10 examples of 'bins in histogram python' in Python

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def bin(X, binsize = 1):
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    """Split X into bins and return the average of each bin as a new set of
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    measurements."""
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    if binsize == 1:
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        return X;
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    else:
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        extra = 0 if pl.size(X, axis = 0) % binsize == 0 else 1
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        dims = [i for i in pl.shape(X)]
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        dims[0] = dims[0] / binsize + extra
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        dims = tuple(dims)
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        X_binned = pl.zeros(dims)
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        for i in xrange(pl.size(X_binned, axis = 0)):
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            X_binned[i] = pl.mean(X[i * binsize:(i + 1) * binsize], axis = 0)
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        return X_binned

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def bin(X, nbins=20):
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    equa0 = (X <= 0.0)
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    equa1 = (X >= 1.0)
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    binned = []
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    groups = range(nbins+1)
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    for g1, g2 in zip(groups, groups[1:]):
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        l, u = g1/float(nbins), g2/float(nbins)
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        binned.append((l < X) & (X <= u))
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    retval = np.concatenate([equa0, equa1] + binned, axis=1)
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    return retval

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def py_histogram(int_vec):
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    hist = {}
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    for val in int_vec:
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        hist[val] = 1 + hist.get(val, 0)
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    return hist

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def histogramValues(values):
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    counts, binEdges = histogram(values, bins=40, density=True)
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    ys = list(counts)
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    xs = []
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    for i in range(len(binEdges) - 1):
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        xs.append((binEdges[i] + binEdges[i + 1]) / 2)
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    return xs, ys

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def bins(self):
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    raise NotImplementedError("bins is not implemented")

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def plot_histogram(values, num_bins=100):
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    """
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    Generates a plot of the histograms of grasps by probability of force closure
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    """
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    bin_edges = np.linspace(np.min(values), np.max(values), num_bins+1)
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    plt.figure()
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    n, bins, patches = plt.hist(values, bin_edges)

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def calculate_histogram(data, bins: int, signal: str):
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    stats = data.statistics['signals'][signal]
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    maximum, minimum = stats['max']['value'], stats['min']['value']
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    width = 3.5 * np.sqrt(stats['σ2']['value']) / (data.sample_count ** (1. / 3))
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    num_bins = bins if bins > 0 else ceil((maximum - minimum) / width)
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    hist = None
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    bin_edges = None
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    for data_chunk in data:
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        if bin_edges is None:
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            hist, bin_edges = np.histogram(data_chunk['signals'][signal]['value'],
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                                           range=(minimum, maximum), bins=num_bins)
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        else:
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            hist += np.histogram(data_chunk['signals'][signal]['value'], bins=bin_edges)[0]
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    return hist, bin_edges

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def fast_hist(a, b, n):
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    k = (a >= 0) & (a < n)
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    return np.bincount(n * a[k].astype(int) + b[k], minlength=n**2).reshape(n, n)

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def hist(data, title='histogram', bins=10, **args):
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    histFig = pyecharts.charts.Bar()
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    histFig.set_global_opts(title_opts=opts.TitleOpts(title=title))
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    y, x = np.histogram(data, bins=bins)
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    x = x.astype(int).astype(str)
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    xlabels = [x[i - 1] + '-' + x[i] for i in range(1, len(x))]
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    histFig.add_xaxis(xlabels)
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    histFig.add_yaxis(data.name, y.tolist(), **args)
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    result = histFig.render_notebook(
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    ) if Config['return_type'] == 'HTML' else histFig
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    return result

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def _histogram(image,
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               min,
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               max,
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               bins):
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    """
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    Delayed wrapping of NumPy's histogram
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    Also reformats the arguments.
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    """
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    return numpy.histogram(image, bins, (min, max))[0]