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Update landing page #22

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add raw data preparation to documentation
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Y0dler committed Oct 4, 2024
commit 2bdbe5674a1254bb2bc414c1ab8e3f00a816baf2
1 change: 1 addition & 0 deletions docs/source/index.rst
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Expand Up @@ -35,6 +35,7 @@ The documentation features various notebooks that demonstrate the usage and inve
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markdown/Installation
markdown/Preparing_raw_data
markdown/Peak_model_composition
markdown/PeakPerformance_validation
markdown/PeakPerformance_workflow
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13 changes: 13 additions & 0 deletions docs/source/markdown/Preparing_raw_data.md
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# Preparing raw data

This step is crucial when using PeakPerformance. Raw data has to be supplied as time series meaning for each signal you want to analyze, save a NumPy array consisting of time in the first dimension and intensity in the second dimension (compare example data in the repository). Both time and intensity should also be NumPy arrays. If you e.g. have time and intensity of a signal as lists, you can use the following code to convert, format, and save them in the correct manner:
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```python
import numpy as np
from pathlib import Path

time_series = np.array([np.array(time), np.array(intensity)])
np.save(Path(r"example_path/time_series.npy"), time_series)
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```

The naming convention of raw data files is `<acquisition name>_<precursor ion m/z or experiment number>_<product ion m/z start>_<product ion m/z end>.npy`. There should be no underscores within the named sections such as `acquisition name`. Essentially, the raw data names include the acquisition and mass trace, thus yielding a recognizable and unique name for each isotopomer/fragment/metabolite/sample. This is of course only relevant when using the pre-manufactured data pipeline and does not apply to user-generated custom data pipelines.
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