commit results

app.py

@@ -2,9 +2,14 @@ import streamlit as st
 from PIL import Image
 import os
 import pandas as pd
+import tomli
 
+pd.options.mode.chained_assignment = None
 
-@st.cache
+st.set_page_config(layout="wide")
+
+
+@st.cache_data
 def load_data():
     df_ind = pd.read_csv("data/df_individuals_score.csv", index_col=[0])
     df_ind = df_ind.drop("region_code", axis=1)
@@ -14,13 +19,30 @@ def load_data():
     ].astype(str)
 
     df_ind = df_ind[df_ind["productive_year"] <= 1800]
-
     return df_ind
 
 
 df_ind = load_data()
 
-st.title("Our History in Data")
+
+def load_region_descriptions():
+    with open("regions.toml", "rb") as toml_file:
+        data = tomli.load(toml_file)
+    return data
+
+
+# Function to get description based on selected region
+def get_region_description(region_data, selected_region):
+    return region_data[selected_region]["description"]
+
+
+region_data = load_region_descriptions()
+
+
+st.sidebar.title("Our History in Data")
+st.sidebar.write(
+    "This project is led by Charles de Dampierre, Folgert Karsdorp, Mike Kestemont, Valentin Thouzeau and Nicolas Baumard"
+)
 
 # Set the global index path
 global_index_path = "data/immaterial_index/figures_trends_R/results"
@@ -41,6 +63,8 @@ maps_path = "data/map_figures"
 
 from region_list import region_list
 
+region_filtered = list(region_list.keys())
+
 index_paths = {}
 
 for region_key in region_list:
@@ -62,9 +86,9 @@ selected_region = st.sidebar.selectbox("Region:", regions, index=regions.index("
 
 # Display the selected region's images vertically
 if selected_region in index_paths:
+    col1, col2 = st.columns(2)
 
     df = df_ind[df_ind["region_name"] == selected_region]
-    df["productive_year"] = round(df["productive_year"], 0)
     df = df.drop(["region_name", "decade"], axis=1)
     df = df[
         [
@@ -76,43 +100,74 @@ if selected_region in index_paths:
     ]
     df = df.sort_values("score", ascending=False)
     df = df.rename(columns={"score": "Number of Catalogs"})
-    df = df.reset_index(drop=True)
-    st.dataframe(df)
 
-    st.write(f"Number of Cultural producers active before 1800: {len(df)}")
-
-    for key, path in index_paths[selected_region].items():
-        if os.path.exists(path):
+    min_date = region_list[selected_region]["time_range"][0]
+    max_date = region_list[selected_region]["time_range"][1]
+    df = df[df["productive_year"] >= min_date]
+    df = df[df["productive_year"] <= max_date]
+    df["productive_year"] = df["productive_year"].astype(int)
+    df = df.reset_index(drop=True)
 
-            if key == "global_index":
-                st.subheader("Global Index")
-                st.image(
-                    Image.open(path), caption=key.capitalize(), use_column_width=True
-                )
-            elif key == "global_index_per_capita":
-                st.subheader("Index per capita")
-                st.image(
-                    Image.open(path), caption=key.capitalize(), use_column_width=True
-                )
-            elif key == "unseen_index":
-                st.subheader("Unsee-Species Index")
-                st.image(
-                    Image.open(path), caption=key.capitalize(), use_column_width=True
-                )
-            elif key == "unseen_index_capita":
-                st.subheader("Unsee-Species per capita Index")
-                st.image(
-                    Image.open(path), caption=key.capitalize(), use_column_width=True
-                )
-            elif key == "population_index":
-                st.subheader("Population Index")
-                st.image(
-                    Image.open(path), caption=key.capitalize(), use_column_width=True
-                )
-            elif key == "map":
-                st.subheader("Maps")
-                st.sidebar.image(
-                    Image.open(path), caption=key.capitalize(), use_column_width=True
+    # Display the data in the left column
+    with col1:
+        st.header("Cultural Producers")
+        st.dataframe(df)
+        st.write(f"Number of Cultural producers active before 1800: {len(df)}")
+
+        for key, path in index_paths[selected_region].items():
+            if os.path.exists(path):
+
+                if key == "global_index":
+                    st.subheader("Global Index")
+                    st.image(
+                        Image.open(path),
+                        caption=key.capitalize(),
+                        use_column_width=True,
+                    )
+                elif key == "global_index_per_capita":
+                    st.subheader("Index per capita")
+                    st.image(
+                        Image.open(path),
+                        caption=key.capitalize(),
+                        use_column_width=True,
+                    )
+                elif key == "unseen_index":
+                    st.subheader("Unsee-Species Index")
+                    st.image(
+                        Image.open(path),
+                        caption=key.capitalize(),
+                        use_column_width=True,
+                    )
+                elif key == "unseen_index_capita":
+                    st.subheader("Unsee-Species per capita Index")
+                    st.image(
+                        Image.open(path),
+                        caption=key.capitalize(),
+                        use_column_width=True,
+                    )
+                elif key == "population_index":
+                    st.subheader("Population Index")
+                    st.image(
+                        Image.open(path),
+                        caption=key.capitalize(),
+                        use_column_width=True,
+                    )
+                elif key == "map":
+                    st.subheader("Maps")
+                    st.sidebar.image(
+                        Image.open(path),
+                        caption=key.capitalize(),
+                        use_column_width=True,
+                    )
+            else:
+                st.write(f"File for {key.capitalize()} does not exist.")
+
+        with col2:
+            try:
+                region_description = get_region_description(
+                    region_data, selected_region
                 )
-        else:
-            st.write(f"File for {key.capitalize()} does not exist.")
+                st.header("Analysis")
+                st.write(f"{region_description}")
+            except:
+                st.write("Analysis not ready yet")

project.toml

@@ -0,0 +1 @@
+

region_list.py

@@ -1,38 +1,37 @@
-region_list = [
-    "Arabic world",
-    "Japan",
-    "Italy",
-    "mediterranean World",
-    "British Islands",
-    "Arabian peninsula",
-    "Northern India",
-    "South East Asia",
-    "Muslim world",
-    "Greek World",
-    "Central Europe",
-    "Ottoman Turkey",
-    "Chinese world",
-    "German world",
-    "Korea",
-    "Spain",
-    "Greece",
-    "Balkans",
-    "Low countries",
-    "Norway",
-    "Portugal",
-    "Ottoman world",
-    "Yangtze",
-    "Sweden",
-    "France",
-    "Slav world",
-    "Nordic countries",
-    "Indian world",
-    "Latin World",
-    "United Kingdom",
-    "Persian world",
-    "Eastern Europe",
-    "Western Europe",
-]
+region_list = {
+    "Arabic world": {"time_range": [600, 1800]},
+    "Italy": {"time_range": [600, 1800]},
+    "Japan": {"time_range": [600, 1800]},
+    "Mediterranean World": {"time_range": [-800, 500]},
+    "British Islands": {"time_range": [600, 1800]},
+    "Arabian peninsula": {"time_range": [600, 1800]},
+    "South East Asia": {"time_range": [-500, 1800]},
+    "Muslim world": {"time_range": [600, 1800]},
+    "Greek World": {"time_range": [-800, 500]},
+    "Central Europe": {"time_range": [600, 1800]},
+    "Ottoman Turkey": {"time_range": [800, 1800]},
+    "Chinese world": {"time_range": [-800, 1800]},
+    "German world": {"time_range": [600, 1800]},
+    "Korea": {"time_range": [600, 1800]},
+    "Spain": {"time_range": [600, 1800]},
+    "Greece": {"time_range": [600, 1800]},
+    "Balkans": {"time_range": [600, 1800]},
+    "Low countries": {"time_range": [600, 1800]},
+    "Norway": {"time_range": [600, 1800]},
+    "Portugal": {"time_range": [600, 1800]},
+    "Ottoman world": {"time_range": [1500, 1800]},
+    "Yangtze": {"time_range": [-800, 1800]},
+    "Sweden": {"time_range": [600, 1800]},
+    "France": {"time_range": [600, 1800]},
+    "Slav world": {"time_range": [600, 1800]},
+    "Nordic countries": {"time_range": [600, 1800]},
+    "Indian world": {"time_range": [-800, 1800]},
+    "Latin World": {"time_range": [-800, 500]},
+    "United Kingdom": {"time_range": [600, 1800]},
+    "Persian world": {"time_range": [-800, 1800]},
+    "Eastern Europe": {"time_range": [600, 1800]},
+    "Western Europe": {"time_range": [600, 1800]},
+}
 
 
 full_region_list = [

regions.toml

@@ -0,0 +1,32 @@
+
+[Japan]
+name = "Japan"
+description = """
+
+Our results converge with qualitative and quantitative assessments of economic development Bassino et al.
+(2019); Farris (2009); Nakabayashi et al. (2020) (see Fig. 10). 
+
+We observe a rise of cultural production from the 7th c. (Nara era) to the 10th c. (Heian era) during which it is estimated that GDP per capita
+increased by 80% Bassino et al. (2019); Nakabayashi et al. (2020). We also observe a continuous rise of
+cultural production from the 16th c. onward during which it is estimated that population tripled (from 10
+to 32 million people), GDP per capita increased by 80%, urbanization was multiplied by 5 from 2.5% to
+12,5%, with the size of Tokyo and Osaka rivaling that of London or Paris Bassino et al. (2019); Nakabayashi
+et al. (2020). Whereas the previous estimates of the GDP per capita showed extended stagnancy between
+the ninth and 14th c., our results converge with the most recent study, showing a sharp decline in the 12th
+century and recovery from the 13th to 16th centuries Nakabayashi et al. (2020). 
+
+In line with this study Nakabayashi et al. (2020), our results also suggest that the recovery of the economic development began in
+the 13th during the Kamakura shogunate, earlier than what was previously estimated.
+Our result also allows to have finer temporal estimation. For instance, we observe that the isolation of
+the country (the sakoku policy) starting in 1633 did not impact economic cultural production. At the other
+end of the period, we can see that cultural production was already very robust before the Meiji reforms
+(1868). 
+
+Also, we document the relative importance of the Kanta and Kansai plains in Japanese history, and
+the progressive shift from the East to the West (see Fig. 11). Despite the political domination of the Kansai
+plain from the 13th onward, the shift in cultural production only occurred during the late 18th c. (Edo
+period)
+
+
+"""
+time_range = [800, 1800]