Schramm 2012: imzML, a common data format for mass spectrometry imaging data

The session

This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.

- Setup values:
Ion image:
- Normalization (normalization): none
- Reduce (m/z window) (reduce): sum
Peak picking:
- Peak picking S/N (snr): 3
- Pixels used for the mean spectrum (0 = all) (sample_pixels): 500
Acquisition:
- Polarity (polarity): negative
You choose: Reduce (m/z window) (reduce), Pixels used for the mean spectrum (0 = all) (sample_pixels).
Ask the scientist: Ion image tolerance (ppm) (ion_ppm).
The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist.
- Mode is now watch: you run the steps.

{
 "ok": true,
 "summary": "9 pixels (3 x 3), m/z 100.0833 to 799.9167, profile, negative, continuous storage, ibd 335976 bytes",
 "metrics": {
  "n_pixels": 9,
  "grid_x": 3,
  "grid_y": 3,
  "n_z": 1,
  "points_per_spectrum_min": 8399,
  "points_per_spectrum_max": 8399,
  "mz_min": 100.08333587646484,
  "mz_max": 799.9166870117188,
  "n_spectra_scanned_for_range": 1,
  "ibd_bytes": 335976,
  "imzml_bytes": 23898,
  "pixel_size_x_um": 100,
  "pixel_size_y_um": 100,
  "polarity_matches_decision": 1
 },
 "data": {
  "storage_mode": "continuous",
  "spectrum_mode": "profile",
  "polarity": "negative",
  "mz_precision": "f",
  "intensity_precision": "f",
  "mz_range_basis": "all spectra"
 },
 "table": {
  "columns": [
   "property",
   "value"
  ],
  "rows": [
   [
    "n_pixels",
    9
   ],
   [
    "grid_x",
    3
   ],
   [
    "grid_y",
    3
   ],
   [
    "pixel_size_x_um",
    100
   ],
   [
    "mz_min",
    100.08333587646484
   ],
   [
    "mz_max",
    799.9166870117188
   ],
   [
    "points_per_spectrum_max",
    8399
   ],
   [
    "ibd_bytes",
    335976
   ],
   [
    "storage_mode",
    "continuous"
   ],
   [
    "spectrum_mode",
    "profile"
   ],
   [
    "polarity",
    "negative"
   ],
   [
    "mz_precision",
    "f"
   ],
   [
    "intensity_precision",
    "f"
   ],
   [
    "mz_range_basis",
    "all spectra"
   ]
  ],
  "n_rows": 14
 }
}

{
 "ok": true,
 "summary": "9 pixels (3 x 3), m/z 100.0833 to 799.9167, profile, negative, processed storage, ibd 604744 bytes",
 "metrics": {
  "n_pixels": 9,
  "grid_x": 3,
  "grid_y": 3,
  "n_z": 1,
  "points_per_spectrum_min": 8399,
  "points_per_spectrum_max": 8399,
  "mz_min": 100.08333587646484,
  "mz_max": 799.9166870117188,
  "n_spectra_scanned_for_range": 9,
  "ibd_bytes": 604744,
  "imzml_bytes": 23929,
  "pixel_size_x_um": 100,
  "pixel_size_y_um": 100,
  "polarity_matches_decision": 1
 },
 "data": {
  "storage_mode": "processed",
  "spectrum_mode": "profile",
  "polarity": "negative",
  "mz_precision": "f",
  "intensity_precision": "f",
  "mz_range_basis": "all spectra"
 },
 "table": {
  "columns": [
   "property",
   "value"
  ],
  "rows": [
   [
    "n_pixels",
    9
   ],
   [
    "grid_x",
    3
   ],
   [
    "grid_y",
    3
   ],
   [
    "pixel_size_x_um",
    100
   ],
   [
    "mz_min",
    100.08333587646484
   ],
   [
    "mz_max",
    799.9166870117188
   ],
   [
    "points_per_spectrum_max",
    8399
   ],
   [
    "ibd_bytes",
    604744
   ],
   [
    "storage_mode",
    "processed"
   ],
   [
    "spectrum_mode",
    "profile"
   ],
   [
    "polarity",
    "negative"
   ],
   [
    "mz_precision",
    "f"
   ],
   [
    "intensity_precision",
    "f"
   ],
   [
    "mz_range_basis",
    "all spectra"
   ]
  ],
  "n_rows": 14
 }
}

{"purpose":"Check if the .ibd sizes match one shared m/z array (continuous) against one m/z array per pixel (processed).","items":[{"name":"continuous_expected_bytes","expression":"16 + 8399*4 + 9*8399*4"},{"name":"processed_expected_bytes","expression":"16 + 9*8399*4*2"},{"name":"size_ratio_processed_to_continuous","expression":"ratio(604744, 335976)"},{"name":"extra_bytes","expression":"604744 - 335976"},{"name":"eight_extra_mz_arrays_bytes","expression":"8*8399*4"}]}

continuous_expected_bytes = 16 + 8399*4 + 9*8399*4 = 335976
processed_expected_bytes = 16 + 9*8399*4*2 = 604744
size_ratio_processed_to_continuous = ratio(604744, 335976) = 1.799961902
extra_bytes = 604744 - 335976 = 268768
eight_extra_mz_arrays_bytes = 8*8399*4 = 268768

{
 "ok": true,
 "summary": "Total ion current (sum of intensities per pixel): mean 161.144, max 243.54 at pixel (3, 3)",
 "metrics": {
  "n_pixels": 9,
  "tic_mean": 161.1443790255365,
  "tic_median": 161.80919044826766,
  "tic_max": 243.53950660310795,
  "tic_min": 108.3959741842164,
  "tic_max_x": 3,
  "tic_max_y": 3,
  "tic_cv": 0.25314754546968943
 },
 "data": {
  "coordinates": "1-based, as in imzML"
 },
 "outputs": [
  {
   "path": "{work}/get_tic_image-1/tic_continuous.png",
   "kind": "image",
   "name": "tic_continuous.png"
  },
  {
   "path": "{work}/get_tic_image-1/tic_continuous.tif",
   "kind": "image",
   "name": "tic_continuous.tif"
  },
  {
   "path": "{work}/get_tic_image-1/tic_continuous_top_pixels.csv",
   "kind": "table",
   "name": "tic_continuous_top_pixels.csv"
  }
 ],
 "table": {
  "columns": [
   "x",
   "y",
   "tic"
  ],
  "rows": [
   [
    3,
    3,
    243.53950660310795
   ],
   [
    1,
    2,
    200.96332770925406
   ],
   [
    2,
    1,
    182.31835420101902
   ],
   [
    2,
    3,
    168.2701814752251
   ],
   [
    3,
    1,
    161.80919044826766
   ],
   [
    2,
    2,
    135.3058417315849
   ],
   [
    1,
    3,
    127.84664447846849
   ],
   [
    1,
    1,
    121.85039039868468
   ],
   [
    3,
    2,
    108.3959741842164
   ]
  ],
  "n_rows": 9,
  "path": "{work}/get_tic_image-1/tic_continuous_top_pixels.csv"
 }
}

{
 "ok": true,
 "summary": "Total ion current (sum of intensities per pixel): mean 161.144, max 243.54 at pixel (3, 3)",
 "metrics": {
  "n_pixels": 9,
  "tic_mean": 161.1443790255365,
  "tic_median": 161.80919044826766,
  "tic_max": 243.53950660310795,
  "tic_min": 108.3959741842164,
  "tic_max_x": 3,
  "tic_max_y": 3,
  "tic_cv": 0.25314754546968943
 },
 "data": {
  "coordinates": "1-based, as in imzML"
 },
 "outputs": [
  {
   "path": "{work}/get_tic_image-2/tic_processed.png",
   "kind": "image",
   "name": "tic_processed.png"
  },
  {
   "path": "{work}/get_tic_image-2/tic_processed.tif",
   "kind": "image",
   "name": "tic_processed.tif"
  },
  {
   "path": "{work}/get_tic_image-2/tic_processed_top_pixels.csv",
   "kind": "table",
   "name": "tic_processed_top_pixels.csv"
  }
 ],
 "table": {
  "columns": [
   "x",
   "y",
   "tic"
  ],
  "rows": [
   [
    3,
    3,
    243.53950660310795
   ],
   [
    1,
    2,
    200.96332770925406
   ],
   [
    2,
    1,
    182.31835420101902
   ],
   [
    2,
    3,
    168.2701814752251
   ],
   [
    3,
    1,
    161.80919044826766
   ],
   [
    2,
    2,
    135.3058417315849
   ],
   [
    1,
    3,
    127.84664447846849
   ],
   [
    1,
    1,
    121.85039039868468
   ],
   [
    3,
    2,
    108.3959741842164
   ]
  ],
  "n_rows": 9,
  "path": "{work}/get_tic_image-2/tic_processed_top_pixels.csv"
 }
}

{
 "ok": true,
 "summary": "Identical spectra: 9 pixels compared, 0 differ, max m/z difference 0, max intensity difference 0 (A is continuous, B is processed)",
 "metrics": {
  "n_pixels_a": 9,
  "n_pixels_b": 9,
  "n_pixels_compared": 9,
  "coordinates_equal": 1,
  "n_pixels_different": 0,
  "n_pixels_different_length": 0,
  "max_abs_mz_difference": 0,
  "max_abs_intensity_difference": 0,
  "identical": 1,
  "ibd_bytes_a": 335976,
  "ibd_bytes_b": 604744
 },
 "data": {
  "storage_a": "continuous",
  "storage_b": "processed"
 }
}

- 2 numbers in the answer match no logged tool result: 16. Run the tool that measures each number, or remove the number, or say how you derived it from logged results.
Fix these problems. Then give the answer again.

{"purpose":"Find the bytes left in each binary file after the m/z and intensity arrays are taken away.","items":[{"name":"continuous_bytes_not_in_arrays","expression":"335976 - (1 + 9) * 8399 * 4"},{"name":"processed_bytes_not_in_arrays","expression":"604744 - (9 + 9) * 8399 * 4"}]}

continuous_bytes_not_in_arrays = 335976 - (1 + 9) * 8399 * 4 = 16
processed_bytes_not_in_arrays = 604744 - (9 + 9) * 8399 * 4 = 16

The final answer

The model wrote this answer. The harness shows it to the scientist after the review.

Settings used, from the decision record: Polarity: negative.

Checks

Review findings

The review recorded 6 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.

Numbers in the answer

The last claim check read 62 numbers in the answer. 62 numbers match a logged result. 0 numbers have no source in the record.

Deviations

The model did not try to change a choice of the scientist.

Failed tool calls

No tool call failed.

Data integrity

Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.

A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.

How to repeat it

Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.

CUVETTE_DATA={data} bash bench/papers/schramm2012-imzml-pyimzml/fetch.sh

Run the same case with Cuvette. The script gives the same answers from bench/papers/schramm2012-imzml-pyimzml/bench.yaml.

cuvette bench papers --papers schramm2012-imzml-pyimzml --models claude:claude-opus-5-5

Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.

1. read_file_info (step n1)

   In Python

   p = ImzMLParser(path)
   then p.polarity, p.spectrum_mode, p.imzmldict, len(p.coordinates), p.mzLengths

   • Run p = pyimzml.ImzMLParser.ImzMLParser('file.imzML').
   • Read len(p.coordinates) for the pixel count and p.imzmldict for the grid and the pixel size.
   • Read p.polarity and p.spectrum_mode. Read max(p.mzLengths) for the points per spectrum.
   • Read the m/z range from p.getspectrum(i)[0].min() and .max().
   • In SMILE MSI File>Open imzML..., then Data>Acquisition & matching... shows the settings.
   • Spectra read for the m/z range = 1
   • Polarity = negative
   • Warning: If you keep the default 200, you get a different result.
   • Note: For a processed file the m/z range comes from evenly spaced spectra, not from all of them. The tool also reads the file sizes.

   The manual route that the harness recorded

   msi_tools.read_file_info(path="{data}/schramm2012-imzml-pyimzml/Example_Continuous.imzML", expected_polarity="negative", max_scan=1)

   The manual route uses the same method. The note in the route gives the known difference.

2. read_file_info (step n2)

   In Python

   p = ImzMLParser(path)
   then p.polarity, p.spectrum_mode, p.imzmldict, len(p.coordinates), p.mzLengths

   • Run p = pyimzml.ImzMLParser.ImzMLParser('file.imzML').
   • Read len(p.coordinates) for the pixel count and p.imzmldict for the grid and the pixel size.
   • Read p.polarity and p.spectrum_mode. Read max(p.mzLengths) for the points per spectrum.
   • Read the m/z range from p.getspectrum(i)[0].min() and .max().
   • In SMILE MSI File>Open imzML..., then Data>Acquisition & matching... shows the settings.
   • Spectra read for the m/z range = 100000
   • Polarity = negative
   • Warning: If you keep the default 200, you get a different result.
   • Note: For a processed file the m/z range comes from evenly spaced spectra, not from all of them. The tool also reads the file sizes.

   The manual route that the harness recorded

   msi_tools.read_file_info(path="{data}/schramm2012-imzml-pyimzml/Example_Processed.imzML", expected_polarity="negative", max_scan=100000)

   The manual route uses the same method. The note in the route gives the known difference.

3. calculate (step n3)

   Run the tool "calculate" with these settings: {"items":[{"name":"continuous_expected_bytes","expression":"16 + 8399*4 + 9*8399*4"},{"name":"processed_expected_bytes","expression":"16 + 9*8399*4*2"},{"name":"size_ratio_processed_to_continuous","expression":"ratio(604744, 335976)"},{"name":"extra_bytes","expression":"604744 - 335976"},{"name":"eight_extra_mz_arrays_bytes","expression":"8*8399*4"}]}.
   - Code only: this step has no route in the program menus. Run it with the script or flow export.

   The harness recorded no manual route for this step.

4. get_tic_image (step n4)

   In Python

   sum(p.getspectrum(i)[1]) for each pixel i, placed at (y - 1, x - 1)

   • For each index i in range(len(p.coordinates)), compute sum(p.getspectrum(i)[1]).
   • Place the value at row y - 1 and column x - 1 of an array of shape (grid y, grid x).
   • In SMILE MSI: the TIC image in the Display panel.

   The manual route that the harness recorded

   msi_tools.get_tic_image(path="{data}/schramm2012-imzml-pyimzml/Example_Continuous.imzML", name="tic_continuous")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

5. get_tic_image (step n5)

   In Python

   sum(p.getspectrum(i)[1]) for each pixel i, placed at (y - 1, x - 1)

   • For each index i in range(len(p.coordinates)), compute sum(p.getspectrum(i)[1]).
   • Place the value at row y - 1 and column x - 1 of an array of shape (grid y, grid x).
   • In SMILE MSI: the TIC image in the Display panel.

   The manual route that the harness recorded

   msi_tools.get_tic_image(path="{data}/schramm2012-imzml-pyimzml/Example_Processed.imzML", name="tic_processed")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

6. compare_files (step n6)

   In Python

   compare p1.getspectrum(i) with p2.getspectrum(i) for every i with numpy.array_equal

   • Open both files with ImzMLParser.
   • For each pixel index i, compare the m/z arrays and the intensity arrays.

   The manual route that the harness recorded

   msi_tools.compare_files(path_a="{data}/schramm2012-imzml-pyimzml/Example_Continuous.imzML", path_b="{data}/schramm2012-imzml-pyimzml/Example_Processed.imzML", max_pixels=0)

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

7. calculate (step n7)

   Run the tool "calculate" with these settings: {"items":[{"name":"continuous_bytes_not_in_arrays","expression":"335976 - (1 + 9) * 8399 * 4"},{"name":"processed_bytes_not_in_arrays","expression":"604744 - (9 + 9) * 8399 * 4"}]}.
   - Code only: this step has no route in the program menus. Run it with the script or flow export.

   The harness recorded no manual route for this step.

Figure

Run facts

The session

This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.

- Setup values:
Ion image:
- Normalization (normalization): none
- Reduce (m/z window) (reduce): sum
Peak picking:
- Peak picking S/N (snr): 3
- Pixels used for the mean spectrum (0 = all) (sample_pixels): 500
Acquisition:
- Polarity (polarity): negative
You choose: Reduce (m/z window) (reduce), Pixels used for the mean spectrum (0 = all) (sample_pixels).
Ask the scientist: Ion image tolerance (ppm) (ion_ppm).
The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist.
- Mode is now watch: you run the steps.

{
 "ok": true,
 "summary": "9 pixels (3 x 3), m/z 100.0833 to 799.9167, profile, negative, continuous storage, ibd 335976 bytes",
 "metrics": {
  "n_pixels": 9,
  "grid_x": 3,
  "grid_y": 3,
  "n_z": 1,
  "points_per_spectrum_min": 8399,
  "points_per_spectrum_max": 8399,
  "mz_min": 100.08333587646484,
  "mz_max": 799.9166870117188,
  "n_spectra_scanned_for_range": 1,
  "ibd_bytes": 335976,
  "imzml_bytes": 23898,
  "pixel_size_x_um": 100,
  "pixel_size_y_um": 100,
  "polarity_matches_decision": 1
 },
 "data": {
  "storage_mode": "continuous",
  "spectrum_mode": "profile",
  "polarity": "negative",
  "mz_precision": "f",
  "intensity_precision": "f",
  "mz_range_basis": "all spectra"
 },
 "table": {
  "columns": [
   "property",
   "value"
  ],
  "rows": [
   [
    "n_pixels",
    9
   ],
   [
    "grid_x",
    3
   ],
   [
    "grid_y",
    3
   ],
   [
    "pixel_size_x_um",
    100
   ],
   [
    "mz_min",
    100.08333587646484
   ],
   [
    "mz_max",
    799.9166870117188
   ],
   [
    "points_per_spectrum_max",
    8399
   ],
   [
    "ibd_bytes",
    335976
   ],
   [
    "storage_mode",
    "continuous"
   ],
   [
    "spectrum_mode",
    "profile"
   ],
   [
    "polarity",
    "negative"
   ],
   [
    "mz_precision",
    "f"
   ],
   [
    "intensity_precision",
    "f"
   ],
   [
    "mz_range_basis",
    "all spectra"
   ]
  ],
  "n_rows": 14
 }
}

{
 "ok": true,
 "summary": "9 pixels (3 x 3), m/z 100.0833 to 799.9167, profile, negative, processed storage, ibd 604744 bytes",
 "metrics": {
  "n_pixels": 9,
  "grid_x": 3,
  "grid_y": 3,
  "n_z": 1,
  "points_per_spectrum_min": 8399,
  "points_per_spectrum_max": 8399,
  "mz_min": 100.08333587646484,
  "mz_max": 799.9166870117188,
  "n_spectra_scanned_for_range": 9,
  "ibd_bytes": 604744,
  "imzml_bytes": 23929,
  "pixel_size_x_um": 100,
  "pixel_size_y_um": 100,
  "polarity_matches_decision": 1
 },
 "data": {
  "storage_mode": "processed",
  "spectrum_mode": "profile",
  "polarity": "negative",
  "mz_precision": "f",
  "intensity_precision": "f",
  "mz_range_basis": "all spectra"
 },
 "table": {
  "columns": [
   "property",
   "value"
  ],
  "rows": [
   [
    "n_pixels",
    9
   ],
   [
    "grid_x",
    3
   ],
   [
    "grid_y",
    3
   ],
   [
    "pixel_size_x_um",
    100
   ],
   [
    "mz_min",
    100.08333587646484
   ],
   [
    "mz_max",
    799.9166870117188
   ],
   [
    "points_per_spectrum_max",
    8399
   ],
   [
    "ibd_bytes",
    604744
   ],
   [
    "storage_mode",
    "processed"
   ],
   [
    "spectrum_mode",
    "profile"
   ],
   [
    "polarity",
    "negative"
   ],
   [
    "mz_precision",
    "f"
   ],
   [
    "intensity_precision",
    "f"
   ],
   [
    "mz_range_basis",
    "all spectra"
   ]
  ],
  "n_rows": 14
 }
}

{
 "ok": true,
 "summary": "Total ion current (sum of intensities per pixel): mean 161.144, max 243.54 at pixel (3, 3)",
 "metrics": {
  "n_pixels": 9,
  "tic_mean": 161.1443790255365,
  "tic_median": 161.80919044826766,
  "tic_max": 243.53950660310795,
  "tic_min": 108.3959741842164,
  "tic_max_x": 3,
  "tic_max_y": 3,
  "tic_cv": 0.25314754546968943
 },
 "data": {
  "coordinates": "1-based, as in imzML"
 },
 "outputs": [
  {
   "path": "{work}/get_tic_image-1/tic_continuous.png",
   "kind": "image",
   "name": "tic_continuous.png"
  },
  {
   "path": "{work}/get_tic_image-1/tic_continuous.tif",
   "kind": "image",
   "name": "tic_continuous.tif"
  },
  {
   "path": "{work}/get_tic_image-1/tic_continuous_top_pixels.csv",
   "kind": "table",
   "name": "tic_continuous_top_pixels.csv"
  }
 ],
 "table": {
  "columns": [
   "x",
   "y",
   "tic"
  ],
  "rows": [
   [
    3,
    3,
    243.53950660310795
   ],
   [
    1,
    2,
    200.96332770925406
   ],
   [
    2,
    1,
    182.31835420101902
   ],
   [
    2,
    3,
    168.2701814752251
   ],
   [
    3,
    1,
    161.80919044826766
   ],
   [
    2,
    2,
    135.3058417315849
   ],
   [
    1,
    3,
    127.84664447846849
   ],
   [
    1,
    1,
    121.85039039868468
   ],
   [
    3,
    2,
    108.3959741842164
   ]
  ],
  "n_rows": 9,
  "path": "{work}/get_tic_image-1/tic_continuous_top_pixels.csv"
 }
}

{
 "ok": true,
 "summary": "Total ion current (sum of intensities per pixel): mean 161.144, max 243.54 at pixel (3, 3)",
 "metrics": {
  "n_pixels": 9,
  "tic_mean": 161.1443790255365,
  "tic_median": 161.80919044826766,
  "tic_max": 243.53950660310795,
  "tic_min": 108.3959741842164,
  "tic_max_x": 3,
  "tic_max_y": 3,
  "tic_cv": 0.25314754546968943
 },
 "data": {
  "coordinates": "1-based, as in imzML"
 },
 "outputs": [
  {
   "path": "{work}/get_tic_image-2/tic_processed.png",
   "kind": "image",
   "name": "tic_processed.png"
  },
  {
   "path": "{work}/get_tic_image-2/tic_processed.tif",
   "kind": "image",
   "name": "tic_processed.tif"
  },
  {
   "path": "{work}/get_tic_image-2/tic_processed_top_pixels.csv",
   "kind": "table",
   "name": "tic_processed_top_pixels.csv"
  }
 ],
 "table": {
  "columns": [
   "x",
   "y",
   "tic"
  ],
  "rows": [
   [
    3,
    3,
    243.53950660310795
   ],
   [
    1,
    2,
    200.96332770925406
   ],
   [
    2,
    1,
    182.31835420101902
   ],
   [
    2,
    3,
    168.2701814752251
   ],
   [
    3,
    1,
    161.80919044826766
   ],
   [
    2,
    2,
    135.3058417315849
   ],
   [
    1,
    3,
    127.84664447846849
   ],
   [
    1,
    1,
    121.85039039868468
   ],
   [
    3,
    2,
    108.3959741842164
   ]
  ],
  "n_rows": 9,
  "path": "{work}/get_tic_image-2/tic_processed_top_pixels.csv"
 }
}

{
 "ok": true,
 "summary": "Identical spectra: 9 pixels compared, 0 differ, max m/z difference 0, max intensity difference 0 (A is continuous, B is processed)",
 "metrics": {
  "n_pixels_a": 9,
  "n_pixels_b": 9,
  "n_pixels_compared": 9,
  "coordinates_equal": 1,
  "n_pixels_different": 0,
  "n_pixels_different_length": 0,
  "max_abs_mz_difference": 0,
  "max_abs_intensity_difference": 0,
  "identical": 1,
  "ibd_bytes_a": 335976,
  "ibd_bytes_b": 604744
 },
 "data": {
  "storage_a": "continuous",
  "storage_b": "processed"
 }
}

{"items":[{"name":"ibd difference","expression":"604744 - 335976"},{"name":"one m/z array bytes (8399 x 4)","expression":"8399*4"},{"name":"extra m/z arrays","expression":"(604744 - 335976)/(8399*4)"},{"name":"ratio ibd","expression":"ratio(604744, 335976)"}]}

ibd difference = 604744 - 335976 = 268768
one m/z array bytes (8399 x 4) = 8399*4 = 33596
extra m/z arrays = (604744 - 335976)/(8399*4) = 8
ratio ibd = ratio(604744, 335976) = 1.799961902

- 2 numbers in the answer match no logged tool result: 32, 31. Run the tool that measures each number, or remove the number, or say how you derived it from logged results.
Fix these problems. Then give the answer again.

{"items":[{"name":"imzML size difference","expression":"23929 - 23898"}]}

imzML size difference = 23929 - 23898 = 31

The final answer

The model wrote this answer. The harness shows it to the scientist after the review.

Settings used, from the decision record: Polarity: negative.

Checks

Review findings

The review recorded 1 finding. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.

Numbers in the answer

The last claim check read 53 numbers in the answer. 53 numbers match a logged result. 0 numbers have no source in the record.

Deviations

The model did not try to change a choice of the scientist.

Failed tool calls

No tool call failed.

Data integrity

Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.

A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.

How to repeat it

Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.

CUVETTE_DATA={data} bash bench/papers/schramm2012-imzml-pyimzml/fetch.sh

Run the same case with Cuvette. The script gives the same answers from bench/papers/schramm2012-imzml-pyimzml/bench.yaml.

cuvette bench papers --papers schramm2012-imzml-pyimzml --models claude:claude-sonnet-5-5

Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.

1. read_file_info (step n1)

   In Python

   p = ImzMLParser(path)
   then p.polarity, p.spectrum_mode, p.imzmldict, len(p.coordinates), p.mzLengths

   • Run p = pyimzml.ImzMLParser.ImzMLParser('file.imzML').
   • Read len(p.coordinates) for the pixel count and p.imzmldict for the grid and the pixel size.
   • Read p.polarity and p.spectrum_mode. Read max(p.mzLengths) for the points per spectrum.
   • Read the m/z range from p.getspectrum(i)[0].min() and .max().
   • In SMILE MSI File>Open imzML..., then Data>Acquisition & matching... shows the settings.
   • Spectra read for the m/z range = 1
   • Polarity = negative
   • Warning: If you keep the default 200, you get a different result.
   • Note: For a processed file the m/z range comes from evenly spaced spectra, not from all of them. The tool also reads the file sizes.

   The manual route that the harness recorded

   msi_tools.read_file_info(path="{data}/schramm2012-imzml-pyimzml/Example_Continuous.imzML", expected_polarity="negative", max_scan=1)

   The manual route uses the same method. The note in the route gives the known difference.

2. read_file_info (step n2)

   In Python

   p = ImzMLParser(path)
   then p.polarity, p.spectrum_mode, p.imzmldict, len(p.coordinates), p.mzLengths

   • Run p = pyimzml.ImzMLParser.ImzMLParser('file.imzML').
   • Read len(p.coordinates) for the pixel count and p.imzmldict for the grid and the pixel size.
   • Read p.polarity and p.spectrum_mode. Read max(p.mzLengths) for the points per spectrum.
   • Read the m/z range from p.getspectrum(i)[0].min() and .max().
   • In SMILE MSI File>Open imzML..., then Data>Acquisition & matching... shows the settings.
   • Spectra read for the m/z range = 100000
   • Polarity = negative
   • Warning: If you keep the default 200, you get a different result.
   • Note: For a processed file the m/z range comes from evenly spaced spectra, not from all of them. The tool also reads the file sizes.

   The manual route that the harness recorded

   msi_tools.read_file_info(path="{data}/schramm2012-imzml-pyimzml/Example_Processed.imzML", expected_polarity="negative", max_scan=100000)

   The manual route uses the same method. The note in the route gives the known difference.

3. get_tic_image (step n3)

   In Python

   sum(p.getspectrum(i)[1]) for each pixel i, placed at (y - 1, x - 1)

   • For each index i in range(len(p.coordinates)), compute sum(p.getspectrum(i)[1]).
   • Place the value at row y - 1 and column x - 1 of an array of shape (grid y, grid x).
   • In SMILE MSI: the TIC image in the Display panel.

   The manual route that the harness recorded

   msi_tools.get_tic_image(path="{data}/schramm2012-imzml-pyimzml/Example_Continuous.imzML", name="tic_continuous")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

4. get_tic_image (step n4)

   In Python

   sum(p.getspectrum(i)[1]) for each pixel i, placed at (y - 1, x - 1)

   • For each index i in range(len(p.coordinates)), compute sum(p.getspectrum(i)[1]).
   • Place the value at row y - 1 and column x - 1 of an array of shape (grid y, grid x).
   • In SMILE MSI: the TIC image in the Display panel.

   The manual route that the harness recorded

   msi_tools.get_tic_image(path="{data}/schramm2012-imzml-pyimzml/Example_Processed.imzML", name="tic_processed")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

5. compare_files (step n5)

   In Python

   compare p1.getspectrum(i) with p2.getspectrum(i) for every i with numpy.array_equal

   • Open both files with ImzMLParser.
   • For each pixel index i, compare the m/z arrays and the intensity arrays.

   The manual route that the harness recorded

   msi_tools.compare_files(path_a="{data}/schramm2012-imzml-pyimzml/Example_Continuous.imzML", path_b="{data}/schramm2012-imzml-pyimzml/Example_Processed.imzML", max_pixels=0)

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

6. calculate (step n6)

   Run the tool "calculate" with these settings: {"items":[{"name":"ibd difference","expression":"604744 - 335976"},{"name":"one m/z array bytes (8399 x 4)","expression":"8399*4"},{"name":"extra m/z arrays","expression":"(604744 - 335976)/(8399*4)"},{"name":"ratio ibd","expression":"ratio(604744, 335976)"}]}.
   - Code only: this step has no route in the program menus. Run it with the script or flow export.

   The harness recorded no manual route for this step.

7. calculate (step n7)

   Run the tool "calculate" with these settings: {"items":[{"name":"imzML size difference","expression":"23929 - 23898"}]}.
   - Code only: this step has no route in the program menus. Run it with the script or flow export.

   The harness recorded no manual route for this step.

Figure

Run facts

The session

This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.

- Setup values:
Ion image:
- Normalization (normalization): none
- Reduce (m/z window) (reduce): sum
Peak picking:
- Peak picking S/N (snr): 3
- Pixels used for the mean spectrum (0 = all) (sample_pixels): 500
Acquisition:
- Polarity (polarity): negative
You choose: Reduce (m/z window) (reduce), Pixels used for the mean spectrum (0 = all) (sample_pixels).
Ask the scientist: Ion image tolerance (ppm) (ion_ppm).
The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist.
- Mode is now watch: you run the steps.

{
 "ok": true,
 "summary": "9 pixels (3 x 3), m/z 100.0833 to 799.9167, profile, negative, continuous storage, ibd 335976 bytes",
 "metrics": {
  "n_pixels": 9,
  "grid_x": 3,
  "grid_y": 3,
  "n_z": 1,
  "points_per_spectrum_min": 8399,
  "points_per_spectrum_max": 8399,
  "mz_min": 100.08333587646484,
  "mz_max": 799.9166870117188,
  "n_spectra_scanned_for_range": 1,
  "ibd_bytes": 335976,
  "imzml_bytes": 23898,
  "pixel_size_x_um": 100,
  "pixel_size_y_um": 100,
  "polarity_matches_decision": 1
 },
 "data": {
  "storage_mode": "continuous",
  "spectrum_mode": "profile",
  "polarity": "negative",
  "mz_precision": "f",
  "intensity_precision": "f",
  "mz_range_basis": "all spectra"
 },
 "table": {
  "columns": [
   "property",
   "value"
  ],
  "rows": [
   [
    "n_pixels",
    9
   ],
   [
    "grid_x",
    3
   ],
   [
    "grid_y",
    3
   ],
   [
    "pixel_size_x_um",
    100
   ],
   [
    "mz_min",
    100.08333587646484
   ],
   [
    "mz_max",
    799.9166870117188
   ],
   [
    "points_per_spectrum_max",
    8399
   ],
   [
    "ibd_bytes",
    335976
   ],
   [
    "storage_mode",
    "continuous"
   ],
   [
    "spectrum_mode",
    "profile"
   ],
   [
    "polarity",
    "negative"
   ],
   [
    "mz_precision",
    "f"
   ],
   [
    "intensity_precision",
    "f"
   ],
   [
    "mz_range_basis",
    "all spectra"
   ]
  ],
  "n_rows": 14
 }
}

{
 "ok": true,
 "summary": "9 pixels (3 x 3), m/z 100.0833 to 799.9167, profile, negative, processed storage, ibd 604744 bytes",
 "metrics": {
  "n_pixels": 9,
  "grid_x": 3,
  "grid_y": 3,
  "n_z": 1,
  "points_per_spectrum_min": 8399,
  "points_per_spectrum_max": 8399,
  "mz_min": 100.08333587646484,
  "mz_max": 799.9166870117188,
  "n_spectra_scanned_for_range": 9,
  "ibd_bytes": 604744,
  "imzml_bytes": 23929,
  "pixel_size_x_um": 100,
  "pixel_size_y_um": 100,
  "polarity_matches_decision": 1
 },
 "data": {
  "storage_mode": "processed",
  "spectrum_mode": "profile",
  "polarity": "negative",
  "mz_precision": "f",
  "intensity_precision": "f",
  "mz_range_basis": "all spectra"
 },
 "table": {
  "columns": [
   "property",
   "value"
  ],
  "rows": [
   [
    "n_pixels",
    9
   ],
   [
    "grid_x",
    3
   ],
   [
    "grid_y",
    3
   ],
   [
    "pixel_size_x_um",
    100
   ],
   [
    "mz_min",
    100.08333587646484
   ],
   [
    "mz_max",
    799.9166870117188
   ],
   [
    "points_per_spectrum_max",
    8399
   ],
   [
    "ibd_bytes",
    604744
   ],
   [
    "storage_mode",
    "processed"
   ],
   [
    "spectrum_mode",
    "profile"
   ],
   [
    "polarity",
    "negative"
   ],
   [
    "mz_precision",
    "f"
   ],
   [
    "intensity_precision",
    "f"
   ],
   [
    "mz_range_basis",
    "all spectra"
   ]
  ],
  "n_rows": 14
 }
}

{
 "ok": true,
 "summary": "Total ion current (sum of intensities per pixel): mean 161.144, max 243.54 at pixel (3, 3)",
 "metrics": {
  "n_pixels": 9,
  "tic_mean": 161.1443790255365,
  "tic_median": 161.80919044826766,
  "tic_max": 243.53950660310795,
  "tic_min": 108.3959741842164,
  "tic_max_x": 3,
  "tic_max_y": 3,
  "tic_cv": 0.25314754546968943
 },
 "data": {
  "coordinates": "1-based, as in imzML"
 },
 "outputs": [
  {
   "path": "{work}/get_tic_image-1/tic_continuous.png",
   "kind": "image",
   "name": "tic_continuous.png"
  },
  {
   "path": "{work}/get_tic_image-1/tic_continuous.tif",
   "kind": "image",
   "name": "tic_continuous.tif"
  },
  {
   "path": "{work}/get_tic_image-1/tic_continuous_top_pixels.csv",
   "kind": "table",
   "name": "tic_continuous_top_pixels.csv"
  }
 ],
 "table": {
  "columns": [
   "x",
   "y",
   "tic"
  ],
  "rows": [
   [
    3,
    3,
    243.53950660310795
   ],
   [
    1,
    2,
    200.96332770925406
   ],
   [
    2,
    1,
    182.31835420101902
   ],
   [
    2,
    3,
    168.2701814752251
   ],
   [
    3,
    1,
    161.80919044826766
   ],
   [
    2,
    2,
    135.3058417315849
   ],
   [
    1,
    3,
    127.84664447846849
   ],
   [
    1,
    1,
    121.85039039868468
   ],
   [
    3,
    2,
    108.3959741842164
   ]
  ],
  "n_rows": 9,
  "path": "{work}/get_tic_image-1/tic_continuous_top_pixels.csv"
 }
}

{
 "ok": true,
 "summary": "Identical spectra: 9 pixels compared, 0 differ, max m/z difference 0, max intensity difference 0 (A is continuous, B is processed)",
 "metrics": {
  "n_pixels_a": 9,
  "n_pixels_b": 9,
  "n_pixels_compared": 9,
  "coordinates_equal": 1,
  "n_pixels_different": 0,
  "n_pixels_different_length": 0,
  "max_abs_mz_difference": 0,
  "max_abs_intensity_difference": 0,
  "identical": 1,
  "ibd_bytes_a": 335976,
  "ibd_bytes_b": 604744
 },
 "data": {
  "storage_a": "continuous",
  "storage_b": "processed"
 }
}

{"items":[{"name":"size_difference_bytes","expression":"604744 - 335976"},{"name":"size_ratio_processed_to_continuous","expression":"ratio(604744, 335976)"},{"name":"points_bytes_per_array_float32","expression":"8399 * 4"},{"name":"tic_mean_check","expression":"161.1443790255365"}],"purpose":"Compute the size difference and ratio of the two data files from the reported byte counts."}

size_difference_bytes = 604744 - 335976 = 268768
size_ratio_processed_to_continuous = ratio(604744, 335976) = 1.799961902
points_bytes_per_array_float32 = 8399 * 4 = 33596
tic_mean_check = 161.1443790255365 = 161.144379

{"items":[{"name":"continuous_expected_bytes","expression":"(1 + 9) * 8399 * 4"},{"name":"processed_expected_bytes","expression":"(9 * 2) * 8399 * 4"},{"name":"continuous_gap_bytes","expression":"335976 - (1 + 9) * 8399 * 4"},{"name":"processed_gap_bytes","expression":"604744 - (9 * 2) * 8399 * 4"}],"purpose":"Check the storage layout that explains the size difference."}

continuous_expected_bytes = (1 + 9) * 8399 * 4 = 335960
processed_expected_bytes = (9 * 2) * 8399 * 4 = 604728
continuous_gap_bytes = 335976 - (1 + 9) * 8399 * 4 = 16
processed_gap_bytes = 604744 - (9 * 2) * 8399 * 4 = 16

- 1 number in the answer match no logged tool result: 32. Run the tool that measures each number, or remove the number, or say how you derived it from logged results.
Fix these problems. Then give the answer again.

The final answer

The model wrote this answer. The harness shows it to the scientist after the review.

Settings used, from the decision record: Polarity: negative.

Checks

Review findings

The review recorded 3 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.

Numbers in the answer

The last claim check read 44 numbers in the answer. 44 numbers match a logged result. 0 numbers have no source in the record.

Deviations

The model did not try to change a choice of the scientist.

Failed tool calls

No tool call failed.

Data integrity

Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.

A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.

How to repeat it

Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.

CUVETTE_DATA={data} bash bench/papers/schramm2012-imzml-pyimzml/fetch.sh

Run the same case with Cuvette. The script gives the same answers from bench/papers/schramm2012-imzml-pyimzml/bench.yaml.

cuvette bench papers --papers schramm2012-imzml-pyimzml --models claude:claude-haiku-5-5

Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.

1. read_file_info (step n1)

   In Python

   p = ImzMLParser(path)
   then p.polarity, p.spectrum_mode, p.imzmldict, len(p.coordinates), p.mzLengths

   • Run p = pyimzml.ImzMLParser.ImzMLParser('file.imzML').
   • Read len(p.coordinates) for the pixel count and p.imzmldict for the grid and the pixel size.
   • Read p.polarity and p.spectrum_mode. Read max(p.mzLengths) for the points per spectrum.
   • Read the m/z range from p.getspectrum(i)[0].min() and .max().
   • In SMILE MSI File>Open imzML..., then Data>Acquisition & matching... shows the settings.
   • Spectra read for the m/z range = 200
   • Polarity = negative
   • Note: For a processed file the m/z range comes from evenly spaced spectra, not from all of them. The tool also reads the file sizes.

   The manual route that the harness recorded

   msi_tools.read_file_info(path="{data}/schramm2012-imzml-pyimzml/Example_Continuous.imzML", expected_polarity="negative", max_scan=200)

   The manual route uses the same method. The note in the route gives the known difference.

2. read_file_info (step n2)

   In Python

   p = ImzMLParser(path)
   then p.polarity, p.spectrum_mode, p.imzmldict, len(p.coordinates), p.mzLengths

   • Run p = pyimzml.ImzMLParser.ImzMLParser('file.imzML').
   • Read len(p.coordinates) for the pixel count and p.imzmldict for the grid and the pixel size.
   • Read p.polarity and p.spectrum_mode. Read max(p.mzLengths) for the points per spectrum.
   • Read the m/z range from p.getspectrum(i)[0].min() and .max().
   • In SMILE MSI File>Open imzML..., then Data>Acquisition & matching... shows the settings.
   • Spectra read for the m/z range = 200
   • Polarity = negative
   • Note: For a processed file the m/z range comes from evenly spaced spectra, not from all of them. The tool also reads the file sizes.

   The manual route that the harness recorded

   msi_tools.read_file_info(path="{data}/schramm2012-imzml-pyimzml/Example_Processed.imzML", expected_polarity="negative", max_scan=200)

   The manual route uses the same method. The note in the route gives the known difference.

3. get_tic_image (step n3)

   In Python

   sum(p.getspectrum(i)[1]) for each pixel i, placed at (y - 1, x - 1)

   • For each index i in range(len(p.coordinates)), compute sum(p.getspectrum(i)[1]).
   • Place the value at row y - 1 and column x - 1 of an array of shape (grid y, grid x).
   • In SMILE MSI: the TIC image in the Display panel.

   The manual route that the harness recorded

   msi_tools.get_tic_image(path="{data}/schramm2012-imzml-pyimzml/Example_Continuous.imzML", name="tic_continuous")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

4. compare_files (step n4)

   In Python

   compare p1.getspectrum(i) with p2.getspectrum(i) for every i with numpy.array_equal

   • Open both files with ImzMLParser.
   • For each pixel index i, compare the m/z arrays and the intensity arrays.

   The manual route that the harness recorded

   msi_tools.compare_files(path_a="{data}/schramm2012-imzml-pyimzml/Example_Continuous.imzML", path_b="{data}/schramm2012-imzml-pyimzml/Example_Processed.imzML", max_pixels=0)

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

5. calculate (step n5)

   Run the tool "calculate" with these settings: {"items":[{"name":"size_difference_bytes","expression":"604744 - 335976"},{"name":"size_ratio_processed_to_continuous","expression":"ratio(604744, 335976)"},{"name":"points_bytes_per_array_float32","expression":"8399 * 4"},{"name":"tic_mean_check","expression":"161.1443790255365"}]}.
   - Code only: this step has no route in the program menus. Run it with the script or flow export.

   The harness recorded no manual route for this step.

6. calculate (step n6)

   Run the tool "calculate" with these settings: {"items":[{"name":"continuous_expected_bytes","expression":"(1 + 9) * 8399 * 4"},{"name":"processed_expected_bytes","expression":"(9 * 2) * 8399 * 4"},{"name":"continuous_gap_bytes","expression":"335976 - (1 + 9) * 8399 * 4"},{"name":"processed_gap_bytes","expression":"604744 - (9 * 2) * 8399 * 4"}]}.
   - Code only: this step has no route in the program menus. Run it with the script or flow export.

   The harness recorded no manual route for this step.

Figure

Run facts

The session

This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.

- Setup values:
Ion image:
- Normalization (normalization): none
- Reduce (m/z window) (reduce): sum
Peak picking:
- Peak picking S/N (snr): 3
- Pixels used for the mean spectrum (0 = all) (sample_pixels): 500
Acquisition:
- Polarity (polarity): negative
You choose: Reduce (m/z window) (reduce), Pixels used for the mean spectrum (0 = all) (sample_pixels).
Ask the scientist: Ion image tolerance (ppm) (ion_ppm).
The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist.
- Mode is now watch: you run the steps.

{
 "ok": true,
 "summary": "9 pixels (3 x 3), m/z 100.0833 to 799.9167, profile, negative, continuous storage, ibd 335976 bytes",
 "metrics": {
  "n_pixels": 9,
  "grid_x": 3,
  "grid_y": 3,
  "n_z": 1,
  "points_per_spectrum_min": 8399,
  "points_per_spectrum_max": 8399,
  "mz_min": 100.08333587646484,
  "mz_max": 799.9166870117188,
  "n_spectra_scanned_for_range": 1,
  "ibd_bytes": 335976,
  "imzml_bytes": 23898,
  "pixel_size_x_um": 100,
  "pixel_size_y_um": 100,
  "polarity_matches_decision": 1
 },
 "data": {
  "storage_mode": "continuous",
  "spectrum_mode": "profile",
  "polarity": "negative",
  "mz_precision": "f",
  "intensity_precision": "f",
  "mz_range_basis": "all spectra"
 },
 "table": {
  "columns": [
   "property",
   "value"
  ],
  "rows": [
   [
    "n_pixels",
    9
   ],
   [
    "grid_x",
    3
   ],
   [
    "grid_y",
    3
   ],
   [
    "pixel_size_x_um",
    100
   ],
   [
    "mz_min",
    100.08333587646484
   ],
   [
    "mz_max",
    799.9166870117188
   ],
   [
    "points_per_spectrum_max",
    8399
   ],
   [
    "ibd_bytes",
    335976
   ],
   [
    "storage_mode",
    "continuous"
   ],
   [
    "spectrum_mode",
    "profile"
   ],
   [
    "polarity",
    "negative"
   ],
   [
    "mz_precision",
    "f"
   ],
   [
    "intensity_precision",
    "f"
   ],
   [
    "mz_range_basis",
    "all spectra"
   ]
  ],
  "n_rows": 14
 }
}

{
 "ok": true,
 "summary": "9 pixels (3 x 3), m/z 100.0833 to 799.9167, profile, negative, processed storage, ibd 604744 bytes",
 "metrics": {
  "n_pixels": 9,
  "grid_x": 3,
  "grid_y": 3,
  "n_z": 1,
  "points_per_spectrum_min": 8399,
  "points_per_spectrum_max": 8399,
  "mz_min": 100.08333587646484,
  "mz_max": 799.9166870117188,
  "n_spectra_scanned_for_range": 9,
  "ibd_bytes": 604744,
  "imzml_bytes": 23929,
  "pixel_size_x_um": 100,
  "pixel_size_y_um": 100,
  "polarity_matches_decision": 1
 },
 "data": {
  "storage_mode": "processed",
  "spectrum_mode": "profile",
  "polarity": "negative",
  "mz_precision": "f",
  "intensity_precision": "f",
  "mz_range_basis": "all spectra"
 },
 "table": {
  "columns": [
   "property",
   "value"
  ],
  "rows": [
   [
    "n_pixels",
    9
   ],
   [
    "grid_x",
    3
   ],
   [
    "grid_y",
    3
   ],
   [
    "pixel_size_x_um",
    100
   ],
   [
    "mz_min",
    100.08333587646484
   ],
   [
    "mz_max",
    799.9166870117188
   ],
   [
    "points_per_spectrum_max",
    8399
   ],
   [
    "ibd_bytes",
    604744
   ],
   [
    "storage_mode",
    "processed"
   ],
   [
    "spectrum_mode",
    "profile"
   ],
   [
    "polarity",
    "negative"
   ],
   [
    "mz_precision",
    "f"
   ],
   [
    "intensity_precision",
    "f"
   ],
   [
    "mz_range_basis",
    "all spectra"
   ]
  ],
  "n_rows": 14
 }
}

- You said what you will do next, but you did not call a tool. Do it now with a tool call, or give your final answer.

{
 "ok": true,
 "summary": "Identical spectra: 9 pixels compared, 0 differ, max m/z difference 0, max intensity difference 0 (A is continuous, B is processed)",
 "metrics": {
  "n_pixels_a": 9,
  "n_pixels_b": 9,
  "n_pixels_compared": 9,
  "coordinates_equal": 1,
  "n_pixels_different": 0,
  "n_pixels_different_length": 0,
  "max_abs_mz_difference": 0,
  "max_abs_intensity_difference": 0,
  "identical": 1,
  "ibd_bytes_a": 335976,
  "ibd_bytes_b": 604744
 },
 "data": {
  "storage_a": "continuous",
  "storage_b": "processed"
 }
}

The final answer

The model wrote this answer. The harness shows it to the scientist after the review.

Settings used, from the decision record: Polarity: negative.

Checks

Review findings

The review recorded 1 finding. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.

Numbers in the answer

The last claim check read 3 numbers in the answer. 3 numbers match a logged result. 0 numbers have no source in the record.

Deviations

The model did not try to change a choice of the scientist.

Failed tool calls

No tool call failed.

Data integrity

Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.

A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.

How to repeat it

Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.

CUVETTE_DATA={data} bash bench/papers/schramm2012-imzml-pyimzml/fetch.sh

Run the same case with Cuvette. The script gives the same answers from bench/papers/schramm2012-imzml-pyimzml/bench.yaml.

cuvette bench papers --papers schramm2012-imzml-pyimzml --models ollama:qwen3:8b

Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.

1. read_file_info (step n1)

   In Python

   p = ImzMLParser(path)
   then p.polarity, p.spectrum_mode, p.imzmldict, len(p.coordinates), p.mzLengths

   • Run p = pyimzml.ImzMLParser.ImzMLParser('file.imzML').
   • Read len(p.coordinates) for the pixel count and p.imzmldict for the grid and the pixel size.
   • Read p.polarity and p.spectrum_mode. Read max(p.mzLengths) for the points per spectrum.
   • Read the m/z range from p.getspectrum(i)[0].min() and .max().
   • In SMILE MSI File>Open imzML..., then Data>Acquisition & matching... shows the settings.
   • Polarity = negative
   • Note: For a processed file the m/z range comes from evenly spaced spectra, not from all of them. The tool also reads the file sizes.

   The manual route that the harness recorded

   msi_tools.read_file_info(path="{data}/schramm2012-imzml-pyimzml/Example_Continuous.imzML", expected_polarity="negative")

   The manual route uses the same method. The note in the route gives the known difference.

2. read_file_info (step n2)

   In Python

   p = ImzMLParser(path)
   then p.polarity, p.spectrum_mode, p.imzmldict, len(p.coordinates), p.mzLengths

   • Run p = pyimzml.ImzMLParser.ImzMLParser('file.imzML').
   • Read len(p.coordinates) for the pixel count and p.imzmldict for the grid and the pixel size.
   • Read p.polarity and p.spectrum_mode. Read max(p.mzLengths) for the points per spectrum.
   • Read the m/z range from p.getspectrum(i)[0].min() and .max().
   • In SMILE MSI File>Open imzML..., then Data>Acquisition & matching... shows the settings.
   • Polarity = negative
   • Note: For a processed file the m/z range comes from evenly spaced spectra, not from all of them. The tool also reads the file sizes.

   The manual route that the harness recorded

   msi_tools.read_file_info(path="{data}/schramm2012-imzml-pyimzml/Example_Processed.imzML", expected_polarity="negative")

   The manual route uses the same method. The note in the route gives the known difference.

3. compare_files (step n3)

   In Python

   compare p1.getspectrum(i) with p2.getspectrum(i) for every i with numpy.array_equal

   • Open both files with ImzMLParser.
   • For each pixel index i, compare the m/z arrays and the intensity arrays.

   The manual route that the harness recorded

   msi_tools.compare_files(path_a="{data}/schramm2012-imzml-pyimzml/Example_Continuous.imzML", path_b="{data}/schramm2012-imzml-pyimzml/Example_Processed.imzML")

   The manual route gives the same numbers. An automatic test in Cuvette checks this.

Figure

Run facts