Add template for slides and setup TravisCI

.coveragerc

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+[run]
+branch = True
+include = code/*, data/*

.travis.yml

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+# vim ft=yaml
+language: python
+
+env:
+  global:
+    - DEPENDS="requirements.txt"
+    - COVERAGE=1
+
+python:
+  - 3.4
+  - 2.7
+
+install:
+  - source tools/travis_tools.sh
+  - virtualenv --python=python venv
+  - source venv/bin/activate
+  - python --version # just to check
+  - retry pip install nose # always
+  - sudo apt-get install libblas-dev liblapack-dev libatlas3gf-base
+  - wheelhouse_pip_install ${DEPENDS}
+  - if [ "${COVERAGE}" == "1" ]; then
+    pip install coverage;
+    pip install coveralls;
+    fi
+
+script:
+  - make coverage
+
+
+after_success:
+  - if [ "${COVERAGE}" == "1" ]; then coveralls; fi

Makefile

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+.PHONY: all clean coverage test
+
+all: clean
+
+clean:
+	find . -name "*.so" -o -name "*.pyc" -o -name "*.pyx.md5" | xargs rm -f
+
+coverage:
+	nosetests code/utils data --with-coverage --cover-package=data  --cover-package=utils
+
+test:
+	nosetests code/utils data
+
+verbose:
+	nosetests -v code/utils data

code/Makefile

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+test:
+	nosetests utils
+
+coverage:
+	nosetests utils --with-coverage --cover-package=utils 

code/utils/pearson.py

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+"""
+This will be your new module with the Pearson correlation function
+
+First try to fill in the "pearson_1d" function.
+
+When you are done with "pearson_1d", you should be able to run the following
+command (from the day5 directory) and see no errors or failures::
+
+    nosetests test_pearson_1d.py
+
+Then, if you are feeling brave, fill in the "pearson_2d" function.  Test with::
+
+    nosetests test_pearson_2d.py
+"""
+# Python 3 compatibility
+from __future__ import print_function, division
+
+import numpy as np
+
+
+def pearson_1d(x, y):
+    """ Pearson product-moment correlation of vectors `x` and `y`
+
+    Parameters
+    ----------
+    x : array shape (N,)
+        One-dimensional array to correlate with `y`
+    y : array shape (N,)
+        One dimensional array to correlate with `x`
+
+    Returns
+    -------
+    r_xy : scalar
+        Pearson product-moment correlation of vectors `x` and `y`.
+    """
+    # Mean-center x -> mc_x
+    mc_x = x - np.mean(x)
+    # Mean-center y -> mc_y
+    mc_y = y - np.mean(y)
+    # a : Get sum of products of mc_x, mc_y
+    a = mc_x.dot(mc_y)
+    # b : Get sum of products of mc_x on mc_x
+    b = mc_x.dot(mc_x)
+    # c : Get sum of products of mc_y on mc_y
+    c = mc_y.dot(mc_y)
+    # return a / (sqrt(b) * sqrt(c))
+    return a / (np.sqrt(b) * np.sqrt(c))
+
+
+def pearson_2d(x, Y):
+    """ Pearson product-moment correlation of vectors `x` and array `Y`
+
+    Parameters
+    ----------
+    x : array shape (N,)
+        One-dimensional array to correlate with every column of `Y`
+    Y : array shape (N, P)
+        2D array where we correlate each column of `Y` with `x`.
+
+    Returns
+    -------
+    r_xy : array shape (P,)
+        Pearson product-moment correlation of vectors `x` and the columns of
+        `Y`, with one correlation value for every column of `Y`.
+    """
+    # Mean-center x -> mc_x
+    mc_x = x - np.mean(x)
+    # Mean-center every column of Y -> mc_Y
+    mean_Y = np.mean(Y, axis=0)
+    mean_Y_expanded = np.tile(mean_Y, (len(x), 1))
+    mc_Y = Y - mean_Y_expanded
+    # (Hint: np.tile, or (advanced, not yet covered) numpy broadcasting)
+    # a : Get sum of products of mc_x and every column of mc_Y
+    a = mc_x.dot(mc_Y)
+    # b : Get sum of products of mc_x on mc_x
+    b = mc_x.dot(mc_x)
+    # c : Get sum of products of every column of mc_Y[:, i] on itself
+    c = np.sum(mc_Y ** 2, axis=0)
+    # return a / (sqrt(b) * sqrt(c))
+    return a / (np.sqrt(b) * np.sqrt(c))
+

code/utils/tests/test_pearson_1d.py

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+"""
+Test Pearson module, pearson_1d function
+
+Run with::
+
+    nosetests test_pearson_1d.py
+
+This is a test module.
+
+It is designed to be run the with the "nose" testing package (via the
+"nosetests" script.
+
+Nose will look for any functions with "test" in their names, and run them.
+Nose reports any errors, or any failures.
+
+A failure is where one of the test conditions run with an "assert" command
+fails.  For example, if I did:
+
+    assert_almost_equal(1, 2)
+
+then this would "fail".
+
+So we use the tests to check that the results of our function are (still) as we
+expect.
+"""
+# Python 3 compatibility
+from __future__ import absolute_import, division, print_function
+
+import numpy as np
+
+from .. import pearson
+
+from numpy.testing import assert_almost_equal
+
+
+def test_pearson_1d():
+    # Test pearson_1d routine
+    x = np.random.rand(22)
+    y = np.random.rand(22)
+    # Does routine give same answer as np.corrcoef?
+    expected = np.corrcoef(x, y)[0, 1]
+    actual = pearson.pearson_1d(x, y)
+    # Did you, gentle user, forget to return the value?
+    if actual is None:
+        raise RuntimeError("function returned None")
+    assert_almost_equal(expected, actual)

code/utils/tests/test_pearson_2d.py

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+"""
+Test Pearson module, pearson_2d function
+
+Run with::
+
+    nosetests test_pearson_2d.py
+"""
+# Python 3 compatibility
+from __future__ import print_function, division
+
+import numpy as np
+
+from .. import pearson
+
+from numpy.testing import assert_almost_equal
+
+
+def test_pearson_2d():
+    # Test pearson_2d routine
+    x = np.random.rand(22)
+    Y = np.random.normal(size=(22, 12))
+    # Does routine give same answers as np.corrcoef?
+    expected = np.corrcoef(x, Y.T)[0, 1:]
+    actual = pearson.pearson_2d(x, Y)
+    # Did you, gentle user, forget to return the value?
+    if actual is None:
+        raise RuntimeError("function returned None")
+    assert_almost_equal(expected, actual)

data/Makefile

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+data:
+	wget http://www.jarrodmillman.com/rcsds/_downloads/ds107_sub001_highres.nii
+
+validate:
+	python data.py

data/data.py

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+from __future__ import print_function, division
+
+import hashlib
+import os
+
+
+d = {'ds107_sub001_highres.nii': "fd733636ae8abe8f0ffbfadedd23896c"}
+
+
+def generate_file_md5(filename, blocksize=2**20):
+    m = hashlib.md5()
+    with open(filename, "rb") as f:
+        while True:
+            buf = f.read(blocksize)
+            if not buf:
+                break
+            m.update(buf)
+    return m.hexdigest()
+
+
+def check_hashes(d):
+    all_good = True
+    for k, v in d.items():
+        digest = generate_file_md5(k)
+        if v == digest:
+            print("The file {0} has the correct hash.".format(k))
+        else:
+            print("ERROR: The file {0} has the WRONG hash!".format(k))
+            all_good = False
+    return all_good
+
+
+if __name__ == "__main__":
+    check_hashes(d)

data/tests/test_data.py

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+from __future__ import absolute_import, division, print_function
+
+import tempfile
+
+from .. import data
+
+
+def test_check_hashes():
+    tf = tempfile.NamedTemporaryFile(delete=False)
+    fname = tf.name
+    with tempfile.NamedTemporaryFile() as temp:
+        temp.write(b'Some data')
+        temp.flush()
+        fname = temp.name
+        d = {fname: "5b82f8bf4df2bfb0e66ccaa7306fd024"}
+        assert data.check_hashes(d)
+        d = {fname: "4b82f8bf4df2bfb0e66ccaa7306fd024"}
+        assert not data.check_hashes(d)

requirements.txt

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+nose==1.3.7
+numpy==1.9.2
+scipy==0.16.0
+matplotlib==1.4.3

slides/.gitignore

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+progress.pdf
+final.pdf

slides/Makefile

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+.PHONY: all clean
+
+all: clean progress.pdf
+
+clean:
+	rm -f progress.pdf
+
+progress.pdf: progress.md
+	pandoc -t beamer -s progress.md -o progress.pdf

slides/README.md

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+# Slides
+
+* http://pandoc.org/demo/example9/producing-slide-shows-with-pandoc.html
+
+## Progress report presentation
+
+Use the above guide (link) to learn how to use pandoc with beamer to produce
+slides.  I've made a template `progress.md` for you to modify.  I've also
+added a `Makefile` with some recipes to build your slides and delete the
+generated files.
+
+## Final report presentation
+
+Create a new file `final.md` and add appropriate entries to the `Makefile`.
+

slides/progress.md

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+% Project Aleph Progress Report
+% Ross Barnowski, Matthew Brett, Jarrod Millman
+% November 12, 2015
+
+# Background
+
+## The Paper
+
+- from OpenFMRI.org
+- ds001
+
+## The Data
+
+- 12 subjects
+- 2 conditions per subject
+
+## The Method
+
+- linear regression
+
+# Initial work
+
+## EDA
+
+- downloaded data
+- simple plots, summary statistics
+
+# Next steps
+
+## Preprocessing / Validation
+
+- PCA
+
+## Statistical Analysis
+
+- linear model

tools/travis_tools.sh

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+# Tools for working with travis-ci
+export WHEELHOUSE="http://travis-wheels.scikit-image.org/"
+
+retry () {
+    # https://gist.github.com/fungusakafungus/1026804
+    local retry_max=5
+    local count=$retry_max
+    while [ $count -gt 0 ]; do
+        "$@" && break
+        count=$(($count - 1))
+        sleep 1
+    done
+
+    [ $count -eq 0 ] && {
+        echo "Retry failed [$retry_max]: $@" >&2
+        return 1
+    }
+    return 0
+}
+
+
+wheelhouse_pip_install() {
+    # Install pip requirements via travis wheelhouse
+    retry pip install --timeout=60 --no-index --find-links $WHEELHOUSE -r $@
+}