Event selections including one that is empty

{
  "n_total": 100000,
  "selections": {
    "A": {
      "n": 

import pandas as pd
import numpy as np
import json

# Read the data
data_path = "data/cms_dimuon_2011.csv"
df = pd.read_csv(data_path)

# Total number of events
n_total = len(df)

# 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(df['eta1']) < 2.4) &
    (np.abs(df['eta2']) < 2.4) &
    (df['pt1'] > 20) &
    (df['pt2'] > 20) &
    (df['Q1'] * df['Q2'] < 0) &
    (df['M'] > 80) &
    (df['M'] < 100)
)
n_A = selection_A.sum()

# Selection B: pt1 > 200 and pt2 > 200 and M < 20
selection_B = (
    (df['pt1'] > 200) &
    (df['pt2'] > 200) &
    (df['M'] < 20)
)
n_B = selection_B.sum()

# Selection C: (type1 == "T" or type2 == "T") and M > 60
selection_C = (
    (df['type1'] == "T") | (df['type2'] == "T") &
    (df['M'] > 60)
)
n_C = selection_C.sum()

# Calculate fractions
frac_A = n_A / n_total if n_total > 0 else 0.0
frac_B = n_B / n_total if n_total > 0 else 0.0
frac_C = n_C / n_total if n_total > 0 else 0.0

# Warnings for empty selections
warnings = []
if n_A == 0:
    warnings.append("empty_selection")
if n_B == 0:
    warnings.append("empty_selection")
if n_C == 0:
    warnings.append("empty_selection")

# Prepare result
result = {
    "n_total": n_total,
    "selections": {
        "A": {"n": n_A, "frac": frac_A},
        "B": {"n": n_B, "frac": frac_B},
        "C": {"n": n_C, "frac": frac_C}
    },
    "warnings": warnings
}

# Save result to JSON
with open("result.json", "w") as f:
    json.dump(result, f, indent=2)

# Save the complete analysis as solution.py
with open("solution.py", "w") as f:
    f.write('''import pandas as pd
import numpy as np
import json

# Read the data
data_path = "data/cms_dimuon_2011.csv"
df = pd.read_csv(data_path)

# Total number of events
n_total = len(df)

# 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(df['eta1']) < 2.4) &
    (np.abs(df['eta2']) < 2.4) &
    (df['pt1'] > 20) &
    (df['pt2'] > 20) &
    (df['Q1'] * df['Q2'] < 0) &
    (df['M'] > 80) &
    (df['M'] < 100)
)
n_A = selection_A.sum()

# Selection B: pt1 > 200 and pt2 > 200 and M < 20
selection_B = (
    (df['pt1'] > 200) &
    (df['pt2'] > 200) &
    (df['M'] < 20)
)
n_B = selection_B.sum()

# Selection C: (type1 == "T" or type2 == "T") and M > 60
selection_C = (
    (df['type1'] == "T") | (df['type2'] == "T") &
    (df['M'] > 60)
)
n_C = selection_C.sum()

# Calculate fractions
frac_A = n_A / n_total if n_total > 0 else 0.0
frac_B = n_B / n_total if n_total > 0 else 0.0
frac_C = n_C / n_total if n_total > 0 else 0.0

# Warnings for empty selections
warnings = []
if n_A == 0:
    warnings.append("empty_selection")
if n_B == 0:
    warnings.append("empty_selection")
if n_C == 0:
    warnings.append("empty_selection")

# Prepare result
result = {
    "n_total": n_total,
    "selections": {
        "A": {"n": n_A, "frac": frac_A},
        "B": {"n": n_B, "frac": frac_B},
        "C": {"n": n_C, "frac": frac_C}
    },
    "warnings": warnings
}

# Save result to JSON
with open("result.json", "w") as f:
    json.dump(result, f, indent=2)
''')

# Submit the result
print("Submission complete.")
print("Assumptions made:")
print("- The data file is correctly formatted and contains no missing values.")
print("- The selections are applied as described in the task.")
print("- The invariant mass M is calculated correctly from the muon pair.")