Streamlit improved

README.md

@@ -8,8 +8,8 @@
 ## Run
 
 * set the start date in [config.toml](src/config.toml)
-* [fetch_v2.py](src/fetch_v2.py): download your Oura data to [data/](data/)
-* [analyze_v2.py](src/analyze_v2.py): analyze your Oura data
+* [fetch.py](src/fetch.py): download your Oura data to [data/](data/)
+* [report.py](src/report.py): analyze your Oura data
 * [app.py](src/app.py): Streamlit visualization
 
 ## Results

cspell-words.txt

@@ -2,6 +2,7 @@
 analize
 autoflake
 autoupdate
+DataFrame
 dayofweek
 figsize
 hypnogram

cspell.config.yaml

@@ -13,6 +13,5 @@ ignorePaths:
   - ".*"
   - "dist"
   - "build"
-  - "node_modules"
   - "requirements.txt"
-  - "api-v1"
+  - "src/deprecated"

src/app.py

@@ -7,48 +7,70 @@
 from pathlib import Path
 
 import altair as alt
+import pandas as pd
 import streamlit as st
 
-from analyze_v2 import prep_data_sleep
+from report import prep_data_sleep
 
 st.set_page_config(page_title="Oura Sleep Report", page_icon=None, layout="wide")
 st.title("Oura Sleep Report")
 
 with (Path("src/config.toml")).open("rb") as f:
     config = tomllib.load(f)
-date_start_default = (dt.datetime.now(tz=dt.UTC) - dt.timedelta(days=21)).date()
+date_start_default = (dt.datetime.now(tz=dt.UTC) - dt.timedelta(weeks=4)).date()
 
 
 df = prep_data_sleep()
 df = df.reset_index()
-df = df.drop(columns=["id"])
+df = df.drop(columns=["bedtime_end", "bedtime_start", "sleep_phase_5_min"])
 df = df.sort_values("day", ascending=False)
 
 
-col1, col2, col3 = st.columns(3)
+col1, col2, col3, col4 = st.columns(4)
 
 sel_start_date = col1.date_input(
     "Start", value=date_start_default, format=config["date_format"]
 )
 if sel_start_date:
-    df = df.query(f"day >= '{sel_start_date}'")
+    df = df.query("day >= @sel_start_date")
+
+st.columns(1)
+
+d = {}
+for prop in ("score", "start of sleep", "end of sleep", "HR average", "HRV average"):
+    d[prop] = {}
+    d[prop]["week even"] = df.query("week_even == True")[prop].mean().round(1)  # type: ignore
+    d[prop]["week uneven"] = df.query("week_even == False")[prop].mean().round(1)  # type: ignore
+    lst = [0, 1, 2, 3, 4]
+    d[prop]["weekdays"] = df.query("dayofweek == @lst")[prop].mean().round(1)  # type: ignore
+    lst = [5, 6]
+    d[prop]["weekend"] = df.query("dayofweek == @lst")[prop].mean().round(1)  # type: ignore
+# st.write(d)
+df2 = pd.DataFrame.from_dict(d)
+st.write(df2.transpose())
 
-sel_weekend = col2.selectbox("Week or Weekend", options=("Su-Th", "Fr-Sa"), index=None)
+
+col1, col2, col3 = st.columns(3)
+sel_week_even = col1.selectbox("Week even.", options=("Even", "Uneven"), index=None)
+if sel_week_even:
+    if sel_week_even == "Even":
+        df = df.query("week_even == True")
+    elif sel_week_even == "Uneven":
+        df = df.query("week_even == False")
+
+sel_weekend = col2.selectbox("Weekend", options=("Weekday", "Weekend"), index=None)
 if sel_weekend:
-    if sel_weekend == "Su-Th":
+    if sel_weekend == "Weekday":  # Su-Th
         lst = [0, 1, 2, 3, 4]
-    elif sel_weekend == "Fr-Sa":
+    elif sel_weekend == "Weekend":  # Fr-Sa
         lst = [5, 6]
-
     df = df.query("dayofweek == @lst")
 
 
-# 0 -> Sunday
-sel_weekday = col3.selectbox(
-    "Weekday", options=("0", "1", "2", "3", "4", "5", "6"), index=None
-)
+day_map = {"Su": 0, "Mo": 1, "Tu": 2, "Wed": 3, "Th": 4, "Fr": 5, "Sa": 6}
+sel_weekday = col3.selectbox("Weekday", options=day_map.keys(), index=None)
 if sel_weekday:
-    df = df.query(f"dayofweek == '{sel_weekday}'")
+    df = df.query(f"dayofweek == {day_map[sel_weekday]}")
 
 
 # c = st.line_chart(data=df, x="day", y="duration of sleep", x_label=None)
@@ -65,17 +87,20 @@
 base = alt.Chart(df).encode(alt.X("day", title=None))
 
 
-for prop in ("duration of sleep", "HR average", "HRV average"):
-    c = base.mark_line().encode(
-        y=alt.Y(prop),
+for prop in ("score", "start of sleep", "sleep total h", "HR average", "HRV average"):
+    ymin = df[prop].min()
+    ymax = df[prop].max()
+    c = base.mark_point(size=100).encode(
+        y=alt.Y(prop, scale=alt.Scale(domain=[ymin, ymax])),
     )
     cr = c.transform_regression("day", prop).mark_line(color="grey", strokeDash=[4, 4])
-    layers = alt.layer(c, cr)  # .resolve_scale(y="independent")
+    cl = c.mark_line()
+    layers = alt.layer(c, cr, cl)  # .resolve_scale(y="independent")
     st.altair_chart(layers, use_container_width=True)  # type: ignore
 
 
 c1 = base.mark_line().encode(
-    y=alt.Y("duration of sleep"),
+    y=alt.Y("sleep total h"),
 )
 c2 = base.mark_line(color="red").encode(
     y=alt.Y("HRV average"),
@@ -84,7 +109,7 @@
 st.altair_chart(layers, use_container_width=True)  # type: ignore
 
 c1 = base.mark_line().encode(
-    y=alt.Y("duration of sleep"),
+    y=alt.Y("sleep total h"),
 )
 c2 = base.mark_line(color="red").encode(
     y=alt.Y("HR average"),
@@ -94,6 +119,30 @@
 
 
 st.columns(1)
+
+st.subheader("relevant data")
+st.dataframe(
+    data=df,
+    hide_index=True,
+    column_config={"day": st.column_config.DateColumn(format=config["date_format"])},
+    column_order=[
+        "day",
+        "start of sleep",
+        "end of sleep",
+        "score",
+        "HR mini",
+        "HR average",
+        "HRV average",
+        "time in bed h",
+        "sleep total h",
+        "sleep rem h",
+        "sleep deep h",
+        "temperature_deviation",
+    ],
+)
+
+
+st.subheader("all data")
 st.dataframe(
     data=df,
     hide_index=True,

src/fetch_v2.py → src/fetch.py

@@ -33,8 +33,6 @@ def fetch_data_summaries() -> None:
     """
     for data_summary_set in ("sleep",):  # , "activity", "readiness"
         print(f"fetching {data_summary_set} data")
-        # url = "https://api.ouraring.com/v1/sleep"
-        # -> last week
         url = f"https://api.ouraring.com/v2/usercollection/{data_summary_set}?start_date={config['date_start']}"
         # start=YYYY-MM-DD
         # end=YYYY-MM-DD
@@ -51,13 +49,7 @@ def fetch_data_summaries() -> None:
             print(f"Error fetching {data_summary_set} data: {e}")
             continue
 
-        # Write raw data to file
-        raw_data_path = Path(f"data/data_raw_{data_summary_set}.json")
-        with raw_data_path.open(mode="w", encoding="utf-8", newline="\n") as fh:
-            fh.write(cont)
-
-        # Write formatted data to file
-        formatted_data_path = Path(f"data/data_formatted_{data_summary_set}.json")
+        formatted_data_path = Path(f"data/data_{data_summary_set}.json")
         with formatted_data_path.open(mode="w", encoding="utf-8", newline="\n") as fh:
             d = json.loads(cont)
             json.dump(d, fh, ensure_ascii=False, sort_keys=False, indent=True)