Minor changes to make sure there is a standard time unit(seconds) used across all tools

Changes to be committed: modified: CHANGELOG.md modified: README modified: README.md modified: conda.recipe/meta.yaml modified: docs/conf.py modified: docs/index.rst modified: docs/notebooks/cohort_data.ipynb.rst modified: docs/notebooks/filters.ipynb.rst modified: py_notebooks/cohort_data.ipynb modified: py_notebooks/filters.ipynb modified: resonate/__init__.py modified: resonate/cohorts.py modified: setup.py modified: tests/cohort_test.py

Minor changes to make sure there is a standard time unit(seconds) used across all tools
Changes to be committed: modified: CHANGELOG.md modified: README modified: README.md modified: conda.recipe/meta.yaml modified: docs/conf.py modified: docs/index.rst modified: docs/notebooks/cohort_data.ipynb.rst modified: docs/notebooks/filters.ipynb.rst modified: py_notebooks/cohort_data.ipynb modified: py_notebooks/filters.ipynb modified: resonate/__init__.py modified: resonate/cohorts.py modified: setup.py modified: tests/cohort_test.py
f636f6ec · Alex Nunes · d2ce46c0 · f636f6ec · f636f6ec · f636f6ec
Commit f636f6ec authored Oct 25, 2018 by Alex Nunes
14 changed files
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,11 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
 and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).

+## [1.0.2]
+
+### Changed
+- All analysis and documentation now uses seconds and not minutes as a timedelta unit
+
 ## [1.0.1]

 ### Fixed

--- a/README
+++ b/README
@@ -45,7 +45,7 @@ OTN has developed a tool which will assist with filtering false detections. The

 This is a very simple tool. It will take an input file of detections and based on an input parameter will identify suspect detections. The suspect detections will be put into a dataframe which the user can examine. There will be enough information for each suspect detection for the user to understand why it was flagged. There is also enough information to be able to reference the detection in the original file if the user wants to see what was happening at the same time.

-The input parameter is a time in minutes. We used 60 minutes as the default as this is what was used in Easton's code. This value can be changed by the user. The output contains a record for each detection for which there has been more than xx minutes since the previous detection (of that tag/animal) and more than the same amount of time until the next detection. It ignores which receiver the detection occurred at. That is all it does, nothing more and nothing less.
+The input parameter is a time in minutes. We used 3600 seconds as the default as this is what was used in Easton's code. This value can be changed by the user. The output contains a record for each detection for which there has been more than xx minutes since the previous detection (of that tag/animal) and more than the same amount of time until the next detection. It ignores which receiver the detection occurred at. That is all it does, nothing more and nothing less.


 Distance Matrix

--- a/README.md
+++ b/README.md
@@ -59,7 +59,7 @@ OTN has developed a tool which will assist with filtering false detections. The

 This is a very simple tool. It will take an input file of detections and based on an input parameter will identify suspect detections. The suspect detections will be put into a dataframe which the user can examine. There will be enough information for each suspect detection for the user to understand why it was flagged. There is also enough information to be able to reference the detection in the original file if the user wants to see what was happening at the same time.

-The input parameter is a time in minutes. We used 60 minutes as the default as this is what was used in Easton's code. This value can be changed by the user. The output contains a record for each detection for which there has been more than xx minutes since the previous detection (of that tag/animal) and more than the same amount of time until the next detection. It ignores which receiver the detection occurred at. That is all it does, nothing more and nothing less.
+The input parameter is a time in minutes. We used 3600 seconds as the default as this is what was used in Easton's code. This value can be changed by the user. The output contains a record for each detection for which there has been more than xx minutes since the previous detection (of that tag/animal) and more than the same amount of time until the next detection. It ignores which receiver the detection occurred at. That is all it does, nothing more and nothing less.


 Distance Matrix

--- a/conda.recipe/meta.yaml
+++ b/conda.recipe/meta.yaml
 package:
  name: resonate
-  version: "1.0.1"
+  version: "1.0.2"

 source:
  git_rev: master

--- a/docs/conf.py
+++ b/docs/conf.py
@@ -76,9 +76,9 @@ copyright = u'2017 Ocean Tracking Network. All Rights Reserved.'
 # built documents.
 #
 # The short X.Y version.
-version = 'v1.0.1'
+version = 'v1.0.2'
 # The full version, including alpha/beta/rc tags.
-release = 'v1.0.1'
+release = 'v1.0.2'

 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

--- a/docs/index.rst
+++ b/docs/index.rst
@@ -66,7 +66,7 @@ OTN has developed a tool which will assist with filtering false detections. The

 This is a very simple tool. It will take an input file of detections and based on an input parameter will identify suspect detections. The suspect detections will be put into a dataframe which the user can examine. There will be enough information for each suspect detection for the user to understand why it was flagged. There is also enough information to be able to reference the detection in the original file if the user wants to see what was happening at the same time.

-The input parameter is a time in minutes. We used 60 minutes as the default as this is what was used in Easton's code. This value can be changed by the user. The output contains a record for each detection for which there has been more than xx minutes since the previous detection (of that tag/animal) and more than the same amount of time until the next detection. It ignores which receiver the detection occurred at. That is all it does, nothing more and nothing less. Details are in :ref:`Filter Tool <filter_page>`.
+The input parameter is a time in minutes. We used 3600 seconds as the default as this is what was used in Easton's code. This value can be changed by the user. The output contains a record for each detection for which there has been more than xx minutes since the previous detection (of that tag/animal) and more than the same amount of time until the next detection. It ignores which receiver the detection occurred at. That is all it does, nothing more and nothing less. Details are in :ref:`Filter Tool <filter_page>`.

 Two other filtering tools are available as well, one based on distance alone and one based on velocity. They can be found at :ref:`Filter Tools <filter_page>` as well.


--- a/docs/notebooks/cohort_data.ipynb.rst
+++ b/docs/notebooks/cohort_data.ipynb.rst
@@ -15,7 +15,7 @@ The function returns a dataframe which you can use to help identify
 animal cohorts. The cohort is created from the compressed data that is a
 result from the ``compress_detections()`` function. Pass the compressed
 dataframe into the ``cohort()`` function along with a time interval in
-minutes (default is 60) to create the cohort dataframe.
+seconds (default is 3600) to create the cohort dataframe.

 .. warning:: 

@@ -28,7 +28,7 @@ minutes (default is 60) to create the cohort dataframe.
    from resonate.compress import compress_detections
    import pandas as pd
    
-    time_interval = 60
+    time_interval = 3600 # in seconds
    
    data = pd.read_csv('/path/to/detections.csv')
    

--- a/docs/notebooks/filters.ipynb.rst
+++ b/docs/notebooks/filters.ipynb.rst
@@ -25,15 +25,15 @@ the user to understand why it was flagged. There is also enough
 information to be able to reference the detection in the original file
 if the user wants to see what was happening at the same time.

-The input parameter is a time in minutes. We used 60 minutes as the
+The input parameter is a time in seconds. We used 3600 seconds as the
 default as this is what was used in Easton’s code. This value can be
 changed by the user. The output contains a record for each detection for
-which there has been more than xx minutes since the previous detection
+which there has been more than xx seconds since the previous detection
 (of that tag/animal) and more than the same amount of time until the
 next detection. It ignores which receiver the detection occurred at.
 That is all it does, nothing more and nothing less.

-Below the interval is set to 60 minutes and is not using a a user
+Below the interval is set to 3600 seconds and is not using a a user
 specified suspect file. The function will also create a distance matrix.

 .. warning:: 
@@ -48,7 +48,7 @@ specified suspect file. The function will also create a distance matrix.
    
    detections = pd.read_csv('/path/to/detections.csv')
    
-    time_interval = 60 # in Minutes
+    time_interval = 3600 # in seconds
    
    SuspectFile = None
    

--- a/py_notebooks/cohort_data.ipynb
+++ b/py_notebooks/cohort_data.ipynb
@@ -10,7 +10,7 @@
    "\n",
    "The tool takes a dataframe of compressed detections and a time parameter in minutes. It identifies groups of animals traveling together. Each station an animal visits is checked for other animals detected there within the specified time period. \n",
    "\n",
-    "The function returns a dataframe which you can use to help identify animal cohorts. The cohort is created from the compressed data that is a result from the `compress_detections()` function. Pass the compressed dataframe into the `cohort()` function along with a time interval in minutes (default is 60) to create the cohort dataframe.\n",
+    "The function returns a dataframe which you can use to help identify animal cohorts. The cohort is created from the compressed data that is a result from the `compress_detections()` function. Pass the compressed dataframe into the `cohort()` function along with a time interval in seconds (default is 3600) to create the cohort dataframe.\n",
    "\n",
    "<span style=\"color:red\">Warning:</span> \n",
    "\n",
@@ -30,7 +30,7 @@
    "from resonate.compress import compress_detections\n",
    "import pandas as pd\n",
    "\n",
-    "time_interval = 60\n",
+    "time_interval = 3600 # in seconds\n",
    "\n",
    "data = pd.read_csv('/path/to/detections.csv')\n",
    "\n",
@@ -63,23 +63,23 @@
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python [conda env:anaconda3]",
   "language": "python",
-   "name": "python2"
+   "name": "conda-env-anaconda3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
-    "version": 2
+    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 2
 }
--- a/py_notebooks/filters.ipynb
+++ b/py_notebooks/filters.ipynb
@@ -15,9 +15,9 @@
    "\n",
    "This is a very simple tool. It will take an input file of detections and based on an input parameter will identify suspect detections. The suspect detections will be put into a dataframe which the user can examine. There will be enough information for each suspect detection for the user to understand why it was flagged. There is also enough information to be able to reference the detection in the original file if the user wants to see what was happening at the same time.\n",
    "\n",
-    "The input parameter is a time in minutes. We used 60 minutes as the default as this is what was used in Easton's code. This value can be changed by the user. The output contains a record for each detection for which there has been more than xx minutes since the previous detection (of that tag/animal) and more than the same amount of time until the next detection. It ignores which receiver the detection occurred at. That is all it does, nothing more and nothing less.\n",
+    "The input parameter is a time in seconds. We used 3600 seconds as the default as this is what was used in Easton's code. This value can be changed by the user. The output contains a record for each detection for which there has been more than xx seconds since the previous detection (of that tag/animal) and more than the same amount of time until the next detection. It ignores which receiver the detection occurred at. That is all it does, nothing more and nothing less.\n",
    "\n",
-    "Below the interval is set to 60 minutes and is not using a a user specified suspect file. The function will also create a distance matrix.\n",
+    "Below the interval is set to 3600 seconds and is not using a a user specified suspect file. The function will also create a distance matrix.\n",
    "\n",
    "<span style=\"color:red\">Warning:</span> \n",
    "\n",
@@ -36,7 +36,7 @@
    "\n",
    "detections = pd.read_csv('/path/to/detections.csv')\n",
    "\n",
-    "time_interval = 60 # in Minutes\n",
+    "time_interval = 3600 # in seconds\n",
    "\n",
    "SuspectFile = None\n",
    "\n",
@@ -211,5 +211,5 @@
  }
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 2
 }
--- a/resonate/__init__.py
+++ b/resonate/__init__.py
 __author__ = "Alex Nunes"
 __credits__ = ["Alex Nunes", "Jon Pye", "Brian Jones", "Marta Mihoff"]
 __license__ = "GPL"
-__version__ = "1.0.1"
+__version__ = "1.0.2"
 __maintainer__ = "Alex Nunes"
 __email__ = "anunes@dal.ca"
 __status__ = "Production"
--- a/resonate/cohorts.py
+++ b/resonate/cohorts.py
 import pandas as pd


-def cohort(compressed_df, interval_time=60):
+def cohort(compressed_df, interval_time=3600):
    """
    Creates a dataframe of cohorts using a compressed detection file

    :param compressed_df: compressed dataframe
-    :param interval_time: cohort detection time interval (in minutes)
+    :param interval_time: cohort detection time interval (in seconds)
    :return: cohort dataframe with the following columns

        * anml_1
@@ -23,7 +23,7 @@ def cohort(compressed_df, interval_time=60):
    """

    # Convert input int interval_time into a timedelta object
-    interval_time = pd.to_timedelta(interval_time, unit='m')
+    interval_time = pd.to_timedelta(interval_time, unit='s')

    # Sort input compressed data file
    cmps = compressed_df.sort_values(['catalognumber', 'seq_num'])

--- a/setup.py
+++ b/setup.py
@@ -12,7 +12,7 @@ with open('LICENSE') as f:

 setup(
    name='resonATe',
-    version='1.0.1',
+    version='1.0.2',
    description='resonate data analysis package',
    long_description=readme,
    author='Alex Nunes',

--- a/tests/cohort_test.py
+++ b/tests/cohort_test.py
 # -*- coding: utf-8 -*-
-from resonate.cohorts import cohort
-from resonate.compress import compress_detections
 import unittest
+
 import pandas as pd
 import pandas.testing as pt
 from colorama import Fore as c
+from resonate.cohorts import cohort
+from resonate.compress import compress_detections


 class CohortTest(unittest.TestCase):

    def test_cohort(self):
-        print(c.YELLOW+'Testing Cohort...'+c.RESET)
-        compressed = compress_detections(pd.read_csv('tests/assertion_files/nsbs.csv'))
-        dfa = cohort(compressed, 60)
+        print(c.YELLOW + 'Testing Cohort...' + c.RESET)
+        compressed = compress_detections(
+            pd.read_csv('tests/assertion_files/nsbs.csv'))
+        dfa = cohort(compressed, 3600)
        dfb = pd.read_csv('tests/assertion_files/nsbs_cohort_60min.csv')
        dfb.anml_2_arrive = pd.to_datetime(dfb.anml_2_arrive)
        dfb.anml_2_depart = pd.to_datetime(dfb.anml_2_depart)
        pt.assert_frame_equal(dfa, dfb)
-        print(c.GREEN+'OK!\n'+c.RESET)
+        print(c.GREEN + 'OK!\n' + c.RESET)


 if __name__ == '__main__':