px.scatter_geo for deep dive regions

app/plot_functions.py

@@ -1,6 +1,5 @@
 # -*- coding: utf-8 -*-
 """Functions for plotting input data and results (cost_data)."""
-import json
 from pathlib import Path
 from typing import Literal
 
@@ -232,30 +231,34 @@ def _choropleth_map_deep_dive_country(
     if custom_data_func_kwargs is None:
         custom_data_func_kwargs = {}
     # subsetting 'df' for the selected deep dive country
-    df = select_subregions(df, deep_dive_country)
+    # missing value removal necessary for wind offshore
+    df = select_subregions(df, deep_dive_country).dropna(subset=color_col)
     # need to calculate custom data befor is03166 column is appended.
     hover_data = custom_data_func(df, **custom_data_func_kwargs)
     # get dataframe with info about iso 3166-2 codes and map them to res_costs
     ddc_info = api.get_dimension("region")
     df["iso3166_code"] = df.index.map(
         pd.Series(ddc_info["iso3166_code"], index=ddc_info["region_name"])
     )
-
-    geojson_file = (
-        Path(__file__).parent.parent.resolve()
-        / "data"
-        / f"{deep_dive_country.lower().replace(' ', '_')}_subregions.geojson"
+    # load representative points data
+    lon_lat = pd.read_csv(
+        (
+            Path(__file__).parent.parent.resolve()
+            / "data"
+            / "subregion_representative_points.csv"
+        )
     )
-    with geojson_file.open("r", encoding="utf-8") as f:
-        subregion_shapes = json.load(f)
+    # merge points to data
+    df = df.merge(lon_lat, left_on="iso3166_code", right_on="iso_3166_2")
 
-    fig = px.choropleth(
-        locations=df["iso3166_code"],
-        featureidkey="properties.iso_3166_2",
+    fig = px.scatter_geo(
+        lon=df["lon"],
+        lat=df["lat"],
         color=df[color_col],
-        geojson=subregion_shapes,
         custom_data=hover_data,
         color_continuous_scale=agora_continuous_color_scale(),
+        size=[15] * len(df.index),
+        opacity=1,
     )
 
     fig.update_geos(

data/subregion_representative_points.csv

@@ -0,0 +1,46 @@
+iso_3166_2,lon,lat
+MA-11,-11.487535149205273,26.291022599999998
+MA-01,-5.434766372913305,35.2181678
+MA-02,-2.3906063870173955,33.5811814
+MA-08,-5.58317463160672,31.28770725
+MA-09,-8.157695785778756,29.7357752
+MA-10,-10.084427221839995,28.385906
+MA-06,-7.799432071783302,33.065160649999996
+MA-07,-8.306409084756796,31.806335949999998
+MA-12,-14.741994773441643,22.735386249999998
+MA-04,-6.328725938578254,34.08533075
+MA-03,-4.847581884998955,33.74727775
+MA-05,-6.280445836675398,32.4092902
+AR-E,-59.41391734740016,-32.09481027848068
+AR-A,-63.2488630052977,-24.188186947408994
+AR-Y,-66.05131162400237,-23.19339019145417
+AR-P,-60.056795466789765,-24.694332167145802
+AR-N,-54.408009579418504,-26.838980814499898
+AR-H,-60.69169607218846,-26.04543832741686
+AR-W,-57.80329050740136,-29.00667836190994
+AR-K,-67.51184361556847,-27.649319756801333
+AR-F,-66.86724287983822,-29.835207004049977
+AR-J,-68.76400612946446,-30.369850969333534
+AR-M,-68.47196659910384,-34.75436330149996
+AR-Q,-69.55620607085012,-38.61386932399996
+AR-U,-68.47608492458708,-43.999489582499905
+AR-R,-66.42178269981838,-39.78144744254544
+AR-Z,-70.40714935693845,-49.193291924999926
+AR-V,-68.09468684709897,-53.841798372999904
+AR-B,-60.113853483913914,-37.10019296649992
+AR-C,-58.44119878492826,-34.642826064273805
+AR-S,-60.97677432423949,-31.164195651608026
+AR-T,-65.39223122835818,-27.06493682212667
+AR-G,-63.37956470637425,-27.840677585851893
+AR-D,-65.95764785447187,-33.936842950791544
+AR-L,-65.8186469775226,-37.06892567313133
+AR-X,-63.68163713228595,-32.214312432772545
+ZA-NC,22.022372549742485,-28.848704728999934
+ZA-NL,30.414604596802526,-28.941989841499947
+ZA-FS,26.606093929174868,-28.668094377346847
+ZA-EC,26.5770656739886,-32.091735934615684
+ZA-LP,29.333422657090793,-23.779038391499938
+ZA-NW,25.24337419152684,-26.37090419793683
+ZA-MP,30.160392889928794,-25.74002980499995
+ZA-WC,22.14966623331297,-32.64267852138539
+ZA-GT,28.020037353796795,-26.0096414140351