Comments (6)
Wow, thanks a lot for this. It is still highly relevant
I've been trying to apply it, using Python's struct library (I'm using the little-endian encoding, i.e. "<I". But I ended up with a few questions, and I'm wondering if you can help me:
- An integer is 4 bytes, i.e. when I convert sample and sampleDark (each is 1800 bytes total) as you suggested, I end up with many more values than 400, even after removing the header (which is 8 bytes according to your instructions). Basically, I would have an 8 byte header, followed by 400 integers, and then 196 bytes that are unknown. For sampleGradient, I end up with too many values as well (1656 bytes total, i.e. 8 byte header, 400 integers and 52 bytes left over). But if instead all the values after the header were integers, then I get 449 integers for sample and sampleDark, and 413 for sampleGradient. That makes it difficult to do the reflectance math. Is it simply that the remaining data is not used? Do you know what's going on?
- I assume that the best way to create a reflectance spectrum from the units of "counts per second" is to normalise it to 0-1? But there must be a fixed maximum and minimum for that? The values that I'm currently getting are huge
- Do you have any idea how to apply the calibration? I guess it's based simply on a scan inside the calibration box, but what is the math behind it?
- Here is some example data:
- sample first few hex bytes:
ba0208070000000018c9a124fc3f2e57aa716475154067b118384dd9407d30dda544906a9753...
- sample, resulting in: -70 (protocol) 2 (signal type: scan) 1800 (length in bytes), then the following integers
614582552 1462648828 1969516970
- sampleDark first few integers:
4269192318 1192275574 2032346525
fromba020807000000007eb476fe76ae10479d252379e65579bccb56d70f85bfecfd6729eb3ebd...
- sampleGradient first few integers:
405697630 247858264 128659535
fromba0278066e0000005e742e185804c60e4f30ab0756c7b26e52e1672bba13a1898f76f0c0b647922...
- Resulting reflectance:
9.45933685e-01 -2.86285788e-01 3.30041416e-02
- sample first few hex bytes:
from scio-read.
I am still trying to work with the code you provided, but I'm not managing to find the bytes you mention. Can you elaborate a bit on how you obtained these and decoded the data? Thanks
from scio-read.
Here is my scan.json
{
"device": {
"deviceBleId": "d40c760000f37b98",
"bleFWVersion": 125,
"serial_number": "CPPCA0031C7PF4616042A6411416A1DF481601QT",
"device_name": "CBRN",
"i2s_tag_config": "20150812-g:PRODUCTION",
"deviceDspId": "82a8b26b2304c2e8",
"aptinaId": "0000f320cc82320b803f1cd6d67ca069",
"firmwareVersion": 151
},
"scan": {
"timestamp": "2023-03-22 08:35:09",
"t_cmos_before": 17.584445075219964,
"t_chip_before": 23.62,
"t_obj_before": 0.0,
"t_cmos_after": 18.294067556060156,
"t_chip_after": 23.87,
"t_obj_after": 0.0,
"sample": "AAAAAEPShV3QZQkQrEMaGczSBHmRAx9vvYWQf5CT3uxpVhfFfJVOz5jTJXqC0cvvR8z9ywCFQEOOoVecSgCpfmu2u9sprzJi9N3htTaj3d4fZncKBXdP1qaEpJsrpMgW1atr1cl49I5GqzNpe7TswE1mYpExw9S_La4DBPEclViGqG2tPRGBmmsSINmXIAQuqArHACwktkEwdtYU91tHXvFkSTaqOMK6r7oVR5sJJ0WnhwOauXg1ww0Si1GMmGrmA2gGp-nlC1FEb3Qlv2Fe7mA7H4OHpQU6Mam82o9ERRN5p24wdDD7jdDIRSdYTNWJv842wNjJ4ccQzts2H_exdWJueUUQ5uraXMhIbOnutdiu2KB8hwpjI1CRI8K4SfJnP9faFN7rDqHc_8oNP-cSZdPBT7BBRBkOWIArV8h0kyQJ3xSKp9ix1s8hbVOUzqCFBizipgKprpsfepL_XCZw__xKv_89QegFDzaSQzMatCUtWytFXN906IqLj2Lh89omtYzasvxYlSA2FC2pdp3NnoPnVk6Tbofg4qouWDxEUlZcsd_fbUgd3zQZeveToG4MiNcmAIUIM8xpnJXeAIDiHkEliSpSrzLIsjeSCZT_S_8YIsUDYDSHDd6D7UZ-0cMNOqNkWu-nLN8Gn5D7CVRmZkZLlfqASh57O2fw77u_Xm1-xmRFOeyxLxqo-IeVy70Dl5csbv5hBCTMn7uWwTywJzZ9vCUg_cjTqPMwO0wKcJCkeVja2H0SvrP-v0AJDpp7XOxGmGmWyzmfwR95N9gHscLKV2_atWGii7XIws2kO7gfgi0QmiFnMLxupvJztHdqeGCKSDkJ_2OEQ9nb2xdCT9M12swTLyRs2ebiExE3Wg6p7_xXDUbgRcA_nAOeHo_cByeJrRWZGP-LoBPCD47Qc8KnwSWhjmMRUxx-TEzjm_oUyNIT1ADtc_olU0Uo82bX39rOMN0-ASMGnzRtvEBl2tampw7GLqHxL4GPsoHncaSdjU258VapGM_NnbrsHtP4QmS-VkrAFcCG1aBM7wg6IjoGiDIW1ltpAq9dHRLs4qHI_1LeplRhVcK1HWG3d_QJfUM0YVQTJXAOA7SCD4KMrTO_eNDRf5Y5KnYmjsHM944QrGHnkB0-RbqwY_L641a7-wjowsGUTYDOneVDUw08_Tsbee_cdUXY0ZKSZn7Ojn916tFY1k2yE52dp-AvALI4ULazX-M8eSzcCyZhVjTV99meyK5TLTjmRrtIffu6A4mctLLnk-nQ0Qe8vka2E7uf7FAD_EfzxnjzMIViFsdMC3IzfTQPNRFF8_58dkt8pKftQF2I8aJVGiBiTWXqy9hMXdun9KvAI2uLB03qttJDezkuS0cNup35cdBke4hHrGyaeTmds4oVmVG2zxvHjRIsZ0NW7HuZYaBiGFxiyM4nDnJqmR5vImQczhFzcN9ACXbzqN_-ry-KZ50WFfEMt_r-eO7YTlsP_WTI-wgITFuoNNpXkYkv0m_VcgnyfGMoj77Kd8ZCQgG0bgtkhom8B_7icnzbV38Dc6Pa1gcuLCRVqD18ZwAPw5_FI7OM4H_6-zmaidTlTYbQ4fJIPcfUQF2EyIbhrEdMenXlosoOOd6gktr9wytydkQ4pqIjpqehJMyD0J4QxM1l080X_Meco1TgRZkfW-EakTCFyceqSGjNM9DXxHZJ-rl8GJzFuPyt58TV70nh5wGSgtxY2IDu7nTkykGZullY9l6IcskvdubPRnB9GpNDfgINOP-lqZQFY_xD2jOHDB0SsGh_YigOT5vv3ARN7O7WE9w8H9WJdtt1_sMeoLVgyzQFSz7dfzlpHPN6BgKNX-4B2yUBYZvrtZ-1iBPtWeH5kSDH34RCuslaX6iySbMx5RHqOlNPlx2y1TgIQ-3YTP9R8Z49_kiVn1XTdpvvBgOv2CWBSwXoP4hQuYWjqckDrsPO7jmvFR6FGfzKKNCfCO3F4ak3pr3hAK4pJs2wHgVHJ-Pe5tXWw0jXPWXZIx4l_cZnCbXoGvgc8QBpt8oj40w3r8uqqKcHzz7qr1PE_rnKaNamErxdMWoHf_qqLbHjIX7bmEFiJQooqZVE5INYTskOKpb-ar2ttEIztEPA_ylOW9kcAnHJNPzGIj4yALjQvqTat20_LALeoh6QGQs1okngMuQS8Kefd6lffi79WUt-YJvHjXL1HneXQRZl2fMTWK3T8TyYyL8-K38jzTwfz2L7xq9wcbYa4HkRuPInsteP1mY3KabgQQRRfG6FD0Gs3Ao7y0HbLbKfjY9LU3ZuN4PA_uqZPmVDVURRzTIpagnHJJkDvZLY4IYwnL--H1qWHLrwWqOntTYPTQPPkStDVoL_qiqsfroZYDgTWr-X5ADKpDumCaYHtZFrilDjzb-6vcUI",
"sample_dark": "AAAAAJ7cyEOGGXV0z3_HN9l7ab-WQSJX9W7SZZAE5TqrenhtdddRkUfmiJJY4umRnpBnbx6fXSkqrJZORByKq64yEaH14PfedIjKK3753vbTRkTG4edPgG7FP_wbLmEOrmwVqFfh0bQbs36L4c5p3M6BFJuO8kod8mYxzAQY_566vwUlq3EFw5zjwHGqmqBtsj7Qa8k-sJXJtEBZibHRzk5ahzP66kLmYwyBOIXuOWp9FDQSMvO29Bq6-G78wvqZD6sHCvIa_ENqN5-llJWGuC8SCfLDMOKVyjpSVFLHPp5N-VuG1xRjD3pc7bBXgXnbdIfelDnvcNgWWBc7jGj1fI2KiFzoZ3x9e-igG3KdZlZ7OzM8tpp9b__DeGZD3Of2Fce3CO3LjqHq7TrLS4ci-cXMGJsQDFTOhdEexwpCLDPMbHIw6tZv2JQgtiY3IsplqsLFbbIp_jGjy2_b52_3jFq19CpZXx1oOIiSFnXGU0fkfI-86cK2Dbz5Qxpq-FElQj8EtvnV5vTofccnz4dLUl7DsB4581e_XNuWun0-FKaVKUVL6-YjJC2hVmLGAD98rS95S36D3MrlAfG6yii_ZUPkAREqKjxnqtWh8q7I77M94Jy078FU00LP-mitSAkrz4Ot0cfx_kdBRb1Y2MtZ6jCffNLFlvRofEo3xFgsdWQnaupUf8CIDjdPBhvd4t4lwBNDsOfGfMn2bKZeHYhc8XnN3BEx6Xkn4_o2VyWOXIiQK3azv82lbcJW817FSdpPnfqb8Tawfz9APU7d1Wyy7Vdf5QK0Xt30hLbhAnJGCtPaihBO4UNh_kjn9pomcP90fPKCRQuR1YxAxYfapyOMfMnkcqlFGkBCdawKwjfXj7YsaH3i0cVWrqeAE7UiS2dbVh4qnx1326sYPVcQ0Yz3xshvuw6SIll0J1Gv8k_z-p8ouUKdDdKSOsGq6AGSb_5Io4FacTH4TVPjbtt6Rd4_WQA8Pmrm-6RV-6R3ACVvQfgBQW9uZamdznugT3ktrOHnUyvM_VnlrzetNzRsORPGwVEVPUfgdCMS6Smk6DQj4331ftJM1UEq4oJlWvTRx3PQgLmuijOjAqRU97CazfUOFvphxycomiL6gh8BfpDjS2kikeafEfWwWteQmiRo4doGXtxacD9ntmOIUq3ifoTHvp-Krw2LXoPf4R7cB-5UvNxqrdO0UB7TouaZNur84pDFZ-R_C7Qniyr-XLoWParH2gCoMnDVJwIKPhGKybGqDTBDLzM38ml7KmPTIum7RTPHBJmDz0g-HuJdBIPLqHVZ5iP2YgGqGanXzKj3ULZhgEIAVcZE3PkhgHL7Anvu_7L69Yx4JNyqo583WROoPmmx-Ec9cystlqqkyQyk3t5IF2jIuYRuicdzLgYORRUQAdz3doUD633TdDFVg-fa3E7hVNBIu-Xzwo4kUikf8iyECVIyLWKXhdY-dZG0K6fIBryf89nOvArshO_wgSE73Gk9_QucHx6WuYHX4cokLCb0j50hc7w0wm4g9NVP_5CMLU0anClyDd9tptLwG9iaxi45__vUDhxBe2FKLNUUmN_BuLIaH1HioR7SCQAmKXZlJTQos0JdL_8AckObf9ZY5FpbNf1x_BIMydlUhTK9MP7f66r7KSX3eOknVFp_GqM9nHMEtH1AFtpjxoE90-eClFmj2XT_Gb-D2MuL4CST0xVVaE-WPMGasXF7ue4JwSGFD3jRefbHU2zJxL3grUYr6pVaHELFAcsgcHv-VbHg2OmQiUTGgne71wWDI8DgjO-pWJTsQe6MixQp_FaKm__gZj2DabZKtfcPVCFaGKFtc0GmwEZ3N8mY6tu-Uw6U8ge4XFIwiuAAS-uw7ib7rl-U3XKcJRXxjic7xtXJFXDFRyQdEv_v3gTWgfVmi89w80OvbMZ67J2oUZi-jfU7lbkxpg5ewwi9p_91u-GDTEMt9PwBHL6G6xH2MOq5gGO5hk2zPvXagD9r_GQ3Mf19I5IeRLwX9lHzl4OqEoiUwQcyaFYCAyuhqEyBTl3mDL77hssc9MmaSpbvsz8Nxcnoy9nBmxkJl3y0m1aY5KM42Fv44bWFyeDsC-8YfIvxWcuMvC94aqyibcXATGtg6tUgtezTz1HefmffL348PMjmpzGpWPdSFllOKNgWALV8lTWeMAJsW3ohNFzaEpTMdwYGKW1t_NhQFxdq-sLQ6F8ejoDTYvQ-I7PlRNxB2_fYZFdQqHItO-yERQeSvb9lbZRcw8cokBKKZIUlOqrzQtl9jFOtGTXH0W0EHEMtISX0CM_E59K5FbaLwDtJiC8ohMzvHL034ukUaTzaKHIA9zo6DXP5NZInDRHbbUys6DP2DHhY5qlyGI9mSAOLivee6wjc9PoXYfWeJO4XgO02znbH",
"sample_gradient": "AAAAAOp1IDt2sDXjiCAmmKUIf8P-UY3KdjodURTElAeQz-_lyjx2QjNRB3wCMQTWamkIV-SLRWeG78QvxiRuSAH2Ayk8mvdE4081PSSL5D-IF2SGJQnJxO9ZTKFOFUbF_0-_txCL670XhA4nV2fCd9q91He_TWyPmSJgkAMfXsfV1qMa8JYVcdLorUfLMNuO-3Q-Odicl_p65vHLimE7RVdvpoM46o41KZOc_Qnrhrtph-R4bd0iw8ThPYb-cHMJNSIuUqbtxx3n509jGTJaBclTJsFSi_VDPQ700vbZRn2bEmHJUAr3O5KXD3BziwZQw844qlwRR6sixZVy3CSfZ1AcJz0nseZn4VfVQ_CwlJLA_9ly4z8YhOKeWcpdEuTeyut2lAlWba8HLnonzMvJmlEy_LAXnUDZp_pndJlhBh8GQXxbr4Z57idajD-n5PjCzxXFAhYVp2IFhw1Iv8tSpf8iPZ9Hu_u3udMke0lrxo6WojdoZjEI9tX8mYXvxclK7a52RgJ3L0HBBuPy10YWNdWsYgo3H2UYAJ_XrYZxO4VMRH7B3NS_zH_J8hS6w8mW_9hXpgNeTV7zdoHO819FTkkcXIthwwLfcomVatcGQcoBGuVx4FCb5hKa8tjj3f96aAjuvGJ8O-VAELU8RBq7AvSumzvr8qVGb1J5PQVeZRowx3sqwK4YWbqS1imDhfTwp2J-JQgfTrIreaQ234P_Uzeofk1mLB7i32ameWG0J6HsixanjwKjXpO7Deywp0aMUDqHYO-nULwo3Rjw_4W4m91H6Rit_H3-u3EGX89xHssqdwtjgDJCfH_zcxn5xnLM8WqBRzNOdCTXnoO5LwB4vBPItwfvqYkFlZYDLh3gDbRiXEIKYpD5GIO_cZd2lexL0WfeD_wcuq3LpuCTlIxeP_8xyBt_rfrSN4G3mD6YyZJr6asqStehI_3JapQkpy6BnyldUYs50CVcXGdv45Ere57EBTKIiE3ze4KkiN1eAG5wp2LTriQdLC9R4Ia0RIPqohAXF3XFyAXX77ZdaQSnhqcq8RvVv_aUfiBTYaEdVYnmW9MhOYTMeKDfC262oF5ImwZTr81OJPtQi6-JjpFBMXfoe8O0ojY1HZeS4fCj0ruAFkFp2SIr1sVFMIlrD5FPVM_5NLfBoX_0iwLVqspycyzFrcWZp6HZdxnIiqfDVYq0G3CLYhOpG8dqNJdQcdmPXZHMTh6Ba7f6oAPEXFFWxUVbaIaq_gudtjParQCE6q-Ucuwk-gWniicvR0p0H7I2WqgvabxVAG192aA7iaCcSddKOZiL8wPoB_ORHy5s-UG-1l-1GJdiEQemJ6ml0w24XWQIBbcAF9Mb1UgHe3yi9n2sixsyti0Wzu5wlQys-YEVjHMW4ggJ6d-55MG0ff05KYvgo0bGElASmu7krMa6fAOr8IPq9YTWZsfrkvrRGvxE2HuPbGetwTvvPPm0OZOY2P5gkoR2ozg2d161Zh-15OyxCvObcBfskH7a7t3b1OyqY8He8cH0qJhb3BVHsv3odVXKZzydItqX5NzEOZ7bsPJ_56CuRa1e3AW-98MoaIoaHO80OrbSg6COhdA0uBuYak1_5ekestdwpIsNWW2P451bcYSCUrw7HcXKMahPk99GRatZYGCp4LzEWrXSwVsRse4F9u_ezZKpLoxyjn_8mw4QL-75qPKdptemyfwUHrpdmnJjctW0SSzeyztucwTnkeKvcpLHblpXbC1-9eCPYDlXua-WmHkVX1XzL9B_sAqpr1TJJW6fUG1mqDNJ8PwCZWBQJRgx3ICds9itU1LPlkhhdbFwtPdhNkaeIB7CGij3MPTQdlle_eBWUC8c-R9cHDz5KceAe2mLsTAZqp6P1o-x9aX_ayzK"
}
}
from scio-read.
@hbsagen What is your question or comment? You just provided a scan file...
from scio-read.
@kebasaa I had no question really. But I am eagerly following this thread, as I want to extract data from the SCIO as well. I thought more data maybe could help somehow :)
from scio-read.
@hbsagen I see. Thanks, but please start a new issue next time you want to contribute something unrelated to this current issue. Your data shows me something interesting at least: It seems like your SCiO is reporting a different number of bytes in sample_gradient. Mine has 1656 bytes, yours seems to have less. I would appreciate some help though, if you have any experience with this kind of reverse-engineering.
@earwickerh Still hoping that you could answer my questions above. 400 values in 1800 bytes somehow doesn't add up, so I'm wondering how that could work... I understood that you're using big-endian decoding. But the hex values you're quoting are nowhere to be found in the data itself, and the code you posted doesn't work. Can you please update it and add some explanations? Thanks
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Related Issues (6)
- How connect scio scanner ? HOT 51
- researcher license HOT 5
- Offer to help HOT 8
- An issue with base64 representation HOT 7
- Data from the literature on the SCiO spectrometer HOT 14
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