Adjusting config of graphs

app.py

@@ -60,7 +60,7 @@ def get_extreme_cases(live_fpmms: pd.DataFrame):
         live_fpmms["id"].isin(markets_with_multiple_samples)
     ]
     selected_markets.sort_values(by="dist_gap_perc", ascending=False, inplace=True)
-    return selected_markets.iloc[0].id, selected_markets.iloc[-1].id
+    return selected_markets.iloc[-1].id, selected_markets.iloc[0].id
 
 
 demo = gr.Blocks()
@@ -77,6 +77,7 @@ with demo:
 
             with gr.Row():
                 gr.Markdown("Best case: a market with a low distribution gap metric")
+            with gr.Row():
                 gr.Markdown(f"Market id = {best_market_id}")
             with gr.Row():
                 best_market_tokens_dist = get_based_tokens_distribution(
@@ -85,6 +86,7 @@ with demo:
 
             with gr.Row():
                 gr.Markdown("Worst case: a market with a high distribution gap metric")
+            with gr.Row():
                 gr.Markdown(f"Market id = {worst_market_id}")
 
             with gr.Row():

tabs/dist_gap.py

@@ -0,0 +1,15 @@
+import pandas as pd
+import gradio as gr
+import matplotlib.pyplot as plt
+import seaborn as sns
+from seaborn import FacetGrid
+import plotly.express as px
+
+
+def plot_top_best_behaviour_markets(markets_data: pd.DataFrame):
+    """Function to paint the top markets with the lowest metric of distribution gap"""
+    sorted_data = markets_data.sort_values(by="dist_gap_perc", ascending=False)
+    top_best_markets = sorted_data[["title", "sample_datetime", "dist_gap_perc"]].head(
+        5
+    )
+    return gr.DataFrame(top_best_markets)

tabs/tokens_votes_dist.py

@@ -8,6 +8,8 @@ import plotly.express as px
 
 def get_based_tokens_distribution(market_id: str, all_markets: pd.DataFrame):
     """Function to paint the evolution of the probability of the outcomes based on the tokens distributions over time"""
+    sns.set_style("darkgrid")
+    sns.set_theme(palette="viridis")
     selected_market = all_markets.loc[all_markets["id"] == market_id]
     ax = selected_market.plot(
         x="sample_datetime",
@@ -18,7 +20,7 @@ def get_based_tokens_distribution(market_id: str, all_markets: pd.DataFrame):
     )
     # add overall title
     plt.title(
-        "Outcomes probability over time based on tokens distributions", fontsize=16
+        "Outcomes probability over time based on tokens distributions", fontsize=8
     )
 
     # add axis titles
@@ -36,6 +38,8 @@ def get_based_tokens_distribution(market_id: str, all_markets: pd.DataFrame):
 
 def get_based_votes_distribution(market_id: str, all_markets: pd.DataFrame):
     """Function to paint the evolution of the probability of the outcomes based on the votes distributions over time"""
+    sns.set_style("darkgrid")
+    sns.set_theme(palette="viridis")
     selected_market = all_markets.loc[all_markets["id"] == market_id]
     ax = selected_market.plot(
         x="sample_datetime",
@@ -45,9 +49,7 @@ def get_based_votes_distribution(market_id: str, all_markets: pd.DataFrame):
         stacked=True,
     )
     # add overall title
-    plt.title(
-        "Outcomes probability over time based on votes distributions", fontsize=16
-    )
+    plt.title("Outcomes probability over time based on votes distributions", fontsize=8)
 
     # add axis titles
     plt.xlabel("Sample date")