Updated some TODOs

Author: duboisa1

iepy/generation/vres/profiles/manager.py

@@ -8,19 +8,18 @@
 import xarray.ufuncs as xu
 import numpy as np
 import pandas as pd
-import dask.array as da
-import dask
-dask.config.set({"array.slicing.split_large_chunks": True})
 
-# from shapely.geometry import Point, Polygon
-# import atlite
 import windpowerlib
 
 from iepy.technologies import get_config_dict, get_config_values
 from iepy.geographics import get_shapes
 
 from iepy import data_path
 
+import dask.array as da
+import dask
+dask.config.set({"array.slicing.split_large_chunks": True})
+
 
 def read_resource_database(spatial_resolution: float) -> xr.Dataset:
     """
@@ -41,7 +40,6 @@ def read_resource_database(spatial_resolution: float) -> xr.Dataset:
     available_res = sorted([float(res) for res in listdir(main_resource_dir)])
     # Find the dataset with the least precise resolution that can accommodate the desired resolution
     dataset_resolution = None
-    # TODO: tis doesn't work if the smaller resolution directory doesn't contain the appropriate region
     for spatial_res in available_res:
         if int(spatial_resolution*1e6) % int(spatial_res*1e6) == 0:
             dataset_resolution = spatial_res
@@ -323,7 +321,7 @@ def get_cap_factor_for_countries(tech: str, countries: List[str], timestamps: pd
             spatial_res = 0.5
             missing_countries = sorted(list(missing_countries))
             which = 'onshore' if get_config_values(tech, ["onshore"]) else 'offshore'
-            shapes_df = get_shapes(missing_countries, which=which)
+            shapes_df = get_shapes(missing_countries, which=which, save=True)
             # TODO: weird userwarning happening on Iceland
             centroids = shapes_df["geometry"].centroid
             points = [(round(p.x / spatial_res) * spatial_res, round(p.y / spatial_res) * spatial_res)
@@ -333,88 +331,3 @@ def get_cap_factor_for_countries(tech: str, countries: List[str], timestamps: pd
             capacity_factors_df = pd.concat([capacity_factors_df, cap_factor_df], axis=1)
 
     return capacity_factors_df[countries].round(precision)
-
-# --- Using atlite --- #
-# def get_cap_factor_for_regions(regions: List[Polygon], start_month: int, end_month: int = None):
-#     """
-#     Return the capacity factor series and generation capacity for pv and wind for a list of regions.
-#
-#     Parameters
-#     ----------
-#     regions: List[Polygon]
-#         List of geographical regions for which we want a capacity factor series
-#     start_month: int
-#         Number of the first month
-#     end_month: int
-#         Number of the last month. If equal to start_month, data will be returned only for one month.
-#         Another way to get this behavior is just no setting end_month and leaving it to None.
-#
-#     Returns
-#     -------
-#     wind_cap_factors: xr.DataArray with coordinates id (i.e. regions) and time
-#         Wind capacity factors for each region in regions
-#     wind_capacities:
-#         Wind generation capacities for each region in regions
-#     pv_cap_factors:
-#         PV capacity factors for each region in regions
-#     pv_capacities:
-#         PV generation capacity for each region in regions
-#     """
-#
-#     if end_month is None:
-#         end_month = start_month
-#
-#     assert start_month <= end_month, \
-#         "ERROR: The number of the end month must be superior to the number of the start month"
-#
-#     cutout_dir = f"{data_path}cutouts/"
-#
-#     cutout_params = dict(years=[2013], months=list(range(start_month, end_month+1)))
-#     cutout = atlite.Cutout("europe-2013-era5", cutout_dir=cutout_dir, **cutout_params)
-#
-#     # Wind
-#     wind_cap_factors, wind_capacities = cutout.wind(shapes=regions, turbine="Vestas_V112_3MW", per_unit=True,
-#                                                     return_capacity=True)
-#
-#     # PV
-#     pv_params = {"panel": "CSi",
-#                  "orientation": {
-#                      "slope": 35.,
-#                      "azimuth": 180.}}
-#     pv_cap_factors, pv_capacities = cutout.pv(shapes=regions, **pv_params, per_unit=True, return_capacity=True)
-#
-#     # Change precision
-#     wind_cap_factors = xr.apply_ufunc(lambda x: np.round(x, 3), wind_cap_factors)
-#     pv_cap_factors = xr.apply_ufunc(lambda x: np.round(x, 3), pv_cap_factors)
-#
-#     return wind_cap_factors, wind_capacities, pv_cap_factors, pv_capacities
-#
-#
-# def get_cap_factor_at_points(points: List[Point], start_month: int, end_month: int = None):
-#     """
-#     Return the capacity factor series and generation capacity for pv and wind for a list of points.
-#
-#     Parameters
-#     ----------
-#     points: List[Point]
-#         Point for which we want a capacity factor series
-#     start_month: int
-#         Number of the first month
-#     end_month: int
-#         Number of the last month. If equal to start_month, data will be returned only for one month.
-#         Another way to get this behavior is just no setting end_month and leaving it to None.
-#
-#     Returns
-#     -------
-#     See 'get_cap_factor_for_regions'
-#
-#     """
-#
-#     resolution = 0.5
-#     # Create a polygon around the point
-#     polygon_df = pd.DataFrame([Polygon([(point.x-resolution, point.y-resolution),
-#                                         (point.x-resolution, point.y+resolution),
-#                                         (point.x+resolution, point.y+resolution),
-#                                         (point.x+resolution, point.y-resolution)]) for point in points],
-#                               index=[(point.x, point.y) for point in points], columns=["region"]).region
-#     return get_cap_factor_for_regions(polygon_df, start_month, end_month)

iepy/geographics/grid_cells.py

@@ -98,7 +98,6 @@ def get_grid_cells(technologies: List[str], resolution: float,
                                   f"shapes for technology {tech}"
 
     # Divide onshore and offshore shapes at a given resolution
-    # TODO: why is taking ages on onshore shape of EU compared to offshore?
     onshore_points, onshore_grid_cells_shapes = [], np.array([])
     if onshore_shape is not None:
         for r in onshore_shape.index:

iepy/geographics/shapes.py

@@ -286,7 +286,9 @@ def get_shapes(region_codes: List[str], which: str = 'onshore_offshore', save: b
 
     # If shapes for those codes were previously computed, output is returned directly from file.
     sorted_name = "".join(sorted(region_codes))
+    print(region_codes)
     hash_name = hashlib.sha224(bytes(sorted_name, 'utf-8')).hexdigest()[:10]
+    print(hash_name)
     fn = f"{data_path}geographics/generated/{hash_name}.geojson"
     if isfile(fn):
         print("ok")

iepy/load/data_retrieval.py

@@ -36,4 +36,4 @@ def download_iea_electricity_consumption(countries, start_year, end_year):
 
 if __name__ == '__main__':
     countries_ = ["CY", "MT"]
-    download_iea_electricity_consumption(countries_, 1990, 2017)
+    download_iea_electricity_consumption(countries_, 1990, 2018)

iepy/topologies/core/manager.py

@@ -98,17 +98,16 @@ def remove_dangling_branches(branches_df: pd.DataFrame(), buses_ids: Union[List[
 def find_closest_links(links_df: pd.DataFrame, new_links_df: pd.DataFrame,
                        distance_upper_bound: float = 1.5) -> pd.Series:
     """
-
-    # TODO: complete
+    TODO: complete
+    Parameters
+    ----------
 
     Returns
     -------
 
-    TODO: what does it return?
-
     Notes
     -----
-    This function is originally copied from PyPSA-Eur script base_network.py
+    This function is originally copied from PyPSA-Eur script base_network.py.
     
 
     """
@@ -154,6 +153,10 @@ def remove_unconnected_components(net: pypsa.Network) -> pypsa.Network:
     Returns
     -------
 
+    Notes
+    -----
+    This function is originally copied from PyPSA-Eur script base_network.py.
+
     """
     _, labels = csgraph.connected_components(net.adjacency_matrix(), directed=False)
     component = pd.Series(labels, index=net.buses.index)

iepy/topologies/tyndp2018.py

@@ -39,7 +39,6 @@ def preprocess(plotting=True) -> None:
     #     - DG (Distributed Generation): prosumers at the centre - small-scale generation, batteries and fuel-switching
     #                                    society engaged and empowered
     #     - GCA (Global Climate Action): full-speed global decarbonisation, large-scale renewables
-    # TODO:  Why are we using NTC 2027 as exiting capacity and not 2020?
     links = pd.read_excel(link_data_fn, sheet_name="NTC", index_col=0, skiprows=[0, 2],
                           usecols=[0, 3, 4, 5, 6, 7, 8, 9, 10],
                           names=["link", "in", "out", "st_in", "st_out", "dg_in", "dg_out", "gca_in", "gca_out"])
@@ -273,22 +272,15 @@ def get_topology(network: pypsa.Network, countries: List[str] = None,
 
         topology_dir = f"{data_path}topologies/tyndp2018/generated/"
         links_fn = f"{topology_dir}links.csv"
-        links = pd.read_csv(links_fn, index_col='id')
-        print(links.p_nom_st.sum())
-        print(links.p_nom_dg.sum())
-        print(links.p_nom_gca.sum())
-        print((links.p_nom_st*links.length).sum())
-        print((links.p_nom_dg*links.length).sum())
-        print((links.p_nom_gca*links.length).sum())
-        exit()
-        diff_st_dg = (links.p_nom_dg - links.p_nom_st).abs()
-        diff_st_gca = (links.p_nom_gca - links.p_nom_st).abs()
-        diff_dg_gca = (links.p_nom_gca - links.p_nom_dg).abs()
+        links_ = pd.read_csv(links_fn, index_col='id')
+        diff_st_dg = (links_.p_nom_dg - links_.p_nom_st).abs()
+        diff_st_gca = (links_.p_nom_gca - links_.p_nom_st).abs()
+        diff_dg_gca = (links_.p_nom_gca - links_.p_nom_dg).abs()
         pd.concat((diff_st_gca, diff_st_dg, diff_dg_gca), axis=1).max(axis=1).plot(kind='bar')
         plt.figure()
-        links.p_nom_st.plot(ls='-', marker='.', c='b', alpha=0.5)
-        links.p_nom_dg.plot(ls='-', marker='.', c='r', alpha=0.5)
-        links.p_nom_gca.plot(ls='-', marker='.', c='g', alpha=0.5)
-        plt.xticks(ticks=range(len(links)), labels=list(links.index), rotation='90')
+        links_.p_nom_st.plot(ls='-', marker='.', c='b', alpha=0.5)
+        links_.p_nom_dg.plot(ls='-', marker='.', c='r', alpha=0.5)
+        links_.p_nom_gca.plot(ls='-', marker='.', c='g', alpha=0.5)
+        plt.xticks(ticks=range(len(links_)), labels=list(links_.index), rotation='90')
         plt.grid()
         plt.show()

setup.py

@@ -1 +1 @@
-# TODO: complete
+# TODO: complete

tests/generation/vres/potentials/enspreso/test_manager.py

@@ -99,10 +99,6 @@ def test_get_capacity_potential_at_points_wrong_country():
         get_capacity_potential_at_points({'wind_onshore': [(0.5, 1.0)]}, 0.5, ['ZZ'])
 
 
-# TODO: add test for 'get_capacity_potential_at_points' and 'get_capacity_potential_for_regions'
-#  if we keep these functions
-
-
 def test_get_capacity_potential_for_countries_wrong_tech():
     with pytest.raises(AssertionError):
         get_capacity_potential_for_countries('wind', ['BE'])