Academic Python — Scientific Computing

Overview

Execute Python 3.12 with a full scientific computing stack pre-installed. You are running inside the container — use python3 directly, no docker exec needed.

When To Use

• User asks to run Python code, analyze data, or create plots
• User needs scientific computation (linear algebra, statistics, symbolic math)
• User wants charts, visualizations, or data processing

CRITICAL: Output File Location

ALWAYS save outputs to /workspace/output/ — this directory is monitored by the UI.

When you save a file, you MUST report the full path in your response so the UI can display it:

保存完成：/workspace/output/chart.png

The UI will automatically detect paths like /workspace/output/xxx.png and display the file.

Quick Execution

Important: Always use /home/user/.venv/bin/python3 for the full scientific stack.

Visualization Example (CORRECT)

/home/user/.venv/bin/python3 << 'PYTHON'
import numpy as np
import matplotlib
matplotlib.use('Agg')  # Required: no display server
import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)

plt.figure(figsize=(10, 6))
plt.plot(x, y, 'b-', linewidth=2)
plt.title('Sine Wave')
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.grid(True)

# MUST save to /workspace/output/
output_path = '/workspace/output/sine_wave.png'
plt.savefig(output_path, dpi=150, bbox_inches='tight')
print(f'图表已保存：{output_path}')
PYTHON

After running, tell the user: “图表已保存到 /workspace/output/sine_wave.png”

Inline (short scripts)

/home/user/.venv/bin/python3 -c "
import numpy as np
x = np.array([1, 2, 3, 4, 5])
print(f'Mean: {np.mean(x):.2f}')
print(f'Std:  {np.std(x):.2f}')
"

Data analysis with Pandas

/home/user/.venv/bin/python3 << 'PYTHON'
import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie'], 'score': [95, 87, 92]}
df = pd.DataFrame(data)
print(df.describe())

# Save results
output_path = '/workspace/output/analysis.csv'
df.to_csv(output_path, index=False)
print(f'数据已保存：{output_path}')
PYTHON

Symbolic math with SymPy

/home/user/.venv/bin/python3 -c "
from sympy import symbols, integrate, diff, latex
x = symbols('x')
f = x**3 + 2*x**2 - x + 1
print(f'f(x)  = {f}')
print(f\"f'(x) = {diff(f, x)}\")
print(f'∫f dx = {integrate(f, x)}')
"

Machine learning with scikit-learn

/home/user/.venv/bin/python3 << 'PYTHON'
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)
print(f"Accuracy: {accuracy_score(y_test, clf.predict(X_test)):.2%}")
PYTHON

Pre-installed Packages

Important Notes

• Always use matplotlib.use('Agg') before importing pyplot (no display server).
• ALWAYS save outputs (plots, CSVs, data) to /workspace/output/ — the UI monitors this directory!
• ALWAYS print the full output path so the UI can detect and display the file.
• For long-running scripts, consider writing progress to stdout.
• R 4.3 with tidyverse is also available: Rscript -e "library(tidyverse); ...".
• pip is available if you need additional packages: pip install <package>.

File Locations