Minor change to filter to avoid copy warning

conda.recipe/meta.yaml

 package:
   name: resonate
-  version: "1.0.6"
+  version: "1.0.7"
 
 source:
   git_rev: master

docs/conf.py

@@ -76,9 +76,9 @@ copyright = u'2017 Ocean Tracking Network. All Rights Reserved.'
 # built documents.
 #
 # The short X.Y version.
-version = 'v1.0.6'
+version = 'v1.0.7'
 # The full version, including alpha/beta/rc tags.
-release = 'v1.0.6'
+release = 'v1.0.7'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/notebooks/abacus_plot.ipynb.rst

@@ -29,3 +29,4 @@ Or use the standard plotting function to save as HTML:
 .. code:: python
 
     abacus_plot(df, ipython_display=False, filename='example.html')
+

docs/notebooks/data_subsetting.ipynb.rst

@@ -59,3 +59,4 @@ you’d like to create a subset from.
     
     # Output the subset data to a new CSV in the indicated directory
     data_column_subset.to_csv(directory+column+"_"+value.replace(" ", "_")+"_"+filename, index=False)
+

docs/notebooks/interval_data.ipynb.rst

@@ -68,3 +68,4 @@ file to a desired location.
 .. code:: python
 
     interval.to_csv('/path/to/output.csv', index=False)
+

resonate/__init__.py

 __author__ = "Alex Nunes"
 __credits__ = ["Alex Nunes", "Jon Pye", "Brian Jones", "Marta Mihoff"]
 __license__ = "GPL"
-__version__ = "1.0.6"
+__version__ = "1.0.7"
 __maintainer__ = "Alex Nunes"
 __email__ = "anunes@dal.ca"
 __status__ = "Production"

resonate/filters.py

@@ -3,6 +3,7 @@ from datetime import datetime, timedelta
 import numpy as np
 import pandas as pd
 from geopy.distance import geodesic
+
 from resonate.library.exceptions import GenericException
 
 
@@ -82,16 +83,17 @@ def filter_detections(detections, suspect_file=None,
         # If the space before + after > min_time_buffer
         # Remove that detection row from the detections and add it to suspect detections.
         # SQL that does this is in load_to_postgresql under createSuspect
-
+        detections = detections.copy(deep=True)
         ind = detections['catalognumber'].unique()
-        detections['datecollected'] = pd.to_datetime(
+        detections.loc[:, 'datecollected'] = pd.to_datetime(
             detections['datecollected'])
         user_int = timedelta(seconds=min_time_buffer)
         good_dets = pd.DataFrame()
         susp_dets = pd.DataFrame()
         grouped = detections.groupby('catalognumber')
         for anm in ind:
-            anm_dets = grouped.get_group(anm)
+            anm_dets = grouped.get_group(anm).sort_values(
+                'datecollected', ascending=True)
             intervals = anm_dets['datecollected'] - \
                 anm_dets['datecollected'].shift(1)
             post_intervals = anm_dets['datecollected'].shift(

setup.py

@@ -12,7 +12,7 @@ with open('LICENSE') as f:
 
 setup(
     name='resonATe',
-    version='1.0.6',
+    version='1.0.7',
     description='resonate data analysis package',
     long_description=readme,
     author='Alex Nunes',