HDDM-0.4/0000775000175000017500000000000012054702235013255 5ustar whykingwhyking00000000000000HDDM-0.4/scripts/0000775000175000017500000000000012054702235014744 5ustar whykingwhyking00000000000000HDDM-0.4/scripts/hddm_demo.py0000775000175000017500000002773712050455454017265 0ustar whykingwhyking00000000000000#!/usr/bin/env python """ brownian() implements one dimensional Brownian motion (i.e. the Wiener process). """ import wx import matplotlib # We want matplotlib to use a wxPython backend if __name__ == "__main__": matplotlib.use('WXAgg') import matplotlib.pyplot as plt from matplotlib.figure import Figure from traits.api import HasTraits, Instance, Range,\ Array, on_trait_change, Property,\ cached_property, Bool, Button, Tuple,\ Any, Int, Str, Float, Delegate, Dict from traitsui.view import View, Item import numpy as np import time import hddm def timer(method): def time_me(self, *args,**kwargs): t0 = time.clock() res = method(self,*args,**kwargs) print "%s took %f secs" % (method.__name__, time.clock() - t0) return res return time_me class DDM(HasTraits): """Drift diffusion model""" # Paremeters z = Range(0, 1., .5) sz = Range(0, 1., .0) v = Range(-4.,4.,.5) sv = Range(0.0,2.,0.0) ter = Range(0,2.,.3) ster = Range(0,2.,.0) a = Range(0.,10.,2.) switch = Bool(False) t_switch = Range(0,2.,.3) v_switch = Range(-20.,20.,1.) intra_sv = Range(0.1,10.,1.) urgency = Range(.1,10.,1.) params = Property(Array, depends_on=['z', 'sz', 'v', 'sv', 'ter', 'ster', 'a']) #, 'switch', 't_switch', 'v_switch', 'intra_sv']) # Distributions drifts = Property(Tuple, depends_on=['params']) rts = Property(Tuple, depends_on=['drifts']) params_dict = Property(Dict) histo = Property(Array, depends_on=['params']) # Total time. T = Float(5.0) # Time step size dt = Float(1e-4) # Number of realizations to generate. num_samples = Int(5000) # Number of drifts to plot iter_plot = Int(50) # Number of histogram bins bins = Int(200) view = View('z', 'sz', 'v', 'sv', 'ter', 'ster', 'a', 'num_samples', 'iter_plot') #, 'switch', 't_switch', 'v_switch', 'intra_sv', 'T') def _get_params_dict(self): d = {'v': self.v, 'V': self.sv, 'z': self.z, 'Z': self.sz, 't': self.ter, 'T': self.ster, 'a': self.a} if self.switch: d['v_switch'] = self.v_switch d['t_switch'] = self.t_switch d['V_switch'] = self.sv return d @cached_property def _get_drifts(self): return hddm.generate._gen_rts_from_simulated_drift(self.params_dict, samples=self.iter_plot, dt=self.dt, intra_sv=self.intra_sv)[1] @cached_property def _get_rts(self): if not self.switch: # Use faster cdf method return hddm.generate.gen_rts(self.params_dict, samples=self.num_samples, range_=(-self.T, self.T)) else: # Simulate individual drifts return hddm.generate._gen_rts_from_simulated_drift(self.params_dict, samples=self.num_samples, dt=self.dt, intra_sv=self.intra_sv)[0] #return hddm.generate.gen_rts(self.params_dict, samples=self.num_samples, range_=(-5, 5), method='drift') @cached_property def _get_histo(self): n, bins = hddm.utils.histogram(self.rts, bins=2 * self.bins, range=(-self.T, self.T)) db = np.array(np.diff(bins), float) return n / db / (n.sum()) def _get_params(self): return np.array([self.a, self.v, self.ter, self.sz, self.sv, self.ster]) class DDMPlot(HasTraits): from hddm.MPLTraits import MPLFigureEditor figure = Instance(Figure, ()) parameters = Instance(DDM, ()) plot_histogram = Bool(True) plot_simple = Bool(False) plot_wiener = Bool(False) plot_lba = Bool(False) plot_drifts = Bool(True) #plot_data = Bool(False) #plot_density = Bool(False) plot_true_density = Bool(False) #plot_density_dist = Bool(False) plot_mean_rt = Bool(False) plot_switch = Bool(False) color_errors = Bool(True) x_analytical = Property(Array) wiener = Property(Array) simple = Property(Array) lba = Property(Array) switch = Property(Array) x_raster = Int(100) data = Array() # Can be set externally external_params = Dict() go = Button('Go') view = View(Item('figure', editor=MPLFigureEditor(), show_label=False), Item('parameters'), Item('plot_histogram'), Item('plot_drifts'), Item('plot_wiener'), #Item('plot_lba'), #Item('plot_data'), #Item('plot_density'), #Item('plot_true_density'), #Item('plot_density_dist'), #Item('plot_switch'), Item('color_errors'), Item('go'), #style='custom', width=800, height=600, resizable=True) def __init__(self, data=None, params=None): super(DDMPlot, self).__init__() plt.hot() # Create plot self.num_axes = 3 self.ax1 = self.figure.add_subplot(411) self.ax2 = self.figure.add_subplot(412, sharex=self.ax1) self.ax3 = self.figure.add_subplot(413, sharex=self.ax1) self.set_figure() #self.update_plot() # Set external data if necessary if data is not None: self.data = data if params is not None: self._set_external_params(params) def _data_changed(self): self.plot_data = True @cached_property def _get_x_analytical(self): return np.linspace(-self.parameters.T, self.parameters.T, self.x_raster) @timer def _get_switch(self): from hddm.sandbox.model import wfpt_switch_like pdf = wfpt_switch_like.rv.pdf(self.x_analytical, self.parameters.v, self.parameters.v_switch, self.parameters.sv, self.parameters.a, self.parameters.z, self.parameters.ter, self.parameters.t_switch, self.parameters.ster) return pdf @timer def _get_wiener(self): pdf = hddm.wfpt.pdf_array(self.x_analytical, self.parameters.v, self.parameters.sv, self.parameters.a, self.parameters.z, self.parameters.sz, self.parameters.ter, self.parameters.ster, 1e-4, logp=False) return pdf @timer def _get_lba(self): return hddm.likelihoods.LBA_like(self.x_analytical, a=self.parameters.a, z=self.parameters.z, v0=self.parameters.v, v1=self.parameters.sz, t=self.parameters.ter, V=self.parameters.sv, logp=False) def _go_fired(self): self.update_plot() def plot_histo(self, x, y, color, max_perc=None): # y consists of lower and upper boundary responses # mid point tells us where to split assert y.shape[0] % 2 == 0, "x_analytical has to be even. Shape is %s " % str(y.shape) mid = y.shape[0] / 2 self.figure.axes[0].plot(x, y[mid:], color=color, lw=2.) # Compute correct EV mean_correct_rt = np.sum(x * y[mid:]) / np.sum(y[mid:]) # [::-1] -> reverse ordering if self.color_errors: color='r' self.figure.axes[2].plot(x, -y[:mid][::-1], color=color, lw=2.) # Compute error EV mean_error_rt = np.sum(x * y[:mid][::-1]) / np.sum(y[:mid][::-1]) if self.plot_mean_rt: self.figure.axes[0].axvline(mean_correct_rt, color=color) if self.color_errors: self.figure.axes[2].axvline(mean_error_rt, color='r') def plot_dens(self): t = np.linspace(0.0, self.parameters.T, self.x_raster) xm, ym = np.meshgrid(t, np.linspace(0, self.parameters.a, 100)) zm = np.zeros_like(xm) # np.zeros((t, 100), dtype=np.float) #print zs i = 0 for xs, ys in zip(xm, ym): j = 0 for x, y in zip(xs, ys): if x <= self.parameters.ter + .05: zm[i, j] = 1. else: zm[i, j] = hddm.wfpt_switch.drift_dens(y, x - self.parameters.ter, self.parameters.v, self.parameters.a, self.parameters.z * self.parameters.a) j += 1 self.figure.axes[1].contourf(xm, ym, zm) @on_trait_change('parameters.params') def update_plot(self): if self.figure.canvas is None: return # Clear all plots for i in range(self.num_axes): self.figure.axes[i].clear() # Set x axis values x = np.linspace(0, self.parameters.T, self.parameters.bins) # Set x axis values for analyticals x_anal = np.linspace(0, self.parameters.T, self.x_raster / 2) # Plot normalized histograms of simulated data if self.plot_histogram: self.plot_histo(x, self.parameters.histo, color='b', max_perc=.99) # Plot normalized histograms of empirical data #if self.plot_data: # histo = hddm.utils.histogram(self.data, bins=2*self.parameters.bins, range=(-self.parameters.T, self.parameters.T), dens=True)[0] # self.plot_histo(x, histo, color='y') # Plot analytical simple likelihood function if self.plot_switch and self.parameters.switch: self.plot_histo(x_anal, self.switch, color='r') if self.plot_lba: self.plot_histo(x_anal, self.lba, color='k') if self.plot_wiener: self.plot_histo(x_anal, self.wiener, color='y') if self.plot_true_density: self.plot_dens() if self.plot_drifts: t = np.linspace(0.0, self.parameters.T, self.parameters.T / 1e-4) drifts = self.parameters.drifts for drift in drifts: # Make sure drift is not longer than x-axis drift_len = len(drift) if len(drift) < len(t) else len(t) if self.color_errors: if drift[drift_len-1] < 0.1: color = 'r' else: color = 'b' else: color = 'b' self.figure.axes[1].plot(t[:drift_len], drift[:drift_len], color, alpha=.5) # Plot analytical simple likelihood function if self.parameters.switch: self.figure.axes[1].axvline(self.parameters.ter+self.parameters.t_switch, color='k', linestyle='--', lw=2) self.set_figure() wx.CallAfter(self.figure.canvas.draw) def set_figure(self): # Set axes limits # TODO: Fix boundary now that we are using true densities total_max = 1. if len(self.figure.axes[0].get_lines()) != 0: for line in self.figure.axes[0].get_lines(): max_point = np.max(line.get_ydata()) if max_point > total_max: total_max = max_point for line in self.figure.axes[2].get_lines(): max_point = np.abs(np.min(line.get_ydata())) if max_point > total_max: total_max = max_point self.figure.axes[0].set_ylim((0, total_max)) self.figure.axes[2].set_ylim((-total_max, 0)) self.figure.axes[0].set_xlim((0, self.parameters.T)) self.figure.axes[2].set_xlim((0, self.parameters.T)) self.figure.axes[1].set_ylim((0, self.parameters.a)) self.figure.subplots_adjust(hspace=0) for ax in self.ax1, self.ax2: plt.setp(ax.get_xticklabels(), visible=False) plt.setp(ax.get_yticklabels(), visible=False) plt.setp(self.ax3.get_yticklabels(), visible=False) self.ax3.set_xlabel('time (secs)') if __name__ == "__main__": ddm = DDMPlot() ddm.configure_traits() HDDM-0.4/scripts/hddm_fit.py0000775000175000017500000000404012050455454017101 0ustar whykingwhyking00000000000000#!/usr/bin/env python import sys import hddm import os try: import argparse except: # fall back to version not using argparse if __name__=='__main__': if len(sys.argv) == 1 or sys.argv[1] == '-h' or sys.argv[1] == '--help': print "hddmfit model.cfg [data.csv]" sys.exit(-1) config_fname = sys.argv[1] data_fname = None if len(sys.argv) == 3: data_fname = sys.argv[2] hddm.utils.parse_config_file(config_fname, data=data_fname) sys.exit(0) if __name__=='__main__': parser = argparse.ArgumentParser(description='HDDM: Hierarchical estimation of drift diffusion models.', add_help=True, version='0.2a') parser.add_argument('--verbose', action="store_true", dest="verbose", help='more verbosity') parser.add_argument('-m', '--map', action="store_true", dest="map", help='find a starting point via MAP estimation') parser.add_argument('-s', '--samples', action="store", dest="samples", type=int, help='samples to generate') parser.add_argument('-b', '--burn', action="store", dest="burn", type=int, help='initial samples to discard as burn-in') parser.add_argument('-t', '--thin', action="store", dest="thin", type=int, help='keep every nth sample') parser.add_argument('-og', action="store_true", dest="only_group_stats", default=False, help='Output only group stats.') parser.add_argument('-np', action='store_true', dest='no_plots', default=False, help='Do not generate output plots.') parser.add_argument('model') parser.add_argument('data') results = parser.parse_args() hddm.utils.parse_config_file(results.model, data=results.data, samples=results.samples, burn=results.burn, thin=results.thin, map=results.map, only_group_stats=results.only_group_stats, plot=not(results.no_plots), verbose=results.verbose) sys.exit(0) HDDM-0.4/LICENSE0000664000175000017500000000277412050455466014303 0ustar whykingwhyking00000000000000Copyright (c) 2012, Thomas V. Wiecki, Imri Sofer All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. The views and conclusions contained in the software and documentation are those of the authors and should not be interpreted as representing official policies, either expressed or implied, of the FreeBSD Project. HDDM-0.4/README.rst0000664000175000017500000001271612054702055014753 0ustar whykingwhyking00000000000000************ Introduction ************ :Date: October 29, 2012 :Author: Thomas V. Wiecki, Imri Sofer, Michael J. Frank :Contact: thomas_wiecki@brown.edu, imri_sofer@brown.edu, michael_frank@brown.edu :Web site: http://ski.clps.brown.edu/hddm_docs :Github: http://github.com/hddm-devs/hddm :Mailing list: https://groups.google.com/group/hddm-users/ :Copyright: This document has been placed in the public domain. :License: HDDM is released under the BSD 2 license. :Version: 0.4 Purpose ======= HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making. Features ======== * Uses hierarchical Bayesian estimation (via PyMC) of DDM parameters to allow simultaneous estimation of subject and group parameters, where individual subjects are assumed to be drawn from a group distribution. HDDM should thus produce better estimates when less RT values are measured compared to other methods using maximum likelihood for individual subjects (i.e. `DMAT`_ or `fast-dm`_). * Heavily optimized likelihood functions for speed (Navarro & Fuss, 2009). * Flexible creation of complex models tailored to specific hypotheses (e.g. estimation of separate drift-rates for different task conditions; or predicted changes in model parameters as a function of other indicators like brain activity). * Easy specification of models via configuration file fosters exchange of models and research results. * Built-in Bayesian hypothesis testing and several convergence and goodness-of-fit diagnostics. Usage ===== Command line ------------ The easiest way to use HDDM is by creating a configuration file for your model: example.conf :: [depends] v = difficulty Then call hddm_fit.py: :: hddm_fit.py --samples 10000 --burn 5000 example.conf mydata.csv Python ------ Of course, you can also use HDDM directly from within Python: :: import hddm # Load data from csv file into a NumPy structured array data = hddm.load_csv('simple_difficulty.csv') # Create a HDDM model multi object model = hddm.HDDM(data, depends_on={'v':'difficulty'}) # Create model and start MCMC sampling model.sample(10000, burn=5000) # Print fitted parameters and other model statistics model.print_stats() # Plot posterior distributions and theoretical RT distributions model.plot_posteriors(save=True) model.plot_posterior_predictive(save=True) Installing ========== See further below on how to install most of the necessary dependencies for the most common platforms. Assuming you have the dependencies, the easiest way to install HDDM is via pip :: pip install kabuki pip install hddm If you already installed a previous version of HDDM, you should upgrade instead :: pip install --upgrade --no-deps kabuki pip install --upgrade --no-deps hddm Dependencies ============ HDDM relies on the following packages: * Python * NumPy * PyMC_ (installation instructions: http://pymc.googlecode.com/svn/doc/installation.html) * kabuki_ * SciPy (optional) * Matplotlib (optional) * Cython_ (optional) Windows ------- The easiest way is to download and install the `Enthought Python Distribution`_ (EPD) which is free for academic use. An untested alternative is the Python(x,y) distribution. Please let us know your experiences if you test this option. We recommend using pip to download and install HDDM. The easiest way to install pip is via easy_install. Start the windows command shell (cmd.exe) and type :: easy_install pip Linux (Debian based, such as Ubuntu) ------------------------------------ Most of HDDM's dependencies are available from your repository, you can install them by typing :: apt-get install python python-dev python-numpy python-scipy python-matplotlib cython python-pip gfortran liblapack-dev which requires sudo rights. Optional dependencies for hddm_demo.py can be installed via :: apt-get install python-wxgtk2.8 python-traitsui OSX --- We recommend installing the `SciPy Superpack`_ maintained by Chris Fonnesbeck. The Superpack requires you to install XCode which apparently does not bundle with gcc anymore (which is required by HDDM). This repository provides some appropriate installers: https://github.com/kennethreitz/osx-gcc-installer/downloads How to cite =========== If HDDM was used in your research, please cite the following: Wiecki, T. V., Sofer, I. and Frank, M. J. at the 44th annual meeting of the Society for Mathematical Psychology, 65, 2011 Getting started =============== Check out the tutorial_ on how to get started. Further information can be found in howto_ and the documentation_. Join our low-traffic `mailing list`_. .. _HDDM: http://code.google.com/p/hddm/ .. _Python: http://www.python.org/ .. _PyMC: http://code.google.com/p/pymc/ .. _Cython: http://www.cython.org/ .. _DMAT: http://ppw.kuleuven.be/okp/software/dmat/ .. _fast-dm: http://seehuhn.de/pages/fast-dm .. _documentation: http://ski.clps.brown.edu/hddm_docs .. _tutorial: http://ski.clps.brown.edu/hddm_docs/tutorial.html .. _howto: http://ski.clps.brown.edu/hddm_docs/howto.html .. _manual: http://ski.clps.brown.edu/hddm_docs/manual.html .. _kabuki: https://github.com/hddm-devs/kabuki .. _Enthought Python Distribution: http://www.enthought.com/products/edudownload.php .. _mailing list: https://groups.google.com/group/hddm-users/ .. _SciPy Superpack: http://fonnesbeck.github.com/ScipySuperpack/ HDDM-0.4/setup.py0000775000175000017500000000306612054702215014775 0ustar whykingwhyking00000000000000from distutils.core import setup from distutils.extension import Extension from glob import glob try: from Cython.Build import cythonize ext_modules = cythonize([Extension('wfpt', ['src/wfpt.pyx']), Extension('lba', ['src/lba.pyx']) ]) except ImportError: ext_modules = [Extension('wfpt', ['src/wfpt.c']), Extension('lba', ['src/lba.c']) ] import numpy as np setup( name='HDDM', version='0.4', author='Thomas V. Wiecki, Imri Sofer, Michael J. Frank', author_email='thomas_wiecki@brown.edu', url='http://github.com/hddm-devs/hddm', packages=['hddm', 'hddm.tests', 'hddm.models'], package_data={'hddm':['examples/*.csv', 'examples/*.conf']}, scripts=['scripts/hddm_fit.py', 'scripts/hddm_demo.py'], description='HDDM is a python module that implements Hierarchical Bayesian estimation of Drift Diffusion Models.', install_requires=['NumPy >=1.5.0', 'SciPy >= 0.6.0', 'kabuki >= 0.4', 'PyMC >= 2.2'], setup_requires=['NumPy >=1.5.0', 'SciPy >= 0.6.0', 'kabuki >= 0.4', 'PyMC >= 2.2'], include_dirs = [np.get_include()], classifiers=[ 'Development Status :: 4 - Beta', 'Environment :: Console', 'Operating System :: OS Independent', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: GNU General Public License (GPL)', 'Programming Language :: Python', 'Topic :: Scientific/Engineering', ], ext_modules = ext_modules ) HDDM-0.4/docs/0000775000175000017500000000000012054702235014205 5ustar whykingwhyking00000000000000HDDM-0.4/docs/tutorial_config_difficulty.rst0000664000175000017500000002107012050455453022354 0ustar whykingwhyking00000000000000.. index:: Tutorial .. _chap_tutorial_config: ***************** A note of caution ***************** Although HDDM tries to make hierarchical Bayesian estimation as easy and automatic as possible, the statistical methods used to estimate the posterior (i.e. Markov-Chain Monte Carlo) rely on certain assumptions (e.g. chain-convergence). Although we encourage everyone to try this software, we would like to stress that you are responsible for making sure that the model actually works. In order to assess whether the necessary conditions for interpreting your results are met you have to have some basic grasp on what those methods do and on what assumptions they rest. There are multiple books introducing you to the world of hierarchical Bayesian estimation. Two of which we like are: `A Practical Course in Bayesian Graphical Modeling`_ by E.J. Wagenmakers and M. Lee `Doing Bayesian Data Analysis\: A Tutorial with R and BUGS`_ by J. Kruschke **************************************** Getting started: Creating a simple model **************************************** Imagine that we collected data from one subject on the moving dots or coherent motion task (e.g., Roitman & Shadlen, 2002). In this task, participants indicate via keypress in which of two directions dots are moving on a screen. Only some of the dots are moving in a coherent direction; depending on the difficulty, more or less of the dots are moving in random directions (see the figure). In our working example, we used two conditions, an easy and a hard condition. In the following, we will walk through the steps on creating a model in HDDM to estimate the underlying psychological decision making parameters of this task. .. figure:: moving_dots.jpg :scale: 20% The easiest way to use HDDM is to create a configuration file. First, you have to prepare your data to be in a specific format (csv). The data that we use here were generated from simulated DDM processes (i.e. they are not real data), so that we know the true underlying generative parameters. The data file can be found in the examples directory and is named simple_difficulty.csv (under Windows, these files can probably be found in ``C:\Python27\Lib\site-packages\hddm\examples``). Lets take a look at what it looks like: .. literalinclude :: ../hddm/examples/simple_difficulty.csv :lines: 1,101-105,579-582 The first line contains the header and specifies which columns contain which data. IMPORTANT: There must be one column named 'rt' and one named 'response'. The rows following the header contain the response made (e.g. 1=correct, 0=error or 1=left, 0=right), followed by the reaction time of the trial, followed by the difficulty of the trial. Note, that 'difficulty' is just an example, you can call them whatever you like. The following configuration file specifies a model in which drift-rate depends on difficulty: .. literalinclude :: ../hddm/examples/simple_difficulty.conf The (optional) tag [depends] specifies DDM parameters that depend on data. In our case, we want to estimate separate drift-rates (v) for the conditions found in the data column 'difficulty'. The optional [mcmc] tag specifies parameter of the Markov chain Monte-Carlo estimation such as how many samples to draw from the posterior and how many samples to discard as burn-in (as in any MCMC case, often it takes the MCMC chains some time to converge to the true posterior so that one would not want to use the initial samples to draw inferences about the true parameters; for details please read up on MCMC approaches). Note that you can also specify these parameters via the command line. Our model specification is now complete and we can fit the model by calling hddmfit: :: hddm_fit.py simple_difficulty.conf simple_difficulty.csv The first argument tells HDDM which model specification to use, the second argument is the data file to apply the model to. Calling hddmfit in this way will generate the following output (note that the numbers will be slightly different each time you run this): :: Creating model... Sampling: 100% [0000000000000000000000000000000000] Iterations: 10000 name mean std 2.5q 25q 50q 75q 97.5 mc_err a : 2.029 0.034 1.953 2.009 2.028 2.049 2.090 0.002 t : 0.297 0.007 0.282 0.292 0.297 0.302 0.311 0.001 v('easy',): 0.992 0.051 0.902 0.953 0.987 1.028 1.102 0.003 v('hard',): 0.522 0.049 0.429 0.485 0.514 0.561 0.612 0.002 logp: -1171.276303 DIC: 2329.069932 The parameters of DDM are usually abbreviated and have the following meaning: * a: threshold * t: non-decision time * v: drift-rate * z: bias (optional) * V: inter-trial variability in drift-rate (optional) * Z: inter-trial variability in bias (optional) * T: inter-trial variability in non-decision time (optional) Because we used simulated data in this example, we know the true parameters that generated the data (i.e. a=2, t=0.3, v_easy=1, v_hard=0.5). As you can see, the mean posterior values are very close to the true parameters -- our estimation worked! However, often we are not only interested in the best fitting value but also how confident we are in that estimation and how good other values are fitting. This is one of advantages of the Bayesian approach -- it gives us the complete posterior distribution, rather than just a single best guess (in principle the maximum likelihood estimate could be only slightly better fit than a range of other values, in which case the posterior would have high variance). As such the next columns are statistics on the shape of the distribution, such as the standard deviation and different quantiles to give you a feel for how certain you can be in the estimates. Lastly, logp and DIC give you a measure of how well the model fits the data. These values are not all that useful if looked at in isolation but they provide a tool to do model comparison. Logp is the summed log-likelihood of the best-fitting values (higher is better). DIC stands for deviance information criterion and is a measure that takes model complexity into account for bayesian models, similar to BIC or AIC (see here for the relationship http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/dicpage.shtml). Lower values are better. :Exercise: Create a new model that ignores the different difficulties (i.e. only estimate a single drift-rate). Compare the resulting DIC score with that of the previous model -- does the increased complexity of the first model result in a sufficient increase in model fit to justify using it? Why does the drift-rate estimate of the second model make sense? Output plots ************ In addition, HDDM generates some useful plots such as the posterior predictive probability density on top of the normalized RT distribution for each condition: .. figure:: ../hddm/examples/plots/simple_difficulty_easy.png :scale: 40% .. figure:: ../hddm/examples/plots/simple_difficulty_hard.png :scale: 40% Note that error responses have been mirrored along the y-axis (on the left) to display both RT distributions in one plot. These plots allow you to see how good the estimation fits your data. Here, we also see that our subject makes more errors and are slower in the difficult condition. This combination is well captured by the reduced estimated drift-rate parameter in this condition. Moreover, HDDM generates the trace and histogram of the posterior samples. As pointed out in the introduction, we can rarely compute the posterior analytically so we have to estimate it. One standard method is MCMC which allows you to draw samples from the posterior. On the left side of the plot we see the trace of this sampling. The main thing to look out for is if the chain drifts around such that the mean value is not stable (i.e. has not converged) or if there are periods where it seems stuck in one place (i.e. proposal distribution too wide). In our case the chain of the parameter "a" (threshold) seems to have converged nicely to the correct value. This is also illustrated in the right side plot which is the histogram of the trace and gives a feel for how to the posterior distribution looks like. In our case, it looks like a normal distribution centered around a value close to 2. .. figure:: ../hddm/examples/plots/simple_difficulty_trace_a.png :scale: 40% Now we are ready for :ref:`part two of the tutorial `. .. _A Practical Course in Bayesian Graphical Modeling: http://www.ejwagenmakers.com/BayesCourse/BayesBook.html .. _Doing Bayesian Data Analysis\: A Tutorial with R and BUGS: http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/ HDDM-0.4/docs/howto.rst0000664000175000017500000004142512051202355016100 0ustar whykingwhyking00000000000000How-to ====== Code subject responses ---------------------- There are two ways you can code subject responses (these are the values you put in the 'response' column in your data file). You can either use accuracy-coding where 1 means correct and 0 means error, or you can use stimulus-coding where 1 means left and 0 means right (this could also code for stimulus A and B instead of left and right). HDDM interprets 0 and 1 responses as lower and upper boundary responses, respectively, so it has no preference one way or another. In most cases it is more direct to use accuracy coding because drift-rate will be directly associated with performance. However, if a certain response direction or stimulus type has a higher probability of being correct and you want to estimate bias, you can *not* use accuracy coding (see the next paragraph for how to include a bias parameter). Instead, you should use stimulus coding. If there is good reason to believe that both stimuli have the same information you should use the HDDMStimCoding model. For this, add a column to your data that codes which stimulus was correct and instantiate the model like this: :: model = hddm.HDDMStimCoding(data, include='z', stim_col='stim', split_param='v') This model expects data to have a column named stim with two distinct identifiers. For identifier 1, drift-rate v will be used while for identifier 2, -v will be used. So ultimately you only estimate one drift-rate. Alternatively you can use bias z and 1-z if you set split_param='z'. See the HDDMStimCoding help doc for more information. Include bias and inter-trial variability ---------------------------------------- Bias and inter-trial variability parameters are optional and can be included as follows: :: model = hddm.HDDM(data, bias=True, include=('sv', 'st', 'sz')) or: :: model = hddm.HDDM(data, include=('z', 'sv', 'st', 'sz')) Where *sv* is inter-trial variability in drift-rate, *st* is inter-trial variability in non-decision time and *sz* is inter-trial variability in starting-point. There is also a convenience argument that is identical to the above. :: model = hddm.HDDM(data, bias=True, include='all') Note that you can also include a subset of parameters. This is relevant because these parameter slow down sampling significantly. In our experience, sv and st often play a bigger role but it really depends on your dataset. I often run models without inter-trial variabilities for exploratory analysis and then later play around with different configurations. If a certain parameter is estimated very close to zero or fails to converge you might want to exclude it (or only include a group-node, see below). Finally, parameter recovery studies show that it requires a lot of trials to get meaningful estimates of these parameters. Have separate parameters for different conditions using depends_on ------------------------------------------------------------------ Most psychological experiments test how different conditions (e.g. drug manipulations) affect certain parameters. You can build arbitrarily complex models using the depends_on keyword. :: model = hddm.HDDM(data, depends_on={'a': 'drug', 'v': ['drug', 'difficulty']}) This will create model in which separate thresholds are estimated for each drug condition and separate drift-rates for different drug conditions and different levels of difficulty. Note that this requires the columns 'drug' and 'difficulty' to be present in your data array. For readability of which parameter coded for what it is often useful to use string identifiers (e.g. drug: off/on rather than drug: 0/1). As you can see, single or multiple columns can supplied as values. Deal with outliers ------------------ HDDM 0.4 (and upwards) enables estimation of a mixture model that enables stable parameter estimation even with outliers present. You can either specify a fixed probability for obtaining an outlier (e.g. 0.05 will assume 5% of the RTs are outliers) or estimate this from the data. In practice, the precise value of p_outlier does not matter. Any value between 0.001 and 0.1, is enough to capture the outliers, and the effect on the recovered paramters is small. To instantiate a model with a fixed probability of getting an outlier run: :: m = hddm.HDDM(data, p_outlier=0.05) To estimate p_outlier from the data, run: :: m = hddm.HDDM(data, include=('p_outlier',)) Under the hood we assume that outliers come from uniform distribution with a fixed density w_outlier (as suggested by Ratcliff and Tuerlinckx, 2002). The resulting likelihood function looks as follows: .. math:: p(RT; v, a, t) = wfpt(RT; v, a, t) * (1-p_{outlier}) + w_{outlier} * p_{outlier} The default value of :math:`w_{outlier}` is 0.1, which is equivalent to uniform distribution from 0 to 5 seconds. However, in practice, the outliers model is applied to all RTs, even the ones which are larger than 5. Estimate a regression model --------------------------- HDDM 0.4 (and upwards) includes a regression model that allows estimation of trial-by-trial influences of a covariate (e.g. a brain measure like fMRI) onto DDM parameters. For example, if your prediction is that activity of a particular brain area has a linear correlation with drift-rate, you could specify the following regression model (make sure to have a column with the brain activity in your data, in our example name this column 'BOLD'): :: # Define regression function (linear in this case) reg_func = lambda args, cols: args[0] + args[1]*cols[:,0] # Define regression descriptor # regression function to use (func, defined above) # args: parameter names (passed to reg_func; v_slope->args[0], # v_inter->args[1]) # covariates: data column to use as the covariate # (in this example, expects a column named # BOLD in the data) # outcome: DDM parameter that will be replaced by trial-by-trial # regressor values (drift-rate v in this case) reg = {'func': reg_func, 'args': ['v_inter','v_slope'], 'covariates': 'BOLD', 'outcome': 'v'} # construct regression model. Second argument must be the # regression descriptor. This model will have new parameters defined # in args above, these can be used in depends_on like any other # parameter. m = hddm.HDDMRegressor(data, reg, depends_on={'v_slope':'trial_type'}) Note that in the last line, the regression coefficients become ordinary model parameters you can use in depends_on. You can also pass a list to covariates if you want to include multiple covariates. E.g.: :: # Define regression function with interaction with exponential # transform reg_func = lambda args, cols: np.exp(args[0] + args[1]*cols[:,0] + args[2]*cols[:,1] + args[3]*cols[:,0]*cols[:,1]) reg = {'func': reg_func, 'args': ['a_intercept','a_slope_cov1', 'a_slope_cov2', 'a_interaction'], 'covariates': 'BOLD', 'outcome': 'a'} Note that these regression coefficients are often hard to estimate and require a lot of data. If you have problems with chain convergance, consider turning the coefficients into group_only_nodes (see above). If you want to estimate two separate regressions, you can also supply a list of regression descriptors to HDDMRegressor: :: m = hddm.HDDMRegressor(data, [reg_a, reg_t]) Make sure to give all regression coefficients different names. Assess model convergence ------------------------ When using MCMC sampling it is critical to make sure that our chains have converged. This basically means that we are sampling from the actual posterior. Unfortunately, there is no 100% fool-proof way to assess whether our chains really converged. In reality, however, if you follow a couple of steps to convince yourself you should be OK in most cases. Look at MC error statistic """""""""""""""""""""""""" When calling: :: model.print_stats() There is a column called MC error. These values should not be smaller then 1% of the posterior std. However, this is a very weak statistic and by no means sufficient to assess convergence. Geweke statistic """""""""""""""" The Geweke statistic is a time-series approach that compares the mean and variance of segments from the beginning and end of a single chain. You can test your model by running: :: from kabuki.analyze import check_geweke print check_geweke(model) This will print `True` if non of the test-statistics is larger than 2 and `False` otherwise. Check the `PyMC documentation` for more information on this test. Visually inspect chains """"""""""""""""""""""" The next thing to look at are the traces of the posteriors. You can plot them by calling: :: model.plot_posteriors() This will create a figure for each parameter in your model (which could be a lot). Here is an example of what a not-converged chain looks like: .. figure:: not_converged_trace.png and an example of what a converged chain looks like: .. figure:: converged_trace.png As you can see, there are striking differences. In the not-converged case, the trace in the upper left corner is very non-stationary. There are also certain periods where no jumps are performed and the chain is stuck (horizontal lines in the trace); this is due to the proposal distribution not being tuned correctly. Secondly, the autocorrelation (lower left plot) is quite high as you can see from the long tails of the distribution. This is further indication that the samples are not independent draws from the posterior. If the chain seems fine otherwise some autocorrelation must not be a big deal. To leverage the problem, increase thinning (see below). Finally, the histogram (right plot) looks rather jagged in the non-converged case. This is our approximation of the marginal posterior distribution for this parameter. Generally, subject and group mean posteriors are normal distributed (see the converged case) while group variability posteriors are Gamma distributed. Posterior predictive analysis """"""""""""""""""""""""""""" Another way to assess how good your model fits the data is to perform posterior predictive analysis: :: model.plot_posterior_predictive() .. TODO: ADD NICE PLOT This will plot the posterior predictive in blue on top of the RT histogram in red for each subject and each condition. Since we are getting a distribution rather than a single parameter in our analysis, the posterior predictive is the average likelihood evaluated over different samples from the posterior. The width of the posterior predictive in light blue corresponds to the standard deviation. R-hat convergence statistic """"""""""""""""""""""""""" Another option to assess chain convergence is to compute the R-hat (Gelman-Rubin) statistic. This requires multiple chains to be run. If all chains converged to the same stationary distribution they should be indistinguishable. The R-hat statistic compares between-chain variance to within-chain variance. To compute the R-hat statistic in kabuki you have to run multiple copies of your model: :: from kabuki.analyze import gelman_rubin models = [] for i in range(5): m = hddm.HDDM(data) m.map() m.sample(5000, burn=1000) models.append(m) gelman_rubin(models) The output is a dictionary that provides the R-hat for each parameter: :: {'a_trans': 1.0028806196268818, 't_trans': 1.0100017175108695, 'v': 1.0232548747719443} What to do about lack of convergence ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ In the simplest case you just need to run a longer chain with more burn-in and more thinning. E.g.: :: model.sample(50000, burn=45000, thin=5) This will cause the first 45000 samples to be discarded. Of the remaining 5000 samples only every 5th sample will be saved. Thus, after sampling our trace will have a length of a 1000 samples. You might also want to find a good starting point for running your chains. This is commonly achieved by finding the maximum posterior (MAP) via optimization. Before sampling, simply call: :: model.map() which will set the starting values to the MAP. Then sample as you would normally. This is a good idea in general. If that still does not work you might want to consider simplifying your model. Certain parameters are just notoriously slow to converge; especially inter-trial variability parameters. The reason is that often individual subjects do not provide enough information to meaningfully estimate these parameters on a per-subject basis. One way around this is to not even try to estimate individual subject parameters and instead use only group nodes. This can be achieved via the group_only_nodes keyword argument: :: model = hddm.HDDM(data, include=['sv', 'st'], group_only_nodes=['sv', 'st']) The resulting model will still have subject nodes for all parameters but sv and st. Perform model comparison ------------------------ We can often come up with different viable hypotheses about which parameters might be influenced by our experimental conditions. Above you can see how you can create these different models using the depends_on keyword. DIC """ To compare which model does a better job at explaining the data you can compare the DIC_ scores (lower is better) emitted when calling: :: model.print_stats() DIC, however, is far from being a perfect measure. So it shouldn't be your only weapon in deciding which model is best. Posterior predictive check """""""""""""""""""""""""" A very elegant method to compare models is to sample new data sets from the estimated model and see how well these simulated data sets corresponds to the actual data on some measurement (e.g. is the mean RT well recovered by this model?). This test is called posterior predictive check and you can run it like this: :: from hddm.utils import post_pred_check post_pred_check(model) This will return a table of statistics which might look like this: :: observed credible quantile SEM mahalanobis mean std 2.5q 25q 50q 75q 97.5q NaN node stat wfpt std_ub 0.353652 1 49.298597 0.000647 0.153912 0.379096 0.165319 0.120420 0.265707 0.354912 0.465269 0.778341 1 mean_lb -0.958116 1 58.200000 0.000400 0.205017 -0.978110 0.097522 -1.206278 -1.030025 -0.971118 -0.911902 -0.811491 0 mean_ub 0.958336 1 51.703407 0.000216 0.090950 0.973042 0.161691 0.699320 0.859808 0.949264 1.067915 1.333156 1 accuracy 0.200000 1 55.700000 0.000005 0.029034 0.197720 0.078529 0.060000 0.140000 0.180000 0.240000 0.380000 0 The rows correspond to the different observed nodes and summary statistics that the model was evaluated on (e.g. mean_lb which represents the mean RT of lower boundary responses)). The columns correspond to the statistics of how the corresponding summary statistic of the real data relates to the simulated data sets. E.g. `wfpt`, `accuracy`, `Observed` represents the accuracy of the observed data. `Quantile` represents in which quantile this mean RT is in the mean RT taken over the simulate data sets. If our model did a great job at recovering we wanted it to produce RTs that have the same mean as our actual data. So the closer this is to the 50th quantile the better. Save and load models -------------------- HDDM models can be saved and reloaded in a separate python session. This is useful if your models need a lot of RAM or you are running models on a cluster. Note that only the traces (i.e. samples) get saved, you do have to recreate the model. :: # 1 load data and create a model data = hddm.load_csv('path_to_my_data') model = hddm.HDDM(data, bias=True) # a very simple model... # 2 add commands for saving traces in a file model.mcmc(dbname='traces.db', db='pickle') # 3 run model. the traces will be saved in the file traces.db in the current working directory (alternatively specify path) model.sample(5000, burn=1000) Now assume that you start a new python session, after the chain started above is completed. :: #4 reconstruct your model data = hddm.load_csv('path_to_my_data') model = hddm.HDDM(data, bias=True) #5 add traces from database model.load_db('traces.db') # not that for this to work you have to be in the same working directory you were in when you started the chain above. otherwise submit full path # now you can access the traces as you can when a chain has just completed # for example, you can access the contents of the chain for parameter v with # len(model.mc.trace("v")[:]) Under the hood, HDDM uses the database backend provided by PyMC. More information on the types of backends and their properties can be found in the `PyMC docs`_. .. _PyMC docs: http://pymc-devs.github.com/pymc/database.html#saving-data-to-disk .. _DIC: http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/dicpage.shtml .. _PyMC documentation: http://pymc-devs.github.com/pymc/modelchecking.html#formal-methods HDDM-0.4/docs/abstract.rst0000664000175000017500000000153312005542775016553 0ustar whykingwhyking00000000000000Abstract ======== Diffusion models are used to infer psychological processes underlying decision making. Efficient open source software to fit these models to data has been made available in different programming languages. However, current estimation methods require an abundance of reaction time measurements to recover meaningful parameters. Here, we present HDDM which stands for Hierarchical Drift Diffusion Modeling and allows fast and easy bayesian estimation of the model parameters using PyMC. Instead of estimating parameters for each subject individually like other tools, HDDM allows simultanious estimation of group and subject parameters and thus requires less data per subject. HDDM does not only provide best fitting parameters, but rather the whole posterior distribution of each parameter which allows intuitive goodness-of-fit statistics. HDDM-0.4/docs/tutorial_python.rst0000664000175000017500000000614012050455453020207 0ustar whykingwhyking00000000000000.. index:: Tutorial .. _chap_tutorial_python: **************************** Specifiying Models in Python **************************** As an alternative to the configuration file, HDDM offers model specification directly from Python. For this, you first import hddm: .. literalinclude :: ../hddm/examples/simple_model.py :lines: 1 Next, we have to load the data into Python. HDDM expects a NumPy structured array which you can either create yourself or load it from a csv file. Information on how to create a proper structured NumPy array can be found here. If you want to load a csv file make sure it is in the proper format outlined above. You can then load the data as follows: .. literalinclude :: ../hddm/examples/simple_model.py :lines: 4 After you loaded the data you can create the model object which is called Multi because it allows you to dynamically create multiple HDDM models depending on your data. In the simplest case, you'll want to create a simple DDM (default): .. literalinclude :: ../hddm/examples/simple_model.py :lines: 7 You may then sample from the posterior distribution by calling: .. literalinclude :: ../hddm/examples/simple_model.py :lines: 10 Depending on the model and amount of data this can take some time. After enough samples were generated, you may want to print some statistics on the screen: .. literalinclude :: ../hddm/examples/simple_model.py :lines: 13 You can currently generate two plots to examine model fit. If you want to see if your chains converged and what the posteriors for each parameter look like you can call: .. literalinclude :: ../hddm/examples/simple_model.py :lines: 16 To see how well the RT distributions are fit by the mean of the posterior distribution we can plot the theoretical RT distribution on top of our empirical RT distribution by calling: .. literalinclude :: ../hddm/examples/simple_model.py :lines: 17 The closer the two distributions look like, the better the fit. Note that the RT distribution for the second response is mirrored on the y-axis. The final program then looks as follows: .. literalinclude :: ../hddm/examples/simple_model.py More complex models can be generated by specifiying different paramters during model creation. Say we wanted to create a model where each subject receives its own set of parameters which are themselves sampled from a group parameter distribution. Morever, as in the example above, we have two trial types in our data, easy and hard. Based on previous research, we assume that difficulty affects drift-rate 'v'. Thus, we want to fit different drift rate parameters for those two conditions while keeping the other parameters fixed across conditions. Finally, we want to use the full DDM with inter-trial variability for drift, non-decision time ter and starting point z. The full model requires integration of these variability parameters and is hence much slower. The model creation and sampling then might look like this (assuming we imported hddm and loaded the data as above): >>> model = hddm.HDDM(data, include=('V','Z','T'), bias=True, depends_on={'v':'difficulty'}) >>> model.sample(10000, burn=5000) HDDM-0.4/docs/manual.rst0000664000175000017500000000123712005542775016226 0ustar whykingwhyking00000000000000hddm Package ============ :mod:`hddm` Package ------------------- .. automodule:: hddm :members: :undoc-members: :show-inheritance: :mod:`model` Module ------------------- .. automodule:: hddm.model :members: :undoc-members: :show-inheritance: :mod:`diag` Module ------------------ .. automodule:: hddm.diag :members: :mod:`generate` Module ---------------------- .. automodule:: hddm.generate :members: :mod:`likelihoods` Module ------------------------- .. automodule:: hddm.likelihoods :members: :undoc-members: :show-inheritance: :mod:`utils` Module ------------------- .. automodule:: hddm.utils :members: HDDM-0.4/docs/tutorial.rst0000664000175000017500000000025712005542775016615 0ustar whykingwhyking00000000000000.. index:: Tutorial .. _chap_tutorial: ######## Tutorial ######## .. toctree:: :maxdepth: 2 tutorial_config_difficulty tutorial_config_subjects tutorial_python HDDM-0.4/docs/conclusions.rst0000664000175000017500000000223112053502541017271 0ustar whykingwhyking00000000000000=========== Conclusions =========== We developed an open-source Python toolbox that allows hierarchical Bayesian parameter estimation of the DDM and LBA that is successfully been used by various psychological research groups. The benefit of this approach is two-fold: (i) improved accuracy of individual subject parameter recover by modeling subject similarity in a hierarchical fashion; and (ii) full Bayesian posterior inference that obsoletes frequentist statistics and confidence intervals. Despite prior methodoligical advancements in this area, a reliable software package implementing this method was previously not available. Special care has been taken to make this software easy to install and use. A simple yet powerful model description mechanism allows the creation of arbitrarily complex models where separate parameters can be estimated for different conditions. Additional features like posterior-predictive-checks allow for model validation and comparison. Finally, to facilitate cross-domain application of this toolbox in the cognitive neurosciences, it is simple to estimate trial-by-trial influences of e.g. brain-measures on individual model parameters. HDDM-0.4/docs/index.rst0000664000175000017500000000072012050455453016050 0ustar whykingwhyking00000000000000.. HDDM documentation master file, created by sphinx-quickstart on Sat Sep 11 16:47:10 2010. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. include :: ../README.rst Table of Contents: ================== .. toctree:: :maxdepth: 2 abstract intro tutorial howto manual Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` HDDM-0.4/docs/intro.rst0000664000175000017500000001677612050455453016116 0ustar whykingwhyking00000000000000============ Introduction ============ *NOTE*: This document is still under development. Diffusion models have established themselves as the de-facto standard for modeling data from simple decision making tasks. Each decision is modeled as a drift process which generates a response once a boundary is crossed. This simple assumption about the underlying psychological process has the intriguing property of reproducing reaction time distributions and choice probability in simple two-choice decision making tasks. Hierarchical Bayesian methods are quickly gaining popularity in cognitive sciences. Traditionally, psychological models where either fit separately to individual subjects (thus not taking similarities of subjects into account) or to the whole group (thus not taking differences of subjects into account). Hierarchical Bayesian methods provide a remedy for this problem by allowing group and subject parameters to be estimated simultaniously at different hierarchies. In essence, subject parameters are assumed to come from a group distribution. In addition, because these methods are Bayesian they deal naturally with uncertainty and variability in the parameter estimations. HDDM_ (Hierarchical Drift Diffusion Modeling) is an open-source software package written in Python_ which allows (i) the construction of hierarchical Bayesian drift models and (ii) the estimation of its posterior parameter distributions via PyMC_. For efficiency, all runtime critical functions are coded in Cython_, heavily optimized and compiled natively. User-defined models can be constructed via a simple configuration files or directly via HDDM library calls. To assess model fit, HDDM generates different statistics and comes with various plotting capabilities. For illustrative purposes, HDDM includes a graphical demo applet which simulates individual drift processes under user-specified parameter combinations. The code is test-covered to assure correct function and is properly documented. Online documentation and tutorials are provided. In sum, the presented software allows researches to construct and fit complex, user-specified models using state-of-the-art estimation methods without requiring a strong computer science or math background. ---------------- Diffusion Models ---------------- HDDM implements two types of drift models, (i) the Ratcliff drift diffusion model (DDM) and (ii) the linear ballistic accumulator (LBA). Both of these models implement decision making as an evidence accumulation process that executes a response upon crossing one of two decision boundaries. The speed of which the accumulation process approaches one of the two boundaries is called the drift rate *v* and influences how swiftly a particular reponse is executed. The distance between the two boundaries (i.e. threshold *a*) influences how much evidence must be accumulated until a reponse is executed. A lower threshold makes responding faster in general but more random while a higher threshold leads to more cautious responding. Reaction time, however, is not solely comprised of the decision making process -- perception, movement initiation and execution all take time and are summarized into one variable called non-decision time *ter*. Ratcliff Drift Diffusion Model ------------------------------ The Ratcliff DDM models decision making in two-choice tasks. Each choice is represented as and upper and lower boundary. A drift process accumulates evidence over time until it crosses one of the two boundaries and initiates the corresponding response. Because there is noise in the drift process, the time of the boundary crossing and the selected response will vary between trials. The starting point of the drift process relative to the two boundaries can influence if one response has a prepotent bias *z*. This pattern gives rise to the reaction time distributions of both choices and will henceforth be called the simple DDM (see figure 1). .. figure:: DDM_drifts_w_labels.svg Later on, it was noticed that this simple DDM could not account for two phenomena observed in decision making tasks -- early and late errors. This lead to the inclusion of inter-trial variability in the drift-rate, the non-decision time and the starting point. Models that take this into account are henceforth called full DDM. Linear Ballistic Accumulator ---------------------------- While the Linear Ballistic Accumulator (LBA) model also belongs to the class of drift models, it separates itself from the Ratcliff DDM in multiple ways. First, instead of one drift process and two boundaries, the LBA contains one drift process for each response. Thus, the LBA can model decision making when more than two responses are possible Moreover, unlike the DDM, the LBA drift process has no intra-trial variance. RT variability is obtained by including inter-trial variability in the drift-rate and the starting point distribution. ------------------------------ Hierarchical Bayesian Modeling ------------------------------ Bayesian estimation of posterior distributions differs from other approaches such as maximum likelihood (ML) by not only finding the one parameter value with the highest probability, but instead estimating the likelihood of each parameter value (i.e. the posterior distribution). The posterior can be computed with Bayes formula: .. math:: P(\theta|data) = \frac{P(data|\theta) * P(\theta)}{P(data)} Where P(data|\theta) is the likelihood and P(\theta) is the prior probability. To compute P(data) we have to integrate (or sum in the discrete case) across all parameter values. Note that in most scenarios this integral is analytically intractable. Sampling methods like Markov-Chain Monte Carlo (MCMC) have been developed that instead approximate the posterior directly by drawing samples from it, rather than analytically solving the above formula. These methods have been used with great success in many different scenarios. The theoretical background of these sampling methods is out of the scope of this paper. Another nice property of the Bayesian method is that it lends itself naturally to a hierarchical design. In such a design, parameters for one distribution can themselves come from a different distribution which allows chaining together of distributions of arbitrary complexity and map the structure of the data onto the model. This hierarchical property has a particular benefit to cognitive modeling where data is often scarce. Traditionally, multiple subjects are tested on the same task and a model is fit to explain the subject's behavior. Using maximum likelihood we can either fit individual models to individual subjects and neglect that they are most likely more similar than not and that what we learn from one subject could be used for inference on other subjects. Alternatively, we could fit one model to all subjects and neglect the individual differences that most likely exist. A hierarchical model helps us to capture this specific design of our data: we can create one distribution for each individual subject parameter but let the parameters for these distributions be modeled according to a group distribution. This way, the individual subject distributions restrict the group distribution and vice versa. ---------------------------------------------- Hierarchical Bayesian Drift Diffusion Modeling ---------------------------------------------- The structure of our simple hierarchical DDM can be appreciated in figure 2. .. figure:: hier_model.svg .. _HDDM: http://github.com/twiecki/hddm .. _Python: http://www.python.org/ .. _PyMC: http://code.google.com/p/pymc/ .. _Cython: http://www.cython.org/ HDDM-0.4/docs/tutorial_config_subjects.rst0000664000175000017500000004171412050455453022043 0ustar whykingwhyking00000000000000.. index:: Tutorial .. _chap_tutorial_config_subjects: *********************************** Creating a hierarchical group model *********************************** Up until now, we have been looking at data that was generated from the same set of parameters. However, in most experiments, we test multiple subjects and may only gather relatively few trials per subject. Traditionally, we would either fit a separate model to each individual subject or fit one model to all subjects. Neither of these approaches are ideal as we will see below. We can expect that subjects will be similar in many ways yet have non-negligable individual differences. If we fit separate models we ignore their similarities and need much more data per subject to make useful inference. If we fit one model to all subjects we ignore their differences and may get a model fit that does not well characterize any individual. It would be best if the model fitting could determine to what extent all subjects are similar to the others in some respects, and use this information as a prior to draw inferences about the paramters of any given subject. The hierarchical approach optimally allocates the information from the group vs the individual depending on the statistics of the data. To illustrate this point, consider the following example: we tested 40 subjects on the above task with the easy and hard condition. For practical reasons, however, we only collected 20 trials per condition. As an example of what happens when trying to fit separate models to each subject, we will run HDDM on the first subject. The file simple_difficulty_subjs_single.csv only contains data from the first subject. Lets run our model and see what happens: :: hddm_fit.py simple_difficulty.conf simple_subjs_difficulty_single.csv Creating model... Sampling: 100% [0000000000000000000000000000000000] Iterations: 10000 name mean std 2.5q 25q 50q 75q 97.5 mc_err a : 1.936 0.152 1.665 1.830 1.926 2.030 2.259 0.007 t : 0.314 0.044 0.214 0.288 0.318 0.347 0.386 0.002 v('easy',): 0.468 0.248 -0.017 0.298 0.473 0.642 0.929 0.009 v('hard',): 0.426 0.234 -0.028 0.268 0.428 0.571 0.869 0.008 logp: -60.214194 DIC: 113.099890 As you can see, the estimates are far worse (especially in the hard condition) and the posterior distributions are much wider indicating a lack of confidence in the estimates. Looking at the posterior predictive and ill-shaped RT distribution makes obvious why fitting a DDM to 10 trials is a fruitless attempt. However, what about the data from the 39 other subjects? We certainly wouldn't expect everyone to have the exact same parameters, but they should be fairly similar. Couldn't we combine the data? This is where the hierarchical approach becomes useful -- we can estimate individual parameters, but at the same time have the parameters feed into a group distribution so that the tiny bit we learn from each subject can be pooled together and constrain the subject fits. Unfortunately, such a model is a little bit more difficult to create in general. Fortunately, HDDM does all of this automatically. Simply running hddm_fit.py on a data file that has a column named 'subj_idx' will make HDDM create a hierarchical model where each subject gets assigned its own subject distribution which is assumed to be distributed around a normal group distribution. Running this example produces the following output: :: >>hddm_fit.py simple_difficulty.conf simple_subjs_difficulty.csv Creating model... Sampling: 100% [00000000000000000000000000000000] Iterations: 20000 name mean std 2.5q 25q 50q 75q 97.5 mc_err a : 2.000 0.026 1.950 1.985 1.996 2.018 2.059 0.002 a0 : 2.002 0.053 1.889 1.977 1.996 2.032 2.120 0.003 a1 : 1.987 0.051 1.870 1.966 1.990 2.011 2.084 0.002 a10 : 2.007 0.049 1.918 1.981 1.997 2.032 2.123 0.003 a11 : 2.009 0.050 1.916 1.981 1.998 2.033 2.122 0.003 a12 : 2.001 0.052 1.898 1.977 1.994 2.028 2.111 0.002 a13 : 2.000 0.052 1.890 1.977 1.994 2.027 2.112 0.002 a14 : 1.995 0.051 1.885 1.973 1.993 2.020 2.104 0.002 a15 : 1.978 0.058 1.829 1.956 1.986 2.009 2.082 0.003 a16 : 2.000 0.050 1.900 1.976 1.995 2.023 2.121 0.002 a17 : 2.011 0.053 1.915 1.980 1.999 2.040 2.142 0.003 a18 : 2.013 0.057 1.906 1.983 2.000 2.045 2.149 0.003 a19 : 2.009 0.054 1.910 1.981 1.999 2.038 2.127 0.003 a2 : 2.003 0.051 1.900 1.980 1.996 2.029 2.119 0.003 a20 : 2.041 0.069 1.951 1.991 2.022 2.076 2.213 0.005 a21 : 1.987 0.054 1.856 1.966 1.990 2.015 2.094 0.003 a22 : 2.003 0.053 1.894 1.976 1.995 2.030 2.112 0.002 a23 : 2.002 0.054 1.886 1.978 1.995 2.029 2.125 0.002 a24 : 1.999 0.049 1.903 1.976 1.994 2.026 2.107 0.002 a25 : 2.012 0.055 1.914 1.983 2.000 2.041 2.139 0.003 a26 : 2.006 0.052 1.906 1.981 1.997 2.033 2.122 0.002 a27 : 1.988 0.053 1.865 1.968 1.991 2.012 2.087 0.003 a28 : 2.015 0.055 1.923 1.983 2.001 2.045 2.147 0.003 a29 : 1.982 0.054 1.853 1.961 1.988 2.010 2.082 0.003 a3 : 1.981 0.056 1.842 1.960 1.988 2.008 2.081 0.003 a30 : 2.013 0.056 1.910 1.983 1.999 2.042 2.147 0.003 a31 : 1.999 0.048 1.901 1.976 1.993 2.024 2.101 0.002 a32 : 1.999 0.050 1.899 1.977 1.994 2.024 2.111 0.002 a33 : 2.017 0.053 1.929 1.985 2.004 2.044 2.153 0.003 a34 : 2.001 0.052 1.898 1.978 1.994 2.023 2.120 0.002 a35 : 2.002 0.052 1.895 1.978 1.995 2.027 2.117 0.002 a36 : 1.983 0.052 1.849 1.964 1.988 2.010 2.077 0.003 a37 : 2.002 0.054 1.890 1.977 1.994 2.029 2.127 0.003 a38 : 1.984 0.055 1.845 1.965 1.991 2.011 2.083 0.003 a39 : 1.975 0.058 1.826 1.954 1.985 2.007 2.071 0.003 a4 : 1.998 0.052 1.886 1.975 1.994 2.025 2.105 0.002 a5 : 2.024 0.056 1.930 1.987 2.008 2.053 2.164 0.003 a6 : 2.003 0.051 1.900 1.980 1.996 2.029 2.120 0.002 a7 : 2.004 0.051 1.901 1.978 1.996 2.030 2.119 0.002 a8 : 1.989 0.051 1.879 1.967 1.990 2.015 2.089 0.002 a9 : 1.984 0.055 1.847 1.964 1.990 2.014 2.083 0.003 avar : 0.042 0.030 0.005 0.016 0.038 0.065 0.103 0.003 t : 0.305 0.006 0.293 0.302 0.305 0.309 0.316 0.000 t0 : 0.308 0.012 0.286 0.301 0.307 0.314 0.337 0.001 t1 : 0.304 0.012 0.279 0.298 0.305 0.311 0.328 0.000 t10 : 0.308 0.012 0.284 0.301 0.307 0.314 0.334 0.001 t11 : 0.308 0.013 0.284 0.301 0.307 0.314 0.341 0.001 t12 : 0.306 0.012 0.281 0.300 0.306 0.312 0.331 0.000 t13 : 0.309 0.013 0.286 0.301 0.307 0.314 0.340 0.001 t14 : 0.304 0.011 0.277 0.299 0.305 0.311 0.327 0.000 t15 : 0.305 0.011 0.279 0.299 0.305 0.311 0.326 0.001 t16 : 0.305 0.012 0.281 0.299 0.305 0.311 0.328 0.000 t17 : 0.307 0.012 0.284 0.300 0.306 0.312 0.333 0.001 t18 : 0.306 0.011 0.283 0.300 0.306 0.312 0.331 0.001 t19 : 0.307 0.012 0.285 0.301 0.307 0.313 0.335 0.001 t2 : 0.306 0.012 0.281 0.299 0.306 0.312 0.329 0.001 t20 : 0.309 0.014 0.287 0.301 0.308 0.315 0.343 0.001 t21 : 0.303 0.011 0.275 0.298 0.304 0.310 0.323 0.001 t22 : 0.306 0.011 0.282 0.300 0.306 0.312 0.331 0.001 t23 : 0.307 0.012 0.284 0.301 0.306 0.313 0.335 0.001 t24 : 0.303 0.012 0.275 0.298 0.304 0.310 0.325 0.001 t25 : 0.305 0.012 0.281 0.299 0.306 0.312 0.330 0.001 t26 : 0.308 0.012 0.286 0.301 0.307 0.313 0.338 0.001 t27 : 0.302 0.012 0.273 0.296 0.303 0.310 0.323 0.001 t28 : 0.307 0.011 0.285 0.300 0.306 0.313 0.332 0.001 t29 : 0.300 0.011 0.272 0.295 0.302 0.308 0.319 0.001 t3 : 0.303 0.011 0.278 0.298 0.304 0.310 0.323 0.001 t30 : 0.308 0.012 0.286 0.301 0.307 0.314 0.338 0.001 t31 : 0.304 0.011 0.280 0.298 0.305 0.311 0.325 0.001 t32 : 0.305 0.011 0.278 0.299 0.305 0.311 0.328 0.001 t33 : 0.304 0.011 0.278 0.298 0.304 0.310 0.325 0.001 t34 : 0.306 0.012 0.281 0.299 0.306 0.312 0.330 0.000 t35 : 0.304 0.012 0.275 0.298 0.304 0.311 0.327 0.001 t36 : 0.299 0.011 0.270 0.293 0.301 0.307 0.316 0.001 t37 : 0.307 0.012 0.283 0.300 0.307 0.313 0.335 0.001 t38 : 0.302 0.011 0.276 0.296 0.303 0.309 0.321 0.001 t39 : 0.306 0.012 0.282 0.299 0.305 0.312 0.332 0.001 t4 : 0.306 0.011 0.283 0.300 0.305 0.312 0.329 0.000 t5 : 0.305 0.011 0.280 0.299 0.305 0.311 0.327 0.001 t6 : 0.306 0.012 0.285 0.300 0.306 0.312 0.334 0.001 t7 : 0.308 0.012 0.285 0.301 0.307 0.313 0.337 0.001 t8 : 0.305 0.012 0.281 0.299 0.306 0.312 0.329 0.001 t9 : 0.299 0.012 0.269 0.294 0.301 0.307 0.318 0.001 tvar : 0.009 0.006 0.002 0.004 0.008 0.013 0.025 0.001 v('easy',) : 0.966 0.050 0.863 0.934 0.969 0.998 1.057 0.004 v('easy',)0 : 0.970 0.092 0.786 0.920 0.971 1.017 1.190 0.004 v('easy',)1 : 0.983 0.091 0.810 0.929 0.983 1.031 1.180 0.005 v('easy',)10 : 0.964 0.093 0.762 0.915 0.970 1.013 1.159 0.004 v('easy',)11 : 0.989 0.093 0.806 0.936 0.988 1.038 1.195 0.005 v('easy',)12 : 0.962 0.085 0.786 0.914 0.967 1.012 1.125 0.004 v('easy',)13 : 0.964 0.090 0.764 0.915 0.971 1.017 1.153 0.005 v('easy',)14 : 0.962 0.089 0.770 0.918 0.967 1.010 1.136 0.004 v('easy',)15 : 0.963 0.090 0.774 0.916 0.967 1.013 1.151 0.004 v('easy',)16 : 0.949 0.091 0.739 0.902 0.960 1.005 1.119 0.004 v('easy',)17 : 0.969 0.089 0.784 0.919 0.968 1.016 1.160 0.005 v('easy',)18 : 0.978 0.089 0.806 0.926 0.979 1.026 1.176 0.004 v('easy',)19 : 0.933 0.101 0.687 0.887 0.950 0.997 1.098 0.006 v('easy',)2 : 0.989 0.093 0.821 0.933 0.983 1.035 1.204 0.005 v('easy',)20 : 0.981 0.094 0.798 0.927 0.978 1.024 1.197 0.005 v('easy',)21 : 0.995 0.096 0.816 0.937 0.990 1.042 1.213 0.005 v('easy',)22 : 0.936 0.097 0.695 0.888 0.952 1.001 1.095 0.005 v('easy',)23 : 0.958 0.088 0.755 0.912 0.968 1.010 1.129 0.005 v('easy',)24 : 0.971 0.085 0.788 0.925 0.974 1.019 1.147 0.004 v('easy',)25 : 0.961 0.089 0.773 0.909 0.968 1.011 1.134 0.004 v('easy',)26 : 0.977 0.087 0.794 0.930 0.977 1.025 1.168 0.004 v('easy',)27 : 0.995 0.096 0.830 0.939 0.987 1.037 1.234 0.005 v('easy',)28 : 0.957 0.090 0.756 0.909 0.966 1.009 1.133 0.004 v('easy',)29 : 0.963 0.093 0.765 0.917 0.970 1.014 1.135 0.005 v('easy',)3 : 0.970 0.093 0.784 0.919 0.972 1.018 1.163 0.004 v('easy',)30 : 0.950 0.093 0.748 0.896 0.961 1.007 1.117 0.005 v('easy',)31 : 0.959 0.087 0.780 0.910 0.965 1.011 1.134 0.004 v('easy',)32 : 0.958 0.087 0.770 0.907 0.965 1.011 1.128 0.004 v('easy',)33 : 0.983 0.093 0.816 0.930 0.982 1.029 1.188 0.005 v('easy',)34 : 0.969 0.088 0.785 0.921 0.973 1.016 1.144 0.004 v('easy',)35 : 0.965 0.086 0.789 0.913 0.971 1.015 1.142 0.004 v('easy',)36 : 0.949 0.090 0.751 0.901 0.961 1.001 1.103 0.005 v('easy',)37 : 0.908 0.111 0.639 0.856 0.933 0.982 1.067 0.007 v('easy',)38 : 0.938 0.091 0.727 0.894 0.954 0.997 1.089 0.005 v('easy',)39 : 0.969 0.091 0.786 0.922 0.975 1.016 1.150 0.005 v('easy',)4 : 0.983 0.090 0.807 0.934 0.979 1.029 1.180 0.004 v('easy',)5 : 0.965 0.087 0.788 0.915 0.969 1.013 1.152 0.004 v('easy',)6 : 0.961 0.087 0.782 0.909 0.966 1.013 1.138 0.004 v('easy',)7 : 0.973 0.088 0.794 0.924 0.975 1.018 1.173 0.004 v('easy',)8 : 0.989 0.095 0.817 0.935 0.982 1.028 1.230 0.005 v('easy',)9 : 0.969 0.089 0.780 0.918 0.972 1.021 1.148 0.004 v('hard',) : 0.401 0.045 0.317 0.370 0.400 0.430 0.500 0.003 v('hard',)0 : 0.488 0.138 0.278 0.387 0.464 0.565 0.803 0.007 v('hard',)1 : 0.424 0.119 0.203 0.354 0.411 0.489 0.695 0.004 v('hard',)10 : 0.333 0.115 0.075 0.273 0.344 0.406 0.551 0.005 v('hard',)11 : 0.393 0.111 0.181 0.322 0.387 0.461 0.626 0.004 v('hard',)12 : 0.400 0.109 0.183 0.336 0.393 0.466 0.632 0.003 v('hard',)13 : 0.444 0.120 0.232 0.365 0.430 0.506 0.727 0.005 v('hard',)14 : 0.385 0.109 0.162 0.321 0.380 0.447 0.618 0.003 v('hard',)15 : 0.387 0.116 0.157 0.320 0.383 0.454 0.629 0.004 v('hard',)16 : 0.379 0.111 0.150 0.313 0.375 0.448 0.614 0.004 v('hard',)17 : 0.435 0.119 0.238 0.350 0.421 0.502 0.717 0.005 v('hard',)18 : 0.374 0.109 0.140 0.313 0.373 0.438 0.597 0.003 v('hard',)19 : 0.423 0.112 0.212 0.350 0.417 0.486 0.654 0.005 v('hard',)2 : 0.396 0.117 0.148 0.327 0.391 0.463 0.653 0.004 v('hard',)20 : 0.422 0.109 0.230 0.351 0.412 0.485 0.675 0.004 v('hard',)21 : 0.437 0.120 0.229 0.357 0.423 0.510 0.705 0.005 v('hard',)22 : 0.414 0.116 0.196 0.342 0.406 0.479 0.671 0.004 v('hard',)23 : 0.375 0.110 0.140 0.312 0.376 0.438 0.590 0.004 v('hard',)24 : 0.303 0.130 -0.010 0.240 0.323 0.388 0.522 0.006 v('hard',)25 : 0.465 0.129 0.248 0.377 0.447 0.546 0.767 0.007 v('hard',)26 : 0.398 0.116 0.167 0.330 0.395 0.460 0.638 0.004 v('hard',)27 : 0.437 0.117 0.223 0.360 0.423 0.501 0.697 0.005 v('hard',)28 : 0.347 0.117 0.081 0.284 0.357 0.420 0.557 0.004 v('hard',)29 : 0.405 0.116 0.202 0.332 0.396 0.471 0.653 0.004 v('hard',)3 : 0.420 0.117 0.193 0.344 0.412 0.489 0.682 0.004 v('hard',)30 : 0.357 0.107 0.111 0.296 0.359 0.421 0.564 0.004 v('hard',)31 : 0.395 0.108 0.182 0.328 0.392 0.454 0.626 0.004 v('hard',)32 : 0.338 0.115 0.079 0.274 0.347 0.410 0.561 0.004 v('hard',)33 : 0.389 0.112 0.159 0.324 0.389 0.456 0.629 0.004 v('hard',)34 : 0.450 0.124 0.254 0.366 0.437 0.519 0.727 0.005 v('hard',)35 : 0.394 0.112 0.172 0.327 0.387 0.461 0.630 0.004 v('hard',)36 : 0.326 0.120 0.045 0.266 0.342 0.397 0.537 0.004 v('hard',)37 : 0.393 0.111 0.164 0.327 0.388 0.459 0.636 0.004 v('hard',)38 : 0.433 0.118 0.223 0.356 0.418 0.497 0.704 0.005 v('hard',)39 : 0.424 0.119 0.202 0.349 0.418 0.490 0.680 0.005 v('hard',)4 : 0.430 0.126 0.201 0.355 0.420 0.499 0.718 0.005 v('hard',)5 : 0.380 0.110 0.144 0.319 0.377 0.446 0.603 0.004 v('hard',)6 : 0.357 0.115 0.114 0.295 0.362 0.423 0.586 0.004 v('hard',)7 : 0.449 0.124 0.238 0.366 0.434 0.527 0.720 0.006 v('hard',)8 : 0.425 0.122 0.214 0.349 0.411 0.494 0.706 0.005 v('hard',)9 : 0.476 0.133 0.264 0.383 0.456 0.553 0.796 0.007 vvar('easy',): 0.111 0.053 0.030 0.068 0.107 0.145 0.221 0.004 vvar('hard',): 0.070 0.047 0.017 0.033 0.058 0.097 0.185 0.004 logp: -1161.693344 DIC: 2882.445038 Plotting t Plotting a Plotting v('hard',) Plotting v('easy',) Plotting posterior predictive... The first you can see when examining the recovered parameter values is that the mean of the group distributions (i.e. a, t, v('hard',) and v('easy',)) is that they match very well the parameters we used to generate the data from. So by pooling the very little data we had on each subject we can make useful inference about the group parameters. The second thing you can see is that individual subject parameters (ending with the index of the subject) are very close to the group mean (this is also indicated by the fact that the var posteriors, the variance of the group distribution -- representing the spread of the individual subject parameters). This property is called *shrinkage*. Intuitively, if we can not make meaningful inference about individual subjects we will assume that they are distributed as the rest of the group. HDDM-0.4/src/0000775000175000017500000000000012054702235014044 5ustar whykingwhyking00000000000000HDDM-0.4/src/integrate.pxi0000664000175000017500000002020212050455454016550 0ustar whykingwhyking00000000000000#cython: embedsignature=True cython: cdivision=True cython: #wraparound=False cython: boundscheck=False import numpy as np cimport numpy as np cimport cython include 'pdf.pxi' cdef double simpson_1D(double x, double v, double sv, double a, double z, double t, double err, double lb_z, double ub_z, int n_sz, double lb_t, double ub_t, int n_st) nogil: #assert ((n_sz&1)==0 and (n_st&1)==0), "n_st and n_sz have to be even" #assert ((ub_t-lb_t)*(ub_z-lb_z)==0 and (n_sz*n_st)==0), "the function is defined for 1D-integration only" cdef double ht, hz cdef int n = max(n_st, n_sz) if n_st==0: #integration over z hz = (ub_z-lb_z)/n ht = 0 lb_t = t ub_t = t else: #integration over t hz = 0 ht = (ub_t-lb_t)/n lb_z = z ub_z = z cdef double S = pdf_sv(x - lb_t, v, sv, a, lb_z, err) cdef double z_tag, t_tag, y cdef int i for i from 1 <= i <= n: z_tag = lb_z + hz * i t_tag = lb_t + ht * i y = pdf_sv(x - t_tag, v, sv, a, z_tag, err) if i&1: #check if i is odd S += (4 * y) else: S += (2 * y) S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y S = S / ((ub_t-lb_t)+(ub_z-lb_z)) #the right function if pdf_sv()/sz or pdf_sv()/st return ((ht+hz) * S / 3) cdef double simpson_2D(double x, double v, double sv, double a, double z, double t, double err, double lb_z, double ub_z, int n_sz, double lb_t, double ub_t, int n_st) nogil: #assert ((n_sz&1)==0 and (n_st&1)==0), "n_st and n_sz have to be even" #assert ((ub_t-lb_t)*(ub_z-lb_z)>0 and (n_sz*n_st)>0), "the function is defined for 2D-integration only, lb_t: %f, ub_t %f, lb_z %f, ub_z %f, n_sz: %d, n_st %d" % (lb_t, ub_t, lb_z, ub_z, n_sz, n_st) cdef double ht cdef double S cdef double t_tag, y cdef int i_t ht = (ub_t-lb_t)/n_st S = simpson_1D(x, v, sv, a, z, lb_t, err, lb_z, ub_z, n_sz, 0, 0, 0) for i_t from 1 <= i_t <= n_st: t_tag = lb_t + ht * i_t y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) if i_t&1: #check if i is odd S += (4 * y) else: S += (2 * y) S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y S = S/ (ub_t-lb_t) return (ht * S / 3) cdef double adaptiveSimpsonsAux(double x, double v, double sv, double a, double z, double t, double pdf_err, double lb_z, double ub_z, double lb_t, double ub_t, double ZT, double simps_err, double S, double f_beg, double f_end, double f_mid, int bottom) nogil: cdef double z_c, z_d, z_e, t_c, t_d, t_e, h cdef double fd, fe cdef double Sleft, Sright, S2 #print "in AdaptiveSimpsAux: lb_z: %f, ub_z: %f, lb_t %f, ub_t %f, f_beg: %f, f_end: %f, bottom: %d" % (lb_z, ub_z, lb_t, ub_t, f_beg, f_end, bottom) if (ub_t-lb_t) == 0: #integration over sz h = ub_z - lb_z z_c = (ub_z + lb_z)/2. z_d = (lb_z + z_c)/2. z_e = (z_c + ub_z)/2. t_c = t t_d = t t_e = t else: #integration over t h = ub_t - lb_t t_c = (ub_t + lb_t)/2. t_d = (lb_t + t_c)/2. t_e = (t_c + ub_t)/2. z_c = z z_d = z z_e = z fd = pdf_sv(x - t_d, v, sv, a, z_d, pdf_err)/ZT fe = pdf_sv(x - t_e, v, sv, a, z_e, pdf_err)/ZT Sleft = (h/12)*(f_beg + 4*fd + f_mid) Sright = (h/12)*(f_mid + 4*fe + f_end) S2 = Sleft + Sright if (bottom <= 0 or fabs(S2 - S) <= 15*simps_err): return S2 + (S2 - S)/15 return adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, lb_z, z_c, lb_t, t_c, ZT, simps_err/2, Sleft, f_beg, f_mid, fd, bottom-1) + \ adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, z_c, ub_z, t_c, ub_t, ZT, simps_err/2, Sright, f_mid, f_end, fe, bottom-1) cdef double adaptiveSimpsons_1D(double x, double v, double sv, double a, double z, double t, double pdf_err, double lb_z, double ub_z, double lb_t, double ub_t, double simps_err, int maxRecursionDepth) nogil: cdef double h if (ub_t - lb_t) == 0: #integration over z lb_t = t ub_t = t h = ub_z - lb_z else: #integration over t h = (ub_t-lb_t) lb_z = z ub_z = z cdef double ZT = h cdef double c_t = (lb_t + ub_t)/2. cdef double c_z = (lb_z + ub_z)/2. cdef double f_beg, f_end, f_mid, S f_beg = pdf_sv(x - lb_t, v, sv, a, lb_z, pdf_err)/ZT f_end = pdf_sv(x - ub_t, v, sv, a, ub_z, pdf_err)/ZT f_mid = pdf_sv(x - c_t, v, sv, a, c_z, pdf_err)/ZT S = (h/6)*(f_beg + 4*f_mid + f_end) cdef double res = adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, lb_z, ub_z, lb_t, ub_t, ZT, simps_err, S, f_beg, f_end, f_mid, maxRecursionDepth) return res cdef double adaptiveSimpsonsAux_2D(double x, double v, double sv, double a, double z, double t, double pdf_err, double err_1d, double lb_z, double ub_z, double lb_t, double ub_t, double st, double err_2d, double S, double f_beg, double f_end, double f_mid, int maxRecursionDepth_sz, int bottom) nogil: cdef double fd, fe cdef double Sleft, Sright, S2 #print "in AdaptiveSimpsAux_2D: lb_z: %f, ub_z: %f, lb_t %f, ub_t %f, f_beg: %f, f_end: %f, bottom: %d" % (lb_z, ub_z, lb_t, ub_t, f_beg, f_end, bottom) cdef double t_c = (ub_t + lb_t)/2. cdef double t_d = (lb_t + t_c)/2. cdef double t_e = (t_c + ub_t)/2. cdef double h = ub_t - lb_t fd = adaptiveSimpsons_1D(x, v, sv, a, z, t_d, pdf_err, lb_z, ub_z, 0, 0, err_1d, maxRecursionDepth_sz)/st fe = adaptiveSimpsons_1D(x, v, sv, a, z, t_e, pdf_err, lb_z, ub_z, 0, 0, err_1d, maxRecursionDepth_sz)/st Sleft = (h/12)*(f_beg + 4*fd + f_mid) Sright = (h/12)*(f_mid + 4*fe + f_end) S2 = Sleft + Sright if (bottom <= 0 or fabs(S2 - S) <= 15*err_2d): return S2 + (S2 - S)/15; return adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, lb_z, ub_z, lb_t, t_c, st, err_2d/2, Sleft, f_beg, f_mid, fd, maxRecursionDepth_sz, bottom-1) + \ adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, lb_z, ub_z, t_c, ub_t, st, err_2d/2, Sright, f_mid, f_end, fe, maxRecursionDepth_sz, bottom-1) cdef double adaptiveSimpsons_2D(double x, double v, double sv, double a, double z, double t, double pdf_err, double lb_z, double ub_z, double lb_t, double ub_t, double simps_err, int maxRecursionDepth_sz, int maxRecursionDepth_st) nogil: cdef double h = (ub_t-lb_t) cdef double st = (ub_t - lb_t) cdef double c_t = (lb_t + ub_t)/2. cdef double c_z = (lb_z + ub_z)/2. cdef double f_beg, f_end, f_mid, S cdef double err_1d = simps_err cdef double err_2d = simps_err f_beg = adaptiveSimpsons_1D(x, v, sv, a, z, lb_t, pdf_err, lb_z, ub_z, 0, 0, err_1d, maxRecursionDepth_sz)/st f_end = adaptiveSimpsons_1D(x, v, sv, a, z, ub_t, pdf_err, lb_z, ub_z, 0, 0, err_1d, maxRecursionDepth_sz)/st f_mid = adaptiveSimpsons_1D(x, v, sv, a, z, (lb_t+ub_t)/2, pdf_err, lb_z, ub_z, 0, 0, err_1d, maxRecursionDepth_sz)/st S = (h/6)*(f_beg + 4*f_mid + f_end) cdef double res = adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, lb_z, ub_z, lb_t, ub_t, st, err_2d, S, f_beg, f_end, f_mid, maxRecursionDepth_sz, maxRecursionDepth_st) return res HDDM-0.4/src/wfpt_full.pyx0000664000175000017500000001310012050455454016607 0ustar whykingwhyking00000000000000#!/usr/bin/python # # Cython version of the Navarro & Fuss, 2009 DDM PDF. Based directly # on the following code by Navarro & Fuss: # http://www.psychocmath.logy.adelaide.edu.au/personalpages/staff/danielnavarro/resources/wfpt.m # # This implementation is about 170 times faT than the matlab # reference version. # # Copyleft Thomas Wiecki (thomas_wiecki[at]brown.edu), 2010 # GPLv3 from copy import copy import numpy as np cimport numpy as np cimport cython from cython.parallel import * include "pdf.pxi" @cython.wraparound(False) @cython.boundscheck(False) # turn of bounds-checking for entire function def wiener_like_full_intrp(np.ndarray[double, ndim=1] x, double v, double V, double a, double z, double sz, double t, double st, double err, int n_st= 10, int n_sz=10, bint use_adaptive=1, double simps_err=1e-8): cdef Py_ssize_t i cdef double p cdef double sum_logp = 0 for i in prange(x.shape[0], nogil=True): p = full_pdf(x[i], v, V, a, z, sz, t, st, err, n_st, n_sz, use_adaptive, simps_err) # If one probability = 0, the log sum will be -Inf if p == 0: with gil: return -np.inf sum_logp += log(p) return sum_logp @cython.wraparound(False) @cython.boundscheck(False) # turn of bounds-checking for entire function def wiener_like_full(np.ndarray[double, ndim=1] x, np.ndarray[double, ndim=1] v, np.ndarray[double, ndim=1] a, np.ndarray[double, ndim=1] z, np.ndarray[double, ndim=1] t, err): cdef Py_ssize_t i cdef double p cdef double sum_logp = 0 for i in prange(x.shape[0], nogil=True): p = pdf_sign(x[i], v[i], a[i], z[i], t[i], err) # If one probability = 0, the log sum will be -Inf if p == 0: with gil: return -np.inf sum_logp += log(p) return sum_logp @cython.wraparound(False) @cython.boundscheck(False) # turn of bounds-checking for entire function def wiener_like_full_multi(np.ndarray[double, ndim=1] x, v, V, a, z, sz, t, st, double err, multi=None): cdef unsigned int size = x.shape[0] cdef unsigned int i cdef double p = 0 if multi is None: return wiener_like_full_intrp(x, v, V, a, z, sz, t, st, err) else: params = {'v':v, 'z':z, 't':t, 'a':a, 'sv':V, 'sz':sz, 'st':st} params_iter = copy(params) for i from 0 <= i < size: for param in multi: params_iter[param] = params[param][i] p += log(full_pdf(x[i], params_iter['v'], params_iter['sv'], params_iter['a'], params_iter['z'], params_iter['sz'], params_iter['t'], params_iter['st'], err)) return p @cython.wraparound(False) @cython.boundscheck(False) # turn of bounds-checking for entire function def gen_rts_from_cdf(double v, double V, double a, double z, double sz, double t, \ double st, int samples=1000, double cdf_lb = -6, double cdf_ub = 6, double dt=1e-2): cdef np.ndarray[double, ndim=1] x = np.arange(cdf_lb, cdf_ub, dt) cdef np.ndarray[double, ndim=1] l_cdf = np.empty(x.shape[0], dtype=np.double) cdef double pdf, rt cdef Py_ssize_t i, j cdef int idx l_cdf[0] = 0 for i in range(x.shape[0]): pdf = full_pdf(x[i], v, V, a, z, sz, 0, 0, 1e-4) l_cdf[i] = l_cdf[i-1] + pdf l_cdf /= l_cdf[x.shape[0]-1] cdef np.ndarray[double, ndim=1] rts = np.empty(samples, dtype=np.double) cdef np.ndarray[double, ndim=1] f = np.random.rand(samples) cdef np.ndarray[double, ndim=1] delay if st!=0: delay = (np.random.rand(samples)*st + (t - st/2.)) for i from 0 <= i < samples: idx = np.searchsorted(l_cdf, f[i]) rt = x[idx] if st==0: rt = rt + np.sign(rt)*t else: rt = rt + np.sign(rt)*delay[i] rts[i] = rt return rts @cython.wraparound(False) @cython.boundscheck(False) # turn of bounds-checking for entire function def wiener_like_full_contaminant(np.ndarray[double, ndim=1] value, np.ndarray[int, ndim=1] cont_x, double gamma, double v, double V, double a, double z, double sz, double t, double st, double t_min, double t_max, double err): """Wiener likelihood function where RTs could come from a separate, uniform contaminant distribution. Reference: Lee, Vandekerckhove, Navarro, & Tuernlinckx (2007) """ cdef Py_ssize_t i cdef double p cdef double sum_logp = 0 cdef int n_cont = np.sum(cont_x) cdef int pos_cont = 0 for i in prange(value.shape[0], nogil=True): if cont_x[i] == 0: p = full_pdf(value[i], v, V, a, z, sz, t, st, err) sum_logp += log(p) elif value[i]>0: pos_cont += 1 # add the log likelihood of the contaminations #first the guesses sum_logp += n_cont*log(gamma*(0.5 * 1./(t_max-t_min))) #then the positive prob_boundary sum_logp += pos_cont*log((1-gamma) * prob_ub(v, a, z) * 1./(t_max-t_min)) #and the negative prob_boundary sum_logp += (n_cont - pos_cont)*log((1-gamma)*(1-prob_ub(v, a, z)) * 1./(t_max-t_min)) return sum_logp def wiener_like_full_single(double x, double v, double V, double a, double z, double sz, double t, double st, double err, int n_st=2, int n_sz=2, bint use_adaptive=1, double simps_err=1e-3): # Wrapper for c-only full_pdf function, only required for unittest return full_pdf(x, v, V, a, z, sz, t, st, err, n_st, n_sz, use_adaptive, simps_err) HDDM-0.4/src/lba.pyx0000664000175000017500000000747112050455454015361 0ustar whykingwhyking00000000000000#cython: embedsignature=True #cython: cdivision=True #cython: wraparound=False #cython: boundscheck=False # # Copyleft Thomas Wiecki (thomas_wiecki[at]brown.edu), 2010 # GPLv3 from __future__ import division from copy import copy import numpy as np cimport numpy as cnp import scipy as sp import scipy.stats from scipy.stats.distributions import norm cimport cython # Define data type DTYPE = np.double ctypedef cnp.double_t DTYPE_t cdef double PI = 3.1415926535897 cdef extern from "math.h": double sin(double) double log(double) double exp(double) double sqrt(double) double fmax(double, double) double pow(double, double) double ceil(double) double floor(double) double fabs(double) double erf(double) cdef inline double norm_pdf(double x): return 0.3989422804014327 * exp(-(x**2/2)) cdef inline double norm_cdf(double x): return .5 * (1 + erf((x)/1.4142135623730951)) cdef DTYPE_t fptcdf_single(DTYPE_t t, double z, double a, double driftrate, double sddrift): cdef double zs=t*sddrift cdef double zu=t*driftrate cdef double aminuszu=a-zu cdef double xx=aminuszu-z cdef double azu=aminuszu/zs cdef double azumax=xx/zs cdef double tmp1 = zs * (norm_pdf(azumax) - norm_pdf(azu)) cdef double tmp2 = (xx * norm_cdf(azumax)) - (aminuszu * norm_cdf(azu)) return 1+(tmp1+tmp2)/z cdef DTYPE_t fptpdf_single(DTYPE_t t, double z, double a, double driftrate, double sddrift): cdef double zs=t*sddrift cdef double zu=t*driftrate cdef double aminuszu=a-zu cdef double azu=aminuszu/zs cdef double azumax=(aminuszu-z)/zs return (driftrate*(norm_cdf(azu)-norm_cdf(azumax)) + sddrift*(norm_pdf(azumax)-norm_pdf(azu)))/z def lba_like(cnp.ndarray[DTYPE_t, ndim=1] value, double z, double a, double ter, double sv, double v0, double v1, bint logp=False, bint normalize_v=False): cdef cnp.ndarray[DTYPE_t, ndim=1] rt = (np.abs(value) - ter) cdef unsigned int nresp = value.shape[0] cdef unsigned int i = 0 cdef cnp.ndarray[DTYPE_t, ndim=1] probs = np.empty_like(value) assert sv >= 0, "sv must be larger than 0" if normalize_v == 1: v1 = 1 - v0 if a <= z: #print "Starting point larger than threshold!" return -np.Inf #print "z: %f, a: %f, ter: %i, v: %f, sv: %f" % (z, a, ter, v[0], sv) for i from 0 <= i < nresp: if value[i] > 0: probs[i] = (1-fptcdf_single(rt[i], z, a, v1, sv)) * \ fptpdf_single(rt[i], z, a, v0, sv) else: probs[i] = (1-fptcdf_single(rt[i], z, a, v0, sv)) * \ fptpdf_single(rt[i], z, a, v1, sv) if logp == 1: return np.sum(np.log(probs)) else: return probs # Backed up if in the future multiple responses are to be supported def lba_like_multi(cnp.ndarray[DTYPE_t, ndim=1] value, cnp.ndarray[DTYPE_t, ndim=1] resps, double z, double a, double ter, cnp.ndarray[DTYPE_t, ndim=1] drift, double sv, unsigned int logp=0): # Rescale parameters so as to fit in the same range as the DDM cdef cnp.ndarray[DTYPE_t, ndim=1] rt = (np.abs(value) - ter) cdef unsigned int nresp = value.shape[0] cdef unsigned int i cdef cnp.ndarray[DTYPE_t, ndim=1] probs = np.empty(nresp, dtype=DTYPE) if a <= z: #print "Starting point larger than threshold!" return -np.Inf #print "z: %f, a: %f, ter: %i, v: %f, sv: %f" % (z, a, ter, v[0], sv) for i from 0 <= i < nresp: if resps[i] == 1: probs[i] = (1-fptcdf_single(rt[i], z, a, drift[1], sv)) * \ fptpdf_single(rt[i], z, a, drift[0], sv) else: probs[i] = (1-fptcdf_single(rt[i], z, a, drift[0], sv)) * \ fptpdf_single(rt[i], z, a, drift[1], sv) if logp == 1: return np.sum(np.log(probs)) else: return probs HDDM-0.4/src/wfpt.c0000664000175000017500000224707112050514011015172 0ustar whykingwhyking00000000000000/* Generated by Cython 0.15.1+ on Tue Nov 13 13:57:42 2012 */ #define PY_SSIZE_T_CLEAN #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02040000 #error Cython requires Python 2.4+. #else #include /* For offsetof */ #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #if PY_VERSION_HEX < 0x02050000 typedef int Py_ssize_t; #define PY_SSIZE_T_MAX INT_MAX #define PY_SSIZE_T_MIN INT_MIN #define PY_FORMAT_SIZE_T "" #define PyInt_FromSsize_t(z) PyInt_FromLong(z) #define PyInt_AsSsize_t(o) __Pyx_PyInt_AsInt(o) #define PyNumber_Index(o) PyNumber_Int(o) #define PyIndex_Check(o) PyNumber_Check(o) #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message) #endif #if PY_VERSION_HEX < 0x02060000 #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt) #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type) #define Py_SIZE(ob) (((PyVarObject*)(ob))->ob_size) #define PyVarObject_HEAD_INIT(type, size) \ PyObject_HEAD_INIT(type) size, #define PyType_Modified(t) typedef struct { void *buf; PyObject *obj; Py_ssize_t len; Py_ssize_t itemsize; int readonly; int ndim; char *format; Py_ssize_t *shape; Py_ssize_t *strides; Py_ssize_t *suboffsets; void *internal; } Py_buffer; #define PyBUF_SIMPLE 0 #define PyBUF_WRITABLE 0x0001 #define PyBUF_FORMAT 0x0004 #define PyBUF_ND 0x0008 #define PyBUF_STRIDES (0x0010 | PyBUF_ND) #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES) #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES) #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES) #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES) #define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE) #define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE) #endif #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ PyCode_New(a, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #endif #if PY_MAJOR_VERSION < 3 && PY_MINOR_VERSION < 6 #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict") #endif #if PY_MAJOR_VERSION >= 3 #define Py_TPFLAGS_CHECKTYPES 0 #define Py_TPFLAGS_HAVE_INDEX 0 #endif #if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3) #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_GET_LENGTH) #define CYTHON_PEP393_ENABLED #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) 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/* "numpy.pxd":722 * # in Cython to enable them only on the right systems. * * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t */ typedef npy_int8 __pyx_t_5numpy_int8_t; /* "numpy.pxd":723 * * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t */ typedef npy_int16 __pyx_t_5numpy_int16_t; /* "numpy.pxd":724 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; /* "numpy.pxd":725 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; /* "numpy.pxd":729 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; /* "numpy.pxd":730 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; /* "numpy.pxd":731 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< * ctypedef npy_uint64 uint64_t * #ctypedef npy_uint96 uint96_t */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; /* "numpy.pxd":732 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< * #ctypedef npy_uint96 uint96_t * #ctypedef npy_uint128 uint128_t */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; /* "numpy.pxd":736 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< * ctypedef npy_float64 float64_t * #ctypedef npy_float80 float80_t */ typedef npy_float32 __pyx_t_5numpy_float32_t; /* "numpy.pxd":737 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< * #ctypedef npy_float80 float80_t * #ctypedef npy_float128 float128_t */ typedef npy_float64 __pyx_t_5numpy_float64_t; /* "numpy.pxd":746 * # The int types are mapped a bit surprising -- * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t # <<<<<<<<<<<<<< * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t */ typedef npy_long __pyx_t_5numpy_int_t; /* "numpy.pxd":747 * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< * ctypedef npy_longlong longlong_t * */ typedef npy_longlong __pyx_t_5numpy_long_t; /* "numpy.pxd":748 * ctypedef npy_long int_t * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< * * ctypedef npy_ulong uint_t */ typedef npy_longlong __pyx_t_5numpy_longlong_t; /* "numpy.pxd":750 * ctypedef npy_longlong longlong_t * * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t */ typedef npy_ulong __pyx_t_5numpy_uint_t; /* "numpy.pxd":751 * * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulonglong_t * */ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; /* "numpy.pxd":752 * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< * * ctypedef npy_intp intp_t */ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; /* "numpy.pxd":754 * ctypedef npy_ulonglong ulonglong_t * * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< * ctypedef npy_uintp uintp_t * */ typedef npy_intp __pyx_t_5numpy_intp_t; /* "numpy.pxd":755 * * ctypedef npy_intp intp_t * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< * * ctypedef npy_double float_t */ typedef npy_uintp __pyx_t_5numpy_uintp_t; /* "numpy.pxd":757 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t */ typedef npy_double __pyx_t_5numpy_float_t; /* "numpy.pxd":758 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< * ctypedef npy_longdouble longdouble_t * */ typedef npy_double __pyx_t_5numpy_double_t; /* "numpy.pxd":759 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; 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} else if ((-PyList_GET_SIZE(o) <= i) & (i < 0)) { PyObject *r = PyList_GET_ITEM(o, PyList_GET_SIZE(o) + i); Py_INCREF(r); return r; } } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } #define __Pyx_GetItemInt_Tuple(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \ __Pyx_GetItemInt_Tuple_Fast(o, i) : \ __Pyx_GetItemInt_Generic(o, to_py_func(i))) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i) { if (likely(o != Py_None)) { if (likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, i); Py_INCREF(r); return r; } else if ((-PyTuple_GET_SIZE(o) <= i) & (i < 0)) { PyObject *r = PyTuple_GET_ITEM(o, PyTuple_GET_SIZE(o) + i); Py_INCREF(r); return r; } } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } #define __Pyx_GetItemInt(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \ __Pyx_GetItemInt_Fast(o, i) : \ __Pyx_GetItemInt_Generic(o, to_py_func(i))) static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i) { if (PyList_CheckExact(o)) { Py_ssize_t n = (likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); if (likely((n >= 0) & (n < PyList_GET_SIZE(o)))) { PyObject *r = PyList_GET_ITEM(o, n); Py_INCREF(r); return r; } } else if (PyTuple_CheckExact(o)) { Py_ssize_t n = (likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); if (likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) { PyObject *r = PyTuple_GET_ITEM(o, n); Py_INCREF(r); return r; } } else if (likely(i >= 0)) { PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; if (likely(m && m->sq_item)) { return m->sq_item(o, i); } } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); static void __Pyx_UnpackTupleError(PyObject *, Py_ssize_t index); /*proto*/ typedef struct { Py_ssize_t shape, strides, suboffsets; } __Pyx_Buf_DimInfo; typedef struct { size_t refcount; Py_buffer pybuffer; } __Pyx_Buffer; typedef struct { __Pyx_Buffer *rcbuffer; char *data; __Pyx_Buf_DimInfo diminfo[8]; } __Pyx_LocalBuf_ND; #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); static void __Pyx_ReleaseBuffer(Py_buffer *view); #else #define __Pyx_GetBuffer PyObject_GetBuffer #define __Pyx_ReleaseBuffer PyBuffer_Release #endif Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level); /*proto*/ static CYTHON_INLINE void __Pyx_RaiseImportError(PyObject *name); static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_Py_intptr_t(Py_intptr_t); #if CYTHON_CCOMPLEX #ifdef __cplusplus #define __Pyx_CREAL(z) ((z).real()) #define __Pyx_CIMAG(z) ((z).imag()) #else #define __Pyx_CREAL(z) (__real__(z)) #define __Pyx_CIMAG(z) (__imag__(z)) #endif #else #define __Pyx_CREAL(z) ((z).real) #define __Pyx_CIMAG(z) ((z).imag) #endif #if defined(_WIN32) && defined(__cplusplus) && CYTHON_CCOMPLEX #define __Pyx_SET_CREAL(z,x) ((z).real(x)) #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) #else #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) #endif static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); #if CYTHON_CCOMPLEX #define __Pyx_c_eqf(a, b) ((a)==(b)) #define __Pyx_c_sumf(a, b) ((a)+(b)) #define __Pyx_c_difff(a, b) ((a)-(b)) #define __Pyx_c_prodf(a, b) ((a)*(b)) #define __Pyx_c_quotf(a, b) ((a)/(b)) #define __Pyx_c_negf(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zerof(z) ((z)==(float)0) #define __Pyx_c_conjf(z) (::std::conj(z)) #if 1 #define __Pyx_c_absf(z) (::std::abs(z)) #define __Pyx_c_powf(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zerof(z) ((z)==0) #define __Pyx_c_conjf(z) (conjf(z)) #if 1 #define __Pyx_c_absf(z) (cabsf(z)) #define __Pyx_c_powf(a, b) (cpowf(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); #endif #endif static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); #if CYTHON_CCOMPLEX #define __Pyx_c_eq(a, b) ((a)==(b)) #define __Pyx_c_sum(a, b) ((a)+(b)) #define __Pyx_c_diff(a, b) ((a)-(b)) #define __Pyx_c_prod(a, b) ((a)*(b)) #define __Pyx_c_quot(a, b) ((a)/(b)) #define __Pyx_c_neg(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero(z) ((z)==(double)0) #define __Pyx_c_conj(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs(z) (::std::abs(z)) #define __Pyx_c_pow(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero(z) ((z)==0) #define __Pyx_c_conj(z) (conj(z)) #if 1 #define __Pyx_c_abs(z) (cabs(z)) #define __Pyx_c_pow(a, b) (cpow(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex); #endif #endif static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject *); static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject *); static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject *); static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject *); static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject *); static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject *); static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject *); static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject *); static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject *); static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject *); static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject *); static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject *); static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject *); static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject *); static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject *); static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject *); static int __Pyx_check_binary_version(void); static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); /*proto*/ static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ typedef struct { int code_line; PyCodeObject* code_object; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; int max_count; __Pyx_CodeObjectCacheEntry* entries; }; static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); static void __pyx_clear_code_object_cache(void); static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno, int __pyx_lineno, const char *__pyx_filename); /*proto*/ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ /* Module declarations from 'cpython.buffer' */ /* Module declarations from 'cpython.ref' */ /* Module declarations from 'libc.stdio' */ /* Module declarations from 'cpython.object' */ /* Module declarations from 'libc.stdlib' */ /* Module declarations from 'numpy' */ /* Module declarations from 'numpy' */ static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ /* Module declarations from 'cython' */ /* Module declarations from 'wfpt' */ static double __pyx_f_4wfpt_ftt_01w(double, double, double); /*proto*/ static double __pyx_f_4wfpt_pdf(double, double, double, double, double); /*proto*/ static double __pyx_f_4wfpt_pdf_sv(double, double, double, double, double, double); /*proto*/ static double __pyx_f_4wfpt_full_pdf(double, double, double, double, double, double, double, double, double, int __pyx_skip_dispatch, struct __pyx_opt_args_4wfpt_full_pdf *__pyx_optional_args); /*proto*/ static double __pyx_f_4wfpt_simpson_1D(double, double, double, double, double, double, double, double, double, int, double, double, int); /*proto*/ static double __pyx_f_4wfpt_simpson_2D(double, double, double, double, double, double, double, double, double, int, double, double, int); /*proto*/ static double __pyx_f_4wfpt_adaptiveSimpsonsAux(double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, int); /*proto*/ static double __pyx_f_4wfpt_adaptiveSimpsons_1D(double, double, double, double, double, double, double, double, double, double, double, double, int); /*proto*/ static double __pyx_f_4wfpt_adaptiveSimpsonsAux_2D(double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, int, int); /*proto*/ static double __pyx_f_4wfpt_adaptiveSimpsons_2D(double, double, double, double, double, double, double, double, double, double, double, double, int, int); /*proto*/ static CYTHON_INLINE int __pyx_f_4wfpt_p_outlier_in_range(double); /*proto*/ static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, 'I', IS_UNSIGNED(int), 0 }; #define __Pyx_MODULE_NAME "wfpt" int __pyx_module_is_main_wfpt = 0; /* Implementation of 'wfpt' */ static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_RuntimeError; static PyObject *__pyx_pf_4wfpt_full_pdf(CYTHON_UNUSED PyObject *__pyx_self, double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_err, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err); /* proto */ static PyObject *__pyx_pf_4wfpt_2pdf_array(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_err, int __pyx_v_logp, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err, double __pyx_v_p_outlier, double __pyx_v_w_outlier); /* proto */ static PyObject *__pyx_pf_4wfpt_4wiener_like(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_err, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err, double __pyx_v_p_outlier, double __pyx_v_w_outlier); /* proto */ static PyObject *__pyx_pf_4wfpt_6wiener_like_multi(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, PyObject *__pyx_v_v, PyObject *__pyx_v_sv, PyObject *__pyx_v_a, PyObject *__pyx_v_z, PyObject *__pyx_v_sz, PyObject *__pyx_v_t, PyObject *__pyx_v_st, double __pyx_v_err, PyObject *__pyx_v_multi, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err, double __pyx_v_p_outlier, double __pyx_v_w_outlier); /* proto */ static PyObject *__pyx_pf_4wfpt_8gen_rts_from_cdf(CYTHON_UNUSED PyObject *__pyx_self, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, int __pyx_v_samples, double __pyx_v_cdf_lb, double __pyx_v_cdf_ub, double __pyx_v_dt); /* proto */ static PyObject *__pyx_pf_4wfpt_10wiener_like_contaminant(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, PyArrayObject *__pyx_v_cont_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_t_min, double __pyx_v_t_max, double __pyx_v_err, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err); /* proto */ static PyObject *__pyx_pf_4wfpt_12gen_cdf_using_pdf(CYTHON_UNUSED PyObject *__pyx_self, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_err, int __pyx_v_N, double __pyx_v_time, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err, double __pyx_v_p_outlier, double __pyx_v_w_outlier); /* proto */ static PyObject *__pyx_pf_4wfpt_14split_cdf(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, PyArrayObject *__pyx_v_data); /* proto */ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ static char __pyx_k_1[] = "at least one of the parameters is out of the support"; static char __pyx_k_5[] = "ndarray is not C contiguous"; static char __pyx_k_7[] = "ndarray is not Fortran contiguous"; static char __pyx_k_9[] = "Non-native byte order not supported"; static char __pyx_k_11[] = "unknown dtype code in numpy.pxd (%d)"; static char __pyx_k_12[] = "Format string allocated too short, see comment in numpy.pxd"; static char __pyx_k_15[] = "Format string allocated too short."; static char __pyx_k_17[] = "scipy.integrate"; static char __pyx_k_18[] = "*"; static char __pyx_k_21[] = "/home/wiecki/working/projects/hddm/src/wfpt.pyx"; static char __pyx_k_30[] = "wiener_like_contaminant"; static char __pyx_k__B[] = "B"; static char __pyx_k__H[] = "H"; static char __pyx_k__I[] = "I"; static char __pyx_k__L[] = "L"; static char __pyx_k__N[] = "N"; static char __pyx_k__O[] = "O"; static char __pyx_k__Q[] = "Q"; static char __pyx_k__a[] = "a"; static char __pyx_k__b[] = "b"; static char __pyx_k__d[] = "d"; static char __pyx_k__f[] = "f"; static char __pyx_k__g[] = "g"; static char __pyx_k__h[] = "h"; static char __pyx_k__i[] = "i"; static char __pyx_k__j[] = "j"; static char __pyx_k__l[] = "l"; static char __pyx_k__p[] = "p"; static char __pyx_k__q[] = "q"; static char __pyx_k__t[] = "t"; static char __pyx_k__v[] = "v"; static char __pyx_k__x[] = "x"; static char __pyx_k__y[] = "y"; static char __pyx_k__z[] = "z"; static char __pyx_k__Zd[] = "Zd"; static char __pyx_k__Zf[] = "Zf"; static char __pyx_k__Zg[] = "Zg"; static char __pyx_k__dt[] = "dt"; static char __pyx_k__lb[] = "lb"; static char __pyx_k__np[] = "np"; static char __pyx_k__rt[] = "rt"; static char __pyx_k__st[] = "st"; static char __pyx_k__sv[] = "sv"; static char __pyx_k__sz[] = "sz"; static char __pyx_k__ub[] = "ub"; static char __pyx_k__err[] = "err"; static char __pyx_k__idx[] = "idx"; static char __pyx_k__inf[] = "inf"; static char __pyx_k__log[] = "log"; static char __pyx_k__pdf[] = "pdf"; static char __pyx_k__rts[] = "rts"; static char __pyx_k__sum[] = "sum"; static char __pyx_k__copy[] = "copy"; static char __pyx_k__data[] = "data"; static char __pyx_k__diff[] = "diff"; static char __pyx_k__hddm[] = "hddm"; static char __pyx_k__logp[] = "logp"; static char __pyx_k__n_st[] = "n_st"; static char __pyx_k__n_sz[] = "n_sz"; static char __pyx_k__rand[] = "rand"; static char __pyx_k__sign[] = "sign"; static char __pyx_k__size[] = "size"; static char __pyx_k__time[] = "time"; static char __pyx_k__wfpt[] = "wfpt"; static char __pyx_k__x_lb[] = "x_lb"; static char __pyx_k__x_ub[] = "x_ub"; static char __pyx_k__array[] = "array"; static char __pyx_k__delay[] = "delay"; static char __pyx_k__dtype[] = "dtype"; static char __pyx_k__empty[] = "empty"; static char __pyx_k__l_cdf[] = "l_cdf"; static char __pyx_k__multi[] = "multi"; static char __pyx_k__numpy[] = "numpy"; static char __pyx_k__param[] = "param"; static char __pyx_k__range[] = "range"; static char __pyx_k__t_max[] = "t_max"; static char __pyx_k__t_min[] = "t_min"; static char __pyx_k__arange[] = "arange"; static char __pyx_k__cdf_lb[] = "cdf_lb"; static char __pyx_k__cdf_ub[] = "cdf_ub"; static char __pyx_k__cont_x[] = "cont_x"; static char __pyx_k__cumsum[] = "cumsum"; static char __pyx_k__double[] = "double"; static char __pyx_k__n_cont[] = "n_cont"; static char __pyx_k__params[] = "params"; static char __pyx_k__random[] = "random"; static char __pyx_k__samples[] = "samples"; static char __pyx_k____main__[] = "__main__"; static char __pyx_k____test__[] = "__test__"; static char __pyx_k__cumtrapz[] = "cumtrapz"; static char __pyx_k__linspace[] = "linspace"; static char __pyx_k__pos_cont[] = "pos_cont"; static char __pyx_k__sum_logp[] = "sum_logp"; static char __pyx_k__cdf_array[] = "cdf_array"; static char __pyx_k__integrate[] = "integrate"; static char __pyx_k__p_outlier[] = "p_outlier"; static char __pyx_k__pdf_array[] = "pdf_array"; static char __pyx_k__simps_err[] = "simps_err"; static char __pyx_k__split_cdf[] = "split_cdf"; static char __pyx_k__w_outlier[] = "w_outlier"; static char __pyx_k__ValueError[] = "ValueError"; static char __pyx_k__wp_outlier[] = "wp_outlier"; static char __pyx_k__concatenate[] = "concatenate"; static char __pyx_k__params_iter[] = "params_iter"; static char __pyx_k__wiener_like[] = "wiener_like"; static char __pyx_k__RuntimeError[] = "RuntimeError"; static char __pyx_k__searchsorted[] = "searchsorted"; static char __pyx_k__use_adaptive[] = "use_adaptive"; static char __pyx_k__gen_rts_from_cdf[] = "gen_rts_from_cdf"; static char __pyx_k__gen_cdf_using_pdf[] = "gen_cdf_using_pdf"; static char __pyx_k__wiener_like_multi[] = "wiener_like_multi"; static PyObject *__pyx_kp_s_1; static PyObject *__pyx_kp_u_11; static PyObject *__pyx_kp_u_12; static PyObject *__pyx_kp_u_15; static PyObject *__pyx_n_s_17; static PyObject *__pyx_n_s_18; static PyObject *__pyx_kp_s_21; static PyObject *__pyx_n_s_30; static PyObject *__pyx_kp_u_5; static PyObject *__pyx_kp_u_7; static PyObject *__pyx_kp_u_9; static PyObject *__pyx_n_s__N; static PyObject *__pyx_n_s__RuntimeError; static PyObject *__pyx_n_s__ValueError; static PyObject *__pyx_n_s____main__; static PyObject *__pyx_n_s____test__; static PyObject *__pyx_n_s__a; static PyObject *__pyx_n_s__arange; static PyObject *__pyx_n_s__array; static PyObject *__pyx_n_s__cdf_array; static PyObject *__pyx_n_s__cdf_lb; static PyObject *__pyx_n_s__cdf_ub; static PyObject *__pyx_n_s__concatenate; static PyObject *__pyx_n_s__cont_x; static PyObject *__pyx_n_s__copy; static PyObject *__pyx_n_s__cumsum; static PyObject *__pyx_n_s__cumtrapz; static PyObject *__pyx_n_s__data; static PyObject *__pyx_n_s__delay; static PyObject *__pyx_n_s__diff; static PyObject *__pyx_n_s__double; static PyObject *__pyx_n_s__dt; static PyObject *__pyx_n_s__dtype; static PyObject *__pyx_n_s__empty; static PyObject *__pyx_n_s__err; static PyObject *__pyx_n_s__f; static PyObject *__pyx_n_s__gen_cdf_using_pdf; static PyObject *__pyx_n_s__gen_rts_from_cdf; static PyObject *__pyx_n_s__hddm; static PyObject *__pyx_n_s__i; static PyObject *__pyx_n_s__idx; static PyObject *__pyx_n_s__inf; static PyObject *__pyx_n_s__integrate; static PyObject *__pyx_n_s__j; static PyObject *__pyx_n_s__l_cdf; static PyObject *__pyx_n_s__lb; static PyObject *__pyx_n_s__linspace; static PyObject *__pyx_n_s__log; static PyObject *__pyx_n_s__logp; static PyObject *__pyx_n_s__multi; static PyObject *__pyx_n_s__n_cont; static PyObject *__pyx_n_s__n_st; static PyObject *__pyx_n_s__n_sz; static PyObject *__pyx_n_s__np; static PyObject *__pyx_n_s__numpy; static PyObject *__pyx_n_s__p; static PyObject *__pyx_n_s__p_outlier; static PyObject *__pyx_n_s__param; static PyObject *__pyx_n_s__params; static PyObject *__pyx_n_s__params_iter; static PyObject *__pyx_n_s__pdf; static PyObject *__pyx_n_s__pdf_array; static PyObject *__pyx_n_s__pos_cont; static PyObject *__pyx_n_s__rand; static PyObject *__pyx_n_s__random; static PyObject *__pyx_n_s__range; static PyObject *__pyx_n_s__rt; static PyObject *__pyx_n_s__rts; static PyObject *__pyx_n_s__samples; static PyObject *__pyx_n_s__searchsorted; static PyObject *__pyx_n_s__sign; static PyObject *__pyx_n_s__simps_err; static PyObject *__pyx_n_s__size; static PyObject *__pyx_n_s__split_cdf; static PyObject *__pyx_n_s__st; static PyObject *__pyx_n_s__sum; static PyObject *__pyx_n_s__sum_logp; static PyObject *__pyx_n_s__sv; static PyObject *__pyx_n_s__sz; static PyObject *__pyx_n_s__t; static PyObject *__pyx_n_s__t_max; static PyObject *__pyx_n_s__t_min; static PyObject *__pyx_n_s__time; static PyObject *__pyx_n_s__ub; static PyObject *__pyx_n_s__use_adaptive; static PyObject *__pyx_n_s__v; static PyObject *__pyx_n_s__w_outlier; static PyObject *__pyx_n_s__wfpt; static PyObject *__pyx_n_s__wiener_like; static PyObject *__pyx_n_s__wiener_like_multi; static PyObject *__pyx_n_s__wp_outlier; static PyObject *__pyx_n_s__x; static PyObject *__pyx_n_s__x_lb; static PyObject *__pyx_n_s__x_ub; static PyObject *__pyx_n_s__y; static PyObject *__pyx_n_s__z; static PyObject *__pyx_int_0; static PyObject *__pyx_int_neg_1; static PyObject *__pyx_int_15; static PyObject *__pyx_k_slice_3; static PyObject *__pyx_k_slice_4; static PyObject *__pyx_k_tuple_2; static PyObject *__pyx_k_tuple_6; static PyObject *__pyx_k_tuple_8; static PyObject *__pyx_k_tuple_10; static PyObject *__pyx_k_tuple_13; static PyObject *__pyx_k_tuple_14; static PyObject *__pyx_k_tuple_16; static PyObject *__pyx_k_tuple_19; static PyObject *__pyx_k_tuple_22; static PyObject *__pyx_k_tuple_24; static PyObject *__pyx_k_tuple_26; static PyObject *__pyx_k_tuple_28; static PyObject *__pyx_k_tuple_31; static PyObject *__pyx_k_tuple_33; static PyObject *__pyx_k_codeobj_20; static PyObject *__pyx_k_codeobj_23; static PyObject *__pyx_k_codeobj_25; static PyObject *__pyx_k_codeobj_27; static PyObject *__pyx_k_codeobj_29; static PyObject *__pyx_k_codeobj_32; static PyObject *__pyx_k_codeobj_34; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":25 * double M_PI * * cdef double ftt_01w(double tt, double w, double err) nogil: # <<<<<<<<<<<<<< * """Compute f(t|0,1,w) for the likelihood of the drift diffusion model using the method * and implementation of Navarro & Fuss, 2009. */ static double __pyx_f_4wfpt_ftt_01w(double __pyx_v_tt, double __pyx_v_w, double __pyx_v_err) { double __pyx_v_kl; double __pyx_v_ks; double __pyx_v_p; int __pyx_v_k; int __pyx_v_K; int __pyx_v_lower; int __pyx_v_upper; double __pyx_r; int __pyx_t_1; int __pyx_t_2; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":33 * * # calculate number of terms needed for large t * if M_PI*tt*err<1: # if error threshold is set low enough # <<<<<<<<<<<<<< * kl=sqrt(-2*log(M_PI*tt*err)/(M_PI**2*tt)) # bound * kl=fmax(kl,1./(M_PI*sqrt(tt))) # ensure boundary conditions met */ __pyx_t_1 = (((M_PI * __pyx_v_tt) * __pyx_v_err) < 1.0); if (__pyx_t_1) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":34 * # calculate number of terms needed for large t * if M_PI*tt*err<1: # if error threshold is set low enough * kl=sqrt(-2*log(M_PI*tt*err)/(M_PI**2*tt)) # bound # <<<<<<<<<<<<<< * kl=fmax(kl,1./(M_PI*sqrt(tt))) # ensure boundary conditions met * else: # if error threshold set too high */ __pyx_v_kl = sqrt(((-2.0 * log(((M_PI * __pyx_v_tt) * __pyx_v_err))) / (pow(M_PI, 2.0) * __pyx_v_tt))); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":35 * if M_PI*tt*err<1: # if error threshold is set low enough * kl=sqrt(-2*log(M_PI*tt*err)/(M_PI**2*tt)) # bound * kl=fmax(kl,1./(M_PI*sqrt(tt))) # ensure boundary conditions met # <<<<<<<<<<<<<< * else: # if error threshold set too high * kl=1./(M_PI*sqrt(tt)) # set to boundary condition */ __pyx_v_kl = fmax(__pyx_v_kl, (1. / (M_PI * sqrt(__pyx_v_tt)))); goto __pyx_L3; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":37 * kl=fmax(kl,1./(M_PI*sqrt(tt))) # ensure boundary conditions met * else: # if error threshold set too high * kl=1./(M_PI*sqrt(tt)) # set to boundary condition # <<<<<<<<<<<<<< * * # calculate number of terms needed for small t */ __pyx_v_kl = (1. / (M_PI * sqrt(__pyx_v_tt))); } __pyx_L3:; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":40 * * # calculate number of terms needed for small t * if 2*sqrt(2*M_PI*tt)*err<1: # if error threshold is set low enough # <<<<<<<<<<<<<< * ks=2+sqrt(-2*tt*log(2*sqrt(2*M_PI*tt)*err)) # bound * ks=fmax(ks,sqrt(tt)+1) # ensure boundary conditions are met */ __pyx_t_1 = (((2.0 * sqrt(((2.0 * M_PI) * __pyx_v_tt))) * __pyx_v_err) < 1.0); if (__pyx_t_1) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":41 * # calculate number of terms needed for small t * if 2*sqrt(2*M_PI*tt)*err<1: # if error threshold is set low enough * ks=2+sqrt(-2*tt*log(2*sqrt(2*M_PI*tt)*err)) # bound # <<<<<<<<<<<<<< * ks=fmax(ks,sqrt(tt)+1) # ensure boundary conditions are met * else: # if error threshold was set too high */ __pyx_v_ks = (2.0 + sqrt(((-2.0 * __pyx_v_tt) * log(((2.0 * sqrt(((2.0 * M_PI) * __pyx_v_tt))) * __pyx_v_err))))); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":42 * if 2*sqrt(2*M_PI*tt)*err<1: # if error threshold is set low enough * ks=2+sqrt(-2*tt*log(2*sqrt(2*M_PI*tt)*err)) # bound * ks=fmax(ks,sqrt(tt)+1) # ensure boundary conditions are met # <<<<<<<<<<<<<< * else: # if error threshold was set too high * ks=2 # minimal kappa for that case */ __pyx_v_ks = fmax(__pyx_v_ks, (sqrt(__pyx_v_tt) + 1.0)); goto __pyx_L4; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":44 * ks=fmax(ks,sqrt(tt)+1) # ensure boundary conditions are met * else: # if error threshold was set too high * ks=2 # minimal kappa for that case # <<<<<<<<<<<<<< * * # compute f(tt|0,1,w) */ __pyx_v_ks = 2.0; } __pyx_L4:; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":47 * * # compute f(tt|0,1,w) * p=0 #initialize density # <<<<<<<<<<<<<< * if ks(ceil(ks)) # round to smallest integer meeting error */ __pyx_v_p = 0.0; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":48 * # compute f(tt|0,1,w) * p=0 #initialize density * if ks(ceil(ks)) # round to smallest integer meeting error * lower = (-floor((K-1)/2.)) */ __pyx_t_1 = (__pyx_v_ks < __pyx_v_kl); if (__pyx_t_1) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":49 * p=0 #initialize density * if ks(ceil(ks)) # round to smallest integer meeting error # <<<<<<<<<<<<<< * lower = (-floor((K-1)/2.)) * upper = (ceil((K-1)/2.)) */ __pyx_v_K = ((int)ceil(__pyx_v_ks)); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":50 * if ks(ceil(ks)) # round to smallest integer meeting error * lower = (-floor((K-1)/2.)) # <<<<<<<<<<<<<< * upper = (ceil((K-1)/2.)) * for k from lower <= k <= upper: # loop over k */ __pyx_v_lower = ((int)(-floor(((__pyx_v_K - 1) / 2.)))); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":51 * K=(ceil(ks)) # round to smallest integer meeting error * lower = (-floor((K-1)/2.)) * upper = (ceil((K-1)/2.)) # <<<<<<<<<<<<<< * for k from lower <= k <= upper: # loop over k * p+=(w+2*k)*exp(-(pow((w+2*k),2))/2/tt) # increment sum */ __pyx_v_upper = ((int)ceil(((__pyx_v_K - 1) / 2.))); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":52 * lower = (-floor((K-1)/2.)) * upper = (ceil((K-1)/2.)) * for k from lower <= k <= upper: # loop over k # <<<<<<<<<<<<<< * p+=(w+2*k)*exp(-(pow((w+2*k),2))/2/tt) # increment sum * p/=sqrt(2*M_PI*pow(tt,3)) # add con_stant term */ __pyx_t_2 = __pyx_v_upper; for (__pyx_v_k = __pyx_v_lower; __pyx_v_k <= __pyx_t_2; __pyx_v_k++) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":53 * upper = (ceil((K-1)/2.)) * for k from lower <= k <= upper: # loop over k * p+=(w+2*k)*exp(-(pow((w+2*k),2))/2/tt) # increment sum # <<<<<<<<<<<<<< * p/=sqrt(2*M_PI*pow(tt,3)) # add con_stant term * */ __pyx_v_p = (__pyx_v_p + ((__pyx_v_w + (2 * __pyx_v_k)) * exp((((-pow((__pyx_v_w + (2 * __pyx_v_k)), 2.0)) / 2.0) / __pyx_v_tt)))); } /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":54 * for k from lower <= k <= upper: # loop over k * p+=(w+2*k)*exp(-(pow((w+2*k),2))/2/tt) # increment sum * p/=sqrt(2*M_PI*pow(tt,3)) # add con_stant term # <<<<<<<<<<<<<< * * else: # if large t is better... */ __pyx_v_p = (__pyx_v_p / sqrt(((2.0 * M_PI) * pow(__pyx_v_tt, 3.0)))); goto __pyx_L5; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":57 * * else: # if large t is better... * K=(ceil(kl)) # round to smallest integer meeting error # <<<<<<<<<<<<<< * for k from 1 <= k <= K: * p+=k*exp(-(pow(k,2))*(M_PI**2)*tt/2)*sin(k*M_PI*w) # increment sum */ __pyx_v_K = ((int)ceil(__pyx_v_kl)); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":58 * else: # if large t is better... * K=(ceil(kl)) # round to smallest integer meeting error * for k from 1 <= k <= K: # <<<<<<<<<<<<<< * p+=k*exp(-(pow(k,2))*(M_PI**2)*tt/2)*sin(k*M_PI*w) # increment sum * p*=M_PI # add con_stant term */ __pyx_t_2 = __pyx_v_K; for (__pyx_v_k = 1; __pyx_v_k <= __pyx_t_2; __pyx_v_k++) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":59 * K=(ceil(kl)) # round to smallest integer meeting error * for k from 1 <= k <= K: * p+=k*exp(-(pow(k,2))*(M_PI**2)*tt/2)*sin(k*M_PI*w) # increment sum # <<<<<<<<<<<<<< * p*=M_PI # add con_stant term * */ __pyx_v_p = (__pyx_v_p + ((__pyx_v_k * exp(((((-pow(__pyx_v_k, 2.0)) * pow(M_PI, 2.0)) * __pyx_v_tt) / 2.0))) * sin(((__pyx_v_k * M_PI) * __pyx_v_w)))); } /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":60 * for k from 1 <= k <= K: * p+=k*exp(-(pow(k,2))*(M_PI**2)*tt/2)*sin(k*M_PI*w) # increment sum * p*=M_PI # add con_stant term # <<<<<<<<<<<<<< * * return p */ __pyx_v_p = (__pyx_v_p * M_PI); } __pyx_L5:; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":62 * p*=M_PI # add con_stant term * * return p # <<<<<<<<<<<<<< * * cdef inline double prob_ub(double v, double a, double z) nogil: */ __pyx_r = __pyx_v_p; goto __pyx_L0; __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":64 * return p * * cdef inline double prob_ub(double v, double a, double z) nogil: # <<<<<<<<<<<<<< * """Probability of hitting upper boundary.""" * return (exp(-2*a*z*v) - 1) / (exp(-2*a*v) - 1) */ static CYTHON_INLINE double __pyx_f_4wfpt_prob_ub(double __pyx_v_v, double __pyx_v_a, double __pyx_v_z) { double __pyx_r; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":66 * cdef inline double prob_ub(double v, double a, double z) nogil: * """Probability of hitting upper boundary.""" * return (exp(-2*a*z*v) - 1) / (exp(-2*a*v) - 1) # <<<<<<<<<<<<<< * * cdef double pdf(double x, double v, double a, double w, double err) nogil: */ __pyx_r = ((exp((((-2.0 * __pyx_v_a) * __pyx_v_z) * __pyx_v_v)) - 1.0) / (exp(((-2.0 * __pyx_v_a) * __pyx_v_v)) - 1.0)); goto __pyx_L0; __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":68 * return (exp(-2*a*z*v) - 1) / (exp(-2*a*v) - 1) * * cdef double pdf(double x, double v, double a, double w, double err) nogil: # <<<<<<<<<<<<<< * """Compute the likelihood of the drift diffusion model f(t|v,a,z) using the method * and implementation of Navarro & Fuss, 2009. */ static double __pyx_f_4wfpt_pdf(double __pyx_v_x, double __pyx_v_v, double __pyx_v_a, double __pyx_v_w, double __pyx_v_err) { double __pyx_v_tt; double __pyx_v_p; double __pyx_r; int __pyx_t_1; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":72 * and implementation of Navarro & Fuss, 2009. * """ * if x <= 0: # <<<<<<<<<<<<<< * return 0 * */ __pyx_t_1 = (__pyx_v_x <= 0.0); if (__pyx_t_1) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":73 * """ * if x <= 0: * return 0 # <<<<<<<<<<<<<< * * cdef double tt = x/a**2 # use normalized time */ __pyx_r = 0.0; goto __pyx_L0; goto __pyx_L3; } __pyx_L3:; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":75 * return 0 * * cdef double tt = x/a**2 # use normalized time # <<<<<<<<<<<<<< * cdef double p = ftt_01w(tt, w, err) #get f(t|0,1,w) * */ __pyx_v_tt = (__pyx_v_x / pow(__pyx_v_a, 2.0)); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":76 * * cdef double tt = x/a**2 # use normalized time * cdef double p = ftt_01w(tt, w, err) #get f(t|0,1,w) # <<<<<<<<<<<<<< * * # convert to f(t|v,a,w) */ __pyx_v_p = __pyx_f_4wfpt_ftt_01w(__pyx_v_tt, __pyx_v_w, __pyx_v_err); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":79 * * # convert to f(t|v,a,w) * return p*exp(-v*a*w -(pow(v,2))*x/2.)/(pow(a,2)) # <<<<<<<<<<<<<< * * cdef double pdf_sv(double x, double v, double sv, double a, double z, double err) nogil: */ __pyx_r = ((__pyx_v_p * exp(((((-__pyx_v_v) * __pyx_v_a) * __pyx_v_w) - ((pow(__pyx_v_v, 2.0) * __pyx_v_x) / 2.)))) / pow(__pyx_v_a, 2.0)); goto __pyx_L0; __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":81 * return p*exp(-v*a*w -(pow(v,2))*x/2.)/(pow(a,2)) * * cdef double pdf_sv(double x, double v, double sv, double a, double z, double err) nogil: # <<<<<<<<<<<<<< * """Compute the likelihood of the drift diffusion model f(t|v,a,z,sv) using the method * and implementation of Navarro & Fuss, 2009. */ static double __pyx_f_4wfpt_pdf_sv(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_err) { double __pyx_v_tt; double __pyx_v_p; double __pyx_r; int __pyx_t_1; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":86 * sv is the std of the drift rate * """ * if x <= 0: # <<<<<<<<<<<<<< * return 0 * */ __pyx_t_1 = (__pyx_v_x <= 0.0); if (__pyx_t_1) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":87 * """ * if x <= 0: * return 0 # <<<<<<<<<<<<<< * * if sv==0: */ __pyx_r = 0.0; goto __pyx_L0; goto __pyx_L3; } __pyx_L3:; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":89 * return 0 * * if sv==0: # <<<<<<<<<<<<<< * return pdf(x, v, a, z, err) * */ __pyx_t_1 = (__pyx_v_sv == 0.0); if (__pyx_t_1) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":90 * * if sv==0: * return pdf(x, v, a, z, err) # <<<<<<<<<<<<<< * * cdef double tt = x/(pow(a,2)) # use normalized time */ __pyx_r = __pyx_f_4wfpt_pdf(__pyx_v_x, __pyx_v_v, __pyx_v_a, __pyx_v_z, __pyx_v_err); goto __pyx_L0; goto __pyx_L4; } __pyx_L4:; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":92 * return pdf(x, v, a, z, err) * * cdef double tt = x/(pow(a,2)) # use normalized time # <<<<<<<<<<<<<< * cdef double p = ftt_01w(tt, z, err) #get f(t|0,1,w) * */ __pyx_v_tt = (__pyx_v_x / pow(__pyx_v_a, 2.0)); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":93 * * cdef double tt = x/(pow(a,2)) # use normalized time * cdef double p = ftt_01w(tt, z, err) #get f(t|0,1,w) # <<<<<<<<<<<<<< * * # convert to f(t|v,a,w) */ __pyx_v_p = __pyx_f_4wfpt_ftt_01w(__pyx_v_tt, __pyx_v_z, __pyx_v_err); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":96 * * # convert to f(t|v,a,w) * return exp(log(p) + ((a*z*sv)**2 - 2*a*v*z - (v**2)*x)/(2*(sv**2)*x+2))/sqrt((sv**2)*x+1)/(a**2) # <<<<<<<<<<<<<< * * cpdef double full_pdf(double x, double v, double sv, double a, double */ __pyx_r = ((exp((log(__pyx_v_p) + (((pow(((__pyx_v_a * __pyx_v_z) * __pyx_v_sv), 2.0) - (((2.0 * __pyx_v_a) * __pyx_v_v) * __pyx_v_z)) - (pow(__pyx_v_v, 2.0) * __pyx_v_x)) / (((2.0 * pow(__pyx_v_sv, 2.0)) * __pyx_v_x) + 2.0)))) / sqrt(((pow(__pyx_v_sv, 2.0) * __pyx_v_x) + 1.0))) / pow(__pyx_v_a, 2.0)); goto __pyx_L0; __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":98 * return exp(log(p) + ((a*z*sv)**2 - 2*a*v*z - (v**2)*x)/(2*(sv**2)*x+2))/sqrt((sv**2)*x+1)/(a**2) * * cpdef double full_pdf(double x, double v, double sv, double a, double # <<<<<<<<<<<<<< * z, double sz, double t, double st, double err, int * n_st=2, int n_sz=2, bint use_adaptive=1, double */ static PyObject *__pyx_pw_4wfpt_1full_pdf(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static double __pyx_f_4wfpt_full_pdf(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_err, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_opt_args_4wfpt_full_pdf *__pyx_optional_args) { int __pyx_v_n_st = ((int)2); int __pyx_v_n_sz = ((int)2); int __pyx_v_use_adaptive = ((int)1); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":101 * z, double sz, double t, double st, double err, int * n_st=2, int n_sz=2, bint use_adaptive=1, double * simps_err=1e-3) nogil: # <<<<<<<<<<<<<< * """full pdf""" * */ double __pyx_v_simps_err = ((double)1e-3); double __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; int __pyx_t_5; int __pyx_t_6; int __pyx_t_7; int __pyx_t_8; int __pyx_t_9; if (__pyx_optional_args) { if (__pyx_optional_args->__pyx_n > 0) { __pyx_v_n_st = __pyx_optional_args->n_st; if (__pyx_optional_args->__pyx_n > 1) { __pyx_v_n_sz = __pyx_optional_args->n_sz; if (__pyx_optional_args->__pyx_n > 2) { __pyx_v_use_adaptive = __pyx_optional_args->use_adaptive; if (__pyx_optional_args->__pyx_n > 3) { __pyx_v_simps_err = __pyx_optional_args->simps_err; } } } } } /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":105 * * # Check if parpameters are valid * if z<0 or z>1 or a<0 or ((fabs(x)-(t-st/2.))<0) or (z+sz/2.>1) or (z-sz/2.<0) or (t-st/2.<0) or (t<0): # <<<<<<<<<<<<<< * return 0 * */ __pyx_t_1 = (__pyx_v_z < 0.0); if (!__pyx_t_1) { __pyx_t_2 = (__pyx_v_z > 1.0); if (!__pyx_t_2) { __pyx_t_3 = (__pyx_v_a < 0.0); if (!__pyx_t_3) { __pyx_t_4 = ((fabs(__pyx_v_x) - (__pyx_v_t - (__pyx_v_st / 2.))) < 0.0); if (!__pyx_t_4) { __pyx_t_5 = ((__pyx_v_z + (__pyx_v_sz / 2.)) > 1.0); if (!__pyx_t_5) { __pyx_t_6 = ((__pyx_v_z - (__pyx_v_sz / 2.)) < 0.0); if (!__pyx_t_6) { __pyx_t_7 = ((__pyx_v_t - (__pyx_v_st / 2.)) < 0.0); if (!__pyx_t_7) { __pyx_t_8 = (__pyx_v_t < 0.0); __pyx_t_9 = __pyx_t_8; } else { __pyx_t_9 = __pyx_t_7; } __pyx_t_7 = __pyx_t_9; } else { __pyx_t_7 = __pyx_t_6; } __pyx_t_6 = __pyx_t_7; } else { __pyx_t_6 = __pyx_t_5; } __pyx_t_5 = __pyx_t_6; } else { __pyx_t_5 = __pyx_t_4; } __pyx_t_4 = __pyx_t_5; } else { __pyx_t_4 = __pyx_t_3; } __pyx_t_3 = __pyx_t_4; } else { __pyx_t_3 = __pyx_t_2; } __pyx_t_2 = __pyx_t_3; } else { __pyx_t_2 = __pyx_t_1; } if (__pyx_t_2) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":106 * # Check if parpameters are valid * if z<0 or z>1 or a<0 or ((fabs(x)-(t-st/2.))<0) or (z+sz/2.>1) or (z-sz/2.<0) or (t-st/2.<0) or (t<0): * return 0 # <<<<<<<<<<<<<< * * # transform x,v,z if x is upper bound response */ __pyx_r = 0.0; goto __pyx_L0; goto __pyx_L3; } __pyx_L3:; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":109 * * # transform x,v,z if x is upper bound response * if x > 0: # <<<<<<<<<<<<<< * v = -v * z = 1.-z */ __pyx_t_2 = (__pyx_v_x > 0.0); if (__pyx_t_2) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":110 * # transform x,v,z if x is upper bound response * if x > 0: * v = -v # <<<<<<<<<<<<<< * z = 1.-z * */ __pyx_v_v = (-__pyx_v_v); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":111 * if x > 0: * v = -v * z = 1.-z # <<<<<<<<<<<<<< * * x = fabs(x) */ __pyx_v_z = (1. - __pyx_v_z); goto __pyx_L4; } __pyx_L4:; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":113 * z = 1.-z * * x = fabs(x) # <<<<<<<<<<<<<< * * if st<1e-3: */ __pyx_v_x = fabs(__pyx_v_x); /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":115 * x = fabs(x) * * if st<1e-3: # <<<<<<<<<<<<<< * st = 0 * if sz <1e-3: */ __pyx_t_2 = (__pyx_v_st < 1e-3); if (__pyx_t_2) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":116 * * if st<1e-3: * st = 0 # <<<<<<<<<<<<<< * if sz <1e-3: * sz = 0 */ __pyx_v_st = 0.0; goto __pyx_L5; } __pyx_L5:; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":117 * if st<1e-3: * st = 0 * if sz <1e-3: # <<<<<<<<<<<<<< * sz = 0 * */ __pyx_t_2 = (__pyx_v_sz < 1e-3); if (__pyx_t_2) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":118 * st = 0 * if sz <1e-3: * sz = 0 # <<<<<<<<<<<<<< * * if (sz==0): */ __pyx_v_sz = 0.0; goto __pyx_L6; } __pyx_L6:; /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":120 * sz = 0 * * if (sz==0): # <<<<<<<<<<<<<< * if (st==0): #sv=0,sz=0,st=0 * return pdf_sv(x - t, v, sv, a, z, err) */ __pyx_t_2 = (__pyx_v_sz == 0.0); if (__pyx_t_2) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":121 * * if (sz==0): * if (st==0): #sv=0,sz=0,st=0 # <<<<<<<<<<<<<< * return pdf_sv(x - t, v, sv, a, z, err) * else: #sv=0,sz=0,st=$ */ __pyx_t_2 = (__pyx_v_st == 0.0); if (__pyx_t_2) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":122 * if (sz==0): * if (st==0): #sv=0,sz=0,st=0 * return pdf_sv(x - t, v, sv, a, z, err) # <<<<<<<<<<<<<< * else: #sv=0,sz=0,st=$ * if use_adaptive>0: */ __pyx_r = __pyx_f_4wfpt_pdf_sv((__pyx_v_x - __pyx_v_t), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_err); goto __pyx_L0; goto __pyx_L8; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":124 * return pdf_sv(x - t, v, sv, a, z, err) * else: #sv=0,sz=0,st=$ * if use_adaptive>0: # <<<<<<<<<<<<<< * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z, z, t-st/2., t+st/2., simps_err, n_st) * else: */ __pyx_t_2 = (__pyx_v_use_adaptive > 0); if (__pyx_t_2) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":125 * else: #sv=0,sz=0,st=$ * if use_adaptive>0: * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z, z, t-st/2., t+st/2., simps_err, n_st) # <<<<<<<<<<<<<< * else: * return simpson_1D(x, v, sv, a, z, t, err, z, z, 0, t-st/2., t+st/2., n_st) */ __pyx_r = __pyx_f_4wfpt_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_err, __pyx_v_z, __pyx_v_z, (__pyx_v_t - (__pyx_v_st / 2.)), (__pyx_v_t + (__pyx_v_st / 2.)), __pyx_v_simps_err, __pyx_v_n_st); goto __pyx_L0; goto __pyx_L9; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":127 * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z, z, t-st/2., t+st/2., simps_err, n_st) * else: * return simpson_1D(x, v, sv, a, z, t, err, z, z, 0, t-st/2., t+st/2., n_st) # <<<<<<<<<<<<<< * * else: #sz=$ */ __pyx_r = __pyx_f_4wfpt_simpson_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_err, __pyx_v_z, __pyx_v_z, 0, (__pyx_v_t - (__pyx_v_st / 2.)), (__pyx_v_t + (__pyx_v_st / 2.)), __pyx_v_n_st); goto __pyx_L0; } __pyx_L9:; } __pyx_L8:; goto __pyx_L7; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":130 * * else: #sz=$ * if (st==0): #sv=0,sz=$,st=0 # <<<<<<<<<<<<<< * if use_adaptive: * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t, t, simps_err, n_sz) */ __pyx_t_2 = (__pyx_v_st == 0.0); if (__pyx_t_2) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":131 * else: #sz=$ * if (st==0): #sv=0,sz=$,st=0 * if use_adaptive: # <<<<<<<<<<<<<< * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t, t, simps_err, n_sz) * else: */ if (__pyx_v_use_adaptive) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":132 * if (st==0): #sv=0,sz=$,st=0 * if use_adaptive: * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t, t, simps_err, n_sz) # <<<<<<<<<<<<<< * else: * return simpson_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t, t , 0) */ __pyx_r = __pyx_f_4wfpt_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_err, (__pyx_v_z - (__pyx_v_sz / 2.)), (__pyx_v_z + (__pyx_v_sz / 2.)), __pyx_v_t, __pyx_v_t, __pyx_v_simps_err, __pyx_v_n_sz); goto __pyx_L0; goto __pyx_L11; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":134 * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t, t, simps_err, n_sz) * else: * return simpson_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t, t , 0) # <<<<<<<<<<<<<< * else: #sv=0,sz=$,st=$ * if use_adaptive: */ __pyx_r = __pyx_f_4wfpt_simpson_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_err, (__pyx_v_z - (__pyx_v_sz / 2.)), (__pyx_v_z + (__pyx_v_sz / 2.)), __pyx_v_n_sz, __pyx_v_t, __pyx_v_t, 0); goto __pyx_L0; } __pyx_L11:; goto __pyx_L10; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":136 * return simpson_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t, t , 0) * else: #sv=0,sz=$,st=$ * if use_adaptive: # <<<<<<<<<<<<<< * return adaptiveSimpsons_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t-st/2., t+st/2., simps_err, n_sz, n_st) * else: */ if (__pyx_v_use_adaptive) { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":137 * else: #sv=0,sz=$,st=$ * if use_adaptive: * return adaptiveSimpsons_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t-st/2., t+st/2., simps_err, n_sz, n_st) # <<<<<<<<<<<<<< * else: * return simpson_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t-st/2., t+st/2., n_st) */ __pyx_r = __pyx_f_4wfpt_adaptiveSimpsons_2D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_err, (__pyx_v_z - (__pyx_v_sz / 2.)), (__pyx_v_z + (__pyx_v_sz / 2.)), (__pyx_v_t - (__pyx_v_st / 2.)), (__pyx_v_t + (__pyx_v_st / 2.)), __pyx_v_simps_err, __pyx_v_n_sz, __pyx_v_n_st); goto __pyx_L0; goto __pyx_L12; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":139 * return adaptiveSimpsons_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t-st/2., t+st/2., simps_err, n_sz, n_st) * else: * return simpson_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t-st/2., t+st/2., n_st) # <<<<<<<<<<<<<< */ __pyx_r = __pyx_f_4wfpt_simpson_2D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_err, (__pyx_v_z - (__pyx_v_sz / 2.)), (__pyx_v_z + (__pyx_v_sz / 2.)), __pyx_v_n_sz, (__pyx_v_t - (__pyx_v_st / 2.)), (__pyx_v_t + (__pyx_v_st / 2.)), __pyx_v_n_st); goto __pyx_L0; } __pyx_L12:; } __pyx_L10:; } __pyx_L7:; __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_4wfpt_1full_pdf(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static char __pyx_doc_4wfpt_full_pdf[] = "full_pdf(double x, double v, double sv, double a, double z, double sz, double t, double st, double err, int n_st=2, int n_sz=2, int use_adaptive=1, double simps_err=0.001) -> double\nfull pdf"; 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__pyx_lineno = 100; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } else { __pyx_v_simps_err = ((double)1e-3); } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("full_pdf", 0, 9, 13, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[1]; __pyx_lineno = 98; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; __Pyx_AddTraceback("wfpt.full_pdf", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; __pyx_r = __pyx_pf_4wfpt_full_pdf(__pyx_self, __pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_sz, __pyx_v_t, __pyx_v_st, __pyx_v_err, __pyx_v_n_st, __pyx_v_n_sz, __pyx_v_use_adaptive, __pyx_v_simps_err); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "/home/wiecki/working/projects/hddm/src/pdf.pxi":98 * return exp(log(p) + ((a*z*sv)**2 - 2*a*v*z - (v**2)*x)/(2*(sv**2)*x+2))/sqrt((sv**2)*x+1)/(a**2) * * cpdef double full_pdf(double x, double v, double sv, double a, double # <<<<<<<<<<<<<< * z, double sz, double t, double st, double err, int * n_st=2, int n_sz=2, bint use_adaptive=1, double */ static PyObject *__pyx_pf_4wfpt_full_pdf(CYTHON_UNUSED PyObject *__pyx_self, double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_err, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err) { PyObject *__pyx_r = NULL; 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__pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":10 * include 'pdf.pxi' * * cdef double simpson_1D(double x, double v, double sv, double a, double z, double t, double err, # <<<<<<<<<<<<<< * double lb_z, double ub_z, int n_sz, double lb_t, double ub_t, int n_st) nogil: * #assert ((n_sz&1)==0 and (n_st&1)==0), "n_st and n_sz have to be even" */ static double __pyx_f_4wfpt_simpson_1D(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_t, double __pyx_v_err, double __pyx_v_lb_z, double __pyx_v_ub_z, int __pyx_v_n_sz, double __pyx_v_lb_t, double __pyx_v_ub_t, int __pyx_v_n_st) { double __pyx_v_ht; double __pyx_v_hz; int __pyx_v_n; double __pyx_v_S; double __pyx_v_z_tag; double __pyx_v_t_tag; double __pyx_v_y; int __pyx_v_i; double __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; int __pyx_t_4; long __pyx_t_5; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":16 * * cdef double ht, hz * cdef int n = max(n_st, n_sz) # <<<<<<<<<<<<<< * if n_st==0: #integration over z * hz = (ub_z-lb_z)/n */ __pyx_t_1 = __pyx_v_n_sz; __pyx_t_2 = __pyx_v_n_st; if ((__pyx_t_1 > __pyx_t_2)) { __pyx_t_3 = __pyx_t_1; } else { __pyx_t_3 = __pyx_t_2; } __pyx_v_n = __pyx_t_3; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":17 * cdef double ht, hz * cdef int n = max(n_st, n_sz) * if n_st==0: #integration over z # <<<<<<<<<<<<<< * hz = (ub_z-lb_z)/n * ht = 0 */ __pyx_t_4 = (__pyx_v_n_st == 0); if (__pyx_t_4) { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":18 * cdef int n = max(n_st, n_sz) * if n_st==0: #integration over z * hz = (ub_z-lb_z)/n # <<<<<<<<<<<<<< * ht = 0 * lb_t = t */ __pyx_v_hz = ((__pyx_v_ub_z - __pyx_v_lb_z) / __pyx_v_n); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":19 * if n_st==0: #integration over z * hz = (ub_z-lb_z)/n * ht = 0 # <<<<<<<<<<<<<< * lb_t = t * ub_t = t */ __pyx_v_ht = 0.0; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":20 * hz = (ub_z-lb_z)/n * ht = 0 * lb_t = t # <<<<<<<<<<<<<< * ub_t = t * else: #integration over t */ __pyx_v_lb_t = __pyx_v_t; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":21 * ht = 0 * lb_t = t * ub_t = t # <<<<<<<<<<<<<< * else: #integration over t * hz = 0 */ __pyx_v_ub_t = __pyx_v_t; goto __pyx_L3; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":23 * ub_t = t * else: #integration over t * hz = 0 # <<<<<<<<<<<<<< * ht = (ub_t-lb_t)/n * lb_z = z */ __pyx_v_hz = 0.0; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":24 * else: #integration over t * hz = 0 * ht = (ub_t-lb_t)/n # <<<<<<<<<<<<<< * lb_z = z * ub_z = z */ __pyx_v_ht = ((__pyx_v_ub_t - __pyx_v_lb_t) / __pyx_v_n); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":25 * hz = 0 * ht = (ub_t-lb_t)/n * lb_z = z # <<<<<<<<<<<<<< * ub_z = z * */ __pyx_v_lb_z = __pyx_v_z; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":26 * ht = (ub_t-lb_t)/n * lb_z = z * ub_z = z # <<<<<<<<<<<<<< * * cdef double S = pdf_sv(x - lb_t, v, sv, a, lb_z, err) */ __pyx_v_ub_z = __pyx_v_z; } __pyx_L3:; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":28 * ub_z = z * * cdef double S = pdf_sv(x - lb_t, v, sv, a, lb_z, err) # <<<<<<<<<<<<<< * cdef double z_tag, t_tag, y * cdef int i */ __pyx_v_S = __pyx_f_4wfpt_pdf_sv((__pyx_v_x - __pyx_v_lb_t), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_lb_z, __pyx_v_err); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":32 * cdef int i * * for i from 1 <= i <= n: # <<<<<<<<<<<<<< * z_tag = lb_z + hz * i * t_tag = lb_t + ht * i */ __pyx_t_3 = __pyx_v_n; for (__pyx_v_i = 1; __pyx_v_i <= __pyx_t_3; __pyx_v_i++) { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":33 * * for i from 1 <= i <= n: * z_tag = lb_z + hz * i # <<<<<<<<<<<<<< * t_tag = lb_t + ht * i * y = pdf_sv(x - t_tag, v, sv, a, z_tag, err) */ __pyx_v_z_tag = (__pyx_v_lb_z + (__pyx_v_hz * __pyx_v_i)); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":34 * for i from 1 <= i <= n: * z_tag = lb_z + hz * i * t_tag = lb_t + ht * i # <<<<<<<<<<<<<< * y = pdf_sv(x - t_tag, v, sv, a, z_tag, err) * if i&1: #check if i is odd */ __pyx_v_t_tag = (__pyx_v_lb_t + (__pyx_v_ht * __pyx_v_i)); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":35 * z_tag = lb_z + hz * i * t_tag = lb_t + ht * i * y = pdf_sv(x - t_tag, v, sv, a, z_tag, err) # <<<<<<<<<<<<<< * if i&1: #check if i is odd * S += (4 * y) */ __pyx_v_y = __pyx_f_4wfpt_pdf_sv((__pyx_v_x - __pyx_v_t_tag), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z_tag, __pyx_v_err); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":36 * t_tag = lb_t + ht * i * y = pdf_sv(x - t_tag, v, sv, a, z_tag, err) * if i&1: #check if i is odd # <<<<<<<<<<<<<< * S += (4 * y) * else: */ __pyx_t_5 = (__pyx_v_i & 1); if (__pyx_t_5) { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":37 * y = pdf_sv(x - t_tag, v, sv, a, z_tag, err) * if i&1: #check if i is odd * S += (4 * y) # <<<<<<<<<<<<<< * else: * S += (2 * y) */ __pyx_v_S = (__pyx_v_S + (4.0 * __pyx_v_y)); goto __pyx_L6; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":39 * S += (4 * y) * else: * S += (2 * y) # <<<<<<<<<<<<<< * S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y * S = S / ((ub_t-lb_t)+(ub_z-lb_z)) #the right function if pdf_sv()/sz or pdf_sv()/st */ __pyx_v_S = (__pyx_v_S + (2.0 * __pyx_v_y)); } __pyx_L6:; } /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":40 * else: * S += (2 * y) * S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y # <<<<<<<<<<<<<< * S = S / ((ub_t-lb_t)+(ub_z-lb_z)) #the right function if pdf_sv()/sz or pdf_sv()/st * */ __pyx_v_S = (__pyx_v_S - __pyx_v_y); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":41 * S += (2 * y) * S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y * S = S / ((ub_t-lb_t)+(ub_z-lb_z)) #the right function if pdf_sv()/sz or pdf_sv()/st # <<<<<<<<<<<<<< * * return ((ht+hz) * S / 3) */ __pyx_v_S = (__pyx_v_S / ((__pyx_v_ub_t - __pyx_v_lb_t) + (__pyx_v_ub_z - __pyx_v_lb_z))); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":43 * S = S / ((ub_t-lb_t)+(ub_z-lb_z)) #the right function if pdf_sv()/sz or pdf_sv()/st * * return ((ht+hz) * S / 3) # <<<<<<<<<<<<<< * * cdef double simpson_2D(double x, double v, double sv, double a, double z, double t, double err, double lb_z, double ub_z, int n_sz, double lb_t, double ub_t, int n_st) nogil: */ __pyx_r = (((__pyx_v_ht + __pyx_v_hz) * __pyx_v_S) / 3.0); goto __pyx_L0; __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":45 * return ((ht+hz) * S / 3) * * cdef double simpson_2D(double x, double v, double sv, double a, double z, double t, double err, double lb_z, double ub_z, int n_sz, double lb_t, double ub_t, int n_st) nogil: # <<<<<<<<<<<<<< * #assert ((n_sz&1)==0 and (n_st&1)==0), "n_st and n_sz have to be even" * #assert ((ub_t-lb_t)*(ub_z-lb_z)>0 and (n_sz*n_st)>0), "the function is defined for 2D-integration only, lb_t: %f, ub_t %f, lb_z %f, ub_z %f, n_sz: %d, n_st %d" % (lb_t, ub_t, lb_z, ub_z, n_sz, n_st) */ static double __pyx_f_4wfpt_simpson_2D(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, CYTHON_UNUSED double __pyx_v_t, double __pyx_v_err, double __pyx_v_lb_z, double __pyx_v_ub_z, int __pyx_v_n_sz, double __pyx_v_lb_t, double __pyx_v_ub_t, int __pyx_v_n_st) { double __pyx_v_ht; double __pyx_v_S; double __pyx_v_t_tag; double __pyx_v_y; int __pyx_v_i_t; double __pyx_r; int __pyx_t_1; long __pyx_t_2; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":54 * cdef int i_t * * ht = (ub_t-lb_t)/n_st # <<<<<<<<<<<<<< * * S = simpson_1D(x, v, sv, a, z, lb_t, err, lb_z, ub_z, n_sz, 0, 0, 0) */ __pyx_v_ht = ((__pyx_v_ub_t - __pyx_v_lb_t) / __pyx_v_n_st); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":56 * ht = (ub_t-lb_t)/n_st * * S = simpson_1D(x, v, sv, a, z, lb_t, err, lb_z, ub_z, n_sz, 0, 0, 0) # <<<<<<<<<<<<<< * * for i_t from 1 <= i_t <= n_st: */ __pyx_v_S = __pyx_f_4wfpt_simpson_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_lb_t, __pyx_v_err, __pyx_v_lb_z, __pyx_v_ub_z, __pyx_v_n_sz, 0.0, 0.0, 0); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":58 * S = simpson_1D(x, v, sv, a, z, lb_t, err, lb_z, ub_z, n_sz, 0, 0, 0) * * for i_t from 1 <= i_t <= n_st: # <<<<<<<<<<<<<< * t_tag = lb_t + ht * i_t * y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) */ __pyx_t_1 = __pyx_v_n_st; for (__pyx_v_i_t = 1; __pyx_v_i_t <= __pyx_t_1; __pyx_v_i_t++) { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":59 * * for i_t from 1 <= i_t <= n_st: * t_tag = lb_t + ht * i_t # <<<<<<<<<<<<<< * y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) * if i_t&1: #check if i is odd */ __pyx_v_t_tag = (__pyx_v_lb_t + (__pyx_v_ht * __pyx_v_i_t)); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":60 * for i_t from 1 <= i_t <= n_st: * t_tag = lb_t + ht * i_t * y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) # <<<<<<<<<<<<<< * if i_t&1: #check if i is odd * S += (4 * y) */ __pyx_v_y = __pyx_f_4wfpt_simpson_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t_tag, __pyx_v_err, __pyx_v_lb_z, __pyx_v_ub_z, __pyx_v_n_sz, 0.0, 0.0, 0); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":61 * t_tag = lb_t + ht * i_t * y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) * if i_t&1: #check if i is odd # <<<<<<<<<<<<<< * S += (4 * y) * else: */ __pyx_t_2 = (__pyx_v_i_t & 1); if (__pyx_t_2) { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":62 * y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) * if i_t&1: #check if i is odd * S += (4 * y) # <<<<<<<<<<<<<< * else: * S += (2 * y) */ __pyx_v_S = (__pyx_v_S + (4.0 * __pyx_v_y)); goto __pyx_L5; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":64 * S += (4 * y) * else: * S += (2 * y) # <<<<<<<<<<<<<< * S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y * S = S/ (ub_t-lb_t) */ __pyx_v_S = (__pyx_v_S + (2.0 * __pyx_v_y)); } __pyx_L5:; } /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":65 * else: * S += (2 * y) * S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y # <<<<<<<<<<<<<< * S = S/ (ub_t-lb_t) * */ __pyx_v_S = (__pyx_v_S - __pyx_v_y); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":66 * S += (2 * y) * S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y * S = S/ (ub_t-lb_t) # <<<<<<<<<<<<<< * * return (ht * S / 3) */ __pyx_v_S = (__pyx_v_S / (__pyx_v_ub_t - __pyx_v_lb_t)); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":68 * S = S/ (ub_t-lb_t) * * return (ht * S / 3) # <<<<<<<<<<<<<< * * cdef double adaptiveSimpsonsAux(double x, double v, double sv, double a, double z, double t, double pdf_err, */ __pyx_r = ((__pyx_v_ht * __pyx_v_S) / 3.0); goto __pyx_L0; __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":70 * return (ht * S / 3) * * cdef double adaptiveSimpsonsAux(double x, double v, double sv, double a, double z, double t, double pdf_err, # <<<<<<<<<<<<<< * double lb_z, double ub_z, double lb_t, double ub_t, double ZT, double simps_err, * double S, double f_beg, double f_end, double f_mid, int bottom) nogil: */ static double __pyx_f_4wfpt_adaptiveSimpsonsAux(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_t, double __pyx_v_pdf_err, double __pyx_v_lb_z, double __pyx_v_ub_z, double __pyx_v_lb_t, double __pyx_v_ub_t, double __pyx_v_ZT, double __pyx_v_simps_err, double __pyx_v_S, double __pyx_v_f_beg, double __pyx_v_f_end, double __pyx_v_f_mid, int __pyx_v_bottom) { double __pyx_v_z_c; double __pyx_v_z_d; double __pyx_v_z_e; double __pyx_v_t_c; double __pyx_v_t_d; double __pyx_v_t_e; double __pyx_v_h; double __pyx_v_fd; double __pyx_v_fe; double __pyx_v_Sleft; double __pyx_v_Sright; double __pyx_v_S2; double __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":79 * #print "in AdaptiveSimpsAux: lb_z: %f, ub_z: %f, lb_t %f, ub_t %f, f_beg: %f, f_end: %f, bottom: %d" % (lb_z, ub_z, lb_t, ub_t, f_beg, f_end, bottom) * * if (ub_t-lb_t) == 0: #integration over sz # <<<<<<<<<<<<<< * h = ub_z - lb_z * z_c = (ub_z + lb_z)/2. */ __pyx_t_1 = ((__pyx_v_ub_t - __pyx_v_lb_t) == 0.0); if (__pyx_t_1) { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":80 * * if (ub_t-lb_t) == 0: #integration over sz * h = ub_z - lb_z # <<<<<<<<<<<<<< * z_c = (ub_z + lb_z)/2. * z_d = (lb_z + z_c)/2. */ __pyx_v_h = (__pyx_v_ub_z - __pyx_v_lb_z); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":81 * if (ub_t-lb_t) == 0: #integration over sz * h = ub_z - lb_z * z_c = (ub_z + lb_z)/2. # <<<<<<<<<<<<<< * z_d = (lb_z + z_c)/2. * z_e = (z_c + ub_z)/2. */ __pyx_v_z_c = ((__pyx_v_ub_z + __pyx_v_lb_z) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":82 * h = ub_z - lb_z * z_c = (ub_z + lb_z)/2. * z_d = (lb_z + z_c)/2. # <<<<<<<<<<<<<< * z_e = (z_c + ub_z)/2. * t_c = t */ __pyx_v_z_d = ((__pyx_v_lb_z + __pyx_v_z_c) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":83 * z_c = (ub_z + lb_z)/2. * z_d = (lb_z + z_c)/2. * z_e = (z_c + ub_z)/2. # <<<<<<<<<<<<<< * t_c = t * t_d = t */ __pyx_v_z_e = ((__pyx_v_z_c + __pyx_v_ub_z) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":84 * z_d = (lb_z + z_c)/2. * z_e = (z_c + ub_z)/2. * t_c = t # <<<<<<<<<<<<<< * t_d = t * t_e = t */ __pyx_v_t_c = __pyx_v_t; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":85 * z_e = (z_c + ub_z)/2. * t_c = t * t_d = t # <<<<<<<<<<<<<< * t_e = t * */ __pyx_v_t_d = __pyx_v_t; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":86 * t_c = t * t_d = t * t_e = t # <<<<<<<<<<<<<< * * else: #integration over t */ __pyx_v_t_e = __pyx_v_t; goto __pyx_L3; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":89 * * else: #integration over t * h = ub_t - lb_t # <<<<<<<<<<<<<< * t_c = (ub_t + lb_t)/2. * t_d = (lb_t + t_c)/2. */ __pyx_v_h = (__pyx_v_ub_t - __pyx_v_lb_t); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":90 * else: #integration over t * h = ub_t - lb_t * t_c = (ub_t + lb_t)/2. # <<<<<<<<<<<<<< * t_d = (lb_t + t_c)/2. * t_e = (t_c + ub_t)/2. */ __pyx_v_t_c = ((__pyx_v_ub_t + __pyx_v_lb_t) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":91 * h = ub_t - lb_t * t_c = (ub_t + lb_t)/2. * t_d = (lb_t + t_c)/2. # <<<<<<<<<<<<<< * t_e = (t_c + ub_t)/2. * z_c = z */ __pyx_v_t_d = ((__pyx_v_lb_t + __pyx_v_t_c) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":92 * t_c = (ub_t + lb_t)/2. * t_d = (lb_t + t_c)/2. * t_e = (t_c + ub_t)/2. # <<<<<<<<<<<<<< * z_c = z * z_d = z */ __pyx_v_t_e = ((__pyx_v_t_c + __pyx_v_ub_t) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":93 * t_d = (lb_t + t_c)/2. * t_e = (t_c + ub_t)/2. * z_c = z # <<<<<<<<<<<<<< * z_d = z * z_e = z */ __pyx_v_z_c = __pyx_v_z; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":94 * t_e = (t_c + ub_t)/2. * z_c = z * z_d = z # <<<<<<<<<<<<<< * z_e = z * */ __pyx_v_z_d = __pyx_v_z; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":95 * z_c = z * z_d = z * z_e = z # <<<<<<<<<<<<<< * * fd = pdf_sv(x - t_d, v, sv, a, z_d, pdf_err)/ZT */ __pyx_v_z_e = __pyx_v_z; } __pyx_L3:; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":97 * z_e = z * * fd = pdf_sv(x - t_d, v, sv, a, z_d, pdf_err)/ZT # <<<<<<<<<<<<<< * fe = pdf_sv(x - t_e, v, sv, a, z_e, pdf_err)/ZT * */ __pyx_v_fd = (__pyx_f_4wfpt_pdf_sv((__pyx_v_x - __pyx_v_t_d), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z_d, __pyx_v_pdf_err) / __pyx_v_ZT); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":98 * * fd = pdf_sv(x - t_d, v, sv, a, z_d, pdf_err)/ZT * fe = pdf_sv(x - t_e, v, sv, a, z_e, pdf_err)/ZT # <<<<<<<<<<<<<< * * Sleft = (h/12)*(f_beg + 4*fd + f_mid) */ __pyx_v_fe = (__pyx_f_4wfpt_pdf_sv((__pyx_v_x - __pyx_v_t_e), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z_e, __pyx_v_pdf_err) / __pyx_v_ZT); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":100 * fe = pdf_sv(x - t_e, v, sv, a, z_e, pdf_err)/ZT * * Sleft = (h/12)*(f_beg + 4*fd + f_mid) # <<<<<<<<<<<<<< * Sright = (h/12)*(f_mid + 4*fe + f_end) * S2 = Sleft + Sright */ __pyx_v_Sleft = ((__pyx_v_h / 12.0) * ((__pyx_v_f_beg + (4.0 * __pyx_v_fd)) + __pyx_v_f_mid)); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":101 * * Sleft = (h/12)*(f_beg + 4*fd + f_mid) * Sright = (h/12)*(f_mid + 4*fe + f_end) # <<<<<<<<<<<<<< * S2 = Sleft + Sright * if (bottom <= 0 or fabs(S2 - S) <= 15*simps_err): */ __pyx_v_Sright = ((__pyx_v_h / 12.0) * ((__pyx_v_f_mid + (4.0 * __pyx_v_fe)) + __pyx_v_f_end)); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":102 * Sleft = (h/12)*(f_beg + 4*fd + f_mid) * Sright = (h/12)*(f_mid + 4*fe + f_end) * S2 = Sleft + Sright # <<<<<<<<<<<<<< * if (bottom <= 0 or fabs(S2 - S) <= 15*simps_err): * return S2 + (S2 - S)/15 */ __pyx_v_S2 = (__pyx_v_Sleft + __pyx_v_Sright); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":103 * Sright = (h/12)*(f_mid + 4*fe + f_end) * S2 = Sleft + Sright * if (bottom <= 0 or fabs(S2 - S) <= 15*simps_err): # <<<<<<<<<<<<<< * return S2 + (S2 - S)/15 * return adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, */ __pyx_t_1 = (__pyx_v_bottom <= 0); if (!__pyx_t_1) { __pyx_t_2 = (fabs((__pyx_v_S2 - __pyx_v_S)) <= (15.0 * __pyx_v_simps_err)); __pyx_t_3 = __pyx_t_2; } else { __pyx_t_3 = __pyx_t_1; } if (__pyx_t_3) { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":104 * S2 = Sleft + Sright * if (bottom <= 0 or fabs(S2 - S) <= 15*simps_err): * return S2 + (S2 - S)/15 # <<<<<<<<<<<<<< * return adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, * lb_z, z_c, lb_t, t_c, ZT, simps_err/2, */ __pyx_r = (__pyx_v_S2 + ((__pyx_v_S2 - __pyx_v_S) / 15.0)); goto __pyx_L0; goto __pyx_L4; } __pyx_L4:; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":110 * adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, * z_c, ub_z, t_c, ub_t, ZT, simps_err/2, * Sright, f_mid, f_end, fe, bottom-1) # <<<<<<<<<<<<<< * * cdef double adaptiveSimpsons_1D(double x, double v, double sv, double a, double z, double t, */ __pyx_r = (__pyx_f_4wfpt_adaptiveSimpsonsAux(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_z_c, __pyx_v_lb_t, __pyx_v_t_c, __pyx_v_ZT, (__pyx_v_simps_err / 2.0), __pyx_v_Sleft, __pyx_v_f_beg, __pyx_v_f_mid, __pyx_v_fd, (__pyx_v_bottom - 1)) + __pyx_f_4wfpt_adaptiveSimpsonsAux(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_pdf_err, __pyx_v_z_c, __pyx_v_ub_z, __pyx_v_t_c, __pyx_v_ub_t, __pyx_v_ZT, (__pyx_v_simps_err / 2.0), __pyx_v_Sright, __pyx_v_f_mid, __pyx_v_f_end, __pyx_v_fe, (__pyx_v_bottom - 1))); goto __pyx_L0; __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":112 * Sright, f_mid, f_end, fe, bottom-1) * * cdef double adaptiveSimpsons_1D(double x, double v, double sv, double a, double z, double t, # <<<<<<<<<<<<<< * double pdf_err, double lb_z, double ub_z, double lb_t, double ub_t, * double simps_err, int maxRecursionDepth) nogil: */ static double __pyx_f_4wfpt_adaptiveSimpsons_1D(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_t, double __pyx_v_pdf_err, double __pyx_v_lb_z, double __pyx_v_ub_z, double __pyx_v_lb_t, double __pyx_v_ub_t, double __pyx_v_simps_err, int __pyx_v_maxRecursionDepth) { double __pyx_v_h; double __pyx_v_ZT; double __pyx_v_c_t; double __pyx_v_c_z; double __pyx_v_f_beg; double __pyx_v_f_end; double __pyx_v_f_mid; double __pyx_v_S; double __pyx_v_res; double __pyx_r; int __pyx_t_1; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":118 * cdef double h * * if (ub_t - lb_t) == 0: #integration over z # <<<<<<<<<<<<<< * lb_t = t * ub_t = t */ __pyx_t_1 = ((__pyx_v_ub_t - __pyx_v_lb_t) == 0.0); if (__pyx_t_1) { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":119 * * if (ub_t - lb_t) == 0: #integration over z * lb_t = t # <<<<<<<<<<<<<< * ub_t = t * h = ub_z - lb_z */ __pyx_v_lb_t = __pyx_v_t; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":120 * if (ub_t - lb_t) == 0: #integration over z * lb_t = t * ub_t = t # <<<<<<<<<<<<<< * h = ub_z - lb_z * else: #integration over t */ __pyx_v_ub_t = __pyx_v_t; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":121 * lb_t = t * ub_t = t * h = ub_z - lb_z # <<<<<<<<<<<<<< * else: #integration over t * h = (ub_t-lb_t) */ __pyx_v_h = (__pyx_v_ub_z - __pyx_v_lb_z); goto __pyx_L3; } /*else*/ { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":123 * h = ub_z - lb_z * else: #integration over t * h = (ub_t-lb_t) # <<<<<<<<<<<<<< * lb_z = z * ub_z = z */ __pyx_v_h = (__pyx_v_ub_t - __pyx_v_lb_t); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":124 * else: #integration over t * h = (ub_t-lb_t) * lb_z = z # <<<<<<<<<<<<<< * ub_z = z * */ __pyx_v_lb_z = __pyx_v_z; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":125 * h = (ub_t-lb_t) * lb_z = z * ub_z = z # <<<<<<<<<<<<<< * * cdef double ZT = h */ __pyx_v_ub_z = __pyx_v_z; } __pyx_L3:; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":127 * ub_z = z * * cdef double ZT = h # <<<<<<<<<<<<<< * cdef double c_t = (lb_t + ub_t)/2. * cdef double c_z = (lb_z + ub_z)/2. */ __pyx_v_ZT = __pyx_v_h; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":128 * * cdef double ZT = h * cdef double c_t = (lb_t + ub_t)/2. # <<<<<<<<<<<<<< * cdef double c_z = (lb_z + ub_z)/2. * */ __pyx_v_c_t = ((__pyx_v_lb_t + __pyx_v_ub_t) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":129 * cdef double ZT = h * cdef double c_t = (lb_t + ub_t)/2. * cdef double c_z = (lb_z + ub_z)/2. # <<<<<<<<<<<<<< * * cdef double f_beg, f_end, f_mid, S */ __pyx_v_c_z = ((__pyx_v_lb_z + __pyx_v_ub_z) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":132 * * cdef double f_beg, f_end, f_mid, S * f_beg = pdf_sv(x - lb_t, v, sv, a, lb_z, pdf_err)/ZT # <<<<<<<<<<<<<< * f_end = pdf_sv(x - ub_t, v, sv, a, ub_z, pdf_err)/ZT * f_mid = pdf_sv(x - c_t, v, sv, a, c_z, pdf_err)/ZT */ __pyx_v_f_beg = (__pyx_f_4wfpt_pdf_sv((__pyx_v_x - __pyx_v_lb_t), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_lb_z, __pyx_v_pdf_err) / __pyx_v_ZT); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":133 * cdef double f_beg, f_end, f_mid, S * f_beg = pdf_sv(x - lb_t, v, sv, a, lb_z, pdf_err)/ZT * f_end = pdf_sv(x - ub_t, v, sv, a, ub_z, pdf_err)/ZT # <<<<<<<<<<<<<< * f_mid = pdf_sv(x - c_t, v, sv, a, c_z, pdf_err)/ZT * S = (h/6)*(f_beg + 4*f_mid + f_end) */ __pyx_v_f_end = (__pyx_f_4wfpt_pdf_sv((__pyx_v_x - __pyx_v_ub_t), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_ub_z, __pyx_v_pdf_err) / __pyx_v_ZT); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":134 * f_beg = pdf_sv(x - lb_t, v, sv, a, lb_z, pdf_err)/ZT * f_end = pdf_sv(x - ub_t, v, sv, a, ub_z, pdf_err)/ZT * f_mid = pdf_sv(x - c_t, v, sv, a, c_z, pdf_err)/ZT # <<<<<<<<<<<<<< * S = (h/6)*(f_beg + 4*f_mid + f_end) * cdef double res = adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, */ __pyx_v_f_mid = (__pyx_f_4wfpt_pdf_sv((__pyx_v_x - __pyx_v_c_t), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_c_z, __pyx_v_pdf_err) / __pyx_v_ZT); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":135 * f_end = pdf_sv(x - ub_t, v, sv, a, ub_z, pdf_err)/ZT * f_mid = pdf_sv(x - c_t, v, sv, a, c_z, pdf_err)/ZT * S = (h/6)*(f_beg + 4*f_mid + f_end) # <<<<<<<<<<<<<< * cdef double res = adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, * lb_z, ub_z, lb_t, ub_t, ZT, simps_err, */ __pyx_v_S = ((__pyx_v_h / 6.0) * ((__pyx_v_f_beg + (4.0 * __pyx_v_f_mid)) + __pyx_v_f_end)); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":138 * cdef double res = adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, * lb_z, ub_z, lb_t, ub_t, ZT, simps_err, * S, f_beg, f_end, f_mid, maxRecursionDepth) # <<<<<<<<<<<<<< * return res * */ __pyx_v_res = __pyx_f_4wfpt_adaptiveSimpsonsAux(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_ub_z, __pyx_v_lb_t, __pyx_v_ub_t, __pyx_v_ZT, __pyx_v_simps_err, __pyx_v_S, __pyx_v_f_beg, __pyx_v_f_end, __pyx_v_f_mid, __pyx_v_maxRecursionDepth); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":139 * lb_z, ub_z, lb_t, ub_t, ZT, simps_err, * S, f_beg, f_end, f_mid, maxRecursionDepth) * return res # <<<<<<<<<<<<<< * * cdef double adaptiveSimpsonsAux_2D(double x, double v, double sv, */ __pyx_r = __pyx_v_res; goto __pyx_L0; __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":141 * return res * * cdef double adaptiveSimpsonsAux_2D(double x, double v, double sv, # <<<<<<<<<<<<<< * double a, double z, double t, double * pdf_err, double err_1d, double lb_z, */ static double __pyx_f_4wfpt_adaptiveSimpsonsAux_2D(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_t, double __pyx_v_pdf_err, double __pyx_v_err_1d, double __pyx_v_lb_z, double __pyx_v_ub_z, double __pyx_v_lb_t, double __pyx_v_ub_t, double __pyx_v_st, double __pyx_v_err_2d, double __pyx_v_S, double __pyx_v_f_beg, double __pyx_v_f_end, double __pyx_v_f_mid, int __pyx_v_maxRecursionDepth_sz, int __pyx_v_bottom) { double __pyx_v_fd; double __pyx_v_fe; double __pyx_v_Sleft; double __pyx_v_Sright; double __pyx_v_S2; double __pyx_v_t_c; double __pyx_v_t_d; double __pyx_v_t_e; double __pyx_v_h; double __pyx_r; int __pyx_t_1; int __pyx_t_2; int __pyx_t_3; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":154 * #print "in AdaptiveSimpsAux_2D: lb_z: %f, ub_z: %f, lb_t %f, ub_t %f, f_beg: %f, f_end: %f, bottom: %d" % (lb_z, ub_z, lb_t, ub_t, f_beg, f_end, bottom) * * cdef double t_c = (ub_t + lb_t)/2. # <<<<<<<<<<<<<< * cdef double t_d = (lb_t + t_c)/2. * cdef double t_e = (t_c + ub_t)/2. */ __pyx_v_t_c = ((__pyx_v_ub_t + __pyx_v_lb_t) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":155 * * cdef double t_c = (ub_t + lb_t)/2. * cdef double t_d = (lb_t + t_c)/2. # <<<<<<<<<<<<<< * cdef double t_e = (t_c + ub_t)/2. * cdef double h = ub_t - lb_t */ __pyx_v_t_d = ((__pyx_v_lb_t + __pyx_v_t_c) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":156 * cdef double t_c = (ub_t + lb_t)/2. * cdef double t_d = (lb_t + t_c)/2. * cdef double t_e = (t_c + ub_t)/2. # <<<<<<<<<<<<<< * cdef double h = ub_t - lb_t * */ __pyx_v_t_e = ((__pyx_v_t_c + __pyx_v_ub_t) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":157 * cdef double t_d = (lb_t + t_c)/2. * cdef double t_e = (t_c + ub_t)/2. * cdef double h = ub_t - lb_t # <<<<<<<<<<<<<< * * fd = adaptiveSimpsons_1D(x, v, sv, a, z, t_d, pdf_err, lb_z, ub_z, */ __pyx_v_h = (__pyx_v_ub_t - __pyx_v_lb_t); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":160 * * fd = adaptiveSimpsons_1D(x, v, sv, a, z, t_d, pdf_err, lb_z, ub_z, * 0, 0, err_1d, maxRecursionDepth_sz)/st # <<<<<<<<<<<<<< * fe = adaptiveSimpsons_1D(x, v, sv, a, z, t_e, pdf_err, lb_z, ub_z, * 0, 0, err_1d, maxRecursionDepth_sz)/st */ __pyx_v_fd = (__pyx_f_4wfpt_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t_d, __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_ub_z, 0.0, 0.0, __pyx_v_err_1d, __pyx_v_maxRecursionDepth_sz) / __pyx_v_st); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":162 * 0, 0, err_1d, maxRecursionDepth_sz)/st * fe = adaptiveSimpsons_1D(x, v, sv, a, z, t_e, pdf_err, lb_z, ub_z, * 0, 0, err_1d, maxRecursionDepth_sz)/st # <<<<<<<<<<<<<< * * Sleft = (h/12)*(f_beg + 4*fd + f_mid) */ __pyx_v_fe = (__pyx_f_4wfpt_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t_e, __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_ub_z, 0.0, 0.0, __pyx_v_err_1d, __pyx_v_maxRecursionDepth_sz) / __pyx_v_st); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":164 * 0, 0, err_1d, maxRecursionDepth_sz)/st * * Sleft = (h/12)*(f_beg + 4*fd + f_mid) # <<<<<<<<<<<<<< * Sright = (h/12)*(f_mid + 4*fe + f_end) * S2 = Sleft + Sright */ __pyx_v_Sleft = ((__pyx_v_h / 12.0) * ((__pyx_v_f_beg + (4.0 * __pyx_v_fd)) + __pyx_v_f_mid)); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":165 * * Sleft = (h/12)*(f_beg + 4*fd + f_mid) * Sright = (h/12)*(f_mid + 4*fe + f_end) # <<<<<<<<<<<<<< * S2 = Sleft + Sright * */ __pyx_v_Sright = ((__pyx_v_h / 12.0) * ((__pyx_v_f_mid + (4.0 * __pyx_v_fe)) + __pyx_v_f_end)); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":166 * Sleft = (h/12)*(f_beg + 4*fd + f_mid) * Sright = (h/12)*(f_mid + 4*fe + f_end) * S2 = Sleft + Sright # <<<<<<<<<<<<<< * * if (bottom <= 0 or fabs(S2 - S) <= 15*err_2d): */ __pyx_v_S2 = (__pyx_v_Sleft + __pyx_v_Sright); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":168 * S2 = Sleft + Sright * * if (bottom <= 0 or fabs(S2 - S) <= 15*err_2d): # <<<<<<<<<<<<<< * return S2 + (S2 - S)/15; * */ __pyx_t_1 = (__pyx_v_bottom <= 0); if (!__pyx_t_1) { __pyx_t_2 = (fabs((__pyx_v_S2 - __pyx_v_S)) <= (15.0 * __pyx_v_err_2d)); __pyx_t_3 = __pyx_t_2; } else { __pyx_t_3 = __pyx_t_1; } if (__pyx_t_3) { /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":169 * * if (bottom <= 0 or fabs(S2 - S) <= 15*err_2d): * return S2 + (S2 - S)/15; # <<<<<<<<<<<<<< * * return adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, */ __pyx_r = (__pyx_v_S2 + ((__pyx_v_S2 - __pyx_v_S) / 15.0)); goto __pyx_L0; goto __pyx_L3; } __pyx_L3:; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":176 * adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, * lb_z, ub_z, t_c, ub_t, st, err_2d/2, * Sright, f_mid, f_end, fe, maxRecursionDepth_sz, bottom-1) # <<<<<<<<<<<<<< * * */ __pyx_r = (__pyx_f_4wfpt_adaptiveSimpsonsAux_2D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_pdf_err, __pyx_v_err_1d, __pyx_v_lb_z, __pyx_v_ub_z, __pyx_v_lb_t, __pyx_v_t_c, __pyx_v_st, (__pyx_v_err_2d / 2.0), __pyx_v_Sleft, __pyx_v_f_beg, __pyx_v_f_mid, __pyx_v_fd, __pyx_v_maxRecursionDepth_sz, (__pyx_v_bottom - 1)) + __pyx_f_4wfpt_adaptiveSimpsonsAux_2D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_pdf_err, __pyx_v_err_1d, __pyx_v_lb_z, __pyx_v_ub_z, __pyx_v_t_c, __pyx_v_ub_t, __pyx_v_st, (__pyx_v_err_2d / 2.0), __pyx_v_Sright, __pyx_v_f_mid, __pyx_v_f_end, __pyx_v_fe, __pyx_v_maxRecursionDepth_sz, (__pyx_v_bottom - 1))); goto __pyx_L0; __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":179 * * * cdef double adaptiveSimpsons_2D(double x, double v, double sv, double a, double z, double t, # <<<<<<<<<<<<<< * double pdf_err, double lb_z, double ub_z, double lb_t, double ub_t, * double simps_err, int maxRecursionDepth_sz, int maxRecursionDepth_st) nogil: */ static double __pyx_f_4wfpt_adaptiveSimpsons_2D(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_t, double __pyx_v_pdf_err, double __pyx_v_lb_z, double __pyx_v_ub_z, double __pyx_v_lb_t, double __pyx_v_ub_t, double __pyx_v_simps_err, int __pyx_v_maxRecursionDepth_sz, int __pyx_v_maxRecursionDepth_st) { double __pyx_v_h; double __pyx_v_st; CYTHON_UNUSED double __pyx_v_c_t; CYTHON_UNUSED double __pyx_v_c_z; double __pyx_v_f_beg; double __pyx_v_f_end; double __pyx_v_f_mid; double __pyx_v_S; double __pyx_v_err_1d; double __pyx_v_err_2d; double __pyx_v_res; double __pyx_r; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":183 * double simps_err, int maxRecursionDepth_sz, int maxRecursionDepth_st) nogil: * * cdef double h = (ub_t-lb_t) # <<<<<<<<<<<<<< * * cdef double st = (ub_t - lb_t) */ __pyx_v_h = (__pyx_v_ub_t - __pyx_v_lb_t); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":185 * cdef double h = (ub_t-lb_t) * * cdef double st = (ub_t - lb_t) # <<<<<<<<<<<<<< * cdef double c_t = (lb_t + ub_t)/2. * cdef double c_z = (lb_z + ub_z)/2. */ __pyx_v_st = (__pyx_v_ub_t - __pyx_v_lb_t); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":186 * * cdef double st = (ub_t - lb_t) * cdef double c_t = (lb_t + ub_t)/2. # <<<<<<<<<<<<<< * cdef double c_z = (lb_z + ub_z)/2. * */ __pyx_v_c_t = ((__pyx_v_lb_t + __pyx_v_ub_t) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":187 * cdef double st = (ub_t - lb_t) * cdef double c_t = (lb_t + ub_t)/2. * cdef double c_z = (lb_z + ub_z)/2. # <<<<<<<<<<<<<< * * cdef double f_beg, f_end, f_mid, S */ __pyx_v_c_z = ((__pyx_v_lb_z + __pyx_v_ub_z) / 2.); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":190 * * cdef double f_beg, f_end, f_mid, S * cdef double err_1d = simps_err # <<<<<<<<<<<<<< * cdef double err_2d = simps_err * */ __pyx_v_err_1d = __pyx_v_simps_err; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":191 * cdef double f_beg, f_end, f_mid, S * cdef double err_1d = simps_err * cdef double err_2d = simps_err # <<<<<<<<<<<<<< * * f_beg = adaptiveSimpsons_1D(x, v, sv, a, z, lb_t, pdf_err, lb_z, ub_z, */ __pyx_v_err_2d = __pyx_v_simps_err; /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":194 * * f_beg = adaptiveSimpsons_1D(x, v, sv, a, z, lb_t, pdf_err, lb_z, ub_z, * 0, 0, err_1d, maxRecursionDepth_sz)/st # <<<<<<<<<<<<<< * * f_end = adaptiveSimpsons_1D(x, v, sv, a, z, ub_t, pdf_err, lb_z, ub_z, */ __pyx_v_f_beg = (__pyx_f_4wfpt_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_lb_t, __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_ub_z, 0.0, 0.0, __pyx_v_err_1d, __pyx_v_maxRecursionDepth_sz) / __pyx_v_st); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":197 * * f_end = adaptiveSimpsons_1D(x, v, sv, a, z, ub_t, pdf_err, lb_z, ub_z, * 0, 0, err_1d, maxRecursionDepth_sz)/st # <<<<<<<<<<<<<< * f_mid = adaptiveSimpsons_1D(x, v, sv, a, z, (lb_t+ub_t)/2, pdf_err, lb_z, ub_z, * 0, 0, err_1d, maxRecursionDepth_sz)/st */ __pyx_v_f_end = (__pyx_f_4wfpt_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_ub_t, __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_ub_z, 0.0, 0.0, __pyx_v_err_1d, __pyx_v_maxRecursionDepth_sz) / __pyx_v_st); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":199 * 0, 0, err_1d, maxRecursionDepth_sz)/st * f_mid = adaptiveSimpsons_1D(x, v, sv, a, z, (lb_t+ub_t)/2, pdf_err, lb_z, ub_z, * 0, 0, err_1d, maxRecursionDepth_sz)/st # <<<<<<<<<<<<<< * S = (h/6)*(f_beg + 4*f_mid + f_end) * cdef double res = adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, */ __pyx_v_f_mid = (__pyx_f_4wfpt_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, ((__pyx_v_lb_t + __pyx_v_ub_t) / 2.0), __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_ub_z, 0.0, 0.0, __pyx_v_err_1d, __pyx_v_maxRecursionDepth_sz) / __pyx_v_st); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":200 * f_mid = adaptiveSimpsons_1D(x, v, sv, a, z, (lb_t+ub_t)/2, pdf_err, lb_z, ub_z, * 0, 0, err_1d, maxRecursionDepth_sz)/st * S = (h/6)*(f_beg + 4*f_mid + f_end) # <<<<<<<<<<<<<< * cdef double res = adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, * lb_z, ub_z, lb_t, ub_t, st, err_2d, */ __pyx_v_S = ((__pyx_v_h / 6.0) * ((__pyx_v_f_beg + (4.0 * __pyx_v_f_mid)) + __pyx_v_f_end)); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":203 * cdef double res = adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, * lb_z, ub_z, lb_t, ub_t, st, err_2d, * S, f_beg, f_end, f_mid, maxRecursionDepth_sz, maxRecursionDepth_st) # <<<<<<<<<<<<<< * return res */ __pyx_v_res = __pyx_f_4wfpt_adaptiveSimpsonsAux_2D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_pdf_err, __pyx_v_err_1d, __pyx_v_lb_z, __pyx_v_ub_z, __pyx_v_lb_t, __pyx_v_ub_t, __pyx_v_st, __pyx_v_err_2d, __pyx_v_S, __pyx_v_f_beg, __pyx_v_f_end, __pyx_v_f_mid, __pyx_v_maxRecursionDepth_sz, __pyx_v_maxRecursionDepth_st); /* "/home/wiecki/working/projects/hddm/src/integrate.pxi":204 * lb_z, ub_z, lb_t, ub_t, st, err_2d, * S, f_beg, f_end, f_mid, maxRecursionDepth_sz, maxRecursionDepth_st) * return res # <<<<<<<<<<<<<< */ __pyx_r = __pyx_v_res; goto __pyx_L0; __pyx_r = 0; __pyx_L0:; return __pyx_r; } /* Python wrapper */ static PyObject *__pyx_pw_4wfpt_3pdf_array(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static char __pyx_doc_4wfpt_2pdf_array[] = "pdf_array(ndarray x, double v, double sv, double a, double z, double sz, double t, double st, double err=0.0001, int logp=0, int n_st=2, int n_sz=2, int use_adaptive=1, double simps_err=0.001, double p_outlier=0, double w_outlier=0)"; static PyMethodDef __pyx_mdef_4wfpt_3pdf_array = {__Pyx_NAMESTR("pdf_array"), (PyCFunction)__pyx_pw_4wfpt_3pdf_array, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_4wfpt_2pdf_array)}; static PyObject *__pyx_pw_4wfpt_3pdf_array(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyArrayObject *__pyx_v_x = 0; 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"" : "s", num_found); } static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AS_STRING(kw_name)); #endif } static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; } else { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) { #else if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) { #endif goto invalid_keyword_type; } else { for (name = first_kw_arg; *name; name++) { #if PY_MAJOR_VERSION >= 3 if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) && PyUnicode_Compare(**name, key) == 0) break; #else if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) && _PyString_Eq(**name, key)) break; #endif } if (*name) { values[name-argnames] = value; } else { for (name=argnames; name != first_kw_arg; name++) { if (**name == key) goto arg_passed_twice; #if PY_MAJOR_VERSION >= 3 if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) && PyUnicode_Compare(**name, key) == 0) goto arg_passed_twice; #else if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) && _PyString_Eq(**name, key)) goto arg_passed_twice; #endif } if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } } } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, **name); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%s() got an unexpected keyword argument '%s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } static int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, const char *name, int exact) { if (!type) { PyErr_Format(PyExc_SystemError, "Missing type object"); return 0; } if (none_allowed && obj == Py_None) return 1; else if (exact) { if (Py_TYPE(obj) == type) return 1; } else { if (PyObject_TypeCheck(obj, type)) return 1; } PyErr_Format(PyExc_TypeError, "Argument '%s' has incorrect type (expected %s, got %s)", name, type->tp_name, Py_TYPE(obj)->tp_name); return 0; } static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { unsigned int n = 1; return *(unsigned char*)(&n) != 0; } static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type) { stack[0].field = &ctx->root; stack[0].parent_offset = 0; ctx->root.type = type; ctx->root.name = "buffer dtype"; ctx->root.offset = 0; ctx->head = stack; ctx->head->field = &ctx->root; ctx->fmt_offset = 0; ctx->head->parent_offset = 0; ctx->new_packmode = '@'; ctx->enc_packmode = '@'; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->is_complex = 0; ctx->is_valid_array = 0; ctx->struct_alignment = 0; while (type->typegroup == 'S') { ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = 0; type = type->fields->type; } } static int __Pyx_BufFmt_ParseNumber(const char** ts) { int count; const char* t = *ts; if (*t < '0' || *t > '9') { return -1; } else { count = *t++ - '0'; while (*t >= '0' && *t < '9') { count *= 10; count += *t++ - '0'; } } *ts = t; return count; } static int __Pyx_BufFmt_ExpectNumber(const char **ts) { int number = __Pyx_BufFmt_ParseNumber(ts); if (number == -1) /* First char was not a digit */ PyErr_Format(PyExc_ValueError,\ "Does not understand character buffer dtype format string ('%c')", **ts); return number; } static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%c'", ch); } static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { switch (ch) { case 'b': return "'char'"; case 'B': return "'unsigned char'"; case 'h': return "'short'"; case 'H': return "'unsigned short'"; case 'i': return "'int'"; case 'I': return "'unsigned int'"; case 'l': return "'long'"; case 'L': return "'unsigned long'"; case 'q': return "'long long'"; case 'Q': return "'unsigned long long'"; case 'f': return (is_complex ? "'complex float'" : "'float'"); case 'd': return (is_complex ? "'complex double'" : "'double'"); case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; case 's': case 'p': return "a string"; case 0: return "end"; default: return "unparseable format string"; } } static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return 2; case 'i': case 'I': case 'l': case 'L': return 4; case 'q': case 'Q': return 8; case 'f': return (is_complex ? 8 : 4); case 'd': return (is_complex ? 16 : 8); case 'g': { PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); return 0; } case 'O': case 'P': return sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { switch (ch) { case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(short); case 'i': case 'I': return sizeof(int); case 'l': case 'L': return sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(float) * (is_complex ? 2 : 1); case 'd': return sizeof(double) * (is_complex ? 2 : 1); case 'g': return sizeof(long double) * (is_complex ? 2 : 1); case 'O': case 'P': return sizeof(void*); default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } typedef struct { char c; short x; } __Pyx_st_short; typedef struct { char c; int x; } __Pyx_st_int; typedef struct { char c; long x; } __Pyx_st_long; typedef struct { char c; float x; } __Pyx_st_float; typedef struct { char c; double x; } __Pyx_st_double; typedef struct { char c; long double x; } __Pyx_st_longdouble; typedef struct { char c; void *x; } __Pyx_st_void_p; #ifdef HAVE_LONG_LONG typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_st_float) - sizeof(float); case 'd': return sizeof(__Pyx_st_double) - sizeof(double); case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } /* These are for computing the padding at the end of the struct to align on the first member of the struct. This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } __Pyx_BufFmt_RaiseExpected(ctx); return -1; } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %"PY_FORMAT_SIZE_T"d but %"PY_FORMAT_SIZE_T"d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; /* Consume from buffer string */ while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; /* breaks both loops as ctx->enc_count == 0 */ } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; /* empty struct */ field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static CYTHON_INLINE PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number; int ndim = ctx->head->field->type->ndim; ; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; while (*ts && *ts != ')') { if (isspace(*ts)) continue; number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case 10: case 13: ++ts; break; case '<': if (!__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': /* substruct */ { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': /* end of substruct; either repeat or move on */ { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } /* fall through */ case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 's': case 'p': if (ctx->enc_type == *ts && got_Z == ctx->is_complex && ctx->enc_packmode == ctx->new_packmode) { ctx->enc_count += ctx->new_count; } else { if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; } ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { buf->buf = NULL; buf->obj = NULL; buf->strides = __Pyx_zeros; buf->shape = __Pyx_zeros; buf->suboffsets = __Pyx_minusones; } static CYTHON_INLINE int __Pyx_GetBufferAndValidate( Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { if (obj == Py_None || obj == NULL) { __Pyx_ZeroBuffer(buf); return 0; } buf->buf = NULL; if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; if (buf->ndim != nd) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", nd, buf->ndim); goto fail; } if (!cast) { __Pyx_BufFmt_Context ctx; __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned)buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%"PY_FORMAT_SIZE_T"d byte%s) does not match size of '%s' (%"PY_FORMAT_SIZE_T"d byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_ZeroBuffer(buf); return -1; } static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { if (info->buf == NULL) return; if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; __Pyx_ReleaseBuffer(info); } static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_Format(PyExc_SystemError, "Missing type object"); return 0; } if (likely(PyObject_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } static void __Pyx_RaiseBufferFallbackError(void) { PyErr_Format(PyExc_ValueError, "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); } static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; } static CYTHON_INLINE void __Pyx_RaiseNoneIndexingError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is unsubscriptable"); } #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { /* cause is unused */ Py_XINCREF(type); Py_XINCREF(value); Py_XINCREF(tb); /* First, check the traceback argument, replacing None with NULL. */ if (tb == Py_None) { Py_DECREF(tb); tb = 0; } else if (tb != NULL && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } /* Next, replace a missing value with None */ if (value == NULL) { value = Py_None; Py_INCREF(value); } #if PY_VERSION_HEX < 0x02050000 if (!PyClass_Check(type)) #else if (!PyType_Check(type)) #endif { /* Raising an instance. The value should be a dummy. */ if (value != Py_None) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } /* Normalize to raise , */ Py_DECREF(value); value = type; #if PY_VERSION_HEX < 0x02050000 if (PyInstance_Check(type)) { type = (PyObject*) ((PyInstanceObject*)type)->in_class; Py_INCREF(type); } else { type = 0; PyErr_SetString(PyExc_TypeError, "raise: exception must be an old-style class or instance"); goto raise_error; } #else type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } #endif } __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else /* Python 3+ */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (!PyExceptionClass_Check(type)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } if (cause) { PyObject *fixed_cause; if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } if (!value) { value = PyObject_CallObject(type, NULL); } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { PyThreadState *tstate = PyThreadState_GET(); PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } } bad: return; } #endif static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %"PY_FORMAT_SIZE_T"d value%s to unpack", index, (index == 1) ? "" : "s"); } static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %"PY_FORMAT_SIZE_T"d)", expected); } static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } static void __Pyx_UnpackTupleError(PyObject *t, Py_ssize_t index) { if (t == Py_None) { __Pyx_RaiseNoneNotIterableError(); } else if (PyTuple_GET_SIZE(t) < index) { __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(t)); } else { __Pyx_RaiseTooManyValuesError(index); } } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); else { PyErr_Format(PyExc_TypeError, "'%100s' does not have the buffer interface", Py_TYPE(obj)->tp_name); return -1; } } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject* obj = view->obj; if (obj) { #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) {PyBuffer_Release(view); return;} #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); Py_DECREF(obj); view->obj = NULL; } } #endif static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level) { PyObject *py_import = 0; PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; py_import = __Pyx_GetAttrString(__pyx_b, "__import__"); if (!py_import) goto bad; if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; #if PY_VERSION_HEX >= 0x02050000 { PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); } #else if (level>0) { PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); goto bad; } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); #endif bad: Py_XDECREF(empty_list); Py_XDECREF(py_import); Py_XDECREF(empty_dict); return module; } static CYTHON_INLINE void __Pyx_RaiseImportError(PyObject *name) { #if PY_MAJOR_VERSION < 3 PyErr_Format(PyExc_ImportError, "cannot import name %.230s", PyString_AsString(name)); #else PyErr_Format(PyExc_ImportError, "cannot import name %S", name); #endif } static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_Py_intptr_t(Py_intptr_t val) { const Py_intptr_t neg_one = (Py_intptr_t)-1, const_zero = (Py_intptr_t)0; const int is_unsigned = const_zero < neg_one; if ((sizeof(Py_intptr_t) == sizeof(char)) || (sizeof(Py_intptr_t) == sizeof(short))) { return PyInt_FromLong((long)val); } else if ((sizeof(Py_intptr_t) == sizeof(int)) || (sizeof(Py_intptr_t) == sizeof(long))) { if (is_unsigned) return PyLong_FromUnsignedLong((unsigned long)val); else return PyInt_FromLong((long)val); } else if (sizeof(Py_intptr_t) == sizeof(PY_LONG_LONG)) { if (is_unsigned) return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG)val); else return PyLong_FromLongLong((PY_LONG_LONG)val); } else { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; return _PyLong_FromByteArray(bytes, sizeof(Py_intptr_t), little, !is_unsigned); } } #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(a, a); case 3: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, a); case 4: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_absf(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(a, a); case 3: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, a); case 4: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_abs(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) { const unsigned char neg_one = (unsigned char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned char" : "value too large to convert to unsigned char"); } return (unsigned char)-1; } return (unsigned char)val; } return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) { const unsigned short neg_one = (unsigned short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned short" : "value too large to convert to unsigned short"); } return (unsigned short)-1; } return (unsigned short)val; } return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) { const unsigned int neg_one = (unsigned int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned int" : "value too large to convert to unsigned int"); } return (unsigned int)-1; } return (unsigned int)val; } return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject* x) { const char neg_one = (char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to char" : "value too large to convert to char"); } return (char)-1; } return (char)val; } return (char)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject* x) { const short neg_one = (short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to short" : "value too large to convert to short"); } return (short)-1; } return (short)val; } return (short)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to int" : "value too large to convert to int"); } return (int)-1; } return (int)val; } return (int)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) { const signed char neg_one = (signed char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed char" : "value too large to convert to signed char"); } return (signed char)-1; } return (signed char)val; } return (signed char)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) { const signed short neg_one = (signed short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed short" : "value too large to convert to signed short"); } return (signed short)-1; } return (signed short)val; } return (signed short)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) { const signed int neg_one = (signed int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed int" : "value too large to convert to signed int"); } return (signed int)-1; } return (signed int)val; } return (signed int)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to int" : "value too large to convert to int"); } return (int)-1; } return (int)val; } return (int)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) { const unsigned long neg_one = (unsigned long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned long"); return (unsigned long)-1; } return (unsigned long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned long"); return (unsigned long)-1; } return (unsigned long)PyLong_AsUnsignedLong(x); } else { return (unsigned long)PyLong_AsLong(x); } } else { unsigned long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned long)-1; val = __Pyx_PyInt_AsUnsignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) { const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (unsigned PY_LONG_LONG)PyLong_AsLongLong(x); } } else { unsigned PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned PY_LONG_LONG)-1; val = __Pyx_PyInt_AsUnsignedLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) { const long neg_one = (long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)PyLong_AsUnsignedLong(x); } else { return (long)PyLong_AsLong(x); } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long)-1; val = __Pyx_PyInt_AsLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (PY_LONG_LONG)PyLong_AsLongLong(x); } } else { PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; val = __Pyx_PyInt_AsLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { const signed long neg_one = (signed long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)PyLong_AsUnsignedLong(x); } else { return (signed long)PyLong_AsLong(x); } } else { signed long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed long)-1; val = __Pyx_PyInt_AsSignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) { const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (signed PY_LONG_LONG)PyLong_AsLongLong(x); } } else { signed PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed PY_LONG_LONG)-1; val = __Pyx_PyInt_AsSignedLongLong(tmp); Py_DECREF(tmp); return val; } } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); #if PY_VERSION_HEX < 0x02050000 return PyErr_Warn(NULL, message); #else return PyErr_WarnEx(NULL, message, 1); #endif } return 0; } #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; py_name = __Pyx_PyIdentifier_FromString(class_name); if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%s.%s is not a type object", module_name, class_name); goto bad; } if (!strict && ((PyTypeObject *)result)->tp_basicsize > (Py_ssize_t)size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); #if PY_VERSION_HEX < 0x02050000 if (PyErr_Warn(NULL, warning) < 0) goto bad; #else if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; #endif } else if (((PyTypeObject *)result)->tp_basicsize != (Py_ssize_t)size) { PyErr_Format(PyExc_ValueError, "%s.%s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; py_name = __Pyx_PyIdentifier_FromString(name); if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif static void __pyx_clear_code_object_cache(void) { __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; int count = __pyx_code_cache.count; int i; if (entries == NULL) { return; } __pyx_code_cache.count = 0; __pyx_code_cache.max_count = 0; __pyx_code_cache.entries = NULL; for (i=0; i= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = (start + end) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int __pyx_clineno, int __pyx_lineno, const char *__pyx_filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(__pyx_filename); #else py_srcfile = PyUnicode_FromString(__pyx_filename); #endif if (!py_srcfile) goto bad; if (__pyx_clineno) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, /*int argcount,*/ 0, /*int kwonlyargcount,*/ 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ __pyx_lineno, /*int firstlineno,*/ __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno, int __pyx_lineno, const char *__pyx_filename) { PyCodeObject *py_code = 0; PyObject *py_globals = 0; PyFrameObject *py_frame = 0; py_code = __pyx_find_code_object(__pyx_clineno ? __pyx_clineno : __pyx_lineno); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, __pyx_clineno, __pyx_lineno, __pyx_filename); if (!py_code) goto bad; __pyx_insert_code_object(__pyx_clineno ? __pyx_clineno : __pyx_lineno, py_code); } py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ py_globals, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = __pyx_lineno; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } /* Type Conversion Functions */ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_VERSION_HEX < 0x03000000 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_VERSION_HEX < 0x03000000 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_VERSION_HEX < 0x03000000 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%s__ returned non-%s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject* x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { #if PY_VERSION_HEX < 0x02050000 if (ival <= LONG_MAX) return PyInt_FromLong((long)ival); else { unsigned char *bytes = (unsigned char *) &ival; int one = 1; int little = (int)*(unsigned char*)&one; return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); } #else return PyInt_FromSize_t(ival); #endif } static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) { unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x); if (unlikely(val == (unsigned PY_LONG_LONG)-1 && PyErr_Occurred())) { return (size_t)-1; } else if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) { PyErr_SetString(PyExc_OverflowError, "value too large to convert to size_t"); return (size_t)-1; } return (size_t)val; } #endif /* Py_PYTHON_H */ HDDM-0.4/src/lba.c0000664000175000017500000105376212050514011014751 0ustar whykingwhyking00000000000000/* Generated by Cython 0.15.1+ on Tue Nov 13 13:57:42 2012 */ #define PY_SSIZE_T_CLEAN #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02040000 #error Cython requires Python 2.4+. #else #include /* For offsetof */ #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #if PY_VERSION_HEX < 0x02050000 typedef int Py_ssize_t; #define PY_SSIZE_T_MAX INT_MAX #define PY_SSIZE_T_MIN INT_MIN #define PY_FORMAT_SIZE_T "" #define PyInt_FromSsize_t(z) PyInt_FromLong(z) #define PyInt_AsSsize_t(o) __Pyx_PyInt_AsInt(o) #define PyNumber_Index(o) PyNumber_Int(o) #define PyIndex_Check(o) PyNumber_Check(o) #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message) #endif #if PY_VERSION_HEX < 0x02060000 #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt) #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type) #define Py_SIZE(ob) (((PyVarObject*)(ob))->ob_size) #define PyVarObject_HEAD_INIT(type, size) \ PyObject_HEAD_INIT(type) size, #define PyType_Modified(t) typedef struct { void *buf; PyObject *obj; Py_ssize_t len; Py_ssize_t itemsize; int readonly; int ndim; char *format; Py_ssize_t *shape; Py_ssize_t *strides; Py_ssize_t *suboffsets; void *internal; } Py_buffer; #define PyBUF_SIMPLE 0 #define PyBUF_WRITABLE 0x0001 #define PyBUF_FORMAT 0x0004 #define PyBUF_ND 0x0008 #define PyBUF_STRIDES (0x0010 | PyBUF_ND) #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES) #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES) #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES) #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES) #define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE) #define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE) #endif #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ PyCode_New(a, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #endif #if PY_MAJOR_VERSION < 3 && PY_MINOR_VERSION < 6 #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict") #endif #if PY_MAJOR_VERSION >= 3 #define Py_TPFLAGS_CHECKTYPES 0 #define Py_TPFLAGS_HAVE_INDEX 0 #endif #if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3) #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_GET_LENGTH) #define CYTHON_PEP393_ENABLED #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) #else #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) #endif #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type #define PyStringObject PyUnicodeObject #define PyString_Type PyUnicode_Type #define PyString_Check PyUnicode_Check #define PyString_CheckExact PyUnicode_CheckExact #endif #if PY_VERSION_HEX < 0x02060000 #define PyBytesObject PyStringObject #define PyBytes_Type PyString_Type #define PyBytes_Check PyString_Check #define PyBytes_CheckExact PyString_CheckExact #define PyBytes_FromString PyString_FromString #define PyBytes_FromStringAndSize PyString_FromStringAndSize #define PyBytes_FromFormat PyString_FromFormat #define PyBytes_DecodeEscape PyString_DecodeEscape #define PyBytes_AsString PyString_AsString #define PyBytes_AsStringAndSize PyString_AsStringAndSize #define PyBytes_Size PyString_Size #define PyBytes_AS_STRING PyString_AS_STRING #define PyBytes_GET_SIZE PyString_GET_SIZE #define PyBytes_Repr PyString_Repr #define PyBytes_Concat PyString_Concat #define PyBytes_ConcatAndDel PyString_ConcatAndDel #endif #if PY_VERSION_HEX < 0x02060000 #define PySet_Check(obj) PyObject_TypeCheck(obj, &PySet_Type) #define PyFrozenSet_Check(obj) PyObject_TypeCheck(obj, &PyFrozenSet_Type) #endif #ifndef PySet_CheckExact #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) #if PY_MAJOR_VERSION >= 3 #define PyIntObject PyLongObject #define PyInt_Type PyLong_Type #define PyInt_Check(op) PyLong_Check(op) #define PyInt_CheckExact(op) PyLong_CheckExact(op) #define PyInt_FromString PyLong_FromString #define PyInt_FromUnicode PyLong_FromUnicode #define PyInt_FromLong PyLong_FromLong #define PyInt_FromSize_t PyLong_FromSize_t #define PyInt_FromSsize_t PyLong_FromSsize_t #define PyInt_AsLong PyLong_AsLong #define PyInt_AS_LONG PyLong_AS_LONG #define PyInt_AsSsize_t PyLong_AsSsize_t #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask #endif #if PY_MAJOR_VERSION >= 3 #define PyBoolObject PyLongObject #endif #if PY_VERSION_HEX < 0x03020000 typedef long Py_hash_t; 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/* "numpy.pxd":752 * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< * * ctypedef npy_intp intp_t */ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; /* "numpy.pxd":754 * ctypedef npy_ulonglong ulonglong_t * * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< * ctypedef npy_uintp uintp_t * */ typedef npy_intp __pyx_t_5numpy_intp_t; /* "numpy.pxd":755 * * ctypedef npy_intp intp_t * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< * * ctypedef npy_double float_t */ typedef npy_uintp __pyx_t_5numpy_uintp_t; /* "numpy.pxd":757 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t */ typedef npy_double __pyx_t_5numpy_float_t; /* "numpy.pxd":758 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< * ctypedef npy_longdouble longdouble_t * */ typedef npy_double __pyx_t_5numpy_double_t; /* "numpy.pxd":759 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; 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if (num_found < num_min) { num_expected = num_min; more_or_less = "at least"; } else { num_expected = num_max; more_or_less = "at most"; } if (exact) { more_or_less = "exactly"; } PyErr_Format(PyExc_TypeError, "%s() takes %s %"PY_FORMAT_SIZE_T"d positional argument%s (%"PY_FORMAT_SIZE_T"d given)", func_name, more_or_less, num_expected, (num_expected == 1) ? "" : "s", num_found); } static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AS_STRING(kw_name)); #endif } static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; } else { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) { #else if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) { #endif goto invalid_keyword_type; } else { for (name = first_kw_arg; *name; name++) { #if PY_MAJOR_VERSION >= 3 if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) && PyUnicode_Compare(**name, key) == 0) break; #else if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) && _PyString_Eq(**name, key)) break; #endif } if (*name) { values[name-argnames] = value; } else { for (name=argnames; name != first_kw_arg; name++) { if (**name == key) goto arg_passed_twice; #if PY_MAJOR_VERSION >= 3 if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) && PyUnicode_Compare(**name, key) == 0) goto arg_passed_twice; #else if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) && _PyString_Eq(**name, key)) goto arg_passed_twice; #endif } if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } } } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, **name); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%s() got an unexpected keyword argument '%s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } static int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, const char *name, int exact) { if (!type) { PyErr_Format(PyExc_SystemError, "Missing type object"); return 0; } if (none_allowed && obj == Py_None) return 1; else if (exact) { if (Py_TYPE(obj) == type) return 1; } else { if (PyObject_TypeCheck(obj, type)) return 1; } PyErr_Format(PyExc_TypeError, "Argument '%s' has incorrect type (expected %s, got %s)", name, type->tp_name, Py_TYPE(obj)->tp_name); return 0; } static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { unsigned int n = 1; return *(unsigned char*)(&n) != 0; } static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type) { stack[0].field = &ctx->root; stack[0].parent_offset = 0; ctx->root.type = type; ctx->root.name = "buffer dtype"; ctx->root.offset = 0; ctx->head = stack; ctx->head->field = &ctx->root; ctx->fmt_offset = 0; ctx->head->parent_offset = 0; ctx->new_packmode = '@'; ctx->enc_packmode = '@'; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->is_complex = 0; ctx->is_valid_array = 0; ctx->struct_alignment = 0; while (type->typegroup == 'S') { ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = 0; type = type->fields->type; } } static int __Pyx_BufFmt_ParseNumber(const char** ts) { int count; const char* t = *ts; if (*t < '0' || *t > '9') { return -1; } else { count = *t++ - '0'; while (*t >= '0' && *t < '9') { count *= 10; count += *t++ - '0'; } } *ts = t; return count; } static int __Pyx_BufFmt_ExpectNumber(const char **ts) { int number = __Pyx_BufFmt_ParseNumber(ts); if (number == -1) /* First char was not a digit */ PyErr_Format(PyExc_ValueError,\ "Does not understand character buffer dtype format string ('%c')", **ts); return number; } static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%c'", ch); } static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { switch (ch) { case 'b': return "'char'"; case 'B': return "'unsigned char'"; case 'h': return "'short'"; case 'H': return "'unsigned short'"; case 'i': return "'int'"; case 'I': return "'unsigned int'"; case 'l': return "'long'"; case 'L': return "'unsigned long'"; case 'q': return "'long long'"; case 'Q': return "'unsigned long long'"; case 'f': return (is_complex ? "'complex float'" : "'float'"); case 'd': return (is_complex ? "'complex double'" : "'double'"); case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; case 's': case 'p': return "a string"; case 0: return "end"; default: return "unparseable format string"; } } static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return 2; case 'i': case 'I': case 'l': case 'L': return 4; case 'q': case 'Q': return 8; case 'f': return (is_complex ? 8 : 4); case 'd': return (is_complex ? 16 : 8); case 'g': { PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); return 0; } case 'O': case 'P': return sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { switch (ch) { case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(short); case 'i': case 'I': return sizeof(int); case 'l': case 'L': return sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(float) * (is_complex ? 2 : 1); case 'd': return sizeof(double) * (is_complex ? 2 : 1); case 'g': return sizeof(long double) * (is_complex ? 2 : 1); case 'O': case 'P': return sizeof(void*); default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } typedef struct { char c; short x; } __Pyx_st_short; typedef struct { char c; int x; } __Pyx_st_int; typedef struct { char c; long x; } __Pyx_st_long; typedef struct { char c; float x; } __Pyx_st_float; typedef struct { char c; double x; } __Pyx_st_double; typedef struct { char c; long double x; } __Pyx_st_longdouble; typedef struct { char c; void *x; } __Pyx_st_void_p; #ifdef HAVE_LONG_LONG typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_st_float) - sizeof(float); case 'd': return sizeof(__Pyx_st_double) - sizeof(double); case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } /* These are for computing the padding at the end of the struct to align on the first member of the struct. This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } __Pyx_BufFmt_RaiseExpected(ctx); return -1; } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %"PY_FORMAT_SIZE_T"d but %"PY_FORMAT_SIZE_T"d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; /* Consume from buffer string */ while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; /* breaks both loops as ctx->enc_count == 0 */ } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; /* empty struct */ field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static CYTHON_INLINE PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number; int ndim = ctx->head->field->type->ndim; ; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; while (*ts && *ts != ')') { if (isspace(*ts)) continue; number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case 10: case 13: ++ts; break; case '<': if (!__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': /* substruct */ { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': /* end of substruct; either repeat or move on */ { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } /* fall through */ case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 's': case 'p': if (ctx->enc_type == *ts && got_Z == ctx->is_complex && ctx->enc_packmode == ctx->new_packmode) { ctx->enc_count += ctx->new_count; } else { if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; } ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { buf->buf = NULL; buf->obj = NULL; buf->strides = __Pyx_zeros; buf->shape = __Pyx_zeros; buf->suboffsets = __Pyx_minusones; } static CYTHON_INLINE int __Pyx_GetBufferAndValidate( Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { if (obj == Py_None || obj == NULL) { __Pyx_ZeroBuffer(buf); return 0; } buf->buf = NULL; if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; if (buf->ndim != nd) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", nd, buf->ndim); goto fail; } if (!cast) { __Pyx_BufFmt_Context ctx; __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned)buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%"PY_FORMAT_SIZE_T"d byte%s) does not match size of '%s' (%"PY_FORMAT_SIZE_T"d byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_ZeroBuffer(buf); return -1; } static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { if (info->buf == NULL) return; if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; __Pyx_ReleaseBuffer(info); } static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) { PyObject *result; result = PyObject_GetAttr(dict, name); if (!result) { if (dict != __pyx_b) { PyErr_Clear(); result = PyObject_GetAttr(__pyx_b, name); } if (!result) { PyErr_SetObject(PyExc_NameError, name); } } return result; } static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_Format(PyExc_SystemError, "Missing type object"); return 0; } if (likely(PyObject_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; } #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { /* cause is unused */ Py_XINCREF(type); Py_XINCREF(value); Py_XINCREF(tb); /* First, check the traceback argument, replacing None with NULL. */ if (tb == Py_None) { Py_DECREF(tb); tb = 0; } else if (tb != NULL && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } /* Next, replace a missing value with None */ if (value == NULL) { value = Py_None; Py_INCREF(value); } #if PY_VERSION_HEX < 0x02050000 if (!PyClass_Check(type)) #else if (!PyType_Check(type)) #endif { /* Raising an instance. The value should be a dummy. */ if (value != Py_None) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } /* Normalize to raise , */ Py_DECREF(value); value = type; #if PY_VERSION_HEX < 0x02050000 if (PyInstance_Check(type)) { type = (PyObject*) ((PyInstanceObject*)type)->in_class; Py_INCREF(type); } else { type = 0; PyErr_SetString(PyExc_TypeError, "raise: exception must be an old-style class or instance"); goto raise_error; } #else type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } #endif } __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else /* Python 3+ */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (!PyExceptionClass_Check(type)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } if (cause) { PyObject *fixed_cause; if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } if (!value) { value = PyObject_CallObject(type, NULL); } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { PyThreadState *tstate = PyThreadState_GET(); PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } } bad: return; } #endif static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %"PY_FORMAT_SIZE_T"d value%s to unpack", index, (index == 1) ? "" : "s"); } static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %"PY_FORMAT_SIZE_T"d)", expected); } static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } static void __Pyx_UnpackTupleError(PyObject *t, Py_ssize_t index) { if (t == Py_None) { __Pyx_RaiseNoneNotIterableError(); } else if (PyTuple_GET_SIZE(t) < index) { __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(t)); } else { __Pyx_RaiseTooManyValuesError(index); } } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); else { PyErr_Format(PyExc_TypeError, "'%100s' does not have the buffer interface", Py_TYPE(obj)->tp_name); return -1; } } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject* obj = view->obj; if (obj) { #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) {PyBuffer_Release(view); return;} #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); Py_DECREF(obj); view->obj = NULL; } } #endif static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level) { PyObject *py_import = 0; PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; py_import = __Pyx_GetAttrString(__pyx_b, "__import__"); if (!py_import) goto bad; if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; #if PY_VERSION_HEX >= 0x02050000 { PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); } #else if (level>0) { PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); goto bad; } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); #endif bad: Py_XDECREF(empty_list); Py_XDECREF(py_import); Py_XDECREF(empty_dict); return module; } static CYTHON_INLINE void __Pyx_RaiseImportError(PyObject *name) { #if PY_MAJOR_VERSION < 3 PyErr_Format(PyExc_ImportError, "cannot import name %.230s", PyString_AsString(name)); #else PyErr_Format(PyExc_ImportError, "cannot import name %S", name); #endif } #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(a, a); case 3: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, a); case 4: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_absf(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(a, a); case 3: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, a); case 4: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_abs(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) { const unsigned char neg_one = (unsigned char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned char" : "value too large to convert to unsigned char"); } return (unsigned char)-1; } return (unsigned char)val; } return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) { const unsigned short neg_one = (unsigned short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned short" : "value too large to convert to unsigned short"); } return (unsigned short)-1; } return (unsigned short)val; } return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) { const unsigned int neg_one = (unsigned int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned int" : "value too large to convert to unsigned int"); } return (unsigned int)-1; } return (unsigned int)val; } return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject* x) { const char neg_one = (char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to char" : "value too large to convert to char"); } return (char)-1; } return (char)val; } return (char)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject* x) { const short neg_one = (short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to short" : "value too large to convert to short"); } return (short)-1; } return (short)val; } return (short)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to int" : "value too large to convert to int"); } return (int)-1; } return (int)val; } return (int)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) { const signed char neg_one = (signed char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed char" : "value too large to convert to signed char"); } return (signed char)-1; } return (signed char)val; } return (signed char)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) { const signed short neg_one = (signed short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed short" : "value too large to convert to signed short"); } return (signed short)-1; } return (signed short)val; } return (signed short)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) { const signed int neg_one = (signed int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed int" : "value too large to convert to signed int"); } return (signed int)-1; } return (signed int)val; } return (signed int)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to int" : "value too large to convert to int"); } return (int)-1; } return (int)val; } return (int)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) { const unsigned long neg_one = (unsigned long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned long"); return (unsigned long)-1; } return (unsigned long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned long"); return (unsigned long)-1; } return (unsigned long)PyLong_AsUnsignedLong(x); } else { return (unsigned long)PyLong_AsLong(x); } } else { unsigned long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned long)-1; val = __Pyx_PyInt_AsUnsignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) { const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (unsigned PY_LONG_LONG)PyLong_AsLongLong(x); } } else { unsigned PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned PY_LONG_LONG)-1; val = __Pyx_PyInt_AsUnsignedLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) { const long neg_one = (long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)PyLong_AsUnsignedLong(x); } else { return (long)PyLong_AsLong(x); } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long)-1; val = __Pyx_PyInt_AsLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (PY_LONG_LONG)PyLong_AsLongLong(x); } } else { PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; val = __Pyx_PyInt_AsLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { const signed long neg_one = (signed long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)PyLong_AsUnsignedLong(x); } else { return (signed long)PyLong_AsLong(x); } } else { signed long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed long)-1; val = __Pyx_PyInt_AsSignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) { const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (signed PY_LONG_LONG)PyLong_AsLongLong(x); } } else { signed PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed PY_LONG_LONG)-1; val = __Pyx_PyInt_AsSignedLongLong(tmp); Py_DECREF(tmp); return val; } } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); #if PY_VERSION_HEX < 0x02050000 return PyErr_Warn(NULL, message); #else return PyErr_WarnEx(NULL, message, 1); #endif } return 0; } #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; py_name = __Pyx_PyIdentifier_FromString(class_name); if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%s.%s is not a type object", module_name, class_name); goto bad; } if (!strict && ((PyTypeObject *)result)->tp_basicsize > (Py_ssize_t)size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); #if PY_VERSION_HEX < 0x02050000 if (PyErr_Warn(NULL, warning) < 0) goto bad; #else if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; #endif } else if (((PyTypeObject *)result)->tp_basicsize != (Py_ssize_t)size) { PyErr_Format(PyExc_ValueError, "%s.%s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; py_name = __Pyx_PyIdentifier_FromString(name); if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif static void __pyx_clear_code_object_cache(void) { __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; int count = __pyx_code_cache.count; int i; if (entries == NULL) { return; } __pyx_code_cache.count = 0; __pyx_code_cache.max_count = 0; __pyx_code_cache.entries = NULL; for (i=0; i= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = (start + end) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int __pyx_clineno, int __pyx_lineno, const char *__pyx_filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(__pyx_filename); #else py_srcfile = PyUnicode_FromString(__pyx_filename); #endif if (!py_srcfile) goto bad; if (__pyx_clineno) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, /*int argcount,*/ 0, /*int kwonlyargcount,*/ 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ __pyx_lineno, /*int firstlineno,*/ __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno, int __pyx_lineno, const char *__pyx_filename) { PyCodeObject *py_code = 0; PyObject *py_globals = 0; PyFrameObject *py_frame = 0; py_code = __pyx_find_code_object(__pyx_clineno ? __pyx_clineno : __pyx_lineno); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, __pyx_clineno, __pyx_lineno, __pyx_filename); if (!py_code) goto bad; __pyx_insert_code_object(__pyx_clineno ? __pyx_clineno : __pyx_lineno, py_code); } py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ py_globals, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = __pyx_lineno; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } /* Type Conversion Functions */ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_VERSION_HEX < 0x03000000 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_VERSION_HEX < 0x03000000 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_VERSION_HEX < 0x03000000 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%s__ returned non-%s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject* x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { #if PY_VERSION_HEX < 0x02050000 if (ival <= LONG_MAX) return PyInt_FromLong((long)ival); else { unsigned char *bytes = (unsigned char *) &ival; int one = 1; int little = (int)*(unsigned char*)&one; return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); } #else return PyInt_FromSize_t(ival); #endif } static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) { unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x); if (unlikely(val == (unsigned PY_LONG_LONG)-1 && PyErr_Occurred())) { return (size_t)-1; } else if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) { PyErr_SetString(PyExc_OverflowError, "value too large to convert to size_t"); return (size_t)-1; } return (size_t)val; } #endif /* Py_PYTHON_H */ HDDM-0.4/src/pdf.pxi0000664000175000017500000001163112050455454015345 0ustar whykingwhyking00000000000000#cython: embedsignature=True #cython: cdivision=True #cython: wraparound=False #cython: boundscheck=False cimport cython #include "integrate.pxi" #from libc.math cimport tan, sin, cos, log, exp, sqrt, fmax, pow, ceil, floor, fabs, M_PI cdef extern from "math.h" nogil: double sin(double) double cos(double) double log(double) double exp(double) double sqrt(double) double fmax(double, double) double pow(double, double) double ceil(double) double floor(double) double fabs(double) double M_PI cdef double ftt_01w(double tt, double w, double err) nogil: """Compute f(t|0,1,w) for the likelihood of the drift diffusion model using the method and implementation of Navarro & Fuss, 2009. """ cdef double kl, ks, p cdef int k, K, lower, upper # calculate number of terms needed for large t if M_PI*tt*err<1: # if error threshold is set low enough kl=sqrt(-2*log(M_PI*tt*err)/(M_PI**2*tt)) # bound kl=fmax(kl,1./(M_PI*sqrt(tt))) # ensure boundary conditions met else: # if error threshold set too high kl=1./(M_PI*sqrt(tt)) # set to boundary condition # calculate number of terms needed for small t if 2*sqrt(2*M_PI*tt)*err<1: # if error threshold is set low enough ks=2+sqrt(-2*tt*log(2*sqrt(2*M_PI*tt)*err)) # bound ks=fmax(ks,sqrt(tt)+1) # ensure boundary conditions are met else: # if error threshold was set too high ks=2 # minimal kappa for that case # compute f(tt|0,1,w) p=0 #initialize density if ks(ceil(ks)) # round to smallest integer meeting error lower = (-floor((K-1)/2.)) upper = (ceil((K-1)/2.)) for k from lower <= k <= upper: # loop over k p+=(w+2*k)*exp(-(pow((w+2*k),2))/2/tt) # increment sum p/=sqrt(2*M_PI*pow(tt,3)) # add con_stant term else: # if large t is better... K=(ceil(kl)) # round to smallest integer meeting error for k from 1 <= k <= K: p+=k*exp(-(pow(k,2))*(M_PI**2)*tt/2)*sin(k*M_PI*w) # increment sum p*=M_PI # add con_stant term return p cdef inline double prob_ub(double v, double a, double z) nogil: """Probability of hitting upper boundary.""" return (exp(-2*a*z*v) - 1) / (exp(-2*a*v) - 1) cdef double pdf(double x, double v, double a, double w, double err) nogil: """Compute the likelihood of the drift diffusion model f(t|v,a,z) using the method and implementation of Navarro & Fuss, 2009. """ if x <= 0: return 0 cdef double tt = x/a**2 # use normalized time cdef double p = ftt_01w(tt, w, err) #get f(t|0,1,w) # convert to f(t|v,a,w) return p*exp(-v*a*w -(pow(v,2))*x/2.)/(pow(a,2)) cdef double pdf_sv(double x, double v, double sv, double a, double z, double err) nogil: """Compute the likelihood of the drift diffusion model f(t|v,a,z,sv) using the method and implementation of Navarro & Fuss, 2009. sv is the std of the drift rate """ if x <= 0: return 0 if sv==0: return pdf(x, v, a, z, err) cdef double tt = x/(pow(a,2)) # use normalized time cdef double p = ftt_01w(tt, z, err) #get f(t|0,1,w) # convert to f(t|v,a,w) return exp(log(p) + ((a*z*sv)**2 - 2*a*v*z - (v**2)*x)/(2*(sv**2)*x+2))/sqrt((sv**2)*x+1)/(a**2) cpdef double full_pdf(double x, double v, double sv, double a, double z, double sz, double t, double st, double err, int n_st=2, int n_sz=2, bint use_adaptive=1, double simps_err=1e-3) nogil: """full pdf""" # Check if parpameters are valid if z<0 or z>1 or a<0 or ((fabs(x)-(t-st/2.))<0) or (z+sz/2.>1) or (z-sz/2.<0) or (t-st/2.<0) or (t<0): return 0 # transform x,v,z if x is upper bound response if x > 0: v = -v z = 1.-z x = fabs(x) if st<1e-3: st = 0 if sz <1e-3: sz = 0 if (sz==0): if (st==0): #sv=0,sz=0,st=0 return pdf_sv(x - t, v, sv, a, z, err) else: #sv=0,sz=0,st=$ if use_adaptive>0: return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z, z, t-st/2., t+st/2., simps_err, n_st) else: return simpson_1D(x, v, sv, a, z, t, err, z, z, 0, t-st/2., t+st/2., n_st) else: #sz=$ if (st==0): #sv=0,sz=$,st=0 if use_adaptive: return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t, t, simps_err, n_sz) else: return simpson_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t, t , 0) else: #sv=0,sz=$,st=$ if use_adaptive: return adaptiveSimpsons_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t-st/2., t+st/2., simps_err, n_sz, n_st) else: return simpson_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t-st/2., t+st/2., n_st) HDDM-0.4/src/wfpt.pyx0000664000175000017500000001643412050455466015605 0ustar whykingwhyking00000000000000#cython: embedsignature=True #cython: cdivision=True #cython: wraparound=False #cython: boundscheck=False # # Cython version of the Navarro & Fuss, 2009 DDM PDF. Based on the following code by Navarro & Fuss: # http://www.psychocmath.logy.adelaide.edu.au/personalpages/staff/danielnavarro/resources/wfpt.m # # This implementation is about 170 times faster than the matlab # reference version. # # Copyleft Thomas Wiecki (thomas_wiecki[at]brown.edu) & Imri Sofer, 2011 # GPLv3 import hddm import scipy.integrate as integrate from copy import copy import numpy as np cimport numpy as np cimport cython from cython.parallel import * #cimport openmp #include "pdf.pxi" include 'integrate.pxi' def pdf_array(np.ndarray[double, ndim=1] x, double v, double sv, double a, double z, double sz, double t, double st, double err=1e-4, bint logp=0, int n_st=2, int n_sz=2, bint use_adaptive=1, double simps_err=1e-3, double p_outlier=0, double w_outlier=0): cdef Py_ssize_t size = x.shape[0] cdef Py_ssize_t i cdef np.ndarray[double, ndim=1] y = np.empty(size, dtype=np.double) for i in prange(size, nogil=True): y[i] = full_pdf(x[i], v, sv, a, z, sz, t, st, err, n_st, n_sz, use_adaptive, simps_err) y = y * (1 - p_outlier) + (w_outlier * p_outlier) if logp==1: return np.log(y) else: return y cdef inline bint p_outlier_in_range(double p_outlier): return (p_outlier >= 0) & (p_outlier <= 1) def wiener_like(np.ndarray[double, ndim=1] x, double v, double sv, double a, double z, double sz, double t, double st, double err, int n_st=10, int n_sz=10, bint use_adaptive=1, double simps_err=1e-8, double p_outlier=0, double w_outlier=0): cdef Py_ssize_t size = x.shape[0] cdef Py_ssize_t i cdef double p cdef double sum_logp = 0 cdef double wp_outlier = w_outlier * p_outlier if not p_outlier_in_range(p_outlier): return -np.inf for i in prange(size, nogil=True, schedule='dynamic'): p = full_pdf(x[i], v, sv, a, z, sz, t, st, err, n_st, n_sz, use_adaptive, simps_err) # If one probability = 0, the log sum will be -Inf p = p * (1 - p_outlier) + wp_outlier if p == 0: with gil: return -np.inf sum_logp += log(p) return sum_logp def wiener_like_multi(np.ndarray[double, ndim=1] x, v, sv, a, z, sz, t, st, double err, multi=None, int n_st=10, int n_sz=10, bint use_adaptive=1, double simps_err=1e-3, double p_outlier=0, double w_outlier=0): cdef Py_ssize_t size = x.shape[0] cdef Py_ssize_t i cdef double p = 0 cdef double sum_logp = 0 cdef double wp_outlier = w_outlier * p_outlier if multi is None: return full_pdf(x, v, sv, a, z, sz, t, st, err) else: params = {'v':v, 'z':z, 't':t, 'a':a, 'sv':sv, 'sz':sz, 'st':st} params_iter = copy(params) for i from 0 <= i < size: for param in multi: params_iter[param] = params[param][i] p = full_pdf(x[i], params_iter['v'], params_iter['sv'], params_iter['a'], params_iter['z'], params_iter['sz'], params_iter['t'], params_iter['st'], err, n_st, n_sz, use_adaptive, simps_err) p = p * (1 - p_outlier) + wp_outlier sum_logp += log(p) return sum_logp def gen_rts_from_cdf(double v, double sv, double a, double z, double sz, double t, \ double st, int samples=1000, double cdf_lb=-6, double cdf_ub=6, double dt=1e-2): cdef np.ndarray[double, ndim=1] x = np.arange(cdf_lb, cdf_ub, dt) cdef np.ndarray[double, ndim=1] l_cdf = np.empty(x.shape[0], dtype=np.double) cdef double pdf, rt cdef Py_ssize_t size = x.shape[0] cdef Py_ssize_t i, j cdef int idx l_cdf[0] = 0 for i from 1 <= i < size: pdf = full_pdf(x[i], v, sv, a, z, sz, 0, 0, 1e-4) l_cdf[i] = l_cdf[i-1] + pdf l_cdf /= l_cdf[x.shape[0]-1] cdef np.ndarray[double, ndim=1] rts = np.empty(samples, dtype=np.double) cdef np.ndarray[double, ndim=1] f = np.random.rand(samples) cdef np.ndarray[double, ndim=1] delay if st!=0: delay = (np.random.rand(samples)*st + (t - st/2.)) for i from 0 <= i < samples: idx = np.searchsorted(l_cdf, f[i]) rt = x[idx] if st==0: rt = rt + np.sign(rt)*t else: rt = rt + np.sign(rt)*delay[i] rts[i] = rt return rts def wiener_like_contaminant(np.ndarray[double, ndim=1] x, np.ndarray[int, ndim=1] cont_x, double v, \ double sv, double a, double z, double sz, double t, double st, double t_min, \ double t_max, double err, int n_st= 10, int n_sz=10, bint use_adaptive=1, \ double simps_err=1e-8): """Wiener likelihood function where RTs could come from a separate, uniform contaminant distribution. Reference: Lee, Vandekerckhove, Navarro, & Tuernlinckx (2007) """ cdef Py_ssize_t size = x.shape[0] cdef Py_ssize_t i cdef double p cdef double sum_logp = 0 cdef int n_cont = np.sum(cont_x) cdef int pos_cont = 0 for i in prange(size, nogil=True): if cont_x[i] == 0: p = full_pdf(x[i], v, sv, a, z, sz, t, st, err, n_st, n_sz, use_adaptive, simps_err) if p == 0: with gil: return -np.inf sum_logp += log(p) # If one probability = 0, the log sum will be -Inf # add the log likelihood of the contaminations sum_logp += n_cont*log(0.5 * 1./(t_max-t_min)) return sum_logp def gen_cdf_using_pdf(double v, double sv, double a, double z, double sz, double t, double st, double err, int N=500, double time=5., int n_st=2, int n_sz=2, bint use_adaptive=1, double simps_err=1e-3, double p_outlier=0, double w_outlier=0): """ generate cdf vector using the pdf """ if (sv < 0) or (a <=0 ) or (z < 0) or (z > 1) or (sz < 0) or (sz > 1) or (z+sz/2.>1) or \ (z-sz/2.<0) or (t-st/2.<0) or (t<0) or (st < 0) or not p_outlier_in_range(p_outlier): raise ValueError("at least one of the parameters is out of the support") cdef np.ndarray[double, ndim=1] x = np.linspace(-time, time, 2*N+1) cdef np.ndarray[double, ndim=1] cdf_array = np.empty(x.shape[0], dtype=np.double) cdef int idx #compute pdf on the real line cdf_array = pdf_array(x, v, sv, a, z, sz, t, st, err, 0, n_st, n_sz, use_adaptive, simps_err, p_outlier, w_outlier) #integrate cdf_array[1:] = integrate.cumtrapz(cdf_array) #normalize cdf_array /= cdf_array[x.shape[0]-1] return x, cdf_array def split_cdf(np.ndarray[double, ndim=1] x, np.ndarray[double, ndim=1] data): #get length of data cdef int N = (len(data) -1) / 2 # lower bound is reversed cdef np.ndarray[double, ndim=1] x_lb = -x[:N][::-1] cdef np.ndarray[double, ndim=1] lb = data[:N][::-1] # lower bound is cumulative in the wrong direction lb = np.cumsum(np.concatenate([np.array([0]), -np.diff(lb)])) cdef np.ndarray[double, ndim=1] x_ub = x[N+1:] cdef np.ndarray[double, ndim=1] ub = data[N+1:] # ub does not start at 0 ub -= ub[0] return (x_lb, lb, x_ub, ub)HDDM-0.4/hddm/0000775000175000017500000000000012054702235014171 5ustar whykingwhyking00000000000000HDDM-0.4/hddm/likelihoods.py0000664000175000017500000001730412051202040017040 0ustar whykingwhyking00000000000000from __future__ import division import pymc as pm import numpy as np from scipy import stats from scipy.stats.mstats import mquantiles np.seterr(divide='ignore') import hddm def wiener_like_contaminant(value, cont_x, v, sv, a, z, sz, t, st, t_min, t_max, err, n_st, n_sz, use_adaptive, simps_err): """Log-likelihood for the simple DDM including contaminants""" return hddm.wfpt.wiener_like_contaminant(value, cont_x.astype(np.int32), v, sv, a, z, sz, t, st, t_min, t_max, err, n_st, n_sz, use_adaptive, simps_err) WienerContaminant = pm.stochastic_from_dist(name="Wiener Simple Diffusion Process", logp=wiener_like_contaminant, dtype=np.float, mv=True) def general_WienerCont(err=1e-4, n_st=2, n_sz=2, use_adaptive=1, simps_err=1e-3): _like = lambda value, cont_x, v, sv, a, z, sz, t, st, t_min, t_max, err=err, n_st=n_st, n_sz=n_sz, \ use_adaptive=use_adaptive, simps_err=simps_err: \ wiener_like_contaminant(value, cont_x, v, sv, a, z, sz, t, st, t_min, t_max,\ err=err, n_st=n_st, n_sz=n_sz, use_adaptive=use_adaptive, simps_err=simps_err) _like.__doc__ = wiener_like_contaminant.__doc__ return pm.stochastic_from_dist(name="Wiener Diffusion Contaminant Process", logp=_like, dtype=np.float, mv=False) def generate_wfpt_stochastic_class(wiener_params=None, sampling_method='cdf', cdf_range=(-5,5), sampling_dt=1e-4): """ create a wfpt stochastic class by creating a pymc nodes and then adding quantile functions. Input: wiener_params - dictonary of wiener_params for wfpt likelihoods sampling_method - an argument used by hddm.generate.gen_rts cdf_range - an argument used by hddm.generate.gen_rts sampling_dt - an argument used by hddm.generate.gen_rts Ouput: wfpt - the wfpt stochastic """ #set wiener_params if wiener_params is None: wiener_params = {'err': 1e-4, 'n_st':2, 'n_sz':2, 'use_adaptive':1, 'simps_err':1e-3, 'w_outlier': 0.1} wp = wiener_params #create likelihood function def wfpt_like(x, v, sv, a, z, sz, t, st, p_outlier=0): return hddm.wfpt.wiener_like(x, v, sv, a, z, sz, t, st, p_outlier=p_outlier, **wp) #create random function def random(v, sv, a, z, sz, t, st, p_outlier, size=None): param_dict = {'v': v, 'z': z, 't': t, 'a': a, 'sz': sz, 'sv': sv, 'st': st} return hddm.generate.gen_rts(param_dict, method=sampling_method, samples=size, dt=sampling_dt, range_=cdf_range) #create pdf function def pdf(self, x): out = hddm.wfpt.pdf_array(x, **self.parents) return out #create cdf function def cdf(self, x): return hddm.cdfdif.dmat_cdf_array(x, w_outlier=wp['w_outlier'], **self.parents) #create wfpt class wfpt = pm.stochastic_from_dist('wfpt', wfpt_like, random=random) #add pdf and cdf_vec to the class wfpt.pdf = pdf wfpt.cdf_vec = lambda self: hddm.wfpt.gen_cdf_using_pdf(time=cdf_range[1], **dict(self.parents.items() + wp.items())) wfpt.cdf = cdf #add quantiles functions add_quantiles_functions_to_pymc_class(wfpt) return wfpt def add_quantiles_functions_to_pymc_class(pymc_class): """ add quantiles methods to a pymc class Input: pymc_class """ #turn pymc node into the final wfpt_node def compute_quantiles_stats(self, quantiles): """ compute quantiles statistics Input: quantiles : sequence the sequence of quantiles, e.g. (0.1, 0.3, 0.5, 0.7, 0.9) """ data = self.value #get proportion of data fall between the quantiles quantiles = np.array(quantiles) pos_proportion = np.diff(np.concatenate((np.array([0.]), quantiles, np.array([1.])))) neg_proportion = pos_proportion[::-1] proportion = np.concatenate((neg_proportion[::-1], pos_proportion)) self._n_samples = len(data) # extract empirical RT at the quantiles ub_emp_rt = mquantiles(data[data>0], prob=quantiles) lb_emp_rt = -mquantiles(-data[data<0], prob=quantiles) self._emp_rt = np.concatenate((lb_emp_rt[::-1], np.array([0.]), ub_emp_rt)) #get frequency of observed values freq_obs = np.zeros(len(proportion)) freq_obs[:len(quantiles)+1] = sum(data<0) * neg_proportion freq_obs[len(quantiles)+1:] = sum(data>0) * pos_proportion self._freq_obs = freq_obs def set_quantiles_stats(self, n_samples, emp_rt, freq_obs): """ set quantiles statistics (used when one do not to compute the statistics from the stochastic's value) """ self._n_samples = n_samples self._emp_rt = emp_rt self._freq_obs = freq_obs def get_quantiles_stats(self): """ get quantiles statistics (after they were computed using compute_quantiles_stats_ """ stats = {'n_samples': self._n_samples, 'emp_rt': self._emp_rt, 'freq_obs': self._freq_obs} return stats def _get_theoretical_proportion(self): #get cdf cdf = self.cdf(self._emp_rt) #get probabilities associated with theoretical RT indices theo_cdf = np.concatenate((np.array([0.]), cdf, np.array([1.]))) #theoretical porportion proportion = np.diff(theo_cdf) #make sure there is no zeros since it causes bugs later on epsi = 1e-6 proportion[proportion <= epsi] = epsi return proportion def chisquare(self): """ compute the chi-square statistic over the stocastic's value """ try: theo_proportion = self._get_theoretical_proportion() except (ValueError, FloatingPointError): return np.inf freq_exp = theo_proportion * self._n_samples score,_ = stats.chisquare(self._freq_obs, freq_exp) return score def gsquare(self): """ compute G^2 (likelihood chi-square) statistic over the stocastic's value Note: this does return the actual G^2, but G^2 up to a constant which depend on the data """ try: theo_proportion = self._get_theoretical_proportion() except ValueError: return -np.inf return 2 * sum(self._freq_obs * np.log(theo_proportion)) def quantiles(self, quantiles=(.1, .3, .5, .7, .9)): quantiles = np.asarray(quantiles) # generate CDF x_lower, cdf_lower, x_upper, cdf_upper = hddm.wfpt.split_cdf(*self.cdf_vec()) # extract theoretical RT indices lower_idx = np.searchsorted(cdf_lower, quantiles*cdf_lower[-1]) upper_idx = np.searchsorted(cdf_upper, quantiles*cdf_upper[-1]) q_vals_lower = x_lower[lower_idx] q_vals_upper = x_upper[upper_idx] q_lower = np.vstack((q_vals_lower, quantiles*cdf_lower[-1])) q_upper = np.vstack((q_vals_upper, quantiles*cdf_upper[-1])) return (q_lower, q_upper) pymc_class.compute_quantiles_stats = compute_quantiles_stats pymc_class.set_quantiles_stats = set_quantiles_stats pymc_class.get_quantiles_stats = get_quantiles_stats pymc_class.chisquare = chisquare pymc_class.gsquare = gsquare pymc_class._get_theoretical_proportion = _get_theoretical_proportion pymc_class.quantiles = quantiles #create default Wfpt class Wfpt = generate_wfpt_stochastic_class() HDDM-0.4/hddm/examples/0000775000175000017500000000000012054702235016007 5ustar whykingwhyking00000000000000HDDM-0.4/hddm/examples/simple_subjs_difficulty_single.csv0000664000175000017500000000126612050455453025016 0ustar whykingwhyking00000000000000response,rt,difficulty,subj_idx 1.0,1.929,easy,0 1.0,1.709,easy,0 0.0,0.917999999998,easy,0 1.0,0.559000000002,easy,0 1.0,1.132,easy,0 1.0,0.815000000002,easy,0 1.0,0.737000000002,easy,0 1.0,0.794000000002,easy,0 1.0,0.661000000002,easy,0 1.0,0.733000000002,easy,0 1.0,1.005,easy,0 1.0,0.836000000002,easy,0 0.0,0.612999999998,easy,0 1.0,1.325,easy,0 1.0,0.681000000002,easy,0 1.0,1.18,hard,0 1.0,2.631,hard,0 1.0,1.547,hard,0 1.0,1.561,hard,0 1.0,1.121,hard,0 1.0,1.462,hard,0 1.0,0.618000000002,hard,0 1.0,0.710000000002,hard,0 0.0,1.209,hard,0 1.0,0.486000000002,hard,0 1.0,0.830000000002,hard,0 1.0,0.822000000002,hard,0 1.0,1.606,hard,0 1.0,0.637000000002,hard,0 1.0,0.758000000002,hard,0 HDDM-0.4/hddm/examples/simple_model.py0000664000175000017500000000075412052770023021036 0ustar whykingwhyking00000000000000import hddm # Load data from csv file into a NumPy structured array data = hddm.load_csv('simple_difficulty.csv') # Create a HDDM model multi object model = hddm.HDDM(data, depends_on={'v':'difficulty'}) # Create model and start MCMC sampling model.sample(10000, burn=5000) # Print fitted parameters and other model statistics model.print_stats() # Plot posterior distributions and theoretical RT distributions model.plot_posteriors(save=True) model.plot_posterior_predictive(save=True) HDDM-0.4/hddm/examples/RatcliffFrank.conf0000664000175000017500000000014512005542775021401 0ustar whykingwhyking00000000000000[model] data = RatcliffFrank.csv [depends] a = da,conf v = conf [mcmc] samples = 10000 burn = 2000 HDDM-0.4/hddm/examples/RatcliffFrank.csv0000664000175000017500000143520312005542775021257 0ustar whykingwhyking00000000000000subj_idx,conf,response,rt,da 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HDDM-0.4/hddm/examples/plot_posterior_quantiles.py0000664000175000017500000000025112050455466023537 0ustar whykingwhyking00000000000000#!/usr/bin/python import hddm import pylab as pl m = hddm.utils.create_test_model(samples=10000, burn=5000, subjs=12, size=200) m.plot_posterior_quantiles() pl.show() HDDM-0.4/hddm/examples/simple_difficulty.csv0000664000175000017500000004613512005542776022260 0ustar whykingwhyking00000000000000response,rt,difficulty 1.0,1.06,hard 1.0,1.052,hard 1.0,1.398,hard 0.0,0.481,hard 1.0,1.798,hard 1.0,0.947,hard 1.0,2.093,hard 1.0,0.999,hard 0.0,1.019,hard 0.0,0.852,hard 1.0,0.907,hard 1.0,1.622,hard 1.0,4.679,hard 0.0,1.448,hard 0.0,1.371,hard 1.0,2.045,hard 0.0,0.847,hard 1.0,0.598,hard 0.0,1.437,hard 1.0,3.056,hard 1.0,1.881,hard 1.0,1.597,hard 1.0,3.643,hard 0.0,1.975,hard 1.0,1.442,hard 0.0,0.821999999998,hard 1.0,2.269,hard 1.0,3.653,hard 1.0,1.01,hard 1.0,2.426,hard 1.0,0.698000000002,hard 1.0,1.37,hard 1.0,1.174,hard 0.0,1.325,hard 1.0,1.127,hard 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1.0,0.809000000002,easy 1.0,2.191,easy 1.0,0.630000000002,easy 1.0,0.968000000002,easy 1.0,0.733000000002,easy 1.0,0.522000000002,easy 1.0,1.011,easy 1.0,0.760000000002,easy HDDM-0.4/hddm/utils.py0000664000175000017500000005370612052770123015715 0ustar whykingwhyking00000000000000from __future__ import division import numpy as np import matplotlib.pyplot as plt import pymc as pm import hddm import sys import kabuki from scipy.stats import scoreatpercentile from scipy.stats.mstats import mquantiles def flip_errors(data): """Flip sign for lower boundary responses. :Arguments: data : numpy.recarray Input array with at least one column named 'RT' and one named 'response' :Returns: data : numpy.recarray Input array with RTs sign flipped where 'response' == 0 """ # Check if data is already flipped if np.any(data['rt'] < 0): return data # Copy data data = data.copy() # Flip sign for lower boundary response idx = data['response'] == 0 data['rt'][idx] = -data['rt'][idx] return data def check_params_valid(**params): a = params.get('a') v = params.get('v') z = params.get('z', .5) t = params.get('t') sv = params.get('sv', 0) st = params.get('st', 0) sz = params.get('sz', 0) if (sv < 0) or (a <= 0) or (z < 0) or (z > 1) or (sz < 0) or (sz > 1) or (z+sz/2. > 1) or \ (z-sz/2. < 0) or (t-st/2. < 0) or (t < 0) or (st < 0): return False else: return True def histogram(a, bins=10, range=None, normed=False, weights=None, density=None): """ Compute the histogram of a set of data. Parameters ---------- a : array_like Input data. The histogram is computed over the flattened array. bins : int or sequence of scalars, optional If `bins` is an int, it defines the number of equal-width bins in the given range (10, by default). If `bins` is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths. range : (float, float), optional The lower and upper range of the bins. If not provided, range is simply ``(a.min(), a.max())``. Values outside the range are ignored. normed : bool, optional This keyword is deprecated in Numpy 1.6 due to confusing/buggy behavior. It will be removed in Numpy 2.0. Use the density keyword instead. If False, the result will contain the number of samples in each bin. If True, the result is the value of the probability *density* function at the bin, normalized such that the *integral* over the range is 1. Note that this latter behavior is known to be buggy with unequal bin widths; use `density` instead. weights : array_like, optional An array of weights, of the same shape as `a`. Each value in `a` only contributes its associated weight towards the bin count (instead of 1). If `normed` is True, the weights are normalized, so that the integral of the density over the range remains 1 density : bool, optional If False, the result will contain the number of samples in each bin. If True, the result is the value of the probability *density* function at the bin, normalized such that the *integral* over the range is 1. Note that the sum of the histogram values will not be equal to 1 unless bins of unity width are chosen; it is not a probability *mass* function. Overrides the `normed` keyword if given. Returns ------- hist : array The values of the histogram. See `normed` and `weights` for a description of the possible semantics. bin_edges : array of dtype float Return the bin edges ``(length(hist)+1)``. See Also -------- histogramdd, bincount, searchsorted, digitize Notes ----- All but the last (righthand-most) bin is half-open. In other words, if `bins` is:: [1, 2, 3, 4] then the first bin is ``[1, 2)`` (including 1, but excluding 2) and the second ``[2, 3)``. The last bin, however, is ``[3, 4]``, which *includes* 4. Examples -------- >>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3]) (array([0, 2, 1]), array([0, 1, 2, 3])) >>> np.histogram(np.arange(4), bins=np.arange(5), density=True) (array([ 0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4])) >>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3]) (array([1, 4, 1]), array([0, 1, 2, 3])) >>> a = np.arange(5) >>> hist, bin_edges = np.histogram(a, density=True) >>> hist array([ 0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5]) >>> hist.sum() 2.4999999999999996 >>> np.sum(hist*np.diff(bin_edges)) 1.0 """ a = np.asarray(a) if weights is not None: weights = np.asarray(weights) if np.any(weights.shape != a.shape): raise ValueError( 'weights should have the same shape as a.') weights = weights.ravel() a = a.ravel() if (range is not None): mn, mx = range if (mn > mx): raise AttributeError( 'max must be larger than min in range parameter.') if not np.iterable(bins): if np.isscalar(bins) and bins < 1: raise ValueError("`bins` should be a positive integer.") if range is None: if a.size == 0: # handle empty arrays. Can't determine range, so use 0-1. range = (0, 1) else: range = (a.min(), a.max()) mn, mx = [mi + 0.0 for mi in range] if mn == mx: mn -= 0.5 mx += 0.5 bins = np.linspace(mn, mx, bins + 1, endpoint=True) else: bins = np.asarray(bins) if (np.diff(bins) < 0).any(): raise AttributeError( 'bins must increase monotonically.') # Histogram is an integer or a float array depending on the weights. if weights is None: ntype = int else: ntype = weights.dtype n = np.zeros(bins.shape, ntype) block = 65536 if weights is None: for i in np.arange(0, len(a), block): sa = np.sort(a[i:i + block]) n += np.r_[sa.searchsorted(bins[:-1], 'left'), \ sa.searchsorted(bins[-1], 'right')] else: zero = np.array(0, dtype=ntype) for i in np.arange(0, len(a), block): tmp_a = a[i:i + block] tmp_w = weights[i:i + block] sorting_index = np.argsort(tmp_a) sa = tmp_a[sorting_index] sw = tmp_w[sorting_index] cw = np.concatenate(([zero, ], sw.cumsum())) bin_index = np.r_[sa.searchsorted(bins[:-1], 'left'), \ sa.searchsorted(bins[-1], 'right')] n += cw[bin_index] n = np.diff(n) if density is not None: if density: db = np.array(np.diff(bins), float) return n / db / n.sum(), bins else: return n, bins else: # deprecated, buggy behavior. Remove for Numpy 2.0 if normed: db = np.array(np.diff(bins), float) return n / (n * db).sum(), bins else: return n, bins def parse_config_file(fname, map=True, mcmc=False, data=None, samples=None, burn=None, thin=None, only_group_stats=False, plot=True, verbose=False): import kabuki import os.path if not os.path.isfile(fname): raise ValueError("%s could not be found." % fname) import ConfigParser config = ConfigParser.ConfigParser() config.optionxform = str config.read(fname) ##################################################### # Parse config file if data is not None: data_fname = data else: try: data_fname = config.get('model', 'data') except (ConfigParser.NoOptionError, ConfigParser.NoSectionError): print "ERROR: No data file specified. Either provide data file as an argument to hddmfit or in the model specification" sys.exit(-1) if not os.path.exists(data_fname): raise IOError("Data file %s not found." % data_fname) data = np.recfromcsv(data_fname) model_name = os.path.splitext(data_fname)[0] try: include = config.get('model', 'include') except (ConfigParser.NoOptionError, ConfigParser.NoSectionError): include = () try: is_group_model = config.getboolean('model', 'is_group_model') except (ConfigParser.NoOptionError, ConfigParser.NoSectionError): is_group_model = None try: bias = config.getboolean('model', 'bias') except (ConfigParser.NoOptionError, ConfigParser.NoSectionError): bias = False try: db = config.get('mcmc', 'db') except (ConfigParser.NoOptionError, ConfigParser.NoSectionError): db = 'ram' try: dbname = config.get('mcmc', 'dbname') except (ConfigParser.NoOptionError, ConfigParser.NoSectionError): dbname = None # MCMC values if samples is None: try: samples = config.getint('mcmc', 'samples') except (ConfigParser.NoOptionError, ConfigParser.NoSectionError): samples = 10000 if burn is None: try: burn = config.getint('mcmc', 'burn') except (ConfigParser.NoOptionError, ConfigParser.NoSectionError): burn = 5000 if thin is None: try: thin = config.getint('mcmc', 'thin') except (ConfigParser.NoOptionError, ConfigParser.NoSectionError): thin = 2 group_params = ['v', 'sv', 'a', 'z', 'sz', 't', 'st'] # Get depends depends = {} for param_name in group_params: try: # Multiple depends can be listed (separated by a comma) depends[param_name] = config.get('depends', param_name).split(',') except (ConfigParser.NoOptionError, ConfigParser.NoSectionError): pass print "Creating model..." m = hddm.HDDM(data, include=include, bias=bias, is_group_model=is_group_model, depends_on=depends) if map: print "Finding good initial values..." m.map() m.mcmc().sample(samples, burn=burn, thin=thin, verbose=verbose) m.print_stats() m.print_stats(fname='stats.txt') print "logp: %f" % m.mc.logp print "DIC: %f" % m.mc.dic if plot: m.plot_posteriors(save=True) print "Plotting posterior predictive..." m.plot_posterior_predictive(save=True) return m def EZ_subjs(data): params = {} # Estimate EZ group parameters v, a, t = EZ_data(data) params['v'] = v params['a'] = a params['t'] = t params['z'] = .5 # Estimate EZ parameters for each subject try: for subj in np.unique(data['subj_idx']): try: v, a, t = EZ_data(data[data['subj_idx'] == subj]) params['v_%i' % subj] = v params['a_%i' % subj] = a params['t_%i' % subj] = t # t-.2 if t-.2>0 else .1 params['z_%i' % subj] = .5 except ValueError: # Subject either had 0%, 50%, or 100% correct, which does not work # with easy. But we can deal with that by just not initializing the # parameters for that one model. params['v_%i' % subj] = None params['a_%i' % subj] = None params['t_%i' % subj] = None params['z_%i' % subj] = None except ValueError: # Data array has no subj_idx -> ignore. pass return params def EZ_param_ranges(data, range_=1.): v, a, t = EZ_data(data) z = .5 param_ranges = {'a_lower': a - range_, 'a_upper': a + range_, 'z_lower': np.min(z - range_, 0), 'z_upper': np.max(z + range_, 1), 't_lower': t - range_ if (t - range_) > 0 else 0., 't_upper': t + range_, 'v_lower': v - range_, 'v_upper': v + range_} return param_ranges def EZ_data(data, s=1): """ Calculate Wagenmaker's EZ-diffusion statistics on data. :Arguments: data : numpy.array Data array with reaction time data. Correct RTs are positive, incorrect RTs are negative. s : float Scaling parameter (default=1) :Returns: (v, a, ter) : tuple drift-rate, threshold and non-decision time :See Also: EZ """ try: rt = data['rt'] except ValueError: rt = data # Compute statistics over data idx_correct = rt > 0 mrt = np.mean(rt[idx_correct]) vrt = np.var(rt[idx_correct]) pc = np.sum(idx_correct) / np.float(rt.shape[0]) # Calculate EZ estimates. return EZ(pc, vrt, mrt, s) def EZ(pc, vrt, mrt, s=1): """ Calculate Wagenmaker's EZ-diffusion statistics. :Parameters: pc : float probability correct. vrt : float variance of response time for correct decisions (only!). mrt : float mean response time for correct decisions (only!). s : float scaling parameter. Default s=1. :Returns: (v, a, ter) : tuple drift-rate, threshold and non-decision time The error RT distribution is assumed identical to the correct RT distrib. Edge corrections are required for cases with Pc=0 or Pc=1. (Pc=.5 is OK) :Assumptions: * The error RT distribution is identical to the correct RT distrib. * z=.5 -- starting point is equidistant from the response boundaries * sv=0 -- across-trial variability in drift rate is negligible * sz=0 -- across-trial variability in starting point is negligible * st=0 -- across-trial range in nondecision time is negligible :Reference: Wagenmakers, E.-J., van der Maas, H. Li. J., & Grasman, R. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14 (1), 3-22. :Example: >>> EZ(.802, .112, .723, s=.1) (0.099938526231301769, 0.13997020267583737, 0.30002997230248141) :See Also: EZ_data """ if (pc == 0 or pc == .5 or pc == 1): raise ValueError('Probability correct is either 0%, 50% or 100%') s2 = s ** 2 logit_p = np.log(pc / (1 - pc)) # Eq. 7 x = ((logit_p * (pc**2 * logit_p - pc * logit_p + pc - .5)) / vrt) v = np.sign(pc - .5) * s * x**.25 # Eq 5 a = (s2 * logit_p) / v y = (-v * a) / s2 # Eq 9 mdt = (a / (2 * v)) * ((1 - np.exp(y)) / (1 + np.exp(y))) # Eq 8 ter = mrt - mdt return (v, a, ter) def hddm_parents_trace(model, obs_node, idx): """Return the parents' value of an wfpt node in index 'idx' (the function is used by ppd_test) """ model.params_include.keys() params = {'a': 0, 'v': 0, 't':0, 'z': 0.5, 'sz': 0, 'st': 0, 'sv': 0} if not np.isscalar(idx): for (key, value) in params.iteritems(): params[key] = np.ones(len(idx)) * value #example for local_name: a,v,t,z.... #example for parent_full_name: v(['cond1'])3 for local_name in model.params_include.keys(): if local_name == 'wfpt': continue parent_full_name = obs_node.parents[local_name].__name__ params[local_name] = model.mc.db.trace(parent_full_name)[idx] return params def _gen_statistics(): """generate different statistical tests from ppd_test.""" statistics = [] ##accuracy test test = {} test['name'] = 'acc' test['func'] = lambda rts: sum(rts > 0) / len(rts) statistics.append(test) ##quantile statistics of absolute response time quantiles = [10, 30, 50, 70, 90] for q in quantiles: test = {} test['name'] = 'q%d' % q test['func'] = lambda rts, q=q: scoreatpercentile(np.abs(rts), q) statistics.append(test) return statistics def gen_ppc_stats(quantiles = (10, 30, 50, 70, 90)): """Generate default statistics for posterior predictive check on RT data. :Returns: OrderedDict mapping statistic name -> function """ from collections import OrderedDict stats = OrderedDict() stats['accuracy'] = lambda x: np.mean(x>0) #upper bound stats stats['mean_ub'] = lambda x: np.mean(x[x>0]) stats['std_ub'] = lambda x: np.std(x[x>0]) for q in quantiles: key = str(q) + 'q' stats[key+'_ub'] = lambda x, q=q: scoreatpercentile(x[x>0], q) if np.any(x>0) else np.nan #lower bound stats stats['mean_lb'] = lambda x: np.mean(x[x<0]) stats['std_lb'] = lambda x: np.std(x[x<0]) for q in quantiles: key = str(q) + 'q' stats[key+'_lb'] = lambda x, q=q: scoreatpercentile(np.abs(x[x<0]), q) if np.any(x<0) else np.nan return stats def post_pred_check(model, **kwargs): """Run posterior predictive check on a model. :Arguments: model : kabuki.Hierarchical Kabuki model over which to compute the ppc on. :Optional: samples : int How many samples to generate for each node. bins : int How many bins to use for computing the histogram. evals : dict User-defined evaluations of the statistics (by default 95 percentile and SEM). :Example: {'percentile': scoreatpercentile} plot : bool Whether to plot the posterior predictive distributions. progress_bar: bool Display progress bar while sampling. :Returns: Hierarchical pandas.DataFrame with the different statistics. """ stats = gen_ppc_stats() return kabuki.analyze.post_pred_check(model, stats=stats, **kwargs) def plot_posteriors(model, **kwargs): """Generate posterior plots for each parameter. This is a wrapper for pymc.Matplot.plot() """ pm.Matplot.plot(model.mc, **kwargs) def data_plot(data, nbins=50): data = hddm.utils.flip_errors(data) plt.figure() plt.hist(data['rt'], nbins) plt.show() def _plot_posterior_quantiles_node(node, axis, quantiles=(.1, .3, .5, .7, .9), samples=100, alpha=.75, hexbin=True, value_range=(0, 5), data_plot_kwargs=None, predictive_plot_kwargs=None): """Plot posterior quantiles for a single node. :Arguments: node : pymc.Node Must be observable. axis : matplotlib.axis handle Axis to plot into. :Optional: value_range : numpy.ndarray Range over which to evaluate the CDF. samples : int (default=10) Number of posterior samples to use. alpha : float (default=.75) Alpha (transparency) of posterior quantiles. hexbin : bool (default=False) Whether to plot posterior quantile density using hexbin. data_plot_kwargs : dict (default=None) Forwarded to data plotting function call. predictive_plot_kwargs : dict (default=None) Forwareded to predictive plotting function call. """ quantiles = np.asarray(quantiles) axis.set_xlim(value_range) axis.set_ylim((0, 1)) theo = np.empty((2, 2, len(quantiles), samples)) for sample in range(samples): kabuki.analyze._parents_to_random_posterior_sample(node) theo[:, :, :, sample] = node.quantiles(quantiles) if hexbin: if predictive_plot_kwargs is None: predictive_plot_kwargs = {'gridsize': 75, 'bins': 'log', 'extent': (value_range[0], value_range[1], 0, 1)} axis.hexbin(theo[:,0,:,:].flatten(), theo[:,1,:,:].flatten(), label='post pred lb', **predictive_plot_kwargs) else: if predictive_plot_kwargs is None: predictive_plot_kwargs = {'alpha': .75} axis.plot(theo[0,0,:,:], theo[0,1,:,:], label='post pred lb', color='b', **predictive_plot_kwargs) axis.plot(theo[1,0,:,:], theo[1,1,:,:], label='post pred ub', color='r', **predictive_plot_kwargs) # Plot data data = node.value color = 'w' if hexbin else 'k' if data_plot_kwargs is None: data_plot_kwargs = {'color': color, 'lw': 2., 'marker': 'o', 'markersize': 7} if len(data) != 0: p_upper = np.mean(data>0) q_lower = mquantiles(-data[data<0], quantiles) q_upper = mquantiles(data[data>0], quantiles) axis.plot(q_lower, quantiles*(1-p_upper), **data_plot_kwargs) axis.plot(q_upper, quantiles*p_upper, **data_plot_kwargs) axis.set_xlabel('RT') axis.set_ylabel('Prob respond') axis.set_ylim(bottom=0) # Likelihood and histogram can only be positive def plot_posterior_quantiles(model, **kwargs): """Plot posterior predictive quantiles. :Arguments: model : HDDM model :Optional: value_range : numpy.ndarray Range over which to evaluate the CDF. samples : int (default=10) Number of posterior samples to use. alpha : float (default=.75) Alpha (transparency) of posterior quantiles. hexbin : bool (default=False) Whether to plot posterior quantile density using hexbin. data_plot_kwargs : dict (default=None) Forwarded to data plotting function call. predictive_plot_kwargs : dict (default=None) Forwareded to predictive plotting function call. columns : int (default=3) How many columns to use for plotting the subjects. save : bool (default=False) Whether to save the figure to a file. path : str (default=None) Save figure into directory prefix """ if 'value_range' not in kwargs: rt = np.abs(model.data['rt']) kwargs['value_range'] = (np.min(rt.min()-.2, 0), rt.max()-.2) kabuki.analyze.plot_posterior_predictive(model, plot_func=_plot_posterior_quantiles_node, required_method='quantiles', **kwargs) def create_test_model(samples=5000, burn=1000, subjs=1, size=100): data, params = hddm.generate.gen_rand_data(subjs=subjs, size=size) m = hddm.HDDM(data) m.sample(samples, burn=burn) return m if __name__ == "__main__": import doctest doctest.testmod() HDDM-0.4/hddm/MPLTraits.py0000664000175000017500000000556412050455453016377 0ustar whykingwhyking00000000000000import wx import matplotlib # We want matplotlib to use a wxPython backend if __name__ == "__main__": matplotlib.use('WXAgg') import matplotlib.pyplot import pylab as pl pl.ion() from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas from matplotlib.figure import Figure from matplotlib.backends.backend_wx import NavigationToolbar2Wx from traits.api import HasTraits, Instance, Range,\ Array, on_trait_change, Property,\ cached_property, Bool from traits.api import Any, Instance from traitsui.wx.editor import Editor from traitsui.basic_editor_factory import BasicEditorFactory try: from IPython.Debugger import Tracer; debug_here = Tracer() except: pass class _MPLFigureEditor(Editor): scrollable = True def init(self, parent): self.control = self._create_canvas(parent) self.set_tooltip() def update_editor(self): pass def _create_canvas(self, parent): """ Create the MPL canvas. """ # The panel lets us add additional controls. panel = wx.Panel(parent, -1, style=wx.CLIP_CHILDREN) sizer = wx.BoxSizer(wx.VERTICAL) panel.SetSizer(sizer) # matplotlib commands to create a canvas mpl_control = FigureCanvas(panel, -1, self.value) sizer.Add(mpl_control, 1, wx.LEFT | wx.TOP | wx.GROW) toolbar = NavigationToolbar2Wx(mpl_control) sizer.Add(toolbar, 0, wx.EXPAND) self.value.canvas.SetMinSize((10,10)) return panel class MPLFigureEditor(BasicEditorFactory): klass = _MPLFigureEditor def main(): # Create a window to demo the editor from enthought.traits.api import HasTraits from enthought.traits.ui.api import View, Item from numpy import sin, cos, linspace, pi class Test(HasTraits): figure = Instance(Figure, ()) test = Range(0,5,1) view = View(Item('figure', editor=MPLFigureEditor(), show_label=False), Item('test'), width=400, height=300, resizable=True) def __init__(self): super(Test, self).__init__() self.axes = self.figure.add_subplot(111) self.t = linspace(0, 2*pi, 200) param = self.test self.line, = self.axes.plot(sin(self.t)*(1+0.5*cos(self.test*self.t)), cos(self.t)*(1+0.5*cos(11*self.t))) def update_plot(self): self.figure.axes[0].clear() self.figure.axes[0].plot(sin(self.t)*(1+0.5*cos(self.test*self.t)), cos(self.t)*(1+0.5*cos(11*self.t))) #self.axes.plot(sin(self.t)*(1+0.5*cos(self.test*self.t)), cos(self.t)*(1+0.5*cos(11*self.t))) #self.axes.redraw_in_frame() wx.CallAfter(self.figure.canvas.draw) def _test_changed(self): self.update_plot() #wx.EVT_IDLE(wx.GetApp(), callback) Test().configure_traits() #if __name__ == "__main__": # main() HDDM-0.4/hddm/tests/0000775000175000017500000000000012054702235015333 5ustar whykingwhyking00000000000000HDDM-0.4/hddm/tests/benchmark.py0000664000175000017500000000740012050455466017647 0ustar whykingwhyking00000000000000import numpy as np from numpy.random import rand import time import pymc as pm import matplotlib.pyplot as plt import pandas as pd import hddm import hddm.diag from scipy.integrate import * from scipy.stats import kstest from scipy.optimize import fmin_powell np.random.seed(3123) def compare_cdf_from_pdf_to_cdf_from_fastdm(repeats=500, bins=25, include=('sv', 'st', 'sz', 'z')): """Comparing the numerical integration of wfpt PDF to fastdm CDF.""" N = 500 np.random.seed(10) diff = np.zeros(repeats) for i in range(repeats): params = hddm.generate.gen_rand_params(include=include) tic1 = time.time() x, cum_pdf = hddm.wfpt.gen_cdf_from_pdf(err=1e-4, N=N, **params) tic2 = time.time() x_cdf, cdf = hddm.wfpt.gen_cdf(N=N, **params) tic3 = time.time() pdf_time = tic2 - tic1 fastdm_time = tic3 - tic2 diff[i] = pdf_time - fastdm_time plt.figure() plt.hist(diff, bins) plt.title('compare gen_cdf_from_pdf to gen_cdf (using fastdm)') plt.xlabel('Difference in time. (positive values mean the fastdm is faster)') def check_outlier_model(seed=None, p_outlier=0.05): """Estimate data which contains outliers""" if seed is not None: np.random.seed(seed) #generate params and data params_true = hddm.generate.gen_rand_params(include=()) data, temp = hddm.generate.gen_rand_data(size=500, params=params_true) data = pd.DataFrame(data) #generating outliers n_outliers = int(len(data) * p_outlier) outliers = data[:n_outliers].copy() #fast outliers outliers.rt[:n_outliers//2] = np.random.rand(n_outliers//2) * (min(abs(data['rt'])) - 0.11) + 0.11 #slow outliers outliers.rt[n_outliers//2:] = np.random.rand(n_outliers - n_outliers//2) * 2 + max(abs(data['rt'])) outliers.response = np.random.randint(0,2,n_outliers) print "generating %d outliers. %f of the dataset" % (n_outliers, float(n_outliers)/(n_outliers + len(data))) print "%d outliers are fast" % sum(outliers.rt < min(data.rt)) print "%d outliers are slow" % sum(outliers.rt > max(data.rt)) #Estimating the data without outliers. this is the best estimation we could get #from this data hm = hddm.HDDMTruncated(data) hm.map() index = ['true', 'estimated'] best_estimate = hm.values df = pd.DataFrame([params_true, hm.values], index=index, dtype=np.float).dropna(1) print "benchmark: MAP of clean data. This is as good as we can get" print df #combine data with outliers data = pd.concat((data, outliers), ignore_index=True) #estimate the data with outlier, to confirm that it is worse hm = hddm.HDDMTruncated(data) hm.map() index = ['best_estimate', 'this_estimate'] df = pd.DataFrame([best_estimate, hm.values], index=index, dtype=np.float).dropna(1) print "MAP with outliers: This is as bas as we can get" print df #MAP with p_outlier as random variable hm = hddm.HDDMTruncated(data,include='p_outlier') hm.map() df = pd.DataFrame([best_estimate, hm.values], index=index, dtype=np.float) df.ix['best_estimate']['p_outlier'] = 0 print "MAP with random p_outlier (Estimated from the data)" print df.dropna(1) #MAP with fixed p_outlier fixed_p_outlier = 0.1 hm = hddm.HDDMTruncated(data, p_outlier=fixed_p_outlier) hm.map() df = pd.DataFrame([best_estimate, hm.values], index=index, dtype=np.float) print "MAP with fixed p_outlier (%.3f) " % fixed_p_outlier print df.dropna(1) #Chi-square hm = hddm.HDDMTruncated(data) hm.optimize(method='chisquare') df = pd.DataFrame([best_estimate, hm.values], index=index, dtype=np.float).dropna(1) print "Chisquare method" print df return data data = check_outlier_model(seed=1, p_outlier=0)HDDM-0.4/hddm/tests/test_models.py0000664000175000017500000004054212053511255020233 0ustar whykingwhyking00000000000000from __future__ import division from copy import copy import itertools import glob import os import unittest import pymc as pm import numpy as np import pandas as pd import nose pd.set_printoptions(precision=4) from nose import SkipTest import hddm from hddm.diag import check_model def diff_model(param, subj=True, num_subjs=10, change=.5, size=500): params_cond_a = {'v':.5, 'a':2., 'z':.5, 't': .3, 'st':0., 'sv':0., 'sz':0.} params_cond_b = copy(params_cond_a) params_cond_b[param] += change params = {'A': params_cond_a, 'B': params_cond_b} data, subj_params = hddm.generate.gen_rand_data(params, subjs=num_subjs, size=size) model = hddm.models.HDDMTruncated(data, depends_on={param:['condition']}, is_group_model=subj) return model class TestMulti(unittest.TestCase): def runTest(self): pass def test_diff_v(self, size=100): m = diff_model('v', subj=False, change=.5, size=size) return m def test_diff_a(self, size=100): m = diff_model('a', subj=False, change=-.5, size=size) return m def test_diff_a_subj(self, size=100): raise SkipTest("Disabled.") m = diff_model('a', subj=True, change=-.5, size=size) return m class TestSingleBreakdown(unittest.TestCase): def __init__(self, *args, **kwargs): super(TestSingleBreakdown, self).__init__(*args, **kwargs) self.iter = 50 self.burn = 10 def runTest(self): return def test_HDDM(self, assert_=False): includes = [[], ['z'],['z', 'sv'],['z', 'st'],['z', 'sz'], ['z', 'sz','st'], ['z', 'sz','st','sv']] model_classes = [hddm.models.HDDMTruncated, hddm.models.HDDM] for include, model_class in itertools.product(includes, model_classes): params = hddm.generate.gen_rand_params(include=include) data, params_true = hddm.generate.gen_rand_data(params, size=500, subjs=1) model = model_class(data, include=include, bias='z' in include, is_group_model=False) model.map() model.sample(self.iter, burn=self.burn) check_model(model.mc, params_true, assert_=assert_) return model.mc def test_HDDM_group(self, assert_=False): includes = [[], ['z'],['z', 'sv'],['z', 'st'],['z', 'sz'], ['z', 'sz','st'], ['z', 'sz','st','sv']] model_classes = [hddm.models.HDDMTruncated, hddm.models.HDDM] for include, model_class in itertools.product(includes, model_classes): params = hddm.generate.gen_rand_params(include=include) data, params_true = hddm.generate.gen_rand_data(params, size=500, subjs=5) model = model_class(data, include=include, bias='z' in include, is_group_model=True) model.approximate_map() model.sample(self.iter, burn=self.burn) check_model(model.mc, params_true, assert_=assert_) return model.mc def test_HDDM_group_only_group_nodes(self, assert_=False): group_only_nodes = [[], ['z'], ['z', 'st'], ['v', 'a']] model_classes = [hddm.models.HDDMTruncated, hddm.models.HDDM] for nodes, model_class in itertools.product(group_only_nodes, model_classes): params = hddm.generate.gen_rand_params(include=nodes) data, params_true = hddm.generate.gen_rand_data(params, size=500, subjs=5) model = model_class(data, include=nodes, group_only_nodes=nodes, is_group_model=True) for node in nodes: self.assertNotIn(node+'_subj', model.nodes_db.index) self.assertIn(node, model.nodes_db.index) def test_cont(self, assert_=False): raise SkipTest("Disabled.") params_true = hddm.generate.gen_rand_params(include=()) data, temp = hddm.generate.gen_rand_data(size=300, params=params_true) data[0]['rt'] = min(abs(data['rt']))/2. data[1]['rt'] = max(abs(data['rt'])) + 0.8 hm = hddm.HDDMContUnif(data, bias=True, is_group_model=False) hm.sample(self.iter, burn=self.burn) check_model(hm.mc, params_true, assert_=assert_) cont_res = hm.cont_report(plot=False) cont_idx = cont_res['cont_idx'] self.assertTrue((0 in cont_idx) and (1 in cont_idx), "did not find the right outliers") self.assertTrue(len(cont_idx)<15, "found too many outliers (%d)" % len(cont_idx)) return hm def test_cont_subj(self, assert_=False): raise SkipTest("Disabled.") data_samples = 200 num_subjs = 2 data, params_true = hddm.generate.gen_rand_subj_data(num_subjs=num_subjs, params=None, size=data_samples, noise=0.0001,include=()) for i in range(num_subjs): data[data_samples*i]['rt'] = min(abs(data['rt']))/2. data[data_samples*i + 1]['rt'] = max(abs(data['rt'])) + 0.8 hm = hddm.models.HDDMContUnif(data, bias=True, is_group_model=True) hm.sample(self.iter, burn=self.burn) check_model(hm.mc, params_true, assert_=assert_) cont_res = hm.cont_report(plot=False) for i in range(num_subjs): cont_idx = cont_res[i]['cont_idx'] self.assertTrue((0 in cont_idx) and (1 in cont_idx), "did not found the right outliers") self.assertTrue(len(cont_idx)<15, "found too many outliers (%d)" % len(cont_idx)) return hm def test_HDDMTruncated_distributions(self): params = hddm.generate.gen_rand_params() data, params_subj = hddm.generate.gen_rand_data(subjs=4, params=params) m = hddm.HDDMTruncated(data) m.sample(self.iter, burn=self.burn) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'], pm.Normal) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['mu'], pm.Normal) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['tau'], pm.Deterministic) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['tau'].parents['x'], pm.Uniform) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['a'], pm.TruncatedNormal) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['a'].parents['mu'], pm.Uniform) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['a'].parents['tau'], pm.Deterministic) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['a'].parents['tau'].parents['x'], pm.Uniform) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['t'], pm.TruncatedNormal) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['t'].parents['tau'], pm.Deterministic) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['t'].parents['tau'].parents['x'], pm.Uniform) def test_HDDM_distributions(self): params = hddm.generate.gen_rand_params() data, params_subj = hddm.generate.gen_rand_data(subjs=4, params=params) m = hddm.HDDM(data) m.sample(self.iter, burn=self.burn) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'], pm.Normal) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['mu'], pm.Normal) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['tau'], pm.Deterministic) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['tau'].parents['x'], pm.Uniform) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['a'], pm.Deterministic) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['a'].parents['x'], pm.Normal) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['a'].parents['x'].parents['mu'], pm.Normal) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['a'].parents['x'].parents['tau'], pm.Deterministic) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['a'].parents['x'].parents['tau'].parents['x'], pm.Uniform) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['t'], pm.Deterministic) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['t'].parents['x'], pm.Normal) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['t'].parents['x'].parents['mu'], pm.Normal) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['t'].parents['x'].parents['tau'], pm.Deterministic) assert isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['t'].parents['x'].parents['tau'].parents['x'], pm.Uniform) def test_HDDMStimCoding(self): params_full, params = hddm.generate.gen_rand_params(cond_dict={'v': [-1, 1], 'z': [.8, .4]}) data, params_subj = hddm.generate.gen_rand_data(params=params_full) m = hddm.HDDMStimCoding(data, stim_col='condition', split_param='v') m.sample(self.iter, burn=self.burn) assert isinstance(m.nodes_db.ix['wfpt(c0)']['node'].parents['v'], pm.Normal) assert isinstance(m.nodes_db.ix['wfpt(c1)']['node'].parents['v'], pm.PyMCObjects.Deterministic) assert isinstance(m.nodes_db.ix['wfpt(c1)']['node'].parents['v'].parents['self'], pm.Normal) m = hddm.HDDMStimCoding(data, stim_col='condition', split_param='z') m.sample(self.iter, burn=self.burn) assert isinstance(m.nodes_db.ix['wfpt(c0)']['node'].parents['z'], pm.CommonDeterministics.InvLogit) assert isinstance(m.nodes_db.ix['wfpt(c1)']['node'].parents['z'], pm.PyMCObjects.Deterministic) assert isinstance(m.nodes_db.ix['wfpt(c1)']['node'].parents['z'].parents['a'], int) assert isinstance(m.nodes_db.ix['wfpt(c1)']['node'].parents['z'].parents['b'], pm.CommonDeterministics.InvLogit) def test_HDDMRegressor(self): reg_func = lambda args, cols: args[0] + args[1]*cols[:,0] reg = {'func': reg_func, 'args':['v_slope','v_inter'], 'covariates': 'cov', 'outcome':'v'} params = hddm.generate.gen_rand_params() data, params_true = hddm.generate.gen_rand_data(params, size=500, subjs=5) data = pd.DataFrame(data) data['cov'] = 1. m = hddm.HDDMRegressor(data, regressor=reg) m.sample(self.iter, burn=self.burn) self.assertTrue(all(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['cols'][:,0] == 1)) self.assertTrue(isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][0], pm.Normal)) self.assertEqual(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][0].__name__, 'v_slope_subj.0') self.assertTrue(isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][1], pm.Normal)) self.assertEqual(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][1].__name__, 'v_inter_subj.0') self.assertEqual(len(np.unique(m.nodes_db.ix['wfpt.0']['node'].parents['v'].value)), 1) def test_HDDMRegressor_no_group(self): reg_func = lambda args, cols: args[0] + args[1]*cols[:,0] reg = {'func': reg_func, 'args':['v_slope','v_inter'], 'covariates': 'cov', 'outcome':'v'} params = hddm.generate.gen_rand_params() data, params_true = hddm.generate.gen_rand_data(params, size=500, subjs=1) data = pd.DataFrame(data) data['cov'] = 1. del data['subj_idx'] m = hddm.HDDMRegressor(data, regressor=reg, is_group_model=False, depends_on={}) m.sample(self.iter, burn=self.burn) print m.nodes_db.index self.assertTrue(all(m.nodes_db.ix['wfpt']['node'].parents['v'].parents['cols'][:,0] == 1)) self.assertTrue(isinstance(m.nodes_db.ix['wfpt']['node'].parents['v'].parents['args'][0], pm.Normal)) self.assertEqual(m.nodes_db.ix['wfpt']['node'].parents['v'].parents['args'][0].__name__, 'v_slope') self.assertTrue(isinstance(m.nodes_db.ix['wfpt']['node'].parents['v'].parents['args'][1], pm.Normal)) self.assertEqual(m.nodes_db.ix['wfpt']['node'].parents['v'].parents['args'][1].__name__, 'v_inter') self.assertEqual(len(np.unique(m.nodes_db.ix['wfpt']['node'].parents['v'].value)), 1) def test_HDDMRegressor_two_covariates(self): reg_func = lambda args, cols: args[0] + args[1]*cols[:,0] + cols[:,1] reg = {'func': reg_func, 'args':['v_slope','v_inter'], 'covariates': ['cov1', 'cov2'], 'outcome':'v'} params = hddm.generate.gen_rand_params() data, params_true = hddm.generate.gen_rand_data(params, size=500, subjs=5) data = pd.DataFrame(data) data['cov1'] = 1. data['cov2'] = -1 m = hddm.HDDMRegressor(data, regressor=reg) m.sample(self.iter, burn=self.burn) self.assertTrue(all(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['cols'][:,0] == 1)) self.assertTrue(all(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['cols'][:,1] == -1)) self.assertTrue(isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][0], pm.Normal)) self.assertEqual(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][0].__name__, 'v_slope_subj.0') self.assertTrue(isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][1], pm.Normal)) self.assertEqual(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][1].__name__, 'v_inter_subj.0') self.assertEqual(len(np.unique(m.nodes_db.ix['wfpt.0']['node'].parents['v'].value)), 1) def test_HDDMRegressor_two_regressors(self): reg_func1 = lambda args, cols: args[0] + args[1]*cols[:,0] reg1 = {'func': reg_func1, 'args':['v_slope','v_inter'], 'covariates': 'cov1', 'outcome':'v'} reg_func2 = lambda args, cols: args[0] + args[1]*cols[:,0] reg2 = {'func': reg_func2, 'args':['a_slope','a_inter'], 'covariates': 'cov2', 'outcome':'a'} params = hddm.generate.gen_rand_params() data, params_true = hddm.generate.gen_rand_data(params, size=500, subjs=5) data = pd.DataFrame(data) data['cov1'] = 1. data['cov2'] = -1 m = hddm.HDDMRegressor(data, regressor=[reg1, reg2]) m.sample(self.iter, burn=self.burn) self.assertTrue(all(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['cols'][:,0] == 1)) self.assertTrue(all(m.nodes_db.ix['wfpt.0']['node'].parents['a'].parents['cols'][:,0] == -1)) self.assertTrue(isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][0], pm.Normal)) self.assertTrue(isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['a'].parents['args'][0], pm.Normal)) self.assertEqual(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][0].__name__, 'v_slope_subj.0') self.assertEqual(m.nodes_db.ix['wfpt.0']['node'].parents['a'].parents['args'][0].__name__, 'a_slope_subj.0') self.assertTrue(isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][1], pm.Normal)) self.assertTrue(isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['a'].parents['args'][1], pm.Normal)) self.assertEqual(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][1].__name__, 'v_inter_subj.0') self.assertEqual(m.nodes_db.ix['wfpt.0']['node'].parents['a'].parents['args'][1].__name__, 'a_inter_subj.0') def test_HDDMRegressorGroupOnly(self): reg_func = lambda args, cols: args[0] + args[1]*cols[:,0] reg = {'func': reg_func, 'args':['v_slope','v_inter'], 'covariates': 'cov', 'outcome':'v'} params = hddm.generate.gen_rand_params() data, params_true = hddm.generate.gen_rand_data(params, size=500, subjs=5) data = pd.DataFrame(data) data['cov'] = 1. m = hddm.HDDMRegressor(data, regressor=reg, group_only_nodes=['v_slope', 'v_inter']) m.sample(self.iter, burn=self.burn) self.assertTrue(all(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['cols'][:,0] == 1)) self.assertTrue(isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][0], pm.Normal)) self.assertEqual(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][0].__name__, 'v_slope') self.assertTrue(isinstance(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][1], pm.Normal)) self.assertEqual(m.nodes_db.ix['wfpt.0']['node'].parents['v'].parents['args'][1].__name__, 'v_inter') self.assertEqual(len(np.unique(m.nodes_db.ix['wfpt.0']['node'].parents['v'].value)), 1) def test_posterior_plots_breakdown(): params = hddm.generate.gen_rand_params() data, params_subj = hddm.generate.gen_rand_data(params=params, subjs=5) m = hddm.HDDM(data) m.sample(200, burn=10) m.plot_posterior_predictive() m.plot_posterior_quantiles() m.plot_posteriors() if __name__=='__main__': print "Run nosetest.py" HDDM-0.4/hddm/tests/matlab_values.py0000664000175000017500000000656612005542776020551 0ustar whykingwhyking00000000000000# Values obtained from running against the Fuss & Navarro 2009 reference implementation vals = [(0.3325402105490861, 0.18224585277734096, 2.0210322268188046, 0.37178992456396914, 0.7513994191503139, 1.6883221884854474, 1.0, 0.082198565245068272), (-0.13074510229340497, 0.44696631528174735, 2.4890334572448456, 0.3816245478330931, 0.9498762676625047, 1.0790903665954314, 0.1, 0.41591722288506577), (0.6056010862462016, 0.16871208306308166, 1.5524386791394562, 0.1286318302691049, 0.19969302867825994, 0.3836652143729468, 1e-07, 0.62063048896871487), (0.3895703269526853, 0.3435094918413777, 2.1750677359226023, 0.25344975464683345, 0.5512703840098271, 3.688305473258689, 0.01, 0.0090709779903460266), (0.42003699302964476, 0.10014569263424461, 2.3012075231555444, 0.0019673685361806803, 0.004527323276278492, 3.009144940928847, 0.0001, 0.0001884560320134888), (-0.5255928511420052, 0.41071488550017876, 2.2389065101119003, 0.2301372155046244, 0.515255710012329, 0.5621315133209579, 1e-05, 1.8640810217155657), (0.008338807811151538, 0.30523807964872274, 2.4560203575759623, 0.032544407891560656, 0.07992972830692877, 3.802538736807515, 1e-05, 0.0030406047220150243), (-0.709463423093855, 0.07788052784603428, 2.0241086070651075, 0.1491208977241244, 0.3018368925766758, 1.213966089059249, 1.0, 0.082017502354950353), (0.460913299169564, 0.2168584116589385, 2.298380160881188, 0.4461371311255309, 1.0253927312113693, 0.25156507383357796, 0.0001, 1.0372891338943479e-05), (-0.6117806447011472, 0.49463268264815274, 1.9686542627132415, 0.25832570907838626, 0.5085540083455858, 1.94831451612509, 0.001, 0.097069006460126561), (0.32861624103817627, 0.12335537773273014, 2.002692863678406, 0.47249065415592595, 0.9462536612328146, 2.796439295540539, 0.001, 0.018459337529972406), (0.021183546657572494, 0.47799029047254493, 2.3569808966748047, 0.4855005752397232, 1.1443155811646561, 2.610216277868382, 0.1, 0.082974887901830302), (0.7175299762398688, 0.3566056886339434, 2.220795104898356, 0.45773365470000554, 1.0165326597050066, 2.1966769477884225, 1e-07, 0.030138660738276948), (-0.7178918281791459, 0.057776773410309845, 1.9358335812071767, 0.10703342161705387, 0.20719889187779905, 1.9963028315012206, 0.01, 0.015193622821588188), (0.38713456753496744, 0.07157449665799648, 2.2096131432869957, 0.017240717413939843, 0.038095315797538463, 3.426246718806154, 1e-07, 0.00089937286951603833), (0.7331877690347837, 0.46552429958519015, 1.8562721149635684, 0.34330970798729826, 0.6372762377331072, 1.6101500341565407, 1.0, 0.071864719581762077), (-0.006430595681853335, 0.1207541730285373, 1.6676102104060564, 0.2604537143639433, 0.4343352734114944, 0.7713437644783603, 1.0, 0.26065820692218827), (-0.5187897244595112, 0.21532637320553233, 2.481938472673327, 0.18482168074390404, 0.4587160400224425, 1.0697490678038166, 1e-09, 0.23165988979463398), (-0.24925121543920914, 0.05753410930092823, 2.1709492103847565, 0.477487812061141, 1.036601788562479, 2.211403925199262, 1e-08, 0.084455290527924637), (-0.5719411096293123, 0.28837526790403306, 2.1929805341020145, 0.2243768947148022, 0.49205416241181843, 1.0791694385505024, 1e-10, 0.27868915917309367)] HDDM-0.4/hddm/tests/__init__.py0000664000175000017500000000011212005542776017446 0ustar whykingwhyking00000000000000import unittest from test_models import * from test_likelihoods import * HDDM-0.4/hddm/tests/test_generate.py0000664000175000017500000000312512050455454020543 0ustar whykingwhyking00000000000000import unittest import hddm from scipy.stats import ks_2samp, kstest import numpy as np from nose import SkipTest class TestGenerate(unittest.TestCase): def test_compare_drift_simulated_data_to_analytic(self): raise SkipTest("The function used KStest in the past, but since Wfpt is not scipy_distribution anymore it does not work") includes = [[],['z', 'sv'],['z', 'st'],['z', 'sz'], ['z', 'sz','st'], ['z', 'sz','st','sv']] Stochastic = hddm.likelihoods.generate_wfpt_stochastic_class(sampling_method='drift') for include in includes: params = hddm.generate.gen_rand_params(include=include) stoch = Stochastic('temp', size=1000, **params) [D, p_value] = kstest(stoch.rvs, stoch.cdf, N=1000) print 'p_value: %f' % p_value self.assertTrue(p_value > 0.05) def test_cdf_samples_to_drift_samples(self): np.random.seed(100) includes = [[],['z', 'sv'],['z', 'st'],['z', 'sz'], ['z', 'sz','st'], ['z', 'sz','st','sv']] for include in includes: params = hddm.generate.gen_rand_params(include=include) Stoch_drift = hddm.likelihoods.generate_wfpt_stochastic_class(sampling_method='drift') Stoch_cdf = hddm.likelihoods.generate_wfpt_stochastic_class(sampling_method='cdf') stoch_drift = Stoch_drift('temp', size=1000, **params) stoch_cdf = Stoch_cdf('temp', size=1000, **params) [D, p_value] = ks_2samp(stoch_cdf.random(), stoch_drift.random()) print 'p_value: %f' % p_value self.assertTrue(p_value > 0.05) HDDM-0.4/hddm/tests/test_likelihoods.py0000664000175000017500000002431612051424250021253 0ustar whykingwhyking00000000000000import numpy as np from numpy.random import rand import scipy as sp import unittest import pymc as pm import hddm from hddm.likelihoods import * from hddm.generate import * from scipy.integrate import * from scipy.stats import kstest from scipy.optimize import fmin_powell from nose import SkipTest np.random.seed(3123) def gen_like_from_matlab(samples=20, seed=3123): # Generate samples from the reference implementation by Navarro & Fuss 2009 # This was used to generate matlab_values.py for comparison with our implementation. import mlabwrap np.random.seed(seed) vals = [] for i in range(samples): v = (rand()-.5)*1.5 t = rand()*.5 a = 1.5+rand() z = .5*rand() z_nonorm = a*z rt = rand()*4 + t err = 10**(round(rand()*-10)) # Test if equal up to the 9th decimal. matlab_wfpt = mlabwrap.mlab.wfpt(rt-t, v, a, z_nonorm, err)[0][0] vals.append((v, t, a, z, z_nonorm, rt, err, matlab_wfpt)) return vals class TestWfpt(unittest.TestCase): def __init__(self, *args, **kwargs): super(TestWfpt, self).__init__(*args, **kwargs) self.samples=5000 self.range_ = (-10,10) def test_pdf_no_matlab(self): # Test if our wfpt pdf implementation yields the same results as the reference implementation by Navarro & Fuss 2009 from matlab_values import vals for v, t, a, z, z_nonorm, rt, err, matlab_wfpt in vals: python_wfpt = hddm.wfpt.full_pdf(-rt, v, 0, a, z, 0, t, 0, err, 0) print v,t,a,z,z_nonorm,rt,err, matlab_wfpt, python_wfpt np.testing.assert_array_almost_equal(matlab_wfpt, python_wfpt, 9) def test_pdf(self): # Test if our wfpt pdf implementation yields the same results as the reference implementation by Navarro & Fuss 2009 try: import mlabwrap except ImportError: raise SkipTest("Could not import mlabwrap, not performing pdf comparison test.") for i in range(500): v = (rand()-.5)*1.5 # TODO! t = rand()*.5 a = 1.5+rand() z = .5*rand() z_nonorm = a*z rt = rand()*4 + t err = 1e-4 # Test if equal up to the 9th decimal. matlab_wfpt = mlabwrap.mlab.wfpt(rt-t, v, a, z_nonorm, err)[0][0] python_wfpt = hddm.wfpt.pdf_array(np.asarray([-rt]), v, 0, a, z, 0, t, 0, err, 0)[0] print v,t,a,z,z_nonorm,rt,err, matlab_wfpt, python_wfpt np.testing.assert_array_almost_equal(matlab_wfpt, python_wfpt, 9) def test_summed_logp(self): params = hddm.generate.gen_rand_params(include=('sv','sz','st')) # Generate random valid RTs rts = params['t'] + params['st'] + rand(50)*2 p = hddm.wfpt.pdf_array(rts, params['v'], params['sv'], params['a'], params['z'], params['sz'], params['t'], params['st'], 1e-4, logp=True) summed_logp = np.sum(p) summed_logp_like = hddm.wfpt.wiener_like(rts, params['v'], params['sv'], params['a'], params['z'], params['sz'], params['t'], params['st'], 1e-4) np.testing.assert_almost_equal(summed_logp, summed_logp_like, 2) self.assertTrue(-np.Inf == hddm.wfpt.wiener_like(np.array([1.,2.,3.,0.]), 1, 0, 2, .5, 0, 0, 0, 1e-4)), "wiener_like_simple should have returned -np.Inf" def test_pdf_sv(self, samples=50): """Test if our wfpt pdf_sv implementation produces the same value as numerical integration over v""" func = lambda v_i,value,err,v,sv,z,a: hddm.wfpt.full_pdf(value, v_i, 0, a, z, 0, 0, 0, err) * norm.pdf(v_i,v,sv) for i in range(50): sv = rand()*0.4+0.1 v = (rand()-.5)*4 t = rand()*.5 a = 1.5+rand() z = .5*rand() rt = rand()*4 + t err = 10**(-3- np.ceil(rand()*12)) # Test if equal up to the 9th decimal. sp_result = quad(func, -np.inf, np.inf, args=(rt,err,v,sv,z,a), epsrel=1e-10, epsabs=1e-10)[0] hddm_result = hddm.wfpt.full_pdf(rt, v, sv, a, z, 0, 0, 0, err) np.testing.assert_array_almost_equal(hddm_result, sp_result) class TestWfptFull(unittest.TestCase): def test_adaptive(self): for i in range(20): sv = rand()*0.4+0.1 v = (rand()-.5)*4 st = rand()*0.3 t = rand()*.5+(st/2) a = 1.5+rand() rt = (rand()*4 + t) * np.sign(rand()) err = 10**-9 sz = rand()*0.3 z = .5*rand()+sz/2 logp = 0#np.floor(rand()*2) n_sz = 60 n_st = 60 my_res = hddm.wfpt.full_pdf(rt,v=v,sv=0,a=a,z=z,sz=0,t=t, st=st,err=err, n_st=5, n_sz=5, use_adaptive=1) res = hddm.wfpt.full_pdf(rt,v=v,sv=0,a=a,z=z,sz=0,t=t, st=st,err=err, n_st=n_st, n_sz=n_sz, use_adaptive=0) print "(%d) rt %f, v: %f, sv: %f, z: %f, sz: %f, t: %f, st: %f a: %f" % (i,rt,v,sv,z,sz,t,st,a) print my_res print res if np.isinf(my_res): my_res = 100 if np.isinf(res): res = 100 self.assertTrue(not np.isnan(my_res)), "Found NaN in the results" self.assertTrue(not np.isnan(res)), "Found NaN in the simulated results" np.testing.assert_array_almost_equal(my_res, res, 3) def test_pdf_integrate_to_one(self): for tests in range(2): sv = rand()*0.4+0.1 v = (rand()-.5)*4 st = rand()*0.3 t = rand()*.5+(st/2) a = 1.5+rand() err = 10**-8 sz = rand()*0.3 z = .5*rand()+sz/2 func = lambda x: np.exp(hddm.wfpt.wiener_like(np.array([x]), v, sv, a, z, sz, t, st, err)) integ, error = sp.integrate.quad(func, a=-5, b=5) np.testing.assert_almost_equal(integ, 1, 2) def test_wiener_like_full_single(self): for i in range(20): sv = rand()*0.4+0.1 v = (rand()-.5)*4 st = rand()*0.3 t = rand()*.5+(st/2) a = 1.5+rand() rt = (rand()*4 + t) * np.sign(rand()) err = 10**-8 sz = rand()*0.3 z = .5*rand()+sz/2 n_sz = 60 n_st = 60 my_res = np.zeros(8) res = np.zeros(8) y_z = np.zeros(n_sz+1); y_t = np.zeros(n_st+1) for vvv in range(2): #test pdf my_res[0+vvv*4] = hddm.wfpt.full_pdf(rt,v=v,sv=sv*vvv,a=a,z=z,sz=0,t=t, st=0,err=err, n_st=n_st, n_sz=n_sz) res[0+vvv*4] = hddm.wfpt.full_pdf(rt,v=v,sv=sv*vvv,a=a,z=z,sz=0,t=t, st=0,err=err, n_st=0, n_sz=0) #test pdf + sz my_res[1+vvv*4] = hddm.wfpt.full_pdf(rt,v=v,sv=sv*vvv,a=a,z=z,sz=sz,t=t, st=0,err=err, n_st=n_st, n_sz=n_sz) hsz = sz/n_sz for j in range(n_sz+1): z_tag = z-sz/2. + hsz*j y_z[j] = hddm.wfpt.full_pdf(rt,v=v,sv=sv*vvv,a=a,z=z_tag,sz=0,t=t, st=0,err=err, n_st=0, n_sz=0)/sz res[1+vvv*4] = simps(y_z, x=None, dx=hsz) #test pdf + st my_res[2+vvv*4] = hddm.wfpt.full_pdf(rt,v=v,sv=sv*vvv,a=a,z=z,sz=0,t=t, st=st,err=err, n_st=n_st, n_sz=n_sz) hst = st/n_st for j in range(n_st+1): t_tag = t-st/2. + hst*j y_t[j] = hddm.wfpt.full_pdf(rt,v=v,sv=sv*vvv,a=a,z=z,sz=0,t=t_tag, st=0,err=err, n_st=0, n_sz=0)/st res[2+vvv*4] = simps(y_t, x=None, dx=hst) #test pdf + sz + st my_res[3+vvv*4] = hddm.wfpt.full_pdf(rt,v=v,sv=sv*vvv,a=a,z=z,sz=sz,t=t, st=st,err=err, n_st=n_st, n_sz=n_sz) hst = st/n_st hsz = sz/n_sz for j_t in range(n_st+1): t_tag = t-st/2. + hst*j_t for j_z in range(n_sz+1): z_tag = z-sz/2. + hsz*j_z y_z[j_z] = hddm.wfpt.full_pdf(rt,v=v,sv=sv*vvv,a=a,z=z_tag,sz=0,t=t_tag, st=0,err=err, n_st=0, n_sz=0)/sz/st y_t[j_t] = simps(y_z, x=None, dx=hsz) res[3+vvv*4] = simps(y_t, x=None, dx=hst) print "(%d) rt %f, v: %f, sv: %f, z: %f, sz: %f, t: %f, st: %f a: %f" % (i,rt,v,sv,z,sz,t,st,a) print my_res print res my_res[np.isinf(my_res)] = 100 res[np.isinf(res)] = 100 self.assertTrue(not any(np.isnan(my_res))), "Found NaN in the results" self.assertTrue(not any(np.isnan(res))), "Found NaN in the simulated results" np.testing.assert_array_almost_equal(my_res, res, 2) def test_failure_mode(self): rt = 0.6 for i in range(2): rt = rt * -1 v = 1 sv = 1 a = 1.5 z = 0.5 sz = 0.2 t = 0.2 st = 0.1 n_st = 10; n_sz =10 z = 1.1 self.assertTrue(hddm.wfpt.full_pdf(rt,v=v,sv=sv,a=a,z=z,sz=sz,t=t, st=st,err=1e-10, n_st=10, n_sz=10)==0) z = -0.1 self.assertTrue(hddm.wfpt.full_pdf(rt,v=v,sv=sv,a=a,z=z,sz=sz,t=t, st=st,err=1e-10, n_st=10, n_sz=10)==0) z = 0.5 z = 0.1 sz = 0.25 self.assertTrue(hddm.wfpt.full_pdf(rt,v=v,sv=sv,a=a,z=z,sz=sz,t=t, st=st,err=1e-10, n_st=10, n_sz=10)==0) z = 0.5 a = -0.1 self.assertTrue(hddm.wfpt.full_pdf(rt,v=v,sv=sv,a=a,z=z,sz=sz,t=t, st=st,err=1e-10, n_st=10, n_sz=10)==0) a = 1.5 t = 0.7 st = 0 self.assertTrue(hddm.wfpt.full_pdf(rt,v=v,sv=sv,a=a,z=z,sz=sz,t=t, st=st,err=1e-10, n_st=10, n_sz=10)==0) t = -0.3 self.assertTrue(hddm.wfpt.full_pdf(rt,v=v,sv=sv,a=a,z=z,sz=sz,t=t, st=st,err=1e-10, n_st=10, n_sz=10)==0) t = 0.1 st = 0.3 self.assertTrue(hddm.wfpt.full_pdf(rt,v=v,sv=sv,a=a,z=z,sz=sz,t=t, st=st,err=1e-10, n_st=10, n_sz=10)==0) t = 0.2 st = 0.1 if __name__=='__main__': print "Run nosetest." HDDM-0.4/hddm/__init__.py0000664000175000017500000000050612054702073016303 0ustar whykingwhyking00000000000000#!/usr/bin/python __docformat__ = 'restructuredtext' __version__ = '0.4' import models import models as model # remain backwards compatibility import likelihoods import generate import utils from models import * import wfpt try: import lba except ImportError: pass from kabuki.utils import load_csv, save_csv HDDM-0.4/hddm/models/0000775000175000017500000000000012054702235015454 5ustar whykingwhyking00000000000000HDDM-0.4/hddm/models/base.py0000664000175000017500000002105112052770072016741 0ustar whykingwhyking00000000000000""" .. module:: HDDM :platform: Agnostic :synopsis: Definition of HDDM models. .. moduleauthor:: Thomas Wiecki Imri Sofer """ from __future__ import division from collections import OrderedDict import numpy as np import pymc as pm import hddm import kabuki import inspect from kabuki.hierarchical import Knode from scipy.optimize import fmin_powell, fmin class AccumulatorModel(kabuki.Hierarchical): def __init__(self, data, **kwargs): # Flip sign for lower boundary RTs data = hddm.utils.flip_errors(data) self.group_only_nodes = kwargs.pop('group_only_nodes', ()) super(AccumulatorModel, self).__init__(data, **kwargs) class HDDMBase(AccumulatorModel): """Implements the hierarchical Ratcliff drift-diffusion model using the Navarro & Fuss likelihood and numerical integration over the variability parameters. :Arguments: data : numpy.recarray Input data with a row for each trial. Must contain the following columns: * 'rt': Reaction time of trial in seconds. * 'response': Binary response (e.g. 0->error, 1->correct) May contain: * 'subj_idx': A unique ID (int) of the subject. * Other user-defined columns that can be used in depends on keyword. :Example: >>> data, params = hddm.generate.gen_rand_data() # gen data >>> model = hddm.HDDM(data) # create object >>> mcmc = model.mcmc() # Create pymc.MCMC object >>> mcmc.sample() # Sample from posterior :Optional: include : iterable Optional parameters to include. These include all inter-trial variability parameters ('sv', 'sz', 'st'), as well as the bias parameter ('z'), and the probability for outliers ('p_outlier'). Can be any combination of 'sv', 'sz', 'st', 'z', and 'p_outlier'. Passing the string 'all' will include all five. Note: Including 'sz' and/or 'st' will increase run time significantly! is_group_model : bool If True, this results in a hierarchical model with separate parameter distributions for each subject. The subject parameter distributions are themselves distributed according to a group parameter distribution. If not provided, this parameter is set to True if data provides a column 'subj_idx' and False otherwise. depends_on : dict Specifies which parameter depends on data of a column in data. For each unique element in that column, a separate set of parameter distributions will be created and applied. Multiple columns can be specified in a sequential container (e.g. list) :Example: >>> hddm.HDDM(data, depends_on={'v':'difficulty'}) Separate drift-rate parameters will be estimated for each difficulty. Requires 'data' to have a column difficulty. bias : bool Whether to allow a bias to be estimated. This is normally used when the responses represent left/right and subjects could develop a bias towards responding right. This is normally never done, however, when the 'response' column codes correct/error. plot_var : bool Plot group variability parameters when calling pymc.Matplot.plot() (i.e. variance of Normal distribution.) wiener_params : dict Parameters for wfpt evaluation and numerical integration. :Parameters: * err: Error bound for wfpt (default 1e-4) * n_st: Maximum depth for numerical integration for st (default 2) * n_sz: Maximum depth for numerical integration for Z (default 2) * use_adaptive: Whether to use adaptive numerical integration (default True) * simps_err: Error bound for Simpson integration (default 1e-3) p_outlier : double (default=0) The probability of outliers in the data. if p_outlier is passed in the 'include' argument, then it is estimated from the data and the value passed using the p_outlier argument is ignored. """ def __init__(self, data, bias=False, include=(), wiener_params=None, p_outlier=0., **kwargs): self._kwargs = kwargs self.include = set(['v', 'a', 't']) if include is not None: if include == 'all': [self.include.add(param) for param in ('z', 'st','sv','sz', 'p_outlier')] elif isinstance(include, str): self.include.add(include) else: [self.include.add(param) for param in include] if bias: self.include.add('z') possible_parameters = ('v', 'a', 't', 'z', 'st', 'sz', 'sv', 'p_outlier') assert self.include.issubset(possible_parameters), """Received and invalid parameter using the 'include' keyword. parameters received: %s parameters allowed: %s """ % (tuple(self.include), possible_parameters) #set wiener params if wiener_params is None: self.wiener_params = {'err': 1e-4, 'n_st':2, 'n_sz':2, 'use_adaptive':1, 'simps_err':1e-3, 'w_outlier': 0.1} else: self.wiener_params = wiener_params wp = self.wiener_params self.p_outlier = p_outlier #set cdf_range cdf_bound = max(np.abs(data['rt'])) + 1; cdf_range = (-cdf_bound, cdf_bound) #set wfpt class self.wfpt_class = hddm.likelihoods.generate_wfpt_stochastic_class(wp, cdf_range=cdf_range) super(HDDMBase, self).__init__(data, **kwargs) def _create_wfpt_parents_dict(self, knodes): wfpt_parents = OrderedDict() wfpt_parents['a'] = knodes['a_bottom'] wfpt_parents['v'] = knodes['v_bottom'] wfpt_parents['t'] = knodes['t_bottom'] wfpt_parents['sv'] = knodes['sv_bottom'] if 'sv' in self.include else 0 wfpt_parents['sz'] = knodes['sz_bottom'] if 'sz' in self.include else 0 wfpt_parents['st'] = knodes['st_bottom'] if 'st' in self.include else 0 wfpt_parents['z'] = knodes['z_bottom'] if 'z' in self.include else 0.5 wfpt_parents['p_outlier'] = knodes['p_outlier_bottom'] if 'p_outlier' in self.include else self.p_outlier return wfpt_parents def _create_wfpt_knode(self, knodes): wfpt_parents = self._create_wfpt_parents_dict(knodes) return Knode(self.wfpt_class, 'wfpt', observed=True, col_name='rt', **wfpt_parents) def create_knodes(self): knodes = self._create_stochastic_knodes(self.include) knodes['wfpt'] = self._create_wfpt_knode(knodes) return knodes.values() def plot_posterior_predictive(self, *args, **kwargs): if 'value_range' not in kwargs: kwargs['value_range'] = np.linspace(-5, 5, 100) kabuki.analyze.plot_posterior_predictive(self, *args, **kwargs) def plot_posterior_quantiles(self, *args, **kwargs): """Plot posterior predictive quantiles. :Arguments: model : HDDM model :Optional: value_range : numpy.ndarray Range over which to evaluate the CDF. samples : int (default=10) Number of posterior samples to use. alpha : float (default=.75) Alpha (transparency) of posterior quantiles. hexbin : bool (default=False) Whether to plot posterior quantile density using hexbin. data_plot_kwargs : dict (default=None) Forwarded to data plotting function call. predictive_plot_kwargs : dict (default=None) Forwareded to predictive plotting function call. columns : int (default=3) How many columns to use for plotting the subjects. save : bool (default=False) Whether to save the figure to a file. path : str (default=None) Save figure into directory prefix """ hddm.utils.plot_posterior_quantiles(self, *args, **kwargs) if __name__ == "__main__": import doctest doctest.testmod() HDDM-0.4/hddm/models/hddm_truncated.py0000664000175000017500000000243312050455466021024 0ustar whykingwhyking00000000000000from collections import OrderedDict from hddm.models import HDDMBase class HDDMTruncated(HDDMBase): def _create_stochastic_knodes(self, include): knodes = OrderedDict() if 'a' in include: knodes.update(self.create_family_trunc_normal('a', lower=1e-3, upper=1e3, value=1)) if 'v' in include: knodes.update(self.create_family_normal('v', value=0)) if 't' in include: knodes.update(self.create_family_trunc_normal('t', lower=1e-3, upper=1e3, value=.01)) if 'sv' in include: # TW: Use kabuki.utils.HalfCauchy, S=10, value=1 instead? knodes.update(self.create_family_trunc_normal('sv', lower=0, upper=1e3, value=1)) #knodes.update(self.create_family_exp('sv', value=1)) if 'sz' in include: knodes.update(self.create_family_trunc_normal('sz', lower=0, upper=1, value=.1)) if 'st' in include: knodes.update(self.create_family_trunc_normal('st', lower=0, upper=1e3, value=.01)) if 'z' in include: knodes.update(self.create_family_trunc_normal('z', lower=0, upper=1, value=.5)) if 'p_outlier' in include: knodes.update(self.create_family_trunc_normal('p_outlier', lower=0, upper=1, value=0.05)) return knodes HDDM-0.4/hddm/models/__init__.py0000664000175000017500000000063612050455466017601 0ustar whykingwhyking00000000000000from base import AccumulatorModel, HDDMBase from hddm_truncated import HDDMTruncated from hddm_transformed import HDDM from hddm_stimcoding import HDDMStimCoding from hddm_regression import HDDMRegressor from hlba_truncated import HLBA __all__ = ['AccumulatorModel', 'HDDMBase', 'HDDMTruncated', 'HDDM', 'HDDMStimCoding', 'HDDMRegressor', 'HLBA' ]HDDM-0.4/hddm/models/hlba_truncated.py0000664000175000017500000000564612050455466021027 0ustar whykingwhyking00000000000000from __future__ import division from collections import OrderedDict import numpy as np from scipy import stats from numpy.random import rand, randn import hddm import kabuki from base import AccumulatorModel from kabuki.hierarchical import Knode from kabuki.distributions import scipy_stochastic class lba_gen(stats.distributions.rv_continuous): def _argcheck(self, *args): return True def _logp(self, x, t, A, b, s, v): return hddm.lba.lba_like(x, A, b, t, s, v, 0, logp=True, normalize_v=True) def _pdf(self, x, t, A, b, s, v): raise NotImplementedError def _rvs(self, t, A, b, s, v): v0 = v v1 = 1 - v; sampled_rts = np.empty(self._size) for i_sample in xrange(self._size): positive = False while not positive: i_v0 = randn()*s + v0 i_v1 = randn()*s + v1 if (i_v0 > 0) or (i_v1 > 0): positive = True if i_v0 > 0: rt0 = (b - rand()*A) / i_v0 + t else: rt0 = np.inf if i_v1 > 0: rt1 = (b - rand()*A) / i_v1 + t else: rt1 = np.inf if rt0 < rt1: sampled_rts[i_sample] = rt0 else: sampled_rts[i_sample] = -rt1 return sampled_rts def random(self, t, A, b, s, v, size): self._size = size return self._rvs(t, A, b, s, v) lba_like = scipy_stochastic(lba_gen, name='lba', longname="", extradoc="") class HLBA(AccumulatorModel): def create_lba_knode(self, knodes): lba_parents = OrderedDict() lba_parents['t'] = knodes['t_bottom'] lba_parents['A'] = knodes['A_bottom'] lba_parents['b'] = knodes['b_bottom'] lba_parents['s'] = knodes['s_bottom'] lba_parents['v'] = knodes['v_bottom'] return Knode(lba_like, 'lba', observed=True, col_name='rt', **lba_parents) def create_knodes(self): """Returns list of model parameters. """ knodes = OrderedDict() knodes.update(self.create_family_trunc_normal('t', lower=1e-3, upper=1e3, value=.01)) knodes.update(self.create_family_trunc_normal('A', lower=1e-3, upper=1e3, value=.2)) knodes.update(self.create_family_trunc_normal('b', lower=1e-3, upper=1e3, value=1.5)) knodes.update(self.create_family_trunc_normal('s', lower=0, upper=1e3, value=1.)) knodes.update(self.create_family_trunc_normal('v', lower=0, upper=1, value=.5)) knodes['wfpt'] = self.create_lba_knode(knodes) return knodes.values() def plot_posterior_predictive(self, *args, **kwargs): if 'value_range' not in kwargs: kwargs['value_range'] = np.linspace(-1.5, 1.5, 100) kabuki.analyze.plot_posterior_predictive(self, *args, **kwargs) HDDM-0.4/hddm/models/hddm_regression.py0000664000175000017500000001532212053533146021207 0ustar whykingwhyking00000000000000from copy import deepcopy import numpy as np from scipy import stats import pymc as pm import hddm from hddm_transformed import HDDM import kabuki from kabuki import Knode from kabuki.distributions import scipy_stochastic class wfpt_regress_gen(stats.distributions.rv_continuous): wiener_params = {'err': 1e-4, 'nT':2, 'nsz':2, 'use_adaptive':1, 'simps_err':1e-3} wp = wiener_params sampling_method = 'drift' dt=1e-4 def _argcheck(self, *args): return True def _logp(self, x, v, sv, a, z, sz, t, st, reg_outcomes, p_outlier): """Log-likelihood for the full DDM using the interpolation method""" return hddm.wfpt.wiener_like_multi(x, v, sv, a, z, sz, t, st, .001, reg_outcomes, p_outlier=p_outlier) def _pdf(self, x, v, sv, a, z, sz, t, st, reg_outcomes, p_outlier): raise NotImplementedError def _rvs(self, v, sv, a, z, sz, t, st, reg_outcomes, p_outlier): param_dict = {'v':v, 'z':z, 't':t, 'a':a, 'sz':sz, 'sv':sv, 'st':st} sampled_rts = np.empty(self._size) for i_sample in xrange(self._size): #get current params for p in reg_outcomes: param_dict[p] = locals()[p][i_sample] #sample sampled_rts[i_sample] = hddm.generate.gen_rts(param_dict, method=self.sampling_method, samples=1, dt=self.dt) return sampled_rts def random(self, v=1., sv=0., a=2, z=.5, sz=.1, t=.3, st=.1, reg_outcomes=None, size=None, p_outlier=0): self._size = len(locals()[reg_outcomes[0]]) return self._rvs(v, sv, a, z, sz, t, st, reg_outcomes) wfpt_reg_like = scipy_stochastic(wfpt_regress_gen, name='wfpt_reg', longname="""Wiener first passage time with regressors likelihood function""", extradoc="") ################################################################################################ class KnodeRegress(kabuki.hierarchical.Knode): def create_node(self, name, kwargs, data): reg = kwargs['regressor'] # order parents according to user-supplied args args = [] for arg in reg['args']: for parent_name, parent in kwargs['parents'].iteritems(): if parent_name.startswith(arg): args.append(parent) parents = {'args': args, 'cols': data[reg['covariates']].values} return self.pymc_node(reg['func'], kwargs['doc'], name, parents=parents) class HDDMRegressor(HDDM): """HDDMRegressor allows estimation of trial-by-trial influences of a covariate (e.g. a brain measure like fMRI) onto DDM parameters. For example, if your prediction is that activity of a particular brain area has a linear correlation with drift-rate, you could specify the following regression model (make sure you have a column with the brain activity in your data, in our example we name this column 'BOLD'): :: # Define regression function (linear in this case) reg_func = lambda args, cols: args[0] + args[1]*cols[:,0] # Define regression descriptor # regression function to use (func, defined above) # args: parameter names (passed to reg_func; v_slope->args[0], # v_inter->args[1]) # covariates: data column to use as the covariate # (in this example, expects a column named # BOLD in the data) # outcome: DDM parameter that will be replaced by trial-by-trial # regressor values (drift-rate v in this case) reg = {'func': reg_func, 'args': ['v_inter','v_slope'], 'covariates': 'BOLD', 'outcome': 'v'} # construct regression model. Second argument must be the # regression descriptor. This model will have new parameters defined # in args above, these can be used in depends_on like any other # parameter. m = hddm.HDDMRegressor(data, reg, depends_on={'v_slope':'trial_type'}) """ def __init__(self, data, regressor=None, **kwargs): """Hierarchical Drift Diffusion Model with regressors """ #create self.regressor and self.reg_outcome regressor = deepcopy(regressor) if isinstance(regressor, dict): regressor = [regressor] self.reg_outcomes = set() # holds all the parameters that are going to modeled as outcome for reg in regressor: if isinstance(reg['args'], str): reg['args'] = [reg['args']] if isinstance(reg['covariates'], str): reg['covariates'] = [reg['covariates']] self.reg_outcomes.add(reg['outcome']) self.regressor = regressor #set wfpt_reg self.wfpt_reg_class = deepcopy(wfpt_reg_like) self.wfpt_reg_class.rv.wiener_params super(HDDMRegressor, self).__init__(data, **kwargs) def _create_wfpt_knode(self, knodes): wfpt_parents = self._create_wfpt_parents_dict(knodes) return Knode(self.wfpt_reg_class, 'wfpt', observed=True, col_name='rt', reg_outcomes=self.reg_outcomes, **wfpt_parents) def _create_stochastic_knodes(self, include): # Create all stochastic knodes except for the ones that we want to replace # with regressors. knodes = super(HDDMRegressor, self)._create_stochastic_knodes(include.difference(self.reg_outcomes)) #create regressor params for i_reg, reg in enumerate(self.regressor): reg_parents = {} for arg in reg['args']: # Create family for each regressor reg_family = self.create_family_normal(arg, value=0) # Store parents to pass to reg node below reg_parents[arg] = reg_family['%s_bottom' % arg] if reg not in self.group_only_nodes and self.is_group_model: # Rename bottom nodes to subj nodes (reg nodes will be bottom) reg_family['%s_subj_reg' % arg] = reg_family.pop('%s_bottom' % arg) knodes.update(reg_family) reg_knode = KnodeRegress(pm.Deterministic, "%s_reg" % reg['outcome'], regressor=reg, col_name=reg['covariates'], subj=self.is_group_model, plot=False, trace=False, hidden=True, **reg_parents) knodes['%s_bottom' % reg['outcome']] = reg_knode return knodes HDDM-0.4/hddm/models/hddm_transformed.py0000664000175000017500000000411712053533073021352 0ustar whykingwhyking00000000000000from collections import OrderedDict import inspect import pymc as pm import kabuki.step_methods as steps from hddm.models import HDDMBase class HDDM(HDDMBase): def __init__(self, *args, **kwargs): self.use_gibbs_for_mean = kwargs.pop('use_gibbs_for_mean', True) self.use_reject_for_std = kwargs.pop('use_reject_for_std', True) super(HDDM, self).__init__(*args, **kwargs) def pre_sample(self): if not self.is_group_model: return # apply gibbs sampler to normal group nodes for name, node_descr in self.iter_group_nodes(): node = node_descr['node'] knode_name = node_descr['knode_name'].replace('_trans', '') if self.use_gibbs_for_mean and isinstance(node, pm.Normal) and knode_name not in self.group_only_nodes: self.mc.use_step_method(steps.kNormalNormal, node) if self.use_reject_for_std and isinstance(node, pm.Uniform) and knode_name not in self.group_only_nodes: self.mc.use_step_method(steps.UniformPriorNormalstd, node) def _create_stochastic_knodes(self, include): knodes = OrderedDict() if 'a' in include: knodes.update(self.create_family_exp('a', value=1)) if 'v' in include: knodes.update(self.create_family_normal('v', value=0)) if 't' in include: knodes.update(self.create_family_exp('t', value=.01)) if 'sv' in include: # TW: Use kabuki.utils.HalfCauchy, S=10, value=1 instead? knodes.update(self.create_family_trunc_normal('sv', lower=0, upper=1e3, value=1)) #knodes.update(self.create_family_exp('sv', value=1)) if 'sz' in include: knodes.update(self.create_family_invlogit('sz', value=.1)) if 'st' in include: knodes.update(self.create_family_exp('st', value=.01)) if 'z' in include: knodes.update(self.create_family_invlogit('z', value=.5)) if 'p_outlier' in include: knodes.update(self.create_family_invlogit('p_outlier', value=0.05)) return knodes HDDM-0.4/hddm/models/hddm_stimcoding.py0000664000175000017500000000720012050456616021166 0ustar whykingwhyking00000000000000from copy import copy import numpy as np from kabuki.hierarchical import Knode from hddm.models import HDDM class HDDMStimCoding(HDDM): """HDDM model that can be used when stimulus coding and estimation of bias (i.e. displacement of starting point z) is required. In that case, the 'resp' column in your data should contain 0 and 1 for the chosen stimulus (or direction), not whether the response was correct or not as you would use in accuracy coding. You then have to provide another column (referred to as stim_col) which contains information about which the correct response was. :Arguments: split_param : str ('v' or 'z') There are two ways to model stimulus coding in the case where both stimuli have equal information (so that there can be no difference in drift): * 'z': Use z for stimulus A and 1-z for stimulus B * 'v': Use drift v for stimulus A and -v for stimulus B stim_col : str Column name for extracting the stimuli to use for splitting. """ def __init__(self, *args, **kwargs): self.stim_col = kwargs.pop('stim_col', 'stim') self.split_param = kwargs.pop('split_param', 'z') if self.split_param == 'z' and 'include' in kwargs: if 'z' not in kwargs['include']: kwargs['include'].append('z') print "Adding z to includes." else: kwargs['include'] = ['z'] print "Adding z to includes." #assert self.stim_col in self.data.columns, "Can not find column named %s" % self.stim_col self.stims = np.sort(np.unique(args[0][self.stim_col])) assert len(self.stims) == 2, "%s must contain two stimulus types" % self.stim_col super(HDDMStimCoding, self).__init__(*args, **kwargs) def _create_wfpt_knode(self, knodes): wfpt_parents = self._create_wfpt_parents_dict(knodes) # Here we use a special Knode (see below) that either inverts v or z # depending on what the correct stimulus was for that trial type. return KnodeWfptStimCoding(self.wfpt_class, 'wfpt', observed=True, col_name='rt', depends=[self.stim_col], split_param=self.split_param, stims=self.stims, stim_col=self.stim_col, **wfpt_parents) class KnodeWfptStimCoding(Knode): def __init__(self, *args, **kwargs): self.split_param = kwargs.pop('split_param') self.stims = kwargs.pop('stims') self.stim_col = kwargs.pop('stim_col') super(KnodeWfptStimCoding, self).__init__(*args, **kwargs) def create_node(self, name, kwargs, data): # the addition of "depends=['stim']" in the call of # KnodeWfptInvZ in HDDMStimCoding makes that data are # submitted splitted by the values of the variable stim the # following lines check if the variable stim is equal to the # value of stim for which z' = 1-z and transforms z if this is # the case (similar to v) if all(data[self.stim_col] == self.stims[0]): if self.split_param == 'z': z = copy(kwargs['z']) kwargs['z'] = 1-z elif self.split_param == 'v': v = copy(kwargs['v']) kwargs['v'] = -v else: raise ValueError('split_var must be either v or z, but is %s' % self.split_var) return self.pymc_node(name, **kwargs) else: return self.pymc_node(name, **kwargs) HDDM-0.4/hddm/generate.py0000664000175000017500000003672212050455466016356 0ustar whykingwhyking00000000000000from __future__ import division import random import kabuki import hddm import numpy as np import pandas as pd from numpy.random import rand from scipy.stats import uniform, norm from copy import copy, deepcopy def gen_single_params_set(include=()): """Returns a dict of DDM parameters with random values for a singel conditin the function is uded by gen_rand_params. :Optional: include : tuple Which optional parameters include. Can be any combination of: * 'z' (bias, default=0.5) * 'sv' (inter-trial drift variability) * 'sz' (inter-trial bias variability) * 'st' (inter-trial non-decision time variability) Special arguments are: * 'all': include all of the above * 'all_inter': include all of the above except 'z' """ params = {} if include == 'all': include = ['z','sv','sz','st'] elif include == 'all_inter': include = ['sv','sz','st'] params['sv'] = rand() if 'sv' in include else 0 params['sz'] = rand()* 0.3 if 'sz' in include else 0 params['st'] = rand()* 0.2 if 'st' in include else 0 params['z'] = .4+rand()*0.2 if 'z' in include else 0.5 # Simple parameters params['v'] = (rand()-.5)*4 params['t'] = 0.2+rand()*0.3+(params['st']/2) params['a'] = 0.5+rand()*1.5 if 'pi' in include or 'gamma' in include: params['pi'] = max(rand()*0.1,0.01) # params['gamma'] = rand() assert hddm.utils.check_params_valid(**params) return params def gen_rand_params(include=(), cond_dict=None, seed=None): """Returns a dict of DDM parameters with random values. :Optional: include : tuple Which optional parameters include. Can be any combination of: * 'z' (bias, default=0.5) * 'sv' (inter-trial drift variability) * 'sz' (inter-trial bias variability) * 'st' (inter-trial non-decision time variability) Special arguments are: * 'all': include all of the above * 'all_inter': include all of the above except 'z' cond_dict : dictionary cond_dict is used when multiple conditions are desired. the dictionary has the form of {param1: [value_1, ... , value_n], param2: [value_1, ... , value_n]} and the function will output n sets of parameters. each set with values from the appropriate place in the dictionary for instance if cond_dict={'v': [0, 0.5, 1]} then 3 parameters set will be created. the first with v=0 the second with v=0.5 and the third with v=1. seed: float random seed Output: if conditions is None: params: dictionary a dictionary holding the parameters values else: cond_params: a dictionary holding the parameters for each one of the conditions, that has the form {'c1': params1, 'c2': params2, ...} it can be used directly as an argument in gen_rand_data. merged_params: a dictionary of parameters that can be used to validate the optimization and learning algorithms. """ #set seed if seed is not None: np.random.seed(seed) #if there is only a single condition then we can use gen_single_params_set if cond_dict is None: return gen_single_params_set(include=include) #generate original parameter set org_params = gen_single_params_set(include) #create a merged set merged_params = org_params.copy() for name in cond_dict.iterkeys(): del merged_params[name] cond_params = {}; n_conds = len(cond_dict.values()[0]) for i in range(n_conds): #create a set of parameters for condition i #put them in i_params, and in cond_params[c#i] i_params = org_params.copy() for name in cond_dict.iterkeys(): i_params[name] = cond_dict[name][i] cond_params['c%d' %i] = i_params #update merged_params merged_params['%s(c%d)' % (name, i)] = cond_dict[name][i] return cond_params, merged_params def gen_antisaccade_rts(params=None, samples_pro=500, samples_anti=500, dt=1e-4, subj_idx=0): if params is None: params = {'v':-2., 'v_switch': 2., 'V_switch': .1, 'a': 2.5, 't': .3, 't_switch': .3, 'z':.5, 'st': 0, 'sz':0, 'sv':0} # Generate prosaccade trials pro_params = copy(params) del pro_params['t_switch'] del pro_params['v_switch'] rts = np.empty(samples_pro+samples_anti, dtype=[('response', np.float), ('rt', np.float), ('instruct', int), ('subj_idx', int)]) pro_rts = gen_rts(pro_params, samples=samples_pro, dt=dt, subj_idx=subj_idx) anti_rts = gen_rts(params, samples=samples_anti, dt=dt, subj_idx=subj_idx, method='drift') rts['instruct'][:samples_pro] = 0 rts['instruct'][samples_pro:] = 1 rts['response'][:samples_pro] = np.array((pro_rts > 0), float) rts['response'][samples_pro:] = np.array((anti_rts > 0), float) rts['rt'][:samples_pro] = np.abs(pro_rts) rts['rt'][samples_pro:] = np.abs(anti_rts) rts['subj_idx'] = subj_idx return rts, params #################################################################### # Functions to generate RT distributions with specified parameters # #################################################################### def gen_rts(params, samples=1000, range_=(-6, 6), dt=1e-3, intra_sv=1., structured=False, subj_idx=None, method='cdf'): """ Returns a numpy.array of randomly simulated RTs from the DDM. :Arguments: params : dict Parameter names and values to use for simulation. :Optional: samples : int Number of RTs to simulate. range_ : tuple Minimum (negative) and maximum (positve) RTs. dt : float Number of steps/sec. intra_sv : float Intra-trial variability. structured : bool Return a structured array with fields 'RT' and 'response'. subj_idx : int If set, append column 'subj_idx' with value subj_idx. method : str Which method to use to simulate the RTs: * 'cdf': fast, uses the inverse of cumulative density function to sample, dt can be 1e-2. * 'drift': slow, simulates each complete drift process, dt should be 1e-4. """ if params.has_key('v_switch') and method != 'drift': print "Warning: Only drift method supports changes in drift-rate. v_switch will be ignored." # Set optional default values if they are not provided for var_param in ('sv', 'sz', 'st'): if var_param not in params: params[var_param] = 0 if 'z' not in params: params['z'] = .5 if 'sv' not in params: params['sv'] = 0 if 'sz' not in params: params['sz'] = 0 #check sample if isinstance(samples, tuple): #this line is because pymc stochastic use tuple for sample size if samples == (): samples = 1 else: samples = samples[0] if method=='cdf_py': rts = _gen_rts_from_cdf(params, samples, range_, dt) elif method=='drift': rts = _gen_rts_from_simulated_drift(params, samples, dt, intra_sv)[0] elif method=='cdf': rts = hddm.wfpt.gen_rts_from_cdf(params['v'],params['sv'],params['a'],params['z'], params['sz'],params['t'],params['st'], samples, range_[0], range_[1], dt) else: raise TypeError, "Sampling method %s not found." % method if not structured: return rts else: if subj_idx is None: data = np.empty(rts.shape, dtype = ([('response', np.float), ('rt', np.float)])) else: data = np.empty(rts.shape, dtype = ([('response', np.float), ('rt', np.float), ('subj_idx', np.float)])) data['subj_idx'] = subj_idx data['response'][rts>0] = 1. data['response'][rts<0] = 0. data['rt'] = np.abs(rts) return data def kabuki_data_to_hddm_data(kabuki_data): "transform data generate by kabuki.generate.gen_rand_data to hddm data" kd = kabuki_data dtype = ([('response', np.int), ('rt', np.float), ('subj_idx', np.int32), ('condition', 'S20')]) data = np.empty(kd.shape, dtype = dtype) rts = kd['data'] data['response'][rts>0] = 1. data['response'][rts<0] = 0. data['rt'] = np.abs(rts) data['condition'] = kd['condition'] data['subj_idx'] = kd['subj_idx'] return data def _gen_rts_from_simulated_drift(params, samples=1000, dt = 1e-4, intra_sv=1.): """Returns simulated RTs from simulating the whole drift-process. :Arguments: params : dict Parameter names and values. :Optional: samlpes : int How many samples to generate. dt : float How many steps/sec. intra_sv : float Intra-trial variability. :SeeAlso: gen_rts """ from numpy.random import rand if samples is None: samples = 1 nn = 1000 a = params['a'] v = params['v'] if params.has_key('v_switch'): switch = True t_switch = params['t_switch']/dt # Hack so that we will always step into a switch nn = int(round(t_switch)) else: switch = False #create delay if params.has_key('st'): start_delay = (uniform.rvs(loc=params['t'], scale=params['st'], size=samples) \ - params['st']/2.) else: start_delay = np.ones(samples)*params['t'] #create starting_points if params.has_key('sz'): starting_points = (uniform.rvs(loc=params['z'], scale=params['sz'], size=samples) \ - params['sz']/2.)*a else: starting_points = np.ones(samples)*params['z']*a rts = np.empty(samples) step_size = np.sqrt(dt)*intra_sv drifts = [] for i_sample in xrange(samples): drift = np.array([]) crossed = False iter = 0 y_0 = starting_points[i_sample] # drifting... if params.has_key('sv') and params['sv'] != 0: drift_rate = norm.rvs(v, params['sv']) else: drift_rate = v if params.has_key('v_switch'): if params.has_key('V_switch') and params['V_switch'] != 0: drift_rate_switch = norm.rvs(params['v_switch'], params['V_switch']) else: drift_rate_switch = params['v_switch'] prob_up = 0.5*(1+np.sqrt(dt)/intra_sv*drift_rate) while (not crossed): # Generate nn steps iter += 1 if iter == 2 and switch: prob_up = 0.5*(1+np.sqrt(dt)/intra_sv*drift_rate_switch) position = ((rand(nn) < prob_up)*2 - 1) * step_size position[0] += y_0 position = np.cumsum(position) # Find boundary crossings cross_idx = np.where((position < 0) | (position > a))[0] drift = np.concatenate((drift, position)) if cross_idx.shape[0]>0: crossed = True else: # If not crossed, set last position as starting point # for next nn steps to continue drift y_0 = position[-1] #find the boundary interception y2 = position[cross_idx[0]] if cross_idx[0]!=0: y1 = position[cross_idx[0]-1] else: y1 = y_0 m = (y2 - y1)/dt # slope # y = m*x + b b = y2 - m*((iter-1)*nn+cross_idx[0])*dt # intercept if y2 < 0: rt = ((0 - b) / m) else: rt = ((a - b) / m) rts[i_sample] = (rt + start_delay[i_sample])*np.sign(y2) delay = start_delay[i_sample]/dt drifts.append(np.concatenate((np.ones(int(delay))*starting_points[i_sample], drift[:int(abs(rt)/dt)]))) return rts, drifts def pdf_with_params(rt, params): """Helper function that calls full_pdf and gets the parameters from the dict params. """ v = params['v']; V= params['sv']; z = params['z']; Z = params['sz']; t = params['t']; T = params['st']; a = params['a'] return hddm.wfpt.full_pdf(rt,v=v,V=V,a=a,z=z,Z=Z,t=t, T=T,err=1e-4, n_st=2, n_sz=2, use_adaptive=1, simps_err=1e-3) def _gen_rts_from_cdf(params, samples=1000): """Returns simulated RTs sampled from the inverse of the CDF. :Arguments: params : dict Parameter names and values. :Optional: samlpes : int How many samples to generate. :SeeAlso: gen_rts """ v = params['v']; V = params['sv']; z = params['z']; Z = params['sz']; t = params['t']; T = params['st']; a = params['a'] return hddm.likelihoods.wfpt.ppf(np.random.rand(samples), args=(v, V, a, z, Z, t, T)) def gen_rand_data(params=None, n_fast_outliers=0, n_slow_outliers=0, **kwargs): """Generate simulated RTs with random parameters. :Optional: params : dict Parameter names and values. If not supplied, takes random values. method : string method to generate samples the rest of the arguments are forwarded to kabuki.generate.gen_rand_data :Returns: data array with RTs parameter values """ if params is None: params = gen_rand_params() from numpy import inf # set valid param ranges bounds = {'a': (0, inf), 'z': (0, 1), 't': (0, inf), 'st': (0, inf), 'sv': (0, inf), 'sz': (0, 1) } # if 'share_noise' not in kwargs: # kwargs['share_noise'] = set(['a','v','t','st','sz','sv','z']) # Create RT data data, subj_params = kabuki.generate.gen_rand_data(hddm.likelihoods.Wfpt, params, check_valid_func=hddm.utils.check_params_valid, bounds=bounds, **kwargs) data = kabuki_data_to_hddm_data(data) #add outliers seed = kwargs.get('seed', None) data = add_outliers(data, n_fast=n_fast_outliers, n_slow=n_slow_outliers, seed=seed) return data, subj_params def add_outliers(data, n_fast, n_slow, seed=None): """add outliers to data. outliers are distrbuted randomly across condition. Input: data - data n_fast/n_slow - numberprobability of fast/slow outliers """ data = pd.DataFrame(data) n_outliers = n_fast + n_slow if n_outliers == 0: return data if seed is not None: np.random.seed(seed) #init outliers DataFrame idx = np.random.permutation(len(data))[:n_outliers] outliers = data.ix[idx].copy() #fast outliers outliers.rt[:n_fast] = np.random.rand(n_fast) * (min(abs(data['rt'])) - 0.1001) + 0.1001 #slow outliers outliers.rt[n_fast:] = np.random.rand(n_slow) * 2 + max(abs(data['rt'])) outliers.response = np.random.randint(0,2,n_outliers) #combine data with outliers data = pd.concat((data, outliers), ignore_index=True) return data HDDM-0.4/hddm/diag.py0000664000175000017500000002172312050455453015457 0ustar whykingwhyking00000000000000from __future__ import division from copy import copy import numpy as np import pymc as pm np.seterr(divide='ignore') from sys import stdout import hddm from scipy.stats import scoreatpercentile from time import time from operator import attrgetter def check_model(model, params_true, assert_=False, conf_interval = 95): """calculate the posterior estimate error if hidden parameters are known (e.g. when simulating data).""" print "checking estimation with %d confidence interval" % conf_interval fail = False nodes = sorted(model.stochastics, key=attrgetter('__name__')) nodes = filter(lambda x:x.shape == (), nodes) for node in nodes: if node.__name__ not in params_true: continue # Skip non-existent params name = node.__name__ trace = model.db.trace(name)[:] est = np.mean(trace) node_median = np.median(trace) truth = params_true[name] lb = (50 - conf_interval/2.) lb_score = scoreatpercentile(trace, lb) ub = (50 + conf_interval/2.) ub_score = scoreatpercentile(trace, ub) fell = np.sum(truth > trace)*1./len(trace) * 100 if lb_score==ub_score: fell = 50 print "%5s: Truth: %6.3f, mean: %6.3f, median: %6.3f, lb: %6.3f, ub: %6.3f, fell in: %.2f" % \ (name, truth, est, node_median, lb_score, ub_score, fell) if (fell < lb) or (fell > ub): fail = True print "the true value of %s is outside of the confidence interval !*!*!*!*!*!*!" % name if assert_: assert (fail==False) ok = not fail return ok def check_rejection(model, assert_=True): """check if the rejection ratio is not too high""" for node in model.stochastics: name = node.__name__ trace = node.trace()[:] rej = np.sum(np.diff(trace)==0) rej_ratio = rej*1.0/len(trace) print "rejection ratio for %s: %.3f" %(name, rej_ratio) if (rej_ratio < 0.5) or (rej_ratio > 0.8): msg = "%s still need to be tuned" % name if assert_: assert 1==0, msg else: print msg def test_params_on_data(params, data, include=(), depends_on=None, conf_interval=95): thin = 1 samples = 10000 burn = 10000 n_iter = burn + samples*thin stdout.flush() if depends_on is None: depends_on = {} if 'pi' in include or 'gamma' in include: m_hddm = hddm.HDDMContaminant(data, bias=True, depends_on=depends_on) else: m_hddm = hddm.HDDM(data, bias=True, include=include, depends_on=depends_on) model = m_hddm.mcmc() #[model.use_step_method(pm.Metropolis, x,proposal_sd=0.1) for x in model.stochastics] i_t = time() model.sample(n_iter, burn=burn, thin=thin) print "sampling took: %.2f seconds" % (time() - i_t) ok = True if check_model(model, params, assert_=False, conf_interval = conf_interval)==False: print "model checking failed. running again" stdout.flush() model.sample(n_iter, burn=burn, thin=thin) if check_model(model, params, assert_=False, conf_interval = conf_interval)==False: print "model checking failed again !!!!!!!!!!!!!!!!!!!!!!!" ok = False res = {} res['params'] = params res['data'] = data res['mc'] = model check_rejection(model, assert_ = False) check_correl(model) stdout.flush() return ok, res def run_accuracy_test(nTimes=20, include=(), stop_when_fail = True): """ run accuracy test nTime times""" n_data = 300 for i_time in range(nTimes): params = hddm.generate.gen_rand_params(include) data,temp = hddm.generate.gen_rand_data(params, samples=n_data) positive = sum(data['response']) print "generated %d data_points (%d positive %d negative)" % (len(data), positive, len(data) - positive) print "testing params: a:%.3f, t:%.3f, v:%.3f, z: %.3f, T: %.3f, V: %.3f Z: %.3f" \ % (params['a'], params['t'], params['v'], params['z'], params['st'], params['sv'], params['sz']) ok, res = test_params_on_data(params, data, include=include) if stop_when_fail and not ok: return res return {} def str_params(params): s = '' keys = params.keys() keys.sort(reverse=True) for name in keys: s = s + "%s: %.3f, " % (name, params[name]) s = s[:-2] + "\n" return s def gen_cond_data_and_params(n_data, n_conds = 3, include = ()): params = hddm.generate.gen_rand_params(include) params_set = {} params_true = copy(params) all_v = np.linspace(min(0,params['v']/2) , max(params['v']*2, 3), n_conds) del params_true['v'] for i in range(n_conds): params_set[i] = copy(params) params_set[i]['v'] = all_v[i] params_true['v(%d,)'%i] = all_v[i] cond_data, temp = hddm.generate.gen_rand_data(params_set, samples=int(n_data/n_conds)) positive = sum(cond_data['response']) print "generated %d data_points (%d positive %d negative)" % \ (len(cond_data), positive, len(cond_data) - positive) print "used params: %s" % str_params(params_true) stdout.flush() return cond_data, params_true def run_simple_test(nTimes=20, stop_when_fail = False): return run_accuracy_test(nTimes) def check_correl(model): nodes = filter(lambda x:x.shape == (), model.stochastics) threshold = 0.05 fail = False for node in nodes: t_lag = np.inf for lag in range(1,min(101, len(node.trace()[:])//2)): corr = pm.diagnostics.autocorr(node.trace()[:], lag) if corr <= threshold: t_lag= lag break if t_lag < np.inf: fail = True print "%s: correlation drop under %f after %d steps" %(node.__name__ , threshold, t_lag) else: print "%s: correlation don't drop under %f!!!!" %(node.__name__ , threshold) ok = not fail return ok def test_acc_full_intrp(include = (), n_conds = 6, use_db=False): from numpy.random import rand burn = 10000 thin = 1 n_samples = 10000 n_iter = n_samples*thin all_wp = [] all_wp = all_wp + [{'err': 1e-5, 'nT':3, 'nZ':3, 'use_adaptive':1, 'simps_err':1e-5}] all_wp = all_wp + [{'err': 1e-5, 'nT':2, 'nZ':2, 'use_adaptive':1, 'simps_err':1e-4}] all_wp = all_wp + [{'err': 1e-4, 'nT':2, 'nZ':2, 'use_adaptive':1, 'simps_err':1e-3}] initial_params = hddm.generate.gen_rand_params(include=include) full_params = copy(initial_params) params_set = {} v_0 = rand() all_v = np.linspace(v_0, max(4,v_0*n_conds), n_conds) for j in range(n_conds): params_set[j] = copy(initial_params) params_set[j]['v'] = all_v[j] full_params['v(%d,)'%j] = params_set[j]['v'] data, params = hddm.generate.gen_rand_data(params_set, samples=150) print "Using the following params: \n %s" % str_params(full_params) i_res={} i_res['params'] = copy(full_params) i_res['all_wp'] = all_wp i_res['data'] = data i_res['sample_time'] = [None]*len(all_wp) i_res['init_time'] = [None]*len(all_wp) i_res['burn_time'] = [None]*len(all_wp) i_res['stats'] = [None]*len(all_wp) i_res['logp'] = [None]*len(all_wp) i_res['dbname'] = [None]*len(all_wp) i_res['mc'] = [None]*len(all_wp) for i_params in range(len(all_wp)): print "working on model %d" % i_params model = hddm.model.HDDM(data, bias=True, wiener_params=all_wp[i_params], include = include, depends_on = {'v':['condition']})#, init_value=params) i_t = time() if use_db: dbname = 'speed.' + str(time()) + '.db' i_res['dbname'][i_params] = dbname[:] else: dbname = None nodes = model.create() mc = pm.MCMC(nodes) i_res['mc'][i_params] = mc [mc.use_step_method(pm.Metropolis, x, proposal_sd=0.5) for x in mc.stochastics] i_t = time() mc.sample(burn+1, burn) d_time = time() - i_t; i_res['burn_time'][i_params] = d_time print "burn phase took %f secs" % d_time i_t = time() mc.sample(n_iter, 0, thin=thin) d_time = time() - i_t; i_res['sample_time'][i_params] = d_time print "sampling took in %f secs" % d_time stdout.flush() check_model(mc, full_params, assert_=False, conf_interval = 95) check_rejection(mc, assert_=False) check_correl(mc) if dbname is not None: mc.db.commit() mc.db.close() return i_res def check_geweke(model, assert_=True): # Test for convergence using geweke method for param in model.group_params.itervalues(): geweke = np.array(pm.geweke(param)) if assert_: assert (np.any(np.abs(geweke[:,1]) < 2)), 'Chain of %s not properly converged'%param return False else: if np.any(np.abs(geweke[:,1]) > 2): print "Chain of %s not properly converged" % param return False return True HDDM-0.4/PKG-INFO0000664000175000017500000000125212054702235014352 0ustar whykingwhyking00000000000000Metadata-Version: 1.1 Name: HDDM Version: 0.4 Summary: HDDM is a python module that implements Hierarchical Bayesian estimation of Drift Diffusion Models. Home-page: http://github.com/hddm-devs/hddm Author: Thomas V. Wiecki, Imri Sofer, Michael J. Frank Author-email: thomas_wiecki@brown.edu License: UNKNOWN Description: UNKNOWN Platform: UNKNOWN Classifier: Development Status :: 4 - Beta Classifier: Environment :: Console Classifier: Operating System :: OS Independent Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: GNU General Public License (GPL) Classifier: Programming Language :: Python Classifier: Topic :: Scientific/Engineering