Enable download of datapackage as zip

app/compose/production/app_postgres/Dockerfile

@@ -10,16 +10,16 @@ RUN mkdir ${CONFIG_ROOT}
 COPY app/requirements/base.txt ${CONFIG_ROOT}/base.txt
 COPY app/requirements/production.txt ${CONFIG_ROOT}/production.txt
 
+# Install gettext utilities for translations
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends gettext-base gettext git && \
+    rm -rf /var/lib/apt/lists/*
 
 RUN pip install --upgrade pip \
     && pip install --no-cache-dir -r ${CONFIG_ROOT}/production.txt
 
 WORKDIR ${APP_ROOT}
 
-# Install gettext utilities for translations
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends gettext-base gettext && \
-    rm -rf /var/lib/apt/lists/*
 
 ADD app/ ${APP_ROOT}
 

app/projects/management/commands/datapackage.py

@@ -1,112 +1,47 @@
 from django.core.management.base import BaseCommand, CommandError
-from projects.models import (
-    Asset,
-    AssetType,
-    TopologyNode,
-    Scenario,
-    Timeseries,
-    ConnectionLink,
-    Bus,
-)
-import pandas as pd
+from projects.models import Scenario
 from pathlib import Path
-import numpy as np
 import shutil
+from oemof.datapackage.datapackage import building
+import datapackage as dp
 
 
 class Command(BaseCommand):
     help = "Convert the given scenarios to datapackages"
 
     def add_arguments(self, parser):
         parser.add_argument("scen_id", nargs="+", type=int)
-
-        parser.add_argument(
-            "--overwrite", action="store_true", help="Overwrite the datapackage"
-        )
+        parser.add_argument("-o", "--outfile", type=str, nargs="?", const="")
 
     def handle(self, *args, **options):
-        overwrite = options["overwrite"]
 
         for scen_id in options["scen_id"]:
             try:
                 scenario = Scenario.objects.get(pk=scen_id)
             except Scenario.DoesNotExist:
                 raise CommandError('Scenario "%s" does not exist' % scen_id)
-
-            destination_path = Path(__file__).resolve().parents[4]
-
-            # Create a folder with a datapackage structure
-            scenario_folder = destination_path / f"scenario_{scen_id}"
-            create_folder = True
-
-            if scenario_folder.exists():
-                if not overwrite:
-                    create_folder = False
-                else:
-                    shutil.rmtree(scenario_folder)
-
-            elements_folder = scenario_folder / "data" / "elements"
-            sequences_folder = scenario_folder / "data" / "sequences"
-
-            if create_folder:
-                # create subfolders
-                (scenario_folder / "scripts").mkdir(parents=True)
-                elements_folder.mkdir(parents=True)
-                sequences_folder.mkdir(parents=True)
-
-            # List all components of the scenario (except the busses)
-            qs_assets = Asset.objects.filter(scenario=scenario)
-            # List all distinct components' assettypes (or facade name)
-            facade_names = qs_assets.distinct().values_list(
-                "asset_type__asset_type", flat=True
+            destination_path = options["outfile"]
+            if destination_path == "":
+                destination_path = Path(__file__).resolve().parents[4]
+            else:
+                destination_path = Path(destination_path)
+
+            scenario_folder = destination_path / f"scenario_{scenario.name}".replace(
+                " ", "_"
             )
+            if scenario_folder.exists():
+                shutil.rmtree(scenario_folder)
 
-            bus_resource_records = []
-            profile_resource_records = {}
-            for facade_name in facade_names:
-                resource_records = []
-                for i, asset in enumerate(
-                    qs_assets.filter(asset_type__asset_type=facade_name)
-                ):
-                    resource_rec, bus_resource_rec, profile_resource_rec = (
-                        asset.to_datapackage()
-                    )
-                    resource_records.append(resource_rec)
-                    # those constitute the busses and sequences used by this asset
-                    bus_resource_records.extend(bus_resource_rec)
-                    profile_resource_records.update(profile_resource_rec)
-
-                if resource_records:
-                    out_path = elements_folder / f"{facade_name}.csv"
-                    Path(out_path).parent.mkdir(parents=True, exist_ok=True)
-                    df = pd.DataFrame(resource_records)
-                    df.to_csv(out_path, index=False)
+            dp_json = scenario_folder / "datapackage.json"
 
-            # Save all unique busses to a elements resource
-            if bus_resource_records:
-                out_path = elements_folder / f"bus.csv"
-                Path(out_path).parent.mkdir(parents=True, exist_ok=True)
-                df = pd.DataFrame(bus_resource_records)
-                df.drop_duplicates("name").to_csv(out_path, index=False)
+            if dp_json.exists():
+                print("Only inferring metadata")
+                p = dp.Package(str(dp_json))
+                building.infer_package_foreign_keys(p, fk_targets=["project"])
+                p.descriptor["resources"].sort(key=lambda x: (x["path"], x["name"]))
+                p.commit()
+                p.save(dp_json)
 
-            # Save all profiles to a sequences resource
-            if profile_resource_records:
-                out_path = sequences_folder / f"profiles.csv"
-                Path(out_path).parent.mkdir(parents=True, exist_ok=True)
-                # add timestamps to the profiles
-                profile_resource_records["timeindex"] = scenario.get_timestamps()
-                try:
-                    df = pd.DataFrame(profile_resource_records)
-                except ValueError as e:
-                    # If not all profiles have the same length we pad the shorter profiles with np.nan
-                    max_len = max(len(v) for v in profile_resource_records.values())
-                    profile_resource_records = {
-                        k: v + [np.nan] * (max_len - len(v))
-                        for k, v in profile_resource_records.items()
-                    }
-                    df = pd.DataFrame(profile_resource_records)
-                    print(
-                        f"Some profiles have more timesteps that other profiles in scenario {scenario.name}({scen_id}) --> the shorter profiles will be expanded with NaN values"
-                    )
-                # TODO check if there are column duplicates
-                df.set_index("timeindex").to_csv(out_path, index=True)
+            else:
+                print("Creating datapackage.json")
+                scenario.to_datapackage(destination_path)