[experiment]
date = 2020-07-30
event count = 31224
run index = 1
sample = 200730_TG_RhoA_Colon_CD45_CD31_Epcam_1
time = 14:01:58

[fluorescence]
bit depth = 16
channel 1 name = 525/50
channel 2 name = 593/46
channel 3 name = 700/75
channel count = 3
channels installed = 3
laser 1 lambda = 488.000000000000
laser 1 power = 1.500000000000
laser 2 lambda = 561.000000000000
laser 2 power = 15.000000000000
laser 3 lambda = 640.000000000000
laser 3 power = 15.000000000000
laser count = 3
lasers installed = 3
sample rate = 625000
samples per event = 353
signal max = 1.000000000000
signal min = -1.000000000000
trace median = 5

[imaging]
flash device = LED (ZMD L1)
flash duration = 2.000000000000
frame rate = 1600.000000000000
pixel size = 0.338999986649
roi position x = 557
roi position y = 456
roi size x = 250
roi size y = 80

[online_contour]
bin area min = 10
bin kernel = 5
bin threshold = -6
image blur = 0
no absdiff = True

[online_filter]
area_ratio max = 1.049999952316
area_ratio min = 1.000000000000
area_ratio soft limit = True
area_um max = 1500.000000000000
area_um min = 10.000000000000
area_um soft limit = False
aspect max = 2.000000000000
aspect min = 1.000000000000
aspect soft limit = True
deform max = 0.200000002980
deform min = 0.000000000000
deform soft limit = True
size_x max = 80.000000000000
size_x min = 1.000000000000
size_x soft limit = False
size_y max = 80.000000000000
size_y min = 1.000000000000
size_y soft limit = False

[setup]
channel width = 20.000000000000
chip region = channel
flow rate = 0.059999998659
flow rate sample = 0.014999999665
flow rate sheath = 0.045000001788
identifier = ZMDD-AcC-0be020-edfbdb
medium = CellCarrierB
module composition = AcCellerator,FluorescenceModule
software version = ShapeIn 2.0.8
temperature = 25.399999618530

[experiment]
date = 2020-07-30
event count = 26789
run index = 2
sample = 200730_TG_RhoA_Colon_CD45_CD31_Epcam_1
time = 14:24:08

[fluorescence]
bit depth = 16
channel 1 name = 525/50
channel 2 name = 593/46
channel 3 name = 700/75
channel count = 3
channels installed = 3
laser 1 lambda = 488.000000000000
laser 1 power = 1.500000000000
laser 2 lambda = 561.000000000000
laser 2 power = 15.000000000000
laser 3 lambda = 640.000000000000
laser 3 power = 15.000000000000
laser count = 3
lasers installed = 3
sample rate = 625000
samples per event = 353
signal max = 1.000000000000
signal min = -1.000000000000
trace median = 5

[imaging]
flash device = LED (ZMD L1)
flash duration = 2.000000000000
frame rate = 1600.000000000000
pixel size = 0.338999986649
roi position x = 557
roi position y = 456
roi size x = 250
roi size y = 80

[online_contour]
bin area min = 10
bin kernel = 5
bin threshold = -6
image blur = 0
no absdiff = True

[online_filter]
area_ratio max = 1.049999952316
area_ratio min = 1.000000000000
area_ratio soft limit = True
area_um max = 1500.000000000000
area_um min = 10.000000000000
area_um soft limit = False
aspect max = 2.000000000000
aspect min = 1.000000000000
aspect soft limit = True
deform max = 0.200000002980
deform min = 0.000000000000
deform soft limit = True
size_x max = 80.000000000000
size_x min = 1.000000000000
size_x soft limit = False
size_y max = 80.000000000000
size_y min = 1.000000000000
size_y soft limit = False

[setup]
channel width = 20.000000000000
chip region = channel
flow rate = 0.059999998659
flow rate sample = 0.014999999665
flow rate sheath = 0.045000001788
identifier = ZMDD-AcC-0be020-edfbdb
medium = CellCarrierB
module composition = AcCellerator,FluorescenceModule
software version = ShapeIn 2.0.8
temperature = 25.700000762939

[
  {
    "libraries": {
      "dckit": "0.14.5",
      "dclab": "0.47.0",
      "h5py": "3.7.0",
      "imageio": "2.10.3",
      "nptdms": "1.6.0",
      "numpy": "1.23.5"
    },
    "python": {
      "build": "tags/v3.8.0:fa919fd, Oct 14 2019 19:37:50",
      "implementation": "CPython",
      "version": "3.8.0"
    },
    "system": {
      "info": "Windows-10-10.0.18362-SP0",
      "machine": "AMD64",
      "name": "Windows",
      "release": "10",
      "version": "10.0.18362"
    },
    "task": {
      "name": "update metadata",
      "new": {
        "experiment:sample": "Ctrl_S8"
      },
      "old": {
        "experiment:sample": "KO_S1"
      }
    },
    "utc": {
      "date": "2023-01-19",
      "time": "22:25:37"
    }
  }
]

{
  "files": [
    {
      "index": 1,
      "md5-5M": "e02ef825c7bbdf3909f02512cc6b7650",
      "name": "Ctrl_S8.rtdc"
    }
  ],
  "libraries": {
    "dclab": "0.47.2",
    "h5py": "3.7.0",
    "numpy": "1.23.5"
  },
  "python": {
    "build": "tags/v3.9.13:6de2ca5, May 17 2022 16:36:42",
    "implementation": "CPython",
    "version": "3.9.13"
  },
  "system": {
    "info": "Windows-10-10.0.18363-SP0",
    "machine": "AMD64",
    "name": "Windows",
    "release": "10",
    "version": "10.0.18363"
  },
  "utc": {
    "date": "2023-01-19",
    "time": "23:07:02"
  }
}

{
  "files": [
    {
      "index": 1,
      "md5-5M": "914d6c12033bea623da23a5b7a630c39",
      "name": "M001_data.rtdc"
    },
    {
      "index": 2,
      "md5-5M": "2edb8708f633e8eaa25b9e8d8fe5dca6",
      "name": "M002_data.rtdc"
    }
  ],
  "libraries": {
    "dclab": "0.47.0",
    "h5py": "3.7.0",
    "imageio": "2.10.3",
    "nptdms": "1.6.0",
    "numpy": "1.23.5"
  },
  "python": {
    "build": "tags/v3.8.0:fa919fd, Oct 14 2019 19:37:50",
    "implementation": "CPython",
    "version": "3.8.0"
  },
  "system": {
    "info": "Windows-10-10.0.18362-SP0",
    "machine": "AMD64",
    "name": "Windows",
    "release": "10",
    "version": "10.0.18362"
  },
  "utc": {
    "date": "2023-01-18",
    "time": "21:49:07"
  }
}

[LOG] number of written datasets 0  14:02:02.155
[LOG] number of written datasets 1000  14:02:42.297
[LOG] number of written datasets 2000  14:03:21.346
[LOG] number of written datasets 3000  14:03:52.521
[LOG] number of written datasets 4000  14:04:40.098
[LOG] number of written datasets 5000  14:05:21.552
[LOG] number of written datasets 6000  14:06:05.522
[LOG] number of written datasets 7000  14:06:28.929
[LOG] number of written datasets 8000  14:07:14.975
[LOG] number of written datasets 9000  14:07:42.710
[LOG] number of written datasets 10000  14:07:43.198
[LOG] number of written datasets 11000  14:07:43.560
[LOG] number of written datasets 12000  14:07:43.795
[LOG] number of written datasets 13000  14:07:44.105
[LOG] number of written datasets 14000  14:07:44.347
[LOG] number of written datasets 15000  14:07:44.695
[LOG] number of written datasets 16000  14:07:44.933
[LOG] number of written datasets 17000  14:07:45.165
[LOG] number of written datasets 18000  14:07:45.491
[LOG] number of written datasets 19000  14:07:45.727
[LOG] number of written datasets 20000  14:07:45.975
[LOG] number of written datasets 21000  14:07:46.235
[LOG] number of written datasets 22000  14:07:46.500
[LOG] number of written datasets 23000  14:08:28.154
[LOG] number of written datasets 24000  14:09:09.930
[LOG] number of written datasets 25000  14:09:30.494
[LOG] number of written datasets 26000  14:10:35.464
[LOG] number of written datasets 27000  14:10:51.214
[LOG] number of written datasets 28000  14:11:48.855
[LOG] number of written datasets 29000  14:12:18.608
[LOG] number of written datasets 30000  14:12:34.355
[LOG] number of written datasets 31000  14:13:20.511

[LOG] number of written datasets 0  14:24:13.570
[LOG] number of written datasets 1000  14:25:19.199
[LOG] number of written datasets 2000  14:26:34.888
[LOG] number of written datasets 3000  14:27:46.312
[LOG] number of written datasets 4000  14:29:00.252
[LOG] number of written datasets 5000  14:30:10.799
[LOG] number of written datasets 6000  14:31:18.832
[LOG] number of written datasets 7000  14:32:19.559
[LOG] number of written datasets 8000  14:33:20.673
[LOG] number of written datasets 9000  14:34:30.895
[LOG] number of written datasets 10000  14:35:40.570
[LOG] number of written datasets 11000  14:36:49.369
[LOG] number of written datasets 12000  14:38:00.465
[LOG] number of written datasets 13000  14:39:10.136
[LOG] number of written datasets 14000  14:40:31.727
[LOG] number of written datasets 15000  14:41:45.452
[LOG] number of written datasets 16000  14:43:02.230
[LOG] number of written datasets 17000  14:44:32.252
[LOG] number of written datasets 18000  14:45:56.908
[LOG] number of written datasets 19000  14:47:18.064
[LOG] number of written datasets 20000  14:48:36.924
[LOG] number of written datasets 21000  14:50:03.006
[LOG] number of written datasets 22000  14:51:27.663
[LOG] number of written datasets 23000  14:52:59.106
[LOG] number of written datasets 24000  14:54:41.805
[LOG] number of written datasets 25000  14:56:40.369
[LOG] number of written datasets 26000  14:59:04.201