IO

The IO subpackage provides a set of utilities for working with all kinds of files that are used in the context of the VdM analysis.

Reading the results from the VdM Framework 

The VdM Framework produces a directory of results, normally with the structure <framework_output dir>/analysed_data/<scan_id>. The scan_id is the familiar <fill_number>_<start_time>_<end_time> folder name. This folder contains all the results from the run, including:

Run conditions (in the cond folder)
Per detector, correction and fit results (following the <detector>/results/correction/ folder structure)

The following code block shows how to read the results from the framework output:

from pathlib import Path
from vdmtools.io import ScanResults

# Path to the framework output directory. All the way to the <scan_id> folder.
framework_output_dir = Path("/path/to/framework/output/analysed_data/<scan_id>")

# Create a ScanResults object from the framework output directory
scan_results = ScanResults(framework_output_dir)

print(scan_results)

The output should look something like

ScanResults from: /path/to/framework/output/analysed_data/<scan_id>
Scan name: <scan-name>
Scan ID: <scan-id>
ScanConditions:
        Fill: <fill-number>
        Run: (<run-number>, <run-number>)
        Scan names: ('<scan-name>', '<scan-name>')
        Scan types: ('<scan-type>', '<scan-type>')
        Scan time windows:
                First  Scan: <start-time> - <end-time>
                Second Scan: <start-time> - <end-time>
        Beta star: <beta-star>
        Angle: <angle>
        Offset: [<offset>, <offset>]
        Particle type B1: <ptype>
        Particle type B2: <ptype>
        COM energy B1 (GeV): <energy>
        COM energy B2 (GeV): <energy>

Results:
        Fits: <set-of-fits-read>
        Detectors: <list-of-detectors-read>
        Corrections: <set-of-corrections-read>

It is also possible to read the results by using the read_one_or_many() function:

from pathlib import Path
from vdmtools.io import read_one_or_many

path = Path("/eos/user/f/flpereir/www/UpdateOnGS8999/8999_no_corr/analysed_data/8999_28Jun23_230143_28Jun23_232943")
scan = read_one_or_many(path, "VdM1")

print(scan)

For the particular result path I gave, the output would be:

ScanResults from: /eos/user/f/flpereir/www/UpdateOnGS8999/8999_no_corr/analysed_data/8999_28Jun23_230143_28Jun23_232943
Scan name: VdM1
Scan ID: 8999_28Jun23_230143_28Jun23_232943
ScanConditions:
        Fill: 8999
        Run: (369802, 369802)
        Scan names: ('X1', 'Y1')
        Scan types: ('NormalX', 'NormalY')
        Scan time windows:
                First  Scan: 2023-06-28 23:01:43 - 2023-06-28 23:17:35
                Second Scan: 2023-06-28 23:29:43 - 2023-06-28 23:45:35
        Beta star: 19.2
        Angle: 0.0
        Offset: [0.0, 0.0]
        Particle type B1: proton
        Particle type B2: proton
        COM energy B1 (GeV): 6799
        COM energy B2 (GeV): 6799

Results:
        Fits: {'DG', 'SG', 'Poly4G'}
        Detectors: ['BCM1F', 'HFET', 'HFOC', 'PLT']
        Corrections: {'noCorr', 'BeamBeam_DynamicBeta'}

Filtering the results 

After obtaining your ScanResults object, you can filter the results using the filter_results_by() method as follows:

dataframe = scan_results.filter_results_by("SG", "PLT", "noCorr")
print(dataframe)

The output would be a pandas DataFrame with the fit and luminosity calibration results for SG fit, PLT detector and no correction applied.

You may also provide additional filters like fit status or covariance matrix status:

dataframe = scan_results.filter_results_by("SG", "PLT", "noCorr", fit_status="good", cov_status="good")
print(dataframe)

The output would now only contain the columns of the results that contain the fit and cov status that are “good” (for fit is 0 and for cov is 3).

IO

Reading the results from the VdM Framework

Filtering the results

Reading the results from the VdM Framework 

Filtering the results 