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Examples/Tests/langmuir/analysis_rcylinder.py

@@ -18,7 +18,7 @@
 
 import matplotlib
 
-matplotlib.use('Agg')
+matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 import yt
 
@@ -27,7 +27,7 @@
 import numpy as np
 from scipy.constants import c, e, epsilon_0, m_e
 
-sys.path.insert(1, '../../../../warpx/Regression/Checksum/')
+sys.path.insert(1, "../../../../warpx/Regression/Checksum/")
 import checksumAPI
 
 # this will be the name of the plot file
@@ -36,56 +36,69 @@
 test_name = os.path.split(os.getcwd())[1]
 
 # Parse test name and check if current correction (psatd.current_correction) is applied
-current_correction = True if re.search('current_correction', fn) else False
+current_correction = True if re.search("current_correction", fn) else False
 
 # Parameters (these parameters must match the parameters in `inputs_rcylinder`)
 epsilon = 0.01
-n = 2.e24
-w0 = 5.e-6
-rmin =   0e-6; rmax = 20.e-6; Nr = 64
+n = 2.0e24
+w0 = 5.0e-6
+rmin = 0e-6
+rmax = 20.0e-6
+Nr = 64
 
 # Plasma frequency
-wp = np.sqrt((n*e**2)/(m_e*epsilon_0))
-kp = wp/c
+wp = np.sqrt((n * e**2) / (m_e * epsilon_0))
+kp = wp / c
 
-def Er( r, epsilon, w0, wp, t) :
+
+def Er(r, epsilon, w0, wp, t):
     """
     Return the radial electric field as an array
     of the same length as r, in the half-plane theta=0
     """
-    Er_array = \
-        epsilon * m_e*c**2/e * 2*r/w0**2 * \
-            np.exp( -r**2/w0**2 ) * np.sin( wp*t )
-    return( Er_array )
+    Er_array = (
+        epsilon
+        * m_e
+        * c**2
+        / e
+        * 2
+        * r
+        / w0**2
+        * np.exp(-(r**2) / w0**2)
+        * np.sin(wp * t)
+    )
+    return Er_array
+
 
 # Read the file
 ds = yt.load(fn)
 t0 = ds.current_time.to_value()
-data = ds.covering_grid(level=0, left_edge=ds.domain_left_edge,
-                        dims=ds.domain_dimensions)
+data = ds.covering_grid(
+    level=0, left_edge=ds.domain_left_edge, dims=ds.domain_dimensions
+)
 
 # Get cell centered coordinates
-dr = (rmax - rmin)/Nr
-coords = np.indices([Nr],'d')
-rr = rmin + (coords[0] + 0.5)*dr
+dr = (rmax - rmin) / Nr
+coords = np.indices([Nr], "d")
+rr = rmin + (coords[0] + 0.5) * dr
 
 # Check the validity of the fields
 overall_max_error = 0
-Er_sim = data[('boxlib','Er')].to_ndarray()[:,0,0]
+Er_sim = data[("boxlib", "Er")].to_ndarray()[:, 0, 0]
 Er_th = Er(rr, epsilon, w0, wp, t0)
-max_error = abs(Er_sim - Er_th).max()/abs(Er_th).max()
-print('Er: Max error: %.2e' %(max_error))
-overall_max_error = max( overall_max_error, max_error )
+max_error = abs(Er_sim - Er_th).max() / abs(Er_th).max()
+print("Er: Max error: %.2e" % (max_error))
+overall_max_error = max(overall_max_error, max_error)
 
 # Plot the last field from the loop (Er at iteration 40)
-plt.subplot2grid((1,2), (0,0))
+plt.subplot2grid((1, 2), (0, 0))
 plt.plot(rr, Er_sim)
-plt.title('Er, last iteration\n(simulation)')
-plt.subplot2grid( (1,2), (0,1) )
+plt.title("Er, last iteration\n(simulation)")
+plt.subplot2grid((1, 2), (0, 1))
 plt.plot(rr, Er_th)
-plt.title('Er, last iteration\n(theory)')
+plt.title("Er, last iteration\n(theory)")
 plt.tight_layout()
-plt.savefig(test_name+'_analysis.png')
+plt.savefig(test_name + "_analysis.png")
 
 error_rel = overall_max_error
 
@@ -94,6 +107,6 @@ def Er( r, epsilon, w0, wp, t) :
 print("error_rel    : " + str(error_rel))
 print("tolerance_rel: " + str(tolerance_rel))
 
-assert( error_rel < tolerance_rel )
+assert error_rel < tolerance_rel
 
 checksumAPI.reset_benchmark(test_name, fn)