Sourcery refactored master branch

cmtj/models/domain_dynamics.py

@@ -172,10 +172,7 @@ def get_unrelaxed_domain_width(self, effective=False):
         """Domain width is based off the effective perpendicular anisotropy.
         We reduce the perpendicular anisotropy by demagnetising field"""
         # Keff = self.Ku - 0.5*mu0*(self.Ms)**2
-        if effective:
-            Keff = self.Ku - (0.5 * mu0) * (self.Ms**2)
-        else:
-            Keff = self.Ku
+        Keff = self.Ku - (0.5 * mu0) * (self.Ms**2) if effective else self.Ku
         return math.sqrt(self.A / Keff)
 
     def set_current_function(self, driver: Callable):

cmtj/models/general_sb.py

@@ -26,9 +26,7 @@ def wrap_fn(*args):
 @njit
 def fast_norm(x):
     """Fast norm function for 1D arrays."""
-    sum_ = 0
-    for x_ in x:
-        sum_ += x_**2
+    sum_ = sum(x_**2 for x_ in x)
     return math.sqrt(sum_)
 
 
@@ -42,8 +40,9 @@ def general_hessian_functional(N: int):
     for i in range(N):
         # indx_i = str(i + 1) # for display purposes
         indx_i = str(i)
-        all_symbols.append(sym.Symbol(r"\theta_" + indx_i))
-        all_symbols.append(sym.Symbol(r"\phi_" + indx_i))
+        all_symbols.extend(
+            (sym.Symbol(r"\theta_" + indx_i), sym.Symbol(r"\phi_" + indx_i))
+        )
     energy_functional_expr = sym.Function("E")(*all_symbols)
     return get_hessian_from_energy_expr(
         N, energy_functional_expr), energy_functional_expr
@@ -76,16 +75,13 @@ def get_hessian_from_energy_expr(N: int, energy_functional_expr: sym.Expr):
             hessian[2 * i + 1][2 * j + 1] = expr
             hessian[2 * j + 1][2 * i + 1] = expr
 
+            s1 = sym.Symbol(r"\theta_" + indx_i)
+            s2 = sym.Symbol(r"\phi_" + indx_j)
+            expr = sym.diff(energy_functional_expr, s1, s2)
             if i == j:
-                s1 = sym.Symbol(r"\theta_" + indx_i)
-                s2 = sym.Symbol(r"\phi_" + indx_j)
-                expr = sym.diff(energy_functional_expr, s1, s2)
                 hessian[2 * i + 1][2 * j] = expr + sym.I * z
                 hessian[2 * i][2 * j + 1] = expr - sym.I * z
             else:
-                s1 = sym.Symbol(r"\theta_" + indx_i)
-                s2 = sym.Symbol(r"\phi_" + indx_j)
-                expr = sym.diff(energy_functional_expr, s1, s2)
                 hessian[2 * i][2 * j + 1] = expr
                 hessian[2 * j + 1][2 * i] = expr
 
@@ -185,11 +181,11 @@ def symbolic_layer_energy(self, H: sym.Matrix, J1top: float,
         top_iec_energy = 0
         bottom_iec_energy = 0
 
-        if not (top_layer is None):
+        if top_layer is not None:
             other_m = top_layer.get_m_sym()
             top_iec_energy = -(J1top / self.thickness) * m.dot(other_m) - (
                 J2top / self.thickness) * m.dot(other_m)**2
-        if not (down_layer is None):
+        if down_layer is not None:
             other_m = down_layer.get_m_sym()
             bottom_iec_energy = -(J1bottom / self.thickness) * m.dot(
                 other_m) - (J2bottom / self.thickness) * m.dot(other_m)**2
@@ -210,11 +206,7 @@ def no_iec_symbolic_layer_energy(self, H: sym.Matrix):
 
     def sb_correction(self):
         omega = sym.Symbol(r'\omega')
-        # we use unreduced gamma
-        # later we don't need to divide omega by 2pi
-        # idk but if we use reduced gamma it numerically breaks lol
-        Z = (omega / gamma) * self.Ms * sym.sin(self.theta) * self.thickness
-        return Z
+        return (omega / gamma) * self.Ms * sym.sin(self.theta) * self.thickness
 
 
 @dataclass
@@ -230,7 +222,7 @@ def __post_init__(self):
         if len(self.layers) != len(self.J2) + 1:
             raise ValueError("Number of layers must be 1 more than J2.")
 
-        id_sets = set([layer._id for layer in self.layers])
+        id_sets = {layer._id for layer in self.layers}
         ideal_set = set(range(len(self.layers)))
         if id_sets != ideal_set:
             raise ValueError("Layer ids must be 0, 1, 2, ... and unique")
@@ -286,7 +278,7 @@ def create_energy(self,
                                                       top_layer, bottom_layer)
         else:
             # surface energy for correct angular gradient
-            for i, layer in enumerate(self.layers):
+            for layer in self.layers:
                 # to avoid dividing J by thickness
                 energy += layer.no_iec_symbolic_layer_energy(
                     H) * layer.thickness
@@ -322,13 +314,12 @@ def create_energy_hessian(self, equilibrium_position: List[float]):
                 hessian[2 * i + 1][2 * j + 1] = expr
                 hessian[2 * j + 1][2 * i + 1] = expr
 
+                expr = sym.diff(sym.diff(energy, theta_i), phi_j)
                 # mixed terms
                 if i == j:
-                    expr = sym.diff(sym.diff(energy, theta_i), phi_j)
                     hessian[2 * i + 1][2 * j] = expr + sym.I * z
                     hessian[2 * i][2 * j + 1] = expr - sym.I * z
                 else:
-                    expr = sym.diff(sym.diff(energy, theta_i), phi_j)
                     hessian[2 * i][2 * j + 1] = expr
                     hessian[2 * j + 1][2 * i] = expr
 
@@ -347,10 +338,8 @@ def get_gradient_expr(self, accel="math"):
         symbols = []
         for layer in self.layers:
             (theta, phi) = layer.get_coord_sym()
-            grad_vector.append(sym.diff(energy, theta))
-            grad_vector.append(sym.diff(energy, phi))
-            symbols.append(theta)
-            symbols.append(phi)
+            grad_vector.extend((sym.diff(energy, theta), sym.diff(energy, phi)))
+            symbols.extend((theta, phi))
         return sym.lambdify(symbols, grad_vector, accel)
 
     def adam_gradient_descent(self,
@@ -508,9 +497,7 @@ def analytical_roots(self):
                                           energy_expression=system_energy,
                                           subs={})
         subs.update(self.get_ms_subs())
-        # substitute all known values
-        solutions = [s.subs(subs) for s in root_expr]
-        return solutions
+        return [s.subs(subs) for s in root_expr]
 
     def get_subs(self, equilibrium_position: List[float]):
         """Returns the substitution dictionary for the energy expression."""
@@ -528,7 +515,7 @@ def get_ms_subs(self):
             r"t_{" + str(layer._id) + r"}": layer.thickness
             for layer in self.layers
         }
-        return {**a, **b}
+        return a | b
 
     def set_H(self, H: VectorObj):
         """Sets the external field."""

cmtj/models/oersted.py

@@ -65,9 +65,7 @@ def ampere_law(self, other_block: 'Block'):
                           (other_block.x, other_block.y))
 
     def __eq__(self, __o: 'Block') -> bool:
-        if (self.ix == __o.ix) and (self.iy == __o.iy) and (self.iz == __o.iz):
-            return True
-        return False
+        return self.ix == __o.ix and self.iy == __o.iy and self.iz == __o.iz
 
 
 class Structure:

cmtj/utils/energy.py

@@ -22,7 +22,7 @@ def compute_from_log(self) -> Dict[str, List[float]]:
         Computes a log of energies over time and returns it
         in the same form of the
         """
-        field_keys = list(set((k[:-1] for k in self.log if "_H" in k)))
+        field_keys = list({k[:-1] for k in self.log if "_H" in k})
         mag_k = (k.replace("_mx", "") for k in self.log if "_mx" in k)
         mag_vectors = {
             k: np.asarray([

cmtj/utils/filters.py

@@ -36,8 +36,7 @@ def butter_bandpass_filter(data: np.ndarray,
             b, a = butter(order, [pass_freq[0], pass_freq[1]],
                           btype="bandpass",
                           analog=False)
-        y = lfilter(b, a, data, zi=None)
-        return y
+        return lfilter(b, a, data, zi=None)
 
     @staticmethod
     def butter_lowpass_filter(data: np.ndarray,
@@ -53,8 +52,7 @@ def butter_lowpass_filter(data: np.ndarray,
         nyq = 0.5 * fs
         normal_cutoff = cutoff / nyq
         b, a = butter(order, normal_cutoff, btype="low", analog=False)
-        y = lfilter(b, a, data, zi=None)
-        return y
+        return lfilter(b, a, data, zi=None)
 
     @staticmethod
     def detrend_axis(arr, axis):
@@ -63,8 +61,4 @@ def detrend_axis(arr, axis):
         :param axis: axis along which to detrend
         """
         medians = np.median(arr, axis=axis)
-        if axis:
-            detrended = (arr.T - medians).T
-        else:
-            detrended = arr - medians
-        return detrended
+        return (arr.T - medians).T if axis else arr - medians

cmtj/utils/plotting.py

@@ -170,7 +170,7 @@ def create_coordinates_plot(axes,
         zs[:, 0] = ys[:, 0]
         zs[:, 1:] = (ys[:, 1:] - ymins[1:]) / dys[1:] * dys[0] + ymins[0]
 
-        axes = [host] + [host.twinx() for i in range(ys.shape[1] - 1)]
+        axes = [host] + [host.twinx() for _ in range(ys.shape[1] - 1)]
         for i, ax in enumerate(axes):
             ax.set_ylim(ymins[i], ymaxs[i])
             ax.spines['top'].set_visible(False)
@@ -196,19 +196,20 @@ def create_coordinates_plot(axes,
             # x-coordinate of the control vertices: at each integer (for the axes) and two inbetween
             # y-coordinate: repeat every point three times, except the first and last only twice
             verts = list(
-                zip([
-                    x for x in np.linspace(
-                        0, len(ys) - 1, len(ys) * 3 - 2, endpoint=True)
-                ],
-                    np.repeat(zs[j, :], 3)[1:-1]))
+                zip(
+                    list(
+                        np.linspace(
+                            0, len(ys) - 1, len(ys) * 3 - 2, endpoint=True
+                        )
+                    ),
+                    np.repeat(zs[j, :], 3)[1:-1],
+                )
+            )
             # for x,y in verts: host.plot(x, y, 'go') # to show the control points of the beziers
             codes = [Path.MOVETO
                      ] + [Path.CURVE4 for _ in range(len(verts) - 1)]
             path = Path(verts, codes)
-            if ys[j, -1] == 0:
-                alpha = alpha_black
-            else:
-                alpha = 0.8
+            alpha = alpha_black if ys[j, -1] == 0 else 0.8
             patch = patches.PathPatch(path,
                                       facecolor='none',
                                       lw=.5,
@@ -277,7 +278,7 @@ def create_stack(ax,
                 verticalalignment='center',
                 fontsize=text_fontsize,
                 zorder=11)
-        if not (angle is None):
+        if angle is not None:
             [dx, dy] = np.dot(rotation_matrix(np.deg2rad(angle)), [x, y])
             x_mid = dx / 2
             y_mid = dy / 2

cmtj/utils/resistance.py

@@ -53,8 +53,7 @@ def calculate_magnetoresistance(Rp: float, Rap: float, m: np.ndarray):
         raise ValueError(
             "The magnetoresistance can only be computed for 2 layers"
             f". Current shape {m.shape}")
-    R = Rp + 0.5 * (Rap - Rp) * np.sum(m[0] * m[1], axis=0)
-    return R
+    return Rp + 0.5 * (Rap - Rp) * np.sum(m[0] * m[1], axis=0)
 
 
 def calculate_resistance_parallel(Rx0: List[float], Ry0: List[float],

cmtj/utils/solvers.py

@@ -43,6 +43,4 @@ def find(self, f, *args, **kwargs):
 
     def find_root(self, f, x0, *args, fprime=None):
         sol = root(f, x0, args=args, jac=fprime, method="hybr")
-        if sol.success:
-            return sol.x[0]
-        return None
+        return sol.x[0] if sol.success else None

examples/networks/training.py

@@ -85,7 +85,6 @@ def __init__(self,
         else:
             raise ValueError(f"Unknown loss type: {loss_type}")
 
-            infeat = 2998 * 80
         self.model = nn.Sequential(
             nn.Flatten(), nn.Linear(in_features=infeat, out_features=2048),
             nn.ReLU(), nn.Dropout(p=1 - keep_prob),
@@ -162,8 +161,7 @@ def validation_epoch_end(self, outputs):
         return {'val_loss': avg_loss}
 
     def configure_optimizers(self):
-        opt = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
-        return opt
+        return torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
 
 
 if __name__ == "__main__":

setup.py

@@ -122,10 +122,7 @@ def build_extensions(self):
                 opts.append("-fvisibility=hidden")
 
         for ext in self.extensions:
-            ext.define_macros = [
-                ("VERSION_INFO",
-                 '"{}"'.format(self.distribution.get_version()))
-            ]
+            ext.define_macros = [("VERSION_INFO", f'"{self.distribution.get_version()}"')]
             ext.extra_compile_args = opts
             ext.extra_link_args = link_opts
         build_ext.build_extensions(self)