Python Report.data_pylab方法代码示例

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def interval_histogram(group, configuration, saccades):    #@UnusedVariable
    interval = saccades[:]['time_passed']

    edges = (2.0 ** numpy.array(range(1, 21))) / 1000
    # centers = (edges[1:]+edges[:-1])/2
    h, edges_ = numpy.histogram(interval, bins=edges, normed=True) #@UnusedVariable
    
    bin_width = numpy.diff(edges);
    hn = h / bin_width;
    
    print 'h', h
    print 'hn', hn
    print 'edges', edges
    print 'width', bin_width
                                
    r = Report()
    attach_description(r, description)
    
    node_id = 'inthist'
    with r.data_pylab(node_id) as pylab:
        pylab.loglog(bin_width, h, 'x-')
        pylab.title('not normalized')
        pylab.xlabel('interval bin width (s)')
        pylab.ylabel('density (s)')
        
    node_id = 'inthistn'
    with r.data_pylab(node_id) as pylab:
        pylab.loglog(bin_width, hn, 'x-')
        pylab.title('normalized by bin width')
        pylab.xlabel('interval bin width (s)')
        pylab.ylabel('density (s)')
        
        
    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def sample_var_time_correlation(
        sample, expdata, configuration, saccades, #@UnusedVariable
        variables, delays, type='pearson'):
    
    # all together
    R, P, labels = get_correlation_matrix(saccades, variables, delays,
                                          type)
    
    #  Significance 
    S = P < 0.01 
    
    nvars = len(variables)
    Rhalf = R[:nvars, :]
    Phalf = P[:nvars, :]
    Shalf = S[:nvars, :]
    
    ylabels = labels[:nvars]
    
    r = Report()
    attach_description(r, create_description(variables, delays, type))
    with r.data_pylab('correlation') as pylab:
        draw_correlation_figure(pylab, labels, ylabels, Rhalf)

    rshow = lambda x: "%+.2f" % x
    r.table('correlation_values', values_to_strings(Rhalf, rshow),
            cols=labels, rows=ylabels, caption="%s coefficient" % type)    
    r.table('pvalues', values_to_strings(Phalf, pvalue_format),
            cols=labels, rows=ylabels, caption="p-values")    

    with r.data_pylab('significance') as pylab:
        draw_significance_figure(pylab, labels, ylabels, Shalf)
    
    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def hist_plots(d):
    # TO
    vars = [ ('C', d.C, {}),
             ('y', cov2corr(d.y_cov, False), {}),
             ('y_dot', cov2corr(d.y_dot_cov, False), {}),
             ('y_dot_sign', cov2corr(d.y_dot_sign_cov, False), {}) ] 

    r = Report()
    f = r.figure(cols=5)
    
    for var in vars:
        label = var[0]
        x = var[1]

        nid = "hist_%s" % label
        with r.data_pylab(nid) as pylab:
            pylab.hist(x.flat, bins=128)
        f.sub(nid, 'histogram of correlation of %s' % label)
            
        order = scale_score(x)
        r.data('order%s' % label, order).display('posneg').add_to(f, 'ordered')

        nid = "hist2_%s" % label
        with r.data_pylab(nid) as pylab:
            pylab.plot(x.flat, order.flat, '.', markersize=0.2)
            pylab.xlabel(label)
            pylab.ylabel('order')
        f.sub(nid, 'histogram of correlation of %s' % label)
        
        h = create_histogram_2d(d.C, x, resolution=128)
        r.data('h2d_%s' % label, numpy.flipud(h.T)).display('scale').add_to(f)
        
    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def create_report(data, image, outdir):
    r = Report('%s_stats' % image)
    
    xcorr = data['results']
    lags = data['lags']
    T = lags * (1.0 / 60) * 1000
    
    mean_xcorr = numpy.mean(xcorr, axis=0)
    min_xcorr = numpy.min(xcorr, axis=0)
    max_xcorr = numpy.max(xcorr, axis=0)
    
    with r.data_pylab('some') as pylab:
        
        for i in range(0, 1000, 50):
            pylab.plot(T, xcorr[i, :], 'x-', label='%d' % i)
    
        pylab.axis([T[0], T[-1], -0.5, 1])
        pylab.xlabel('delay (ms)')
        pylab.ylabel('autocorrelation')
        pylab.legend()
    
    with r.data_pylab('mean_xcorr') as pylab:
        pylab.plot(T, mean_xcorr, 'x-')
        
        pylab.plot([T[0], T[-1]], [0, 0], 'k-')
        pylab.plot([0, 0], [-0.5, 1], 'k-')
        pylab.axis([T[0], T[-1], -0.5, 1.1])
        
        pylab.xlabel('delay (ms)')
        pylab.ylabel('autocorrelation')
        
        
    with r.data_pylab('various') as pylab:
        pylab.plot(T, mean_xcorr, 'gx-', label='mean')
        pylab.plot(T, min_xcorr, 'bx-', label='min')
        pylab.plot(T, max_xcorr, 'rx-', label='max')
        
        pylab.plot([T[0], T[-1]], [0, 0], 'k-')
        pylab.plot([0, 0], [-0.5, 1], 'k-')
        pylab.axis([T[0], T[-1], -0.5, 1.1])
        
        pylab.xlabel('delay (ms)')
        pylab.ylabel('autocorrelation')
        pylab.legend()
        
    f = r.figure()
    f.sub('some', caption='Autocorrelation of some receptors')
    f.sub('mean_xcorr', caption='Mean autocorrelation')
    
    f.sub('various', caption='Mean,min,max')
    
    filename = os.path.join(outdir, r.id + '.html')
    resources = os.path.join(outdir, 'images')
    print 'Writing to %s' % filename
    r.to_html(filename, resources)
    
    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def main():
    N = 100
    num_svds = 8

    radius_deg = 180

    kernels = [identity, linear01_sat, pow3_sat, pow7_sat]
#    kernels = [linear01_sat, pow3_sat, pow7_sat]

    r = Report('eig analysis')
#    warps_desc = ", ".join(['%.2f' % x for x in warps])
    caption = """ This figure shows that on S^1 things can be warped easily.
    The initial distribution of {N} points, with radius {radius_deg}.
    """.format(**locals())

    f = r.figure(caption=caption)
    mime = 'application/pdf'
    figsize = (4, 3)
    with r.data_pylab('kernels', mime=mime, figsize=figsize) as pylab:

        for kernel in kernels:
            x = np.linspace(-1, +1, 256)
            y = kernel(x)
            pylab.plot(x, y, label=kernel.__name__)
        pylab.axis([-1, 1, -1, 1])
        pylab.xlabel('Cosine between orientations')
        pylab.ylabel('Correlation')
        pylab.legend(loc='lower right')

    r.last().add_to(f, caption='Correlation kernels')

    for ndim in [2, 3]:
        S = get_distribution(ndim, N, radius_deg)
        C = cosines_from_directions(S)
        D = distances_from_directions(S)
        assert np.degrees(D.max()) <= 2 * radius_deg

        with r.data_pylab('svds%d' % ndim,
                          mime=mime, figsize=figsize) as pylab:
            for kernel in kernels:
                Cw = kernel(C)
                # TODO: 
                # Cw = cos(kernel(D))
                s = svds(Cw, num_svds)
                pylab.semilogy(s, 'x-', label=kernel.__name__)
            pylab.legend(loc='center right')
        r.last().add_to(f,
            caption='Singular value for different kernels (ndim=%d)' % ndim)

    filename = 'cbc_demos/kernels.html'
    print("Writing to %r." % filename)
    r.to_html(filename)

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def group_var_joint(group, configuration, saccades, #@UnusedVariable
                    var1, delay1, var2, delay2):    
    
    var1delay = get_delay_desc_string(delay1)
    var2delay = get_delay_desc_string(delay2)
    
    r = Report()
    attach_description(r, description.format(var1=var1, var2=var2,
                            var1delay=var1delay, var2delay=var2delay))
    
    node_id = 'joint_%s%d_%s%d' % (var1.id, delay1, var2.id, delay2)
    with r.data_pylab(node_id) as pylab:
    
        colors = ['r', 'g', 'b', 'm', 'k'] * 50        
        for sample, saccades_for_sample in iterate_over_samples(saccades): #@UnusedVariable
            x = saccades_for_sample[var1.field]
            y = saccades_for_sample[var2.field]
            x, y = get_delayed(x, delay1, y, delay2)
            
            color = colors.pop()            
            pylab.plot(x, y, "%s." % color, markersize=MS)
            
        pylab.axis([var1.interesting[0], var1.interesting[1],
                    var2.interesting[0], var2.interesting[1]]
                    )
         
        pylab.xlabel('%s (%s)' % (var1.name, var1.unit))
        pylab.ylabel('%s (%s)' % (var2.name, var2.unit))
        
    node_id += "_log"
    with r.data_pylab(node_id) as pylab:
    
        colors = ['r', 'g', 'b', 'm', 'k'] * 50        
        for sample, saccades_for_sample in iterate_over_samples(saccades): #@UnusedVariable
            x = saccades_for_sample[var1.field]
            y = saccades_for_sample[var2.field]
            x, y = get_delayed(x, delay1, y, delay2)
            
            color = colors.pop()            
            pylab.loglog(x, y, "%s." % color, markersize=MS)
            
        pylab.axis([var1.interesting[0], var1.interesting[1],
                    var2.interesting[0], var2.interesting[1]]
                    )
         
        pylab.xlabel('%s (%s)' % (var1.name, var1.unit))
        pylab.ylabel('%s (%s)' % (var2.name, var2.unit))
        
    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def group_var_hist(group, configuration, saccades, variable): #@UnusedVariable
    lb = variable.interesting[0]
    ub = variable.interesting[1]
    
    x = saccades[variable.field]
    
    if variable.mod:
        M = variable.interesting[1]
        x = numpy.fmod(x + M, M)

    hist, bin_edges = numpy.histogram(x, bins=variable.density_bins,
                range=variable.interesting, normed=True)

    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
    
    r = Report()
    attach_description(r, description.format(var=variable, ub=ub, lb=lb))

    with r.data_pylab('histogram') as pylab:
        pylab.plot(bin_centers, hist, 'b-')
        
        pylab.ylabel('density')
        pylab.xlabel('%s (%s)' % (variable.name, variable.unit))
        
        pylab.axis([lb, ub, 0, variable.density_max_y])
        
    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def sample_var_hist(sample, expdata, configuration, #@UnusedVariable
                    saccades, variable):
    lb = variable.interesting[0]
    ub = variable.interesting[1]
    
    x = saccades[variable.field]
    if variable.mod:
        M = variable.interesting[1]
        x = numpy.fmod(x + M, M)
    
    # TODO: we don't strictly enforce the bounds and we do not compute
    # how many are left out
    hist, bin_edges = numpy.histogram(x, bins=variable.density_bins,
                range=variable.interesting, normed=True)

    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
     
    r = Report()
    attach_description(r, description.format(var=variable, ub=ub, lb=lb))
    with r.data_pylab('histogram') as pylab:
        pylab.plot(bin_centers, hist, 'b-')
        
        pylab.ylabel('density')
        pylab.xlabel('%s (%s)' % (variable.name, variable.unit))
        
        pylab.axis([lb, ub, 0, variable.density_max_y])
    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def plot_simulated_sample_trajectories(
                    sample, exp_data, configuration, saccades): #@UnusedVariable
    x = [0]
    y = [0]
    theta = 0 
    for saccade in saccades:
        dt = saccade['time_passed']
        xp = x[-1] + numpy.cos(theta) * dt * v
        yp = y[-1] + numpy.sin(theta) * dt * v
        
        x.append(xp)
        y.append(yp)
        
        theta += numpy.radians(saccade['amplitude']) * saccade['sign']
        
    r = Report()
    attach_description(r, description)
    
    with r.data_pylab('simulated_trajectory') as pylab:
        pylab.plot(x, y, 'b-')
        pylab.xlabel('x position (m)') 
        pylab.ylabel('y position (m)')
        pylab.axis('equal')
        
    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def plot_raw_trajectories(sample, exp_data): #@UnusedVariable
    thetas = numpy.radians(exp_data[:]['orientation'])
    T = exp_data[:]['timestamp']
    
    x = [0]
    y = [0]
    
    dt = T[1] - T[0]  
    for i in range(len(thetas)):
        theta = thetas[i]
        xp = x[-1] + numpy.cos(theta) * dt * v
        yp = y[-1] + numpy.sin(theta) * dt * v
        
        x.append(xp)
        y.append(yp)        
        
    r = Report()
    attach_description(r, description)
    with r.data_pylab('simulated_trajectory') as pylab:
        pylab.plot(x, y, 'b-') 
        pylab.xlabel('x position (m)') 
        pylab.ylabel('y position (m)')
        pylab.axis('equal')

    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def group_sign_hist(group, configuration, saccades): #@UnusedVariable
    r = Report()
    attach_description(r, description)

    left_percentage = []
    for sample, saccades_for_sample in iterate_over_samples(saccades): #@UnusedVariable
        
        sign = saccades_for_sample['sign']
        left, = numpy.nonzero(sign == +1)
        
        perc = len(left) * 100.0 / len(sign)
        
        left_percentage.append(perc)

    left_percentage = numpy.array(left_percentage) 
    N = len(left_percentage)
    with r.data_pylab('sign_hist') as pylab:
        R = range(N)
         
        right_percentage = -left_percentage + 100
        
        pylab.bar(left=R, height=left_percentage, color='b')
        pylab.bar(left=R, height=right_percentage, bottom=left_percentage,
                  color='#faacb6')
        
        pylab.plot([0], [0])
        pylab.ylabel('percentage of left turns')
        pylab.xlabel('sample')
        pylab.axis([0, N, 0, 100])
        
        
    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def compute_general_statistics(id, db, samples, interval_function,
                               signal, signal_component):
    r = Report(id)
    
    x = get_all_data_for_signal(db, samples, interval_function,
                                signal, signal_component)
    
    limit = 0.3
    
    perc = [0.001, limit, 1, 10, 25, 50, 75, 90, 99, 100 - limit, 100 - 0.001]
    xp = map(lambda p: "%.3f" % scipy.stats.scoreatpercentile(x, p), perc)
    
    lower = scipy.stats.scoreatpercentile(x, limit)
    upper = scipy.stats.scoreatpercentile(x, 100 - limit)
    
    f = r.figure()
    
    with r.data_pylab('histogram') as pylab:
        bins = numpy. linspace(lower, upper, 100)
        pylab.hist(x, bins=bins)
        
    f.sub('histogram')

    
    labels = map(lambda p: "%.3f%%" % p, perc)
    
    
    r.table("percentiles", data=[xp], cols=labels, caption="Percentiles")
    
    r.table("stats", data=[[x.mean(), x.std()]], cols=['mean', 'std.dev.'],
            caption="Other statistics")

    print "Computing correlation..."
    corr, lags = xcorr(x, maxlag=20)
    print "...done."

    with r.data_pylab('cross_correlation') as pylab:
        delta = (1.0 / 60) * lags * 1000;
        pylab.plot(delta, corr, 'o-')
        pylab.axis([min(delta), max(delta), -0.7, 1.1])
        pylab.xlabel('delay (ms)')
    
    f = r.figure()
    f.sub('cross_correlation')

     
    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def create_report_delayed(exp_id, delayed, description):

    delays = numpy.array(sorted(delayed.keys()))

    r = Report(exp_id)
    r.text("description", description)

    f = r.figure(cols=3)

    # max and sum of correlation for each delay
    # corr_max = []
    corr_mean = []

    for delay in delays:
        data = delayed[delay]

        a = data["action_image_correlation"]

        id = "delay%d" % delay

        # rr = r.node('delay%d' % delay)
        r.data(id, a).data_rgb("retina", add_reflines(posneg(values2retina(a))))

        corr_mean.append(numpy.abs(a).mean())

        caption = "delay: %d (max: %.3f, sum: %f)" % (delay, numpy.abs(a).max(), numpy.abs(a).sum())
        f.sub(id, caption=caption)

    timestamp2ms = lambda x: x * (1.0 / 60) * 1000

    peak = numpy.argmax(corr_mean)
    peak_ms = timestamp2ms(delays[peak])
    with r.data_pylab("mean") as pylab:
        T = timestamp2ms(delays)
        pylab.plot(T, corr_mean, "o-")
        pylab.ylabel("mean correlation field")
        pylab.xlabel("delay (ms) ")

        a = pylab.axis()

        pylab.plot([0, 0], [a[2], a[3]], "k-")

        y = a[2] + (a[3] - a[2]) * 0.1
        pylab.text(+5, y, "causal", horizontalalignment="left")
        pylab.text(-5, y, "non causal", horizontalalignment="right")

        pylab.plot([peak_ms, peak_ms], [a[2], max(corr_mean)], "b--")

        y = a[2] + (a[3] - a[2]) * 0.2
        pylab.text(peak_ms + 10, y, "%d ms" % peak_ms, horizontalalignment="left")

    f = r.figure("stats")
    f.sub("mean")

    a = delayed[int(delays[peak])]["action_image_correlation"]
    r.data_rgb("best_delay", add_reflines(posneg(values2retina(a))))

    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def sample_var_joint(sample, expdata, configuration, saccades, #@UnusedVariable
                     var1, delay1, var2, delay2):
    x = saccades[var1.field]
    y = saccades[var2.field]
    x, y = get_delayed(x, delay1, y, delay2)

    var1delay = get_delay_desc_string(delay1)
    var2delay = get_delay_desc_string(delay2)
    
    r = Report()
    attach_description(r, description.format(var1=var1, var2=var2,
                                             var1delay=var1delay,
                                             var2delay=var2delay))
    node_id = 'joint_%s%d_%s%d' % (var1.id, delay1, var2.id, delay2)
    with r.data_pylab(node_id) as pylab:
            
        pylab.plot(x, y, "b.", markersize=MS)
            
        pylab.axis([var1.interesting[0], var1.interesting[1],
                    var2.interesting[0], var2.interesting[1]]
                    )
         
        pylab.xlabel('%s (%s)' % (var1.name, var1.unit))
        pylab.ylabel('%s (%s)' % (var2.name, var2.unit))
        
    node_id += '_log'
    with r.data_pylab(node_id) as pylab:
            
        pylab.loglog(x, y, "b.", markersize=MS)
            
        pylab.axis([var1.interesting[0], var1.interesting[1],
                    var2.interesting[0], var2.interesting[1]]
                    )
         
        pylab.xlabel('%s (%s)' % (var1.name, var1.unit))
        pylab.ylabel('%s (%s)' % (var2.name, var2.unit))
    
    return r

# 需要导入模块: from reprep import Report [as 别名]
# 或者: from reprep.Report import data_pylab [as 别名]
def raw_theta_hist(sample, exp_data): #@UnusedVariable
    thetas = numpy.fmod(exp_data[:]['orientation'] + 360, 360)    
        
    r = Report()
    attach_description(r, description)
    with r.data_pylab('simulated_trajectory') as pylab:
        pylab.hist(thetas, bins=90, normed=True) 
        pylab.xlabel('orientation (degrees)') 
        pylab.ylabel('density')
        # TODO: choose ymax
        a = pylab.axis()
        pylab.axis([0, 360, 0, a[3]])

    return r