feat: add time span filters to historical and aggregated data

ui/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "valencianow"
-version = "0.1.0"
+version = "0.2.0"
 requires-python = ">=3.12"
 dependencies = [
     "streamlit>=1.32.2,<2",

ui/src/valencianow/app.py

@@ -18,26 +18,35 @@
 
 def aggregated_sensor_data(data_now: pd.DataFrame, label: str) -> None:
     info = data.PIPES[label]
-    st.markdown("## ➕ individual sensor data")
+    st.markdown("## ➕ Individual sensor data")
     with st.form(f"aggregated-sensor-{label}"):
         sensor = st.selectbox(
-            "🔢 Select a sensor to show its data (sensor numbers in map tooltips)",
+            "🔢 Select a sensor to show its data (sensor ids in map tooltips)",
             sorted(data_now.sensor.values),
         )
+        timespan = st.radio(
+            "Select a time span: ",
+            ["Today", "Last Week", "Last Month", "Last Year"],
+            index=2,
+            horizontal=True,
+        )
+
         if st.form_submit_button("🔎 Find sensor data", use_container_width=True):
             components.historical_graph(
-                info["hist_pipe"], sensor, info["hist_meas"], info["hist_y"]
+                info["hist_pipe"], timespan, sensor, info["hist_meas"], info["hist_y"]
             )
-            st.markdown("#### aggregated data")
-            sensor_col_1, sensor_col_2 = st.columns(2)
-            with sensor_col_1:
-                components.per_day_graph(
-                    info["per_day_pipe"], sensor, info["per_day_y"]
-                )
-            with sensor_col_2:
-                components.per_day_of_week_graph(
-                    info["per_dow_pipe"], sensor, info["per_dow_y"]
-                )
+            if timespan != "Today":
+                st.markdown(f"#### Aggregated data ({timespan})")
+                sensor_col_1, sensor_col_2 = st.columns(2)
+                with sensor_col_1:
+                    components.per_day_graph(
+                        info["per_day_pipe"], sensor, timespan, info["per_day_y"]
+                    )
+                if timespan != "Last Week":
+                    with sensor_col_2:
+                        components.per_day_of_week_graph(
+                            info["per_dow_pipe"], sensor, timespan, info["per_dow_y"]
+                        )
 
 
 def render_tab_car(tab) -> None:

ui/src/valencianow/components.py

@@ -57,24 +57,46 @@ def reset_date_filter(date: str | None, reset) -> str | None:
     return date
 
 
-def historical_graph(pipe: str, sensor: str, measurement: str, y_axis: str) -> None:
-    data_sensor = data.load_data(pipe, None, sensor)
+def historical_graph(
+    pipe: str, timespan: str, sensor: str, measurement: str, y_axis: str
+) -> None:
+    data_sensor = data.load_data(pipe, None, sensor, filter_timespan=timespan)
     if data_sensor is not None:
-        st.markdown(f"#### historical data: {measurement}")
+        st.markdown(f"#### Historical data: {measurement} ({timespan})")
         data_sensor = data_sensor.sort_values(by="datetime")
-        fig = px.line(data_sensor, x="datetime", y=y_axis, markers=True)
+        fig = px.line(
+            data_sensor, x="datetime", y=y_axis, markers=True, line_shape="spline"
+        )
         st.plotly_chart(fig, theme="streamlit", use_container_width=True)
 
 
-def per_day_graph(pipe: str, sensor: str, y_axis):
-    st.markdown("**📅 data by day**")
-    data_agg_sensor = data.load_data(pipe, None, sensor)
-    fig = px.bar(data_agg_sensor, x="day", y=y_axis)
+def per_day_graph(pipe: str, sensor: str, timespan: str, y_axis):
+    st.markdown("**📅 Data by day**")
+    data_agg_sensor = data.load_data(pipe, None, sensor, filter_timespan=timespan)
+    fig = px.bar(
+        data_agg_sensor,
+        x="day",
+        y=y_axis,
+        hover_data={"day": "|%A - %B %d, %Y"},
+    )
+    fig.update_xaxes(tickformat="%a - %b %d")
     st.plotly_chart(fig, theme="streamlit", use_container_width=True)
 
 
-def per_day_of_week_graph(pipe: str, sensor: str, y_axis):
-    st.markdown("**📅 data by day of week (1 is Monday)**")
+def per_day_of_week_graph(pipe: str, sensor: str, timespan: str, y_axis):
+    st.markdown("**📅 Data by day of week**")
     data_agg_week_sensor = data.load_data(pipe, None, sensor)
+    day_name_map = {
+        1: "Monday",
+        2: "Tuesday",
+        3: "Wednesday",
+        4: "Thursday",
+        5: "Friday",
+        6: "Saturday",
+        7: "Sunday",
+    }
+    data_agg_week_sensor["day_of_week"] = data_agg_week_sensor["day_of_week"].map(
+        day_name_map
+    )
     fig = px.bar(data_agg_week_sensor, x="day_of_week", y=y_axis)
     st.plotly_chart(fig, theme="streamlit", use_container_width=True)

ui/src/valencianow/config.py

@@ -9,7 +9,7 @@
 LOGGER.addHandler(logger_handler)
 
 
-APP_NAME = "valencia-now"
+APP_NAME = "Valencia-Now"
 
 TINYBIRD_API = "https://api.tinybird.co/v0/pipes/"
 TINYBIRD_TOKEN = os.environ["TINYBIRD_TOKEN_VLC"]

ui/src/valencianow/data.py

@@ -1,5 +1,5 @@
 import urllib.parse
-from datetime import datetime
+from datetime import datetime, timedelta, timezone
 
 # Streamlit Cloud won't install the package, so we can't do:
 # from valencianow import config
@@ -70,12 +70,26 @@ def _date_to_utc(date: str) -> str:
     return utc_datetime.strftime("%Y-%m-%d %H:%M:%S")
 
 
+def _min_date(current_date: datetime, timespan: str) -> str:
+    output = current_date
+    if timespan == "Today":
+        output -= timedelta(days=1)
+    elif timespan == "Last Week":
+        output -= timedelta(days=7)
+    elif timespan == "Last Month":
+        output -= timedelta(days=31)
+    elif timespan == "Last Year":
+        output -= timedelta(days=365)
+    return output.strftime("%Y-%m-%d %H:%M:%S")
+
+
 # caching data for some time to reduce the number of Tinybird API hits
 @st.cache_data(ttl=config.DATA_CACHE_SECONDS)
 def load_data(
     pipe_name: str,  # the name of the Tinybird PIPE
     filter_max_date: str | None,
     filter_sensor: int | None = None,
+    filter_timespan: str | None = None,
     local_time: bool = False,
 ) -> pd.DataFrame | None:
     """Load data from the given Tinybird pipe name.
@@ -88,6 +102,12 @@ def load_data(
         if not local_time:
             filter_max_date = _date_to_utc(filter_max_date)
         params["date"] = filter_max_date
+    if filter_timespan:
+        if filter_max_date:
+            max_date = datetime.strptime(filter_max_date, "%Y-%m-%d %H:%M:%S")
+        else:
+            max_date = datetime.now(timezone.utc)
+        params["min_date"] = _min_date(max_date, filter_timespan)
     if filter_sensor:
         params["sensor"] = filter_sensor
     logger.info(f"Retrieving {pipe_name} data from Tinybird: {params}")

ui/src/valencianow/maps.py

@@ -1,6 +1,5 @@
-"""functions to build all the maps shown in the application
+"""functions to build all the maps shown in the application"""
 
-"""
 import pandas as pd
 import pydeck as pdk
 import streamlit as st
@@ -46,8 +45,8 @@ def traffic_now_elevation(data: pd.DataFrame, is_bike=False) -> None:
     max_ih = MAX_IH_BIKE if is_bike else MAX_IH_CAR
     label = LABEL_BIKE if is_bike else LABEL_CAR
     scale = 5 if is_bike else 0.5
-    tooltip = "🔢 sensor id: {sensor} \n⏱" + label + "s/hour: {ih}"
-    tooltip += "\n📅 updated: {date}"
+    tooltip = "🔢 Sensor id: {sensor} \n⏱ " + label.capitalize() + "s/hour: {ih}"
+    tooltip += "\n📅 Updated: {date}"
     return pdk.Deck(
         # map_style=None,  # type: ignore
         map_style=pdk.map_styles.SATELLITE,
@@ -94,7 +93,7 @@ def air_now_scatterplot(data: pd.DataFrame):
             1: [162, 91, 164],
         }  # type: ignore
     )
-    tooltip = "🔢 sensor: {sensor} \n 🍃 ICA: {ica} \n 📅 updated: {date}"
+    tooltip = "🔢 Sensor: {sensor} \n 🍃 ICA: {ica} \n 📅 Updated: {date}"
     return pdk.Deck(
         map_style=pdk.map_styles.SATELLITE,
         map_provider="mapbox",