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Merged
loganbvh merged 1 commit into from
Jun 8, 2022
Merged

Update black #19

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4 changes: 2 additions & 2 deletions sequencing/calibration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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]
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14 changes: 7 additions & 7 deletions sequencing/pulses.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)


Expand Down Expand Up @@ -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])
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