diff --git a/latest/_modules/compass/landice/tests/calving_dt_convergence/dt_convergence_test.html b/latest/_modules/compass/landice/tests/calving_dt_convergence/dt_convergence_test.html index 647b843cd0..1a1b131012 100644 --- a/latest/_modules/compass/landice/tests/calving_dt_convergence/dt_convergence_test.html +++ b/latest/_modules/compass/landice/tests/calving_dt_convergence/dt_convergence_test.html @@ -112,13 +112,14 @@

Source code for compass.landice.tests.calving_dt_convergence.dt_convergence_test

-from compass.testcase import TestCase
-from compass.landice.tests.calving_dt_convergence.run_model import RunModel
+import matplotlib.cm
+import matplotlib.pyplot as plt
+import netCDF4
 # from compass.validate import compare_variables  # not currently used
 import numpy
-import netCDF4
-import matplotlib.pyplot as plt
-import matplotlib.cm
+
+from compass.landice.tests.calving_dt_convergence.run_model import RunModel
+from compass.testcase import TestCase
 
 
 

@@ -146,7 +147,7 @@ 
Source code for compass.landice.tests.calving_dt_convergence.dt_convergence_
         test_group : compass.landice.tests.calving_dt_convergence.CalvingDtConvergence
             The test group that this test case belongs to
 
-        """
+        """  # noqa: E501
         self.name = f'calving_dt_convergence_test_{mesh}_{calving}_{velo}'
         subdir = f'{mesh}.{calving}.{velo}'
         super().__init__(test_group=test_group, name=self.name, subdir=subdir)
@@ -198,7 +199,8 @@ 
Source code for compass.landice.tests.calving_dt_convergence.dt_convergence_
         ax[3].set_ylabel('# warnings', color='c')
         ax2 = ax[3].twinx()
         ax2.set_ylabel('fraction with warnings', color='g')
-        colors = matplotlib.cm.jet(numpy.linspace(0, 1, len(self.fractions)))
+        jet = matplotlib.colormaps['jet']
+        colors = jet(numpy.linspace(0, 1, len(self.fractions)))
         nWarn = numpy.zeros([len(self.fractions)])
         nTimesteps = numpy.zeros([len(self.fractions)])
 
@@ -212,7 +214,7 @@ 
Source code for compass.landice.tests.calving_dt_convergence.dt_convergence_
             ax[0].plot(yr[1:], calv[1:], '-', label=f'{frac:.2f}',
                        color=colors[i])
 
-            ax[1].plot(yr[1:], (calv[1:]*deltat[1:]).cumsum(), '-',
+            ax[1].plot(yr[1:], (calv[1:] * deltat[1:]).cumsum(), '-',
                        color=colors[i])
 
             ratio = f.variables['dtCalvingCFLratio'][:]
@@ -228,7 +230,7 @@ 
Source code for compass.landice.tests.calving_dt_convergence.dt_convergence_
             nWarn[i] = logcontents.count("WARNING: Failed to ablate")
             nTimesteps[i] = logcontents.count("Starting timestep number")
             ax[3].plot(frac, nWarn[i], 'co')
-            ax2.plot(frac, nWarn[i]/nTimesteps[i], 'gx')
+            ax2.plot(frac, nWarn[i] / nTimesteps[i], 'gx')
 
             f.close()
             i += 1
diff --git a/latest/_modules/compass/landice/tests/eismint2/standard_experiments/visualize.html b/latest/_modules/compass/landice/tests/eismint2/standard_experiments/visualize.html
index 51a9c14c89..2284e0382b 100644
--- a/latest/_modules/compass/landice/tests/eismint2/standard_experiments/visualize.html
+++ b/latest/_modules/compass/landice/tests/eismint2/standard_experiments/visualize.html
@@ -114,8 +114,9 @@
              
   
Source code for compass.landice.tests.eismint2.standard_experiments.visualize
 import datetime
-import netCDF4
+
 import matplotlib.pyplot as plt
+import netCDF4
 import numpy as np
 from scipy.interpolate import griddata
 
@@ -138,7 +139,7 @@ 
Source code for compass.landice.tests.eismint2.standard_experiments.visualiz
         ----------
         test_case : compass.landice.tests.eismint2.standard_experiments.StandardExperiments
             The test case this step belongs to
-        """
+        """  # noqa: E501
         super().__init__(test_case=test_case, name='visualize')

 
 
@@ -196,8 +197,8 @@ 
Source code for compass.landice.tests.eismint2.standard_experiments.visualiz
 
     # open supplied MPAS output file and get variables needed
     filein = netCDF4.Dataset(filename, 'r')
-    xCell = filein.variables['xCell'][:]/1000.0
-    yCell = filein.variables['yCell'][:]/1000.0
+    xCell = filein.variables['xCell'][:] / 1000.0
+    yCell = filein.variables['yCell'][:] / 1000.0
     xtime = filein.variables['xtime'][:]
     nCells = len(filein.dimensions['nCells'])
     nVertLevels = len(filein.dimensions['nVertLevels'])
@@ -229,16 +230,17 @@ 
Source code for compass.landice.tests.eismint2.standard_experiments.visualiz
 
     # make an educated guess about how big the markers should be.
     if nCells**0.5 < 100.0:
-        markersize = max(int(round(3600.0/(nCells**0.5))), 1)
+        markersize = max(int(round(3600.0 / (nCells**0.5))), 1)
         # use hexes if the points are big enough, otherwise just dots
         markershape = 'h'
     else:
-        markersize = max(int(round(1800.0/(nCells**0.5))), 1)
+        markersize = max(int(round(1800.0 / (nCells**0.5))), 1)
         markershape = '.'
     logger.info('Using a markersize of {}'.format(markersize))
 
     fig = plt.figure(1, facecolor='w')
-    fig.suptitle('Payne et al. Fig. 1, 3, 6, 9, or 11', fontsize=10, fontweight='bold')
+    fig.suptitle('Payne et al. Fig. 1, 3, 6, 9, or 11', fontsize=10,
+                 fontweight='bold')
 
     iceIndices = np.where(thickness[timelev, :] > 10.0)[0]
     plt.scatter(xCell[iceIndices], yCell[iceIndices], markersize,
@@ -264,7 +266,8 @@ 
Source code for compass.landice.tests.eismint2.standard_experiments.visualiz
         plt.savefig('EISMINT2-{}-basaltemp.png'.format(experiment), dpi=150)
 
     # ================
-    # STEADY STATE MAPS -  panels b and c are switched and with incorrect units in the paper
+    # STEADY STATE MAPS -  panels b and c are switched and with incorrect
+    #                      units in the paper
     # ================
     fig = plt.figure(2, facecolor='w', figsize=(12, 6), dpi=72)
     fig.suptitle('Payne et al. Fig. 2 or 4', fontsize=10, fontweight='bold')
@@ -278,7 +281,7 @@ 
Source code for compass.landice.tests.eismint2.standard_experiments.visualiz
                 edgecolors='none')
 
     # add contours of ice thickness over the top
-    contour_intervals = np.linspace(0.0, 5000.0,  int(5000.0/250.0)+1)
+    contour_intervals = np.linspace(0.0, 5000.0, int(5000.0 / 250.0) + 1)
     _contour_mpas(thickness[timelev, :], nCells, xCell, yCell,
                   contour_levs=contour_intervals)
 
@@ -293,8 +296,8 @@ 
Source code for compass.landice.tests.eismint2.standard_experiments.visualiz
 
     flux = np.zeros((nCells,))
     for k in range(nVertLevels):
-        speedLevel = (uReconstructX[timelev, :, k:k+2].mean(axis=1)**2 +
-                      uReconstructY[timelev, :, k:k+2].mean(axis=1)**2)**0.5
+        speedLevel = (uReconstructX[timelev, :, k:k + 2].mean(axis=1)**2 +
+                      uReconstructY[timelev, :, k:k + 2].mean(axis=1)**2)**0.5
         flux += speedLevel * thickness[timelev, :] * layerThicknessFractions[k]
 
     plt.scatter(xCell[iceIndices], yCell[iceIndices], markersize,
@@ -302,8 +305,8 @@ 
Source code for compass.landice.tests.eismint2.standard_experiments.visualiz
                 edgecolors='none')
 
     # add contours over the top
-    contour_intervals = np.linspace(0.0, 20.0,  11)
-    _contour_mpas(flux * 3600.0*24.0*365.0 / 10000.0, nCells, xCell, yCell,
+    contour_intervals = np.linspace(0.0, 20.0, 11)
+    _contour_mpas(flux * 3600.0 * 24.0 * 365.0 / 10000.0, nCells, xCell, yCell,
                   contour_levs=contour_intervals)
     ax.set_aspect('equal')
     plt.title('Final flux (m$^2$ a$^{-1}$ / 10000)')
@@ -325,7 +328,7 @@ 
Source code for compass.landice.tests.eismint2.standard_experiments.visualiz
     if flwa[timelev, :, :].max() > 0.0:
         # NOT SURE WHICH LEVEL FLWA SHOULD COME FROM - so taking column average
         _contour_mpas(
-            flwa[timelev, :, :].mean(axis=1) * 3600.0*24.0*365.0 / 1.0e-17,
+            flwa[timelev, :, :].mean(axis=1) * 3600.0 * 24.0 * 365.0 / 1.0e-17,
             nCells, xCell, yCell)
     ax.set_aspect('equal')
     # Note: the paper's figure claims units of 10$^{-25}$ Pa$^{-3}$ a$^{-1}$
@@ -341,7 +344,8 @@ 
Source code for compass.landice.tests.eismint2.standard_experiments.visualiz
     # DIVIDE EVOLUTION TIME SERIES
     # ================
     fig = plt.figure(3, facecolor='w')
-    fig.suptitle('Payne et al. Fig. 5, 7, or 8', fontsize=10, fontweight='bold')
+    fig.suptitle('Payne et al. Fig. 5, 7, or 8', fontsize=10,
+                 fontweight='bold')
 
     # get indices for given time
     if experiment == 'b':
@@ -358,14 +362,15 @@ 
Source code for compass.landice.tests.eismint2.standard_experiments.visualiz
     # panel a - thickness
     fig.add_subplot(211)
     timeInd = np.nonzero(years <= endTime)[0][0:]
-    plt.plot(years[timeInd]/1000.0, thickness[timeInd, divideIndex], 'k.-')
+    plt.plot(years[timeInd] / 1000.0, thickness[timeInd, divideIndex], 'k.-')
     plt.ylabel('Thickness (m)')
 
     # panel b - basal temperature
     fig.add_subplot(212)
     # skip the first index cause basalTemperature isn't calculated then
     timeInd = np.nonzero(years <= endTime)[0][1:]
-    plt.plot(years[timeInd]/1000.0, basalTemperature[timeInd, divideIndex], 'k.-')
+    plt.plot(years[timeInd] / 1000.0, basalTemperature[timeInd, divideIndex],
+             'k.-')
     plt.ylabel('Basal temperature (K)')
     plt.xlabel('Time (kyr)')
 
@@ -422,8 +427,8 @@ 
Source code for compass.landice.tests.eismint2.standard_experiments.visualiz
                  'min/mean/max of community', fontsize=10, fontweight='bold')
 
     fig.add_subplot(151)
-    volume = ((thickness[timelev, iceIndices] * areaCell[iceIndices]).sum()
-              / 1000.0**3 / 10.0**6)
+    volume = ((thickness[timelev, iceIndices] * areaCell[iceIndices]).sum() /
+              1000.0**3 / 10.0**6)
     # benchmark results
     plt.plot(np.zeros((3,)), bench['volume'], 'k*')
     if bench['stattype'] == 'relative':
@@ -600,7 +605,7 @@ 
Source code for compass.landice.tests.eismint2.standard_experiments.visualiz
     if len(contour_levs) == 1:
         im = plt.contour(xi, yi, zi)
     else:
-        im = plt.contour(xi, yi, zi, contour_levs, cmap=plt.cm.jet)
+        im = plt.contour(xi, yi, zi, contour_levs, cmap=plt.get_cmap('jet'))
 
     # to see the raw data on top
     # plt.scatter(xCell, yCell, c=temperature[timelev,:,-1], s=100,
diff --git a/latest/_modules/compass/ocean/tests/global_convergence/cosine_bell/analysis.html b/latest/_modules/compass/ocean/tests/global_convergence/cosine_bell/analysis.html
index 33d2e1a1ae..21cca7c4dd 100644
--- a/latest/_modules/compass/ocean/tests/global_convergence/cosine_bell/analysis.html
+++ b/latest/_modules/compass/ocean/tests/global_convergence/cosine_bell/analysis.html
@@ -256,21 +256,20 @@ 
Source code for compass.ocean.tests.global_convergence.cosine_bell.analysis<
         # find time since the beginning of run
         ds = xr.open_dataset(f'{mesh_name}_output.nc')
         for j in range(len(ds.xtime)):
-            tt = str(ds.xtime[j].values)
-            tt.rfind('_')
-            DY = float(tt[10:12]) - 1
-            if DY == pd:
+            time_string = str(np.strings.decode(ds.xtime[j].values))
+            day = float(time_string[8:10]) - 1
+            if day == pd:
                 sliceTime = j
                 break
-        HR = float(tt[13:15])
-        MN = float(tt[16:18])
-        t = 86400.0 * DY + HR * 3600. + MN
+        hour = float(time_string[11:13])
+        min = float(time_string[14:16])
+        t = 86400.0 * day + hour * 3600. + min
         # find new location of blob center
         # center is based on equatorial velocity
-        R = init.sphere_radius
-        distTrav = 2.0 * 3.14159265 * R / (86400.0 * pd) * t
+        r = init.sphere_radius
+        distTrav = 2.0 * np.pi * r / (86400.0 * pd) * t
         # distance in radians is
-        distRad = distTrav / R
+        distRad = distTrav / r
         newLon = lonCent + distRad
         if newLon > 2.0 * np.pi:
             newLon -= 2.0 * np.pi
@@ -279,12 +278,12 @@ 
Source code for compass.ocean.tests.global_convergence.cosine_bell.analysis<
         tracer = np.zeros_like(init.tracer1[0, :, 0].values)
         latC = init.latCell.values
         lonC = init.lonCell.values
-        temp = R * np.arccos(np.sin(latCent) * np.sin(latC) +
-                             np.cos(latCent) * np.cos(latC) * np.cos(
-            lonC - newLon))
+        temp = r * np.arccos(np.sin(latCent) * np.sin(latC) +
+                             (np.cos(latCent) * np.cos(latC) *
+                              np.cos(lonC - newLon)))
         mask = temp < radius
         tracer[mask] = psi0 / 2.0 * (
-            1.0 + np.cos(3.1415926 * temp[mask] / radius))
+            1.0 + np.cos(np.pi * temp[mask] / radius))
 
         # oad forward mode data
         tracerF = ds.tracer1[sliceTime, :, 0].values
@@ -292,7 +291,7 @@ 
Source code for compass.ocean.tests.global_convergence.cosine_bell.analysis<
 
         init.close()
         ds.close()
-        return rmseValue, init.dims['nCells']

+        return rmseValue, init.sizes['nCells']
diff --git a/latest/_modules/compass/ocean/tests/sphere_transport/process_output.html b/latest/_modules/compass/ocean/tests/sphere_transport/process_output.html index f66fe2f6b1..9f38df01c7 100644 --- a/latest/_modules/compass/ocean/tests/sphere_transport/process_output.html +++ b/latest/_modules/compass/ocean/tests/sphere_transport/process_output.html @@ -112,12 +112,13 @@

Source code for compass.ocean.tests.sphere_transport.process_output

-import matplotlib.pyplot as plt
-from matplotlib.gridspec import GridSpec
-from matplotlib.colors import ListedColormap
+from importlib import resources
+
 import matplotlib
+import matplotlib.pyplot as plt
 import numpy as np
-from importlib import resources
+from matplotlib.colors import ListedColormap
+from matplotlib.gridspec import GridSpec
 
 
 def appx_mesh_size(dataset):
@@ -385,8 +386,8 @@ 
Source code for compass.ocean.tests.sphere_transport.process_output
map_file_found = False
     try:
         with resources.open_text(
-                "compass.ocean.tests.sphere_transport.resources", cmap_filename) \
-                as f:
+                "compass.ocean.tests.sphere_transport.resources",
+                cmap_filename) as f:
             flines = f.readlines()
         map_file_found = True
     except BaseException:
@@ -402,7 +403,7 @@ 
Source code for compass.ocean.tests.sphere_transport.process_output
result = ListedColormap(rgb, name=cmap_filename)
     else:
         print("error reading ncl colormap. using matplotlib default instead.")
-        result = matplotlib.cm.get_cmap()
+        result = matplotlib.pyplot.get_cmap()
     return result

 
 
@@ -492,8 +493,7 @@ 
Source code for compass.ocean.tests.sphere_transport.process_output
cmap="seismic",
                              vmin=diffmin,
                              vmax=diffmax)
-    lcm = axes[9].tricontourf(xc, yc, dataset.variables["layerThickness"]
-                              [0, :, 1])
+    axes[9].tricontourf(xc, yc, dataset.variables["layerThickness"][0, :, 1])
     axes[9].set_ylabel('layer thickness')
     axes[10].tricontourf(xc, yc, dataset.variables["layerThickness"]
                          [0, :, 1])
@@ -517,8 +517,8 @@ 
Source code for compass.ocean.tests.sphere_transport.process_output
axes[9 + i].set_xticklabels(xticklabels)
     for i in range(9):
         axes[i].set_xticklabels([])
-    cb1 = fig.colorbar(cm, ax=axes[8])
-    cb2 = fig.colorbar(tcm, ax=axes[5])
+    fig.colorbar(cm, ax=axes[8])
+    fig.colorbar(tcm, ax=axes[5])
     # cb3 = fig.colorbar(lcm, ax=axes[11])
     fig.suptitle(tcname)

 
diff --git a/latest/_modules/compass/ocean/tests/tides/analysis.html b/latest/_modules/compass/ocean/tests/tides/analysis.html
index e2322fcc5a..d5fd0335d0 100644
--- a/latest/_modules/compass/ocean/tests/tides/analysis.html
+++ b/latest/_modules/compass/ocean/tests/tides/analysis.html
@@ -112,17 +112,18 @@
            

              
   
Source code for compass.ocean.tests.tides.analysis
-from compass.step import Step
+import os
 
-import netCDF4
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-import numpy as np
 import cartopy.crs as ccrs
 import cartopy.feature as cfeature
-import os
+import matplotlib.pyplot as plt
+import netCDF4
+import numpy as np
+from matplotlib import colormaps
 from mpas_tools.logging import check_call
 
+from compass.step import Step
+
 
 

 [docs]
@@ -227,7 +228,7 @@ 
Source code for compass.ocean.tests.tides.analysis
# Write coordinate file for OTPS2
         f = open('lat_lon', 'w')
         for i in range(nCells):
-            f.write(str(lat_grid[i])+'  '+str(lon_grid[i])+'\n')
+            f.write(f'{lat_grid[i]}  {lon_grid[i]} \n')
         f.close()

 
 
@@ -248,7 +249,7 @@ 
Source code for compass.ocean.tests.tides.analysis
'comment': '! 2. latitude/longitude/<time> file'},
                      {'inp': 'z',
                       'comment': '! 3. z/U/V/u/v'},
-                     {'inp':  con,
+                     {'inp': con,
                       'comment': '! 4. tidal constituents to include'},
                      {'inp': 'AP',
                       'comment': '! 5. AP/RI'},
@@ -264,10 +265,10 @@ 
Source code for compass.ocean.tests.tides.analysis
os.mkdir('inputs')
 
             # Write the setup_con file
-            f = open('inputs/'+con+'_setup', 'w')
+            f = open(f'inputs/{con}_setup', 'w')
             for line in lines:
                 spaces = 28 - len(line['inp'])
-                f.write(line['inp'] + spaces*' ' + line['comment'] + '\n')
+                f.write(line['inp'] + spaces * ' ' + line['comment'] + '\n')
             f.close()
 
             # Write the Model_atlas_con file
@@ -313,18 +314,18 @@ 
Source code for compass.ocean.tests.tides.analysis
line_sp = line.split()
                 if line_sp[2] != '*************':
                     val = float(line_sp[2])
-                    self.mesh_AP[con]['amp'][start+i] = val
+                    self.mesh_AP[con]['amp'][start + i] = val
                 else:
-                    self.mesh_AP[con]['amp'][start+i] = -9999
+                    self.mesh_AP[con]['amp'][start + i] = -9999
 
                 if line_sp[3] != 'Site':
                     val = float(line_sp[3])
                     if val < 0:
                         val = val + 360.0
-                    self.mesh_AP[con]['phase'][start+i] = val
+                    self.mesh_AP[con]['phase'][start + i] = val
 
                 else:
-                    self.mesh_AP[con]['phase'][start+i] = -9999

+                    self.mesh_AP[con]['phase'][start + i] = -9999

 
 
 

@@ -397,7 +398,7 @@ 
Source code for compass.ocean.tests.tides.analysis
        """
 
         plt.switch_backend('agg')
-        cmap_reversed = cm.get_cmap('Spectral_r')
+        cmap_reversed = colormaps['Spectral_r']
 
         # Initialize plotting variables
         TW = 2                         # Tick width
@@ -410,8 +411,8 @@ 
Source code for compass.ocean.tests.tides.analysis
lon_grid = np.mod(data_nc.variables['lonCell'][:] + np.pi,
                           2.0 * np.pi) - np.pi
-        lon_grid = lon_grid*180.0/np.pi
-        lat_grid = data_nc.variables['latCell'][:]*180.0/np.pi
+        lon_grid = lon_grid * 180.0 / np.pi
+        lat_grid = data_nc.variables['latCell'][:] * 180.0 / np.pi
 
         nCells = lon_grid.size
         data1 = np.zeros((nCells))
@@ -429,14 +430,14 @@ 
Source code for compass.ocean.tests.tides.analysis
# Use these to fix up the plots
         subplot_ticks = [[np.linspace(0, 0.65, 10), np.linspace(0, 0.65, 10),
                           np.linspace(0, 0.13, 10), np.linspace(0, 0.13, 10)],
-                         [np.linspace(0, 1.4,  10), np.linspace(0, 1.4,  10),
+                         [np.linspace(0, 1.40, 10), np.linspace(0, 1.40, 10),
                           np.linspace(0, 0.22, 10), np.linspace(0, 0.25, 10)],
                          [np.linspace(0, 0.22, 10), np.linspace(0, 0.22, 10),
                           np.linspace(0, 0.05, 10), np.linspace(0, 0.05, 10)],
-                         [np.linspace(0, 0.5,  10), np.linspace(0, 0.5,  10),
+                         [np.linspace(0, 0.50, 10), np.linspace(0, 0.50, 10),
                           np.linspace(0, 0.08, 10), np.linspace(0, 0.08, 10)],
-                         [np.linspace(0, 0.7,  10), np.linspace(0, 0.7,  10),
-                          np.linspace(0, 0.5,  10), np.linspace(0, 0.5,  10)]]
+                         [np.linspace(0, 0.70, 10), np.linspace(0, 0.70, 10),
+                          np.linspace(0, 0.50, 10), np.linspace(0, 0.50, 10)]]
 
         for i, con in enumerate(constituent_list):
 
@@ -453,39 +454,45 @@ 
Source code for compass.ocean.tests.tides.analysis
data2_phase[:] = data_nc.variables[
                 f'{con}Phase{self.tpxo_version}'][:]
 
-            data1_phase = data1_phase*np.pi/180.0
-            data2_phase = data2_phase*np.pi/180.0
+            data1_phase = data1_phase * np.pi / 180.0
+            data2_phase = data2_phase * np.pi / 180.0
 
             # Calculate RMSE values
-            rmse_amp = 0.5*(data1 - data2)**2
-            rmse_com = 0.5*(data2**2 + data1**2) \
-                - data1*data2*np.cos(data2_phase - data1_phase)
+            rmse_amp = 0.5 * (data1 - data2)**2
+            rmse_com = 0.5 * (data2**2 + data1**2) \
+                - data1 * data2 * np.cos(data2_phase - data1_phase)
 
             # Calculate mean (global) values
-            idx = np.where((depth > 20)
-                           & (rmse_com < 1000) & (rmse_amp < 1000))
+            idx = np.where((depth > 20) & (rmse_com < 1000) &
+                           (rmse_amp < 1000))
             area_tot = np.sum(area[idx])
-            glo_rmse_amp = np.sqrt(np.sum(rmse_amp[idx]*area[idx])/area_tot)
-            glo_rmse_com = np.sqrt(np.sum(rmse_com[idx]*area[idx])/area_tot)
+            glo_rmse_amp = np.sqrt(np.sum(rmse_amp[idx] * area[idx]) /
+                                   area_tot)
+            glo_rmse_com = np.sqrt(np.sum(rmse_com[idx] * area[idx]) /
+                                   area_tot)
             print('Global RMSE (Amp) = ', glo_rmse_amp)
             print('Global RMSE (Com) = ', glo_rmse_com)
 
             # Calculate shallow RMSE (<=1000m)
-            idx = np.where((depth > 20) & (depth < 1000)
-                           & (np.abs(lat_grid) < 66)
-                           & (rmse_com < 1000) & (rmse_amp < 1000))
+            idx = np.where((depth > 20) & (depth < 1000) &
+                           (np.abs(lat_grid) < 66) &
+                           (rmse_com < 1000) & (rmse_amp < 1000))
             area_tot = np.sum(area[idx])
-            shal_rmse_amp = np.sqrt(np.sum(rmse_amp[idx]*area[idx])/area_tot)
-            shal_rmse_com = np.sqrt(np.sum(rmse_com[idx]*area[idx])/area_tot)
+            shal_rmse_amp = np.sqrt(np.sum(rmse_amp[idx] * area[idx]) /
+                                    area_tot)
+            shal_rmse_com = np.sqrt(np.sum(rmse_com[idx] * area[idx]) /
+                                    area_tot)
             print('Shallow RMSE (Amp) = ', shal_rmse_amp)
             print('Shallow RMSE (Com) = ', shal_rmse_com)
 
             # Calculate deep RMSE (>1000m)
-            idx = np.where((depth >= 1000) & (np.abs(lat_grid) < 66)
-                           & (rmse_com < 1000) & (rmse_amp < 1000))
+            idx = np.where((depth >= 1000) & (np.abs(lat_grid) < 66) &
+                           (rmse_com < 1000) & (rmse_amp < 1000))
             area_tot = np.sum(area[idx])
-            deep_rmse_amp = np.sqrt(np.sum(rmse_amp[idx]*area[idx])/area_tot)
-            deep_rmse_com = np.sqrt(np.sum(rmse_com[idx]*area[idx])/area_tot)
+            deep_rmse_amp = np.sqrt(np.sum(rmse_amp[idx] * area[idx]) /
+                                    area_tot)
+            deep_rmse_com = np.sqrt(np.sum(rmse_com[idx] * area[idx]) /
+                                    area_tot)
             print('Deep RMSE (Amp) = ', deep_rmse_amp)
             print('Deep RMSE (Com) = ', deep_rmse_com)
 
@@ -500,7 +507,7 @@ 
Source code for compass.ocean.tests.tides.analysis
f'{con} RMSE (Complex) [m]']
 
             for subplot in range(0, 4):
-                ax = fig.add_subplot(2, 2, subplot+1,
+                ax = fig.add_subplot(2, 2, subplot + 1,
                                      projection=ccrs.PlateCarree())
                 ax.set_title(subplot_title[subplot], fontsize=20)
                 levels = subplot_ticks[i][subplot][:]
@@ -549,9 +556,9 @@ 
Source code for compass.ocean.tests.tides.analysis
cbar.ax.tick_params(labelsize=16)
 
             fig.tight_layout()
-            global_err = str(round(glo_rmse_com*100, 3))
-            deep_err = str(round(deep_rmse_com*100, 3))
-            shallow_err = str(round(shal_rmse_com*100, 3))
+            global_err = str(round(glo_rmse_com * 100, 3))
+            deep_err = str(round(deep_rmse_com * 100, 3))
+            shallow_err = str(round(shal_rmse_com * 100, 3))
             fig.suptitle(f'Complex RMSE: Global = {global_err} cm; '
                          f'Deep = {deep_err} cm; '
                          f'Shallow = {shallow_err} cm',
@@ -575,7 +582,7 @@ 
Source code for compass.ocean.tests.tides.analysis
# Setup chunking for TPXO extraction with large meshes
         indices = np.arange(self.nCells)
-        nchunks = np.ceil(self.nCells/200000)
+        nchunks = np.ceil(self.nCells / 200000)
         index_chunks = np.array_split(indices, nchunks)
 
         # Initialize data structure for TPXO values