Version bump

MANIFEST.in

@@ -5,8 +5,29 @@ include *.yaml
 include *.rst
 include *.ini
 include *.csv
+include *.npz
 include carculator_utils/data/*.csv
 include carculator_utils/data/*.json
 include carculator_utils/data/*.yaml
-include carculator_utils/data/*.yml
+include carculator_utils/data/driving_cycles/*.csv
+include carculator_utils/data/driving_cycles/*.yaml
+include carculator_utils/data/efficiency/*.yaml
+include carculator_utils/data/electricity/*.csv
+include carculator_utils/data/electricity/*.yaml
+include carculator_utils/data/emission_factors/*.yaml
+include carculator_utils/data/emission_factors/bus/*.csv
+include carculator_utils/data/emission_factors/car/*.csv
+include carculator_utils/data/emission_factors/truck/*.csv
+include carculator_utils/data/emission_factors/two-wheeler/*.csv
+include carculator_utils/data/export/*.csv
+include carculator_utils/data/export/*.yaml
+include carculator_utils/data/export/*.json
+include carculator_utils/data/fuel/*.csv
+include carculator_utils/data/fuel/*.yaml
+include carculator_utils/data/gradient/*.csv
+include carculator_utils/data/IAM/*.csv
+include carculator_utils/data/IAM/*.npz
+include carculator_utils/data/lci/*.xlsx
+include carculator_utils/data/lcia/*.csv
+include carculator_utils/data/lcia/*.yaml
 include LICENSE

carculator_utils/__init__.py

@@ -18,7 +18,7 @@
     "ExportInventory",
     "VehicleInputParameters",
 )
-__version__ = (1, 2, 0, "dev0")
+__version__ = (1, 2, 0, "dev1")
 
 from pathlib import Path
 

carculator_utils/driving_cycles.py

@@ -1,7 +1,7 @@
 """
-driving_cycles.py loads a driving cycle based on
+driving_cycles.py loads a driving_cycles based on
 the name specific by the user.
-The driving cycle returned is a numpy
+The driving_cycles returned is a numpy
 array with speed levels (in km/h) for each
 second of driving.
 """
@@ -15,7 +15,7 @@
 
 from . import DATA_DIR
 
-FILEPATH_DC_SPECS = DATA_DIR / "driving cycle" / "dc_specs.yaml"
+FILEPATH_DC_SPECS = DATA_DIR / "driving_cycles" / "dc_specs.yaml"
 
 
 def detect_vehicle_type(vehicle_sizes: List[str]) -> str:
@@ -35,9 +35,9 @@ def detect_vehicle_type(vehicle_sizes: List[str]) -> str:
 
 
 def get_driving_cycle_specs() -> dict:
-    """Get driving cycle specifications.
+    """Get driving_cycles specifications.
 
-    :returns: A dictionary with driving cycle specifications.
+    :returns: A dictionary with driving_cycles specifications.
     :rtype: dict
 
     """
@@ -51,7 +51,7 @@ def get_dc_column_number(
 ) -> List[int]:
     """
     Loads YAML file that contains the column number.
-    Return the column number given a vehicle type and driving cycle name.
+    Return the column number given a vehicle type and driving_cycles name.
     """
 
     dc_specs = get_driving_cycle_specs()
@@ -94,18 +94,18 @@ def get_data(
         return dc
 
     except KeyError as err:
-        print(err, "The specified driving cycle could not be found.")
+        print(err, "The specified driving_cycles could not be found.")
         raise
 
 
 def get_standard_driving_cycle_and_gradient(
     vehicle_type: str, vehicle_sizes: List[str], name: str
 ) -> Tuple[np.ndarray, np.ndarray]:
-    """Get driving cycle and gradient data as a Pandas `Series`.
+    """Get driving_cycles and gradient data as a Pandas `Series`.
 
     Driving cycles are given as km/h per second up to 3200 seconds.
 
-    :param name: The name of the driving cycle.
+    :param name: The name of the driving_cycles.
     e.g., WLTC (Worldwide harmonized Light vehicles Test Cycles)
     :type name: str
 
@@ -115,12 +115,12 @@ def get_standard_driving_cycle_and_gradient(
 
     """
 
-    filepath_dc = DATA_DIR / "driving cycle" / f"{vehicle_type}.csv"
+    filepath_dc = DATA_DIR / "driving_cycles" / f"{vehicle_type}.csv"
     filepath_gradient = DATA_DIR / "gradient" / f"{vehicle_type}.csv"
 
     # definition of columns to select in the CSV file
     # each column corresponds to a size class
-    # since the driving cycle is simulated for each size class
+    # since the driving_cycles is simulated for each size class
     return (
         get_data(filepath_dc, vehicle_type, vehicle_sizes, name),
         get_data(filepath_gradient, vehicle_type, vehicle_sizes, name),

carculator_utils/energy_consumption.py

@@ -43,7 +43,7 @@ def __(obj: Union[np.ndarray, xr.DataArray]) -> Union[np.ndarray, xr.DataArray]:
 
 
 def get_default_driving_cycle_name(vehicle_type) -> str:
-    """Get the default driving cycle name"""
+    """Get the default driving_cycles name"""
     return list(get_driving_cycle_specs()["columns"][vehicle_type].keys())[0]
 
 
@@ -121,9 +121,9 @@ def convert_to_xr(data):
 class EnergyConsumptionModel:
     """
     Calculate energy consumption of a vehicle for a
-    given driving cycle and vehicle parameters.
+    given driving_cycles and vehicle parameters.
 
-    Based on a selected driving cycle, this class calculates
+    Based on a selected driving_cycles, this class calculates
     the acceleration needed and provides
     two methods:
 
@@ -140,7 +140,7 @@ class EnergyConsumptionModel:
     :type rho_air: float
     :param gradient: Road gradient per second of driving, in degrees.
     None by default. Should be passed as an array of length equal
-    to the length of the driving cycle.
+    to the length of the driving_cycles.
     :type gradient: numpy.ndarray
 
     :ivar rho_air: Mass per unit volume of air. Value of 1.204 at 23C (test temperature for WLTC).
@@ -196,7 +196,7 @@ def __init__(
 
         assert len(self.cycle) == len(
             self.gradient
-        ), "The length of the driving cycle and the gradient must be the same."
+        ), "The length of the driving_cycles and the gradient must be the same."
 
         # Unit conversion km/h to m/s
         self.velocity = np.where(np.isnan(self.cycle), 0, (self.cycle * 1000) / 3600)
@@ -295,7 +295,7 @@ def calculate_hvac_energy(
 
     def find_last_driving_second(self) -> ndarray:
         """
-        Find the last second of the driving cycle that is not zero.
+        Find the last second of the driving_cycles that is not zero.
         """
 
         # find last index where velocity is greater than 0
@@ -617,7 +617,7 @@ def motive_energy_per_km(
 
         auxiliary_energy = np.where(self.velocity > 0, auxiliary_energy, 0)
 
-        # if first dimension is 1, resize it to the length of the driving cycle
+        # if first dimension is 1, resize it to the length of the driving_cycles
         if auxiliary_energy.shape[0] == 1:
             auxiliary_energy = np.resize(
                 auxiliary_energy, (self.velocity.shape[0], *auxiliary_energy.shape[1:])

carculator_utils/export.py

@@ -172,7 +172,7 @@ def get_simapro_technosphere() -> Dict[Tuple[str, str], str]:
 
 def rename_mapping(filename: str) -> Dict[str, str]:
     """
-    Load the file rename_powertrains.yml and return a dictionary
+    Load the file rename_powertrains.yaml and return a dictionary
     """
     with open(DATA_DIR / "export" / filename, encoding="utf-8") as f:
         rename_map = yaml.safe_load(f)
@@ -192,8 +192,8 @@ def __init__(
         self.array: xr.DataArray = array
         self.indices: Dict[int, Tuple[str, str, str, str]] = indices
         self.vm = vehicle_model
-        self.rename_pwt = rename_mapping("rename_powertrains.yml")
-        self.rename_parameters = rename_mapping("rename_parameters.yml")
+        self.rename_pwt = rename_mapping("rename_powertrains.yaml")
+        self.rename_parameters = rename_mapping("rename_parameters.yaml")
         self.rename_vehicles()
         self.rev_rename_pwt = {v: k for k, v in self.rename_pwt.items()}
         self.db_name: str = db_name
@@ -463,15 +463,15 @@ def format_data_for_lci_for_simapro(
         self, data: List[Dict], ei_version: str
     ) -> List[List]:
         # not all biosphere flows exist in simapro
-        # load list from `simapro_blacklist.yml`
+        # load list from `simapro_blacklist.yaml`
         with open(
-            DATA_DIR / "export" / "simapro_blacklist.yml", "r", encoding="utf-8"
+            DATA_DIR / "export" / "simapro_blacklist.yaml", "r", encoding="utf-8"
         ) as f:
             blacklist = yaml.safe_load(f)
 
-        # load fields list from `simapro_fields.yml`
+        # load fields list from `simapro_fields.yaml`
         with open(
-            DATA_DIR / "export" / "simapro_fields.yml", "r", encoding="utf-8"
+            DATA_DIR / "export" / "simapro_fields.yaml", "r", encoding="utf-8"
         ) as f:
             fields = yaml.safe_load(f)
 

carculator_utils/hot_emissions.py

@@ -14,7 +14,7 @@
 
 from . import DATA_DIR
 
-FILEPATH_DC_SPECS = DATA_DIR / "driving cycle" / "dc_specs.yaml"
+FILEPATH_DC_SPECS = DATA_DIR / "driving_cycles" / "dc_specs.yaml"
 
 MAP_PWT = {
     "Human": "BEV",
@@ -194,7 +194,7 @@ def get_hot_emissions(
 
         :param powertrain_type: "diesel", "petrol" or "CNG"
         :param euro_class: integer, corresponding to the EURO pollution class
-        :param energy_consumption: tank-to-wheel energy consumption for each second of the driving cycle
+        :param energy_consumption: tank-to-wheel energy consumption for each second of the driving_cycles
         :param yearly_km: annual mileage, to calculate cold start emissions
         :return: Pollutants emission per km driven, per air compartment.
         """
@@ -227,7 +227,7 @@ def get_hot_emissions(
 
         distance = self.velocity.sum(dim="second") / 3600
 
-        # Emissions for each second of the driving cycle equal:
+        # Emissions for each second of the driving_cycles equal:
         # a * energy consumption
         # with a being a coefficient given by fitting HBEFA 4.1 data
         # the fitting of emissions function of energy consumption
@@ -313,7 +313,7 @@ def get_hot_emissions(
 
             # Cold start and soak emissions are defined per start and stop
             # Therefore, we need to normalize per km
-            # And add cold start emissions to the first second of the driving cycle
+            # And add cold start emissions to the first second of the driving_cycles
 
             yearly_km = yearly_km.transpose("value", "year", "powertrain", "size")
 
@@ -327,7 +327,7 @@ def get_hot_emissions(
                 * non_exhaust.sel(type="cold start").values
             )
 
-            # And add soak emissions to the last second of the driving cycle
+            # And add soak emissions to the last second of the driving_cycles
             emissions.loc[
                 dict(
                     second=emissions.second.values[-1],
@@ -340,7 +340,7 @@ def get_hot_emissions(
 
             # Diurnal emissions are defined in g/day
             # And need to be evenly distributed
-            # throughout the driving cycle
+            # throughout the driving_cycles
 
             daily_km_to_year = distance / (yearly_km / 365)
 
@@ -351,7 +351,7 @@ def get_hot_emissions(
             )
 
             # Running losses are in g/km (no conversion needed)
-            # And need to be evenly distributed throughout the driving cycle
+            # And need to be evenly distributed throughout the driving_cycles
 
             emissions.loc[dict(component=non_exhaust.component.values)] += (
                 _(distance)

carculator_utils/inventory.py

@@ -330,9 +330,9 @@ def get_split_indices(self):
         :return: list of indices
         :rtype: list
         """
-        # read `impact_source_categories.yml` file
+        # read `impact_source_categories.yaml` file
         with open(
-            DATA_DIR / "lcia" / "impact_source_categories.yml", "r", encoding="utf-8"
+            DATA_DIR / "lcia" / "impact_source_categories.yaml", "r", encoding="utf-8"
         ) as stream:
             source_cats = yaml.safe_load(stream)
 

carculator_utils/model.py

@@ -35,8 +35,8 @@ class VehicleModel:
     all the vehicles parameters.
 
     :ivar array: multi-dimensional numpy-like array that contains parameters' value(s)
-    :ivar cycle: name of a driving cycle, or custom driving cycle
-    :ivar gradient: series of gradients, for each second of the driving cycle
+    :ivar cycle: name of a driving_cycles, or custom driving_cycles
+    :ivar gradient: series of gradients, for each second of the driving_cycles
     :ivar energy_storage: dictionary with selection of battery chemistry for each powertrain
 
     """
@@ -68,8 +68,8 @@ def __init__(
         """
         :param array: multi-dimensional numpy-like array that contains parameters' value(s)
         :param country: country code
-        :param cycle: name of a driving cycle, or custom driving cycle
-        :param gradient: series of gradients, for each second of the driving cycle
+        :param cycle: name of a driving_cycles, or custom driving_cycles
+        :param gradient: series of gradients, for each second of the driving_cycles
         :param energy_storage: dictionary with selection of battery chemistry, capacity and origin for each powertrain-size-year combination
         :param electric_utility_factor: fraction of electricity that is generated from renewable sources
         :param drop_hybrids: boolean, if True, hybrid vehicles are dropped from the inventory
@@ -1329,10 +1329,10 @@ def set_ttw_efficiency(self) -> None:
 
     def set_hot_emissions(self) -> None:
         """
-        Calculate hot pollutant emissions based on ``driving cycle``.
-        The driving cycle is passed to the :class:`HotEmissionsModel` class
+        Calculate hot pollutant emissions based on ``driving_cycles``.
+        The driving_cycles is passed to the :class:`HotEmissionsModel` class
         and :meth:`get_emissions_per_powertrain`
-        return emissions per substance per second of driving cycle.
+        return emissions per substance per second of driving_cycles.
         Those are summed up and divided by
         the distance driven, to obtain emissions, in kg per km.
         :return: Does not return anything. Modifies ``self.array`` in place.
@@ -1442,11 +1442,11 @@ def set_particulates_emission(self) -> None:
 
     def set_noise_emissions(self) -> None:
         """
-        Calculate noise emissions based on ``driving cycle``.
-        The driving cycle is passed to the :class:`NoiseEmissionsModel` class
+        Calculate noise emissions based on ``driving_cycles``.
+        The driving_cycles is passed to the :class:`NoiseEmissionsModel` class
         and :meth:`get_sound_power_per_compartment`
         returns emissions per compartment type ("rural", "non-urban" and "urban")
-        per second of driving cycle.
+        per second of driving_cycles.
 
         Noise emissions are not differentiated by size classes at the moment,
         but only by powertrain "type"