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UI tweaks for match columns step + auto hide empty columns

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Matt
2025-10-04 09:48:14 -04:00
parent dadcf3b6c6
commit 4953355b91
178 changed files with 580 additions and 61798 deletions
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440
inventory-server/old/metrics/sales-forecasts.js
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@@ -1,440 +0,0 @@
const { outputProgress, formatElapsedTime, estimateRemaining, calculateRate, logError } = require('./utils/progress');
const { getConnection } = require('./utils/db');
async function calculateSalesForecasts(startTime, totalProducts, processedCount = 0, isCancelled = false) {
const connection = await getConnection();
let success = false;
let processedOrders = 0;
try {
if (isCancelled) {
outputProgress({
status: 'cancelled',
operation: 'Sales forecasts calculation cancelled',
current: processedCount,
total: totalProducts,
elapsed: formatElapsedTime(startTime),
remaining: null,
rate: calculateRate(startTime, processedCount),
percentage: ((processedCount / totalProducts) * 100).toFixed(1),
timing: {
start_time: new Date(startTime).toISOString(),
end_time: new Date().toISOString(),
elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
}
});
return {
processedProducts: processedCount,
processedOrders: 0,
processedPurchaseOrders: 0,
success
};
}
// Get order count that will be processed
const orderCount = await connection.query(`
SELECT COUNT(*) as count
FROM orders o
WHERE o.canceled = false
AND o.date >= CURRENT_DATE - INTERVAL '90 days'
`);
processedOrders = parseInt(orderCount.rows[0].count);
outputProgress({
status: 'running',
operation: 'Starting sales forecasts calculation',
current: processedCount,
total: totalProducts,
elapsed: formatElapsedTime(startTime),
remaining: estimateRemaining(startTime, processedCount, totalProducts),
rate: calculateRate(startTime, processedCount),
percentage: ((processedCount / totalProducts) * 100).toFixed(1),
timing: {
start_time: new Date(startTime).toISOString(),
end_time: new Date().toISOString(),
elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
}
});
// First, create a temporary table for forecast dates
await connection.query(`
CREATE TEMPORARY TABLE IF NOT EXISTS temp_forecast_dates (
forecast_date DATE,
day_of_week INT,
month INT,
PRIMARY KEY (forecast_date)
)
`);
await connection.query(`
INSERT INTO temp_forecast_dates
SELECT
CURRENT_DATE + (n || ' days')::INTERVAL as forecast_date,
EXTRACT(DOW FROM CURRENT_DATE + (n || ' days')::INTERVAL) + 1 as day_of_week,
EXTRACT(MONTH FROM CURRENT_DATE + (n || ' days')::INTERVAL) as month
FROM (
SELECT a.n + b.n * 10 as n
FROM
(SELECT 0 as n UNION SELECT 1 UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION
SELECT 5 UNION SELECT 6 UNION SELECT 7 UNION SELECT 8 UNION SELECT 9) a,
(SELECT 0 as n UNION SELECT 1 UNION SELECT 2) b
ORDER BY n
LIMIT 31
) numbers
`);
processedCount = Math.floor(totalProducts * 0.92);
outputProgress({
status: 'running',
operation: 'Forecast dates prepared, calculating daily sales stats',
current: processedCount,
total: totalProducts,
elapsed: formatElapsedTime(startTime),
remaining: estimateRemaining(startTime, processedCount, totalProducts),
rate: calculateRate(startTime, processedCount),
percentage: ((processedCount / totalProducts) * 100).toFixed(1),
timing: {
start_time: new Date(startTime).toISOString(),
end_time: new Date().toISOString(),
elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
}
});
if (isCancelled) return {
processedProducts: processedCount,
processedOrders,
processedPurchaseOrders: 0,
success
};
// Create temporary table for daily sales stats
await connection.query(`
CREATE TEMPORARY TABLE temp_daily_sales AS
SELECT
o.pid,
EXTRACT(DOW FROM o.date) + 1 as day_of_week,
SUM(o.quantity) as daily_quantity,
SUM(o.price * o.quantity) as daily_revenue,
COUNT(DISTINCT DATE(o.date)) as day_count
FROM orders o
WHERE o.canceled = false
AND o.date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY o.pid, EXTRACT(DOW FROM o.date) + 1
`);
processedCount = Math.floor(totalProducts * 0.94);
outputProgress({
status: 'running',
operation: 'Daily sales stats calculated, preparing product stats',
current: processedCount,
total: totalProducts,
elapsed: formatElapsedTime(startTime),
remaining: estimateRemaining(startTime, processedCount, totalProducts),
rate: calculateRate(startTime, processedCount),
percentage: ((processedCount / totalProducts) * 100).toFixed(1),
timing: {
start_time: new Date(startTime).toISOString(),
end_time: new Date().toISOString(),
elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
}
});
if (isCancelled) return {
processedProducts: processedCount,
processedOrders,
processedPurchaseOrders: 0,
success
};
// Create temporary table for product stats
await connection.query(`
CREATE TEMPORARY TABLE temp_product_stats AS
SELECT
pid,
AVG(daily_revenue) as overall_avg_revenue,
SUM(day_count) as total_days
FROM temp_daily_sales
GROUP BY pid
`);
processedCount = Math.floor(totalProducts * 0.96);
outputProgress({
status: 'running',
operation: 'Product stats prepared, calculating product-level forecasts',
current: processedCount,
total: totalProducts,
elapsed: formatElapsedTime(startTime),
remaining: estimateRemaining(startTime, processedCount, totalProducts),
rate: calculateRate(startTime, processedCount),
percentage: ((processedCount / totalProducts) * 100).toFixed(1),
timing: {
start_time: new Date(startTime).toISOString(),
end_time: new Date().toISOString(),
elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
}
});
if (isCancelled) return {
processedProducts: processedCount,
processedOrders,
processedPurchaseOrders: 0,
success
};
// Calculate product-level forecasts
await connection.query(`
INSERT INTO sales_forecasts (
pid,
forecast_date,
forecast_quantity,
confidence_level,
created_at
)
WITH daily_stats AS (
SELECT
ds.pid,
AVG(ds.daily_quantity) as avg_daily_qty,
STDDEV(ds.daily_quantity) as std_daily_qty,
COUNT(DISTINCT ds.day_count) as data_points,
SUM(ds.day_count) as total_days,
AVG(ds.daily_revenue) as avg_daily_revenue,
STDDEV(ds.daily_revenue) as std_daily_revenue,
MIN(ds.daily_quantity) as min_daily_qty,
MAX(ds.daily_quantity) as max_daily_qty,
-- Calculate variance without using LAG
COALESCE(
STDDEV(ds.daily_quantity) / NULLIF(AVG(ds.daily_quantity), 0),
0
) as daily_variance_ratio
FROM temp_daily_sales ds
GROUP BY ds.pid
HAVING AVG(ds.daily_quantity) > 0
)
SELECT
ds.pid,
fd.forecast_date,
GREATEST(0,
ROUND(
ds.avg_daily_qty *
(1 + COALESCE(sf.seasonality_factor, 0))
)
) as forecast_quantity,
CASE
WHEN ds.total_days >= 60 AND ds.daily_variance_ratio < 0.5 THEN 90
WHEN ds.total_days >= 60 THEN 85
WHEN ds.total_days >= 30 AND ds.daily_variance_ratio < 0.5 THEN 80
WHEN ds.total_days >= 30 THEN 75
WHEN ds.total_days >= 14 AND ds.daily_variance_ratio < 0.5 THEN 70
WHEN ds.total_days >= 14 THEN 65
ELSE 60
END as confidence_level,
NOW() as created_at
FROM daily_stats ds
JOIN temp_product_stats ps ON ds.pid = ps.pid
CROSS JOIN temp_forecast_dates fd
LEFT JOIN sales_seasonality sf ON fd.month = sf.month
GROUP BY ds.pid, fd.forecast_date, ps.overall_avg_revenue, sf.seasonality_factor,
ds.avg_daily_qty, ds.std_daily_qty, ds.avg_daily_qty, ds.total_days, ds.daily_variance_ratio
ON CONFLICT (pid, forecast_date) DO UPDATE
SET
forecast_quantity = EXCLUDED.forecast_quantity,
confidence_level = EXCLUDED.confidence_level,
created_at = NOW()
`);
processedCount = Math.floor(totalProducts * 0.98);
outputProgress({
status: 'running',
operation: 'Product forecasts calculated, preparing category stats',
current: processedCount,
total: totalProducts,
elapsed: formatElapsedTime(startTime),
remaining: estimateRemaining(startTime, processedCount, totalProducts),
rate: calculateRate(startTime, processedCount),
percentage: ((processedCount / totalProducts) * 100).toFixed(1),
timing: {
start_time: new Date(startTime).toISOString(),
end_time: new Date().toISOString(),
elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
}
});
if (isCancelled) return {
processedProducts: processedCount,
processedOrders,
processedPurchaseOrders: 0,
success
};
// Create temporary table for category stats
await connection.query(`
CREATE TEMPORARY TABLE temp_category_sales AS
SELECT
pc.cat_id,
EXTRACT(DOW FROM o.date) + 1 as day_of_week,
SUM(o.quantity) as daily_quantity,
SUM(o.price * o.quantity) as daily_revenue,
COUNT(DISTINCT DATE(o.date)) as day_count
FROM orders o
JOIN product_categories pc ON o.pid = pc.pid
WHERE o.canceled = false
AND o.date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY pc.cat_id, EXTRACT(DOW FROM o.date) + 1
`);
await connection.query(`
CREATE TEMPORARY TABLE temp_category_stats AS
SELECT
cat_id,
AVG(daily_revenue) as overall_avg_revenue,
SUM(day_count) as total_days
FROM temp_category_sales
GROUP BY cat_id
`);
processedCount = Math.floor(totalProducts * 0.99);
outputProgress({
status: 'running',
operation: 'Category stats prepared, calculating category-level forecasts',
current: processedCount,
total: totalProducts,
elapsed: formatElapsedTime(startTime),
remaining: estimateRemaining(startTime, processedCount, totalProducts),
rate: calculateRate(startTime, processedCount),
percentage: ((processedCount / totalProducts) * 100).toFixed(1),
timing: {
start_time: new Date(startTime).toISOString(),
end_time: new Date().toISOString(),
elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
}
});
if (isCancelled) return {
processedProducts: processedCount,
processedOrders,
processedPurchaseOrders: 0,
success
};
// Calculate category-level forecasts
await connection.query(`
INSERT INTO category_forecasts (
category_id,
forecast_date,
forecast_units,
forecast_revenue,
confidence_level,
created_at
)
SELECT
cs.cat_id::bigint as category_id,
fd.forecast_date,
GREATEST(0,
ROUND(AVG(cs.daily_quantity) *
(1 + COALESCE(sf.seasonality_factor, 0)))
) as forecast_units,
GREATEST(0,
COALESCE(
CASE
WHEN SUM(cs.day_count) >= 4 THEN AVG(cs.daily_revenue)
ELSE ct.overall_avg_revenue
END *
(1 + COALESCE(sf.seasonality_factor, 0)),
0
)
) as forecast_revenue,
CASE
WHEN ct.total_days >= 60 THEN 90
WHEN ct.total_days >= 30 THEN 80
WHEN ct.total_days >= 14 THEN 70
ELSE 60
END as confidence_level,
NOW() as created_at
FROM temp_category_sales cs
JOIN temp_category_stats ct ON cs.cat_id = ct.cat_id
CROSS JOIN temp_forecast_dates fd
LEFT JOIN sales_seasonality sf ON fd.month = sf.month
GROUP BY
cs.cat_id,
fd.forecast_date,
ct.overall_avg_revenue,
ct.total_days,
sf.seasonality_factor,
sf.month
HAVING AVG(cs.daily_quantity) > 0
ON CONFLICT (category_id, forecast_date) DO UPDATE
SET
forecast_units = EXCLUDED.forecast_units,
forecast_revenue = EXCLUDED.forecast_revenue,
confidence_level = EXCLUDED.confidence_level,
created_at = NOW()
`);
// Clean up temporary tables
await connection.query(`
DROP TABLE IF EXISTS temp_forecast_dates;
DROP TABLE IF EXISTS temp_daily_sales;
DROP TABLE IF EXISTS temp_product_stats;
DROP TABLE IF EXISTS temp_category_sales;
DROP TABLE IF EXISTS temp_category_stats;
`);
processedCount = Math.floor(totalProducts * 1.0);
outputProgress({
status: 'running',
operation: 'Category forecasts calculated and temporary tables cleaned up',
current: processedCount,
total: totalProducts,
elapsed: formatElapsedTime(startTime),
remaining: estimateRemaining(startTime, processedCount, totalProducts),
rate: calculateRate(startTime, processedCount),
percentage: ((processedCount / totalProducts) * 100).toFixed(1),
timing: {
start_time: new Date(startTime).toISOString(),
end_time: new Date().toISOString(),
elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
}
});
// If we get here, everything completed successfully
success = true;
// Update calculate_status
await connection.query(`
INSERT INTO calculate_status (module_name, last_calculation_timestamp)
VALUES ('sales_forecasts', NOW())
ON CONFLICT (module_name) DO UPDATE
SET last_calculation_timestamp = NOW()
`);
return {
processedProducts: processedCount,
processedOrders,
processedPurchaseOrders: 0,
success
};
} catch (error) {
success = false;
logError(error, 'Error calculating sales forecasts');
throw error;
} finally {
if (connection) {
try {
// Ensure temporary tables are cleaned up
await connection.query(`
DROP TABLE IF EXISTS temp_forecast_dates;
DROP TABLE IF EXISTS temp_daily_sales;
DROP TABLE IF EXISTS temp_product_stats;
DROP TABLE IF EXISTS temp_category_sales;
DROP TABLE IF EXISTS temp_category_stats;
`);
} catch (err) {
console.error('Error cleaning up temporary tables:', err);
}
connection.release();
}
}
}
module.exports = calculateSalesForecasts;