latools/helpers/plot.py
def histograms(dat, keys=None, bins=25, logy=False, cmap=None, ncol=4):
    """
    Plot histograms of all items in dat.

    Parameters
    ----------
    dat : dict
        Data in {key: array} pairs.
    keys : arra-like
        The keys in dat that you want to plot. If None,
        all are plotted.
    bins : int
        The number of bins in each histogram (default = 25)
    logy : bool
        If true, y axis is a log scale.
    cmap : dict
        The colours that the different items should be. If None,
        all are grey.

    Returns
    -------
    fig, axes
    """
    if keys is None:
        keys = dat.keys()

    ncol = int(ncol)
    nrow = calc_nrow(len(keys), ncol)

    fig, axs = plt.subplots(nrow, 4, figsize=[ncol * 2, nrow * 2])

    pn = 0
    for k, ax in zip(keys, axs.flat):
        tmp = nominal_values(dat[k])
        x = tmp[~np.isnan(tmp)]

        if cmap is not None:
            c = cmap[k]
        else:
            c = (0, 0, 0, 0.5)
        ax.hist(x, bins=bins, color=c)

        if logy:
            ax.set_yscale('log')
            ylab = '$log_{10}(n)$'
        else:
            ylab = 'n'

        ax.set_ylim(1, ax.get_ylim()[1])

        if ax.is_first_col():
            ax.set_ylabel(ylab)

        ax.set_yticklabels([])

        ax.text(.95, .95, k, ha='right', va='top', transform=ax.transAxes)

        pn += 1

    for ax in axs.flat[pn:]:
        ax.set_visible(False)
    fig.tight_layout()

    return fig, axs
def histograms(dat, keys=None, bins=25, logy=False, cmap=None, ncol=4):
    """
    Plot histograms of all items in dat.

    Parameters
    ----------
    dat : dict
        Data in {key: array} pairs.
    keys : arra-like
        The keys in dat that you want to plot. If None,
        all are plotted.
    bins : int
        The number of bins in each histogram (default = 25)
    logy : bool
        If true, y axis is a log scale.
    cmap : dict
        The colours that the different items should be. If None,
        all are grey.

    Returns
    -------
    fig, axes
    """
    if keys is None:
        keys = dat.keys()

    ncol = int(ncol)
    nrow = calc_nrow(len(keys), ncol)

    fig, axs = plt.subplots(nrow, 4, figsize=[ncol * 2, nrow * 2])

    pn = 0
    for k, ax in zip(keys, axs.flat):
        tmp = nominal_values(dat[k])
        x = tmp[~np.isnan(tmp)]

        if cmap is not None:
            c = cmap[k]
        else:
            c = (0, 0, 0, 0.5)
        ax.hist(x, bins=bins, color=c)

        if logy:
            ax.set_yscale('log')
            ylab = '$log_{10}(n)$'
        else:
            ylab = 'n'

        ax.set_ylim(1, ax.get_ylim()[1])

        if ax.is_first_col():
            ax.set_ylabel(ylab)

        ax.set_yticklabels([])

        ax.text(.95, .95, k, ha='right', va='top', transform=ax.transAxes)

        pn += 1

    for ax in axs.flat[pn:]:
        ax.set_visible(False)
    fig.tight_layout()

    return fig, axs
Plot histograms of all items in dat.

    Parameters
    ----------
    dat : dict
        Data in {key: array} pairs.
    keys : arra-like
        The keys in dat that you want to plot. If None,
        all are plotted.
    bins : int
        The number of bins in each histogram (default = 25)
    logy : bool
        If true, y axis is a log scale.
    cmap : dict
        The colours that the different items should be. If None,
        all are grey.

    Returns
    -------
    fig, axes