updated live data and average time evolution graph

live_data/markets_live_data.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ef505e34ed9cf0b28b18b2ba22952bc89c7c70a40dae9ce5ce4e2141898d4010
-size 140888
+oid sha256:69a3fffac1b1e11e818cdf3c709fd3006d6f93107df947693548a05bc66f337d
+size 145777

live_data/markets_live_data_sample.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6a6f9834eccc4663ea7f95de171ad40c199376474b6c3f311c7e7407ebcafc9e
-size 129910
+oid sha256:ef505e34ed9cf0b28b18b2ba22952bc89c7c70a40dae9ce5ce4e2141898d4010
+size 140888

tabs/dist_gap.py

@@ -2,7 +2,7 @@ import pandas as pd
 import gradio as gr
 import matplotlib.pyplot as plt
 import seaborn as sns
-from seaborn import FacetGrid
+from datetime import datetime, UTC
 import plotly.express as px
 
 HEIGHT = 300
@@ -49,20 +49,36 @@ def get_dist_gap_timeline_plotly(market_id: str, all_markets: pd.DataFrame) -> g
 
 
 def get_avg_gap_time_evolution(all_markets: pd.DataFrame) -> gr.Plot:
+    # filter by the opening datetime
+    current = pd.Timestamp("today")
+
+    recent_markets = all_markets.loc[all_markets["opening_datetime"] > current]
+    recent_markets["creation_datetime"] = recent_markets["creationTimestamp"].apply(
+        lambda x: datetime.fromtimestamp(int(x))
+    )
+
+    recent_markets["creation_date"] = pd.to_datetime(
+        recent_markets["creation_datetime"]
+    ).dt.date
     avg_dist_gap_perc = (
-        all_markets.groupby("sample_date")["dist_gap_perc"].mean().reset_index()
+        recent_markets.groupby(["sample_date", "creation_date"])["dist_gap_perc"]
+        .mean()
+        .reset_index()
     )
-    avg_dist_gap_perc["sample_date"] = avg_dist_gap_perc["sample_date"].astype(str)
+    avg_dist_gap_perc["creation_date"] = avg_dist_gap_perc["creation_date"].astype(str)
     avg_dist_gap_perc.rename(
         columns={"dist_gap_perc": "mean_dist_gap_perc"}, inplace=True
     )
-    fig = px.line(avg_dist_gap_perc, x="sample_date", y="mean_dist_gap_perc")
+    fig = px.line(
+        avg_dist_gap_perc,
+        x="sample_date",
+        y="mean_dist_gap_perc",
+        color="creation_date",
+    )
     fig.update_layout(
-        xaxis_title="Day of the sample",
+        xaxis_title="Day the samples were collected",
         yaxis_title="Mean dist gap percentage (%)",
     )
-    # fig.update_layout(width=WIDTH, height=HEIGHT)
-    # fig.update_xaxes(tickangle=45)
     fig.update_xaxes(tickformat="%b-%d-%Y")
     return gr.Plot(value=fig)