Fix typos (#562)

autotest/la_tests.py

@@ -477,7 +477,7 @@ def ends_freyberg_dev():
     ax.set_xlabel("new obs group")
     ax.set_title("ensemble variance analysis for three Freyberg predictions",loc="left")
     plt.tight_layout()
-    plt.savefig("precent.pdf")
+    plt.savefig("percent.pdf")
     plt.close("all")
 
 

autotest/pst_from_tests.py

@@ -299,7 +299,7 @@ def freyberg_test(tmp_path):
     sfodf_c.columns = sfodf_c.columns.str.lower()
     assert (sfrobs_p == sfodf_c.loc[sfrobs_p.index,
                                     sfrobs_p.columns]).all().all(), (
-        "Mis-match between expected and processed obs values\n",
+        "Mismatch between expected and processed obs values\n",
         sfrobs_p.head(),
         sfodf_c.loc[sfrobs_p.index, sfrobs_p.columns].head())
 
@@ -338,7 +338,7 @@ def freyberg_test(tmp_path):
     sfodf_c.columns = sfodf_c.columns.str.lower()
     assert (sfrobs_p == sfodf_c.loc[sfrobs_p.index,
                                     sfrobs_p.columns]).all().all(), (
-        "Mis-match between expected and processed obs values")
+        "Mismatch between expected and processed obs values")
     obsnmes = pd.concat([df.obgnme for df in pf.obs_dfs]).unique()
     assert all([gp in obsnmes for gp in ['qaquifer', 'qout']])
     pf.post_py_cmds.append(
@@ -758,7 +758,7 @@ def mf6_freyberg_test(tmp_path):
     # add the function call to make generic to the forward run script
     pf.add_py_function(__file__, "generic_function()", is_pre_cmd=False)
 
-    # add a function that isnt going to be called directly
+    # add a function that isn't going to be called directly
     pf.add_py_function(__file__, "another_generic_function(some_arg)",
                        is_pre_cmd=None)
 
@@ -2913,7 +2913,7 @@ def test_add_array_parameters(self):
             os.chdir(self.dest_ws)
             # first delete the model file in the template ws
             model_file.unlink()
-            # manually apply a multipler
+            # manually apply a multiplier
             mult = 4
             mult_values = np.loadtxt(mult_file)
             mult_values[:] = mult
@@ -2991,7 +2991,7 @@ def test_add_list_parameters(self):
             os.chdir(self.dest_ws)
             # first delete the model file in the template ws
             model_file.unlink()
-            # manually apply a multipler
+            # manually apply a multiplier
             mult = 4
             mult_df = pd.read_csv(mult_file)
             # no idea why '3' is the column with multipliers and 'parval1_3' isn't
@@ -3079,7 +3079,7 @@ def test_add_array_parameters_pps_grid(self):
                     # first delete the model file in the template ws
                     model_file = df['model_file'].values[mult2model_row]
                     os.remove(model_file)
-                    # manually apply a multipler
+                    # manually apply a multiplier
                     mult = 4
                     if par_type != "pilotpoints":
                         mult_values = np.loadtxt(mult_file)
@@ -3194,7 +3194,7 @@ def test_add_array_parameters_to_file_list(self):
         for file in array_file_input:
             shutil.copy(self.array_file, Path(self.dest_ws, file))
 
-        # single 2D zone array applied to each file in filesnames
+        # single 2D zone array applied to each file in filenames
         self.pf.add_parameters(filenames=array_file_input, par_type='zone',
                                zone_array=self.zone_array,
                                par_name_base=tag,  # basename for parameters that are set up
@@ -3211,7 +3211,7 @@ def test_add_array_parameters_to_file_list(self):
         # first delete the model file in the template ws
         for model_file in df['model_file']:
             os.remove(model_file)
-        # manually apply a multipler
+        # manually apply a multiplier
         mult = 4
         mult_values = np.loadtxt(mult_file)
         mult_values[:] = mult
@@ -3761,7 +3761,7 @@ def usg_freyberg_test(tmp_path):
     for layer in layers:
         df_lay = df.loc[df.layer==layer,:].copy()
         df_lay.sort_values(by="node")
-        #substract off the min node number so that each layers node dict starts at zero
+        #subtract off the min node number so that each layers node dict starts at zero
         df_lay.loc[:,"node"] = df_lay.node - df_lay.node.min()
         print(df_lay)
         srd = {n:xy for n,xy in zip(df_lay.node.values,df_lay.xy.values)}
@@ -4078,7 +4078,7 @@ def mf6_subdir_test(tmp_path):
     # add the function call to make generic to the forward run script
     pf.add_py_function(__file__, f"generic_function('{sd}')",is_pre_cmd=False)
 
-    # add a function that isnt going to be called directly
+    # add a function that isn't going to be called directly
     pf.add_py_function(__file__, "another_generic_function(some_arg)",is_pre_cmd=None)
 
     # pf.post_py_cmds.append("generic_function()")
@@ -5218,7 +5218,7 @@ def plot_thresh(m_d):
 
 def test_array_fmt(tmp_path):
     from pyemu.utils.pst_from import _load_array_get_fmt
-    # psuedo ff option
+    # pseudo ff option
     with open(Path(tmp_path, "test.dat"), 'w') as fp:
         fp.write("       3.000      3.0000      03.000\n"
                  "         3.0      3.0000      03.000")
@@ -5239,14 +5239,14 @@ def test_array_fmt(tmp_path):
     arr, fmt = _load_array_get_fmt(Path(tmp_path, "test.dat"))
     assert fmt == ''.join([" %11.8F"] * 3)
     assert arr.sum(axis=1).sum() == 18
-    # tru space delim option -- sep passed
+    # true space delim option -- sep passed
     with open(Path(tmp_path, "test.dat"), 'w') as fp:
         fp.write("3.000 3.00000 03.000\n"
                  "3.0 3.0000 03.000")
     arr, fmt = _load_array_get_fmt(Path(tmp_path, "test.dat"), sep=' ')
     assert fmt == "%7.5F"
     assert arr.sum(axis=1).sum() == 18
-    # tru space delim option with sep None
+    # true space delim option with sep None
     with open(Path(tmp_path, "test.dat"), 'w') as fp:
         fp.write("3.000 3.00000 03.000\n"
                  "3.0 3.0000 03.000")

autotest/pst_tests.py

@@ -272,7 +272,7 @@ def derivative_increment_test():
     import pyemu
 
     pst = pyemu.Pst(os.path.join("pst", "inctest.pst"))
-    pst.calculate_pertubations()
+    pst.calculate_perturbations()
 
 
 def pestpp_args_test(tmp_path):

autotest/smoother/10par_xsec/_master_stdout.dat

@@ -1,6 +1,6 @@
 
 
-             pestpp-swp - a parameteric sweep utility
+             pestpp-swp - a parametric sweep utility
                      for PEST(++) datasets 
 
                  by the PEST++ development team

autotest/utils_tests.py

@@ -2136,7 +2136,7 @@ def maha_pdc_summary_test(tmp_path):  # todo add back in? currently super slowww
     import pyemu
     Path(tmp_path).mkdir(exist_ok=True)
     l1_critical_value = 6.4 #chi squared value at df=1,p=0.01
-    l2_critical_value = 9.2 #chi sqaured value at df=2,p=0.01
+    l2_critical_value = 9.2 #chi squared value at df=2,p=0.01
     pst_file = os.path.join("la", "pest.pst")
     shutil.copy(pst_file, tmp_path)
     pst = pyemu.Pst(os.path.join(tmp_path, "pest.pst"))
@@ -2730,14 +2730,14 @@ def pypestworker_test():
     num_workers=5
 
     # looper over and start the workers - in this
-    # case they dont need unique dirs since they arent writing
+    # case they dont need unique dirs since they aren't writing
     # anything
     procs = []
     for i in range(num_workers):
         pp = mp.Process(target=ppw_worker,args=(i,case,t_d,host,port,frun))
         pp.start()
         procs.append(pp)
-    # if everyhing worked, the the workers should recieve the 
+    # if everything worked, the the workers should receive the 
     # shutdown signal from the master and exit gracefully...
     for pp in procs:
         pp.join()
@@ -3221,7 +3221,7 @@ def gpr_zdt1_test():
     #return
 
     # looper over and start the workers - in this
-    # case they dont need unique dirs since they arent writing
+    # case they dont need unique dirs since they aren't writing
     # anything
     procs = []
     # try this test with 1 worker as an edge case
@@ -3230,7 +3230,7 @@ def gpr_zdt1_test():
         pp = mp.Process(target=gpr_zdt1_ppw)
         pp.start()
         procs.append(pp)
-    # if everyhing worked, the the workers should recieve the 
+    # if everything worked, the the workers should receive the 
     # shutdown signal from the master and exit gracefully...
     for pp in procs:
         pp.join()

examples/MatrixCovariance_demo.ipynb

@@ -113,7 +113,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Convience methods of `Matrix`\n",
+    "# Convenience methods of `Matrix`\n",
     "\n",
     "several cool things are implemented in `Matrix` and accessed through `@property` decorated methods.  For example, the SVD components of a `Matrix` object are simply accessed by name.  The SVD routine is called on demand and the components are cast to `Matrix` objects, all opaque to the user:"
    ]

examples/Schurexample_freyberg.ipynb

@@ -39,7 +39,7 @@
     "## Model background\n",
     "This example is based on the synthetic classroom model of Freyberg(1988).  The  model is a 2-dimensional MODFLOW model with 1 layer,  40 rows, and 20 columns.  The model has 2 stress periods: an initial steady-state stress period used for calibration, and a 5-year transient stress period.  The calibration period uses the recharge and well flux of Freyberg(1988); the last stress period use 25% less recharge and 25% more pumping to represent future conditions for a forecast period.\n",
     "\n",
-    "The inverse problem has 761 parameters: hydraulic conductivity of each active model cell, calibration and forecast period recharge multipliers, storage and specific yield, calibration and forecast well flux for each of the six wells, and river bed conductance for each 40 cells with river-type boundary conditions.  The inverse problem has 12 head obseravtions, measured at the end of the steady-state calibration period.  The forecasts of interest include the sw-gw exchange flux during both stress periods (observations named ```sw_gw_0``` and ``sw_gw_1``), and the water level in well cell 6 located in at row 28 column 5 at the end of the stress periods (observations named ```or28c05_0``` and ```or28c05_1```).  The forecasts are included in the Jacobian matrix as zero-weight observations. The model files, pest control file and previously-calculated jacobian matrix are in the `freyberg/` folder\n",
+    "The inverse problem has 761 parameters: hydraulic conductivity of each active model cell, calibration and forecast period recharge multipliers, storage and specific yield, calibration and forecast well flux for each of the six wells, and river bed conductance for each 40 cells with river-type boundary conditions.  The inverse problem has 12 head observations, measured at the end of the steady-state calibration period.  The forecasts of interest include the sw-gw exchange flux during both stress periods (observations named ```sw_gw_0``` and ``sw_gw_1``), and the water level in well cell 6 located in at row 28 column 5 at the end of the stress periods (observations named ```or28c05_0``` and ```or28c05_1```).  The forecasts are included in the Jacobian matrix as zero-weight observations. The model files, pest control file and previously-calculated jacobian matrix are in the `freyberg/` folder\n",
     "\n",
     "\n",
     "Freyberg, David L. \"AN EXERCISE IN GROUND‐WATER MODEL CALIBRATION AND PREDICTION.\" Groundwater 26.3 (1988): 350-360."
@@ -65,7 +65,7 @@
    "outputs": [],
    "source": [
     "# Because this model is old -- it predates flopy's modelgrid implementation. \n",
-    "# And because modelgrid has been implemented without backward compatability \n",
+    "# And because modelgrid has been implemented without backward compatibility \n",
     "# the modelgrid object is not constructed properly. \n",
     "# - We will use some sneaky pyemu to get things to how they should be \n",
     "import pyemu\n",
@@ -166,7 +166,7 @@
    "source": [
     "# just set the path and filename for the jco file\n",
     "jco = os.path.join(\"Freyberg\",\"freyberg.jcb\") \n",
-    "# use the jco name with extention \"pst\" for the control file\n",
+    "# use the jco name with extension \"pst\" for the control file\n",
     "pst = pyemu.Pst(jco.replace(\".jcb\",\".pst\"))     \n",
     "# get the list of forecast names from the pest++ argument\n",
     "la = pyemu.Schur(jco=jco, pst=pst, verbose=\"schur_example.log\")\n",

examples/Schurexample_henry.ipynb

@@ -27,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The inverse problem has 603 parameters: 600 hydraulic conductivity pilot points, 1 global hydraulic conductivity, 1 specified flux multiplier for history matching and 1 specified flux multiplier for forecast conditions.  The inverse problem has 36 obseravtions (21 heads and 15 concentrations) measured at the end of the steady-state calibration period.  The forecasts of interest of the distance from the left model edge to the 10% seawater concentration in the basal model layer and the concentration at location 10.  Both of there forecasts are \"measured\" at the end of the forecast stress period.  The forecasts are both in the Jacobian matrix as zero-weight observations named `pd_ten` and `C_obs10_2`.I previously calculated the jacobian matrix, which is in the `henry/` folder, along with the PEST control file.\n",
+    "The inverse problem has 603 parameters: 600 hydraulic conductivity pilot points, 1 global hydraulic conductivity, 1 specified flux multiplier for history matching and 1 specified flux multiplier for forecast conditions.  The inverse problem has 36 observations (21 heads and 15 concentrations) measured at the end of the steady-state calibration period.  The forecasts of interest of the distance from the left model edge to the 10% seawater concentration in the basal model layer and the concentration at location 10.  Both of there forecasts are \"measured\" at the end of the forecast stress period.  The forecasts are both in the Jacobian matrix as zero-weight observations named `pd_ten` and `C_obs10_2`.I previously calculated the jacobian matrix, which is in the `henry/` folder, along with the PEST control file.\n",
     "\n"
    ]
   },
@@ -222,7 +222,7 @@
     "## parameter contribution to forecast uncertainty\n",
     "\n",
     "\n",
-    "Lets look at which parameters are contributing most to forecast uncertainty, which we estimate as the decrese in forecast uncertainty from \"perfect\" knowledge of one or more parameters.  for demostration purposes, lets group the hydraulic conductivity parameters by row."
+    "Lets look at which parameters are contributing most to forecast uncertainty, which we estimate as the decrease in forecast uncertainty from \"perfect\" knowledge of one or more parameters.  for demonstration purposes, lets group the hydraulic conductivity parameters by row."
    ]
   },
   {