generate baseflow figure

python/events/main.py

@@ -1,43 +1,81 @@
 from hydrotools.events.baseflow import eckhardt as bf
 from hydrotools.nwis_client.iv import IVDataService
+import plotly.graph_objects as go
+import numpy as np
+import pandas as pd
 
-# Get some streamflow data
-client = IVDataService()
-df = client.get(
-    startDT="2020-10-01",
-    endDT="2021-09-30T23:55",
-    sites="02146470"
-)
-
-# Important: Streamflow data should be continuous with
-#   no missing data and a DateTimeIndex
-TIME_SCALE = "1h"
-df = df.set_index("value_time").resample(TIME_SCALE).first().ffill()
-
-# Let's rename 'value' to 'total_flow'
-df = df.rename(columns={"value": "total_flow"})
-
-# Perform baseflow separation
-#   This catchment is pretty small, so we'll use a
-#   12-hour recession time-scale
-results = bf.separate_baseflow(
-    df["total_flow"],
-    output_time_scale = TIME_SCALE,
-    recession_time_scale = "12h"
+def main():
+    # Get some streamflow data
+    client = IVDataService()
+    df = client.get(
+        startDT="2020-10-01",
+        endDT="2021-09-30T23:55",
+        sites="02146470"
     )
 
-# Look at the 12-hour filter parameters
-print(f"Recession constant, a: {results.recession_constant:.4f}")
-print(f"Maximum baseflow index, bfi_max: {results.maximum_baseflow_index:.4f}")
+    # Important: Streamflow data should be continuous with
+    #   no missing data and a DateTimeIndex
+    TIME_SCALE = "1h"
+    df = df.set_index("value_time").resample(TIME_SCALE).first().ffill()
 
-# Store the resulting baseflow in original DataFrame
-df["baseflow"] = results.values
-print(df.head())
+    # Let's rename 'value' to 'total_flow'
+    df = df.rename(columns={"value": "total_flow"})
 
-import plotly.graph_objects as go
+    # Perform baseflow separation
+    #   This catchment is pretty small, so we'll use a
+    #   12-hour recession time-scale
+    results = bf.separate_baseflow(
+        df["total_flow"],
+        output_time_scale = TIME_SCALE,
+        recession_time_scale = "12h"
+        )
+
+    # Look at the 12-hour filter parameters
+    print(f"Recession constant, a: {results.recession_constant:.4f}")
+    print(f"Maximum baseflow index, bfi_max: {results.maximum_baseflow_index:.4f}")
+
+    # Store the resulting baseflow in original DataFrame
+    df["baseflow"] = results.values
+    print(df.head())
+
+    df2 = df.loc["2021-02-11":"2021-02-17"]
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df2.index, y=df2.total_flow, name="Total Flow"))
+    fig.add_trace(go.Scatter(x=df2.index, y=df2.baseflow, name="Baseflow"))
+    fig.update_layout(
+        yaxis=dict(
+            title=dict(
+                text=r"$\text{ Discharge } \text{ (foot }^3 \text{ s }^{-1})$"
+            ),
+            showline=True,
+            linecolor="black",
+            tickmode="array",
+            tickvals=np.arange(0.0, 400.0, 50.0),
+            linewidth=2,
+            ticks="outside",
+            range=[0.0, 351.0],
+            tickwidth=2
+        ),
+        xaxis=dict(
+            showline=True,
+            linecolor="black",
+            linewidth=2,
+            ticks="outside",
+            tickmode="array",
+            tickvals=pd.date_range("2021-02-11", periods=8),
+            range=["2021-02-11", "2021-02-17T00:15"],
+            tickwidth=2
+        ),
+        plot_bgcolor="white",
+        margin=dict(
+            l=50,
+            r=50,
+            b=50,
+            t=50,
+            pad=10
+        )
+    )
+    fig.write_image("baseflow_hydrograph.png", width=960, height=540, scale=2)
 
-df2 = df.loc["2021-02-11":"2021-02-17"]
-fig = go.Figure()
-fig.add_trace(go.Scatter(x=df2.index, y=df2.total_flow))
-fig.add_trace(go.Scatter(x=df2.index, y=df2.baseflow))
-fig.show()
+if __name__ == "__main__":
+    main()