Get rid of Emojis #193

README.md

@@ -145,7 +145,7 @@ In the relay's terminal, you should see roughly this sequence:
 
 ```
 [RELAY] listening on socket: "baton.sock"
-✓ Successfully created named pipe listener
+[OK] Successfully created named pipe listener
 [...] TCP - Successfully connected to iMotions server.
 [...] TX - packet len=50 text="E;1;PilotDataSync;;;;;AltitudeSync;1250.5;1250.5\r\n"
 ```

inference/README.md

@@ -48,15 +48,15 @@ No other manual package installation is required.
 The scripts are meant to be run in this order. Every command below is written to be run **from
 the project root** (`PilotDataSynchronization/`), with the virtual environment activated.
 
-| Step | Script | Purpose | Input | Output |
-|---|---|---|---|---|
-| 1 | `inference/Data/data_logger.py` | Collects live telemetry from the relay over TCP and appends it to a raw CSV | Telemetry socket stream | `inference/Data/raw_flight_data.csv` |
-| 1b | `inference/generate_balanced_data.py` | (Optional, no hardware needed) Generates synthetic, class-balanced flight data for testing the pipeline | none | `inference/Data/synthetic_flight_data.csv` |
-| 2 | `inference/label_generator.py` | Applies rule-based thresholds (altitude, speed, vertical speed, roll, g-force, heading change) to assign one of 13 event labels per row | `inference/Data/raw_flight_data.csv` | `inference/Data/labeled_flight_data.csv` |
-| 3 | `inference/validate_labels.py` | Sanity-checks the labeled dataset: required columns, valid label set, data ranges, spot-checks, and label distribution | `inference/Data/labeled_flight_data.csv` | Console report only (exit code 0/1) |
-| 4 | `inference/prepare_data.py` | (Optional) Cleans, median-fills, standardizes, and splits the labeled data into train/test CSVs — independent of the training step below | `inference/Data/labeled_flight_data.csv` | `inference/dataset/*.csv`, `label_mapping.json`, `scaler_params.json` |
-| 5 | `inference/train_model.py` | Trains a Random Forest classifier (80/20 stratified split) and reports accuracy/precision/recall | `inference/Data/labeled_flight_data.csv` | `inference/Models/bestModel.pkl`, `inference/Models/finalModel.pkl`, `inference/dataset/test.csv` |
-| 6 | `inference/test_model.py` | Loads a trained model, runs inference, and (if ground-truth labels are present) evaluates it | Trained model + held-out test data (see below) | `inference/predictions_output.csv`, `inference/evaluation_metrics.json` |
+| Step | Script                                | Purpose                                                                                                                                  | Input                                          | Output                                                                                            |
+| ---- | ------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------------------------------------------- |
+| 1    | `inference/Data/data_logger.py`       | Collects live telemetry from the relay over TCP and appends it to a raw CSV                                                              | Telemetry socket stream                        | `inference/Data/raw_flight_data.csv`                                                              |
+| 1b   | `inference/generate_balanced_data.py` | (Optional, no hardware needed) Generates synthetic, class-balanced flight data for testing the pipeline                                  | none                                           | `inference/Data/synthetic_flight_data.csv`                                                        |
+| 2    | `inference/label_generator.py`        | Applies rule-based thresholds (altitude, speed, vertical speed, roll, g-force, heading change) to assign one of 13 event labels per row  | `inference/Data/raw_flight_data.csv`           | `inference/Data/labeled_flight_data.csv`                                                          |
+| 3    | `inference/validate_labels.py`        | Sanity-checks the labeled dataset: required columns, valid label set, data ranges, spot-checks, and label distribution                   | `inference/Data/labeled_flight_data.csv`       | Console report only (exit code 0/1)                                                               |
+| 4    | `inference/prepare_data.py`           | (Optional) Cleans, median-fills, standardizes, and splits the labeled data into train/test CSVs — independent of the training step below | `inference/Data/labeled_flight_data.csv`       | `inference/dataset/*.csv`, `label_mapping.json`, `scaler_params.json`                             |
+| 5    | `inference/train_model.py`            | Trains a Random Forest classifier (80/20 stratified split) and reports accuracy/precision/recall                                         | `inference/Data/labeled_flight_data.csv`       | `inference/Models/bestModel.pkl`, `inference/Models/finalModel.pkl`, `inference/dataset/test.csv` |
+| 6    | `inference/test_model.py`             | Loads a trained model, runs inference, and (if ground-truth labels are present) evaluates it                                             | Trained model + held-out test data (see below) | `inference/predictions_output.csv`, `inference/evaluation_metrics.json`                           |
 
 ### Step 4 and step 5/6 are independent
 
@@ -105,25 +105,30 @@ label_generator.py`); the scripts resolve their own input/output paths relative
 The system labels data with 13 event types:
 
 **Flight phases**
+
 - `TAXI` — Ground operations (altitude < 50ft, speed < 30 knots)
 - `TAKEOFF` — Transition from ground to air (low altitude, climbing, 50-100 knots)
 - `CRUISE` — Steady flight at altitude (altitude > 3000ft, stable vertical speed)
 - `APPROACH` — Descending for landing (500-3000ft, descending)
 - `LANDING` — Final approach and touchdown (altitude < 500ft, descending)
 
 **Maneuver events**
+
 - `TURN_LEFT` — Left turn (roll < -5° or heading change < -3°/s)
 - `TURN_RIGHT` — Right turn (roll > 5° or heading change > 3°/s)
 
 **Speed events**
+
 - `HIGH_SPEED` — Velocity > 200 knots
 - `LOW_SPEED` — Velocity < 60 knots (while airborne)
 
 **Altitude events**
+
 - `HIGH_ALTITUDE` — Altitude > 10,000 feet
 - `LOW_ALTITUDE` — Altitude < 1,000 feet (while airborne)
 
 **Special events**
+
 - `HIGH_G_FORCE` — G-force > 1.5g
 - `NORMAL_FLIGHT` — Default steady flight (none of the above conditions)
 
@@ -140,6 +145,7 @@ class distribution, splits into train/test sets, saves the held-out test split t
 set, prints accuracy/precision/recall, and saves the trained model.
 
 Model parameters:
+
 - `n_estimators`: 100 trees
 - `max_depth`: None (unlimited depth)
 - `min_samples_split`: 2
@@ -153,7 +159,7 @@ Model parameters:
 `inference/Models/finalModel.pkl`. It looks for test data in this order:
 `inference/dataset/test.csv` (the held-out split `train_model.py` saves — the model never trained
 on these rows), then `inference/Data/labeled_flight_data.csv` (fallback; this is the full dataset
-the model *did* train on, so metrics from this fallback measure memorization, not generalization),
+the model _did_ train on, so metrics from this fallback measure memorization, not generalization),
 then `inference/labeled_flight_data.csv`. Run `train_model.py` before `test_model.py` so the
 held-out split exists and the first candidate is used.
 
@@ -163,14 +169,6 @@ held-out split exists and the first candidate is used.
   1b** — `generate_balanced_data.py` writes synthetic data to `inference/Data/synthetic_flight_data.csv`
   rather than overwriting the real collected `raw_flight_data.csv`. Copy or rename it to
   `raw_flight_data.csv` before running `label_generator.py`.
-- **`UnicodeEncodeError: 'charmap' codec can't encode character '✓'` on Windows** — several
-  scripts print a ✓ character, and the default Windows console codepage (cp1252) can't encode it.
-  Set `PYTHONUTF8=1` before running (e.g. `set PYTHONUTF8=1` in cmd.exe,
-  `$env:PYTHONUTF8=1` in PowerShell, `export PYTHONUTF8=1` in bash), or run `chcp 65001` first.
-- **`ValueError: Missing required columns` in `label_generator.py`, `validate_labels.py`, or
-  `prepare_data.py`** — the input CSV is missing one of `altitude`, `velocity`, `vertical_speed`,
-  `heading`, `roll`, `g_force` (and `pitch`/`yaw`/`event_label` for later steps). Check the header
-  row of your CSV against `CSV_FIELDS` in `Data/data_logger.py`.
 - **`validate_labels.py` reports "Negative velocity values found" or missing label categories** —
   this is expected with small or synthetic datasets that don't exercise every flight phase (e.g.
   `TAKEOFF`, `APPROACH`, `LANDING`, `LOW_ALTITUDE` require specific altitude/speed/vertical-speed
@@ -180,7 +178,7 @@ held-out split exists and the first candidate is used.
   produce `inference/Models/bestModel.pkl`.
 - **`inference/.venv` not picked up / `uv run` uses the wrong Python** — make sure you're running
   `uv` commands from inside `inference/` (where `pyproject.toml` lives), or pass `--project
-  inference` from the repository root.
+inference` from the repository root.
 
 ## Files
 

inference/label_generator.py

@@ -275,7 +275,7 @@ def _validate_labels(self, df: pd.DataFrame) -> None:
         if unexpected_labels:
             logger.warning(f"Found unexpected labels: {unexpected_labels}")
 
-        logger.info("✓ All rows have valid labels")
+        logger.info("[OK] All rows have valid labels")
 
     def _print_label_statistics(self, df: pd.DataFrame) -> None:
         """
@@ -304,8 +304,8 @@ def main():
     """Main execution function."""
     # Define paths relative to inference folder
     inference_dir = Path(__file__).parent
-    input_file = inference_dir / 'Data' / 'raw_flight_data.csv'  # ← input file
-    output_file = inference_dir / 'Data' / 'labeled_flight_data.csv'  # ← output file
+    input_file = inference_dir / 'Data' / 'raw_flight_data.csv'  # input file
+    output_file = inference_dir / 'Data' / 'labeled_flight_data.csv'  # output file
 
     logger.info("="*60)
     logger.info("Flight Event Labeling System")
@@ -329,7 +329,7 @@ def main():
         logger.error(f"Labeling failed: {e}")
         raise
 
-    logger.info("\n✓ Labeling process completed successfully")
+    logger.info("\n[OK] Labeling process completed successfully")
 
 
 if __name__ == '__main__':

inference/test_model.py

@@ -58,7 +58,7 @@ def load_model(self, model_path: Path):
             raise FileNotFoundError(f"Model file not found: {model_path}")
 
         self.model = joblib.load(model_path)
-        logger.info(f"✓ Model loaded successfully: {type(self.model).__name__}")
+        logger.info(f"[OK] Model loaded successfully: {type(self.model).__name__}")
 
         # Display model parameters
         if hasattr(self.model, 'n_estimators'):
@@ -82,7 +82,7 @@ def load_test_data(self, data_path: Path) -> pd.DataFrame:
             raise FileNotFoundError(f"Test data file not found: {data_path}")
 
         df = pd.read_csv(data_path)
-        logger.info(f"✓ Loaded {len(df)} test samples")
+        logger.info(f"[OK] Loaded {len(df)} test samples")
 
         # Verify required feature columns exist
         missing_cols = [col for col in self.feature_columns if col not in df.columns]
@@ -92,11 +92,11 @@ def load_test_data(self, data_path: Path) -> pd.DataFrame:
         # Check if labels exist (for evaluation)
         has_labels = self.target_column in df.columns
         if has_labels:
-            logger.info(f"✓ Test data contains ground truth labels")
+            logger.info(f"[OK] Test data contains ground truth labels")
             logger.info("\nActual label distribution:")
             print(df[self.target_column].value_counts().to_string())
         else:
-            logger.warning("⚠ Test data does not contain labels (evaluation will be skipped)")
+            logger.warning("[WARN] Test data does not contain labels (evaluation will be skipped)")
 
         return df
 
@@ -123,7 +123,7 @@ def run_inference(self, df: pd.DataFrame) -> np.ndarray:
         logger.info(f"\nGenerating predictions for {len(X_test)} samples...")
         predictions = self.model.predict(X_test)
 
-        logger.info("✓ Inference completed")
+        logger.info("[OK] Inference completed")
         logger.info(f"\nPredicted label distribution:")
         unique, counts = np.unique(predictions, return_counts=True)
         for label, count in zip(unique, counts):
@@ -159,19 +159,19 @@ def evaluate_predictions(self, y_true: pd.Series, y_pred: np.ndarray) -> dict:
         }
 
         # Display evaluation metrics
-        logger.info("\n📊 Evaluation Metrics:")
+        logger.info("\n[METRICS] Evaluation Metrics:")
         logger.info("="*60)
         logger.info(f"  Accuracy:  {accuracy:.4f} ({accuracy*100:.2f}%)")
         logger.info(f"  Precision: {precision:.4f} ({precision*100:.2f}%)")
         logger.info(f"  Recall:    {recall:.4f} ({recall*100:.2f}%)")
         logger.info("="*60)
 
         # Display detailed classification report
-        logger.info("\n📋 Detailed Classification Report:")
+        logger.info("\n[REPORT] Detailed Classification Report:")
         print("\n" + classification_report(y_true, y_pred, zero_division=0))
 
         # Display confusion matrix
-        logger.info("🔢 Confusion Matrix:")
+        logger.info("[MATRIX] Confusion Matrix:")
         cm = confusion_matrix(y_true, y_pred)
         classes = sorted(y_true.unique())
 
@@ -192,7 +192,7 @@ def save_predictions(self, df: pd.DataFrame, predictions: np.ndarray, output_pat
             predictions: Predicted labels
             output_path: Path to save predictions
         """
-        logger.info(f"\n💾 Saving predictions to: {output_path}")
+        logger.info(f"\n[SAVE] Saving predictions to: {output_path}")
 
         # Create output DataFrame with original data and predictions
         output_df = df.copy()
@@ -208,7 +208,7 @@ def save_predictions(self, df: pd.DataFrame, predictions: np.ndarray, output_pat
         output_path.parent.mkdir(parents=True, exist_ok=True)
         output_df.to_csv(output_path, index=False)
 
-        logger.info(f"✓ Saved {len(output_df)} predictions")
+        logger.info(f"[OK] Saved {len(output_df)} predictions")
         logger.info(f"  Columns: {', '.join(output_df.columns.tolist())}")
 
     def save_metrics(self, metrics: dict, output_path: Path):
@@ -221,14 +221,14 @@ def save_metrics(self, metrics: dict, output_path: Path):
         """
         import json
 
-        logger.info(f"\n💾 Saving metrics to: {output_path}")
+        logger.info(f"\n[SAVE] Saving metrics to: {output_path}")
 
         output_path.parent.mkdir(parents=True, exist_ok=True)
 
         with output_path.open('w') as f:
             json.dump(metrics, f, indent=2)
 
-        logger.info("✓ Metrics saved successfully")
+        logger.info("[OK] Metrics saved successfully")
 
 
 def main():
@@ -303,30 +303,30 @@ def main():
             # Save metrics
             tester.save_metrics(metrics, output_metrics)
         else:
-            logger.info("\n⚠ Skipping evaluation (no ground truth labels available)")
+            logger.info("\n[WARN] Skipping evaluation (no ground truth labels available)")
 
         # Save predictions
         tester.save_predictions(test_data, predictions, output_predictions)
 
         # Final summary
         logger.info("\n" + "="*60)
-        logger.info("✅ Testing Complete!")
+        logger.info("[OK] Testing Complete!")
         logger.info("="*60)
         logger.info(f"\nModel used: {model_path}")
         logger.info(f"Test data: {data_path_used}")
         logger.info(f"Predictions saved: {output_predictions}")
 
         if metrics:
             logger.info(f"Metrics saved: {output_metrics}")
-            logger.info(f"\n📊 Final Results:")
+            logger.info(f"\n[METRICS] Final Results:")
             logger.info(f"  Accuracy:  {metrics['accuracy']:.4f}")
             logger.info(f"  Precision: {metrics['precision']:.4f}")
             logger.info(f"  Recall:    {metrics['recall']:.4f}")
 
-        logger.info("\n✓ All outputs saved in inference/ folder")
+        logger.info("\n[OK] All outputs saved in inference/ folder")
 
     except Exception as e:
-        logger.error(f"\n❌ Testing failed: {e}")
+        logger.error(f"\n[ERROR] Testing failed: {e}")
         raise
 
 

inference/train_model.py

@@ -153,7 +153,7 @@ def train_model(self, X_train: pd.DataFrame, y_train: pd.Series):
         logger.info(f"\nTraining on {len(X_train)} samples...")
         self.model.fit(X_train, y_train)
 
-        logger.info("✓ Model training completed")
+        logger.info("[OK] Model training completed")
 
         # Display feature importance
         self._display_feature_importance()
@@ -239,10 +239,10 @@ def save_model(self, models_dir: Path):
         logger.info("\nSaving trained models:")
 
         joblib.dump(self.model, best_model_path)
-        logger.info(f"  ✓ Saved: {best_model_path}")
+        logger.info(f"  [OK] Saved: {best_model_path}")
 
         joblib.dump(self.model, final_model_path)
-        logger.info(f"  ✓ Saved: {final_model_path}")
+        logger.info(f"  [OK] Saved: {final_model_path}")
 
 
 def main():

relay/src/state.rs

@@ -379,11 +379,11 @@ impl State {
                     return Ok(());
                 }
                 Ok(l) => {
-                    println!("✓ Successfully created named pipe listener");
+                    println!("[OK] Successfully created named pipe listener");
                     l
                 }
                 Err(e) => {
-                    eprintln!("✗ Failed to create listener: {} (kind: {:?})", e, e.kind());
+                    eprintln!("[ERROR] Failed to create listener: {} (kind: {:?})", e, e.kind());
                     return Err(anyhow!("Failed to create listener: {}", e));
                 }
             };

relay/src/view.rs

@@ -58,7 +58,7 @@ fn spawn_error_message(state: &State) -> Option<UIElement> {
         .error_message
         .as_ref()
         .map(|err| {
-            container(text(format!("⚠️ {}", err)))
+            container(text(format!("[WARN] {}", err)))
                 .padding(10)
                 .width(Length::Fill)
                 .style(container::rounded_box)