plot_utils.py 50.9 KB
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# -*- coding: utf-8 -*-
"""
Created on Thu May 05 13:29:12 2016

@author: Suhas Somnath
"""
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from __future__ import division # int/int = float
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from warnings import warn
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import os
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import h5py
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import scipy
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import matplotlib.pyplot as plt
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from matplotlib.colors import LinearSegmentedColormap
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from mpl_toolkits.axes_grid1 import ImageGrid
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import numpy as np
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from ..analysis.utils.be_loop import loop_fit_function
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from ..io.hdf_utils import reshape_to_Ndims, get_formatted_labels
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def set_tick_font_size(axes, font_size):
    """
    Sets the font size of the ticks in the provided axes

    Parameters
    ----------
    axes : matplotlib.pyplot.axis object or list of axis objects
        axes to set font sizes
    font_size : unigned int
        Font size
    """

    def __set_axis_tick(axis):
        """
        Sets the font sizes to the x and y axis in the given axis object

        Parameters
        ----------
        axis : matplotlib.pyplot.axis object
            axis to set font sizes
        """
        for tick in axis.xaxis.get_major_ticks():
            tick.label.set_fontsize(font_size)
        for tick in axis.yaxis.get_major_ticks():
            tick.label.set_fontsize(font_size)

    if hasattr(axes, '__iter__'):
        for axis in axes:
            __set_axis_tick(axis)
    else:
        __set_axis_tick(axes)

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def cmap_jet_white_center():
    """
    Generates the jet colormap with a white center

    Returns
    -------
    white_jet : matplotlib.colors.LinearSegmentedColormap object
        color map object that can be used in place of plt.cm.jet
    """
    # For red - central column is like brightness
    # For blue - last column is like brightness
    cdict = {'red': ((0.00, 0.0, 0.0),
                     (0.30, 0.0, 0.0),
                     (0.50, 1.0, 1.0),
                     (0.90, 1.0, 1.0),
                     (1.00, 0.5, 1.0)),
             'green': ((0.00, 0.0, 0.0),
                       (0.10, 0.0, 0.0),
                       (0.42, 1.0, 1.0),
                       (0.58, 1.0, 1.0),
                       (0.90, 0.0, 0.0),
                       (1.00, 0.0, 0.0)),
             'blue': ((0.00, 0.0, 0.5),
                      (0.10, 1.0, 1.0),
                      (0.50, 1.0, 1.0),
                      (0.70, 0.0, 0.0),
                      (1.00, 0.0, 0.0))
             }
    return LinearSegmentedColormap('white_jet', cdict)
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def cmap_hot_desaturated():
    hot_desaturated = [(1, (255, 76, 76, 255)),
                       (0.857, (107, 0, 0, 255)),
                       (0.714, (255, 96, 0, 255)),
                       (0.571, (255, 255, 0, 255)),
                       (0.429, (0, 127, 0, 255)),
                       (0.285, (0, 255, 255, 255)),
                       (0.143, (0, 0, 91, 255)),
                       (0, (71, 71, 219, 255))]

    cdict = {'red': tuple([(dist, colors[0]/255.0, colors[0]/255.0) for (dist, colors) in hot_desaturated][::-1]),
             'green': tuple([(dist, colors[1]/255.0, colors[1]/255.0) for (dist, colors) in hot_desaturated][::-1]),
             'blue': tuple([(dist, colors[2]/255.0, colors[2]/255.0) for (dist, colors) in hot_desaturated][::-1])}

    return LinearSegmentedColormap('hot_desaturated', cdict)



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def discrete_cmap(num_bins, base_cmap=plt.cm.jet):
    """
    Create an N-bin discrete colormap from the specified input map

    Parameters
    ----------
    num_bins : unsigned int
        Number of discrete bins
    base_cmap : matplotlib.colors.LinearSegmentedColormap object
        Base color map to discretize

    Returns
    -------
    new_cmap : matplotlib.colors.LinearSegmentedColormap object
        Discretized color map

    Credits
    -------
    Jake VanderPlas
    License: BSD-style
    """

    base = plt.cm.get_cmap(base_cmap)
    color_list = base(np.linspace(0, 1, num_bins))
    cmap_name = base.name + str(num_bins)
    return base.from_list(cmap_name, color_list, num_bins)

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def plotLoopFitNGuess(Vdc, ds_proj_loops, ds_guess, ds_fit, title=''):
    '''
    Plots the loop guess, fit, source projected loops for a single cycle

    Parameters
    ----------
    Vdc - 1D float numpy array
        DC offset vector (unshifted)
    ds_proj_loops - 2D numpy array
        Projected loops arranged as [position, Vdc]
    ds_guess - 1D compound numpy array
        Loop guesses arranged as [position]
    ds_fit - 1D compound numpy array
        Loop fits arranged as [position]
    title - (Optional) String / unicode
        Title for the figure

    Returns
    ----------
    fig - matplotlib.pyplot.figure object
        Figure handle
    axes - 2D array of matplotlib.pyplot.axis handles
        handles to axes in the 2d figure
    '''
    shift_ind = int(-1 * len(Vdc) / 4)
    Vdc_shifted = np.roll(Vdc, shift_ind)

    num_plots = np.min([5, int(np.sqrt(ds_proj_loops.shape[0]))])
    fig, axes = plt.subplots(nrows=num_plots, ncols=num_plots, figsize=(18, 18))
    positions = np.linspace(0, ds_proj_loops.shape[0] - 1, num_plots ** 2, dtype=np.int)
    for ax, pos in zip(axes.flat, positions):
        ax.plot(Vdc, ds_proj_loops[pos, :], 'k', label='Raw')
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        ax.plot(Vdc_shifted, loop_fit_function(Vdc_shifted, np.array(list(ds_guess[pos]))), 'g', label='guess')
        ax.plot(Vdc_shifted, loop_fit_function(Vdc_shifted, np.array(list(ds_fit[pos]))), 'r--', label='Fit')
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        ax.set_xlabel('V_DC (V)')
        ax.set_ylabel('PR (a.u.)')
        ax.set_title('Loop ' + str(pos))
    ax.legend()
    fig.suptitle(title)
    fig.tight_layout()

    return fig, axes
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###############################################################################

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def rainbowPlot(ax, ao_vec, ai_vec, num_steps=32, cmap=plt.cm.jet, **kwargs):
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    """
    Plots the input against the output waveform (typically loops).
    The color of the curve changes as a function of time using the jet colorscheme

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    Parameters
    ----------
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    ax : axis handle
        Axis to plot the curve
    ao_vec : 1D float numpy array
        vector that forms the X axis
    ai_vec : 1D float numpy array
        vector that forms the Y axis
    num_steps : unsigned int (Optional)
        Number of discrete color steps
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    cmap : matplotlib.colors.LinearSegmentedColormap object
        Colormap to be used
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    """
    pts_per_step = int(len(ai_vec) / num_steps)
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    for step in range(num_steps - 1):
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        ax.plot(ao_vec[step * pts_per_step:(step + 1) * pts_per_step],
                ai_vec[step * pts_per_step:(step + 1) * pts_per_step],
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                color=cmap(255 * step / num_steps), **kwargs)
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    # plot the remainder:
    ax.plot(ao_vec[(num_steps - 1) * pts_per_step:],
            ai_vec[(num_steps - 1) * pts_per_step:],
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            color=cmap(255 * num_steps / num_steps), **kwargs)
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    """
    CS3=plt.contourf([[0,0],[0,0]], range(0,310),cmap=plt.cm.jet)
    fig.colorbar(CS3)"""


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def plot_line_family(ax, x_axis, line_family, line_names=None, label_prefix='Line', label_suffix='', cmap=plt.cm.jet, **kwargs):
    """
    Plots a family of lines with a sequence of colors

    Parameters
    ----------
    ax : axis handle
        Axis to plot the curve
    x_axis : array-like
        Values to plot against
    line_family : 2D numpy array
        family of curves arranged as [curve_index, features]
    line_names : array-like
        array of string or numbers that represent the identity of each curve in the family
    label_prefix : string / unicode
        prefix for the legend (before the index of the curve)
    label_suffix : string / unicode
        suffix for the legend (after the index of the curve)
    cmap : matplotlib.colors.LinearSegmentedColormap object
        Colormap to be used
    """
    num_lines = line_family.shape[0]

    if line_names is None:
        line_names = ['{} {} {}'.format(label_prefix, line_ind, label_suffix) for line_ind in range(num_lines)]
    else:
        if len(line_names) != num_lines:
            warn('Line names of different length compared to provided dataset')
            line_names = ['{} {} {}'.format(label_prefix, line_ind, label_suffix) for line_ind in range(num_lines)]

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    for line_ind in range(num_lines):
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        ax.plot(x_axis, line_family[line_ind],
                label=line_names[line_ind],
                color=cmap(int(255 * line_ind / (num_lines - 1))), **kwargs)



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def plot_map(axis, data, stdevs=2, show_colorbar=False, **kwargs):
    """
    Plots a 2d map with a tight z axis, with or without color bars.
    Note that the direction of the y axis is flipped if the color bar is required

    Parameters
    ----------
    axis : matplotlib.pyplot.axis object
        Axis to plot this map onto
    data : 2D real numpy array
        Data to be plotted
    stdevs : unsigned int (Optional. Default = 2)
        Number of standard deviations to consider for plotting
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    Returns
    -------
    """
    data_mean = np.mean(data)
    data_std = np.std(data)
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    im = axis.imshow(data, interpolation='none',
                     vmin=data_mean - stdevs * data_std,
                     vmax=data_mean + stdevs * data_std,
                     **kwargs)
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    axis.set_aspect('auto')

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    return im
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###############################################################################

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def plotLoops(excit_wfm, h5_loops, h5_pos=None, central_resp_size=None,
              evenly_spaced=True, plots_on_side=5, rainbow_plot=True,
              x_label='', y_label='', subtitles='Eigenvector', title=None):
    """
    Plots loops from up to 25 evenly spaced positions

    Parameters
    -----------
    excit_wfm : 1D numpy float array
        Excitation waveform in the time domain
    h5_loops : float HDF5 dataset reference or 2D numpy array
        Dataset containing data arranged as (pixel, time)
    h5_pos : HDF5 dataset reference or 2D numpy array
        Dataset containing position indices
    central_resp_size : (optional) unsigned integer
        Number of responce sample points from the center of the waveform to show in plots. Useful for SPORC
    evenly_spaced : boolean
        Evenly spaced positions or first N positions
    plots_on_side : unsigned int
        Number of plots on each side
    rainbow_plot : (optional) Boolean
        Plot the lines as a function of spectral index (eg. time)
    x_label : (optional) String
        X Label for all plots
    y_label : (optional) String
        Y label for all plots
    subtitles : (optional) String
        prefix for title over each plot
    title : (optional) String
        Main plot title

    Returns
    ---------
    fig, axes
    """

    plots_on_side = min(abs(plots_on_side), 5)
    num_pos = h5_loops.shape[0]
    sq_num_plots = min(plots_on_side, int(round(num_pos ** 0.5)))
    if evenly_spaced:
        chosen_pos = np.linspace(0, num_pos - 1, sq_num_plots ** 2, dtype=int)
    else:
        chosen_pos = np.arange(sq_num_plots ** 2, dtype=int)

    fig, axes = plt.subplots(nrows=sq_num_plots, ncols=sq_num_plots, figsize=(12, 12))
    axes_lin = axes.flat

    cent_ind = int(0.5 * h5_loops.shape[1])
    if central_resp_size:
        sz = int(0.5 * central_resp_size)
        l_resp_ind = cent_ind - sz
        r_resp_ind = cent_ind + sz
    else:
        l_resp_ind = 0
        r_resp_ind = h5_loops.shape[1]

    for count, posn in enumerate(chosen_pos):
        if rainbow_plot:
            rainbowPlot(axes_lin[count], excit_wfm[l_resp_ind:r_resp_ind], h5_loops[posn, l_resp_ind:r_resp_ind])
        else:
            axes_lin[count].plot(excit_wfm[l_resp_ind:r_resp_ind], h5_loops[posn, l_resp_ind:r_resp_ind])

        if type(h5_pos) != type(None):
            # print 'Row ' + str(h5_pos[posn,1]) + ' Col ' + str(h5_pos[posn,0])
            axes_lin[count].set_title('Row ' + str(h5_pos[posn, 1]) + ' Col ' + str(h5_pos[posn, 0]), fontsize=12)
        else:
            axes_lin[count].set_title(subtitles + ' ' + str(posn), fontsize=12)

        if count % sq_num_plots == 0:
            axes_lin[count].set_ylabel(y_label, fontsize=12)
        if count >= (sq_num_plots - 1) * sq_num_plots:
            axes_lin[count].set_xlabel(x_label, fontsize=12)
        axes_lin[count].axis('tight')
        axes_lin[count].set_aspect('auto')
        axes_lin[count].ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
    if title:
        fig.suptitle(title, fontsize=14)
    plt.tight_layout()
    return fig, axes
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def plotVSsnapshots(resp_mat, title='', stdevs=2, save_path=None):
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    """
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    Plots the spatial distribution of the response at evenly spaced UDVS steps
    
    Parameters
    -------------
    resp_mat : 3D numpy array
        SHO responses arranged as [udvs_step, rows, cols]
    title : (Optional) String
        Super title for the plots - Preferably the group name
    stdevs : (Optional) string
        Number of standard deviations from the mean to be used to clip the color axis
    save_path : (Optional) String
        Absolute path to write the figure to
        
    Returns
    ----------
    None
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    """
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    num_udvs = resp_mat.shape[2]
    if num_udvs >= 9:
        tot_plots = 9
    elif num_udvs >= 4:
        tot_plots = 4
    else:
        tot_plots = 1
    delta_pos = int(np.ceil(num_udvs/tot_plots)) 
    
    fig, axes = plt.subplots(nrows=int(tot_plots**0.5),ncols=int(tot_plots**0.5),
                             sharex=True, sharey=True, figsize=(12, 12)) 
    if tot_plots > 1:    
        axes_lin = axes.reshape(tot_plots)
    else:
        axes_lin = axes
    
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    for count, posn in enumerate(range(0,num_udvs, delta_pos)):
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        snapshot = np.squeeze(resp_mat[:,:,posn])
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        amp_mean = np.mean(snapshot) 
        amp_std = np.std(snapshot)
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        ndims = len(snapshot.shape)
        if ndims == 2:
            axes_lin[count].imshow(snapshot, vmin=amp_mean-stdevs*amp_std, vmax=amp_mean+stdevs*amp_std)
        elif ndims == 1:
            np.clip(snapshot,amp_mean-stdevs*amp_std,amp_mean+stdevs*amp_std,snapshot)
            axes_lin[count].plot(snapshot)
        axes_lin[count].axis('tight')
        axes_lin[count].set_aspect('auto')
        axes_lin[count].set_title('UDVS Step #' + str(posn))
    
    fig.suptitle(title)
    plt.tight_layout()
    if save_path:
        fig.savefig(save_path, format='png', dpi=300)

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def plotSpectrograms(eigenvectors, num_comps=4, title='Eigenvectors', xlabel='Step', stdevs=2,
                     show_colorbar=True):
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    """
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    Plots the provided spectrograms from SVD V vector

    Parameters:
    -------------
    eigenvectors : 3D numpy complex matrices
        Eigenvectors rearranged as - [row, col, component]


    xaxis : 1D real numpy array
        The vector to plot against
    num_comps : int
        Number of components to plot
    title : String
        Title to plot above everything else
    xlabel : String
        Label for x axis
    stdevs : int
        Number of standard deviations to consider for plotting

    Returns:
    ---------
    fig, axes
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    """
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    import matplotlib.pyplot as plt
    fig_h, fig_w = (4, 4 + show_colorbar * 1.00)
    p_rows = int(np.ceil(np.sqrt(num_comps)))
    p_cols = int(np.floor(num_comps / p_rows))
    fig201, axes201 = plt.subplots(p_rows, p_cols, figsize=(p_cols * fig_w, p_rows * fig_h))
    fig201.subplots_adjust(hspace=0.4, wspace=0.4)
    fig201.canvas.set_window_title(title)

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    for index in range(num_comps):
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        cur_map = np.transpose(eigenvectors[index, :, :])
        ax = axes201.flat[index]
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        mean = np.mean(cur_map)
        std = np.std(cur_map)
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        ax.imshow(cur_map, cmap='jet',
                  vmin=mean - stdevs * std,
                  vmax=mean + stdevs * std)
        ax.set_title('Eigenvector: %d' % (index + 1))
        ax.set_aspect('auto')
        ax.set_xlabel(xlabel)
        ax.axis('tight')

    return fig201, axes201


###############################################################################

def plotBEspectrograms(eigenvectors, num_comps=4, title='Eigenvectors', xlabel='UDVS Step', stdevs=2):
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    """
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    Plots the provided spectrograms from SVD V vector

    Parameters:
    -------------
    eigenvectors : 3D numpy complex matrices
        Eigenvectors rearranged as - [row, col, component]


    xaxis : 1D real numpy array
        The vector to plot against
    num_comps : int
        Number of components to plot
    title : String
        Title to plot above everything else
    xlabel : String
        Label for x axis
    stdevs : int
        Number of standard deviations to consider for plotting

    Returns:
    ---------
    fig, axes
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    """
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    fig201, axes201 = plt.subplots(2, num_comps, figsize=(4 * num_comps, 8))
    fig201.subplots_adjust(hspace=0.4, wspace=0.4)
    fig201.canvas.set_window_title(title)

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    for index in range(num_comps):
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        cur_map = np.transpose(eigenvectors[index, :, :])
        axes = [axes201.flat[index], axes201.flat[index + num_comps]]
        funcs = [np.abs, np.angle]
        labels = ['Amplitude', 'Phase']
        for func, lab, ax in zip(funcs, labels, axes):
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            amp_mean = np.mean(func(cur_map))
            amp_std = np.std(func(cur_map))
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            ax.imshow(func(cur_map), cmap='inferno',
                      vmin=amp_mean - stdevs * amp_std,
                      vmax=amp_mean + stdevs * amp_std)
            ax.set_title('Eigenvector: %d - %s' % (index + 1, lab))
            ax.set_aspect('auto')
        ax.set_xlabel(xlabel)

    return fig201, axes201


###############################################################################

def plotBEeigenvectors(eigenvectors, num_comps=4, xlabel=''):
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    """
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    Plots the provided spectrograms from SVD V vector

    Parameters:
    -------------
    eigenvectors : 3D numpy complex matrices
        Eigenvectors rearranged as - [row, col, component]


    xaxis : 1D real numpy array
        The vector to plot against
    num_comps : int
        Number of components to plot
    title : String
        Title to plot above everything else
    xlabel : String
        Label for x axis
    stdevs : int
        Number of standard deviations to consider for plotting

    Returns:
    ---------
    fig, axes
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    """
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    funcs = [np.abs, np.angle]
    labels = ['Amplitude', 'Phase']

    fig201, axes201 = plt.subplots(len(funcs), num_comps, figsize=(num_comps * 4, 4 * len(funcs)))
    fig201.subplots_adjust(hspace=0.4, wspace=0.4)
    fig201.canvas.set_window_title("Eigenvectors")

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    for index in range(num_comps):
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        cur_map = eigenvectors[index, :]
        #         axes = [axes201.flat[index], axes201.flat[index+num_comps], axes201.flat[index+2*num_comps], axes201.flat[index+3*num_comps]]
        axes = [axes201.flat[index], axes201.flat[index + num_comps]]
        for func, lab, ax in zip(funcs, labels, axes):
            ax.plot(func(cur_map))
            ax.set_title('Eigenvector: %d - %s' % (index + 1, lab))
        ax.set_xlabel(xlabel)
    fig201.tight_layout()

    return fig201, axes201


###############################################################################

def plotBELoops(xaxis, xlabel, amp_mat, phase_mat, num_comps, title=None):
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    """
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    Plots the provided loops from the SHO. Replace / merge with function in BESHOUtils

    Parameters:
    -------------
    xaxis : 1D real numpy array
        The vector to plot against
    xlabel : string
        Label for x axis
    amp_mat : 2D real numpy array
        Amplitude matrix arranged as [points, component]
    phase_mat : 2D real numpy array
        Phase matrix arranged as [points, component]
    num_comps : int
        Number of components to plot
    title : String
        Title to plot above everything else

    Returns:
    ---------
    fig, axes
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    """
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    fig201, axes201 = plt.subplots(2, num_comps, figsize=(4 * num_comps, 6))
    fig201.subplots_adjust(hspace=0.4, wspace=0.4)
    fig201.canvas.set_window_title(title)

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    for index in range(num_comps):
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        axes = [axes201.flat[index], axes201.flat[index + num_comps]]
        resp_vecs = [amp_mat[index, :], phase_mat[index, :]]
        resp_titles = ['Amplitude', 'Phase']

        for ax, resp, titl in zip(axes, resp_vecs, resp_titles):
            ax.plot(xaxis, resp)
            ax.set_title('%s %d' % (titl, index + 1))
            ax.set_aspect('auto')
            ax.set_xlabel(xlabel)

    fig201.tight_layout()
    return fig201, axes201


###############################################################################

def plotScree(S, title='Scree'):
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    """
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    Plots the S or scree

    Parameters:
    -------------
    S : 1D real numpy array
        The S vector from SVD

    Returns:
    ---------
    fig, axes
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    """
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    fig203 = plt.figure(figsize=(6.5, 6))
    axes203 = fig203.add_axes([0.1, 0.1, .8, .8])  # left, bottom, width, height (range 0 to 1)
    axes203.loglog(np.arange(len(S)) + 1, S, 'b', marker='*')
    axes203.set_xlabel('Principal Component')
    axes203.set_ylabel('Variance')
    axes203.set_title(title)
    axes203.set_xlim(left=1, right=len(S))
    axes203.set_ylim(bottom=np.min(S), top=np.max(S))
    fig203.canvas.set_window_title("Scree")

    return fig203, axes203


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# ###############################################################################


def plot_map_stack(map_stack, num_comps=4, stdevs=2, color_bar_mode=None,
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                   title='Component', heading='Map Stack', **kwargs):
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    """
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    Plots the provided stack of maps
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    Parameters:
    -------------
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    map_stack : 3D real numpy array
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        structured as [rows, cols, component]
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    num_comps : unsigned int
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        Number of components to plot
    stdevs : int
        Number of standard deviations to consider for plotting
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    color_bar_mode : String, Optional
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        Options are None, single or each. Default None
    title : String or list of strings
        The titles for each of the plots.
        If a single string is provided, the plot titles become ['title 01', title 02', ...].
        if a list of strings (equal to the number of components) are provided, these are used instead.
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    Returns:
    ---------
    fig, axes
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    """
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    if isinstance(title, list):
        if len(title) > num_comps:
            # remove additional titles
            title = title[:num_comps]
        elif len(title) < num_comps:
            # add titles
            title = title + ['Component' + ' ' + str(x) for x in range(len(title), num_comps)]
    else:
        if not isinstance(title, str):
            title = 'Component'
        title = [title + ' ' + str(x) for x in range(num_comps)]

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    fig_h, fig_w = (4, 4)
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    p_rows = int(np.floor(np.sqrt(num_comps)))
    p_cols = int(np.ceil(num_comps / p_rows))
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    if p_rows*p_cols < num_comps:
        p_cols += 1
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    fig202 = plt.figure(figsize=(p_cols * fig_w, p_rows * fig_h))
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    axes202 = ImageGrid(fig202, 111, nrows_ncols=(p_rows, p_cols),
                        cbar_mode=color_bar_mode,
                        cbar_pad='1%',
                        cbar_size='5%',
                        axes_pad=(0.1*fig_w, 0.07*fig_h))
    # fig202, axes202 = plt.subplots(p_cols, p_rows, figsize=(p_cols * fig_w, p_rows * fig_h))
    # fig202.subplots_adjust(hspace=0.4, wspace=0.4)
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    fig202.canvas.set_window_title(heading)
    fig202.suptitle(heading, fontsize=16)
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    for index, subtitle in enumerate(title):
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        im = plot_map(axes202[index],
                      map_stack[:, :, index],
                      stdevs=stdevs,
                      show_colorbar=False, **kwargs)
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        axes202[index].set_title(subtitle)
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        if color_bar_mode is 'each':
            axes202.cbar_axes[index].colorbar(im)

    if color_bar_mode is 'single':
        axes202.cbar_axes[0].colorbar(im)
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    return fig202, axes202

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# TODO: The label and units for the main dataset itself are missing in most cases! - ie. I don't know that the data is 'Current' and 'nA'


def plot_cluster_results(h5_group, y_spec_label):
    h5_labels = h5_group['Labels']
    try:
        h5_mean_resp = h5_group['Mean_Response']
    except KeyError:
        # old PySPM format:
        h5_mean_resp = h5_group['Centroids']

    # Reshape the mean response to N dimensions
    mean_response, success = reshape_to_Ndims(h5_mean_resp)

    # unfortunately, we cannot use the above function for the labels
    # However, we will assume that the position values are linked to the labels:
    h5_pos_vals = h5_labels.file[h5_labels.attrs['Position_Values']]
    h5_pos_inds = h5_labels.file[h5_labels.attrs['Position_Indices']]

    # Reshape the labels correctly:
    pos_dims = []
    for col in range(h5_pos_inds.shape[1]):
        pos_dims.append(np.unique(h5_pos_inds[:, col]).size)

    pos_ticks = [h5_pos_vals[:pos_dims[0], 0], h5_pos_vals[slice(0,None,pos_dims[0]), 1]]
    # prepare the axes ticks for the map

    pos_dims.reverse()  # go from slowest to fastest
    pos_dims = tuple(pos_dims)
    label_mat = np.reshape(h5_labels.value, pos_dims)

    # Figure out the correct units and labels for mean response:
    h5_spec_vals = h5_mean_resp.file[h5_mean_resp.attrs['Spectroscopic_Values']]
    x_spec_label = get_formatted_labels(h5_spec_vals)[0]

    # Figure out the correct axes labels for label map:
    pos_labels = get_formatted_labels(h5_pos_vals)

    plotClusterResults(label_mat, mean_response, spec_val=np.squeeze(h5_spec_vals[0]),
                       spec_label=x_spec_label, resp_label=y_spec_label,
                       pos_labels=pos_labels, pos_ticks=pos_ticks)
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###############################################################################
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def plotClusterResults(label_mat, mean_response, spec_val=None, cmap=plt.cm.jet,
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                       spec_label='Spectroscopic Value', resp_label='Response',
                       pos_labels=('X', 'Y'), pos_ticks=None):
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    """
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    Plot the cluster labels and mean response for each cluster

    Parameters
    ----------
    label_mat : 2D ndarray or h5py.Dataset of ints
        Spatial map of cluster labels structured as [rows, cols]
    mean_response : 2D ndarray or h5py.Dataset
        Mean value of each cluster over all samples 
        arranged as [cluster number, features]
    spec_val :  1D ndarray or h5py.Dataset of floats, optional
        X axis to plot the centroids against
        If no value is specified, the data is plotted against the index
    cmap : plt.cm object or str, optional
        Colormap to use for the labels map and the centroid.
        Advised to pick a map where the centroid plots show clearly.
        Default = matplotlib.pyplot.cm.jet
    spec_label : str, optional
        Label to use for X axis on cluster centroid plot
        Default = 'Spectroscopic Value'
    resp_label : str, optional
        Label to use for Y axis on cluster centroid plot
         Default = 'Response'
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    pos_labels : array_like of str, optional
        Labels to use for the X and Y axes on the Label map
        Default = ('X', 'Y')
    pos_ticks : array_like of int
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    Returns
    -------
    fig : Figure
        Figure containing the plots
    axes : 1D array_like of axes objects
        Axes of the individual plots within `fig`
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    """
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    def __plotCentroids(centroids, ax, spec_val, spec_label, y_label, cmap, title=None):
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        plot_line_family(ax, spec_val, centroids, label_prefix='Cluster', cmap=cmap)
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        ax.set_ylabel(y_label)
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        # ax.legend(loc='best')
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        if title:
            ax.set_title(title)
            ax.set_xlabel(spec_label)

    if type(spec_val) == type(None):
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        spec_val = np.arange(mean_response.shape[1])
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    if mean_response.dtype in [np.complex64, np.complex128, np.complex]:
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        fig = plt.figure(figsize=(12, 8))
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        ax_map = plt.subplot2grid((2, 12), (0, 0), colspan=6, rowspan=2)
        ax_amp = plt.subplot2grid((2, 12), (0, 6), colspan=4)
        ax_phase = plt.subplot2grid((2, 12), (1, 6), colspan=4)
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        axes = [ax_map, ax_amp, ax_phase]

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        __plotCentroids(np.abs(mean_response), ax_amp, spec_val, spec_label,
                        resp_label + ' - Amplitude', cmap, 'Mean Response')
        __plotCentroids(np.angle(mean_response), ax_phase, spec_val, spec_label,
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                        resp_label + ' - Phase', cmap)
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        plot_handles, plot_labels = ax_amp.get_legend_handles_labels()

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    else:
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        fig = plt.figure(figsize=(12, 8))
        ax_map = plt.subplot2grid((1, 12), (0, 0), colspan=6)
        ax_resp = plt.subplot2grid((1, 12), (0, 6), colspan=4)
        axes = [ax_map, ax_resp]
        __plotCentroids(mean_response, ax_resp, spec_val, spec_label,
                        resp_label, cmap, 'Mean Response')
        plot_handles, plot_labels = ax_resp.get_legend_handles_labels()

    fleg = plt.figlegend(plot_handles, plot_labels, loc='center right',
                         borderaxespad=0.0)
    num_clusters = mean_response.shape[0]
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    if isinstance(label_mat, h5py.Dataset):
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        """
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        Reshape label_mat based on linked positions
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        """
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        pos = label_mat.file[label_mat.attrs['Position_Indices']]
        nx = len(np.unique(pos[:, 0]))
        ny = len(np.unique(pos[:, 1]))
        label_mat = label_mat[()].reshape(nx, ny)
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    # im = ax_map.imshow(label_mat, interpolation='none')
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    ax_map.set_xlabel(pos_labels[0])
    ax_map.set_ylabel(pos_labels[1])

    if pos_ticks is not None:
        x_ticks = np.linspace(0, label_mat.shape[1] - 1, 5, dtype=np.uint16)
        y_ticks = np.linspace(0, label_mat.shape[0] - 1, 5, dtype=np.uint16)
        ax_map.set_xticks(x_ticks)
        ax_map.set_yticks(y_ticks)
        ax_map.set_xticklabels(pos_ticks[0][x_ticks])
        ax_map.set_yticklabels(pos_ticks[1][y_ticks])

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    """divider = make_axes_locatable(ax_map)
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    cax = divider.append_axes("right", size="5%", pad=0.05)  # space for colorbar
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    fig.colorbar(im, cax=cax, ticks=np.arange(num_clusters),
                 cmap=discrete_cmap(num_clusters, base_cmap=plt.cm.jet))
    ax_map.axis('tight')"""
    pcol0 = ax_map.pcolor(label_mat, cmap=discrete_cmap(num_clusters, base_cmap=plt.cm.jet))
    fig.colorbar(pcol0, ax=ax_map, ticks=np.arange(num_clusters))
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    ax_map.axis('tight')
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    ax_map.set_aspect('auto')
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    ax_map.set_title('Cluster Label Map')

    fig.tight_layout()
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    fig.canvas.set_window_title('Cluster results')
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    return fig, axes

###############################################################################

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def plotKMeansClusters(label_mat, cluster_centroids, max_centroids=4,
                       spec_val=None, x_label='Excitation (a.u.)', y_label='Response (a.u.)'):
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    """
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    Plots the provided labels mat and centroids from clustering
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    Parameters
    ----------
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    label_mat : 2D int numpy array
                structured as [rows, cols]
    cluster_centroids: 2D real numpy array
                       structured as [cluster,features]
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    max_centroids : unsigned int
                    Number of centroids to plot
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    spec_val :  array-like
        X axis to plot the centroids against
        If no value is specified, the data is plotted against the index
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    x_label : String / unicode
              X label for centroid plots
    y_label : String / unicode
              Y label for centroid plots
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    Returns
    -------
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    fig
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    """
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    if max_centroids < 5:
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        fig501 = plt.figure(figsize=(20, 10))
        fax1 = plt.subplot2grid((2, 4), (0, 0), colspan=2, rowspan=2)
        fax2 = plt.subplot2grid((2, 4), (0, 2))
        fax3 = plt.subplot2grid((2, 4), (0, 3))
        fax4 = plt.subplot2grid((2, 4), (1, 2))
        fax5 = plt.subplot2grid((2, 4), (1, 3))
        fig501.tight_layout()
        axes_handles = [fax1, fax2, fax3, fax4, fax5]

    else:
        fig501 = plt.figure(figsize=(20, 10))
        # make subplot for cluster map
        fax1 = plt.subplot2grid((3, 6), (0, 0), colspan=3, rowspan=3)  # For cluster map
        fax1.set_xmargin(0.50)
        # make subplot for cluster centers
        fax2 = plt.subplot2grid((3, 6), (0, 3))
        fax3 = plt.subplot2grid((3, 6), (0, 4))
        fax4 = plt.subplot2grid((3, 6), (0, 5))
        fax5 = plt.subplot2grid((3, 6), (1, 3))
        fax6 = plt.subplot2grid((3, 6), (1, 4))
        fax7 = plt.subplot2grid((3, 6), (1, 5))
        fax8 = plt.subplot2grid((3, 6), (2, 3))
        fax9 = plt.subplot2grid((3, 6), (2, 4))
        fax10 = plt.subplot2grid((3, 6), (2, 5))
        fig501.tight_layout()
        axes_handles = [fax1, fax2, fax3, fax4, fax5, fax6, fax7, fax8, fax9, fax10]

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    # First plot the labels map:
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    pcol0 = fax1.pcolor(label_mat, cmap=discrete_cmap(cluster_centroids.shape[0],
                                                      base_cmap=plt.cm.jet))
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    fig501.colorbar(pcol0, ax=fax1, ticks=np.arange(cluster_centroids.shape[0]))
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    fax1.axis('tight')
    fax1.set_aspect('auto')
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    fax1.set_title('Cluster Label Map')
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    """im = fax1.imshow(label_mat, interpolation='none')
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    divider = make_axes_locatable(fax1)
    cax = divider.append_axes("right", size="5%", pad=0.05)  # space for colorbar
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    plt.colorbar(im, cax=cax)"""

    if spec_val is None and cluster_centroids.ndim == 2:
        spec_val = np.arange(cluster_centroids.shape[1])
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    # Plot results
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    for ax, index in zip(axes_handles[1: max_centroids + 1], np.arange(max_centroids)):
        if cluster_centroids.ndim == 2:
            ax.plot(spec_val, cluster_centroids[index, :],
                    color=plt.cm.jet(int(255 * index / (cluster_centroids.shape[0] - 1))))
            ax.set_xlabel(x_label)
            ax.set_ylabel(y_label)
        elif cluster_centroids.ndim == 3:
            plot_map(ax, cluster_centroids[index], show_colorbar=True)
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        ax.set_title('Centroid: %d' % index)
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    fig501.subplots_adjust(hspace=0.60, wspace=0.60)
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    fig501.tight_layout()
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    return fig501


###############################################################################

def plotClusterDendrograms(label_mat, e_vals, num_comp, num_cluster, mode='Full', last=None,
                           sort_type='distance', sort_mode=True):
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    """
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    Creates and plots the dendrograms for the given label_mat and
    eigenvalues

    Parameters
    -------------
    label_mat : 2D real numpy array
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        structured as [rows, cols], from KMeans clustering
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    e_vals: 3D real numpy array of eigenvalues
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        structured as [component, rows, cols]
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    num_comps : int
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        Number of components used to make eigenvalues
    num_cluster : int
        Number of cluster used to make the label_mat
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    mode: str, optional
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        How should the dendrograms be created.
        "Full" -- use all clusters when creating the dendrograms
        "Truncated" -- stop showing clusters after 'last'
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    last: int, optional - should be provided when using "Truncated"
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        How many merged clusters should be shown when using
        "Truncated" mode
    sort_type: {'count', 'distance'}, optional
        What type of sorting should be used when plotting the
        dendrograms.  Options are:
        count - Uses the count_sort from scipy.cluster.hierachy.dendrogram
        distance - Uses the distance_sort from scipy.cluster.hierachy.dendrogram
    sort_mode: {False, True, 'ascending', 'descending'}, optional
        For the chosen sort_type, which mode should be used.
        False - Does no sorting
        'ascending' or True - The child with the minimum of the chosen sort
        parameter is plotted first
        'descending' - The child with the maximum of the chosen sort parameter is
        plotted first
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    Returns
    ---------
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    fig : matplotlib.pyplot Figure object
        Figure containing the dendrogram
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    """
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    if mode == 'Truncated' and not last:
        warn('Warning: Truncated dendrograms requested, but no last cluster given.  Reverting to full dendrograms.')
        mode = 'Full'

    if mode == 'Full':
        print 'Creating full dendrogram from clusters'
        mode = None
    elif mode == 'Truncated':
        print 'Creating truncated dendrogram from clusters.  Will stop at {}.'.format(last)
        mode = 'lastp'
        show_contracted = True
    else:
        raise ValueError('Error: Unknown mode requested for plotting dendrograms. mode={}'.format(mode))

    c_sort = False
    d_sort = False
    if sort_type == 'count':
        c_sort = sort_mode
        if c_sort == 'descending':
            c_sort = 'descendent'
    elif sort_type == 'distance':
        d_sort = sort_mode

    centroid_mat = np.zeros([num_cluster, num_comp])
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    for k1 in range(num_cluster):
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        [i_x, i_y] = np.where(label_mat == k1)
        u_stack = np.zeros([len(i_x), num_comp])
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        for k2 in range(len(i_x)):
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            u_stack[k2, :] = np.abs(e_vals[i_x[k2], i_y[k2], :num_comp])

        centroid_mat[k1, :] = np.mean(u_stack, 0)


        # Get the distrance between cluster means
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    distance_mat = scipy.spatial.distance.pdist(centroid_mat)
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    # get hierachical pairings of clusters
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    linkage_pairing = scipy.cluster.hierarchy.linkage(distance_mat, 'weighted')
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    linkage_pairing[:, 3] = linkage_pairing[:, 3] / max(linkage_pairing[:, 3])

    fig = plt.figure()
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    scipy.cluster.hierarchy.dendrogram(linkage_pairing, p=last, truncate_mode=mode,
                                       count_sort=c_sort, distance_sort=d_sort,
                                       leaf_rotation=90)
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    fig.axes[0].set_title('Dendrogram')
    fig.axes[0].set_xlabel('Index or (cluster size)')
    fig.axes[0].set_ylabel('Distance')

    return fig


def plot1DSpectrum(data_vec, freq, title, figure_path=None):
    """
    Plots the Step averaged BE response

    Parameters
    ------------
    data_vec : 1D numpy array
        Response of one BE pulse
    freq : 1D numpy array
        BE frequency that serves as the X axis of the plot
    title : String
        Plot group name
    figure_path : String / Unicode
        Absolute path of the file to write the figure to

    Returns
    ---------
    fig : Matplotlib.pyplot figure
        Figure handle
    ax : Matplotlib.pyplot axis
        Axis handle
    """
    if len(data_vec) != len(freq):
        #         print '1D:',data_vec.shape, freq.shape
        warn('plot2DSpectrogram: Incompatible data sizes!!!!')
        return
    freq = freq * 1E-3  # to kHz
    fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True);
    ax[0].plot(freq, np.abs(data_vec) * 1E+3)
    ax[0].set_title('Amplitude (mV)')
    # ax[0].set_xlabel('Frequency (kHz)')
    ax[1].plot(freq, np.angle(data_vec) * 180 / np.pi)
    ax[1].set_title('Phase (deg)')
    ax[1].set_xlabel('Frequency (kHz)')
    fig.suptitle(title + ': mean UDVS, mean spatial response')
    if figure_path:
        plt.savefig(figure_path, format='png', dpi=300)
    return (fig, ax)


###############################################################################

def plot2DSpectrogram(mean_spectrogram, freq, title, figure_path=None):
    """
    Plots the position averaged spectrogram

    Parameters
    ------------
    mean_spectrogram : 2D numpy complex array
        Means spectrogram arranged as [frequency, UDVS step]
    freq : 1D numpy float array
        BE frequency that serves as the X axis of the plot
    title : String
        Plot group name
    figure_path : String / Unicode
        Absolute path of the file to write the figure to

    Returns
    ---------
    fig : Matplotlib.pyplot figure
        Figure handle
    ax : Matplotlib.pyplot axis
        Axis handle
    """
    if mean_spectrogram.shape[1] != len(freq):
        #  print '2D:',mean_spectrogram.shape, freq.shape
        warn('plot2DSpectrogram: Incompatible data sizes!!!!')
        return
    freq = freq * 1E-3  # to kHz
    fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True);
    # print mean_spectrogram.shape
    # print freq.shape
    ax[0].imshow(np.abs(mean_spectrogram), interpolation='nearest',
                 extent=[freq[0], freq[-1], mean_spectrogram.shape[0], 0])
    ax[0].set_title('Amplitude')
    # ax[0].set_xticks(freq)
    # ax[0].set_ylabel('UDVS Step')
    ax[0].axis('tight')
    ax[1].imshow(np.angle(mean_spectrogram), interpolation='nearest',
                 extent=[freq[0], freq[-1], mean_spectrogram.shape[0], 0])
    ax[1].set_title('Phase')
    ax[1].set_xlabel('Frequency (kHz)')
    # ax[0].set_ylabel('UDVS Step')
    ax[1].axis('tight')
    fig.suptitle(title)
    if figure_path:
        plt.savefig(figure_path, format='png', dpi=300)
    return (fig, ax)


###############################################################################

def plotHistgrams(p_hist, p_hbins, title, figure_path=None):
    """
    Plots the position averaged spectrogram

    Parameters
    ------------
    p_hist : 2D numpy array
        histogram data arranged as [physical quantity, frequency bin]
    p_hbins : 1D numpy array
        BE frequency that serves as the X axis of the plot
    title : String
        Plot group name
    figure_path : String / Unicode
        Absolute path of the file to write the figure to

    Returns
    ---------
    fig : Matplotlib.pyplot figure
        Figure handle
    """

    base_fig_size = 7
    h_fig = base_fig_size
    w_fig = base_fig_size * 4

    fig = plt.figure(figsize=(w_fig, h_fig))
    fig.suptitle(title)
    iplot = 0

    p_Nx, p_Ny = np.amax(p_hbins, axis=1) + 1

    p_hist = np.reshape(p_hist, (4, p_Ny, p_Nx))

    iplot += 1
    p_plot_title = 'Spectral BEHistogram Amp (log10 of counts)'
    p_plot = fig.add_subplot(1, 4, iplot, title=p_plot_title)
    p_im = p_plot.imshow(np.rot90(np.log10(p_hist[0])), interpolation='nearest')
    p_plot.axis('tight')
    fig.colorbar(p_im, fraction=0.1)

    iplot += 1
    p_plot_title = 'Spectral BEHistogram Phase (log10 of counts)'
    p_plot = fig.add_subplot(1, 4, iplot, title=p_plot_title)
    p_im = p_plot.imshow(np.rot90(np.log10(p_hist[1])), interpolation='nearest')
    p_plot.axis('tight')
    fig.colorbar(p_im, fraction=0.1)

    iplot += 1
    p_plot_title = 'Spectral BEHistogram Real (log10 of counts)'
    p_plot = fig.add_subplot(1, 4, iplot, title=p_plot_title)
    p_im = p_plot.imshow(np.rot90(np.log10(p_hist[2])), interpolation='nearest')
    p_plot.axis('tight')
    fig.colorbar(p_im, fraction=0.1)

    iplot += 1
    p_plot_title = 'Spectral BEHistogram Imag (log10 of counts)'
    p_plot = fig.add_subplot(1, 4, iplot, title=p_plot_title)
    p_im = p_plot.imshow(np.rot90(np.log10(p_hist[3])), interpolation='nearest')
    p_plot.axis('tight')
    fig.colorbar(p_im, fraction=0.1)

    if figure_path:
        plt.savefig(figure_path, format='png')

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    return fig


def plotSHOLoops(dc_vec, resp_mat, x_label='', y_label='', title=None, save_path=None):
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    """
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    Plots BE loops from up to 9 positions (evenly separated)

    Parameters
    -----------
    dc_vec : 1D numpy array
        X axis - DC offset / AC amplitude
    resp_mat : real 2D numpy array
        containing quantity such as amplitude or phase organized as
        [position, spectroscopic index]
    x_label : (optional) String
        X Label for all plots
    y_label : (optional) String
        Y label for all plots
    title : (optional) String
        Main plot title
    save_path : (Optional) String
        Absolute path to write the figure to

    Returns
    -----------
    None
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    """
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    num_pos = resp_mat.shape[0]
    if num_pos >= 9:
        tot_plots = 9
    elif num_pos >= 4:
        tot_plots = 4
    else:
        tot_plots = 1
    delta_pos = int(np.ceil(num_pos / tot_plots))

    fig, axes = plt.subplots(nrows=int(tot_plots ** 0.5), ncols=int(tot_plots ** 0.5),
                             figsize=(12, 12))
    if tot_plots > 1:
        axes_lin = axes.reshape(tot_plots)
    else:
        axes_lin = axes

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    for count, posn in enumerate(range(0, num_pos, delta_pos)):
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        axes_lin[count].plot(dc_vec, np.squeeze(resp_mat[posn, :]))
        axes_lin[count].set_title('Pixel #' + str(posn))
        axes_lin[count].set_xlabel(x_label)
        axes_lin[count].set_ylabel(y_label)
        axes_lin[count].axis('tight')
        axes_lin[count].set_aspect('auto')

    fig.suptitle(title)
    fig.tight_layout()
    if save_path:
        fig.savefig(save_path, format='png', dpi=300)


def visualizeSHOResults(h5_main, save_plots=True, show_plots=True):
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    """
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    Plots some loops, amplitude, phase maps for BE-Line and BEPS datasets.\n
    Note: The file MUST contain SHO fit gusses at the very least

    Parameters
    ----------
    h5_main : HDF5 Dataset
        dataset to be plotted
    save_plots : (Optional) Boolean
        Whether or not to save plots to files in the same directory as the h5 file
    show_plots : (Optional) Boolean
        Whether or not to display the plots on the screen

    Returns
    -------
    None
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    """
    plt_path = None

    print('Creating plots of SHO Results from {}.'.format(h5_main.name))
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    h5_file = h5_main.file

    expt_type = h5_file.attrs['data_type']
    if expt_type not in ['BEPSData', 'BELineData']:
        warn('Unsupported data format')
        return
    isBEPS = expt_type == 'BEPSData'

    (folder_path, basename) = os.path.split(h5_file.filename)
    basename, _ = os.path.splitext(basename)

    sho_grp = h5_main.parent
    chan_grp = sho_grp.parent

    grp_name = '_'.join(chan_grp.name[1:].split('/'))
    grp_name = '_'.join([grp_name, sho_grp.name.split('/')[-1].split('-')[0], h5_main.name.split('/')[-1]])

    try:
        h5_pos = h5_file[h5_main.attrs['Position_Indices']]
    except KeyError:
        print('No Position_Indices found as attribute of {}'.format(h5_main.name))
        print('Rows and columns will be calculated from dataset shape.')
        num_rows = int(np.floor((np.sqrt(h5_main.shape[0]))))
        num_cols = int(np.reshape(h5_main, [num_rows, -1, h5_main.shape[1]]).shape[1])
    else:
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        num_rows = len(np.unique(h5_pos[:, 0]))
        num_cols = len(np.unique(h5_pos[:, 1]))
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    try:
        h5_spec_inds = h5_file[h5_main.attrs['Spectroscopic_Indices']]
        h5_spec_vals = h5_file[h5_main.attrs['Spectroscopic_Values']]
    # except KeyError:
    #     warn('No Spectrosocpic Datasets found as attribute of {}'.format(h5_main.name))
    #     raise
    except:
        raise

    # Assume that there's enough memory to load all the guesses into memory
    amp_mat = h5_main['Amplitude [V]'] * 1000  # convert to mV ahead of time
    freq_mat = h5_main['Frequency [Hz]'] / 1000
    q_mat = h5_main['Quality Factor']
    phase_mat = h5_main['Phase [rad]']
    rsqr_mat = h5_main['R2 Criterion']

    if isBEPS:
        meas_type = chan_grp.parent.attrs['VS_mode']
        # basically 3 kinds for now - DC/current, AC, UD - lets ignore this
        if meas_type == 'load user defined VS Wave from file':
            warn('Not handling custom experiments for now')
            h5_file.close()
            return

        # Plot amplitude and phase maps at one or more UDVS steps

        if meas_type == 'AC modulation mode with time reversal':
            center = int(h5_spec_vals.shape[1] * 0.5)
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            ac_vec = np.squeeze(h5_spec_vals[h5_spec_vals.attrs['AC_Amplitude']][0:center])
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            forw_resp = np.squeeze(amp_mat[:, slice(0, center)])
            plt_title = grp_name + '_Forward_Loops'
            if save_plots:
                plt_path = os.path.join(folder_path, basename + '_' + plt_title + '.png')
            plotSHOLoops(ac_vec, forw_resp, 'AC Amplitude', 'Amplitude', title=plt_title, save_path=plt_path)
            rev_resp = np.squeeze(amp_mat[:, slice(center, None)])
            plt_title = grp_name + '_Reverse_Loops'
            if save_plots:
                plt_path = os.path.join(folder_path, basename + '_' + plt_title + '.png')
            plotSHOLoops(ac_vec, rev_resp, 'AC Amplitude', 'Amplitude', title=plt_title, save_path=plt_path)
            plt_title = grp_name + '_Forward_Snaps'
            if save_plots:
                plt_path = os.path.join(folder_path, basename + '_' + plt_title + '.png')
            plotVSsnapshots(forw_resp.reshape(num_rows, num_cols, forw_resp.shape[1]), title=plt_title,
                            save_path=plt_path)
            plt_title = grp_name + '_Reverse_Snaps'
            if save_plots:
                plt_path = os.path.join(folder_path, basename + '_' + plt_title + '.png')
            plotVSsnapshots(rev_resp.reshape(num_rows, num_cols, rev_resp.shape[1]), title=plt_title,
                            save_path=plt_path)
        else:
            # plot loops at a few locations
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            dc_vec = np.squeeze(h5_spec_vals[h5_spec_vals.attrs['DC_Offset']])
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            if chan_grp.parent.attrs['VS_measure_in_field_loops'] == 'in and out-of-field':

                in_phase = np.squeeze(phase_mat[:, slice(0, None, 2)])
                in_amp = np.squeeze(amp_mat[:, slice(0, None, 2)])
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                dc_vec = np.squeeze(dc_vec[slice(0, None, 2)])
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                plt_title = grp_name + '_In_Field_Loops'
                if save_plots:
                    plt_path = os.path.join(folder_path, basename + '_' + plt_title + '.png')
                plotSHOLoops(dc_vec, in_phase * in_amp, 'DC Bias', 'Piezoresponse (a.u.)', title=plt_title,
                             save_path=plt_path)
                out_phase = np.squeeze(phase_mat[:, slice(1, None, 2)])
                out_amp = np.squeeze(amp_mat[:, slice(1, None, 2)])
                plt_title = grp_name + '_Out_of_Field_Loops'
                if save_plots:
                    plt_path = os.path.join(folder_path, basename + '_' + plt_title + '.png')
                plotSHOLoops(dc_vec, out_phase * out_amp, 'DC Bias', 'Piezoresponse (a.u.)', title=plt_title,
                             save_path=plt_path)
                # print 'trying to reshape', in_phase.shape, 'into', in_phase.shape[0],',',num_rows,',',num_cols
                plt_title = grp_name + '_In_Field_Snaps'
                if save_plots:
                    plt_path = os.path.join(folder_path, basename + '_' + plt_title + '.png')
                plotVSsnapshots(in_phase.reshape(num_rows, num_cols, in_phase.shape[1]), title=plt_title,
                                save_path=plt_path)
                plt_title = grp_name + '_Out_of_Field_Snaps'
                if save_plots:
                    plt_path = os.path.join(folder_path, basename + '_' + plt_title + '.png')
                plotVSsnapshots(out_phase.reshape(num_rows, num_cols, out_phase.shape[1]), title=plt_title,
                                save_path=plt_path)
            else:
                plt_title = grp_name + '_Loops'
                if save_plots:
                    plt_path = os.path.join(folder_path, basename + '_' + plt_title + '.png')
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                plotSHOLoops(dc_vec, phase_mat * amp_mat, 'DC Bias', 'Piezoresponse (a.u.)', title=plt_title,
                             save_path=plt_path)
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                plt_title = grp_name + '_Snaps'
                if save_plots:
                    plt_path = os.path.join(folder_path, basename + '_' + plt_title + '.png')
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                plotVSsnapshots(phase_mat.reshape(num_rows, num_cols, phase_mat.shape[1]), title=plt_title,
                                save_path=plt_path)
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    else:  # BE-Line can only visualize the amplitude and phase maps:
        amp_mat = amp_mat.reshape(num_rows, num_cols)
        freq_mat = freq_mat.reshape(num_rows, num_cols)
        q_mat = q_mat.reshape(num_rows, num_cols)
        phase_mat = phase_mat.reshape(num_rows, num_cols)
        rsqr_mat = rsqr_mat.reshape(num_rows, num_cols)
        if save_plots:
            plt_path = os.path.join(folder_path, basename + '_' + grp_name + 'Maps.png')
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        fig_ms, ax_ms = plot_map_stack(np.dstack((amp_mat, freq_mat, q_mat, phase_mat, rsqr_mat)),
                                       num_comps=5, color_bar_mode='each', heading=grp_name,
                                       title=['Amplitude (mV)', 'Frequency (kHz)', 'Quality Factor', 'Phase (deg)',
                                              'R^2 Criterion'], cmap=cmap_jet_white_center())
        fig_ms.savefig(plt_path, format='png', dpi=300)
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    if show_plots:
        plt.show()

    plt.close('all')