Change PyEmu's RNG to be mostly locally scoped

autotest/emulator_tests.py

@@ -24,7 +24,7 @@
 def generate_synth_data(num_realizations=100, num_observations=10):
 
     # generate synth data
-    data = np.random.normal(size=(num_realizations,num_observations))
+    data = pyemu.en.rng.normal(size=(num_realizations,num_observations))
     data = pd.DataFrame(data,columns=[f"obs{i}" for i in range(10)])
     # dummy observation data
     obsdata = pd.DataFrame(index=data.columns, columns=["obsnme","obsval","weight","obgnme"])
@@ -450,8 +450,8 @@ def test_autoencoder_basic():
     from pyemu.emulators.dsiae import AutoEncoder
 
     # Create simple synthetic data
-    np.random.seed(42)
-    X = np.random.randn(50, 10).astype(np.float32)  # 50 samples, 10 features
+    rng = np.random.RandomState(42)
+    X = rng.standard_normal((50, 10,)).astype(np.float32)  # 50 samples, 10 features
 
     # Test initialization
     ae = AutoEncoder(input_dim=10, latent_dim=3, hidden_dims=(8, 4))
@@ -477,8 +477,8 @@ def test_autoencoder_pandas_input():
     from pyemu.emulators.dsiae import AutoEncoder
 
     # Create pandas DataFrame
-    np.random.seed(42)
-    data = pd.DataFrame(np.random.randn(30, 8),
+    rng = np.random.RandomState(42)
+    data = pd.DataFrame(rng.standard_normal((30, 8,)),
                        columns=[f'feature_{i}' for i in range(8)],
                        index=[f'sample_{i}' for i in range(30)])
 
@@ -631,8 +631,8 @@ def test_autoencoder_basic():
     from pyemu.emulators.dsiae import AutoEncoder
 
     # Create simple synthetic data
-    np.random.seed(42)
-    X = np.random.randn(50, 10).astype(np.float32)  # 50 samples, 10 features
+    rng = np.random.RandomState(42)
+    X = rng.standard_normal((50, 10,)).astype(np.float32)  # 50 samples, 10 features
 
     # Test initialization
     ae = AutoEncoder(input_dim=10, latent_dim=3, hidden_dims=(8, 4))
@@ -658,8 +658,8 @@ def test_autoencoder_pandas_input():
     from pyemu.emulators.dsiae import AutoEncoder
 
     # Create pandas DataFrame
-    np.random.seed(42)
-    data = pd.DataFrame(np.random.randn(30, 8),
+    rng = np.random.RandomState(42)
+    data = pd.DataFrame(rng.standard_normal((30, 8,)),
                        columns=[f'feature_{i}' for i in range(8)],
                        index=[f'sample_{i}' for i in range(30)])
 
@@ -705,7 +705,7 @@ def test_dsiae_save_load(tmp_path):
     # 1. Generate synthetic data
     num_realizations = 50
     num_observations = 20
-    data = np.random.normal(size=(num_realizations, num_observations))
+    data = pyemu.en.rng.normal(size=(num_realizations, num_observations))
     data_df = pd.DataFrame(data, columns=[f"obs{i}" for i in range(num_observations)])
 
     # 2. Initialize and fit DSIAE
@@ -725,7 +725,7 @@ def test_dsiae_save_load(tmp_path):
     # The predict method takes pvals which are latent space values
 
     # Generate random latent vectors
-    new_pvals = np.random.normal(size=(5, latent_dim))
+    new_pvals = pyemu.en.rng.normal(size=(5, latent_dim))
     new_pvals_df = pd.DataFrame(new_pvals, columns=[f"latent_{i}" for i in range(latent_dim)])
 
     # Predict with original model
@@ -795,7 +795,7 @@ def test_gpr_basic(tmp_path):
     x = np.linspace(0.0, 10.0, 20)
     y = 2.0 * x + 1.0
     # Add small noise (very small so interpolation is almost exact)
-    # y += np.random.normal(0, 0.001, 20) 
+    # y += pyemu.en.rng.normal(0, 0.001, 20) 
 
     df = pd.DataFrame({'x': x, 'y': y})
 
@@ -994,10 +994,10 @@ def test_lpfa_synth(tmp_path):
     t = np.linspace(0, 10, 50)
     data = []
     n_real = 30
-    np.random.seed(42)
+    rng = np.random.RandomState(42)
     for i in range(n_real):
-        phase = np.random.uniform(0, 2*np.pi)
-        amp = np.random.uniform(0.8, 1.2)
+        phase = rng.uniform(0, 2*np.pi)
+        amp = rng.uniform(0.8, 1.2)
         # Inputs (history)
         hist = amp * np.sin(t[:10] + phase)
         # Outputs (forecast)

autotest/en_tests.py

@@ -665,7 +665,7 @@ def binary_test(tmp_path):
     obs_names = ["o{0}".format(i) for i in range(nobs)]
     pst = pyemu.Pst.from_par_obs_names(par_names,obs_names)
     # array to write (mimicing ensemble)
-    arr = np.random.random((nreal,npar))
+    arr = pyemu.en.rng.random((nreal,npar))
     df = pd.DataFrame(data=arr,columns=par_names,index=[str(i) for i in range(nreal)])
     pe = pyemu.ParameterEnsemble(pst=pst, df=df)
 
@@ -695,7 +695,7 @@ def binary_test(tmp_path):
     assert d.max().max() < 1.0e-10
 
     # big ensemble should default to dense .bin write
-    pe3 = pd.DataFrame(np.random.rand(10, int(2e6)))
+    pe3 = pd.DataFrame(pyemu.en.rng.random((10, int(2e6,))))
     pe3.columns = "parameter_number_" + pe3.columns.astype(str)
     pe3 = pe3.rename(index={9:'base'})
     pst = pyemu.Pst.from_par_obs_names(pe3.columns, obs_names)

autotest/full_meal_deal_tests_ignore.py

@@ -302,7 +302,7 @@ def freyberg_kl_pp_compare():
     hds_nz_obs = hds_obs.loc[hds_obs.ij.apply(lambda x: x in obs_locs.ij.values),"obsnme"]
     print(hds_nz_obs)
     obs.loc[hds_nz_obs,"weight"] = 1.0
-    obs.loc[hds_nz_obs,"obsval"] += np.random.normal(0.0,1.0,len(hds_nz_obs))
+    obs.loc[hds_nz_obs,"obsval"] += pyemu.en.rng.normal(0.0,1.0,len(hds_nz_obs))
     ph.pst.control_data.noptmax = 6
     ph.pst.parameter_data.loc[ph.pst.parameter_data.pargp!="pp_hk0","partrans"] = "fixed"
     ph.pst.write(os.path.join(new_model_ws,"pest_pp.pst"))

autotest/la_tests.py

@@ -15,7 +15,7 @@ def schur_test_nonpest():
     onames = ["o1","o2","o3","o4"]
     npar = len(pnames)
     nobs = len(onames)
-    j_arr = np.random.random((nobs,npar))
+    j_arr = pyemu.en.rng.random((nobs,npar))
     jco = Jco(x=j_arr,row_names=onames,col_names=pnames)
     parcov = Cov(x=np.eye(npar),names=pnames)
     obscov = Cov(x=np.eye(nobs),names=onames)
@@ -49,7 +49,7 @@ def schur_test_nonpest():
     print(s.get_added_obs_importance({"group1": ["o1", "o3"]},reset_zero_weight=0.0))
     print(s.get_removed_obs_importance({"group1":["o1","o3"]}))
 
-    forecasts = Matrix(x=np.random.random((1,npar)),row_names=[forecasts],col_names=pnames)
+    forecasts = Matrix(x=pyemu.en.rng.random((1,npar)),row_names=[forecasts],col_names=pnames)
 
     sc = Schur(jco=jco,forecasts=forecasts.T,parcov=parcov,obscov=obscov)
     ffile = os.path.join("temp","forecasts.jcb")
@@ -132,7 +132,7 @@ def errvar_test_nonpest():
     onames = ["o1","o2","o3","o4"]
     npar = len(pnames)
     nobs = len(onames)
-    j_arr = np.random.random((nobs,npar))
+    j_arr = pyemu.en.rng.random((nobs,npar))
     jco = Matrix(x=j_arr,row_names=onames,col_names=pnames)
     parcov = Cov(x=np.eye(npar),names=pnames)
     obscov = Cov(x=np.eye(nobs),names=onames)
@@ -325,7 +325,7 @@ def inf2():
     onames = inpst.obs_names
     npar = inpst.npar
     nobs = inpst.nobs
-    j_arr = np.random.random((nobs,npar))
+    j_arr = pyemu.en.rng.random((nobs,npar))
     parcov = mhand.Cov(x=np.eye(npar),names=pnames)
     obscov = mhand.Cov(x=np.eye(nobs),names=onames)
     jco = mhand.Jco.from_binary(inpst.filename.replace(".pst",".jcb"))

autotest/mat_tests.py

@@ -243,7 +243,7 @@ def cov_identity_test():
     import pyemu
     n = 100
     names = ["name_{0}".format(i) for i in range(n)]
-    arr = np.random.random(n*n)
+    arr = pyemu.en.rng.random(n*n)
     arr.resize((n,n))
     cov = pyemu.Cov(x=arr*arr.transpose(),names=names)
     cov *= 2.0
@@ -268,7 +268,7 @@ def hadamard_product_test(copy_mat_temp):
     assert hp.x.sum() == 0.0
 
     c = pyemu.Cov(x=np.ones((jco.shape[0],1)),names=jco.row_names,isdiagonal=True)
-    r = pyemu.Matrix(x=np.random.rand(c.shape[0],c.shape[0]),
+    r = pyemu.Matrix(x=pyemu.en.rng.random((c.shape[0],c.shape[0],)),
                      row_names=c.row_names,col_names=c.col_names)
     hp = c.hadamard_product(r)
     assert np.abs(hp.x.sum() - np.diagonal(r.x).sum()) < 1.0e-6
@@ -282,7 +282,7 @@ def get_diag_test():
     n = 100
     col_names = ["cname_{0}".format(i) for i in range(n)]
     row_names = ["rname_{0}".format(i) for i in range(n)]
-    arr = np.random.random(n*n)
+    arr = pyemu.en.rng.random(n*n)
     arr.resize((n,n))
     mat = pyemu.Matrix(x=arr,row_names=row_names,
                        col_names=col_names)
@@ -451,7 +451,7 @@ def coo_test(setup_empty_mat_temp):
     rnames = ["row_{0}".format(i) for i in range(nrow)]
     cnames = ["col_{0}".format(i) for i in range(ncol)]
 
-    x = np.random.random((nrow,ncol))
+    x = pyemu.en.rng.random((nrow,ncol))
 
     m = pyemu.Matrix(x=x,row_names=rnames, col_names=cnames)
     assert m.shape[0] == len(rnames)
@@ -511,7 +511,7 @@ def df_test():
     rnames = ["row_{0}".format(i) for i in range(nrow)]
     cnames = ["col_{0}".format(i) for i in range(ncol)]
 
-    x = np.random.random((nrow, ncol))
+    x = pyemu.en.rng.random((nrow, ncol))
 
     m = pyemu.Matrix(x=x, row_names=rnames, col_names=cnames)
 
@@ -552,7 +552,7 @@ def dense_mat_format_test(setup_empty_mat_temp):
     rnames = [long_str+"row_{0}".format(i) for i in range(nrow)]
     cnames = [long_str+"col_{0}".format(i) for i in range(ncol)]
 
-    arr = np.random.random((nrow,ncol))
+    arr = pyemu.en.rng.random((nrow,ncol))
     matfile = os.path.join(wd, "dense.bin")
     m = pyemu.Matrix(x=arr, row_names=rnames, col_names=cnames)
     f = m.to_dense(matfile, close=True)

autotest/mc_tests_ignore.py

@@ -92,7 +92,7 @@ def gaussian_draw_test():
     vals = mc.pst.parameter_data.parval1.values
     cov = Cov.from_parameter_data(mc.pst)
     start = datetime.now()
-    val_array = np.random.multivariate_normal(vals, cov.as_2d,num_reals)
+    val_array = pyemu.en.rng.multivariate_normal(vals, cov.as_2d,num_reals)
     print(datetime.now() - start)
 
     start = datetime.now()
@@ -125,7 +125,7 @@ def from_dataframe_test():
     pst = jco.replace(".jcb",".pst")
     mc = MonteCarlo(jco=jco,pst=pst)
     names = ["par_{0}".format(_) for _ in range(10)]
-    df = pd.DataFrame(np.random.random((10,mc.pst.npar)),columns=mc.pst.par_names)
+    df = pd.DataFrame(pyemu.en.rng.random((10,mc.pst.npar)),columns=mc.pst.par_names)
     mc.parensemble = ParameterEnsemble.from_dataframe(df=df,pst=mc.pst)
     print(mc.parensemble.shape)
     mc.project_parensemble()
@@ -193,7 +193,7 @@ def ensemble_seed_test():
 
     pe1.reseed()
     pe1.draw(cov,num_reals=10)
-    #np.random.seed(1111)
+    #pyemu.en.rng = np.random.RandomState(1111)
     pe2.reseed()
     pe2.draw(cov,num_reals=10)
     assert (pe1-pe2).apply(np.abs).as_matrix().max() == 0.0
@@ -383,7 +383,7 @@ def par_diagonal_draw_test():
     vals = mc.pst.parameter_data.parval1.values
     cov = Cov.from_parameter_data(mc.pst)
     start = datetime.now()
-    val_array = np.random.multivariate_normal(vals, cov.as_2d,num_reals)
+    val_array = pyemu.en.rng.multivariate_normal(vals, cov.as_2d,num_reals)
     print(datetime.now() - start)
 
     start = datetime.now()
@@ -463,8 +463,8 @@ def homegrown_draw_test():
 
     pst.parameter_data.loc[:,"partrans"] = "none"
     par = pst.parameter_data
-    par.loc[:,"x"] = np.random.random(npar) * 10.0
-    par.loc[:, "y"] = np.random.random(npar) * 10.0
+    par.loc[:,"x"] = pyemu.en.rng.random(npar) * 10.0
+    par.loc[:, "y"] = pyemu.en.rng.random(npar) * 10.0
 
     par.loc[pst.par_names[0], "pargp"] = "zero"
     par.loc[pst.par_names[1:10],"pargp"] = "one"
@@ -525,8 +525,8 @@ def ensemble_covariance_test():
 
     pst.parameter_data.loc[:, "partrans"] = "none"
     par = pst.parameter_data
-    par.loc[:, "x"] = np.random.random(npar) * 10.0
-    par.loc[:, "y"] = np.random.random(npar) * 10.0
+    par.loc[:, "x"] = pyemu.en.rng.random(npar) * 10.0
+    par.loc[:, "y"] = pyemu.en.rng.random(npar) * 10.0
 
     cov = gs.covariance_matrix(par.x, par.y, par.parnme)
     num_reals = 100000
@@ -612,8 +612,8 @@ def to_from_binary_test():
 
     pst.parameter_data.loc[:, "partrans"] = "none"
     par = pst.parameter_data
-    par.loc[:, "x"] = np.random.random(npar) * 10.0
-    par.loc[:, "y"] = np.random.random(npar) * 10.0
+    par.loc[:, "x"] = pyemu.en.rng.random(npar) * 10.0
+    par.loc[:, "y"] = pyemu.en.rng.random(npar) * 10.0
 
     cov = gs.covariance_matrix(par.x, par.y, par.parnme)
     num_reals = 1000
@@ -687,8 +687,8 @@ def sparse_draw_test():
 
     pst.parameter_data.loc[:, "partrans"] = "none"
     par = pst.parameter_data
-    par.loc[:, "x"] = np.random.random(npar) * 10.0
-    par.loc[:, "y"] = np.random.random(npar) * 10.0
+    par.loc[:, "x"] = pyemu.en.rng.random(npar) * 10.0
+    par.loc[:, "y"] = pyemu.en.rng.random(npar) * 10.0
 
     par.loc[pst.par_names[0], "pargp"] = "zero"
     par.loc[pst.par_names[1:10], "pargp"] = "one"

autotest/metrics_tests.py

@@ -5,10 +5,10 @@ def res_and_ens_test():
     import pyemu
 
     # make some fake residuals
-    np.random.seed(42)
+    rng = np.random.RandomState(42)
     t = np.linspace(1,20, 200)
     obs = t/10 * np.sin(np.pi*t)
-    mod = obs+np.random.randn(200)*.5    
+    mod = obs+rng.standard_normal((200,))*.5    
     obsnames = ['ob_t_{:03d}'.format(i) for i in range(len(t))]
     obsgroups = ['start_grp' if i<80 else 'end_grp' for i in range(len(t))]
     res = pd.DataFrame({'name':obsnames,
@@ -18,11 +18,11 @@ def res_and_ens_test():
                         'residual':obs-mod,
                         'weight':np.ones(len(t))})
     res.set_index(res['name'], inplace=True)
-    np.random.seed(98)
-    res.weight =  [float(i>.5) for i in np.random.random(200)]
+    rng = np.random.RandomState(98)
+    res.weight =  [float(i>.5) for i in rng.random(200)]
 
     # and an ensemble version
-    ens = pd.DataFrame(np.tile(obs,(10,1))+np.random.randn(10,200)*.5, columns=obsnames)
+    ens = pd.DataFrame(np.tile(obs,(10,1))+rng.standard_normal((10,200,))*.5, columns=obsnames)
     ens.loc['base'] = mod
 
     # cook up a PEST file for obs and weights