Fixed issues indicated by flake8

src/pyimpspec/analysis/drt/bht.py

@@ -777,7 +777,7 @@ def _single_hilbert_transform_estimate(
                 kw = {}
             else:
                 kw = {"category": OptimizeWarning}
-            
+
             with warnings.catch_warnings(**kw):
                 warnings.simplefilter("ignore")
                 res: OptimizeResult = minimize(
@@ -1243,10 +1243,10 @@ def calculate_drt_bht(
 
         A_im: NDArray[float64] = _compute_A_im(w, tau)
         prog.increment()
-        
+
         A_H_re: NDArray[float64] = _compute_A_H_re(w, tau)
         prog.increment()
-        
+
         A_H_im: NDArray[float64] = _compute_A_H_im(w, tau)
         prog.increment()
 

src/pyimpspec/analysis/drt/tr_nnls.py

@@ -450,7 +450,7 @@ def calculate_drt_tr_nnls(
     if not isinstance(mode, str):
         raise TypeError(f"Expected a string instead of {mode=}")
     elif mode not in _MODES:
-        raise ValueError(f"Valid mode values: '" + "', '".join(_MODES))
+        raise ValueError("Valid mode values: '" + "', '".join(_MODES))
 
     if not _is_floating(lambda_value):
         raise TypeError(f"Expected a float instead of {lambda_value=}")

src/pyimpspec/analysis/drt/utility.py

@@ -116,7 +116,7 @@ def _l_curve_corner_search(
 
     def update_lambda(lambdas: List[float], index: int):
         if index not in (1, 2):
-            raise ValueError(f"Expected the index to be 1 or 2")
+            raise ValueError("Expected the index to be 1 or 2")
 
         xs: NDArray[float64] = log(lambdas)
 

src/pyimpspec/analysis/kramers_kronig/algorithms/utility/osculating_circle.py

@@ -187,18 +187,18 @@ def calculate_curvatures(Z: ComplexImpedances) -> NDArray[float64]:
         NDArray[float64]
     """
     kappa: NDArray[float64] = zeros((Z.size - 2, 3), dtype=float64)
-    
+
     a: NDArray[float64] = zeros((Z.size - 2, 2), dtype=float64)
-    a[:,0] = (Z[1:-1] - Z[:-2]).real
-    a[:,1] = (Z[1:-1] - Z[:-2]).imag
+    a[:, 0] = (Z[1:-1] - Z[:-2]).real
+    a[:, 1] = (Z[1:-1] - Z[:-2]).imag
 
     b: NDArray[float64] = zeros((Z.size - 2, 2), dtype=float64)
-    b[:,0] = (Z[2:] - Z[1:-1]).real
-    b[:,1] = (Z[2:] - Z[1:-1]).imag
+    b[:, 0] = (Z[2:] - Z[1:-1]).real
+    b[:, 1] = (Z[2:] - Z[1:-1]).imag
 
     c: NDArray[float64] = zeros((Z.size - 2, 2), dtype=float64)
-    c[:,0] = (Z[2:] - Z[:-2]).real
-    c[:,1] = (Z[2:] - Z[:-2]).imag
+    c[:, 0] = (Z[2:] - Z[:-2]).real
+    c[:, 1] = (Z[2:] - Z[:-2]).imag
 
     a_dot_b: NDArray[float64] = zeros(Z.size - 2, dtype=float64)
     a_norm: NDArray[float64] = zeros(Z.size - 2, dtype=float64)
@@ -209,12 +209,12 @@ def calculate_curvatures(Z: ComplexImpedances) -> NDArray[float64]:
 
     i: int
     for i in range(0, a.shape[0]):
-        a_dot_b[i] = dot(a[i,:], b[i,:])
-        a_norm[i] = norm(a[i,:])
-        b_norm[i] = norm(b[i,:])
+        a_dot_b[i] = dot(a[i, :], b[i, :])
+        a_norm[i] = norm(a[i, :])
+        b_norm[i] = norm(b[i, :])
         a_norm_dot_b_norm[i] = dot(a_norm[i], b_norm[i])
         cos_alpha[i] = a_dot_b[i] / a_norm_dot_b_norm[i]
-        c_norm[i] = norm(c[i,:])
+        c_norm[i] = norm(c[i, :])
 
     indices: NDArray[int64] = argwhere(cos_alpha < -1.0).flatten()
     if indices.size > 0:
@@ -235,12 +235,12 @@ def calculate_curvatures(Z: ComplexImpedances) -> NDArray[float64]:
     abs_kappa: NDArray[float64] = 2 * sin(alpha) / c_norm
 
     matrix: NDArray[float64] = ones((Z.size, 3), dtype=float64)
-    matrix[:,0] = Z.real
-    matrix[:,1] = -Z.imag
+    matrix[:, 0] = Z.real
+    matrix[:, 1] = -Z.imag
     determinant: NDArray[float64] = zeros(Z.size - 2, dtype=float64)
 
     for i in range(0, a.shape[0]):
-        determinant[i] = det(matrix[i:i+3,:])
+        determinant[i] = det(matrix[i:i+3, :])
 
     kappa: NDArray[float64] = abs_kappa * sign(determinant)
 

src/pyimpspec/analysis/kramers_kronig/algorithms/utility/pseudo_chi_squared.py

@@ -154,9 +154,9 @@ def _fit_intersecting_lines(
                     p0=(s1, o1, s2, o2),
                     maxfev=1000,
                 )[0]
-            except RuntimeError as e1:
+            except RuntimeError:
                 continue
-            except TypeError as e2:
+            except TypeError:
                 break
 
             return tuple(p)
@@ -191,7 +191,7 @@ def _approximate_transition_and_end_point(
 
     # Some test implementations may have a very significant drop in
     # X²ps at high num_RC when the number of points per decade is low.
-    while (log_pseudo_chisqrs[i-1] -  log_pseudo_chisqrs[i]) > 1.0:
+    while (log_pseudo_chisqrs[i-1] - log_pseudo_chisqrs[i]) > 1.0:
         i -= 1
 
     max_num_RC: int = int(max(num_RCs[:i]))

src/pyimpspec/analysis/kramers_kronig/exploratory.py

@@ -434,7 +434,7 @@ def _calculate_statistic(
     if target_num_RC > 0:
         i: int = argmin(abs(x - target_num_RC))
 
-        return mean(y[i : i + 2])
+        return mean(y[i:i + 2])
 
     # Unable to estimate the target num_RC at
     # log(fits.log_F_ext) = 0 for some reason.
@@ -617,8 +617,8 @@ def _fit_cubic_and_interpolate(
         i = max((0, k - 2 * delta))
 
     if (k - i) == (2 * delta) and (k - i) > 3:
-        x = x[i : k + 1]
-        y = y[i : k + 1]
+        x = x[i:k + 1]
+        y = y[i:k + 1]
 
     p: Tuple[float, float, float, float] = _fit_cubic_function(x, y)
 
@@ -772,8 +772,8 @@ def backup_approach(x: NDArray[float64], y: NDArray[float64]):
             m = min(argwhere(smooth_y <= max(rel_std)).flatten())
             n = max(argwhere(smooth_y >= min(rel_std)).flatten())
             ax2.plot(
-                smooth_x[m : n + 1],
-                smooth_y[m : n + 1],
+                smooth_x[m:n + 1],
+                smooth_y[m:n + 1],
                 color="green",
                 linestyle=":",
             )
@@ -799,8 +799,8 @@ def backup_approach(x: NDArray[float64], y: NDArray[float64]):
         m = min(argwhere(smooth_y <= max(y)).flatten())
         n = max(argwhere(smooth_y >= min(y)).flatten())
         ax1.plot(
-            smooth_x[m : n + 1],
-            smooth_y[m : n + 1],
+            smooth_x[m:n + 1],
+            smooth_y[m:n + 1],
             color="black",
             linestyle=":",
         )

src/pyimpspec/analysis/kramers_kronig/least_squares.py

@@ -85,7 +85,7 @@ def _add_resistance_to_A_matrix(
     i: int = 0
 
     if test == "complex":
-        A[0 : m // 2, i] = 1
+        A[0:m // 2, i] = 1
     elif test == "real":
         A[0:m, i] = 1
 
@@ -117,8 +117,8 @@ def _add_kth_variables_to_A_matrix(
 
     m: int = A.shape[0]
     if test == "complex":
-        A[0 : m // 2, i] = c.real
-        A[m // 2 :, i] = c.imag
+        A[0:m // 2, i] = c.real
+        A[m // 2:, i] = c.imag
     elif test == "real":
         A[0:m, i] = c.real
     else:
@@ -135,7 +135,7 @@ def _add_capacitance_to_A_matrix(
     m: int = A.shape[0]
 
     if test == "complex":
-        A[m // 2 :, i] = w if admittance else (-1 / w)
+        A[m // 2:, i] = w if admittance else (-1 / w)
     elif test == "imaginary":
         A[:, i] = w if admittance else (-1 / w)
 
@@ -150,7 +150,7 @@ def _add_inductance_to_A_matrix(
     m: int = A.shape[0]
 
     if test == "complex":
-        A[m // 2 :, i] = (1 / w) if admittance else w
+        A[m // 2:, i] = (1 / w) if admittance else w
     elif test == "imaginary":
         A[:, i] = (1 / w) if admittance else w
 
@@ -227,8 +227,8 @@ def _add_values_to_b_vector(
     m: int = b.shape[0]
 
     if test == "complex":
-        b[0 : m // 2] = (Z_exp ** (-1 if admittance else 1)).real
-        b[m // 2 :] = (Z_exp ** (-1 if admittance else 1)).imag
+        b[0:m // 2] = (Z_exp ** (-1 if admittance else 1)).real
+        b[m // 2:] = (Z_exp ** (-1 if admittance else 1)).imag
     elif test == "real":
         b[0:m] = (Z_exp ** (-1 if admittance else 1)).real
     else:

src/pyimpspec/analysis/zhit/smoothing/modified_sinc.py

@@ -262,7 +262,7 @@ def _extend_data(
     At each end, m extrapolated points are appended.
     """
     extended_data: NDArray[float64] = zeros(len(data) + 2 * m, dtype=float64)
-    extended_data[m : m + len(data)] = data
+    extended_data[m:m + len(data)] = data
 
     lin_reg: LinearRegression = LinearRegression()
 
@@ -376,7 +376,7 @@ def _smooth(
         kernel,
     )
 
-    return extended_smoothed[radius : radius + len(data)]
+    return extended_smoothed[radius:radius + len(data)]
 
 
 def _smooth_like_savgol(

src/pyimpspec/circuit/base.py

@@ -1282,7 +1282,7 @@ def _get_elements_recursive(self) -> List[Element]:
                 )
 
         if len(elements) != len(set(elements)):
-            raise ValueError(f"Detected duplicate elements")
+            raise ValueError("Detected duplicate elements")
 
         return elements
 

src/pyimpspec/circuit/parser.py

@@ -102,7 +102,7 @@ def process(self, string: str, version: int = -1) -> Circuit:
         if (
             string == ""
             or string == "[]"
-            or (string.startswith("!") and string[string.find("!", 1) + 1 :] == "[]")
+            or (string.startswith("!") and string[string.find("!", 1) + 1:] == "[]")
         ):
             self.push_stack(Series([]))
         else:

src/pyimpspec/circuit/registry.py

@@ -69,7 +69,7 @@ def _initialized():
 
     key: str
     element: Type[Element]
-    for  key, element in _DEFAULT_ELEMENTS.items():
+    for key, element in _DEFAULT_ELEMENTS.items():
         _DEFAULT_ELEMENT_PARAMETERS[key] = element.get_default_values().copy()
 
 

src/pyimpspec/circuit/tokenizer.py

@@ -304,7 +304,7 @@ def push(self, Class: Type[Token]):
         if Class is Number or Class is FixedNumber:
             value = float(self._value)
         else:
-            value = self._original[self._start : self._end]
+            value = self._original[self._start:self._end]
 
         self._tokens.append(Class(self._start, self._end, value))
         self._start = self._index

src/pyimpspec/cli/test.py

@@ -190,7 +190,7 @@ def exploratory_tests(
             raise err
         else:
             raise ValueError(
-                f"Expected to successfully test at least one representation"
+                "Expected to successfully test at least one representation"
             )
 
     test: KramersKronigResult

src/pyimpspec/cli/utility.py

@@ -263,7 +263,7 @@ def _parse_identity(identity: str) -> Tuple[str, dict]:
 
     i: int = identity.rfind(":")
     if ":" in identity and i > max(map(lambda s: identity.rfind(s), ("}", "]", ")"))):
-        for arg in identity[i + 1 :].split(","):
+        for arg in identity[i + 1:].split(","):
             key, value = arg.split("=")
             if key in kwarg_types:
                 kwargs[key] = kwarg_types[key](value)

src/pyimpspec/data/__init__.py

@@ -183,7 +183,7 @@ def parse_data(
     if not isinstance(data_sets, list):
         raise TypeError(f"Expected a list instead of {data_sets=}")
     elif len(data_sets) == 0:
-        raise ValueError(f"Expected a list with at least one element")
+        raise ValueError("Expected a list with at least one element")
     elif not all(map(lambda _: isinstance(_, DataSet), data_sets)):
         raise TypeError(f"Expected a list of DataSet instead of {data_sets=}")
     elif not all(map(lambda _: _.get_label().strip() != "", data_sets)):

src/pyimpspec/data/data_set.py

@@ -135,7 +135,7 @@ def __init__(
             )
         elif not (len(frequencies) == len(impedances) > 0):
             raise ValueError(
-                f"Expected the frequency and impedance arrays to be of equal length and have at least 1 element each"
+                "Expected the frequency and impedance arrays to be of equal length and have at least 1 element each"
             )
         elif len(unique(frequencies)) != len(frequencies):
             raise ValueError(
@@ -395,7 +395,7 @@ def average(cls, data_sets: List["DataSet"], label: str = "Average") -> "DataSet
                 frequencies,
             )
         ):
-            raise ValueError(f"Expected all frequency arrays to have the same values")
+            raise ValueError("Expected all frequency arrays to have the same values")
 
         Z: ComplexImpedances = mean(array(impedances), axis=0)
 

src/pyimpspec/data/formats/pssession.py

@@ -59,7 +59,7 @@ def parse_pssession(path: Union[str, Path]) -> List[DataSet]:
     json: dict = loads(contents[i:j])
     if "Measurements" not in json:
         raise UnsupportedFileFormat(
-            f"Expected to find a 'Measurements' key in the file"
+            "Expected to find a 'Measurements' key in the file"
         )
 
     dataframes: Dict[str, DataFrame] = {}

src/pyimpspec/plot/mpl/kramers_kronig.py

@@ -354,8 +354,8 @@ def _plot_suggestion_method_1(
         j = max(argwhere(y >= -0.05).flatten())
         axes[0].axhline(p[0] + p[3], color=COLOR_RED, linestyle=":")
         axes[0].plot(
-            x[i : j + 1],
-            y[i : j + 1],
+            x[i:j + 1],
+            y[i:j + 1],
             color=COLOR_RED,
             linestyle=":",
         )