Getting Started#

XRTpy is a Python package developed for the analysis of observations made by the X-Ray Telescope (XRT) on board the Hinode spacecraft. This page is intended for new users of XRTpy. For background information about XRT, please refer to the SolarSoft XRT Analysis Guide.

XRTpy Objects#

XRTpy currently provides access to the following core classes:

  • xrtpy.response.Channel

  • xrtpy.response.EffectiveAreaFundamental

  • xrtpy.response.TemperatureResponseFundamental

It also includes functionality to:

  • Derive temperature and emission measure from image pairs,

  • Sharpen images using the Point Spread Function (PSF),

  • Correct synoptic images for the light leak (visible stray light).

Visit our Examples page for step-by-step Jupyter notebook guides on how to use each feature.

Channel#

The Channel class describes the configuration of a specific XRT filter channel. It includes details for the Charge-Coupled Device (CCD), Entrance Filter, Focal Plane Filters, Geometry, and Mirrors.

Effective Area#

XRTpy calculates the effective area for each XRT filter channel, accounting for time-dependent contamination on the CCD. For more details, refer to the SolarSoft XRT Analysis Guide.

Temperature Response#

XRTpy calculates the temperature response of XRT filter channels using the CHIANTI atomic database (version 10.0) and coronal abundances (Feldman [2014]). This produces a response function as a function of temperature, using an assumed emission model (Narukage et al. [2011], Narukage et al. [2014]).

Deriving Temperature and Emission Measure#

The temperature_from_filter_ratio function allows you to derive plasma temperature and emission measure from a pair of XRT images using the filter-ratio method. This mirrors the logic in the SolarSoft IDL routine of the same name. A usage example is available in the Examples section.

Image Deconvolution with the PSF#

The deconvolve function applies image deconvolution using the instrument’s Point Spread Function (PSF) to sharpen XRT images. This is especially useful for recovering detail around bright or sharp solar structures.

Light Leak Correction#

The remove_lightleak function subtracts visible stray light from XRT synoptic composite images. This correction improves the quality of long-term coronal evolution studies. See our Examples section for how to use this function.

Abundance Model Options#

By default, XRTpy uses CHIANTI coronal abundances (Feldman [2014]). You may also choose:

  • "hybrid" — based on Fludra and Schmelz [1999]

  • "photospheric" — based on Grevesse et al. [2007]

To use a different abundance model:

from xrtpy.response import TemperatureResponseFundamental

TemperatureResponseFundamental(
    "Al-poly", "2022-07-04T23:43:12", abundance_model="hybrid"
)

You may also pass the abundance_model keyword to temperature_from_filter_ratio.

Note

In the future, XRTpy may support additional emission model libraries beyond CHIANTI. This feature is planned for the v0.6.0 release, expected later in 2025. Stay tuned for exciting new capabilities!

Tools#

The xrtpy.response.tools module includes helpful utility functions to streamline workflows. It includes the following:

Generate Temperature Responses#

Use the generate_temperature_responses tool to compute the temperature response for one or more filters — including combinations like "Al-poly/Ti-poly" — with a single command.

This function returns a list of TemperatureResponseFundamental objects, one for each specified filter.

Function:

from xrtpy.response.tools import generate_temperature_responses

responses = generate_temperature_responses(
    filters=["Al-poly", "Be-thick", "Al-poly/Ti-poly"],
    obs_date="2020-07-04T00:00:00",
    abundance="Hybrid",
)

for resp in responses:
    print(f"Filter: {resp.filter_name}")
    print(f"  Temperatures: {resp.temperature[:3]}")
    print(f"  Response: {resp.response[:3]}")

Example Output:

Filter: Al-poly
  Temperatures: [100000. 112201.9 125892.516] K
  Response: [8.34e-31 1.07e-30 1.53e-30] cm5 DN / (pix s)

Filter: Be-thick
  Temperatures: [100000. 112201.9 125892.516] K
  Response: [0.00e+00 1.73e-94 2.43e-84] cm5 DN / (pix s)

Filter: Al-poly/Ti-poly
  Temperatures: [100000. 112201.9 125892.516] K
  Response: [5.34e-34 7.24e-34 1.11e-33] cm5 DN / (pix s)

Each response object has the following attributes:

  • filter_name — Filter label (e.g., "Be-thick" or "Al-poly/Ti-poly")

  • temperature — Temperature grid (Astropy Quantity in K)

  • response — Temperature response function (Astropy Quantity in cm⁵ DN / (pix s))

This tool is useful on its own, but it also serves as a foundation for upcoming Differential Emission Measure (DEM) workflows in XRTpy.

Data Products#

XRT data products are available through the XRT website. These include:

  • Level 1 Data — Calibrated data using xrt_prep, in instrumental DN units.

  • Level 2 Synoptics — Composite images from the synoptic observing program.

For more information, visit the XRT data products page.

Double Filter Combinations#

XRTpy now supports double filter combinations such as "Al-poly/Ti-poly" in both the EffectiveAreaFundamental and TemperatureResponseFundamental classes.

Examples:

from xrtpy.response import EffectiveAreaFundamental, TemperatureResponseFundamental

eff_area = EffectiveAreaFundamental(
    "Al-poly/Ti-poly", "2020-08-17T09:00:00", abundance_model="photospheric"
)
temp_resp = TemperatureResponseFundamental(
    "C-poly/Ti-poly", "2025-07-10T12:00:00", abundance_model="coronal"
)

The following combinations are currently supported:

  • "Al-poly/Al-mesh"

  • "Al-poly/Ti-poly"

  • "Al-poly/Al-thick"

  • "Al-poly/Be-thick"

  • "C-poly/Ti-poly"

Note

Only a subset of double filter combinations is currently supported. If you’d like to request additional combinations, please open an issue on GitHub or email the development team.

X-Ray Filter Channels#

XRT uses two filter wheels to configure the imaging filter channel. Each wheel includes several filters and an open slot:

Filter Wheel 1:

  • Open

  • Al-poly

  • C-poly

  • Be-thin

  • Be-med

  • Al-med

Filter Wheel 2:

  • Open

  • Al-mesh

  • Ti-poly

  • G-band (visible)

  • Al-thick

  • Be-thick

Note

  • Each wheel has an Open slot used when the filter is in the opposite wheel.

  • XRTpy does not support G-band image processing or response calculations.

Filter names in XRTpy are passed as strings like 'Ti-poly'.

References#

Velasquez, J., Murphy, N., Reeves, K. K., Slavin, J., Weber, M., & Barnes, W. (2024). XRTpy: A Hinode-X-Ray Telescope Python Package. JOSS, 9(100), 6396. https://doi.org/10.21105/joss.06396