'S_ijmu^2 (D^2)',
'?', # 'S_ij',
'log10(s^-1)', # 'Log_10 (A_ij)',
'?', # 'Lovas/AST Intensity',
'cm^-1', # 'E_L (cm^-1)',
'K', # 'E_L (K)',
'cm^-1', # 'E_U (cm^-1)',
'K', # 'E_U (K)',
'?', # 'HFS int',
None, # 'Upper State Degeneracy',
None, # 'Molecule Tag',
None, # 'Quantum Number Code',
None
] # 'Linelist']
column_names_original = column_names[:]
#~ column_names = [i.lower() for i in column_names]
#~ for i in _np.arange(len(column_names)):
#~ column_names[i] = column_names[i].replace('nrao recommended', 'nrao_rec')
#~ column_names[i] = column_names[i].replace('chemical name', 'name')
#~ if 'meas freq err' in column_names[i]:
#~ column_names[i] = 'mferr'
#~ elif 'meas freq' in column_names[i]:
#~ column_names[i] = 'mfreq'
#~ elif 'freq err' in column_names[i]:
#~ column_names[i] = 'ferr'
#~ elif 'freq' in column_names[i]:
#~ column_names[i] = 'freq'
#~ column_names[i] = column_names[i].replace('resolved qns', 'resqn')
#~ column_names[i] = column_names[i].replace('unresolved quantum numbers', 'resqn')
column_names = [
'species', 'nrao_rec', 'name', 'ofreq', 'oferr', 'mfreq', 'mferr',
'res_qn', 'uresqn', 'cdmsjplint', 'sijmu2', 'Sij', 'logaij',
'lovasastint', 'el_cm', 'el_k', 'eu_cm', 'eu_k', 'hfsint', 'gu',
'tag', 'qncode', 'list'
]
results = Table(data=rows, names=column_names, dtypes=column_dtypes)
for i in _np.arange(len(column_units)):
results.field(i).units = column_units[i]
return results
else:
print('Nothing else than astropy.table output is implemented atm')
return results
def _parse_results(data, output='astropy.table'):
"""
Only one output type at the moment, the astropy.table table
"""
#TODO : what if results are empty
if output == 'astropy.table':
if not use_astropy:
#~ print('Astropy not installed, try other output format')
raise(ImportError('Astropy not installed, try other output format'))
# get each line (i.e. each molecule)
rows = data.split('\n')
# get the names of the columns
column_names = rows[0]
column_names = column_names.split(':')
# clean them up a bit
for i in _np.arange(len(column_names)):
column_names[i] = column_names[i].replace('<br>', ' ')
column_names[i] = column_names[i].replace('<sub>', '_')
column_names[i] = column_names[i].replace('<sup>', '^')
column_names[i] = column_names[i].replace('</sup>', '')
column_names[i] = column_names[i].replace('</sub>', '')
column_names[i] = column_names[i].replace('μ', 'mu')
column_names[i] = column_names[i].replace('sid[0] is null', '')
column_names[i] = column_names[i].replace('sid[0] is null', '')
"""
Column Names should now be:
['Species',
'NRAO Recommended',
'Chemical Name',
'Freq-GHz',
'Freq Err',
'Meas Freq-GHz',
'Meas Freq Err',
'Resolved QNs',
'Unresolved Quantum Numbers',
'CDMS/JPL Intensity',
'S_ijmu^2 (D^2)',
'S_ij',
'Log_10 (A_ij)',
'Lovas/AST Intensity',
'E_L (cm^-1)',
'E_L (K)',
'E_U (cm^-1)',
'E_U (K)',
'HFS int',
'Upper State Degeneracy',
'Molecule Tag',
'Quantum Number Code',
'Linelist']
"""
rows = rows[1:-1]
rows = [i.split(':') for i in rows]
rows = _np.array(rows)
rows[rows == ''] = -999999
#~ print column_names
#~ return rows
column_dtypes = ['str', # 'Species',
'str', # 'NRAO Recommended',
'str', # 'Chemical Name',
'float', # 'Freq-GHz',
'float', # 'Freq Err',
'float', # 'Meas Freq-GHz',
'float', # 'Meas Freq Err',
'str', # 'Resolved QNs',
'str', # 'Unresolved Quantum Numbers',
'float', # 'CDMS/JPL Intensity',
'float', # 'S_ijmu^2 (D^2)',
'float', # 'S_ij',
'float', # 'Log_10 (A_ij)',
'str', # 'Lovas/AST Intensity',
'float', # 'E_L (cm^-1)',
'float', # 'E_L (K)',
'float', # 'E_U (cm^-1)',
'float', # 'E_U (K)',
'float', # 'HFS int',
'float', # 'Upper State Degeneracy',
'int', # 'Molecule Tag',
'int', # 'Quantum Number Code',
'str'] # 'Linelist']
funit = str(column_names[3][-3:])
column_units = [None, # 'Species',
None, # 'NRAO Recommended',
None, # 'Chemical Name',
funit, # 'Freq-GHz',
funit, # 'Freq Err',
funit, # 'Meas Freq-GHz',
funit, # 'Meas Freq Err',
None, # 'Resolved QNs',
None, # 'Unresolved Quantum Numbers',
'?', # 'CDMS/JPL Intensity',
'Debye^2', # 'S_ijmu^2 (D^2)',
'?', # 'S_ij',
'log10(s^-1)', # 'Log_10 (A_ij)',
'?', # 'Lovas/AST Intensity',
'cm^-1', # 'E_L (cm^-1)',
'K', # 'E_L (K)',
'cm^-1', # 'E_U (cm^-1)',
'K', # 'E_U (K)',
'?', # 'HFS int',
None, # 'Upper State Degeneracy',
None, # 'Molecule Tag',
None, # 'Quantum Number Code',
None] # 'Linelist']
column_names_original = column_names[:]
#~ column_names = [i.lower() for i in column_names]
#~ for i in _np.arange(len(column_names)):
#~ column_names[i] = column_names[i].replace('nrao recommended', 'nrao_rec')
#~ column_names[i] = column_names[i].replace('chemical name', 'name')
#~ if 'meas freq err' in column_names[i]:
#~ column_names[i] = 'mferr'
#~ elif 'meas freq' in column_names[i]:
#~ column_names[i] = 'mfreq'
#~ elif 'freq err' in column_names[i]:
#~ column_names[i] = 'ferr'
#~ elif 'freq' in column_names[i]:
#~ column_names[i] = 'freq'
#~ column_names[i] = column_names[i].replace('resolved qns', 'resqn')
#~ column_names[i] = column_names[i].replace('unresolved quantum numbers', 'resqn')
column_names = ['species',
'nrao_rec',
'name',
'ofreq',
'oferr',
'mfreq',
'mferr',
'res_qn',
'uresqn',
'cdmsjplint',
'sijmu2',
'Sij',
'logaij',
'lovasastint',
'el_cm',
'el_k',
'eu_cm',
'eu_k',
'hfsint',
'gu',
'tag',
'qncode',
'list']
results = Table(data = rows ,
names = column_names,
dtypes = column_dtypes)
for i in _np.arange(len(column_units)):
results.field(i).units = column_units[i]
return results
else:
print('Nothing else than astropy.table output is implemented atm')
return results
def main():
print 'loading full catalog...'
step_1 = timer()
fll_n = 'results/full_120_1.2_0.5_0.083_20-21_1.cat'
full_cat = fits.open(fll_n)
full_db = Table(full_cat[2].data)
full_db = full_db.to_pandas()
print 'loading merged catalog...'
step_2 = timer()
print 'elapsed time {}'.format(step_2 - step_1)
mrgd_n = 'results/merged_120_1.2_0.5_0.083_20-21_1.cat'
merged_cat = fits.open(mrgd_n)
merged_db = Table(merged_cat[2].data)
print 'getting "/h from miliarc/year'
step_3 = timer()
print 'elapsed time {}'.format(step_3 - step_2)
pmalpha = Series(merged_db.field('PMALPHA_J2000') / 8.75e6) # 8.75e6
pmdelta = Series(merged_db.field('PMDELTA_J2000') / 8.75e6)
pmealpha = Series(merged_db.field('PMALPHAERR_J2000') / 8.75e6)
pmedelta = Series(merged_db.field('PMDELTAERR_J2000') / 8.75e6)
print 'getting proper motions'
step_4 = timer()
print 'elapsed time {}'.format(step_4 - step_3)
pm = Series(sqrt(array(pmalpha**2 + pmdelta**2), dtype=float))
pme = Series(sqrt(array(pmealpha**2 + pmedelta**2), dtype=float))
print 'creating new DataFrame'
step_5 = timer()
print 'elapsed time {}'.format(step_5 - step_4)
method_j = []
method_a_p = Process(target=method_a,
args=(
full_db,
pm,
pme,
pmalpha,
pmdelta,
pmealpha,
pmedelta,
))
method_j.append(method_a_p)
method_a_p.start()
method_b_p = Process(target=method_b,
args=(
full_db,
pm,
pme,
pmalpha,
pmdelta,
pmealpha,
pmedelta,
))
method_j.append(method_b_p)
method_b_p.start()
active_method = list([job.is_alive() for job in method_j])
while True in active_method:
active_method = list([job.is_alive() for job in method_j])
pass
step_6 = timer()
print 'elapsed time for a and b {}'.format(step_6 - step_5)
