From 293c4816836b1bc2e3d1c8a37222e815d9167aa0 Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Tue, 20 Aug 2024 19:58:47 +0200
Subject: [PATCH 01/21] Implemented UltraNest to example_16, double checked
 until run_fit()

---
 examples/example_16/ob08092-o4_MN.yaml        |   1 +
 .../example_16/ob08092-o4_minimal_MN.yaml     |   1 +
 examples/example_16/ulens_model_fit.py        | 126 +++++++++++++++---
 3 files changed, 112 insertions(+), 16 deletions(-)

diff --git a/examples/example_16/ob08092-o4_MN.yaml b/examples/example_16/ob08092-o4_MN.yaml
index e15f5890..aef22261 100644
--- a/examples/example_16/ob08092-o4_MN.yaml
+++ b/examples/example_16/ob08092-o4_MN.yaml
@@ -1,5 +1,6 @@
 photometry_files:
     data/OB08092/phot_ob08092_O4.dat
+fit_method: UltraNest  # options: EMCEE, MultiNest, UltraNest (... dynesty)
 prior_limits:
     t_0: [2455379.4, 2455379.76]
     u_0: [0.3, 0.65]
diff --git a/examples/example_16/ob08092-o4_minimal_MN.yaml b/examples/example_16/ob08092-o4_minimal_MN.yaml
index 9a998a07..d8347564 100644
--- a/examples/example_16/ob08092-o4_minimal_MN.yaml
+++ b/examples/example_16/ob08092-o4_minimal_MN.yaml
@@ -1,5 +1,6 @@
 photometry_files:
     data/OB08092/phot_ob08092_O4.dat
+fit_method: UltraNest  # options: EMCEE, MultiNest, UltraNest (... dynesty)
 prior_limits:
     t_0: [2455379.4, 2455379.76]
     u_0: [0.46, 0.65]
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 3beb2495..9ad28d5a 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -33,6 +33,10 @@
     from pymultinest.analyse import Analyzer
 except Exception:
     import_failed.add("pymultinest")
+try:
+    import ultranest
+except Exception:
+    import_failed.add("ultranest")
 
 try:
     import MulensModel as mm
@@ -69,6 +73,18 @@ class UlensModelFit(object):
             ``'scale_errorbars': {'factor': kappa, 'minimum': epsilon}``
             to scale uncertainties.
 
+        fit_method: *str*
+            Method of fitting. Currently accepted values are ``EMCEE``,
+            ``MultiNest``, and ``UltraNest``. If not provided, the script
+            will guess it based on other parameters: ``EMCEE`` is selected
+            if ``starting_parameters`` are provided and ``MultiNest`` is
+            selected if ``prior_limits`` are provided.
+
+            Webpage of each method:
+            - EMCEE: https://emcee.readthedocs.io/en/stable/
+            - MultiNest: https://johannesbuchner.github.io/PyMultiNest/
+            - UltraNest: https://johannesbuchner.github.io/UltraNest/
+
         starting_parameters: *dict*
             Starting values of the parameters.
             It also indicates the EMCEE fitting mode.
@@ -108,12 +124,13 @@ class UlensModelFit(object):
 
         prior_limits: *dict*
             Upper and lower limits of parameters.
-            It also indicates the pyMultiNest fitting mode.
+            It only applies to pyMultiNest and UltraNest fitting. However,
+            if fit_method is not given, pyMultiNest will be applied.
 
             Keys are MulensModel parameters and values are lists of two floats
             each (alternatively a string giving 2 floats can be provided - see
             example below). Currently, no informative priors are allowed for
-            pyMultiNest fitting. Example input:
+            pyMultiNest and UltraNest fitting. Example input:
 
             .. code-block:: python
 
@@ -157,7 +174,7 @@ class UlensModelFit(object):
         min_values: *dict*
             Minimum values of parameters that define the prior, e.g.,
             ``{'t_E': 0.}``. Note that the these are only limits of a prior.
-            Functional form of priors can be defines in ``fit_constraints``.
+            Functional form of priors can be defined in ``fit_constraints``.
             It works only for EMCEE fitting.
 
         max_values: *dict*
@@ -333,7 +350,7 @@ class UlensModelFit(object):
     """
 
     def __init__(
-            self, photometry_files,
+            self, photometry_files, fit_method=None,
             starting_parameters=None, prior_limits=None, model=None,
             fixed_parameters=None,
             min_values=None, max_values=None, fitting_parameters=None,
@@ -341,6 +358,7 @@ def __init__(
     ):
         self._check_MM_version()
         self._photometry_files = photometry_files
+        self._fit_method = fit_method
         self._starting_parameters_input = starting_parameters
         self._prior_limits = prior_limits
         self._model_parameters = model
@@ -355,7 +373,10 @@ def __init__(
         self._which_task()
         self._set_default_parameters()
         if self._task == 'fit':
-            self._guess_fitting_method()
+            if self._fit_method is None:
+                self._guess_fitting_method()
+            else:
+                self._check_fitting_method()
             self._check_starting_parameters_type()
             self._set_fit_parameters_unsorted()
         self._check_imports()
@@ -419,7 +440,7 @@ def _which_task(self):
 
     def _check_unnecessary_settings_plot(self):
         """
-        Make sure that there arent' too many parameters specified for:
+        Make sure that there aren't too many parameters specified for:
         self._task == 'plot'
         """
         keys = ['_starting_parameters_input', '_min_values', '_max_values',
@@ -506,7 +527,7 @@ def _guess_fitting_method(self):
                     "which makes impossible to choose the fitting method. "
                     "These settings indicate EMCEE and pyMultiNest "
                     "respectively, and cannot be both set.")
-            method = "MultiNest"
+            method = "MultiNest"  # UltraNest is also supported
         if method is None:
             raise ValueError(
                 "No fitting method chosen. You can chose either 'EMCEE' or "
@@ -514,12 +535,27 @@ def _guess_fitting_method(self):
                 "starting_parameters or prior_limits, respectively.")
         self._fit_method = method
 
+    def _check_fitting_method(self):
+        """
+        Check if fitting method is consistent with the settings.
+        """
+        if self._fit_method == "EMCEE":
+            if self._starting_parameters_input is None:
+                raise ValueError(
+                    "EMCEE fitting method requires starting_parameters.")
+        elif self._fit_method in ["MultiNest", "UltraNest"]:
+            if self._prior_limits is None:
+                raise ValueError(
+                    "pyMultiNest fitting method requires prior_limits.")
+        else:
+            raise ValueError("Invalid fitting method was inserted.")
+
     def _check_starting_parameters_type(self):
         """
         Check if starting parameters are read from file or
         will be drawn from distributions specified.
         """
-        if self._fit_method == "MultiNest":
+        if self._fit_method in ["MultiNest", "UltraNest"]:
             return
 
         if 'file' in self._starting_parameters_input:
@@ -547,7 +583,7 @@ def _set_fit_parameters_unsorted(self):
             else:
                 raise ValueError(
                     'unexpected: ' + str(self._starting_parameters_type))
-        elif self._fit_method == "MultiNest":
+        elif self._fit_method in ["MultiNest", "UltraNest"]:
             unsorted_keys = self._prior_limits.keys()
         else:
             raise ValueError('unexpected method error')
@@ -576,6 +612,8 @@ def _check_imports(self):
                 required_packages.add('emcee')
             elif self._fit_method == "MultiNest":
                 required_packages.add('pymultinest')
+            elif self._fit_method == "Ultranest":
+                required_packages.add('cython')
 
             if self._plots is not None and 'triangle' in self._plots:
                 required_packages.add('corner')
@@ -844,7 +882,7 @@ def _check_plots_parameters_trace(self):
             raise ValueError(
                 'Unknown settings for "trace" plot: {:}'.format(unknown))
 
-        if self._fit_method == "MultiNest":
+        if self._fit_method in ["MultiNest", "UltraNest"]:
             raise ValueError(
                 'Trace plot cannot be requested for MultiNest fit')
 
@@ -897,7 +935,7 @@ def _check_other_fit_parameters(self):
         """
         Check if there aren't any other inconsistencies between settings
         """
-        if self._fit_method == "MultiNest":
+        if self._fit_method in ["MultiNest", "UltraNest"]:
             if self._min_values is not None or self._max_values is not None:
                 raise ValueError("In MultiNest fitting you cannot set "
                                  "min_values or max_values")
@@ -1145,7 +1183,7 @@ def _parse_fitting_parameters(self):
         if self._fit_method == 'EMCEE':
             self._parse_fitting_parameters_EMCEE()
             self._get_n_walkers()
-        elif self._fit_method == 'MultiNest':
+        elif self._fit_method in ['MultiNest', 'UltraNest']:
             self._parse_fitting_parameters_MultiNest()
         else:
             raise ValueError('internal inconsistency')
@@ -1374,7 +1412,7 @@ def _set_prior_limits(self):
         """
         if self._fit_method == 'EMCEE':
             self._set_prior_limits_EMCEE()
-        elif self._fit_method == 'MultiNest':
+        elif self._fit_method in ['MultiNest', 'UltraNest']:
             self._set_prior_limits_MultiNest()
         else:
             raise ValueError('internal bug')
@@ -1459,7 +1497,7 @@ def _parse_fit_constraints(self):
         """
         Parse the fitting constraints that are not simple limits on parameters
         """
-        if self._fit_method == 'MultiNest':
+        if self._fit_method in ['MultiNest', 'UltraNest']:
             if self._fit_constraints is not None:
                 raise NotImplementedError(
                     "Currently no fit_constraints are implemented for "
@@ -1485,7 +1523,8 @@ def _check_fit_constraints(self):
         """
         Run checks on self._fit_constraints
         """
-        if self._fit_constraints is not None and self._fit_method == 'MultiNest':
+        lst_check = ['MultiNest', 'UltraNest']
+        if self._fit_constraints is not None and self._fit_method == lst_check:
             raise NotImplementedError(
                 "Currently no fit_constraints are implemented for MultiNest "
                 "fit. Please contact Radek Poleski with a specific request.")
@@ -1947,7 +1986,7 @@ def _get_example_parameters(self):
         elif self._task == 'fit':
             if self._fit_method == 'EMCEE':
                 parameters.update(self._get_example_parameters_EMCEE())
-            elif self._fit_method == 'MultiNest':
+            elif self._fit_method in ['MultiNest', 'UltraNest']:
                 means = 0.5 * (self._max_values + self._min_values)
                 parameters.update(dict(zip(self._fit_parameters, means)))
                 if "x_caustic_in" in self._fit_parameters:
@@ -2418,6 +2457,8 @@ def _setup_fit(self):
             self._setup_fit_EMCEE()
         elif self._fit_method == 'MultiNest':
             self._setup_fit_MultiNest()
+        elif self._fit_method == 'UltraNest':
+            self._setup_fit_UltraNest()
         else:
             raise ValueError('internal bug')
 
@@ -2441,6 +2482,15 @@ def _setup_fit_MultiNest(self):
         if self._return_fluxes:
             self._kwargs_MultiNest['n_params'] += self._n_fluxes
 
+    def _setup_fit_UltraNest(self):
+        """
+        Setup UltraNest fit -- Raphael
+        """
+        self._sampler = ultranest.ReactiveNestedSampler(
+            self._fit_parameters,
+            self._ln_like, transform=self._transform_unit_cube_un
+        )
+
     def _transform_unit_cube(self, cube, n_dims, n_params):
         """
         Transform MulitNest unit cube to microlensing parameters.
@@ -2477,7 +2527,34 @@ def _transform_unit_cube(self, cube, n_dims, n_params):
                 cube[i] = fluxes[i-n_dims]
             self._last_fluxes = fluxes
 
+    def _transform_unit_cube_un(self, cube, n_dims=3, n_params=3):
+        """
+        Transform UltraNest unit cube to microlensing parameters.
+
+        [...]
+        """
+        cube_out = self._min_values + cube[:n_dims] * self._range_values
+
+        # Raphael, check: are "x_caustic_in" lines needed here?
+
+        # if hasattr(self, '_last_theta'):
+        #     self._prev_last_theta = self._last_theta.copy()
+        # else:
+        #     self._prev_last_theta = cube_out
+        # self._last_ln_like = self._ln_like(cube_out)
+        # self._last_theta = cube_out
+
+        # Raphael, check: are self._return_fluxes lines needed here?
+        if self._return_fluxes:
+            fluxes = self._get_fluxes()
+            for i in range(n_dims, n_params):
+                cube[i] = fluxes[i-n_dims]
+            self._last_fluxes = fluxes
+
+        return cube_out
+
     def _ln_like_MN(self, theta, n_dim, n_params, lnew):
+        # def _ln_like_MN(self, theta, n_dim=3, n_params=3, lnew=-1.e300):
         """
         Calculate likelihood and save if its best model.
         This is used for MultiNest fitting.
@@ -2512,6 +2589,8 @@ def _run_fit(self):
             self._run_fit_EMCEE()
         elif self._fit_method == 'MultiNest':
             self._run_fit_MultiNest()
+        elif self._fit_method == 'UltraNest':
+            self._run_fit_UltraNest()
         else:
             raise ValueError('internal bug')
 
@@ -2527,6 +2606,12 @@ def _run_fit_MultiNest(self):
         """
         mn_run(**self._kwargs_MultiNest)
 
+    def _run_fit_UltraNest(self):
+        """
+        Run Ultranest fit
+        """
+        self.un_result = self._sampler.run()
+
     def _finish_fit(self):
         """
         Make the things that are necessary after the fit is done.
@@ -2602,6 +2687,15 @@ def _parse_results(self):
                 self._save_posterior_EMCEE()
         elif self._fit_method == "MultiNest":
             self._parse_results_MultiNest()
+        elif self._fit_method == "UltraNest":
+            result = self.un_result['maximum_likelihood']['point']
+            stdev = self.un_result['posterior']['stdev']
+            print("\n--\nUltraNest results:")
+            for (i, param) in enumerate(self.un_result['paramnames']):
+                print(param, ' =', result[i], ' +/-', stdev[i])
+            print('logl =', self.un_result['maximum_likelihood']['logl'])
+            print('logz =', self.un_result['logz'], '+/-',
+                  self.un_result['logzerr'])
         else:
             raise ValueError('internal bug')
 

From 294a31a10abbc4eec4cd274e689703882d1404c3 Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Wed, 21 Aug 2024 12:28:09 +0200
Subject: [PATCH 02/21] Finished parsing fitting_parameters for UltraNest

---
 examples/example_16/ob08092-o4_MN.yaml |   9 +-
 examples/example_16/ulens_model_fit.py | 110 ++++++++++++++++++++-----
 2 files changed, 95 insertions(+), 24 deletions(-)

diff --git a/examples/example_16/ob08092-o4_MN.yaml b/examples/example_16/ob08092-o4_MN.yaml
index aef22261..45944b97 100644
--- a/examples/example_16/ob08092-o4_MN.yaml
+++ b/examples/example_16/ob08092-o4_MN.yaml
@@ -6,11 +6,16 @@ prior_limits:
     u_0: [0.3, 0.65]
     t_E: 16. 19.6
 fitting_parameters:
+    # MultiNest only (basename, multimodal, evidence tolerance)
     basename: out_ob08092_O4_MN-
     multimodal: True
+    # evidence tolerance: 0.5
+    # UltraNest only (log directory, derived parameter names, show_status)
+    log directory: ultranest_outputs/
+    # derived parameter names: source_flux blending_flux
+    show_status: True
+    # `n_live_points` can be used in MultiNest and UltraNest
     n_live_points: 1000
-# Default settings of other parameters:
-#    evidence tolerance: 0.5
 plots:
     best model:
         file: out_ob08092_O4_MN_model.png
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 9ad28d5a..527d290b 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -227,6 +227,22 @@ class UlensModelFit(object):
             ``evidence tolerance`` (*float*) - requested tolerance of ln(Z)
             calculation; default is 0.5 and should work well in most cases.
 
+            Third - UltraNest. There are no required parameters, but a few
+            can be provided. Currently accepted ones are:
+
+            ``log directory`` (*str*) - where to store output files. If given,
+            there is a check if directory exists. If not given, no outputs
+            are saved.
+
+            ``derived parameter names`` (*str*) - names of additional derived
+            parameters created by transform... Continue here!
+
+            ``show_status`` (*bool*) - whether to show integration progress
+            as a status line or not
+
+            ``n_live_points`` (*int*) - number of live points, default value
+            is 400.
+
         fit_constraints: *dict*
             Constraints on model other than minimal and maximal values.
 
@@ -884,7 +900,7 @@ def _check_plots_parameters_trace(self):
 
         if self._fit_method in ["MultiNest", "UltraNest"]:
             raise ValueError(
-                'Trace plot cannot be requested for MultiNest fit')
+                f'Trace plot cannot be requested for {self._fit_method}.')
 
         self._parse_plots_parameter_shift_t_0(self._plots['trace'])
 
@@ -937,8 +953,8 @@ def _check_other_fit_parameters(self):
         """
         if self._fit_method in ["MultiNest", "UltraNest"]:
             if self._min_values is not None or self._max_values is not None:
-                raise ValueError("In MultiNest fitting you cannot set "
-                                 "min_values or max_values")
+                raise ValueError("In {:} fitting you cannot set min_values "
+                                 "or max_values".format(self._fit_method))
 
     def _parse_methods(self, methods):
         """
@@ -1165,11 +1181,12 @@ def _get_parameters_latex(self):
 
         if self._fit_constraints is not None:
             if 'posterior parsing' in self._fit_constraints:
-                if 'abs' in self._fit_constraints['posterior parsing']:
-                    settings = self._fit_constraints['posterior parsing']['abs']
-                    if not isinstance(settings, list):
-                        raise ValueError("Error: fit_constraints -> posterior parsing -> abs - list expected")
-                    for key in self._fit_constraints['posterior parsing']['abs']:
+                settings = self._fit_constraints['posterior parsing']
+                if 'abs' in settings:
+                    if not isinstance(settings['abs'], list):
+                        raise ValueError("Error: fit_constraints -> posterior"
+                                         " parsing -> abs - list expected")
+                    for key in settings['abs']:
                         conversion[key] = "|" + conversion[key] + "|"
 
         self._fit_parameters_latex = [
@@ -1183,8 +1200,10 @@ def _parse_fitting_parameters(self):
         if self._fit_method == 'EMCEE':
             self._parse_fitting_parameters_EMCEE()
             self._get_n_walkers()
-        elif self._fit_method in ['MultiNest', 'UltraNest']:
+        elif self._fit_method == 'MultiNest':
             self._parse_fitting_parameters_MultiNest()
+        elif self._fit_method == 'UltraNest':
+            self._parse_fitting_parameters_UltraNest()
         else:
             raise ValueError('internal inconsistency')
 
@@ -1258,8 +1277,8 @@ def _check_required_and_allowed_parameters(self, required, allowed):
 
         for required_ in required:
             if required_ not in settings:
-                raise ValueError('EMCEE method requires fitting parameter: ' +
-                                 required_)
+                msg = '{:} method requires fitting parameter: {:}'
+                raise ValueError(msg.format(self._fit_method, required_))
 
         if len(set(settings.keys()) - set(full)) > 0:
             raise ValueError('Unexpected fitting parameters: ' +
@@ -1338,6 +1357,11 @@ def _parse_fitting_parameters_MultiNest(self):
         floats = ['sampling efficiency', 'evidence tolerance']
         allowed = strings + bools + ints + floats
 
+        only_UltraNest = ['log directory', 'derived parameter names',
+                          'show status']
+        for item in only_UltraNest:
+            settings.pop(item, None)
+
         self._check_required_and_allowed_parameters(required, allowed)
         self._check_parameters_types(settings, bools, ints, floats, strings)
 
@@ -1349,7 +1373,7 @@ def _parse_fitting_parameters_MultiNest(self):
         same_keys = ["multimodal", "n_live_points"]
         keys = {**keys, **{key: key for key in same_keys}}
 
-        self._set_dict_safetly(self._kwargs_MultiNest, settings, keys)
+        self._set_dict_safely(self._kwargs_MultiNest, settings, keys)
 
         self._kwargs_MultiNest['importance_nested_sampling'] = (
             not self._kwargs_MultiNest['multimodal'])
@@ -1361,7 +1385,7 @@ def _parse_fitting_parameters_MultiNest(self):
             self._MN_temporary_files = True
         self._check_output_files_MultiNest()
 
-    def _set_dict_safetly(self, target, source, keys_mapping):
+    def _set_dict_safely(self, target, source, keys_mapping):
         """
         For each key in keys_mapping (*dict*) check if it is in
         source (*dict*). If it is, then set
@@ -1406,12 +1430,51 @@ def _check_output_files_MultiNest(self):
             message += "(unless you kill this process)!!!\n"
             warnings.warn(message + str(existing) + "\n")
 
+    def _parse_fitting_parameters_UltraNest(self):
+        """
+        make sure MultiNest fitting parameters are properly defined
+        """
+        self._kwargs_UltraNest = dict()
+        self._kwargs_UltraNest['viz_callback'] = False
+
+        settings = self._fitting_parameters
+        if settings is None:
+            settings = dict()
+
+        required = []
+        bools = ['show_status']
+        ints = ['n_live_points']
+        strings = ['log directory', 'derived parameter names']
+        floats = []
+        allowed = strings + bools + ints + floats
+
+        only_MultiNest = ['basename', 'multimodal', 'evidence tolerance']
+        for item in only_MultiNest:
+            settings.pop(item, None)
+
+        self._check_required_and_allowed_parameters(required, allowed)
+        self._check_parameters_types(settings, bools, ints, floats, strings)
+        value = settings.pop("log directory")
+        if value is not None:
+            if path.exists(value) and path.isdir(value):
+                self._un_log_dir = value
+            else:
+                raise ValueError("log directory value in fitting_parameters"
+                                 "does not exist.")
+        value = settings.pop("derived parameter names", "")
+        self._un_derived_params_names = value.split()
+
+        keys = {"n_live_points": "min_num_live_points"}
+        same_keys = ["show_status"]
+        keys = {**keys, **{key: key for key in same_keys}}
+        self._set_dict_safely(self._kwargs_UltraNest, settings, keys)
+
     def _set_prior_limits(self):
         """
         Set minimum and maximum values of the prior space
         """
         if self._fit_method == 'EMCEE':
-            self._set_prior_limits_EMCEE()
+            self._set_prior_limits_EMCEE()  # Raphael: never happens?
         elif self._fit_method in ['MultiNest', 'UltraNest']:
             self._set_prior_limits_MultiNest()
         else:
@@ -2488,7 +2551,9 @@ def _setup_fit_UltraNest(self):
         """
         self._sampler = ultranest.ReactiveNestedSampler(
             self._fit_parameters,
-            self._ln_like, transform=self._transform_unit_cube_un
+            self._ln_like, transform=self._transform_unit_cube_un,
+            derived_param_names=self._un_derived_params_names,
+            log_dir=self._un_log_dir
         )
 
     def _transform_unit_cube(self, cube, n_dims, n_params):
@@ -2610,7 +2675,7 @@ def _run_fit_UltraNest(self):
         """
         Run Ultranest fit
         """
-        self.un_result = self._sampler.run()
+        self._un_result = self._sampler.run(**self._kwargs_UltraNest)
 
     def _finish_fit(self):
         """
@@ -2688,14 +2753,15 @@ def _parse_results(self):
         elif self._fit_method == "MultiNest":
             self._parse_results_MultiNest()
         elif self._fit_method == "UltraNest":
-            result = self.un_result['maximum_likelihood']['point']
-            stdev = self.un_result['posterior']['stdev']
+            # self._sampler.print_results()
+            result = self._un_result['maximum_likelihood']['point']
+            stdev = self._un_result['posterior']['stdev']
             print("\n--\nUltraNest results:")
-            for (i, param) in enumerate(self.un_result['paramnames']):
+            for (i, param) in enumerate(self._un_result['paramnames']):
                 print(param, ' =', result[i], ' +/-', stdev[i])
-            print('logl =', self.un_result['maximum_likelihood']['logl'])
-            print('logz =', self.un_result['logz'], '+/-',
-                  self.un_result['logzerr'])
+            print('logl =', self._un_result['maximum_likelihood']['logl'])
+            print('logz =', self._un_result['logz'], '+/-',
+                  self._un_result['logzerr'])
         else:
             raise ValueError('internal bug')
 

From fbb5885c4d46663f2e7b406fb4625411ded5ad32 Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Wed, 21 Aug 2024 18:30:43 +0200
Subject: [PATCH 03/21] Removed repeated check of self._fit_constraints, minor
 changes in description

---
 examples/example_16/ulens_model_fit.py | 37 +++++++++++---------------
 1 file changed, 16 insertions(+), 21 deletions(-)

diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 527d290b..bac7b038 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -184,7 +184,7 @@ class UlensModelFit(object):
 
         fitting_parameters: *dict*
             Parameters of the fit function. They depend on the method used -
-            we discuss EMCEE and pyMultiNest below.
+            we discuss EMCEE, pyMultiNest and UltraNest below.
 
             First - EMCEE. The required parameter is ``n_steps``.
             You can also specify ``n_burn`` and ``n_walkers``. The ``n_burn``
@@ -218,7 +218,7 @@ class UlensModelFit(object):
             the posterior to be detected and reported separately?
 
             ``n_live_points`` (*int*) - number of live points, default value
-            is 400.
+            is 400. Also valid for UltraNest.
 
             ``sampling efficiency`` (*float*) - requested sampling efficiency.
             MultiNest documentation suggests 0.8 (default value) for parameter
@@ -235,13 +235,15 @@ class UlensModelFit(object):
             are saved.
 
             ``derived parameter names`` (*str*) - names of additional derived
-            parameters created by transform... Continue here!
+            parameters created by transform. In microlensing, they are usually
+            the source(s) and blending fluxes. If not given, they are ignored
+            in the transform function.
 
             ``show_status`` (*bool*) - whether to show integration progress
-            as a status line or not
+            as a status line or not. Default is *True*.
 
-            ``n_live_points`` (*int*) - number of live points, default value
-            is 400.
+            ``n_live_points`` (*int*) - minimum number of live points
+            throughout the run. Default value is 400.
 
         fit_constraints: *dict*
             Constraints on model other than minimal and maximal values.
@@ -1474,7 +1476,7 @@ def _set_prior_limits(self):
         Set minimum and maximum values of the prior space
         """
         if self._fit_method == 'EMCEE':
-            self._set_prior_limits_EMCEE()  # Raphael: never happens?
+            self._set_prior_limits_EMCEE()
         elif self._fit_method in ['MultiNest', 'UltraNest']:
             self._set_prior_limits_MultiNest()
         else:
@@ -1531,7 +1533,7 @@ def _set_prior_limits_MultiNest(self):
         max_values = []
         for parameter in self._fit_parameters:
             if parameter not in self._prior_limits:
-                raise ValueError("interal issue")
+                raise ValueError("internal issue")
             values = self._prior_limits[parameter]
             if isinstance(values, str):
                 values = values.split()
@@ -1560,13 +1562,7 @@ def _parse_fit_constraints(self):
         """
         Parse the fitting constraints that are not simple limits on parameters
         """
-        if self._fit_method in ['MultiNest', 'UltraNest']:
-            if self._fit_constraints is not None:
-                raise NotImplementedError(
-                    "Currently no fit_constraints are implemented for "
-                    "MultiNest fit. Please contact Radek Poleski with "
-                    "a specific request.")
-
+        self._check_fit_constraints()
         self._prior_t_E = None
         self._priors = None
 
@@ -1574,7 +1570,6 @@ def _parse_fit_constraints(self):
             self._set_default_fit_constraints()
             return
 
-        self._check_fit_constraints()
         self._parse_fit_constraints_keys()
         self._parse_fit_constraints_fluxes()
         self._parse_fit_constraints_posterior()
@@ -1587,15 +1582,15 @@ def _check_fit_constraints(self):
         Run checks on self._fit_constraints
         """
         lst_check = ['MultiNest', 'UltraNest']
-        if self._fit_constraints is not None and self._fit_method == lst_check:
-            raise NotImplementedError(
-                "Currently no fit_constraints are implemented for MultiNest "
-                "fit. Please contact Radek Poleski with a specific request.")
+        if self._fit_constraints is not None and self._fit_method in lst_check:
+            msg = "Currently no fit_constraints are implemented for {:} " + \
+                  "fit. Please contact Radek Poleski with a specific request."
+            raise NotImplementedError(msg.format(self._fit_method))
 
         if isinstance(self._fit_constraints, list):
             raise TypeError(
                 "In version 0.5.0 we've changed type of 'fit_constraints' " +
-                "from list to dict. Please correct you input and re-run " +
+                "from list to dict. Please correct your input and re-run " +
                 "the code. Most probably what you need is:\n" +
                 "fit_constraints = {'no_negative_blending_flux': True}")
 

From 9e9b14eb865d12d72cb8e9034ee49f1b90c54f7b Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Thu, 22 Aug 2024 11:05:26 +0200
Subject: [PATCH 04/21] Checked entire code for changes, only missing parse
 UltraNest results

---
 examples/example_16/ob08092-o4_MN.yaml |  2 +-
 examples/example_16/ulens_model_fit.py | 49 ++++++++++++++------------
 2 files changed, 27 insertions(+), 24 deletions(-)

diff --git a/examples/example_16/ob08092-o4_MN.yaml b/examples/example_16/ob08092-o4_MN.yaml
index 45944b97..a3be9ea3 100644
--- a/examples/example_16/ob08092-o4_MN.yaml
+++ b/examples/example_16/ob08092-o4_MN.yaml
@@ -12,7 +12,7 @@ fitting_parameters:
     # evidence tolerance: 0.5
     # UltraNest only (log directory, derived parameter names, show_status)
     log directory: ultranest_outputs/
-    # derived parameter names: source_flux blending_flux
+    derived parameter names: source_flux blending_flux
     show_status: True
     # `n_live_points` can be used in MultiNest and UltraNest
     n_live_points: 1000
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index bac7b038..6d6a4f5c 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -1637,7 +1637,7 @@ def _check_color_constraints_conflict(self, allowed_keys_color):
                     str(used_keys.intersection(allowed_keys_color)-{'color'}))
 
     def _parse_fit_constraints_fluxes(self):
-        """msg +=
+        """
         Process each constraint fit_constraints.
         """
         for key, value in self._fit_constraints.items():
@@ -1723,10 +1723,8 @@ def _parse_fit_constraints_prior(self):
         priors = dict()
         for (key, value) in self._fit_constraints['prior'].items():
             if key == 't_E':
-                if value == "Mroz et al. 2017":
-                    self._prior_t_E = 'Mroz+17'
-                elif value == "Mroz et al. 2020":
-                    self._prior_t_E = 'Mroz+20'
+                if value in ["Mroz et al. 2017", "Mroz et al. 2020"]:
+                    self._prior_t_E = value.replace(" et al. 20", "+")
                 else:
                     raise ValueError("Unrecognized t_E prior: " + value)
                 self._read_prior_t_E_data()
@@ -1761,15 +1759,15 @@ def _parse_fit_constraints_posterior(self):
             return
 
         if self._fit_method != "EMCEE":
-            raise ValueError('Input in "posterior parsing" is allowed only for EMCEE')
+            raise ValueError('Input in "posterior parsing" is allowed only'
+                             ' for EMCEE')
 
         allowed_keys = {"abs"}
         settings = self._fit_constraints['posterior parsing']
         unknown = set(settings.keys()) - allowed_keys
         if len(unknown) > 0:
-            raise KeyError(
-                "Unrecognized key in fit_constraints -> 'posterior parsing': " +
-                str(unknown))
+            msg = "Unrecognized key in fit_constraints -> 'posterior parsing':"
+            raise KeyError(msg + ' ' + str(unknown))
 
         if 'abs' in settings:
             self._parse_posterior_abs = settings['abs']
@@ -2542,8 +2540,16 @@ def _setup_fit_MultiNest(self):
 
     def _setup_fit_UltraNest(self):
         """
-        Setup UltraNest fit -- Raphael
+        Prepare UltraNest fit, declaring sampler instance
         """
+        if self._return_fluxes and len(self._un_derived_params_names) == 0:
+            self._un_derived_params_names = ["source_flux", "blending_flux"]
+            if self._n_fluxes > 2:
+                self._un_derived_params_names[0] += "_1"
+                for i in range(2, self._n_fluxes):
+                    name = "source_flux_{:}".format(i)
+                    self._un_derived_params_names.insert(i-1, name)
+
         self._sampler = ultranest.ReactiveNestedSampler(
             self._fit_parameters,
             self._ln_like, transform=self._transform_unit_cube_un,
@@ -2587,34 +2593,29 @@ def _transform_unit_cube(self, cube, n_dims, n_params):
                 cube[i] = fluxes[i-n_dims]
             self._last_fluxes = fluxes
 
-    def _transform_unit_cube_un(self, cube, n_dims=3, n_params=3):
+    def _transform_unit_cube_un(self, cube):
         """
         Transform UltraNest unit cube to microlensing parameters.
 
-        [...]
+        Based on _transform_unit_cube() for MultiNest method and tutorials
+        from UltraNest documentation:
+        https://johannesbuchner.github.io/UltraNest/example-sine-line.html
         """
+        n_dims = self._n_fit_parameters
+        n_params = n_dims + self._n_fluxes * self._return_fluxes
         cube_out = self._min_values + cube[:n_dims] * self._range_values
 
-        # Raphael, check: are "x_caustic_in" lines needed here?
-
-        # if hasattr(self, '_last_theta'):
-        #     self._prev_last_theta = self._last_theta.copy()
-        # else:
-        #     self._prev_last_theta = cube_out
-        # self._last_ln_like = self._ln_like(cube_out)
-        # self._last_theta = cube_out
+        # Check with Radek: are "x_caustic_in" lines needed here?
 
-        # Raphael, check: are self._return_fluxes lines needed here?
         if self._return_fluxes:
             fluxes = self._get_fluxes()
             for i in range(n_dims, n_params):
-                cube[i] = fluxes[i-n_dims]
+                cube_out = np.append(cube_out, fluxes[i-n_dims])
             self._last_fluxes = fluxes
 
         return cube_out
 
     def _ln_like_MN(self, theta, n_dim, n_params, lnew):
-        # def _ln_like_MN(self, theta, n_dim=3, n_params=3, lnew=-1.e300):
         """
         Calculate likelihood and save if its best model.
         This is used for MultiNest fitting.
@@ -2748,6 +2749,8 @@ def _parse_results(self):
         elif self._fit_method == "MultiNest":
             self._parse_results_MultiNest()
         elif self._fit_method == "UltraNest":
+            # Raphael: continue from here...
+
             # self._sampler.print_results()
             result = self._un_result['maximum_likelihood']['point']
             stdev = self._un_result['posterior']['stdev']

From 2e29836fcb04836ce5aa3dc208b9a3786d35f9f1 Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Thu, 22 Aug 2024 14:05:17 +0200
Subject: [PATCH 05/21] Replaced _un to _UltraNest, started to parse results

---
 examples/example_16/ulens_model_fit.py | 85 +++++++++++++-------------
 1 file changed, 43 insertions(+), 42 deletions(-)

diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 6d6a4f5c..1d925e6c 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -563,8 +563,8 @@ def _check_fitting_method(self):
                     "EMCEE fitting method requires starting_parameters.")
         elif self._fit_method in ["MultiNest", "UltraNest"]:
             if self._prior_limits is None:
-                raise ValueError(
-                    "pyMultiNest fitting method requires prior_limits.")
+                msg = "{:} fitting method requires prior_limits."
+                raise ValueError(msg.format(self._fit_method))
         else:
             raise ValueError("Invalid fitting method was inserted.")
 
@@ -955,8 +955,8 @@ def _check_other_fit_parameters(self):
         """
         if self._fit_method in ["MultiNest", "UltraNest"]:
             if self._min_values is not None or self._max_values is not None:
-                raise ValueError("In {:} fitting you cannot set min_values "
-                                 "or max_values".format(self._fit_method))
+                msg = "In {:} fitting you cannot set min_values or max_values"
+                raise ValueError(msg.format(self._fit_method))
 
     def _parse_methods(self, methods):
         """
@@ -1459,12 +1459,12 @@ def _parse_fitting_parameters_UltraNest(self):
         value = settings.pop("log directory")
         if value is not None:
             if path.exists(value) and path.isdir(value):
-                self._un_log_dir = value
+                self._log_dir_UltraNest = value
             else:
                 raise ValueError("log directory value in fitting_parameters"
                                  "does not exist.")
         value = settings.pop("derived parameter names", "")
-        self._un_derived_params_names = value.split()
+        self._derived_params_UltraNest = value.split()
 
         keys = {"n_live_points": "min_num_live_points"}
         same_keys = ["show_status"]
@@ -2542,19 +2542,19 @@ def _setup_fit_UltraNest(self):
         """
         Prepare UltraNest fit, declaring sampler instance
         """
-        if self._return_fluxes and len(self._un_derived_params_names) == 0:
-            self._un_derived_params_names = ["source_flux", "blending_flux"]
+        if self._return_fluxes and len(self._derived_params_UltraNest) == 0:
+            self._derived_params_UltraNest = ["source_flux", "blending_flux"]
             if self._n_fluxes > 2:
-                self._un_derived_params_names[0] += "_1"
+                self._derived_params_UltraNest[0] += "_1"
                 for i in range(2, self._n_fluxes):
                     name = "source_flux_{:}".format(i)
-                    self._un_derived_params_names.insert(i-1, name)
+                    self._derived_params_UltraNest.insert(i-1, name)
 
         self._sampler = ultranest.ReactiveNestedSampler(
             self._fit_parameters,
-            self._ln_like, transform=self._transform_unit_cube_un,
-            derived_param_names=self._un_derived_params_names,
-            log_dir=self._un_log_dir
+            self._ln_like, transform=self._transform_unit_cube_UltraNest,
+            derived_param_names=self._derived_params_UltraNest,
+            log_dir=self._log_dir_UltraNest
         )
 
     def _transform_unit_cube(self, cube, n_dims, n_params):
@@ -2593,28 +2593,6 @@ def _transform_unit_cube(self, cube, n_dims, n_params):
                 cube[i] = fluxes[i-n_dims]
             self._last_fluxes = fluxes
 
-    def _transform_unit_cube_un(self, cube):
-        """
-        Transform UltraNest unit cube to microlensing parameters.
-
-        Based on _transform_unit_cube() for MultiNest method and tutorials
-        from UltraNest documentation:
-        https://johannesbuchner.github.io/UltraNest/example-sine-line.html
-        """
-        n_dims = self._n_fit_parameters
-        n_params = n_dims + self._n_fluxes * self._return_fluxes
-        cube_out = self._min_values + cube[:n_dims] * self._range_values
-
-        # Check with Radek: are "x_caustic_in" lines needed here?
-
-        if self._return_fluxes:
-            fluxes = self._get_fluxes()
-            for i in range(n_dims, n_params):
-                cube_out = np.append(cube_out, fluxes[i-n_dims])
-            self._last_fluxes = fluxes
-
-        return cube_out
-
     def _ln_like_MN(self, theta, n_dim, n_params, lnew):
         """
         Calculate likelihood and save if its best model.
@@ -2642,6 +2620,28 @@ def _ln_like_MN(self, theta, n_dim, n_params, lnew):
 
         return ln_like
 
+    def _transform_unit_cube_UltraNest(self, cube):
+        """
+        Transform UltraNest unit cube to microlensing parameters.
+
+        Based on _transform_unit_cube() for MultiNest method and tutorials
+        from UltraNest documentation:
+        https://johannesbuchner.github.io/UltraNest/example-sine-line.html
+        """
+        n_dims = self._n_fit_parameters
+        n_params = n_dims + self._n_fluxes * self._return_fluxes
+        cube_out = self._min_values + cube[:n_dims] * self._range_values
+
+        # Check with Radek: are "x_caustic_in" lines needed here?
+
+        if self._return_fluxes:
+            fluxes = self._get_fluxes()
+            for i in range(n_dims, n_params):
+                cube_out = np.append(cube_out, fluxes[i-n_dims])
+            self._last_fluxes = fluxes
+
+        return cube_out
+
     def _run_fit(self):
         """
         Call the method that does the fit.
@@ -2671,7 +2671,7 @@ def _run_fit_UltraNest(self):
         """
         Run Ultranest fit
         """
-        self._un_result = self._sampler.run(**self._kwargs_UltraNest)
+        self._result_UltraNest = self._sampler.run(**self._kwargs_UltraNest)
 
     def _finish_fit(self):
         """
@@ -2752,14 +2752,15 @@ def _parse_results(self):
             # Raphael: continue from here...
 
             # self._sampler.print_results()
-            result = self._un_result['maximum_likelihood']['point']
-            stdev = self._un_result['posterior']['stdev']
+            result = self._result_UltraNest['maximum_likelihood']['point']
+            stdev = self._result_UltraNest['posterior']['stdev']
+            log_like = self._result_UltraNest['maximum_likelihood']['logl']
             print("\n--\nUltraNest results:")
-            for (i, param) in enumerate(self._un_result['paramnames']):
+            for (i, param) in enumerate(self._result_UltraNest['paramnames']):
                 print(param, ' =', result[i], ' +/-', stdev[i])
-            print('logl =', self._un_result['maximum_likelihood']['logl'])
-            print('logz =', self._un_result['logz'], '+/-',
-                  self._un_result['logzerr'])
+            print('logl =', log_like)
+            print('logz =', self._result_UltraNest['logz'], '+/-',
+                  self._result_UltraNest['logzerr'])
         else:
             raise ValueError('internal bug')
 

From b590824056fb5e8560ed7da2543ebf525197a690 Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Thu, 22 Aug 2024 16:55:14 +0200
Subject: [PATCH 06/21] Finished parse_results for UltraNest, still make a
 separate YAML file

---
 examples/example_16/ob08092-o4_MN.yaml |  6 +++
 examples/example_16/ulens_model_fit.py | 57 ++++++++++++++++++--------
 2 files changed, 45 insertions(+), 18 deletions(-)

diff --git a/examples/example_16/ob08092-o4_MN.yaml b/examples/example_16/ob08092-o4_MN.yaml
index a3be9ea3..2166f393 100644
--- a/examples/example_16/ob08092-o4_MN.yaml
+++ b/examples/example_16/ob08092-o4_MN.yaml
@@ -19,5 +19,11 @@ fitting_parameters:
 plots:
     best model:
         file: out_ob08092_O4_MN_model.png
+        second Y scale:
+            magnifications: optimal
     triangle:
         file: out_ob08092_O4_MN_triangle.png
+        shift t_0: False
+other_output:
+    yaml output:
+        file name: ob03235_2_all_results_UN.yaml
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 1d925e6c..4551ec50 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -2749,18 +2749,7 @@ def _parse_results(self):
         elif self._fit_method == "MultiNest":
             self._parse_results_MultiNest()
         elif self._fit_method == "UltraNest":
-            # Raphael: continue from here...
-
-            # self._sampler.print_results()
-            result = self._result_UltraNest['maximum_likelihood']['point']
-            stdev = self._result_UltraNest['posterior']['stdev']
-            log_like = self._result_UltraNest['maximum_likelihood']['logl']
-            print("\n--\nUltraNest results:")
-            for (i, param) in enumerate(self._result_UltraNest['paramnames']):
-                print(param, ' =', result[i], ' +/-', stdev[i])
-            print('logl =', log_like)
-            print('logz =', self._result_UltraNest['logz'], '+/-',
-                  self._result_UltraNest['logzerr'])
+            self._parse_results_UltraNest()
         else:
             raise ValueError('internal bug')
 
@@ -2857,7 +2846,7 @@ def _print_results(self, data, names="parameters", mode=None):
 
         if self._fit_method == "EMCEE":
             results = self._get_weighted_percentile(data)
-        elif self._fit_method == "MultiNest":
+        elif self._fit_method in ["MultiNest", "UltraNest"]:
             if mode is None:
                 weights = self._samples_flat_weights
             else:
@@ -2906,7 +2895,7 @@ def _print_yaml_results(self, data, names="parameters", mode=None):
 
         if self._fit_method == "EMCEE":
             results = self._get_weighted_percentile(data)
-        elif self._fit_method == "MultiNest":
+        elif self._fit_method in ["MultiNest", "UltraNest"]:
             if mode is None:
                 weights = self._samples_flat_weights
             else:
@@ -3220,12 +3209,44 @@ def _extract_posterior_samples_MultiNest(self):
 
     def _get_fluxes_to_print_MultiNest(self):
         """
-        prepare values to be printed for EMCEE fitting
+        prepare flux values to be printed for MultiNest and Ultranest fitting
         """
-        index = 2 + self._n_fit_parameters
-        data = self._analyzer_data[:, index:]
+        if self._fit_method == "MultiNest":
+            index = 2 + self._n_fit_parameters
+            data = self._analyzer_data
+        elif self._fit_method == "UltraNest":
+            index = self._n_fit_parameters
+            data = self._result_UltraNest['weighted_samples']['points']
+
+        return data[:, index:]
 
-        return data
+    def _parse_results_UltraNest(self):
+        """
+        Parse results of UltraNest fitting.
+        Functions that print and save EMCEE results are also called here.
+        """
+        # re-weighted posterior samples
+        # self._samples_flat = self._result_UltraNest['samples'][:, :-2]
+        # weighted samples from the posterior
+        weighted_samples = self._result_UltraNest['weighted_samples']
+        self._samples_flat = weighted_samples['points'][:, :-2]
+        self._samples_flat_weights = weighted_samples['weights']
+        self._sampler.print_results()
+
+        max_like = self._result_UltraNest['maximum_likelihood']
+        self._best_model_ln_prob = max_like['logl']
+        self._best_model_theta = max_like['point'][:self._n_fit_parameters]
+        self._best_model_fluxes = max_like['point'][self._n_fit_parameters:]
+        self._parse_results_MultiNest_singlemode()
+
+        self._shift_t_0_in_samples()
+        self._print_best_model()
+        if self._yaml_results:
+            self._print_yaml_best_model()
+            ln_ev = self._result_UltraNest['logz_single']
+            ln_ev_err = self._result_UltraNest['logzerr_single']
+            lns = "  ln_ev: [{:.5f}, +{:.5f}, -{:.5f}]"
+            print(lns.format(ln_ev, ln_ev_err, ln_ev_err), **self._yaml_kwargs)
 
     def _write_residuals(self):
         """

From 5e31f20f79cd9b7dae94348c7e1164a83d701256 Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Fri, 23 Aug 2024 00:09:15 +0200
Subject: [PATCH 07/21] Added separate YAML file for UltraNest

---
 examples/example_16/ob03235_2_full.yaml |  2 ++
 examples/example_16/ob08092-o4_MN.yaml  | 14 +++-----------
 examples/example_16/ob08092-o4_UN.yaml  | 23 +++++++++++++++++++++++
 examples/example_16/ulens_model_fit.py  |  2 +-
 4 files changed, 29 insertions(+), 12 deletions(-)
 create mode 100644 examples/example_16/ob08092-o4_UN.yaml

diff --git a/examples/example_16/ob03235_2_full.yaml b/examples/example_16/ob03235_2_full.yaml
index 3be65d64..130d321e 100644
--- a/examples/example_16/ob03235_2_full.yaml
+++ b/examples/example_16/ob03235_2_full.yaml
@@ -5,6 +5,8 @@ photometry_files:
 #    - {file_name: some_K2_data.txt, phot_fmt: flux, bandpass: 'Kp', ephemerides_file: K2_ephemeris_01.dat}
 # For files with time starting with 245...:
 #    - {..., add_2450000: False}
+fit_method: EMCEE
+# Method options: EMCEE (default), MultiNest, UltraNest
 model:
     methods: 2452800. point_source 2452833. VBBL 2452845. point_source 2452860.
     default method: point_source_point_lens
diff --git a/examples/example_16/ob08092-o4_MN.yaml b/examples/example_16/ob08092-o4_MN.yaml
index 2166f393..103d526c 100644
--- a/examples/example_16/ob08092-o4_MN.yaml
+++ b/examples/example_16/ob08092-o4_MN.yaml
@@ -1,29 +1,21 @@
 photometry_files:
     data/OB08092/phot_ob08092_O4.dat
-fit_method: UltraNest  # options: EMCEE, MultiNest, UltraNest (... dynesty)
+fit_method: MultiNest
 prior_limits:
     t_0: [2455379.4, 2455379.76]
     u_0: [0.3, 0.65]
     t_E: 16. 19.6
 fitting_parameters:
-    # MultiNest only (basename, multimodal, evidence tolerance)
     basename: out_ob08092_O4_MN-
     multimodal: True
+    # Default settings of other parameters:
     # evidence tolerance: 0.5
-    # UltraNest only (log directory, derived parameter names, show_status)
-    log directory: ultranest_outputs/
-    derived parameter names: source_flux blending_flux
-    show_status: True
-    # `n_live_points` can be used in MultiNest and UltraNest
     n_live_points: 1000
 plots:
     best model:
         file: out_ob08092_O4_MN_model.png
-        second Y scale:
-            magnifications: optimal
     triangle:
         file: out_ob08092_O4_MN_triangle.png
-        shift t_0: False
 other_output:
     yaml output:
-        file name: ob03235_2_all_results_UN.yaml
+        file name: ob08092_O4_MN_all_results.yaml
diff --git a/examples/example_16/ob08092-o4_UN.yaml b/examples/example_16/ob08092-o4_UN.yaml
new file mode 100644
index 00000000..b555d5b9
--- /dev/null
+++ b/examples/example_16/ob08092-o4_UN.yaml
@@ -0,0 +1,23 @@
+photometry_files:
+    data/OB08092/phot_ob08092_O4.dat
+fit_method: UltraNest
+prior_limits:
+    t_0: [2455379.4, 2455379.76]
+    u_0: [0.3, 0.65]
+    t_E: 16. 19.6
+fitting_parameters:
+    log directory: ultranest_outputs/
+    derived parameter names: source_flux blending_flux
+    show_status: True
+    n_live_points: 1000
+plots:
+    best model:
+        file: out_ob08092_O4_UN_model.png
+        second Y scale:
+            magnifications: optimal
+    triangle:
+        file: out_ob08092_O4_UN_triangle.png
+        shift t_0: False
+other_output:
+    yaml output:
+        file name: ob08092_O4_UN_all_results.yaml
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 4551ec50..387583fd 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -2629,7 +2629,7 @@ def _transform_unit_cube_UltraNest(self, cube):
         https://johannesbuchner.github.io/UltraNest/example-sine-line.html
         """
         n_dims = self._n_fit_parameters
-        n_params = n_dims + self._n_fluxes * self._return_fluxes
+        n_params = n_dims + self._n_fluxes_per_dataset * self._return_fluxes
         cube_out = self._min_values + cube[:n_dims] * self._range_values
 
         # Check with Radek: are "x_caustic_in" lines needed here?

From d63d6446cc59136333a2d549414be2ae6549889a Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Fri, 23 Aug 2024 13:23:05 +0200
Subject: [PATCH 08/21] Minor corrections to UltraNest parsing

---
 examples/example_16/ob08092-o4_minimal_MN.yaml |  3 ++-
 examples/example_16/ulens_model_fit.py         | 17 +++++++++++------
 2 files changed, 13 insertions(+), 7 deletions(-)

diff --git a/examples/example_16/ob08092-o4_minimal_MN.yaml b/examples/example_16/ob08092-o4_minimal_MN.yaml
index d8347564..7e95e488 100644
--- a/examples/example_16/ob08092-o4_minimal_MN.yaml
+++ b/examples/example_16/ob08092-o4_minimal_MN.yaml
@@ -1,6 +1,7 @@
 photometry_files:
     data/OB08092/phot_ob08092_O4.dat
-fit_method: UltraNest  # options: EMCEE, MultiNest, UltraNest (... dynesty)
+fit_method: MultiNest
+# fit_method options if prior_limits are given: MultiNest and UltraNest
 prior_limits:
     t_0: [2455379.4, 2455379.76]
     u_0: [0.46, 0.65]
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 387583fd..504b9879 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -1456,13 +1456,14 @@ def _parse_fitting_parameters_UltraNest(self):
 
         self._check_required_and_allowed_parameters(required, allowed)
         self._check_parameters_types(settings, bools, ints, floats, strings)
-        value = settings.pop("log directory")
-        if value is not None:
-            if path.exists(value) and path.isdir(value):
-                self._log_dir_UltraNest = value
-            else:
+        self._log_dir_UltraNest = settings.pop("log directory", None)
+        if self._log_dir_UltraNest is not None:
+            if not path.exists(self._log_dir_UltraNest):
                 raise ValueError("log directory value in fitting_parameters"
                                  "does not exist.")
+            elif not path.isdir(self._log_dir_UltraNest):
+                raise ValueError("log directory value in fitting_parameters"
+                                 "exists, but it is a file.")
         value = settings.pop("derived parameter names", "")
         self._derived_params_UltraNest = value.split()
 
@@ -2671,6 +2672,9 @@ def _run_fit_UltraNest(self):
         """
         Run Ultranest fit
         """
+        self._kwargs_UltraNest['dlogz'] = 100.  # 0.5... 100. took 15min
+        self._kwargs_UltraNest['dKL'] = 100.  # 0.5
+        self._kwargs_UltraNest['frac_remain'] = 0.005
         self._result_UltraNest = self._sampler.run(**self._kwargs_UltraNest)
 
     def _finish_fit(self):
@@ -3229,7 +3233,8 @@ def _parse_results_UltraNest(self):
         # self._samples_flat = self._result_UltraNest['samples'][:, :-2]
         # weighted samples from the posterior
         weighted_samples = self._result_UltraNest['weighted_samples']
-        self._samples_flat = weighted_samples['points'][:, :-2]
+        index = self._n_fit_parameters
+        self._samples_flat = weighted_samples['points'][:, :index]
         self._samples_flat_weights = weighted_samples['weights']
         self._sampler.print_results()
 

From f3510969d5ba46862a40a124da28fe45c9acaa80 Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Mon, 26 Aug 2024 19:30:28 +0200
Subject: [PATCH 09/21] Last changes before PR, added parameters to UltraNest,
 yaml files

---
 examples/example_16/ob03235_2_full.yaml | 22 ++++++++++++++-
 examples/example_16/ob08092-o4_UN.yaml  |  8 ++++++
 examples/example_16/ulens_model_fit.py  | 36 ++++++++++++++++++-------
 3 files changed, 56 insertions(+), 10 deletions(-)

diff --git a/examples/example_16/ob03235_2_full.yaml b/examples/example_16/ob03235_2_full.yaml
index 130d321e..c0a61d25 100644
--- a/examples/example_16/ob03235_2_full.yaml
+++ b/examples/example_16/ob03235_2_full.yaml
@@ -6,7 +6,12 @@ photometry_files:
 # For files with time starting with 245...:
 #    - {..., add_2450000: False}
 fit_method: EMCEE
-# Method options: EMCEE (default), MultiNest, UltraNest
+# Options: EMCEE, MultiNest, UltraNest.
+# If not given, EMCEE is used if starting_parameters are given or MultiNest
+# if prior_limits are given.
+# If MultiNest/UltraNest is selected: starting_parameters, fit_constraints,
+# min_values and max_value cannot be provided. The trace plot cannot be
+# generated as well.
 model:
     methods: 2452800. point_source 2452833. VBBL 2452845. point_source 2452860.
     default method: point_source_point_lens
@@ -78,12 +83,27 @@ max_values:
     s: 2.
     alpha: 360.
 fitting_parameters:
+    ## EMCEE only
     n_walkers: 20
     n_steps: 4
     n_burn:  2
     progress: True
     posterior file: ob03235_2_models.npy
     posterior file fluxes: all
+    ## MultiNest only (basename, multimodal, evidence tolerance)
+    # basename: out_ob08092_O4_MN-
+    # multimodal: True
+    # # evidence tolerance: 0.5
+    ## UltraNest only (log directory, derived parameter names, show_status,
+    ## dlogz, frac_remain, max_num_improvement_loops)
+    # log directory: ultranest_outputs/
+    # derived parameter names: source_flux blending_flux
+    # show_status: True
+    # dlogz: 2.
+    # frac_remain: 0.5
+    # max_num_improvement_loops: 0
+    ## Both for MultiNest and UltraNest (number of live points)
+    # n_live_points: 20
 plots:
     best model:
         # You can skip the line below - the light curve will be plotted on screen.
diff --git a/examples/example_16/ob08092-o4_UN.yaml b/examples/example_16/ob08092-o4_UN.yaml
index b555d5b9..2cd8b6a0 100644
--- a/examples/example_16/ob08092-o4_UN.yaml
+++ b/examples/example_16/ob08092-o4_UN.yaml
@@ -10,6 +10,14 @@ fitting_parameters:
     derived parameter names: source_flux blending_flux
     show_status: True
     n_live_points: 1000
+    # `n_live_points` can also be named `min_num_live_points`
+    # If it is smaller than 40, `cluster_num_live_points` is also reduced.
+    # UltraNest may increase n_live_points if it is too low to achieve the
+    # logz accuracy (default=0.5). It can be avoided increasing dlogz:
+    dlogz: 2.
+    # The parameters below can reduce runtime (default is -1 and 0.01)
+    # frac_remain: 0.5
+    # max_num_improvement_loops: 0
 plots:
     best model:
         file: out_ob08092_O4_UN_model.png
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 504b9879..10083054 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -242,9 +242,22 @@ class UlensModelFit(object):
             ``show_status`` (*bool*) - whether to show integration progress
             as a status line or not. Default is *True*.
 
-            ``n_live_points`` (*int*) - minimum number of live points
+            ``min_num_live_points`` (*int*) - minimum number of live points
             throughout the run. Default value is 400.
 
+            ``dlogz`` (*float*) - Target evidence uncertainty, in order to
+            obtain a logz error below a threshold. Default value is 0.5.
+            It can be increased to allow `min_num_live_points` values below:
+            sqrt(iterations) / dlogz = sqrt(1000) / 0.5 ~ 64.
+
+            ``frac_remain`` (*float*) - Integrate until this fraction of the
+            integral is left in the remainder. Numbers smaller than 0.01
+            ensure that peaks are discovered, higher numbers can be set if
+            the posterior is simple. Default value is 0.01.
+
+            ``max_num_improvement_loops`` (*int*) - Limit the number of times
+            the algorithm is repeated to improve. Default value is -1.
+
         fit_constraints: *dict*
             Constraints on model other than minimal and maximal values.
 
@@ -1360,7 +1373,8 @@ def _parse_fitting_parameters_MultiNest(self):
         allowed = strings + bools + ints + floats
 
         only_UltraNest = ['log directory', 'derived parameter names',
-                          'show status']
+                          'show status', 'dlogz', 'frac_remain',
+                          'max_num_improvement_loops']
         for item in only_UltraNest:
             settings.pop(item, None)
 
@@ -1434,7 +1448,7 @@ def _check_output_files_MultiNest(self):
 
     def _parse_fitting_parameters_UltraNest(self):
         """
-        make sure MultiNest fitting parameters are properly defined
+        Make sure UltraNest fitting parameters are properly defined
         """
         self._kwargs_UltraNest = dict()
         self._kwargs_UltraNest['viz_callback'] = False
@@ -1445,9 +1459,11 @@ def _parse_fitting_parameters_UltraNest(self):
 
         required = []
         bools = ['show_status']
-        ints = ['n_live_points']
+        ints = ['min_num_live_points', 'max_num_improvement_loops']
+        if 'n_live_points' in settings:
+            ints[0] = 'n_live_points'
         strings = ['log directory', 'derived parameter names']
-        floats = []
+        floats = ['dlogz', 'frac_remain']
         allowed = strings + bools + ints + floats
 
         only_MultiNest = ['basename', 'multimodal', 'evidence tolerance']
@@ -1468,7 +1484,8 @@ def _parse_fitting_parameters_UltraNest(self):
         self._derived_params_UltraNest = value.split()
 
         keys = {"n_live_points": "min_num_live_points"}
-        same_keys = ["show_status"]
+        same_keys = ["min_num_live_points", 'max_num_improvement_loops',
+                     "show_status", "dlogz", "frac_remain"]
         keys = {**keys, **{key: key for key in same_keys}}
         self._set_dict_safely(self._kwargs_UltraNest, settings, keys)
 
@@ -2672,9 +2689,10 @@ def _run_fit_UltraNest(self):
         """
         Run Ultranest fit
         """
-        self._kwargs_UltraNest['dlogz'] = 100.  # 0.5... 100. took 15min
-        self._kwargs_UltraNest['dKL'] = 100.  # 0.5
-        self._kwargs_UltraNest['frac_remain'] = 0.005
+        min_n_live = self._kwargs_UltraNest['min_num_live_points']
+        cluster_n_live = 40 if min_n_live >= 40 else min_n_live
+        self._kwargs_UltraNest['cluster_num_live_points'] = cluster_n_live
+
         self._result_UltraNest = self._sampler.run(**self._kwargs_UltraNest)
 
     def _finish_fit(self):

From a05311eca09f8b659534d4257076ebc3f6d45c2d Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Mon, 26 Aug 2024 22:30:00 +0200
Subject: [PATCH 10/21] Minor changes before pull request

---
 examples/example_16/ulens_model_fit.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 10083054..1a850bb0 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -2558,7 +2558,10 @@ def _setup_fit_MultiNest(self):
 
     def _setup_fit_UltraNest(self):
         """
-        Prepare UltraNest fit, declaring sampler instance
+        Prepare UltraNest fit, declaring sampler instance.
+        If the names of the derived parameters are not given, the source
+        and blending fluxes are assigned, with the former containing _1
+        and _2 in case of multiple sources.
         """
         if self._return_fluxes and len(self._derived_params_UltraNest) == 0:
             self._derived_params_UltraNest = ["source_flux", "blending_flux"]
@@ -2650,7 +2653,7 @@ def _transform_unit_cube_UltraNest(self, cube):
         n_params = n_dims + self._n_fluxes_per_dataset * self._return_fluxes
         cube_out = self._min_values + cube[:n_dims] * self._range_values
 
-        # Check with Radek: are "x_caustic_in" lines needed here?
+        # Check: are "x_caustic_in" lines needed as in line 2597?
 
         if self._return_fluxes:
             fluxes = self._get_fluxes()
@@ -3247,9 +3250,9 @@ def _parse_results_UltraNest(self):
         Parse results of UltraNest fitting.
         Functions that print and save EMCEE results are also called here.
         """
-        # re-weighted posterior samples
+        # re-weighted posterior samples:
         # self._samples_flat = self._result_UltraNest['samples'][:, :-2]
-        # weighted samples from the posterior
+        # weighted samples from the posterior:
         weighted_samples = self._result_UltraNest['weighted_samples']
         index = self._n_fit_parameters
         self._samples_flat = weighted_samples['points'][:, :index]

From cd3d78b25e44e0f384c98b0753afb66aaee501e7 Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Tue, 27 Aug 2024 12:12:27 +0200
Subject: [PATCH 11/21] Minor corrections to comments in  yaml files

---
 examples/example_16/ob03235_2_full.yaml | 6 +++---
 examples/example_16/ob08092-o4_UN.yaml  | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/example_16/ob03235_2_full.yaml b/examples/example_16/ob03235_2_full.yaml
index c0a61d25..c812a993 100644
--- a/examples/example_16/ob03235_2_full.yaml
+++ b/examples/example_16/ob03235_2_full.yaml
@@ -90,12 +90,12 @@ fitting_parameters:
     progress: True
     posterior file: ob03235_2_models.npy
     posterior file fluxes: all
-    ## MultiNest only (basename, multimodal, evidence tolerance)
+    ## MultiNest only (basename, multimodal, evidence tolerance=0.5)
     # basename: out_ob08092_O4_MN-
     # multimodal: True
-    # # evidence tolerance: 0.5
+    # evidence tolerance: 0.5
     ## UltraNest only (log directory, derived parameter names, show_status,
-    ## dlogz, frac_remain, max_num_improvement_loops)
+    ## dlogz=0.5, frac_remain=0.01, max_num_improvement_loops=-1)
     # log directory: ultranest_outputs/
     # derived parameter names: source_flux blending_flux
     # show_status: True
diff --git a/examples/example_16/ob08092-o4_UN.yaml b/examples/example_16/ob08092-o4_UN.yaml
index 2cd8b6a0..5e63d7e6 100644
--- a/examples/example_16/ob08092-o4_UN.yaml
+++ b/examples/example_16/ob08092-o4_UN.yaml
@@ -15,7 +15,7 @@ fitting_parameters:
     # UltraNest may increase n_live_points if it is too low to achieve the
     # logz accuracy (default=0.5). It can be avoided increasing dlogz:
     dlogz: 2.
-    # The parameters below can reduce runtime (default is -1 and 0.01)
+    # The parameters below can reduce runtime (default is 0.01 and -1)
     # frac_remain: 0.5
     # max_num_improvement_loops: 0
 plots:

From 74d944baaa3437c2931870a740e47ee1445cea0c Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Mon, 9 Sep 2024 17:55:05 +0200
Subject: [PATCH 12/21] ex16 - UltraNest, adding Radek comments 1 to 5

---
 examples/example_16/ob03235_2_full.yaml |  4 +-
 examples/example_16/ulens_model_fit.py  | 53 ++++++++++++++++++-------
 2 files changed, 41 insertions(+), 16 deletions(-)

diff --git a/examples/example_16/ob03235_2_full.yaml b/examples/example_16/ob03235_2_full.yaml
index c812a993..3ecf7fd9 100644
--- a/examples/example_16/ob03235_2_full.yaml
+++ b/examples/example_16/ob03235_2_full.yaml
@@ -7,8 +7,8 @@ photometry_files:
 #    - {..., add_2450000: False}
 fit_method: EMCEE
 # Options: EMCEE, MultiNest, UltraNest.
-# If not given, EMCEE is used if starting_parameters are given or MultiNest
-# if prior_limits are given.
+# If not given, EMCEE is used if starting_parameters are given; if prior_limits
+# are given, MultiNest or UltraNest is used depending on fitting_parameters.
 # If MultiNest/UltraNest is selected: starting_parameters, fit_constraints,
 # min_values and max_value cannot be provided. The trace plot cannot be
 # generated as well.
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 1a850bb0..c2abe62c 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -77,8 +77,9 @@ class UlensModelFit(object):
             Method of fitting. Currently accepted values are ``EMCEE``,
             ``MultiNest``, and ``UltraNest``. If not provided, the script
             will guess it based on other parameters: ``EMCEE`` is selected
-            if ``starting_parameters`` are provided and ``MultiNest`` is
-            selected if ``prior_limits`` are provided.
+            if ``starting_parameters`` are provided. If ``prior_limits`` are
+            provided, either ``MultiNest`` or ``UltraNest`` will be selected
+            depending on the ``fitting_parameters``.
 
             Webpage of each method:
             - EMCEE: https://emcee.readthedocs.io/en/stable/
@@ -124,8 +125,7 @@ class UlensModelFit(object):
 
         prior_limits: *dict*
             Upper and lower limits of parameters.
-            It only applies to pyMultiNest and UltraNest fitting. However,
-            if fit_method is not given, pyMultiNest will be applied.
+            It only applies to pyMultiNest and UltraNest fitting.
 
             Keys are MulensModel parameters and values are lists of two floats
             each (alternatively a string giving 2 floats can be provided - see
@@ -381,15 +381,15 @@ class UlensModelFit(object):
     """
 
     def __init__(
-            self, photometry_files, fit_method=None,
+            self, photometry_files,
             starting_parameters=None, prior_limits=None, model=None,
             fixed_parameters=None,
             min_values=None, max_values=None, fitting_parameters=None,
-            fit_constraints=None, plots=None, other_output=None
+            fit_constraints=None, plots=None, other_output=None,
+            fit_method=None
     ):
         self._check_MM_version()
         self._photometry_files = photometry_files
-        self._fit_method = fit_method
         self._starting_parameters_input = starting_parameters
         self._prior_limits = prior_limits
         self._model_parameters = model
@@ -400,6 +400,7 @@ def __init__(
         self._fit_constraints = fit_constraints
         self._plots = plots
         self._other_output = other_output
+        self._fit_method = fit_method
 
         self._which_task()
         self._set_default_parameters()
@@ -407,6 +408,7 @@ def __init__(
             if self._fit_method is None:
                 self._guess_fitting_method()
             else:
+                self._fit_method = self._fit_method.lower()
                 self._check_fitting_method()
             self._check_starting_parameters_type()
             self._set_fit_parameters_unsorted()
@@ -558,7 +560,7 @@ def _guess_fitting_method(self):
                     "which makes impossible to choose the fitting method. "
                     "These settings indicate EMCEE and pyMultiNest "
                     "respectively, and cannot be both set.")
-            method = "MultiNest"  # UltraNest is also supported
+            method = self._guess_MultiNest_or_UltraNest()
         if method is None:
             raise ValueError(
                 "No fitting method chosen. You can chose either 'EMCEE' or "
@@ -566,15 +568,38 @@ def _guess_fitting_method(self):
                 "starting_parameters or prior_limits, respectively.")
         self._fit_method = method
 
+    def _guess_MultiNest_or_UltraNest(self):
+        """
+        Guess fit_method between MultiNest or UltraNest, based on the
+        provided fitting_parameters.
+        """
+        args_MultiNest = ['basename', 'multimodal', 'evidence tolerance',
+                          'n_live_points']
+        if all([key in args_MultiNest for key in self._fitting_parameters]):
+            return "MultiNest"
+
+        args_UltraNest = ['log directory', 'derived parameter names',
+                          'show_status', 'dlogz', 'frac_remain',
+                          'max_num_improvement_loops', 'n_live_points']
+        if all([key in args_UltraNest for key in self._fitting_parameters]):
+            return "UltraNest"
+
+        raise ValueError(
+            "Cannot guess fitting method. Provide more parameters in "
+            "fitting_parameters.")
+
     def _check_fitting_method(self):
         """
         Check if fitting method is consistent with the settings.
         """
-        if self._fit_method == "EMCEE":
+        if self._fit_method == "emcee":
+            self._fit_method = "EMCEE"
             if self._starting_parameters_input is None:
                 raise ValueError(
                     "EMCEE fitting method requires starting_parameters.")
-        elif self._fit_method in ["MultiNest", "UltraNest"]:
+        elif self._fit_method in ["multinest", "ultranest"]:
+            self._fit_method = self._fit_method.capitalize()
+            self._fit_method = self._fit_method.replace("nest", "Nest")
             if self._prior_limits is None:
                 msg = "{:} fitting method requires prior_limits."
                 raise ValueError(msg.format(self._fit_method))
@@ -643,8 +668,8 @@ def _check_imports(self):
                 required_packages.add('emcee')
             elif self._fit_method == "MultiNest":
                 required_packages.add('pymultinest')
-            elif self._fit_method == "Ultranest":
-                required_packages.add('cython')
+            elif self._fit_method == "UltraNest":
+                required_packages.add('ultranest')
 
             if self._plots is not None and 'triangle' in self._plots:
                 required_packages.add('corner')
@@ -1373,7 +1398,7 @@ def _parse_fitting_parameters_MultiNest(self):
         allowed = strings + bools + ints + floats
 
         only_UltraNest = ['log directory', 'derived parameter names',
-                          'show status', 'dlogz', 'frac_remain',
+                          'show_status', 'dlogz', 'frac_remain',
                           'max_num_improvement_loops']
         for item in only_UltraNest:
             settings.pop(item, None)
@@ -2692,7 +2717,7 @@ def _run_fit_UltraNest(self):
         """
         Run Ultranest fit
         """
-        min_n_live = self._kwargs_UltraNest['min_num_live_points']
+        min_n_live = self._kwargs_UltraNest.get("min_num_live_points", 400)
         cluster_n_live = 40 if min_n_live >= 40 else min_n_live
         self._kwargs_UltraNest['cluster_num_live_points'] = cluster_n_live
 

From 38372d4fdbe5ebe9c20f6d446b399357cd702771 Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Mon, 9 Sep 2024 19:30:23 +0200
Subject: [PATCH 13/21] ex16 - UltraNest, added Radek comments, except 8

---
 examples/example_16/ob03235_2_full.yaml | 15 +++++++--
 examples/example_16/ob08092-o4_UN.yaml  |  2 +-
 examples/example_16/ulens_model_fit.py  | 42 +++++++++----------------
 3 files changed, 29 insertions(+), 30 deletions(-)

diff --git a/examples/example_16/ob03235_2_full.yaml b/examples/example_16/ob03235_2_full.yaml
index 3ecf7fd9..5bfb44e9 100644
--- a/examples/example_16/ob03235_2_full.yaml
+++ b/examples/example_16/ob03235_2_full.yaml
@@ -43,6 +43,17 @@ starting_parameters:
     #   file: ob08092-o4_starting_file_input.txt
     #   parameters: t_0 u_0 t_E
     # See also ob08092-o4_starting_file.yaml
+# prior_limits:
+#     t_0: [2452847.5, 2452848.5]
+#     u_0: [0.08, 0.18]
+#     t_E: 58.7 64.7
+#     rho: [0.0004, 0.0018]
+#     pi_E_N: [-0.05, 0.05]
+#     pi_E_E: [-0.05, 0.05]
+#     s: [1.077, 1.125]
+#     q: [0.001, 0.02]
+#     alpha: [219.4, 229.4]
+#     # Only MultiNest and UltraNest, cannot be given with starting_parameters
 fit_constraints:
     negative_blending_flux_sigma_mag: 20.  
     #one can specify for which dataset soft constrain on blending flux should be applied: sigma dataset_label(s)
@@ -94,10 +105,10 @@ fitting_parameters:
     # basename: out_ob08092_O4_MN-
     # multimodal: True
     # evidence tolerance: 0.5
-    ## UltraNest only (log directory, derived parameter names, show_status,
+    ## UltraNest only (log directory, derived param names, show_status,
     ## dlogz=0.5, frac_remain=0.01, max_num_improvement_loops=-1)
     # log directory: ultranest_outputs/
-    # derived parameter names: source_flux blending_flux
+    # derived param names: flux_s_1 flux_b_1
     # show_status: True
     # dlogz: 2.
     # frac_remain: 0.5
diff --git a/examples/example_16/ob08092-o4_UN.yaml b/examples/example_16/ob08092-o4_UN.yaml
index 5e63d7e6..c699545c 100644
--- a/examples/example_16/ob08092-o4_UN.yaml
+++ b/examples/example_16/ob08092-o4_UN.yaml
@@ -7,7 +7,7 @@ prior_limits:
     t_E: 16. 19.6
 fitting_parameters:
     log directory: ultranest_outputs/
-    derived parameter names: source_flux blending_flux
+    derived param names: flux_s_1 flux_b_1
     show_status: True
     n_live_points: 1000
     # `n_live_points` can also be named `min_num_live_points`
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index c2abe62c..b7830323 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -234,7 +234,7 @@ class UlensModelFit(object):
             there is a check if directory exists. If not given, no outputs
             are saved.
 
-            ``derived parameter names`` (*str*) - names of additional derived
+            ``derived param names`` (*str*) - names of additional derived
             parameters created by transform. In microlensing, they are usually
             the source(s) and blending fluxes. If not given, they are ignored
             in the transform function.
@@ -578,7 +578,7 @@ def _guess_MultiNest_or_UltraNest(self):
         if all([key in args_MultiNest for key in self._fitting_parameters]):
             return "MultiNest"
 
-        args_UltraNest = ['log directory', 'derived parameter names',
+        args_UltraNest = ['log directory', 'derived param names',
                           'show_status', 'dlogz', 'frac_remain',
                           'max_num_improvement_loops', 'n_live_points']
         if all([key in args_UltraNest for key in self._fitting_parameters]):
@@ -1397,12 +1397,6 @@ def _parse_fitting_parameters_MultiNest(self):
         floats = ['sampling efficiency', 'evidence tolerance']
         allowed = strings + bools + ints + floats
 
-        only_UltraNest = ['log directory', 'derived parameter names',
-                          'show_status', 'dlogz', 'frac_remain',
-                          'max_num_improvement_loops']
-        for item in only_UltraNest:
-            settings.pop(item, None)
-
         self._check_required_and_allowed_parameters(required, allowed)
         self._check_parameters_types(settings, bools, ints, floats, strings)
 
@@ -1478,7 +1472,7 @@ def _parse_fitting_parameters_UltraNest(self):
         self._kwargs_UltraNest = dict()
         self._kwargs_UltraNest['viz_callback'] = False
 
-        settings = self._fitting_parameters
+        settings = self._fitting_parameters.copy()
         if settings is None:
             settings = dict()
 
@@ -1487,14 +1481,10 @@ def _parse_fitting_parameters_UltraNest(self):
         ints = ['min_num_live_points', 'max_num_improvement_loops']
         if 'n_live_points' in settings:
             ints[0] = 'n_live_points'
-        strings = ['log directory', 'derived parameter names']
+        strings = ['log directory', 'derived param names']
         floats = ['dlogz', 'frac_remain']
         allowed = strings + bools + ints + floats
 
-        only_MultiNest = ['basename', 'multimodal', 'evidence tolerance']
-        for item in only_MultiNest:
-            settings.pop(item, None)
-
         self._check_required_and_allowed_parameters(required, allowed)
         self._check_parameters_types(settings, bools, ints, floats, strings)
         self._log_dir_UltraNest = settings.pop("log directory", None)
@@ -1505,8 +1495,8 @@ def _parse_fitting_parameters_UltraNest(self):
             elif not path.isdir(self._log_dir_UltraNest):
                 raise ValueError("log directory value in fitting_parameters"
                                  "exists, but it is a file.")
-        value = settings.pop("derived parameter names", "")
-        self._derived_params_UltraNest = value.split()
+        value = settings.pop("derived param names", "")
+        self._derived_param_names_UltraNest = value.split()
 
         keys = {"n_live_points": "min_num_live_points"}
         same_keys = ["min_num_live_points", 'max_num_improvement_loops',
@@ -2585,21 +2575,19 @@ def _setup_fit_UltraNest(self):
         """
         Prepare UltraNest fit, declaring sampler instance.
         If the names of the derived parameters are not given, the source
-        and blending fluxes are assigned, with the former containing _1
-        and _2 in case of multiple sources.
+        and blending fluxes are assigned, with indexes depending on the
+        number of sources (s1, s2) and datasets (_1, _2).
         """
-        if self._return_fluxes and len(self._derived_params_UltraNest) == 0:
-            self._derived_params_UltraNest = ["source_flux", "blending_flux"]
-            if self._n_fluxes > 2:
-                self._derived_params_UltraNest[0] += "_1"
-                for i in range(2, self._n_fluxes):
-                    name = "source_flux_{:}".format(i)
-                    self._derived_params_UltraNest.insert(i-1, name)
+        if self._return_fluxes:
+            if len(self._derived_param_names_UltraNest) == 0:
+                if self._flux_names is None:
+                    self._flux_names = self._get_fluxes_names_to_print()
+                self._derived_param_names_UltraNest = self._flux_names
 
         self._sampler = ultranest.ReactiveNestedSampler(
             self._fit_parameters,
             self._ln_like, transform=self._transform_unit_cube_UltraNest,
-            derived_param_names=self._derived_params_UltraNest,
+            derived_param_names=self._derived_param_names_UltraNest,
             log_dir=self._log_dir_UltraNest
         )
 
@@ -2678,7 +2666,7 @@ def _transform_unit_cube_UltraNest(self, cube):
         n_params = n_dims + self._n_fluxes_per_dataset * self._return_fluxes
         cube_out = self._min_values + cube[:n_dims] * self._range_values
 
-        # Check: are "x_caustic_in" lines needed as in line 2597?
+        # TBD: add "x_caustic_in" checks as in line 2610...
 
         if self._return_fluxes:
             fluxes = self._get_fluxes()

From 315b0d9f9f83e518575d46dd69491e5ee2594928 Mon Sep 17 00:00:00 2001
From: radek_poleski <radek.poleski@gmail.com>
Date: Sun, 15 Sep 2024 16:31:42 +0200
Subject: [PATCH 14/21] ex16: param -> parameters

---
 examples/example_16/ulens_model_fit.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index b7830323..829941a6 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -234,7 +234,7 @@ class UlensModelFit(object):
             there is a check if directory exists. If not given, no outputs
             are saved.
 
-            ``derived param names`` (*str*) - names of additional derived
+            ``derived parameters names`` (*str*) - names of additional derived
             parameters created by transform. In microlensing, they are usually
             the source(s) and blending fluxes. If not given, they are ignored
             in the transform function.
@@ -578,7 +578,7 @@ def _guess_MultiNest_or_UltraNest(self):
         if all([key in args_MultiNest for key in self._fitting_parameters]):
             return "MultiNest"
 
-        args_UltraNest = ['log directory', 'derived param names',
+        args_UltraNest = ['log directory', 'derived parameters names',
                           'show_status', 'dlogz', 'frac_remain',
                           'max_num_improvement_loops', 'n_live_points']
         if all([key in args_UltraNest for key in self._fitting_parameters]):
@@ -1481,7 +1481,7 @@ def _parse_fitting_parameters_UltraNest(self):
         ints = ['min_num_live_points', 'max_num_improvement_loops']
         if 'n_live_points' in settings:
             ints[0] = 'n_live_points'
-        strings = ['log directory', 'derived param names']
+        strings = ['log directory', 'derived parameters names']
         floats = ['dlogz', 'frac_remain']
         allowed = strings + bools + ints + floats
 
@@ -1495,7 +1495,7 @@ def _parse_fitting_parameters_UltraNest(self):
             elif not path.isdir(self._log_dir_UltraNest):
                 raise ValueError("log directory value in fitting_parameters"
                                  "exists, but it is a file.")
-        value = settings.pop("derived param names", "")
+        value = settings.pop("derived parameters names", "")
         self._derived_param_names_UltraNest = value.split()
 
         keys = {"n_live_points": "min_num_live_points"}

From b489b2b341349e713d963bb469179b541b1b4a80 Mon Sep 17 00:00:00 2001
From: radek_poleski <radek.poleski@gmail.com>
Date: Sun, 15 Sep 2024 16:33:26 +0200
Subject: [PATCH 15/21] ex16 - version update

---
 examples/example_16/ulens_model_fit.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 829941a6..c93b2c23 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -43,7 +43,7 @@
 except Exception:
     raise ImportError('\nYou have to install MulensModel first!\n')
 
-__version__ = '0.38.0'
+__version__ = '0.39.0'
 
 
 class UlensModelFit(object):

From 3f5b367060633dc027fa85fb009e8f86f6625875 Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Mon, 16 Sep 2024 17:28:22 +0200
Subject: [PATCH 16/21] Added UltraNest to make transform in same function as
 MultiNest

---
 examples/example_16/ob03235_2_full.yaml |  4 +-
 examples/example_16/ob08092-o4_UN.yaml  |  2 +-
 examples/example_16/ulens_model_fit.py  | 62 +++++++++++--------------
 3 files changed, 29 insertions(+), 39 deletions(-)

diff --git a/examples/example_16/ob03235_2_full.yaml b/examples/example_16/ob03235_2_full.yaml
index 5bfb44e9..dc1239b8 100644
--- a/examples/example_16/ob03235_2_full.yaml
+++ b/examples/example_16/ob03235_2_full.yaml
@@ -105,10 +105,10 @@ fitting_parameters:
     # basename: out_ob08092_O4_MN-
     # multimodal: True
     # evidence tolerance: 0.5
-    ## UltraNest only (log directory, derived param names, show_status,
+    ## UltraNest only (log directory, derived parameter names, show_status,
     ## dlogz=0.5, frac_remain=0.01, max_num_improvement_loops=-1)
     # log directory: ultranest_outputs/
-    # derived param names: flux_s_1 flux_b_1
+    # derived parameter names: flux_s_1 flux_b_1
     # show_status: True
     # dlogz: 2.
     # frac_remain: 0.5
diff --git a/examples/example_16/ob08092-o4_UN.yaml b/examples/example_16/ob08092-o4_UN.yaml
index c699545c..44a9d6a3 100644
--- a/examples/example_16/ob08092-o4_UN.yaml
+++ b/examples/example_16/ob08092-o4_UN.yaml
@@ -7,7 +7,7 @@ prior_limits:
     t_E: 16. 19.6
 fitting_parameters:
     log directory: ultranest_outputs/
-    derived param names: flux_s_1 flux_b_1
+    derived parameter names: flux_s_1 flux_b_1
     show_status: True
     n_live_points: 1000
     # `n_live_points` can also be named `min_num_live_points`
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index c93b2c23..d2a39253 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -234,7 +234,7 @@ class UlensModelFit(object):
             there is a check if directory exists. If not given, no outputs
             are saved.
 
-            ``derived parameters names`` (*str*) - names of additional derived
+            ``derived parameter names`` (*str*) - names of additional derived
             parameters created by transform. In microlensing, they are usually
             the source(s) and blending fluxes. If not given, they are ignored
             in the transform function.
@@ -578,7 +578,7 @@ def _guess_MultiNest_or_UltraNest(self):
         if all([key in args_MultiNest for key in self._fitting_parameters]):
             return "MultiNest"
 
-        args_UltraNest = ['log directory', 'derived parameters names',
+        args_UltraNest = ['log directory', 'derived parameter names',
                           'show_status', 'dlogz', 'frac_remain',
                           'max_num_improvement_loops', 'n_live_points']
         if all([key in args_UltraNest for key in self._fitting_parameters]):
@@ -1481,7 +1481,7 @@ def _parse_fitting_parameters_UltraNest(self):
         ints = ['min_num_live_points', 'max_num_improvement_loops']
         if 'n_live_points' in settings:
             ints[0] = 'n_live_points'
-        strings = ['log directory', 'derived parameters names']
+        strings = ['log directory', 'derived parameter names']
         floats = ['dlogz', 'frac_remain']
         allowed = strings + bools + ints + floats
 
@@ -1495,7 +1495,7 @@ def _parse_fitting_parameters_UltraNest(self):
             elif not path.isdir(self._log_dir_UltraNest):
                 raise ValueError("log directory value in fitting_parameters"
                                  "exists, but it is a file.")
-        value = settings.pop("derived parameters names", "")
+        value = settings.pop("derived parameter names", "")
         self._derived_param_names_UltraNest = value.split()
 
         keys = {"n_live_points": "min_num_live_points"}
@@ -2584,28 +2584,34 @@ def _setup_fit_UltraNest(self):
                     self._flux_names = self._get_fluxes_names_to_print()
                 self._derived_param_names_UltraNest = self._flux_names
 
+        n_dims = self._n_fit_parameters
+        n_params = n_dims + self._n_fluxes_per_dataset * self._return_fluxes
+        t_kwargs = {'n_dims': n_dims, 'n_params': n_params}
         self._sampler = ultranest.ReactiveNestedSampler(
-            self._fit_parameters,
-            self._ln_like, transform=self._transform_unit_cube_UltraNest,
+            self._fit_parameters, self._ln_like,
+            transform=lambda cube: self._transform_unit_cube(cube, **t_kwargs),
             derived_param_names=self._derived_param_names_UltraNest,
             log_dir=self._log_dir_UltraNest
         )
 
     def _transform_unit_cube(self, cube, n_dims, n_params):
         """
-        Transform MulitNest unit cube to microlensing parameters.
+        Transform MultiNest/UltraNest unit cube to microlensing parameters.
 
-        Based on SafePrior() in
+        MultiNest: based on SafePrior() in
         https://github.com/JohannesBuchner/PyMultiNest/blob/master/
         pymultinest/solve.py
+        UltraNest: based on the above and UltraNest documentation
+        https://johannesbuchner.github.io/UltraNest/example-sine-line.html
 
         NOTE: We call self._ln_like() here (and remember the result)
         because in MultiNest you can add fluxes only in "prior" function,
         not in likelihood function.
         """
         cube_out = self._min_values + cube[:n_dims] * self._range_values
-        for i in range(n_dims):
-            cube[i] = cube_out[i]
+        if self._fit_method == "MultiNest":
+            for i in range(n_dims):
+                cube[i] = cube_out[i]
 
         if "x_caustic_in" in self._model.parameters.parameters:
             self._set_model_parameters(cube_out)
@@ -2613,19 +2619,25 @@ def _transform_unit_cube(self, cube, n_dims, n_params):
                 self._last_ln_like = -1.e300
                 self._last_theta = cube_out
                 if self._return_fluxes:
-                    for i in range(n_dims, n_params):
-                        cube[i] = 0.
                     self._last_fluxes = np.zeros(n_params - n_dims)
-                return
+                    if self._fit_method == "MultiNest":
+                        for i in range(n_dims, n_params):
+                            cube[i] = 0.
+                        return
+                    cube_out = np.append(cube_out, self._last_fluxes)
+                    return cube_out
 
         self._last_ln_like = self._ln_like(cube_out)
         self._last_theta = cube_out
 
         if self._return_fluxes:
             fluxes = self._get_fluxes()
+            self._last_fluxes = fluxes
+            if self._fit_method == "UltraNest":
+                cube_out = np.append(cube_out, fluxes)
+                return cube_out
             for i in range(n_dims, n_params):
                 cube[i] = fluxes[i-n_dims]
-            self._last_fluxes = fluxes
 
     def _ln_like_MN(self, theta, n_dim, n_params, lnew):
         """
@@ -2654,28 +2666,6 @@ def _ln_like_MN(self, theta, n_dim, n_params, lnew):
 
         return ln_like
 
-    def _transform_unit_cube_UltraNest(self, cube):
-        """
-        Transform UltraNest unit cube to microlensing parameters.
-
-        Based on _transform_unit_cube() for MultiNest method and tutorials
-        from UltraNest documentation:
-        https://johannesbuchner.github.io/UltraNest/example-sine-line.html
-        """
-        n_dims = self._n_fit_parameters
-        n_params = n_dims + self._n_fluxes_per_dataset * self._return_fluxes
-        cube_out = self._min_values + cube[:n_dims] * self._range_values
-
-        # TBD: add "x_caustic_in" checks as in line 2610...
-
-        if self._return_fluxes:
-            fluxes = self._get_fluxes()
-            for i in range(n_dims, n_params):
-                cube_out = np.append(cube_out, fluxes[i-n_dims])
-            self._last_fluxes = fluxes
-
-        return cube_out
-
     def _run_fit(self):
         """
         Call the method that does the fit.

From 31e2cfb222873dcdd1e17e24227e5f71119cdaaf Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Tue, 17 Sep 2024 12:48:11 +0200
Subject: [PATCH 17/21] Added prior in blending_flux and t_E for UltraNest

---
 examples/example_16/ob08092-o4_UN.yaml |  4 +++
 examples/example_16/ulens_model_fit.py | 43 ++++++++++++++++++--------
 2 files changed, 34 insertions(+), 13 deletions(-)

diff --git a/examples/example_16/ob08092-o4_UN.yaml b/examples/example_16/ob08092-o4_UN.yaml
index 44a9d6a3..dba6bb2d 100644
--- a/examples/example_16/ob08092-o4_UN.yaml
+++ b/examples/example_16/ob08092-o4_UN.yaml
@@ -5,6 +5,10 @@ prior_limits:
     t_0: [2455379.4, 2455379.76]
     u_0: [0.3, 0.65]
     t_E: 16. 19.6
+fit_constraints:
+    negative_blending_flux_sigma_mag: 20.
+    prior:
+        t_E: Mroz et al. 2020
 fitting_parameters:
     log directory: ultranest_outputs/
     derived parameter names: flux_s_1 flux_b_1
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index d2a39253..d29769e4 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -1595,7 +1595,6 @@ def _parse_fit_constraints(self):
         """
         Parse the fitting constraints that are not simple limits on parameters
         """
-        self._check_fit_constraints()
         self._prior_t_E = None
         self._priors = None
 
@@ -1603,6 +1602,7 @@ def _parse_fit_constraints(self):
             self._set_default_fit_constraints()
             return
 
+        self._check_fit_constraints()
         self._parse_fit_constraints_keys()
         self._parse_fit_constraints_fluxes()
         self._parse_fit_constraints_posterior()
@@ -1614,11 +1614,22 @@ def _check_fit_constraints(self):
         """
         Run checks on self._fit_constraints
         """
-        lst_check = ['MultiNest', 'UltraNest']
-        if self._fit_constraints is not None and self._fit_method in lst_check:
-            msg = "Currently no fit_constraints are implemented for {:} " + \
-                  "fit. Please contact Radek Poleski with a specific request."
-            raise NotImplementedError(msg.format(self._fit_method))
+        if self._fit_method == 'MultiNest':
+            raise NotImplementedError(
+                "Currently no fit_constraints are implemented for MultiNest "
+                "fit. Please contact Radek Poleski with a specific request.")
+
+        if self._fit_method == 'UltraNest':
+            allowed_keys = {'negative_blending_flux_sigma_mag', 'prior'}
+            used_keys = set(self._fit_constraints.keys())
+            if not used_keys.issubset(allowed_keys):
+                raise NotImplementedError(
+                    "The supported fit_constraints options for UltraNest are"
+                    " `negative_blending_flux_sigma_mag` and `prior`.")
+            if 'prior' in used_keys:
+                if set(self._fit_constraints['prior'].keys()) != {'t_E'}:
+                    raise ValueError(
+                        "Only `t_E` is allowed in fit_constraints['prior'].")
 
         if isinstance(self._fit_constraints, list):
             raise TypeError(
@@ -2233,9 +2244,12 @@ def _ln_prob(self, theta):
 
         NOTE: we're using np.log(), i.e., natural logarithms.
         """
-        ln_prior = self._ln_prior(theta)
-        if not np.isfinite(ln_prior):
-            return self._return_ln_prob(-np.inf)
+        if self._fit_method == "EMCEE":
+            ln_prior = self._ln_prior(theta)
+            if not np.isfinite(ln_prior):
+                return self._return_ln_prob(-np.inf)
+        elif self._fit_method == "UltraNest":
+            ln_prior = self._ln_prior_t_E() if self._prior_t_E else 0.
 
         ln_like = self._ln_like(theta)
         if not np.isfinite(ln_like):
@@ -2251,9 +2265,11 @@ def _ln_prob(self, theta):
 
         ln_prob += ln_prior_flux
 
-        self._update_best_model_EMCEE(ln_prob, theta, fluxes)
+        if self._fit_method == "EMCEE":
+            self._update_best_model_EMCEE(ln_prob, theta, fluxes)
+            return self._return_ln_prob(ln_prob, fluxes)
 
-        return self._return_ln_prob(ln_prob, fluxes)
+        return ln_prob
 
     def _return_ln_prob(self, value, fluxes=None):
         """
@@ -2487,7 +2503,8 @@ def _run_flux_checks_ln_prior(self, fluxes):
                 return outside
 
         inside += self._apply_negative_blending_flux_sigma_mag_prior(fluxes)
-        inside += self._apply_color_prior(fluxes)
+        if self._fit_method == "EMCEE":
+            inside += self._apply_color_prior(fluxes)
 
         return inside
 
@@ -2588,7 +2605,7 @@ def _setup_fit_UltraNest(self):
         n_params = n_dims + self._n_fluxes_per_dataset * self._return_fluxes
         t_kwargs = {'n_dims': n_dims, 'n_params': n_params}
         self._sampler = ultranest.ReactiveNestedSampler(
-            self._fit_parameters, self._ln_like,
+            self._fit_parameters, self._ln_prob,
             transform=lambda cube: self._transform_unit_cube(cube, **t_kwargs),
             derived_param_names=self._derived_param_names_UltraNest,
             log_dir=self._log_dir_UltraNest

From d0376cf23191e0d147027c5e2f2a8febecb9839a Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Tue, 17 Sep 2024 12:50:12 +0200
Subject: [PATCH 18/21] Declare _parse_posterior_abs when posterior parsing is
 not used

---
 examples/example_16/ulens_model_fit.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index d29769e4..fc929a8d 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -1800,6 +1800,7 @@ def _parse_fit_constraints_posterior(self):
         Parse constraints on what is done with posterior.
         """
         if 'posterior parsing' not in self._fit_constraints:
+            self._parse_posterior_abs = list()
             return
 
         if self._fit_method != "EMCEE":

From adad7697d9dd9454bf321d793c3fffa703094b21 Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Tue, 17 Sep 2024 15:37:32 +0200
Subject: [PATCH 19/21] Added check of derived parameter names for UltraNest

---
 examples/example_16/ulens_model_fit.py | 30 +++++++++++++++++++-------
 1 file changed, 22 insertions(+), 8 deletions(-)

diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index fc929a8d..54b9e68f 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -706,7 +706,6 @@ def run_fit(self):
         self._check_ulens_model_parameters()
         self._get_parameters_ordered()
         self._get_parameters_latex()
-        self._parse_fitting_parameters()
         self._set_prior_limits()
         self._parse_fit_constraints()
         if self._fit_method == "EMCEE":
@@ -714,6 +713,7 @@ def run_fit(self):
 
         self._check_fixed_parameters()
         self._make_model_and_event()
+        self._parse_fitting_parameters()
         if self._fit_method == "EMCEE":
             self._get_starting_parameters()
 
@@ -1488,15 +1488,9 @@ def _parse_fitting_parameters_UltraNest(self):
         self._check_required_and_allowed_parameters(required, allowed)
         self._check_parameters_types(settings, bools, ints, floats, strings)
         self._log_dir_UltraNest = settings.pop("log directory", None)
-        if self._log_dir_UltraNest is not None:
-            if not path.exists(self._log_dir_UltraNest):
-                raise ValueError("log directory value in fitting_parameters"
-                                 "does not exist.")
-            elif not path.isdir(self._log_dir_UltraNest):
-                raise ValueError("log directory value in fitting_parameters"
-                                 "exists, but it is a file.")
         value = settings.pop("derived parameter names", "")
         self._derived_param_names_UltraNest = value.split()
+        self._check_dir_and_parameter_names_Ultranest()
 
         keys = {"n_live_points": "min_num_live_points"}
         same_keys = ["min_num_live_points", 'max_num_improvement_loops',
@@ -1504,6 +1498,26 @@ def _parse_fitting_parameters_UltraNest(self):
         keys = {**keys, **{key: key for key in same_keys}}
         self._set_dict_safely(self._kwargs_UltraNest, settings, keys)
 
+    def _check_dir_and_parameter_names_Ultranest(self):
+        """
+        Checks if the path to `log directory` exists and is a directory,
+        and also if the number of `derived parameter names` matches the
+        number of derived fluxes.
+        """
+        if self._log_dir_UltraNest is not None:
+            if not path.exists(self._log_dir_UltraNest):
+                raise ValueError("log directory value in fitting_parameters"
+                                 "does not exist.")
+            elif not path.isdir(self._log_dir_UltraNest):
+                raise ValueError("log directory value in fitting_parameters"
+                                 "exists, but it is a file.")
+
+        n_datasets = len(self._datasets)
+        n_fluxes = self._n_fluxes_per_dataset
+        if len(self._derived_param_names_UltraNest) != n_datasets * n_fluxes:
+            raise ValueError("The number of `derived parameter names` must "
+                             "match the number of derived fluxes.")
+
     def _set_prior_limits(self):
         """
         Set minimum and maximum values of the prior space

From 1a211e541f53233e698702b097dbb4e89af7b257 Mon Sep 17 00:00:00 2001
From: "Raphael A. P. Oliveira" <roliveira.astro@gmail.com>
Date: Tue, 17 Sep 2024 16:25:20 +0200
Subject: [PATCH 20/21] Minor changes to full YAML file, removed
 _n_walkers=None

---
 examples/example_16/ob03235_2_full.yaml | 13 +++++++------
 examples/example_16/ulens_model_fit.py  |  6 ++----
 2 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/examples/example_16/ob03235_2_full.yaml b/examples/example_16/ob03235_2_full.yaml
index dc1239b8..23780c8e 100644
--- a/examples/example_16/ob03235_2_full.yaml
+++ b/examples/example_16/ob03235_2_full.yaml
@@ -7,11 +7,12 @@ photometry_files:
 #    - {..., add_2450000: False}
 fit_method: EMCEE
 # Options: EMCEE, MultiNest, UltraNest.
-# If not given, EMCEE is used if starting_parameters are given; if prior_limits
-# are given, MultiNest or UltraNest is used depending on fitting_parameters.
-# If MultiNest/UltraNest is selected: starting_parameters, fit_constraints,
-# min_values and max_value cannot be provided. The trace plot cannot be
-# generated as well.
+# If fit_method is not given, EMCEE is used if starting_parameters are given;
+# and MultiNest or UltraNest is used if prior_limits are given, depending on
+# the combination of fitting_parameters.
+# If MultiNest/UltraNest is used, this file cannot have starting_parameters,
+# fit_constraints (except negative_blending_flux_sigma_mag and prior->t_E for
+# UltraNest), min_values, max_values and plots->trace.
 model:
     methods: 2452800. point_source 2452833. VBBL 2452845. point_source 2452860.
     default method: point_source_point_lens
@@ -108,7 +109,7 @@ fitting_parameters:
     ## UltraNest only (log directory, derived parameter names, show_status,
     ## dlogz=0.5, frac_remain=0.01, max_num_improvement_loops=-1)
     # log directory: ultranest_outputs/
-    # derived parameter names: flux_s_1 flux_b_1
+    # derived parameter names: flux_s_1 flux_b_1 flux_s_2 flux_b_2
     # show_status: True
     # dlogz: 2.
     # frac_remain: 0.5
diff --git a/examples/example_16/ulens_model_fit.py b/examples/example_16/ulens_model_fit.py
index 54b9e68f..5bdfe6d2 100644
--- a/examples/example_16/ulens_model_fit.py
+++ b/examples/example_16/ulens_model_fit.py
@@ -1372,11 +1372,9 @@ def _get_n_walkers(self):
         if 'n_walkers' in self._fitting_parameters:
             self._n_walkers = self._fitting_parameters['n_walkers']
         else:
-            if self._starting_parameters_type == 'file':
-                self._n_walkers = None
-            elif self._starting_parameters_type == 'draw':
+            if self._starting_parameters_type == 'draw':
                 self._n_walkers = 4 * self._n_fit_parameters
-            else:
+            elif self._starting_parameters_type != 'file':
                 raise ValueError(
                     'Unexpected: ' + self._starting_parameters_type)
 

From d45acf24ffa7d76b9846ae0074a4e218b74af58f Mon Sep 17 00:00:00 2001
From: radek_poleski <radek.poleski@gmail.com>
Date: Sun, 22 Sep 2024 17:11:14 +0200
Subject: [PATCH 21/21] ex16 - minor

---
 examples/example_16/ob08092-o4_UN.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/example_16/ob08092-o4_UN.yaml b/examples/example_16/ob08092-o4_UN.yaml
index dba6bb2d..2cf18914 100644
--- a/examples/example_16/ob08092-o4_UN.yaml
+++ b/examples/example_16/ob08092-o4_UN.yaml
@@ -10,7 +10,7 @@ fit_constraints:
     prior:
         t_E: Mroz et al. 2020
 fitting_parameters:
-    log directory: ultranest_outputs/
+    log directory: outputs_ultranest/
     derived parameter names: flux_s_1 flux_b_1
     show_status: True
     n_live_points: 1000