From df4eb85a19078e32709d66f9b868222bdebc925c Mon Sep 17 00:00:00 2001
From: Fabian <fab.hof@gmx.de>
Date: Fri, 18 Nov 2022 09:08:07 +0100
Subject: [PATCH] fix typos

---
 doc/data.csv                            |  4 ++--
 doc/installation.rst                    |  2 +-
 doc/release_notes.rst                   |  2 +-
 doc/spatial_resolution.rst              |  2 +-
 scripts/add_existing_baseyear.py        |  6 +++---
 scripts/build_industry_sector_ratios.py |  4 ++--
 scripts/build_retro_cost.py             | 16 ++++++++--------
 scripts/helper.py                       |  2 +-
 scripts/plot_summary.py                 |  4 ++--
 scripts/prepare_sector_network.py       |  6 +++---
 10 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/doc/data.csv b/doc/data.csv
index 2c6ac8c8..49fbc536 100644
--- a/doc/data.csv
+++ b/doc/data.csv
@@ -23,7 +23,7 @@ Floor area missing in hotmaps building stock data,floor_area_missing.csv,unknown
 Comparative level investment,comparative_level_investment.csv,Eurostat,https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Comparative_price_levels_for_investment
 Electricity taxes,electricity_taxes_eu.csv,Eurostat,https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en
 Building topologies and corresponding standard values,tabula-calculator-calcsetbuilding.csv,unknown,https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx
-Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unkown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
+Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unknown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
 District heating most countries,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees,,
-District heating missing countries,district_heat_share.csv,unkown,https://www.euroheat.org/knowledge-hub/country-profiles,,
+District heating missing countries,district_heat_share.csv,unknown,https://www.euroheat.org/knowledge-hub/country-profiles,,
 
diff --git a/doc/installation.rst b/doc/installation.rst
index 6637cdfd..f5cb7c7a 100644
--- a/doc/installation.rst
+++ b/doc/installation.rst
@@ -54,7 +54,7 @@ The requirements are the same as `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>
 xarray version >= 0.15.1, you will need the latest master branch of
 atlite version 0.0.2.
 
-You can create an enviroment using the environment.yaml file in pypsa-eur/envs:
+You can create an environment using the environment.yaml file in pypsa-eur/envs:
 
 .. code:: bash
 
diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index 51c533f4..51e9b43d 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -28,7 +28,7 @@ incorporates retrofitting options to hydrogen.
 
 * New rule ``cluster_gas_network`` that clusters the gas transmission network
   data to the model resolution. Cross-regional pipeline capacities are aggregated
-  (while pressure and diameter compability is ignored), intra-regional pipelines
+  (while pressure and diameter compatibility is ignored), intra-regional pipelines
   are dropped. Lengths are recalculated based on the regions' centroids.
 
 * With the option ``sector: gas_network:``, the existing gas network is
diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index d410065a..ff11cc39 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -15,7 +15,7 @@ The total number of nodes for Europe is set in the ``config.yaml`` file
 under ``clusters``. The number of nodes can vary between 37, the number
 of independent countries / synchronous areas, and several
 hundred. With 200-300 nodes the model needs 100-150 GB RAM to solve
-with a commerical solver like Gurobi.
+with a commercial solver like Gurobi.
 
 
 Not all of the sectors are at the full nodal resolution, and some
diff --git a/scripts/add_existing_baseyear.py b/scripts/add_existing_baseyear.py
index 0c60288a..e5b7e1d1 100644
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@@ -165,11 +165,11 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
     df_agg.loc[biomass_i, 'DateOut'] = df_agg.loc[biomass_i, 'DateOut'].fillna(dateout)
 
 
-    # drop assets which are already phased out / decomissioned
+    # drop assets which are already phased out / decommissioned
     phased_out = df_agg[df_agg["DateOut"]<baseyear].index
     df_agg.drop(phased_out, inplace=True)
 
-    # calculate remaining lifetime before phase-out (+1 because assumming
+    # calculate remaining lifetime before phase-out (+1 because assuming
     # phase out date at the end of the year)
     df_agg["lifetime"] = df_agg.DateOut - df_agg.DateIn + 1
 
@@ -251,7 +251,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
                     # existing capacities are split evenly among regions in every country
                     inv_ind = [i for i in inv_busmap[ind]]
 
-                    # for offshore the spliting only inludes coastal regions
+                    # for offshore the splitting only includes coastal regions
                     inv_ind = [i for i in inv_ind if (i + name_suffix) in n.generators.index]
 
                     p_max_pu = n.generators_t.p_max_pu[[i + name_suffix for i in inv_ind]]
diff --git a/scripts/build_industry_sector_ratios.py b/scripts/build_industry_sector_ratios.py
index d1dbe9d8..d2a9ca48 100644
--- a/scripts/build_industry_sector_ratios.py
+++ b/scripts/build_industry_sector_ratios.py
@@ -618,7 +618,7 @@ def nonmetalic_mineral_products():
     # (c) clinker production (kilns),
     # (d) Grinding, packaging.
     # (b)+(c) represent 94% of fec. So (a) is joined to (b) and (d) is joined to (c).
-    # Temperatures above 1400C are required for procesing limestone and sand into clinker.
+    # Temperatures above 1400C are required for processing limestone and sand into clinker.
     # Everything (except current electricity and heat consumption and existing biomass)
     # is transformed into methane for high T.
 
@@ -1110,7 +1110,7 @@ def non_ferrous_metals():
 
     # Aluminium secondary route
 
-    # All is coverted into secondary route fully electrified.
+    # All is converted into secondary route fully electrified.
 
     sector = "Aluminium - secondary production"
 
diff --git a/scripts/build_retro_cost.py b/scripts/build_retro_cost.py
index bec71a53..129c4156 100644
--- a/scripts/build_retro_cost.py
+++ b/scripts/build_retro_cost.py
@@ -33,7 +33,7 @@ The basic equations:
 
         E_space = H_losses - H_gains
 
-    Heat losses constitute from the losses through heat trasmission (H_tr [W/m²K])
+    Heat losses constitute from the losses through heat transmission (H_tr [W/m²K])
     (this includes heat transfer through building elements and thermal bridges)
     and losses by ventilation (H_ve [W/m²K]):
 
@@ -71,7 +71,7 @@ import xarray as xr
 
 # thermal conductivity standard value
 k = 0.035
-# strenght of relative retrofitting depending on the component
+# strength of relative retrofitting depending on the component
 # determined by historical data of insulation thickness for retrofitting
 l_weight = pd.DataFrame({"weight": [1.95, 1.48, 1.]},
                         index=["Roof", "Wall", "Floor"])
@@ -89,8 +89,8 @@ tau_H_0 = 30
 # constant parameter alpha_H_0 [-] according to EN 13790 seasonal method
 alpha_H_0 = 0.8
 
-# paramter for solar heat load during heating season -------------------------
-# tabular standard values table p.8 in documenation
+# parameter for solar heat load during heating season -------------------------
+# tabular standard values table p.8 in documentation
 external_shading = 0.6     # vertical orientation: fraction of window area shaded [-]
 frame_area_fraction = 0.3  # fraction of frame area of window [-]
 non_perpendicular = 0.9    # reduction factor, considering radiation non perpendicular to the glazing[-]
@@ -279,7 +279,7 @@ def prepare_building_stock_data():
 def prepare_building_topology(u_values, same_building_topology=True):
     """
     reads in typical building topologies (e.g. average surface of building elements)
-    and typical losses trough thermal bridging and air ventilation
+    and typical losses through thermal bridging and air ventilation
     """
 
     data_tabula = pd.read_csv(snakemake.input.data_tabula,
@@ -585,7 +585,7 @@ def map_to_lstrength(l_strength, df):
 
 def calculate_heat_losses(u_values, data_tabula, l_strength, temperature_factor):
     """
-    calculates total annual heat losses Q_ht for different insulation thiknesses
+    calculates total annual heat losses Q_ht for different insulation thicknesses
     (l_strength), depening on current insulation state (u_values), standard
     building topologies and air ventilation from TABULA (data_tabula) and
     the accumulated difference between internal and external temperature
@@ -790,7 +790,7 @@ def sample_dE_costs_area(area, area_tot, costs, dE_space, countries,
 
     # drop not considered countries
     cost_dE = cost_dE.reindex(countries,level=0)
-    # get share of residential and sevice floor area
+    # get share of residential and service floor area
     sec_w = area_tot.value / area_tot.value.groupby(level=0).sum()
     # get the total cost-energy-savings weight by sector area
     tot = (cost_dE.mul(sec_w, axis=0).groupby(level="country_code").sum()
@@ -863,7 +863,7 @@ if __name__ == "__main__":
     data_tabula = prepare_building_topology(u_values)
     # costs for retrofitting -------------------------------------------------
     cost_retro, window_assumptions, cost_w, tax_w = prepare_cost_retro(country_iso_dic)
-    # temperature dependend parameters
+    # temperature dependent parameters
     d_heat, temperature_factor = prepare_temperature_data()
 
 
diff --git a/scripts/helper.py b/scripts/helper.py
index 48e28e71..e6ddfd4a 100644
--- a/scripts/helper.py
+++ b/scripts/helper.py
@@ -25,7 +25,7 @@ def override_component_attrs(directory):
 
     Returns
     -------
-    Dictionary of overriden component attributes.
+    Dictionary of overridden component attributes.
     """
 
     attrs = Dict({k : v.copy() for k,v in component_attrs.items()})
diff --git a/scripts/plot_summary.py b/scripts/plot_summary.py
index b36c2ec5..be08a3af 100644
--- a/scripts/plot_summary.py
+++ b/scripts/plot_summary.py
@@ -401,7 +401,7 @@ def plot_carbon_budget_distribution(input_eurostat):
 
     ax1.plot(emissions, color='black', linewidth=3, label=None)
 
-    #plot commited and uder-discussion targets
+    #plot committed and uder-discussion targets
     #(notice that historical emissions include all countries in the
     # network, but targets refer to EU)
     ax1.plot([2020],[0.8*emissions[1990]],
@@ -427,7 +427,7 @@ def plot_carbon_budget_distribution(input_eurostat):
 
     ax1.plot([2050],[0.125*emissions[1990]],'ro',
                      marker='*', markersize=12, markerfacecolor='black',
-                     markeredgecolor='black', label='EU commited target')
+                     markeredgecolor='black', label='EU committed target')
 
     ax1.legend(fancybox=True, fontsize=18, loc=(0.01,0.01),
                        facecolor='white', frameon=True)
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 244f615b..6c153f16 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1382,7 +1382,7 @@ def add_land_transport(n, costs):
 
 def build_heat_demand(n):
 
-    # copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
+    # copy forward the daily average heat demand into each hour, so it can be multiplied by the intraday profile
     daily_space_heat_demand = xr.open_dataarray(snakemake.input.heat_demand_total).to_pandas().reindex(index=n.snapshots, method="ffill")
 
     intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
@@ -1724,7 +1724,7 @@ def add_heat(n, costs):
 
             # minimum heat demand 'dE' after retrofitting in units of original heat demand (values between 0-1)
             dE = retro_data.loc[(ct, sec), ("dE")]
-            # get addtional energy savings 'dE_diff' between the different retrofitting strengths/generators at one node
+            # get additional energy savings 'dE_diff' between the different retrofitting strengths/generators at one node
             dE_diff = abs(dE.diff()).fillna(1-dE.iloc[0])
             # convert costs Euro/m^2 -> Euro/MWh
             capital_cost =  retro_data.loc[(ct, sec), ("cost")] * floor_area_node / \
@@ -2565,7 +2565,7 @@ def set_temporal_aggregation(n, opts, solver_name):
         if m is not None:
             n = average_every_nhours(n, m.group(0))
             break
-        # representive snapshots
+        # representative snapshots
         m = re.match(r"(^\d+)sn$", o, re.IGNORECASE)
         if m is not None:
             sn = int(m[1])