diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 0c2f8f3..22de2f0 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,3 +1,13 @@
+v0.2.2
+======
+
+2020/11/09
+
+* allows passing matplotlib axes to urbanaccess.plot.plot_net()
+* adds flexibility to calendar/date handling (calendar_dates.txt now supported)
+* improves GTFS downloading (solves issue where requests were rejected due to missing user agent header)
+* improves text encoding support
+
 v0.2.1
 ======
 
diff --git a/README.rst b/README.rst
index 03af455..2391036 100644
--- a/README.rst
+++ b/README.rst
@@ -51,7 +51,7 @@ Citation and academic literature
 
 To cite this tool and for a complete description of the UrbanAccess methodology see the paper below:
 
-`Samuel D. Blanchard and Paul Waddell. 2017. "UrbanAccess: Generalized Methodology for Measuring Regional Accessibility with an Integrated Pedestrian and Transit Network." Transportation Research Record: Journal of the Transportation Research Board. No. 2653. pp. 35–44. <http://trrjournalonline.trb.org/doi/pdf/10.3141/2653-05>`__
+`Samuel D. Blanchard and Paul Waddell. 2017. "UrbanAccess: Generalized Methodology for Measuring Regional Accessibility with an Integrated Pedestrian and Transit Network." Transportation Research Record: Journal of the Transportation Research Board. No. 2653. pp. 35–44. <https://journals.sagepub.com/doi/pdf/10.3141/2653-05>`__
 
 For other related literature see `here <https://udst.github.io/urbanaccess/introduction.html#citation-and-academic-literature>`__.
 
@@ -113,9 +113,8 @@ Minimum GTFS data requirements
 
 The minimum `GTFS data
 types <https://developers.google.com/transit/gtfs/>`__ required to use
-UrbanAccess are: ``stop_times``, ``stops``, ``routes``, ``calendar``,
-and ``trips`` however if there is no ``calendar``, ``calendar_dates``
-can be used as a replacement.
+UrbanAccess are: ``stop_times``, ``stops``, ``routes`` and ``trips`` and
+one of either ``calendar`` or ``calendar_dates``.
 
 Related UDST libraries
 ----------------------
diff --git a/docs/source/conf.py b/docs/source/conf.py
index f365eb5..a073f17 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -30,8 +30,8 @@
 project = u'UrbanAccess'
 author = u'UrbanSim Inc.'
 copyright = u'{}, {}'.format(datetime.now().year, author)
-version = u'0.2.1'
-release = u'0.2.1'
+version = u'0.2.2'
+release = u'0.2.2'
 language = None
 
 # List of patterns to ignore when looking for source files.
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 5b6133a..3c6cec9 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -3,7 +3,7 @@ UrbanAccess
 
 A tool for computing GTFS transit and OSM pedestrian networks for accessibility analysis.
 
-v0.2.1, released August 28, 2020.
+v0.2.2, released November 9, 2020.
 
 Contents
 --------
diff --git a/docs/source/introduction.rst b/docs/source/introduction.rst
index 0f8e0ed..2274b91 100644
--- a/docs/source/introduction.rst
+++ b/docs/source/introduction.rst
@@ -39,7 +39,7 @@ A `demo <https://github.com/UDST/urbanaccess/tree/master/demo>`__ is available a
 Minimum GTFS data requirements
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The minimum `GTFS data types <https://developers.google.com/transit/gtfs/>`__ required to use UrbanAccess are: ``stop_times``, ``stops``, ``routes``, ``calendar``, and ``trips``. If you are using a feed that does not have or utilize a calendar you may use the ``calendar_dates`` file instead of ``calendar`` with the ``calendar_dates_lookup`` parameter :ref:`here <transit-network>`.
+The minimum `GTFS data types <https://developers.google.com/transit/gtfs/>`__ required to use UrbanAccess are: ``stop_times``, ``stops``, ``routes``, and ``trips`` and either ``calendar`` or ``calendar_dates``. If you are using a feed that does not have or utilize a calendar you may use the ``calendar_dates`` file instead of ``calendar`` with the ``calendar_dates_lookup`` parameter :ref:`here <transit-network>`.
 
 License
 ~~~~~~~~
@@ -51,11 +51,11 @@ Citation and academic literature
 
 To cite this tool and for a complete description of the UrbanAccess methodology see the paper below:
 
-`Samuel D. Blanchard and Paul Waddell. 2017. "UrbanAccess: Generalized Methodology for Measuring Regional Accessibility with an Integrated Pedestrian and Transit Network." Transportation Research Record: Journal of the Transportation Research Board. No. 2653. pp. 35–44. <http://trrjournalonline.trb.org/doi/pdf/10.3141/2653-05>`__
+`Samuel D. Blanchard and Paul Waddell. 2017. "UrbanAccess: Generalized Methodology for Measuring Regional Accessibility with an Integrated Pedestrian and Transit Network." Transportation Research Record: Journal of the Transportation Research Board. No. 2653. pp. 35–44. <https://journals.sagepub.com/doi/pdf/10.3141/2653-05>`__
 
 For a detailed use case of the tool see the following paper:
 
-`Samuel D. Blanchard and Paul Waddell. 2017. "Assessment of Regional Transit Accessibility in the San Francisco Bay Area of California with UrbanAccess." Transportation Research Record: Journal of the Transportation Research Board. No. 2654. pp. 45–54. <http://trrjournalonline.trb.org/doi/abs/10.3141/2654-06>`__
+`Samuel D. Blanchard and Paul Waddell. 2017. "Assessment of Regional Transit Accessibility in the San Francisco Bay Area of California with UrbanAccess." Transportation Research Record: Journal of the Transportation Research Board. No. 2654. pp. 45–54. <https://journals.sagepub.com/doi/pdf/10.3141/2654-06>`__
 
 Reporting bugs
 ~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/requirements-dev.txt b/requirements-dev.txt
index fbaf9c8..d578087 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -7,6 +7,7 @@ pycodestyle
 # testing demo notebook
 jupyter
 cartopy  # requires conda
+pyepsg
 
 # building documentation
 numpydoc
diff --git a/setup.py b/setup.py
index 1e75e96..297359e 100644
--- a/setup.py
+++ b/setup.py
@@ -9,7 +9,7 @@
 
 setup(
     name='urbanaccess',
-    version='0.2.1',
+    version='0.2.2',
     license='AGPL',
     description=description,
     long_description=long_description,
diff --git a/urbanaccess/__init__.py b/urbanaccess/__init__.py
index c45aee2..4c015c1 100644
--- a/urbanaccess/__init__.py
+++ b/urbanaccess/__init__.py
@@ -9,6 +9,6 @@
 from .gtfsfeeds import *
 from .plot import *
 
-__version__ = "0.2.1"
+__version__ = "0.2.2"
 
 version = __version__
diff --git a/urbanaccess/config.py b/urbanaccess/config.py
index 421458c..cba8f25 100644
--- a/urbanaccess/config.py
+++ b/urbanaccess/config.py
@@ -16,11 +16,12 @@ def _format_check(settings):
     """
 
     valid_keys = ['data_folder', 'logs_folder', 'log_file',
-                  'log_console', 'log_name', 'log_filename', 'gtfs_api']
+                  'log_console', 'log_name', 'log_filename',
+                  'txt_encoding', 'gtfs_api']
 
     for key in settings.keys():
         if key not in valid_keys:
-            raise ValueError('{} not found in list of valid configuation '
+            raise ValueError('{} not found in list of valid configuration '
                              'keys'.format(key))
         if not isinstance(key, str):
             raise ValueError('{} must be a string'.format(key))
@@ -42,13 +43,17 @@ class urbanaccess_config(object):
     logs_folder : str
         location to write log files
     log_file : bool
-        if true, save log output to a log file in logs_folder
+        if True, save log output to a log file in logs_folder
     log_console : bool
-        if true, print log output to the console
+        if True, print log output to the console
     log_name : str
         name of the logger
     log_filename : str
         name of the log file
+    txt_encoding : str
+        default text encoding used by the GTFS files, to be passed to
+        Python's open() function. Must be a valid encoding recognized by
+        Python codecs.
     gtfs_api : dict
         dictionary of the name of the GTFS API service as the key and
         the GTFS API server root URL as the value to pass to the GTFS loader
@@ -61,6 +66,7 @@ def __init__(self,
                  log_console=False,
                  log_name='urbanaccess',
                  log_filename='urbanaccess',
+                 txt_encoding='utf-8',
                  gtfs_api={'gtfsdataexch': (
                          'http://www.gtfs-data-exchange.com/'
                          'api/agencies?format=csv')}):
@@ -71,6 +77,7 @@ def __init__(self,
         self.log_console = log_console
         self.log_name = log_name
         self.log_filename = log_filename
+        self.txt_encoding = txt_encoding
         self.gtfs_api = gtfs_api
 
     @classmethod
@@ -110,6 +117,7 @@ def from_yaml(cls, configdir='configs',
                        log_name=yaml_config.get('log_name', 'urbanaccess'),
                        log_filename=yaml_config.get('log_filename',
                                                     'urbanaccess'),
+                       txt_encoding=yaml_config.get('txt_encoding', 'utf-8'),
                        gtfs_api=yaml_config.get('gtfs_api', {
                            'gtfsdataexch':
                                ('http://www.gtfs-data-exchange.com/'
@@ -128,6 +136,7 @@ def to_dict(self):
                 'log_console': self.log_console,
                 'log_name': self.log_name,
                 'log_filename': self.log_filename,
+                'txt_encoding': self.txt_encoding,
                 'gtfs_api': self.gtfs_api,
                 }
 
diff --git a/urbanaccess/gtfs/load.py b/urbanaccess/gtfs/load.py
index 0facd16..43b88af 100644
--- a/urbanaccess/gtfs/load.py
+++ b/urbanaccess/gtfs/load.py
@@ -4,6 +4,7 @@
 import time
 import pandas as pd
 import six
+import logging as lg
 
 from urbanaccess import config
 from urbanaccess.utils import log
@@ -20,7 +21,7 @@ def _standardize_txt(csv_rootpath=os.path.join(config.settings.data_folder,
     Parameters
     ----------
     csv_rootpath : str, optional
-        root path where all gtfs feeds that make up a contiguous metropolitan
+        root path where all GTFS feeds that make up a contiguous metropolitan
         area are stored
 
     Returns
@@ -59,6 +60,7 @@ def _txt_encoder_check(gtfsfiles_to_use,
     """
     # UnicodeDecodeError
     start_time = time.time()
+    log('Checking GTFS text file for encoding issues...')
 
     folderlist = [foldername for foldername in os.listdir(csv_rootpath) if
                   os.path.isdir(os.path.join(csv_rootpath, foldername))]
@@ -74,14 +76,16 @@ def _txt_encoder_check(gtfsfiles_to_use,
         for textfile in textfilelist:
             if textfile in gtfsfiles_to_use:
                 # Read from file
-                file_open = open(os.path.join(csv_rootpath, folder, textfile))
+                file_path = os.path.join(csv_rootpath, folder, textfile)
+                file_open = open(file_path)
                 raw = file_open.read()
                 file_open.close()
                 if raw.startswith(codecs.BOM_UTF8):
+                    msg = 'Correcting encoding issue in: {}...'
+                    log(msg.format(file_path))
                     raw = raw.replace(codecs.BOM_UTF8, '', 1)
                     # Write to file
-                    file_open = open(
-                        os.path.join(csv_rootpath, folder, textfile), 'w')
+                    file_open = open(file_path, 'w')
                     file_open.write(raw)
                     file_open.close()
 
@@ -100,9 +104,9 @@ def _txt_header_whitespace_check(gtfsfiles_to_use,
     Parameters
     ----------
     gtfsfiles_to_use : list
-        list of gtfs feed txt files to utilize
+        list of GTFS feed txt files to utilize
     csv_rootpath : str, optional
-        root path where all gtfs feeds that make up a contiguous metropolitan
+        root path where all GTFS feeds that make up a contiguous metropolitan
         area are stored
 
     Returns
@@ -111,6 +115,11 @@ def _txt_header_whitespace_check(gtfsfiles_to_use,
     """
     start_time = time.time()
 
+    txt_encoding = config.settings.txt_encoding
+    msg = ('Checking GTFS text file header whitespace... '
+           'Reading files using encoding: {} set in configuration.')
+    log(msg.format(txt_encoding))
+
     folderlist = [foldername for foldername in os.listdir(csv_rootpath) if
                   os.path.isdir(os.path.join(csv_rootpath, foldername))]
 
@@ -124,25 +133,41 @@ def _txt_header_whitespace_check(gtfsfiles_to_use,
 
         for textfile in textfilelist:
             if textfile in gtfsfiles_to_use:
+                file_path = os.path.join(csv_rootpath, folder, textfile)
                 # Read from file
-                with open(os.path.join(csv_rootpath, folder, textfile)) as f:
-                    lines = f.readlines()
-                lines[0] = re.sub(r'\s+', '', lines[0]) + '\n'
-                # Write to file
                 try:
-                    with open(os.path.join(csv_rootpath, folder, textfile),
-                              'w') as f:
-                        f.writelines(lines)
-                except Exception:
-                    log('Unable to read {}. Check that file is not currently'
-                        'being read or is not already in memory as this is '
-                        'likely the cause of the error.'
-                        ''.format(os.path.join(csv_rootpath,
-                                               folder, textfile)))
-    log(
-        'GTFS text file header whitespace check completed. Took {:,'
-        '.2f} seconds'.format(
-            time.time() - start_time))
+                    if six.PY2:
+                        with open(file_path) as f:
+                            lines = f.readlines()
+                    else:
+                        # read with default 'utf-8' encoding
+                        with open(
+                                file_path,
+                                encoding=txt_encoding) as f:
+                            lines = f.readlines()
+                    line_wo_whitespace = re.sub(r'\s+', '', lines[0]) + '\n'
+                    # only write the file if there are changes to be made
+                    if lines[0] != line_wo_whitespace:
+                        msg = 'Removing whitespace from header(s) in: {}...'
+                        log(msg.format(file_path))
+                        lines[0] = line_wo_whitespace
+                        # Write to file
+                        if six.PY2:
+                            with open(
+                                    file_path, 'w') as f:
+                                f.writelines(lines)
+                        else:
+                            # write with default 'utf-8' encoding
+                            with open(
+                                    file_path, 'w',
+                                    encoding=txt_encoding) as f:
+                                f.writelines(lines)
+                except Exception as e:
+                    msg = 'Unable to process: {}. Exception: {}'
+                    raise Exception(log(msg.format(file_path, e),
+                                        level=lg.ERROR))
+    log('GTFS text file header whitespace check completed. '
+        'Took {:,.2f} seconds'.format(time.time() - start_time))
 
 
 def gtfsfeed_to_df(gtfsfeed_path=None, validation=False, verbose=True,
@@ -156,7 +181,7 @@ def gtfsfeed_to_df(gtfsfeed_path=None, validation=False, verbose=True,
     Parameters
     ----------
     gtfsfeed_path : str, optional
-        root path where all gtfs feeds that make up a contiguous metropolitan
+        root path where all GTFS feeds that make up a contiguous metropolitan
         area are stored
     validation : bool
         if true, the validation check on stops checking for stops outside
@@ -236,8 +261,20 @@ def gtfsfeed_to_df(gtfsfeed_path=None, validation=False, verbose=True,
                         os.listdir(os.path.join(gtfsfeed_path, folder)) if
                         textfilename.endswith(".txt")]
         required_gtfsfiles = ['stops.txt', 'routes.txt', 'trips.txt',
-                              'stop_times.txt', 'calendar.txt']
-        optional_gtfsfiles = ['agency.txt', 'calendar_dates.txt']
+                              'stop_times.txt']
+        optional_gtfsfiles = ['agency.txt']
+        # either calendar or calendar_dates is required
+        calendar_gtfsfiles = ['calendar.txt', 'calendar_dates.txt']
+
+        calendar_files = [i for i in calendar_gtfsfiles if i in textfilelist]
+        if len(calendar_files) == 0:
+            error_msg = (
+                'at least one of `calendar.txt` or `calendar_dates.txt` is '
+                'required to complete a GTFS dataset but neither was found in '
+                'folder {}')
+            raise ValueError(error_msg.format(os.path.join(
+                gtfsfeed_path, folder)))
+
         for required_file in required_gtfsfiles:
             if required_file not in textfilelist:
                 raise ValueError(
@@ -263,10 +300,32 @@ def gtfsfeed_to_df(gtfsfeed_path=None, validation=False, verbose=True,
                 stop_times_df = utils_format._read_gtfs_stop_times(
                     textfile_path=os.path.join(gtfsfeed_path, folder),
                     textfile=textfile)
+
+        for textfile in calendar_files:
+            # use both calendar and calendar_dates if they exist, otherwise
+            # if only one of them exists use the one that exists and set the
+            # other one that does not exist to a blank df
             if textfile == 'calendar.txt':
                 calendar_df = utils_format._read_gtfs_calendar(
                     textfile_path=os.path.join(gtfsfeed_path, folder),
                     textfile=textfile)
+                # if only calendar, set calendar_dates as blank
+                # with default required columns
+                if len(calendar_files) == 1:
+                    calendar_dates_df = pd.DataFrame(
+                        columns=['service_id', 'dates', 'exception_type'])
+            else:
+                calendar_dates_df = utils_format._read_gtfs_calendar_dates(
+                    textfile_path=os.path.join(gtfsfeed_path, folder),
+                    textfile=textfile)
+                # if only calendar_dates, set calendar as blank
+                # with default required columns
+                if len(calendar_files) == 1:
+                    calendar_df = pd.DataFrame(
+                        columns=['service_id', 'monday',
+                                 'tuesday', 'wednesday', 'thursday',
+                                 'friday', 'saturday', 'sunday',
+                                 'start_date', 'end_date'])
 
         for textfile in optional_gtfsfiles:
             if textfile == 'agency.txt':
@@ -276,13 +335,6 @@ def gtfsfeed_to_df(gtfsfeed_path=None, validation=False, verbose=True,
                         textfile=textfile)
                 else:
                     agency_df = pd.DataFrame()
-            if textfile == 'calendar_dates.txt':
-                if textfile in textfilelist:
-                    calendar_dates_df = utils_format._read_gtfs_calendar_dates(
-                        textfile_path=os.path.join(gtfsfeed_path, folder),
-                        textfile=textfile)
-                else:
-                    calendar_dates_df = pd.DataFrame()
 
         stops_df, routes_df, trips_df, stop_times_df, calendar_df, \
             calendar_dates_df = (utils_format
diff --git a/urbanaccess/gtfs/network.py b/urbanaccess/gtfs/network.py
index fa6592b..39b6725 100644
--- a/urbanaccess/gtfs/network.py
+++ b/urbanaccess/gtfs/network.py
@@ -58,7 +58,7 @@ def create_transit_net(gtfsfeeds_dfs, day,
         DataFrame for the same time period stored in the
         gtfsfeeds_dfs object it will be used instead of re-calculated
     save_processed_gtfs : bool, optional
-        if true, all processed gtfs DataFrames will
+        if true, all processed GTFS DataFrames will
         be stored to disk in a hdf5 file
     save_dir : str, optional
         directory to save the hdf5 file
@@ -97,10 +97,15 @@ def create_transit_net(gtfsfeeds_dfs, day,
             level=lg.WARNING)
     if gtfsfeeds_dfs is None:
         raise ValueError('gtfsfeeds_dfs is None')
-    if gtfsfeeds_dfs.trips.empty or gtfsfeeds_dfs.calendar.empty or \
-            gtfsfeeds_dfs.stop_times.empty or gtfsfeeds_dfs.stops.empty:
-        raise ValueError('one of the gtfsfeeds_dfs object trips, calendar, '
-                         'stops, or stop_times were found to be empty.')
+    error_msg = ('one of the following gtfsfeeds_dfs objects {} were '
+                 'found to be empty.')
+    if gtfsfeeds_dfs.trips.empty or gtfsfeeds_dfs.stop_times.empty or \
+            gtfsfeeds_dfs.stops.empty:
+        error_msg_case_1 = 'trips, stops, or stop_times'
+        raise ValueError(error_msg.format(error_msg_case_1))
+    if gtfsfeeds_dfs.calendar.empty and gtfsfeeds_dfs.calendar_dates.empty:
+        error_msg_case_2 = 'calendar or calendar_dates'
+        raise ValueError(error_msg.format(error_msg_case_2))
     if not isinstance(overwrite_existing_stop_times_int, bool):
         raise ValueError('overwrite_existing_stop_times_int must be bool')
     if not isinstance(use_existing_stop_times_int, bool):
@@ -117,6 +122,9 @@ def create_transit_net(gtfsfeeds_dfs, day,
     if 'direction_id' not in gtfsfeeds_dfs.trips.columns:
         columns.remove('direction_id')
 
+    # TODO: support use case where only calendar_dates is in use: make 'day'
+    #  optional as None but require either day or calendar_dates_lookup
+    #  to exist but both are not required
     calendar_selected_trips_df = _trip_schedule_selector(
         input_trips_df=gtfsfeeds_dfs.trips[columns],
         input_calendar_df=gtfsfeeds_dfs.calendar,
@@ -129,8 +137,7 @@ def create_transit_net(gtfsfeeds_dfs, day,
             is False:
         gtfsfeeds_dfs.stop_times_int = _interpolate_stop_times(
             stop_times_df=gtfsfeeds_dfs.stop_times,
-            calendar_selected_trips_df=calendar_selected_trips_df,
-            day=day)
+            calendar_selected_trips_df=calendar_selected_trips_df)
 
         gtfsfeeds_dfs.stop_times_int = _time_difference(
             stop_times_df=gtfsfeeds_dfs.stop_times_int)
@@ -209,7 +216,7 @@ def _trip_schedule_selector(input_trips_df, input_calendar_df,
         day in the GTFS calendar
     calendar_dates_lookup : dict, optional
         dictionary of the lookup column (key) as a string and corresponding
-        string (value) a s string or list of strings to use to subset trips
+        string (value) as string or list of strings to use to subset trips
         using the calendar_dates DataFrame. Search will be exact. If none,
         then the calendar_dates DataFrame will not be used to select trips
         that are not in the calendar DataFrame. Note search will select all
@@ -369,18 +376,19 @@ def _trip_schedule_selector(input_trips_df, input_calendar_df,
                 subset_result = input_calendar_dates_df[
                     input_calendar_dates_df[col_name_key].str.match(
                         text, case=False, na=False)]
-                feed_id_list = subset_result['unique_feed_id'].unique()
-                for index, id in enumerate(feed_id_list):
-                    feed_id_list[index] = ' '.join(id.split('_')[:-1])
+                if len(subset_result) != 0:
+                    feed_id_list = subset_result['unique_feed_id'].unique()
+                    for index, id in enumerate(feed_id_list):
+                        feed_id_list[index] = ' '.join(id.split('_')[:-1])
 
-                log('Found {:,} records that matched query: column: {} and '
-                    'string: {} for GTFS feed(s): {}'.format(len(
-                        subset_result),
-                        col_name_key,
-                        text,
-                        feed_id_list))
+                    log('Found {:,} records that matched query: column: {} '
+                        'and string: {} for GTFS feed(s): {}'.format(len(
+                            subset_result),
+                            col_name_key,
+                            text,
+                            feed_id_list))
 
-                subset_result_df = subset_result_df.append(subset_result)
+                    subset_result_df = subset_result_df.append(subset_result)
 
         subset_result_df.drop_duplicates(inplace=True)
         subset_result_df = subset_result_df[['unique_service_id']]
@@ -428,7 +436,7 @@ def _trip_schedule_selector(input_trips_df, input_calendar_df,
     return calendar_selected_trips_df
 
 
-def _interpolate_stop_times(stop_times_df, calendar_selected_trips_df, day):
+def _interpolate_stop_times(stop_times_df, calendar_selected_trips_df):
     """
     Interpolate missing stop times using a linear
     interpolator between known stop times
@@ -439,10 +447,6 @@ def _interpolate_stop_times(stop_times_df, calendar_selected_trips_df, day):
         stop times DataFrame
     calendar_selected_trips_df : pandas.DataFrame
         DataFrame of trips that run on specific day
-    day : {'friday','monday','saturday','sunday','thursday','tuesday',
-    'wednesday'}
-        day of the week to extract transit schedule from that corresponds
-        to the day in the GTFS calendar
 
     Returns
     -------
@@ -480,7 +484,7 @@ def _interpolate_stop_times(stop_times_df, calendar_selected_trips_df, day):
         'unique_trip_id'].unique().tolist()
     # select trip ids that match the trips in the
     # calendar_selected_trips_df -- resulting df will be stop times
-    # only for trips that run on the service day of interest
+    # only for trips that run on the service day or dates of interest
     stop_times_df = stop_times_df[
         stop_times_df['unique_trip_id'].isin(uniquetriplist)]
 
@@ -498,10 +502,10 @@ def _interpolate_stop_times(stop_times_df, calendar_selected_trips_df, day):
             level=lg.WARNING)
         log('Starting departure stop time interpolation...')
         log(
-            'Departure time records missing from trips following {} '
-            'schedule: {:,} ({:.2f} percent of {:,} total '
+            'Departure time records missing from trips following the '
+            'specified schedule: {:,} ({:.2f} percent of {:,} total '
             'records)'.format(
-                day, missing_stop_times_count,
+                missing_stop_times_count,
                 (missing_stop_times_count / len(stop_times_df)) * 100,
                 len(stop_times_df['departure_time_sec'])))
 
@@ -510,8 +514,8 @@ def _interpolate_stop_times(stop_times_df, calendar_selected_trips_df, day):
     else:
 
         log('There are no departure time records missing from trips '
-            'following {} schedule. There are no records to '
-            'interpolate.'.format(day))
+            'following the specified schedule. There are no records to '
+            'interpolate.')
 
     # Find trips with more than one missing time
     # Note: all trip ids have at least 1 null departure time because the
diff --git a/urbanaccess/gtfs/utils_format.py b/urbanaccess/gtfs/utils_format.py
index 6ed694d..65d71ef 100644
--- a/urbanaccess/gtfs/utils_format.py
+++ b/urbanaccess/gtfs/utils_format.py
@@ -179,13 +179,11 @@ def _read_gtfs_calendar(textfile_path, textfile):
 
     df = pd.read_csv(os.path.join(textfile_path, textfile),
                      dtype={'service_id': object}, low_memory=False)
-
     if len(df) == 0:
-        error_msg = ('{} has no records. This could indicate that this feed '
-                     'is using calendar_dates.txt for service_ids. If so, '
-                     'make a dummy row in calendar.txt to proceed.')
-        raise ValueError(error_msg.format(os.path.join(textfile_path,
-                                                       textfile)))
+        warning_msg = ('{} has no records. This could indicate that this feed '
+                       'is using calendar_dates.txt for service_ids.')
+        log(warning_msg.format(os.path.join(
+            textfile_path, textfile)), level=lg.WARNING)
 
     columnlist = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday',
                   'saturday', 'sunday']
@@ -217,8 +215,11 @@ def _read_gtfs_calendar_dates(textfile_path, textfile):
     df = pd.read_csv(os.path.join(textfile_path, textfile),
                      dtype={'service_id': object}, low_memory=False)
     if len(df) == 0:
-        raise ValueError('{} has no records'.format(os.path.join(
-            textfile_path, textfile)))
+        warning_msg = ('{} has no records. This could indicate that this feed '
+                       'is using calendar.txt for service_ids.')
+        log(warning_msg.format(os.path.join(
+            textfile_path, textfile)), level=lg.WARNING)
+
     # remove any extra whitespace in column names
     df.rename(columns=lambda x: x.strip(), inplace=True)
     return df
diff --git a/urbanaccess/gtfsfeeds.py b/urbanaccess/gtfsfeeds.py
index 0eb5373..26acb02 100644
--- a/urbanaccess/gtfsfeeds.py
+++ b/urbanaccess/gtfsfeeds.py
@@ -5,7 +5,7 @@
 import os
 import logging as lg
 import time
-from six.moves.urllib.request import urlopen
+from six.moves.urllib import request
 
 from urbanaccess.utils import log
 from urbanaccess import config
@@ -78,9 +78,11 @@ def from_yaml(cls, gtfsfeeddir=os.path.join(config.settings.data_folder,
             for value in yaml_config['gtfs_feeds'][key]:
                 if not isinstance(value, str):
                     raise ValueError('{} must be a string'.format(value))
-
-        if (pd.Series(
-                yaml_config['gtfs_feeds'].values()).value_counts() != 1).all():
+        unique_url_count = len(
+            pd.DataFrame.from_dict(yaml_config['gtfs_feeds'], orient='index')[
+                0].unique())
+        url_count = len(yaml_config['gtfs_feeds'])
+        if unique_url_count != url_count:
             raise ValueError(
                 'duplicate values were found when the passed add_dict '
                 'dictionary was added to the existing dictionary. Feed URL '
@@ -439,7 +441,7 @@ def download(data_folder=os.path.join(config.settings.data_folder),
                     raise ValueError('{} must be a string'.format(value))
 
         for key, value in feed_dict.items():
-            if value in feed_dict.gtfs_feeds.values():
+            if value in feeds.gtfs_feeds.values():
                 raise ValueError(
                     'duplicate values were found when the passed add_dict '
                     'dictionary was added to the existing dictionary. Feed '
@@ -458,70 +460,81 @@ def download(data_folder=os.path.join(config.settings.data_folder),
     if not os.path.exists(download_folder):
         os.makedirs(download_folder)
         log('{} does not exist. Directory was created'.format(download_folder))
-    log('{} GTFS feeds will be downloaded here: {}'.format(
+    log('{:,} GTFS feed(s) will be downloaded here: {}'.format(
         len(feeds.gtfs_feeds), download_folder))
 
     start_time1 = time.time()
+    msg_no_connection_w_status = ('Unable to connect. URL at {} returned '
+                                  'status code {} and no data')
+    msg_no_connection = 'Unable to connect to: {}. Error: {}'
+    msg_download_succeed = ('{} GTFS feed downloaded successfully. '
+                            'Took {:,.2f} seconds for {:,.1f}KB')
     # TODO: add file counter and print number to user
     for feed_name_key, feed_url_value in feeds.gtfs_feeds.items():
         start_time2 = time.time()
         zipfile_path = ''.join([download_folder, '/', feed_name_key, '.zip'])
 
-        if 'http' in feed_url_value:
-            status_code = urlopen(feed_url_value).getcode()
-            if status_code == 200:
-                file = urlopen(feed_url_value)
-
-                _zipfile_type_check(file=file,
-                                    feed_url_value=feed_url_value)
+        # add default user-agent header in request to avoid 403 Errors
+        opener = request.build_opener()
+        opener.addheaders = [('User-agent', '')]
+        request.install_opener(opener)
 
-                with open(zipfile_path, "wb") as local_file:
-                    local_file.write(file.read())
-                log(
-                    '{} GTFS feed downloaded successfully. Took {:,'
-                    '.2f} seconds for {:,.1f}KB'.format(
-                        feed_name_key, time.time() - start_time2,
-                        os.path.getsize(zipfile_path)))
-            elif status_code in [429, 504]:
-                log(
-                    'URL at {} returned status code {} and no data. '
-                    'Re-trying request in {:.2f} seconds.'.format(
-                        feed_url_value, status_code, error_pause_duration),
-                    level=lg.WARNING)
-                time.sleep(error_pause_duration)
-                try:
-                    file = urlopen(feed_url_value)
+        if 'http' in feed_url_value:
+            try:
+                status_code = request.urlopen(feed_url_value).getcode()
+                if status_code == 200:
+                    file = request.urlopen(feed_url_value)
 
                     _zipfile_type_check(file=file,
                                         feed_url_value=feed_url_value)
 
                     with open(zipfile_path, "wb") as local_file:
                         local_file.write(file.read())
-                except Exception:
-                    log('Unable to connect. URL at {} returned status code '
-                        '{} and no data'.format(feed_url_value, status_code),
+                    log(msg_download_succeed.format(
+                        feed_name_key, time.time() - start_time2,
+                        os.path.getsize(zipfile_path)))
+                elif status_code in [429, 504]:
+                    msg = ('URL at {} returned status code {} and no data. '
+                           'Re-trying request in {:.2f} seconds.')
+                    log(msg.format(feed_url_value, status_code,
+                                   error_pause_duration),
+                        level=lg.WARNING)
+                    time.sleep(error_pause_duration)
+                    try:
+                        file = request.urlopen(feed_url_value)
+
+                        _zipfile_type_check(file=file,
+                                            feed_url_value=feed_url_value)
+
+                        with open(zipfile_path, "wb") as local_file:
+                            local_file.write(file.read())
+                    except Exception:
+                        log(msg_no_connection_w_status.format(
+                            feed_url_value, status_code),
+                            level=lg.ERROR)
+                else:
+                    log(msg_no_connection_w_status.format(
+                        feed_url_value, status_code),
                         level=lg.ERROR)
-            else:
-                log(
-                    'Unable to connect. URL at {} returned status code {} '
-                    'and no data'.format(
-                        feed_url_value, status_code), level=lg.ERROR)
+            except Exception:
+                log(msg_no_connection.format(
+                    feed_url_value, traceback.format_exc()),
+                    level=lg.ERROR)
         else:
             try:
-                file = urlopen(feed_url_value)
+                file = request.urlopen(feed_url_value)
                 _zipfile_type_check(file=file,
                                     feed_url_value=feed_url_value)
-                with open(
-                        ''.join([download_folder, '/', feed_name_key, '.zip']),
-                        "wb") as local_file:
+                file_path = ''.join(
+                    [download_folder, '/', feed_name_key, '.zip'])
+                with open(file_path, "wb") as local_file:
                     local_file.write(file.read())
-                log(
-                    '{} GTFS feed downloaded successfully. Took {:,'
-                    '.2f} seconds for {:,.1f}KB'.format(
-                        feed_name_key, time.time() - start_time2,
-                        os.path.getsize(zipfile_path)))
+                log(msg_download_succeed.format(
+                    feed_name_key, time.time() - start_time2,
+                    os.path.getsize(zipfile_path)))
             except Exception:
-                log('Unable to connect: {}'.format(traceback.format_exc()),
+                log(msg_no_connection.format(
+                    feed_url_value, traceback.format_exc()),
                     level=lg.ERROR)
 
     log('GTFS feed download completed. Took {:,.2f} seconds'.format(
diff --git a/urbanaccess/plot.py b/urbanaccess/plot.py
index 69dbae3..ab24594 100644
--- a/urbanaccess/plot.py
+++ b/urbanaccess/plot.py
@@ -13,7 +13,8 @@ def plot_net(nodes, edges, x_col=None, y_col=None, from_col=None,
              fig_height=6, margin=0.02,
              edge_color='#999999', edge_linewidth=1, edge_alpha=1,
              node_color='black', node_size=15, node_alpha=1,
-             node_edgecolor='none', node_zorder=3, nodes_only=False):
+             node_edgecolor='none', node_zorder=3, nodes_only=False,
+             ax=None):
     """
     plot urbanaccess network nodes and edges
 
@@ -59,6 +60,9 @@ def plot_net(nodes, edges, x_col=None, y_col=None, from_col=None,
         nodes under the edges, 3 will plot nodes on top
     nodes_only : bool
         if true only the nodes will plot
+    ax :  matplotlib.axes._subplots.AxesSubplot, optional
+        matplotlib axes, as given by, for example, plt.subplot.
+        Use to specify the projection.
 
     Returns
     -------
@@ -117,8 +121,11 @@ def plot_net(nodes, edges, x_col=None, y_col=None, from_col=None,
                           'in a negative value or 0'))
     bbox_aspect_ratio = (y_max - y_min) / (x_max - x_min)
 
-    fig, ax = plt.subplots(figsize=(fig_height / bbox_aspect_ratio,
-                                    fig_height))
+    if ax is None:
+        fig, ax = plt.subplots(figsize=(fig_height / bbox_aspect_ratio,
+                                        fig_height))
+    else:
+        fig = ax.figure
 
     if nodes_only is False:
         # TODO: optimize for speed by calculating only for edges that are
diff --git a/urbanaccess/tests/conftest.py b/urbanaccess/tests/conftest.py
new file mode 100644
index 0000000..5ca8297
--- /dev/null
+++ b/urbanaccess/tests/conftest.py
@@ -0,0 +1,799 @@
+import pytest
+import os
+import pandas as pd
+import numpy as np
+
+
+@pytest.fixture
+def agency_feed_1():
+    data = {
+        'agency_id': 'agency a',
+        'agency_name': 'agency a city a',
+        'agency_url': 'http://www.agency_a.org',
+        'agency_timezone': 'America/Los_Angeles',
+        'agency_phone': '(000) 000-0000'
+    }
+    index = range(1)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def agency_feed_2():
+    data = {
+        'agency_id': ['agency b bus', 'agency b rail'],
+        'agency_name': ['agency b district 1', 'agency b district 2'],
+        'agency_url': ['http://www.agency_b.org', 'http://www.agency_b.org'],
+        'agency_timezone': ['America/Los_Angeles', 'America/Los_Angeles'],
+        'agency_phone': ['(000) 000-0000', '(000) 000-0000']
+    }
+    index = range(2)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def agency_feed_3():
+    data = {
+        'agency_id': '',
+        'agency_name': 'agency c',
+        'agency_url': 'http://www.agency_c.org',
+        'agency_timezone': 'America/Los_Angeles',
+        'agency_phone': '(000) 000-0000'
+    }
+    index = range(1)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def agency_feed_4():
+    data = {
+        'agency_id': ['agency 1', 'agency 2', 'agency 3'],
+        'agency_name': ['agency 1 bus', 'agency 2 rail', 'agency 3 metro'],
+        'agency_url': ['http://www.agency_1.org', 'http://www.agency_2.org',
+                       'http://www.agency_2.org'],
+        'agency_timezone': ['America/Los_Angeles', 'America/Los_Angeles',
+                            'America/Los_Angeles'],
+        'agency_phone': ['(000) 000-0000', '(000) 000-0000', '(000) 000-0000']
+    }
+    index = range(3)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def routes_feed_1():
+    data = {
+        'agency_id': ['agency a'] * 4,
+        'route_id': ['10-101', '11-101', '12-101', '13-101'],
+        'route_short_name': ['10', '11', 'red', 'blue'],
+        'route_long_name': ['ave a local', 'ave a express', 'red line',
+                            'blue line'],
+        'route_type': [3, 3, 1, 1]
+    }
+
+    index = range(4)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def routes_feed_2():
+    data = {
+        'agency_id': ['agency b bus', 'agency b bus', 'agency b rail',
+                      'agency b rail'],
+        'route_id': ['40-4', '40-4x', 'r-2', 'r-2ext'],
+        'route_short_name': ['40', '40', 'red', 'red-ext'],
+        'route_long_name': ['ave a local', 'ave a express', 'red line',
+                            'red line extension'],
+        'route_type': [3, 3, 1, 1]
+    }
+
+    index = range(4)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def routes_feed_4():
+    data = {
+        'agency_id': ['agency 1', 'agency 1', 'agency 2', 'agency 2',
+                      'agency 3', 'agency 3'],
+        'route_id': ['a1x', 'a1', 'a2x', 'a2', 'a3x', 'a3'],
+        'route_short_name': ['1x', '1', '2x', '2', '3x', '3'],
+        'route_long_name': ['1 express', '1 local', '2 express',
+                            '2 local', '3 express', '3 local'],
+        'route_type': [3, 3, 3, 3, 3, 3]
+    }
+
+    index = range(6)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def stops_feed_1():
+    data = {
+        'stop_id': ['1', '2', '3', '4', '5', '6',
+                    '7', '8', '9'],
+        'stop_name': ['ave a', 'ave b', 'ave c', 'ave d', 'ave e', 'ave f',
+                      '1st st', '2nd st', '3rd st'],
+        'stop_lat': [37.797484, 37.774963, 37.803664, 37.80787, 37.828415,
+                     37.844601, 37.664174, 37.591208, 37.905628],
+        'stop_lon': [-122.265609, -122.224274, -122.271604, -122.269029,
+                     -122.267227, -122.251793, -122.444116, -122.017867,
+                     -122.067423],
+        'location_type': [1, 1, 1, 1, 1, 1,
+                          2, 2, 2],
+        'wheelchair_boarding': [1, 0, 0, 0, 0, 0,
+                                1, 1, 1]
+    }
+
+    index = range(9)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def stops_feed_2():
+    data = {
+        'stop_id': ['60', '61', '62', '63', '64', '65',
+                    '66', '67', '68',
+                    '600', '601', '602', '603', '604', '605', '606'],
+        'stop_name': ['ave m', 'ave n', 'ave o', 'ave p', 'ave q', 'ave r',
+                      '10th st', '11th st', '12th st',
+                      '121th st', '122th st', '123th st', '124th st',
+                      '125th st', '126th st', '127th st'],
+        'stop_lat': [38.797484, 38.774963, 38.803664, 38.80787, 38.828415,
+                     38.844601, 38.664174, 38.591208, 38.905628,
+                     38.603664, 38.60787, 38.628415,
+                     38.644601, 38.660000, 38.691208, 38.605628],
+        'stop_lon': [-121.265609, -121.224274, -121.271604, -121.269029,
+                     -121.267227, -121.251793, -121.444116, -121.017867,
+                     -121.067423, -122.271604, -122.269029, -122.267227,
+                     -122.251793, -122.444116, -122.017867, -122.067423],
+        'location_type': [1, 1, 1, 1, 1, 1,
+                          2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+        'wheelchair_boarding': [1, 0, 0, 0, 0, 0,
+                                1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+    }
+
+    index = range(16)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def stops_feed_4():
+    data = {
+        'stop_id': ['70', '71', '72', '73', '74', '75',
+                    '76', '77', '78'],
+        'stop_name': ['station 1', 'station 2', 'station 3', 'station 4',
+                      'station 5', 'station 6',
+                      'station 7', 'station 8', 'station 9'],
+        'stop_lat': [20.797484, 20.774963, 20.803664, 20.80787, 20.828415,
+                     20.844601, 20.664174, 20.591208, 20.905628],
+        'stop_lon': [-100.265609, -100.224274, -100.271604, -100.269029,
+                     -100.267227, -100.251793, -100.444116, -100.017867,
+                     -100.067423]
+    }
+
+    index = range(9)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def trips_feed_1():
+    data = {
+        'route_id': ['10-101', '10-101', '10-101', '10-101',
+                     '11-101', '11-101',
+                     '12-101', '12-101',
+                     '13-101', '13-101'],
+        'trip_id': ['a1', 'a2', 'a3', 'a4',
+                    'b1', 'b2',
+                    'c1', 'c2',
+                    'd1', 'd2'],
+        'service_id': ['weekday-1', 'weekday-1', 'weekday-1', 'weekday-1',
+                       'weekday-2', 'weekday-2',
+                       'weekday-3', 'weekday-3',
+                       'weekend-1', 'weekend-1'],
+        'direction_id': [1, 0, 1, 0,
+                         1, 0,
+                         1, 0,
+                         1, 0],
+        'wheelchair_accessible': [1, 1, 1, 1,
+                                  0, 0,
+                                  0, 0,
+                                  0, 0],
+        'bikes_allowed': [1, 1, 1, 1,
+                          0, 0,
+                          0, 0,
+                          0, 0]
+    }
+
+    index = range(10)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def trips_feed_2():
+    data = {
+
+        'route_id': ['40-4', '40-4', '40-4', '40-4',
+                     '40-4x', '40-4x',
+                     'r-2', 'r-2',
+                     'r-2ext', 'r-2ext'],
+        'trip_id': ['11', '12', '13', '14',
+                    '21', '22',
+                    '31', '32',
+                    '41', '42'],
+        'service_id': ['weekday-1', 'weekday-1', 'weekday-1', 'weekday-1',
+                       'weekday-2', 'weekday-2',
+                       'weekday-3', 'weekday-3',
+                       'weekend-1', 'weekend-1']
+    }
+
+    index = range(10)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def trips_feed_4():
+    data = {
+        'route_id': ['a1x', 'a1x', 'a1x', 'a1x',
+                     'a1', 'a1',
+                     'a2x', 'a2x',
+                     'a2', 'a2',
+                     'a3x', 'a3x',
+                     'a3', 'a3'],
+        'trip_id': ['a131', 'a132', 'a133', 'a134',
+                    'a135', 'a136',
+                    'a237', 'a238',
+                    'a239', 'a240',
+                    'a341', 'a342',
+                    'a343', 'a344'],
+        'service_id': ['wk-1', 'wk-1', 'wk-1', 'wk-1',
+                       'wk-1', 'wk-1',
+                       'wk-1', 'wk-1',
+                       'wk-1', 'wk-1',
+                       'wk-1', 'wk-1',
+                       'wk-1', 'wk-1']
+    }
+
+    index = range(14)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def calendar_feed_1():
+    data = {
+        'service_id': ['weekday-1',
+                       'weekday-2',
+                       'weekday-3',
+                       'weekend-1'],
+        'monday': [1, 1, 1, 0],
+        'tuesday': [1, 1, 1, 0],
+        'wednesday': [1, 1, 1, 0],
+        'thursday': [1, 1, 1, 0],
+        'friday': [1, 1, 1, 0],
+        'saturday': [0, 0, 0, 1],
+        'sunday': [0, 0, 0, 1],
+        'start_date': [20161224] * 4,
+        'end_date': [20170318] * 4}
+
+    index = range(4)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def calendar_feed_2():
+    data = {
+        'service_id': ['weekday-1',
+                       'weekday-2',
+                       'weekday-3',
+                       'weekend-1'],
+        'monday': [1, 1, 1, 0],
+        'tuesday': [1, 1, 1, 0],
+        'wednesday': [1, 1, 1, 0],
+        'thursday': [1, 1, 1, 0],
+        'friday': [1, 1, 1, 0],
+        'saturday': [0, 0, 0, 1],
+        'sunday': [0, 0, 0, 1],
+        'start_date': [20161224] * 4,
+        'end_date': [20170318] * 4}
+
+    index = range(4)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def calendar_feed_4():
+    data = {
+        'service_id': ['wk-1'],
+        'monday': [1],
+        'tuesday': [1],
+        'wednesday': [1],
+        'thursday': [1],
+        'friday': [1],
+        'saturday': [0],
+        'sunday': [0],
+        'start_date': [20161224],
+        'end_date': [20170318]}
+
+    index = range(1)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def calendar_empty():
+    columns = {'service_id',
+               'monday',
+               'tuesday',
+               'wednesday',
+               'thursday',
+               'friday',
+               'saturday',
+               'sunday',
+               'start_date',
+               'end_date'}
+
+    df = pd.DataFrame(columns=columns)
+    return df
+
+
+@pytest.fixture
+def calendar_dates_feed_1():
+    data = {
+        'service_id': ['weekday-1',
+                       'weekday-2',
+                       'weekday-3',
+                       'weekend-1'],
+        'date': [20161224, 20170318, 20160424, 20161230],
+        'exception_type': [1, 2, 1, 1]}
+
+    index = range(4)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def calendar_dates_feed_2():
+    data = {
+        'service_id': ['weekday-1',
+                       'weekday-2',
+                       'weekday-3',
+                       'weekend-1'],
+        'date': [20161224, 20170318, 20160424, 20161230],
+        'exception_type': [1, 2, 1, 1],
+        'schedule_type': ['WD', 'WD', 'WD', 'SA']
+    }
+
+    index = range(4)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def calendar_dates_feed_4():
+    data = {
+        'service_id': ['wk-1'],
+        'date': [20161224],
+        'exception_type': [1]}
+
+    index = range(1)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def stop_times_feed_1():
+    data = {
+        'trip_id': ['a1', 'a1', 'a1', 'a1', 'a1', 'a1',
+                    'a2', 'a2', 'a2', 'a2', 'a2', 'a2',
+                    'a3', 'a3', 'a3', 'a3', 'a3', 'a3',
+                    'a4', 'a4', 'a4', 'a4', 'a4', 'a4',
+                    'b1', 'b1', 'b1', 'b1', 'b1', 'b1',
+                    'b2', 'b2', 'b2', 'b2', 'b2', 'b2',
+                    'c1', 'c1', 'c1', 'c1', 'c1', 'c1',
+                    'c2', 'c2', 'c2', 'c2', 'c2', 'c2',
+                    'd1', 'd1', 'd1',
+                    'd2', 'd2', 'd2'],
+        'stop_id': ['1', '2', '3', '4', '5', '6',
+                    '6', '5', '4', '3', '2', '1',
+                    '1', '2', '3', '4', '5', '6',
+                    '6', '5', '4', '3', '2', '1',
+                    '1', '2', '3', '4', '5', '6',
+                    '6', '5', '4', '3', '2', '1',
+                    '1', '2', '3', '4', '5', '6',
+                    '6', '5', '4', '3', '2', '1',
+                    '7', '8', '9',
+                    '9', '8', '7'],
+        'arrival_time': ['06:15:00', '06:20:00', np.nan, np.nan, '06:35:00',
+                         '06:40:00',
+                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                         np.nan, '06:40:00',
+                         '08:15:00', '08:20:00', '08:25:00', '08:30:00',
+                         '08:35:00', '08:40:00',
+                         '13:15:00', '13:20:00', '13:25:00', '13:30:00',
+                         '13:35:00', '13:40:00',
+                         '06:15:00', '06:20:00', np.nan, np.nan, '06:35:00',
+                         '06:40:00',
+                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                         np.nan, '06:40:00',
+                         '26:15:00', '26:20:00', np.nan, np.nan, '26:35:00',
+                         '26:40:00',
+                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                         np.nan, '06:40:00',
+                         '06:15:00', '06:20:00', '06:25:00',
+                         '06:15:00', '06:20:00', '06:25:00'],
+        'departure_time': ['06:15:00', '06:20:00', np.nan, np.nan,
+                           '06:35:00', '06:40:00',
+                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                           np.nan, '06:40:00',
+                           '08:15:00', '08:20:00', '08:25:00', '08:30:00',
+                           '08:35:00', '08:40:00',
+                           '13:15:00', '13:20:00', '13:25:00', '13:30:00',
+                           '13:35:00', '13:40:00',
+                           '06:15:00', '06:20:00', np.nan, np.nan,
+                           '06:35:00', '06:40:00',
+                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                           np.nan, '06:40:00',
+                           '26:15:00', '26:20:00', np.nan, np.nan,
+                           '26:35:00', '26:40:00',
+                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                           np.nan, '06:40:00',
+                           '06:15:00', '06:20:00', '06:25:00',
+                           '06:15:00', '06:20:00', '06:25:00'],
+        'stop_sequence': [1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3,
+                          1, 2, 3],
+        'pickup_type': [0, 0, 0, 0, 0, 0,
+                        0, 0, 0, 0, 0, 0,
+                        1, 1, 1, 1, 1, 1,
+                        0, 0, 0, 0, 0, 0,
+                        0, 0, 0, 0, 0, 0,
+                        0, 0, 0, 0, 0, 0,
+                        0, 0, 0, 0, 0, 0,
+                        0, 0, 0, 0, 0, 0,
+                        0, 0, 0,
+                        0, 0, 0],
+        'drop_off_type': [0, 0, 0, 0, 0, 0,
+                          0, 0, 0, 0, 0, 0,
+                          1, 1, 1, 1, 1, 1,
+                          0, 0, 0, 0, 0, 0,
+                          0, 0, 0, 0, 0, 0,
+                          0, 0, 0, 0, 0, 0,
+                          0, 0, 0, 0, 0, 0,
+                          0, 0, 0, 0, 0, 0,
+                          0, 0, 0,
+                          0, 0, 0]
+    }
+    index = range(54)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def stop_times_feed_2():
+    data = {
+        'trip_id': ['11', '11', '11', '11', '11', '11',
+                    '12', '12', '12', '12', '12', '12',
+                    '13', '13', '13', '13', '13', '13',
+                    '14', '14', '14', '14', '14', '14',
+                    '21', '21', '21', '21', '21', '21',
+                    '22', '22', '22', '22', '22', '22',
+                    '31', '31', '31', '31', '31', '31',
+                    '32', '32', '32', '32', '32', '32',
+                    '41', '41', '41',
+                    '42', '42', '42'],
+        'stop_id': ['60', '61', '62', '63', '64', '65',
+                    '65', '64', '63', '62', '61', '60',
+                    '60', '61', '62', '63', '64', '65',
+                    '65', '64', '63', '62', '61', '60',
+                    '60', '61', '62', '63', '64', '65',
+                    '65', '64', '63', '62', '61', '60',
+                    '600', '601', '602', '603', '604', '605',
+                    '606', '605', '604', '603', '602', '601',
+                    '66', '67', '68',
+                    '68', '67', '66'],
+        'arrival_time': ['06:15:00', '06:20:00', np.nan, np.nan, '06:35:00',
+                         '06:40:00',
+                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                         np.nan, '06:40:00',
+                         '08:15:00', '08:20:00', '08:25:00', '08:30:00',
+                         '08:35:00', '08:40:00',
+                         '13:15:00', '13:20:00', '13:25:00', '13:30:00',
+                         '13:35:00', '13:40:00',
+                         '06:15:00', '06:20:00', np.nan, np.nan, '06:35:00',
+                         '06:40:00',
+                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                         np.nan, '06:40:00',
+                         '26:15:00', '26:20:00', np.nan, np.nan, '26:35:00',
+                         '26:40:00',
+                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                         np.nan, '06:40:00',
+                         '06:15:00', '06:20:00', '06:25:00',
+                         '06:15:00', '06:20:00', '06:25:00'],
+        'departure_time': ['06:15:00', '06:20:00', np.nan, np.nan,
+                           '06:35:00', '06:40:00',
+                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                           np.nan, '06:40:00',
+                           '08:15:00', '08:20:00', '08:25:00', '08:30:00',
+                           '08:35:00', '08:40:00',
+                           '13:15:00', '13:20:00', '13:25:00', '13:30:00',
+                           '13:35:00', '13:40:00',
+                           '06:15:00', '06:20:00', np.nan, np.nan,
+                           '06:35:00', '06:40:00',
+                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                           np.nan, '06:40:00',
+                           '26:15:00', '26:20:00', np.nan, np.nan,
+                           '26:35:00', '26:40:00',
+                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                           np.nan, '06:40:00',
+                           '06:15:00', '06:20:00', '06:25:00',
+                           '06:15:00', '06:20:00', '06:25:00'],
+        'stop_sequence': [1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3,
+                          1, 2, 3]
+    }
+    index = range(54)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture
+def stop_times_feed_4():
+    data = {
+        'trip_id': ['a131', 'a131', 'a131', 'a131', 'a131', 'a131',
+                    'a132', 'a132', 'a132', 'a132', 'a132', 'a132',
+                    'a133', 'a133', 'a133', 'a133', 'a133', 'a133',
+                    'a134', 'a134', 'a134', 'a134', 'a134', 'a134',
+                    'a135', 'a135', 'a135', 'a135', 'a135', 'a135',
+                    'a136', 'a136', 'a136', 'a136', 'a136', 'a136',
+                    'a237', 'a237', 'a237', 'a237', 'a237', 'a237',
+                    'a238', 'a238', 'a238', 'a238', 'a238', 'a238',
+                    'a239', 'a239', 'a239',
+                    'a240', 'a240', 'a240',
+                    'a341', 'a341', 'a341',
+                    'a342', 'a342', 'a342',
+                    'a343', 'a343', 'a343',
+                    'a344', 'a344', 'a344'],
+        'stop_id': ['70', '71', '72', '73', '74', '75',
+                    '75', '74', '73', '72', '71', '70',
+                    '70', '71', '72', '73', '74', '75',
+                    '75', '74', '73', '72', '71', '70',
+                    '70', '71', '72', '73', '74', '75',
+                    '75', '74', '73', '72', '71', '70',
+                    '70', '71', '72', '73', '74', '75',
+                    '75', '74', '73', '72', '71', '70',
+                    '76', '77', '78',
+                    '78', '77', '76',
+                    '76', '77', '78',
+                    '78', '77', '76',
+                    '76', '77', '78',
+                    '78', '77', '76'],
+
+        'arrival_time': ['06:15:00', '06:20:00', np.nan, np.nan, '06:35:00',
+                         '06:40:00',
+                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                         np.nan, '06:40:00',
+                         '08:15:00', '08:20:00', '08:25:00', '08:30:00',
+                         '08:35:00', '08:40:00',
+                         '13:15:00', '13:20:00', '13:25:00', '13:30:00',
+                         '13:35:00', '13:40:00',
+                         '06:15:00', '06:20:00', np.nan, np.nan, '06:35:00',
+                         '06:40:00',
+                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                         np.nan, '06:40:00',
+                         '26:15:00', '26:20:00', np.nan, np.nan, '26:35:00',
+                         '26:40:00',
+                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                         np.nan, '06:40:00',
+                         '06:15:00', '06:20:00', '06:25:00',
+                         '06:15:00', '06:20:00', '06:25:00',
+                         '06:15:00', '06:20:00', '06:25:00',
+                         '06:15:00', '06:20:00', '06:25:00',
+                         '06:15:00', '06:20:00', '06:25:00',
+                         '06:15:00', '06:20:00', '06:25:00'],
+        'departure_time': ['06:15:00', '06:20:00', np.nan, np.nan,
+                           '06:35:00', '06:40:00',
+                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                           np.nan, '06:40:00',
+                           '08:15:00', '08:20:00', '08:25:00', '08:30:00',
+                           '08:35:00', '08:40:00',
+                           '13:15:00', '13:20:00', '13:25:00', '13:30:00',
+                           '13:35:00', '13:40:00',
+                           '06:15:00', '06:20:00', np.nan, np.nan,
+                           '06:35:00', '06:40:00',
+                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                           np.nan, '06:40:00',
+                           '26:15:00', '26:20:00', np.nan, np.nan,
+                           '26:35:00', '26:40:00',
+                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
+                           np.nan, '06:40:00',
+                           '06:15:00', '06:20:00', '06:25:00',
+                           '06:15:00', '06:20:00', '06:25:00',
+                           '06:15:00', '06:20:00', '06:25:00',
+                           '06:15:00', '06:20:00', '06:25:00',
+                           '06:15:00', '06:20:00', '06:25:00',
+                           '06:15:00', '06:20:00', '06:25:00'],
+        'stop_sequence': [1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3, 4, 5, 6,
+                          1, 2, 3,
+                          1, 2, 3,
+                          1, 2, 3,
+                          1, 2, 3,
+                          1, 2, 3,
+                          1, 2, 3]
+    }
+    index = range(66)
+
+    df = pd.DataFrame(data, index)
+    return df
+
+
+@pytest.fixture()
+def agency_a_feed_on_disk_wo_calendar_dates(
+        tmpdir,
+        agency_feed_1, stop_times_feed_1, stops_feed_1,
+        routes_feed_1, trips_feed_1, calendar_feed_1):
+    feed_file_dict = {'agency': agency_feed_1,
+                      'stop_times': stop_times_feed_1,
+                      'stops': stops_feed_1,
+                      'routes': routes_feed_1,
+                      'trips': trips_feed_1,
+                      'calendar': calendar_feed_1}
+    feed_path = os.path.join(tmpdir.strpath, 'agency_a_wo_calendar_dates')
+    os.makedirs(feed_path)
+    print('writing test data to dir: {}'.format(feed_path))
+    for feed_file, feed_df in feed_file_dict.items():
+        feed_file_name = '{}.txt'.format(feed_file)
+        feed_df.to_csv(os.path.join(feed_path, feed_file_name), index=False)
+    return feed_path
+
+
+@pytest.fixture()
+def agency_a_feed_on_disk_wo_calendar(
+        tmpdir,
+        agency_feed_1, stop_times_feed_1, stops_feed_1,
+        routes_feed_1, trips_feed_1, calendar_dates_feed_1):
+    feed_file_dict = {'agency': agency_feed_1,
+                      'stop_times': stop_times_feed_1,
+                      'stops': stops_feed_1,
+                      'routes': routes_feed_1,
+                      'trips': trips_feed_1,
+                      'calendar_dates': calendar_dates_feed_1}
+    feed_path = os.path.join(tmpdir.strpath, 'agency_a_wo_calendar')
+    os.makedirs(feed_path)
+    print('writing test data to dir: {}'.format(feed_path))
+    for feed_file, feed_df in feed_file_dict.items():
+        feed_file_name = '{}.txt'.format(feed_file)
+        feed_df.to_csv(os.path.join(feed_path, feed_file_name), index=False)
+    return feed_path
+
+
+@pytest.fixture()
+def agency_a_feed_on_disk_w_calendar_and_calendar_dates(
+        tmpdir,
+        agency_feed_1, stop_times_feed_1, stops_feed_1,
+        routes_feed_1, trips_feed_1, calendar_feed_1, calendar_dates_feed_1):
+    feed_file_dict = {'agency': agency_feed_1,
+                      'stop_times': stop_times_feed_1,
+                      'stops': stops_feed_1,
+                      'routes': routes_feed_1,
+                      'trips': trips_feed_1,
+                      'calendar': calendar_feed_1,
+                      'calendar_dates': calendar_dates_feed_1}
+    feed_path = os.path.join(tmpdir.strpath, 'agency_a_w_both_calendars')
+    os.makedirs(feed_path)
+    print('writing test data to dir: {}'.format(feed_path))
+    for feed_file, feed_df in feed_file_dict.items():
+        feed_file_name = '{}.txt'.format(feed_file)
+        feed_df.to_csv(os.path.join(feed_path, feed_file_name), index=False)
+    return feed_path
+
+
+@pytest.fixture()
+def agency_a_feed_on_disk_wo_calendar_and_calendar_dates(
+        tmpdir,
+        agency_feed_1, stop_times_feed_1, stops_feed_1,
+        routes_feed_1, trips_feed_1):
+    feed_file_dict = {'agency': agency_feed_1,
+                      'stop_times': stop_times_feed_1,
+                      'stops': stops_feed_1,
+                      'routes': routes_feed_1,
+                      'trips': trips_feed_1}
+    feed_path = os.path.join(tmpdir.strpath, 'agency_a_wo_both_calendar')
+    os.makedirs(feed_path)
+    print('writing test data to dir: {}'.format(feed_path))
+    for feed_file, feed_df in feed_file_dict.items():
+        feed_file_name = '{}.txt'.format(feed_file)
+        feed_df.to_csv(os.path.join(feed_path, feed_file_name), index=False)
+    return feed_path
+
+
+@pytest.fixture()
+def agency_a_feed_on_disk_wo_req_file(
+        tmpdir,
+        agency_feed_1, stop_times_feed_1, stops_feed_1,
+        routes_feed_1, calendar_feed_1):
+    feed_file_dict = {'agency': agency_feed_1,
+                      'stop_times': stop_times_feed_1,
+                      'stops': stops_feed_1,
+                      'routes': routes_feed_1,
+                      'calendar': calendar_feed_1}
+    feed_path = os.path.join(tmpdir.strpath, 'agency_a_wo_req_file')
+    os.makedirs(feed_path)
+    print('writing test data to dir: {}'.format(feed_path))
+    for feed_file, feed_df in feed_file_dict.items():
+        feed_file_name = '{}.txt'.format(feed_file)
+        feed_df.to_csv(os.path.join(feed_path, feed_file_name), index=False)
+    return feed_path
+
+
+@pytest.fixture()
+def agency_a_feed_on_disk_wo_agency(
+        tmpdir,
+        agency_feed_1, stop_times_feed_1, stops_feed_1,
+        routes_feed_1, trips_feed_1, calendar_feed_1):
+    feed_file_dict = {'stop_times': stop_times_feed_1,
+                      'stops': stops_feed_1,
+                      'routes': routes_feed_1,
+                      'trips': trips_feed_1,
+                      'calendar': calendar_feed_1}
+    feed_path = os.path.join(tmpdir.strpath, 'agency_a_wo_agency')
+    os.makedirs(feed_path)
+    print('writing test data to dir: {}'.format(feed_path))
+    for feed_file, feed_df in feed_file_dict.items():
+        feed_file_name = '{}.txt'.format(feed_file)
+        feed_df.to_csv(os.path.join(feed_path, feed_file_name), index=False)
+    return feed_path
diff --git a/urbanaccess/tests/integration/integration_sandiego.py b/urbanaccess/tests/integration/integration_sandiego.py
index e6459a1..e3fd242 100644
--- a/urbanaccess/tests/integration/integration_sandiego.py
+++ b/urbanaccess/tests/integration/integration_sandiego.py
@@ -1,9 +1,12 @@
 import os
 import time
-import pandas as pd
 
 import matplotlib
+
 matplotlib.use('agg')
+import matplotlib.pyplot as plt
+
+import cartopy.crs as ccrs
 
 import urbanaccess
 
@@ -22,19 +25,6 @@
 
 urbanaccess.gtfsfeeds.download(data_folder=root_path)
 
-# create dummy calendar.txt file because
-dummy_txt_file = os.path.join(root_path,
-                              'gtfsfeed_text',
-                              'MTS',
-                              'calendar.txt')
-
-data = {'service_id': -99, 'monday': 0, 'tuesday': 0, 'wednesday': 0,
-        'thursday': 0, 'friday': 0, 'saturday': 0, 'sunday': 0}
-
-index = range(1)
-
-pd.DataFrame(data, index).to_csv(dummy_txt_file, index=False)
-
 validation = True
 verbose = True
 # small bbox for testing purposes
@@ -56,6 +46,13 @@
                            'schedule_type': 'WD'},
     timerange=['07:00:00', '10:00:00'])
 
+# This is the standard map projection for California
+teale_albers = ccrs.AlbersEqualArea(
+    false_northing=-4000000.0, false_easting=0,
+    central_longitude=-120.0, central_latitude=0,
+    standard_parallels=(34.0, 40.5))
+teale_albers_ax = plt.axes(projection=teale_albers)
+
 urbanaccess.plot.plot_net(nodes=transit_net.transit_nodes,
                           edges=transit_net.transit_edges,
                           bbox=bbox,
@@ -68,7 +65,8 @@
                           node_alpha=1,
                           node_edgecolor='none',
                           node_zorder=3,
-                          nodes_only=False)
+                          nodes_only=False,
+                          ax=teale_albers_ax)
 
 print('{} integration test completed successfully. Took {:,'
       '.2f} seconds'.format(name, time.time() - start_time))
diff --git a/urbanaccess/tests/test_gtfs_load.py b/urbanaccess/tests/test_gtfs_load.py
new file mode 100644
index 0000000..f813f27
--- /dev/null
+++ b/urbanaccess/tests/test_gtfs_load.py
@@ -0,0 +1,233 @@
+# coding=utf-8
+import pytest
+import pandas as pd
+import os
+import six
+import codecs
+import sys
+
+import urbanaccess.gtfs.load as gtfs_load
+from urbanaccess.gtfs.gtfsfeeds_dataframe import urbanaccess_gtfs_df
+
+
+@pytest.fixture
+def expected_urbanaccess_gtfs_df_keys():
+    expected_keys = ['stops', 'routes', 'trips', 'stop_times',
+                     'calendar', 'calendar_dates', 'stop_times_int',
+                     'headways']
+    return expected_keys.sort()
+
+
+@pytest.fixture
+def test_txt_files(tmpdir):
+    # test file that does not need to be fixed
+    do_not_fix_txt = os.path.join(tmpdir.strpath, 'agency.txt')
+    data = ['name,text\n', '  Circulação  , áéíóúüñ¿¡ \n']
+    if six.PY2:
+        with open(do_not_fix_txt, 'w') as f:
+            f.writelines(data)
+    else:
+        with open(do_not_fix_txt, 'w', encoding='utf-8') as f:
+            f.writelines(data)
+
+    # test file that does need to be fixed
+    fix_txt = os.path.join(tmpdir.strpath, 'calendar.txt')
+    data = ['  name  , text \n', '  Circulação  , áéíóúüñ¿¡ \n']
+    if six.PY2:
+        with open(fix_txt, 'w') as f:
+            f.writelines(data)
+    else:
+        with open(fix_txt, 'w', encoding='utf-8') as f:
+            f.writelines(data)
+
+    fix_txt_wBOM = os.path.join(tmpdir.strpath, 'calendar_dates.txt')
+    if six.PY2:
+        data = [codecs.BOM_UTF8,
+                '  name  , text \n',
+                '  Circulação  , áéíóúüñ¿¡ \n']
+        with open(fix_txt_wBOM, 'w') as f:
+            f.writelines(data)
+    else:
+        data = [str(codecs.BOM_UTF8),
+                '  name  , text \n',
+                '  Circulação  , áéíóúüñ¿¡ \n']
+        with open(fix_txt_wBOM, 'w', encoding='utf-8') as f:
+            f.writelines(data)
+
+    return tmpdir.strpath, do_not_fix_txt, fix_txt, fix_txt_wBOM
+
+
+@pytest.fixture
+def test_txt_files_to_use():
+    gtfsfiles_to_use = ['stops.txt', 'routes.txt', 'trips.txt',
+                        'stop_times.txt', 'calendar.txt',
+                        'agency.txt', 'calendar_dates.txt']
+    return gtfsfiles_to_use
+
+
+def test_txt_standardization(test_txt_files):
+    root_dir, do_not_fix_txt, fix_txt, fix_txt_wBOM = test_txt_files
+
+    gtfs_load._standardize_txt(csv_rootpath=root_dir)
+
+    df = pd.read_csv(fix_txt)
+    assert list(df.columns) == list(df.columns.str.strip())
+
+    df = pd.read_csv(fix_txt_wBOM)
+    assert list(df.columns) == list(df.columns.str.strip())
+
+
+def test_txt_header_whitespace_check(test_txt_files, test_txt_files_to_use):
+    root_dir, do_not_fix_txt, fix_txt, fix_txt_wBOM = test_txt_files
+
+    gtfs_load._txt_header_whitespace_check(
+        gtfsfiles_to_use=test_txt_files_to_use,
+        csv_rootpath=root_dir)
+
+    # only check 'fix_txt' as 'fix_txt_wBOM' would need to be
+    # fixed by _txt_encoder_check first
+    df = pd.read_csv(fix_txt)
+    assert list(df.columns) == list(df.columns.str.strip())
+
+
+@pytest.mark.skipif(
+    sys.version_info >= (3, 0), reason="requires python < 3.0")
+def test_txt_encoder_check(test_txt_files, test_txt_files_to_use):
+    root_dir, do_not_fix_txt, fix_txt, fix_txt_wBOM = test_txt_files
+
+    gtfs_load._txt_encoder_check(
+        gtfsfiles_to_use=test_txt_files_to_use,
+        csv_rootpath=root_dir)
+
+    with open(fix_txt_wBOM, 'r') as f:
+        raw = f.read()
+    assert raw.startswith(codecs.BOM_UTF8) is False
+
+
+def test_loadgtfsfeed_to_df_wo_calendar(
+        agency_a_feed_on_disk_wo_calendar,
+        expected_urbanaccess_gtfs_df_keys):
+    feed_dir = agency_a_feed_on_disk_wo_calendar
+    loaded_feeds = gtfs_load.gtfsfeed_to_df(
+        gtfsfeed_path=feed_dir,
+        validation=False,
+        verbose=True,
+        bbox=None,
+        remove_stops_outsidebbox=False,
+        append_definitions=False)
+    assert isinstance(loaded_feeds, urbanaccess_gtfs_df)
+    urbanaccess_gtfs_df_info = vars(loaded_feeds)
+    expected_dfs = ['stops', 'routes', 'trips', 'stop_times',
+                    'calendar_dates']
+    assert expected_urbanaccess_gtfs_df_keys == list(
+        urbanaccess_gtfs_df_info.keys()).sort()
+    for key, value in urbanaccess_gtfs_df_info.items():
+        assert isinstance(value, pd.core.frame.DataFrame)
+        # check that df is not empty
+        if key in expected_dfs:
+            assert value.empty is False
+
+
+def test_loadgtfsfeed_to_df_wo_calendar_dates(
+        agency_a_feed_on_disk_wo_calendar_dates,
+        expected_urbanaccess_gtfs_df_keys):
+    feed_dir = agency_a_feed_on_disk_wo_calendar_dates
+    loaded_feeds = gtfs_load.gtfsfeed_to_df(
+        gtfsfeed_path=feed_dir,
+        validation=False,
+        verbose=True,
+        bbox=None,
+        remove_stops_outsidebbox=False,
+        append_definitions=False)
+    assert isinstance(loaded_feeds, urbanaccess_gtfs_df)
+    urbanaccess_gtfs_df_info = vars(loaded_feeds)
+    expected_dfs = ['stops', 'routes', 'trips', 'stop_times',
+                    'calendar']
+    assert expected_urbanaccess_gtfs_df_keys == list(
+        urbanaccess_gtfs_df_info.keys()).sort()
+    for key, value in urbanaccess_gtfs_df_info.items():
+        assert isinstance(value, pd.core.frame.DataFrame)
+        # check that df is not empty
+        if key in expected_dfs:
+            assert value.empty is False
+
+
+def test_loadgtfsfeed_to_df_w_calendar_and_calendar_dates(
+        agency_a_feed_on_disk_w_calendar_and_calendar_dates,
+        expected_urbanaccess_gtfs_df_keys):
+    feed_dir = agency_a_feed_on_disk_w_calendar_and_calendar_dates
+    loaded_feeds = gtfs_load.gtfsfeed_to_df(
+        gtfsfeed_path=feed_dir,
+        validation=False,
+        verbose=True,
+        bbox=None,
+        remove_stops_outsidebbox=False,
+        append_definitions=False)
+    assert isinstance(loaded_feeds, urbanaccess_gtfs_df)
+    urbanaccess_gtfs_df_info = vars(loaded_feeds)
+    expected_dfs = ['stops', 'routes', 'trips', 'stop_times',
+                    'calendar', 'calendar_dates']
+    assert expected_urbanaccess_gtfs_df_keys == list(
+        urbanaccess_gtfs_df_info.keys()).sort()
+    for key, value in urbanaccess_gtfs_df_info.items():
+        assert isinstance(value, pd.core.frame.DataFrame)
+        # check that df is not empty
+        if key in expected_dfs:
+            assert value.empty is False
+
+
+def test_loadgtfsfeed_to_df_wo_calendar_and_calendar_dates(
+        agency_a_feed_on_disk_wo_calendar_and_calendar_dates):
+    feed_dir = agency_a_feed_on_disk_wo_calendar_and_calendar_dates
+    with pytest.raises(ValueError) as excinfo:
+        loaded_feeds = gtfs_load.gtfsfeed_to_df(
+            gtfsfeed_path=feed_dir,
+            validation=False,
+            verbose=True,
+            bbox=None,
+            remove_stops_outsidebbox=False,
+            append_definitions=False)
+    expected_error = (
+        "at least one of `calendar.txt` or `calendar_dates.txt` is required "
+        "to complete a GTFS dataset but neither was found in folder")
+    assert expected_error in str(excinfo.value)
+
+
+def test_loadgtfsfeed_to_df_wo_req_file(
+        agency_a_feed_on_disk_wo_req_file):
+    feed_dir = agency_a_feed_on_disk_wo_req_file
+    with pytest.raises(ValueError) as excinfo:
+        loaded_feeds = gtfs_load.gtfsfeed_to_df(
+            gtfsfeed_path=feed_dir,
+            validation=False,
+            verbose=True,
+            bbox=None,
+            remove_stops_outsidebbox=False,
+            append_definitions=False)
+    expected_error = (
+        "trips.txt is a required GTFS text file and was not found in folder")
+    assert expected_error in str(excinfo.value)
+
+
+def test_loadgtfsfeed_to_df_wo_agency(
+        agency_a_feed_on_disk_wo_agency,
+        expected_urbanaccess_gtfs_df_keys):
+    feed_dir = agency_a_feed_on_disk_wo_agency
+    loaded_feeds = gtfs_load.gtfsfeed_to_df(
+        gtfsfeed_path=feed_dir,
+        validation=False,
+        verbose=True,
+        bbox=None,
+        remove_stops_outsidebbox=False,
+        append_definitions=False)
+    assert isinstance(loaded_feeds, urbanaccess_gtfs_df)
+    urbanaccess_gtfs_df_info = vars(loaded_feeds)
+    expected_dfs = ['stops', 'routes', 'trips', 'stop_times',
+                    'calendar']
+    assert expected_urbanaccess_gtfs_df_keys == list(
+        urbanaccess_gtfs_df_info.keys()).sort()
+    for key, value in urbanaccess_gtfs_df_info.items():
+        assert isinstance(value, pd.core.frame.DataFrame)
+        # check that df is not empty
+        if key in expected_dfs:
+            assert value.empty is False
diff --git a/urbanaccess/tests/test_gtfs_network.py b/urbanaccess/tests/test_gtfs_network.py
index 4bb7f71..01de1c8 100644
--- a/urbanaccess/tests/test_gtfs_network.py
+++ b/urbanaccess/tests/test_gtfs_network.py
@@ -1,7 +1,31 @@
 import pytest
 import pandas as pd
 import numpy as np
-from urbanaccess.gtfs import network
+
+import urbanaccess.gtfs.network as gtfs_network
+import urbanaccess.gtfs.load as gtfs_load
+from urbanaccess.network import urbanaccess_network
+
+
+@pytest.fixture
+def expected_urbanaccess_network_keys():
+    expected_keys = ['transit_nodes', 'transit_edges', 'net_connector_edges',
+                     'osm_nodes', 'osm_edges', 'net_nodes', 'net_edges']
+    return expected_keys.sort()
+
+
+@pytest.fixture
+def gtfs_feed_wo_calendar_dates(
+        tmpdir, agency_a_feed_on_disk_wo_calendar_dates):
+    feed_dir = agency_a_feed_on_disk_wo_calendar_dates
+    loaded_feeds = gtfs_load.gtfsfeed_to_df(
+        gtfsfeed_path=feed_dir,
+        validation=False,
+        verbose=True,
+        bbox=None,
+        remove_stops_outsidebbox=False,
+        append_definitions=False)
+    return loaded_feeds
 
 
 @pytest.fixture
@@ -81,8 +105,73 @@ def stop_times_interpolated():
     return df
 
 
+def test_create_transit_net_wo_calendar_dates(
+        tmpdir, gtfs_feed_wo_calendar_dates,
+        expected_urbanaccess_network_keys):
+    transit_net = gtfs_network.create_transit_net(
+        gtfs_feed_wo_calendar_dates, day='monday',
+        timerange=['07:00:00', '10:00:00'],
+        calendar_dates_lookup=None,
+        overwrite_existing_stop_times_int=False,
+        use_existing_stop_times_int=False,
+        save_processed_gtfs=False,
+        save_dir=tmpdir,
+        save_filename=None)
+    assert isinstance(transit_net, urbanaccess_network)
+    urbanaccess_network_info = vars(transit_net)
+    expected_dfs = ['transit_nodes', 'transit_edges']
+    assert expected_urbanaccess_network_keys == list(
+        urbanaccess_network_info.keys()).sort()
+    for key, value in urbanaccess_network_info.items():
+        assert isinstance(value, pd.core.frame.DataFrame)
+        # check that df is not empty
+        if key in expected_dfs:
+            assert value.empty is False
+
+
+def test_create_transit_net_wo_req_file(
+        tmpdir, gtfs_feed_wo_calendar_dates):
+    # set trips df to blank df for test
+    gtfs_feed_wo_calendar_dates.trips = pd.DataFrame()
+    with pytest.raises(ValueError) as excinfo:
+        transit_net = gtfs_network.create_transit_net(
+            gtfs_feed_wo_calendar_dates, day='monday',
+            timerange=['07:00:00', '10:00:00'],
+            calendar_dates_lookup=None,
+            overwrite_existing_stop_times_int=False,
+            use_existing_stop_times_int=False,
+            save_processed_gtfs=False,
+            save_dir=tmpdir,
+            save_filename=None)
+    expected_error = (
+        "one of the following gtfsfeeds_dfs objects trips, stops, "
+        "or stop_times were found to be empty.")
+    assert expected_error in str(excinfo.value)
+
+
+def test_create_transit_net_wo_calendar_and_calendar_dates(
+        tmpdir, gtfs_feed_wo_calendar_dates):
+    # set calendar_dates and calendar dfs to blank df for test
+    gtfs_feed_wo_calendar_dates.calendar_dates = pd.DataFrame()
+    gtfs_feed_wo_calendar_dates.calendar = pd.DataFrame()
+    with pytest.raises(ValueError) as excinfo:
+        transit_net = gtfs_network.create_transit_net(
+            gtfs_feed_wo_calendar_dates, day='monday',
+            timerange=['07:00:00', '10:00:00'],
+            calendar_dates_lookup=None,
+            overwrite_existing_stop_times_int=False,
+            use_existing_stop_times_int=False,
+            save_processed_gtfs=False,
+            save_dir=tmpdir,
+            save_filename=None)
+    expected_error = (
+        "one of the following gtfsfeeds_dfs objects calendar or "
+        "calendar_dates were found to be empty.")
+    assert expected_error in str(excinfo.value)
+
+
 def test_interpolator(stop_times, calendar):
-    df = network._interpolate_stop_times(stop_times, calendar, day='monday')
+    df = gtfs_network._interpolate_stop_times(stop_times, calendar)
 
     # unique_trip_id should be generated
     assert df.loc[1, 'unique_trip_id'] == 'a_citytrains'
@@ -121,7 +210,7 @@ def test_skip_interpolator(stop_times, calendar):
 
     stop_times['departure_time_sec'] = series
 
-    df = network._interpolate_stop_times(stop_times, calendar, day='monday')
+    df = gtfs_network._interpolate_stop_times(stop_times, calendar)
 
     # everything should be the same,
     # with one row dropped for calendar day filter
@@ -132,7 +221,7 @@ def test_skip_interpolator(stop_times, calendar):
 
 
 def test_edge_reformatter(stop_times_interpolated):
-    df = network._format_transit_net_edge(stop_times_interpolated)
+    df = gtfs_network._format_transit_net_edge(stop_times_interpolated)
 
     # length of edge df should be 16
     assert len(df) == 16
diff --git a/urbanaccess/tests/test_gtfs_utils_format.py b/urbanaccess/tests/test_gtfs_utils_format.py
index 659b872..4857c9f 100644
--- a/urbanaccess/tests/test_gtfs_utils_format.py
+++ b/urbanaccess/tests/test_gtfs_utils_format.py
@@ -1,689 +1,11 @@
 import pytest
 import pandas as pd
-import numpy as np
 import os
 from re import sub
 
 from urbanaccess.gtfs import utils_format
 
 
-@pytest.fixture
-def agency_feed_1():
-    data = {
-        'agency_id': 'agency a',
-        'agency_name': 'agency a city a',
-        'agency_url': 'http://www.agency_a.org',
-        'agency_timezone': 'America/Los_Angeles',
-        'agency_phone': '(000) 000-0000'
-    }
-    index = range(1)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def agency_feed_2():
-    data = {
-        'agency_id': ['agency b bus', 'agency b rail'],
-        'agency_name': ['agency b district 1', 'agency b district 2'],
-        'agency_url': ['http://www.agency_b.org', 'http://www.agency_b.org'],
-        'agency_timezone': ['America/Los_Angeles', 'America/Los_Angeles'],
-        'agency_phone': ['(000) 000-0000', '(000) 000-0000']
-    }
-    index = range(2)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def agency_feed_3():
-    data = {
-        'agency_id': '',
-        'agency_name': 'agency c',
-        'agency_url': 'http://www.agency_c.org',
-        'agency_timezone': 'America/Los_Angeles',
-        'agency_phone': '(000) 000-0000'
-    }
-    index = range(1)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def agency_feed_4():
-    data = {
-        'agency_id': ['agency 1', 'agency 2', 'agency 3'],
-        'agency_name': ['agency 1 bus', 'agency 2 rail', 'agency 3 metro'],
-        'agency_url': ['http://www.agency_1.org', 'http://www.agency_2.org',
-                       'http://www.agency_2.org'],
-        'agency_timezone': ['America/Los_Angeles', 'America/Los_Angeles',
-                            'America/Los_Angeles'],
-        'agency_phone': ['(000) 000-0000', '(000) 000-0000', '(000) 000-0000']
-    }
-    index = range(3)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def routes_feed_1():
-    data = {
-        'agency_id': ['agency a'] * 4,
-        'route_id': ['10-101', '11-101', '12-101', '13-101'],
-        'route_short_name': ['10', '11', 'red', 'blue'],
-        'route_long_name': ['ave a local', 'ave a express', 'red line',
-                            'blue line'],
-        'route_type': [3, 3, 1, 1]
-    }
-
-    index = range(4)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def routes_feed_2():
-    data = {
-        'agency_id': ['agency b bus', 'agency b bus', 'agency b rail',
-                      'agency b rail'],
-        'route_id': ['40-4', '40-4x', 'r-2', 'r-2ext'],
-        'route_short_name': ['40', '40', 'red', 'red-ext'],
-        'route_long_name': ['ave a local', 'ave a express', 'red line',
-                            'red line extension'],
-        'route_type': [3, 3, 1, 1]
-    }
-
-    index = range(4)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def routes_feed_4():
-    data = {
-        'agency_id': ['agency 1', 'agency 1', 'agency 2', 'agency 2',
-                      'agency 3', 'agency 3'],
-        'route_id': ['a1x', 'a1', 'a2x', 'a2', 'a3x', 'a3'],
-        'route_short_name': ['1x', '1', '2x', '2', '3x', '3'],
-        'route_long_name': ['1 express', '1 local', '2 express',
-                            '2 local', '3 express', '3 local'],
-        'route_type': [3, 3, 3, 3, 3, 3]
-    }
-
-    index = range(6)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def stops_feed_1():
-    data = {
-        'stop_id': ['1', '2', '3', '4', '5', '6',
-                    '7', '8', '9'],
-        'stop_name': ['ave a', 'ave b', 'ave c', 'ave d', 'ave e', 'ave f',
-                      '1st st', '2nd st', '3rd st'],
-        'stop_lat': [37.797484, 37.774963, 37.803664, 37.80787, 37.828415,
-                     37.844601, 37.664174, 37.591208, 37.905628],
-        'stop_lon': [-122.265609, -122.224274, -122.271604, -122.269029,
-                     -122.267227, -122.251793, -122.444116, -122.017867,
-                     -122.067423],
-        'location_type': [1, 1, 1, 1, 1, 1,
-                          2, 2, 2],
-        'wheelchair_boarding': [1, 0, 0, 0, 0, 0,
-                                1, 1, 1]
-    }
-
-    index = range(9)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def stops_feed_2():
-    data = {
-        'stop_id': ['60', '61', '62', '63', '64', '65',
-                    '66', '67', '68',
-                    '600', '601', '602', '603', '604', '605', '606'],
-        'stop_name': ['ave m', 'ave n', 'ave o', 'ave p', 'ave q', 'ave r',
-                      '10th st', '11th st', '12th st',
-                      '121th st', '122th st', '123th st', '124th st',
-                      '125th st', '126th st', '127th st'],
-        'stop_lat': [38.797484, 38.774963, 38.803664, 38.80787, 38.828415,
-                     38.844601, 38.664174, 38.591208, 38.905628,
-                     38.603664, 38.60787, 38.628415,
-                     38.644601, 38.660000, 38.691208, 38.605628],
-        'stop_lon': [-121.265609, -121.224274, -121.271604, -121.269029,
-                     -121.267227, -121.251793, -121.444116, -121.017867,
-                     -121.067423, -122.271604, -122.269029, -122.267227,
-                     -122.251793, -122.444116, -122.017867, -122.067423],
-        'location_type': [1, 1, 1, 1, 1, 1,
-                          2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
-        'wheelchair_boarding': [1, 0, 0, 0, 0, 0,
-                                1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
-    }
-
-    index = range(16)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def stops_feed_4():
-    data = {
-        'stop_id': ['70', '71', '72', '73', '74', '75',
-                    '76', '77', '78'],
-        'stop_name': ['station 1', 'station 2', 'station 3', 'station 4',
-                      'station 5', 'station 6',
-                      'station 7', 'station 8', 'station 9'],
-        'stop_lat': [20.797484, 20.774963, 20.803664, 20.80787, 20.828415,
-                     20.844601, 20.664174, 20.591208, 20.905628],
-        'stop_lon': [-100.265609, -100.224274, -100.271604, -100.269029,
-                     -100.267227, -100.251793, -100.444116, -100.017867,
-                     -100.067423]
-    }
-
-    index = range(9)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def trips_feed_1():
-    data = {
-        'route_id': ['10-101', '10-101', '10-101', '10-101',
-                     '11-101', '11-101',
-                     '12-101', '12-101',
-                     '13-101', '13-101'],
-        'trip_id': ['a1', 'a2', 'a3', 'a4',
-                    'b1', 'b2',
-                    'c1', 'c2',
-                    'd1', 'd2'],
-        'service_id': ['weekday-1', 'weekday-1', 'weekday-1', 'weekday-1',
-                       'weekday-2', 'weekday-2',
-                       'weekday-3', 'weekday-3',
-                       'weekend-1', 'weekend-1'],
-        'direction_id': [1, 0, 1, 0,
-                         1, 0,
-                         1, 0,
-                         1, 0],
-        'wheelchair_accessible': [1, 1, 1, 1,
-                                  0, 0,
-                                  0, 0,
-                                  0, 0],
-        'bikes_allowed': [1, 1, 1, 1,
-                          0, 0,
-                          0, 0,
-                          0, 0]
-    }
-
-    index = range(10)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def trips_feed_2():
-    data = {
-
-        'route_id': ['40-4', '40-4', '40-4', '40-4',
-                     '40-4x', '40-4x',
-                     'r-2', 'r-2',
-                     'r-2ext', 'r-2ext'],
-        'trip_id': ['11', '12', '13', '14',
-                    '21', '22',
-                    '31', '32',
-                    '41', '42'],
-        'service_id': ['weekday-1', 'weekday-1', 'weekday-1', 'weekday-1',
-                       'weekday-2', 'weekday-2',
-                       'weekday-3', 'weekday-3',
-                       'weekend-1', 'weekend-1']
-    }
-
-    index = range(10)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def trips_feed_4():
-    data = {
-        'route_id': ['a1x', 'a1x', 'a1x', 'a1x',
-                     'a1', 'a1',
-                     'a2x', 'a2x',
-                     'a2', 'a2',
-                     'a3x', 'a3x',
-                     'a3', 'a3'],
-        'trip_id': ['a131', 'a132', 'a133', 'a134',
-                    'a135', 'a136',
-                    'a237', 'a238',
-                    'a239', 'a240',
-                    'a341', 'a342',
-                    'a343', 'a344'],
-        'service_id': ['wk-1', 'wk-1', 'wk-1', 'wk-1',
-                       'wk-1', 'wk-1',
-                       'wk-1', 'wk-1',
-                       'wk-1', 'wk-1',
-                       'wk-1', 'wk-1',
-                       'wk-1', 'wk-1']
-    }
-
-    index = range(14)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def calendar_feed_1():
-    data = {
-        'service_id': ['weekday-1',
-                       'weekday-2',
-                       'weekday-3',
-                       'weekend-1'],
-        'monday': [1, 1, 1, 0],
-        'tuesday': [1, 1, 1, 0],
-        'wednesday': [1, 1, 1, 0],
-        'thursday': [1, 1, 1, 0],
-        'friday': [1, 1, 1, 0],
-        'saturday': [0, 0, 0, 1],
-        'sunday': [0, 0, 0, 1],
-        'start_date': [20161224] * 4,
-        'end_date': [20170318] * 4}
-
-    index = range(4)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def calendar_feed_2():
-    data = {
-        'service_id': ['weekday-1',
-                       'weekday-2',
-                       'weekday-3',
-                       'weekend-1'],
-        'monday': [1, 1, 1, 0],
-        'tuesday': [1, 1, 1, 0],
-        'wednesday': [1, 1, 1, 0],
-        'thursday': [1, 1, 1, 0],
-        'friday': [1, 1, 1, 0],
-        'saturday': [0, 0, 0, 1],
-        'sunday': [0, 0, 0, 1],
-        'start_date': [20161224] * 4,
-        'end_date': [20170318] * 4}
-
-    index = range(4)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def calendar_feed_4():
-    data = {
-        'service_id': ['wk-1'],
-        'monday': [1],
-        'tuesday': [1],
-        'wednesday': [1],
-        'thursday': [1],
-        'friday': [1],
-        'saturday': [0],
-        'sunday': [0],
-        'start_date': [20161224],
-        'end_date': [20170318]}
-
-    index = range(1)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def calendar_empty():
-    columns = {'service_id',
-               'monday',
-               'tuesday',
-               'wednesday',
-               'thursday',
-               'friday',
-               'saturday',
-               'sunday',
-               'start_date',
-               'end_date'}
-
-    df = pd.DataFrame(columns=columns)
-    return df
-
-
-@pytest.fixture
-def calendar_dates_feed_1():
-    data = {
-        'service_id': ['weekday-1',
-                       'weekday-2',
-                       'weekday-3',
-                       'weekend-1'],
-        'date': [20161224, 20170318, 20160424, 20161230],
-        'exception_type': [1, 2, 1, 1]}
-
-    index = range(4)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def calendar_dates_feed_2():
-    data = {
-        'service_id': ['weekday-1',
-                       'weekday-2',
-                       'weekday-3',
-                       'weekend-1'],
-        'date': [20161224, 20170318, 20160424, 20161230],
-        'exception_type': [1, 2, 1, 1],
-        'schedule_type': ['WD', 'WD', 'WD', 'SA']
-    }
-
-    index = range(4)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def calendar_dates_feed_4():
-    data = {
-        'service_id': ['wk-1'],
-        'date': [20161224],
-        'exception_type': [1]}
-
-    index = range(1)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def stop_times_feed_1():
-    data = {
-        'trip_id': ['a1', 'a1', 'a1', 'a1', 'a1', 'a1',
-                    'a2', 'a2', 'a2', 'a2', 'a2', 'a2',
-                    'a3', 'a3', 'a3', 'a3', 'a3', 'a3',
-                    'a4', 'a4', 'a4', 'a4', 'a4', 'a4',
-                    'b1', 'b1', 'b1', 'b1', 'b1', 'b1',
-                    'b2', 'b2', 'b2', 'b2', 'b2', 'b2',
-                    'c1', 'c1', 'c1', 'c1', 'c1', 'c1',
-                    'c2', 'c2', 'c2', 'c2', 'c2', 'c2',
-                    'd1', 'd1', 'd1',
-                    'd2', 'd2', 'd2'],
-        'stop_id': ['1', '2', '3', '4', '5', '6',
-                    '6', '5', '4', '3', '2', '1',
-                    '1', '2', '3', '4', '5', '6',
-                    '6', '5', '4', '3', '2', '1',
-                    '1', '2', '3', '4', '5', '6',
-                    '6', '5', '4', '3', '2', '1',
-                    '1', '2', '3', '4', '5', '6',
-                    '6', '5', '4', '3', '2', '1',
-                    '7', '8', '9',
-                    '9', '8', '7'],
-        'arrival_time': ['06:15:00', '06:20:00', np.nan, np.nan, '06:35:00',
-                         '06:40:00',
-                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                         np.nan, '06:40:00',
-                         '08:15:00', '08:20:00', '08:25:00', '08:30:00',
-                         '08:35:00', '08:40:00',
-                         '13:15:00', '13:20:00', '13:25:00', '13:30:00',
-                         '13:35:00', '13:40:00',
-                         '06:15:00', '06:20:00', np.nan, np.nan, '06:35:00',
-                         '06:40:00',
-                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                         np.nan, '06:40:00',
-                         '26:15:00', '26:20:00', np.nan, np.nan, '26:35:00',
-                         '26:40:00',
-                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                         np.nan, '06:40:00',
-                         '06:15:00', '06:20:00', '06:25:00',
-                         '06:15:00', '06:20:00', '06:25:00'],
-        'departure_time': ['06:15:00', '06:20:00', np.nan, np.nan,
-                           '06:35:00', '06:40:00',
-                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                           np.nan, '06:40:00',
-                           '08:15:00', '08:20:00', '08:25:00', '08:30:00',
-                           '08:35:00', '08:40:00',
-                           '13:15:00', '13:20:00', '13:25:00', '13:30:00',
-                           '13:35:00', '13:40:00',
-                           '06:15:00', '06:20:00', np.nan, np.nan,
-                           '06:35:00', '06:40:00',
-                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                           np.nan, '06:40:00',
-                           '26:15:00', '26:20:00', np.nan, np.nan,
-                           '26:35:00', '26:40:00',
-                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                           np.nan, '06:40:00',
-                           '06:15:00', '06:20:00', '06:25:00',
-                           '06:15:00', '06:20:00', '06:25:00'],
-        'stop_sequence': [1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3,
-                          1, 2, 3],
-        'pickup_type': [0, 0, 0, 0, 0, 0,
-                        0, 0, 0, 0, 0, 0,
-                        1, 1, 1, 1, 1, 1,
-                        0, 0, 0, 0, 0, 0,
-                        0, 0, 0, 0, 0, 0,
-                        0, 0, 0, 0, 0, 0,
-                        0, 0, 0, 0, 0, 0,
-                        0, 0, 0, 0, 0, 0,
-                        0, 0, 0,
-                        0, 0, 0],
-        'drop_off_type': [0, 0, 0, 0, 0, 0,
-                          0, 0, 0, 0, 0, 0,
-                          1, 1, 1, 1, 1, 1,
-                          0, 0, 0, 0, 0, 0,
-                          0, 0, 0, 0, 0, 0,
-                          0, 0, 0, 0, 0, 0,
-                          0, 0, 0, 0, 0, 0,
-                          0, 0, 0, 0, 0, 0,
-                          0, 0, 0,
-                          0, 0, 0]
-    }
-    index = range(54)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def stop_times_feed_2():
-    data = {
-        'trip_id': ['11', '11', '11', '11', '11', '11',
-                    '12', '12', '12', '12', '12', '12',
-                    '13', '13', '13', '13', '13', '13',
-                    '14', '14', '14', '14', '14', '14',
-                    '21', '21', '21', '21', '21', '21',
-                    '22', '22', '22', '22', '22', '22',
-                    '31', '31', '31', '31', '31', '31',
-                    '32', '32', '32', '32', '32', '32',
-                    '41', '41', '41',
-                    '42', '42', '42'],
-        'stop_id': ['60', '61', '62', '63', '64', '65',
-                    '65', '64', '63', '62', '61', '60',
-                    '60', '61', '62', '63', '64', '65',
-                    '65', '64', '63', '62', '61', '60',
-                    '60', '61', '62', '63', '64', '65',
-                    '65', '64', '63', '62', '61', '60',
-                    '600', '601', '602', '603', '604', '605',
-                    '606', '605', '604', '603', '602', '601',
-                    '66', '67', '68',
-                    '68', '67', '66'],
-        'arrival_time': ['06:15:00', '06:20:00', np.nan, np.nan, '06:35:00',
-                         '06:40:00',
-                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                         np.nan, '06:40:00',
-                         '08:15:00', '08:20:00', '08:25:00', '08:30:00',
-                         '08:35:00', '08:40:00',
-                         '13:15:00', '13:20:00', '13:25:00', '13:30:00',
-                         '13:35:00', '13:40:00',
-                         '06:15:00', '06:20:00', np.nan, np.nan, '06:35:00',
-                         '06:40:00',
-                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                         np.nan, '06:40:00',
-                         '26:15:00', '26:20:00', np.nan, np.nan, '26:35:00',
-                         '26:40:00',
-                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                         np.nan, '06:40:00',
-                         '06:15:00', '06:20:00', '06:25:00',
-                         '06:15:00', '06:20:00', '06:25:00'],
-        'departure_time': ['06:15:00', '06:20:00', np.nan, np.nan,
-                           '06:35:00', '06:40:00',
-                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                           np.nan, '06:40:00',
-                           '08:15:00', '08:20:00', '08:25:00', '08:30:00',
-                           '08:35:00', '08:40:00',
-                           '13:15:00', '13:20:00', '13:25:00', '13:30:00',
-                           '13:35:00', '13:40:00',
-                           '06:15:00', '06:20:00', np.nan, np.nan,
-                           '06:35:00', '06:40:00',
-                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                           np.nan, '06:40:00',
-                           '26:15:00', '26:20:00', np.nan, np.nan,
-                           '26:35:00', '26:40:00',
-                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                           np.nan, '06:40:00',
-                           '06:15:00', '06:20:00', '06:25:00',
-                           '06:15:00', '06:20:00', '06:25:00'],
-        'stop_sequence': [1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3,
-                          1, 2, 3]
-    }
-    index = range(54)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
-@pytest.fixture
-def stop_times_feed_4():
-    data = {
-        'trip_id': ['a131', 'a131', 'a131', 'a131', 'a131', 'a131',
-                    'a132', 'a132', 'a132', 'a132', 'a132', 'a132',
-                    'a133', 'a133', 'a133', 'a133', 'a133', 'a133',
-                    'a134', 'a134', 'a134', 'a134', 'a134', 'a134',
-                    'a135', 'a135', 'a135', 'a135', 'a135', 'a135',
-                    'a136', 'a136', 'a136', 'a136', 'a136', 'a136',
-                    'a237', 'a237', 'a237', 'a237', 'a237', 'a237',
-                    'a238', 'a238', 'a238', 'a238', 'a238', 'a238',
-                    'a239', 'a239', 'a239',
-                    'a240', 'a240', 'a240',
-                    'a341', 'a341', 'a341',
-                    'a342', 'a342', 'a342',
-                    'a343', 'a343', 'a343',
-                    'a344', 'a344', 'a344'],
-        'stop_id': ['70', '71', '72', '73', '74', '75',
-                    '75', '74', '73', '72', '71', '70',
-                    '70', '71', '72', '73', '74', '75',
-                    '75', '74', '73', '72', '71', '70',
-                    '70', '71', '72', '73', '74', '75',
-                    '75', '74', '73', '72', '71', '70',
-                    '70', '71', '72', '73', '74', '75',
-                    '75', '74', '73', '72', '71', '70',
-                    '76', '77', '78',
-                    '78', '77', '76',
-                    '76', '77', '78',
-                    '78', '77', '76',
-                    '76', '77', '78',
-                    '78', '77', '76'],
-
-        'arrival_time': ['06:15:00', '06:20:00', np.nan, np.nan, '06:35:00',
-                         '06:40:00',
-                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                         np.nan, '06:40:00',
-                         '08:15:00', '08:20:00', '08:25:00', '08:30:00',
-                         '08:35:00', '08:40:00',
-                         '13:15:00', '13:20:00', '13:25:00', '13:30:00',
-                         '13:35:00', '13:40:00',
-                         '06:15:00', '06:20:00', np.nan, np.nan, '06:35:00',
-                         '06:40:00',
-                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                         np.nan, '06:40:00',
-                         '26:15:00', '26:20:00', np.nan, np.nan, '26:35:00',
-                         '26:40:00',
-                         '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                         np.nan, '06:40:00',
-                         '06:15:00', '06:20:00', '06:25:00',
-                         '06:15:00', '06:20:00', '06:25:00',
-                         '06:15:00', '06:20:00', '06:25:00',
-                         '06:15:00', '06:20:00', '06:25:00',
-                         '06:15:00', '06:20:00', '06:25:00',
-                         '06:15:00', '06:20:00', '06:25:00'],
-        'departure_time': ['06:15:00', '06:20:00', np.nan, np.nan,
-                           '06:35:00', '06:40:00',
-                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                           np.nan, '06:40:00',
-                           '08:15:00', '08:20:00', '08:25:00', '08:30:00',
-                           '08:35:00', '08:40:00',
-                           '13:15:00', '13:20:00', '13:25:00', '13:30:00',
-                           '13:35:00', '13:40:00',
-                           '06:15:00', '06:20:00', np.nan, np.nan,
-                           '06:35:00', '06:40:00',
-                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                           np.nan, '06:40:00',
-                           '26:15:00', '26:20:00', np.nan, np.nan,
-                           '26:35:00', '26:40:00',
-                           '06:15:00', '06:20:00', '06:25:00', '06:30:00',
-                           np.nan, '06:40:00',
-                           '06:15:00', '06:20:00', '06:25:00',
-                           '06:15:00', '06:20:00', '06:25:00',
-                           '06:15:00', '06:20:00', '06:25:00',
-                           '06:15:00', '06:20:00', '06:25:00',
-                           '06:15:00', '06:20:00', '06:25:00',
-                           '06:15:00', '06:20:00', '06:25:00'],
-        'stop_sequence': [1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3, 4, 5, 6,
-                          1, 2, 3,
-                          1, 2, 3,
-                          1, 2, 3,
-                          1, 2, 3,
-                          1, 2, 3,
-                          1, 2, 3]
-    }
-    index = range(66)
-
-    df = pd.DataFrame(data, index)
-    return df
-
-
 @pytest.fixture
 def folder_feed_1():
     return r'/data/gtfs_feeds/agency_a'
diff --git a/urbanaccess/tests/test_gtfsfeeds.py b/urbanaccess/tests/test_gtfsfeeds.py
new file mode 100644
index 0000000..dacf5be
--- /dev/null
+++ b/urbanaccess/tests/test_gtfsfeeds.py
@@ -0,0 +1,219 @@
+import pytest
+import os
+import pandas as pd
+import yaml
+
+from urbanaccess import gtfsfeeds
+from urbanaccess.gtfsfeeds import feeds
+
+
+@pytest.fixture
+def feed_dict1():
+    return {
+        'ac transit':
+            'http://www.actransit.org/wp-content/uploads/GTFSJune182017B.zip'}
+
+
+@pytest.fixture
+def feed_dict2():
+    return {
+        'Bay Area Rapid Transit':
+            'http://www.gtfs-data-exchange.com/agency/bay-area-rapid-transit'
+            '/latest.zip'}
+
+
+@pytest.fixture
+def feed_dict3():
+    return {
+        'ac transit': 'http://www.actransit.org/wp-content/uploads'
+                      '/GTFSJune182017B.zip',
+        'Bay Area Rapid Transit':
+            'http://www.gtfs-data-exchange.com/agency/bay-area-rapid-transit'
+            '/latest.zip'}
+
+
+@pytest.fixture
+def feed_yaml(tmpdir):
+    yaml_dict = {
+        'gtfs_feeds': {
+            'ac transit': 'http://www.actransit.org/wp-content/uploads'
+                          '/GTFSJune182017B.zip',
+            'Bay Area Rapid Transit':
+                'http://www.gtfs-data-exchange.com/agency/bay-area-rapid'
+                '-transit/latest.zip'}}
+
+    yaml_path = os.path.join(tmpdir.strpath, 'gtfsfeeds.yaml')
+    with open(yaml_path, 'w') as f:
+        yaml.dump(yaml_dict, f, default_flow_style=False)
+    return tmpdir.strpath
+
+
+def test_feed_object():
+    assert isinstance(gtfsfeeds.feeds, gtfsfeeds.urbanaccess_gtfsfeeds)
+    assert isinstance(feeds.to_dict(), dict)
+
+
+def test_add_feed(feed_dict1, feed_dict2):
+    feeds.add_feed(add_dict=feed_dict1)
+    assert len(feeds.gtfs_feeds.keys()) == 1
+    feeds.add_feed(add_dict=feed_dict2)
+    assert len(feeds.gtfs_feeds.keys()) == 2
+    feed_dict_replace = {'Bay Area Rapid Transit': 'test'}
+    feeds.add_feed(add_dict=feed_dict_replace, replace=True)
+
+    for key, value in feeds.gtfs_feeds.items():
+        if key == 'Bay Area Rapid Transit':
+            assert value == 'test'
+    assert isinstance(feeds, gtfsfeeds.urbanaccess_gtfsfeeds)
+    # clear feeds from global memory
+    feeds.remove_feed(remove_all=True)
+
+
+def test_remove_feed(feed_dict3):
+    feeds.add_feed(add_dict=feed_dict3)
+    feeds.remove_feed(del_key='ac transit')
+    assert len(feeds.gtfs_feeds.keys()) == 1
+    assert 'ac transit' not in feeds.gtfs_feeds.keys()
+    feeds.remove_feed(remove_all=True)
+    assert len(feeds.gtfs_feeds.keys()) == 0
+    assert isinstance(feeds, gtfsfeeds.urbanaccess_gtfsfeeds)
+    # clear feeds from global memory
+    feeds.remove_feed(remove_all=True)
+
+
+def test_to_yaml_feed(tmpdir, feed_dict3):
+    feeds.add_feed(add_dict=feed_dict3)
+    feeds.to_yaml(tmpdir.strpath, overwrite=True)
+
+    yaml_path = os.path.join(tmpdir.strpath, 'gtfsfeeds.yaml')
+    with open(yaml_path, 'r') as f:
+        yaml_config = yaml.load(f)
+    assert yaml_config['gtfs_feeds'] == feed_dict3
+    # clear feeds from global memory
+    feeds.remove_feed(remove_all=True)
+
+
+def test_from_yaml_feed(feed_yaml):
+    yaml_path = feed_yaml
+    feeds_from_yaml = feeds.from_yaml(yaml_path, 'gtfsfeeds.yaml')
+
+    assert isinstance(feeds_from_yaml, gtfsfeeds.urbanaccess_gtfsfeeds)
+    assert len(feeds_from_yaml.gtfs_feeds.keys()) == 2
+
+    valid_feed = ('http://www.gtfs-data-exchange.com/'
+                  'agency/bay-area-rapid-transit/latest.zip')
+    assert feeds_from_yaml.gtfs_feeds['Bay Area Rapid Transit'] == valid_feed
+
+    valid_feed = ('http://www.actransit.org/wp-content/'
+                  'uploads/GTFSJune182017B.zip')
+    assert feeds_from_yaml.gtfs_feeds['ac transit'] == valid_feed
+    # clear feeds from global memory
+    feeds.remove_feed(remove_all=True)
+
+
+def test_search_contains_gtfs_data_exchange():
+    search_result = gtfsfeeds.search(api='gtfsdataexch',
+                                     search_text=['ac transit', 'santa rosa'],
+                                     search_field=None, match='contains',
+                                     add_feed=False, overwrite_feed=False)
+
+    assert isinstance(search_result, pd.DataFrame)
+    assert search_result.empty is False
+    assert len(search_result) == 2
+
+    col_list = ['dataexchange_url', 'dataexchange_id', 'name']
+    for col in col_list:
+        assert col in search_result.columns
+        assert search_result[col].isnull().all() == False  # noqa
+
+    value_list = ['ac-transit', 'santa-rosa-citybus']
+    for value in value_list:
+        assert value in list(search_result['dataexchange_id'])
+
+
+def test_search_contains_add_feed_gtfs_data_exchange():
+    gtfsfeeds.search(api='gtfsdataexch',
+                     search_text='ac transit',
+                     search_field=None, match='contains',
+                     add_feed=True, overwrite_feed=False)
+
+    assert len(feeds.gtfs_feeds.keys()) == 1
+    assert 'AC Transit' in feeds.gtfs_feeds.keys()
+
+    # test overwrite feed
+    gtfsfeeds.search(api='gtfsdataexch',
+                     search_text='Bay Area Rapid Transit',
+                     search_field=None, match='exact',
+                     add_feed=True, overwrite_feed=True)
+
+    assert len(feeds.gtfs_feeds.keys()) == 1
+    assert 'Bay Area Rapid Transit' in feeds.gtfs_feeds.keys()
+    # clear feeds from global memory
+    feeds.remove_feed(remove_all=True)
+
+
+def test_search_exact_search_field_gtfs_data_exchange():
+    # test search field
+    search_result = gtfsfeeds.search(api='gtfsdataexch',
+                                     search_text='San Francisco Bay Area',
+                                     search_field=['area'], match='exact',
+                                     add_feed=False, overwrite_feed=False)
+    assert len(search_result) == 8
+
+
+def test_download_gtfs_feed_via_feed_object(feed_dict3, tmpdir):
+    feeds.add_feed(add_dict=feed_dict3)
+    tmp_path = tmpdir.strpath
+    gtfsfeeds.download(data_folder=tmp_path)
+
+    filelist = ['ac transit.zip', 'Bay Area Rapid Transit.zip']
+    txtlist = ['calendar.txt', 'routes.txt', 'stop_times.txt',
+               'stops.txt', 'trips.txt']
+    zip_path = os.path.join(tmp_path, 'gtfsfeed_zips')
+    txt_path = os.path.join(tmp_path, 'gtfsfeed_text')
+    for zipfile in filelist:
+        assert os.path.exists(os.path.join(zip_path, zipfile)) is True
+    for folder in filelist:
+        check_path = os.path.join(txt_path, folder.replace('.zip', ''))
+        assert os.path.exists(check_path) is True
+        for txt in txtlist:
+            check_path = os.path.join(
+                txt_path, folder.replace('.zip', ''), txt)
+            assert os.path.exists(check_path) is True
+    # clear feeds from global memory
+    feeds.remove_feed(remove_all=True)
+
+
+def test_download_gtfs_feed_via_feed_name_and_dict(tmpdir):
+    tmp_path = tmpdir.strpath
+    gtfsfeeds.download(
+        data_folder=tmp_path,
+        feed_name='test_agency',
+        feed_url=('http://www.gtfs-data-exchange.com/'
+                  'agency/bay-area-rapid-transit/latest.zip'),
+        feed_dict=None,
+        error_pause_duration=5, delete_zips=False)
+
+    gtfsfeeds.download(
+        data_folder=tmp_path,
+        feed_dict={
+            'test_agency_dict': 'http://www.gtfs-data-exchange.com/agency/'
+                                'ac-transit/latest.zip'},
+        error_pause_duration=5, delete_zips=False)
+
+    filelist = ['test_agency.zip', 'test_agency_dict.zip']
+    txtlist = ['calendar.txt', 'routes.txt', 'stop_times.txt',
+               'stops.txt', 'trips.txt']
+    zip_path = os.path.join(tmp_path, 'gtfsfeed_zips')
+    txt_path = os.path.join(tmp_path, 'gtfsfeed_text')
+    for zipfile in filelist:
+        assert os.path.exists(os.path.join(zip_path, zipfile)) is True
+    for folder in filelist:
+        check_path = os.path.join(txt_path, folder.replace('.zip', ''))
+        assert os.path.exists(check_path) is True
+        for txt in txtlist:
+            check_path = os.path.join(
+                txt_path, folder.replace('.zip', ''), txt)
+            assert os.path.exists(check_path) is True
+    # clear feeds from global memory
+    feeds.remove_feed(remove_all=True)
diff --git a/urbanaccess/tests/test_osm_network.py b/urbanaccess/tests/test_osm_network.py
index 10ffad7..cbcf516 100644
--- a/urbanaccess/tests/test_osm_network.py
+++ b/urbanaccess/tests/test_osm_network.py
@@ -9,11 +9,11 @@ def bbox1():
 
 
 def test_column_names(bbox1):
-    nodes, edges = ua_network_from_bbox(bbox=bbox1, network_type='walk',
-                                        timeout=180, memory=None,
-                                        max_query_area_size=50 * 1000 * 50 *
-                                                            1000,
-                                        remove_lcn=False)  # noqa
+    nodes, edges = ua_network_from_bbox(
+        bbox=bbox1, network_type='walk',
+        timeout=180, memory=None,
+        max_query_area_size=50 * 1000 * 50 * 1000,
+        remove_lcn=False)
     col_list = ['x', 'y', 'id']
     for col in col_list:
         assert col in nodes.columns
diff --git a/urbanaccess/utils.py b/urbanaccess/utils.py
index e8fbf9e..d5f0420 100644
--- a/urbanaccess/utils.py
+++ b/urbanaccess/utils.py
@@ -63,7 +63,7 @@ def log(message, level=None, name=None, filename=None):
 
         # convert message to ascii for proper console display in windows
         # terminals
-        message = unicodedata.normalize('NFKD', unicode(message)).encode(
+        message = unicodedata.normalize('NFKD', str(message)).encode(
             'ascii', errors='replace').decode()
         print(message)
         sys.stdout = standard_out