Accessing stellar spectra¶
In the previous notebook we saw how to configure our DataClass to open ESPRESSO observations. In this, we will see how we can interact with the stellar spectra and the header keywords.
In [27]:
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from pathlib import Path
from ASTRA.data_objects.DataClass import DataClass
from ASTRA.Instruments import ESPRESSO
inst_options = {}
data_in_path = list(Path("/home/amiguel/spectra_collection/ESPRESSO/proxima").glob("*.fits"))
data = DataClass(data_in_path, instrument=ESPRESSO, instrument_options=inst_options, storage_path="")
from pathlib import Path
from ASTRA.data_objects.DataClass import DataClass
from ASTRA.Instruments import ESPRESSO
inst_options = {}
data_in_path = list(Path("/home/amiguel/spectra_collection/ESPRESSO/proxima").glob("*.fits"))
data = DataClass(data_in_path, instrument=ESPRESSO, instrument_options=inst_options, storage_path="")
2025-04-14 21:53:35.443 | DEBUG | ASTRA.utils.UserConfigs:receive_user_inputs:216 - Generating internal configs of - 2025-04-14 21:53:35.444 | INFO | ASTRA.utils.UserConfigs:receive_user_inputs:221 - Checking for any parameter that will take default value 2025-04-14 21:53:35.445 | DEBUG | ASTRA.utils.UserConfigs:receive_user_inputs:228 - Configuration <SAVE_DISK_SPACE> using the default value: DISK_SAVE_MODE.DISABLED 2025-04-14 21:53:35.446 | DEBUG | ASTRA.utils.UserConfigs:receive_user_inputs:228 - Configuration <WORKING_MODE> using the default value: WORKING_MODE.ONE_SHOT 2025-04-14 21:53:35.447 | INFO | ASTRA.data_objects.DataClass:__init__:126 - DataClass opening 3 files from a list/tuple 2025-04-14 21:53:35.448 | INFO | ASTRA.base_models.Frame:__init__:253 - Creating frame from: /home/amiguel/spectra_collection/ESPRESSO/proxima/r.ESPRE.2019-07-03T01:43:39.634_S2D_A.fits 2025-04-14 21:53:35.449 | WARNING | ASTRA.Components.SpectrumComponent:regenerate_order_status:96 - Resetting order status of Frame - ESPRESSO 2025-04-14 21:53:35.466 | DEBUG | ASTRA.base_models.Frame:assess_bad_orders:711 - Rejecting spectral orders 2025-04-14 21:53:35.468 | INFO | ASTRA.base_models.Frame:assess_bad_orders:741 - Frame -553975442344993652 rejected 37 orders for having SNR smaller than 5: 0-36 2025-04-14 21:53:35.469 | INFO | ASTRA.base_models.Frame:__init__:253 - Creating frame from: /home/amiguel/spectra_collection/ESPRESSO/proxima/r.ESPRE.2019-07-14T02:07:49.063_S2D_A.fits 2025-04-14 21:53:35.471 | WARNING | ASTRA.Components.SpectrumComponent:regenerate_order_status:96 - Resetting order status of Frame - ESPRESSO 2025-04-14 21:53:35.488 | DEBUG | ASTRA.base_models.Frame:assess_bad_orders:711 - Rejecting spectral orders 2025-04-14 21:53:35.490 | INFO | ASTRA.base_models.Frame:assess_bad_orders:741 - Frame -2975062627551510774 rejected 15 orders for having SNR smaller than 5: 0-12, 32-33 2025-04-14 21:53:35.492 | INFO | ASTRA.base_models.Frame:__init__:253 - Creating frame from: /home/amiguel/spectra_collection/ESPRESSO/proxima/r.ESPRE.2019-07-20T01:43:40.032_S2D_A.fits 2025-04-14 21:53:35.493 | WARNING | ASTRA.Components.SpectrumComponent:regenerate_order_status:96 - Resetting order status of Frame - ESPRESSO 2025-04-14 21:53:35.508 | DEBUG | ASTRA.base_models.Frame:assess_bad_orders:711 - Rejecting spectral orders 2025-04-14 21:53:35.511 | INFO | ASTRA.base_models.Frame:assess_bad_orders:741 - Frame 3535164837059905149 rejected 16 orders for having SNR smaller than 5: 0-13, 32-33 2025-04-14 21:53:35.513 | DEBUG | ASTRA.data_objects.DataClass:__init__:154 - Selected 3 observations from disk 2025-04-14 21:53:35.514 | INFO | ASTRA.data_objects.DataClass:_collect_MetaData:369 - Collecting MetaData from the observations 2025-04-14 21:53:35.515 | WARNING | ASTRA.data_objects.Target:__init__:73 - Target dictionary not found in <None> 2025-04-14 21:53:35.515 | DEBUG | ASTRA.data_objects.Target:clean_targ_list:98 - Parsing through loaded OBJECTs 2025-04-14 21:53:35.517 | INFO | ASTRA.data_objects.Target:__init__:92 - Validated target to be V V645 Cen 2025-04-14 21:53:35.517 | INFO | ASTRA.data_objects.DataClass:show_loadedData_table:885 - -------------------------------------------------------------------- -------------------------------------------------------------------- subInstrument Total OBS Valid OBS [warnings] INVALID OBS -------------------------------------------------------------------- ESPRESSO18 0 0 [0] 0 ESPRESSO19 3 3 [0] 0 Total 3 3 [0] 0 -------------------------------------------------------------------- 2025-04-14 21:53:35.518 | INFO | ASTRA.data_objects.DataClass:load_instrument_extra_information:894 - Checking if the instrument has extra data to load 2025-04-14 21:53:35.519 | INFO | ASTRA.data_objects.DataClass:load_instrument_extra_information:901 - Current instrument does not need to load anything from the outside
In ASTRA, each observation is called a Frame, with a corresponding (unique) identifier (constructed from the hash of the filename, can take negative values). Furthermore, the division between multiple datasets of each instrument (known as sub-Instruments, e.g., ESPRESSO18, ESPRESSO19) is done automatically.
Through the DataClass interface we can ask for:
- Data for a corresponding frameID
- Data for a given sub-Instrument
- All data that was loaded
Furthermore, at runtime we can reject observations (either by user-imposed conditions or by internal QC checks).
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from ASTRA.utils.spectral_conditions import FNAME_condition
print(data)
print("Loaded sub-Instruments", data.get_subInstruments_with_valid_frames())
print("Current frameIDs", data.get_valid_frameIDS())
print("Current (rejected) frameIDs", data.get_invalid_frameIDs())
condition = FNAME_condition(data_in_path[0].name)
data.reject_observations(conditions=condition)
print("---//---")
print("Current frameIDs", data.get_valid_frameIDS())
print("Current (rejected) frameIDs", data.get_invalid_frameIDs())
from ASTRA.utils.spectral_conditions import FNAME_condition
print(data)
print("Loaded sub-Instruments", data.get_subInstruments_with_valid_frames())
print("Current frameIDs", data.get_valid_frameIDS())
print("Current (rejected) frameIDs", data.get_invalid_frameIDs())
condition = FNAME_condition(data_in_path[0].name)
data.reject_observations(conditions=condition)
print("---//---")
print("Current frameIDs", data.get_valid_frameIDS())
print("Current (rejected) frameIDs", data.get_invalid_frameIDs())
2025-04-14 21:53:35.531 | DEBUG | ASTRA.base_models.Frame:add_to_status:574 - Updating Frame (r.ESPRE.2019-07-03T01:43:39.634_S2D_A.fits) status to USER_BLOCKED: Filename rejected 2025-04-14 21:53:35.532 | INFO | ASTRA.data_objects.DataClass:reject_observations:292 - User conditions removed 1 / 3 frames 2025-04-14 21:53:35.533 | INFO | ASTRA.data_objects.DataClass:reject_observations:298 - Updated observation on disk! 2025-04-14 21:53:35.534 | INFO | ASTRA.data_objects.DataClass:show_loadedData_table:885 - -------------------------------------------------------------------- -------------------------------------------------------------------- subInstrument Total OBS Valid OBS [warnings] INVALID OBS -------------------------------------------------------------------- ESPRESSO18 0 0 [0] 0 ESPRESSO19 3 2 [0] 1 Total 3 2 [0] 1 --------------------------------------------------------------------
Data Class from <class 'ASTRA.Instruments.ESPRESSO.ESPRESSO'> holding 0 OBS from ESPRESSO18, 3 OBS from ESPRESSO19 Loaded sub-Instruments ['ESPRESSO19'] Current frameIDs [-553975442344993652, -2975062627551510774, 3535164837059905149] Current (rejected) frameIDs [] ---//--- Current frameIDs [-2975062627551510774, 3535164837059905149] Current (rejected) frameIDs [-553975442344993652]
Collect header keywords¶
By default, ASTRA collects some header keywords under a common name across multiple instruments, standardizing the information needed to construct the stellar template (see here for a full list). If needed, the collect_KW_observations also accepts a Condition object to select the observations from which we will load header information.
Some values have an internal name that allows easy access:
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available_subInstruments = data.get_subInstruments_with_valid_frames()
ccf_rv = data.collect_KW_observations("DRS_RV", subInstruments=available_subInstruments)
available_subInstruments = data.get_subInstruments_with_valid_frames()
ccf_rv = data.collect_KW_observations("DRS_RV", subInstruments=available_subInstruments)
Some of the internal values are stored as astropy.Quantity data products, which can be converted to float values through:
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from ASTRA.utils.units import convert_data, kilometer_second
convert_data(data=ccf_rv, as_value=True, new_units= kilometer_second)
from ASTRA.utils.units import convert_data, kilometer_second
convert_data(data=ccf_rv, as_value=True, new_units= kilometer_second)
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[-21.3856633997537, -21.3845961288029]
In alternative, we can ask ASTRA to load the keywords directly from the header
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data.collect_KW_observations("ESO QC CCF RV", subInstruments=available_subInstruments, from_header=True)
data.collect_KW_observations("ESO QC CCF RV", subInstruments=available_subInstruments, from_header=True)
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[-21.3856633997537, -21.3845961288029]
Accessing stellar spectra¶
Through the DataClass interface we can directly access the wavelength, flux and uncertainties of the stellar spectra. Furthermore, we also gain access to a pixel-mask, containing the pixels that were rejected by the multiple processes
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import matplotlib.pyplot as plt
fig, axis = plt.subplots()
for frameID in data.get_valid_frameIDS():
wave, flux, err, mask = data.get_frame_OBS_order(frameID=frameID,
order=50,
)
good_pixel_mask = ~mask
axis.scatter(wave[good_pixel_mask], flux[good_pixel_mask], s=5)
axis.set_xlabel(r"wavelength $[\AA]$")
axis.set_ylabel("Flux")
import matplotlib.pyplot as plt
fig, axis = plt.subplots()
for frameID in data.get_valid_frameIDS():
wave, flux, err, mask = data.get_frame_OBS_order(frameID=frameID,
order=50,
)
good_pixel_mask = ~mask
axis.scatter(wave[good_pixel_mask], flux[good_pixel_mask], s=5)
axis.set_xlabel(r"wavelength $[\AA]$")
axis.set_ylabel("Flux")
Out[37]:
Text(0, 0.5, 'Flux')
