testing if CI fails

hippylib · Mar 16, 2024 · f80fc13 · f80fc13
1 parent 94ab33c
commit f80fc13
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Showing 4 changed files with 22 additions and 56 deletions.
diff --git a/example/poisson_example.py b/example/poisson_example.py
@@ -7,8 +7,6 @@
 import sys
 import os
 import dolfinx.fem.petsc
-import pickle
-import time
 
 from matplotlib import pyplot as plt
 
@@ -108,9 +106,6 @@ def run_inversion(nx : int, ny : int, noise_variance : float, prior_param : dict
     hpx.parRandom.normal(1.,noise)
     prior.sample(noise,m0)
 
-    # hpx.modelVerify(comm,model,m0,is_quadratic=False,misfit_only=True,verbose=(rank == 0))
-    # hpx.modelVerify(comm,model,m0,is_quadratic=False,misfit_only=False,verbose=(rank == 0))
-
 
     eps, err_grad, err_H,rel_symm_error = hpx.modelVerify(comm,model,m0,is_quadratic=False,misfit_only=True,verbose=(rank == 0))
 
@@ -120,12 +115,7 @@ def run_inversion(nx : int, ny : int, noise_variance : float, prior_param : dict
     eps, err_grad, err_H,rel_symm_error = hpx.modelVerify(comm,model,m0,is_quadratic=False,misfit_only=False,verbose=(rank == 0))
 
     data_misfit_False = {"eps":eps,"err_grad":err_grad, "err_H": err_H, "sym_Hessian_value":rel_symm_error}
-
-    # if(rank == 0):
-    #     print(err_grad,'\n')    
-    #     print(err_H)
-        # plt.show()  
-
+
     # # #######################################
 
     prior_mean_copy = prior.generate_parameter(0)

diff --git a/example/sfsi_toy_gaussian.py b/example/sfsi_toy_gaussian.py
@@ -8,10 +8,6 @@
 import os
 import dolfinx.fem.petsc
 
-# for validation purposes only - to write out modelVerify results
-import pickle
-
-
 from matplotlib import pyplot as plt
 
 sys.path.append( os.environ.get('HIPPYLIBX_BASE_DIR', "../") )
@@ -129,41 +125,12 @@ def run_inversion(nx : int, ny : int, noise_variance : float, prior_param : dict
 
     eps, err_grad, err_H,rel_symm_error = hpx.modelVerify(comm,model,m0,is_quadratic=False,misfit_only=True,verbose=(rank == 0))
 
-    # if(rank == 0):
     data_misfit_True = {"eps":eps,"err_grad":err_grad, "err_H": err_H, "sym_Hessian_value":rel_symm_error}
 
-        # if(comm.size == 1):        
-        #     os.makedirs('../hippylibX/test',exist_ok=True)
-        #     with open('../hippylibX/test/outputs_qpact_1_proc_misfit_True.pickle','wb') as f:
-                # pickle.dump(data,f)
-
-        # if(comm.size == 4):        
-        #     os.makedirs('../hippylibX/test',exist_ok=True)
-        #     with open('../hippylibX/test/outputs_qpact_4_proc_misfit_True.pickle','wb') as f:
-        #         pickle.dump(data,f)
-
-
     eps, err_grad, err_H,rel_symm_error = hpx.modelVerify(comm,model,m0,is_quadratic=False,misfit_only=False,verbose=(rank == 0))
 
-    # if(rank == 0):
     data_misfit_False = {"eps":eps,"err_grad":err_grad, "err_H": err_H, "sym_Hessian_value":rel_symm_error}
-
-        # if(comm.size == 1):        
-        #     os.makedirs('../hippylibX/test',exist_ok=True)
-        #     with open('../hippylibX/test/outputs_qpact_1_proc_misfit_False.pickle','wb') as f:
-        #         pickle.dump(data,f)
-
-        # if(comm.size == 4):        
-        #     os.makedirs('../hippylibX/test',exist_ok=True)
-        #     with open('../hippylibX/test/outputs_qpact_4_proc_misfit_False.pickle','wb') as f:
-        #         pickle.dump(data,f)
-
-
-    # if(rank == 0):
-    #     print(err_grad,'\n')
-    #     print(err_H)
-        # plt.show()  
-
+
     # #######################################
 
     prior_mean_copy = prior.generate_parameter(0)

diff --git a/hippylibX/modeling/modelVerify.py b/hippylibX/modeling/modelVerify.py
@@ -94,12 +94,9 @@ def modelVerify(comm : mpi4py.MPI.Intracomm, model, m0 : dlx.la.Vector, is_quadr
         err.destroy()
 
     if verbose:
-        modelVerifyPlotErrors(comm, misfit_only,is_quadratic, eps, err_grad, err_H)
+        modelVerifyPlotErrors(is_quadratic, eps, err_grad, err_H)
 
-        #comm and misfit_only are being passed for plotting purposes only-
-        #the title of the plot for saved figures.
-        #has to be removed for the final version.
-
+
     temp_petsc_vec_grad_x.destroy()
 
     xx = model.generate_vector(PARAMETER)
@@ -128,7 +125,7 @@ def modelVerify(comm : mpi4py.MPI.Intracomm, model, m0 : dlx.la.Vector, is_quadr
     return eps, err_grad, err_H, rel_symm_error
 
 
-def modelVerifyPlotErrors(comm, misfit_only, is_quadratic : bool, eps : np.ndarray, err_grad : np.ndarray, err_H : np.ndarray) -> None:
+def modelVerifyPlotErrors(is_quadratic : bool, eps : np.ndarray, err_grad : np.ndarray, err_H : np.ndarray) -> None:
     try:
         import matplotlib.pyplot as plt
     except:
@@ -142,7 +139,6 @@ def modelVerifyPlotErrors(comm, misfit_only, is_quadratic : bool, eps : np.ndarr
         plt.subplot(122)
         plt.loglog(eps[0], err_H[0], "-ob", [10*eps[0], eps[0], 0.1*eps[0]], [err_H[0],err_H[0],err_H[0]], "-.k")
         plt.title("FD Hessian Check")
-        # plt.savefig(f"result_with_misfit_{misfit_only}_using_{comm.size}_procs.png")
     else:  
         plt.figure()
         plt.subplot(121)
@@ -151,4 +147,3 @@ def modelVerifyPlotErrors(comm, misfit_only, is_quadratic : bool, eps : np.ndarr
         plt.subplot(122)
         plt.loglog(eps, err_H, "-ob", eps, eps*(err_H[0]/eps[0]), "-.k")
         plt.title("FD Hessian Check")
-        # plt.savefig(f"result_with_misfit_{misfit_only}_using_{comm.size}_procs.png")
diff --git a/hippylibX/test/testing_suite_file.py b/hippylibX/test/testing_suite_file.py
@@ -13,21 +13,35 @@
 
 from example import poisson_example, sfsi_toy_gaussian
 
+# def data_parser(data):
+#         eps = data["eps"]
+#         err_grad = data['err_grad']
+#         err_H = data['err_H']
+#         sym_Hessian_value = data['sym_Hessian_value']
+
+#         slope_grad_coeffs = np.polyfit(np.log(eps[20:30]), np.log(err_grad[20:30]), 1)
+#         slope_grad = slope_grad_coeffs[0]
+
+#         slope_H_coeffs = np.polyfit(np.log(eps[20:30]), np.log(err_H[20:30]), 1)
+#         slope_H = slope_H_coeffs[0]
+
+#         return sym_Hessian_value, slope_grad, slope_H
+
+
 def data_parser(data):
         eps = data["eps"]
         err_grad = data['err_grad']
         err_H = data['err_H']
         sym_Hessian_value = data['sym_Hessian_value']
 
-        slope_grad_coeffs = np.polyfit(np.log(eps[20:30]), np.log(err_grad[20:30]), 1)
+        slope_grad_coeffs = np.polyfit(np.log(eps[10:]), np.log(err_grad[10:]), 1)
         slope_grad = slope_grad_coeffs[0]
 
-        slope_H_coeffs = np.polyfit(np.log(eps[20:30]), np.log(err_H[20:30]), 1)
+        slope_H_coeffs = np.polyfit(np.log(eps[10:]), np.log(err_H[10:]), 1)
         slope_H = slope_H_coeffs[0]
 
         return sym_Hessian_value, slope_grad, slope_H
 
-
 class Test_runner:
     def __init__(self):
         self.result = unittest.TestResult()