feat: add aggregator integration test scripts

scripts/aggregator/config/general.yaml

@@ -0,0 +1,37 @@
+analysis:
+  n_cores: 1
+psf:
+  use_fft_default: false              # 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.
+hpc:
+  hpc_mode: false
+  iterations_per_full_update: 5000
+adapt:
+  adapt_minimum_percent: 0.01
+  adapt_noise_limit: 100000000.0
+inversion:
+  check_reconstruction: false       # 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.
+  use_positive_only_solver: false    # 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.
+  no_regularization_add_to_curvature_diag_value: 1.0e-8 # 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.
+  positive_only_uses_p_initial: false  # If True, the positive-only solver of an inversion's uses an initial guess of the reconstructed data's values as which values should be positive, speeding up the solver.
+  use_border_relocator: false          # If True, by default a pixelization's border is used to relocate all pixels outside its border to the border.
+numba:
+  cache: true
+  nopython: true
+  parallel: false
+  use_numba: true
+output:
+  force_pickle_overwrite: false
+  info_whitespace_length: 80
+  log_file: output.log
+  log_level: INFO
+  log_to_file: false
+  model_results_decimal_places: 3
+  remove_files: false
+  samples_to_csv: false
+profiling:
+  perform: true
+  repeats: 1
+test:
+  check_likelihood_function: false  # if True, when a search is resumed the likelihood of a previous sample is recalculated to ensure it is consistent with the previous run.
+  exception_override: false
+  disable_positions_lh_inversion_check: false

scripts/aggregator/config/non_linear.yaml

@@ -0,0 +1,75 @@
+mock:
+  MockOptimizer:
+    initialize:
+      method: prior
+    printing:
+      silence: false
+
+  MockSearch:
+    initialize:
+      method: prior
+    printing:
+      silence: false
+    search: {}
+
+nest:
+  DynestyDynamic:
+    general:
+      acceptance_ratio_threshold: 0.1
+      bootstrap: null
+      bound: multi
+      enlarge: null
+      first_update: null
+      fmove: 0.9
+      max_move: 100
+      sample: auto
+      sampling_efficiency: 0.5
+      slices: 5
+      terminate_at_acceptance_ratio: false
+      update_interval: null
+      walks: 25
+    initialize:
+      method: prior
+    parallel:
+      force_x1_cpu: false
+      number_of_cores: 1
+    printing:
+      silence: false
+
+  DynestyStatic:
+    parallel:
+      number_of_cores: 1
+    initialize:
+      method: prior
+    inversion:
+      acceptance_ratio_threshold: 0.05
+      const_efficiency_mode: true
+      evidence_tolerance: 100.0
+      multimodal: false
+      n_live_points: 50
+      sampling_efficiency: 0.3
+      terminate_at_acceptance_ratio: false
+    printing:
+      silence: false
+    search:
+      const_efficiency_mode: false
+      evidence_tolerance: 0.5
+      importance_nested_sampling: false
+      max_iter: 0
+      max_modes: 100
+      mode_tolerance: -1.0e+90
+      multimodal: false
+      n_live_points: 50
+      sampling_efficiency: 0.5
+    settings:
+      context: 0
+      init_MPI: false
+      log_zero: -1.0e+100
+      n_iter_before_update: 5
+      null_log_evidence: -1.0e+90
+      resume: true
+      seed: -1.0
+      stagger_resampling_likelihood: true
+      verbose: false
+      write_output: true
+

scripts/aggregator/config/notation.yaml

@@ -0,0 +1,83 @@
+label:
+  label:
+    alpha: \alpha
+    angle_binary: \theta
+    beta: \beta
+    break_radius: \theta_{\rm B}
+    centre_0: y
+    centre_1: x
+    coefficient: \lambda
+    contribution_factor: \omega_{\rm 0}
+    core_radius: C_{\rm r}
+    core_radius_0: C_{rm r0}
+    core_radius_1: C_{\rm r1}
+    effective_radius: R_{\rm eff}
+    einstein_radius: \theta_{\rm Ein}
+    ell_comps_0: \epsilon_{\rm 1}
+    ell_comps_1: \epsilon_{\rm 2}
+    multipole_comps_0: M_{\rm 1}
+    multipole_comps_1: M_{\rm 2}
+    flux: F
+    gamma: \gamma
+    gamma_1: \gamma
+    gamma_2: \gamma
+    inner_coefficient: \lambda_{\rm 1}
+    inner_slope: t_{\rm 1}
+    intensity: I_{\rm b}
+    kappa: \kappa
+    kappa_s: \kappa_{\rm s}
+    log10m_vir: log_{\rm 10}(m_{vir})
+    m: m
+    mass: M
+    mass_at_200: M_{\rm 200}
+    mass_ratio: M_{\rm ratio}
+    mass_to_light_gradient: \Gamma
+    mass_to_light_ratio: \Psi
+    mass_to_light_ratio_base: \Psi_{\rm base}
+    mass_to_light_radius: R_{\rm ref}
+    noise_factor: \omega_{\rm 1}
+    noise_power: \omega{\rm 2}
+    noise_scale: \sigma_{\rm 1}
+    normalization_scale: n
+    outer_coefficient: \lambda_{\rm 2}
+    outer_slope: t_{\rm 2}
+    overdens: \Delta_{\rm vir}
+    pixels: N_{\rm pix}
+    radius_break: R_{\rm b}
+    redshift: z
+    redshift_object: z_{\rm obj}
+    redshift_source: z_{\rm src}
+    scale_radius: R_{\rm s}
+    scatter: \sigma
+    separation: s
+    sersic_index: n
+    shape_0: y_{\rm pix}
+    shape_1: x_{\rm pix}
+    sigma: \sigma
+    signal_scale: V
+    sky_scale: \sigma_{\rm 0}
+    slope: \gamma
+    truncation_radius: R_{\rm t}
+    weight_floor: W_{\rm f}
+    weight_power: W_{\rm p}
+  superscript:
+    ExternalShear: ext
+    Pixelization: pix
+    Point: point
+    Redshift: ''
+    Regularization: reg
+label_format:
+  format:
+    angular_diameter_distance_to_earth: '{:.2f}'
+    concentration: '{:.2f}'
+    einstein_mass: '{:.4e}'
+    einstein_radius: '{:.2f}'
+    kpc_per_arcsec: '{:.2f}'
+    luminosity: '{:.4e}'
+    m: '{:.1f}'
+    mass: '{:.4e}'
+    mass_at_truncation_radius: '{:.4e}'
+    radius: '{:.2f}'
+    redshift: '{:.2f}'
+    rho: '{:.2f}'
+    sersic_luminosity: '{:.4e}'

scripts/aggregator/config/output.yaml

@@ -0,0 +1,63 @@
+# Determines whether files saved by the search are output to the hard-disk. This is true both when saving to the
+# directory structure and when saving to database.
+
+# Files can be listed name: bool where the name is the name of the file without a suffix (e.g. model not model.json)
+# and bool is true or false.
+
+# If a given file is not listed then the default value is used.
+
+default: true # If true then files which are not explicitly listed here are output anyway. If false then they are not.
+
+### Samples ###
+
+# The `samples.csv`file contains every sampled value of every free parameter with its log likelihood and weight.
+
+# This file is often large, therefore disabling it can significantly reduce hard-disk space use.
+
+# `samples.csv` is used to perform marginalization, infer model parameter errors and do other analysis of the search
+# chains. Even if output of `samples.csv` is disabled, these tasks are still performed by the fit and output to
+# the `samples_summary.json` file. However, without a `samples.csv` file these types of tasks cannot be performed
+# after the fit is complete, for example via the database.
+
+samples: true
+
+# The `samples.csv` file contains every accepted sampled value of every free parameter with its log likelihood and
+# weight. For certain searches, the majority of samples have a very low weight, which has no numerical impact on the
+# results of the model-fit. However, these samples are still output to the `samples.csv` file, taking up hard-disk space
+# and slowing down analysis of the samples (e.g. via the database).
+
+# The `samples_weight_threshold` below specifies the threshold value of the weight such that samples with a weight
+# below this value are not output to the `samples.csv` file. This can be used to reduce the size of the `samples.csv`
+# file and speed up analysis of the samples.
+
+# Note that for many searches (e.g. MCMC) all samples have equal weight, and thus this threshold has no impact and
+# there is no simple way to save hard-disk space. However, for nested sampling, the majority of samples have a very
+# low weight and this threshold can be used to save hard-disk space.
+
+# Set value to empty (e.g. delete 1.0e-10 below) to disable this feature.
+
+samples_weight_threshold: 1.0e-10
+
+### Search Internal ###
+
+# The search internal folder which contains a saved state of the non-linear search, as a .pickle or .dill file.
+
+# If the entry below is false, the folder is still output during the model-fit, as it is required to resume the fit
+# from where it left off. Therefore, settings `false` below does not impact model-fitting checkpointing and resumption.
+# Instead, the search internal folder is deleted once the fit is completed.
+
+# The search internal folder file is often large, therefore deleting it after a fit is complete can significantly
+# reduce hard-disk space use.
+
+# The search internal representation (e.g. what you can load from the output .pickle file) may have additional
+# quantities specific to the non-linear search that you are interested in inspecting. Deleting the folder means this
+# information is list.
+
+search_internal: false
+
+# Other Files:
+
+covariance: false # `covariance.csv`: The [free parameters x free parameters] covariance matrix.
+data: true # `data.json`: The value of every data point in the data.
+noise_map: true # `noise_map.json`: The value of every RMS noise map value.
+