testing out optional reqs

.travis.yml

@@ -20,7 +20,7 @@ install:
   scipy matplotlib seaborn scikit-learn
 - source activate testenv
 - pip install -r requirements.txt
-- python setup.py install
+- pip install -e . [speech-decoding, efficient-learning]
 script: py.test
 notifications:
   slack:

MANIFEST.in

@@ -1 +1,2 @@
 include quail/data/*
+include requirements.txt

quail/load.py

@@ -3,19 +3,22 @@
 from builtins import zip
 from builtins import str
 from builtins import range
-from sqlalchemy import create_engine, MetaData, Table
 import json
 import re
 import csv
-import pandas as pd
-import numpy as np
-from .egg import Egg, FriedEgg
-import dill
 import pickle
 import os
+import pandas as pd
+import numpy as np
 import deepdish as dd
+from .egg import Egg, FriedEgg
 from .helpers import parse_egg, stack_eggs
 
+try:
+    from sqlalchemy import create_engine, MetaData, Table
+except:
+    pass
+
 def load(filepath, update=True):
     """
     Loads eggs, fried eggs ands example data

requirements.txt

@@ -1,16 +1,8 @@
-seaborn>=0.7.1
-matplotlib>=1.5.1
-scipy>=0.17.1
 numpy>=1.10.4
-pandas
-grpcio
-sqlalchemy
-dill
-requests
-pydub
-google-cloud-speech
-pathos
+scipy>=0.17.1
+matplotlib>=1.5.1
+seaborn>=0.7.1
+pandas>=0.18.1
 future
 six
 deepdish
-joblib

setup.py

@@ -2,34 +2,40 @@
 
 from setuptools import setup, find_packages
 
+DESCRIPTION = 'A python toolbox for analyzing and plotting free recall data'
+LONG_DESCRIPTION = """\
+Quail is a Python package that facilitates analyses of behavioral data from memory experiments. (The current focus is on free recall experiments.) Key features include:
+
+- Serial position curves (probability of recalling items presented at each presentation position)
+Probability of Nth recall curves (probability of recalling items at each presentation position as the Nth recall in the recall sequence)
+- Lag-Conditional Response Probability curves (probability of transitioning between items in the recall sequence, as a function of their relative presentation positions)
+- Clustering metrics (e.g. single-number summaries of how often participants transition from recalling a word to another related word, where "related" can be user-defined.)
+- Many nice plotting functions
+- Convenience functions for loading in data
+- Automatically parse speech data (audio files) using wrappers for the Google Cloud Speech to Text API
+
+The intended user of this toolbox is a memory researcher who seeks an easy way to analyze and visualize data from free recall psychology experiments.
+"""
+
+with open('requirements.txt') as f:
+    REQUIREMENTS = f.read().splitlines()
+
+EXTRAS_REQUIRE={
+    'speech-decoding': ["pydub", "google-cloud-speech<0.31dev,>=0.30.0", "google-cloud>=0.32.0,<0.34.0"],
+    'efficient-learning': ["sqlalchemy"],
+}
+
 setup(
     name='quail',
     version='0.2.0',
-    description='A python toolbox for analyzing and plotting free recall data',
-    long_description=' ',
+    description=DESCRIPTION,
+    long_description=LONG_DESCRIPTION,
     author='Contextual Dynamics Lab',
     author_email='contextualdynamics@gmail.com',
     url='https://github.com/ContextLab/quail',
     license='MIT',
     packages=find_packages(exclude=('tests', 'docs', 'paper')),
     include_package_data=True,
-    install_requires=[
-        "seaborn>=0.7.1",
-        "matplotlib>=1.5.1",
-        "scipy>=0.17.1",
-        "numpy>=1.10.4",
-        "pandas",
-        "grpcio",
-        "sqlalchemy",
-        "dill",
-        "requests",
-        "pydub",
-        "google-cloud-speech<0.31dev,>=0.30.0",
-        "google-cloud>=0.32.0,<0.34.0",
-        "pathos",
-        "future",
-        "six",
-        "deepdish",
-        "joblib",
-    ]
+    install_requires=REQUIREMENTS,
+    extras_require=EXTRAS_REQUIRE,
 )