create testbed file

testbed.py

@@ -0,0 +1,84 @@
+from farmOS import farmOS
+
+hostname = "farmos.wagframe.duckdns.org"
+username = "matze"
+password = "m6ChEHx5gMqgctr8dpb3fhATZbQS8hv4"
+
+# Create the client.
+farm_client = farmOS(
+    hostname=hostname,
+    client_id = "jupyter", # Optional. The default oauth client_id "farm" is enabled on all farmOS servers.
+)
+import json
+# Authorize the client, save the token.
+# A scope can be specified, but will default to the default scope set when initializing the client.
+token = farm_client.authorize(username, password)
+
+import pandas as pd
+
+
+def get_related_logs_of_type(asset, type): 
+    response = farm_client.resource.get('log', type)
+    ret = []
+    for log in response['data']:
+        assets = log['relationships']['asset']['data']
+        for asset in assets:
+            if asset['id'] == id:
+                ret.append(log)
+    return ret
+
+def get_related_quantities_of_measure(log, measure):
+    quantities = log['relationships']['quantity']['data']
+    ret = []
+    for quant_ref in quantities:
+        quant_id = quant_ref['id']
+        quant = farm_client.resource.get_id('quantity', 'standard', quant_id)
+        if quant['data']['attributes']['measure'] == measure:
+            ret.append(quant)
+    return ret
+
+# Beispiel für eine API-Abfrage mit farm_client
+response = farm_client.resource.get('log', 'harvest')
+
+logs = []
+for item in response['data']:
+    # plant name und fläche holen
+    assets = item['relationships']['asset']['data']
+    plant_name = ''
+    plant_area = 0.0
+    for asset in assets:
+        if asset['type'] == 'asset--plant':    
+            plant_id = asset['id']
+            seedings = get_related_logs_of_type(plant_id, 'seeding')
+            for seeding in seedings:
+                area_quants = get_related_quantities_of_measure(seeding, 'area')
+                for area_quant in area_quants:
+                    plant_area += float(area_quant['data']['attributes']['value']['decimal'])
+            plant = farm_client.asset.get_id('plant', plant_id)
+            plant_name = plant['data']['attributes']['name']
+            
+    
+    # log aufbauen
+    log = {
+        'ID': item['id'],
+        'Name': item['attributes']['name'],
+        'Timestamp': item['attributes']['timestamp'],
+        'Plant': plant_name,
+        'Area': plant_area,
+        'Status': item['attributes']['status'],
+        'Revision Created': item['attributes']['revision_created'],
+        'Link': item['links']['self']['href']
+    }
+    logs.append(log)
+
+# Erstelle ein DataFrame aus den Harvest Logs
+df = pd.DataFrame(logs)
+
+# Zeige die Tabelle an
+print(df)
+
+# Speichere die Tabelle in einer CSV-Datei
+csv_file_path = 'resources/harvest_logs.csv'
+df.to_csv(csv_file_path, index=False)
+
+print(f"Die Tabelle wurde erfolgreich in der Datei '{csv_file_path}' gespeichert.")