# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
import contextlib
import copy
import hashlib
import logging
import os
import re
import urllib
from functools import partial
from os.path import exists
from pathlib import Path
from shutil import copyfile
import pandas as pd
import pytz
import requests
import yaml
from snakemake.utils import update_config
from tqdm import tqdm
logger = logging.getLogger(__name__)
REGION_COLS = ["geometry", "name", "x", "y", "country"]
def copy_default_files(workflow):
default_files = {
"config/config.default.yaml": "config/config.yaml",
"config/scenarios.template.yaml": "config/scenarios.yaml",
}
for template, target in default_files.items():
target = os.path.join(workflow.current_basedir, target)
template = os.path.join(workflow.current_basedir, template)
if not exists(target) and exists(template):
copyfile(template, target)
def get_scenarios(run):
scenario_config = run.get("scenarios", {})
if run["name"] and scenario_config.get("enable"):
fn = Path(scenario_config["file"])
if fn.exists():
scenarios = yaml.safe_load(fn.read_text())
if run["name"] == "all":
run["name"] = list(scenarios.keys())
return scenarios
return {}
def get_rdir(run):
scenario_config = run.get("scenarios", {})
if run["name"] and scenario_config.get("enable"):
RDIR = "{run}/"
elif run["name"]:
RDIR = run["name"] + "/"
else:
RDIR = ""
prefix = run.get("prefix", "")
if prefix:
RDIR = f"{prefix}/{RDIR}"
return RDIR
def get_run_path(fn, dir, rdir, shared_resources, exclude_from_shared):
"""
Dynamically provide paths based on shared resources and filename.
Use this function for snakemake rule inputs or outputs that should be
optionally shared across runs or created individually for each run.
Parameters
----------
fn : str
The filename for the path to be generated.
dir : str
The base directory.
rdir : str
Relative directory for non-shared resources.
shared_resources : str or bool
Specifies which resources should be shared.
- If string is "base", special handling for shared "base" resources (see notes).
- If random string other than "base", this folder is used instead of the `rdir` keyword.
- If boolean, directly specifies if the resource is shared.
exclude_from_shared: list
List of filenames to exclude from shared resources. Only relevant if shared_resources is "base".
Returns
-------
str
Full path where the resource should be stored.
Notes
-----
Special case for "base" allows no wildcards other than "technology", "year"
and "scope" and excludes filenames starting with "networks/elec" or
"add_electricity". All other resources are shared.
"""
if shared_resources == "base":
pattern = r"\{([^{}]+)\}"
existing_wildcards = set(re.findall(pattern, fn))
irrelevant_wildcards = {"technology", "year", "scope", "kind"}
no_relevant_wildcards = not existing_wildcards - irrelevant_wildcards
not_shared_rule = (
not fn.startswith("networks/elec")
and not fn.startswith("add_electricity")
and not any(fn.startswith(ex) for ex in exclude_from_shared)
)
is_shared = no_relevant_wildcards and not_shared_rule
rdir = "" if is_shared else rdir
elif isinstance(shared_resources, str):
rdir = shared_resources + "/"
elif isinstance(shared_resources, bool):
rdir = "" if shared_resources else rdir
else:
raise ValueError(
"shared_resources must be a boolean, str, or 'base' for special handling."
)
return f"{dir}{rdir}{fn}"
def path_provider(dir, rdir, shared_resources, exclude_from_shared):
"""
Returns a partial function that dynamically provides paths based on shared
resources and the filename.
Returns
-------
partial function
A partial function that takes a filename as input and
returns the path to the file based on the shared_resources parameter.
"""
return partial(
get_run_path,
dir=dir,
rdir=rdir,
shared_resources=shared_resources,
exclude_from_shared=exclude_from_shared,
)
def get_opt(opts, expr, flags=None):
"""
Return the first option matching the regular expression.
The regular expression is case-insensitive by default.
"""
if flags is None:
flags = re.IGNORECASE
for o in opts:
match = re.match(expr, o, flags=flags)
if match:
return match.group(0)
return None
def find_opt(opts, expr):
"""
Return if available the float after the expression.
"""
for o in opts:
if expr in o:
m = re.findall(r"m?\d+(?:[\.p]\d+)?", o)
if len(m) > 0:
return True, float(m[-1].replace("p", ".").replace("m", "-"))
else:
return True, None
return False, None
# Define a context manager to temporarily mute print statements
@contextlib.contextmanager
def mute_print():
with open(os.devnull, "w") as devnull:
with contextlib.redirect_stdout(devnull):
yield
def set_scenario_config(snakemake):
scenario = snakemake.config["run"].get("scenarios", {})
if scenario.get("enable") and "run" in snakemake.wildcards.keys():
try:
with open(scenario["file"], "r") as f:
scenario_config = yaml.safe_load(f)
except FileNotFoundError:
# fallback for mock_snakemake
script_dir = Path(__file__).parent.resolve()
root_dir = script_dir.parent
with open(root_dir / scenario["file"], "r") as f:
scenario_config = yaml.safe_load(f)
update_config(snakemake.config, scenario_config[snakemake.wildcards.run])
def configure_logging(snakemake, skip_handlers=False):
"""
Configure the basic behaviour for the logging module.
Note: Must only be called once from the __main__ section of a script.
The setup includes printing log messages to STDERR and to a log file defined
by either (in priority order): snakemake.log.python, snakemake.log[0] or "logs/{rulename}.log".
Additional keywords from logging.basicConfig are accepted via the snakemake configuration
file under snakemake.config.logging.
Parameters
----------
snakemake : snakemake object
Your snakemake object containing a snakemake.config and snakemake.log.
skip_handlers : True | False (default)
Do (not) skip the default handlers created for redirecting output to STDERR and file.
"""
import logging
import sys
kwargs = snakemake.config.get("logging", dict()).copy()
kwargs.setdefault("level", "INFO")
if skip_handlers is False:
fallback_path = Path(__file__).parent.joinpath(
"..", "logs", f"{snakemake.rule}.log"
)
logfile = snakemake.log.get(
"python", snakemake.log[0] if snakemake.log else fallback_path
)
kwargs.update(
{
"handlers": [
# Prefer the 'python' log, otherwise take the first log for each
# Snakemake rule
logging.FileHandler(logfile),
logging.StreamHandler(),
]
}
)
logging.basicConfig(**kwargs)
# Setup a function to handle uncaught exceptions and include them with their stacktrace into logfiles
def handle_exception(exc_type, exc_value, exc_traceback):
# Log the exception
logger = logging.getLogger()
logger.error(
"Uncaught exception", exc_info=(exc_type, exc_value, exc_traceback)
)
sys.excepthook = handle_exception
def update_p_nom_max(n):
# if extendable carriers (solar/onwind/...) have capacity >= 0,
# e.g. existing assets from the OPSD project are included to the network,
# the installed capacity might exceed the expansion limit.
# Hence, we update the assumptions.
n.generators.p_nom_max = n.generators[["p_nom_min", "p_nom_max"]].max(1)
def aggregate_p_nom(n):
return pd.concat(
[
n.generators.groupby("carrier").p_nom_opt.sum(),
n.storage_units.groupby("carrier").p_nom_opt.sum(),
n.links.groupby("carrier").p_nom_opt.sum(),
n.loads_t.p.groupby(n.loads.carrier, axis=1).sum().mean(),
]
)
def aggregate_p(n):
return pd.concat(
[
n.generators_t.p.sum().groupby(n.generators.carrier).sum(),
n.storage_units_t.p.sum().groupby(n.storage_units.carrier).sum(),
n.stores_t.p.sum().groupby(n.stores.carrier).sum(),
-n.loads_t.p.sum().groupby(n.loads.carrier).sum(),
]
)
def aggregate_e_nom(n):
return pd.concat(
[
(n.storage_units["p_nom_opt"] * n.storage_units["max_hours"])
.groupby(n.storage_units["carrier"])
.sum(),
n.stores["e_nom_opt"].groupby(n.stores.carrier).sum(),
]
)
def aggregate_p_curtailed(n):
return pd.concat(
[
(
(
n.generators_t.p_max_pu.sum().multiply(n.generators.p_nom_opt)
- n.generators_t.p.sum()
)
.groupby(n.generators.carrier)
.sum()
),
(
(n.storage_units_t.inflow.sum() - n.storage_units_t.p.sum())
.groupby(n.storage_units.carrier)
.sum()
),
]
)
def aggregate_costs(n, flatten=False, opts=None, existing_only=False):
components = dict(
Link=("p_nom", "p0"),
Generator=("p_nom", "p"),
StorageUnit=("p_nom", "p"),
Store=("e_nom", "p"),
Line=("s_nom", None),
Transformer=("s_nom", None),
)
costs = {}
for c, (p_nom, p_attr) in zip(
n.iterate_components(components.keys(), skip_empty=False), components.values()
):
if c.df.empty:
continue
if not existing_only:
p_nom += "_opt"
costs[(c.list_name, "capital")] = (
(c.df[p_nom] * c.df.capital_cost).groupby(c.df.carrier).sum()
)
if p_attr is not None:
p = c.pnl[p_attr].sum()
if c.name == "StorageUnit":
p = p.loc[p > 0]
costs[(c.list_name, "marginal")] = (
(p * c.df.marginal_cost).groupby(c.df.carrier).sum()
)
costs = pd.concat(costs)
if flatten:
assert opts is not None
conv_techs = opts["conv_techs"]
costs = costs.reset_index(level=0, drop=True)
costs = costs["capital"].add(
costs["marginal"].rename({t: t + " marginal" for t in conv_techs}),
fill_value=0.0,
)
return costs
def progress_retrieve(url, file, disable=False):
if disable:
urllib.request.urlretrieve(url, file)
else:
with tqdm(unit="B", unit_scale=True, unit_divisor=1024, miniters=1) as t:
def update_to(b=1, bsize=1, tsize=None):
if tsize is not None:
t.total = tsize
t.update(b * bsize - t.n)
urllib.request.urlretrieve(url, file, reporthook=update_to)
def mock_snakemake(
rulename,
root_dir=None,
configfiles=None,
submodule_dir="workflow/submodules/pypsa-eur",
**wildcards,
):
"""
This function is expected to be executed from the 'scripts'-directory of '
the snakemake project. It returns a snakemake.script.Snakemake object,
based on the Snakefile.
If a rule has wildcards, you have to specify them in **wildcards.
Parameters
----------
rulename: str
name of the rule for which the snakemake object should be generated
root_dir: str/path-like
path to the root directory of the snakemake project
configfiles: list, str
list of configfiles to be used to update the config
submodule_dir: str, Path
in case PyPSA-Eur is used as a submodule, submodule_dir is
the path of pypsa-eur relative to the project directory.
**wildcards:
keyword arguments fixing the wildcards. Only necessary if wildcards are
needed.
"""
import os
import snakemake as sm
from pypsa.descriptors import Dict
from snakemake.api import Workflow
from snakemake.common import SNAKEFILE_CHOICES
from snakemake.script import Snakemake
from snakemake.settings.types import (
ConfigSettings,
DAGSettings,
ResourceSettings,
StorageSettings,
WorkflowSettings,
)
script_dir = Path(__file__).parent.resolve()
if root_dir is None:
root_dir = script_dir.parent
else:
root_dir = Path(root_dir).resolve()
user_in_script_dir = Path.cwd().resolve() == script_dir
if str(submodule_dir) in __file__:
# the submodule_dir path is only need to locate the project dir
os.chdir(Path(__file__[: __file__.find(str(submodule_dir))]))
elif user_in_script_dir:
os.chdir(root_dir)
elif Path.cwd().resolve() != root_dir:
raise RuntimeError(
"mock_snakemake has to be run from the repository root"
f" {root_dir} or scripts directory {script_dir}"
)
try:
for p in SNAKEFILE_CHOICES:
if os.path.exists(p):
snakefile = p
break
if configfiles is None:
configfiles = []
elif isinstance(configfiles, str):
configfiles = [configfiles]
resource_settings = ResourceSettings()
config_settings = ConfigSettings(configfiles=map(Path, configfiles))
workflow_settings = WorkflowSettings()
storage_settings = StorageSettings()
dag_settings = DAGSettings(rerun_triggers=[])
workflow = Workflow(
config_settings,
resource_settings,
workflow_settings,
storage_settings,
dag_settings,
storage_provider_settings=dict(),
)
workflow.include(snakefile)
if configfiles:
for f in configfiles:
if not os.path.exists(f):
raise FileNotFoundError(f"Config file {f} does not exist.")
workflow.configfile(f)
workflow.global_resources = {}
rule = workflow.get_rule(rulename)
dag = sm.dag.DAG(workflow, rules=[rule])
wc = Dict(wildcards)
job = sm.jobs.Job(rule, dag, wc)
def make_accessable(*ios):
for io in ios:
for i, _ in enumerate(io):
io[i] = os.path.abspath(io[i])
make_accessable(job.input, job.output, job.log)
snakemake = Snakemake(
job.input,
job.output,
job.params,
job.wildcards,
job.threads,
job.resources,
job.log,
job.dag.workflow.config,
job.rule.name,
None,
)
# create log and output dir if not existent
for path in list(snakemake.log) + list(snakemake.output):
Path(path).parent.mkdir(parents=True, exist_ok=True)
finally:
if user_in_script_dir:
os.chdir(script_dir)
return snakemake
def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
"""
Give a 24*7 long list of weekly hourly profiles, generate this for each
country for the period dt_index, taking account of time zones and summer
time.
"""
weekly_profile = pd.Series(weekly_profile, range(24 * 7))
week_df = pd.DataFrame(index=dt_index, columns=nodes)
for node in nodes:
timezone = pytz.timezone(pytz.country_timezones[node[:2]][0])
tz_dt_index = dt_index.tz_convert(timezone)
week_df[node] = [24 * dt.weekday() + dt.hour for dt in tz_dt_index]
week_df[node] = week_df[node].map(weekly_profile)
week_df = week_df.tz_localize(localize)
return week_df
def parse(infix):
"""
Recursively parse a chained wildcard expression into a dictionary or a YAML
object.
Parameters
----------
list_to_parse : list
The list to parse.
Returns
-------
dict or YAML object
The parsed list.
"""
if len(infix) == 1:
return yaml.safe_load(infix[0])
else:
return {infix.pop(0): parse(infix)}
def update_config_from_wildcards(config, w, inplace=True):
"""
Parses configuration settings from wildcards and updates the config.
"""
if not inplace:
config = copy.deepcopy(config)
if w.get("opts"):
opts = w.opts.split("-")
if nhours := get_opt(opts, r"^\d+(h|seg)$"):
config["clustering"]["temporal"]["resolution_elec"] = nhours
co2l_enable, co2l_value = find_opt(opts, "Co2L")
if co2l_enable:
config["electricity"]["co2limit_enable"] = True
if co2l_value is not None:
config["electricity"]["co2limit"] = (
co2l_value * config["electricity"]["co2base"]
)
gasl_enable, gasl_value = find_opt(opts, "CH4L")
if gasl_enable:
config["electricity"]["gaslimit_enable"] = True
if gasl_value is not None:
config["electricity"]["gaslimit"] = gasl_value * 1e6
if "Ept" in opts:
config["costs"]["emission_prices"]["co2_monthly_prices"] = True
ep_enable, ep_value = find_opt(opts, "Ep")
if ep_enable:
config["costs"]["emission_prices"]["enable"] = True
if ep_value is not None:
config["costs"]["emission_prices"]["co2"] = ep_value
if "ATK" in opts:
config["autarky"]["enable"] = True
if "ATKc" in opts:
config["autarky"]["by_country"] = True
attr_lookup = {
"p": "p_nom_max",
"e": "e_nom_max",
"c": "capital_cost",
"m": "marginal_cost",
}
for o in opts:
flags = ["+e", "+p", "+m", "+c"]
if all(flag not in o for flag in flags):
continue
carrier, attr_factor = o.split("+")
attr = attr_lookup[attr_factor[0]]
factor = float(attr_factor[1:])
if not isinstance(config["adjustments"]["electricity"], dict):
config["adjustments"]["electricity"] = dict()
update_config(
config["adjustments"]["electricity"], {attr: {carrier: factor}}
)
if w.get("sector_opts"):
opts = w.sector_opts.split("-")
if "T" in opts:
config["sector"]["transport"] = True
if "H" in opts:
config["sector"]["heating"] = True
if "B" in opts:
config["sector"]["biomass"] = True
if "I" in opts:
config["sector"]["industry"] = True
if "A" in opts:
config["sector"]["agriculture"] = True
if "CCL" in opts:
config["solving"]["constraints"]["CCL"] = True
eq_value = get_opt(opts, r"^EQ+\d*\.?\d+(c|)")
for o in opts:
if eq_value is not None:
config["solving"]["constraints"]["EQ"] = eq_value
elif "EQ" in o:
config["solving"]["constraints"]["EQ"] = True
break
if "BAU" in opts:
config["solving"]["constraints"]["BAU"] = True
if "SAFE" in opts:
config["solving"]["constraints"]["SAFE"] = True
if nhours := get_opt(opts, r"^\d+(h|sn|seg)$"):
config["clustering"]["temporal"]["resolution_sector"] = nhours
if "decentral" in opts:
config["sector"]["electricity_transmission_grid"] = False
if "noH2network" in opts:
config["sector"]["H2_network"] = False
if "nowasteheat" in opts:
config["sector"]["use_fischer_tropsch_waste_heat"] = False
config["sector"]["use_methanolisation_waste_heat"] = False
config["sector"]["use_haber_bosch_waste_heat"] = False
config["sector"]["use_methanation_waste_heat"] = False
config["sector"]["use_fuel_cell_waste_heat"] = False
config["sector"]["use_electrolysis_waste_heat"] = False
if "nodistrict" in opts:
config["sector"]["district_heating"]["progress"] = 0.0
dg_enable, dg_factor = find_opt(opts, "dist")
if dg_enable:
config["sector"]["electricity_distribution_grid"] = True
if dg_factor is not None:
config["sector"][
"electricity_distribution_grid_cost_factor"
] = dg_factor
if "biomasstransport" in opts:
config["sector"]["biomass_transport"] = True
_, maxext = find_opt(opts, "linemaxext")
if maxext is not None:
config["lines"]["max_extension"] = maxext * 1e3
config["links"]["max_extension"] = maxext * 1e3
_, co2l_value = find_opt(opts, "Co2L")
if co2l_value is not None:
config["co2_budget"] = float(co2l_value)
if co2_distribution := get_opt(opts, r"^(cb)\d+(\.\d+)?(ex|be)$"):
config["co2_budget"] = co2_distribution
if co2_budget := get_opt(opts, r"^(cb)\d+(\.\d+)?$"):
config["co2_budget"] = float(co2_budget[2:])
attr_lookup = {
"p": "p_nom_max",
"e": "e_nom_max",
"c": "capital_cost",
"m": "marginal_cost",
}
for o in opts:
flags = ["+e", "+p", "+m", "+c"]
if all(flag not in o for flag in flags):
continue
carrier, attr_factor = o.split("+")
attr = attr_lookup[attr_factor[0]]
factor = float(attr_factor[1:])
if not isinstance(config["adjustments"]["sector"], dict):
config["adjustments"]["sector"] = dict()
update_config(config["adjustments"]["sector"], {attr: {carrier: factor}})
_, sdr_value = find_opt(opts, "sdr")
if sdr_value is not None:
config["costs"]["social_discountrate"] = sdr_value / 100
_, seq_limit = find_opt(opts, "seq")
if seq_limit is not None:
config["sector"]["co2_sequestration_potential"] = seq_limit
# any config option can be represented in wildcard
for o in opts:
if o.startswith("CF+"):
infix = o.split("+")[1:]
update_config(config, parse(infix))
if not inplace:
return config
def get_checksum_from_zenodo(file_url):
parts = file_url.split("/")
record_id = parts[parts.index("records") + 1]
filename = parts[-1]
response = requests.get(f"https://zenodo.org/api/records/{record_id}", timeout=30)
response.raise_for_status()
data = response.json()
for file in data["files"]:
if file["key"] == filename:
return file["checksum"]
return None
def validate_checksum(file_path, zenodo_url=None, checksum=None):
"""
Validate file checksum against provided or Zenodo-retrieved checksum.
Calculates the hash of a file using 64KB chunks. Compares it against a
given checksum or one from a Zenodo URL.
Parameters
----------
file_path : str
Path to the file for checksum validation.
zenodo_url : str, optional
URL of the file on Zenodo to fetch the checksum.
checksum : str, optional
Checksum (format 'hash_type:checksum_value') for validation.
Raises
------
AssertionError
If the checksum does not match, or if neither `checksum` nor `zenodo_url` is provided.
Examples
--------
>>> validate_checksum("/path/to/file", checksum="md5:abc123...")
>>> validate_checksum(
... "/path/to/file",
... zenodo_url="https://zenodo.org/records/12345/files/example.txt",
... )
If the checksum is invalid, an AssertionError will be raised.
"""
assert checksum or zenodo_url, "Either checksum or zenodo_url must be provided"
if zenodo_url:
checksum = get_checksum_from_zenodo(zenodo_url)
hash_type, checksum = checksum.split(":")
hasher = hashlib.new(hash_type)
with open(file_path, "rb") as f:
for chunk in iter(lambda: f.read(65536), b""): # 64kb chunks
hasher.update(chunk)
calculated_checksum = hasher.hexdigest()
assert (
calculated_checksum == checksum
), "Checksum is invalid. This may be due to an incomplete download. Delete the file and re-execute the rule."
def get_snapshots(snapshots, drop_leap_day=False, freq="h", **kwargs):
"""
Returns pandas DateTimeIndex potentially without leap days.
"""
time = pd.date_range(freq=freq, **snapshots, **kwargs)
if drop_leap_day and time.is_leap_year.any():
time = time[~((time.month == 2) & (time.day == 29))]
return time