Add aggregate_orders() function

- the function queries the database and aggregates the ad-hoc orders
  by pixel and time steps into a demand time series
- implement "heavy" integration tests for `aggregate_orders()`
- make `pandas` a package dependency
- streamline the `Config`

pyproject.toml

@@ -31,6 +31,7 @@ python = "^3.8"
 Shapely = "^1.7.1"
 alembic = "^1.4.2"
 click = "^7.1.2"
+pandas = "^1.1.0"
 psycopg2 = "^2.8.5"  # adapter for PostgreSQL
 sqlalchemy = "^1.3.18"
 utm = "^0.7.0"
@@ -40,7 +41,6 @@ utm = "^0.7.0"
 jupyterlab = { version="^2.2.2", optional=true }
 nb_black = { version="^1.0.7", optional=true }
 numpy = { version="^1.19.1", optional=true }
-pandas = { version="^1.1.0", optional=true }
 pytz = { version="^2020.1", optional=true }
 
 [tool.poetry.extras]
@@ -48,7 +48,6 @@ research = [
     "jupyterlab",
     "nb_black",
     "numpy",
-    "pandas",
     "pytz",
 ]