Added Linter

.github/workflows/black.yml

@@ -0,0 +1,13 @@
+name: Lint
+
+on: [push, pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: psf/black@stable
+        with:
+            src: "./src"
+            jupyter: false

src/LangevinFunctions.py

@@ -64,6 +64,7 @@ def generate_multiple_molecules(n):
                         limits = 0
                     else:
                         coordinates[n] = single_track[:cutlimit, :]
+                        limits = 1
                 else:
                     coordinates[n] = single_track
                     limits = 1

src/MSDFunctions.py

@@ -61,7 +61,7 @@ def DSigma2_OLSF_BootStrap(self, coordinates, dT, R=1./6, n_d=1, maxiter=100, mi
             D_err[i], var_err[i] = self.DSigma2_OLSF(new_coordinates, dT, R=R, n_d=n_d)
         return np.std(D_err), np.std(var_err)
 
-    def DSigma2_OLSF(self, coordinates, dT, R=1./6, n_d=1, maxiter=100, min_points=10):
+    def DSigma2_OLSF(self, coordinates, dT, R=1./6, n_d=1, maxiter=100, min_points=10, supress_warning=False):
         """
         Compute diffusion coefficient estimate, and estimate of the
         dynamic localisation error, using the OLSF MSD approach.
@@ -118,13 +118,14 @@ def DSigma2_OLSF(self, coordinates, dT, R=1./6, n_d=1, maxiter=100, min_points=1
                 rho = rho[:np.max(np.hstack([pa, pb]))]
                 if not donea:
                     A = np.vstack((np.ones(pa[-1]), np.arange(1, pa[-1] + 1))).T
-                    B = rho[:A.shape[0]]
+                    B = np.asarray(rho[:A.shape[0]])
                     if B.shape[0] != A.shape[0]:
                         donea = True
                         doneb = True
                         D = np.nan
                         var = np.nan
-                        print('OLSF did not converge in {} iterations'.format(maxiter))
+                        if supress_warning == False:
+                            print('OLSF did not converge in {} iterations'.format(maxiter))
                         return D, var
                     X = np.linalg.lstsq(A, B, rcond=None)[0]
                     aa, ba = X
@@ -137,13 +138,14 @@ def DSigma2_OLSF(self, coordinates, dT, R=1./6, n_d=1, maxiter=100, min_points=1
                     pa = np.hstack([pa, newpa])
                 if not doneb:
                     A = np.vstack((np.ones(pb[-1]), np.arange(1, pb[-1] + 1))).T
-                    B = rho[:int(pb[-1])]
+                    B = np.asarray(rho[:int(pb[-1])])
                     if B.shape[0] != A.shape[0]:
                         donea = True
                         doneb = True
                         D = np.nan
                         var = np.nan
-                        print('OLSF did not converge in {} iterations'.format(maxiter))
+                        if supress_warning == False:
+                            print('OLSF did not converge in {} iterations'.format(maxiter))
                         return D, var
                     X = np.linalg.lstsq(A, B, rcond=None)[0]
                     ab, bb = X