Merge pull request #250 from choderalab/remove-pymbar-datasets

Remove abandoned pymbar-datasets code

pymbar/tests/test_covariance.py

@@ -22,12 +22,7 @@ def load_exponentials(n_states, n_samples):
     return name, u_kn, N_k_output, s_n
 
 def _test(data_generator):
-    try:
-        name, U, N_k, s_n = data_generator()
-    except IOError as exc:
-        raise(SkipTest("Cannot load dataset; skipping test.  Try downloading pymbar-datasets GitHub repository and setting PYMBAR-DATASETS environment variable.  Error was '%s'" % exc))
-    except ImportError as exc:
-        raise(SkipTest("Error importing pytables to load dataset; skipping test. Error was '%s'" % exc))
+    name, U, N_k, s_n = data_generator()
     print(name)
     mbar = pymbar.MBAR(U, N_k)
     fij1, dfij1, Theta_ij = mbar.getFreeEnergyDifferences(uncertainty_method="svd")
@@ -45,31 +40,20 @@ def _test(data_generator):
 
     eq(fij0, fij1, decimal=8)
     eq(dfij0, dfij1, decimal=8)
-    
+
     eq(fij0, fij2, decimal=8)
     eq(dfij0, dfij2, decimal=8)
-    
+
 
 
 def test_100x100_oscillators():
     data_generator = lambda : load_oscillators(100, 100)
     _test(data_generator)
-    
+
 def test_200x50_oscillators():
     data_generator = lambda : load_oscillators(200, 50)
     _test(data_generator)
-    
+
 def test_200x50_exponentials():
     data_generator = lambda : load_exponentials(200, 50)
-    _test(data_generator)    
-
-
-def test_gas():
-    raise(SkipTest("skip"))
-    data_generator = pymbar.testsystems.pymbar_datasets.load_gas_data
-    _test(data_generator)
-
-def test_8proteins():
-    raise(SkipTest("skip"))
-    data_generator = pymbar.testsystems.pymbar_datasets.load_8proteins_data
     _test(data_generator)

pymbar/tests/test_mbar_solvers.py

@@ -29,12 +29,7 @@ def load_exponentials(n_states, n_samples, provide_test=False):
     return returns
 
 def _test(data_generator):
-    try:
-        name, U, N_k, s_n = data_generator()
-    except IOError as exc:
-        raise(SkipTest("Cannot load dataset; skipping test.  Try downloading pymbar-datasets GitHub repository and setting PYMBAR-DATASETS environment variable.  Error was '%s'" % exc))
-    except ImportError as exc:
-        raise(SkipTest("Error importing pytables to load external dataset; skipping test. Error was '%s'" % exc))
+    name, U, N_k, s_n = data_generator()
     print(name)
     mbar = pymbar.MBAR(U, N_k)
     eq(pymbar.mbar_solvers.mbar_gradient(U, N_k, mbar.f_k), np.zeros(N_k.shape), decimal=8)
@@ -45,41 +40,28 @@ def _test(data_generator):
     # Test against old MBAR code.
     mbar0 = pymbar.old_mbar.MBAR(U, N_k)
     eq(mbar.f_k, mbar0.f_k, decimal=8)
-    eq(np.exp(mbar.Log_W_nk), np.exp(mbar0.Log_W_nk), decimal=5)    
+    eq(np.exp(mbar.Log_W_nk), np.exp(mbar0.Log_W_nk), decimal=5)
 
 
 def test_100x100_oscillators():
     data_generator = lambda : load_oscillators(100, 100)
     _test(data_generator)
-    
+
 def test_200x50_oscillators():
     data_generator = lambda : load_oscillators(200, 50)
     _test(data_generator)
-    
+
 def test_200x50_exponentials():
     data_generator = lambda : load_exponentials(200, 50)
-    _test(data_generator)    
-
-def test_gas():
-    data_generator = pymbar.testsystems.pymbar_datasets.load_gas_data
-    _test(data_generator)
-
-def test_8proteins():
-    data_generator = pymbar.testsystems.pymbar_datasets.load_8proteins_data
     _test(data_generator)
 
-def test_k69():
-    data_generator = pymbar.testsystems.pymbar_datasets.load_k69_data
-    _test(data_generator)
-
-
 def test_subsampling():
     name, u_kn, N_k, s_n = load_exponentials(5, 20000)
     mbar = pymbar.MBAR(u_kn, N_k)
     u_kn_sub, N_k_sub = pymbar.mbar_solvers.subsample_data(u_kn, N_k, s_n, 2)
     mbar_sub = pymbar.MBAR(u_kn_sub, N_k_sub)
     eq(mbar.f_k, mbar_sub.f_k, decimal=2)
-    
+
 def test_protocols():
     '''Test that free energy is moderatley equal to analytical solution, independent of solver protocols'''
     #Supress the warnings when jacobian and Hessian information is not used in a specific solver
@@ -92,13 +74,13 @@ def test_protocols():
     fa = fa[1:] - fa[0]
 
     #scipy.optimize.minimize methods, same ones that are checked for in mbar_solvers.py
-    subsampling_protocols = ["L-BFGS-B", "dogleg", "CG", "BFGS", "Newton-CG", "TNC", "trust-ncg", "SLSQP"] 
+    subsampling_protocols = ["L-BFGS-B", "dogleg", "CG", "BFGS", "Newton-CG", "TNC", "trust-ncg", "SLSQP"]
     solver_protocols = ['hybr', 'lm'] #scipy.optimize.root methods. Omitting methods which do not use the Jacobian
     for subsampling_protocol in subsampling_protocols:
         for solver_protocol in solver_protocols:
             #Solve MBAR with zeros for initial weights
             mbar = pymbar.MBAR(u_kn, N_k, subsampling_protocol=({'method':subsampling_protocol},), solver_protocol=({'method':solver_protocol},))
-            #Solve MBAR with the correct f_k used for the inital weights 
+            #Solve MBAR with the correct f_k used for the inital weights
             mbar = pymbar.MBAR(u_kn, N_k, initial_f_k=mbar.f_k, subsampling_protocol=({'method':subsampling_protocol},), solver_protocol=({'method':solver_protocol},))
             fe, fe_sigma, Theta_ij = mbar.getFreeEnergyDifferences()
             fe, fe_sigma = fe[0,1:], fe_sigma[0,1:]

pymbar/testsystems/__init__.py

@@ -4,4 +4,3 @@
 from pymbar.testsystems.exponential_distributions import ExponentialTestCase
 from pymbar.testsystems.timeseries import correlated_timeseries_example
 from pymbar.testsystems.gaussian_work import gaussian_work_example
-from pymbar.testsystems.pymbar_datasets import load_gas_data, load_8proteins_data