Add extract_definitions() function

waternsw_grabber/waternsw_grabber.py

@@ -242,6 +242,128 @@ def telemetered_bore_downloader(bore_ids, start_date, end_date, download_dir):
     driver.quit()
 
 
+def extract_definitions(input_dir, output_dir):
+    """Extract variable and quality metadata from bore records.
+
+    Args:
+        input_dir:   path to downloaded zip archives
+        output_dir:  path to save csv files
+    """
+
+    # Get telemetered site data
+    csv_name = os.path.join(
+        os.path.dirname(os.path.dirname(__file__)), 'data',
+        'telemetered-sites.csv')
+    master = pd.read_csv(csv_name, index_col=0)
+
+    # Find zip files
+    zip_names = [f for f in os.listdir(input_dir) if f.endswith('.zip')]
+
+    # Prepare output directory
+    os.makedirs(output_dir, exist_ok=True)
+
+    for zip_name in tqdm(zip_names):
+        # Skip duplicate downloads
+        if re.search(r'\([0-9]+\)', zip_name):
+            continue
+
+        # Rename '.part' file if zip was not correctly downloaded
+        if os.path.getsize(os.path.join(input_dir, zip_name)) == 0:
+            shutil.move(
+                os.path.join(input_dir, zip_name) + '.part',
+                os.path.join(input_dir, zip_name))
+
+        # Read csv file inside zip archive
+        df = pd.read_csv(
+            os.path.join(input_dir, zip_name),
+            header=2,
+            skiprows=[3],
+            parse_dates=['Date'],
+            compression='zip',
+            dayfirst=True,
+            nrows=100)
+
+        # Extract metadata from last column
+        keys = ['Sites:', 'Variables:', 'Qualities:']
+        meta = {k: [] for k in keys}
+        for i, row in df.iterrows():
+            line = row.values[-1]
+            if line in keys:
+                header = True
+                var = line
+            elif line == ' ':
+                continue
+            else:
+                meta[var].append(line)
+
+        # Get bore specifics
+        site_data = meta['Sites:'][0]
+        lat = float(re.search(r'(?<=Lat:)\S+', site_data).group())
+        lon = float(re.search(r'(?<=Long:)\S+', site_data).group())
+        elev = float(re.search(r'(?<=Elev:).+(?=m)', site_data).group())
+        address = re.search(r'(?<=\d\.\d\.\d - ).+(?=\sLat)',
+                            site_data).group()
+        bore_id = re.search(r'^\S+', site_data).group()
+        site, hole, pipe = bore_id.split('.')
+
+        sites = pd.DataFrame()
+        sites['ID'] = [bore_id]
+        sites['Site'] = [site]
+        sites['Hole'] = [hole]
+        sites['Pipe'] = [pipe]
+        sites['Lat'] = [lat]
+        sites['Lon'] = [lon]
+        sites['Elev'] = [elev]
+        sites['Address'] = [address]
+        sites = sites.set_index('ID')
+
+        # Get basin from master site dataframe
+        sites['Basin name'] = master.loc[sites.index, 'Basin name']
+        sites['Basin code'] = master.loc[sites.index, 'Basin code']
+
+        # Save variable definitions
+        variables = pd.DataFrame(
+            [v.split(' - ', 1) for v in meta['Variables:']])
+        variables.columns = ['Code', 'Description']
+        variables['Code'] = variables['Code'].astype(int)
+        variables = variables.set_index('Code')
+
+        # Save quality definitions
+        qualities = pd.DataFrame(
+            [q.split(' - ', 1) for q in meta['Qualities:']])
+        qualities.columns = ['Code', 'Description']
+        qualities['Code'] = qualities['Code'].astype(int)
+        qualities = qualities.set_index('Code')
+
+        # Update existing values
+        csv_name_s = os.path.join(output_dir, 'sites.csv')
+        csv_name_v = os.path.join(output_dir, 'variables.csv')
+        csv_name_q = os.path.join(output_dir, 'qualities.csv')
+
+        try:
+            sites = sites.append(pd.read_csv(csv_name_s, index_col=0))
+            sites = sites.drop_duplicates().sort_index()
+        except FileNotFoundError:
+            pass
+
+        try:
+            variables = variables.append(pd.read_csv(csv_name_v, index_col=0))
+            variables = variables.drop_duplicates().sort_index()
+        except FileNotFoundError:
+            pass
+
+        try:
+            variables = variables.append(pd.read_csv(csv_name_q, index_col=0))
+            qualities = qualities.drop_duplicates().sort_index()
+        except FileNotFoundError:
+            pass
+
+        # Export updated tables
+        sites.to_csv(csv_name_s)
+        variables.to_csv(csv_name_v)
+        qualities.to_csv(csv_name_q)
+
+
 def extract_records(input_dir, output_dir, clean_up=False):
     """Extract downloaded bore records.
 
@@ -274,6 +396,23 @@ def extract_records(input_dir, output_dir, clean_up=False):
                 os.path.join(input_dir, zip_name) + '.part',
                 os.path.join(input_dir, zip_name))
 
+        # Read header
+        header = pd.read_csv(
+            os.path.join(input_dir, zip_name), compression='zip', nrows=3)
+
+        # Remove comments
+        header = header.iloc[:, 1:-1].T
+
+        # Apply product codes to all columns
+        header.iloc[1::2, 0] = header.iloc[::2, 0].values
+        header[0] = header[0].astype(float).astype(int).astype(str)
+
+        # Move quality label
+        header.iloc[1::2, 1] = header.iloc[1::2, 2]
+
+        # Combine labels
+        columns = [' '.join(c) for c in header.iloc[:, :-1].values]
+
         # Read csv file inside zip archive
         df = pd.read_csv(
             os.path.join(input_dir, zip_name),
@@ -283,32 +422,14 @@ def extract_records(input_dir, output_dir, clean_up=False):
             compression='zip',
             dayfirst=True)
 
+        # Update column names
+        df.columns = ['Date time'] + columns + ['Metadata']
+
         # Get bore specifics
-        meta = df.iloc[1, -1]
+        meta = df['Metadata'].iloc[1]
-        lat = float(re.search(r'(?<=Lat:)\S+', meta).group())
-        lon = float(re.search(r'(?<=Long:)\S+', meta).group())
-        elev = float(re.search(r'(?<=Elev:).+(?=m)', meta).group())
-        address = re.search(r'(?<=\d\.\d\.\d - ).+(?=\sLat)', meta).group()
         bore_id = re.search(r'^\S+', meta).group()
         site, hole, pipe = bore_id.split('.')
-
+        df.drop(columns='Metadata')
-        # FIXME: detect basin automatically
-        basin_id = 'MB'
-
-        # Rename columns
-        df = df.rename(
-            columns={
-                'Date': 'Date time',
-                'Bore level below MP': 'Below Measuring Point',
-                'GW Level - m AHD': 'Above Sea Level'
-            })
-
-        # Select output columns
-        df = df[[
-            'Date time',
-            'Below Measuring Point',
-            'Above Sea Level',
-        ]]
 
         # Set date index for resampling
         df.index = df['Date time']
@@ -325,15 +446,6 @@ def extract_records(input_dir, output_dir, clean_up=False):
                 df = df.resample('1w').mean()
                 df['Date time'] = df.index
 
-            # Add bore specifics to dataframe
-            df['Site'] = site
-            df['Hole'] = hole
-            df['Pipe'] = pipe
-            df['Lat'] = lat
-            df['Lon'] = lon
-            df['Elev'] = elev
-            df['Basin'] = basin_id
-
             master[period] = pd.concat([master[period], df])
 
         if clean_up:
@@ -341,12 +453,6 @@ def extract_records(input_dir, output_dir, clean_up=False):
             os.remove(os.path.join(input_dir, zip_name))
 
     for period in periods:
-        # Set column order
-        master[period] = master[period][[
-            'Date time', 'Basin', 'Site', 'Hole', 'Pipe',
-            'Below Measuring Point', 'Above Sea Level', 'Lat', 'Lon', 'Elev'
-        ]]
-
         # Get latest date from dataframe
         latest_date = master[period]['Date time'].iloc[-1].strftime('%Y-%m-%d')
         csv_name = os.path.join(