2020-02-18 11:05:27 +00:00
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import numpy as np
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2020-03-30 09:40:19 +00:00
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lookup_tables_u16 = []
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2020-04-23 22:38:05 +00:00
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lookup_tables_u8 = []
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2019-12-03 23:00:54 +00:00
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2020-04-01 10:04:42 +00:00
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ADC_RESOLUTION = 4096
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OUTPUT_RESOLUTION = 2 ** 16 - 1
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2019-12-03 23:00:54 +00:00
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# linear to exponential conversion
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2020-02-18 11:08:15 +00:00
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values = np.linspace(0, 1, num=ADC_RESOLUTION)
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values = np.power(values, 2) * OUTPUT_RESOLUTION
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2019-12-03 23:00:54 +00:00
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2020-03-30 09:40:19 +00:00
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lookup_tables_u16.append(('linear_to_exp', values))
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2019-12-03 23:44:46 +00:00
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# Left pan Lookup table
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2020-02-18 11:08:15 +00:00
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l_pan = np.linspace(0, 1, num=ADC_RESOLUTION)
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r_pan = np.linspace(0, 1, num=ADC_RESOLUTION)
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2019-12-03 23:44:46 +00:00
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2020-02-18 11:08:15 +00:00
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l_pan = np.sin(l_pan * (np.pi / 2.0))
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r_pan = np.cos(r_pan * (np.pi / 2.0))
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2019-12-03 23:44:46 +00:00
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2020-02-18 11:05:27 +00:00
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l_pan = np.round(l_pan * OUTPUT_RESOLUTION)
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r_pan = np.round(r_pan * OUTPUT_RESOLUTION)
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2019-12-03 23:44:46 +00:00
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2020-03-30 09:40:19 +00:00
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lookup_tables_u16.append(('left_sin_pan', l_pan))
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lookup_tables_u16.append(('right_cos_pan', r_pan))
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2019-12-03 23:44:46 +00:00
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2020-04-23 22:38:05 +00:00
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# led gamma correction
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2020-04-24 09:48:02 +00:00
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gamma = 2.4
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2020-04-23 22:38:05 +00:00
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max_in = 255
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2020-04-24 09:48:02 +00:00
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max_out = 511
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2020-04-23 22:38:05 +00:00
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input_vals = np.linspace(0, max_in, num=max_in + 1)
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gamma_correction = ((input_vals / max_in) ** gamma) * max_out + 0.5
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2020-04-23 22:56:39 +00:00
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lookup_tables_u16.append(('led_gamma', np.floor(gamma_correction)))
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2020-04-23 22:38:05 +00:00
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