Esempio n. 1
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def plot_bold_contrast(data_dir, num_groups, trial_duration, num_trials, p_b_e,
                       p_x_e, p_e_e, p_e_i, p_i_i, p_i_e):
    file_desc='wta.groups.%d.duration.%0.3f.p_b_e.%0.3f.p_x_e.%0.3f.p_e_e.%0.3f.p_e_i.%0.3f.p_i_i.%0.3f.p_i_e.%0.3f' %\
              (num_groups, trial_duration, p_b_e, p_x_e, p_e_e, p_e_i, p_i_i, p_i_e)
    file_prefix = os.path.join(data_dir, file_desc)
    trial_contrast = np.zeros([num_trials, 1])
    trial_max_bold = np.zeros(num_trials)
    trial_max_bold_exc = np.zeros(num_trials)
    for i in range(num_trials):
        file_name = '%s.trial.%d.h5' % (file_prefix, i)
        print('opening %s' % file_name)
        data = FileInfo(file_name)
        trial_contrast[i] = abs(data.input_freq[0] - data.input_freq[1]) / sum(
            data.input_freq)
        trial_max_bold[i] = np.max(data.voxel_rec['y'][0])
        trial_max_bold_exc[i] = np.max(data.voxel_exc_rec['y'][0])

    clf = LinearRegression()
    clf.fit(trial_contrast, trial_max_bold)
    a = clf.coef_[0]
    b = clf.intercept_

    fig = plt.figure()
    plt.plot(trial_contrast, trial_max_bold, 'x')
    x_min = np.min(trial_contrast)
    x_max = np.max(trial_contrast)
    plt.plot([x_min, x_max], [a * x_min + b, a * x_max + b], '--')
    plt.xlabel('Input Contrast')
    plt.ylabel('Max BOLD')
    plt.show()

    clf = LinearRegression()
    clf.fit(trial_contrast, trial_max_bold_exc)
    a = clf.coef_[0]
    b = clf.intercept_

    fig = plt.figure()
    plt.plot(trial_contrast, trial_max_bold_exc, 'x')
    x_min = np.min(trial_contrast)
    x_max = np.max(trial_contrast)
    plt.plot([x_min, x_max], [a * x_min + b, a * x_max + b], '--')
    plt.xlabel('Input Contrast')
    plt.ylabel('Max BOLD')
    plt.title('Exc only')
    plt.show()
Esempio n. 2
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def test_ridge_vs_lstsq():
    """On alpha=0., Ridge and OLS yield the same solution."""

    # we need more samples than features
    n_samples, n_features = 5, 4
    np.random.seed(0)
    y = np.random.randn(n_samples)
    X = np.random.randn(n_samples, n_features)

    ridge = Ridge(alpha=0.)
    ols = LinearRegression()

    ridge.fit(X, y)
    ols.fit (X, y)
    assert_almost_equal(ridge.coef_, ols.coef_)

    ridge.fit(X, y, fit_intercept=False)
    ols.fit (X, y, fit_intercept=False)
    assert_almost_equal(ridge.coef_, ols.coef_)
Esempio n. 3
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def test_ridge_vs_lstsq():
    """On alpha=0., Ridge and OLS yield the same solution."""

    # we need more samples than features
    n_samples, n_features = 5, 4
    np.random.seed(0)
    y = np.random.randn(n_samples)
    X = np.random.randn(n_samples, n_features)

    ridge = Ridge(alpha=0.)
    ols = LinearRegression()

    ridge.fit(X, y)
    ols.fit(X, y)
    assert_almost_equal(ridge.coef_, ols.coef_)

    ridge.fit(X, y, fit_intercept=False)
    ols.fit(X, y, fit_intercept=False)
    assert_almost_equal(ridge.coef_, ols.coef_)
Esempio n. 4
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def plot_bold_contrast(data_dir, num_groups, trial_duration, num_trials, p_b_e,p_x_e,p_e_e,p_e_i,p_i_i,p_i_e):
    file_desc='wta.groups.%d.duration.%0.3f.p_b_e.%0.3f.p_x_e.%0.3f.p_e_e.%0.3f.p_e_i.%0.3f.p_i_i.%0.3f.p_i_e.%0.3f' %\
              (num_groups, trial_duration, p_b_e, p_x_e, p_e_e, p_e_i, p_i_i, p_i_e)
    file_prefix=os.path.join(data_dir,file_desc)
    trial_contrast=np.zeros([num_trials,1])
    trial_max_bold=np.zeros(num_trials)
    trial_max_bold_exc=np.zeros(num_trials)
    for i in range(num_trials):
        file_name='%s.trial.%d.h5' % (file_prefix, i)
        print('opening %s' % file_name)
        data=FileInfo(file_name)
        trial_contrast[i]=abs(data.input_freq[0]-data.input_freq[1])/sum(data.input_freq)
        trial_max_bold[i]=np.max(data.voxel_rec['y'][0])
        trial_max_bold_exc[i]=np.max(data.voxel_exc_rec['y'][0])

    clf=LinearRegression()
    clf.fit(trial_contrast,trial_max_bold)
    a=clf.coef_[0]
    b=clf.intercept_

    fig=plt.figure()
    plt.plot(trial_contrast, trial_max_bold, 'x')
    x_min=np.min(trial_contrast)
    x_max=np.max(trial_contrast)
    plt.plot([x_min,x_max],[a*x_min+b,a*x_max+b],'--')
    plt.xlabel('Input Contrast')
    plt.ylabel('Max BOLD')
    plt.show()

    clf=LinearRegression()
    clf.fit(trial_contrast,trial_max_bold_exc)
    a=clf.coef_[0]
    b=clf.intercept_

    fig=plt.figure()
    plt.plot(trial_contrast, trial_max_bold_exc, 'x')
    x_min=np.min(trial_contrast)
    x_max=np.max(trial_contrast)
    plt.plot([x_min,x_max],[a*x_min+b,a*x_max+b],'--')
    plt.xlabel('Input Contrast')
    plt.ylabel('Max BOLD')
    plt.title('Exc only')
    plt.show()
Esempio n. 5
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def create_bold_report(reports_dir,
                       trial_contrast,
                       trial_max_bold,
                       trial_max_rate,
                       trial_rt,
                       regenerate_plot=True):

    report_info = Struct()

    clf = LinearRegression()
    clf.fit(trial_contrast, trial_max_bold)
    a = clf.coef_[0]
    b = clf.intercept_
    report_info.bold_contrast_slope = a
    report_info.bold_contrast_intercept = b
    report_info.bold_contrast_r_sqr = clf.score(trial_contrast, trial_max_bold)

    furl = 'img/contrast_bold.png'
    fname = os.path.join(reports_dir, furl)
    report_info.contrast_bold_url = furl
    if regenerate_plot or not os.path.exists(fname):
        fig = plt.figure()
        plt.plot(trial_contrast, trial_max_bold, 'x')
        x_min = np.min(trial_contrast)
        x_max = np.max(trial_contrast)
        plt.plot([x_min, x_max], [a * x_min + b, a * x_max + b], '--')
        plt.xlabel('Input Contrast')
        plt.ylabel('Max BOLD')
        save_to_png(fig, fname)
        save_to_eps(fig, os.path.join(reports_dir, 'img/contrast_bold.eps'))
        plt.close()

    clf = LinearRegression()
    clf.fit(trial_max_rate, trial_max_bold)
    a = clf.coef_[0]
    b = clf.intercept_
    report_info.bold_firing_rate_slope = a
    report_info.bold_firing_rate_intercept = b
    report_info.bold_firing_rate_r_sqr = clf.score(trial_max_rate,
                                                   trial_max_bold)

    furl = 'img/firing_rate_bold.png'
    fname = os.path.join(reports_dir, furl)
    report_info.firing_rate_bold_url = furl
    if regenerate_plot or not os.path.exists(fname):
        fig = plt.figure()
        plt.plot(trial_max_rate, trial_max_bold, 'x')
        x_min = np.min(trial_max_rate)
        x_max = np.max(trial_max_rate)
        plt.plot([x_min, x_max], [a * x_min + b, a * x_max + b], '--')
        plt.xlabel('Max Firing Rate')
        plt.ylabel('Max BOLD')
        save_to_png(fig, fname)
        save_to_eps(fig, os.path.join(reports_dir, 'img/firing_rate_bold.eps'))
        plt.close()

    clf = LinearRegression()
    clf.fit(trial_rt, trial_max_bold)
    a = clf.coef_[0]
    b = clf.intercept_
    report_info.bold_rt_slope = a
    report_info.bold_rt_intercept = b
    report_info.bold_rt_r_sqr = clf.score(trial_rt, trial_max_bold)

    furl = 'img/response_time_bold.png'
    fname = os.path.join(reports_dir, furl)
    report_info.response_time_bold_url = furl
    if regenerate_plot or not os.path.exists(fname):
        fig = plt.figure()
        plt.plot(trial_rt, trial_max_bold, 'x')
        x_min = np.min(trial_rt)
        x_max = np.max(trial_rt)
        plt.plot([x_min, x_max], [a * x_min + b, a * x_max + b], '--')
        plt.xlabel('Response Time')
        plt.ylabel('Max BOLD')
        save_to_png(fig, fname)
        save_to_eps(fig, os.path.join(reports_dir,
                                      'img/response_time_bold.eps'))
        plt.close()

    return report_info
Esempio n. 6
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def create_wta_network_report(file_prefix,
                              contrast_range,
                              num_trials,
                              reports_dir,
                              edesc,
                              regenerate_network_plots=True,
                              regenerate_trial_plots=True):

    make_report_dirs(reports_dir)

    report_info = Struct()
    report_info.edesc = edesc
    report_info.trials = []

    (data_dir, data_file_prefix) = os.path.split(file_prefix)

    total_trials = num_trials * len(contrast_range)
    trial_contrast = np.zeros([total_trials, 1])
    trial_max_bold = np.zeros(total_trials)
    trial_max_input = np.zeros([total_trials, 1])
    trial_max_rate = np.zeros([total_trials, 1])
    trial_rt = np.zeros([total_trials, 1])

    report_info.contrast_accuracy = np.zeros([len(contrast_range), 1])

    max_bold = []
    for j, contrast in enumerate(contrast_range):
        contrast_max_bold = []
        report_info.contrast_accuracy[j] = 0.0
        for i in range(num_trials):
            file_name = '%s.contrast.%0.4f.trial.%d.h5' % (file_prefix,
                                                           contrast, i)
            print('opening %s' % file_name)
            data = FileInfo(file_name)

            if not i:
                report_info.wta_params = data.wta_params
                report_info.voxel_params = data.voxel_params
                report_info.num_groups = data.num_groups
                report_info.trial_duration = data.trial_duration
                report_info.background_rate = data.background_rate
                report_info.stim_start_time = data.stim_start_time
                report_info.stim_end_time = data.stim_end_time
                report_info.network_group_size = data.network_group_size
                report_info.background_input_size = data.background_input_size
                report_info.task_input_size = data.task_input_size

            trial_idx = j * num_trials + i
            trial = create_trial_report(
                data,
                reports_dir,
                contrast,
                i,
                regenerate_plots=regenerate_trial_plots)
            trial_contrast[trial_idx] = trial.input_contrast
            if not math.isnan(trial.max_bold):
                trial_max_bold[trial_idx] = trial.max_bold
            else:
                if j > 1 and max_bold[j - 1] < 1.0 and max_bold[j - 2] < 1.0:
                    trial_max_bold[trial_idx] = max_bold[j - 1] + (
                        max_bold[j - 1] - max_bold[j - 2])
                elif j > 0 and max_bold[j - 1] < 1.0:
                    trial_max_bold[trial_idx] = max_bold[j - 1] * 2.0
                else:
                    trial_max_bold[trial_idx] = 1.0

            report_info.contrast_accuracy[j] += trial.correct

            trial_max_input[trial_idx] = trial.max_input
            trial_max_rate[trial_idx] = trial.max_rate
            trial_rt[trial_idx] = trial.rt
            report_info.trials.append(trial)

            contrast_max_bold.append(trial_max_bold[trial_idx])
        report_info.contrast_accuracy[j] /= float(num_trials)
        mean_contrast_bold = np.mean(np.array(contrast_max_bold))
        max_bold.append(mean_contrast_bold)

    clf = LinearRegression()
    clf.fit(trial_max_input, trial_max_rate)
    a = clf.coef_[0]
    b = clf.intercept_
    report_info.io_slope = a
    report_info.io_intercept = b
    report_info.io_r_sqr = clf.score(trial_max_input, trial_max_rate)

    furl = 'img/input_output_rate.png'
    fname = os.path.join(reports_dir, furl)
    report_info.input_output_rate_url = furl
    if regenerate_network_plots or not os.path.exists(fname):
        fig = plt.figure()
        plt.plot(trial_max_input, trial_max_rate, 'x')
        x_min = np.min(trial_max_input)
        x_max = np.max(trial_max_input)
        plt.plot([x_min, x_max], [x_min, x_max], '--')
        plt.plot([x_min, x_max], [a * x_min + b, a * x_max + b], '--')
        plt.xlabel('Max Input Rate')
        plt.ylabel('Max Population Rate')
        save_to_png(fig, fname)
        save_to_eps(fig, os.path.join(reports_dir,
                                      'img/input_output_rate.eps'))
        plt.close()

    report_info.bold = create_bold_report(
        reports_dir,
        trial_contrast,
        trial_max_bold,
        trial_max_rate,
        trial_rt,
        regenerate_plot=regenerate_network_plots)

    report_info.roc = create_roc_report(
        file_prefix,
        report_info.num_groups,
        contrast_range,
        num_trials,
        reports_dir,
        regenerate_plot=regenerate_network_plots)

    #create report
    template_file = 'wta_network_instance.html'
    env = Environment(loader=FileSystemLoader(TEMPLATE_DIR))
    template = env.get_template(template_file)

    output_file = 'wta_network.%s.html' % data_file_prefix
    fname = os.path.join(reports_dir, output_file)
    stream = template.stream(rinfo=report_info)
    stream.dump(fname)

    return report_info
Esempio n. 7
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def create_bold_report(reports_dir, trial_contrast, trial_max_bold, trial_max_rate, trial_rt, regenerate_plot=True):

    report_info=Struct()

    clf=LinearRegression()
    clf.fit(trial_contrast,trial_max_bold)
    a=clf.coef_[0]
    b=clf.intercept_
    report_info.bold_contrast_slope=a
    report_info.bold_contrast_intercept=b
    report_info.bold_contrast_r_sqr=clf.score(trial_contrast,trial_max_bold)

    furl='img/contrast_bold.png'
    fname=os.path.join(reports_dir, furl)
    report_info.contrast_bold_url=furl
    if regenerate_plot or not os.path.exists(fname):
        fig=plt.figure()
        plt.plot(trial_contrast, trial_max_bold, 'x')
        x_min=np.min(trial_contrast)
        x_max=np.max(trial_contrast)
        plt.plot([x_min,x_max],[a*x_min+b,a*x_max+b],'--')
        plt.xlabel('Input Contrast')
        plt.ylabel('Max BOLD')
        save_to_png(fig, fname)
        save_to_eps(fig, os.path.join(reports_dir, 'img/contrast_bold.eps'))
        plt.close()

    clf=LinearRegression()
    clf.fit(trial_max_rate,trial_max_bold)
    a=clf.coef_[0]
    b=clf.intercept_
    report_info.bold_firing_rate_slope=a
    report_info.bold_firing_rate_intercept=b
    report_info.bold_firing_rate_r_sqr=clf.score(trial_max_rate,trial_max_bold)

    furl='img/firing_rate_bold.png'
    fname=os.path.join(reports_dir, furl)
    report_info.firing_rate_bold_url=furl
    if regenerate_plot or not os.path.exists(fname):
        fig=plt.figure()
        plt.plot(trial_max_rate, trial_max_bold, 'x')
        x_min=np.min(trial_max_rate)
        x_max=np.max(trial_max_rate)
        plt.plot([x_min,x_max],[a*x_min+b,a*x_max+b],'--')
        plt.xlabel('Max Firing Rate')
        plt.ylabel('Max BOLD')
        save_to_png(fig, fname)
        save_to_eps(fig, os.path.join(reports_dir, 'img/firing_rate_bold.eps'))
        plt.close()

    clf=LinearRegression()
    clf.fit(trial_rt,trial_max_bold)
    a=clf.coef_[0]
    b=clf.intercept_
    report_info.bold_rt_slope=a
    report_info.bold_rt_intercept=b
    report_info.bold_rt_r_sqr=clf.score(trial_rt,trial_max_bold)

    furl='img/response_time_bold.png'
    fname=os.path.join(reports_dir, furl)
    report_info.response_time_bold_url=furl
    if regenerate_plot or not os.path.exists(fname):
        fig=plt.figure()
        plt.plot(trial_rt, trial_max_bold, 'x')
        x_min=np.min(trial_rt)
        x_max=np.max(trial_rt)
        plt.plot([x_min,x_max],[a*x_min+b,a*x_max+b],'--')
        plt.xlabel('Response Time')
        plt.ylabel('Max BOLD')
        save_to_png(fig, fname)
        save_to_eps(fig, os.path.join(reports_dir, 'img/response_time_bold.eps'))
        plt.close()

    return report_info
Esempio n. 8
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def create_wta_network_report(file_prefix, contrast_range, num_trials, reports_dir, edesc, regenerate_network_plots=True,
                              regenerate_trial_plots=True):

    make_report_dirs(reports_dir)

    report_info=Struct()
    report_info.edesc=edesc
    report_info.trials=[]

    (data_dir, data_file_prefix) = os.path.split(file_prefix)

    total_trials=num_trials*len(contrast_range)
    trial_contrast=np.zeros([total_trials,1])
    trial_max_bold=np.zeros(total_trials)
    trial_max_input=np.zeros([total_trials,1])
    trial_max_rate=np.zeros([total_trials,1])
    trial_rt=np.zeros([total_trials,1])

    report_info.contrast_accuracy=np.zeros([len(contrast_range),1])

    max_bold=[]
    for j,contrast in enumerate(contrast_range):
        contrast_max_bold=[]
        report_info.contrast_accuracy[j]=0.0
        for i in range(num_trials):
            file_name='%s.contrast.%0.4f.trial.%d.h5' % (file_prefix, contrast, i)
            print('opening %s' % file_name)
            data=FileInfo(file_name)

            if not i:
                report_info.wta_params=data.wta_params
                report_info.voxel_params=data.voxel_params
                report_info.num_groups=data.num_groups
                report_info.trial_duration=data.trial_duration
                report_info.background_rate=data.background_rate
                report_info.stim_start_time=data.stim_start_time
                report_info.stim_end_time=data.stim_end_time
                report_info.network_group_size=data.network_group_size
                report_info.background_input_size=data.background_input_size
                report_info.task_input_size=data.task_input_size

            trial_idx=j*num_trials+i
            trial = create_trial_report(data, reports_dir, contrast, i, regenerate_plots=regenerate_trial_plots)
            trial_contrast[trial_idx]=trial.input_contrast
            if not math.isnan(trial.max_bold):
                trial_max_bold[trial_idx]=trial.max_bold
            else:
                if j>1 and max_bold[j-1]<1.0 and max_bold[j-2]<1.0:
                    trial_max_bold[trial_idx]=max_bold[j-1]+(max_bold[j-1]-max_bold[j-2])
                elif j>0 and max_bold[j-1]<1.0:
                    trial_max_bold[trial_idx]=max_bold[j-1]*2.0
                else:
                    trial_max_bold[trial_idx]=1.0

            report_info.contrast_accuracy[j]+=trial.correct

            trial_max_input[trial_idx]=trial.max_input
            trial_max_rate[trial_idx]=trial.max_rate
            trial_rt[trial_idx]=trial.rt
            report_info.trials.append(trial)

            contrast_max_bold.append(trial_max_bold[trial_idx])
        report_info.contrast_accuracy[j]/=float(num_trials)
        mean_contrast_bold=np.mean(np.array(contrast_max_bold))
        max_bold.append(mean_contrast_bold)

    clf=LinearRegression()
    clf.fit(trial_max_input,trial_max_rate)
    a=clf.coef_[0]
    b=clf.intercept_
    report_info.io_slope=a
    report_info.io_intercept=b
    report_info.io_r_sqr=clf.score(trial_max_input,trial_max_rate)

    furl='img/input_output_rate.png'
    fname=os.path.join(reports_dir, furl)
    report_info.input_output_rate_url=furl
    if regenerate_network_plots or not os.path.exists(fname):
        fig=plt.figure()
        plt.plot(trial_max_input, trial_max_rate, 'x')
        x_min=np.min(trial_max_input)
        x_max=np.max(trial_max_input)
        plt.plot([x_min,x_max],[x_min,x_max],'--')
        plt.plot([x_min,x_max],[a*x_min+b,a*x_max+b],'--')
        plt.xlabel('Max Input Rate')
        plt.ylabel('Max Population Rate')
        save_to_png(fig, fname)
        save_to_eps(fig, os.path.join(reports_dir, 'img/input_output_rate.eps'))
        plt.close()

    report_info.bold=create_bold_report(reports_dir, trial_contrast, trial_max_bold, trial_max_rate, trial_rt,
        regenerate_plot=regenerate_network_plots)

    report_info.roc=create_roc_report(file_prefix, report_info.num_groups, contrast_range, num_trials, reports_dir,
        regenerate_plot=regenerate_network_plots)

    #create report
    template_file='wta_network_instance.html'
    env = Environment(loader=FileSystemLoader(TEMPLATE_DIR))
    template=env.get_template(template_file)

    output_file='wta_network.%s.html' % data_file_prefix
    fname=os.path.join(reports_dir,output_file)
    stream=template.stream(rinfo=report_info)
    stream.dump(fname)

    return report_info