updated code in a few files to make it more understandable, also used .join method at a place to increase the speed.

toolboxes/AntennaPatterns/plot_fields.py

@@ -58,9 +58,9 @@ if epsr:
     wavelength = v1 / f
 
 # Print some useful information
-logger.info('Centre frequency: {} GHz'.format(f / 1e9))
+logger.info(f'Centre frequency: {f / 1000000000.0} GHz')
 if epsr:
-    logger.info('Critical angle for Er {} is {} degrees'.format(epsr, thetac))
+    logger.info(f'Critical angle for Er {epsr} is {thetac} degrees')
     logger.info('Wavelength: {:.3f} m'.format(wavelength))
     logger.info('Observation distance(s) from {:.3f} m ({:.1f} wavelengths) to {:.3f} m ({:.1f} wavelengths)'.format(radii[0], radii[0] / wavelength, radii[-1], radii[-1] / wavelength))
     logger.info('Theoretical boundary between reactive & radiating near-field (0.62*sqrt((D^3/wavelength): {:.3f} m'.format(0.62 * np.sqrt((D**3) / wavelength)))

toolboxes/DebyeFit/Debye_Fit.py

@@ -119,9 +119,7 @@ class Relaxation(object):
         print(f"Approximating {self.name}"
               f" using {self.number_of_debye_poles} Debye poles")
         print(f"{self.name} parameters: ")
-        s = ''
-        for k, v in self.params.items():
-            s += f"{k:10s} = {v}\n"
+        s = ''.join(f"{k:10s} = {v}\n" for k, v in self.params.items())
         print(s)
         return f'{self.name}:\n{s}'
 
@@ -231,16 +229,14 @@ class Relaxation(object):
             print("_" * 65)
 
         # Print the Debye expnasion in a gprMax format
-        material_prop = []
-        material_prop.append("#material: {} {} {} {} {}\n".format(ee, self.sigma,
-                                                                  self.mu,
-                                                                  self.mu_sigma,
-                                                                  self.material_name))
+        material_prop = [
+            f"#material: {ee} {self.sigma} {self.mu} {self.mu_sigma} {self.material_name}\n"
+        ]
         print(material_prop[0], end="")
-        dispersion_prop = "#add_dispersion_debye: {}".format(len(tau))
+        dispersion_prop = f"#add_dispersion_debye: {len(tau)}"
         for i in range(len(tau)):
-            dispersion_prop += " {} {}".format(weights[i], 10**tau[i])
-        dispersion_prop += " {}".format(self.material_name)
+            dispersion_prop += f" {weights[i]} {10**tau[i]}"
+        dispersion_prop += f" {self.material_name}"
         print(dispersion_prop)
         material_prop.append(dispersion_prop + '\n')
         return material_prop
@@ -327,11 +323,10 @@ class Relaxation(object):
         else:
             sys.exit("Cannot save material properties "
                      f"in {os.path.join(fdir, 'my_materials.txt')}!")
-        fileH = open(file_path, "a")
-        fileH.write(f"## {output[0].split(' ')[-1]}")
-        fileH.writelines(output)
-        fileH.write("\n")
-        fileH.close()
+        with open(file_path, "a") as fileH:
+            fileH.write(f"## {output[0].split(' ')[-1]}")
+            fileH.writelines(output)
+            fileH.write("\n")
         print(f"Material properties save at: {file_path}")
 
 
@@ -562,7 +557,7 @@ class Crim(Relaxation):
               f" using {self.number_of_debye_poles} Debye poles")
         print("CRIM parameters: ")
         for i in range(len(self.volumetric_fractions)):
-            print("Material {}.:".format(i+1))
+            print(f"Material {i + 1}.:")
             print("---------------------------------")
             print(f"{'Vol. fraction':>27s} = {self.volumetric_fractions[i]}")
             print(f"{'e_inf':>27s} = {self.materials[i][0]}")

toolboxes/DebyeFit/optimization.py

@@ -456,7 +456,6 @@ def DLS(logt, rl, im, freq):
                     d.imag, x[np.newaxis].T).T[0]
     cost_i = np.sum(np.abs(ip-im))/len(im)
     ee = np.mean(rl - rp)
-    if ee < 1:
-        ee = 1
+    ee = max(ee, 1)
     cost_r = np.sum(np.abs(rp + ee - rl))/len(im)
     return cost_i, cost_r, x, ee, rp, ip

toolboxes/Plotting/plot_Ascan.py

@@ -79,11 +79,10 @@ def mpl_plot(filename, outputs=Rx.defaultoutputs, fft=False, save=False):
         time = np.linspace(0, (iterations - 1) * dt, num=iterations)
 
         # Check for single output component when doing a FFT
-        if fft:
-            if not len(outputs) == 1:
-                logger.exception('A single output must be specified when using ' +
-                                 'the -fft option')
-                raise ValueError
+        if fft and not len(outputs) == 1:
+            logger.exception('A single output must be specified when using ' +
+                             'the -fft option')
+            raise ValueError
 
         # New plot for each receiver
         for rx in range(1, nrx + 1):

toolboxes/Plotting/plot_source_wave.py

@@ -86,14 +86,14 @@ def mpl_plot(w, timewindow, dt, iterations, fft=False, save=False):
     logging.info(f'Type: {w.type}')
     logging.info(f'Maximum (absolute) amplitude: {np.max(np.abs(waveform)):g}')
 
-    if w.freq and not w.type == 'gaussian' and not w.type == 'impulse':
+    if w.freq and w.type != 'gaussian' and w.type != 'impulse':
         logging.info(f'Centre frequency: {w.freq:g} Hz')
 
-    if (w.type == 'gaussian' or w.type == 'gaussiandot' or w.type == 'gaussiandotnorm'
-        or w.type == 'gaussianprime' or w.type == 'gaussiandoubleprime'):
+    if w.type in ['gaussian', 'gaussiandot', 'gaussiandotnorm',
+                  'gaussianprime', 'gaussiandoubleprime']:
         delay = 1 / w.freq
         logging.info(f'Time to centre of pulse: {delay:g} s')
-    elif w.type == 'gaussiandotdot' or w.type == 'gaussiandotdotnorm' or w.type == 'ricker':
+    elif w.type in ['gaussiandotdot', 'gaussiandotdotnorm', 'ricker']:
         delay = np.sqrt(2) / w.freq
         logging.info(f'Time to centre of pulse: {delay:g} s')
 

toolboxes/STLtoVoxel/perimeter.py

@@ -36,12 +36,11 @@ def generate_y(p1, p2, x):
 
 def paint_y_axis(lines, pixels, x):
     is_black = False
-    target_ys = list(map(lambda line: int(generate_y(line[0], line[1], x)), lines))
-    target_ys.sort()
+    target_ys = sorted(
+        map(lambda line: int(generate_y(line[0], line[1], x)), lines)
+    )
     if len(target_ys) % 2:
-        distances = []
-        for i in range(len(target_ys) - 1):
-            distances.append(target_ys[i+1] - target_ys[i])
+        distances = [target_ys[i+1] - target_ys[i] for i in range(len(target_ys) - 1)]
         # https://stackoverflow.com/a/17952763
         min_idx = -min((x, -i) for i, x in enumerate(distances))[1]
         del target_ys[min_idx]
@@ -54,7 +53,7 @@ def paint_y_axis(lines, pixels, x):
         pixels[target_y][x] = True
         is_black = not is_black
         yi = target_y
-    assert is_black is False, 'an error has occured at x%s' % x
+    assert is_black is False, f'an error has occured at x{x}'
 
 
 def generate_line_events(line_list):

toolboxes/STLtoVoxel/slice.py

@@ -1,3 +1,4 @@
+import itertools
 import multiprocessing as mp
 import sys
 
@@ -90,10 +91,7 @@ def triangle_to_intersecting_lines(triangle, height, pixels, lines):
             y = int(same[0][1])
             pixels[y][x] = True
     else:
-        cross_lines = []
-        for a in above:
-            for b in below:
-                cross_lines.append((b, a))
+        cross_lines = [(b, a) for a, b in itertools.product(above, below)]
         side1 = where_line_crosses_z(cross_lines[0][0], cross_lines[0][1], height)
         side2 = where_line_crosses_z(cross_lines[1][0], cross_lines[1][1], height)
         lines.append((side1, side2))