Merge 4d41c51 into fc3fda7

README.md

@@ -28,10 +28,10 @@ For help of how to use the command line/terminal, click the hyperlink correspond
 
 The above step is done to ensure that the compatible version of docutils packages (version 0.12) is installed. 
 
-7. Finally, in this terminal, run `example_scripts/example_runScript.py`, located in the fitbenchmarking folder. This example script fit benchmarks Mantid using all the available minimizers. The resulting tables can be found in `example_scripts/results`.
+7. Finally, in this terminal, run `example_scripts/example_runScript_mantid.py`, located in the fitbenchmarking folder. This example script fit benchmarks Mantid using all the available minimizers. The resulting tables can be found in `example_scripts/results`.
 
 ## FitBenchmarking Scipy
-The `example_runScripts.py` file can be changed such that it benchmarks minimizers supported by scipy instead of mantid (details provided in the file itself).
+The `example_runScripts.py` file is designed to benchmark minimizers supported by software/libraries that provide straightforward cross-platform Python install; as of know this mean SciPy (more details provided in the file itself).
 
 For this to work scipy version 0.17 or higher is needed (which includes needed [curve_fit](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html) support). **The Linux distributions we have tested against so far have all included scipy 0.17+ (0.17 is from Feb 2016).**
 
@@ -54,6 +54,16 @@ Mantid on Windows is shipped with Python. The above steps can also be done from
 terminal, in which case please ensure that you are upgrading against Python
 installed with Mantid, which by default is located in `C:\MantidInstall\bin`.
 
+## FitBenchmarking SasView
+The `example_runScripts_SasView.py` file is designed to benchmark minimizers supported by SasView (Bumps).
+
+In order to do so, Bumps, sasmodels, lxml and sascalc need to be installed. Note that Bumps, sasmodels and lxml can be installed via `pip` commands. However, as of this writing, sascalc is not an independent package yet and, therefore, cannot be installed via `pip`. Thus, sascalc is now included in FitBenchmarking under the folder `fitbenchmarking/sas`.
+
+To install Bumps, sasmodels and lxml, run the following command on console:
+1. `python -m pip install bumps`
+2. `python -m pip install sasmodels`
+3. `python -m pip install lxml`
+
 ## Description
 The tool creates a table/tables that shows a comparison between the different minimizers available in a fitting software (e.g. scipy or mantid), based on their accuracy and/or runtimes.
 An example of a table is:

benchmark_problems/SAS_modelling/1D/data_files/cyl_400_20.txt

@@ -0,0 +1,22 @@
+<X>   <Y>
+0  -1.#IND
+0.025  125.852
+0.05  53.6662
+0.075  26.0733
+0.1  11.8935
+0.125  4.61714
+0.15  1.29983
+0.175  0.171347
+0.2  0.0417614
+0.225  0.172719
+0.25  0.247876
+0.275  0.20301
+0.3  0.104599
+0.325  0.0285595
+0.35  0.00213344
+0.375  0.0137511
+0.4  0.0312374
+0.425  0.0350328
+0.45  0.0243172
+0.475  0.00923067
+0.5 0.00121297

benchmark_problems/SAS_modelling/1D/data_files/cyl_400_40.txt

@@ -0,0 +1,56 @@
+<X>   <Y>
+0  -1.#IND
+0.00925926  1246.59
+0.0185185  612.143
+0.0277778  361.142
+0.037037  211.601
+0.0462963  122.127
+0.0555556  65.2385
+0.0648148  30.8914
+0.0740741  12.4737
+0.0833333  3.51371
+0.0925926  0.721835
+0.101852  0.583607
+0.111111  1.31084
+0.12037  1.9432
+0.12963  1.94286
+0.138889  1.58912
+0.148148  0.987076
+0.157407  0.456678
+0.166667  0.147595
+0.175926  0.027441
+0.185185  0.0999575
+0.194444  0.198717
+0.203704  0.277667
+0.212963  0.288172
+0.222222  0.220056
+0.231481  0.139378
+0.240741  0.0541106
+0.25  0.0140158
+0.259259  0.0132187
+0.268519  0.0336301
+0.277778  0.0672911
+0.287037  0.0788983
+0.296296  0.0764438
+0.305556  0.0555445
+0.314815  0.0280548
+0.324074  0.0111798
+0.333333  0.00156156
+0.342593  0.00830883
+0.351852  0.0186266
+0.361111  0.0275426
+0.37037  0.03192
+0.37963  0.0255329
+0.388889  0.0175216
+0.398148  0.0073075
+0.407407  0.0016631
+0.416667  0.00224153
+0.425926  0.0051335
+0.435185  0.0112914
+0.444444  0.0138209
+0.453704  0.0137453
+0.462963  0.0106682
+0.472222  0.00532472
+0.481481  0.00230646
+0.490741  0.000335344
+0.5  0.00177224

benchmark_problems/SAS_modelling/1D/prob_def_1.txt

@@ -0,0 +1,6 @@
+# An example data set for SasView 1D data
+name = 'Problem Def 1'
+input_file = 'cyl_400_20.txt'
+function ='name=cylinder,radius=35.0,length=350.0,background=0.0,scale=1.0,sld=4.0,sld_solvent=1.0'
+parameter_ranges = 'radius.range(1,50);length.range(1,500)'
+description = ''

benchmark_problems/SAS_modelling/1D/prob_def_2.txt

@@ -0,0 +1,6 @@
+# An example data set for SasView 1D data
+name = 'Problem Def 2'
+input_file = 'cyl_400_40.txt'
+function ='name=cylinder,radius=35.0,length=350.0,background=0.0,scale=1.0,sld=4.0,sld_solvent=1.0'
+parameter_ranges = 'radius.range(1,50);length.range(1,500)'
+description = ''

example_scripts/SasView_example.py

@@ -0,0 +1,87 @@
+from sasmodels.core import load_model
+from sasmodels.bumps_model import Model, Experiment
+from sasmodels.data import load_data, empty_data1D, Data1D
+
+from sasmodels.models.broad_peak import Iq
+
+from bumps.names import *
+from bumps.fitters import fit
+from bumps.formatnum import format_uncertainty
+
+import matplotlib.pyplot as plt
+
+import os
+
+current_path = os.path.realpath(__file__)
+dir_path = os.path.dirname(current_path)
+main_dir = os.path.dirname(dir_path)
+oneD_data_dir = os.path.join(main_dir, 'benchmark_problems', '1D_data', 'data_files', 'cyl_400_20.txt')
+
+test_data = load_data(oneD_data_dir)
+test_data.dy = 0.2*test_data.y
+
+# print(type(test_data))
+
+data_1D = Data1D(x=test_data.x, y=test_data.y, dy=test_data.dy)
+
+print(type(data_1D.x))
+
+kernel = load_model('cylinder')
+
+# model_test = load_model('sphere')
+
+# kernel = load_model('broad_peak')
+# print(type(test_load))
+#We set some errors for demonstration
+
+# x_data = empty_data1D(test_data.x)
+# print(x_data.x)
+# print(type(x_data))
+# print(test_data.qmin)
+# print(test_data.y)
+# print(type(data_1D))
+
+pars = dict(radius=35,
+            length=350,
+            background=0.0,
+            scale=1.0,
+            sld=4.0,
+            sld_solvent=1.0)
+
+model = Model(kernel, **pars)
+# print(model.parameters())
+# model = Model(kernel)
+
+# SET THE FITTING PARAMETERS
+model.radius.range(1, 50)
+model.length.range(1, 500)
+
+# M = Experiment(data=data_1D, model=model)
+M = Experiment(data=test_data, model=model)
+
+param_initial = M.parameters()
+radius_initial = param_initial['radius']
+
+problem = FitProblem(M)
+
+print("Initial chisq", problem.chisq_str())
+# problem.plot()
+# problem.summarize()
+# pylab.show()
+# plt.show()
+result = fit(problem, method='dream')
+
+# print(M.theory())
+#
+# print(test_data.y)
+
+print("Final chisq", problem.chisq_str())
+for k, v, dv in zip(problem.labels(), result.x, result.dx):
+    print(k, ":", format_uncertainty(v, dv))
+
+
+# problem.plot()
+# print(model.state())
+# print(problem.y)
+# plt.show()
+# print((M.parameters()))

example_scripts/example_runScripts.py

@@ -51,6 +51,7 @@
 benchmark_probs_dir = os.path.join(fitbenchmarking_folder,
                                    'benchmark_problems')
 
+
 """
 Modify results_dir to specify where the results of the fit should be saved
 If left as None, they will be saved in a "results" folder in the working dir
@@ -75,7 +76,7 @@
 # Do this, in this example file, by selecting sub-folders in benchmark_probs_dir
 # "Muon_data" works for mantid minimizers
 # problem_sets = ["Neutron_data", "NIST/average_difficulty"]
-# problem_sets = ["CUTEst", "Muon_data", "Neutron_data", "NIST/average_difficulty", "NIST/high_difficulty", "NIST/low_difficulty"]
+# problem_sets = ["CUTEst", "Muon", "Neutron", "NIST/average_difficulty", "NIST/high_difficulty", "NIST/low_difficulty"]
 
 problem_sets = ["NIST/low_difficulty"]
 

example_scripts/example_runScripts_SasView.py

@@ -0,0 +1,126 @@
+
+
+from __future__ import (absolute_import, division, print_function)
+import os
+import sys
+
+# Avoid reaching the maximum recursion depth by setting recursion limit
+# This is useful when running multiple data set benchmarking
+# Otherwise recursion limit is reached and the interpreter throws an error
+sys.setrecursionlimit(10000)
+
+# Insert path to where the scripts are located, relative to
+# the example_scripts folder
+current_path = os.path.dirname(os.path.realpath(__file__))
+fitbenchmarking_folder = os.path.abspath(os.path.join(current_path, os.pardir))
+scripts_folder = os.path.join(fitbenchmarking_folder, 'fitbenchmarking')
+sys.path.insert(0, scripts_folder)
+sys.path.insert(1, fitbenchmarking_folder)
+
+try:
+    import bumps
+except:
+    print('******************************************\n'
+          'Bumps is not yet installed on your computer\n'
+          'To install, type the following command:\n'
+          'python -m pip install bumps\n'
+          '******************************************')
+    sys.exit()
+
+try:
+    import sasmodels.data
+except:
+    print('******************************************\n'
+          'sasmodels is not yet installed on your computer\n'
+          'To install, type the following command:\n'
+          'python -m pip install sasmodels\n'
+          '******************************************')
+    sys.exit()
+
+try:
+    import sas
+except:
+    print('******************************************\n'
+          'sas is not yet installed on your computer\n'
+          'To install, clone a version of SasView from https://github.com/SasView/sasview\n'
+          'After that, copy a folder called "sas" inside the sub-folder sasview/src to the fitbenchmarking directory\n'
+          '******************************************')
+    sys.exit()
+
+from fitting_benchmarking import do_fitting_benchmark as fitBenchmarking
+from results_output import save_results_tables as printTables
+
+# SPECIFY THE SOFTWARE/PACKAGE CONTAINING THE MINIMIZERS YOU WANT TO BENCHMARK
+# software = 'mantid'
+software = 'sasview'
+software_options = {'software': software}
+
+# User defined minimizers
+custom_minimizers = {"mantid": ["BFGS", "Simplex"],
+              "scipy": ["lm", "trf", "dogbox"],
+              "sasview": ["amoeba"]}
+# custom_minimizers = None
+# "amoeba", "lm", "newton", "de", "pt", "mp"
+
+# SPECIFY THE MINIMIZERS YOU WANT TO BENCHMARK, AND AS A MINIMUM FOR THE SOFTWARE YOU SPECIFIED ABOVE
+if len(sys.argv) > 1:
+  # Read custom minimizer options from file
+  software_options['minimizer_options'] = current_path + sys.argv[1]
+elif custom_minimizers:
+  # Custom minimizer options:
+  software_options['minimizer_options'] = custom_minimizers
+else:
+  # Using default minimizers from
+  # fitbenchmarking/fitbenchmarking/minimizers_list_default.json
+  software_options['minimizer_options'] = None
+
+
+# Benchmark problem directories
+benchmark_probs_dir = os.path.join(fitbenchmarking_folder,
+                                   'benchmark_problems')
+
+"""
+Modify results_dir to specify where the results of the fit should be saved
+If left as None, they will be saved in a "results" folder in the working dir
+If the full path is not given results_dir is created relative to the working dir
+"""
+results_dir = None
+
+# Whether to use errors in the fitting process
+use_errors = True
+
+# Parameters of how the final tables are colored
+# e.g. lower that 1.1 -> light yellow, higher than 3 -> dark red
+# Change these values to suit your needs
+color_scale = [(1.1, 'ranking-top-1'),
+               (1.33, 'ranking-top-2'),
+               (1.75, 'ranking-med-3'),
+               (3, 'ranking-low-4'),
+               (float('nan'), 'ranking-low-5')]
+
+# ADD WHICH PROBLEM SETS TO TEST AGAINST HERE
+# Do this, in this example file, by selecting sub-folders in benchmark_probs_dir
+# "Muon_data" works for mantid minimizers
+# problem_sets = ["Neutron_data", "NIST/average_difficulty"]
+# problem_sets = ["CUTEst", "Muon", "Neutron", "NIST/average_difficulty", "NIST/high_difficulty", "NIST/low_difficulty"]
+problem_sets = ["SAS_modelling/1D"]
+for sub_dir in problem_sets:
+  # generate group label/name used for problem set
+  label = sub_dir.replace('/', '_')
+
+  # Problem data directory
+  data_dir = os.path.join(benchmark_probs_dir, sub_dir)
+
+  print('\nRunning the benchmarking on the {} problem set\n'.format(label))
+  results_per_group, results_dir = fitBenchmarking(group_name=label, software_options=software_options,
+                                                   data_dir=data_dir,
+                                                   use_errors=use_errors, results_dir=results_dir)
+
+  print('\nProducing output for the {} problem set\n'.format(label))
+  for idx, group_results in enumerate(results_per_group):
+    # Display the runtime and accuracy results in a table
+    printTables(software_options, group_results,
+                group_name=label, use_errors=use_errors,
+                color_scale=color_scale, results_dir=results_dir)
+
+  print('\nCompleted benchmarking for {} problem set\n'.format(sub_dir))

example_scripts/example_runScripts_expert.py

@@ -31,7 +31,7 @@
 from resproc import visual_pages
 
 # SPECIFY THE SOFTWARE/PACKAGE CONTAINING THE MINIMIZERS YOU WANT TO BENCHMARK
-software = ['mantid', 'scipy']
+software = ['scipy']
 software_options = {'software': software}
 
 # User defined minimizers
@@ -80,7 +80,7 @@
 # Do this, in this example file, by selecting sub-folders in benchmark_probs_dir
 # "Muon_data" works for mantid minimizers
 # problem_sets = ["Neutron_data", "NIST/average_difficulty"]
-problem_sets = ["Neutron_data"]
+problem_sets = ["CUTEst"]
 for sub_dir in problem_sets:
     # generate group group_name/name used for problem set
     group_name = sub_dir.replace('/', '_')