fix: 💄 stylize

filters-kf-basic.ipynb

@@ -13,9 +13,9 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "from numpy import random\n",
                 "import matplotlib.pyplot as plt\n",
-                "import numpy as np"
+                "import numpy as np\n",
+                "from numpy import random"
             ]
         },
         {

filters/bayes.py

@@ -60,8 +60,7 @@ def prior_estimate(x0, model, zs, R, show_steps=False):
         priors[i], estimates[i], ps[i] = prior, x.mean, x.var
         if show_steps:
             if i == 0:
-                print_header
-            print_steps(prior, z, x)
+                print_steps(prior, z, x)
             if i == len(zs) - 1:
                 print_conclusion(z, x)
     return (priors, estimates, ps)

filters/fusion.py

@@ -1,13 +1,14 @@
 import sys
 
-from numpy import random
 import numpy as np
+from numpy import random
 
 from .helpers import KFSaver
 from .kalman import KalmanFilter
 from .kalman_ukf import UnscentedKalmanFilter
 from .sigma_points import JulierSigmas
 
+
 sys.path.append('..')
 from models.const_vel import FCV
 

filters/ghk.py

@@ -490,7 +490,8 @@ def critical_damping_params(theta, order=2):
     """
 
     if theta < 0 or theta > 1:
-        raise ValueError('theta must be between 0 and 1')
+        error_message = 'theta must be between 0 and 1'
+        raise ValueError(error_message)
 
     if order == 2:
         return (1.0 - theta**2, (1.0 - theta) ** 2)
@@ -502,7 +503,8 @@ def critical_damping_params(theta, order=2):
             0.5 * (1 - theta) ** 3,
         )
 
-    raise ValueError(f'bad order specified: {order}')
+    error_message = f'bad order specified: {order}'
+    raise ValueError(error_message)
 
 
 def benedict_bornder_constants(g, critical=False):

filters/ghq.py

@@ -1,6 +1,6 @@
+import numpy as np
 from scipy import linalg
 from scipy import stats
-import numpy as np
 
 
 def gh_packed_pc(x, fmm_param):

filters/helpers.py

@@ -1,7 +1,7 @@
 from collections import defaultdict
 from collections import deque
-from copy import deepcopy
 import contextlib
+from copy import deepcopy
 import inspect
 
 import numpy as np
@@ -75,7 +75,8 @@ def to_array(self, flatten=False):
             except Exception as e:
                 # get back to lists so we are in a valid state
                 self.__dict__.update(self._DL)
-                raise ValueError(f'could not convert {key} into np.array') from e
+                error_message = f'could not convert {key} into np.array'
+                raise ValueError(error_message) from e
         if flatten:
             self.flatten()
 

filters/imm.py

@@ -20,7 +20,8 @@ class IMMEstimator:
     def __init__(self, filters, mu, M):
         self.num_f = len(filters)
         if self.num_f < 2:
-            raise ValueError('filters must contain at least two filters')
+            error_message = 'filters must contain at least two filters'
+            raise ValueError(error_message)
 
         self.filters = filters
         self.mu = np.asarray(mu) / np.sum(mu)
@@ -70,7 +71,7 @@ def predict(self, u=None):
 
         # compute mixed initial conditions
         xs, Ps = [], []
-        for i, (f, w) in enumerate(zip(self.filters, self.omega.T)):
+        for _, (_, w) in enumerate(zip(self.filters, self.omega.T)):
             x = np.zeros(self.x.shape)
             for kf, wj in zip(self.filters, w):
                 x += kf.x * wj
@@ -141,10 +142,12 @@ class MMAEFilterBank:
 
     def __init__(self, filters, p, dim_x, H=None):
         if len(filters) != len(p):
-            raise ValueError('length of filters and p must be the same')
+            error_message = 'length of filters and p must be the same'
+            raise ValueError(error_message)
 
         if dim_x < 1:
-            raise ValueError('dim_x must be >= 1')
+            error_message = 'dim_x must be >= 1'
+            raise ValueError(error_message)
 
         self.filters = filters
         self.p = np.asarray(p)

filters/kalman.py

@@ -1,11 +1,13 @@
+from __future__ import annotations
+
 from copy import deepcopy
-from typing import Union
 import math
 import sys
+from typing import Union
 
+import numpy as np
 from numpy import linalg
 from scipy import stats
-import numpy as np
 
 from .helpers import pretty_str
 from .transformers import reshape_z
@@ -65,11 +67,14 @@ def __init__(self, dim_x: int, dim_z: int, dim_u: int = 0, _alpha_sq: float = 1.
         Dan Simon. "Optimal State Estimation." John Wiley & Sons. p. 208-212. (2006)
         """
         if dim_x < 1:
-            raise ValueError('dim_x ≥ 1')
+            error_message = 'dim_x ≥ 1'
+            raise ValueError(error_message)
         if dim_z < 1:
-            raise ValueError('dim_z ≥ 1')
+            error_message = 'dim_z ≥ 1'
+            raise ValueError(error_message)
         if dim_u < 0:
-            raise ValueError('dim_u ≥ 0')
+            error_message = 'dim_u ≥ 0'
+            raise ValueError(error_message)
 
         self.dim_x = dim_x
         self.dim_z = dim_z
@@ -122,10 +127,10 @@ def __init__(self, dim_x: int, dim_z: int, dim_u: int = 0, _alpha_sq: float = 1.
 
     def predict(
         self,
-        F: Union[bool, np.ndarray] = None,
-        G: Union[bool, np.ndarray] = None,
-        u: Union[bool, np.array] = None,
-        Q: Union[bool, np.ndarray] = None,
+        F: bool | np.ndarray = None,
+        G: bool | np.ndarray = None,
+        u: bool | np.array = None,
+        Q: bool | np.ndarray = None,
     ):
         """Predict next state using the Kalman filter state propagation equations.
 
@@ -318,7 +323,8 @@ def rts_smoother(self, Xs, Ps, Fs=None, Qs=None, inv=linalg.inv):
         means and covariances computed by a Kalman filter. The usual input would come from the output of `batch_filter()`.
         """
         if len(Xs) != len(Ps):
-            raise ValueError('length of Xs and Ps must be the same')
+            error_message = 'length of Xs and Ps must be the same'
+            raise ValueError(error_message)
 
         n = Xs.shape[0]
         dim_x = Xs.shape[1]
@@ -402,7 +408,8 @@ def alpha(self):
     @alpha.setter
     def alpha(self, value):
         if not np.isscalar(value) or value < 1:
-            raise ValueError('alpha must be a float greater than 1')
+            error_message = 'alpha must be a float greater than 1'
+            raise ValueError(error_message)
 
         self._alpha_sq = value**2
 

filters/kalman_ckf.py

@@ -2,9 +2,9 @@
 import math
 import sys
 
+import numpy as np
 from numpy import linalg
 from scipy import stats
-import numpy as np
 
 from .helpers import pretty_str
 from .sigma_points import SphericalRadialSigmas

filters/kalman_ekf.py

@@ -1,14 +1,13 @@
 from __future__ import annotations
 
 from copy import deepcopy
-from typing import Callable
-from typing import Union
 import math
 import sys
+from typing import Callable
 
+import numpy as np
 from numpy import linalg
 from scipy import stats
-import numpy as np
 
 from .helpers import pretty_str
 from .transformers import reshape_z

filters/kalman_enkf.py

@@ -1,8 +1,8 @@
 from copy import deepcopy
 
+import numpy as np
 from numpy import linalg
 from numpy import random
-import numpy as np
 
 from .helpers import pretty_str
 

filters/kalman_fm.py

@@ -17,7 +17,8 @@ class FadingMemoryFilter:
 
     def __init__(self, x0, dt, order, beta):
         if order < 0 or order > 2:
-            raise ValueError('order must be between 0 and 2')
+            error_message = 'order must be between 0 and 2'
+            raise ValueError(error_message)
 
         if np.isscalar(x0):
             self.x = np.zeros(order + 1)

filters/kalman_hinf.py

@@ -1,7 +1,7 @@
 from copy import deepcopy
 
-from numpy import linalg
 import numpy as np
+from numpy import linalg
 
 from .helpers import pretty_str
 

filters/kalman_ukf.py

@@ -2,9 +2,9 @@
 import math
 import sys
 
+import numpy as np
 from numpy import linalg
 from scipy import stats
-import numpy as np
 
 from .helpers import pretty_str
 from .kalman import KalmanFilter

filters/lsq.py

@@ -17,7 +17,8 @@ class LeastSquaresFilter:
 
     def __init__(self, dt, order, noise_sigma=0.0):
         if order < 0 or order > 2:
-            raise ValueError('order must be between 0 and 2')
+            error_message = 'order must be between 0 and 2'
+            raise ValueError(error_message)
 
         self.dt = dt
 

filters/particle.py

@@ -1,6 +1,6 @@
+import numpy as np
 from numpy import random
 from scipy import stats
-import numpy as np
 
 
 class ParticleFilter:

filters/resamplers.py

@@ -1,5 +1,5 @@
-from numpy import random
 import numpy as np
+from numpy import random
 
 
 def resample_from_index(particles, weights, indexes):

filters/sigma_points.py

@@ -1,5 +1,5 @@
-from scipy import linalg
 import numpy as np
+from scipy import linalg
 
 from .helpers import pretty_str
 
@@ -50,7 +50,8 @@ def sigma_points(self, x, P):
         """
 
         if self.n != np.size(x):
-            raise ValueError(f'expected size(x) {self.n}, but size is {np.size(x)}')
+            error_message = f'expected size(x) {self.n}, but size is {np.size(x)}'
+            raise ValueError(error_message)
 
         n = self.n
 
@@ -121,7 +122,8 @@ def sigma_points(self, x, P):
         """
 
         if self.n != np.size(x):
-            raise ValueError(f'expected size(x) {self.n}, but size is {np.size(x)}')
+            error_message = f'expected size(x) {self.n}, but size is {np.size(x)}'
+            raise ValueError(error_message)
 
         if np.isscalar(x):
             x = np.asarray([x])
@@ -205,7 +207,8 @@ def sigma_points(self, x, P):
         """
 
         if self.n != np.size(x):
-            raise ValueError(f'expected size(x) {self.n}, but size is {np.size(x)}')
+            error_message = f'expected size(x) {self.n}, but size is {np.size(x)}'
+            raise ValueError(error_message)
 
         n = self.n
 
@@ -220,9 +223,8 @@ def sigma_points(self, x, P):
         Istar = np.array([[-1 / np.sqrt(2 * λ), 1 / np.sqrt(2 * λ)]])
 
         for d in range(2, n + 1):
-            row = np.ones((1, Istar.shape[1] + 1)) * 1.0 / np.sqrt(λ * d * (d + 1))  # pylint: disable=unsubscriptable-object
+            row = np.ones((1, Istar.shape[1] + 1)) * 1.0 / np.sqrt(λ * d * (d + 1))
             row[0, -1] = -d / np.sqrt(λ * d * (d + 1))
-            Istar = np.r_[np.c_[Istar, np.zeros((Istar.shape[0]))], row]  # pylint: disable=unsubscriptable-object
 
         _I = np.sqrt(n) * Istar
         scaled_unitary = U.T @ _I
@@ -282,7 +284,8 @@ def sigma_points(self, x, P):
         Arasaratnam, I, Haykin, S. "Cubature Kalman Filters," IEEE Transactions on Automatic Control, 2009, pp 1254-1269, vol 54, No 6
         """
         if self.n != np.size(x):
-            raise ValueError(f'expected size(x) {self.n}, but size is {np.size(x)}')
+            error_message = f'expected size(x) {self.n}, but size is {np.size(x)}'
+            raise ValueError(error_message)
 
         # dimension of P is
         n = self.n

filters/smoothers.py

@@ -1,5 +1,5 @@
-from numpy import linalg
 import numpy as np
+from numpy import linalg
 
 from .helpers import pretty_str
 from .kalman import KalmanFilter

filters/stats.py

@@ -1,7 +1,8 @@
 from collections import namedtuple
 
-from numpy import linalg
 import numpy as np
+from numpy import linalg
+
 
 gaussian = namedtuple('Gaussian', ['mean', 'var'])  # noqa: PYI024
 gaussian.__repr__ = lambda s: f'N(μ={s[0]:.3f}, σ²={s[1] ** 2:.3f})'

filters/transformers.py

@@ -1,5 +1,5 @@
-from scipy import linalg
 import numpy as np
+from scipy import linalg
 
 
 def reshape_z(z, dim_z, ndim):
@@ -30,9 +30,8 @@ def reshape_z(z, dim_z, ndim):
         z = z.T
 
     if z.shape != (dim_z, 1):
-        raise ValueError(
-            f'z (shape {z.shape}) must be convertible to shape ({dim_z}, 1)'
-        )
+        error_message = f'z (shape {z.shape}) must be convertible to shape ({dim_z}, 1)'
+        raise ValueError(error_message)
 
     if ndim == 1:
         z = z[:, 0]

models/const_acc.py

@@ -1,10 +1,11 @@
 import sys
 
-from scipy import linalg
 import numpy as np
+from scipy import linalg
 
 from .noise import white_noise_discrete
 
+
 sys.path.append('..')
 from filters.kalman import KalmanFilter
 
@@ -41,7 +42,7 @@ def FCA(dim, dt):
     return F
 
 
-def KFCA3d(P, R, Q=0, dt=1, x=[0, 0, 0]):
+def KFCA3d(P, R, Q=0, dt=1, x=(0, 0, 0)):
     if type(x) == list:
         x = np.array(x)
     dim_x = len(x)