Feature/result str

autofit/config/general.ini

@@ -4,6 +4,7 @@ log_file=output.log
 log_level=INFO
 samples_to_csv=False
 model_results_decimal_places=3
+info_whitespace_length=80
 remove_files=False
 force_pickle_overwrite=False
 identifier_version=4

autofit/mapper/prior_model/abstract.py

@@ -27,13 +27,12 @@
 from autofit.text import formatter as frm
 from autofit.text.formatter import TextFormatter
 from autofit.tools.util import split_paths
+from autofit.tools.util import info_whitespace
 
 logger = logging.getLogger(
     __name__
 )
 
-LINE_LENGTH = 80
-
 
 class Limits:
     @staticmethod
@@ -1554,8 +1553,9 @@ def info(self) -> str:
         parameter of the overall model.
         This information is extracted from each priors *model_info* property.
         """
+
         formatter = TextFormatter(
-            line_length=LINE_LENGTH
+            line_length=info_whitespace()
         )
 
         for t in self.path_instance_tuples_for_class(
@@ -1603,7 +1603,7 @@ def parameterization(self) -> str:
         from .prior_model import PriorModel
 
         formatter = TextFormatter(
-            line_length=LINE_LENGTH
+            line_length=info_whitespace()
         )
 
         for t in self.path_instance_tuples_for_class(

autofit/text/samples_text.py

@@ -4,7 +4,6 @@
 
 logger = logging.getLogger(__name__)
 
-
 def values_from_samples(samples, median_pdf_model):
 
     if median_pdf_model:
@@ -15,7 +14,8 @@ def values_from_samples(samples, median_pdf_model):
 def summary(
     samples, sigma=3.0, median_pdf_model=True, indent=1, line_length=None
 ) -> str:
-    """ Create a string summarizing the results of the `NonLinearSearch` at an input sigma value.
+    """
+    Create a string summarizing the results of the `NonLinearSearch` at an input sigma value.
 
     This function is used for creating the model.results files of a non-linear search.
 

autofit/text/text_util.py

@@ -1,6 +1,6 @@
 from autoconf import conf
 from autofit.text import formatter as frm, samples_text
-
+from autofit.tools.util import info_whitespace
 
 def padding(item, target=6):
     string = str(item)
@@ -14,7 +14,6 @@ def result_info_from(samples) -> str:
     Output the full model.results file, which include the most-likely model, most-probable model at 1 and 3
     sigma confidence and information on the maximum log likelihood.
     """
-
     results = []
 
     if hasattr(samples, "log_evidence"):
@@ -24,7 +23,7 @@ def result_info_from(samples) -> str:
                 frm.add_whitespace(
                     str0="Bayesian Evidence ",
                     str1="{:.8f}".format(samples.log_evidence),
-                    whitespace=90
+                    whitespace=info_whitespace()
                 )
             ]
             results += ["\n"]
@@ -33,15 +32,15 @@ def result_info_from(samples) -> str:
         frm.add_whitespace(
             str0="Maximum Log Likelihood ",
             str1="{:.8f}".format(max(samples.log_likelihood_list)),
-            whitespace=90
+            whitespace=info_whitespace()
         )
     ]
     results += ["\n"]
     results += [
         frm.add_whitespace(
             str0="Maximum Log Posterior ",
             str1="{:.8f}".format(max(samples.log_posterior_list)),
-            whitespace=90
+            whitespace=info_whitespace()
         )
     ]
     results += ["\n"]
@@ -50,7 +49,7 @@ def result_info_from(samples) -> str:
 
     results += ["Maximum Log Likelihood Model:\n\n"]
 
-    formatter = frm.TextFormatter()
+    formatter = frm.TextFormatter(line_length=info_whitespace())
 
     for i, prior_path in enumerate(samples.model.unique_prior_paths):
         formatter.add(
@@ -62,21 +61,21 @@ def result_info_from(samples) -> str:
 
         if samples.pdf_converged:
 
-            results += samples_text.summary(samples=samples, sigma=3.0, indent=4, line_length=90)
+            results += samples_text.summary(samples=samples, sigma=3.0, indent=4, line_length=info_whitespace())
             results += ["\n"]
-            results += samples_text.summary(samples=samples, sigma=1.0, indent=4, line_length=90)
+            results += samples_text.summary(samples=samples, sigma=1.0, indent=4, line_length=info_whitespace())
 
         else:
 
             results += [
                 "\n WARNING: The samples have not converged enough to compute a PDF and model errors. \n "
                 "The model below over estimates errors. \n\n"
             ]
-            results += samples_text.summary(samples=samples, sigma=1.0, indent=4, line_length=90)
+            results += samples_text.summary(samples=samples, sigma=1.0, indent=4, line_length=info_whitespace())
 
         results += ["\n\ninstances\n"]
 
-    formatter = frm.TextFormatter()
+    formatter = frm.TextFormatter(line_length=info_whitespace())
 
     for t in samples.model.path_float_tuples:
         formatter.add(*t)

autofit/tools/util.py

@@ -8,6 +8,7 @@
 
 import numpy as np
 
+from autoconf import conf
 
 def split_paths(func):
     """
@@ -141,3 +142,7 @@ def numpy_array_from_json(file_path: str):
     """
     with open(file_path, "r") as f:
         return np.asarray(json.load(f))
+
+
+def info_whitespace():
+    return conf.instance["general"]["output"]["info_whitespace_length"]

test_autofit/config/general.ini

@@ -4,6 +4,7 @@ log_file=output.log
 log_level=INFO
 samples_to_csv=False
 model_results_decimal_places=3
+info_whitespace_length=80
 remove_files=True
 force_pickle_overwrite=False
 identifier_version=3

test_autofit/non_linear/test_analysis.py

@@ -213,7 +213,7 @@ def test_set_number_of_cores(
     multi_analysis.n_cores = 1
     assert multi_analysis._log_likelihood_function.__name__ == "_summed_log_likelihood"
 
-    multi_analysis.n_cores = 1
+    multi_analysis.n_cores = 2
     assert isinstance(
         multi_analysis._log_likelihood_function,
         AnalysisPool

test_autofit/text/test_text_util.py

@@ -37,7 +37,7 @@ def test__results_to_file(samples):
         samples=samples,
     )
 
-    assert  "Maximum Log Likelihood                                                                    1.00000000\n" in result_info
+    assert  "Maximum Log Likelihood                                                          1.00000000\n" in result_info
 
 def test__search_summary_to_file(model):
     file_search_summary = path.join(text_path, "search.summary")