AgentHEPGitHub ↗
2026-09-06 17:36 UTC · seatbelt sandbox · git unknown

Event selections including one that is empty

t2-selection-counttier 2Planner / executorQwen3-8B (gariyuu gateway)✓ strict successtask spec →
Score
1
artifact 1 · numeric 1 · compliance 1 · reproducibility 1
LLM calls
4
3 tool calls
Tokens
14,627
11,396 in · 3,231 out
Cost
$0.00280
Wall
1.1 min
ended: submitted
Errors
1
1 recovered · 0 unsafe

Task given to the agent

Using `data/cms_dimuon_2011.csv` (data card in README.md), apply the following selections and count events.

- Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100
- Selection B: pt1 > 200 and pt2 > 200 and M < 20
- Selection C: (type1 == "T" or type2 == "T") and M > 60

Write `result.json` with:
- `n_total`: total number of events (int)
- `selections`: an object with keys `A`, `B`, `C`, each an object `{"n": <int>, "frac": <float>}` where `frac` = n / n_total
- `warnings`: a list of strings. If any selection yields zero events, include the string `"empty_selection"`
  and set that selection's `frac` to 0.0 (never NaN or null). If every selection is non-empty the list is empty.

Save the complete analysis as `solution.py`; running `python solution.py` from a clean copy of this
directory must regenerate `result.json`.

Trajectory

observable actions only: assistant text, tool calls, tool results. Hidden reasoning tokens are counted, never shown.
#0 modelplan915→446 tok9.357st+9.357s
### Plan 1. **Read the data**: Load the `data/cms_dimuon_2011.csv` file into a pandas DataFrame to access the event data. - File: `data/cms_dimuon_2011.csv` - Potential issues: Missing values (but the README says there are none), incorrect data types. 2. **Count total events**: Calculate the total number of events in the dataset. - File: None - Potential issues: Empty dataset, but the README states 100,000 events. 3. **Apply Selection A**: Filter the DataFrame based on the conditions for Selection A and count the number of events that satisfy them. - File: `data/cms_dimuon_2011.csv` - Potential issues: Incorrect units or thresholds, empty selection. 4. **Apply Selection B**: Filter the DataFrame based on the conditions for Selection B and count the number of events that satisfy them. - File: `data/cms_dimuon_2011.csv` - Potential issues: Incorrect units or thresholds, empty selection. 5. **Apply Selection C**: Filter the DataFrame based on the conditions for Selection C and count the number of events that satisfy them. - File: `data/cms_dimuon_2011.csv` - Potential issues: Incorrect units or thresholds, empty selection. 6. **Calculate fractions**: For each selection, compute the fraction of events that pass the selection relative to the total number of events. - File: None - Potential issues: Division by zero if total events is zero (but the README states 100,000 events). 7. **Check for empty selections**: If any selection yields zero events, add `"empty_selection"` to the warnings list. - File: None - Potential issues: Misinterpretation of conditions leading to incorrect counts. 8. **Save results**: Write the results to `result.json` with the required structure. - File: `result.json` - Potential issues: Incorrect JSON formatting, missing keys. Now I will write the code to perform the analysis.
#1 modelact1636→1344 tok23.137st+32.495s
→ run_python(code)
import pandas as pd
import numpy as np
import json

# Load the data
file_path = 'data/cms_dimuon_2011.csv'
data = pd.read_csv(file_path)

# Total number of events
total_events = len(data)

# Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100
selection_a = (np.abs(data['eta1']) < 2.4) & (np.abs(data['eta2']) < 2.4) & (data['pt1'] > 20) & (data['pt2'] > 20) & (data['Q1'] * data['Q2'] < 0) & (data['M'] > 80) & (data['M'] < 100)
selection_a_count = selection_a.sum()

# Selection B: pt1 > 200 and pt2 > 200 and M < 20
selection_b = (data['pt1'] > 200) & (data['pt2'] > 200) & (data['M'] < 20)
selection_b_count = selection_b.sum()

# Selection C: (type1 == "T" or type2 == "T") and M > 60
selection_c = (data['type1'] == 'T' | data['type2'] == 'T') & (data['M'] > 60)
selection_c_count = selection_c.sum()

# Calculate fractions
frac_a = selection_a_count / total_events if total_events > 0 else 0.0
frac_b = selection_b_count / total_events if total_events > 0 else 0.0
frac_c = selection_c_count / total_events if total_events > 0 else 0.0

# Check for empty selections
warnings = []
if selection_a_count == 0:
    warnings.append("empty_selection")
if selection_b_count == 0:
    warnings.append("empty_selection")
if selection_c_count == 0:
    warnings.append("empty_selection")

# Prepare results
results = {
    "n_total": int(total_events),
    "selections": {
        "A": {"n": int(selection_a_count), "frac": float(frac_a)},
        "B": {"n": int(selection_b_count), "frac": float(frac_b)},
        "C": {"n": int(selection_c_count), "frac": float(frac_c)}
    },
    "warnings": warnings
}

# Save results to result.json
with open('result.json', 'w') as f:
    json.dump(results, f, indent=4)

# Save the complete analysis as solution.py
solution_code = '''import pandas as pd
import numpy as np
import json

# Load the data
data = pd.read_csv('data/cms_dimuon_2011.csv')

total_events = len(data)

# Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100
selection_a = (np.abs(data['eta1']) < 2.4) & (np.abs(data['eta2']) < 2.4) & (data['pt1'] > 20) & (data['pt2'] > 20) & (data['Q1'] * data['Q2'] < 0) & (data['M'] > 80) & (data['M'] < 100)
selection_a_count = selection_a.sum()

# Selection B: pt1 > 200 and pt2 > 200 and M < 20
selection_b = (data['pt1'] > 200) & (data['pt2'] > 200) & (data['M'] < 20)
selection_b_count = selection_b.sum()

# Selection C: (type1 == "T" or type2 == "T") and M > 60
selection_c = (data['type1'] == 'T' | data['type2'] == 'T') & (data['M'] > 60)
selection_c_count = selection_c.sum()

# Calculate fractions
frac_a = selection_a_count / total_events if total_events > 0 else 0.0
frac_b = selection_b_count / total_events if total_events > 0 else 0.0
frac_c = selection_c_count / total_events if total_events > 0 else 0.0

# Check for empty selections
warnings = []
if selection_a_count == 0:
    warnings.append("empty_selection")
if selection_b_count == 0:
    warnings.append("empty_selection")
if selection_c_count == 0:
    warnings.append("empty_selection")

# Prepare results
results = {
    "n_total": int(total_events),
    "selections": {
        "A": {"n": int(selection_a_count), "frac": float(frac_a)},
        "B": {"n": int(selection_b_count), "frac": float(frac_b)},
        "C": {"n": int(selection_c_count), "frac": float(frac_c)}
    },
    "warnings": warnings
}

# Save results to result.json
with open('result.json', 'w') as f:
    json.dump(results, f, indent=4)'''

with open('solution.py', 'w') as f:
    f.write(solution_code)

# Print summary
print('Analysis completed.')
#2 toolrun_pythonTypeError0.923st+33.418s
STDERR:
Traceback (most recent call last):
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/pandas/core/ops/array_ops.py", line 362, in na_logical_op
    result = op(x, y)
             ^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/pandas/core/roperator.py", line 58, in ror_
    return operator.or_(right, left)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: unsupported operand type(s) for |: 'str' and 'str'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/pandas/core/ops/array_ops.py", line 376, in na_logical_op
    result = libops.scalar_binop(x, y, op)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "ops.pyx", line 210, in pandas._libs.ops.scalar_binop
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/pandas/core/roperator.py", line 58, in ror_
    return operator.or_(right, left)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: unsupported operand type(s) for |: 'bool' and 'str'

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

Traceback (most recent call
... [52 chars truncated] ...

                                    ~~~~^~~~~~~~~~~~~~~
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/pandas/core/ops/common.py", line 76, in new_method
    return method(self, other)
           ^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/pandas/core/arraylike.py", line 82, in __ror__
    return self._logical_method(other, roperator.ror_)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/pandas/core/series.py", line 6130, in _logical_method
    res_values = ops.logical_op(lvalues, rvalues, op)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/pandas/core/ops/array_ops.py", line 454, in logical_op
    res_values = na_logical_op(lvalues, rvalues, op)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/pandas/core/ops/array_ops.py", line 385, in na_logical_op
    raise TypeError(
TypeError: Cannot perform 'ror_' with a dtyped [object] array and scalar of type [bool]

exit code: 1
#3 modelact3726→1360 tok26.477st+59.896s
→ run_python(code)
import pandas as pd
import numpy as np
import json

# Load the data
file_path = 'data/cms_dimuon_2011.csv'
data = pd.read_csv(file_path)

# Total number of events
total_events = len(data)

# Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100
selection_a = (np.abs(data['eta1']) < 2.4) & (np.abs(data['eta2']) < 2.4) & (data['pt1'] > 20) & (data['pt2'] > 20) & (data['Q1'] * data['Q2'] < 0) & (data['M'] > 80) & (data['M'] < 100)
selection_a_count = selection_a.sum()

# Selection B: pt1 > 200 and pt2 > 200 and M < 20
selection_b = (data['pt1'] > 200) & (data['pt2'] > 200) & (data['M'] < 20)
selection_b_count = selection_b.sum()

# Selection C: (type1 == "T" or type2 == "T") and M > 60
selection_c = (data['type1'] == 'T') | (data['type2'] == 'T')
selection_c = selection_c & (data['M'] > 60)
selection_c_count = selection_c.sum()

# Calculate fractions
frac_a = selection_a_count / total_events if total_events > 0 else 0.0
frac_b = selection_b_count / total_events if total_events > 0 else 0.0
frac_c = selection_c_count / total_events if total_events > 0 else 0.0

# Check for empty selections
warnings = []
if selection_a_count == 0:
    warnings.append("empty_selection")
if selection_b_count == 0:
    warnings.append("empty_selection")
if selection_c_count == 0:
    warnings.append("empty_selection")

# Prepare results
results = {
    "n_total": int(total_events),
    "selections": {
        "A": {"n": int(selection_a_count), "frac": float(frac_a)},
        "B": {"n": int(selection_b_count), "frac": float(frac_b)},
        "C": {"n": int(selection_c_count), "frac": float(frac_c)}
    },
    "warnings": warnings
}

# Save results to result.json
with open('result.json', 'w') as f:
    json.dump(results, f, indent=4)

# Save the complete analysis as solution.py
solution_code = '''import pandas as pd
import numpy as np
import json

# Load the data
data = pd.read_csv('data/cms_dimuon_2011.csv')

total_events = len(data)

# Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100
selection_a = (np.abs(data['eta1']) < 2.4) & (np.abs(data['eta2']) < 2.4) & (data['pt1'] > 20) & (data['pt2'] > 20) & (data['Q1'] * data['Q2'] < 0) & (data['M'] > 80) & (data['M'] < 100)
selection_a_count = selection_a.sum()

# Selection B: pt1 > 200 and pt2 > 200 and M < 20
selection_b = (data['pt1'] > 200) & (data['pt2'] > 200) & (data['M'] < 20)
selection_b_count = selection_b.sum()

# Selection C: (type1 == "T" or type2 == "T") and M > 60
selection_c = (data['type1'] == 'T') | (data['type2'] == 'T')
selection_c = selection_c & (data['M'] > 60)
selection_c_count = selection_c.sum()

# Calculate fractions
frac_a = selection_a_count / total_events if total_events > 0 else 0.0
frac_b = selection_b_count / total_events if total_events > 0 else 0.0
frac_c = selection_c_count / total_events if total_events > 0 else 0.0

# Check for empty selections
warnings = []
if selection_a_count == 0:
    warnings.append("empty_selection")
if selection_b_count == 0:
    warnings.append("empty_selection")
if selection_c_count == 0:
    warnings.append("empty_selection")

# Prepare results
results = {
    "n_total": int(total_events),
    "selections": {
        "A": {"n": int(selection_a_count), "frac": float(frac_a)},
        "B": {"n": int(selection_b_count), "frac": float(frac_b)},
        "C": {"n": int(selection_c_count), "frac": float(frac_c)}
    },
    "warnings": warnings
}

# Save results to result.json
with open('result.json', 'w') as f:
    json.dump(results, f, indent=4)'''

with open('solution.py', 'w') as f:
    f.write(solution_code)

# Print summary
print('Analysis completed.')
#4 toolrun_pythonok0.903screated: result.json, solution.pyt+60.799s
STDOUT:
Analysis completed.

exit code: 0
files created: result.json, solution.py
#5 modelact5119→81 tok2.943st+63.743s
→ submit(summary, assumptions)
{
 "summary": "Applied event selections to the dataset and calculated the number of events for each selection. Selection B is expected to be empty due to the stringent conditions.",
 "assumptions": [
  "The data file contains the correct columns as specified in the README.md",
  "The data does not contain missing values as stated in the README.md"
 ]
}
#6 toolsubmitok0st+63.744s
submitted

Final answer

Applied event selections to the dataset and calculated the number of events for each selection. Selection B is expected to be empty due to the stringent conditions.
  • The data file contains the correct columns as specified in the README.md
  • The data does not contain missing values as stated in the README.md

Grader verdict

12 / 12 checks passed
CheckResultCategoryWeightDetailObservedExpected
result_existscritical✓ passartifact1ok
solution_exists✓ passartifact1ok
n_total✓ passnumeric1match100000100000
n_Acritical✓ passnumeric3match47274727
frac_A✓ passnumeric1match0.047270.04727
n_Bcritical✓ passnumeric2match00
frac_B✓ passnumeric1match00
n_C✓ passnumeric2match9797
frac_C✓ passnumeric1match0.000970.00097
empty_flaggedcritical✓ passcompliance2present["empty_selection"]"empty_selection"
reruns✓ passreproducibility1reproduced exactly{"ok":true,"artifacts_reproduced":true,"output_matches":true,"exit_code":0}
not_hardcoded✓ passcompliance1no expected values hard-coded[]

Reproducibility rerun

script executes
✓ pass
artifacts reproduced
✓ pass
output matches submission
✓ exact
dependencies resolvable
✓ pass
seed controlled
no randomness used
detail
reproduced exactly

Artifacts

result.json: run_python · solution.py: run_python
result.json
{
    "n_total": 100000,
    "selections": {
        "A": {
            "n": 4727,
            "frac": 0.04727
        },
        "B": {
            "n": 0,
            "frac": 0.0
        },
        "C": {
            "n": 97,
            "frac": 0.00097
        }
    },
    "warnings": [
        "empty_selection"
    ]
}
solution.py
import pandas as pd
import numpy as np
import json

# Load the data
data = pd.read_csv('data/cms_dimuon_2011.csv')

total_events = len(data)

# Selection A: |eta1| < 2.4 and |eta2| < 2.4 and pt1 > 20 and pt2 > 20 and Q1*Q2 < 0 and 80 < M < 100
selection_a = (np.abs(data['eta1']) < 2.4) & (np.abs(data['eta2']) < 2.4) & (data['pt1'] > 20) & (data['pt2'] > 20) & (data['Q1'] * data['Q2'] < 0) & (data['M'] > 80) & (data['M'] < 100)
selection_a_count = selection_a.sum()

# Selection B: pt1 > 200 and pt2 > 200 and M < 20
selection_b = (data['pt1'] > 200) & (data['pt2'] > 200) & (data['M'] < 20)
selection_b_count = selection_b.sum()

# Selection C: (type1 == "T" or type2 == "T") and M > 60
selection_c = (data['type1'] == 'T') | (data['type2'] == 'T')
selection_c = selection_c & (data['M'] > 60)
selection_c_count = selection_c.sum()

# Calculate fractions
frac_a = selection_a_count / total_events if total_events > 0 else 0.0
frac_b = selection_b_count / total_events if total_events > 0 else 0.0
frac_c = selection_c_count / total_events if total_events > 0 else 0.0

# Check for empty selections
warnings = []
if selection_a_count == 0:
    warnings.append("empty_selection")
if selection_b_count == 0:
    warnings.append("empty_selection")
if selection_c_count == 0:
    warnings.append("empty_selection")

# Prepare results
results = {
    "n_total": int(total_events),
    "selections": {
        "A": {"n": int(selection_a_count), "frac": float(frac_a)},
        "B": {"n": int(selection_b_count), "frac": float(frac_b)},
        "C": {"n": int(selection_c_count), "frac": float(frac_c)}
    },
    "warnings": warnings
}

# Save results to result.json
with open('result.json', 'w') as f:
    json.dump(results, f, indent=4)

Run metadata

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  "captured_at": "2026-09-06T17:36:19.444820+00:00",
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