Merge pull request #15 from fabiansinz/master

lgtm

.coveragerc

@@ -0,0 +1,2 @@
+[run]
+source = thunderfish

.coveralls.yml

@@ -0,0 +1 @@
+repo_token: b2SXUJqkxFFdvCWzQEHqCWS4Rxh5tUgAo

.travis.yml

@@ -0,0 +1,29 @@
+sudo: false
+
+language: python
+
+python:
+  - 2.7
+
+addons:
+  apt:
+    packages:
+    - python-numpy
+    - python-scipy
+    - python-matplotlib
+    - python-pandas
+    - python-tk
+
+virtualenv:
+    system_site_packages: true
+
+install:
+  - pip install git+https://github.com/G-Node/python-odml.git
+  - pip install -r requirements.txt
+  - pip install nose nose-cov python-coveralls
+# command to run tests
+script:
+  - nosetests -vv --with-coverage --cover-package=thunderfish
+
+after_success:
+  - coveralls

README.md

@@ -1,3 +1,6 @@
+[![Build Status](https://travis-ci.org/bendalab/thunderfish.svg?branch=master)](https://travis-ci.org/bendalab/thunderfish)
+[![Coverage Status](https://coveralls.io/repos/github/bendalab/thunderfish/badge.svg?branch=master)](https://coveralls.io/github/bendalab/thunderfish?branch=master)
+
 # thunderfish
 
 this project is meant to analyze transects, segregate fishes in wave or pulse type fishes

requirements.txt

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+numpy
+matplotlib
+scipy
+seaborn
+ipython
+audioread

setup.py

@@ -1,8 +1,9 @@
 from distutils.core import setup
+from setuptools import find_packages
 
 setup(name='thunderfish',
       version='0.5',
-      packages=['thunderfish'],
+      packages=find_packages(exclude=['contrib', 'doc', 'tests*']),
       description='Python scripts for analyzing recordings of e-fish electric fields.',
       author='Juan F. Sehuanes, Till Raab, Fabian Sinz, Jan Benda, Joerg Henninger, Jan Grewe',
       requires=['matplotlib', 'numpy', 'scipy', 'seaborn', 'IPython', 'audioread']

tests/test_auxiliary.py

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+import numpy as np
+from nose.tools import assert_true
+
+from thunderfish.Auxiliary import peakdet
+
+
+def test_peakdet():
+    """Tests whether peakdet works corretly"""
+    x = np.zeros(1000)
+    peaks = np.random.randint(0,len(x), size=20)
+    peaks.sort()
+    while np.any(np.diff(peaks).min() < 2):
+        peaks = np.random.randint(0,len(x), size=20)
+        peaks.sort()
+    x[peaks] = 5
+    maxtab, maxidx, _, _ = peakdet(x, 2)
+    assert_true(np.all(maxidx == peaks), "Peaks were not detected correctly")