PyAutoNervesBoard

12# version:3#   python_version_check: False  # uncomment to suppress the Python version warning4#                                # if running on a non-recommended Python (anything other than 3.12 / 3.13).56updates:7  iterations_per_quick_update: 1e99 # Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.8  iterations_per_full_update: 1e99  # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.9  quick_update_background: false    # If True, perform_quick_update runs on a background daemon thread so the sampler is never blocked while visuals render. Requires the Analysis subclass to set `supports_background_update = True`.10  live_visual_update: false         # If True, quick-update visuals are pushed to a live surface (a Jupyter cell when in a kernel, a matplotlib viewer subprocess otherwise) in addition to being written to disk.11psf:12  use_fft_default: true              # If True, PSFs are convolved using FFTs by default, which is faster and uses less memory in all cases except for very small PSFs, False uses direct convolution.13grid:14  max_evaluation_grid_size: 1000   # An evaluation grid whose shape is adaptive chosen is used to compute quantities like critical curves, this integer is the max size of the grid ensuring faster run times.15inversion:16  check_reconstruction: true        # If True, the inversion's reconstruction is checked to ensure the solution of a meshs's mapper is not an invalid solution where the values are all the same.17  use_positive_only_solver: true      # If True, inversion's use a positive-only linear algebra solver by default, which is slower but prevents unphysical negative values in the reconstructed solutuion.18  use_edge_zeroed_pixels : true       # If True, the edge pixels of a pixelization are set to zero, which prevents unphysical values in the reconstructed solution at the edge of the pixelization.19  no_regularization_add_to_curvature_diag_value : 1.0e-3 # The default value added to the curvature matrix's diagonal when regularization is not applied to a linear object, which prevents inversion's failing due to the matrix being singular.20  use_border_relocator: false       # If True, by default a pixelization's border is used to relocate all pixels outside its border to the border.21  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.22hpc:23  hpc_mode: false                   # If True, use HPC mode, which disables GUI visualization, logging to screen and other settings which are not suited to running on a super computer.24  iterations_per_quick_update: 10000 #  Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.25  iterations_per_full_update: 1e99  # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.26adapt:27  adapt_minimum_percent: 0.0128  adapt_noise_limit: 100000000.029numba:30  use_numba: true31  cache: true32  nopython: true33  parallel: false34output:35  force_pickle_overwrite: false     #   force_pickle_overwrite: false     # If True, pickle files output by a search (e.g. samples.pickle) are recreated when a new model-fit is performed.36  force_visualize_overwrite: false # If True, visualization images output by a search (e.g. subplots of the fit) are recreated when a new model-fit is performed.37  info_whitespace_length: 80        # Length of whitespace between the parameter names and values in the model.info / result.info38  log_level: INFO                   # The level of information output by logging.39  log_to_file: false                # If True, outputs the non-linear search log to a file (and not printed to screen).40  log_file: output.log              # The name of the file the logged output is written to (in the non-linear search output folder)41  model_results_decimal_places: 3   # Number of decimal places estimated parameter values / errors are output in model.results.42  remove_files: false               # If True, all output files of a non-linear search (e.g. samples, visualization, etc.) are deleted once the model-fit has completed, such that only the .zip file remains.43  samples_to_csv: true              # If True, non-linear search samples are written to a .csv file.44  unconverged_sample_size : 100     # If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors.45parallel:46  warn_environment_variables: true  # If True, a warning is displayed when the search's number of CPU > 1 and enviromment variables related to threading are also > 1.4748profiling:49  parallel_profile: false           # If True, the parallelization of the fit is profiled outputting a cPython graph.50  repeats: 1                        # The number of repeat function calls used to measure run-times when profiling.51structures:52  native_binned_only: false           # If True, data structures are only stored in their native and binned format. This is used to reduce memory usage in autocti.53test:54  check_likelihood_function: true   # if True, when a search is resumed the likelihood of a previous sample is recalculated to ensure it is consistent with the previous run.55  lh_timeout_seconds:               # If a float is input, the log_likelihood_function call is timed out after this many seconds, to diagnose infinite loops. Default is None, meaning no timeout.56version:57  # The compatibility FLOOR: the oldest library release whose API this58  # workspace's scripts require. Preferred over workspace_version59  # (autonerves/workspace.py). Bump DELIBERATELY — only when a script60  # starts needing new API — never per release. Must always name an61  # INSTALLABLE (non-yanked) release.62  minimum_library_version: 2026.7.9.163  workspace_version_check: True     # If False, bypass the workspace/library version check. Set to False on `main`-branch clones — `main` updates faster than releases, so mismatches are expected and not actionable.

1# Workspace overrides for the library latent toggles. The PyAutoGalaxy2# library defaults `total_galaxy_0_flux` to `true` (no instrument inputs3# needed) and `total_galaxy_0_flux_mujy` to `false`. Enabling the µJy4# variant here means a workspace fit produces microjansky output as long5# as the user also passes `magzero` to `ag.AnalysisImaging(...)` — without6# `magzero` it returns NaN + one warning per process.7#8# Run `scripts/guides/results/latent_variables.py` for a tutorial on9# what each key means and `scripts/guides/units/flux.py` for how to10# convert a raw-flux latent to microjanskies in post.1112# `total_galaxy_0_flux` — total integrated flux of `fit.galaxies[0]` in13# the fit's raw image units. No instrument inputs required.14total_galaxy_0_flux: true1516# `total_galaxy_0_flux_mujy` — same flux converted to microjanskies via17# magzero. Requires `magzero` via Analysis kwargs; returns NaN + one18# warning per process if missing.19total_galaxy_0_flux_mujy: true

1version: 12disable_existing_loggers: false34handlers:5  console:6    class: logging.StreamHandler7    level: INFO8    stream: ext://sys.stdout9    formatter: formatter1011root:12  level: INFO13  handlers: [ console ]1415formatters:16  formatter:17    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'

1# The notation configs define the labels of every model parameter and its derived quantities, which are used when2# visualizing results (for example labeling the axis of the PDF triangle plots output by a non-linear search).345# label: The label given to the each parameter, for plots like PDF corner plots.67# For example, if `centre=x`, the plot axis will be labeled 'x'.8910# superscript: the superscript used on certain plots that show the results of different model-components.1112# For example, if `Gaussian=g`, plots where the parameters of the Gaussian model-component have superscript `g`.1314label:15  label:16    sigma: \sigma17    alpha: \alpha18    angle_binary: \theta19    beta: \beta20    break_radius: \theta_{\rm B}21    centre_0: y22    centre_1: x23    coefficient: \lambda24    core_radius: C_{\rm r}25    core_radius_0: C_{rm r0}26    core_radius_1: C_{\rm r1}27    effective_radius: R_{\rm eff}28    einstein_radius: \theta_{\rm Ein}29    ell_comps_0: \epsilon_{\rm 1}30    ell_comps_1: \epsilon_{\rm 2}31    multipole_comps_0: M_{\rm 1}32    multipole_comps_1: M_{\rm 2}33    flux: F34    gamma: \gamma35    gamma_1: \gamma36    gamma_2: \gamma37    inner_coefficient: \lambda_{\rm 1}38    inner_slope: t_{\rm 1}39    intensity: I_{\rm b}40    kappa: \kappa41    kappa_s: \kappa_{\rm s}42    log10m_vir: log_{\rm 10}(m_{vir})43    m: m44    mass: M45    mass_at_200: M_{\rm 200}46    mass_ratio: M_{\rm ratio}47    mass_to_light_gradient: \Gamma48    mass_to_light_ratio: \Psi49    mass_to_light_ratio_base: \Psi_{\rm base}50    mass_to_light_radius: R_{\rm ref}51    noise_factor: \omega_{\rm 1}52    noise_power: \omega{\rm 2}53    noise_scale: \sigma_{\rm 1}54    normalization_scale: n55    outer_coefficient: \lambda_{\rm 2}56    outer_slope: t_{\rm 2}57    overdens: \Delta_{\rm vir}58    pixels: N_{\rm pix}59    radius_break: R_{\rm b}60    redshift: z61    redshift_object: z_{\rm obj}62    redshift_source: z_{\rm src}63    scale_radius: R_{\rm s}64    scatter: \sigma65    separation: s66    sersic_index: n67    shape_0: y_{\rm pix}68    shape_1: x_{\rm pix}69    signal_scale: V70    sky_scale: \sigma_{\rm 0}71    slope: \gamma72    truncation_radius: R_{\rm t}73    weight_floor: W_{\rm f}74    weight_power: W_{\rm p}75  superscript:76    ExternalShear: ext77    Mesh: mesh78    Point: point79    SMBH: smbh80    Redshift: z81    Regularization: reg82    InputDeflections: defl8384# label_format: The format certain parameters are output as in output files like the `model.results` file.8586# For example, if `centre={:.2d}`, the format of the centre parameter in results files will use this Python format.8788label_format:89  format:90    sigma: '{:.4f}'91    alpha: '{:.4f}'92    angle_binary: '{:.4f}'93    angular_diameter_distance_to_earth: '{:.4f}'94    beta: '{:.4f}'95    c_2: '{:.4f}'96    centre_0: '{:.4f}'97    centre_1: '{:.4f}'98    coefficient: '{:.4f}'99    concentration: '{:.4f}'100    core_radius: '{:.4f}'101    core_radius_0: '{:.4f}'102    core_radius_1: '{:.4f}'103    effective_radius: '{:.4f}'104    einstein_mass: '{:.4e}'105    einstein_radius: '{:.4f}'106    ell_comps_0: '{:.4f}'107    ell_comps_1: '{:.4f}'108    multipole_comps_0: '{:.4f}'109    multipole_comps_1: '{:.4f}'110    flux: '{:.4e}'111    gamma: '{:.4f}'112    inner_coefficient: '{:.4f}'113    inner_slope: '{:.4f}'114    intensity: '{:.4f}'115    kappa: '{:.4f}'116    kappa_s: '{:.4f}'117    kpc_per_arcsec: '{:.4f}'118    log10m_vir: '{:.4f}'119    luminosity: '{:.4e}'120    m: '{:.1f}'121    mass: '{:.4e}'122    mass_at_200: '{:.4e}'123    mass_at_truncation_radius: '{:.4e}'124    mass_ratio: '{:.4f}'125    mass_to_light_gradient: '{:.4f}'126    mass_to_light_ratio: '{:.4f}'127    n_x: '{:.1d}'128    n_y: '{:.1d}'129    noise_factor: '{:.3f}'130    noise_power: '{:.3f}'131    noise_scale: '{:.3f}'132    normalization_scale: '{:.4f}'133    outer_coefficient: '{:.4f}'134    outer_slope: '{:.4f}'135    overdens: '{:.4f}'136    pixels: '{:.4f}'137    radius: '{:.4f}'138    radius_break: '{:.4f}'139    redshift: '{:.4f}'140    redshift_object: '{:.4f}'141    redshift_source: '{:.4f}'142    rho: '{:.4f}'143    scale_radius: '{:.4f}'144    separation: '{:.4f}'145    sersic_index: '{:.4f}'146    shape_0: '{:.4f}'147    shape_1: '{:.4f}'148    signal_scale: '{:.4f}'149    sky_scale: '{:.4f}'150    slope: '{:.4f}'151    truncation_radius: '{:.4f}'152    weight_floor: '{:.4f}'153    weight_power: '{:.4f}'

1# Determines whether files saved by the search are output to the hard-disk. This is true both when saving to the2# directory structure and when saving to database.34default: true # If true then files which are not explicitly listed here are output anyway. If false then they are not.56### Samples ###78# The `samples.csv`file contains every sampled value of every free parameter with its log likelihood and weight.910# This file is often large, therefore disabling it can significantly reduce hard-disk space use.1112# `samples.csv` is used to perform marginalization, infer model parameter errors and do other analysis of the search13# chains. Even if output of `samples.csv` is disabled, these tasks are still performed by the fit and output to14# the `samples_summary.json` file. However, without a `samples.csv` file these types of tasks cannot be performed15# after the fit is complete, for example via the database.1617samples: true1819# The `samples.csv` file contains every accepted sampled value of every free parameter with its log likelihood and20# weight. For certain searches, the majority of samples have a very low weight and have no numerical impact on the21# results of the model-fit. However, these samples are still output to the `samples.csv` file, taking up hard-disk22# space and slowing down analysis of the samples (e.g. via the database).2324# The `samples_weight_threshold` below specifies the threshold value of the weight such that samples with a weight25# below this value are not output to the `samples.csv` file. This can be used to reduce the size of the `samples.csv`26# file and speed up analysis of the samples.2728# For many searches (e.g. MCMC) all samples have an equal weight of 1.0, and this threshold therefore has no impact.29# For these searches, there is no simple way to save hard-disk space. This input is more suited to nested sampling,30# where the majority of samples have a very low weight..3132# Set value to empty (e.g. delete 1.0e-10 below) to disable this feature.3334samples_weight_threshold: 1.0e-103536### Search Internal ###3738# The search internal folder which contains a saved state of the non-linear search in its internal reprsenetation,39# as a .pickle or .dill file.4041# For example, for the nested sampling dynesty, this .dill file is the `DynestySampler` object which is used to42# perform sampling, and it therefore contains all internal dynesty representations of the results, samples, weights, etc.4344# If the entry below is false, the folder is still output during the model-fit, as it is required to resume the fit45# from where it left off. Therefore, settings `false` below does not impact model-fitting checkpointing and resumption.46# Instead, the search internal folder is deleted once the fit is completed.4748# The search internal folder file is often large, therefore deleting it after a fit is complete can significantly49# reduce hard-disk space use.5051# The search internal representation that can be loaded from the .dill file has many additional quantities specific to52# the non-linear search that the standardized autofit forms do not. For example, for emcee, it contains information on53# every walker. This information is required to do certain analyes and make certain plots, therefore deleting the54# folder means this information is list.5556search_internal: false5758### Start Point ###5960# If an Initalizer is used to provide a start point for the non-linear search, visualization of that start point can be61# output to hard-disk to show the user the initial model-fit that is used to start the search. This visualization is62# the visualizer wrapped in the Analysis class, and therefore should show things like the quality of the fit63# to the data and the residuals at the start point.6465start_point: true6667### Latent Variables ###6869# A latent variable is not a model parameter but can be derived from the model. Its value and errors may be of interest70# and aid in the interpretation of a model-fit.7172# For example, for the simple 1D Gaussian example, it could be the full-width half maximum (FWHM) of the Gaussian. This73# is not included in the model but can be easily derived from the Gaussian's sigma value.7475# By overwriting an Analysis class's `compute_latent_variables` method we can manually specify latent variables that76# are calculated and output to a `latent.csv` file, which mirrors the `samples.csv` file. The `latent.csv` file has77# the same weight resampling performed on the `samples.csv` file, controlled via the `samples_weight_threshold` above.7879# There may also be a `latent.results` and `latent_summary.json` files output, which the inputs below control whether80# they are output and how often.8182# Outputting latent variables manually after a fit is complete is simple, just call83# the `analysis.compute_latent_variables()` function.8485# For many use cases, the best set up may be to disable autofit latent variable output during the fit and perform it86# manually after completing a successful model-fit. This will save computational run time by not computing latent87# variables during a any model-fit which is unsuccessful.8889latent_during_fit: false # Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.90latent_after_fit: true # If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.91latent_draw_via_pdf : true # Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.92latent_draw_via_pdf_size : 100 # The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.93latent_csv: true # Whether to ouptut the `latent.csv` file.94latent_results: true # Whether to output the `latent.results` file.9596# Other Files:9798search_log: true # `search.log`: logging produced whilst running the fit method99100model_graph: false # `model.graph`: graphical-model factor graph summary. Only meaningful for graphical models (autofit.graphical); empty for ordinary PriorModel/Collection fits, so disabled by default.101102# `model.png`: the model figure drawn beside `model.info` (af.ModelPlotter). Opt-in until the103# model-figures epic's phase-3 acceptance renders pass. Unlike other keys an absent key means off.104model_figure: false

1# Curated examples rendered to executed markdown pages (markdown/) with their2# real output images, so they can be read on GitHub. Built manually by3# PyAutoHands's generate_markdown.py — see that module's docstring for the4# rules (never TEST_MODE; features/ scripts never rendered; list order is5# execution order and index order).6- script: start_here.py7  max_minutes: 1208- script: scripts/imaging/start_here.py9  max_minutes: 24010- script: scripts/imaging/simulator.py11  max_minutes: 4512- script: scripts/imaging/likelihood_function.py13  max_minutes: 4514- script: scripts/imaging/fit.py15  max_minutes: 4516- script: scripts/imaging/modeling.py17  max_minutes: 24018- script: scripts/interferometer/start_here.py19  max_minutes: 24020- script: scripts/interferometer/simulator.py21  max_minutes: 4522- script: scripts/interferometer/likelihood_function.py23  max_minutes: 4524- script: scripts/interferometer/fit.py25  max_minutes: 4526- script: scripts/interferometer/modeling.py27  max_minutes: 30028- script: scripts/multi_dataset/start_here.py29  max_minutes: 12030- script: scripts/multi_dataset/simulator.py31  max_minutes: 4532- script: scripts/multi_dataset/modeling.py33  max_minutes: 24034- script: scripts/ellipse/simulator.py35  max_minutes: 4536- script: scripts/ellipse/fit.py37  max_minutes: 4538# The "Multiple Ellipses" + "Masking" sections are the two heaviest cells here:39# 10 and 11 sequential DynestyStatic/Drawer fits respectively. Measured 2026-08-2040# on 1 core: ~32s per fit, flat in major_axis (0.3" and 3.6" both 32s), so the41# worst cell is ~6 min and the whole page ~13 min. An earlier attempt was42# abandoned on a 7200s CellTimeoutError and recorded as "genuinely multi-hour" —43# that diagnosis does not reproduce; 120 is ~20x headroom over the measured cell.44- script: scripts/ellipse/modeling.py45  max_minutes: 12046- script: scripts/guides/galaxies.py47  max_minutes: 4548- script: scripts/guides/data_structures.py49  max_minutes: 45

1# Scripts to skip during automated runs (smoke tests, pre-release checks, CI).2# Each entry is matched against script paths:3#   - Entries with '/' do a substring match against the file path4#   - Entries without '/' match the file stem exactly5# Add an inline # comment to document the reason for skipping.6#7# SLOW-skip convention:8#   Entries tagged `# SLOW <YYYY-MM-DD> - <reason>` mark scripts that are9#   skipped because they exceed the per-script timeout cap (300s by10#   default; 1800s for mode=release runs). These are11#   NOT permanent skips — every mega-run surfaces them with a loud warning12#   banner. Fix the performance issue and remove the SLOW marker.13#14# NEEDS_FIX convention:15#   Entries tagged `# NEEDS_FIX <YYYY-MM-DD> - <reason>` mark scripts that16#   are broken and parked as a to-do list. Like SLOW-skips, these are NOT17#   permanent skips — every mega-run surfaces them with a loud warning18#   banner. Investigate the failure, fix the underlying bug, and remove19#   the NEEDS_FIX marker.2021- gui/light_centre # GUI scripts cannot be run22- gui/mask_extra_galaxies # GUI scripts cannot be run23- gui/mask # GUI scripts cannot be run24- gui/extra_galaxies_centres # GUI scripts cannot be run25- fits_make # Test mode does not output .fits images.26- png_make # Test mode does not output .png images.27- guides/plot/searches # Test mode breaks search visualization.28- imaging/features/shapelets/modeling # SLOW 2026-07-14 - real-search JAX shapelet fit exceeds the 1800s mode=release cap (>30min); speedup tracked by the Profiling Agent (PyAutoHeart#72). Not a bug.

1# Per-script environment variable configuration for the RELEASE-FIDELITY2# validation run (Heart's workspace-validation.yml, mode=release — the M33# wheel-based release-fidelity path). Distinct from profile_smoke.yaml, which is the4# `smoke` profile used by the per-PR CI gate.5#6# The `release` profile trades speed for fidelity: it is run once per release7# rehearsal against the TestPyPI wheels, not on every PR, so it can afford a8# reduced (not bypassed) sampler and real fit output/visualization/checks.9# Spec + acceptance table: PyAutoHeart/docs/release_validation.md.10#11# "defaults" are applied to every script on top of the inherited environment.12# Every var this profile cares about is given an EXPLICIT value in "defaults"13# (not left absent) — the runner only ever *sets* keys it's given, it never14# clears unrelated inherited env vars, so an absent key silently falls through15# to whatever the calling process already had (a leftover smoke-mode "1" from16# an earlier step, a developer's local shell, ...). Pinning everything here17# makes the profile self-contained regardless of the caller's environment.18#19# "overrides" should normally `set:` a var away from this profile's own default.20# Use `unset:` only when the script genuinely needs the variable absent, such as21# guides that must not run under PyAuto test mode at all.22#23# Pattern convention (same as no_run.yaml):24#   - Patterns containing '/' do a substring match against the file path25#   - Patterns without '/' match the file stem exactly2627defaults:28  PYAUTO_TEST_MODE: "1"                     # reduced iterations (real sampler), not bypassed29  PYAUTO_SKIP_FIT_OUTPUT: "0"               # real fit output (release fidelity, not smoke)30  PYAUTO_SKIP_VISUALIZATION: "0"            # real visualization (release fidelity, not smoke)31  PYAUTO_SKIP_CHECKS: "0"                   # real checks (release fidelity, not smoke)32  PYAUTO_SMALL_DATASETS: "1"                # cap grids/masks to 15x15, reduce MGE gaussians33  PYAUTO_DISABLE_JAX: "0"                   # JAX enabled (release fidelity, not smoke)34  PYAUTO_FAST_PLOTS: "1"                    # skip tight_layout() + critical curve/caustic overlays35  PYAUTO_SKIP_WORKSPACE_VERSION_CHECK: "1"  # TestPyPI dev version won't match the workspace pin36  JAX_ENABLE_X64: "True"                    # enable 64-bit precision in JAX37  NUMBA_CACHE_DIR: "/tmp/numba_cache"       # writable cache dir for numba38  MPLCONFIGDIR: "/tmp/matplotlib"           # writable config dir for matplotlib3940overrides:41  # Non-simulator scripts that load committed FITS data need full-size42  # datasets to avoid shape mismatch with pre-existing 100x100 data.43  - pattern: "guides/"44    set: { PYAUTO_SMALL_DATASETS: "0" }45  # (guides/results/ formerly unset PYAUTO_TEST_MODE here; that intent now46  # lives in-file as '# ENV: real_search' declarations on the scripts, which47  # apply in every profile — the override became redundant.)48  #49  # fits_make / png_make produce .fits / .png outputs from real fits, so they50  # need PYAUTO_FAST_PLOTS forced off (it would otherwise close every figure51  # without saving via the subplot_save / save_figure short-circuit in52  # autoarray/plot/utils.py). PYAUTO_SKIP_VISUALIZATION is already "0" above.53  - pattern: fits_make54    set: { PYAUTO_FAST_PLOTS: "0" }55  - pattern: png_make56    set: { PYAUTO_FAST_PLOTS: "0" }

1# Per-script environment variable configuration for automated runs2# (smoke tests, pre-release checks, CI).3#4# "defaults" are applied to every script on top of the inherited environment.5# "overrides" selectively unset or replace vars for matching path patterns.6#7# Pattern convention (same as no_run.yaml):8#   - Patterns containing '/' do a substring match against the file path9#   - Patterns without '/' match the file stem exactly1011defaults:12  PYAUTO_TEST_MODE: "2"                     # 0=normal, 1=reduced iterations, 2=skip sampler (fastest)13  PYAUTO_SKIP_FIT_OUTPUT: "1"               # Skip pre/post-fit I/O, VRAM profiling, result text14  PYAUTO_SKIP_VISUALIZATION: "1"            # Skip fit visualization and plotting15  PYAUTO_SKIP_CHECKS: "1"                   # Skip mesh validation, position checks, weight thresholds16  PYAUTO_SMALL_DATASETS: "1"                # Cap grids/masks to 15x15, reduce MGE gaussians17  PYAUTO_DISABLE_JAX: "1"                   # Force use_jax=False, avoid JIT compilation overhead18  PYAUTO_FAST_PLOTS: "1"                    # Skip tight_layout() + critical curve/caustic overlays19  JAX_ENABLE_X64: "True"                    # Enable 64-bit precision in JAX20  NUMBA_CACHE_DIR: "/tmp/numba_cache"       # Writable cache dir for numba21  MPLCONFIGDIR: "/tmp/matplotlib"           # Writable config dir for matplotlib2223overrides:24  # The `guides/` PYAUTO_SMALL_DATASETS unset migrated to in-file25  # `# ENV: full_datasets` declarations on each guides/ script (#187 Stage 2).26  # PYAUTO_TEST_MODE / PYAUTO_FAST_PLOTS below likewise migrated to `# ENV:`27  # declarations; the non-declarable PYAUTO_SKIP_* vars remain here.28  # guides/results/start_here.py must produce real samples so the example29  # scripts that read from `output/results_folder` afterwards30  # (data_fitting, queries, models, samples_via_aggregator) find a31  # non-empty aggregator. Covers all scripts under guides/results/.32  # (PYAUTO_TEST_MODE moved to `# ENV: real_search`.)33  - pattern: "guides/results/"34    unset: [PYAUTO_SKIP_FIT_OUTPUT]35  # fits_make / png_make produce .fits / .png outputs from real fits, so they need36  # visualization turned on. (PYAUTO_FAST_PLOTS moved to `# ENV: real_plots`.)37  - pattern: fits_make38    unset: [PYAUTO_SKIP_VISUALIZATION]39  - pattern: png_make40    unset: [PYAUTO_SKIP_VISUALIZATION]

1# Notebook stems that should run when PyAutoHands's generate / run pipeline2# is invoked with --visualise. Used to refresh notebook output cells in main.3#4# Format: flat list of notebook stems (no extension, no path).5# An empty list means no notebooks need re-visualisation in this workspace.6#7# This file overrides PyAutoHands/autohands/config/visualise_notebooks.yaml8# for this workspace. Add or remove entries here, not there.910[]

1# The settings of a parallelized grid search of non-linear searches.23parallel:4  number_of_cores: 3 # The number of cores the search is parallelized over by default, using Python multiprocessing.5  step_size: 0.1     # The default step size of each grid search parameter, in terms of unit values of the priors.

1Basis: {}