ffc08d0629
- 40 FlexMeasures assets (10 PV, 10 Bat, 10 Chg, 10 EV) - Geolocated on Martinique - Documentation: architecture, deployment, concepts - Standards: Flex Ready, S2, OpenADR, EPEX SPOT - R&D tools: HAMLET, OPLEM, lemlab - Map patch: Mapbox -> OpenStreetMap
97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
#!/usr/bin/env python3
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"""Re-localise les assets Cariflex sur la Martinique."""
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import subprocess, random
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def run_sql(query):
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subprocess.run([
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"docker", "exec", "flexmeasures-db", "psql", "-U", "flexmeasures", "-d", "flexmeasures",
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"-t", "-c", query
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], capture_output=True, text=True)
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# Martinique bounding box (approximate)
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# Lat: 14.38 to 14.87, Lon: -61.25 to -60.82
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# But we need to avoid the sea - use known land areas
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# Realistic zones on Martinique (lat, lon) - major towns and industrial zones
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ZONES = [
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# Fort-de-France area (capital, dense urban)
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(14.6091, -61.2155), # Fort-de-France center
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(14.6150, -61.2080), # Fort-de-France north
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(14.6050, -61.2200), # Fort-de-France south
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(14.6200, -61.2000), # Fort-de-France east
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(14.5950, -61.2100), # Fort-de-France west
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# Le Lamentin (airport, industrial zone)
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(14.6100, -61.0100), # Le Lamentin center
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(14.6150, -61.0050), # Airport area
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(14.6050, -61.0200), # Industrial zone
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# Sainte-Marie (north-east)
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(14.7800, -60.9800), # Sainte-Marie town
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(14.7900, -60.9700), # North coast
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# Le Robert (north-east coast)
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(14.6800, -60.9400), # Le Robert town
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# Le François (south-east coast)
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(14.6600, -60.9000), # Le François town
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# Le Vauclin (south-east tip)
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(14.5500, -60.8400), # Le Vauclin
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# Le Marin (south)
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(14.4700, -60.8700), # Le Marin town
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# Sainte-Anne (south coast)
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(14.4400, -60.8500), # Sainte-Anne
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# Le Diamant (south-west)
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(14.4800, -61.0200), # Le Diamant
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# Les Anses-d'Arlet (south-west coast)
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(14.5000, -61.0800), # Les Anses-d'Arlet
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# Sainte-Luce (south-east)
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(14.4600, -60.9300), # Sainte-Luce
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# Le Lorrain (north)
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(14.7200, -60.9300), # Le Lorrain
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]
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random.seed(42) # Reproducible
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# Shuffle zones and pick 10 for each asset type (with some overlap for co-location)
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random.shuffle(ZONES)
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# Get all assets ordered by type and name
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result = subprocess.run([
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"docker", "exec", "flexmeasures-db", "psql", "-U", "flexmeasures", "-d", "flexmeasures",
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"-t", "-c", """
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SELECT g.id, g.name, gt.name as type_name
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FROM generic_asset g
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JOIN generic_asset_type gt ON g.generic_asset_type_id = gt.id
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ORDER BY gt.id, g.name;
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"""
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], capture_output=True, text=True)
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assets = []
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for line in result.stdout.strip().split('\n'):
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parts = [p.strip() for p in line.split('|')]
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if len(parts) == 3:
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assets.append((int(parts[0]), parts[1], parts[2]))
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# Assign random locations per type (4 types, 10 assets each)
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type_groups = {}
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for aid, name, atype in assets:
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if atype not in type_groups:
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type_groups[atype] = []
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type_groups[atype].append((aid, name))
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zone_idx = 0
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for atype, group in type_groups.items():
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# Pick 10 random zones for this type (zones can repeat slightly for density)
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type_zones = [ZONES[i % len(ZONES)] for i in range(zone_idx, zone_idx + 10)]
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random.shuffle(type_zones)
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zone_idx += 10
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for i, (aid, name) in enumerate(group):
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lat, lon = type_zones[i]
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# Add small random jitter (±0.005 degrees ≈ 500m)
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lat += random.uniform(-0.005, 0.005)
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lon += random.uniform(-0.005, 0.005)
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run_sql("UPDATE generic_asset SET latitude = {}, longitude = {} WHERE id = {};".format(
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round(lat, 6), round(lon, 6), aid))
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print(" {} ({}) -> {:.4f}, {:.4f}".format(name, atype[:3], lat, lon))
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print("\nDone!")
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