Pandas deprecations - offset warning

config.default.yaml

@@ -48,7 +48,7 @@ demand_data:
   update_data: true # if true, the workflow downloads the energy balances data saved in data/demand/unsd/data again. Turn on for the first run.
   base_year: 2019
 
-  other_industries: false # Whether or not to include industries that are not specified. some countries have has exageratted numbers, check carefully.
+  other_industries: false # Whether or not to include industries that are not specified. some countries have has exaggerated numbers, check carefully.
   aluminium_year: 2019 # Year of the aluminium demand data specified in `data/AL_production.csv`
 
 
@@ -57,12 +57,12 @@ enable:
   retrieve_irena: true  #If true, downloads the IRENA data
 
 fossil_reserves:
-  oil: 100 #TWh Maybe reduntant
+  oil: 100 #TWh Maybe redundant
 
 
 export:
   h2export: [10] # Yearly export demand in TWh
-  store: true # [True, False] # specifies wether an export store to balance demand is implemented
+  store: true # [True, False] # specifies whether an export store to balance demand is implemented
   store_capital_costs: "no_costs" # ["standard_costs", "no_costs"] # specifies the costs of the export store. "standard_costs" takes CAPEX of "hydrogen storage tank type 1 including compressor"
   export_profile: "ship" # use "ship" or "constant"
   ship:
@@ -157,7 +157,7 @@ sector:
   district_heating:
     potential: 0.3 #maximum fraction of urban demand which can be supplied by district heating
       #increase of today's district heating demand to potential maximum district heating share
-      #progress = 0 means today's district heating share, progress=-1 means maxumzm fraction of urban demand is supplied by district heating
+      #progress = 0 means today's district heating share, progress=-1 means maximum fraction of urban demand is supplied by district heating
     progress: 1
       #2020: 0.0
       #2030: 0.3
@@ -219,7 +219,7 @@ sector:
   efficiency_heat_gas_to_elec: 0.9
 
   dynamic_transport:
-    enable: false # If "True", then the BEV and FCEV shares are obtained depening on the "Co2L"-wildcard (e.g. "Co2L0.70: 0.10"). If "False", then the shares are obtained depending on the "demand" wildcard and "planning_horizons" wildcard as listed below (e.g. "DF_2050: 0.08")
+    enable: false # If "True", then the BEV and FCEV shares are obtained depending on the "Co2L"-wildcard (e.g. "Co2L0.70: 0.10"). If "False", then the shares are obtained depending on the "demand" wildcard and "planning_horizons" wildcard as listed below (e.g. "DF_2050: 0.08")
     land_transport_electric_share:
       Co2L2.0: 0.00
       Co2L1.0: 0.01

config.pypsa-earth.yaml

@@ -203,7 +203,7 @@ atlite:
       dy: 0.3  # cutout resolution
       # The cutout time is automatically set by the snapshot range. See `snapshot:` option above and 'build_cutout.py'.
       # time: ["2013-01-01", "2014-01-01"]  # to manually specify a different weather year (~70 years available)
-      # The cutout spatial extent [x,y] is automatically set by country selection. See `countires:` option above and 'build_cutout.py'.
+      # The cutout spatial extent [x,y] is automatically set by country selection. See `countries:` option above and 'build_cutout.py'.
       # x: [-12., 35.]  # set cutout range manual, instead of automatic by boundaries of country
       # y: [33., 72]    # manual set cutout range
 
@@ -352,8 +352,8 @@ monte_carlo:
     add_to_snakefile: false # When set to true, enables Monte Carlo sampling
     samples: 9 # number of optimizations. Note that number of samples when using scipy has to be the square of a prime number
     sampling_strategy: "chaospy"  # "pydoe2", "chaospy", "scipy", packages that are supported
-    seed: 42 # set seedling for reproducibilty
-  # Uncertanties on any PyPSA object are specified by declaring the specific PyPSA object under the key 'uncertainties'.
+    seed: 42 # set seedling for reproducibility
+  # Uncertainties on any PyPSA object are specified by declaring the specific PyPSA object under the key 'uncertainties'.
   # For each PyPSA object, the 'type' and 'args' keys represent the type of distribution and its argument, respectively.
   # Supported distributions types are uniform, normal, lognormal, triangle, beta and gamma.
   # The arguments of the distribution are passed using the key 'args'  as follows, tailored by distribution type

scripts/add_export.py

@@ -14,7 +14,6 @@
 
 import logging
 import os
-from pathlib import Path
 
 import geopandas as gpd
 import numpy as np
@@ -164,7 +163,7 @@ def create_export_profile():
 
     # Resample to temporal resolution defined in wildcard "sopts" with pandas resample
     sopts = snakemake.wildcards.sopts.split("-")
-    export_profile = export_profile.resample(sopts[0]).mean()
+    export_profile = export_profile.resample(sopts[0].casefold()).mean()
 
     # revise logger msg
     export_type = snakemake.params.export_profile

scripts/build_industry_demand.py

@@ -10,9 +10,8 @@
 import os
 from itertools import product
 
-import numpy as np
 import pandas as pd
-from helpers import read_csv_nafix, sets_path_to_root, three_2_two_digits_country
+from helpers import mock_snakemake, read_csv_nafix, sets_path_to_root
 
 _logger = logging.getLogger(__name__)
 
@@ -50,8 +49,6 @@ def country_to_nodal(industrial_production, keys):
 
 if __name__ == "__main__":
     if "snakemake" not in globals():
-        from helpers import mock_snakemake, sets_path_to_root
-
         os.chdir(os.path.dirname(os.path.abspath(__file__)))
 
         snakemake = mock_snakemake(
@@ -125,7 +122,7 @@ def country_to_nodal(industrial_production, keys):
             snakemake.input["base_industry_totals"], index_col=[0, 1]
         )
 
-        production_base = cagr.applymap(lambda x: 1)
+        production_base = cagr.map(lambda x: 1)
         production_tom = production_base * growth_factors
 
         # non-used line; commented out

scripts/prepare_sector_network.py

@@ -2250,7 +2250,7 @@ def average_every_nhours(n, offset):
     # logger.info(f'Resampling the network to {offset}')
     m = n.copy(with_time=False)
 
-    snapshot_weightings = n.snapshot_weightings.resample(offset).sum()
+    snapshot_weightings = n.snapshot_weightings.resample(offset.casefold()).sum()
     m.set_snapshots(snapshot_weightings.index)
     m.snapshot_weightings = snapshot_weightings
 
@@ -2259,11 +2259,11 @@ def average_every_nhours(n, offset):
         for k, df in c.pnl.items():
             if not df.empty:
                 if c.list_name == "stores" and k == "e_max_pu":
-                    pnl[k] = df.resample(offset).min()
+                    pnl[k] = df.resample(offset.casefold()).min()
                 elif c.list_name == "stores" and k == "e_min_pu":
-                    pnl[k] = df.resample(offset).max()
+                    pnl[k] = df.resample(offset.casefold()).max()
                 else:
-                    pnl[k] = df.resample(offset).mean()
+                    pnl[k] = df.resample(offset.casefold()).mean()
 
     return m
 

test/config.test1.yaml

@@ -48,20 +48,20 @@ demand_data:
   update_data: true # if true, the workflow downloads the energy balances data saved in data/demand/unsd/data again. Turn on for the first run.
   base_year: 2019
 
-  other_industries: false # Whether or not to include industries that are not specified. some countries have has exageratted numbers, check carefully.
+  other_industries: false # Whether or not to include industries that are not specified. some countries have has exaggerated numbers, check carefully.
   aluminium_year: 2019 # Year of the aluminium demand data specified in `data/AL_production.csv`
 
 
 enable:
   retrieve_cost_data: true # if true, the workflow overwrites the cost data saved in data/costs again
 
 fossil_reserves:
-  oil: 100 #TWh Maybe reduntant
+  oil: 100 #TWh Maybe redundant
 
 
 export:
   h2export: [120] # Yearly export demand in TWh
-  store: true # [True, False] # specifies wether an export store to balance demand is implemented
+  store: true # [True, False] # specifies whether an export store to balance demand is implemented
   store_capital_costs: "no_costs" # ["standard_costs", "no_costs"] # specifies the costs of the export store "standard_costs" takes CAPEX of "hydrogen storage tank type 1 including compressor"
   export_profile: "ship" # use "ship" or "constant"
   ship:
@@ -156,7 +156,7 @@ sector:
   district_heating:
     potential: 0.3 #maximum fraction of urban demand which can be supplied by district heating
       #increase of today's district heating demand to potential maximum district heating share
-      #progress = 0 means today's district heating share, progress=-1 means maxumzm fraction of urban demand is supplied by district heating
+      #progress = 0 means today's district heating share, progress=-1 means maximum fraction of urban demand is supplied by district heating
     progress: 1
       #2020: 0.0
       #2030: 0.3
@@ -217,7 +217,7 @@ sector:
   efficiency_heat_gas_to_elec: 0.9
 
   dynamic_transport:
-    enable: false # If "True", then the BEV and FCEV shares are obtained depening on the "Co2L"-wildcard (e.g. "Co2L0.70: 0.10"). If "False", then the shares are obtained depending on the "demand" wildcard and "planning_horizons" wildcard as listed below (e.g. "DF_2050: 0.08")
+    enable: false # If "True", then the BEV and FCEV shares are obtained depending on the "Co2L"-wildcard (e.g. "Co2L0.70: 0.10"). If "False", then the shares are obtained depending on the "demand" wildcard and "planning_horizons" wildcard as listed below (e.g. "DF_2050: 0.08")
     land_transport_electric_share:
       Co2L2.0: 0.00
       Co2L1.0: 0.01