alexbw

# A = model.trans_distn.A.copy()

labels_norep, _ = pymouse.get_norep(np.hstack(model.stateseqs))
A = build_frequentist_statemap(labels_norep)

import spectral_clustering.spclust as sp
labels_norep, _ = pymouse.get_norep(np.hstack(model.stateseqs))
used_labels = np.argwhere(np.bincount(labels_norep).astype('float32')/len(labels_norep) > 0.001).ravel()
A = A[np.ix_(used_labels,used_labels)]

alexbw

_,roi = get_behavior(store_name, experiments=list(train_experiments), array_names=["rect_ROI"])
roi = roi['rect_ROI']
height,width = roi[train_experiments[0]]
Parallel(n_jobs=24)(delayed(pymouse.show_syllable)(labels,
                                           images, df,
                                           height=height,
                                           width=width,
                                           which_syllable=which_syllable,
                                           num_instances_to_grab=45,
                                           desired_length=120) for which_syllable in np.unique(labels));

alexbw

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alexbw

alexbw

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-9d64abf5b3b8> in <module>()
     11                                   kappa=kappa,
     12                                   train_experiments=train_experiments,
---> 13                                   test_experiments=test_experiments)

/home/dattalab/Code/pymouse/scripts/train_model_generalAR.py in hmm(out_file, params, store_name, train_experiments, test_experiments, datadimension, n_cpu, n_iter, max_frames_per_mouse, pca, use_ard, windowsize, featurefn, random_seed, model_dtype, **kwargs)
    182     samples = []
    183     for itr in progprint_xrange(n_iter):

alexbw

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alexbw

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

alexbw

https://plus.google.com/hangouts/_/guoxccx7jaijdnbofw453tim2ia

alexbw

function [tsOffsets, instantRate] = calcCrossCorrelogram(ts1, ts2, window)
    % INPUT:
    % ts1 - timestamp array, center of cross-correlogram
    % ts2 - timestamp array
    % window - the window of the timestamps to compute, e.g. [-0.1 0.1] for
    %           0.1 seconds around each spike in ts1.
    %
    % OUTPUT:
    % tsOffsets - the offsets from each spike in ts1 that has spikes nearby
    % instantRate - 1/ISI of each spike in ts1 that has spikes nearby

alexbw

// Setting up the Audio Session... 
	// Initialize and configure the audio session, and add an interuption listener
	AudioSessionInitialize(NULL, NULL, sessionInterruptionListener, self);
	
	UInt32 audioCategory = kAudioSessionCategory_PlayAndRecord;
	AudioSessionSetProperty(kAudioSessionProperty_AudioCategory, sizeof(audioCategory), &audioCategory);
	
	// Allow iPod audio to continue to play while the app is active.
	UInt32 flag = 1;
	AudioSessionSetProperty(kAudioSessionProperty_OverrideCategoryMixWithOthers, sizeof(flag), &flag);