Fix code generation errors for complex meteorological analysis

- Add specific guidance for wind rose diagrams with polar projections
- Improve correlation analysis error handling and data validation
- Add statistical analysis patterns for wind speed thresholds
- Fix data validation with proper early exits and error messages
- Prevent common plotting errors for complex visualizations

Addresses failures in:
- Wind direction vs pollution rose diagrams
- Meteorological factor correlation analysis
- Wind speed threshold analysis
- Unicode column name handling

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

new_system_prompt.txt

@@ -69,13 +69,32 @@ PLOT GENERATION (MANDATORY FOR PLOTS):
 CRITICAL CODING PRACTICES:
 
 DATA VALIDATION & SAFETY:
-- Always check if DataFrames/Series are empty before operations: if df.empty: return
+- Always check if DataFrames/Series are empty before operations: if df.empty: answer = "No data available"; exit()
 - Use .dropna() to handle missing values or .fillna() with appropriate defaults
-- Validate column names exist before accessing: if 'column' in df.columns
+- Validate column names exist before accessing: if 'column' in df.columns: else: answer = "Column not found"
 - Check data types before operations: df['col'].dtype, isinstance() checks
 - Handle edge cases: empty results, single row/column DataFrames, all NaN columns
 - Use .copy() when modifying DataFrames to avoid SettingWithCopyWarning
 
+SPECIFIC PLOT TYPE REQUIREMENTS:
+
+WIND ROSE DIAGRAMS:
+- Use matplotlib polar projection: fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))
+- Create proper wind direction bins: wind_bins = np.arange(0, 361, 30) 
+- Handle wind direction (0-360 degrees): convert to radians with np.radians()
+- Group by wind direction bins and calculate statistics properly
+
+CORRELATION ANALYSIS:
+- Always use .dropna() before correlation: df_clean = df.dropna(subset=[col1, col2])
+- Handle correlation matrices properly: corr = df_clean.corr()
+- Check for sufficient data: if len(df_clean) < 10: answer = "Insufficient data"
+- Use .abs() on Series, not float values: corr_series.abs() not float_value.abs()
+
+STATISTICAL ANALYSIS:
+- For wind speed thresholds: use boolean indexing df[df['WS (m/s)'] > 5.0]
+- For meteorological factors: handle missing weather data gracefully  
+- For time-based analysis: ensure proper datetime conversion and filtering
+
 VARIABLE & TYPE HANDLING:
 - Use descriptive variable names (avoid single letters in complex operations)
 - Ensure all variables are defined before use - initialize with defaults