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snow_white GaiaID str fix #42

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snow_white GaiaID str fix #42

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7fa07b0
made Gaia sourceID a str for comparison and expanded limits of second…
NPGFusillo Dec 11, 2024
720cae5
small typo fix
NPGFusillo Dec 11, 2024
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11 changes: 6 additions & 5 deletions python/astra/pipelines/snow_white/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,9 +134,10 @@ def snow_white(
log_H=np.array(tl[u'logg_H']).astype(float)
eT_H=np.array(tl[u'eteff_H']).astype(float)
elog_H=np.array(tl[u'elogg_H']).astype(float)
if spectrum.source.gaia_dr3_source_id in sourceID: #if there is a photometric solution use that as starting point
first_T=T_H[sourceID==spectrum.source.gaia_dr3_source_id][0]
first_g=log_H[sourceID==spectrum.source.gaia_dr3_source_id][0]*100
GaiaID=str(spectrum.source.gaia_dr3_source_id)
if GaiaID in sourceID: #if there is a photometric solution use that as starting point
first_T=T_H[sourceID==GaiaID][0]
first_g=log_H[sourceID==GaiaID][0]*100
initial=1
if first_T > 80000:
first_T=80000
Expand Down Expand Up @@ -201,12 +202,12 @@ def snow_white(
if first_T <=13000.:
tmp_Tg,tmp_chi= grid_param[grid_param[:,0]>13000.], grid_chi[grid_param[:,0]>13000.]
second_T= tmp_Tg[tmp_chi==np.min(tmp_chi)][0][0]
fit_params['teff'] = lmfit.Parameter(name="teff",value=second_T,min=13000,max=80000)
fit_params['teff'] = lmfit.Parameter(name="teff",value=second_T,min=12000,max=80000)
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I understand fixing the string comparison bug we discussed by email, but I’m confused by the logic of splitting up to 12,000 K in one optimisation and then 14,000 K in another. What am I missing here?

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Hi Andy,
Unless I messed up I did not change where the split happens. I still look for a secondary solution starting below 13000 if the first one was hotter and above 13000 if the first solution was colder.

What I did was make the maximum value in the fit less a of strict wall. So yes I am looking for a solution above 13000 so I start the fit from the best initial value above 13000, but the fit may still only converge below 13000 so I am now giving some margin to go below 13000 during the fit so we don't just get a bunch of exactly Teff=13000K for fits that would converge just below it.

Similarly I expanded the maximum limit to 14000 if I am looking for a second solution starting below 13000.


elif first_T >13000.:
tmp_Tg,tmp_chi= grid_param[grid_param[:,0]<13000.], grid_chi[grid_param[:,0]<13000.]
second_T= tmp_Tg[tmp_chi==np.min(tmp_chi)][0][0]
fit_params['teff'] = lmfit.Parameter(name="teff",value=second_T,min=3000,max=13000)
fit_params['teff'] = lmfit.Parameter(name="teff",value=second_T,min=3000,max=14000)

if second_T>=16000 and second_T<=40000:
line_crop = np.loadtxt(os.path.join(PIPELINE_DATA_DIR, 'line_crop.dat'),skiprows=1)
Expand Down