sammosummo

"""Contains functions for generating and using equal-loudness contours for
side-presented steady pure tones according to the ISO/IEC 226 standard.

"""


__author__ = 'Sam Mathias'
__version__ = 1.0

sammosummo

"""This is the docstring for the example.py module.  Modules names should
have short, all-lowercase names.  The module name may have underscores if
this improves readability.

Every module should have a docstring at the very top of the file.  The
module's docstring may extend over multiple lines.  If your docstring does
extend over multiple lines, the closing three quotation marks must be on
a line by itself, preferably preceeded by a blank line.

"""

sammosummo

# -*- coding: utf-8 -*-
"""
Created on Tue Dec 10 17:41:11 2013

@author: smathias
"""
import matplotlib.pyplot as plt
import numpy as np

class Kaernbach1991:

sammosummo

# -*- coding: utf-8 -*-
"""
Created on Wed Nov 20 15:22:22 2013

@author: smathias
"""

import brian
import brian.hears as b
import numpy as np

sammosummo

"""Bayesian hierarchical generalised linear signal detection models for
yes-no experiments.

"""
import numpy as np
import pandas as pd
import patsy
import pymc3 as pm
import theano
import theano.tensor as T

sammosummo

"""Model of working-memory capacity for change-detection tasks.

This script implements a "slots-based" model of working memory capacity based
on the Cowan formula, modified to allow lapses and formalized in a Bayesian
framework.

Data are organised into a pandas data frame in which each row represents a
subject, and columns contain covariates or observations. "Covariates" can be
categorical or continuous. "Observations" are counts of trials and are named
`{x}_{y}` where `{x}` is a single uppercase letter indicating the count type

sammosummo

"""Simple fully contained script to fit a Bayesian hierarchical logistic
regression model using PyMC3.

"""
import theano
import matplotlib
matplotlib.use('Agg')  # seems to be necessary on Unix
import numpy as np
import pandas as pd
import pymc3 as pm

sammosummo

import numpy as np
import brian
import brian.hears as hears

def irn(dur, f, g, n, method='s'):
    """Generate IRN with a duration dur in seconds, a pitch at f (either a
    single value for a flat pitch, or an iterable for a contour) in Hz, a gain
    of g (-1 <= g <= 1) and n iterations. Since this is done in the time
    domain, processing time gets longer with larger n."""
    x = hears.whitenoise(dur * brian.second) #input signal

sammosummo

# -*- coding: utf-8 -*-

from warnings import warn
import numpy as np
from scipy.signal import butter, lfilter
from scipy.stats import norm, uniform
import matplotlib.pyplot as plt
import brian
import brian.hears as hears

sammosummo

import hddm
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from tqdm import tqdm


def setupfig():
    """Tweak for the target journal.