From 28b395cf6d86e6257ad0036ab6d048abc40e75e3 Mon Sep 17 00:00:00 2001
From: Dan Howe <d.howe@wrl.unsw.edu.au>
Date: Tue, 3 Dec 2019 14:11:19 +1100
Subject: [PATCH] Add support for ocean time time series

---
 mhl-waverider/mhl_waverider.py | 41 +++++++++++++++++++++++++---------
 1 file changed, 31 insertions(+), 10 deletions(-)

diff --git a/mhl-waverider/mhl_waverider.py b/mhl-waverider/mhl_waverider.py
index 9f5f45d..93f59e4 100644
--- a/mhl-waverider/mhl_waverider.py
+++ b/mhl-waverider/mhl_waverider.py
@@ -13,27 +13,48 @@ url = 'http://www.mhl.nsw.gov.au/data/realtime/timeseries/'
 txt_names = [
     'SYDDOW.WaveHeight.csv.dat.txt',
     'SYDDOW.WavePeriod.csv.dat.txt',
+    'OceanTide-213470.Level.csv.dat.txt',
 ]
 
-for txt_name in txt_names:
-    # Read text file from MHL website
-    df = pd.read_csv(url + txt_name, index_col=0, parse_dates=True)
 
-    # Create name for exported data
-    csv_name = os.path.join(input_dir, txt_name.replace('.dat.txt', ''))
+def update_master(output_dir, csv_name, df):
+    """Update master csv time series with new data.
+
+    Args:
+        output_dir (str): path to time series directory
+        csv_name (str):   name of time series file
+        df (dataframe):   dataframe with new time seies data
+
+    Returns:
+        None
+    """
     try:
-        # Load local master table
-        master = pd.read_csv(csv_name, index_col=0, parse_dates=True)
+        # Load local master table if it exists
+        master = pd.read_csv(os.path.join(output_dir, csv_name),
+                             index_col=0,
+                             parse_dates=True)
 
-        # Only include timestamps that do not already exist
-        df = df[~df.index.isin(master.index)]
+        # Remove existing rows if same timestamps appear in new dataframe
+        master = master[~master.index.isin(df.index)]
 
         # Update master
         master = master.append(df)
 
+        # Make sure timestamps are in order
+        master = master.sort_index()
+
     except FileNotFoundError:
         # Create new master table if none exists
         master = df
 
     # Export master table
-    master.to_csv(csv_name)
+    master.to_csv(os.path.join(output_dir, csv_name))
+
+
+for txt_name in txt_names:
+    # Read text file from MHL website
+    df = pd.read_csv(url + txt_name, index_col=0, parse_dates=True)
+
+    # Update local copy
+    csv_name = txt_name.replace('.dat.txt', '')
+    master = update_master(output_dir, csv_name, df)