Derived dimuon kinematics on real CMS data
0.8083
3
11,534
$0.00226
1.3 min
2
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
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
# Read the data
data_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(data_path)
# Compute derived kinematics
def compute_kinematics(row):
# Calculate dimuon system properties
px_total = row['px1'] + row['px2']
py_total = row['py1'] + row['py2']
pt_ll = np.sqrt(px_total**2 + py_total**2)
E_total = row['E1'] + row['E2']
pz_total = row['pz1'] + row['pz2']
y_ll = 0.5 * np.log((E_total + pz_total) / (E_total - pz_total))
# Calculate delta_phi
phi_diff = abs(row['phi1'] - row['phi2'])
delta_phi = phi_diff if phi_diff <= np.pi else 2 * np.pi - phi_diff
# Calculate delta_r
delta_eta = abs(row['eta1'] - row['eta2'])
delta_r = np.sqrt(delta_eta**2 + delta_phi**2)
return pd.Series({
'pt_ll': pt_ll,
'y_ll': y_ll,
'delta_phi': delta_phi,
'delta_r': delta_r
})
# Apply the function to each row
kinematics_df = df.apply(compute_kinematics, axis=1)
# Combine with original data
result_df = pd.concat([df[['Run', 'Event']], kinematics_df], axis=1)
# Save the kinematics CSV
result_df.to_csv('kinematics.csv', index=False)
# Compute statistics for result.json
n_events = len(result_df)
mean_pt_ll = result_df['pt_ll'].mean()
mean_delta_r = result_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (result_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = result_df['y_ll'].abs().max()
# Save result.json
result_json = {
'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:
f.write(str(result_json))
# Plot histogram of pt_ll
plt.figure(figsize=(8, 6))
plt.hist(result_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('Transverse Momentum (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of Dimuon Transverse Momentum')
plt.grid(axis='y', alpha=0.75)
plt.savefig('figures/pt_ll.svg')
# Create solution.py
solution_code = """
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
# Read the data
data_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(data_path)
# Compute derived kinematics
def compute_kinematics(row):
# Calculate dimuon system properties
px_total = row['px1'] + row['px2']
py_total = row['py1'] + row['py2']
pt_ll = np.sqrt(px_total**2 + py_total**2)
E_total = row['E1'] + row['E2']
pz_total = row['pz1'] + row['pz2']
y_ll = 0.5 * np.log((E_total + pz_total) / (E_total - pz_total))
# Calculate delta_phi
phi_diff = abs(row['phi1'] - row['phi2'])
delta_phi = phi_diff if phi_diff <= np.pi else 2 * np.pi - phi_diff
# Calculate delta_r
delta_eta = abs(row['eta1'] - row['eta2'])
delta_r = np.sqrt(delta_eta**2 + delta_phi**2)
return pd.Series({
'pt_ll': pt_ll,
'y_ll': y_ll,
'delta_phi': delta_phi,
'delta_r': delta_r
})
# Apply the function to each row
kinematics_df = df.apply(compute_kinematics, axis=1)
# Combine with original data
result_df = pd.concat([df[['Run', 'Event']], kinematics_df], axis=1)
# Save the kinematics CSV
result_df.to_csv('kinematics.csv', index=False)
# Compute statistics for result.json
n_events = len(result_df)
mean_pt_ll = result_df['pt_ll'].mean()
mean_delta_r = result_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (result_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = result_df['y_ll'].abs().max()
# Save result.json
result_json = {
'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:
f.write(str(result_json))
# Plot histogram of pt_ll
plt.figure(figsize=(8, 6))
plt.hist(result_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('Transverse Momentum (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of Dimuon Transverse Momentum')
plt.grid(axis='y', alpha=0.75)
plt.savefig('figures/pt_ll.svg')
"""
with open('solution.py', 'w') as f:
f.write(solution_code)
# Submit the results
print("All deliverables have been generated.")
print("Assumptions made:")
print("- The data file is correctly formatted and contains no missing values.")
print("- The calculation of rapidity uses the correct formula.")
print("- The azimuthal separation is wrapped into [0, pi].")
print("- The delta_r is calculated using the Euclidean distance in eta and phi.")
```
wrote 4740 chars to solution.py
STDERR:
Traceback (most recent call last):
File "/Users/gariyuu/Projects/agenthep/runs/20260906T174945-t3-dimuon-kinematics-self_debug-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/20260906T174945-t3-dimuon-kinematics-self_debug-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 70, 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/agenthep/.venv
... [94 chars truncated] ...
bda *args, **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/20260906T174945-t3-dimuon-kinematics-self_debug-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 created: kinematics.csv, result.json```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
# Read the data
data_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(data_path)
# Compute derived kinematics
def compute_kinematics(row):
# Calculate dimuon system properties
px_total = row['px1'] + row['px2']
py_total = row['py1'] + row['py2']
pt_ll = np.sqrt(px_total**2 + py_total**2)
E_total = row['E1'] + row['E2']
pz_total = row['pz1'] + row['pz2']
y_ll = 0.5 * np.log((E_total + pz_total) / (E_total - pz_total))
# Calculate delta_phi
phi_diff = abs(row['phi1'] - row['phi2'])
delta_phi = phi_diff if phi_diff <= np.pi else 2 * np.pi - phi_diff
# Calculate delta_r
delta_eta = abs(row['eta1'] - row['eta2'])
delta_r = np.sqrt(delta_eta**2 + delta_phi**2)
return pd.Series({
'pt_ll': pt_ll,
'y_ll': y_ll,
'delta_phi': delta_phi,
'delta_r': delta_r
})
# Apply the function to each row
kinematics_df = df.apply(compute_kinematics, axis=1)
# Combine with original data
result_df = pd.concat([df[['Run', 'Event']], kinematics_df], axis=1)
# Save the kinematics CSV
result_df.to_csv('kinematics.csv', index=False)
# Compute statistics for result.json
n_events = len(result_df)
mean_pt_ll = result_df['pt_ll'].mean()
mean_delta_r = result_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (result_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = result_df['y_ll'].abs().max()
# Save result.json
result_json = {
'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:
f.write(str(result_json))
# Plot histogram of pt_ll
plt.figure(figsize=(8, 6))
plt.hist(result_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('Transverse Momentum (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of Dimuon Transverse Momentum')
plt.grid(axis='y', alpha=0.75)
plt.savefig('figures/pt_ll.svg')
# Ensure the figures directory exists
os.makedirs('figures', exist_ok=True)
```
wrote 2200 chars to solution.py
STDERR:
Traceback (most recent call last):
File "/Users/gariyuu/Projects/agenthep/runs/20260906T174945-t3-dimuon-kinematics-self_debug-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/20260906T174945-t3-dimuon-kinematics-self_debug-gariyuu-qwen3-8b-r0/workdir/_cell.py", line 70, 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/agenthep/.venv
... [95 chars truncated] ...
da *args, **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/20260906T174945-t3-dimuon-kinematics-self_debug-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```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
# Read the data
data_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(data_path)
# Compute derived kinematics
def compute_kinematics(row):
# Calculate dimuon system properties
px_total = row['px1'] + row['px2']
py_total = row['py1'] + row['py2']
pt_ll = np.sqrt(px_total**2 + py_total**2)
E_total = row['E1'] + row['E2']
pz_total = row['pz1'] + row['pz2']
y_ll = 0.5 * np.log((E_total + pz_total) / (E_total - pz_total))
# Calculate delta_phi
phi_diff = abs(row['phi1'] - row['phi2'])
delta_phi = phi_diff if phi_diff <= np.pi else 2 * np.pi - phi_diff
# Calculate delta_r
delta_eta = abs(row['eta1'] - row['eta2'])
delta_r = np.sqrt(delta_eta**2 + delta_phi**2)
return pd.Series({
'pt_ll': pt_ll,
'y_ll': y_ll,
'delta_phi': delta_phi,
'delta_r': delta_r
})
# Apply the function to each row
kinematics_df = df.apply(compute_kinematics, axis=1)
# Combine with original data
result_df = pd.concat([df[['Run', 'Event']], kinematics_df], axis=1)
# Save the kinematics CSV
result_df.to_csv('kinematics.csv', index=False)
# Compute statistics for result.json
n_events = len(result_df)
mean_pt_ll = result_df['pt_ll'].mean()
mean_delta_r = result_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (result_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = result_df['y_ll'].abs().max()
# Save result.json
result_json = {
'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:
f.write(str(result_json))
# Plot histogram of pt_ll
plt.figure(figsize=(8, 6))
plt.hist(result_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('Transverse Momentum (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of Dimuon Transverse Momentum')
plt.grid(axis='y', alpha=0.75)
# Ensure the figures directory exists
os.makedirs('figures', exist_ok=True)
plt.savefig('figures/pt_ll.svg')
```
wrote 2200 chars to solution.py
exit code: 0 files created: figures/pt_ll.svg files modified: kinematics.csv, result.json
Final answer
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
# Read the data
data_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(data_path)
# Compute derived kinematics
def compute_kinematics(row):
# Calculate dimuon system properties
px_total = row['px1'] + row['px2']
py_total = row['py1'] + row['py2']
pt_ll = np.sqrt(px_total**2 + py_total**2)
E_total = row['E1'] + row['E2']
pz_total = row['pz1'] + row['pz2']
y_ll = 0.5 * np.log((E_total + pz_total) / (E_total - pz_total))
# Calculate delta_phi
phi_diff = abs(row['phi1'] - row['phi2'])
delta_phi = phi_diff if phi_diff <= np.pi else 2 * np.pi - phi_diff
# Calculate delta_r
delta_eta = abs(row['eta1'] - row['eta2'])
delta_r = np.sqrt(delta_eta**2 + delta_phi**2)
return pd.Series({
'pt_ll': pt_ll,
'y_ll': y_ll,
'delta_phi': delta_phi,
'delta_r': delta_r
})
# Apply the function to each row
kinematics_df = df.apply(compute_kinematics, axis=1)
# Combine with original data
result_df = pd.concat([df[['Run', 'Event']], kinematics_df], axis=1)
# Save the kinematics CSV
result_df.to_csv('kinematics.csv', index=False)
# Compute statistics for result.json
n_events = len(result_df)
mean_pt_ll = result_df['pt_ll'].mean()
mean_delta_r = result_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (result_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = result_df['y_ll'].abs().max()
# Save result.json
result_json = {
'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:
f.write(str(result_json))
# Plot histogram of pt_ll
plt.figure(figsize=(8, 6))
plt.hist(result_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('Transverse Momentum (GeV)')
plt.ylabel('Number
Grader verdict
| Check | Result | Category | Weight | Detail | Observed | Expected |
|---|---|---|---|---|---|---|
| kin_existscritical | ✓ pass | artifact | 1 | ok | ||
| result_exists | ✗ fail | artifact | 1 | unparseable json: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| figure_exists | ✓ pass | artifact | 1 | ok | ||
| solution_exists | ✓ pass | artifact | 1 | ok | ||
| kin_columns | ✓ pass | numeric | 1 | all present | ["Run","Event","pt_ll","y_ll","delta_phi","delta_r"] | |
| pt_llcritical | ✓ pass | numeric | 2 | match | {"n_mismatch":0,"max_abs_diff":9.094947017729282e-13} | {"atol":0.001,"rtol":0} |
| y_ll | ✓ pass | numeric | 2 | match | {"n_mismatch":0,"max_abs_diff":0} | {"atol":0.001,"rtol":0} |
| delta_phicritical | ✓ pass | numeric | 3 | match | {"n_mismatch":0,"max_abs_diff":0} | {"atol":0.0001,"rtol":0} |
| delta_r | ✓ pass | numeric | 2 | match | {"n_mismatch":0,"max_abs_diff":8.881784197001252e-16} | {"atol":0.0001,"rtol":0} |
| mean_pt | ✗ fail | numeric | 1 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| mean_dr | ✗ fail | numeric | 1 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| frac_dphi | ✗ fail | numeric | 2 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| max_y | ✗ fail | numeric | 1 | grader error: JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1) | ||
| svg_unit | ✓ pass | plot | 1 | labels present | ["Matplotlib v3.9.2, https://matplotlib.org/","0","25","50","75","100","125","15 | ["GeV"] |
| svg_drawn | ✓ pass | plot | 1 | drawn | 78 | ">=5" |
| reruns | ✓ pass | reproducibility | 1 | rerun output differs from submission: ['result.json: JSONDecodeError'] | {"ok":true,"artifacts_reproduced":true,"output_matches":false,"exit_code":0} | |
| not_hardcoded | ✓ pass | compliance | 1 | no expected values hard-coded | [] |
Reproducibility rerun
✓ pass
✓ pass
✗ differs
✓ pass
no randomness used
rerun output differs from submission: ['result.json: JSONDecodeError']
Artifacts
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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
# Read the data
data_path = 'data/cms_dimuon_2011.csv'
df = pd.read_csv(data_path)
# Compute derived kinematics
def compute_kinematics(row):
# Calculate dimuon system properties
px_total = row['px1'] + row['px2']
py_total = row['py1'] + row['py2']
pt_ll = np.sqrt(px_total**2 + py_total**2)
E_total = row['E1'] + row['E2']
pz_total = row['pz1'] + row['pz2']
y_ll = 0.5 * np.log((E_total + pz_total) / (E_total - pz_total))
# Calculate delta_phi
phi_diff = abs(row['phi1'] - row['phi2'])
delta_phi = phi_diff if phi_diff <= np.pi else 2 * np.pi - phi_diff
# Calculate delta_r
delta_eta = abs(row['eta1'] - row['eta2'])
delta_r = np.sqrt(delta_eta**2 + delta_phi**2)
return pd.Series({
'pt_ll': pt_ll,
'y_ll': y_ll,
'delta_phi': delta_phi,
'delta_r': delta_r
})
# Apply the function to each row
kinematics_df = df.apply(compute_kinematics, axis=1)
# Combine with original data
result_df = pd.concat([df[['Run', 'Event']], kinematics_df], axis=1)
# Save the kinematics CSV
result_df.to_csv('kinematics.csv', index=False)
# Compute statistics for result.json
n_events = len(result_df)
mean_pt_ll = result_df['pt_ll'].mean()
mean_delta_r = result_df['delta_r'].mean()
frac_delta_phi_gt_2p5 = (result_df['delta_phi'] > 2.5).sum() / n_events
max_abs_y_ll = result_df['y_ll'].abs().max()
# Save result.json
result_json = {
'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:
f.write(str(result_json))
# Plot histogram of pt_ll
plt.figure(figsize=(8, 6))
plt.hist(result_df['pt_ll'], bins=50, range=(0, 200), color='blue', edgecolor='black')
plt.xlabel('Transverse Momentum (GeV)')
plt.ylabel('Number of Events')
plt.title('Distribution of Dimuon Transverse Momentum')
plt.grid(axis='y', alpha=0.75)
# Ensure the figures directory exists
os.makedirs('figures', exist_ok=True)
plt.savefig('figures/pt_ll.svg')
figures/pt_ll.svg
Run metadata
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"provider": "openai_compat",
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"temperature": 0,
"max_tokens": 2500,
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