Skip plt output in doctests

qutil/signal_processing/fourier_space.py

@@ -159,10 +159,10 @@ def derivative(x, f, order: int = 0, overwrite_x: bool = False,
     >>> import matplotlib.pyplot as plt
     >>> xfd, f = derivative(xf, f, order=1)
     >>> xtd = np.gradient(xt, t)
-    >>> plt.plot(t, 5 * np.cos(5 * t), label='Analytical')
-    >>> plt.plot(t, xtd, label='Finite differences')
-    >>> plt.plot(t, np.fft.irfft(xfd, n=n), label='FFT')
-    >>> plt.legend(); plt.xlabel('$t$'); plt.ylabel('$dx/dt$')
+    >>> plt.plot(t, 5 * np.cos(5 * t), label='Analytical')  # doctest: +SKIP
+    >>> plt.plot(t, xtd, label='Finite differences')  # doctest: +SKIP
+    >>> plt.plot(t, np.fft.irfft(xfd, n=n), label='FFT')  # doctest: +SKIP
+    >>> plt.legend(); plt.xlabel('$t$'); plt.ylabel('$dx/dt$')  # doctest: +SKIP
 
     Integrals:
 
@@ -170,10 +170,11 @@ def derivative(x, f, order: int = 0, overwrite_x: bool = False,
     >>> xfii, f = derivative(xf, f, order=-2)
     >>> xti = integrate.cumulative_simpson(xt, x=t, initial=0)
     >>> xtii = integrate.cumulative_simpson(xti, x=t, initial=0)
-    >>> plt.plot(t, -xt / 25, label='Analytical')
-    >>> plt.plot(t, xtii - np.linspace(0, 1, n), label='Finite differences')  # cheating
-    >>> plt.plot(t, np.fft.irfft(xfii, n=n), label='FFT')
-    >>> plt.legend(); plt.xlabel('$t$'); plt.ylabel(r'$\iint x dt$')
+    >>> plt.plot(t, -xt / 25, label='Analytical')  # doctest: +SKIP
+    >>> # cheating a bit here because indefinite integration is tricky
+    >>> plt.plot(t, xtii - np.linspace(0, 1, n), label='Finite differences')  # doctest: +SKIP
+    >>> plt.plot(t, np.fft.irfft(xfii, n=n), label='FFT')  # doctest: +SKIP
+    >>> plt.legend(); plt.xlabel('$t$'); plt.ylabel(r'$\iint x dt$')  # doctest: +SKIP
 
     Note that the backtransform method naturally only works well for
     periodic data.