Problem with using directional method "PREDICTED"

[SiduoLi2020]

Hello! I use crYOLO for filament picking. I have chosen "Activate filament mode" and the crYOLO model. Although there were no errors when I was running predictions, the log indicates that I can't use the directional method "PREDICTED":

########## The directional method "PREDICTED" can't be used as your model is not a filament model.You need to retrain your picking model. Fall back to old directional method "CONVOLUTION". ##########

How to use directional method "PREDICTED"? Also I want to konw weather PREDICTED is more precise than CONVOLUTION ?

By the way, tutorials document of Install napari and the boxmanager plugin is incorrect. I cannot install napari by conda while the command pip install napari can work. :)

[thorstenwagner]

Hi,

thanks for getting in touch :-)

How to use directional method "PREDICTED"?

how did you train the crYOLO model? You need to train with filament annotations to get a filament model which can use "PREDICTED". The general model is only for single particles.

"How to use directional method "PREDICTED"? Also I want to know weather PREDICTED is more precise than CONVOLUTION

I didn't do a systematic comparison, but PREDICTED should be more flexible regarding the filament shape. CONVOLUTION expects that the filament has certain width.

By the way, tutorials document of Install napari and the boxmanager plugin is incorrect. I cannot install napari by conda while the command pip install napari can work. :)

Can you tell me what kind of error you saw? I just redid the installation as described in the tutorial and its working on my side.

Best, Thorsten

[SiduoLi2020]

Thanks for your response! Perhaps the napari is cause of all problems.

Can you tell me what kind of error you saw? I just redid the installation as described in the tutorial and its working on my side.

I still cannot install napari by conda. Here is the log.

conda create -y -n napari-cryolo -c conda-forge python=3.10 napari=0.4.17 pyqt pip An HTTP error occurred when trying to retrieve this URL. HTTP errors are often intermittent, and a simple retry will get you on your way. If your current network has https://repo.anaconda.com blocked, please file a support request with your network engineering team. 'https//repo.anaconda.com/pkgs/main/linux-64'

I also tried download napari in dependent , it stopped in solving enviromnet.

conda install -c conda-forge napari=0.4.17`
Collecting package metadata (current_repodata.json): / DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): conda.anaconda.org:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): conda.anaconda.org:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443                                                                                                                      
DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/r/linux-64/current_repodata.json HTTP/1.1" 304 0                                                                                      | DEBUG:urllib3.connectionpool:https://conda.anaconda.org:443 "GET /conda-forge/noarch/current_repodata.json HTTP/1.1" 200 None
DEBUG:urllib3.connectionpool:https://conda.anaconda.org:443 "GET /conda-forge/linux-64/current_repodata.json HTTP/1.1" 200 None                                                                             - DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/main/noarch/current_repodata.json HTTP/1.1" 304 0                                                                                     - DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/main/linux-64/current_repodata.json HTTP/1.1" 304 0                                                                                   
DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/r/noarch/current_repodata.json HTTP/1.1" 304 0                                                                                        done
Solving environment: unsuccessful initial attempt using frozen solve. Retrying with flexible solve.
Solving environment: unsuccessful attempt using repodata from current_repodata.json, retrying with next repodata source.
Collecting package metadata (repodata.json): | DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): conda.anaconda.org:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): conda.anaconda.org:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): repo.anaconda.com:443                                                                                                                       DEBUG:urllib3.connectionpool:https://conda.anaconda.org:443 "GET /conda-forge/noarch/repodata.json HTTP/1.1" 200 None                                                                                       DEBUG:urllib3.connectionpool:https://conda.anaconda.org:443 "GET /conda-forge/linux-64/repodata.json HTTP/1.1" 200 None                                                                                     DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/main/linux-64/repodata.json HTTP/1.1" 304 0
DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/r/linux-64/repodata.json HTTP/1.1" 304 0                                                                                              | DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/main/noarch/repodata.json HTTP/1.1" 304 0                                                                                             / DEBUG:urllib3.connectionpool:https://repo.anaconda.com:443 "GET /pkgs/r/noarch/repodata.json HTTP/1.1" 304 0                                                                                                done
Solving environment: \

I'm not sure if all this is caused by network issues or the absence of this package on conda：

conda install napari`
PackagesNotFoundError: The following packages are not available from current channels:
- napari

Finally I try to download it by pip, thanksfully it works. （napari 0.4.18）

how did you train the crYOLO model? You need to train with filament annotations to get a filament model which can use "PREDICTED". The general model is only for single particles.

Is it because I deleted so many things that cryolo can't recognize the training data as filament?

Maybe the napari installed from pip have some bug. I cannot load .cox data into CRYOLO when i start trainning.

#####################################################
Important debugging information.
In case of any problems, please provide this information.
#####################################################
/home/em/anaconda3/envs/cryolo/bin/cryolo_gui.py train 
-c config_cryolo.json 
-w 5 
-nc 16 
--gpu_fraction 1.0 
-e 10 
-lft 2 
--seed 10 
#####################################################
###############################################
New version of crYOLO available
Local version:       1.8.4
Latest version:      1.9.6
More information here:
 https://cryolo.readthedocs.io/en/latest/changes.html
###############################################
###############################################
The following training image sizes were detected:
4096 x 4096 ( N: 19 )

crYOLO will train in mode: SQUARE
###############################################
Reading old CBOX format file

2023-09-28 22:37:44.408501: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation.
Using TensorFlow backend.
2023-09-28 22:37:45.391510: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2000000000 Hz
2023-09-28 22:37:45.392759: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55c37ef32190 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2023-09-28 22:37:45.392783: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2023-09-28 22:37:45.396331: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2023-09-28 22:37:45.632035: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55c37e424c40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-09-28 22:37:45.632077: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5
2023-09-28 22:37:45.633273: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1666] Found device 0 with properties:
name: NVIDIA GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
pciBusID: 0000:1a:00.0
2023-09-28 22:37:45.633341: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2023-09-28 22:37:45.645550: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2023-09-28 22:37:45.674337: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2023-09-28 22:37:45.674665: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2023-09-28 22:37:45.675267: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.11
2023-09-28 22:37:45.676324: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2023-09-28 22:37:45.676471: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2023-09-28 22:37:45.678536: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1794] Adding visible gpu devices: 0
2023-09-28 22:37:45.678600: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2023-09-28 22:37:46.174525: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1206] Device interconnect StreamExecutor with strength 1 edge matrix:
2023-09-28 22:37:46.174567: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1212]      0
2023-09-28 22:37:46.174575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1225] 0:   N
2023-09-28 22:37:46.176901: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1351] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6624 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:1a:00.0, compute capability: 7.5)
Traceback (most recent call last):
  File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2895, in get_loc
    return self._engine.get_loc(casted_key)
  File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/hashtable_class_helper.pxi", line 1675, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas/_libs/hashtable_class_helper.pxi", line 1683, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: '_CoordinateZ'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/CoordsIO.py", line 230, in read_cbox_boxfile
    z=starfile['cryolo']['_CoordinateZ'][i],
  File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/pandas/core/frame.py", line 2906, in __getitem__
    indexer = self.columns.get_loc(key)
  File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 2897, in get_loc
    raise KeyError(key) from err
KeyError: '_CoordinateZ'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/em/anaconda3/envs/cryolo/bin/cryolo_gui.py", line 8, in <module>
    sys.exit(_main_())
  File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/cryolo_main.py", line 455, in _main_
    Gooey(
  File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/gooey/python_bindings/gooey_decorator.py", line 134, in <lambda>
    return lambda *args, **kwargs: func(*args, **kwargs)
  File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/cryolo_main.py", line 424, in main
    train.main(args)
  File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/train.py", line 516, in main
    parse_dict = preprocess.parse_annotation(
  File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/preprocessing.py", line 150, in parse_annotation
    filaments = CoordsIO.read_cbox_boxfile(boxpath, int(cell_h))
  File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/CoordsIO.py", line 265, in read_cbox_boxfile
    return read_cbox_boxfile_old(path)
  File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/cryolo/CoordsIO.py", line 273, in read_cbox_boxfile_old
    boxreader = np.atleast_2d(np.genfromtxt(path))
  File "/home/em/anaconda3/envs/cryolo/lib/python3.8/site-packages/numpy/lib/npyio.py", line 2103, in genfromtxt
    raise ValueError(errmsg)
ValueError: Some errors were detected !
    Line #3 (got 2 columns instead of 1)
    Line #13 (got 5 columns instead of 1)
    Line #14 (got 5 columns instead of 1)
    Line #15 (got 5 columns instead of 1)
    Line #16 (got 5 columns instead of 1)
    Line #17 (got 5 columns instead of 1)
    Line #18 (got 5 columns instead of 1)
    Line #19 (got 5 columns instead of 1)
    Line #20 (got 5 columns instead of 1)
    Line #21 (got 5 columns instead of 1)
    Line #22 (got 5 columns instead of 1)
    Line #23 (got 5 columns instead of 1)
    Line #24 (got 5 columns instead of 1)
    Line #25 (got 5 columns instead of 1)
    Line #26 (got 5 columns instead of 1)
    Line #27 (got 5 columns instead of 1)
    Line #28 (got 5 columns instead of 1)
    Line #38 (got 5 columns instead of 1)
    Line #39 (got 5 columns instead of 1)
    Line #40 (got 5 columns instead of 1)
    Line #41 (got 5 columns instead of 1)
    Line #42 (got 5 columns instead of 1)
    Line #43 (got 5 columns instead of 1)
    Line #44 (got 5 columns instead of 1)
    Line #45 (got 5 columns instead of 1)
    Line #46 (got 5 columns instead of 1)
    Line #47 (got 5 columns instead of 1)
    Line #48 (got 5 columns instead of 1)
    Line #49 (got 5 columns instead of 1)
    Line #50 (got 5 columns instead of 1)
    Line #51 (got 5 columns instead of 1)
    Line #52 (got 5 columns instead of 1)
    Line #53 (got 5 columns instead of 1)
    Line #54 (got 5 columns instead of 1)
    Line #55 (got 5 columns instead of 1)
    Line #56 (got 5 columns instead of 1)
    Line #57 (got 5 columns instead of 1)
    Line #58 (got 5 columns instead of 1)
    Line #59 (got 5 columns instead of 1)
    Line #60 (got 5 columns instead of 1)
    Line #61 (got 5 columns instead of 1)
    Line #62 (got 5 columns instead of 1)
    Line #63 (got 5 columns instead of 1)
    Line #64 (got 5 columns instead of 1)
    Line #65 (got 5 columns instead of 1)
    Line #66 (got 5 columns instead of 1)
    Line #67 (got 5 columns instead of 1)
    Line #68 (got 5 columns instead of 1)
    Line #69 (got 5 columns instead of 1)
    Line #70 (got 5 columns instead of 1)
    Line #71 (got 5 columns instead of 1)
    Line #72 (got 5 columns instead of 1)
    Line #73 (got 5 columns instead of 1)
    Line #74 (got 5 columns instead of 1)
    Line #75 (got 5 columns instead of 1)
    Line #76 (got 5 columns instead of 1)
    Line #77 (got 5 columns instead of 1)
    Line #78 (got 5 columns instead of 1)
    Line #79 (got 5 columns instead of 1)
    Line #80 (got 5 columns instead of 1)
    Line #81 (got 5 columns instead of 1)
    Line #82 (got 5 columns instead of 1)
    Line #83 (got 5 columns instead of 1)
    Line #84 (got 5 columns instead of 1)
    Line #85 (got 5 columns instead of 1)
    Line #86 (got 5 columns instead of 1)
    Line #87 (got 5 columns instead of 1)
    Line #88 (got 5 columns instead of 1)
    Line #89 (got 5 columns instead of 1)
    Line #90 (got 5 columns instead of 1)
    Line #91 (got 5 columns instead of 1)
    Line #92 (got 5 columns instead of 1)
    Line #93 (got 5 columns instead of 1)
    Line #94 (got 5 columns instead of 1)
    Line #95 (got 5 columns instead of 1)
    Line #96 (got 5 columns instead of 1)
    Line #97 (got 5 columns instead of 1)
    Line #98 (got 5 columns instead of 1)
    Line #99 (got 5 columns instead of 1)
    Line #100 (got 5 columns instead of 1)
    Line #101 (got 5 columns instead of 1)
    Line #102 (got 5 columns instead of 1)
    Line #103 (got 5 columns instead of 1)
    Line #104 (got 5 columns instead of 1)
    Line #105 (got 5 columns instead of 1)
    Line #106 (got 5 columns instead of 1)
    Line #107 (got 5 columns instead of 1)
    Line #108 (got 5 columns instead of 1)
    Line #109 (got 5 columns instead of 1)
    Line #110 (got 5 columns instead of 1)
    Line #111 (got 5 columns instead of 1)
    Line #112 (got 5 columns instead of 1)
    Line #113 (got 5 columns instead of 1)
    Line #114 (got 5 columns instead of 1)
    Line #115 (got 5 columns instead of 1)
    Line #116 (got 5 columns instead of 1)
    Line #117 (got 5 columns instead of 1)
    Line #118 (got 5 columns instead of 1)
    Line #119 (got 5 columns instead of 1)

I check the .cox file：

data_global

_cbox_format_version   1.0 

data_filament_vertices

loop_
_CoordinateX #1
_CoordinateY #2
_filamentid #3
_Width #4
_Height #5
 1167.069071  1648.989192  0.000000  500.000000  500.000000 
  221.764288   235.665864  0.000000  500.000000  500.000000 
 1709.229167  1384.859914  1.000000  500.000000  500.000000 
 2585.026245   175.425853  1.000000  500.000000  500.000000 
 1139.265989   370.047426  2.000000  500.000000  500.000000 
 1343.155256   193.961241  2.000000  500.000000  500.000000 
 2306.995427  1964.090786  3.000000  500.000000  500.000000 
 4007.617267  1556.312252  3.000000  500.000000  500.000000 
 1199.506000  2724.041690  4.000000  500.000000  500.000000 
 1273.647551  4021.518843  4.000000  500.000000  500.000000 
  471.992024  2612.829363  5.000000  500.000000  500.000000 
  967.813651  3993.715761  5.000000  500.000000  500.000000 
 1662.890697  2413.573943  6.000000  500.000000  500.000000 
 2793.549359  2700.872455  6.000000  500.000000  500.000000 
  921.475181  2571.124740  7.000000  500.000000  500.000000 
 2093.838466  3919.574210  7.000000  500.000000  500.000000 

data_cryolo

loop_
_CoordinateX #1
_CoordinateY #2
_filamentid #3
_Width #4
_Height #5
 1398.989192   917.069071  0.000000  500.000000  500.000000 
 1281.212248   838.293672  0.000000  500.000000  500.000000 
 1163.435304   759.518274  0.000000  500.000000  500.000000 
 1045.658360   680.742875  0.000000  500.000000  500.000000 
  927.881416   601.967476  0.000000  500.000000  500.000000 
  810.104472   523.192078  0.000000  500.000000  500.000000 
  692.327528   444.416679  0.000000  500.000000  500.000000 
  574.550584   365.641281  0.000000  500.000000  500.000000 
  456.773640   286.865882  0.000000  500.000000  500.000000 
  338.996696   208.090484  0.000000  500.000000  500.000000 
  221.219752   129.315085  0.000000  500.000000  500.000000 
  103.442808    50.539686  0.000000  500.000000  500.000000 
  -14.334136   -28.235712  0.000000  500.000000  500.000000 
 1134.859914  1459.229167  1.000000  500.000000  500.000000 
 1013.916508  1546.808875  1.000000  500.000000  500.000000 
  892.973102  1634.388583  1.000000  500.000000  500.000000 
  772.029696  1721.968290  1.000000  500.000000  500.000000 
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   46.369259  2247.446538  1.000000  500.000000  500.000000 
  -74.574147  2335.026245  1.000000  500.000000  500.000000 
  120.047426   889.265989  2.000000  500.000000  500.000000 
  -56.038759  1093.155256  2.000000  500.000000  500.000000 
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 1476.219975  3049.024834  3.000000  500.000000  500.000000 
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 2433.495529   769.172122  7.000000  500.000000  500.000000 
 2545.866318   866.869062  7.000000  500.000000  500.000000 
 2658.237107   964.566002  7.000000  500.000000  500.000000 
 2770.607896  1062.262943  7.000000  500.000000  500.000000 
 2882.978686  1159.959883  7.000000  500.000000  500.000000 
 2995.349475  1257.656824  7.000000  500.000000  500.000000 
 3107.720264  1355.353764  7.000000  500.000000  500.000000 
 3220.091053  1453.050704  7.000000  500.000000  500.000000 
 3332.461842  1550.747645  7.000000  500.000000  500.000000 
 3444.832631  1648.444585  7.000000  500.000000  500.000000 
 3557.203421  1746.141526  7.000000  500.000000  500.000000 
 3669.574210  1843.838466  7.000000  500.000000  500.000000 

data_cryolo_include

loop_
_slice_index #1

I changed the format, keeping only the X and Y coordinates of the box and the size of the box：

1398.989192 917.0690710000001   500.0   500.0
1281.212248 838.293672  500.0   500.0
1163.4353039999999  759.518274  500.0   500.0
1045.65836  680.742875  500.0   500.0
927.8814160000001   601.967476  500.0   500.0
810.104472  523.192078  500.0   500.0
692.327528  444.416679  500.0   500.0
574.550584  365.641281  500.0   500.0
456.77364000000006  286.865882  500.0   500.0
338.996696  208.090484  500.0   500.0
221.219752  129.315085  500.0   500.0
103.442808  50.539685999999996  500.0   500.0
-14.334135999999999 -28.235712  500.0   500.0
1134.8599140000001  1459.229167 500.0   500.0
1013.916508 1546.808875 500.0   500.0
892.9731019999999   1634.388583 500.0   500.0
772.029696  1721.9682899999998  500.0   500.0
651.0862900000001   1809.547998 500.0   500.0
530.142884  1897.1277059999998  500.0   500.0
409.199478  1984.707414 500.0   500.0
288.256072  2072.2871219999997  500.0   500.0
167.31266599999998  2159.86683  500.0   500.0
46.369259   2247.4465379999997  500.0   500.0
-74.574147  2335.026245 500.0   500.0
120.047426  889.2659890000001   500.0   500.0
-56.038759  1093.155256 500.0   500.0
1714.090786 2056.995427 500.0   500.0
1680.1092420000002  2198.713914 500.0   500.0
1646.1276970000001  2340.4324   500.0   500.0
1612.146153 2482.1508870000002  500.0   500.0
1578.164608 2623.869374 500.0   500.0
1544.183064 2765.58786  500.0   500.0
1510.201519 2907.3063469999997  500.0   500.0
1476.219975 3049.024834 500.0   500.0
1442.23843  3190.74332  500.0   500.0
1408.256886 3332.461807 500.0   500.0
1374.275341 3474.180294 500.0   500.0
1340.293797 3615.8987799999995  500.0   500.0
1306.312252 3757.617267 500.0   500.0
2474.04169  949.506 500.0   500.0
2618.205818 957.74395   500.0   500.0
2762.3699460000003  965.9819    500.0   500.0
2906.5340739999997  974.21985   500.0   500.0
3050.698203 982.4578    500.0   500.0
3194.862331 990.695751  500.0   500.0
3339.026459 998.9337009999999   500.0   500.0
3483.1905869999996  1007.171651 500.0   500.0
3627.354715 1015.409601 500.0   500.0
3771.518843 1023.647551 500.0   500.0
2362.829363 221.99202400000001  500.0   500.0
2500.918002 271.574187  500.0   500.0
2639.0066420000003  321.15635   500.0   500.0
2777.095282 370.738512  500.0   500.0
2915.1839219999997  420.320675  500.0   500.0
3053.272562 469.902838  500.0   500.0
3191.361202 519.485 500.0   500.0
3329.449842 569.0671629999999   500.0   500.0
3467.538482 618.649326  500.0   500.0
3605.627121 668.231488  500.0   500.0
3743.715761 717.8136509999999   500.0   500.0
2163.573943 1412.890697 500.0   500.0
2199.486257 1554.22303  500.0   500.0
2235.398571 1695.555363 500.0   500.0
2271.3108850000003  1836.887695 500.0   500.0
2307.223199 1978.2200280000002  500.0   500.0
2343.1355129999997  2119.552361 500.0   500.0
2379.047827 2260.8846940000003  500.0   500.0
2414.960141 2402.217027 500.0   500.0
2450.872455 2543.549359 500.0   500.0
2321.1247399999997  671.4751809999999   500.0   500.0
2433.495529 769.172122  500.0   500.0
2545.866318 866.8690619999999   500.0   500.0
2658.237107 964.5660019999999   500.0   500.0
2770.607896 1062.262943 500.0   500.0
2882.978686 1159.959883 500.0   500.0
2995.349475 1257.656824 500.0   500.0
3107.720264 1355.353764 500.0   500.0
3220.091053 1453.050704 500.0   500.0
3332.461842 1550.747645 500.0   500.0
3444.832631 1648.444585 500.0   500.0
3557.203421 1746.141526 500.0   500.0
3669.57421  1843.838466 500.0   500.0

With the new format, cryolo train can work.