From 6d81bb17a37b019341056ccd1fdc49ffb0e90962 Mon Sep 17 00:00:00 2001 From: Zeb Nicholls Date: Thu, 17 Mar 2022 21:45:57 +1100 Subject: [PATCH] Add units agnostic harmonisation (#42) * Add failing test of units handling via standard interfaces * Add test of convenience handling method * Pass tests of single method * Fix tests * Add first test of multiple timeseries handling * Pass first multi timeseries test * Pass tests of handling multiple timeseries * Add tests of error handling * Avoid warning * Format * Satisfy stickler * Update CI dependencies * Typo * Fix dependencies again * Test multiple matching overrides * Test default decision tree propogation * Finalise last test * Add example notebook * Docstring * Format * Appease stickler --- .github/workflows/ci-cd-workflow.yml | 8 +- Makefile | 4 +- aneris/convenience.py | 175 +++ aneris/errors.py | 16 + aneris/methods.py | 25 +- doc/source/convenience.ipynb | 1628 +++++++++++++++++++++++ doc/source/tutorial.ipynb | 1815 +++++++++++++++++++++++++- setup.py | 1 + tests/test_convenience.py | 652 +++++++++ tests/test_harmonize.py | 39 + 10 files changed, 4333 insertions(+), 30 deletions(-) create mode 100644 aneris/convenience.py create mode 100644 aneris/errors.py create mode 100644 doc/source/convenience.ipynb create mode 100644 tests/test_convenience.py diff --git a/.github/workflows/ci-cd-workflow.yml b/.github/workflows/ci-cd-workflow.yml index e740a65..9954a7b 100644 --- a/.github/workflows/ci-cd-workflow.yml +++ b/.github/workflows/ci-cd-workflow.yml @@ -42,7 +42,7 @@ jobs: conda env update --file ci/environment-conda-default.yml conda env update --file ci/environment-conda-forge.yml conda env update --file doc/environment.yml - pip install -e .[tests,deploy] + pip install -e .[tests,deploy,units] # if we want to remove stickler # - name: Run format and linting tests # shell: bash -l {0} @@ -103,7 +103,7 @@ jobs: conda env update --file ci/environment-conda-default.yml conda env update --file ci/environment-conda-forge.yml conda env update --file doc/environment.yml - pip install -e .[tests,deploy] + pip install -e .[tests,deploy,units] - name: Install ipopt (${{ runner.os }}) # see https://github.com/conda-forge/ipopt-feedstock/issues/55 if: startsWith(runner.os, 'Windows') @@ -161,7 +161,7 @@ jobs: conda env update --file ci/environment-conda-forge.yml conda env update --file doc/environment.yml conda install -q pandas==${{ matrix.pandas-version }} - pip install .[tests] + pip install .[tests,units] - name: Run tests shell: bash -l {0} run: | @@ -206,7 +206,7 @@ jobs: conda env update --file ci/environment-conda-default.yml conda env update --file ci/environment-conda-forge.yml conda env update --file doc/environment.yml - pip install -e .[tests,deploy] + pip install -e .[tests,deploy,units] - name: Download data shell: bash -l {0} env: diff --git a/Makefile b/Makefile index 43ccc46..d474f0b 100644 --- a/Makefile +++ b/Makefile @@ -87,14 +87,14 @@ docs: $(VENV_DIR) ## make the docs .PHONY: virtual-environment virtual-environment: $(VENV_DIR) ## make virtual environment for development -$(VENV_DIR): $(CI_ENVIRONMENT_CONDA_DEFAULT_FILE) $(CI_ENVIRONMENT_CONDA_FORGE_FILE) $(ENVIRONMENT_DOC_FILE) +$(VENV_DIR): setup.py $(CI_ENVIRONMENT_CONDA_DEFAULT_FILE) $(CI_ENVIRONMENT_CONDA_FORGE_FILE) $(ENVIRONMENT_DOC_FILE) $(CONDA_EXE) config --add channels conda-forge # sets conda-forge as highest priority # install requirements $(CONDA_EXE) env update --name $(CONDA_DEFAULT_ENV) --file $(CI_ENVIRONMENT_CONDA_DEFAULT_FILE) $(CONDA_EXE) env update --name $(CONDA_DEFAULT_ENV) --file $(CI_ENVIRONMENT_CONDA_FORGE_FILE) $(CONDA_EXE) env update --name $(CONDA_DEFAULT_ENV) --file $(ENVIRONMENT_DOC_FILE) # Install development setup - $(VENV_DIR)/bin/pip install -e .[tests,deploy] + $(VENV_DIR)/bin/pip install -e .[tests,deploy,units] touch $(VENV_DIR) .PHONY: release-on-conda diff --git a/aneris/convenience.py b/aneris/convenience.py new file mode 100644 index 0000000..0a9397b --- /dev/null +++ b/aneris/convenience.py @@ -0,0 +1,175 @@ +from openscm_units import unit_registry + +from .harmonize import Harmonizer, default_methods +from .errors import ( + AmbiguousHarmonisationMethod, + MissingHarmonisationYear, + MissingHistoricalError, +) +from .methods import harmonize_factors + + +def harmonise_all(scenarios, history, harmonisation_year, overrides=None): + """ + Harmonise all timeseries in ``scenarios`` to match ``history`` + + Parameters + ---------- + scenarios : :obj:`pd.DataFrame` + :obj:`pd.DataFrame` containing the timeseries to be harmonised + + history : :obj:`pd.DataFrame` + :obj:`pd.DataFrame` containing the historical timeseries to which + ``scenarios`` should be harmonised + + harmonisation_year : int + The year in which ``scenarios`` should be harmonised to ``history`` + + overrides : :obj:`pd.DataFrame` + If not provided, the default aneris decision tree is used. Otherwise, + ``overrides`` must be a :obj:`pd.DataFrame` containing any + specifications for overriding the default aneris methods. Each row + specifies one override. The override method is specified in the + "method" columns. The other columns specify which of the timeseries in + ``scenarios`` should use this override by specifying metadata to match ( + e.g. variable, region). If a cell has a null value (evaluated using + `pd.isnull()`) then that scenario characteristic will not be used for + filtering for that override e.g. if you have a row with "method" equal + to "constant_ratio", region equal to "World" and variable is null then + all timeseries in the World region will use the "constant_ratio" + method. In contrast, if you have a row with "method" equal to + "constant_ratio", region equal to "World" and variable is + "Emissions|CO2" then only timeseries with variable equal to + "Emissions|CO2" and region equal to "World" will use the + "constant_ratio" method. + + Returns + ------- + :obj:`pd.DataFrame` + The harmonised timeseries + + Notes + ----- + This interface is nowhere near as sophisticated as aneris' other + interfaces. It simply harmonises timeseries, it does not check sectoral + sums or other possible errors which can arise when harmonising. If you need + such features, do not use this interface. + + Raises + ------ + MissingHistoricalError + No historical data is provided for a given timeseries + + MissingHarmonisationYear + A value for the harmonisation year is missing or is null in ``history`` + + AmbiguousHarmonisationMethod + ``overrides`` do not uniquely specify the harmonisation method for a + given timeseries + """ + # use groupby to maintain indexes, not sure if there's a better way because + # this will likely be super slow + res = scenarios.groupby(scenarios.index.names).apply( + _harmonise_single, history, harmonisation_year, overrides + ) + + return res + + +def _harmonise_single(timeseries, history, harmonisation_year, overrides): + assert timeseries.shape[0] == 1 + # unclear why we don't use pyam or scmdata for filtering + mdata = { + k: v for k, v in zip(timeseries.index.names, timeseries.index.to_list()[0]) + } + + variable = mdata["variable"] + region = mdata["region"] + + hist_variable = history.index.get_level_values("variable") == variable + hist_region = history.index.get_level_values("region") == region + relevant_hist = history[hist_variable & hist_region] + + if relevant_hist.empty: + error_msg = "No historical data for `{}` `{}`".format(region, variable) + raise MissingHistoricalError(error_msg) + + if harmonisation_year not in relevant_hist: + error_msg = "No historical data for year {} for `{}` `{}`".format( + harmonisation_year, region, variable + ) + raise MissingHarmonisationYear(error_msg) + + if relevant_hist[harmonisation_year].isnull().all(): + error_msg = "Historical data is null for year {} for `{}` `{}`".format( + harmonisation_year, region, variable + ) + raise MissingHarmonisationYear(error_msg) + + # convert units + hist_unit = relevant_hist.index.get_level_values("unit").unique()[0] + relevant_hist = _convert_units( + relevant_hist, current_unit=hist_unit, target_unit=mdata["unit"] + ) + # set index for rest of processing (as units are now consistent) + relevant_hist.index = timeseries.index.copy() + + if overrides is not None: + method = overrides.copy() + for key, value in mdata.items(): + if key in method: + method = method[(method[key] == value) | method[key].isnull()] + + if overrides is not None and method.shape[0] > 1: + error_msg = ( + "Ambiguous harmonisation overrides for metdata `{}`, the " + "following methods match: {}".format(mdata, method) + ) + raise AmbiguousHarmonisationMethod( + "More than one override for metadata: {}".format(mdata) + ) + + if overrides is None or method.empty: + default, _ = default_methods( + relevant_hist, timeseries, base_year=harmonisation_year + ) + method_to_use = default.values[0] + + else: + method_to_use = method["method"].values[0] + + return _harmonise_aligned( + timeseries, relevant_hist, harmonisation_year, method_to_use + ) + + +def _convert_units(inp, current_unit, target_unit): + # would be simpler using scmdata or pyam + out = inp.copy() + out.iloc[:, :] = ( + (out.values * unit_registry(current_unit)).to(target_unit).magnitude + ) + out = out.reset_index("unit") + out["unit"] = target_unit + out = out.set_index("unit", append=True) + + return out + + +def _harmonise_aligned(timeseries, history, harmonisation_year, method): + # seems odd that the methods are stored in a class instance + harmonise_func = Harmonizer._methods[method] + delta = _get_delta(timeseries, history, method, harmonisation_year) + + return harmonise_func(timeseries, delta, harmonize_year=harmonisation_year) + + +def _get_delta(timeseries, history, method, harmonisation_year): + if method == "budget": + return history + + offset, ratio = harmonize_factors(timeseries, history, harmonisation_year) + if "ratio" in method: + return ratio + + return offset diff --git a/aneris/errors.py b/aneris/errors.py new file mode 100644 index 0000000..e5fae02 --- /dev/null +++ b/aneris/errors.py @@ -0,0 +1,16 @@ +class AmbiguousHarmonisationMethod(ValueError): + """ + Error raised when harmonisation methods are ambiguous + """ + + +class MissingHistoricalError(ValueError): + """ + Error raised when historical data is missing + """ + + +class MissingHarmonisationYear(ValueError): + """ + Error raised when the harmonisation year is missing + """ diff --git a/aneris/methods.py b/aneris/methods.py index f99fb4d..8d9243b 100644 --- a/aneris/methods.py +++ b/aneris/methods.py @@ -139,9 +139,10 @@ def reduce_offset(df, offset, final_year='2050', harmonize_year='2015'): df = df.copy() yi, yf = int(harmonize_year), int(final_year) numcols = utils.numcols(df) + numcols_int = [int(v) for v in numcols] # get factors that reduce from 1 to 0; factors before base year are > 1 f = lambda year: -(year - yi) / float(yf - yi) + 1 - factors = [f(int(year)) if year <= final_year else 0.0 for year in numcols] + factors = [f(year) if year <= yf else 0.0 for year in numcols_int] # add existing values to offset time series offsets = pd.DataFrame(np.outer(offset, factors), columns=numcols, index=offset.index) @@ -171,17 +172,19 @@ def reduce_ratio(df, ratios, final_year='2050', harmonize_year='2015'): df = df.copy() yi, yf = int(harmonize_year), int(final_year) numcols = utils.numcols(df) + numcols_int = [int(v) for v in numcols] # get factors that reduce from 1 to 0, but replace with 1s in years prior # to harmonization f = lambda year: -(year - yi) / float(yf - yi) + 1 - prefactors = [f(int(harmonize_year)) - for year in numcols if year < harmonize_year] - postfactors = [f(int(year)) if year <= final_year else 0.0 - for year in numcols if year >= harmonize_year] + prefactors = [f(yi) + for year in numcols_int if year < yi] + postfactors = [f(year) if year <= yf else 0.0 + for year in numcols_int if year >= yi] factors = prefactors + postfactors # multiply existing values by ratio time series ratios = pd.DataFrame(np.outer(ratios - 1, factors), columns=numcols, index=ratios.index) + 1 + df[numcols] = df[numcols] * ratios return df @@ -402,7 +405,9 @@ def default_method_choice( return 'constant_offset' else: # is this co2? - if row.gas == 'CO2': + # ZN: This gas dependence isn't documented in the default + # decision tree + if hasattr(row, "gas") and row.gas == 'CO2': return ratio_method # is cov big? if np.isfinite(row['cov']) and row['cov'] > luc_cov_threshold: @@ -469,8 +474,12 @@ def default_methods(hist, model, base_year, method_choice=None, **kwargs): kwargs['luc_cov_threshold'] = 10 y = str(base_year) - h = hist[y] - m = model[y] + try: + h = hist[base_year] + m = model[base_year] + except KeyError: + h = hist[y] + m = model[y] dH = (h - m).abs() / h f = h / m dM = (model.max(axis=1) - model.min(axis=1)).abs() / model.max(axis=1) diff --git a/doc/source/convenience.ipynb b/doc/source/convenience.ipynb new file mode 100644 index 0000000..5de6460 --- /dev/null +++ b/doc/source/convenience.ipynb @@ -0,0 +1,1628 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8dfb7a5e", + "metadata": {}, + "source": [ + "# Convenience\n", + "\n", + "This is an example of aneris' `convenience` module. This module doesn't have anywhere near the error checking of aneris' other features, but it does make it slightly simpler to calibrate timeseries and it adds unit handling onto aneris' harmonisation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c8009cf2", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "import aneris.tutorial\n", + "import aneris.convenience" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "98a00062", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"figure.figsize\"] = (12, 8)" + ] + }, + { + "cell_type": "markdown", + "id": "b961c1bd", + "metadata": {}, + "source": [ + "We start by loading some dummy data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "15dbb45b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelScenarioRegionVariableUnit20052010202020302040205020602070208020902100
0modelsspnregioncprefix|Emissions|BC|sector1|suffixMt BC/yr10.011.012.013.014.015.016.017.018.019.020.0
1modelsspnregioncprefix|Emissions|BC|sector2|suffixMt BC/yr11.012.013.014.015.016.017.018.019.020.021.0
2modelsspnregioncprefix|Emissions|BC|suffixMt BC/yr21.023.025.027.029.031.033.035.037.039.041.0
3modelsspnWorldprefix|Emissions|BC|sector1|suffixMt BC/yr10.011.012.013.014.015.016.017.018.019.020.0
4modelsspnWorldprefix|Emissions|BC|sector2|suffixMt BC/yr11.012.013.014.015.016.017.018.019.020.021.0
5modelsspnWorldprefix|Emissions|BC|suffixMt BC/yr21.023.025.027.029.031.033.035.037.039.041.0
\n", + "
" + ], + "text/plain": [ + " Model Scenario Region Variable Unit \\\n", + "0 model sspn regionc prefix|Emissions|BC|sector1|suffix Mt BC/yr \n", + "1 model sspn regionc prefix|Emissions|BC|sector2|suffix Mt BC/yr \n", + "2 model sspn regionc prefix|Emissions|BC|suffix Mt BC/yr \n", + "3 model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr \n", + "4 model sspn World prefix|Emissions|BC|sector2|suffix Mt BC/yr \n", + "5 model sspn World prefix|Emissions|BC|suffix Mt BC/yr \n", + "\n", + " 2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 \n", + "0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 \n", + "1 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 \n", + "2 21.0 23.0 25.0 27.0 29.0 31.0 33.0 35.0 37.0 39.0 41.0 \n", + "3 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 \n", + "4 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 \n", + "5 21.0 23.0 25.0 27.0 29.0 31.0 33.0 35.0 37.0 39.0 41.0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model, hist, _ = aneris.tutorial.load_data()\n", + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5abe03a9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelScenarioRegionVariableUnit200020012002200320042005
0Historyscenregionaprefix|Emissions|BC|sector1|suffixMt BC/yr123456
1Historyscenregionaprefix|Emissions|BC|sector2|suffixMt BC/yr234567
2Historyscenregionaprefix|Emissions|BC|suffixMt BC/yr35791113
3Historyscenregionbprefix|Emissions|BC|sector1|suffixMt BC/yr345678
4Historyscenregionbprefix|Emissions|BC|sector2|suffixMt BC/yr456789
5Historyscenregionbprefix|Emissions|BC|suffixMt BC/yr7911131517
6HistoryscenWorldprefix|Emissions|BC|sector1|suffixMt BC/yr468101214
7HistoryscenWorldprefix|Emissions|BC|sector2|suffixMt BC/yr6810121416
8HistoryscenWorldprefix|Emissions|BC|suffixMt BC/yr101418222630
\n", + "
" + ], + "text/plain": [ + " Model Scenario Region Variable Unit \\\n", + "0 History scen regiona prefix|Emissions|BC|sector1|suffix Mt BC/yr \n", + "1 History scen regiona prefix|Emissions|BC|sector2|suffix Mt BC/yr \n", + "2 History scen regiona prefix|Emissions|BC|suffix Mt BC/yr \n", + "3 History scen regionb prefix|Emissions|BC|sector1|suffix Mt BC/yr \n", + "4 History scen regionb prefix|Emissions|BC|sector2|suffix Mt BC/yr \n", + "5 History scen regionb prefix|Emissions|BC|suffix Mt BC/yr \n", + "6 History scen World prefix|Emissions|BC|sector1|suffix Mt BC/yr \n", + "7 History scen World prefix|Emissions|BC|sector2|suffix Mt BC/yr \n", + "8 History scen World prefix|Emissions|BC|suffix Mt BC/yr \n", + "\n", + " 2000 2001 2002 2003 2004 2005 \n", + "0 1 2 3 4 5 6 \n", + "1 2 3 4 5 6 7 \n", + "2 3 5 7 9 11 13 \n", + "3 3 4 5 6 7 8 \n", + "4 4 5 6 7 8 9 \n", + "5 7 9 11 13 15 17 \n", + "6 4 6 8 10 12 14 \n", + "7 6 8 10 12 14 16 \n", + "8 10 14 18 22 26 30 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hist" + ] + }, + { + "cell_type": "markdown", + "id": "8de09726", + "metadata": {}, + "source": [ + "The data must be set up slightly differently to use the convenience methods (it should match the format provided by [scmdata](https://github.com/openscm/scmdata) and [pyam](https://github.com/IAMconsortium/pyam) aka the IAMC style)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "717e01ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
20052010202020302040205020602070208020902100
modelscenarioregionvariableunit
modelsspnregioncprefix|Emissions|BC|sector1|suffixMt BC/yr10.011.012.013.014.015.016.017.018.019.020.0
prefix|Emissions|BC|sector2|suffixMt BC/yr11.012.013.014.015.016.017.018.019.020.021.0
prefix|Emissions|BC|suffixMt BC/yr21.023.025.027.029.031.033.035.037.039.041.0
Worldprefix|Emissions|BC|sector1|suffixMt BC/yr10.011.012.013.014.015.016.017.018.019.020.0
prefix|Emissions|BC|sector2|suffixMt BC/yr11.012.013.014.015.016.017.018.019.020.021.0
prefix|Emissions|BC|suffixMt BC/yr21.023.025.027.029.031.033.035.037.039.041.0
\n", + "
" + ], + "text/plain": [ + " 2005 \\\n", + "model scenario region variable unit \n", + "model sspn regionc prefix|Emissions|BC|sector1|suffix Mt BC/yr 10.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 11.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 21.0 \n", + " World prefix|Emissions|BC|sector1|suffix Mt BC/yr 10.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 11.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 21.0 \n", + "\n", + " 2010 \\\n", + "model scenario region variable unit \n", + "model sspn regionc prefix|Emissions|BC|sector1|suffix Mt BC/yr 11.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 12.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 23.0 \n", + " World prefix|Emissions|BC|sector1|suffix Mt BC/yr 11.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 12.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 23.0 \n", + "\n", + " 2020 \\\n", + "model scenario region variable unit \n", + "model sspn regionc prefix|Emissions|BC|sector1|suffix Mt BC/yr 12.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 13.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 25.0 \n", + " World prefix|Emissions|BC|sector1|suffix Mt BC/yr 12.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 13.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 25.0 \n", + "\n", + " 2030 \\\n", + "model scenario region variable unit \n", + "model sspn regionc prefix|Emissions|BC|sector1|suffix Mt BC/yr 13.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 14.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 27.0 \n", + " World prefix|Emissions|BC|sector1|suffix Mt BC/yr 13.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 14.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 27.0 \n", + "\n", + " 2040 \\\n", + "model scenario region variable unit \n", + "model sspn regionc prefix|Emissions|BC|sector1|suffix Mt BC/yr 14.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 15.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 29.0 \n", + " World prefix|Emissions|BC|sector1|suffix Mt BC/yr 14.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 15.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 29.0 \n", + "\n", + " 2050 \\\n", + "model scenario region variable unit \n", + "model sspn regionc prefix|Emissions|BC|sector1|suffix Mt BC/yr 15.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 16.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 31.0 \n", + " World prefix|Emissions|BC|sector1|suffix Mt BC/yr 15.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 16.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 31.0 \n", + "\n", + " 2060 \\\n", + "model scenario region variable unit \n", + "model sspn regionc prefix|Emissions|BC|sector1|suffix Mt BC/yr 16.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 17.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 33.0 \n", + " World prefix|Emissions|BC|sector1|suffix Mt BC/yr 16.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 17.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 33.0 \n", + "\n", + " 2070 \\\n", + "model scenario region variable unit \n", + "model sspn regionc prefix|Emissions|BC|sector1|suffix Mt BC/yr 17.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 18.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 35.0 \n", + " World prefix|Emissions|BC|sector1|suffix Mt BC/yr 17.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 18.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 35.0 \n", + "\n", + " 2080 \\\n", + "model scenario region variable unit \n", + "model sspn regionc prefix|Emissions|BC|sector1|suffix Mt BC/yr 18.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 19.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 37.0 \n", + " World prefix|Emissions|BC|sector1|suffix Mt BC/yr 18.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 19.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 37.0 \n", + "\n", + " 2090 2100 \n", + "model scenario region variable unit \n", + "model sspn regionc prefix|Emissions|BC|sector1|suffix Mt BC/yr 19.0 20.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 20.0 21.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 39.0 41.0 \n", + " World prefix|Emissions|BC|sector1|suffix Mt BC/yr 19.0 20.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 20.0 21.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 39.0 41.0 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def convert_to_iamc_style(inp, idx=(\"model\", \"scenario\", \"region\", \"variable\", \"unit\")):\n", + " out = inp.copy()\n", + " out.columns = out.columns.str.lower()\n", + " out = out.set_index(list(idx))\n", + " out.columns = out.columns.map(int)\n", + " \n", + " return out\n", + "\n", + "hist_iamc_style = convert_to_iamc_style(hist)\n", + "model_iamc_style = convert_to_iamc_style(model)\n", + "model_iamc_style" + ] + }, + { + "cell_type": "markdown", + "id": "2cf42a87", + "metadata": {}, + "source": [ + "We're also going to only harmonise the World data." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ea4ec78a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
20052010202020302040205020602070208020902100
modelscenarioregionvariableunit
modelsspnWorldprefix|Emissions|BC|sector1|suffixMt BC/yr10.011.012.013.014.015.016.017.018.019.020.0
prefix|Emissions|BC|sector2|suffixMt BC/yr11.012.013.014.015.016.017.018.019.020.021.0
prefix|Emissions|BC|suffixMt BC/yr21.023.025.027.029.031.033.035.037.039.041.0
\n", + "
" + ], + "text/plain": [ + " 2005 2010 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 10.0 11.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 11.0 12.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 21.0 23.0 \n", + "\n", + " 2020 2030 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 12.0 13.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 13.0 14.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 25.0 27.0 \n", + "\n", + " 2040 2050 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 14.0 15.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 15.0 16.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 29.0 31.0 \n", + "\n", + " 2060 2070 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 16.0 17.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 17.0 18.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 33.0 35.0 \n", + "\n", + " 2080 2090 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 18.0 19.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 19.0 20.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 37.0 39.0 \n", + "\n", + " 2100 \n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 20.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 21.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 41.0 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hist_iamc_style = hist_iamc_style[hist_iamc_style.index.get_level_values(\"region\") == \"World\"]\n", + "model_iamc_style = model_iamc_style[model_iamc_style.index.get_level_values(\"region\") == \"World\"]\n", + "model_iamc_style" + ] + }, + { + "cell_type": "markdown", + "id": "4580e81f", + "metadata": {}, + "source": [ + "Finally, we alter the units of the historical data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7ad5f9a2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
200020012002200320042005
modelscenarioregionvariableunit
HistoryscenWorldprefix|Emissions|BC|sector1|suffixkt BC / yr400060008000100001200014000
prefix|Emissions|BC|sector2|suffixkt BC / yr6000800010000120001400016000
prefix|Emissions|BC|suffixkt BC / yr100001400018000220002600030000
\n", + "
" + ], + "text/plain": [ + " 2000 \\\n", + "model scenario region variable unit \n", + "History scen World prefix|Emissions|BC|sector1|suffix kt BC / yr 4000 \n", + " prefix|Emissions|BC|sector2|suffix kt BC / yr 6000 \n", + " prefix|Emissions|BC|suffix kt BC / yr 10000 \n", + "\n", + " 2001 \\\n", + "model scenario region variable unit \n", + "History scen World prefix|Emissions|BC|sector1|suffix kt BC / yr 6000 \n", + " prefix|Emissions|BC|sector2|suffix kt BC / yr 8000 \n", + " prefix|Emissions|BC|suffix kt BC / yr 14000 \n", + "\n", + " 2002 \\\n", + "model scenario region variable unit \n", + "History scen World prefix|Emissions|BC|sector1|suffix kt BC / yr 8000 \n", + " prefix|Emissions|BC|sector2|suffix kt BC / yr 10000 \n", + " prefix|Emissions|BC|suffix kt BC / yr 18000 \n", + "\n", + " 2003 \\\n", + "model scenario region variable unit \n", + "History scen World prefix|Emissions|BC|sector1|suffix kt BC / yr 10000 \n", + " prefix|Emissions|BC|sector2|suffix kt BC / yr 12000 \n", + " prefix|Emissions|BC|suffix kt BC / yr 22000 \n", + "\n", + " 2004 \\\n", + "model scenario region variable unit \n", + "History scen World prefix|Emissions|BC|sector1|suffix kt BC / yr 12000 \n", + " prefix|Emissions|BC|sector2|suffix kt BC / yr 14000 \n", + " prefix|Emissions|BC|suffix kt BC / yr 26000 \n", + "\n", + " 2005 \n", + "model scenario region variable unit \n", + "History scen World prefix|Emissions|BC|sector1|suffix kt BC / yr 14000 \n", + " prefix|Emissions|BC|sector2|suffix kt BC / yr 16000 \n", + " prefix|Emissions|BC|suffix kt BC / yr 30000 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hist_iamc_style *= 1000\n", + "hist_iamc_style.index = hist_iamc_style.index.set_levels([\"kt BC / yr\"], level=\"unit\")\n", + "hist_iamc_style" + ] + }, + { + "cell_type": "markdown", + "id": "3be14d62", + "metadata": {}, + "source": [ + "Now we harmonise the data using the convenience methods. Note how the historical data's units have been converted to the input data's units before harmonisation." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d75af5d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
20052010202020302040205020602070208020902100
modelscenarioregionvariableunit
modelsspnWorldprefix|Emissions|BC|sector1|suffixMt BC/yr14.015.10666715.84000016.46666716.98666717.40000017.70666717.90666718.019.020.0
prefix|Emissions|BC|sector2|suffixMt BC/yr16.017.09090917.72727318.24242418.63636418.90909119.06060619.09090919.020.021.0
prefix|Emissions|BC|suffixMt BC/yr30.032.20000033.57142934.71428635.62857136.31428636.77142937.00000037.039.041.0
\n", + "
" + ], + "text/plain": [ + " 2005 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 14.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 16.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 30.0 \n", + "\n", + " 2010 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 15.106667 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 17.090909 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 32.200000 \n", + "\n", + " 2020 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 15.840000 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 17.727273 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 33.571429 \n", + "\n", + " 2030 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 16.466667 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 18.242424 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 34.714286 \n", + "\n", + " 2040 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 16.986667 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 18.636364 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 35.628571 \n", + "\n", + " 2050 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 17.400000 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 18.909091 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 36.314286 \n", + "\n", + " 2060 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 17.706667 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 19.060606 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 36.771429 \n", + "\n", + " 2070 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 17.906667 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 19.090909 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 37.000000 \n", + "\n", + " 2080 2090 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 18.0 19.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 19.0 20.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 37.0 39.0 \n", + "\n", + " 2100 \n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 20.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 21.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 41.0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_harmonised = aneris.convenience.harmonise_all(\n", + " scenarios=model_iamc_style,\n", + " history=hist_iamc_style,\n", + " harmonisation_year=2005,\n", + ")\n", + "model_harmonised" + ] + }, + { + "cell_type": "markdown", + "id": "d5b5b90e", + "metadata": {}, + "source": [ + "Make a plot to examine (doing this without scmdata/pyam is fiddly)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e9646c7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model_iamc_style_pdf = model_iamc_style.copy()\n", + "model_iamc_style_pdf[\"harmonised\"] = False\n", + "\n", + "model_harmonised_pdf = model_harmonised.copy()\n", + "model_harmonised_pdf[\"harmonised\"] = True\n", + "\n", + "pd.concat([model_iamc_style_pdf, model_harmonised_pdf]).groupby([\"harmonised\", \"variable\"]).mean().T.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "dabefbe4", + "metadata": {}, + "source": [ + "The above plot makes clear that the default harmonisation method is `reduce_ratio_2080`. We can override this using the `overrides` argument, which takes a pandas DataFrame as input." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "40bff52b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
variablemethod
0prefix|Emissions|BC|suffixreduce_offset_2030
1prefix|Emissions|BC|sector1|suffixreduce_ratio_2100
\n", + "
" + ], + "text/plain": [ + " variable method\n", + "0 prefix|Emissions|BC|suffix reduce_offset_2030\n", + "1 prefix|Emissions|BC|sector1|suffix reduce_ratio_2100" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "overrides = pd.DataFrame([\n", + " {\"variable\": \"prefix|Emissions|BC|suffix\", \"method\": \"reduce_offset_2030\"},\n", + " {\"variable\": \"prefix|Emissions|BC|sector1|suffix\", \"method\": \"reduce_ratio_2100\"},\n", + "])\n", + "overrides" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "816b78a7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
20052010202020302040205020602070208020902100
modelscenarioregionvariableunit
modelsspnWorldprefix|Emissions|BC|sector1|suffixMt BC/yr14.015.16842116.04210516.83157917.53684218.15789518.69473719.14736819.51578919.820.0
prefix|Emissions|BC|sector2|suffixMt BC/yr16.017.09090917.72727318.24242418.63636418.90909119.06060619.09090919.00000020.021.0
prefix|Emissions|BC|suffixMt BC/yr30.030.20000028.60000027.00000029.00000031.00000033.00000035.00000037.00000039.041.0
\n", + "
" + ], + "text/plain": [ + " 2005 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 14.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 16.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 30.0 \n", + "\n", + " 2010 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 15.168421 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 17.090909 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 30.200000 \n", + "\n", + " 2020 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 16.042105 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 17.727273 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 28.600000 \n", + "\n", + " 2030 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 16.831579 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 18.242424 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 27.000000 \n", + "\n", + " 2040 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 17.536842 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 18.636364 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 29.000000 \n", + "\n", + " 2050 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 18.157895 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 18.909091 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 31.000000 \n", + "\n", + " 2060 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 18.694737 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 19.060606 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 33.000000 \n", + "\n", + " 2070 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 19.147368 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 19.090909 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 35.000000 \n", + "\n", + " 2080 \\\n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 19.515789 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 19.000000 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 37.000000 \n", + "\n", + " 2090 2100 \n", + "model scenario region variable unit \n", + "model sspn World prefix|Emissions|BC|sector1|suffix Mt BC/yr 19.8 20.0 \n", + " prefix|Emissions|BC|sector2|suffix Mt BC/yr 20.0 21.0 \n", + " prefix|Emissions|BC|suffix Mt BC/yr 39.0 41.0 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_harmonised_overrides = aneris.convenience.harmonise_all(\n", + " scenarios=model_iamc_style,\n", + " history=hist_iamc_style,\n", + " harmonisation_year=2005,\n", + " overrides=overrides\n", + ")\n", + "model_harmonised_overrides" + ] + }, + { + "cell_type": "markdown", + "id": "9c298532", + "metadata": {}, + "source": [ + "A quick plot shows the change in output as a result of overriding the method." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a2e6339e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "model_iamc_style_pdf = model_iamc_style.copy()\n", + "model_iamc_style_pdf[\"harmonised\"] = False\n", + "\n", + "model_harmonised_overrides_pdf = model_harmonised_overrides.copy()\n", + "model_harmonised_overrides_pdf[\"harmonised\"] = True\n", + "\n", + "pd.concat([model_iamc_style_pdf, model_harmonised_overrides_pdf]).groupby([\"harmonised\", \"variable\"]).mean().T.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "716c23e3", + "metadata": {}, + "source": [ + "It should be noted that the sectoral sum is no longer valid. The convenience methods do not include the sectoral sum checks that aneris' other methods do. They are designed for a quick way to harmonise timeseries without all the extra checks. The extra checks are only provided by aneris' other interfaces." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/source/tutorial.ipynb b/doc/source/tutorial.ipynb index 31d80dc..3f1f7b5 100644 --- a/doc/source/tutorial.ipynb +++ b/doc/source/tutorial.ipynb @@ -48,9 +48,33 @@ }, "outputs": [ { - "output_type": "stream", "name": "stderr", - "text": "INFO:root:Downselecting prefix|suffix variables\nINFO:root:Translating to standard format\nINFO:root:Aggregating historical values to native regions\nINFO:root:Harmonizing (with example methods):\nINFO:root: method default \\\nregion gas sector units \nregionc BC prefix|sector1|suffix kt reduce_ratio_2100 reduce_ratio_2050 \n prefix|sector2|suffix kt reduce_ratio_2050 reduce_ratio_2050 \n prefix|suffix kt reduce_ratio_2050 reduce_ratio_2050 \n\n override \nregion gas sector units \nregionc BC prefix|sector1|suffix kt reduce_ratio_2100 \n prefix|sector2|suffix kt NaN \n prefix|suffix kt NaN \nINFO:root:and override methods:\nINFO:root:region gas sector units\nregionc BC prefix|sector1|suffix kt reduce_ratio_2100\nName: method, dtype: object\nINFO:root:Harmonizing with reduce_ratio_2100\nINFO:root:Harmonizing with reduce_ratio_2050\nWARNING:root:Removing sector aggregates. Recalculating with harmonized totals.\nINFO:root:Translating to IAMC template\n" + "output_type": "stream", + "text": [ + "INFO:root:Downselecting prefix|suffix variables\n", + "INFO:root:Translating to standard format\n", + "INFO:root:Aggregating historical values to native regions\n", + "INFO:root:Harmonizing (with example methods):\n", + "INFO:root: method default \\\n", + "region gas sector units \n", + "regionc BC prefix|sector1|suffix kt reduce_ratio_2100 reduce_ratio_2050 \n", + " prefix|sector2|suffix kt reduce_ratio_2050 reduce_ratio_2050 \n", + " prefix|suffix kt reduce_ratio_2050 reduce_ratio_2050 \n", + "\n", + " override \n", + "region gas sector units \n", + "regionc BC prefix|sector1|suffix kt reduce_ratio_2100 \n", + " prefix|sector2|suffix kt NaN \n", + " prefix|suffix kt NaN \n", + "INFO:root:and override methods:\n", + "INFO:root:region gas sector units\n", + "regionc BC prefix|sector1|suffix kt reduce_ratio_2100\n", + "Name: method, dtype: object\n", + "INFO:root:Harmonizing with reduce_ratio_2100\n", + "INFO:root:Harmonizing with reduce_ratio_2050\n", + "WARNING:root:Removing sector aggregates. Recalculating with harmonized totals.\n", + "INFO:root:Translating to IAMC template\n" + ] } ], "source": [ @@ -94,13 +118,109 @@ "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": " Model Scenario Region Variable Year \\\n0 History scen World prefix|Emissions|BC|sector1|suffix 2000 \n1 History scen World prefix|Emissions|BC|sector2|suffix 2000 \n2 History scen World prefix|Emissions|BC|suffix 2000 \n3 model sspn World prefix|Emissions|BC|sector1|suffix 2000 \n4 model sspn World prefix|Emissions|BC|sector2|suffix 2000 \n\n Emissions Label \n0 4.0 History prefix|Emissions|BC|sector1|suffix \n1 6.0 History prefix|Emissions|BC|sector2|suffix \n2 10.0 History prefix|Emissions|BC|suffix \n3 NaN model prefix|Emissions|BC|sector1|suffix \n4 NaN model prefix|Emissions|BC|sector2|suffix ", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ModelScenarioRegionVariableYearEmissionsLabel
0HistoryscenWorldprefix|Emissions|BC|sector1|suffix20004.0History prefix|Emissions|BC|sector1|suffix
1HistoryscenWorldprefix|Emissions|BC|sector2|suffix20006.0History prefix|Emissions|BC|sector2|suffix
2HistoryscenWorldprefix|Emissions|BC|suffix200010.0History prefix|Emissions|BC|suffix
3modelsspnWorldprefix|Emissions|BC|sector1|suffix2000NaNmodel prefix|Emissions|BC|sector1|suffix
4modelsspnWorldprefix|Emissions|BC|sector2|suffix2000NaNmodel prefix|Emissions|BC|sector2|suffix
\n
" + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelScenarioRegionVariableYearEmissionsLabel
0HistoryscenWorldprefix|Emissions|BC|sector1|suffix20004.0History prefix|Emissions|BC|sector1|suffix
1HistoryscenWorldprefix|Emissions|BC|sector2|suffix20006.0History prefix|Emissions|BC|sector2|suffix
2HistoryscenWorldprefix|Emissions|BC|suffix200010.0History prefix|Emissions|BC|suffix
3modelsspnWorldprefix|Emissions|BC|sector1|suffix2000NaNmodel prefix|Emissions|BC|sector1|suffix
4modelsspnWorldprefix|Emissions|BC|sector2|suffix2000NaNmodel prefix|Emissions|BC|sector2|suffix
\n", + "
" + ], + "text/plain": [ + " Model Scenario Region Variable Year \\\n", + "0 History scen World prefix|Emissions|BC|sector1|suffix 2000 \n", + "1 History scen World prefix|Emissions|BC|sector2|suffix 2000 \n", + "2 History scen World prefix|Emissions|BC|suffix 2000 \n", + "3 model sspn World prefix|Emissions|BC|sector1|suffix 2000 \n", + "4 model sspn World prefix|Emissions|BC|sector2|suffix 2000 \n", + "\n", + " Emissions Label \n", + "0 4.0 History prefix|Emissions|BC|sector1|suffix \n", + "1 6.0 History prefix|Emissions|BC|sector2|suffix \n", + "2 10.0 History prefix|Emissions|BC|suffix \n", + "3 NaN model prefix|Emissions|BC|sector1|suffix \n", + "4 NaN model prefix|Emissions|BC|sector2|suffix " + ] }, + "execution_count": 5, "metadata": {}, - "execution_count": 5 + "output_type": "execute_result" } ], "source": [ @@ -113,23 +233,1686 @@ "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, + "execution_count": 6, "metadata": {}, - "execution_count": 6 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { - "text/plain": "
", - "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "image/png": "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\n" + "image/png": "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\n", + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ @@ -161,9 +1944,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3-final" + "version": "3.9.5" } }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} diff --git a/setup.py b/setup.py index a37999a..9abad7b 100755 --- a/setup.py +++ b/setup.py @@ -34,6 +34,7 @@ EXTRA_REQUIREMENTS = { 'tests': ['pytest', 'coverage', 'coveralls', 'pytest', 'pytest-cov'], 'deploy': ['twine', 'setuptools', 'wheel'], + 'units': ['openscm-units'] } diff --git a/tests/test_convenience.py b/tests/test_convenience.py new file mode 100644 index 0000000..b3c6a9d --- /dev/null +++ b/tests/test_convenience.py @@ -0,0 +1,652 @@ +import re + +import numpy as np +import numpy.testing as npt +import pandas as pd +import pandas.testing as pdt +import pytest + +from aneris.convenience import harmonise_all +from aneris.errors import ( + AmbiguousHarmonisationMethod, + MissingHarmonisationYear, + MissingHistoricalError, +) + +pytest.importorskip("pint") +import pint.errors + + +@pytest.mark.parametrize( + "method,exp_res", + ( + ( + "constant_ratio", + { + 2010: 10 * 1.1, + 2030: 5 * 1.1, + 2050: 3 * 1.1, + 2100: 1 * 1.1, + }, + ), + ( + "reduce_ratio_2050", + { + 2010: 11, + 2030: 5 * 1.05, + 2050: 3, + 2100: 1, + }, + ), + ( + "reduce_ratio_2030", + { + 2010: 11, + 2030: 5, + 2050: 3, + 2100: 1, + }, + ), + ( + "reduce_ratio_2150", + { + 2010: 11, + 2030: 5 * (1 + 0.1 * (140 - 20) / 140), + 2050: 3 * (1 + 0.1 * (140 - 40) / 140), + 2100: 1 * (1 + 0.1 * (140 - 90) / 140), + }, + ), + ( + "constant_offset", + { + 2010: 10 + 1, + 2030: 5 + 1, + 2050: 3 + 1, + 2100: 1 + 1, + }, + ), + ( + "reduce_offset_2050", + { + 2010: 11, + 2030: 5 + 0.5, + 2050: 3, + 2100: 1, + }, + ), + ( + "reduce_offset_2030", + { + 2010: 11, + 2030: 5, + 2050: 3, + 2100: 1, + }, + ), + ( + "reduce_offset_2150", + { + 2010: 11, + 2030: 5 + 1 * (140 - 20) / 140, + 2050: 3 + 1 * (140 - 40) / 140, + 2100: 1 + 1 * (140 - 90) / 140, + }, + ), + ( + "model_zero", + { + 2010: 10 + 1, + 2030: 5 + 1, + 2050: 3 + 1, + 2100: 1 + 1, + }, + ), + ( + "hist_zero", + { + 2010: 10, + 2030: 5, + 2050: 3, + 2100: 1, + }, + ), + ), +) +def test_different_unit_handling(method, exp_res): + idx = ["variable", "unit", "region", "model", "scenario"] + + hist = pd.DataFrame( + { + "variable": ["Emissions|CO2"], + "unit": ["MtC / yr"], + "region": ["World"], + "model": ["CEDS"], + "scenario": ["historical"], + 2010: [11000], + } + ).set_index(idx) + + scenario = pd.DataFrame( + { + "variable": ["Emissions|CO2"], + "unit": ["GtC / yr"], + "region": ["World"], + "model": ["IAM"], + "scenario": ["abc"], + 2010: [10], + 2030: [5], + 2050: [3], + 2100: [1], + } + ).set_index(idx) + + overrides = [{"variable": "Emissions|CO2", "method": method}] + overrides = pd.DataFrame(overrides) + + res = harmonise_all( + scenarios=scenario, + history=hist, + harmonisation_year=2010, + overrides=overrides, + ) + + for year, val in exp_res.items(): + npt.assert_allclose(res[year], val) + + +@pytest.fixture() +def hist_df(): + idx = ["variable", "unit", "region", "model", "scenario"] + + hist = pd.DataFrame( + { + "variable": ["Emissions|CO2", "Emissions|CH4"], + "unit": ["MtCO2 / yr", "MtCH4 / yr"], + "region": ["World"] * 2, + "model": ["CEDS"] * 2, + "scenario": ["historical"] * 2, + 2010: [11000 * 44 / 12, 200], + 2015: [12000 * 44 / 12, 250], + 2020: [13000 * 44 / 12, 300], + } + ).set_index(idx) + + return hist + + +@pytest.fixture() +def scenarios_df(): + idx = ["variable", "unit", "region", "model", "scenario"] + + scenario = pd.DataFrame( + { + "variable": ["Emissions|CO2", "Emissions|CH4"], + "unit": ["GtC / yr", "GtCH4 / yr"], + "region": ["World"] * 2, + "model": ["IAM"] * 2, + "scenario": ["abc"] * 2, + 2010: [10, 0.1], + 2015: [11, 0.15], + 2020: [5, 0.25], + 2030: [5, 0.1], + 2050: [3, 0.05], + 2100: [1, 0.03], + } + ).set_index(idx) + + return scenario + + +@pytest.mark.parametrize("extra_col", (False, "mip_era")) +@pytest.mark.parametrize( + "harmonisation_year,scales", + ( + (2010, [1.1, 2]), + (2015, [12 / 11, 25 / 15]), + ), +) +def test_different_unit_handling_multiple_timeseries_constant_ratio( + hist_df, + scenarios_df, + extra_col, + harmonisation_year, + scales, +): + if extra_col: + scenarios_df[extra_col] = "test" + scenarios_df = scenarios_df.set_index(extra_col, append=True) + + exp = scenarios_df.multiply(scales, axis=0) + + overrides = [{"method": "constant_ratio"}] + overrides = pd.DataFrame(overrides) + + res = harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=harmonisation_year, + overrides=overrides, + ) + + pdt.assert_frame_equal(res, exp) + + +@pytest.mark.parametrize( + "harmonisation_year,offset", + ( + (2010, [1, 0.1]), + (2015, [1, 0.1]), + (2020, [8, 0.05]), + ), +) +def test_different_unit_handling_multiple_timeseries_constant_offset( + hist_df, + scenarios_df, + harmonisation_year, + offset, +): + exp = scenarios_df.add(offset, axis=0) + + overrides = [{"method": "constant_offset"}] + overrides = pd.DataFrame(overrides) + + res = harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=harmonisation_year, + overrides=overrides, + ) + + pdt.assert_frame_equal(res, exp) + + +def test_different_unit_handling_multiple_timeseries_overrides( + hist_df, + scenarios_df, +): + harmonisation_year = 2015 + + exp = scenarios_df.sort_index() + for r in exp.index: + for c in exp: + if "CO2" in r[0]: + harm_year_ratio = 12 / 11 + + if c >= 2050: + sf = 1 + elif c <= 2015: + # this custom pre-harmonisation year logic doesn't apply to + # offsets which seems surprising + sf = harm_year_ratio + else: + sf = 1 + ( + (harm_year_ratio - 1) * (2050 - c) / (2050 - harmonisation_year) + ) + + exp.loc[r, c] *= sf + else: + harm_year_offset = 0.1 + + if c >= 2030: + of = 0 + else: + of = harm_year_offset * (2030 - c) / (2030 - harmonisation_year) + + exp.loc[r, c] += of + + overrides = [ + {"variable": "Emissions|CO2", "method": "reduce_ratio_2050"}, + {"variable": "Emissions|CH4", "method": "reduce_offset_2030"}, + ] + overrides = pd.DataFrame(overrides) + + res = harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=harmonisation_year, + overrides=overrides, + ) + + pdt.assert_frame_equal(res, exp, check_like=True) + + +def test_raise_if_variable_not_in_hist(hist_df, scenarios_df): + hist_df = hist_df[~hist_df.index.get_level_values("variable").str.endswith("CO2")] + + error_msg = re.escape("No historical data for `World` `Emissions|CO2`") + with pytest.raises(MissingHistoricalError, match=error_msg): + harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=2010, + overrides=pd.DataFrame([{"method": "constant_ratio"}]), + ) + + +def test_raise_if_region_not_in_hist(hist_df, scenarios_df): + hist_df = hist_df[~hist_df.index.get_level_values("region").str.startswith("World")] + + error_msg = re.escape("No historical data for `World` `Emissions|CH4`") + with pytest.raises(MissingHistoricalError, match=error_msg): + harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=2010, + overrides=pd.DataFrame([{"method": "constant_ratio"}]), + ) + + +def test_raise_if_incompatible_unit(hist_df, scenarios_df): + scenarios_df = scenarios_df.reset_index("unit") + scenarios_df["unit"] = "Mt CO2 / yr" + scenarios_df = scenarios_df.set_index("unit", append=True) + + error_msg = re.escape( + "Cannot convert from 'megatCH4 / a' ([mass] * [methane] / [time]) to " + "'CO2 * megametric_ton / a' ([carbon] * [mass] / [time])" + ) + with pytest.raises(pint.errors.DimensionalityError, match=error_msg): + harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=2010, + overrides=pd.DataFrame([{"method": "constant_ratio"}]), + ) + + +def test_raise_if_undefined_unit(hist_df, scenarios_df): + scenarios_df = scenarios_df.reset_index("unit") + scenarios_df["unit"] = "Mt CO2eq / yr" + scenarios_df = scenarios_df.set_index("unit", append=True) + + with pytest.raises(pint.errors.UndefinedUnitError): + harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=2010, + overrides=pd.DataFrame([{"method": "constant_ratio"}]), + ) + + +def test_raise_if_harmonisation_year_missing(hist_df, scenarios_df): + hist_df = hist_df.drop(2015, axis="columns") + + error_msg = re.escape( + "No historical data for year 2015 for `World` `Emissions|CH4`" + ) + with pytest.raises(MissingHarmonisationYear, match=error_msg): + harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=2015, + overrides=pd.DataFrame([{"method": "constant_ratio"}]), + ) + + +def test_raise_if_harmonisation_year_nan(hist_df, scenarios_df): + hist_df.loc[ + hist_df.index.get_level_values("variable").str.endswith("CO2"), 2015 + ] = np.nan + + error_msg = re.escape( + "Historical data is null for year 2015 for `World` `Emissions|CO2`" + ) + with pytest.raises(MissingHarmonisationYear, match=error_msg): + harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=2015, + overrides=pd.DataFrame([{"method": "constant_ratio"}]), + ) + + +def test_override_multi_level(hist_df, scenarios_df): + asia_hist = hist_df * 0.7 + asia_hist.index = asia_hist.index.set_levels(["World|R5.2ASIA"], level="region") + + hist_df = pd.concat([hist_df, asia_hist]) + + asia = scenarios_df.copy() + asia.index = asia.index.set_levels(["World|R5.2ASIA"], level="region") + + model_2 = scenarios_df.copy() + model_2.index = model_2.index.set_levels(["FaNCY"], level="model") + + scenario_2 = scenarios_df.copy() + scenario_2.index = scenario_2.index.set_levels(["EMF33 quick"], level="scenario") + + scenarios_df = pd.concat([scenarios_df, asia, model_2, scenario_2]) + + overrides = pd.DataFrame( + [ + { + "variable": "Emissions|CO2", + "region": "World", + "model": "IAM", + "scenario": "abc", + "method": "constant_ratio", + }, + { + "variable": "Emissions|CH4", + "region": "World", + "model": "IAM", + "scenario": "abc", + "method": "constant_offset", + }, + { + "variable": "Emissions|CO2", + "region": "World|R5.2ASIA", + "model": "IAM", + "scenario": "abc", + "method": "reduce_ratio_2030", + }, + { + "variable": "Emissions|CH4", + "region": "World|R5.2ASIA", + "model": "IAM", + "scenario": "abc", + "method": "reduce_ratio_2050", + }, + { + "variable": "Emissions|CO2", + "region": "World", + "model": "FaNCY", + "scenario": "abc", + "method": "reduce_ratio_2070", + }, + { + "variable": "Emissions|CH4", + "region": "World", + "model": "FaNCY", + "scenario": "abc", + "method": "reduce_ratio_2090", + }, + { + "variable": "Emissions|CO2", + "region": "World", + "model": "IAM", + "scenario": "EMF33 quick", + "method": "reduce_offset_2050", + }, + { + "variable": "Emissions|CH4", + "region": "World", + "model": "IAM", + "scenario": "EMF33 quick", + "method": "reduce_offset_2070", + }, + ] + ) + + res = harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=2015, + overrides=overrides, + ) + + co2_rows = res.index.get_level_values("variable") == "Emissions|CO2" + world_rows = res.index.get_level_values("region") == "World" + fancy_rows = res.index.get_level_values("model") == "FaNCY" + emf33_rows = res.index.get_level_values("scenario") == "EMF33 quick" + + atol = 1e-4 + pick_rows = co2_rows & world_rows & ~fancy_rows & ~emf33_rows + npt.assert_allclose( + res.loc[pick_rows, :], + 12 / 11 * scenarios_df.loc[pick_rows, :], + atol=atol, + ) + npt.assert_allclose( + res.loc[~co2_rows & world_rows & ~fancy_rows & ~emf33_rows, :], + 0.1 + scenarios_df.loc[~co2_rows & world_rows & ~fancy_rows & ~emf33_rows, :], + atol=atol, + ) + + npt.assert_allclose( + res.loc[co2_rows & ~world_rows & ~fancy_rows & ~emf33_rows, :].squeeze(), + [7.636363, 8.4, 4.21212121, 5, 3, 1], + atol=atol, + ) + npt.assert_allclose( + res.loc[~co2_rows & ~world_rows & ~fancy_rows & ~emf33_rows, :].squeeze(), + [0.11667, 0.175, 0.285714, 0.109524, 0.05, 0.03], + atol=atol, + ) + + npt.assert_allclose( + res.loc[co2_rows & world_rows & fancy_rows & ~emf33_rows, :].squeeze(), + [10.909090, 12, 5.413233, 5.330579, 3.099174, 1], + atol=atol, + ) + npt.assert_allclose( + res.loc[~co2_rows & world_rows & fancy_rows & ~emf33_rows, :].squeeze(), + [0.16667, 0.25, 0.405555, 0.15333, 0.067777, 0.03], + atol=atol, + ) + + npt.assert_allclose( + res.loc[co2_rows & world_rows & ~fancy_rows & emf33_rows, :].squeeze(), + [11.142857, 12, 5.857143, 5.571429, 3, 1], + atol=atol, + ) + npt.assert_allclose( + res.loc[~co2_rows & world_rows & ~fancy_rows & emf33_rows, :].squeeze(), + [0.2090909, 0.25, 0.340909, 0.172727, 0.086364, 0.03], + atol=atol, + ) + + +@pytest.mark.parametrize( + "overrides", + ( + pd.DataFrame( + [ + {"region": "World", "method": "constant_ratio"}, + {"region": "World", "method": "constant_offset"}, + ] + ), + pd.DataFrame( + [ + { + "region": "World", + "variable": "Emissions|CH4", + "method": "constant_ratio", + }, + {"region": "World", "method": "constant_offset"}, + ] + ), + pd.DataFrame( + [ + {"variable": "Emissions|CH4", "method": "constant_ratio"}, + {"variable": "Emissions|CH4", "method": "reduce_offset_2030"}, + ] + ), + pd.DataFrame( + [ + {"variable": "Emissions|CH4", "method": "constant_ratio"}, + { + "variable": "Emissions|CH4", + "model": "IAM", + "method": "reduce_offset_2030", + }, + ] + ), + ), +) +def test_multiple_matching_overrides(hist_df, scenarios_df, overrides): + with pytest.raises( + AmbiguousHarmonisationMethod, match="More than one override for metadata" + ): + harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=2015, + overrides=overrides, + ) + + +def test_defaults(hist_df, scenarios_df): + co2_afolu = scenarios_df[ + scenarios_df.index.get_level_values("variable") == "Emissions|CO2" + ].copy() + co2_afolu = co2_afolu.reset_index() + co2_afolu["variable"] = "Emissions|CO2|AFOLU" + co2_afolu = co2_afolu.set_index(scenarios_df.index.names) + co2_afolu.iloc[:, :] = [2, 0.5, -1, -1.5, -2, -3] + + bc_afolu = scenarios_df[ + scenarios_df.index.get_level_values("variable") == "Emissions|CO2" + ].copy() + bc_afolu = bc_afolu.reset_index() + bc_afolu["variable"] = "Emissions|BC|AFOLU" + bc_afolu["unit"] = "Mt BC / yr" + bc_afolu = bc_afolu.set_index(scenarios_df.index.names) + bc_afolu.iloc[:, :] = [30, 33, 40, 42, 36, 24] + + scenarios_df = pd.concat([scenarios_df, co2_afolu, bc_afolu]) + + co2_afolu_hist = hist_df[ + hist_df.index.get_level_values("variable") == "Emissions|CO2" + ].copy() + co2_afolu_hist = co2_afolu_hist.reset_index() + co2_afolu_hist["variable"] = "Emissions|CO2|AFOLU" + co2_afolu_hist = co2_afolu_hist.set_index(hist_df.index.names) + co2_afolu_hist.iloc[:, :] = [ + 1.5 * 44000 / 12, + 1.6 * 44000 / 12, + 1.7 * 44000 / 12, + ] + + bc_afolu_hist = hist_df[ + hist_df.index.get_level_values("variable") == "Emissions|CO2" + ].copy() + bc_afolu_hist = bc_afolu_hist.reset_index() + bc_afolu_hist["variable"] = "Emissions|BC|AFOLU" + bc_afolu_hist["unit"] = "Gt BC / yr" + bc_afolu_hist = bc_afolu_hist.set_index(hist_df.index.names) + bc_afolu_hist.iloc[:, :] = [20, 35, 28] + + hist_df = pd.concat([hist_df, co2_afolu_hist, bc_afolu_hist]) + + res = harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=2015, + ) + + exp = harmonise_all( + scenarios=scenarios_df, + history=hist_df, + harmonisation_year=2015, + overrides=pd.DataFrame( + [ + {"variable": "Emissions|CO2", "method": "reduce_ratio_2080"}, + {"variable": "Emissions|CH4", "method": "reduce_ratio_2080"}, + {"variable": "Emissions|CO2|AFOLU", "method": "reduce_ratio_2100"}, + {"variable": "Emissions|BC|AFOLU", "method": "constant_ratio"}, + ] + ), + ) + + pdt.assert_frame_equal(res, exp, check_like=True) diff --git a/tests/test_harmonize.py b/tests/test_harmonize.py index d69806c..a7b35ce 100644 --- a/tests/test_harmonize.py +++ b/tests/test_harmonize.py @@ -1,4 +1,5 @@ import pandas as pd +import pytest import numpy as np import numpy.testing as npt @@ -159,6 +160,44 @@ def test_harmonize_reduce_ratio(): npt.assert_array_almost_equal(obs, exp) +@pytest.mark.xfail(reason="standard interfaces can't handle units") +def test_harmonize_reduce_ratio_different_units(): + df = _df.copy() + hist = _hist.copy() + hist /= 1000 + hist.index = hist.index.set_levels(["kt"], "units") + + methods = _methods.copy() + h = harmonize.Harmonizer(df, hist) + + tf = 2050 + + method = 'reduce_ratio_{}'.format(tf) + methods['method'] = [method] * nvals + res = h.harmonize(overrides=methods['method']) + + # base year + obs = res['2015'] + exp = hist['2015'] + # should come back with input units + obs_units = obs.index.get_level_values("units") + df_units = df.index.get_level_values("units") + assert (obs_units == df_units).all() + npt.assert_array_almost_equal(obs, exp) + + # future year + obs = res['2040'] + ratio = _hist['2015'] / _df['2015'] + exp = _df['2040'] * (ratio + _t_frac(tf) * (1 - ratio)) + npt.assert_array_almost_equal(obs, exp) + + # future year + if tf < 2060: + obs = res['2060'] + exp = _df['2060'] + npt.assert_array_almost_equal(obs, exp) + + def test_harmonize_mix(): df = _df.copy() hist = _hist.copy()