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Clean up formatting of spectra_generator.md and related .py files #354

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Dec 4, 2024
Merged

Clean up formatting of spectra_generator.md and related .py files #354

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db088ff
organize imports. remove unused.
troyraen Dec 3, 2024
6406fb1
Run autopep8 with --max-line-length=100
troyraen Dec 3, 2024
1f5e8d6
Wrap long lines of text
troyraen Dec 4, 2024
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33 changes: 15 additions & 18 deletions spectroscopy/code_src/data_structures_spec.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,5 @@
#setup to store the light curves in a data structure
# setup to store the light curves in a data structure
import pandas as pd
from astropy.table import vstack
from astropy.timeseries import TimeSeries


class MultiIndexDFObject:
Expand All @@ -12,35 +10,36 @@ class MultiIndexDFObject:
--------
# Initialize Pandas MultiIndex data frame for storing the light curve
df_lc = MultiIndexDFObject()

#make a single multiindex dataframe
dfsingle = pd.DataFrame(dict(flux=[0.1], err=[0.1], time=[time_mjd], objectid=[ccount + 1], /
band=[mission], label=lab)).set_index(["objectid", "label", "band", "time"])

# Append to existing MultiIndex light curve object
df_lc.append(dfsingle)

#Show the contents
df_lc.data

"""

def __init__(self, data=None):
"""Create a MultiIndex DataFrame that is empty if data is None, else contains the data.

Parameters
----------
data : pd.DataFrame, optional
Dataframe to store in the `data` attribute.
"""
index = ["objectid", "label", "filter", "mission"]
columns = ["wave", "flux" , "err","instrument"]
columns = ["wave", "flux", "err", "instrument"]
self.data = pd.DataFrame(columns=index + columns).set_index(index)
if data is not None:
self.append(data)
def append(self,x):

def append(self, x):
"""Add a new band of light curve data to the dataframe

Parameters
----------
x : Pandas dataframe
Expand All @@ -52,7 +51,7 @@ def append(self,x):
else:
# assume x is a pd.DataFrame
new_data = x

# if either new_data or self.data is empty we should not try to concat
if new_data.empty:
# leave self.data as is
Expand All @@ -61,18 +60,16 @@ def append(self,x):
# replace self.data with new_data
self.data = new_data
return

# if we get here, both new_data and self.data contain data, so concat
self.data = pd.concat([self.data, new_data])
def remove(self,x):

def remove(self, x):
""" Drop a light curve from the dataframe

Parameters
----------
x : list of values
Index values identifying rows to be dropped.
"""
self.data = self.data.drop(x)


118 changes: 57 additions & 61 deletions spectroscopy/code_src/desi_functions.py
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Original file line number Diff line number Diff line change
@@ -1,19 +1,14 @@
import os

import numpy as np

import astropy.units as u
import numpy as np
import pandas as pd
from astropy import nddata
from astropy.coordinates import SkyCoord
from astropy.table import Table
from astropy import nddata

import pandas as pd

from sparcl.client import SparclClient
from specutils import Spectrum1D

from data_structures_spec import MultiIndexDFObject

from specutils import Spectrum1D

def DESIBOSS_get_spec(sample_table, search_radius_arcsec):
'''
Expand All @@ -32,74 +27,75 @@ def DESIBOSS_get_spec(sample_table, search_radius_arcsec):
-------
df_lc : MultiIndexDFObject
The main data structure to store all spectra

'''


## Set up client
# Set up client
client = SparclClient()
#print(client.all_datasets) # print data sets
## Initialize multi-index object:
# print(client.all_datasets) # print data sets

# Initialize multi-index object:
df_spec = MultiIndexDFObject()



for stab in sample_table:
## Search
data_releases = ['DESI-EDR','BOSS-DR16']
#data_releases = ['DESI-EDR','BOSS-DR16','SDSS-DR16'] # we want to use DR17 directly using SDSS query

# Search
data_releases = ['DESI-EDR', 'BOSS-DR16']
# data_releases = ['DESI-EDR','BOSS-DR16','SDSS-DR16'] # we want to use DR17 directly using SDSS query

search_coords = stab["coord"]
dra = (search_radius_arcsec*u.arcsec).to(u.degree)
ddec = (search_radius_arcsec*u.arcsec).to(u.degree)
out = ['sparcl_id', 'ra', 'dec', 'redshift', 'spectype', 'data_release', 'redshift_err']
cons = {'spectype': ['GALAXY','STAR','QSO'],
cons = {'spectype': ['GALAXY', 'STAR', 'QSO'],
'data_release': data_releases,
#'redshift': [0.5, 0.9],
'ra' : [search_coords.ra.deg-dra.value , search_coords.ra.deg+dra.value ],
'dec' : [search_coords.dec.deg-ddec.value , search_coords.dec.deg+ddec.value ]
}
found_I = client.find(outfields=out, constraints=cons, limit=20) # search
#print(found_I)
## Extract nice table and the spectra
# 'redshift': [0.5, 0.9],
'ra': [search_coords.ra.deg-dra.value, search_coords.ra.deg+dra.value],
'dec': [search_coords.dec.deg-ddec.value, search_coords.dec.deg+ddec.value]
}
found_I = client.find(outfields=out, constraints=cons, limit=20) # search
# print(found_I)

# Extract nice table and the spectra
if len(found_I.records) > 0:
result_tab = Table(names=found_I.records[0].keys() , dtype=[ type(found_I.records[0][key]) for key in found_I.records[0].keys()])
_ = [ result_tab.add_row([f[key] for key in f.keys()]) for f in found_I.records]

sep = [search_coords.separation(SkyCoord(tt["ra"], tt["dec"], unit=u.deg, frame='icrs')).to(u.arcsecond).value for tt in result_tab]
result_tab = Table(names=found_I.records[0].keys(), dtype=[type(
found_I.records[0][key]) for key in found_I.records[0].keys()])
_ = [result_tab.add_row([f[key] for key in f.keys()]) for f in found_I.records]

sep = [search_coords.separation(SkyCoord(tt["ra"], tt["dec"], unit=u.deg, frame='icrs')).to(
u.arcsecond).value for tt in result_tab]
result_tab["separation"] = sep
## Retrieve Spectra

# Retrieve Spectra
inc = ['sparcl_id', 'specid', 'data_release', 'redshift', 'flux',
'wavelength', 'model', 'ivar', 'mask', 'spectype', 'ra', 'dec']
results_I = client.retrieve(uuid_list=found_I.ids, include=inc)
specs = [Spectrum1D(spectral_axis = r.wavelength*u.AA,
flux = np.array(r.flux)* 10**-17 * u.Unit('erg cm-2 s-1 AA-1'),
uncertainty = nddata.InverseVariance(np.array(r.ivar)),
redshift = r.redshift,
mask = r.mask)
for r in results_I.records]


## Choose objects
specs = [Spectrum1D(spectral_axis=r.wavelength*u.AA,
flux=np.array(r.flux) * 10**-17 * u.Unit('erg cm-2 s-1 AA-1'),
uncertainty=nddata.InverseVariance(np.array(r.ivar)),
redshift=r.redshift,
mask=r.mask)
for r in results_I.records]

# Choose objects
for dr in data_releases:

sel = np.where(result_tab["data_release"] == dr)[0]
if len(sel) > 0: # found entry
idx_closest = sel[ np.where(result_tab["separation"][sel] == np.nanmin(result_tab["separation"][sel]))[0][0] ]

if len(sel) > 0: # found entry
idx_closest = sel[np.where(result_tab["separation"][sel] == np.nanmin(
result_tab["separation"][sel]))[0][0]]

# create MultiIndex Object
dfsingle = pd.DataFrame(dict(wave=[specs[idx_closest].spectral_axis] ,
flux=[specs[idx_closest].flux],
err=[np.sqrt(1/specs[idx_closest].uncertainty.quantity)],
label=[stab["label"]],
objectid=[stab["objectid"]],
mission=[dr],
instrument=[dr],
filter=["optical"],
)).set_index(["objectid", "label", "filter", "mission"])
dfsingle = pd.DataFrame(dict(wave=[specs[idx_closest].spectral_axis],
flux=[specs[idx_closest].flux],
err=[
np.sqrt(1/specs[idx_closest].uncertainty.quantity)],
label=[stab["label"]],
objectid=[stab["objectid"]],
mission=[dr],
instrument=[dr],
filter=["optical"],
)).set_index(["objectid", "label", "filter", "mission"])
df_spec.append(dfsingle)
return(df_spec)

return (df_spec)
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