From 1752cd6249af62ba40790ba8a4a34ba9822fe60f Mon Sep 17 00:00:00 2001 From: Logan Bishop-Van Horn Date: Wed, 8 Jun 2022 09:23:14 -0700 Subject: [PATCH] Update black --- sequencing/calibration.py | 4 ++-- sequencing/pulses.py | 14 +++++++------- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/sequencing/calibration.py b/sequencing/calibration.py index b4cb039..ba1a128 100644 --- a/sequencing/calibration.py +++ b/sequencing/calibration.py @@ -44,8 +44,8 @@ def sine(xs, amp=1, f0=1, phi=0, ofs=0.5): def fit_displacement(xs, ys): def displacement(xs, xscale=1.0, amp=1, ofs=0, n=0): alphas = xs * xscale - nbars = alphas ** 2 - return ofs + amp * nbars ** n / factorial(int(n)) * np.exp(-nbars) + nbars = alphas**2 + return ofs + amp * nbars**n / factorial(int(n)) * np.exp(-nbars) if xs[-1] > xs[0]: amp = ys[0] - ys[-1] diff --git a/sequencing/pulses.py b/sequencing/pulses.py index 548240d..273315a 100644 --- a/sequencing/pulses.py +++ b/sequencing/pulses.py @@ -89,20 +89,20 @@ def array_pulse( def gaussian_wave(sigma, chop=4): ts = np.linspace(-chop // 2 * sigma, chop // 2 * sigma, int(chop * sigma // 4) * 4) - P = np.exp(-(ts ** 2) / (2.0 * sigma ** 2)) + P = np.exp(-(ts**2) / (2.0 * sigma**2)) ofs = P[0] return (P - ofs) / (1 - ofs) def gaussian_deriv_wave(sigma, chop=4): ts = np.linspace(-chop // 2 * sigma, chop // 2 * sigma, int(chop * sigma // 4) * 4) - ofs = np.exp(-ts[0] ** 2 / (2 * sigma ** 2)) - return (0.25 / sigma ** 2) * ts * np.exp(-(ts ** 2) / (2 * sigma ** 2)) / (1 - ofs) + ofs = np.exp(-ts[0] ** 2 / (2 * sigma**2)) + return (0.25 / sigma**2) * ts * np.exp(-(ts**2) / (2 * sigma**2)) / (1 - ofs) def gaussian_chop(t, sigma, t0): - P = np.exp(-(t ** 2) / (2.0 * sigma ** 2)) - ofs = np.exp(-(t0 ** 2) / (2.0 * sigma ** 2)) + P = np.exp(-(t**2) / (2.0 * sigma**2)) + ofs = np.exp(-(t0**2) / (2.0 * sigma**2)) return (P - ofs) / (1 - ofs) @@ -135,9 +135,9 @@ def _ring_up(ts): if np.abs(ts) < ramp_offset: return 1.0 elif ts > ramp_offset: - return np.exp(-((ts - ramp_offset) ** 2) / (2.0 * sigma ** 2)) + return np.exp(-((ts - ramp_offset) ** 2) / (2.0 * sigma**2)) else: # ts < ramp_offset - return np.exp(-((ts + ramp_offset) ** 2) / (2.0 * sigma ** 2)) + return np.exp(-((ts + ramp_offset) ** 2) / (2.0 * sigma**2)) ts = np.linspace(-length + 1, 0, length) P = np.array([_ring_up(t) for t in ts])