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import matplotlib.pyplot as plt
import matplotlib as mpl
%matplotlib inline
#%config InlineBackend.figure_format='retina' # for hi-dpi displays
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%run patternlib
Here we define the pattern parameters. For a description of each paramenter refer
to the compute_pattern() documentation in patternlib.py.
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d = {
'Xm': [21, 21.72, 44.87, 68.2, 91.45, 114.72, 137.87, 160.97, 184.04],
'Ym': [21.72, 44.87, 68.2, 91.45, 114.72, 137.87, 160.97, 184.04],
'center_x': 0,
'center_y': 0,
'dark_all': False,
'focal': 0.032,
'grid': True,
'ncols': 8,
'nospot': False,
'nrows': 1,
'phase_factor': 85,
'phase_max': 2.0,
'phase_wrap_neg': True,
'phase_wrap_pos': True,
'pitch_x': 18,
'pitch_y': 18,
'ref_spot': 0,
'ref_spot_dark': False,
'rotation': 1,
'spotsize': 30.0,
'steer_horiz': True,
'steer_lw': 2,
'steer_pad': 4,
'steer_vmax': 95,
'test_pattern': False,
'wavelen': 532e-09,
'stretch': True}
We generate the pattern (a 2D array of unit8 values):
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a = compute_pattern(**d)
and plot it:
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fig, ax = plt.subplots(figsize=(8, 5))
im = plt.imshow(a, interpolation='none', cmap='magma',
vmin=0, vmax=255)#, norm=mpl.colors.LogNorm(1, 255))
plt.colorbar()
#plt.tight_layout()
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fig, ax = plt.subplots(figsize=(7, 4))
im = plt.imshow(a, interpolation='none', cmap='Greys_r',
vmin=0, vmax=255)#, norm=mpl.colors.LogNorm(1, 255))
plt.colorbar()
#plt.tight_layout()
plt.xlim(480, 320)
plt.ylim(240, 360)
ax.get_yaxis().set_tick_params(direction='out')
ax.get_xaxis().set_tick_params(direction='out')
plt.savefig('../figures/SI_figure_LCOS_pattern.png', bbox_inches='tight', dpi=300)
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