Fixes

3DGI · Feb 18, 2024 · 9b26c1b · 9b26c1b
1 parent d3e13d6
commit 9b26c1b
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diff --git a/dashboard/dashboard_server/dashboard.py b/dashboard/dashboard_server/dashboard.py
@@ -66,7 +66,7 @@ def set_neighborhoods_value(available_options):
     html.Div([
         html.Div(
             children=[
-                html.H5(children="Municipality:"),
+                html.H5(children="Gemeente"),
                 dcc.Dropdown(
                     id="municipalities-dropdown",
                     options=neighborhoods.index.get_level_values(0).unique(),
@@ -77,7 +77,7 @@ def set_neighborhoods_value(available_options):
         ),
         html.Div(
             children=[
-                html.H5(children="Neighborhood:"),
+                html.H5(children="Buurt"),
                 dcc.Dropdown(
                     id="neighborhoods-dropdown",
                     value="Bezuidenhout-Midden",

diff --git a/report/_quarto.yml b/report/_quarto.yml
@@ -7,9 +7,8 @@ lang: en
 
 format:
   html:
-    code-fold: true
+    echo: false
   pdf: default
-  ipynb: default
 
 jupyter: python3
 

diff --git a/report/report_nl.qmd b/report/report_nl.qmd
@@ -1,8 +1,23 @@
 ---
 title: "Wijklabels"
-author: "Balázs Dukai, Ravi Peters"
+author: 
+  - name: "Balázs Dukai"
+    email: "[email protected]"
+    affiliation:
+      - name: "3DGI"
+        url: "https://3dgi.nl"
+  - name: "Ravi Peters"
+    email: "[email protected]"
+    affiliation:
+      - name: "3DGI"
+        url: "https://3dgi.nl"
 date: last-modified
 bibliography: references.bib
+license: "CC BY"
+copyright: 
+  holder: "3DGI"
+  year: "2024"
+lang: "nl"
 ---
 
 ```{python}
@@ -250,6 +265,14 @@ Als we onze validatie beperken tot die woningen waar het juiste label wel bestaa
 
 Voor deze subset van de data, is de gemiddelde labelafwijking in de door ons geschatte labels ten opzichte van de @_EP_2023 labels **-0,6**, met een standaardafwijking van **1,6**. Dit is ongeveer twee keer zo laag als de labelafwijking voor de gehele dataset.
 
+### Buurten
+
+Kies een gemeente en een buurt om de geschatte energielabels te bekijken.
+
+```{=html}
+<iframe width="780" height="600" src="https://dev.3dgi.xyz/wijklabels/"></iframe>
+```
+
 # Conclusies
 
 Gemiddeld liggen de door ons geschatte labels één label onder het echte label, met een standaardafwijking van twee labels.

diff --git a/src/wijklabels/report.py b/src/wijklabels/report.py
@@ -192,7 +192,7 @@ def _plot_dist(grouped, t):
             plt.axhline(y=0.0, color='#154273', linestyle='-')
             plt.grid(visible=True, which="major", axis="y", zorder=0)
             plt.title(f"{aggregate_id_column.title()}: {aggregate_id}\nNr. woningen: {len(b)}", fontsize=10)
-            plt.suptitle("Afwijking van de geschatt labels van de EP-Online labels", fontsize=14)
+            plt.suptitle("Afwijking van de geschatte labels van de EP-Online labels", fontsize=14)
             plt.tight_layout()
             plt.savefig(f"{dir_plots}/{aggregate_level}_{filename}_dist{t}.png")
             plt.close()

diff --git a/src/wijklabels/validate.py b/src/wijklabels/validate.py
@@ -274,25 +274,25 @@ def _ep_in_dist(df_dist, row):
         # Plot NL
         log.info(f"Writing plot of the Netherlands to {p}")
         plot_comparison(df_with_truth, p, aggregate_level=AggregateUnit.NL)
-        log.info(f"Writing plots of municipalities to {p}")
-        plot_comparison(df_with_truth, p, aggregate_level=AggregateUnit.GEMEENTE)
-        log.info(f"Writing plots of wijken to {p}")
-        plot_comparison(df_with_truth, p, aggregate_level=AggregateUnit.WIJK)
-        log.info(f"Writing plots of neighborhoods to {p}")
-        plot_comparison(df_with_truth, p, aggregate_level=AggregateUnit.BUURT)
+        # log.info(f"Writing plots of municipalities to {p}")
+        # plot_comparison(df_with_truth, p, aggregate_level=AggregateUnit.GEMEENTE)
+        # log.info(f"Writing plots of wijken to {p}")
+        # plot_comparison(df_with_truth, p, aggregate_level=AggregateUnit.WIJK)
+        # log.info(f"Writing plots of neighborhoods to {p}")
+        # plot_comparison(df_with_truth, p, aggregate_level=AggregateUnit.BUURT)
 
         p = PATH_OUTPUT_DIR.joinpath("plots_possible")
         p.mkdir(parents=True, exist_ok=True)
 
         # Plot NL
         log.info(f"Writing plot of the Netherlands to {p}")
         plot_comparison(possible_labels, p, aggregate_level=AggregateUnit.NL)
-        log.info(f"Writing plots of municipalities to {p}")
-        plot_comparison(possible_labels, p, aggregate_level=AggregateUnit.GEMEENTE)
-        log.info(f"Writing plots of wijken to {p}")
-        plot_comparison(possible_labels, p, aggregate_level=AggregateUnit.WIJK)
-        log.info(f"Writing plots of neighborhoods to {p}")
-        plot_comparison(possible_labels, p, aggregate_level=AggregateUnit.BUURT)
+        # log.info(f"Writing plots of municipalities to {p}")
+        # plot_comparison(possible_labels, p, aggregate_level=AggregateUnit.GEMEENTE)
+        # log.info(f"Writing plots of wijken to {p}")
+        # plot_comparison(possible_labels, p, aggregate_level=AggregateUnit.WIJK)
+        # log.info(f"Writing plots of neighborhoods to {p}")
+        # plot_comparison(possible_labels, p, aggregate_level=AggregateUnit.BUURT)
 
 
 if __name__ == "__main__":

diff --git a/tmp/analyse_ep_online.py b/tmp/analyse_ep_online.py
@@ -5,6 +5,7 @@
 import pandas as pd
 import psycopg
 import matplotlib.pyplot as plt
+import numpy as np
 
 from wijklabels.load import EPLoader
 from wijklabels.woningtype import Bouwperiode, WoningtypePreNTA8800
@@ -29,6 +30,14 @@
 
 if __name__ == '__main__':
     args = parser.parse_args()
+    # args = parser.parse_args([
+    #     "/data/energylabel-ep-online/v20231101_v2_csv_subset.csv",
+    #     "-d", "postgres",
+    #     "--host", "localhost",
+    #     "-p", "8001",
+    #     "-u", "postgres",
+    #     "--password", "password"
+    # ])
     bouwperiode = True
     woningtype = True
     coverage = True
@@ -95,31 +104,29 @@
             periods_sorted_pretty
         ]
 
+
+
         fig = plt.figure(figsize=(9, 7))
-        plt.barh(
-            y=bag_df_bouwperiode_dist.index,
-            width=bag_df_bouwperiode_dist.bouwperiode.values * -100,
+        x = np.arange(len(bag_df_bouwperiode_dist.index))
+        plt.bar(
+            x=x,
+            height=bag_df_bouwperiode_dist.bouwperiode.values * 100,
             label="BAG",
-            zorder=3
+            zorder=3,
+            width=0.25
         )
-        plt.barh(
-            y=ep_online_bouwperiode_dist.index,
-            width=ep_online_bouwperiode_dist.bouwperiode.values * 100,
+        plt.bar(
+            x=x + 0.25,
+            height=ep_online_bouwperiode_dist.bouwperiode.values * 100,
             label="EP-Online",
-            zorder=3
+            zorder=3,
+            width=0.25
         )
         plt.legend(loc="best")
         plt.grid(which="major", axis="x", zorder=0)
-        plt.xticks(
-            ticks=range(-50, 60, 10),
-            labels=[str(i) for i in range(-50, 60, 10)]
-        )
-        plt.xticks(
-            ticks=range(-55, 60, 10),
-            minor=True,
-        )
-        plt.xlabel("Percentage (%) van het hele dataset")
-        plt.ylabel("Bouwperiode")
+        plt.ylabel("Percentage (%) van het hele dataset")
+        plt.xticks(x+0.125, bag_df_bouwperiode_dist.index)
+        plt.xlabel("Bouwperiode")
         plt.suptitle("Spreiding van woningen per bouwperiode", fontsize=14)
         plt.savefig("bouwperiode_dist.png")
         plt.close()
@@ -142,31 +149,27 @@
             normalize=True
         )
 
-        fig = plt.figure(figsize=(12, 7))
-        plt.barh(
-            y=bag_df_woningtype_dist.index,
-            width=bag_df_woningtype_dist.woningtype.values * -100,
+        fig = plt.figure(figsize=(9, 7))
+        x = np.arange(len(bag_df_woningtype_dist.index))
+        plt.bar(
+            x=x,
+            height=bag_df_woningtype_dist.woningtype.values * 100,
             label="BAG",
-            zorder=3
+            zorder=3,
+            width=0.25
         )
-        plt.barh(
-            y=ep_online_woningtype_dist.index,
-            width=ep_online_woningtype_dist.woningtype.values * 100,
+        plt.bar(
+            x=x+0.25,
+            height=ep_online_woningtype_dist.woningtype.values * 100,
             label="EP-Online",
-            zorder=3
+            zorder=3,
+            width=0.25
         )
         plt.legend(loc="best")
         plt.grid(which="major", axis="x", zorder=0)
-        plt.xticks(
-            ticks=range(-60, 70, 10),
-            labels=[str(i) for i in range(-60, 70, 10)],
-        )
-        plt.xticks(
-            ticks=range(-65, 70, 5),
-            minor=True,
-        )
-        plt.xlabel("Percentage (%) van het hele dataset")
-        plt.ylabel("woningtype")
+        plt.ylabel("Percentage (%) van het hele dataset")
+        plt.xticks(x + 0.125, bag_df_woningtype_dist.index)
+        plt.xlabel("woningtype")
         plt.suptitle("Spreiding van woningen per woningtype", fontsize=14)
         plt.savefig("woningtype_dist.png")
         plt.close()