print(np.polynomial.polynomial.polyfit(loopRange[1:], ts[1:], 1))
if (True):
ts = []
for loop in loopRange:
beads = []
for i in range(loop):
beads.append(N.Bead(50, 50))
N.XY(beads, img)
for i in range(20):
N.Calibrate(beads, [imgc[i]], i)
N.ComputeCalibration(beads)
N.XYZ(beads, img)
print(beads[0])
start = time.time()
for i in range(100):
N.XYZ(beads, img)
ts.append(time.time() - start)
plt.plot(loopRange, np.array(ts), marker="o", label="CPU - Numpy")
print(np.polynomial.polynomial.polyfit(loopRange[1:], ts[1:], 1))
plt.title("Time to run 100 frames")
plt.ylabel("Time(s)")
plt.xlabel("Bead Number")
plt.grid()
plt.legend()
mm.SetZ(sz)
T.ComputeCalibration(beads)
plt.title('Calibration R')
plt.imshow(np.flip(beads[0].Rc, axis=0), cmap="gray")
plt.show()
# test
zts = np.arange(500, 4500, 50)
z0s = []
for z in zts:
mm.SetZ(sz + z)
for t in range(100):
img = mm.Get()
T.XYZ(beads, img)
z0s.append(beads[0].z)
xs = []
ys = []
yerr = []
for i, z in enumerate(zts):
x = sz + z
data = z0s[i * 100:(i * 100 + 100)]
xs.append(x)
ys.append(np.mean(data) - x)
yerr.append(np.std(data))
plt.plot(z0s, marker="o")
plt.grid()
plt.show()
