Initial commit

.gitignore

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+*.py[co]
+*~
+*.DS_Store
+_build/
+auto_examples/
+gen_api/

README.md

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+## shablona
+A bare-bones template for Python packages, ready for use with setuptools (PyPI), pip, and py.test.
+
+### Using this as a template
+Let's assume that you want to create a small scientific Python project called `smallish`.
+
+To use this repository as a template, click the green "use this template" button on the front page of the "shablona" repository.
+
+In "Repository name" enter the name of your project. For example, enter `smallish` here. After that, you can hit the "Create repository from template" button.
+
+You should then be able to clone the new repo into your machine. You will want to change the names of the files. For example, you will want to move `shablona/shablona.py` to be called `smallish/smallish.py`
+```
+git mv shablona smallish
+git mv smallish/shablona.py smallish/smallish.py
+git mv smallish/tests/test_shablona.py smallish/tests/test_smallish.py
+```
+
+Make a commit recording these changes. Something like:
+```
+git commit -a -m "Moved names from `shablona` to `smallish`"
+```
+
+You will want to edit a few more places that still have `shablona` in them. Type the following to see where all these files are:
+```
+git grep shablona
+```
+
+You can replace `shablona` for `smallish` quickly with:
+```
+git grep -l 'shablona' | xargs sed -i 's/shablona/smallish/g'
+```
+
+Edit `shablona/__init__.py`, and `shablona/version.py` with the information specific to your project.
+
+This very file (`README.md`) should be edited to reflect what your project is about.
+
+At this point, make another commit, and continue to develop your own code based on this template.
+
+
+### Contributing
+If you wish to make any changes (e.g. add documentation, tests, continuous integration, etc.), please follow the [Shablona](https://github.com/uwescience/shablona) template.

requirements.txt

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+# Per https://stackoverflow.com/questions/43658870/requirements-txt-vs-setup-py
+
+# requirements.txt
+#
+# installs dependencies from ./setup.py, and the package itself,
+# in editable mode
+-e .
+
+# (the -e above is optional). you could also just install the package
+# normally with just the line below (after uncommenting)
+# .

setup.py

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+import sys
+import os
+from setuptools import setup, find_packages
+PACKAGES = find_packages()
+
+# Get version and release info, which is all stored in shablona/version.py
+ver_file = os.path.join('shablona', 'version.py')
+with open(ver_file) as f:
+    exec(f.read())
+
+# Give setuptools a hint to complain if it's too old a version
+# 24.2.0 added the python_requires option
+# Should match pyproject.toml
+SETUP_REQUIRES = ['setuptools >= 24.2.0']
+# This enables setuptools to install wheel on-the-fly
+SETUP_REQUIRES += ['wheel'] if 'bdist_wheel' in sys.argv else []
+
+opts = dict(name=NAME,
+            maintainer=MAINTAINER,
+            maintainer_email=MAINTAINER_EMAIL,
+            description=DESCRIPTION,
+            long_description=LONG_DESCRIPTION,
+            url=URL,
+            download_url=DOWNLOAD_URL,
+            license=LICENSE,
+            classifiers=CLASSIFIERS,
+            author=AUTHOR,
+            author_email=AUTHOR_EMAIL,
+            platforms=PLATFORMS,
+            version=VERSION,
+            packages=PACKAGES,
+            package_data=PACKAGE_DATA,
+            install_requires=REQUIRES,
+            python_requires=PYTHON_REQUIRES,
+            setup_requires=SETUP_REQUIRES,
+            requires=REQUIRES)
+
+
+if __name__ == '__main__':
+    setup(**opts)

shablona/__init__.py

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+from .shablona import *  # noqa

shablona/shablona.py

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+
+from __future__ import absolute_import, division, print_function
+import numpy as np
+import pandas as pd
+import scipy.optimize as opt
+from scipy.special import erf
+
+
+def transform_data(data):
+    """
+    Function that takes experimental data and gives us the
+    dependent/independent variables for analysis.
+    Parameters
+    ----------
+    data : Pandas DataFrame or string.
+        If this is a DataFrame, it should have the columns `contrast1` and
+        `answer` from which the dependent and independent variables will be
+        extracted. If this is a string, it should be the full path to a csv
+        file that contains data that can be read into a DataFrame with this
+        specification.
+    Returns
+    -------
+    x : array
+        The unique contrast differences.
+    y : array
+        The proportion of '2' answers in each contrast difference
+    n : array
+        The number of trials in each x,y condition
+    """
+    if isinstance(data, str):
+        data = pd.read_csv(data)
+
+    contrast1 = data['contrast1']
+    answers = data['answer']
+
+    x = np.unique(contrast1)
+    y = []
+    n = []
+
+    for c in x:
+        idx = np.where(contrast1 == c)
+        n.append(float(len(idx[0])))
+        answer1 = len(np.where(answers[idx[0]] == 1)[0])
+        y.append(answer1 / n[-1])
+    return x, y, n
+
+
+def cumgauss(x, mu, sigma):
+    """
+    The cumulative Gaussian at x, for the distribution with mean mu and
+    standard deviation sigma.
+    Parameters
+    ----------
+    x : float or array
+       The values of x over which to evaluate the cumulative Gaussian function
+    mu : float
+       The mean parameter. Determines the x value at which the y value is 0.5
+    sigma : float
+       The variance parameter. Determines the slope of the curve at the point
+       of Deflection
+    Returns
+    -------
+    g : float or array
+        The cumulative gaussian with mean $\\mu$ and variance $\\sigma$
+        evaluated at all points in `x`.
+    Notes
+    -----
+    Based on:
+    http://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution_function
+    The cumulative Gaussian function is defined as:
+    .. math::
+        \\Phi(x) = \\frac{1}{2} [1 + erf(\\frac{x}{\\sqrt{2}})]
+    Where, $erf$, the error function is defined as:
+    .. math::
+        erf(x) = \\frac{1}{\\sqrt{\\pi}} \\int_{-x}^{x} e^{t^2} dt
+    """
+    return 0.5 * (1 + erf((x - mu) / (np.sqrt(2) * sigma)))
+
+
+def opt_err_func(params, x, y, func):
+    """
+    Error function for fitting a function using non-linear optimization.
+    Parameters
+    ----------
+    params : tuple
+        A tuple with the parameters of `func` according to their order of
+        input
+    x : float array
+        An independent variable.
+    y : float array
+        The dependent variable.
+    func : function
+        A function with inputs: `(x, *params)`
+    Returns
+    -------
+    float array
+        The marginals of the fit to x/y given the params
+    """
+    return y - func(x, *params)
+
+
+class Model(object):
+    """Class for fitting cumulative Gaussian functions to data"""
+    def __init__(self, func=cumgauss):
+        """ Initialize a model object.
+        Parameters
+        ----------
+        data : Pandas DataFrame
+            Data from a subjective contrast judgement experiment
+        func : callable, optional
+            A function that relates x and y through a set of parameters.
+            Default: :func:`cumgauss`
+        """
+        self.func = func
+
+    def fit(self, x, y, initial=[0.5, 1]):
+        """
+        Fit a Model to data.
+        Parameters
+        ----------
+        x : float or array
+           The independent variable: contrast values presented in the
+           experiment
+        y : float or array
+           The dependent variable
+        Returns
+        -------
+        fit : :class:`Fit` instance
+            A :class:`Fit` object that contains the parameters of the model.
+        """
+        params, _ = opt.leastsq(opt_err_func, initial,
+                                args=(x, y, self.func))
+        return Fit(self, params)
+
+
+class Fit(object):
+    """
+    Class for representing a fit of a model to data
+    """
+    def __init__(self, model, params):
+        """
+        Initialize a :class:`Fit` object.
+        Parameters
+        ----------
+        model : a :class:`Model` instance
+            An object representing the model used
+        params : array or list
+            The parameters of the model evaluated for the data
+        """
+        self.model = model
+        self.params = params
+
+    def predict(self, x):
+        """
+        Predict values of the dependent variable based on values of the
+        indpendent variable.
+        Parameters
+        ----------
+        x : float or array
+            Values of the independent variable. Can be values presented in
+            the experiment. For out-of-sample prediction (e.g. in
+            cross-validation), these can be values
+            that were not presented in the experiment.
+        Returns
+        -------
+        y : float or array
+            Predicted values of the dependent variable, corresponding to
+            values of the independent variable.
+        """
+        return self.model.func(x, *self.params)

shablona/tests/test_shablona.py

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+import os.path as op
+import numpy.testing as npt
+import shablona as ece
+
+data_path = op.join(ece.__path__[0], 'data')
+#Load data like: op.join(data_path, 'mydatafile.dat')
+
+
+def test_trivial():
+    """
+    Should always pass. Just used to ensure that py.test is setup correctly.
+    """
+    npt.assert_equal(np.array([1, 1, 1]), np.array([1, 1, 1]))

shablona/version.py

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+from os.path import join as pjoin
+
+# Format expected by setup.py and doc/source/conf.py: string of form "X.Y.Z"
+_version_major = 0
+_version_minor = 1
+_version_micro = ''  # use '' for first of series, number for 1 and above
+_version_extra = 'dev'
+# _version_extra = ''  # Uncomment this for full releases
+
+# Construct full version string from these.
+_ver = [_version_major, _version_minor]
+if _version_micro:
+    _ver.append(_version_micro)
+if _version_extra:
+    _ver.append(_version_extra)
+
+__version__ = '.'.join(map(str, _ver))
+
+CLASSIFIERS = ["Development Status :: 3 - Alpha",
+               "Environment :: Console",
+               "Intended Audience :: Science/Research",
+               "License :: OSI Approved :: MIT License",
+               "Operating System :: OS Independent",
+               "Programming Language :: Python",
+               "Topic :: Scientific/Engineering"]
+
+# Description should be a one-liner:
+description = "shablona: Put a one-liner description of your code here"
+# Long description will go up on the pypi page
+long_description = """
+shablona: Put a longer description of your code here.
+"""
+
+NAME = "shablona"
+MAINTAINER = "Graham Findlay"
+MAINTAINER_EMAIL = "[email protected]"
+DESCRIPTION = description
+LONG_DESCRIPTION = long_description
+URL = "http://github.com/CSC-UW/shablona"
+DOWNLOAD_URL = ""
+LICENSE = "MIT"
+AUTHOR = "Graham Findlay"
+AUTHOR_EMAIL = "[email protected]"
+PLATFORMS = "OS Independent"
+MAJOR = _version_major
+MINOR = _version_minor
+MICRO = _version_micro
+VERSION = __version__
+PACKAGE_DATA = {'shablona': [pjoin('data', '*')]}
+REQUIRES = ["numpy"]
+PYTHON_REQUIRES = ">= 3.7"