Timeseries analysis for neuroscience data

There might be something not right about the last two figures here

http://nipy.org/nitime/examples/granger_fmri.html

Those are missing the body of the graph, which is all white.

I believe this is in the file doc/examples/granger_fmri.py.

I have times series with 166800 samples (raw MEG data).

alg.dpss_windows(166800, 4, 8)

fails. However, in Matlab it works.

any idea of how to fix this?

In nipype, tests are breaking in Python 2.7, 3.4, due to the @ operator:

./../../virtualenv/python2.7.15/lib/python2.7/site-packages/py/_path/local.py:668: in pyimport
    __import__(modname)
nipype/interfaces/nitime/__init__.py:5: in <module>
    from .analysis import (CoherenceAnalyzerInputSpec, CoherenceAnalyzerOutputSpec,
nipype/interfaces/nitime/analysis.py:28: in <module>
    package_check('nitime')
nipype/utils/misc.py:180: in package_check
    mod = __import__(pkg_name)
../../../virtualenv/python2.7.15/lib/python2.7/site-packages/nitime/__init__.py:26: in <module>
    from . import algorithms
../../../virtualenv/python2.7.15/lib/python2.7/site-packages/nitime/algorithms/__init__.py:62: in <module>
    from nitime.algorithms.event_related import *
E     File "/home/travis/virtualenv/python2.7.15/lib/python2.7/site-packages/nitime/algorithms/event_related.py", line 60
E       h = np.array(linalg.pinv(X.T @ X) @ X.T @ y.T)
E                                    ^
E   SyntaxError: invalid syntax

This error happens with numpy installed. It also happens when building in readthedocs projects having nitime as a requirement and autodocs enabled.

Collecting numpy (from nitime)
  Using cached numpy-1.10.4-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Collecting nitime
  Downloading nitime-0.6.tar.gz (10.0MB)
    100% |████████████████████████████████| 10.0MB 55kB/s 
    Complete output from command python setup.py egg_info:
    Traceback (most recent call last):
      File "<string>", line 20, in <module>
      File "/private/var/folders/hw/7bn8tjn96vd58k7sg1ptt82c0000gn/T/pip-build-l6NHiN/nitime/setup.py", line 17, in <module>
        exec(f.read())
      File "<string>", line 2, in <module>
      File "nitime/__init__.py", line 26, in <module>
        from . import algorithms
      File "nitime/algorithms/__init__.py", line 55, in <module>
        from nitime.algorithms.spectral import *
      File "nitime/algorithms/spectral.py", line 10, in <module>
        import numpy as np
    ImportError: No module named numpy

    ----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in /private/var/folders/hw/7bn8tjn96vd58k7sg1ptt82c0000gn/T/pip-build-l6NHiN/nitime

Here's a set of patches that make nitime imports faster and cleaner by deferring the matplotlib and scipy imports (in a few places) until they are actually needed.

Mostly, I want this so that I can import nitime.timeseries without pulling in matplotlib and scipy.

Without this PR wall time for imports is 1.2 - 1.9 seconds:

16:30@nitime(master)$ time python -c "import nitime" 

real    0m1.879s
user    0m0.870s
sys     0m0.415s

With this PR:

16:31@nitime(lazy-imports)$ time python -c "import nitime" 

real    0m0.425s
user    0m0.242s
sys     0m0.111s

Which is pretty damn good, considering that on this system:

16:57@nitime(lazy-imports)$ time  python -c "import numpy" 

real    0m0.385s
user    0m0.206s
sys     0m0.108s

(In particular - there's about a 300ms advantage of lazyloading numpy.testing.nosetools!)

Here are some import analyses - using this lazy loading saves us from importing ~500 modules up front.

In [2]: import sys; snn = set([k for k in sys.modules]); len(snn)
Out[2]: 461 # set of modules - no nitime

In [3]: import nitime; sn = set([k for k in sys.modules]); len(sn)
Out[3]: 629 # set of modules with nitime (this is 1102 without lazy loading)

In [4]: nitime.test(); snt = set([k for k in sys.modules]); len(sn)
Out[4]: 1540 # set of modules after nitime.test() (1542 without lazy loading)

The functionality of lazyimports.LazyImport is generic enough to allow the lazily imported module to act as the module in almost every way (tab completion, introspection for docstrings and sources) except reloading is not supported.

For skeptics - add a line such as bogus.parameter : True to the end of your ~/.matplotlib/matplotlibrc - which will cause a Bad Key user warning from matplotlib complaint on import. Then ::

In [1]: import sys

In [2]: import matplotlib.mlab as mlab

Bad key "bogus.parameter" on line 374 in
/home/pi/.matplotlib/matplotlibrc.
You probably need to get an updated matplotlibrc file from
http://matplotlib.sf.net/_static/matplotlibrc or from the matplotlib source
distribution

In [3]: mlab
Out[3]: <module 'matplotlib.mlab' from '.../site-packages/matplotlib/mlab.pyc'>

In [4]: mlab.
Display all 107 possibilities? (y or n)n

In [6]: [sys.modules.pop(k) for k in sys.modules.keys() if 'matplotlib' in k];

In [7]: from nitime.lazyimports import mlab

In [8]: mlab

Bad key "bogus.parameter" on line 374 in
/home/pi/.matplotlib/matplotlibrc.
You probably need to get an updated matplotlibrc file from
http://matplotlib.sf.net/_static/matplotlibrc or from the matplotlib source
distribution
Out[8]: <module 'matplotlib.mlab' from '.../site-packages/matplotlib/mlab.pyc'>

In [9]: mlab.
Display all 107 possibilities? (y or n)n

In particular - note that for the lazy case, the actual import of matplotlib did not happen until after In [8] - which imported the module and called the repr on it.

As a side note (on making reload() work): The following code (a bit more convoluted than what's in this PR) gets closer to being able to reload, but I haven't been able to figure out what machinery is missing to make it fully work. Perhaps @fperez has an idea, but it's not a big deal.

import nitime.descriptors as desc
from types import ModuleType as module

class LazyImport(module):
    def __init__(self, modname):
        #module.__init__(self,modname,"foo")
        self.__lazyname__= modname
        self.__name__= modname
    @desc.auto_attr # one-time property
    def __lazyimported__(self):
        name = module.__getattribute__(self,'__lazyname__')
        return __import__(name, fromlist=name.split('.'))
    def __getattribute__(self,x):
        return module.__getattribute__(self,'__lazyimported__').__getattribute__(x)
    def __repr__(self):
        return module.__getattribute__(self,'__lazyimported__').__repr__()

Dear all, When I run a script like that:

>>>sampling_rate=1000
>>>freq_idx_G
Out[7]: array([40, 41])
>>>G.frequencies.shape[0]
Out[8]: 513
>>>g1 = np.mean(G.causality_xy[:, :, freq_idx_G], -1)

it met the following memory error (freq_idx_G=):

---------------------------------------------------------------------------
MemoryError                               Traceback (most recent call last)
<ipython-input-6-b3dd332ebe13> in <module>()
----> 1 g1 = np.mean(G.causality_xy[:, :, freq_idx_G], -1)

/home/qdong/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/nitime/descriptors.pyc in __get__(self, obj, type)
    138         # Errors in the following line are errors in setting a
    139         # OneTimeProperty
--> 140         val = self.getter(obj)
    141 
    142         setattr(obj, self.name, val)

/home/qdong/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/nitime/analysis/granger.pyc in causality_xy(self)
    202     @desc.setattr_on_read
    203     def causality_xy(self):
--> 204         return self._dict2arr('gc_xy')
    205 
    206     @desc.setattr_on_read

/home/qdong/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/nitime/analysis/granger.pyc in _dict2arr(self, key)
    191         arr = np.empty((self._n_process,
    192                         self._n_process,
--> 193                         self.frequencies.shape[0]))
    194 
    195         arr.fill(np.nan)

MemoryError: 

Can anyone give me some tips? Thanks!

Running Sphinx v1.1.2 /Library/Frameworks/EPD64.framework/Versions/7.2/lib/python2.7/site-packages/matplotlib/init.py:908: UserWarning: This call to matplotlib.use() has no effect because the the backend has already been chosen; matplotlib.use() must be called before pylab, matplotlib.pyplot, or matplotlib.backends is imported for the first time.

if warn: warnings.warn(_use_error_msg) WARNING: extension 'ipython_console_highlighting' has no setup() function; is it really a Sphinx extension module? loading pickled environment... not yet created building [html]: targets for 71 source files that are out of date updating environment: 71 added, 0 changed, 0 removed /Users/arokem/projects/nitime/doc/sphinxext/docscrape.py:117: UserWarning: Unknown section Note warn("Unknown section %s" % key) /Users/arokem/projects/nitime/doc/sphinxext/docscrape.py:117: UserWarning: Unknown section Warning warn("Unknown section %s" % key) /Users/arokem/projects/nitime/doc/sphinxext/docscrape.py:117: UserWarning: Unknown section Example warn("Unknown section %s" % key) reading sources... [ 29%] api/generated/nitime.lazyimports
Exception occurred: File "/Library/Frameworks/EPD64.framework/Versions/7.2/lib/python2.7/site-packages/sphinx/environment.py", line 828, in read_doc pickle.dump(doctree, f, pickle.HIGHEST_PROTOCOL) PicklingError: Can't pickle <type 'module'>: attribute lookup builtin.module failed The full traceback has been saved in /var/folders/sf/3b6q6p1d7518rpb4882pzsxw0000gn/T/sphinx-err-9UpBlB.log, if you want to report the issue to the developers. Please also report this if it was a user error, so that a better error message can be provided next time. Either send bugs to the mailing list at http://groups.google.com/group/sphinx-dev/, or report them in the tracker at http://bitbucket.org/birkenfeld/sphinx/issues/. Thanks! make: *** [htmlonly] Error 1

This branch contains a major reorganization of algorithms.py as a sub-module of the library. The main idea is to change the layout of the library, making it slightly more developer-friendly. I am asking for a review of this, in the hopes of getting comments on the general structure. The idea is to adopt a similar structure for timeseries.py, utils.py, analysis.py and viz.py.

It also includes completion of 100% test coverage for almost all of the algorithms sub-module. Some of it is just smoke testing, but I have added quite a bit of actual tests for spectral and coherence, as well as for autoregressive.

The one bit that is still not entirely covered by the tests is algorithms.wavelet. I am not so sure how to use these functions. Maybe someone with a better idea (Kilian?) can take a look and add tests for this sub-module?

Hi,

The build fails with the following due to change in Sphinx's API change add_directive function. Log below:

$ sphinx-build doc html-no-exec  
Running Sphinx v3.2.0
/home/nilesh/ups/nitime/doc/conf.py:34: MatplotlibDeprecationWarning: 
The mpl_toolkits.axes_grid module was deprecated in Matplotlib 2.1 and will be removed two minor releases later. Use mpl_toolkits.axes_grid1 and mpl_toolkits.axisartist, which provide the same functionality instead.
  __import__(package, fromlist=parts)
WARNING: while setting up extension ipython_console_highlighting: extension 'ipython_console_highlighting' has no setup() function; is it really a Sphinx extension module?

Exception occurred:
  File "/home/nilesh/ups/nitime/doc/sphinxext/only_directives.py", line 40, in setup
    app.add_directive('htmlonly', html_only_directive, True, (0, 0, 0))
TypeError: add_directive() takes from 3 to 4 positional arguments but 5 were given
The full traceback has been saved in /tmp/sphinx-err-z9obztzm.log, if you want to report the issue to the developers.
Please also report this if it was a user error, so that a better error message can be provided next time.
A bug report can be filed in the tracker at <https://github.com/sphinx-doc/sphinx/issues>. Thanks!

This is an attempt to fix it along with adding in sphinx build to travis tests so as to ensure this works OK with future commits as well.

Under nitime/data/, there is a fmri_timeseries.csv file with 31 different areas. Can you give me more information about this file? For example, how did the data come from, or where did it come from?

Thank you!

with this PR, adding and subtracting values which aren't TimeArrays first converts them and gives them the unit of the time array. For example

In [1]: import nitime

In [2]: nitime.TimeArray(1) + 1 Out[2]: 2.0 s

In [3]: nitime.TimeArray(1, time_unit='ms') + 1 Out[3]: 2.0 ms

In [4]: nitime.TimeArray(1, time_unit='ms') + 1 + nitime.TimeArray(1) Out[4]: 1002.0 ms

In [5]: a = nitime.TimeArray(1)

In [6]: a Out[6]: 1.0 s

In [7]: a.convert_unit('ms')

In [8]: a Out[8]: 1000.0 ms

In [9]: a+1 Out[9]: 1001.0 ms

Hi,

In maintaining the NixOS package for nitime we noticed that the test test_FilterAnalyzer fails once we bump scipy to 1.8.0:

_____________________________ test_FilterAnalyzer ______________________________

    def test_FilterAnalyzer():
        """Testing the FilterAnalyzer """
        t = np.arange(np.pi / 100, 10 * np.pi, np.pi / 100)
        fast = np.sin(50 * t) + 10
        slow = np.sin(10 * t) - 20
    
        fast_mean = np.mean(fast)
        slow_mean = np.mean(slow)
    
        fast_ts = ts.TimeSeries(data=fast, sampling_rate=np.pi)
        slow_ts = ts.TimeSeries(data=slow, sampling_rate=np.pi)
    
        #Make sure that the DC is preserved
        f_slow = nta.FilterAnalyzer(slow_ts, ub=0.6)
        f_fast = nta.FilterAnalyzer(fast_ts, lb=0.6)
    
        npt.assert_almost_equal(f_slow.filtered_fourier.data.mean(),
                                slow_mean,
                                decimal=2)
    
        npt.assert_almost_equal(f_slow.filtered_boxcar.data.mean(),
                                slow_mean,
                                decimal=2)
    
        npt.assert_almost_equal(f_slow.fir.data.mean(),
                                slow_mean)
    
        npt.assert_almost_equal(f_slow.iir.data.mean(),
                                slow_mean)
    
        npt.assert_almost_equal(f_fast.filtered_fourier.data.mean(),
                                10)
    
        npt.assert_almost_equal(f_fast.filtered_boxcar.data.mean(),
                                10,
                                decimal=2)
    
        npt.assert_almost_equal(f_fast.fir.data.mean(),
                                10)
    
        npt.assert_almost_equal(f_fast.iir.data.mean(),
                                10)
    
        #Check that things work with a two-channel time-series:
        T2 = ts.TimeSeries(np.vstack([fast, slow]), sampling_rate=np.pi)
        f_both = nta.FilterAnalyzer(T2, ub=1.0, lb=0.1)
        #These are rather basic tests:
        npt.assert_equal(f_both.fir.shape, T2.shape)
>       npt.assert_equal(f_both.iir.shape, T2.shape)

Full build log available at https://hydra.nixos.org/log/12f43cyblp08zbjc5psd8ayxxmq3if72-python3.9-nitime-0.9.drv where all the python dependency versions can be seen. This is on an x86_64 linux system.

First of all, I still need to read more carefully the references, so I might be wrong.

In the Multitaper coherence estimation tutorial, the confidence interval are computed (t975_limit and t025_limit), but they are not printed or visualized in any way later. It turns out that t025_limit contains many negative values, but coherence is constrained to be within [0, 1].

Is there anything going wrong?

Hi,

I noticed that the following code returns freqs which is all 0s:

    freqs, d_psd = tsa.periodogram(ar_seq, Fs=1., normalize=False)

I believe it is this line (in algorithms/spectral.py) that is causing the issue:

freqs = np.linspace(0, Fs // 2, Fn)

Should it be Fs / 2 instead?

Version: nitime 0.9 Installed via conda

great code thanks may you clarify : will it work for multivariate time series prediction both regression and classification 1 where all values are continues values 2 or even will it work for multivariate time series where values are mixture of continues and categorical values for example 2 dimensions have continues values and 3 dimensions are categorical values

color        weight     gender  height  age  

1 black 56 m 160 34 2 white 77 f 170 54 3 yellow 87 m 167 43 4 white 55 m 198 72 5 white 88 f 176 32

Hello,

I installed nitime in general using the command window but afterwards was made aware that it has to be installed directly in Jupyter because otherwise it doesn't work there. However, when I try to install it in Jupyter (using: "! pip install nitime") , the kernel just remains busy and nothing happens (I gave it 3 hours). Weird thing is that I tried the same command to install another random package ("geocoder") en this immediately worked.

Does anyone know why nitime won't install?

Thanks in advance!

i'd like to perform harmonic analysis with two different p-values on the same signal. all other parameters the same. it's a huge waste to call utils.detect_lines twice i would think. FFT has to be done twice etc. is there a workaround where i can save partial results? how easy would it be to add a method which input multiple p-values? thanks!