Issue with build_cutout using alite

[Rock910]

Using my config.yaml file, I get an error in the build_cutout, which is related to alite

INFO:root:Preparing cutout with parameters {'module': ['sarah', 'era5'], 'x': slice(-12.0, 45.0, None), 'y': slice(33.0, 65, None), 'dx': 0.2, 'dy': 0.2, 'time': slice('2016-01-01', '2016-02-01', None), 'sarah_interpolate': False, 'sarah_dir': None, 'features': ['influx', 'temperature']}. INFO:atlite.cutout:Building new cutout cutouts/europe-2013-sarah.nc INFO:atlite.data:Storing temporary files in /tmp/tmpd4bvedp8 INFO:atlite.data:Calculating and writing with module sarah: Traceback (most recent call last): File "/home/ubuntu/pypsa-eur/.snakemake/scripts/tmpw6pc4v_v.build_cutout.py", line 134, in cutout.prepare(features=features) File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/atlite/data.py", line 102, in wrapper res = func(*args, **kwargs) File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/atlite/data.py", line 164, in cutout_prepare ds = get_features(cutout, module, missing_features, tmpdir=tmpdir) File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/atlite/data.py", line 46, in get_features datasets = compute(datasets) File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/dask/base.py", line 599, in compute results = schedule(dsk, keys, **kwargs) File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/dask/threaded.py", line 89, in get results = get_async( File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/dask/local.py", line 511, in get_async raise_exception(exc, tb) File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/dask/local.py", line 319, in reraise raise exc File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/dask/local.py", line 224, in execute_task result = _execute_task(task, data) File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/dask/core.py", line 119, in _execute_task return func((_execute_task(a, cache) for a in args)) File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/dask/utils.py", line 73, in apply return func(args, kwargs) File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/atlite/datasets/sarah.py", line 198, in get_data files = get_filenames(sarah_dir, coords) File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/atlite/datasets/sarah.py", line 73, in get_filenames dict(sis=_filenames_starting_with("SIS"), sid=_filenames_starting_with("SID")), File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/site-packages/atlite/datasets/sarah.py", line 62, in _filenames_starting_with pattern = os.path.join(sarah_dir, "", f"{name}.nc") File "/home/ubuntu/anaconda3/envs/pypsa-eur/lib/python3.10/posixpath.py", line 76, in join a = os.fspath(a) TypeError: expected str, bytes or os.PathLike object, not NoneType [Mon Apr 17 15:44:37 2023] Error in rule build_cutout: jobid: 14 input: resources/regions_onshore.geojson, resources/regions_offshore.geojson output: cutouts/europe-2013-sarah.nc log: logs/build_cutout/europe-2013-sarah.log (check log file(s) for error details)

# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0

version: 0.7.0
tutorial: false

logging:
  level: INFO
  format: '%(levelname)s:%(name)s:%(message)s'

run:
  name: "" # use this to keep track of runs with different settings
  shared_cutouts: false # set to true to share the default cutout(s) across runs


scenario:
  simpl: ['']
  ll: ['copt']
  clusters: [37]
  #clusters: [37, 128, 256, 512, 1024]
  opts: [Co2L-1H]
  #opts: [Co2L-3H]

countries: ['AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK']

snapshots:
 # start: "2013-01-01"
  start: "2016-01-17"
  end: "2016-01-23"
  inclusive: 'left' # include start, not end

enable:
  prepare_links_p_nom: false
  retrieve_databundle: true
  retrieve_cost_data: true
  build_cutout: true
  #build_cutout: false
  retrieve_cutout: false
  #retrieve_cutout: true
  build_natura_raster: false
  retrieve_natura_raster: true
  custom_busmap: false

electricity:
  voltages: [220., 300., 380.]
  gaslimit: false # global gas usage limit of X MWh_th
  co2limit: 7.75e+7 # 0.05 * 3.1e9*0.5
  co2base: 1.487e+9
  agg_p_nom_limits: data/agg_p_nom_minmax.csv

  operational_reserve: # like https://genxproject.github.io/GenX/dev/core/#Reserves
    activate: false
    epsilon_load: 0.02 # share of total load
    epsilon_vres: 0.02 # share of total renewable supply
    contingency: 4000 # fixed capacity in MW

  max_hours:
    battery: 6
    H2: 168

  extendable_carriers:
    Generator: []
  #  Generator: [solar, onwind, offwind-ac, offwind-dc, OCGT]
    StorageUnit: [battery] # battery, H2
    Store: [battery, H2]
    Link: [] # H2 pipeline

  # use pandas query strings here, e.g. Country not in ['Germany']
  powerplants_filter: (DateOut >= 2022 or DateOut != DateOut)
  # use pandas query strings here, e.g. Country in ['Germany']
  custom_powerplants: false

  conventional_carriers: [nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass]
  renewable_carriers: [solar, onwind, offwind-ac, offwind-dc, hydro]

  estimate_renewable_capacities:
    enable: true
    # Add capacities from OPSD data
    from_opsd: true
    # Renewable capacities are based on existing capacities reported by IRENA
    year: 2020
    # Artificially limit maximum capacities to factor * (IRENA capacities),
    # i.e. 110% of <years>'s capacities => expansion_limit: 1.1
    # false: Use estimated renewable potentials determine by the workflow
    expansion_limit: false
    technology_mapping:
      # Wind is the Fueltype in powerplantmatching, onwind, offwind-{ac,dc} the carrier in PyPSA-Eur
      Offshore: [offwind-ac, offwind-dc]
      Onshore: [onwind]
      PV: [solar]

atlite:
  nprocesses: 4
  show_progress: false # false saves time
  cutouts:
    # use 'base' to determine geographical bounds and time span from config
    # base:
      # module: era5
    europe-2013-era5:
      module: era5 # in priority order
      x: [-12., 35.]
      y: [33., 72]
      dx: 0.3
      dy: 0.3
      time: ['2016-01-01', '2016-02-01']
      #time: ['2013', '2013']
    europe-2013-sarah:
      module: [sarah, era5] # in priority order
      x: [-12., 45.]
      y: [33., 65]
      dx: 0.2
      dy: 0.2
      time: ['2016-01-01', '2016-02-01']
     # time: ['2013', '2013']
      sarah_interpolate: false
      sarah_dir:
      features: [influx, temperature]


renewable:
  onwind:
    cutout: europe-2013-era5
    resource:
      method: wind
      turbine: Vestas_V112_3MW
    capacity_per_sqkm: 3 # ScholzPhd Tab 4.3.1: 10MW/km^2 and assuming 30% fraction of the already restricted
    # area is available for installation of wind generators due to competing land use and likely public
    # acceptance issues.
    # correction_factor: 0.93
    corine:
      # Scholz, Y. (2012). Renewable energy based electricity supply at low costs:
      #  development of the REMix model and application for Europe. ( p.42 / p.28)
      grid_codes: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32]
      distance: 1000
      distance_grid_codes: [1, 2, 3, 4, 5, 6]
    natura: true
    excluder_resolution: 100
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
  offwind-ac:
    cutout: europe-2013-era5
    resource:
      method: wind
      turbine: NREL_ReferenceTurbine_5MW_offshore
    capacity_per_sqkm: 2 # ScholzPhd Tab 4.3.1: 10MW/km^2 and assuming 20% fraction of the already restricted
    # area is available for installation of wind generators due to competing land use and likely public
    # acceptance issues.
    correction_factor: 0.8855
    # proxy for wake losses
    # from 10.1016/j.energy.2018.08.153
    # until done more rigorously in #153
    corine: [44, 255]
    natura: true
    ship_threshold: 400
    max_depth: 50
    max_shore_distance: 30000
    excluder_resolution: 200
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
  offwind-dc:
    cutout: europe-2013-era5
    resource:
      method: wind
      turbine: NREL_ReferenceTurbine_5MW_offshore
    capacity_per_sqkm: 2 # ScholzPhd Tab 4.3.1: 10MW/km^2 and assuming 20% fraction of the already restricted
    # area is available for installation of wind generators due to competing land use and likely public
    # acceptance issues.
    correction_factor: 0.8855
    # proxy for wake losses
    # from 10.1016/j.energy.2018.08.153
    # until done more rigorously in #153
    corine: [44, 255]
    natura: true
    ship_threshold: 400
    max_depth: 50
    min_shore_distance: 30000
    excluder_resolution: 200
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
  solar:
    cutout: europe-2013-sarah
    resource:
      method: pv
      panel: CSi
      orientation:
        slope: 35.
        azimuth: 180.
    capacity_per_sqkm: 1.7 # ScholzPhd Tab 4.3.1: 170 MW/km^2 and assuming 1% of the area can be used for solar PV panels
    # Correction factor determined by comparing uncorrected area-weighted full-load hours to those
    # published in Supplementary Data to
    # Pietzcker, Robert Carl, et al. "Using the sun to decarbonize the power
    # sector: The economic potential of photovoltaics and concentrating solar
    # power." Applied Energy 135 (2014): 704-720.
    # This correction factor of 0.854337 may be in order if using reanalysis data.
    # for discussion refer to https://github.com/PyPSA/pypsa-eur/pull/304
    # correction_factor: 0.854337
    corine: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 26, 31, 32]
    natura: true
    excluder_resolution: 100
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
  hydro:
    cutout: europe-2013-era5
    carriers: [ror, PHS, hydro]
    PHS_max_hours: 6
    hydro_max_hours: "energy_capacity_totals_by_country" # one of energy_capacity_totals_by_country, estimate_by_large_installations or a float
    clip_min_inflow: 1.0

conventional:
  nuclear:
    p_max_pu: "data/nuclear_p_max_pu.csv" # float of file name

lines:
  types:
    220.: "Al/St 240/40 2-bundle 220.0"
    300.: "Al/St 240/40 3-bundle 300.0"
    380.: "Al/St 240/40 4-bundle 380.0"
  s_max_pu: 0.7
  s_nom_max: .inf
  length_factor: 1.25
  under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity

links:
  p_max_pu: 1.0
  p_nom_max: .inf
  include_tyndp: true
  under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity

transformers:
  x: 0.1
  s_nom: 2000.
  type: ''

load:
  power_statistics: true # only for files from <2019; set false in order to get ENTSOE transparency data
  interpolate_limit: 3 # data gaps up until this size are interpolated linearly
  time_shift_for_large_gaps: 1w # data gaps up until this size are copied by copying from
  manual_adjustments: true # false
  scaling_factor: 1.0

costs:
  year: 2030
  version: v0.5.0
  rooftop_share: 0.14  # based on the potentials, assuming  (0.1 kW/m2 and 10 m2/person)
  fill_values:
    FOM: 0
    VOM: 0
    efficiency: 1
    fuel: 0
    investment: 0
    lifetime: 25
    "CO2 intensity": 0
    "discount rate": 0.07
  marginal_cost:
    solar: 0.01
    onwind: 0.015
    offwind: 0.015
    hydro: 0.
    H2: 0.
    electrolysis: 0.
    fuel cell: 0.
    battery: 0.
    battery inverter: 0.
  emission_prices: # in currency per tonne emission, only used with the option Ep
    co2: 0.

clustering:
  simplify_network:
    to_substations: false # network is simplified to nodes with positive or negative power injection (i.e. substations or offwind connections)
    algorithm: kmeans # choose from: [hac, kmeans]
    feature: solar+onwind-time # only for hac. choose from: [solar+onwind-time, solar+onwind-cap, solar-time, solar-cap, solar+offwind-cap] etc.
    exclude_carriers: []
    remove_stubs: true
    remove_stubs_across_borders: true
  cluster_network:
    algorithm: kmeans
    feature: solar+onwind-time
    exclude_carriers: []
  aggregation_strategies:
    generators:
      p_nom_max: sum # use "min" for more conservative assumptions
      p_nom_min: sum
      p_min_pu: mean
      marginal_cost: mean
      committable: any
      ramp_limit_up: max
      ramp_limit_down: max
      efficiency: mean

solving:
  options:
    formulation: kirchhoff
    load_shedding: false
    noisy_costs: true
    min_iterations: 4
    max_iterations: 6
    clip_p_max_pu: 0.01
    skip_iterations: true
    track_iterations: false
    #nhours: 10
  solver:
    name: cbc
   # threads: 4
   # method: 2 # barrier
   # crossover: 0
   # BarConvTol: 1.e-5
   # FeasibilityTol: 1.e-6
   # AggFill: 0
   # PreDual: 0
   # GURO_PAR_BARDENSETHRESH: 200
  # solver:
  #   name: cplex
  #   threads: 4
  #   lpmethod: 4 # barrier
  #   solutiontype: 2 # non basic solution, ie no crossover
  #   barrier.convergetol: 1.e-5
  #   feasopt.tolerance: 1.e-6

plotting:
  map:
    figsize: [7, 7]
    boundaries: [-10.2, 29, 35, 72]
    p_nom:
      bus_size_factor: 5.e+4
      linewidth_factor: 3.e+3

  costs_max: 800
  costs_threshold: 1

  energy_max: 15000.
  energy_min: -10000.
  energy_threshold: 50.

  vre_techs: ["onwind", "offwind-ac", "offwind-dc", "solar", "ror"]
  conv_techs: ["OCGT", "CCGT", "Nuclear", "Coal"]
  storage_techs: ["hydro+PHS", "battery", "H2"]
  load_carriers: ["AC load"]
  AC_carriers: ["AC line", "AC transformer"]
  link_carriers: ["DC line", "Converter AC-DC"]
  tech_colors:
    "onwind": "#235ebc"
    "onshore wind": "#235ebc"
    'offwind': "#6895dd"
    'offwind-ac': "#6895dd"
    'offshore wind': "#6895dd"
    'offshore wind ac': "#6895dd"
    'offwind-dc': "#74c6f2"
    'offshore wind dc': "#74c6f2"
    "hydro": "#08ad97"
    "hydro+PHS": "#08ad97"
    "PHS": "#08ad97"
    "hydro reservoir": "#08ad97"
    'hydroelectricity': '#08ad97'
    "ror": "#4adbc8"
    "run of river": "#4adbc8"
    'solar': "#f9d002"
    'solar PV': "#f9d002"
    'solar thermal': '#ffef60'
    'biomass': '#0c6013'
    'solid biomass': '#06540d'
    'biogas': '#23932d'
    'waste': '#68896b'
    'geothermal': '#ba91b1'
    "OCGT": "#d35050"
    "gas": "#d35050"
    "natural gas": "#d35050"
    "CCGT": "#b20101"
    "nuclear": "#ff9000"
    "coal": "#707070"
    "lignite": "#9e5a01"
    "oil": "#262626"
    "H2": "#ea048a"
    "hydrogen storage": "#ea048a"
    "battery": "#b8ea04"
    "Electric load": "#f9d002"
    "electricity": "#f9d002"
    "lines": "#70af1d"
    "transmission lines": "#70af1d"
    "AC-AC": "#70af1d"
    "AC line": "#70af1d"
    "links": "#8a1caf"
    "HVDC links": "#8a1caf"
    "DC-DC": "#8a1caf"
    "DC link": "#8a1caf"
  nice_names:
    OCGT: "Open-Cycle Gas"
    CCGT: "Combined-Cycle Gas"
    offwind-ac: "Offshore Wind (AC)"
    offwind-dc: "Offshore Wind (DC)"
    onwind: "Onshore Wind"
    solar: "Solar"
    PHS: "Pumped Hydro Storage"
    hydro: "Reservoir & Dam"
    battery: "Battery Storage"
    H2: "Hydrogen Storage"
    lines: "Transmission Lines"
    ror: "Run of River"

Your Environment

I'm using PyPSA-eur v0.7.0

• The atlite version used:
• How you installed atlite (conda, pip or github):
• Operating System:
• My environment:
  (output of `conda list`)

  ```

packages in environment at /home/ubuntu/anaconda3/envs/pypsa-eur:

Name Version Build Channel

_libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge affine 2.4.0 pyhd8ed1ab_0 conda-forge alsa-lib 1.2.8 h166bdaf_0 conda-forge ampl-mp 3.1.0 h2cc385e_1006 conda-forge amply 0.1.5 pyhd8ed1ab_0 conda-forge appdirs 1.4.4 pyh9f0ad1d_0 conda-forge arrow-cpp 11.0.0 ha770c72_13_cpu conda-forge asttokens 2.2.1 pyhd8ed1ab_0 conda-forge atlite 0.2.10 pyhd8ed1ab_0 conda-forge attr 2.5.1 h166bdaf_1 conda-forge attrs 22.2.0 pyh71513ae_0 conda-forge aws-c-auth 0.6.26 hf365957_1 conda-forge aws-c-cal 0.5.21 h48707d8_2 conda-forge aws-c-common 0.8.14 h0b41bf4_0 conda-forge aws-c-compression 0.2.16 h03acc5a_5 conda-forge aws-c-event-stream 0.2.20 h00877a2_4 conda-forge aws-c-http 0.7.6 hf342b9f_0 conda-forge aws-c-io 0.13.19 h5b20300_3 conda-forge aws-c-mqtt 0.8.6 hc4349f7_12 conda-forge aws-c-s3 0.2.7 h909e904_1 conda-forge aws-c-sdkutils 0.1.8 h03acc5a_0 conda-forge aws-checksums 0.1.14 h03acc5a_5 conda-forge aws-crt-cpp 0.19.8 hf7fbfca_12 conda-forge aws-sdk-cpp 1.10.57 h17c43bd_8 conda-forge backcall 0.2.0 pyh9f0ad1d_0 conda-forge backports 1.0 pyhd8ed1ab_3 conda-forge backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge beautifulsoup4 4.12.0 pyha770c72_0 conda-forge blosc 1.21.3 hafa529b_0 conda-forge bokeh 2.4.3 pyhd8ed1ab_3 conda-forge boost-cpp 1.78.0 h75c5d50_1 conda-forge bottleneck 1.3.7 py310h0a54255_0 conda-forge branca 0.6.0 pyhd8ed1ab_0 conda-forge brotli 1.0.9 h166bdaf_8 conda-forge brotli-bin 1.0.9 h166bdaf_8 conda-forge brotlipy 0.7.0 py310h5764c6d_1005 conda-forge bzip2 1.0.8 h7f98852_4 conda-forge c-ares 1.18.1 h7f98852_0 conda-forge ca-certificates 2022.12.7 ha878542_0 conda-forge cairo 1.16.0 ha61ee94_1014 conda-forge cartopy 0.21.1 py310h7eb24ba_1 conda-forge cdsapi 0.6.1 pyhd8ed1ab_0 conda-forge certifi 2022.12.7 pyhd8ed1ab_0 conda-forge cffi 1.15.1 py310h255011f_3 conda-forge cfitsio 4.2.0 hd9d235c_0 conda-forge cftime 1.6.2 py310hde88566_1 conda-forge charset-normalizer 3.1.0 pyhd8ed1ab_0 conda-forge click 8.1.3 unix_pyhd8ed1ab_2 conda-forge click-plugins 1.1.1 py_0 conda-forge cligj 0.7.2 pyhd8ed1ab_1 conda-forge cloudpickle 2.2.1 pyhd8ed1ab_0 conda-forge coin-or-cbc 2.10.8 h3786ebc_0 conda-forge coin-or-cgl 0.60.6 h6f57e76_2 conda-forge coin-or-clp 1.17.7 hc56784d_2 conda-forge coin-or-osi 0.108.7 h2720bb7_2 conda-forge coin-or-utils 2.11.6 h202d8b1_2 conda-forge coincbc 2.10.8 0_metapackage conda-forge colorama 0.4.6 pyhd8ed1ab_0 conda-forge configargparse 1.5.3 pyhd8ed1ab_0 conda-forge connection_pool 0.0.3 pyhd3deb0d_0 conda-forge country_converter 1.0.0 pyhd8ed1ab_1 conda-forge countrycode 0.2 pypi_0 pypi cryptography 40.0.1 py310h34c0648_0 conda-forge curl 7.88.1 hdc1c0ab_1 conda-forge cycler 0.11.0 pyhd8ed1ab_0 conda-forge cytoolz 0.12.0 py310h5764c6d_1 conda-forge dask 2023.3.2 pyhd8ed1ab_0 conda-forge dask-core 2023.3.2 pyhd8ed1ab_0 conda-forge datrie 0.8.2 py310h5764c6d_6 conda-forge dbus 1.13.6 h5008d03_3 conda-forge decorator 5.1.1 pyhd8ed1ab_0 conda-forge deprecation 2.1.0 pyh9f0ad1d_0 conda-forge descartes 1.1.0 py_4 conda-forge distributed 2023.3.2 pyhd8ed1ab_0 conda-forge distro 1.8.0 pyhd8ed1ab_0 conda-forge docutils 0.19 py310hff52083_1 conda-forge dpath 2.1.5 py310hff52083_0 conda-forge entsoe-py 0.5.8 pyhd8ed1ab_0 conda-forge et_xmlfile 1.1.0 pyhd8ed1ab_0 conda-forge exceptiongroup 1.1.1 pyhd8ed1ab_0 conda-forge executing 1.2.0 pyhd8ed1ab_0 conda-forge expat 2.5.0 hcb278e6_1 conda-forge fftw 3.3.10 nompi_hf0379b8_106 conda-forge filelock 3.10.7 pyhd8ed1ab_0 conda-forge fiona 1.9.2 py310ha325b7b_0 conda-forge folium 0.14.0 pyhd8ed1ab_0 conda-forge font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge font-ttf-inconsolata 3.000 h77eed37_0 conda-forge font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge font-ttf-ubuntu 0.83 hab24e00_0 conda-forge fontconfig 2.14.2 h14ed4e7_0 conda-forge fonts-conda-ecosystem 1 0 conda-forge fonts-conda-forge 1 0 conda-forge fonttools 4.39.3 py310h1fa729e_0 conda-forge freetype 2.12.1 hca18f0e_1 conda-forge freexl 1.0.6 h166bdaf_1 conda-forge fsspec 2023.3.0 pyhd8ed1ab_1 conda-forge gdal 3.6.3 py310hc1b7723_1 conda-forge geographiclib 1.52 pyhd8ed1ab_0 conda-forge geojson-rewind 1.0.2 pyhd8ed1ab_0 conda-forge geopandas 0.12.2 pyhd8ed1ab_0 conda-forge geopandas-base 0.12.2 pyha770c72_0 conda-forge geopy 2.3.0 pyhd8ed1ab_0 conda-forge geos 3.11.2 hcb278e6_0 conda-forge geotiff 1.7.1 hb963b44_7 conda-forge gettext 0.21.1 h27087fc_0 conda-forge gflags 2.2.2 he1b5a44_1004 conda-forge giflib 5.2.1 h0b41bf4_3 conda-forge gitdb 4.0.10 pyhd8ed1ab_0 conda-forge gitpython 3.1.31 pyhd8ed1ab_0 conda-forge glib 2.74.1 h6239696_1 conda-forge glib-tools 2.74.1 h6239696_1 conda-forge glog 0.6.0 h6f12383_0 conda-forge graphite2 1.3.13 h58526e2_1001 conda-forge gst-plugins-base 1.22.0 h4243ec0_2 conda-forge gstreamer 1.22.0 h25f0c4b_2 conda-forge gstreamer-orc 0.4.33 h166bdaf_0 conda-forge harfbuzz 6.0.0 h8e241bc_0 conda-forge hdf4 4.2.15 h501b40f_6 conda-forge hdf5 1.12.2 nompi_h4df4325_101 conda-forge heapdict 1.0.1 py_0 conda-forge highspy 1.5.0.dev0 pypi_0 pypi humanfriendly 10.0 py310hff52083_4 conda-forge icu 70.1 h27087fc_0 conda-forge idna 3.4 pyhd8ed1ab_0 conda-forge importlib-metadata 6.1.0 pyha770c72_0 conda-forge importlib_metadata 6.1.0 hd8ed1ab_0 conda-forge importlib_resources 5.12.0 pyhd8ed1ab_0 conda-forge iniconfig 2.0.0 pyhd8ed1ab_0 conda-forge ipopt 3.14.11 hf9e1ecf_0 conda-forge ipython 8.12.0 pyh41d4057_0 conda-forge jack 1.9.22 h11f4161_0 conda-forge jedi 0.18.2 pyhd8ed1ab_0 conda-forge jinja2 3.1.2 pyhd8ed1ab_1 conda-forge joblib 1.2.0 pyhd8ed1ab_0 conda-forge json-c 0.16 hc379101_0 conda-forge jsonschema 4.17.3 pyhd8ed1ab_0 conda-forge jupyter_core 5.3.0 py310hff52083_0 conda-forge kealib 1.5.0 ha7026e8_0 conda-forge keyutils 1.6.1 h166bdaf_0 conda-forge kiwisolver 1.4.4 py310hbf28c38_1 conda-forge krb5 1.20.1 h81ceb04_0 conda-forge lame 3.100 h166bdaf_1003 conda-forge lcms2 2.15 haa2dc70_1 conda-forge ld_impl_linux-64 2.40 h41732ed_0 conda-forge lerc 4.0.0 h27087fc_0 conda-forge libabseil 20230125.0 cxx17_hcb278e6_1 conda-forge libaec 1.0.6 hcb278e6_1 conda-forge libarrow 11.0.0 h93537a5_13_cpu conda-forge libblas 3.9.0 16_linux64_openblas conda-forge libbrotlicommon 1.0.9 h166bdaf_8 conda-forge libbrotlidec 1.0.9 h166bdaf_8 conda-forge libbrotlienc 1.0.9 h166bdaf_8 conda-forge libcap 2.67 he9d0100_0 conda-forge libcblas 3.9.0 16_linux64_openblas conda-forge libclang 15.0.7 default_had23c3d_1 conda-forge libclang13 15.0.7 default_h3e3d535_1 conda-forge libcrc32c 1.1.2 h9c3ff4c_0 conda-forge libcups 2.3.3 h36d4200_3 conda-forge libcurl 7.88.1 hdc1c0ab_1 conda-forge libdb 6.2.32 h9c3ff4c_0 conda-forge libdeflate 1.17 h0b41bf4_0 conda-forge libedit 3.1.20191231 he28a2e2_2 conda-forge libev 4.33 h516909a_1 conda-forge libevent 2.1.10 h28343ad_4 conda-forge libexpat 2.5.0 hcb278e6_1 conda-forge libffi 3.4.2 h7f98852_5 conda-forge libflac 1.4.2 h27087fc_0 conda-forge libgcc-ng 12.2.0 h65d4601_19 conda-forge libgcrypt 1.10.1 h166bdaf_0 conda-forge libgdal 3.6.3 h93ed92d_1 conda-forge libgfortran-ng 12.2.0 h69a702a_19 conda-forge libgfortran5 12.2.0 h337968e_19 conda-forge libglib 2.74.1 h606061b_1 conda-forge libgomp 12.2.0 h65d4601_19 conda-forge libgoogle-cloud 2.8.0 h0bc5f78_1 conda-forge libgpg-error 1.46 h620e276_0 conda-forge libgrpc 1.52.1 hcf146ea_1 conda-forge libiconv 1.17 h166bdaf_0 conda-forge libjpeg-turbo 2.1.5.1 h0b41bf4_0 conda-forge libkml 1.3.0 h37653c0_1015 conda-forge liblapack 3.9.0 16_linux64_openblas conda-forge liblapacke 3.9.0 16_linux64_openblas conda-forge libllvm15 15.0.7 hadd5161_1 conda-forge libnetcdf 4.9.1 nompi_hd2e9713_102 conda-forge libnghttp2 1.52.0 h61bc06f_0 conda-forge libnsl 2.0.0 h7f98852_0 conda-forge libnuma 2.0.16 h0b41bf4_1 conda-forge libogg 1.3.4 h7f98852_1 conda-forge libopenblas 0.3.21 pthreads_h78a6416_3 conda-forge libopus 1.3.1 h7f98852_1 conda-forge libpng 1.6.39 h753d276_0 conda-forge libpq 15.2 hb675445_0 conda-forge libprotobuf 3.21.12 h3eb15da_0 conda-forge librttopo 1.1.0 h0d5128d_13 conda-forge libsndfile 1.2.0 hb75c966_0 conda-forge libspatialindex 1.9.3 h9c3ff4c_4 conda-forge libspatialite 5.0.1 h2d2bb50_24 conda-forge libsqlite 3.40.0 h753d276_0 conda-forge libssh2 1.10.0 hf14f497_3 conda-forge libstdcxx-ng 12.2.0 h46fd767_19 conda-forge libsystemd0 253 h8c4010b_1 conda-forge libthrift 0.18.1 h5e4af38_0 conda-forge libtiff 4.5.0 hddfeb54_5 conda-forge libtool 2.4.7 h27087fc_0 conda-forge libudev1 253 h0b41bf4_1 conda-forge libutf8proc 2.8.0 h166bdaf_0 conda-forge libuuid 2.38.1 h0b41bf4_0 conda-forge libvorbis 1.3.7 h9c3ff4c_0 conda-forge libwebp-base 1.3.0 h0b41bf4_0 conda-forge libxcb 1.13 h7f98852_1004 conda-forge libxkbcommon 1.5.0 h79f4944_1 conda-forge libxml2 2.10.3 hca2bb57_4 conda-forge libxslt 1.1.37 h873f0b0_0 conda-forge libzip 1.9.2 hc929e4a_1 conda-forge libzlib 1.2.13 h166bdaf_4 conda-forge linopy 0.1.4 pyhd8ed1ab_0 conda-forge locket 1.0.0 pyhd8ed1ab_0 conda-forge lxml 4.9.2 py310hbdc0903_0 conda-forge lz4 4.3.2 py310h0cfdcf0_0 conda-forge lz4-c 1.9.4 hcb278e6_0 conda-forge lzo 2.10 h516909a_1000 conda-forge mapclassify 2.5.0 pyhd8ed1ab_1 conda-forge markupsafe 2.1.2 py310h1fa729e_0 conda-forge matplotlib 3.5.3 py310hff52083_2 conda-forge matplotlib-base 3.5.3 py310h8d5ebf3_2 conda-forge matplotlib-inline 0.1.6 pyhd8ed1ab_0 conda-forge memory_profiler 0.61.0 pyhd8ed1ab_0 conda-forge metis 5.1.0 h58526e2_1006 conda-forge mpg123 1.31.3 hcb278e6_0 conda-forge msgpack-python 1.0.5 py310hdf3cbec_0 conda-forge mumps-include 5.2.1 ha770c72_11 conda-forge mumps-seq 5.2.1 h2104b81_11 conda-forge munch 2.5.0 py_0 conda-forge munkres 1.1.4 pyh9f0ad1d_0 conda-forge mysql-common 8.0.32 ha901b37_1 conda-forge mysql-libs 8.0.32 hd7da12d_1 conda-forge nbformat 5.8.0 pyhd8ed1ab_0 conda-forge ncurses 6.3 h27087fc_1 conda-forge netcdf4 1.6.3 nompi_py310h0feb132_100 conda-forge networkx 3.0 pyhd8ed1ab_0 conda-forge nomkl 1.0 h5ca1d4c_0 conda-forge nspr 4.35 h27087fc_0 conda-forge nss 3.89 he45b914_0 conda-forge numexpr 2.8.3 py310hf05e7a9_101 conda-forge numpy 1.23.5 py310h53a5b5f_0 conda-forge openjdk 17.0.3 h4335b31_6 conda-forge openjpeg 2.5.0 hfec8fc6_2 conda-forge openpyxl 3.1.1 py310h1fa729e_0 conda-forge openssl 3.1.0 h0b41bf4_0 conda-forge orc 1.8.3 hfdbbad2_0 conda-forge packaging 23.0 pyhd8ed1ab_0 conda-forge pandas 1.5.3 py310h9b08913_1 conda-forge parquet-cpp 1.5.1 2 conda-forge parso 0.8.3 pyhd8ed1ab_0 conda-forge partd 1.3.0 pyhd8ed1ab_0 conda-forge patsy 0.5.3 pyhd8ed1ab_0 conda-forge pcre2 10.40 hc3806b6_0 conda-forge pexpect 4.8.0 pyh1a96a4e_2 conda-forge pickleshare 0.7.5 py_1003 conda-forge pillow 9.4.0 py310h065c6d2_2 conda-forge pip 23.0.1 pyhd8ed1ab_0 conda-forge pixman 0.40.0 h36c2ea0_0 conda-forge pkgutil-resolve-name 1.3.10 pyhd8ed1ab_0 conda-forge plac 1.3.5 pyhd8ed1ab_0 conda-forge platformdirs 3.2.0 pyhd8ed1ab_0 conda-forge pluggy 1.0.0 pyhd8ed1ab_5 conda-forge ply 3.11 py_1 conda-forge pooch 1.7.0 pyha770c72_3 conda-forge poppler 23.03.0 hf052cbe_1 conda-forge poppler-data 0.4.12 hd8ed1ab_0 conda-forge postgresql 15.2 h3248436_0 conda-forge powerplantmatching 0.5.6 pyhd8ed1ab_0 conda-forge progressbar2 4.2.0 pyhd8ed1ab_0 conda-forge proj 9.1.1 h8ffa02c_2 conda-forge prompt-toolkit 3.0.38 pyha770c72_0 conda-forge prompt_toolkit 3.0.38 hd8ed1ab_0 conda-forge psutil 5.9.4 py310h5764c6d_0 conda-forge pthread-stubs 0.4 h36c2ea0_1001 conda-forge ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge pulp 2.7.0 py310hff52083_0 conda-forge pulseaudio 16.1 hcb278e6_3 conda-forge pulseaudio-client 16.1 h5195f5e_3 conda-forge pulseaudio-daemon 16.1 ha8d29e2_3 conda-forge pure_eval 0.2.2 pyhd8ed1ab_0 conda-forge pyarrow 11.0.0 py310h633f555_13_cpu conda-forge pybind11 2.10.4 pypi_0 pypi pycountry 22.3.5 pyhd8ed1ab_0 conda-forge pycparser 2.21 pyhd8ed1ab_0 conda-forge pygments 2.14.0 pyhd8ed1ab_0 conda-forge pyomo 6.5.0 py310heca2aa9_0 conda-forge pyopenssl 23.1.1 pyhd8ed1ab_0 conda-forge pyparsing 3.0.9 pyhd8ed1ab_0 conda-forge pyproj 3.5.0 py310h15e2413_0 conda-forge pypsa 0.22.1 pyhd8ed1ab_0 conda-forge pyqt 5.15.7 py310hab646b1_3 conda-forge pyqt5-sip 12.11.0 py310heca2aa9_3 conda-forge pyrsistent 0.19.3 py310h1fa729e_0 conda-forge pyshp 2.3.1 pyhd8ed1ab_0 conda-forge pysocks 1.7.1 pyha2e5f31_6 conda-forge pytables 3.7.0 py310hb60b9b2_3 conda-forge pytest 7.2.2 pyhd8ed1ab_0 conda-forge python 3.10.10 he550d4f_0_cpython conda-forge python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge python-fastjsonschema 2.16.3 pyhd8ed1ab_0 conda-forge python-utils 3.5.2 pyhd8ed1ab_0 conda-forge python_abi 3.10 3_cp310 conda-forge pytz 2023.3 pyhd8ed1ab_0 conda-forge pyxlsb 1.0.10 pyhd8ed1ab_0 conda-forge pyyaml 6.0 py310h5764c6d_5 conda-forge qt-main 5.15.8 h67dfc38_7 conda-forge rasterio 1.3.6 py310h3e853a9_0 conda-forge re2 2023.02.02 hcb278e6_0 conda-forge readline 8.2 h8228510_1 conda-forge requests 2.28.2 pyhd8ed1ab_1 conda-forge reretry 0.11.8 pyhd8ed1ab_0 conda-forge rtree 1.0.1 py310hbdcdc62_1 conda-forge s2n 1.3.41 h3358134_0 conda-forge scikit-learn 1.2.2 py310h41b6a48_1 conda-forge scipy 1.10.1 py310h8deb116_0 conda-forge scotch 6.0.9 hb2e6521_2 conda-forge seaborn 0.12.2 hd8ed1ab_0 conda-forge seaborn-base 0.12.2 pyhd8ed1ab_0 conda-forge setuptools 67.6.1 pyhd8ed1ab_0 conda-forge setuptools-scm 7.1.0 pyhd8ed1ab_0 conda-forge setuptools_scm 7.1.0 hd8ed1ab_0 conda-forge shapely 2.0.1 py310h056c13c_1 conda-forge sip 6.7.7 py310heca2aa9_1 conda-forge six 1.16.0 pyh6c4a22f_0 conda-forge smart_open 6.3.0 pyhd8ed1ab_1 conda-forge smmap 3.0.5 pyh44b312d_0 conda-forge snakemake-minimal 7.25.0 pyhdfd78af_0 bioconda snappy 1.1.10 h9fff704_0 conda-forge snuggs 1.4.7 py_0 conda-forge sortedcontainers 2.4.0 pyhd8ed1ab_0 conda-forge soupsieve 2.3.2.post1 pyhd8ed1ab_0 conda-forge sqlite 3.40.0 h4ff8645_0 conda-forge stack_data 0.6.2 pyhd8ed1ab_0 conda-forge statsmodels 0.13.5 py310hde88566_2 conda-forge stopit 1.1.2 py_0 conda-forge tabula-py 2.6.0 py310hff52083_0 conda-forge tabulate 0.9.0 pyhd8ed1ab_1 conda-forge tblib 1.7.0 pyhd8ed1ab_0 conda-forge threadpoolctl 3.1.0 pyh8a188c0_0 conda-forge throttler 1.2.1 pyhd8ed1ab_0 conda-forge tiledb 2.13.2 hd532e3d_0 conda-forge tk 8.6.12 h27826a3_0 conda-forge toml 0.10.2 pyhd8ed1ab_0 conda-forge tomli 2.0.1 pyhd8ed1ab_0 conda-forge toolz 0.12.0 pyhd8ed1ab_0 conda-forge toposort 1.10 pyhd8ed1ab_0 conda-forge tornado 6.2 py310h5764c6d_1 conda-forge tqdm 4.65.0 pyhd8ed1ab_1 conda-forge traitlets 5.9.0 pyhd8ed1ab_0 conda-forge tsam 2.2.2 pypi_0 pypi typing-extensions 4.5.0 hd8ed1ab_0 conda-forge typing_extensions 4.5.0 pyha770c72_0 conda-forge tzcode 2023c h0b41bf4_0 conda-forge tzdata 2023c h71feb2d_0 conda-forge ucx 1.14.0 ha0ee010_0 conda-forge unicodedata2 15.0.0 py310h5764c6d_0 conda-forge unidecode 1.3.6 pyhd8ed1ab_0 conda-forge unixodbc 2.3.10 h583eb01_0 conda-forge urllib3 1.26.15 pyhd8ed1ab_0 conda-forge vresutils 0.3.1 pypi_0 pypi wcwidth 0.2.6 pyhd8ed1ab_0 conda-forge wheel 0.40.0 pyhd8ed1ab_0 conda-forge wrapt 1.15.0 py310h1fa729e_0 conda-forge xarray 2023.3.0 pyhd8ed1ab_0 conda-forge xcb-util 0.4.0 h166bdaf_0 conda-forge xcb-util-image 0.4.0 h166bdaf_0 conda-forge xcb-util-keysyms 0.4.0 h166bdaf_0 conda-forge xcb-util-renderutil 0.3.9 h166bdaf_0 conda-forge xcb-util-wm 0.4.1 h166bdaf_0 conda-forge xerces-c 3.2.4 h55805fa_1 conda-forge xkeyboard-config 2.38 h0b41bf4_0 conda-forge xlrd 2.0.1 pyhd8ed1ab_3 conda-forge xorg-fixesproto 5.0 h7f98852_1002 conda-forge xorg-inputproto 2.3.2 h7f98852_1002 conda-forge xorg-kbproto 1.0.7 h7f98852_1002 conda-forge xorg-libice 1.0.10 h7f98852_0 conda-forge xorg-libsm 1.2.3 hd9c2040_1000 conda-forge xorg-libx11 1.8.4 h0b41bf4_0 conda-forge xorg-libxau 1.0.9 h7f98852_0 conda-forge xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge xorg-libxext 1.3.4 h0b41bf4_2 conda-forge xorg-libxfixes 5.0.3 h7f98852_1004 conda-forge xorg-libxi 1.7.10 h7f98852_0 conda-forge xorg-libxrender 0.9.10 h7f98852_1003 conda-forge xorg-libxtst 1.2.3 h7f98852_1002 conda-forge xorg-recordproto 1.14.2 h7f98852_1002 conda-forge xorg-renderproto 0.11.1 h7f98852_1002 conda-forge xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge xorg-xf86vidmodeproto 2.3.1 h7f98852_1002 conda-forge xorg-xproto 7.0.31 h7f98852_1007 conda-forge xyzservices 2023.2.0 pyhd8ed1ab_0 conda-forge xz 5.2.6 h166bdaf_0 conda-forge yaml 0.2.5 h7f98852_2 conda-forge yte 1.5.1 py310hff52083_1 conda-forge zict 2.2.0 pyhd8ed1ab_0 conda-forge zipp 3.15.0 pyhd8ed1ab_0 conda-forge zlib 1.2.13 h166bdaf_4 conda-forge zstd 1.5.2 h3eb15da_6 conda-forge

    <!-- output of `conda list` -->
  ```
</details>

Thank you!

[pz-max]

@Rock910, creating new cutouts usually needs some extra attention. Did this Sarah example work for you? I can also recommend testing the ERA5 cutout if you haven't tried yet.

[euronion]

Hi @Rock910 ,

Building the cutouts is a tedious task. For ERA5 the data can be downloaded automatically as long as you have registered with the CDSAPI. For SARAH data (and creation of the SARAH cutout), you have to download the raw SARAH data first and pass the raw data folder to PyPSA-EUR via this line in the config.yaml:

      sarah_dir:

Right now it is empty (in your config above). Empty means NULL, and that creates the error you see.

[Rock910]

Thank you for your reply, would there be any way to have a prebuilt SARAH cutout for the year 2016 instead? I was trying to get the data but it looks like the total size is 7.5 TiB.

[euronion]

I can't offer you a prebuilt cutout for 2016.

The SARAH data should not be 7.5 TiB, you might have selected the wrong product or maybe you did not limit the temporal range for which to download the raw data.

If you select the right product and limit the temporal range for which you download the variable, you end up with a more reasonable 98 GiB of size for the raw data, see here:

[euronion]

Hi @Rock910

Did you have success with downloading and building a SARAH cutout?