Merge pull request #8 from ClimateImpactLab/flake8fixes

Flake8fixes

Author: kitschwarz

.github/workflows/pythonpackage.yaml

@@ -4,7 +4,7 @@ on:
   pull_request:
   push:
     branches:
-      - main
+      - flake8fixes
 
 jobs:
 

dscim/__init__.py

@@ -201,7 +201,6 @@ def menu_factory(self, menu_key, sector, kwargs=None):
             climate_options.update(**climate_kwargs)
 
         # Set up
-        dir_name = os.path.dirname(os.path.abspath(__file__))
         sector_config = self.param_dict["sectors"].get(sector)
         sector_config.update({k: v for k, v in global_options.items()})
         sector_config.update(

dscim/cli.py

@@ -78,6 +78,8 @@ def cli(config_file, menu_order, log_level, local, pro=False):
         path_to_scheduler = os.path.join(os.getenv("SCRATCH"), "scheduler.json")
         client = Client(scheduler_file=path_to_scheduler)
 
+    print(logger, client)
+
     if menu_order == "":
         waiter.execute_order()
     else:
@@ -86,12 +88,9 @@ def cli(config_file, menu_order, log_level, local, pro=False):
 
         menus = global_parameters["menu_type"]
         discounts = global_parameters["discounting_type"]
-        course = global_parameters["course"]
         pulse_year = waiter.param_dict["climate"]["pulse_year"]
 
-        for menu, discount, sector, level in itertools.product(
-            menus, discounts, sectors, levels
-        ):
+        for menu, discount, sector in itertools.product(menus, discounts, sectors):
             obj = waiter.menu_factory(
                 menu_key=menu,
                 sector=sector,

dscim/diagnostics/batch_maps.py

@@ -31,7 +31,7 @@ def make_map(
     # create figure
     fig, ax = plt.subplots(figsize=figsize, facecolor="white")
 
-    if color_max == None:
+    if color_max is None:
         max_val = max(abs(df[colname].max()), abs(df[colname].min()))
         color_min, color_max = -max_val, max_val
 
@@ -54,15 +54,15 @@ def make_map(
     fig.text(0.5, 0.08, title, ha="center", va="center", rotation=0, fontsize=18)
 
     ax.set_axis_off()
-    if maxmin == True:
+    if maxmin:
         plt.annotate(
             text=f"min: {df[colname].min()}, max: {df[colname].max()}",
             xy=location,
             xycoords="axes fraction",
             fontsize=10,
         )
 
-    if save_path != None:
+    if save_path is not None:
         os.makedirs(save_path, exist_ok=True)
         fig.savefig(f"{save_path}/{name_file}", dpi=200, bbox_inches="tight")
 
@@ -119,22 +119,22 @@ def batch_maps(
     if gcm == "mean":
         damages = damages.weighted(weights).mean(dim="gcm")
     elif gcm == "ce":
-        damages = c_equivalence(risk.damages, dims=["gcm"])
+        damages = c_equivalence(damages, dims=["gcm"])
     else:
         damages = damages.sel({"gcm": gcm})
 
     merged = xr.merge([damages, gdppc]).to_dataframe().reset_index()
 
     shp_file = gpd.read_file(
-        f"/shares/gcp/climate/_spatial_data/world-combo-new-nytimes/new_shapefile.shp"
+        "/shares/gcp/climate/_spatial_data/world-combo-new-nytimes/new_shapefile.shp"
     )
 
     data = shp_file.merge(merged, left_on=["hierid"], right_on=["region"])
 
     # generate variables of interest
     data["damages_inc_share"] = data["damages"] / data["gdppc"] * 100
 
-    if plot == False:
+    if not plot:
         return data
     else:
 

dscim/diagnostics/compare_sccs.py

@@ -25,7 +25,7 @@ def compare_sccs(
             this = this.sel(
                 gas="CO2_Fossil", model="IIASA GDP", rcp="ssp370", drop=True
             ).rename({"simulation": "runid"})
-        except:
+        except KeyError:
             # for rff calculations
             if ("simulation" in this.dims) and (len(this.simulation) == 1):
                 this = this.sel(simulation=1, drop=True)
@@ -37,9 +37,9 @@ def compare_sccs(
         else:
             this = this.quantile(quantiles, "runid")
 
-        try:
+        if "discrate" in this.dims:
             this = this.sel(discrate=0.02, drop=True)
-        except:
+        else:
             pass
 
         this = this.sel(weitzman_parameter=wp, drop=True)

dscim/diagnostics/crayola_plots.py

@@ -106,7 +106,7 @@ def crayola_plots(
             ["year", "ssp", "rcp", "model", "temp_bins"], as_index=False
         )[damages_col_name].mean()
     else:
-        raise NotImplemented(f"{var} is not a defined option")
+        raise NotImplementedError(f"{var} is not a defined option")
 
     with sns.plotting_context("paper", font_scale=1.3):
         sns.set_style("white")
@@ -180,4 +180,4 @@ def crayola_plots(
                 title = f"{var}_{recipe.discounting_type}_{recipe.NAME}_{recipe.sector}_{ssp}.png"
                 g.savefig(os.path.join(save_plot_path, title))
 
-    return (global_damages, global_consumption)
+    return (global_damages, recipe.global_consumption)

dscim/diagnostics/damage_function.py

@@ -55,7 +55,7 @@ def get_legacy(sector, filepath, scale):
     coefs = temps.join(coefs).reset_index().rename(columns={"index": "year"})
 
     # Predict y_hat
-    coefs[f"y_hat"] = (
+    coefs["y_hat"] = (
         coefs.cons + coefs.anomaly * coefs.beta_1 + coefs.anomaly**2 * coefs.beta_2
     )
 
@@ -136,7 +136,7 @@ def damage_function(
         )
 
         # subset
-        if subset_dict != None:
+        if subset_dict is not None:
             for col, val in subset_dict.items():
                 points_file = points_file.loc[points_file[col].isin(val)]
             fit_file = fit_file.sel(subset_dict)
@@ -174,7 +174,7 @@ def damage_function(
         )
 
     # grab legacy function if passed
-    if legacy != None:
+    if legacy is not None:
         fit["legacy"] = get_legacy(sector, filepath=legacy[0], scale=legacy[1])
         fit["legacy"] = fit["legacy"].loc[fit["legacy"].year == year]
         fit["legacy"]["y_hat_scaled"] = fit["legacy"]["y_hat"].apply(
@@ -195,7 +195,7 @@ def damage_function(
 
         print(f"{recipe} : # of points is {len(points[recipe])}")
 
-        if scatter == True:
+        if scatter:
 
             sns.scatterplot(
                 data=points[recipe]
@@ -207,7 +207,7 @@ def damage_function(
                 ]
                 .sort_values("hue"),
                 x=x_var,
-                y=f"damages_scaled",
+                y="damages_scaled",
                 hue="hue",
                 palette=palette,
                 s=6,
@@ -230,7 +230,7 @@ def damage_function(
             ax=ax[0][i],
         )
 
-        if legacy != None:
+        if legacy is not None:
 
             sns.lineplot(
                 data=fit["legacy"],
@@ -243,19 +243,19 @@ def damage_function(
 
         ax[0][i].set_xlabel(x_var)
         ax[0][i].set_title(f"Recipe: {recipe}")
-        if attributes == True:
+        if attributes:
             ax[0][i].annotate(
                 attrs[recipe], (0.0, -0.1), xycoords="axes fraction", fontsize=6
             )
 
-    ltext = "\n paper damage function = grey dotted" if legacy != None else ""
+    ltext = "\n paper damage function = grey dotted" if legacy is not None else ""
     fig.suptitle(
         f"{sector}: {discounting} discounting \n Damage functions in {year} {ltext}"
     )
 
     ax[0][0].set_ylabel(f'Damages in {"{:.0e}".format(scale)} 2019 USD')
 
-    if save_path != None:
+    if save_path is not None:
 
         os.makedirs(save_path, exist_ok=True)
         plt.savefig(f"{save_path}/{sector}_{discounting}_{year}_damage_function.pdf")

dscim/diagnostics/damage_inc_share_boxplots.py

@@ -1,6 +1,8 @@
 import xarray as xr
 import pandas as pd
-import os, sys, yaml
+import os
+import sys
+import yaml
 
 USER = os.getenv("USER")
 from dscim.utils.functions import get_model_weights, US_territories
@@ -34,7 +36,7 @@ def income_boxplot(
         delta_var
     ]
 
-    if USA == True:
+    if USA:
         # subset US data
         US_IRs = [
             i for i in socioec.region.values if any(j in i for j in US_territories())
@@ -104,7 +106,7 @@ def income_boxplot(
             }
         )
 
-    bxp = ax.bxp(stats, showmeans=True, meanline=False)
+    ax.bxp(stats, showmeans=True, meanline=False)
     ax.set_ylabel(f"{year} damages as a share of present-day GDP")
     ax.set_xlabel("Decile of present-day GDP per capita")
     ax.spines["top"].set_visible(False)

dscim/diagnostics/discount_rates.py

@@ -2,7 +2,8 @@
 import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
-import os, sys
+import os
+import sys
 
 
 def plot_implicit_rates(

dscim/diagnostics/equity_risk_premiums.py

@@ -1,4 +1,5 @@
-import os, sys
+import os
+import sys
 import numpy as np
 import pandas as pd
 import xarray as xr
@@ -169,7 +170,7 @@ def mumbai_plots(
         for ax in g.axes:
             ax.set_ylabel("2019 PPP-adjusted USD")
             ax.set_xlabel("GMST")
-            if (premium == "equity") or (title == False):
+            if (premium == "equity") or (not title):
                 ax.set_title("")
 
         title_items = "_".join([ssp, model, str(year)]).replace(" ", "_")