Final Updates for XRTpy JOSS Submission (#267)

* Update the two references -ssr

* Remove .py from class name reference in joss paper.

* Updated effective area plot-fine detail

* Updated temperature response-fine detail

* Updated temperature response plot reference comment

* Updated effective area plot reference comment

* Updated effective area units

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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joss/paper.bib

@@ -55,7 +55,7 @@ @ARTICLE{Golub:2007
 @ARTICLE{Grevesse:2007,
   author = {{Grevesse}, N. and {Asplund}, M. and {Sauval}, A.~J.},
   title = "{The Solar Chemical Composition}",
-  journal = {\ssr},
+  journal = {Space Science Reviews},
   keywords = {Sun: abundances, photosphere, corona},
   year = 2007,
   month = jun,
@@ -72,7 +72,7 @@ @ARTICLE{Guidoni:2015
 Plowman, J. E. and Yoshimura, K.},
   title = "{Temperature and Electron Density Diagnostics of a
 Candle-flame-shaped Flare}",
-  journal = {\ssr},
+  journal = {Space Science Reviews},
   keywords = {magnetic reconnection; shock waves; Sun: flares; Sun: UV radiation; Sun: X-rays; gamma rays},
   year = 2015,
   month = Feb,

joss/paper.md

@@ -1,5 +1,5 @@
 ---
-title: 'XRTpy : A Hinode-X-Ray Telescope Python Package'
+title: 'XRTpy: A Hinode-X-Ray Telescope Python Package'
 
 tags:
   - Python
@@ -83,17 +83,17 @@ The package is structured into distinct modules, each serving a specific purpose
 
  - `xrtpy.image_correction.deconvolve`: Deconvolution is a powerful technique for improving image sharpness. The `deconvolve` function applies deconvolution to XRT images, effectively reducing blurring effects caused by the telescope's point spread function.
 
- - `xrtpy.image_correction.remove_lightleak.py`: The `remove_lightleak` function in this module eliminates light leak (visible stray light) from XRT synoptic composite images. This results in cleaner and more precise images suitable for in-depth analysis.
+ - `xrtpy.image_correction.remove_lightleak`: The `remove_lightleak` function in this module eliminates light leak (visible stray light) from XRT synoptic composite images. This results in cleaner and more precise images suitable for in-depth analysis.
 
 XRTpy supports multiple elemental abundance sets, including CHIANTI coronal abundances [@Feldman:1992], hybrid abundances (based on [@Fludra-and-Schmelz:1999] and [@Schmelz:2012]), and photospheric abundances (based on [@Grevesse:2007], [@Scott:2015], and [@Asplund:2009]).
 
 Researchers have the flexibility to select the abundance model that best aligns with their research requirements.
 
 XRTpy's capabilities are designed to empower researchers and scientists to fully exploit the potential of XRT data, offering the scientific community a unique opportunity to study the Sun's dynamic and complex behavior in a user-friendly and efficient manner.
 
-![The Effective area for all XRT filters plotted using XRTpy. .\label{fig:Figure 1}](xrtpy_effective_area_plot.pdf)
+![This graph displays the effective area for all X-ray focal-plane filters used in the XRT, plotted across a wavelength range of 0 to 70 angstroms. Each filter, represented by a unique color, shows distinct peaks that are important for choosing the best filter based on the wavelength being observed, and the curves demonstrate the instrument's ability to distinguish between different X-ray wavelengths. .\label{fig:Figure 1}](xrtpy_effective_area_plot.pdf)
 
-![The temperature response is plotted for all XRT filters using XRTpy.  The plot also shows the effects of using different abundance models from CHIANTI for each filter. .\label{fig:Figure 2}](xrtpy_temperature_response_plot.pdf)
+![The temperature response is plotted for all XRT X-ray focal-plane filters using XRTpy. Each curve represents the total instrument response as a function of temperature, integrated with different CHIANTI abundance models: Coronal (solid lines), Hybrid (dashed lines), and Photospheric (dotted lines). Highlights the sensitivity variations under different coronal conditions. .\label{fig:Figure 2}](xrtpy_temperature_response_plot.pdf)
 
 ![In Figure 3, the application of the `temperature_from_filter_ratio` function is illustrated, demonstrating its role in calculating electron temperature and volume emission measure through filter ratios. The dataset, collected on January 28, 2011, between 01:31:55 and 01:32:05 UTC, comprises two images captured with specific filters. These images offer unique insights into solar conditions during the observed moments, as shown by [@Guidoni:2015]. .\label{fig:Figure 3}](xrtpy_temperature_from_filter_ratio_plot.pdf)