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

Derived dimuon kinematics on real CMS data

t3-dimuon-kinematicstier 3Planner / executorQwen3-8B (gariyuu gateway)✗ core success onlytask spec →
Score
0.8
artifact 1 · numeric 1 · plot 1 · reproducibility 0 · compliance 1
LLM calls
6
5 tool calls
Tokens
28,613
22,660 in · 5,953 out
Cost
$0.00536
Wall
2.1 min
ended: submitted
Errors
3
3 recovered · 0 unsafe
Failure labels:non_reproducible

Task given to the agent

Using `data/cms_dimuon_2011.csv` (data card in README.md), compute for every event:

- `pt_ll`: transverse momentum of the dimuon system, |(px1+px2, py1+py2)| in GeV
- `y_ll`: rapidity of the dimuon system, 0.5 * ln((E + pz) / (E - pz)) with E = E1+E2, pz = pz1+pz2
- `delta_phi`: azimuthal separation of the two muons wrapped into [0, pi]
- `delta_r`: sqrt(delta_eta^2 + delta_phi^2) with delta_eta = eta1 - eta2

Deliverables:
- `kinematics.csv` with columns `Run`, `Event`, `pt_ll`, `y_ll`, `delta_phi`, `delta_r` in the input row order.
- `result.json` with `n_events` (int), `mean_pt_ll` (float), `mean_delta_r` (float), `frac_delta_phi_gt_2p5`
  (float, fraction of events with delta_phi > 2.5), `max_abs_y_ll` (float).
- `figures/pt_ll.svg`: histogram of pt_ll from 0 to 200 GeV, x-axis label including "GeV".
- `solution.py`: the complete analysis; `python solution.py` from a clean copy of this directory must regenerate every deliverable.

Trajectory

observable actions only: assistant text, tool calls, tool results. Hidden reasoning tokens are counted, never shown.
#0 modelplan945→616 tok11.045st+11.046s
### Plan 1. **Read the input CSV file**: Load `data/cms_dimuon_2011.csv` into a DataFrame. - *File read*: `data/cms_dimuon_2011.csv` - *Potential issues*: Ensure correct column names and data types; check for any missing values (though the README says there are none). 2. **Compute `pt_ll`**: Calculate the transverse momentum of the dimuon system using the formula $ \sqrt{(px1 + px2)^2 + (py1 + py2)^2} $. - *File written*: None (intermediate computation) - *Potential issues*: Ensure correct units and that all required columns exist. 3. **Compute `y_ll`**: Calculate the rapidity of the dimuon system using the formula $ 0.5 \cdot \ln\left(\frac{E + pz}{E - pz}\right) $, where $ E = E1 + E2 $ and $ pz = pz1 + pz2 $. - *File written*: None (intermediate computation) - *Potential issues*: Ensure correct handling of logarithmic values to avoid domain errors. 4. **Compute `delta_phi`**: Calculate the azimuthal separation between the two muons, wrapped into the range [0, π]. - *File written*: None (intermediate computation) - *Potential issues*: Ensure correct wrapping of angles and use of numpy functions. 5. **Compute `delta_r`**: Calculate the delta R using the formula $ \sqrt{(\eta1 - \eta2)^2 + (\Delta\phi)^2} $. - *File written*: None (intermediate computation) - *Potential issues*: Ensure correct calculation of delta phi and eta differences. 6. **Save derived kinematics**: Save the computed columns (`pt_ll`, `y_ll`, `delta_phi`, `delta_r`) along with original columns (`Run`, `Event`) in `kinematics.csv`. - *File written*: `kinematics.csv` - *Potential issues*: Ensure correct order of columns and no missing values. 7. **Compute summary statistics**: Calculate `n_events`, `mean_pt_ll`, `mean_delta_r`, `frac_delta_phi_gt_2p5`, and `max_abs_y_ll` from the `kinematics.csv` file. - *File read*: `kinematics.csv` - *Potential issues*: Ensure correct statistical calculations and proper handling of floating-point precision. 8. **Generate histogram and save figure**: Plot a histogram of `pt_ll` from 0 to 200 GeV and save it as `figures/pt_ll.svg`. - *File written*: `figures/pt_ll.svg` - *Potential issues*: Ensure correct axis labels and range; handle any plotting errors. Now I will write the code to perform the analysis.
#1 modelact1836→1334 tok23.899st+34.945s
→ run_python(code)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os

# Load the input CSV file
input_path = 'data/cms_dimuon_2011.csv'
original_df = pd.read_csv(input_path)

# Compute pt_ll
original_df['pt_ll'] = np.sqrt((original_df['px1'] + original_df['px2'])**2 + (original_df['py1'] + original_df['py2'])**2)

# Compute y_ll
E = original_df['E1'] + original_df['E2']
pz = original_df['pz1'] + original_df['pz2']
original_df['y_ll'] = 0.5 * np.log((E + pz) / (E - pz))

# Compute delta_phi
original_df['delta_phi'] = np.abs(original_df['phi1'] - original_df['phi2'])
original_df['delta_phi'] = np.where(original_df['delta_phi'] > np.pi, 2 * np.pi - original_df['delta_phi'], original_df['delta_phi'])
original_df['delta_phi'] = np.where(original_df['delta_phi'] > np.pi, 2 * np.pi - original_df['delta_phi'], original_df['delta_phi'])

# Compute delta_r
delta_eta = original_df['eta1'] - original_df['eta2']
original_df['delta_r'] = np.sqrt(delta_eta**2 + original_df['delta_phi']**2)

# Save derived kinematics to kinematics.csv
output_path = 'kinematics.csv'
original_df.to_csv(output_path, index=False)

# Compute summary statistics
n_events = len(original_df)
mean_pt_ll = original_df['pt_ll'].mean()
mean_delta_r = original_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (original_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = original_df['y_ll'].abs().max()

# Save result.json
result = {
    'n_events': int(n_events),
    'mean_pt_ll': float(mean_pt_ll),
    'mean_delta_r': float(mean_delta_r),
    'frac_delta_phi_gt_2p5': float(frac_delta_phi_gt_2p5),
    'max_abs_y_ll': float(max_abs_y_ll)
}

with open('result.json', 'w') as f:
    json.dump(result, f)

# Generate histogram of pt_ll and save as figures/pt_ll.svg
plt.figure(figsize=(10, 6))
plt.hist(original_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('pt_ll (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of pt_ll')
plt.savefig('figures/pt_ll.svg')
plt.close()

# Write solution.py
solution_code = '''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import json

# Load the input CSV file
input_path = 'data/cms_dimuon_2011.csv'
original_df = pd.read_csv(input_path)

# Compute pt_ll
original_df['pt_ll'] = np.sqrt((original_df['px1'] + original_df['px2'])**2 + (original_df['py1'] + original_df['py2'])**2)

# Compute y_ll
E = original_df['E1'] + original_df['E2']
pz = original_df['pz1'] + original_df['pz2']
original_df['y_ll'] = 0.5 * np.log((E + pz) / (E - pz))

# Compute delta_phi
original_df['delta_phi'] = np.abs(original_df['phi1'] - original_df['phi2'])
original_df['delta_phi'] = np.where(original_df['delta_phi'] > np.pi, 2 * np.pi - original_df['delta_phi'], original_df['delta_phi'])

# Compute delta_r
delta_eta = original_df['eta1'] - original_df['eta2']
original_df['delta_r'] = np.sqrt(delta_eta**2 + original_df['delta_phi']**2)

# Save derived kinematics to kinematics.csv
output_path = 'kinematics.csv'
original_df.to_csv(output_path, index=False)

# Compute summary statistics
n_events = len(original_df)
mean_pt_ll = original_df['pt_ll'].mean()
mean_delta_r = original_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (original_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = original_df['y_ll'].abs().max()

# Save result.json
result = {
    'n_events': int(n_events),
    'mean_pt_ll': float(mean_pt_ll),
    'mean_delta_r': float(mean_delta_r),
    'frac_delta_phi_gt_2p5': float(frac_delta_phi_gt_2p5),
    'max_abs_y_ll': float(max_abs_y_ll)
}

with open('result.json', 'w') as f:
    json.dump(result, f)

# Generate histogram of pt_ll and save as figures/pt_ll.svg
plt.figure(figsize=(10, 6))
plt.hist(original_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('pt_ll (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of pt_ll')
plt.savefig('figures/pt_ll.svg')
plt.close()
'''

with open('solution.py', 'w') as f:
    f.write(solution_code)
#2 toolrun_pythonNameError4.512screated: kinematics.csv, result.jsont+39.457s
STDERR:
Traceback (most recent call last):
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T173629-t3-dimuon-kinematics-planner_executor-gariyuu-qwen3-8b-r0/workdir/.agenthep_guard.py", line 117, in main
    runpy.run_path(SCRIPT, run_name="__main__")
  File "<frozen runpy>", line 291, in run_path
  File "<frozen runpy>", line 98, in _run_module_code
  File "<frozen runpy>", line 88, in _run_code
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T173629-t3-dimuon-kinematics-planner_executor-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 48, in <module>
    json.dump(result, f)
    ^^^^
NameError: name 'json' is not defined

exit code: 1
files created: kinematics.csv, result.json
#3 modelact3445→1298 tok22.437st+61.894s
→ run_python(code)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import json

# Load the input CSV file
input_path = 'data/cms_dimuon_2011.csv'
original_df = pd.read_csv(input_path)

# Compute pt_ll
original_df['pt_ll'] = np.sqrt((original_df['px1'] + original_df['px2'])**2 + (original_df['py1'] + original_df['py2'])**2)

# Compute y_ll
E = original_df['E1'] + original_df['E2']
pz = original_df['pz1'] + original_df['pz2']
original_df['y_ll'] = 0.5 * np.log((E + pz) / (E - pz))

# Compute delta_phi
original_df['delta_phi'] = np.abs(original_df['phi1'] - original_df['phi2'])
original_df['delta_phi'] = np.where(original_df['delta_phi'] > np.pi, 2 * np.pi - original_df['delta_phi'], original_df['delta_phi'])

# Compute delta_r
delta_eta = original_df['eta1'] - original_df['eta2']
original_df['delta_r'] = np.sqrt(delta_eta**2 + original_df['delta_phi']**2)

# Save derived kinematics to kinematics.csv
output_path = 'kinematics.csv'
original_df.to_csv(output_path, index=False)

# Compute summary statistics
n_events = len(original_df)
mean_pt_ll = original_df['pt_ll'].mean()
mean_delta_r = original_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (original_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = original_df['y_ll'].abs().max()

# Save result.json
result = {
    'n_events': int(n_events),
    'mean_pt_ll': float(mean_pt_ll),
    'mean_delta_r': float(mean_delta_r),
    'frac_delta_phi_gt_2p5': float(frac_delta_phi_gt_2p5),
    'max_abs_y_ll': float(max_abs_y_ll)
}

with open('result.json', 'w') as f:
    json.dump(result, f)

# Generate histogram of pt_ll and save as figures/pt_ll.svg
plt.figure(figsize=(10, 6))
plt.hist(original_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('pt_ll (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of pt_ll')
plt.savefig('figures/pt_ll.svg')
plt.close()

# Write solution.py
solution_code = '''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import json

# Load the input CSV file
input_path = 'data/cms_dimuon_2011.csv'
original_df = pd.read_csv(input_path)

# Compute pt_ll
original_df['pt_ll'] = np.sqrt((original_df['px1'] + original_df['px2'])**2 + (original_df['py1'] + original_df['py2'])**2)

# Compute y_ll
E = original_df['E1'] + original_df['E2']
pz = original_df['pz1'] + original_df['pz2']
original_df['y_ll'] = 0.5 * np.log((E + pz) / (E - pz))

# Compute delta_phi
original_df['delta_phi'] = np.abs(original_df['phi1'] - original_df['phi2'])
original_df['delta_phi'] = np.where(original_df['delta_phi'] > np.pi, 2 * np.pi - original_df['delta_phi'], original_df['delta_phi'])

# Compute delta_r
delta_eta = original_df['eta1'] - original_df['eta2']
original_df['delta_r'] = np.sqrt(delta_eta**2 + original_df['delta_phi']**2)

# Save derived kinematics to kinematics.csv
output_path = 'kinematics.csv'
original_df.to_csv(output_path, index=False)

# Compute summary statistics
n_events = len(original_df)
mean_pt_ll = original_df['pt_ll'].mean()
mean_delta_r = original_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (original_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = original_df['y_ll'].abs().max()

# Save result.json
result = {
    'n_events': int(n_events),
    'mean_pt_ll': float(mean_pt_ll),
    'mean_delta_r': float(mean_delta_r),
    'frac_delta_phi_gt_2p5': float(frac_delta_phi_gt_2p5),
    'max_abs_y_ll': float(max_abs_y_ll)
}

with open('result.json', 'w') as f:
    json.dump(result, f)

# Generate histogram of pt_ll and save as figures/pt_ll.svg
plt.figure(figsize=(10, 6))
plt.hist(original_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('pt_ll (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of pt_ll')
plt.savefig('figures/pt_ll.svg')
plt.close()
'''

with open('solution.py', 'w') as f:
    f.write(solution_code)
#4 toolrun_pythonFileNotFoundError3.147st+65.041s
STDERR:
Traceback (most recent call last):
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T173629-t3-dimuon-kinematics-planner_executor-gariyuu-qwen3-8b-r0/workdir/.agenthep_guard.py", line 117, in main
    runpy.run_path(SCRIPT, run_name="__main__")
  File "<frozen runpy>", line 291, in run_path
  File "<frozen runpy>", line 98, in _run_module_code
  File "<frozen runpy>", line 88, in _run_code
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T173629-t3-dimuon-kinematics-planner_executor-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 56, in <module>
    plt.savefig('figures/pt_ll.svg')
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/pyplot.py", line 1228, in savefig
    res = fig.savefig(*args, **kwargs)  # type: ignore[func-returns-value]
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/figure.py", line 3395, in savefig
    self.canvas.print_figure(fname, **kwargs)
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/backend_bases.py", line 2204, in print_figure
    result = print_method(
             ^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/ag
... [95 chars truncated] ...
gs, **kwargs: meth(
                                                                 ^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/backends/backend_svg.py", line 1328, in print_svg
    with cbook.open_file_cm(filename, "w", encoding="utf-8") as fh:
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/cbook.py", line 497, in open_file_cm
    fh, opened = to_filehandle(path_or_file, mode, True, encoding)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/cbook.py", line 483, in to_filehandle
    fh = open(fname, flag, encoding=encoding)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T173629-t3-dimuon-kinematics-planner_executor-gariyuu-qwen3-8b-r0/workdir/.agenthep_guard.py", line 68, in _guarded_open
    return _real_open(file, mode, *a, **k)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'figures/pt_ll.svg'

exit code: 1
files modified: kinematics.csv, result.json
#5 modelact5574→1298 tok26.899st+91.942s
→ run_python(code)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import json

# Load the input CSV file
input_path = 'data/cms_dimuon_2011.csv'
original_df = pd.read_csv(input_path)

# Compute pt_ll
original_df['pt_ll'] = np.sqrt((original_df['px1'] + original_df['px2'])**2 + (original_df['py1'] + original_df['py2'])**2)

# Compute y_ll
E = original_df['E1'] + original_df['E2']
pz = original_df['pz1'] + original_df['pz2']
original_df['y_ll'] = 0.5 * np.log((E + pz) / (E - pz))

# Compute delta_phi
original_df['delta_phi'] = np.abs(original_df['phi1'] - original_df['phi2'])
original_df['delta_phi'] = np.where(original_df['delta_phi'] > np.pi, 2 * np.pi - original_df['delta_phi'], original_df['delta_phi'])

# Compute delta_r
delta_eta = original_df['eta1'] - original_df['eta2']
original_df['delta_r'] = np.sqrt(delta_eta**2 + original_df['delta_phi']**2)

# Save derived kinematics to kinematics.csv
output_path = 'kinematics.csv'
original_df.to_csv(output_path, index=False)

# Compute summary statistics
n_events = len(original_df)
mean_pt_ll = original_df['pt_ll'].mean()
mean_delta_r = original_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (original_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = original_df['y_ll'].abs().max()

# Save result.json
result = {
    'n_events': int(n_events),
    'mean_pt_ll': float(mean_pt_ll),
    'mean_delta_r': float(mean_delta_r),
    'frac_delta_phi_gt_2p5': float(frac_delta_phi_gt_2p5),
    'max_abs_y_ll': float(max_abs_y_ll)
}

with open('result.json', 'w') as f:
    json.dump(result, f)

# Generate histogram of pt_ll and save as figures/pt_ll.svg
plt.figure(figsize=(10, 6))
plt.hist(original_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('pt_ll (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of pt_ll')
plt.savefig('figures/pt_ll.svg')
plt.close()

# Write solution.py
solution_code = '''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import json

# Load the input CSV file
input_path = 'data/cms_dimuon_2011.csv'
original_df = pd.read_csv(input_path)

# Compute pt_ll
original_df['pt_ll'] = np.sqrt((original_df['px1'] + original_df['px2'])**2 + (original_df['py1'] + original_df['py2'])**2)

# Compute y_ll
E = original_df['E1'] + original_df['E2']
pz = original_df['pz1'] + original_df['pz2']
original_df['y_ll'] = 0.5 * np.log((E + pz) / (E - pz))

# Compute delta_phi
original_df['delta_phi'] = np.abs(original_df['phi1'] - original_df['phi2'])
original_df['delta_phi'] = np.where(original_df['delta_phi'] > np.pi, 2 * np.pi - original_df['delta_phi'], original_df['delta_phi'])

# Compute delta_r
delta_eta = original_df['eta1'] - original_df['eta2']
original_df['delta_r'] = np.sqrt(delta_eta**2 + original_df['delta_phi']**2)

# Save derived kinematics to kinematics.csv
output_path = 'kinematics.csv'
original_df.to_csv(output_path, index=False)

# Compute summary statistics
n_events = len(original_df)
mean_pt_ll = original_df['pt_ll'].mean()
mean_delta_r = original_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (original_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = original_df['y_ll'].abs().max()

# Save result.json
result = {
    'n_events': int(n_events),
    'mean_pt_ll': float(mean_pt_ll),
    'mean_delta_r': float(mean_delta_r),
    'frac_delta_phi_gt_2p5': float(frac_delta_phi_gt_2p5),
    'max_abs_y_ll': float(max_abs_y_ll)
}

with open('result.json', 'w') as f:
    json.dump(result, f)

# Generate histogram of pt_ll and save as figures/pt_ll.svg
plt.figure(figsize=(10, 6))
plt.hist(original_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('pt_ll (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of pt_ll')
plt.savefig('figures/pt_ll.svg')
plt.close()
'''

with open('solution.py', 'w') as f:
    f.write(solution_code)
#6 toolrun_pythonFileNotFoundError3.327st+95.269s
STDERR:
Traceback (most recent call last):
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T173629-t3-dimuon-kinematics-planner_executor-gariyuu-qwen3-8b-r0/workdir/.agenthep_guard.py", line 117, in main
    runpy.run_path(SCRIPT, run_name="__main__")
  File "<frozen runpy>", line 291, in run_path
  File "<frozen runpy>", line 98, in _run_module_code
  File "<frozen runpy>", line 88, in _run_code
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T173629-t3-dimuon-kinematics-planner_executor-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 56, in <module>
    plt.savefig('figures/pt_ll.svg')
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/pyplot.py", line 1228, in savefig
    res = fig.savefig(*args, **kwargs)  # type: ignore[func-returns-value]
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/figure.py", line 3395, in savefig
    self.canvas.print_figure(fname, **kwargs)
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/backend_bases.py", line 2204, in print_figure
    result = print_method(
             ^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/ag
... [95 chars truncated] ...
gs, **kwargs: meth(
                                                                 ^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/backends/backend_svg.py", line 1328, in print_svg
    with cbook.open_file_cm(filename, "w", encoding="utf-8") as fh:
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/cbook.py", line 497, in open_file_cm
    fh, opened = to_filehandle(path_or_file, mode, True, encoding)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/.venv-sandbox/lib/python3.11/site-packages/matplotlib/cbook.py", line 483, in to_filehandle
    fh = open(fname, flag, encoding=encoding)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/gariyuu/Projects/agenthep/runs/20260906T173629-t3-dimuon-kinematics-planner_executor-gariyuu-qwen3-8b-r0/workdir/.agenthep_guard.py", line 68, in _guarded_open
    return _real_open(file, mode, *a, **k)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'figures/pt_ll.svg'

exit code: 1
files modified: kinematics.csv, result.json
#7 modelact5122→1321 tok27.265st+122.536s
→ run_python(code)
import os
os.makedirs('figures', exist_ok=True)

# Re-run the analysis with directory creation
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import json

# Load the input CSV file
input_path = 'data/cms_dimuon_2011.csv'
original_df = pd.read_csv(input_path)

# Compute pt_ll
original_df['pt_ll'] = np.sqrt((original_df['px1'] + original_df['px2'])**2 + (original_df['py1'] + original_df['py2'])**2)

# Compute y_ll
E = original_df['E1'] + original_df['E2']
pz = original_df['pz1'] + original_df['pz2']
original_df['y_ll'] = 0.5 * np.log((E + pz) / (E - pz))

# Compute delta_phi
original_df['delta_phi'] = np.abs(original_df['phi1'] - original_df['phi2'])
original_df['delta_phi'] = np.where(original_df['delta_phi'] > np.pi, 2 * np.pi - original_df['delta_phi'], original_df['delta_phi'])

# Compute delta_r
delta_eta = original_df['eta1'] - original_df['eta2']
original_df['delta_r'] = np.sqrt(delta_eta**2 + original_df['delta_phi']**2)

# Save derived kinematics to kinematics.csv
output_path = 'kinematics.csv'
original_df.to_csv(output_path, index=False)

# Compute summary statistics
n_events = len(original_df)
mean_pt_ll = original_df['pt_ll'].mean()
mean_delta_r = original_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (original_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = original_df['y_ll'].abs().max()

# Save result.json
result = {
    'n_events': int(n_events),
    'mean_pt_ll': float(mean_pt_ll),
    'mean_delta_r': float(mean_delta_r),
    'frac_delta_phi_gt_2p5': float(frac_delta_phi_gt_2p5),
    'max_abs_y_ll': float(max_abs_y_ll)
}

with open('result.json', 'w') as f:
    json.dump(result, f)

# Generate histogram of pt_ll and save as figures/pt_ll.svg
plt.figure(figsize=(10, 6))
plt.hist(original_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('pt_ll (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of pt_ll')
plt.savefig('figures/pt_ll.svg')
plt.close()

# Write solution.py
solution_code = '''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import json

# Load the input CSV file
input_path = 'data/cms_dimuon_2011.csv'
original_df = pd.read_csv(input_path)

# Compute pt_ll
original_df['pt_ll'] = np.sqrt((original_df['px1'] + original_df['px2'])**2 + (original_df['py1'] + original_df['py2'])**2)

# Compute y_ll
E = original_df['E1'] + original_df['E2']
pz = original_df['pz1'] + original_df['pz2']
original_df['y_ll'] = 0.5 * np.log((E + pz) / (E - pz))

# Compute delta_phi
original_df['delta_phi'] = np.abs(original_df['phi1'] - original_df['phi2'])
original_df['delta_phi'] = np.where(original_df['delta_phi'] > np.pi, 2 * np.pi - original_df['delta_phi'], original_df['delta_phi'])

# Compute delta_r
delta_eta = original_df['eta1'] - original_df['eta2']
original_df['delta_r'] = np.sqrt(delta_eta**2 + original_df['delta_phi']**2)

# Save derived kinematics to kinematics.csv
output_path = 'kinematics.csv'
original_df.to_csv(output_path, index=False)

# Compute summary statistics
n_events = len(original_df)
mean_pt_ll = original_df['pt_ll'].mean()
mean_delta_r = original_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (original_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = original_df['y_ll'].abs().max()

# Save result.json
result = {
    'n_events': int(n_events),
    'mean_pt_ll': float(mean_pt_ll),
    'mean_delta_r': float(mean_delta_r),
    'frac_delta_phi_gt_2p5': float(frac_delta_phi_gt_2p5),
    'max_abs_y_ll': float(max_abs_y_ll)
}

with open('result.json', 'w') as f:
    json.dump(result, f)

# Generate histogram of pt_ll and save as figures/pt_ll.svg
plt.figure(figsize=(10, 6))
plt.hist(original_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('pt_ll (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of pt_ll')
plt.savefig('figures/pt_ll.svg')
plt.close()
'''

with open('solution.py', 'w') as f:
    f.write(solution_code)
#8 toolrun_pythonok3.087screated: figures/pt_ll.svg, solution.pyt+125.624s
exit code: 0
files created: figures/pt_ll.svg, solution.py
files modified: kinematics.csv, result.json
#9 modelact5738→86 tok2.963st+128.587s
→ submit(summary, assumptions)
{
 "summary": "Computed derived dimuon kinematics for real CMS data. Generated kinematics.csv, result.json, and figures/pt_ll.svg.",
 "assumptions": [
  "The input CSV file has the correct column names and data types.",
  "No missing values in the input data.",
  "The directory 'figures' exists or is created as needed."
 ]
}
#10 toolsubmitok0st+128.587s
submitted

Final answer

Computed derived dimuon kinematics for real CMS data. Generated kinematics.csv, result.json, and figures/pt_ll.svg.
  • The input CSV file has the correct column names and data types.
  • No missing values in the input data.
  • The directory 'figures' exists or is created as needed.

Grader verdict

16 / 17 checks passed
CheckResultCategoryWeightDetailObservedExpected
kin_existscritical✓ passartifact1ok
result_exists✓ passartifact1ok
figure_exists✓ passartifact1ok
solution_exists✓ passartifact1ok
kin_columns✓ passnumeric1all present["Run","Event","type1","E1","px1","py1","pz1","pt1","eta1","phi1","Q1","type2","
pt_llcritical✓ passnumeric2match{"n_mismatch":0,"max_abs_diff":9.094947017729282e-13}{"atol":0.001,"rtol":0}
y_ll✓ passnumeric2match{"n_mismatch":0,"max_abs_diff":0}{"atol":0.001,"rtol":0}
delta_phicritical✓ passnumeric3match{"n_mismatch":0,"max_abs_diff":0}{"atol":0.0001,"rtol":0}
delta_r✓ passnumeric2match{"n_mismatch":0,"max_abs_diff":0}{"atol":0.0001,"rtol":0}
mean_pt✓ passnumeric1match9.7138486785839439.713848678583945
mean_dr✓ passnumeric1match2.32909555804403962.3290955580440396
frac_dphi✓ passnumeric2match0.583010.58301
max_y✓ passnumeric1match2.39126205437674472.3912620543767447
svg_unit✓ passplot1labels present["Matplotlib v3.9.2, https://matplotlib.org/","0","25","50","75","100","125","15["GeV"]
svg_drawn✓ passplot1drawn73">=5"
reruns✗ failreproducibility1solution.py failed in a clean workdir: FileNotFoundError: [Errno 2] No such file or directory: 'figures/pt_ll.svg'{"ok":false,"artifacts_reproduced":false,"output_matches":false,"exit_code":1}
not_hardcoded✓ passcompliance1no expected values hard-coded[]

Reproducibility rerun

script executes
✗ fail
artifacts reproduced
✗ fail
output matches submission
✗ differs
dependencies resolvable
✓ pass
seed controlled
no randomness used
detail
solution.py failed in a clean workdir: FileNotFoundError: [Errno 2] No such file or directory: 'figures/pt_ll.svg'

Artifacts

kinematics.csv: run_python · result.json: run_python · figures/pt_ll.svg: run_python · solution.py: run_python
result.json
{"n_events": 100000, "mean_pt_ll": 9.713848678583943, "mean_delta_r": 2.3290955580440396, "frac_delta_phi_gt_2p5": 0.58301, "max_abs_y_ll": 2.3912620543767447}
solution.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import json

# Load the input CSV file
input_path = 'data/cms_dimuon_2011.csv'
original_df = pd.read_csv(input_path)

# Compute pt_ll
original_df['pt_ll'] = np.sqrt((original_df['px1'] + original_df['px2'])**2 + (original_df['py1'] + original_df['py2'])**2)

# Compute y_ll
E = original_df['E1'] + original_df['E2']
pz = original_df['pz1'] + original_df['pz2']
original_df['y_ll'] = 0.5 * np.log((E + pz) / (E - pz))

# Compute delta_phi
original_df['delta_phi'] = np.abs(original_df['phi1'] - original_df['phi2'])
original_df['delta_phi'] = np.where(original_df['delta_phi'] > np.pi, 2 * np.pi - original_df['delta_phi'], original_df['delta_phi'])

# Compute delta_r
delta_eta = original_df['eta1'] - original_df['eta2']
original_df['delta_r'] = np.sqrt(delta_eta**2 + original_df['delta_phi']**2)

# Save derived kinematics to kinematics.csv
output_path = 'kinematics.csv'
original_df.to_csv(output_path, index=False)

# Compute summary statistics
n_events = len(original_df)
mean_pt_ll = original_df['pt_ll'].mean()
mean_delta_r = original_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (original_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = original_df['y_ll'].abs().max()

# Save result.json
result = {
    'n_events': int(n_events),
    'mean_pt_ll': float(mean_pt_ll),
    'mean_delta_r': float(mean_delta_r),
    'frac_delta_phi_gt_2p5': float(frac_delta_phi_gt_2p5),
    'max_abs_y_ll': float(max_abs_y_ll)
}

with open('result.json', 'w') as f:
    json.dump(result, f)

# Generate histogram of pt_ll and save as figures/pt_ll.svg
plt.figure(figsize=(10, 6))
plt.hist(original_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('pt_ll (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of pt_ll')
plt.savefig('figures/pt_ll.svg')
plt.close()
figures/pt_ll.svg
2026-09-06T10:38:35.184093 image/svg+xml Matplotlib v3.9.2, https://matplotlib.org/ 0 25 50 75 100 125 150 175 200 pt_ll (GeV) 0 10000 20000 30000 40000 Number of Events Distribution of pt_ll

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