inlab-geo · jwhhh · Dec 14, 2022 · Dec 12, 2022 · Dec 12, 2022 · Dec 13, 2022
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -2,6 +2,14 @@
 
 <!--next-version-placeholder-->
 
+## v0.1.2.dev23 (14/12/2022)
+
+### CoFI Core
+
+- [#91](https://github.com/inlab-geo/cofi/issues/91) Raise warning when people set solver params that are not in optional list
+- [#97](https://github.com/inlab-geo/cofi/issues/97) Make walkers_start_pos a property of InversionOptions instead of BaseProblem
+- [#98](https://github.com/inlab-geo/cofi/issues/98) Typo, wording fixes; shorten error messages
+
 ## v0.1.2.dev22 (23/11/2022)
 
 ### CoFI Core

diff --git a/docs/Makefile b/docs/Makefile
@@ -7,11 +7,19 @@ SPHINXOPTS    ?=
 SPHINXBUILD   ?= sphinx-build
 SOURCEDIR     = source
 BUILDDIR      = build
+EXAMPLESDIR	  = examples
+TUTORIALSDIR  = tutorials
+GENERATEDDIR  = generated
 
 # Put it first so that "make" without argument is like "make help".
 help:
 	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
 
+clean_all:
+	@$(SPHINXBUILD) -M clean "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	rm -rf $(SOURCEDIR)/$(EXAMPLESDIR)/$(GENERATEDDIR)/*
+	rm -rf $(SOURCEDIR)/$(TUTORIALSDIR)/$(GENERATEDDIR)/*
+
 .PHONY: help Makefile
 
 # Catch-all target: route all unknown targets to Sphinx using the new

diff --git a/docs/source/api/generated/cofi.BaseProblem.rst b/docs/source/api/generated/cofi.BaseProblem.rst
@@ -349,15 +349,6 @@ cofi.BaseProblem
 
 
 
-
-
-
-
-
-
-
-
-
 
 
 
@@ -655,13 +646,6 @@ cofi.BaseProblem
 
 
 
-    .. automethod:: BaseProblem.set_walkers_starting_pos
-
-
-
-
-
-
     .. automethod:: BaseProblem.suggest_tools
 
 
@@ -748,10 +732,6 @@ cofi.BaseProblem
 
     .. autoattribute:: BaseProblem.residual_defined
 
-    .. autoattribute:: BaseProblem.walkers_starting_pos
-
-    .. autoattribute:: BaseProblem.walkers_starting_pos_defined
-
 
 :ref:`back to top <top_BaseProblem>`
 

diff --git a/docs/source/cofi-examples b/docs/source/cofi-examples
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -127,7 +127,6 @@
     "ignore_pattern": "._lib.py|_preprocessing.py",
     "pypandoc": True,
     "download_all_examples": False,
-    "backreferences_dir"  : "api/generated/backreferences",
     "doc_module": "cofi",
 }
 

diff --git a/...xamples/generated/field_data/images/sphx_glr_linear_regression_sealevel_006.png b/...xamples/generated/field_data/images/sphx_glr_linear_regression_sealevel_006.png
diff --git a/...xamples/generated/field_data/images/sphx_glr_linear_regression_sealevel_007.png b/...xamples/generated/field_data/images/sphx_glr_linear_regression_sealevel_007.png
diff --git a/...xamples/generated/field_data/images/sphx_glr_linear_regression_sealevel_008.png b/...xamples/generated/field_data/images/sphx_glr_linear_regression_sealevel_008.png
diff --git a/...xamples/generated/field_data/images/sphx_glr_linear_regression_sealevel_009.png b/...xamples/generated/field_data/images/sphx_glr_linear_regression_sealevel_009.png
diff --git a/...generated/field_data/images/thumb/sphx_glr_linear_regression_sealevel_thumb.png b/...generated/field_data/images/thumb/sphx_glr_linear_regression_sealevel_thumb.png
diff --git a/docs/source/examples/generated/field_data/linear_regression_sealevel.ipynb b/docs/source/examples/generated/field_data/linear_regression_sealevel.ipynb
@@ -583,7 +583,7 @@
       },
       "outputs": [],
       "source": [
-        "######## CoFI BaseProblem - provide additional information\ninv_problem.set_log_prior(log_prior)\ninv_problem.set_log_likelihood(log_likelihood)\ninv_problem.set_walkers_starting_pos(walkers_start)\n\n######## CoFI InversionOptions - get a different tool\ninv_options_3 = InversionOptions()\ninv_options_3.set_tool(\"emcee\")      # Here we use to Affine Invariant McMC sampler from Goodman and Weare (2010).\ninv_options_3.set_params(nwalkers=nwalkers, nsteps=nsteps, progress=True)\n\n######## CoFI Inversion - run it\ninv_3 = Inversion(inv_problem, inv_options_3)\ninv_result_3 = inv_3.run()\n\n######## CoFI InversionResult - check result\nprint(f\"The inversion result from `emcee`:\")\ninv_result_3.summary()"
+        "######## CoFI BaseProblem - provide additional information\ninv_problem.set_log_prior(log_prior)\ninv_problem.set_log_likelihood(log_likelihood)\ninv_problem.set_model_shape(ndim)\n\n######## CoFI InversionOptions - get a different tool\ninv_options_3 = InversionOptions()\ninv_options_3.set_tool(\"emcee\")      # Here we use to Affine Invariant McMC sampler from Goodman and Weare (2010).\ninv_options_3.set_params(nwalkers=nwalkers, nsteps=nsteps, progress=True, initial_state=walkers_start)\n\n######## CoFI Inversion - run it\ninv_3 = Inversion(inv_problem, inv_options_3)\ninv_result_3 = inv_3.run()\n\n######## CoFI InversionResult - check result\nprint(f\"The inversion result from `emcee`:\")\ninv_result_3.summary()"
       ]
     },
     {

diff --git a/docs/source/examples/generated/field_data/linear_regression_sealevel.py b/docs/source/examples/generated/field_data/linear_regression_sealevel.py
@@ -702,12 +702,12 @@ def log_prior(model):    # uniform distribution
 ######## CoFI BaseProblem - provide additional information
 inv_problem.set_log_prior(log_prior)
 inv_problem.set_log_likelihood(log_likelihood)
-inv_problem.set_walkers_starting_pos(walkers_start)
+inv_problem.set_model_shape(ndim)
 
 ######## CoFI InversionOptions - get a different tool
 inv_options_3 = InversionOptions()
 inv_options_3.set_tool("emcee")      # Here we use to Affine Invariant McMC sampler from Goodman and Weare (2010).
-inv_options_3.set_params(nwalkers=nwalkers, nsteps=nsteps, progress=True)
+inv_options_3.set_params(nwalkers=nwalkers, nsteps=nsteps, progress=True, initial_state=walkers_start)
 
 ######## CoFI Inversion - run it
 inv_3 = Inversion(inv_problem, inv_options_3)

diff --git a/docs/source/examples/generated/field_data/linear_regression_sealevel.py.md5 b/docs/source/examples/generated/field_data/linear_regression_sealevel.py.md5
@@ -1 +1 @@
-2cb73ba6d1076e3d54021e6c5ffe7f0c
+eb7d98418661f81519e47d3aa3f5881d
diff --git a/docs/source/examples/generated/field_data/linear_regression_sealevel.rst b/docs/source/examples/generated/field_data/linear_regression_sealevel.rst
diff --git a/docs/source/examples/generated/field_data/linear_regression_sealevel_codeobj.pickle b/docs/source/examples/generated/field_data/linear_regression_sealevel_codeobj.pickle
diff --git a/docs/source/examples/generated/field_data/pygimli_dcip_century_tri_mesh.rst b/docs/source/examples/generated/field_data/pygimli_dcip_century_tri_mesh.rst
diff --git a/docs/source/examples/generated/field_data/pygimli_dcip_century_tri_mesh_codeobj.pickle b/docs/source/examples/generated/field_data/pygimli_dcip_century_tri_mesh_codeobj.pickle
diff --git a/docs/source/examples/generated/field_data/sg_execution_times.rst b/docs/source/examples/generated/field_data/sg_execution_times.rst
@@ -5,10 +5,10 @@
 
 Computation times
 =================
-**04:23.827** total execution time for **examples_generated_field_data** files:
+**04:21.481** total execution time for **examples_generated_field_data** files:
 
 +-----------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_generated_field_data_linear_regression_sealevel.py` (``linear_regression_sealevel.py``)       | 02:35.673 | 0.0 MB |
+| :ref:`sphx_glr_examples_generated_field_data_linear_regression_sealevel.py` (``linear_regression_sealevel.py``)       | 02:32.564 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_generated_field_data_pygimli_dcip_century_tri_mesh.py` (``pygimli_dcip_century_tri_mesh.py``) | 01:48.154 | 0.0 MB |
+| :ref:`sphx_glr_examples_generated_field_data_pygimli_dcip_century_tri_mesh.py` (``pygimli_dcip_century_tri_mesh.py``) | 01:48.917 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/source/examples/generated/synth_data/fmm_tomography.ipynb b/docs/source/examples/generated/synth_data/fmm_tomography.ipynb
@@ -192,7 +192,7 @@
       },
       "outputs": [],
       "source": [
-        "my_options = cofi.InversionOptions()\n\n# cofi's own simple newton's matrix-based optimization solver\nmy_options.set_tool(\"cofi.simple_newton\")\nmy_options.set_params(max_iterations=6, step_length=1, verbose=True)"
+        "my_options = cofi.InversionOptions()\n\n# cofi's own simple newton's matrix-based optimization solver\nmy_options.set_tool(\"cofi.simple_newton\")\nmy_options.set_params(num_iterations=6, step_length=1, verbose=True)"
       ]
     },
     {

diff --git a/docs/source/examples/generated/synth_data/fmm_tomography.py b/docs/source/examples/generated/synth_data/fmm_tomography.py
@@ -246,7 +246,7 @@ def hessian(slowness, reg, sigma, reduce_data=None):        # reduce_data=(idx_f
 
 # cofi's own simple newton's matrix-based optimization solver
 my_options.set_tool("cofi.simple_newton")
-my_options.set_params(max_iterations=6, step_length=1, verbose=True)
+my_options.set_params(num_iterations=6, step_length=1, verbose=True)
 
 ######################################################################
 #

diff --git a/docs/source/examples/generated/synth_data/fmm_tomography.py.md5 b/docs/source/examples/generated/synth_data/fmm_tomography.py.md5
@@ -1 +1 @@
-ec27105e84301116c44b0e9ce8763399
+a49ef5a42993d8cb1fb9e3f4941d107c
diff --git a/docs/source/examples/generated/synth_data/fmm_tomography.rst b/docs/source/examples/generated/synth_data/fmm_tomography.rst
@@ -403,7 +403,7 @@ Review what information is included in the ``BaseProblem`` object:
     ---------------------------------------------------------------------
     List of functions/properties that can be further set for the problem:
     ( not all of these may be relevant to your inversion workflow )
-    ['log_posterior', 'log_posterior_with_blobs', 'log_likelihood', 'log_prior', 'hessian_times_vector', 'residual', 'jacobian', 'jacobian_times_vector', 'data_misfit', 'regularization', 'regularization_matrix', 'regularization_factor', 'forward', 'data', 'data_covariance', 'data_covariance_inv', 'walkers_starting_pos', 'blobs_dtype', 'bounds', 'constraints']
+    ['log_posterior', 'log_posterior_with_blobs', 'log_likelihood', 'log_prior', 'hessian_times_vector', 'residual', 'jacobian', 'jacobian_times_vector', 'data_misfit', 'regularization', 'regularization_matrix', 'regularization_factor', 'forward', 'data', 'data_covariance', 'data_covariance_inv', 'blobs_dtype', 'bounds', 'constraints']
 
 
 
@@ -423,7 +423,7 @@ Review what information is included in the ``BaseProblem`` object:
 
     # cofi's own simple newton's matrix-based optimization solver
     my_options.set_tool("cofi.simple_newton")
-    my_options.set_params(max_iterations=6, step_length=1, verbose=True)
+    my_options.set_params(num_iterations=6, step_length=1, verbose=True)
 
 
 
@@ -459,11 +459,11 @@ Review what’s been defined for the inversion we are about to run:
     Use `suggest_solving_methods()` to check available solving methods.
     -----------------------------
     Backend tool: `cofi.simple_newton` - CoFI's own solver - simple Newton's approach (for testing mainly)
-    References: ['https://en.wikipedia.org/wiki/Newton%27s_method_in_optimization', 'https://en.wikipedia.org/wiki/Backtracking_line_search']
+    References: ['https://en.wikipedia.org/wiki/Newton%27s_method_in_optimization']
     Use `suggest_tools()` to check available backend tools.
     -----------------------------
     Solver-specific parameters: 
-    max_iterations = 6
+    num_iterations = 6
     step_length = 1
     verbose = True
     Use `suggest_solver_params()` to check required/optional solver-specific parameters.
@@ -608,7 +608,7 @@ sphinx_gallery_thumbnail_number = -1
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  7.992 seconds)
+   **Total running time of the script:** ( 0 minutes  9.712 seconds)
 
 
 .. _sphx_glr_download_examples_generated_synth_data_fmm_tomography.py:

diff --git a/docs/source/examples/generated/synth_data/fmm_tomography_codeobj.pickle b/docs/source/examples/generated/synth_data/fmm_tomography_codeobj.pickle
diff --git a/docs/source/examples/generated/synth_data/linear_regression.ipynb b/docs/source/examples/generated/synth_data/linear_regression.ipynb
@@ -435,7 +435,7 @@
       },
       "outputs": [],
       "source": [
-        "######## Provide additional information\ninv_problem.set_log_prior(log_prior)\ninv_problem.set_log_likelihood(log_likelihood)\ninv_problem.set_walkers_starting_pos(walkers_start)\n\n######## Set a different tool\ninv_options_3 = InversionOptions()\ninv_options_3.set_tool(\"emcee\")\ninv_options_3.set_params(nwalkers=nwalkers, nsteps=nsteps)\n\n######## Run it\ninv_3 = Inversion(inv_problem, inv_options_3)\ninv_result_3 = inv_3.run()\n\n######## Check result\nprint(f\"The inversion result from `emcee`:\")\ninv_result_3.summary()"
+        "######## Provide additional information\ninv_problem.set_log_prior(log_prior)\ninv_problem.set_log_likelihood(log_likelihood)\ninv_problem.set_model_shape(ndim)\n\n######## Set a different tool\ninv_options_3 = InversionOptions()\ninv_options_3.set_tool(\"emcee\")\ninv_options_3.set_params(nwalkers=nwalkers, nsteps=nsteps, initial_state=walkers_start)\n\n######## Run it\ninv_3 = Inversion(inv_problem, inv_options_3)\ninv_result_3 = inv_3.run()\n\n######## Check result\nprint(f\"The inversion result from `emcee`:\")\ninv_result_3.summary()"
       ]
     },
     {

diff --git a/docs/source/examples/generated/synth_data/linear_regression.py b/docs/source/examples/generated/synth_data/linear_regression.py
@@ -744,12 +744,12 @@ def log_prior(model):    # uniform distribution
 ######## Provide additional information
 inv_problem.set_log_prior(log_prior)
 inv_problem.set_log_likelihood(log_likelihood)
-inv_problem.set_walkers_starting_pos(walkers_start)
+inv_problem.set_model_shape(ndim)
 
 ######## Set a different tool
 inv_options_3 = InversionOptions()
 inv_options_3.set_tool("emcee")
-inv_options_3.set_params(nwalkers=nwalkers, nsteps=nsteps)
+inv_options_3.set_params(nwalkers=nwalkers, nsteps=nsteps, initial_state=walkers_start)
 
 ######## Run it
 inv_3 = Inversion(inv_problem, inv_options_3)

diff --git a/docs/source/examples/generated/synth_data/linear_regression.py.md5 b/docs/source/examples/generated/synth_data/linear_regression.py.md5
@@ -1 +1 @@
-33db0120b2daa10a0b2bd7002bd17a30
+8b64a5826889c93ac6d0ca3bb5f3ea3e
diff --git a/docs/source/examples/generated/synth_data/linear_regression.rst b/docs/source/examples/generated/synth_data/linear_regression.rst
@@ -241,7 +241,7 @@ Recall that the function we are going to fit is:
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7fdd1e701660>
+    <matplotlib.legend.Legend object at 0x7f2d28db09a0>
 
 
 
@@ -310,7 +310,7 @@ object.
     ---------------------------------------------------------------------
     List of functions/properties that can be further set for the problem:
     ( not all of these may be relevant to your inversion workflow )
-    ['objective', 'log_posterior', 'log_posterior_with_blobs', 'log_likelihood', 'log_prior', 'gradient', 'hessian', 'hessian_times_vector', 'residual', 'jacobian_times_vector', 'data_misfit', 'regularization', 'regularization_matrix', 'regularization_factor', 'forward', 'data_covariance', 'data_covariance_inv', 'initial_model', 'model_shape', 'walkers_starting_pos', 'blobs_dtype', 'bounds', 'constraints']
+    ['objective', 'log_posterior', 'log_posterior_with_blobs', 'log_likelihood', 'log_prior', 'gradient', 'hessian', 'hessian_times_vector', 'residual', 'jacobian_times_vector', 'data_misfit', 'regularization', 'regularization_matrix', 'regularization_factor', 'forward', 'data_covariance', 'data_covariance_inv', 'initial_model', 'model_shape', 'blobs_dtype', 'bounds', 'constraints']
 
 
 
@@ -389,7 +389,7 @@ categories of inversion approaches you’d like to use.
 
  .. code-block:: none
 
-    Here's a complete list of inversion solvers supported by CoFI (grouped by methods):
+    Here's a complete list of inversion tools supported by CoFI (grouped by methods):
     {
         "optimization": [
             "scipy.optimize.minimize",
@@ -478,7 +478,7 @@ function shows available options and set your desired backend solver.
     Use `InversionOptions.set_tool(tool_name)` to set a specific tool from above
     Use `InversionOptions.set_solving_method(method_name)` to change solving method
     Use `InversionOptions.unset_solving_method()` if you'd like to see more options
-    Check CoFI documentation 'Advanced Usage' section for how to plug in your own solver
+    Check CoFI documentation 'Advanced Usage' section for how to plug in your own tool or solver
 
 
 
@@ -578,7 +578,7 @@ and is an engine to actually perform the inversion.
     ['jacobian_times_vector']
     List of functions/properties that can be further set for the problem:
     ( not all of these may be relevant to your inversion workflow )
-    ['objective', 'log_posterior', 'log_posterior_with_blobs', 'log_likelihood', 'log_prior', 'gradient', 'hessian', 'hessian_times_vector', 'residual', 'jacobian_times_vector', 'data_misfit', 'regularization', 'regularization_matrix', 'regularization_factor', 'forward', 'data_covariance', 'data_covariance_inv', 'initial_model', 'model_shape', 'walkers_starting_pos', 'blobs_dtype', 'bounds', 'constraints']
+    ['objective', 'log_posterior', 'log_posterior_with_blobs', 'log_likelihood', 'log_prior', 'gradient', 'hessian', 'hessian_times_vector', 'residual', 'jacobian_times_vector', 'data_misfit', 'regularization', 'regularization_matrix', 'regularization_factor', 'forward', 'data_covariance', 'data_covariance_inv', 'initial_model', 'model_shape', 'blobs_dtype', 'bounds', 'constraints']
 
 
 
@@ -718,9 +718,9 @@ any).
     ['jacobian_times_vector']
     List of functions/properties that can be further set for the problem:
     ( not all of these may be relevant to your inversion workflow )
-    ['objective', 'log_posterior', 'log_posterior_with_blobs', 'log_likelihood', 'log_prior', 'gradient', 'hessian', 'hessian_times_vector', 'residual', 'jacobian_times_vector', 'data_misfit', 'regularization', 'regularization_matrix', 'regularization_factor', 'forward', 'data_covariance', 'data_covariance_inv', 'initial_model', 'model_shape', 'walkers_starting_pos', 'blobs_dtype', 'bounds', 'constraints']
+    ['objective', 'log_posterior', 'log_posterior_with_blobs', 'log_likelihood', 'log_prior', 'gradient', 'hessian', 'hessian_times_vector', 'residual', 'jacobian_times_vector', 'data_misfit', 'regularization', 'regularization_matrix', 'regularization_factor', 'forward', 'data_covariance', 'data_covariance_inv', 'initial_model', 'model_shape', 'blobs_dtype', 'bounds', 'constraints']
     List of functions/properties got used by the backend tool:
-    ['data', 'jacobian']
+    ['jacobian', 'data']
 
 
 
@@ -765,7 +765,7 @@ ground truth.
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7fdd02a31a50>
+    <matplotlib.legend.Legend object at 0x7f2d5580fee0>
 
 
 
@@ -970,7 +970,7 @@ CoFI.
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7fdd1e6fe800>
+    <matplotlib.legend.Legend object at 0x7f2d30a65f00>
 
 
 
@@ -1214,12 +1214,12 @@ Finally, we attach all above information to our ``BaseProblem`` and
     ######## Provide additional information
     inv_problem.set_log_prior(log_prior)
     inv_problem.set_log_likelihood(log_likelihood)
-    inv_problem.set_walkers_starting_pos(walkers_start)
+    inv_problem.set_model_shape(ndim)
 
     ######## Set a different tool
     inv_options_3 = InversionOptions()
     inv_options_3.set_tool("emcee")
-    inv_options_3.set_params(nwalkers=nwalkers, nsteps=nsteps)
+    inv_options_3.set_params(nwalkers=nwalkers, nsteps=nsteps, initial_state=walkers_start)
 
     ######## Run it
     inv_3 = Inversion(inv_problem, inv_options_3)
@@ -1483,7 +1483,7 @@ posterior ensemble and compare to the data.
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7fdd2d5982b0>
+    <matplotlib.legend.Legend object at 0x7f2d5c77ef80>
 
 
 
@@ -1656,7 +1656,7 @@ sphinx_gallery_thumbnail_number = -1
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  7.269 seconds)
+   **Total running time of the script:** ( 0 minutes  7.315 seconds)
 
 
 .. _sphx_glr_download_examples_generated_synth_data_linear_regression.py:

diff --git a/docs/source/examples/generated/synth_data/linear_regression_codeobj.pickle b/docs/source/examples/generated/synth_data/linear_regression_codeobj.pickle
diff --git a/docs/source/examples/generated/synth_data/pygimli_dcip.rst b/docs/source/examples/generated/synth_data/pygimli_dcip.rst
@@ -684,7 +684,7 @@ the problem in ``cofi`` by setting the problem information for a
 
  .. code-block:: none
 
-    Based on what you've provided so far, here are possible solvers:
+    Based on what you've provided so far, here are possible tools:
     {
         "optimization": [
             "scipy.optimize.minimize",
@@ -812,26 +812,28 @@ the problem in ``cofi`` by setting the problem information for a
 
  .. code-block:: none
 
-    Iteration #0, objective value: 40.95771199605638
-    Iteration #1, objective value: 33.00676105436523
-    Iteration #2, objective value: 27.517003420370493
-    Iteration #3, objective value: 23.856374111765216
-    Iteration #4, objective value: 21.50039198382915
-    Iteration #5, objective value: 20.031913540512758
-    Iteration #6, objective value: 19.94374499736552
-    Iteration #7, objective value: 19.827200872787692
-    Iteration #8, objective value: 19.68751418779837
-    Iteration #9, objective value: 19.529728014788613
-    Iteration #10, objective value: 19.358622701101357
-    Iteration #11, objective value: 19.178021476346988
-    Iteration #12, objective value: 18.990944837951876
-    Iteration #13, objective value: 18.80075206647256
-    Iteration #14, objective value: 18.61164377301345
-    Iteration #15, objective value: 18.428027398821495
-    Iteration #16, objective value: 18.253580046588773
-    Iteration #17, objective value: 18.090719054250933
-    Iteration #18, objective value: 17.940812351147578
-    Iteration #19, objective value: 17.80478600956866
+    /home/jiawen/cofi/src/cofi/solvers/base_solver.py:267: UserWarning: the following options are defined but not in parameter list for the chosen tool: ['lr']
+      warnings.warn(
+    Iteration #0, objective value: 40.95771199605861
+    Iteration #1, objective value: 33.006761054365505
+    Iteration #2, objective value: 27.517003420367388
+    Iteration #3, objective value: 23.856374111760857
+    Iteration #4, objective value: 21.50039198382136
+    Iteration #5, objective value: 20.03191354052629
+    Iteration #6, objective value: 19.94374499737532
+    Iteration #7, objective value: 19.827200872779063
+    Iteration #8, objective value: 19.687514187805156
+    Iteration #9, objective value: 19.529728014807965
+    Iteration #10, objective value: 19.35862270111142
+    Iteration #11, objective value: 19.178021476356154
+    Iteration #12, objective value: 18.990944837953872
+    Iteration #13, objective value: 18.800752066476278
+    Iteration #14, objective value: 18.61164377300838
+    Iteration #15, objective value: 18.428027398836804
+    Iteration #16, objective value: 18.253580046609493
+    Iteration #17, objective value: 18.090719054270597
+    Iteration #18, objective value: 17.94081235117483
+    Iteration #19, objective value: 17.804786009588636
 
 
 
@@ -914,7 +916,7 @@ sphinx_gallery_thumbnail_number = -1
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  57.612 seconds)
+   **Total running time of the script:** ( 3 minutes  4.958 seconds)
 
 
 .. _sphx_glr_download_examples_generated_synth_data_pygimli_dcip.py:

diff --git a/docs/source/examples/generated/synth_data/pygimli_dcip_codeobj.pickle b/docs/source/examples/generated/synth_data/pygimli_dcip_codeobj.pickle