pyGXrender 0.0.2.3

This code computes the 2D maps of the solar gyroresonance and free-free microwave emission using the models of active regions created by the GX Simulator.

gximagecomputing / pyGXrender

This code computes 2D maps of the solar microwave (gyroresonance and free-free) and EUV (spectral lines) emission, using models of active regions created by the GX Simulator (requires SolarSoft GX_Simulator package).

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PyPI Package Name

PyPI distribution name: pyGXrender

Python import package:

import gxrender

Install from PyPI:

pip install pyGXrender

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Quick Start

See the example files:

• ./examples/idl/RenderExampleMW.pro
• ./examples/idl/RenderExampleEUV.pro
• ./examples/python/cli/RenderExampleMW.py

Examples folder layout:

examples/
├── idl/
│   ├── RenderExampleMW.pro
│   ├── RenderExampleEUV.pro
│   ├── InterpolateEBTELexample.pro
│   └── compile_local_idl
└── python/
    ├── cli/
    │   ├── RenderExampleMW.py
    │   └── RenderExampleEUV.py
    └── sdk/
        ├── sdk_render_mw.py
        └── sdk_render_euv.py

Note: Sample GX Simulator model and EBTEL data are not included.

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Python Environment Notes

Imports (PYTHONPATH)

If running scripts directly from the repository checkout (without installing the package), add src to PYTHONPATH:

PYTHONPATH=src python examples/python/cli/RenderExampleMW.py --help

If installed with pip, this is usually not needed:

pip install .

Writable Config/Cache Directories (SunPy/Matplotlib)

Some environments have non-writable default user config/cache folders. In that case, use writable overrides:

SUNPY_CONFIGDIR=/tmp/sunpy_cfg MPLCONFIGDIR=/tmp/mpl_cfg python examples/python/cli/RenderExampleMW.py --help

If needed, create those folders first:

mkdir -p /tmp/sunpy_cfg /tmp/mpl_cfg

This avoids runtime errors such as:

• Could not write to SUNPY_CONFIGDIR=...
• Matplotlib/fontconfig cache permission warnings

Generated Docs (Sphinx, Optional)

A lightweight Sphinx docs pipeline is available for generated API/reference pages (SDK, CLI workflow modules, viewer module).

Build locally:

pip install -r docs/requirements.txt
make docs-html

Open:

• docs/_build/html/index.html

EBTEL Table Path (GXIMAGECOMPUTING_EBTEL_PATH)

The shared Python workflow/API now requires the EBTEL choice to be explicit:

• pass a real .sav path to use DEM/DDM tables
• pass "" to disable DEM/DDM tables and use the native-library fallback path

The example CLI frontends still honor GXIMAGECOMPUTING_EBTEL_PATH as a convenience fallback, but they emit a warning when they do so.

If you want to use that example-layer convenience, set one environment variable once per shell session:

export GXIMAGECOMPUTING_EBTEL_PATH="$SSW/packages/gx_simulator/euv/ebtel/ebtel_ss.sav"

If your SolarSoft installation does not define $SSW, use an absolute path:

export GXIMAGECOMPUTING_EBTEL_PATH="/full/path/to/ssw/packages/gx_simulator/euv/ebtel/ebtel_ss.sav"

Then run an example CLI:

python examples/python/cli/RenderExampleMW.py --model-path /path/to/your.chr.sav --model-format auto

MW Rendering: CLI and Programmatic Usage

gxrender-mw is an example-oriented CLI entrypoint. If you omit science inputs such as the frequency list, pixel scale, plasma scalars, or SHtable, it will fill the repository's demonstration defaults and emit warnings describing each assumption.

The shared workflow function gxrender.workflows.render_mw.run(...) and the SDK entrypoint gxrender.render_mw_maps(...) are stricter: they expect those science inputs to be explicit.

If you installed the package (pip install . or pip install -e .), use the installed CLI:

gxrender-mw \
  --model-path /path/to/your.chr.h5 \
  --ebtel-path "" \
  --dx 2.0 --dy 2.0 \
  --frequencies-ghz 5.8 6.2 6.6 7.0 \
  --tbase 1.0e6 --nbase 1.0e8 --q0 0.0217 --a 0.3 --b 2.7 \
  --observer solar-orbiter \
  --output-dir /tmp \
  --output-format h5

Optional EBTEL-enabled run:

gxrender-mw \
  --model-path /path/to/your.chr.h5 \
  --ebtel-path /full/path/to/ebtel_ss.sav \
  --output-dir /tmp \
  --output-format h5

You can also call the same workflow from Python:

from argparse import Namespace
from gxrender.workflows.render_mw import run

args = Namespace(
    model_path="/path/to/your.chr.h5",
    model_format="auto",
    ebtel_path="",
    output_dir="/tmp",
    output_name=None,
    output_format="h5",
    omp_threads=None,
    xc=None,
    yc=None,
    observer="earth",
    dx=2.0,
    dy=2.0,
    pixel_scale_arcsec=None,
    nx=None,
    ny=None,
    xrange=None,
    yrange=None,
    frequencies_ghz=[5.8, 6.2, 6.6, 7.0],
    tbase=1.0e6,
    nbase=1.0e8,
    q0=0.0217,
    a=0.3,
    b=2.7,
    corona_mode=0,
    shtable=None,
    shtable_path=None,
    force_isothermal=False,
    interpol_b=False,
    analytical_nt=False,
)
run(args)

Professional SDK Usage (Programmatic, No CLI/argparse Coupling)

For application integration, prefer the SDK layer in gxrender.sdk (also re-exported at package root). This avoids argparse.Namespace-style calls and provides typed option objects for MW and EUV rendering.

The SDK does not inject science defaults anymore. Callers are expected to provide explicit plasma scalars, MW frequencies, map pixel scale, and an explicit EBTEL choice (path or ""). The example scripts under examples/python/ remain the place where demonstration defaults are supplied with warnings.

Available SDK entry points:

• gxrender.render_mw_maps(...)
• gxrender.render_euv_maps(...)
• gxrender.MWRenderOptions
• gxrender.EUVRenderOptions
• gxrender.MapGeometry
• gxrender.ObserverOverrides
• gxrender.CoronalPlasmaParameters

MW SDK example:

from gxrender import (
    CoronalPlasmaParameters,
    MapGeometry,
    MWRenderOptions,
    ObserverOverrides,
    render_mw_maps,
)

result = render_mw_maps(
    MWRenderOptions(
        model_path="/path/to/model.chr.sav",
        model_format="sav",
        ebtel_path="/path/to/ebtel.sav",
        output_dir="/tmp/gxrender",
        output_format="h5",
        freqlist_ghz=[5.8, 6.4, 7.2, 8.0, 10.0, 12.0],
        plasma=CoronalPlasmaParameters(
            tbase=1.5e6,
            nbase=2.5e8,
            q0=0.03,
            a=0.4,
            b=2.2,
            mode=0,
            shtable=[
                [1.0, 1.0, 1.0, 1.1, 1.2, 1.3, 1.4],
                [1.0, 1.0, 1.0, 1.1, 1.2, 1.3, 1.4],
                [1.0, 1.0, 1.0, 1.1, 1.2, 1.3, 1.4],
                [1.1, 1.1, 1.1, 1.21, 1.32, 1.43, 1.54],
                [1.2, 1.2, 1.2, 1.32, 1.44, 1.56, 1.68],
                [1.3, 1.3, 1.3, 1.43, 1.56, 1.69, 1.82],
                [1.4, 1.4, 1.4, 1.54, 1.68, 1.82, 0.1],
            ],
        ),
        geometry=MapGeometry(dx=2.0, dy=2.0),
        observer=ObserverOverrides(dsun_cm=None, lonc_deg=None, b0sun_deg=None),
        save_outputs=False,   # in-memory workflow (no files written)
        write_preview=False,  # ignored when save_outputs=False, but explicit is clearer
        verbose=False,  # no CLI-style printing
    )
)

print(result.outputs.h5_path)      # None (save_outputs=False)
print(result.freqlist_ghz[:3])
ti_cube = result.ti                # [ny, nx, nf]
tv_cube = result.tv                # [ny, nx, nf]
print(result.geometry.nx, result.geometry.ny)

EUV SDK example:

from gxrender import CoronalPlasmaParameters, EUVRenderOptions, MapGeometry, ObserverOverrides, render_euv_maps

result = render_euv_maps(
    EUVRenderOptions(
        model_path="/path/to/model.chr.sav",
        model_format="sav",
        ebtel_path="/path/to/ebtel.sav",
        response_sav="/path/to/resp_aia_20251126T153431.sav",
        output_dir="/tmp/gxrender",
        geometry=MapGeometry(dx=2.0, dy=2.0),
        plasma=CoronalPlasmaParameters(
            tbase=1.5e6,
            nbase=2.5e8,
            q0=0.03,
            a=0.4,
            b=2.2,
        ),
        observer=ObserverOverrides(
            dsun_cm=14763359700479.328,
            lonc_deg=-17.0574058213,
            b0sun_deg=1.4406505929155138,
        ),
        save_outputs=False,
        write_preview=False,
        verbose=False,
    )
)

print(result.outputs.h5_path)     # None (in-memory render)
print(result.response.channels)
flux_corona = result.flux_corona  # [ny, nx, nch]
flux_tr = result.flux_tr          # [ny, nx, nch]
print(result.outputs.save_outputs, result.outputs.write_preview)

Notes:

• The SDK reuses the same rendering engines as the CLI workflows, so CLI and SDK behavior stay aligned.
• CLI entry points remain useful for quick tests and demonstrations.
• The SDK returns typed dataclasses (MWRenderResult, EUVRenderResult) for a stronger contract than raw dictionaries.
• Set save_outputs=False for fully in-memory rendering; set write_preview=False to skip preview PNG generation.
• write_preview is only used when save_outputs=True.
• MW CLI users can override the default science parameters directly with:
  • --frequencies-ghz
  • --tbase
  • --nbase
  • --q0
  • --a
  • --b
  • --selective-heating
  • --shtable-path
  • --corona-mode
  • --force-isothermal
  • --interpol-b
  • --analytical-nt
• CoronalPlasmaParameters is shared by MW and EUV and includes the selective-heating connectivity table (shtable), so the same plasma/heating configuration can be used in both render paths.
• By default no selective-heating table is applied. To enable selective heating without providing a custom table, set --selective-heating in the CLI or selective_heating=True in CoronalPlasmaParameters; the standard 7x7 table will then be used with a warning.

Observer Metadata Overrides (MW and EUV, Python CLI)

For parity/debugging workflows, both render CLIs support explicit overrides for observer/model metadata before calling the native DLL/shared library:

• --observer (resolved with SunPy at the true model observation time)
• --dsun-cm
• --lonc-deg
• --b0sun-deg

Observer resolution priority is:

1. CLI observer name (--observer)
2. Saved observer state in the model metadata
   • current pyAMPP files: observer/ephemeris/* first, then observer/pb0r/*
   • older headers: HGLN/HGLT/DSUN or CRLN/CRLT/DSUN
3. Observer name stored in the model metadata
4. CLI triad overrides (--dsun-cm, --lonc-deg, --b0sun-deg) applied on top of the restored/default observer
5. Earth fallback

The resolved geometry is applied before automatic center/FOV inference, so it also affect default xc/yc and FOV calculations unless you pass explicit map geometry (--xc, --yc, --dx, --dy, --nx, --ny, etc.).

MW example:

gxrender-mw \
  --model-path /path/to/your.chr.sav \
  --ebtel-path /path/to/ebtel.sav \
  --observer stereo-a

EUV example:

python examples/python/cli/RenderExampleEUV.py \
  --model-path /path/to/your.chr.sav \
  --model-format sav \
  --ebtel-path /path/to/ebtel.sav \
  --response-sav /path/to/resp_aia_20251126T153431.sav \
  --observer "solar orbiter"

Repository Test Fixtures

Large review/test fixtures are distributed separately from the code repository in:

• https://github.com/suncast-org/pyGXrender-test-data

Recommended layout:

@SUNCAST-ORG/
  gximagecomputing/
  pyGXrender-test-data/

Install the default dataset with:

cd ../pyGXrender-test-data
scripts/install_dataset.sh

The Python shell examples and data-dependent test utilities will auto-detect fixtures from:

../pyGXrender-test-data/raw

or from GXRENDER_TEST_DATA_ROOT if you prefer a different location.

The default installer populates:

• raw/models/
• raw/responses/
• raw/ebtel/

Fixture Provenance and Regeneration

The published fixture dataset is reproducible. The model, response, and EBTEL inputs can be regenerated independently.

EBTEL tables

The packaged EBTEL tables are also available from the upstream GX Simulator / SolarSoft distribution. In a standard SSW installation they are typically located under:

$SSW/packages/gx_simulator/euv/ebtel/

The external fixture repository provides a packaged copy for reproducible testing, but you may also point the examples and tests at your own local SSW/GX Simulator installation.

test.chr.sav

The SAV fixture can be regenerated with the original IDL gx_fov2box command stored inside the file metadata:

gx_fov2box, '26-Nov-25 15:47:52', CENTER_ARCSEC=[ -280, -230], DX_KM= 1400, EUV= 1, OUT_DIR='/Users/gelu/Library/CloudStorage/Dropbox/@Projects/sim4fasr/gx_models', SIZE_PIX=[ 150, 100, 100], TMP_DIR='/Users/gelu/Library/CloudStorage/Dropbox/@Projects/sim4fasr/jsoc_cache', UV= 1, CEA= 1

This requires an SSW/IDL environment with GX Simulator installed.

test.chr.h5

The HDF fixture can be regenerated from the Python gx-fov2box command stored in the HDF metadata:

gx-fov2box --time 2025-11-26T15:47:52 --coords -280.0 -230.0 --hpc --cea --box-dims 150 100 100 --dx-km 1400.000000 --pad-frac 0.1000 --data-dir /Users/gelu/Library/CloudStorage/Dropbox/@Projects/sim4fasr/jsoc_cache --gxmodel-dir /Users/gelu/Library/CloudStorage/Dropbox/@Projects/sim4fasr/gx_models --euv --uv --save-potential --save-bounds --save-nas --save-gen --save-chr --observer-name earth --stop-after chr

This requires a working pyAMPP / gx-fov2box installation and access to the input caches referenced by the command.

IDL response files

The response fixtures in pyGXrender-test-data were generated in IDL for the test-model epoch using:

• idlcode/LoadEUVresponse.pro
• local/GenerateTestEUVResponses.pro

The local helper loops over supported instruments and writes date-tagged files such as:

• resp_aia_20251126T153431.sav

To regenerate them:

@/path/to/gximagecomputing/examples/idl/compile_local_idl
.compile '/path/to/gximagecomputing/local/GenerateTestEUVResponses.pro'
GenerateTestEUVResponses

This requires an SSW/IDL environment with GX Simulator and the relevant SolarSoft response routines installed. The response generation time is tied to the test-model observation epoch (2025-11-26T15:34:31), even though the original gx_fov2box request time stored in the model provenance is 2025-11-26T15:47:52.

Render Map Viewer GUI (gxrender-map-view)

Interactive viewer for rendered MW and EUV map products.

Supported input formats:

• Python-rendered HDF5 map containers (.h5, .hdf5)
  • MW schema: maps/data + maps/freqlist_ghz
  • EUV schema: maps/data + maps/channel_ids (+ optional maps/component_ids)
• IDL-rendered map containers (.sav, .xdr)
  • Combined map containers
  • EUV mapcorona / maptr style containers

CLI usage:

gxrender-map-view /path/to/rendered_maps.h5

Optional initial index (frequency index for MW, channel index for EUV):

gxrender-map-view /path/to/rendered_maps.h5 --start-index 0

Optional solar grid spacing (defaults to 10 degrees; use 0 to disable):

gxrender-map-view /path/to/rendered_maps.h5 --grid-deg 5

Examples:

MW HDF5 output:

gxrender-map-view /tmp/gximagecomputing_validation_groundtruth/test.chr.h5_py_mw_maps.h5

EUV HDF5 output:

gxrender-map-view /tmp/gximagecomputing_validation_groundtruth/test.chr.h5_py_euv_maps.h5

IDL MW SAV output:

gxrender-map-view /tmp/gximagecomputing_validation_groundtruth/test.chr.sav_idl_mw_maps.sav

IDL EUV SAV output:

gxrender-map-view /tmp/gximagecomputing_validation_groundtruth/test.chr.sav_idl_euv_maps.sav

Viewer behavior:

• Displays two synchronized panels (left/right components)
  • MW: TI and TV
  • EUV: GX (TR) and GX (Corona) (normalized to this order when possible)
• For Python-rendered HDF5, reads the stored SunPy-ready WCS header from metadata/wcs_header
• Performs no observer or geometry reconstruction for HDF5 inputs
• Uses map-appropriate units
  • MW: K
  • EUV: DN s^-1 pix^-1
• Replaces NaN/Inf pixels with 0 for robust display
• Draws a solar coordinate grid by default (--grid-deg 10)
• Provides per-panel controls:
  • intensity range slider
  • log scaling toggle
• Provides a shared axis slider:
  • MW: frequency index
  • EUV: channel index

Notes:

• For EUV HDF5 files, the slider moves across channels (e.g. A94, A131, ...)
• For IDL SAV files, the viewer auto-detects MW vs EUV from map IDs
• If your environment has restrictive config/cache permissions, set:

SUNPY_CONFIGDIR=/tmp/sunpy_cfg MPLCONFIGDIR=/tmp/mpl_cfg gxrender-map-view /path/to/rendered_maps.h5

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Python Data Branches (Non-Interfering)

The Python API now treats CHR inputs as two explicit branches that both normalize to the same internal ChromoModel representation consumed by the rendering library:

• IDL branch: load_model_sav(...) for GX Simulator .sav CHR models.
• pyAMPP branch: load_model_hdf(...) for current pyAMPP .h5 CHR models (/chromo group).

Both branches are converted into one internal data layout before calling the native renderer, so loader-specific format changes do not leak into rendering logic.

Loader Observer Contract

The Python and IDL model loaders now follow the same observer-precedence contract:

• explicit scalar overrides win:
  • DSun
  • lonC
  • b0Sun
• otherwise, if recompute_observer_ephemeris is requested, the loader recomputes observer ephemeris from time using the requested observer name, or the saved observer name when it is a standard resolvable observer
• otherwise, the loader uses observer geometry recovered from the file itself
• only if the file does not contain usable observer geometry does the loader fall back to implicit Earth defaults

This means saved non-Earth HDF5/SAV products are loaded as-saved by default. Recompute mode is an explicit opt-in reinterpretation step.

For pyAMPP HDF5 inputs, the file-first observer geometry follows the saved pyAMPP convention directly:

• observer/pb0r/b0_deg and observer/pb0r/l0_deg are treated as the saved B0/L0 observer angles
• these are true heliographic Stonyhurst observer angles
• in particular, observer/pb0r/l0_deg is the saved Stonyhurst L0, not CRLN_OBS and not the model lonC
• observer/pb0r/rsun_arcsec is the saved apparent solar radius for that observer geometry

So gxrender does not define a new saved-file observer convention here; it follows the one already written by pyAMPP.

On the IDL HDF path, this is handled by ConvertToGX.pro preserving the saved HDF5 /observer subtree as a separate nested box.observer structure, adapting dynamically to whatever keys are present. The original box.index header is treated as the saved birth-certificate header and is not rewritten from /observer/*.

If a caller needs both the strict DLL-ready model structure and the saved observer metadata without reading the input model twice:

• IDL: LoadGXmodel, modelfile, observer_struct=observer, index_struct=index
• Python HDF5: load_model_hdf_with_observer(...)
• Python SAV: load_model_sav_with_observer(...)

These return the normal DLL-ready model plus the saved observer metadata group as stored in the input file. This keeps saved LOS state and saved FOV metadata available for later image-geometry decisions without changing the DLL model or simbox conventions.

For Python, the returned tuple is:

• model: the unchanged DLL-ready model structure
• model_dt: the NumPy dtype describing that structure
• header: resolved loader metadata after file-first defaults, recompute, and/or explicit overrides
• observer: the saved observer metadata group as stored in the file

For the IDL render examples, non-Earth LOS and automatic FOV handling now run through the shared geometry helper suite in idlcode/GXObserverGeometry.pro:

• GXResolveObserverGeometry
• GXComputeInscribingFOV
• GXResolveSimboxFromObserverAndModel

This keeps the DLL-facing model and simbox conventions unchanged while making the IDL path follow the same saved-observer and saved-FOV logic as the Python renderer.

By default, the IDL render examples use the saved observer/fov rectangle when it is present and no explicit observer or view overrides were requested. To force a fresh inscribing-FOV computation instead, pass /AUTO_FOV to the IDL render example. USE_SAVED_FOV remains available as a backward-compatible alias, but the preferred interface is:

• default: use saved observer/fov when present
• /AUTO_FOV: recompute the observer-aligned inscribing FOV from geometry

Parity expectations for this contract are:

• default file-first loading is expected to be numerically equivalent between the Python and IDL loaders for the same saved input model
• explicit scalar overrides (DSun, lonC, b0Sun) are expected to be numerically equivalent between the Python and IDL loaders
• recompute_observer_ephemeris is not expected to be numerically identical between Python and IDL, because each side uses its own ephemeris engine

In other words, recompute mode is a deliberate observer reinterpretation feature, not a cross-language bitwise parity mode. For reproducible parity checks, use the saved file observer state or explicit scalar overrides.

Native SAV -> HDF5 Conversion (No pyAMPP Dependency)

Use the built-in CLI to convert a GX CHR .sav model into canonical HDF5:

gx-sav2h5 \
  --sav-path /path/to/input.NAS.CHR.sav \
  --out-h5 /path/to/output.NAS.CHR.h5

Optional: seed from an existing HDF5 template while still rewriting model groups:

gx-sav2h5 \
  --sav-path /path/to/input.NAS.CHR.sav \
  --out-h5 /path/to/output.NAS.CHR.h5 \
  --template-h5 /path/to/template.h5

Internal Validation Workflows

Repository-internal parity/regression procedures (including IDL/Python parity and comparison scripts under tests/) are documented in tests/README.md.

---

Building Native Library (Linux/macOS)

The source/makefile supports platform-aware builds and copies outputs into ./binaries.

Build

cd source
make

Outputs

• Linux: binaries/RenderGRFF.so
• macOS arm64: binaries/RenderGRFF_arm64.so
• macOS x86_64: binaries/RenderGRFF_x86_64.so

macOS prerequisites

Install OpenMP runtime (Homebrew):

brew install libomp

If Homebrew is in a non-default prefix, set include/link flags explicitly:

make CPPFLAGS='-I/opt/homebrew/opt/libomp/include' LDFLAGS='-L/opt/homebrew/opt/libomp/lib'

Binary Wheel Releases

For maintainers: release process and exact publish commands are documented in RELEASING.md. CI workflow: .github/workflows/build_wheels.yml

---

Microwave Emission Maps

Step 1: Load the GX Simulator Model

model = LoadGXmodel(modelfile [, newTime=newTime])

• modelfile: GX Simulator model file name (must include field line info and chromospheric part).
• newTime: Optional date/time (accepted by anytim()), rotates the model to new date/time if specified.

---

Step 2: Load the EBTEL Tables

ebtel = LoadEBTEL(ebtelfile [, DEM=DEM, DDM=DDM])

• ebtelfile: GX Simulator sav file with EBTEL table(s) (DEM and/or DDM).
• If ebtelfile='': DEM, DDM and coronal heating model are not used (coronal plasma described by constant temperature and barometric height profile of density).
• /DEM and /DDM keywords: Use when both DEM and DDM tables are present.
• If only one table exists, keywords are ignored.

---

Step 3: Define Map Size, Position and Frequencies

simbox = MakeSimulationBox(xc, yc, dx, dy, Nx, Ny, freqlist [, rot=rot, /parallel, /exact, Nthreads=Nthreads])

• xc, yc: Center (helioprojective x, y) in arcseconds.
• dx, dy: Output resolution, arcseconds.
• Nx, Ny: Map size (pixels).
• freqlist: 1D array of emission frequencies (GHz).
• rot: Optional rotation angle (degrees, counterclockwise). The center of the resulting map is still at (xc, yc), and the x and y coordinates correspond to the rotated coordinate system
• /parallel: Render with parallel projection (all lines of sight are parallel to each other) (default: perspective, all lines of sight intersect at the observer's location).
• /exact: Use with /parallel, controls conversion to kilometers. If not set (default), the conversion from arcseconds to kilometers in the parallel projection is performed using the distance from the observer to the center of the Sun. If set, the conversion is performed using the actual distance from the observer to the considered active region.
• Nthreads: Number of processor threads (≤ available processors). Default: a system-defined value (typically, the number of available processors).

---

Step 4: Define Coronal Plasma Parameters

coronaparms = DefineCoronaParams(Tbase, nbase, Q0, a, b [, /force_isothermal, /analyticalNT])

• Tbase: Plasma temperature (K).
• nbase: Base plasma density (cm^{-3}) at the bottom of the simulation box.
• Q0, a, b: Coronal heating model (applies to closed field lines), where heating rate:

  Q = Q0*(B/B0)^a / (L/L0)^b
  

• /force_isothermal: Ignore multi-thermal formulae given in the paper of Fleishman, Kuznetsov & Landi (2021), use the moments of the DEM or DDM distribution (if both DEM and DDM are provided, the DDM moments are used). This option improves the computation speed greatly, although the results become less accurate.
• /analyticalNT: Use analytical formula for voxels with heating parameters outside EBTEL table bounds.

Tbase and nbase are used to find the plasma parameters in the voxels associated with open field lines, or, if the keyword /analyticalNT is not set, the heating parameters (Q, L) in closed field lines which are beyond the boundaries of the EBTEL table. In such voxels, the plasma temperature is set to Tbase, and the plasma density is computed using nbase, Tbase, and the barometric formula.

---

Step 5: Prepare Output Memory Structure

outspace = ReserveOutputSpace(simbox)

---

Step 6: (Optional) Selective Heating Table

Prepare SHtable (2D double array) to define selective heating for coronal magnetic field lines.

---

Main Microwave Computation

Call the main executable module (RenderGRFF_32.dll, RenderGRFF_64.dll, or RenderGRFF.so) via call_external:

r = call_external(libname, 'ComputeMW', model, ebtel, simbox, coronaparms, outspace [, SHtable])

• libname: Name of executable library
• Remaining arguments: Structures from above steps

Output Structure

• outspace.TI & outspace.TV: Brightness temperatures for Stokes parameters I and V (in K). Each field is a 3D array with Nx * Ny * Nf elements, where Nx and Ny are the x and y sizes of the computed maps, and Nf is the number of the emission frequencies.
• To convert:

  ConvertToMaps, outspace, simbox, model, mapI, mapV [, /flux]
  

  • mapI, mapV: SolarSoft multi-frequency map objects
  • If /flux is specified, unit changes to sfu/pix
• Additional fields: outspace.flagsAll, outspace.flagsCorona (see Computation Statistics).

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Example Usage

See: examples/idl/RenderExampleMW.pro (sample data not included).

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EUV Emission Maps

Steps are similar, but with differences noted below.

Step 1: Load the GX Simulator Model

model = LoadGXmodel(modelfile [, newTime=newTime])

(see microwave section above for description)

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Step 2: Load the EBTEL Tables

ebtel = LoadEBTEL(ebtelfile)

• Includes EBTEL tables for DEM (corona and transition region).
• If ebtelfile='': DEM and heating model not used.

The keyword /DDM should not be used, because the EUV emission depends on the DEM only.

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Step 3: Load Instrumental Response Function

response = LoadEUVresponse(model.obstime [, instrument, evenorm=evenorm, chiantifix=chiantifix])

• model.obstime: Observation time from LoadGXmodel
• instrument: Choose from 'AIA', 'AIA2', 'TRACE', 'SXT', 'SOLO-FSI', 'SOLO-HRI', 'STEREO-A', 'STEREO-B' (default 'AIA').
• evenorm, chiantifix: AIA parameters, default=1 (see SolarSoft aia_get_response.pro).

For backward compatibility, LoadEUVresponse also accepts a full model structure and will read its OBSTIME field, but the preferred interface is to pass the time directly.

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Step 4: Define EUV Map Size and Position

simbox = MakeSimulationBoxEUV(xc, yc, dx, dy, Nx, Ny [, /parallel, /exact, Nthreads=Nthreads])

• Channels: All specified by instrument's response table (cannot select individual channels).
• Emission computed as observed from Earth's distance. Thus for Solar Orbiter and STEREO the map position and pixel size should be corrected accordingly

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Step 5: Define Coronal Plasma Parameters

coronaparms = DefineCoronaParams(Tbase, nbase, Q0, a, b [, /analyticalNT])

• /force_isothermal has no effect for EUV emission.
• Other parameters, see microwave emission above.

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Step 6: Prepare Output Memory Structure

outspace = ReserveOutputSpaceEUV(simbox, response)

• simbox from above, response from LoadEUVresponse

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Step 7: (Optional) Selective Heating Table

Prepare SHtable as described above.

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Main EUV Computation

Call the main executable module as:

r = call_external(libname, 'ComputeEUV', model, ebtel, response, simbox, coronaparms, outspace [, SHtable])

Output Structure

• outspace.fluxCorona, outspace.fluxTR: Computed EUV fluxes (units: DN s^{-1} pix^{-1})

• To convert:

  ConvertToMapsEUV, outspace, simbox, model, response, mapCorona, mapTR
  

  • mapCorona, mapTR: SolarSoft multi-channel maps

• Flags information: outspace.flagsAll, outspace.flagsCorona (see Computation Statistics)

Example Usage

See: examples/idl/RenderExampleEUV.pro (sample data not included).

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Selective Heating Table (SHtable)

Both microwave and EUV emission computations may utilize the optional selective heating table (SHtable):

• A 2D array (double precision), with 7 * 7 elements:

  [number of closed field lines, number of simulation epochs]
  

• Default value: 1.0 for all of the elements. Each element of that table represents the factor applied to the heating rate Q for the field lines connecting specific regions at the photosphere; see the 'Selective Heating Mask' panel in GX Simulator. The SHtable table is supposed to be symmetric, i.e., SHtable[j, i]=SHtable[i, j]; asymmetric tables are accepted but the result will likely have no sense.
• Used in both ComputeMW and ComputeEUV calls when provided.

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Computation Statistics: Output Flags

The output structure contains fields for computation statistics:

flagsAll (length=6):

Index	Meaning
0	Total number of voxels crossed by lines-of-sight
1	Number of voxels in chromospheric part of model (crossed)
2	Number of voxels (crossed by the LOS) associated with closed field lines (known loop length L and average magnetic field B_avg). flagsAll[2]=flagsAll[3]+flagsAll[4]+flagsAll[5]
3	Voxels (crossed and closed field lines) with EBTEL table hits (both L and Q within table)
4	Voxels (crossed and closed field lines) missing EBTEL table due to loop length (L is beyond the table)
5	Voxels (crossed and closed field lines) missing EBTEL table due to heating rate (Q out of bounds)

flagsCorona (length=6):

Similar to flagsAll, but refers only to the coronal part of the model.

• flagsCorona[0]: Total number of voxels in the coronal part crossed by lines-of-sight
• flagsCorona[1]: always zero

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References

For detailed theory and formulae, see the relevant publications, especially:

• Fleishman, Kuznetsov & Landi (2021)

For questions or issues, please open a GitHub issue or contact the author.

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