# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
import contextlib
import logging
import os
import urllib
from pathlib import Path
import pandas as pd
import pytz
import yaml
from pypsa.components import component_attrs, components
from pypsa.descriptors import Dict
from tqdm import tqdm
logger = logging.getLogger(__name__)
REGION_COLS = ["geometry", "name", "x", "y", "country"]
# 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 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
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)
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, configfiles=[], **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
configfiles: list, str
list of configfiles to be used to update the config
**wildcards:
keyword arguments fixing the wildcards. Only necessary if wildcards are
needed.
"""
import os
import snakemake as sm
from packaging.version import Version, parse
from pypsa.descriptors import Dict
from snakemake.script import Snakemake
script_dir = Path(__file__).parent.resolve()
root_dir = script_dir.parent
user_in_script_dir = Path.cwd().resolve() == script_dir
if 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 sm.SNAKEFILE_CHOICES:
if os.path.exists(p):
snakefile = p
break
kwargs = (
dict(rerun_triggers=[]) if parse(sm.__version__) > Version("7.7.0") else {}
)
if isinstance(configfiles, str):
configfiles = [configfiles]
workflow = sm.Workflow(snakefile, overwrite_configfiles=configfiles, **kwargs)
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 range(len(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(l):
if len(l) == 1:
return yaml.safe_load(l[0])
else:
return {l.pop(0): parse(l)}
def update_config_with_sector_opts(config, sector_opts):
from snakemake.utils import update_config
for o in sector_opts.split("-"):
if o.startswith("CF+"):
l = o.split("+")[1:]
update_config(config, parse(l))