Merge pull request #29 from aring1988/master

Made necessary adjustments and removed unnecessary function

monetio/obs/pams.py

@@ -5,7 +5,7 @@
 import pandas as pd
 
 
-def open_dataset(filename):
+def add_data(filename):
     """ Opens a json file, returns data array
 
     Parameters
@@ -17,12 +17,48 @@ def open_dataset(filename):
     -----------------
     data: Pandas DataFrame
             DataFrame with all pertinent information
-
+            Date and Time are Datetime Objects
     """
 
     jsonf = open_json(filename)
-    data = get_data(jsonf)
+    dataf = jsonf['Data']
+    data = pd.DataFrame.from_dict(dataf)
+
+    # Combining state code, county code, and site number into one column
+    data['siteid'] = data.state_code.astype(str).str.zfill(
+        2)+data.county_code.astype(str).str.zfill(3)+data.site_number.astype(str).str.zfill(4)
+
+    # Combining date and time into one column
+    data['datetime_local'] = pd.to_datetime(data['date_local']+' '+data['time_local'])
+    data['datetime_utc'] = pd.to_datetime(data['date_gmt']+' '+data['time_gmt'])
+
+    # Renaming columns
+    data = data.rename(columns={'sample_measurement': 'obs',
+                                'units_of_measure': 'units', 'units_of_measure_code': 'unit_code'})
 
+    # Dropping some columns, and reordering columns
+    data = data.drop(columns=['state_code', 'county_code', 'site_number', 'datum',
+                              'qualifier', 'uncertainty', 'county', 'state', 'date_of_last_change',
+                              'date_local', 'time_local', 'date_gmt', 'time_gmt', 'poc', 'unit_code',
+                              'sample_duration_code', 'method_code'])
+    cols = data.columns.tolist()
+    cols.insert(0, cols.pop(cols.index('siteid')))
+    cols.insert(1, cols.pop(cols.index('latitude')))
+    cols.insert(2, cols.pop(cols.index('longitude')))
+    cols.insert(3, cols.pop(cols.index('datetime_local')))
+    cols.insert(4, cols.pop(cols.index('datetime_utc')))
+    data = data.reindex(columns=cols)
+
+    # Adjusting parameter units
+    units = data.units.unique()
+    for i in units:
+        con = data.units == i
+        if i.upper() == 'Parts per billion Carbon'.upper():
+            data.loc[con, 'units'] = 'ppbC'
+        if i == 'Parts per billion':
+            data.loc[con, 'units'] = 'ppb'
+        if i == 'Parts per million':
+            data.loc[con, 'units'] = 'ppm'
     return data
 
 
@@ -48,80 +84,26 @@ def open_json(filename):
     return jsonf
 
 
-def get_header(jsonf):
+def get_header(filename):
     """Finds basic header information in json file
 
 
     Parameters
     ----------------
-    jsonf: dictionary
+    filename: string
+           Full file path for json file
 
     Results
     ----------------
     header: Pandas DataFrame
 
     """
-
+    jsonf = open_json(filename)
     header = jsonf['Header']
     header = pd.DataFrame.from_dict(header)
     return header
 
 
-def get_data(jsonf):
-    """ Finds data in json file
-
-    Parameters
-    ----------------
-    jsonf: dictionary
-
-    Results
-    ----------------
-    data: Pandas DataFrame
-             DataFrame containing pertinent information
-             Date and Time are Datetime Objects
-
-    """
-    dataf = jsonf['Data']
-    data = pd.DataFrame.from_dict(dataf)
-
-    # Combining state code, county code, and site number into one column
-    data['siteid'] = data.state_code.astype(str).str.zfill(
-        2)+data.county_code.astype(str).str.zfill(3)+data.site_number.astype(str).str.zfill(4)
-
-    # Combining date and time into one column
-    data['datetime_local'] = pd.to_datetime(data['date_local']+' '+data['time_local'])
-    data['datetime_utc'] = pd.to_datetime(data['date_gmt']+' '+data['time_gmt'])
-
-    # Renaming columns
-    data = data.rename(columns={'sample_measurement': 'obs',
-                                'units_of_measure': 'units', 'units_of_measure_code': 'unit_code'})
-
-    # Dropping some columns, and reordering columns
-    data = data.drop(columns=['state_code', 'county_code', 'site_number', 'datum',
-                              'qualifier', 'uncertainty', 'county', 'state', 'date_of_last_change',
-                              'date_local', 'time_local', 'date_gmt', 'time_gmt', 'poc', 'unit_code',
-                              'sample_duration_code', 'method_code'])
-    cols = data.columns.tolist()
-    cols.insert(0, cols.pop(cols.index('siteid')))
-    cols.insert(1, cols.pop(cols.index('latitude')))
-    cols.insert(2, cols.pop(cols.index('longitude')))
-    cols.insert(3, cols.pop(cols.index('datetime_local')))
-    cols.insert(4, cols.pop(cols.index('datetime_utc')))
-    data = data.reindex(columns=cols)
-
-    # Adjusting parameter units
-    units = data.units.unique()
-    for i in units:
-        con = data.units == i
-        if i.upper() == 'Parts per billion Carbon'.upper():
-            data.loc[con, 'units'] = 'ppbC'
-        if i == 'Parts per billion':
-            data.loc[con, 'units'] = 'ppb'
-        if i == 'Parts per million':
-            data.loc[con, 'units'] = 'ppm'
-    return data
-
-
 def write_csv(array, filename):
     """Writes the data array to a csv file