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vguz_v2/graph_table_query.py

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#!/usr/bin/env python3
"""
Table Query - Data Access Layer
================================
Abstracts data sources (SQLite, CSV, etc.) for graph module.
Provides adapter pattern for different data sources while maintaining
consistent interface for plotting functions.
Features:
- Abstract DataAdapter interface
- SQLiteAdapter for database files
- CSVAdapter for CSV files
- Column utilities and labels
- Data alignment for drift comparison
Author: Kynsight
Version: 1.0.0
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import List, Dict, Tuple, Optional, Any
from dataclasses import dataclass
from pathlib import Path
import sqlite3
import numpy as np
# =============================================================================
# Data Structures
# =============================================================================
@dataclass
class SessionInfo:
"""Session metadata."""
session_id: str
session_name: str
created_at: str
description: str
run_count: int
@dataclass
class RunInfo:
"""Run metadata."""
session_id: str
session_name: str
run_number: int
sample_count: int
start_time_ns: int
end_time_ns: int
duration_ms: float
command_name: str = "Unknown" # UART or I2C command name
@dataclass
class TelemetryData:
"""Decoded telemetry data (source-agnostic)."""
session_id: str
session_name: str
run_no: int
command_name: str = "Unknown" # UART or I2C command name
# Time axes
t_ns: Optional[np.ndarray] = None
time_ms: Optional[np.ndarray] = None
# UART decoded data
motor_current: Optional[np.ndarray] = None
encoder_value: Optional[np.ndarray] = None
relative_encoder_value: Optional[np.ndarray] = None
v24_pec_diff: Optional[np.ndarray] = None
pwm: Optional[np.ndarray] = None
# I2C decoded data
i2c_raw14: Optional[np.ndarray] = None
i2c_zero_raw14: Optional[np.ndarray] = None
i2c_delta_raw14: Optional[np.ndarray] = None
i2c_angle_deg: Optional[np.ndarray] = None
i2c_zero_angle_deg: Optional[np.ndarray] = None
# Derived data
angular_velocity: Optional[np.ndarray] = None
angular_acceleration: Optional[np.ndarray] = None
# =============================================================================
# Abstract Adapter
# =============================================================================
class DataAdapter(ABC):
"""
Abstract data source adapter.
Implement this interface for each data source type (SQLite, CSV, etc.)
All adapters provide the same interface for loading telemetry data.
"""
@abstractmethod
def connect(self) -> bool:
"""
Connect to data source.
Returns:
True on success, False on failure
"""
pass
@abstractmethod
def close(self):
"""Close connection to data source."""
pass
@abstractmethod
def get_sessions(self) -> List[SessionInfo]:
"""
Get all sessions from data source.
Returns:
List of SessionInfo objects
"""
pass
@abstractmethod
def get_runs(self, session_id: str) -> List[RunInfo]:
"""
Get all runs for a specific session.
Args:
session_id: Session identifier
Returns:
List of RunInfo objects
"""
pass
@abstractmethod
def load_run_data(self, session_id: str, run_no: int) -> Optional[TelemetryData]:
"""
Load telemetry data for one run.
Args:
session_id: Session identifier
run_no: Run number
Returns:
TelemetryData object or None on error
"""
pass
# =============================================================================
# SQLite Adapter
# =============================================================================
class SQLiteAdapter(DataAdapter):
"""
SQLite database adapter.
Works with decoded_telemetry table schema.
"""
def __init__(self, db_path: str):
"""
Initialize SQLite adapter.
Args:
db_path: Path to SQLite database file
"""
self.db_path = db_path
self._conn: Optional[sqlite3.Connection] = None
def connect(self) -> bool:
"""Open database connection."""
try:
self._conn = sqlite3.connect(self.db_path)
self._conn.row_factory = sqlite3.Row
return True
except Exception as e:
print(f"[SQLite ERROR] Failed to connect: {e}")
return False
def close(self):
"""Close database connection."""
if self._conn:
self._conn.close()
self._conn = None
def get_sessions(self) -> List[SessionInfo]:
"""Get all sessions from database."""
if not self._conn:
return []
try:
cursor = self._conn.cursor()
cursor.execute("""
SELECT
s.session_id,
s.session_name,
s.created_at,
s.description,
COUNT(DISTINCT t.run_no) as run_count
FROM sessions s
LEFT JOIN telemetry_decoded t ON s.session_id = t.session_id
GROUP BY s.session_id
ORDER BY s.created_at DESC
""")
sessions = []
for row in cursor.fetchall():
sessions.append(SessionInfo(
session_id=row['session_id'],
session_name=row['session_name'] or row['session_id'],
created_at=row['created_at'] or '',
description=row['description'] or '',
run_count=row['run_count'] or 0
))
return sessions
except Exception as e:
print(f"[SQLite ERROR] Failed to get sessions: {e}")
return []
def get_runs(self, session_id: str) -> List[RunInfo]:
"""Get all runs for a session."""
if not self._conn:
return []
try:
cursor = self._conn.cursor()
cursor.execute("""
SELECT
t.session_id,
t.session_name,
t.run_no,
t.run_command_id,
COUNT(*) as sample_count,
MIN(t.t_ns) as start_time_ns,
MAX(t.t_ns) as end_time_ns,
COALESCE(u.command_name, i.command_name, 'Unknown') as command_name
FROM telemetry_decoded t
LEFT JOIN uart_commands u ON t.run_command_id = u.command_id
LEFT JOIN i2c_commands i ON t.run_command_id = i.command_id
WHERE t.session_id = ?
GROUP BY t.session_id, t.run_no, t.session_name, t.run_command_id
ORDER BY t.run_no
""", (session_id,))
runs = []
for row in cursor.fetchall():
duration_ns = row['end_time_ns'] - row['start_time_ns']
duration_ms = duration_ns / 1_000_000.0
runs.append(RunInfo(
session_id=row['session_id'],
session_name=row['session_name'],
run_number=row['run_no'],
sample_count=row['sample_count'],
start_time_ns=row['start_time_ns'],
end_time_ns=row['end_time_ns'],
duration_ms=duration_ms,
command_name=row['command_name']
))
return runs
except Exception as e:
print(f"[SQLite ERROR] Failed to get runs: {e}")
return []
def load_run_data(self, session_id: str, run_no: int) -> Optional[TelemetryData]:
"""Load telemetry data for one run."""
if not self._conn:
return None
try:
cursor = self._conn.cursor()
cursor.execute("""
SELECT
t.session_name,
t.t_ns,
t.time_ms,
t.motor_current,
t.encoder_value,
t.relative_encoder_value,
t.v24_pec_diff,
t.pwm,
t.i2c_raw14,
t.i2c_zero_raw14,
t.i2c_delta_raw14,
t.i2c_angle_deg,
t.i2c_zero_angle_deg,
t.angular_velocity,
t.angular_acceleration,
COALESCE(u.command_name, i.command_name, 'Unknown') as command_name
FROM telemetry_decoded t
LEFT JOIN uart_commands u ON t.run_command_id = u.command_id
LEFT JOIN i2c_commands i ON t.run_command_id = i.command_id
WHERE t.session_id = ? AND t.run_no = ?
ORDER BY t.t_ns
""", (session_id, run_no))
rows = cursor.fetchall()
if not rows:
return None
# Get session_name and command_name from first row
session_name = rows[0]['session_name']
command_name = rows[0]['command_name']
# Extract columns
data = TelemetryData(
session_id=session_id,
session_name=session_name,
run_no=run_no,
command_name=command_name,
t_ns=self._extract_column(rows, 't_ns', dtype=np.int64),
time_ms=self._extract_column(rows, 'time_ms', dtype=np.int64),
motor_current=self._extract_column(rows, 'motor_current', dtype=np.float32),
encoder_value=self._extract_column(rows, 'encoder_value', dtype=np.float32),
relative_encoder_value=self._extract_column(rows, 'relative_encoder_value', dtype=np.float32),
v24_pec_diff=self._extract_column(rows, 'v24_pec_diff', dtype=np.float32),
pwm=self._extract_column(rows, 'pwm', dtype=np.float32),
i2c_raw14=self._extract_column(rows, 'i2c_raw14', dtype=np.float32),
i2c_zero_raw14=self._extract_column(rows, 'i2c_zero_raw14', dtype=np.float32),
i2c_delta_raw14=self._extract_column(rows, 'i2c_delta_raw14', dtype=np.float32),
i2c_angle_deg=self._extract_column(rows, 'i2c_angle_deg', dtype=np.float32),
i2c_zero_angle_deg=self._extract_column(rows, 'i2c_zero_angle_deg', dtype=np.float32),
angular_velocity=self._extract_column(rows, 'angular_velocity', dtype=np.float32),
angular_acceleration=self._extract_column(rows, 'angular_acceleration', dtype=np.float32)
)
return data
except Exception as e:
print(f"[SQLite ERROR] Failed to load run data: {e}")
return None
def _extract_column(self, rows: List, column: str, dtype=np.float32) -> Optional[np.ndarray]:
"""
Extract column from rows, handling NULL values.
Returns None if all values are NULL.
"""
values = [row[column] for row in rows]
# Check if any non-NULL values
if all(v is None for v in values):
return None
# Replace None with NaN
values = [v if v is not None else np.nan for v in values]
return np.array(values, dtype=dtype)
# =============================================================================
# CSV Adapter
# =============================================================================
class CSVAdapter(DataAdapter):
"""
CSV file adapter.
Expected CSV format:
session_id,session_name,run_no,t_ns,time_ms,motor_current,encoder_value,...
Single CSV file containing all sessions and runs.
"""
def __init__(self, csv_path: str):
"""
Initialize CSV adapter.
Args:
csv_path: Path to CSV file
"""
self.csv_path = csv_path
self._df = None # pandas DataFrame
def connect(self) -> bool:
"""Load CSV file."""
try:
import pandas as pd
self._df = pd.read_csv(self.csv_path)
# Validate required columns
required = ['session_id', 'session_name', 'run_no']
missing = [col for col in required if col not in self._df.columns]
if missing:
print(f"[CSV ERROR] Missing required columns: {missing}")
return False
return True
except Exception as e:
print(f"[CSV ERROR] Failed to load CSV: {e}")
return False
def close(self):
"""Release DataFrame."""
self._df = None
def get_sessions(self) -> List[SessionInfo]:
"""Get all sessions from CSV."""
if self._df is None:
return []
try:
# Group by session_id
session_groups = self._df.groupby('session_id')
sessions = []
for session_id, group in session_groups:
run_count = group['run_no'].nunique()
# Try to get created_at if column exists
created_at = ''
if 'created_at' in group.columns and len(group) > 0:
created_at = str(group['created_at'].iloc[0])
sessions.append(SessionInfo(
session_id=str(session_id),
created_at=created_at,
description='', # Not in CSV
run_count=run_count
))
return sessions
except Exception as e:
print(f"[CSV ERROR] Failed to get sessions: {e}")
return []
def get_runs(self, session_id: str) -> List[RunInfo]:
"""Get all runs for a session."""
if self._df is None:
return []
try:
# Filter by session
session_df = self._df[self._df['session_id'] == session_id]
# Group by run_no
run_groups = session_df.groupby('run_no')
runs = []
for run_no, group in run_groups:
sample_count = len(group)
# Get session_name
session_name = str(group['session_name'].iloc[0]) if 'session_name' in group.columns else ''
# Get time info
if 't_ns' in group.columns:
start_time_ns = int(group['t_ns'].min())
end_time_ns = int(group['t_ns'].max())
duration_ms = (end_time_ns - start_time_ns) / 1_000_000.0
else:
start_time_ns = 0
end_time_ns = 0
duration_ms = 0.0
runs.append(RunInfo(
session_id=session_id,
session_name=session_name,
run_number=int(run_no),
sample_count=sample_count,
start_time_ns=start_time_ns,
end_time_ns=end_time_ns,
duration_ms=duration_ms
))
# Sort by run_number
runs.sort(key=lambda r: r.run_number)
return runs
except Exception as e:
print(f"[CSV ERROR] Failed to get runs: {e}")
return []
def load_run_data(self, session_id: str, run_no: int) -> Optional[TelemetryData]:
"""Load telemetry data for one run."""
if self._df is None:
return None
try:
# Filter by session and run
run_df = self._df[
(self._df['session_id'] == session_id) &
(self._df['run_no'] == run_no)
]
if len(run_df) == 0:
return None
# Sort by timestamp
if 't_ns' in run_df.columns:
run_df = run_df.sort_values('t_ns')
# Get session_name
session_name = str(run_df['session_name'].iloc[0]) if 'session_name' in run_df.columns else ''
# Extract columns
data = TelemetryData(
session_id=session_id,
session_name=session_name,
run_no=run_no,
t_ns=self._extract_column_csv(run_df, 't_ns', dtype=np.int64),
time_ms=self._extract_column_csv(run_df, 'time_ms', dtype=np.int64),
motor_current=self._extract_column_csv(run_df, 'motor_current', dtype=np.float32),
encoder_value=self._extract_column_csv(run_df, 'encoder_value', dtype=np.float32),
relative_encoder_value=self._extract_column_csv(run_df, 'relative_encoder_value', dtype=np.float32),
v24_pec_diff=self._extract_column_csv(run_df, 'v24_pec_diff', dtype=np.float32),
pwm=self._extract_column_csv(run_df, 'pwm', dtype=np.float32),
i2c_raw14=self._extract_column_csv(run_df, 'i2c_raw14', dtype=np.float32),
i2c_zero_raw14=self._extract_column_csv(run_df, 'i2c_zero_raw14', dtype=np.float32),
i2c_delta_raw14=self._extract_column_csv(run_df, 'i2c_delta_raw14', dtype=np.float32),
i2c_angle_deg=self._extract_column_csv(run_df, 'i2c_angle_deg', dtype=np.float32),
i2c_zero_angle_deg=self._extract_column_csv(run_df, 'i2c_zero_angle_deg', dtype=np.float32),
angular_velocity=self._extract_column_csv(run_df, 'angular_velocity', dtype=np.float32),
angular_acceleration=self._extract_column_csv(run_df, 'angular_acceleration', dtype=np.float32)
)
return data
except Exception as e:
print(f"[CSV ERROR] Failed to load run data: {e}")
return None
def _extract_column_csv(self, df, column: str, dtype=np.float32) -> Optional[np.ndarray]:
"""
Extract column from DataFrame, handling missing columns and NaN.
Returns None if column doesn't exist or all values are NaN.
"""
if column not in df.columns:
return None
values = df[column].values
# Check if all NaN
if np.all(np.isnan(values.astype(float, errors='ignore'))):
return None
return values.astype(dtype)
# =============================================================================
# Column Utilities
# =============================================================================
def get_available_columns() -> List[str]:
"""
Get list of all plottable columns.
Returns:
List of column names
"""
return [
't_ns',
'time_ms',
'motor_current',
'encoder_value',
'relative_encoder_value',
'v24_pec_diff',
'pwm',
'i2c_raw14',
'i2c_zero_raw14',
'i2c_delta_raw14',
'i2c_angle_deg',
'i2c_zero_angle_deg',
'angular_velocity',
'angular_acceleration'
]
def get_column_label(column: str) -> str:
"""
Get human-readable label for column.
Args:
column: Column name
Returns:
Human-readable label
"""
labels = {
't_ns': 'Time (ns)',
'time_ms': 'Time (ms)',
'motor_current': 'Motor Current (A)',
'encoder_value': 'Encoder Value',
'relative_encoder_value': 'Relative Encoder',
'v24_pec_diff': 'V24 PEC Diff (V)',
'pwm': 'PWM (%)',
'i2c_angle_deg': 'Angle (°)',
'i2c_zero_angle_deg': 'Zero Angle (°)',
'angular_velocity': 'Angular Velocity (°/s)',
'angular_acceleration': 'Angular Acceleration (°/s²)'
}
return labels.get(column, column.replace('_', ' ').title())
def get_column_groups() -> Dict[str, List[str]]:
"""
Get columns grouped by category.
Returns:
Dictionary of category -> list of columns
"""
return {
'Time': ['t_ns', 'time_ms'],
'UART': ['motor_current', 'encoder_value', 'relative_encoder_value',
'v24_pec_diff', 'pwm'],
'I2C': ['i2c_angle_deg', 'i2c_zero_angle_deg'],
'Derived': ['angular_velocity', 'angular_acceleration']
}
def get_default_columns() -> List[str]:
"""
Get default selected columns.
Returns:
List of column names to check by default
"""
return [
'time_ms',
'motor_current',
'encoder_value',
'relative_encoder_value',
'pwm',
'i2c_angle_deg',
'angular_velocity'
]
# =============================================================================
# Data Alignment
# =============================================================================
def align_data_to_reference(
data_list: List[TelemetryData],
x_column: str,
reference_index: int = 0
) -> List[TelemetryData]:
"""
Align multiple runs to reference run's X-axis.
Useful for drift comparison when runs have different timestamps
or sampling rates.
Args:
data_list: List of TelemetryData objects
x_column: Column name to use as X-axis
reference_index: Index of reference run (default: 0)
Returns:
List of TelemetryData objects aligned to reference
"""
if reference_index >= len(data_list):
reference_index = 0
reference = data_list[reference_index]
ref_x = getattr(reference, x_column, None)
if ref_x is None:
return data_list
aligned_list = [reference] # Reference stays as-is
for idx, data in enumerate(data_list):
if idx == reference_index:
continue
x_data = getattr(data, x_column, None)
if x_data is None:
aligned_list.append(data)
continue
# Create aligned data object
aligned = TelemetryData(
session_id=data.session_id,
session_name=data.session_name,
run_no=data.run_no
)
# Interpolate all columns to reference X
for col in get_available_columns():
if col == x_column:
# X column uses reference
setattr(aligned, col, ref_x)
else:
y_data = getattr(data, col, None)
if y_data is not None and len(y_data) == len(x_data):
# Interpolate
y_aligned = np.interp(ref_x, x_data, y_data)
setattr(aligned, col, y_aligned)
aligned_list.append(aligned)
return aligned_list
# =============================================================================
# Demo
# =============================================================================
if __name__ == "__main__":
print("Table Query - Data Access Layer")
print("=" * 60)
print()
print("Available Adapters:")
print(" • SQLiteAdapter - for .db files")
print(" • CSVAdapter - for .csv files")
print()
print("Column Utilities:")
print(f" • {len(get_available_columns())} plottable columns")
print(f" • {len(get_column_groups())} categories")
print(f" • {len(get_default_columns())} default selections")
print()
print("Usage:")
print(" from table_query import SQLiteAdapter, CSVAdapter")
print(" adapter = SQLiteAdapter('./database/ehinge.db')")
print(" adapter.connect()")
print(" sessions = adapter.get_sessions()")
print(" data = adapter.load_run_data('Session_A', 1)")
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