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Day 1-2 (07/11-12)
- Day 1: Reviewed papers Chris sent, met with Chris and Eske on Zoom to discuss expectations, project outline on wiki page
- Day 2: Set up FASRC account. Followed the "User Quick Start Guide" on FASRC page
- set up 2FA, FASRC VPN, terminal access, and watched a few data transfer videos
- Used rsync to load Eske's target directory /n/holystore01/LABS/stubbs_lab/Lab/Auxtel_data/spectrum_data onto computer
- Went into lab and met Eske, Ali, and Mark in person. Very cool people.
- Opened Jupyter notebook; looked up astropy documentation to open fits files
- Successfully opened fits file to view contents inside, retrieved and displayed table data, displayed file header information, and plotted data
Day 3 (07/13)
- In Jupyter notebook, filtered all the fits files in '/spectrum_data' to print out star name and date/time of observation
- Stored names of files in record type that are all associated with one star; looks like there are 4 observed stars in the dataset Eske gave me.
Day 4 (07/14)
- Found data files missing the table of equivalent widths that Eske gave me
spec_data_2022062800336.fits HD205905 2022-06-29
- Plotted H2O and O2 equivalent widths against air masses for each star on each night (four stars on four nights based on data Eske gave me)
- Noticed that some files do not have O2 data (may have variants like O2(Z) or O2(B), though)
First Impressions of Data:
- The equivalent widths of H20 seem to have much more variability than those of O2
- May be some outliers in the data (particularly for the first two plots with the negative equivalent widths, may need to check on those data points
Findings
Links to Notebook:
Github repo of project code: https://github.com/ariscjj/stubbs
In notebook: https://github.com/ariscjj/stubbs/blob/master/Extract_H20.ipynb (added Chris as a collaborator on the github)
07/19/2022
- Met with Chris over zoom, got a few tasks to do:
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ObsId failiure mode notes -
Plot H- alpha, H-beta- should have no dependence on airmass; for quality control
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Separate O2 lines (B and Z) lines
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perform five sigma clipping (probably in sci py or astropy to clean data); data trimming
five sigma clipping: remove outliers outside of five STDs of data
If in future there are uncertainties in equivalent widths to be reported, compare reported with experimental STDs
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Add error bars to plots, error bars represent underlying Gaussian distribution
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create linear fit through data
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a * (airmass) + b * \sqrt{airmass} + c
create a polynomial fit
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Create list of questions for data reduction
Where are the uncertainties in equivalent widths?
Why are there different O2 lines for some plots and not others?
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Keep track of stars and airmass span
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07/21/22
- successfully fit linear models to the data, can grab equations, and the R^2 value
- Managed to fit the data to a single equation of the 1/2 order (a * x ^ (1/2) + b) but not (a * x ^ (1/2) + b * x + c)
- attempting to fit the polynomial data, having some bugs fo fitting a polynomial to the 1/2 order and the first order
- bugs fixed, have (a * x ^ (1/2) + b * x + c) functions for each type of molecule's equivalent widths against airmass
- Met with Chris and Eske to discuss new tasks for the week (more details can be found in personal .md file "Meeting 0722")
- Continue working on the "Data_Clipping.ipynb" notebook to try to create a table that has a column of files and their missing information (missing data or negative eq widths)
- Created a dictionary of files that stores the file name and an array with error messages → converted into an 2D array of filename and errors
- Successfully created the table to output in Jupyter notebook, but having difficulties exporting an image of the data table, which would be nice to have. Tried the following:
from PIL import Image
import imgkit
import dataframe_image as dfi
which did not work even after I installed new packages - Here's what I have now:
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