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Migrate .report.legacy code from message_data dev branch #254

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Migrate .report.legacy code from message_data dev branch #254

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b2ec285
Add .report.legacy.advanced_hydro_tables
khaeru Nov 26, 2024
7305110
Update .report.legacy.postprocess
khaeru Nov 26, 2024
54723b5
Update .report.legacy.pp_utils
khaeru Nov 26, 2024
0ecdecb
Update .report.legacy.iamc_tree
khaeru Nov 26, 2024
7db8a76
Update .report.legacy.iamc_report_hackathon
khaeru Nov 26, 2024
dd4182f
Update .report.legacy.default_tables
khaeru Nov 26, 2024
a350708
Update .report.legacy.ENGAGE_SSP2_v417_tables
khaeru Nov 26, 2024
8328823
Exclude tests/report/legacy/ from ruff checking
khaeru Nov 26, 2024
a6cd8a4
Add .tests.report.legacy.test_iamc_report_hackathon
khaeru Nov 26, 2024
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Add .tests.report.legacy.test_pp_utils
khaeru Nov 26, 2024
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359 changes: 4 additions & 355 deletions message_ix_models/report/legacy/ENGAGE_SSP2_v417_tables.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
import pandas as pd

from . import pp_utils
import message_data.tools.post_processing.pp_utils as pp_utils

pp = None
mu = None
Expand Down Expand Up @@ -941,357 +941,6 @@ def retr_pop(units):
return df


@_register
def retr_price(
units_CPrc_co2,
units_CPrc_co2_outp,
units_energy,
units_energy_outp,
units_CPrc_c,
conv_CPrc_co2_to_c,
units_agri,
):
"""Price: Prices for Emissions, Energy and Land-Use.

Parameters
----------

units_CPrc_co2 : str
Units to which carbon price (in CO2 equivalent) should be converted.
units_CPrc_co2_outp : str
Units in which carbon price (in CO2 equivalent) should be reported in.
units_energy : str
Units to which energy variables should be converted.
units_energy_outp : str
Units to which variables should be converted.
units_CPrc_c : str
Units to which carbon price (in carbon equivalent) should be converted.
conv_CPrc_co2_to_c : number
Conversion factor for carbon price from CO2 to C.
units_agri : str
Units to which agricultural variables should be converted.
"""

dfs = []

# ----------------------
# Calculate Carbon Price
# ----------------------

vars = {}
vars["Carbon"] = pp.cprc(units_CPrc_co2)
dfs.append(pp_utils.make_outputdf(vars, units_CPrc_co2_outp, param="max"))

# -------------------------------------------------
# Calculate Final Energy Prices incl. carbon prices
# -------------------------------------------------

# Carbon price must also be multiplied by factor as retrieved
# prices are already converted to $/GJ

_cbprc = pp.cprc(units_CPrc_c) * conv_CPrc_co2_to_c
vars = {}
# Final Energy prices are reported including the carbon price
vars["Final Energy|Residential|Electricity"] = pp.eneprc(
enefilter={"commodity": ["electr"], "level": ["final"]}, units=units_energy
)

vars["Final Energy|Residential|Gases|Natural Gas"] = pp.eneprc(
enefilter={"commodity": ["gas"], "level": ["final"]}, units=units_energy
)

vars["Final Energy|Residential|Liquids|Biomass"] = pp.eneprc(
enefilter={"commodity": ["ethanol"], "level": ["final"]}, units=units_energy
)

vars["Final Energy|Residential|Liquids|Oil"] = pp.eneprc(
enefilter={"commodity": ["lightoil"], "level": ["final"]}, units=units_energy
)

vars["Final Energy|Residential|Solids|Biomass"] = pp.eneprc(
enefilter={"commodity": ["biomass"], "level": ["final"]}, units=units_energy
)

vars["Final Energy|Residential|Solids|Coal"] = pp.eneprc(
enefilter={"commodity": ["coal"], "level": ["final"]}, units=units_energy
)

# -------------------------------------------------
# Calculate Final Energy Prices excl. carbon prices
# -------------------------------------------------

vars["Final Energy wo carbon price|Residential|Electricity"] = pp.eneprc(
enefilter={"commodity": ["electr"], "level": ["final"]}, units=units_energy
)

vars["Final Energy wo carbon price|Residential|Gases|Natural Gas"] = (
pp.eneprc(
enefilter={"commodity": ["gas"], "level": ["final"]}, units=units_energy
)
- _cbprc * mu["crbcnt_gas"]
)

vars["Final Energy wo carbon price|Residential|Liquids|Biomass"] = pp.eneprc(
enefilter={"commodity": ["ethanol"], "level": ["final"]}, units=units_energy
)

vars["Final Energy wo carbon price|Residential|Liquids|Oil"] = (
pp.eneprc(
enefilter={"commodity": ["lightoil"], "level": ["final"]},
units=units_energy,
)
- _cbprc * mu["crbcnt_oil"]
)

vars["Final Energy wo carbon price|Residential|Solids|Biomass"] = pp.eneprc(
enefilter={"commodity": ["biomass"], "level": ["final"]}, units=units_energy
)

vars["Final Energy wo carbon price|Residential|Solids|Coal"] = (
pp.eneprc(
enefilter={"commodity": ["coal"], "level": ["final"]}, units=units_energy
)
- _cbprc * mu["crbcnt_coal"]
)

# ---------------------------------------------------
# Calculate Primary Energy Prices excl. carbon prices
# ---------------------------------------------------

# Primary energy prices are reported excluding the carbon price, hence the
# subtraction of the carbonprice * the carbon content of the fuel

vars["Primary Energy w carbon price|Biomass"] = pp.eneprc(
enefilter={"commodity": ["biomass"], "level": ["primary"]}, units=units_energy
)

vars["Primary Energy|Biomass"] = vars["Primary Energy w carbon price|Biomass"]

# -----------------------------------------------------
# Calculate Secondary Energy Prices excl. carbon prices
# -----------------------------------------------------

# Secondary energy prices are reported excluding the carbon price, hence the
# subtraction of the carbonprice * the carbon content of the fuel

# vars["Secondary Energy|Gases|Natural Gas"] =pp.eneprc(
# enefilter={"commodity": [""],
# "level": ["secondary"]},
# units=units_energy)

# vars["Secondary Energy|Liquids"] = pp.eneprc(
# enefilter={"commodity": [""],
# "level": ["secondary"]},
# units=units_energy)

vars["Secondary Energy|Liquids"] = pp_utils._make_zero()

# vars["Secondary Energy|Solids|Biomass"] = pp.eneprc(
# enefilter={"commodity": [""],
# "level": ["secondary"]},
# units=units_energy)

# vars["Secondary Energy|Solids|Coal"] = pp.eneprc(
# enefilter={"commodity": [""],
# "level": ["secondary"]},
# units=units_energy) - _cbprc * crbcnt_coal

# Secondary energy prices are reported including the carbon price
vars["Secondary Energy w carbon price|Liquids"] = pp_utils._make_zero()

dfs.append(pp_utils.make_outputdf(vars, units_energy_outp, param="max"))

# ------------------------------------------------------
# Calculate Energy Prices for variables where the GLOBAL
# value variable differs from regional value variable
# ------------------------------------------------------

vars = {}

# Primary energy prices are reported excluding the carbon price, hence the
# subtraction of the carbonprice * the carbon content of the fuel

# Coal uses primary-coal for regions and import-coal for GLB
vars["Primary Energy w carbon price|Coal"] = pd.concat(
[
pp_utils.rem_glb(
pp.eneprc(
enefilter={"commodity": ["coal"], "level": ["primary"]},
units=units_energy,
)
),
pp_utils.rem_reg(
pp.eneprc(
enefilter={"commodity": ["coal"], "level": ["import"]},
units=units_energy,
)
),
],
sort=True,
)

vars["Primary Energy|Coal"] = vars["Primary Energy w carbon price|Coal"].subtract(
pp_utils.rem_glb(_cbprc) * mu["crbcnt_coal"], fill_value=0
)

# Gas uses primary-gas for regions and import-LNG for GLB
# Note: GLOBAL region uses LNG prices
vars["Primary Energy w carbon price|Gas"] = pd.concat(
[
pp_utils.rem_glb(
pp.eneprc(
enefilter={"commodity": ["gas"], "level": ["primary"]},
units=units_energy,
)
),
pp_utils.rem_reg(
pp.eneprc(
enefilter={"commodity": ["LNG"], "level": ["import"]},
units=units_energy,
)
),
],
sort=True,
)

vars["Primary Energy|Gas"] = vars["Primary Energy w carbon price|Gas"].subtract(
pp_utils.rem_glb(_cbprc) * mu["crbcnt_gas"], fill_value=0
)

# Oil uses primary-crudeoil for regions and import-crudeoil for GLB
vars["Primary Energy w carbon price|Oil"] = pd.concat(
[
pp_utils.rem_glb(
pp.eneprc(
enefilter={"commodity": ["crudeoil"], "level": ["secondary"]},
units=units_energy,
)
),
pp_utils.rem_reg(
pp.eneprc(
enefilter={"commodity": ["crudeoil"], "level": ["import"]},
units=units_energy,
)
),
],
sort=True,
)

vars["Primary Energy|Oil"] = vars["Primary Energy w carbon price|Oil"].subtract(
pp_utils.rem_glb(_cbprc) * mu["crbcnt_oil"], fill_value=0
)

# Secondary energy prices are reported including the carbon price
# Oil uses secondary-lightoil for regions and import-lightoil for GLB
vars["Secondary Energy w carbon price|Liquids|Oil"] = pd.concat(
[
pp_utils.rem_glb(
pp.eneprc(
enefilter={"commodity": ["lightoil"], "level": ["secondary"]},
units=units_energy,
)
),
pp_utils.rem_reg(
pp.eneprc(
enefilter={"commodity": ["lightoil"], "level": ["import"]},
units=units_energy,
)
),
],
sort=True,
)

vars["Secondary Energy|Liquids|Oil"] = vars[
"Secondary Energy w carbon price|Liquids|Oil"
].subtract(pp_utils.rem_glb(_cbprc) * mu["crbcnt_oil"], fill_value=0)

# Biomass uses secondary-ethanol for regions and import-ethanol for GLB
vars["Secondary Energy w carbon price|Liquids|Biomass"] = pd.concat(
[
pp_utils.rem_glb(
pp.eneprc(
enefilter={"commodity": ["ethanol"], "level": ["primary"]},
units=units_energy,
)
),
pp_utils.rem_reg(
pp.eneprc(
enefilter={"commodity": ["ethanol"], "level": ["import"]},
units=units_energy,
)
),
],
sort=True,
)

# Carbon-price is NOT subtracted
vars["Secondary Energy|Liquids|Biomass"] = vars[
"Secondary Energy w carbon price|Liquids|Biomass"
]

# Hydrogen uses secondary-hydrogen for regions and import-lh2 for GLB
vars["Secondary Energy w carbon price|Hydrogen"] = pd.concat(
[
pp_utils.rem_glb(
pp.eneprc(
enefilter={"commodity": ["hydrogen"], "level": ["secondary"]},
units=units_energy,
)
),
pp_utils.rem_reg(
pp.eneprc(
enefilter={"commodity": ["lh2"], "level": ["import"]},
units=units_energy,
)
),
],
sort=True,
)

# Carbon-price is NOT subtracted
vars["Secondary Energy|Hydrogen"] = vars["Secondary Energy w carbon price|Hydrogen"]

dfs.append(pp_utils.make_outputdf(vars, units_energy_outp, glb=False))

# ------------------------------------------------------
# Calculate Energy Prices for variables where the GLOBAL
# value is set to ZERO
# ------------------------------------------------------

vars = {}

vars["Secondary Energy|Electricity"] = pp.eneprc(
enefilter={"commodity": ["electr"], "level": ["secondary"]}, units=units_energy
)

vars["Secondary Energy w carbon price|Electricity"] = pp.eneprc(
enefilter={"commodity": ["electr"], "level": ["secondary"]}, units=units_energy
)

dfs.append(pp_utils.make_outputdf(vars, units_energy_outp, glb=False))

# ----------------------------------------------------
# Calculate Agricultural Prices where the global price
# is derived using a formula provided by GLOBIOM
# ----------------------------------------------------

vars = {}

# Define the technology which represents the quantities used to derive
# sums accross regions values.
scale_tec = "Agricultural Production|Non-Energy Crops"

vars["Agriculture|Non-Energy Crops and Livestock|Index"] = pp.retrieve_lu_price(
"Price|Agriculture|Non-Energy Crops and Livestock|Index", scale_tec
)
vars["Agriculture|Non-Energy Crops|Index"] = pp.retrieve_lu_price(
"Price|Agriculture|Non-Energy Crops|Index", scale_tec
)
dfs.append(pp_utils.make_outputdf(vars, units_agri, glb=False))

return pd.concat(dfs, sort=True)


@_register
def retr_kyoto(units):
"""Emissions: Kyoto Gases.
Expand Down Expand Up @@ -3862,9 +3511,9 @@ def retr_pe(units, method=None):
# Primary Energy Biomass
# ----------------------

vars["Biomass"] = pp.out(
["biomass_imp", "land_use_biomass"], units, outfilter={"commodity": ["biomass"]}
) - pp.inp(["biomass_exp"], units, inpfilter={"commodity": ["biomass"]})
vars["Biomass"] = pp.land_out(
lu_out_filter={"level": ["land_use"], "commodity": ["bioenergy"]}, units=units
)

# Note OFR 20180412: Correction inserted. scrubber output per vintage cannot
# be divided by powerplant eff per vintage. In most cases this does work except
Expand Down
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