Align wrt master.

docs/extract.rst

@@ -59,6 +59,7 @@ Concentration Pathway (RCP)—and a year (e.g., SSP1, Base, 2035).
 
     A summary report of the main variables of the scenarios
     selected is generated automatically after each database export.
+    There is also an `online dashboard <https://premisedash-6f5a0259c487.herokuapp.com/>`_.
     You can also generate it manually:
 
 .. python::
@@ -141,8 +142,9 @@ If you wish to clear that cache folder, do:
 From ecospold2 files
 --------------------
 
-To extract from a set of ecospold2 files, you need to point to the location of those files
-in `source_file_path`, as well as indicate the database format in `source_type`:
+To extract from a set of ecospold2 files, you need to point to the location of
+those files in `source_file_path`, as well as indicate the database format in
+`source_type`:
 
 .. code-block:: python
 
@@ -600,7 +602,6 @@ They are necessary to model the distribution of hydrogen:
 * via truck, in a liquid state
 * hydrogen refuelling station
 
-
 Small and large storage solutions are also provided:
 * high pressure hydrogen storage tank
 * geological storage tank
@@ -642,19 +643,18 @@ Hydrogen turbine
 
 A dataset for a hydrogen turbine is also imported, to model the production of electricity
 from hydrogen, with an efficiency of 51%. The efficiency of the H2-fed gas turbine is based
-on the parameters of Ozawa_ et al. (2019), consulted here: LCI_H2_turbine_.
+on the parameters of Ozawa_ et al. (2019), accessible here: LCI_H2_turbine_.
 
 .. _Ozawa: https://doi.org/10.1016/j.ijhydene.2019.02.230
 .. _LCI_H2_turbine: https://github.com/polca/premise/blob/master/premise/data/additional_inventories/lci-hydrogen-turbine.xlsx
 
 
-
 Biofuels
 --------
 
 Inventories for energy crops- and residues-based production of bioethanol and biodiesel
-are imported, and can be consulted here: LCI_biofuels_. They include the farming of the crop,
-the conversion of hte biomass to fuel, as well as its distribution. The conversion process
+are imported, and can be accessed here: LCI_biofuels_. They include the farming of the crop,
+the conversion of the biomass to fuel, as well as its distribution. The conversion process
 often leads to the production of co-products (dried distiller's grain, electricity, CO2, bagasse.).
 Hence, energy, economic and system expansion partitioning approaches are available.
 These inventories originate from several different sources
@@ -854,7 +854,7 @@ pump heat and excess heat), which are found under the following names, for each
   carbon dioxide, captured from atmosphere, with a sorbent-based direct air capture system, 100ktCO2, with heat pump heat, and grid electricity            all IAM regions
  ======================================================================================================================================================= ==================
 
-Note that only solid sorbent DAC can use waste heat, as teh heat requirement for liquid solvent DAC
+Note that only solid sorbent DAC can use waste heat, as the heat requirement for liquid solvent DAC
 is too high (~900 C)
 
 Li-ion batteries

docs/introduction.rst

@@ -24,7 +24,7 @@ Workflow
 As illustrated in the workflow diagram above, *premise* follows an Extract, Transform, Load (ETL_) process:
 
 Extract the ecoinvent database from a Brightway_ project or from ecospold2_ files.
-Expand the database by adding additional inventories related to future production pathways for certain commodities, such as electricity, steel, cement, etc.
+Expand the database by adding additional inventories for future production pathways for certain commodities, such as electricity, steel, cement, etc.
 Modify the ecoinvent database, focusing primarily on process efficiency improvements and market adjustments.
 Load the updated database back into a Brightway project or export it as a set of CSV files, such as Simapro CSV files.
 
@@ -79,8 +79,9 @@ Additionally, we provided a summary of the main characteristics of each scenario
 
 .. _CarbonBrief: https://www.carbonbrief.org/explainer-how-shared-socioeconomic-pathways-explore-future-climate-change
 
-You can however use any other scenario files generated by REMIND or IMAGE. If you wish to use an IAM file
-which has not been generated by either of these two models, you should refer to the **Mapping** section.
+You can however use any other scenario files generated by REMIND or IMAGE.
+If you wish to use an IAM file which has not been generated by either of these
+two models, you should refer to the **Mapping** section.
 
 .. _maintainers: mailto:[email protected]
 

docs/transform.rst

@@ -93,6 +93,11 @@ Efficiency adjustment
 
 The energy conversion efficiency of power plant datasets for specific technologies is adjusted
 to align with the efficiency changes indicated by the IAM scenario.
+Two approaches are posisble:
+* application of a scaling factor to the inputs of the dataset relative to the current efficiency
+* application of a scaling factor to the inputs of the dataset to match the absolute efficiency given by the IAM scenario
+
+The first approach (default) preserves
 
 Combustion-based powerplants
 ----------------------------
@@ -791,7 +796,7 @@ Typical fuel inputs for these process are natural gas, coal, coal-based coke.
 Emissions of (fossil) CO2 are scaled accordingly.
 
 Regarding the production of secondary steel (using EAF),
-*premise* adjusts the input of electricity based on teh scaling factor
+*premise* adjusts the input of electricity based on the scaling factor
 provided by the IAM scenario.
 
 
@@ -823,7 +828,7 @@ from the steel sector in a given region and year is sequestered and stored,
 The datatset used to that effect is from Meunier_ et al., 2020.
 The dataset described the capture of CO2 from a cement plant, not a steel mill,
 but it is assumed to be an acceptable approximation since the CO2 concentration
-in teh flue gases should not be significantly different.
+in the flue gases should not be significantly different.
 
 To that dataset, *premise* adds another dataset that models the storage
 of the CO2 underground, from Volkart_ et al, 2013.

premise/__init__.py

@@ -1,5 +1,5 @@
 __all__ = ("NewDatabase", "clear_cache", "get_regions_definition")
-__version__ = (2, 0, 0, "dev0")
+__version__ = (2, 0, 0, "dev1")
 
 
 from .new_database import NewDatabase

premise/electricity.py

@@ -32,7 +32,6 @@
 from .utils import get_efficiency_solar_photovoltaics, rescale_exchanges
 from .validation import ElectricityValidation
 
-LOSS_PER_COUNTRY = DATA_DIR / "electricity" / "losses_per_country.csv"
 POWERPLANT_TECHS = VARIABLES_DIR / "electricity_variables.yaml"
 
 logger = create_logger("electricity")
@@ -141,7 +140,7 @@ def get_production_weighted_losses(
         )
 
         transf_loss += (
-            dict_loss["Transformation loss high voltage"]
+            dict_loss.get("Transformation loss high voltage", 0)
             * dict_loss["Production volume"]
         )
 
@@ -169,12 +168,13 @@ def get_production_weighted_losses(
                 "Production volume": 0,
             },
         )
+
         transf_loss += (
-            dict_loss["Transformation loss medium voltage"]
+            dict_loss.get("Transformation loss medium voltage", 0)
             * dict_loss["Production volume"]
         )
         distr_loss += (
-            dict_loss["Transmission loss to medium voltage"]
+            dict_loss.get("Transmission loss to medium voltage", 0)
             * dict_loss["Production volume"]
         )
         cumul_prod += dict_loss["Production volume"]
@@ -195,11 +195,11 @@ def get_production_weighted_losses(
             },
         )
         transf_loss += (
-            dict_loss["Transformation loss low voltage"]
+            dict_loss.get("Transformation loss low voltage", 0)
             * dict_loss["Production volume"]
         )
         distr_loss += (
-            dict_loss["Transmission loss to low voltage"]
+            dict_loss.get("Transmission loss to low voltage", 0)
             * dict_loss["Production volume"]
         )
         cumul_prod += dict_loss["Production volume"]

premise/marginal_mixes.py

@@ -102,10 +102,11 @@ def fetch_volume_change(data: xr.DataArray, start_year: int, end_year: int) -> n
     """
     Calculate the volume change of a market.
     """
+
     return (
         (
-            data.sel(year=end_year).sum(dim="variables")
-            - data.sel(year=start_year).sum(dim="variables")
+            data.interp(year=end_year).sum(dim="variables")
+            - data.interp(year=start_year).sum(dim="variables")
         )
         / (end_year - start_year)
     ).values

premise/new_database.py

@@ -9,11 +9,8 @@
 import multiprocessing
 import os
 import pickle
-import sys
 from datetime import date
-from functools import partial
 from multiprocessing import Pool as ProcessPool
-from multiprocessing import cpu_count
 from multiprocessing.pool import ThreadPool as Pool
 from pathlib import Path
 from typing import List, Union

tests/test_electricity.py

@@ -9,7 +9,6 @@
 from premise.electricity import Electricity
 from premise.filesystem_constants import DATA_DIR
 
-LOSS_PER_COUNTRY = DATA_DIR / "electricity" / "losses_per_country.csv"
 LHV_FUELS = DATA_DIR / "fuels_lower_heating_value.txt"