refactor(sql_explorer): Replace Plotly with SVG rendering for plots
Replaces the Plotly-based graph generation in the SQL Explorer with direct SVG rendering within an HTML template, similar to the exercise progress sparklines. - Modifies `routes/sql_explorer.py` endpoints (`plot_query`, `plot_unsaved_query`) to fetch raw data instead of using pandas/Plotly. - Adds `utils.prepare_svg_plot_data` to process raw SQL results, determine plot type (scatter, line, bar, table), normalize data, and prepare it for SVG. - Creates `templates/partials/sql_explorer/svg_plot.html` to render the SVG plot with axes, ticks, labels, and basic tooltips. - Removes the `generate_plot` function's usage for SQL Explorer and the direct dependency on Plotly for this feature.
This commit is contained in:
Peter Stockings
230
utils.py
230
utils.py
@@ -3,7 +3,9 @@ from datetime import datetime, date, timedelta
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import numpy as np
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import pandas as pd
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import plotly.express as px
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import plotly.io as pio
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import plotly.io as pio # Keep for now, might remove later if generate_plot is fully replaced
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import math
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from decimal import Decimal
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def convert_str_to_date(date_str, format='%Y-%m-%d'):
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try:
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@@ -141,4 +143,228 @@ def calculate_estimated_1rm(weight, repetitions):
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if repetitions == 0: # Avoid division by zero
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return 0
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estimated_1rm = round((100 * int(weight)) / (101.3 - 2.67123 * repetitions), 0)
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return int(estimated_1rm)
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return int(estimated_1rm)
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def _is_numeric(val):
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"""Check if a value is numeric (int, float, Decimal)."""
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return isinstance(val, (int, float, Decimal))
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def _is_datetime(val):
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"""Check if a value is a date or datetime object."""
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return isinstance(val, (date, datetime))
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def _get_column_type(results, column_name):
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"""Determine the effective type of a column (numeric, datetime, categorical)."""
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numeric_count = 0
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datetime_count = 0
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total_count = 0
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for row in results:
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val = row.get(column_name)
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if val is not None:
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total_count += 1
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if _is_numeric(val):
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numeric_count += 1
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elif _is_datetime(val):
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datetime_count += 1
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if total_count == 0: return 'categorical' # Default if all null or empty
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if numeric_count / total_count > 0.8: return 'numeric' # Allow some non-numeric noise
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if datetime_count / total_count > 0.8: return 'datetime'
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return 'categorical'
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def _normalize_value(value, min_val, range_val, target_max):
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"""Normalize a value to a target range (e.g., SVG coordinate)."""
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if range_val == 0: return target_max / 2 # Avoid division by zero, place in middle
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return ((value - min_val) / range_val) * target_max
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def prepare_svg_plot_data(results, columns, title):
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"""
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Prepares data from raw SQL results for SVG plotting.
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Determines plot type and scales data.
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"""
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if not results:
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raise ValueError("No data provided for plotting.")
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num_columns = len(columns)
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plot_type = 'table' # Default if no suitable plot found
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plot_data = {}
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x_col, y_col = None, None
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x_type, y_type = None, None
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# --- Determine Plot Type and Columns ---
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if num_columns == 1:
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x_col = columns[0]
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x_type = _get_column_type(results, x_col)
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if x_type == 'numeric':
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plot_type = 'histogram'
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else:
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plot_type = 'bar_count' # Bar chart of value counts
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elif num_columns >= 2:
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# Prioritize common patterns
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x_col, y_col = columns[0], columns[1]
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x_type = _get_column_type(results, x_col)
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y_type = _get_column_type(results, y_col)
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if x_type == 'numeric' and y_type == 'numeric':
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plot_type = 'scatter'
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elif x_type == 'datetime' and y_type == 'numeric':
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plot_type = 'line' # Treat datetime as numeric for position
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elif x_type == 'categorical' and y_type == 'numeric':
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plot_type = 'bar'
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elif x_type == 'numeric' and y_type == 'categorical':
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# Could do horizontal bar, but let's stick to vertical for now
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plot_type = 'bar' # Treat numeric as category label, categorical as value (count?) - less common
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# Or maybe swap? Let's assume categorical X, numeric Y is more likely intended
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x_col, y_col = columns[1], columns[0] # Try swapping
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x_type, y_type = y_type, x_type
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if not (x_type == 'categorical' and y_type == 'numeric'):
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plot_type = 'table' # Revert if swap didn't help
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else: # Other combinations (datetime/cat, cat/cat, etc.) default to table
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plot_type = 'table'
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# --- Basic SVG Setup ---
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vb_width = 500
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vb_height = 300
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margin = {'top': 20, 'right': 20, 'bottom': 50, 'left': 60} # Increased bottom/left for labels/axes
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draw_width = vb_width - margin['left'] - margin['right']
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draw_height = vb_height - margin['top'] - margin['bottom']
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plot_data = {
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'title': title,
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'plot_type': plot_type,
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'vb_width': vb_width,
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'vb_height': vb_height,
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'margin': margin,
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'draw_width': draw_width,
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'draw_height': draw_height,
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'x_axis_label': x_col or '',
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'y_axis_label': y_col or '',
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'plots': [],
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'x_ticks': [],
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'y_ticks': [],
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'original_results': results, # Keep original for table fallback
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'original_columns': columns
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}
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if plot_type == 'table':
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return plot_data # No further processing needed for table fallback
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# --- Data Extraction and Scaling (Specific to Plot Type) ---
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points = []
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x_values_raw = []
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y_values_raw = []
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# Extract relevant data, handling potential type issues
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for row in results:
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x_val_raw = row.get(x_col)
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y_val_raw = row.get(y_col)
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# Convert datetimes to numeric representation (e.g., days since min date)
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if x_type == 'datetime':
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x_values_raw.append(x_val_raw) # Keep original dates for range calculation
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elif _is_numeric(x_val_raw):
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x_values_raw.append(float(x_val_raw)) # Convert Decimal to float
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# Add handling for categorical X if needed (e.g., bar chart)
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if y_type == 'numeric':
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if _is_numeric(y_val_raw):
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y_values_raw.append(float(y_val_raw))
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else:
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y_values_raw.append(None) # Mark non-numeric Y as None
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# Add handling for categorical Y if needed
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if not x_values_raw or not y_values_raw:
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plot_data['plot_type'] = 'table' # Fallback if essential data is missing
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return plot_data
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# Calculate ranges (handle datetime separately)
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if x_type == 'datetime':
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valid_dates = [d for d in x_values_raw if d is not None]
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if not valid_dates:
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plot_data['plot_type'] = 'table'; return plot_data
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min_x_dt, max_x_dt = min(valid_dates), max(valid_dates)
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# Convert dates to days since min_date for numerical scaling
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total_days = (max_x_dt - min_x_dt).days
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x_values_numeric = [(d - min_x_dt).days if d is not None else None for d in x_values_raw]
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min_x, max_x = 0, total_days
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else: # Numeric or Categorical (treat categorical index as numeric for now)
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valid_x = [x for x in x_values_raw if x is not None]
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if not valid_x:
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plot_data['plot_type'] = 'table'; return plot_data
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min_x, max_x = min(valid_x), max(valid_x)
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x_values_numeric = x_values_raw # Already numeric (or will be treated as such)
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valid_y = [y for y in y_values_raw if y is not None]
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if not valid_y:
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plot_data['plot_type'] = 'table'; return plot_data
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min_y, max_y = min(valid_y), max(valid_y)
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range_x = max_x - min_x
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range_y = max_y - min_y
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# Scale points
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for i, row in enumerate(results):
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x_num = x_values_numeric[i]
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y_num = y_values_raw[i] # Use original list which might have None
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if x_num is None or y_num is None: continue # Skip points with missing essential data
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# Scale X to drawing width, Y to drawing height (inverted Y for SVG)
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scaled_x = margin['left'] + _normalize_value(x_num, min_x, range_x, draw_width)
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scaled_y = margin['top'] + draw_height - _normalize_value(y_num, min_y, range_y, draw_height)
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points.append({
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'x': scaled_x,
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'y': scaled_y,
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'original': row # Store original row data for tooltips
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})
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# --- Generate Ticks ---
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num_ticks = 5 # Desired number of ticks
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# X Ticks
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x_ticks = []
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if range_x >= 0:
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step_x = (max_x - min_x) / (num_ticks -1) if num_ticks > 1 and range_x > 0 else 0
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for i in range(num_ticks):
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tick_val_raw = min_x + i * step_x
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tick_pos = margin['left'] + _normalize_value(tick_val_raw, min_x, range_x, draw_width)
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label = ""
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if x_type == 'datetime':
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tick_date = min_x_dt + timedelta(days=tick_val_raw)
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label = tick_date.strftime('%Y-%m-%d') # Format date label
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else: # Numeric
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label = f"{tick_val_raw:.1f}" if isinstance(tick_val_raw, float) else str(tick_val_raw)
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x_ticks.append({'value': tick_val_raw, 'label': label, 'position': tick_pos})
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# Y Ticks
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y_ticks = []
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if range_y >= 0:
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step_y = (max_y - min_y) / (num_ticks - 1) if num_ticks > 1 and range_y > 0 else 0
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for i in range(num_ticks):
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tick_val = min_y + i * step_y
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tick_pos = margin['top'] + draw_height - _normalize_value(tick_val, min_y, range_y, draw_height)
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label = f"{tick_val:.1f}" if isinstance(tick_val, float) else str(tick_val)
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y_ticks.append({'value': tick_val, 'label': label, 'position': tick_pos})
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# --- Finalize Plot Data ---
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# For now, put all points into one series
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plot_data['plots'].append({
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'label': f'{y_col} vs {x_col}',
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'color': '#388fed', # Default color
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'points': points
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})
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plot_data['x_ticks'] = x_ticks
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plot_data['y_ticks'] = y_ticks
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# Add specific adjustments for plot types if needed (e.g., bar width)
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if plot_type == 'bar':
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# Calculate bar width based on number of bars/categories
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# This needs more refinement based on how categorical X is handled
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plot_data['bar_width'] = draw_width / len(points) * 0.8 if points else 10
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return plot_data
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