15/12/2024 - 21/12/2024

18/12/2024 20:32

I'm testing how changing the external trigger rate affects the data rate for the nalu system. I still don't know how to construct events from the UDP packets, so the true event rate is unknown with these tests. Also keep in mind I'm not bothering to adjust the fine rate tuning knob on the gate generator.

Gate Generator Knob Setting (Hz)	Oscilloscope Measured Rate (Hz)	MIDAS Data Rate (MB/s)	Screenshot Link
1	<10	0.25	Link
10	18	2.7	Link
100	191	14.1	Link
1000	1850	21	Link
10000	17855	17.6	Link
100000	172700	17.7	Link

It seems the highest achievable rate is about 21 MB/s. But it is indeed responding to the external trigger in some way.

19/12/2024 01:25

I took some data using the NaluScope with varying gate generator (external trigger) rate:

Gate Generator Knob Setting (Hz)	Oscilloscope Measured Rate (Hz)
1	<10
10	18
100	191
1000	1850
10000	17855
100000	172700

The goal being to see how external trigger rate affects the rate of events digitized.

19/12/2024 01:33

Here is the structure of an event read in from an aquisition:

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       2095, 2094, 2078, 2028, 2069, 2106, 2088, 2072, 2025, 2009, 2082,
       2078, 2067, 2068, 2058, 2028, 2054, 2024, 2075, 2074, 2080, 2056,
       2046, 2071, 2076, 2054, 2044, 2078, 2085, 2002, 2032, 2040, 2035,
       2033, 2046, 2050, 2025, 2033, 2078, 2071, 2040, 2005, 2026, 2044,
       2040, 2003, 2040, 2046, 2032, 2055, 2076, 2054, 2025, 2040, 2069,
       2082, 2088, 2048, 2046, 2052, 1984, 1664, 1573, 1573, 1620, 1572,
       1558, 1529, 1518, 1534, 1541, 1538, 1480, 1491, 1516, 1520, 1477,
       1529, 1519, 1459, 1488, 1486, 1500, 1517, 1516, 1590, 1516, 1566,
       1554, 1518, 1517, 1528, 1516, 1455, 1446, 1486, 1466, 1488, 1468,
       1467, 1452, 1486, 1467, 1510, 1526, 1463, 1492, 1487, 1451, 1491,
       1506, 1468, 1491, 1496, 1516, 1493, 1481, 1487, 1493, 1492, 1490,
       1470, 1469, 1484, 1502, 1495, 1488, 1457, 1504, 1520, 1489, 1516,
       1492, 1535, 1568, 1509, 1503, 1488, 1536, 1540, 1516, 1490, 1517,
       1502, 1515, 1501, 1540, 1544, 1539, 1547, 1522, 1524, 1541, 1566,
       1540, 1538, 1542, 1524, 1542, 1536, 1568, 1506, 1518, 1554, 1528,
       1548, 1536, 1559, 1550, 1510, 1542, 1505, 1510, 1517, 1526, 1555,
       1568, 1556, 1552, 1506, 1536, 1537, 1558, 1576, 1540, 1483, 1497,
       1503, 1542, 1525, 1515, 1531, 1528, 1491, 1526, 1540, 1523, 1542,
       1568, 1554, 1530, 1522, 1550, 1550, 1527, 1516, 1524, 1498, 1518,
       1534, 1525, 1532, 1541, 1570, 1528, 1549, 1561, 1542, 1544, 1559,
       1569, 1510, 1527, 1542, 1568, 1557, 1528, 1542, 1556, 1565, 1563,
       1584, 1602, 1507, 1566, 1550, 1518, 1550, 1546, 1547, 1567, 1560,
       1593, 1564, 1574, 1614, 1618, 1644, 1616, 1607, 1638, 1634, 1598,
       1616, 1599, 1616, 1619, 1594, 1605, 1612, 1596, 1628, 1619, 1608,
       1640, 1592, 1626, 1645, 1579, 1606, 1643, 1606, 1617, 1592, 1619,
       1635, 1619, 1624, 1544, 1658, 1673, 1646, 1669, 1650, 1640, 1593,
       1612, 1630, 1637, 1644, 1606, 1628, 1620, 1646, 1629, 1635, 1646,
       1641, 1644, 1657, 1598, 1622, 1622, 1620, 1620, 1608, 1640, 1629,
       1619, 1614, 1619, 1670, 1632, 1596, 1598, 1626, 1610, 1638, 1604,
       1644, 1622, 1625, 1622, 1618, 1615, 1644, 1662, 1617, 1645, 1618,
       1611, 1618, 1607, 1613, 1617, 1619, 1612, 1609, 1623, 1624, 1646,
       1607, 1597, 1640, 1667, 1648, 1647, 1630, 1644, 1606, 1606, 1620,
       1624, 1616, 1597, 1646, 1615, 1661, 1650, 1647, 1640, 1602, 1603,
       1595, 1586, 1644, 1621, 1630, 1616, 1584, 1632, 1624, 1608, 1662,
       1641, 1656, 1645, 1593, 1593, 1567, 1608, 1632, 1594, 1643, 1667,
       1645, 1624, 1616, 1632, 1646, 1660, 1628, 1640, 1593, 1618, 1645,
       1594, 1628, 1631, 1593, 1615, 1594, 1614, 1646, 1656, 1648, 1620,
       1656, 1656, 1666, 1670, 1664, 1694, 1616, 1626, 1614, 1660, 1618,
       1617, 1659, 1606, 1660, 1622, 1662, 1654, 1566, 1614, 1634, 1596,
       1630, 1619, 1646, 1595, 1608, 1656, 1645, 1605, 1644, 1621, 1673,
       1635, 1618, 1621, 1616, 1645, 1606, 1622, 1618, 1608, 1579, 1618,
       1619, 1616, 1672, 1640, 1645, 1623, 1590, 1635, 1623, 1616, 1631,
       1632, 1618, 1610, 1585, 1620, 1621, 1604, 1749, 1622, 1632, 1656,
       1627, 1623, 1618, 1645, 1595, 1614, 1622, 1667, 1606, 1620, 1648,
       1619, 1669, 1614, 1640, 1655, 1616, 1638, 1602, 1642, 1625, 1618,
       1667, 1621, 1618, 1646, 1624, 1646], dtype=uint16), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64)], 'timing': [[], [], [10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []], 'time': [array([], dtype=float64), array([], dtype=float64), array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,
        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
       117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
       130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
       143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,
       156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
       169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
       195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,
       208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,
       221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
       234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,
       247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,
       260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,
       273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,
       286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,
       299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,
       312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,
       325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,
       338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,
       351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,
       364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,
       377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,
       390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,
       403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,
       416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,
       429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441,
       442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454,
       455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,
       468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,
       481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493,
       494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506,
       507, 508, 509, 510, 511]), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64)], 'created_at': 0, 'pkg_num': 0, 'event_num': 0, 'name': None}

{'window_labels': [[], [], [32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []], 'evt_window_labels': [[], [], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []], 'data': [array([], dtype=float64), array([], dtype=float64), array([2053, 2108, 2102, 2078, 2073, 2092, 2099, 2090, 2058, 2074, 2076,
       2095, 2094, 2078, 2028, 2069, 2106, 2088, 2072, 2025, 2009, 2082,
       2078, 2067, 2068, 2058, 2028, 2054, 2024, 2075, 2074, 2080, 2056,
       2046, 2071, 2076, 2054, 2044, 2078, 2085, 2002, 2032, 2040, 2035,
       2033, 2046, 2050, 2025, 2033, 2078, 2071, 2040, 2005, 2026, 2044,
       2040, 2003, 2040, 2046, 2032, 2055, 2076, 2054, 2025, 2040, 2069,
       2082, 2088, 2048, 2046, 2052, 1984, 1664, 1573, 1573, 1620, 1572,
       1558, 1529, 1518, 1534, 1541, 1538, 1480, 1491, 1516, 1520, 1477,
       1529, 1519, 1459, 1488, 1486, 1500, 1517, 1516, 1590, 1516, 1566,
       1554, 1518, 1517, 1528, 1516, 1455, 1446, 1486, 1466, 1488, 1468,
       1467, 1452, 1486, 1467, 1510, 1526, 1463, 1492, 1487, 1451, 1491,
       1506, 1468, 1491, 1496, 1516, 1493, 1481, 1487, 1493, 1492, 1490,
       1470, 1469, 1484, 1502, 1495, 1488, 1457, 1504, 1520, 1489, 1516,
       1492, 1535, 1568, 1509, 1503, 1488, 1536, 1540, 1516, 1490, 1517,
       1502, 1515, 1501, 1540, 1544, 1539, 1547, 1522, 1524, 1541, 1566,
       1540, 1538, 1542, 1524, 1542, 1536, 1568, 1506, 1518, 1554, 1528,
       1548, 1536, 1559, 1550, 1510, 1542, 1505, 1510, 1517, 1526, 1555,
       1568, 1556, 1552, 1506, 1536, 1537, 1558, 1576, 1540, 1483, 1497,
       1503, 1542, 1525, 1515, 1531, 1528, 1491, 1526, 1540, 1523, 1542,
       1568, 1554, 1530, 1522, 1550, 1550, 1527, 1516, 1524, 1498, 1518,
       1534, 1525, 1532, 1541, 1570, 1528, 1549, 1561, 1542, 1544, 1559,
       1569, 1510, 1527, 1542, 1568, 1557, 1528, 1542, 1556, 1565, 1563,
       1584, 1602, 1507, 1566, 1550, 1518, 1550, 1546, 1547, 1567, 1560,
       1593, 1564, 1574, 1614, 1618, 1644, 1616, 1607, 1638, 1634, 1598,
       1616, 1599, 1616, 1619, 1594, 1605, 1612, 1596, 1628, 1619, 1608,
       1640, 1592, 1626, 1645, 1579, 1606, 1643, 1606, 1617, 1592, 1619,
       1635, 1619, 1624, 1544, 1658, 1673, 1646, 1669, 1650, 1640, 1593,
       1612, 1630, 1637, 1644, 1606, 1628, 1620, 1646, 1629, 1635, 1646,
       1641, 1644, 1657, 1598, 1622, 1622, 1620, 1620, 1608, 1640, 1629,
       1619, 1614, 1619, 1670, 1632, 1596, 1598, 1626, 1610, 1638, 1604,
       1644, 1622, 1625, 1622, 1618, 1615, 1644, 1662, 1617, 1645, 1618,
       1611, 1618, 1607, 1613, 1617, 1619, 1612, 1609, 1623, 1624, 1646,
       1607, 1597, 1640, 1667, 1648, 1647, 1630, 1644, 1606, 1606, 1620,
       1624, 1616, 1597, 1646, 1615, 1661, 1650, 1647, 1640, 1602, 1603,
       1595, 1586, 1644, 1621, 1630, 1616, 1584, 1632, 1624, 1608, 1662,
       1641, 1656, 1645, 1593, 1593, 1567, 1608, 1632, 1594, 1643, 1667,
       1645, 1624, 1616, 1632, 1646, 1660, 1628, 1640, 1593, 1618, 1645,
       1594, 1628, 1631, 1593, 1615, 1594, 1614, 1646, 1656, 1648, 1620,
       1656, 1656, 1666, 1670, 1664, 1694, 1616, 1626, 1614, 1660, 1618,
       1617, 1659, 1606, 1660, 1622, 1662, 1654, 1566, 1614, 1634, 1596,
       1630, 1619, 1646, 1595, 1608, 1656, 1645, 1605, 1644, 1621, 1673,
       1635, 1618, 1621, 1616, 1645, 1606, 1622, 1618, 1608, 1579, 1618,
       1619, 1616, 1672, 1640, 1645, 1623, 1590, 1635, 1623, 1616, 1631,
       1632, 1618, 1610, 1585, 1620, 1621, 1604, 1749, 1622, 1632, 1656,
       1627, 1623, 1618, 1645, 1595, 1614, 1622, 1667, 1606, 1620, 1648,
       1619, 1669, 1614, 1640, 1655, 1616, 1638, 1602, 1642, 1625, 1618,
       1667, 1621, 1618, 1646, 1624, 1646], dtype=uint16), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64)], 'timing': [[], [], [10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []], 'time': [array([], dtype=float64), array([], dtype=float64), array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,
        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
       117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
       130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
       143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,
       156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
       169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
       195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,
       208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,
       221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
       234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,
       247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,
       260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,
       273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,
       286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,
       299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,
       312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,
       325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,
       338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,
       351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,
       364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,
       377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,
       390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,
       403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,
       416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,
       429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441,
       442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454,
       455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,
       468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,
       481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493,
       494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506,
       507, 508, 509, 510, 511]), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64), array([], dtype=float64)], 'created_at': 0, 'pkg_num': 0, 'event_num': 0, 'name': None}

It appears that the section

'timing': [[], [], [10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]

'timing': [[], [], [10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814, 10488814], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]

has some of timestamps. I'm guessing they're the current clock cycle stored in some register. There's an array for each channel. I don't know why each channel has 16 copies of the timestamp; I checked that these 16 numbers are always equal to each other in any event.

I modified this notebook:
https://github.com/NaluScientific/naluexamples/blob/main/examples/opening_acquisitions.ipynb

To have this code loop through events and create a histogram of the differences between timestamps in

import matplotlib.pyplot as plt
import numpy as np
from naludaq.backend import DiskAcquisition

# Path to the acquisition data
ACQ_PATH = r"/home/pioneer/nalu_stuff/first_project/2024-12-18 21-08-39.934460"

# Initialize a list to store timestamps for each channel
channel_timestamps = []
max_timestamp_value = 16777216  # Assumed power of 2 (2^24)

# DiskAcquisition is recommended to be used as a context manager for safety
with DiskAcquisition(ACQ_PATH) as acq:
    print("Board model:", acq.params["model"])
    print("Number of events:", len(acq))
    print("Pedestals stored:", acq.pedestals is not None)

    # Loop through all events and collect the first timing entry from non-empty channels
    for EVENT_INDEX in range(len(acq)):
        EVENT = acq[EVENT_INDEX]
        if EVENT.get("data", None) is None:
            print(f"Event {EVENT_INDEX} is corrupted!")
        else:
            # Get the timing data for the event
            timing = EVENT.get("timing", [])
            
            # Iterate through each channel's timing data
            for channel_idx, channel_timing in enumerate(timing):
                if channel_timing:  # Only consider non-empty timing data
                    first_timestamp = channel_timing[0]
                    
                    # Check if all values in the timing array are the same
                    if len(set(channel_timing)) > 1:
                        print(f"Warning: Channel {channel_idx} has inconsistent timing values!")
                    
                    # Store the first timestamp for this channel
                    channel_timestamps.append(first_timestamp)

# Compute the differential time between successive events
if len(channel_timestamps) > 1:
    diff_times = np.diff(channel_timestamps)  # Computes the difference between successive timestamps
    
    # Filter out negative values from diff_times
    diff_times = diff_times[diff_times >= 0]

    #Printing
    np.set_printoptions(threshold=np.inf)
    print(channel_timestamps)
    print(diff_times)

    # Plotting the differential times as a histogram
    plt.hist(diff_times, bins=50, edgecolor='black')
    plt.xlabel("Time Difference")
    plt.ylabel("Frequency")
    plt.title("Time Between Events (~1 Hz external trigger)")
    plt.show()
else:
    print("Not enough timestamps to calculate differentials!")

import matplotlib.pyplot as plt
import numpy as np
from naludaq.backend import DiskAcquisition

# Path to the acquisition data
ACQ_PATH = r"/home/pioneer/nalu_stuff/first_project/2024-12-18 21-08-39.934460"

# Initialize a list to store timestamps for each channel
channel_timestamps = []
max_timestamp_value = 16777216  # Assumed power of 2 (2^24)

# DiskAcquisition is recommended to be used as a context manager for safety
with DiskAcquisition(ACQ_PATH) as acq:
    print("Board model:", acq.params["model"])
    print("Number of events:", len(acq))
    print("Pedestals stored:", acq.pedestals is not None)

    # Loop through all events and collect the first timing entry from non-empty channels
    for EVENT_INDEX in range(len(acq)):
        EVENT = acq[EVENT_INDEX]
        if EVENT.get("data", None) is None:
            print(f"Event {EVENT_INDEX} is corrupted!")
        else:
            # Get the timing data for the event
            timing = EVENT.get("timing", [])
            
            # Iterate through each channel's timing data
            for channel_idx, channel_timing in enumerate(timing):
                if channel_timing:  # Only consider non-empty timing data
                    first_timestamp = channel_timing[0]
                    
                    # Check if all values in the timing array are the same
                    if len(set(channel_timing)) > 1:
                        print(f"Warning: Channel {channel_idx} has inconsistent timing values!")
                    
                    # Store the first timestamp for this channel
                    channel_timestamps.append(first_timestamp)

# Compute the differential time between successive events
if len(channel_timestamps) > 1:
    diff_times = np.diff(channel_timestamps)  # Computes the difference between successive timestamps
    
    # Filter out negative values from diff_times
    diff_times = diff_times[diff_times >= 0]

    #Printing
    np.set_printoptions(threshold=np.inf)
    print(channel_timestamps)
    print(diff_times)

    # Plotting the differential times as a histogram
    plt.hist(diff_times, bins=50, edgecolor='black')
    plt.xlabel("Time Difference")
    plt.ylabel("Frequency")
    plt.title("Time Between Events (~1 Hz external trigger)")
    plt.show()
else:
    print("Not enough timestamps to calculate differentials!")

19/12/2024 01:39

There seems to be a maximum to the timing value (I'm guessing it's 2^24 -1), after this the values seem to loop back around. Subsequently, I just ignored negative values when plotting the diff histograms below.

[16543325, 16546825, 16550325, 16553825, 16557325, 16560826, 16564326, 16567826, 16571326, 16574826, 16578326, 16581826, 16585326, 16588826, 16592326, 16595827, 16599327, 16602827, 16606327, 16609827, 16613327, 16616827, 16620327, 16623827, 16627328, 16630827, 16634328, 16637828, 16641328, 16644827, 16648328, 16651828, 16655328, 16658828, 16662328, 16665828, 16669328, 16672828, 16676328, 16679828, 16683328, 16686829, 16690329, 16693829, 16697329, 16700829, 16704329, 16707829, 16711329, 16714829, 16718329, 16721830, 16725330, 16728830, 16732330, 16735830, 16739330, 16742830, 16746330, 16749830, 16753331, 16756831, 16760331, 16763831, 16767331, 16770831, 16774331, 615, 4115,

[16543325, 16546825, 16550325, 16553825, 16557325, 16560826, 16564326, 16567826, 16571326, 16574826, 16578326, 16581826, 16585326, 16588826, 16592326, 16595827, 16599327, 16602827, 16606327, 16609827, 16613327, 16616827, 16620327, 16623827, 16627328, 16630827, 16634328, 16637828, 16641328, 16644827, 16648328, 16651828, 16655328, 16658828, 16662328, 16665828, 16669328, 16672828, 16676328, 16679828, 16683328, 16686829, 16690329, 16693829, 16697329, 16700829, 16704329, 16707829, 16711329, 16714829, 16718329, 16721830, 16725330, 16728830, 16732330, 16735830, 16739330, 16742830, 16746330, 16749830, 16753331, 16756831, 16760331, 16763831, 16767331, 16770831, 16774331, 615, 4115,

19/12/2024 01:39

Here are the plots:

I only took a few seconds of data for each rate; So each aquistion is somewhere between 3 and 10 seconds of data taking.

19/12/2024 01:45

I'm not understanding the timing differences. They do appear to get smaller the higher the rate (though the ~1 Hz to ~100 Hz region all have about the same time differences). However, they don't scale correctly. I would expect the time difference between 1850Hz and 17855Hz to scale by about a factor of 10, but it's more like a factor of 5. Furthermore the jump from 17855Hz to 172700 Hz should scale by a factor of 10, but don't. Mayb there's some maximum rate the board can handle for external trigger which is bottlenecking things at higher rates.

21/12/2024 20:52

I did some "fine control" on the gate generator to change the rate. I did a sort of "binary search" to maximize the data rate based on the trigger rate. We know the optimal event rate is around 2kHz, so my start is at about 1kHz.

The way I did this way I just left a midas run going while I adjusted the trigger rate with the "fine control", reading the trigger rate off the oscilliscope.

21/12/2024 21:22

The "binary search" results confused me:

Window size 62, all channels enabled:

Gate Generator Knob Setting (Hz)	Oscilloscope Measured Rate (Hz)	MIDAS Data Rate (MB/s)	Screenshot Link
1	<10	0.25	Screenshot
10	18	2.7	Screenshot
100	191	14.1	Screenshot
1000	1850	21	Screenshot
10000	17855	17.6	Screenshot
100000	172700	17.7	Screenshot
1000	1002	20.1	Screenshot
1000	2980	19.6	Screenshot
1000	2000	20.7	Screenshot
1000	1502	19.6	Screenshot
1000	1748	20.7	Screenshot
1000	1850	20.5	Screenshot
100	497	18.1	Screenshot
100	798	17.1	Screenshot

So I am taking a more systematic approach next.

21/12/2024 21:52

I realized I never was really explicitly setting the channels for these tests, so I went and used the method:

        if self.channels:
            get_readout_controller(self.board).set_readout_channels(self.channels)

        if self.channels:
            get_readout_controller(self.board).set_readout_channels(self.channels)

to set it and verified with:

        print(get_readout_controller(self.board).get_readout_channels())

        print(get_readout_controller(self.board).get_readout_channels())

If we specify no channels, the print statement prints all channels are enabled for readout (which makes sense given out data rates). I.e. all previous tests were with 32 channels, but I was already operating under that assumption.

21/12/2024 22:48

I've "consolidated" my data into 3 tables with corresponding plots

Window size 32, all channels enabled:

Gate Generator Knob Setting (Hz)	Target Trigger Rate (Hz)	Oscilloscope Measured Rate (Hz)	MIDAS Data Rate (MB/s)	Screenshot Link
1	-	<10	0.25	Link
10	-	18	2.7	Link
100	100	101	14.7	Link
100	250	249	14.2	Link
100	500	497	18.1	Link
100	750	798	17.1	Link
1000	1000	1002	20.1	Link
1000	1250	1246	20.2	Link
1000	1500	1502	19.6	Link
1000	1750	1748	20.7	Link
1000	2000	2000	20.7	Link
1000	2500	2501	18.8	Link
1000	2750	2745	19.9	Link
1000	3000	2980	19.6	Link
10000	-	17855	17.6	Link
100000	-	172700	17.7	Link

Window size 1, all channels enabled

Gate Generator Knob Setting (Hz)	Target Trigger Rate (Hz)	Oscilloscope Measured Rate (Hz)	MIDAS Data Rate (MB/s)	Screenshot Link
1	1	1	0.24	Link
100	100	101	0.24	Link
1000	1000	973	2.29	Link
10000	10000	9416	22.1	Link
10000	20000	20375	47.9	Link
10000	22500	22390	52.7	Link
10000	23750	23770	54.2	Link
10000	25000	25024	54.7	Link
10000	30000	30337	54.7	Link
100000	100000	92289	54.7	Link

Window size 1, 1 channel enabled:

Gate Generator Knob Setting (Hz)	Target Trigger Rate (Hz)	Oscilloscope Measured Rate (Hz)	MIDAS Data Rate (MB/s)	Screenshot Link
100	100	108	0.01	Link
1000	1000	1041	0.07	Link
10000	10000	10077	0.74	Link
100000	100000	98657	7.26	Link
100000	200000	200000	13.1	Link
100000	500000	499530	14.8	Link
100000	625000	623000	15.2	Link
100000	700000	689000	16.8	Link
100000	750000	752600	18.1	Link
100000	875000	872400	15.9	Link
1000000	1000000	891670	16.3	Link