plot_utils.py 47.8 KB
Newer Older
Somnath, Suhas's avatar
Somnath, Suhas committed
1
2
3
4
5
6
# -*- coding: utf-8 -*-
"""
Created on Thu May 05 13:29:12 2016

@author: Suhas Somnath
"""
Chris Smith's avatar
Chris Smith committed
7
# TODO: All general plotting functions should support data with 1, 2, or 3 spatial dimensions.
Chris Smith's avatar
Chris Smith committed
8

9
from __future__ import division, print_function, absolute_import, unicode_literals
10
11

import inspect
12
from warnings import warn
13

Chris Smith's avatar
merged    
Chris Smith committed
14
import h5py
15
import matplotlib.pyplot as plt
16
17
import numpy as np
import scipy
18
from scipy.signal import blackman
19
from matplotlib.colors import LinearSegmentedColormap
20
from mpl_toolkits.axes_grid1 import ImageGrid
21

22
from ..io.hdf_utils import reshape_to_Ndims, get_formatted_labels
23

Somnath, Suhas's avatar
Somnath, Suhas committed
24

25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
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)

Somnath, Suhas's avatar
Somnath, Suhas committed
57

58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
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)
87

Chris Smith's avatar
Chris Smith committed
88

Somnath, Suhas's avatar
Somnath, Suhas committed
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
def cmap_from_rgba(name, interp_vals, normalization_val):
    """
    Generates a colormap given a matlab-style interpolation table

    Parameters
    ----------
    name : String / Unicode
        Name of the desired colormap
    interp_vals : List of tuples
        Interpolation table that describes the desired color map. Each entry in the table should be described as:
        (position in the colorbar, (red, green, blue, alpha))
        The position in the color bar, red, green, blue, and alpha vary from 0 to the normalization value
    normalization_val : number
        The common maximum value for the position in the color bar, red, green, blue, and alpha

    Returns
    -------
    new_cmap : matplotlib.colors.LinearSegmentedColormap object
        desired color map
    """

    normalization_val = np.round(1.0 * normalization_val)

    cdict = {'red': tuple([(dist / normalization_val, colors[0] / normalization_val, colors[0] / normalization_val)
                           for (dist, colors) in interp_vals][::-1]),
             'green': tuple([(dist / normalization_val, colors[1] / normalization_val, colors[1] / normalization_val)
                             for (dist, colors) in interp_vals][::-1]),
             'blue': tuple([(dist / normalization_val, colors[2] / normalization_val, colors[2] / normalization_val)
                            for (dist, colors) in interp_vals][::-1]),
             'alpha': tuple([(dist / normalization_val, colors[3] / normalization_val, colors[3] / normalization_val)
                            for (dist, colors) in interp_vals][::-1])}

    return LinearSegmentedColormap(name, cdict)


def make_linear_alpha_cmap(name, solid_color, normalization_val, min_alpha=0, max_alpha=1):
    """
    Generates a transparent to opaque color map based on a single solid color

    Parameters
    ----------
    name : String / Unicode
        Name of the desired colormap
    solid_color : List of numbers
        red, green, blue, and alpha values for a specific color
    normalization_val : number
        The common maximum value for the red, green, blue, and alpha values. This is 1 in matplotlib
    min_alpha : float (optional. Default = 0 : ie- transparent)
        Lowest alpha value for the bottom of the color bar
    max_alpha : float (optional. Default = 1 : ie- opaque)
        Highest alpha value for the top of the color bar

    Returns
    -------
    new_cmap : matplotlib.colors.LinearSegmentedColormap object
        transparent to opaque color map based on the provided color
    """
    solid_color = np.array(solid_color) / normalization_val * 1.0
    interp_table = [(1.0, (solid_color[0], solid_color[1], solid_color[2], max_alpha)),
                    (0, (solid_color[0], solid_color[1], solid_color[2], min_alpha))]
    return cmap_from_rgba(name, interp_table, 1)
Chris Smith's avatar
Chris Smith committed
150
151


Somnath, Suhas's avatar
Somnath, Suhas committed
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
def cmap_hot_desaturated():
    """
    Returns a desaturated color map based on the hot colormap

    Returns
    -------
    new_cmap : matplotlib.colors.LinearSegmentedColormap object
        Desaturated version of the hot color map
    """
    hot_desaturated = [(255.0, (255, 76, 76, 255)),
                       (218.5, (107, 0, 0, 255)),
                       (182.1, (255, 96, 0, 255)),
                       (145.6, (255, 255, 0, 255)),
                       (109.4, (0, 127, 0, 255)),
                       (72.675, (0, 255, 255, 255)),
                       (36.5, (0, 0, 91, 255)),
                       (0, (71, 71, 219, 255))]

    return cmap_from_rgba('hot_desaturated', hot_desaturated, 255)
Chris Smith's avatar
Chris Smith committed
171
172


173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
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

Chris Smith's avatar
Chris Smith committed
189
190
191
192
193
    Notes
    -----
    Jake VanderPlas License: BSD-style
    https://gist.github.com/jakevdp/91077b0cae40f8f8244a

194
195
196
197
198
199
200
    """

    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)

201

Chris Smith's avatar
Chris Smith committed
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
def _add_loop_parameters(axes, switching_coef_vec):
    """
    Add the loop parameters for the given loop to a list of axes

    Parameters
    ----------
    axes : list of matplotlib.pyplo.axes
        Plot axes to add the coeffients to
    switching_coef_vec : 1D numpy.ndarray
        Array of loop parameters arranged by position

    Returns
    -------
    axes : list of matplotlib.pyplo.axes
    """
    positions = np.linspace(0, switching_coef_vec.shape[0] - 1, len(axes.flat), dtype=np.int)

    for ax, pos in zip(axes.flat, positions):
        ax.axvline(switching_coef_vec[pos]['V+'], c='k', label='V+')
        ax.axvline(switching_coef_vec[pos]['V-'], c='r', label='V-')
        ax.axvline(switching_coef_vec[pos]['Nucleation Bias 1'], c='k', ls=':', label='Nucleation Bias 1')
        ax.axvline(switching_coef_vec[pos]['Nucleation Bias 2'], c='r', ls=':', label='Nucleation Bias 2')
        ax.axhline(switching_coef_vec[pos]['R+'], c='k', ls='-.', label='R+')
        ax.axhline(switching_coef_vec[pos]['R-'], c='r', ls='-.', label='R-')

    return axes
228

229
def rainbow_plot(ax, ao_vec, ai_vec, num_steps=32, cmap=plt.cm.jet, **kwargs):
Somnath, Suhas's avatar
Somnath, Suhas committed
230
231
232
233
    """
    Plots the input against the output waveform (typically loops).
    The color of the curve changes as a function of time using the jet colorscheme

234
235
    Parameters
    ----------
Somnath, Suhas's avatar
Somnath, Suhas committed
236
237
238
239
240
241
242
243
    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
244
245
    cmap : matplotlib.colors.LinearSegmentedColormap object
        Colormap to be used
Somnath, Suhas's avatar
Somnath, Suhas committed
246
247
    """
    pts_per_step = int(len(ai_vec) / num_steps)
248
    for step in range(num_steps - 1):
Somnath, Suhas's avatar
Somnath, Suhas committed
249
250
        ax.plot(ao_vec[step * pts_per_step:(step + 1) * pts_per_step],
                ai_vec[step * pts_per_step:(step + 1) * pts_per_step],
251
                color=cmap(255 * step / num_steps), **kwargs)
Somnath, Suhas's avatar
Somnath, Suhas committed
252
253
254
    # plot the remainder:
    ax.plot(ao_vec[(num_steps - 1) * pts_per_step:],
            ai_vec[(num_steps - 1) * pts_per_step:],
255
            color=cmap(255 * num_steps / num_steps), **kwargs)
Somnath, Suhas's avatar
Somnath, Suhas committed
256
257
258
259
260
    """
    CS3=plt.contourf([[0,0],[0,0]], range(0,310),cmap=plt.cm.jet)
    fig.colorbar(CS3)"""


261
262
def plot_line_family(axis, x_axis, line_family, line_names=None, label_prefix='Line', label_suffix='', cmap=plt.cm.jet,
                     **kwargs):
263
264
265
266
267
    """
    Plots a family of lines with a sequence of colors

    Parameters
    ----------
268
    axis : axis handle
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
        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)]

292
    for line_ind in range(num_lines):
293
294
295
        axis.plot(x_axis, line_family[line_ind],
                  label=line_names[line_ind],
                  color=cmap(int(255 * line_ind / (num_lines - 1))), **kwargs)
296
297


Chris Smith's avatar
Chris Smith committed
298
def plot_map(axis, data, stdevs=2, origin='lower', **kwargs):
299
300
301
302
303
304
305
306
307
308
309
310
    """
    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
Chris Smith's avatar
Chris Smith committed
311
312
313
314
    origin : str
        Where should the origin of the image data be located.  'lower' sets the origin to the
        bottom left, 'upper' sets it to the upper left.
        Default 'lower'
315

316
317
318
319
320
    Returns
    -------
    """
    data_mean = np.mean(data)
    data_std = np.std(data)
321
322
323
    im = axis.imshow(data, interpolation='none',
                     vmin=data_mean - stdevs * data_std,
                     vmax=data_mean + stdevs * data_std,
324
                     origin=origin,
325
                     **kwargs)
326
327
    axis.set_aspect('auto')

328
    return im
329

330

331
332
def plot_loops(excit_wfm, datasets, line_colors=[], dataset_names=[], evenly_spaced=True, plots_on_side=5, x_label='',
               y_label='', subtitles='Position', title='', central_resp_size=None, use_rainbow_plots=False, h5_pos=None):
333
    # TODO: Allow multiple excitation waveforms
Somnath, Suhas's avatar
Somnath, Suhas committed
334
    """
335
    Plots loops from multiple datasets from up to 25 evenly spaced positions
Somnath, Suhas's avatar
Somnath, Suhas committed
336
337
338
339
340

    Parameters
    -----------
    excit_wfm : 1D numpy float array
        Excitation waveform in the time domain
341
342
343
344
345
346
    datasets : list of 2D numpy arrays or 2D hyp5.Dataset objects
        Datasets containing data arranged as (pixel, time)
    line_colors : list of strings
        Colors to be used for each of the datasets
    dataset_names : (Optional) list of strings
        Names of the different datasets to be compared
Somnath, Suhas's avatar
Somnath, Suhas committed
347
348
349
350
351
352
353
354
    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
355
    use_rainbow_plots : (optional) Boolean
Somnath, Suhas's avatar
Somnath, Suhas committed
356
357
358
359
360
361
362
363
364
365
366
367
368
369
        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
    """
370
    if type(datasets) in [h5py.Dataset, np.ndarray]:
371
372
373
        # can be numpy array or h5py.dataset
        num_pos = datasets.shape[0]
        num_points = datasets.shape[1]
Somnath, Suhas's avatar
Somnath, Suhas committed
374
        datasets = [datasets]
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
        line_colors = ['b']
        dataset_names = ['Default']
    else:
        # First check if the datasets are correctly shaped:
        num_pos_es = list()
        num_points_es = list()
        for dataset in datasets:
            num_pos_es.append(dataset.shape[0])
            num_points_es.append(dataset.shape[1])
        num_pos_es = np.array(num_pos_es)
        num_points_es = np.array(num_points_es)
        if np.unique(num_pos_es).size > 1 or np.unique(num_points_es).size > 1:
            warn('Datasets of incompatible sizes')
            return
        num_pos = np.unique(num_pos_es)[0]
        num_points = np.unique(num_points_es)[0]

        # Next the identification of datasets:
        if len(dataset_names) > len(datasets):
            # remove additional titles
            dataset_names = dataset_names[:len(datasets)]
        elif len(dataset_names) < len(datasets):
            # add titles
            dataset_names = dataset_names + ['Dataset' + ' ' + str(x) for x in range(len(dataset_names), len(datasets))]
        if len(line_colors) != len(datasets):
            color_list = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'pink', 'brown', 'orange']
            if len(datasets) < len(color_list):
                remaining_colors = [x for x in color_list if x not in line_colors]
                line_colors += remaining_colors[:len(datasets) - len(color_list)]
            else:
                warn('Insufficient number of line colors provided')
                return


    if excit_wfm.size != num_points:
        warn('Length of excitation waveform not compatible with second axis of datasets')
        return
Somnath, Suhas's avatar
Somnath, Suhas committed
412
413

    plots_on_side = min(abs(plots_on_side), 5)
414

Somnath, Suhas's avatar
Somnath, Suhas committed
415
416
417
418
419
420
    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)

421
    fig, axes = plt.subplots(nrows=sq_num_plots, ncols=sq_num_plots, sharex=True, figsize=(12, 12))
422
    axes_lin = axes.flatten()
Somnath, Suhas's avatar
Somnath, Suhas committed
423

424
    cent_ind = int(0.5 * excit_wfm.size)
Somnath, Suhas's avatar
Somnath, Suhas committed
425
426
427
428
429
430
    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
431
        r_resp_ind = excit_wfm.size
Somnath, Suhas's avatar
Somnath, Suhas committed
432
433

    for count, posn in enumerate(chosen_pos):
434
435
        if use_rainbow_plots and len(datasets) == 1:
            rainbow_plot(axes_lin[count], excit_wfm[l_resp_ind:r_resp_ind], datasets[0][posn, l_resp_ind:r_resp_ind])
Somnath, Suhas's avatar
Somnath, Suhas committed
436
        else:
437
438
439
            for dataset, col_val in zip(datasets, line_colors):
                axes_lin[count].plot(excit_wfm[l_resp_ind:r_resp_ind], dataset[posn, l_resp_ind:r_resp_ind], color=col_val)
        if h5_pos is not None:
440
            # print('Row ' + str(h5_pos[posn,1]) + ' Col ' + str(h5_pos[posn,0]))
Somnath, Suhas's avatar
Somnath, Suhas committed
441
442
443
444
445
446
447
448
449
450
451
            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))
452
453
    if len(datasets) > 1:
        axes_lin[count].legend(dataset_names, loc='best')
Somnath, Suhas's avatar
Somnath, Suhas committed
454
455
456
457
    if title:
        fig.suptitle(title, fontsize=14)
    plt.tight_layout()
    return fig, axes
Chris Smith's avatar
merged    
Chris Smith committed
458

Somnath, Suhas's avatar
Somnath, Suhas committed
459
460
###############################################################################

461
462

def plot_complex_map_stack(map_stack, num_comps=4, title='Eigenvectors', xlabel='UDVS Step', stdevs=2):
Somnath, Suhas's avatar
Somnath, Suhas committed
463
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
464
465
    Plots the provided spectrograms from SVD V vector

Chris Smith's avatar
Chris Smith committed
466
    Parameters
Somnath, Suhas's avatar
Somnath, Suhas committed
467
    -------------
468
    map_stack : 3D numpy complex matrices
Somnath, Suhas's avatar
Somnath, Suhas committed
469
470
471
472
473
474
475
476
477
478
        Eigenvectors rearranged as - [row, col, component]
    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

Chris Smith's avatar
Chris Smith committed
479
    Returns
Somnath, Suhas's avatar
Somnath, Suhas committed
480
481
    ---------
    fig, axes
Somnath, Suhas's avatar
Somnath, Suhas committed
482
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
483
484
485
486
    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)

487
    for index in range(num_comps):
488
        cur_map = np.transpose(map_stack[index, :, :])
Somnath, Suhas's avatar
Somnath, Suhas committed
489
490
491
492
        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):
493
494
            amp_mean = np.mean(func(cur_map))
            amp_std = np.std(func(cur_map))
Somnath, Suhas's avatar
Somnath, Suhas committed
495
496
497
498
499
500
501
502
503
504
505
506
            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


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

507
def plot_complex_loop_stack(loop_stack, x_axis, heading='BE Loops', subtitle='Eigenvector', num_comps=4, x_label=''):
Somnath, Suhas's avatar
Somnath, Suhas committed
508
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
509
510
    Plots the provided spectrograms from SVD V vector

Chris Smith's avatar
Chris Smith committed
511
    Parameters
Somnath, Suhas's avatar
Somnath, Suhas committed
512
    -------------
513
514
515
    loop_stack : 3D numpy complex matrices
        Loops rearranged as - [component, points]
    x_axis : 1D real numpy array
Somnath, Suhas's avatar
Somnath, Suhas committed
516
517
518
519
520
        The vector to plot against
    num_comps : int
        Number of components to plot
    title : String
        Title to plot above everything else
521
    x_label : String
Somnath, Suhas's avatar
Somnath, Suhas committed
522
523
524
525
        Label for x axis
    stdevs : int
        Number of standard deviations to consider for plotting

Chris Smith's avatar
Chris Smith committed
526
    Returns
Somnath, Suhas's avatar
Somnath, Suhas committed
527
528
    ---------
    fig, axes
Somnath, Suhas's avatar
Somnath, Suhas committed
529
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
530
531
532
533
534
    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)
535
    fig201.canvas.set_window_title(heading)
Somnath, Suhas's avatar
Somnath, Suhas committed
536

537
    for index in range(num_comps):
538
        cur_map = loop_stack[index, :]
Somnath, Suhas's avatar
Somnath, Suhas committed
539
540
        axes = [axes201.flat[index], axes201.flat[index + num_comps]]
        for func, lab, ax in zip(funcs, labels, axes):
541
542
543
            ax.plot(x_axis, func(cur_map))
            ax.set_title('%s: %d - %s' % (subtitle, index + 1, lab))
        ax.set_xlabel(x_label)
Somnath, Suhas's avatar
Somnath, Suhas committed
544
545
546
547
548
549
550
    fig201.tight_layout()

    return fig201, axes201

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


551
def plotScree(scree, title='Scree'):
Somnath, Suhas's avatar
Somnath, Suhas committed
552
    """
553
    Plots the scree or scree
Somnath, Suhas's avatar
Somnath, Suhas committed
554

Chris Smith's avatar
Chris Smith committed
555
    Parameters
Somnath, Suhas's avatar
Somnath, Suhas committed
556
    -------------
557
558
    scree : 1D real numpy array
        The scree vector from SVD
Somnath, Suhas's avatar
Somnath, Suhas committed
559

Chris Smith's avatar
Chris Smith committed
560
    Returns
Somnath, Suhas's avatar
Somnath, Suhas committed
561
562
    ---------
    fig, axes
Somnath, Suhas's avatar
Somnath, Suhas committed
563
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
564
565
    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)
566
    axes203.loglog(np.arange(len(scree)) + 1, scree, 'b', marker='*')
Somnath, Suhas's avatar
Somnath, Suhas committed
567
568
569
    axes203.set_xlabel('Principal Component')
    axes203.set_ylabel('Variance')
    axes203.set_title(title)
570
571
    axes203.set_xlim(left=1, right=len(scree))
    axes203.set_ylim(bottom=np.min(scree), top=np.max(scree))
Somnath, Suhas's avatar
Somnath, Suhas committed
572
573
574
575
576
    fig203.canvas.set_window_title("Scree")

    return fig203, axes203


577
578
579
# ###############################################################################


580
def plot_map_stack(map_stack, num_comps=9, stdevs=2, color_bar_mode=None, evenly_spaced=False,
Chris Smith's avatar
Chris Smith committed
581
                   title='Component', heading='Map Stack', fig_mult=(4, 4), pad_mult=(0.1, 0.07), **kwargs):
Somnath, Suhas's avatar
Somnath, Suhas committed
582
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
583
    Plots the provided stack of maps
Somnath, Suhas's avatar
Somnath, Suhas committed
584

Chris Smith's avatar
Chris Smith committed
585
    Parameters
Somnath, Suhas's avatar
Somnath, Suhas committed
586
    -------------
Somnath, Suhas's avatar
Somnath, Suhas committed
587
    map_stack : 3D real numpy array
Somnath, Suhas's avatar
Somnath, Suhas committed
588
        structured as [rows, cols, component]
589
    num_comps : unsigned int
Somnath, Suhas's avatar
Somnath, Suhas committed
590
591
592
        Number of components to plot
    stdevs : int
        Number of standard deviations to consider for plotting
593
    color_bar_mode : String, Optional
594
595
596
597
598
        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.
599
600
601
602
603
    heading : String
        ###Insert description here### Default 'Map Stack'
    fig_mult : length 2 array_like of uints
        Size multipliers for the figure.  Figure size is calculated as (num_rows*`fig_mult[0]`, num_cols*`fig_mult[1]`).
        Default (4, 4)
Chris Smith's avatar
Chris Smith committed
604
605
606
607
608
609
610
    pad_mult : length 2 array_like of floats
        Multipliers for the axis padding between plots in the stack.  Padding is calculated as
        (pad_mult[0]*fig_mult[1], pad_mult[1]*fig_mult[0]) for the width and height padding respectively.
        Default (0.1, 0.07)
    kwargs : dictionary
        Keyword arguments to be passed to either matplotlib.pyplot.figure, mpl_toolkits.axes_grid1.ImageGrid, or
        pycroscopy.vis.plot_utils.plot_map.  See specific function documentation for the relavent options.
Somnath, Suhas's avatar
Somnath, Suhas committed
611

Chris Smith's avatar
Chris Smith committed
612
    Returns
Somnath, Suhas's avatar
Somnath, Suhas committed
613
614
    ---------
    fig, axes
Somnath, Suhas's avatar
Somnath, Suhas committed
615
    """
616
617
618
619
620
621
622
623
    num_comps = abs(num_comps)
    num_comps = min(num_comps, map_stack.shape[-1])

    if evenly_spaced:
        chosen_pos = np.linspace(0, map_stack.shape[-1] - 1, num_comps, dtype=int)
    else:
        chosen_pos = np.arange(num_comps, dtype=int)

624
625
626
627
628
629
    if isinstance(title, list):
        if len(title) > num_comps:
            # remove additional titles
            title = title[:num_comps]
        elif len(title) < num_comps:
            # add titles
630
            title += ['Component' + ' ' + str(x) for x in range(len(title), num_comps)]
631
632
633
    else:
        if not isinstance(title, str):
            title = 'Component'
634
        title = [title + ' ' + str(x) for x in chosen_pos]
635

636
    fig_h, fig_w = fig_mult
637
638
    p_rows = int(np.floor(np.sqrt(num_comps)))
    p_cols = int(np.ceil(num_comps / p_rows))
639
640
    if p_rows*p_cols < num_comps:
        p_cols += 1
Chris Smith's avatar
Chris Smith committed
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664

    pad_w, pad_h = pad_mult

    '''
    Set defaults for kwargs to the figure creation and extract any non-default values from current kwargs
    '''
    figkwargs = dict()
    for key in inspect.getargspec(plt.figure).args:
        if key in kwargs:
            figkwargs.update({key: kwargs.pop(key)})

    fig202 = plt.figure(figsize=(p_cols * fig_w, p_rows * fig_h), **figkwargs)

    '''
    Set defaults for kwargs to the ImageGrid and extract any non-default values from current kwargs
    '''
    igkwargs = {'cbar_pad': '1%',
                'cbar_size': '5%',
                'cbar_location': 'right',
                'direction': 'row',
                'add_all': True,
                'share_all': False,
                'aspect': True,
                'label_mode': 'L'}
Somnath, Suhas's avatar
Somnath, Suhas committed
665
    for key in igkwargs.keys():
Chris Smith's avatar
Chris Smith committed
666
667
668
        if key in kwargs:
            igkwargs.update({key: kwargs.pop(key)})

669
670
    axes202 = ImageGrid(fig202, 111, nrows_ncols=(p_rows, p_cols),
                        cbar_mode=color_bar_mode,
Chris Smith's avatar
Chris Smith committed
671
672
                        axes_pad=(pad_w*fig_w, pad_h*fig_h),
                        **igkwargs)
Somnath, Suhas's avatar
Somnath, Suhas committed
673
674
    fig202.canvas.set_window_title(heading)
    fig202.suptitle(heading, fontsize=16)
Somnath, Suhas's avatar
Somnath, Suhas committed
675

676
677
    for count, index, subtitle in zip(range(chosen_pos.size), chosen_pos, title):
        im = plot_map(axes202[count],
678
                      map_stack[:, :, index],
679
680
                      stdevs=stdevs, **kwargs)
        axes202[count].set_title(subtitle)
681
        if color_bar_mode is 'each':
682
            axes202.cbar_axes[count].colorbar(im)
683
684
685

    if color_bar_mode is 'single':
        axes202.cbar_axes[0].colorbar(im)
Somnath, Suhas's avatar
Somnath, Suhas committed
686
687
688

    return fig202, axes202

689

690
691
def plot_cluster_h5_group(h5_group, y_spec_label, centroids_together=True):
    """
Chris Smith's avatar
Chris Smith committed
692
    Plots the cluster labels and mean response for each cluster
693

Chris Smith's avatar
Chris Smith committed
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
    Parameters
    ----------
    h5_group : h5py.Datagroup object
        H5 group containing the labels and mean response
    y_spec_label : str
        Label to use for Y axis on cluster centroid plot
    centroids_together : Boolean, optional - default = True
        Whether or nor to plot all centroids together on the same plot

    Returns
    -------
    fig : Figure
        Figure containing the plots
    axes : 1D array_like of axes objects
        Axes of the individual plots within `fig`
    """
710
    # TODO: The quantity and units for the main dataset itself are missing in most cases!
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
    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)
744
    # TODO: cleaner x and y axes labels instead of 0.0000125 etc.
745

746
747
748
749
750
751
752
    if centroids_together:
        return plot_cluster_results_together(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)
    else:
        return plot_cluster_results_separate(label_mat, mean_response, max_centroids=4, x_label=x_spec_label,
                                             spec_val=np.squeeze(h5_spec_vals[0]), y_label=y_spec_label)
Somnath, Suhas's avatar
Somnath, Suhas committed
753
754

###############################################################################
755
756


757
758
759
def plot_cluster_results_together(label_mat, mean_response, spec_val=None, cmap=plt.cm.jet,
                                  spec_label='Spectroscopic Value', resp_label='Response',
                                  pos_labels=('X', 'Y'), pos_ticks=None):
Somnath, Suhas's avatar
Somnath, Suhas committed
760
    """
761
    Plot the cluster labels and mean response for each cluster in separate plots
Chris Smith's avatar
Chris Smith committed
762
763
764
765
766

    Parameters
    ----------
    label_mat : 2D ndarray or h5py.Dataset of ints
        Spatial map of cluster labels structured as [rows, cols]
767
    mean_response : 2D array or h5py.Dataset
Chris Smith's avatar
Chris Smith committed
768
769
        Mean value of each cluster over all samples 
        arranged as [cluster number, features]
770
    spec_val :  1D array or h5py.Dataset of floats, optional
Chris Smith's avatar
Chris Smith committed
771
772
773
774
775
776
777
778
779
780
781
782
        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'
Chris Smith's avatar
Chris Smith committed
783
784
785
786
    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
Chris Smith's avatar
Chris Smith committed
787
788
789
790
791
792
793

    Returns
    -------
    fig : Figure
        Figure containing the plots
    axes : 1D array_like of axes objects
        Axes of the individual plots within `fig`
Somnath, Suhas's avatar
Somnath, Suhas committed
794
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
795

796
    def __plot_centroids(centroids, ax, spec_val, spec_label, y_label, cmap, title=None):
797
        plot_line_family(ax, spec_val, centroids, label_prefix='Cluster', cmap=cmap)
Somnath, Suhas's avatar
Somnath, Suhas committed
798
        ax.set_ylabel(y_label)
Chris Smith's avatar
Chris Smith committed
799
        # ax.legend(loc='best')
Somnath, Suhas's avatar
Somnath, Suhas committed
800
801
802
803
804
        if title:
            ax.set_title(title)
            ax.set_xlabel(spec_label)

    if type(spec_val) == type(None):
Chris Smith's avatar
Chris Smith committed
805
        spec_val = np.arange(mean_response.shape[1])
Somnath, Suhas's avatar
Somnath, Suhas committed
806

Chris Smith's avatar
Chris Smith committed
807
    if mean_response.dtype in [np.complex64, np.complex128, np.complex]:
Somnath, Suhas's avatar
Somnath, Suhas committed
808
        fig = plt.figure(figsize=(12, 8))
Chris Smith's avatar
Chris Smith committed
809
810
811
        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)
Somnath, Suhas's avatar
Somnath, Suhas committed
812
813
        axes = [ax_map, ax_amp, ax_phase]

814
        __plot_centroids(np.abs(mean_response), ax_amp, spec_val, spec_label,
Chris Smith's avatar
Chris Smith committed
815
                        resp_label + ' - Amplitude', cmap, 'Mean Response')
816
        __plot_centroids(np.angle(mean_response), ax_phase, spec_val, spec_label,
Somnath, Suhas's avatar
Somnath, Suhas committed
817
                        resp_label + ' - Phase', cmap)
Chris Smith's avatar
Chris Smith committed
818
819
        plot_handles, plot_labels = ax_amp.get_legend_handles_labels()

Somnath, Suhas's avatar
Somnath, Suhas committed
820
    else:
Chris Smith's avatar
Chris Smith committed
821
822
823
824
        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]
825
        __plot_centroids(mean_response, ax_resp, spec_val, spec_label,
Chris Smith's avatar
Chris Smith committed
826
827
828
829
830
831
                        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]
Somnath, Suhas's avatar
Somnath, Suhas committed
832
833

    if isinstance(label_mat, h5py.Dataset):
Somnath, Suhas's avatar
Somnath, Suhas committed
834
        """
Somnath, Suhas's avatar
Somnath, Suhas committed
835
        Reshape label_mat based on linked positions
Somnath, Suhas's avatar
Somnath, Suhas committed
836
        """
Somnath, Suhas's avatar
Somnath, Suhas committed
837
838
839
840
        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)
841

Chris Smith's avatar
Chris Smith committed
842
    # im = ax_map.imshow(label_mat, interpolation='none')
843
844
845
846
847
848
849
850
851
852
853
    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])

854
    """divider = make_axes_locatable(ax_map)
Somnath, Suhas's avatar
Somnath, Suhas committed
855
    cax = divider.append_axes("right", size="5%", pad=0.05)  # space for colorbar
856
857
858
859
860
    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))
Somnath, Suhas's avatar
Somnath, Suhas committed
861
    ax_map.axis('tight')
862
    ax_map.set_aspect('auto')
Somnath, Suhas's avatar
Somnath, Suhas committed
863
864
865
    ax_map.set_title('Cluster Label Map')

    fig.tight_layout()
Chris Smith's avatar
Chris Smith committed
866
    fig.canvas.set_window_title('Cluster results')
Somnath, Suhas's avatar
Somnath, Suhas committed
867
868
869
870
871

    return fig, axes

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

872

873
874
def plot_cluster_results_separate(label_mat, cluster_centroids, max_centroids=4,
                                  spec_val=None, x_label='Excitation (a.u.)', y_label='Response (a.u.)'):
Somnath, Suhas's avatar
Somnath, Suhas committed
875
    """
876
    Plots the provided labels mat and centroids from clustering
Somnath, Suhas's avatar
Somnath, Suhas committed
877

878
879
    Parameters
    ----------
Somnath, Suhas's avatar
Somnath, Suhas committed
880
881
882
883
    label_mat : 2D int numpy array
                structured as [rows, cols]
    cluster_centroids: 2D real numpy array
                       structured as [cluster,features]
884
885
    max_centroids : unsigned int
                    Number of centroids to plot
886
887
888
    spec_val :  array-like
        X axis to plot the centroids against
        If no value is specified, the data is plotted against the index
889
890
891
892
    x_label : String / unicode
              X label for centroid plots
    y_label : String / unicode
              Y label for centroid plots
Somnath, Suhas's avatar
Somnath, Suhas committed
893

894
895
    Returns
    -------
Somnath, Suhas's avatar
Somnath, Suhas committed
896
    fig
Somnath, Suhas's avatar
Somnath, Suhas committed
897
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
898

899
    if max_centroids < 5:
Somnath, Suhas's avatar
Somnath, Suhas committed
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927

        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]

928
    # First plot the labels map:
929
930
    pcol0 = fax1.pcolor(label_mat, cmap=discrete_cmap(cluster_centroids.shape[0],
                                                      base_cmap=plt.cm.jet))
931
    fig501.colorbar(pcol0, ax=fax1, ticks=np.arange(cluster_centroids.shape[0]))
932
933
    fax1.axis('tight')
    fax1.set_aspect('auto')
934
    fax1.set_title('Cluster Label Map')
935
    """im = fax1.imshow(label_mat, interpolation='none')
936
937
    divider = make_axes_locatable(fax1)
    cax = divider.append_axes("right", size="5%", pad=0.05)  # space for colorbar
938
939
940
941
    plt.colorbar(im, cax=cax)"""

    if spec_val is None and cluster_centroids.ndim == 2:
        spec_val = np.arange(cluster_centroids.shape[1])
942
943

    # Plot results
944
945
946
947
948
949
950
    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:
951
            plot_map(ax, cluster_centroids[index])
952
        ax.set_title('Centroid: %d' % index)
Somnath, Suhas's avatar
Somnath, Suhas committed
953
954

    fig501.subplots_adjust(hspace=0.60, wspace=0.60)
955
    fig501.tight_layout()
Somnath, Suhas's avatar
Somnath, Suhas committed
956
957
958
959
960
961

    return fig501


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

962
963
def plot_cluster_dendrogram(label_mat, e_vals, num_comp, num_cluster, mode='Full', last=None,
                            sort_type='distance', sort_mode=True):
Somnath, Suhas's avatar
Somnath, Suhas committed
964
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
965
966
967
968
969
970
    Creates and plots the dendrograms for the given label_mat and
    eigenvalues

    Parameters
    -------------
    label_mat : 2D real numpy array
971
        structured as [rows, cols], from KMeans clustering
Somnath, Suhas's avatar
Somnath, Suhas committed
972
    e_vals: 3D real numpy array of eigenvalues
973
        structured as [component, rows, cols]
974
    num_comp : int
975
976
977
        Number of components used to make eigenvalues
    num_cluster : int
        Number of cluster used to make the label_mat
Somnath, Suhas's avatar
Somnath, Suhas committed
978
    mode: str, optional
979
980
981
        How should the dendrograms be created.
        "Full" -- use all clusters when creating the dendrograms
        "Truncated" -- stop showing clusters after 'last'
Somnath, Suhas's avatar
Somnath, Suhas committed
982
    last: int, optional - should be provided when using "Truncated"
983
984
985
986
987
988
989
990
991
992
993
994
995
996
        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
Somnath, Suhas's avatar
Somnath, Suhas committed
997
998
999

    Returns
    ---------
1000
1001
    fig : matplotlib.pyplot Figure object
        Figure containing the dendrogram
Somnath, Suhas's avatar
Somnath, Suhas committed
1002
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
1003
1004
1005
1006
1007
    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':
1008
        print('Creating full dendrogram from clusters')
Somnath, Suhas's avatar
Somnath, Suhas committed
1009
1010
        mode = None
    elif mode == 'Truncated':
1011
        print('Creating truncated dendrogram from clusters.  Will stop at {}.'.format(last))
Somnath, Suhas's avatar
Somnath, Suhas committed
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
        mode = 'lastp'
    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])
1026
    for k1 in range(num_cluster):
Somnath, Suhas's avatar
Somnath, Suhas committed
1027
1028
        [i_x, i_y] = np.where(label_mat == k1)
        u_stack = np.zeros([len(i_x), num_comp])
1029
        for k2 in range(len(i_x)):
Somnath, Suhas's avatar
Somnath, Suhas committed
1030
1031
1032
1033
            u_stack[k2, :] = np.abs(e_vals[i_x[k2], i_y[k2], :num_comp])

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

1034
    # Get the distrance between cluster means
1035
    distance_mat = scipy.spatial.distance.pdist(centroid_mat)
Somnath, Suhas's avatar
Somnath, Suhas committed
1036
1037

    # get hierachical pairings of clusters
1038
    linkage_pairing = scipy.cluster.hierarchy.linkage(distance_mat, 'weighted')
Somnath, Suhas's avatar
Somnath, Suhas committed
1039
1040
1041
    linkage_pairing[:, 3] = linkage_pairing[:, 3] / max(linkage_pairing[:, 3])

    fig = plt.figure()
1042
1043
1044
    scipy.cluster.hierarchy.dendrogram(linkage_pairing, p=last, truncate_mode=mode,
                                       count_sort=c_sort, distance_sort=d_sort,
                                       leaf_rotation=90)
Somnath, Suhas's avatar
Somnath, Suhas committed
1045
1046
1047
1048
1049
1050
1051
1052

    fig.axes[0].set_title('Dendrogram')
    fig.axes[0].set_xlabel('Index or (cluster size)')
    fig.axes[0].set_ylabel('Distance')

    return fig


1053
def plot_1d_spectrum(data_vec, freq, title, figure_path=None):
Somnath, Suhas's avatar
Somnath, Suhas committed
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
    """
    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):
1076
1077
        warn('plot_1d_spectrum: Incompatible data sizes!!!!')
        print('1D:', data_vec.shape, freq.shape)
Somnath, Suhas's avatar
Somnath, Suhas committed
1078
        return
1079
1080
    freq *= 1E-3  # to kHz
    fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)
Somnath, Suhas's avatar
Somnath, Suhas committed
1081
1082
1083
1084
1085
1086
1087
1088
    ax[0].plot(freq, np.abs(data_vec) * 1E+3)
    ax[0].set_title('Amplitude (mV)')
    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)
1089
    return
Somnath, Suhas's avatar
Somnath, Suhas committed
1090
1091
1092
1093


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

1094
def plot_2d_spectrogram(mean_spectrogram, freq, title, cmap=None, figure_path=None, **kwargs):
Somnath, Suhas's avatar
Somnath, Suhas committed
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
    """
    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
1106
1107
    cmap : matplotlib.colors.LinearSegmentedColormap object
        color map. Default = plt.cm.jet
Somnath, Suhas's avatar
Somnath, Suhas committed
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
    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):
1119
1120
        warn('plot_2d_spectrogram: Incompatible data sizes!!!!')
        print('2D:', mean_spectrogram.shape, freq.shape)
Somnath, Suhas's avatar
Somnath, Suhas committed
1121
        return
1122
1123
1124
1125
1126
1127

    """cmap = kwargs.get('cmap')
    kwargs.pop('cmap')"""
    if cmap is None:  # unpack from kwargs instead
        col_map = plt.cm.jet  # overriding default

1128
1129
    freq *= 1E-3  # to kHz
    fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)
1130
1131
    # print(mean_spectrogram.shape)
    # print(freq.shape)
1132
1133
    ax[0].imshow(np.abs(mean_spectrogram), interpolation='nearest', cmap=col_map,
                 extent=[freq[0], freq[-1], mean_spectrogram.shape[0], 0], **kwargs)
Somnath, Suhas's avatar
Somnath, Suhas committed
1134
1135
1136
1137
    ax[0].set_title('Amplitude')
    # ax[0].set_xticks(freq)
    # ax[0].set_ylabel('UDVS Step')
    ax[0].axis('tight')
1138
1139
    ax[1].imshow(np.angle(mean_spectrogram), interpolation='nearest', cmap=col_map,
                 extent=[freq[0], freq[-1], mean_spectrogram.shape[0], 0], **kwargs)
Somnath, Suhas's avatar
Somnath, Suhas committed
1140
1141
1142
1143
1144
1145
1146
    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)
1147
    return fig, ax
Somnath, Suhas's avatar
Somnath, Suhas committed
1148
1149
1150

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

1151
1152

def plot_histgrams(p_hist, p_hbins, title, figure_path=None):
Somnath, Suhas's avatar
Somnath, Suhas committed
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
    """
    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')

1216
    return fig
1217
1218


1219
def plot_image_cleaning_results(raw_image, clean_image, stdevs=2, heading='Image Cleaning Results',
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
                                fig_mult=(4, 4), fig_args={}, **kwargs):
    """
    
    Parameters
    ----------
    raw_image
    clean_image
    stdevs
    color_bar_mode
    fig_mult
    fig_args
    heading

    Returns
    -------

    """
    plot_args = {'cbar_pad': '2.0%', 'cbar_size': '4%', 'hor_axis_pad': 0.115, 'vert_axis_pad': 0.1,
                 'sup_title_size': 26, 'sub_title_size': 22, 'show_x_y_ticks': False, 'show_tick_marks': False,
                 'x_y_tick_font_size': 18, 'cbar_tick_font_size': 18}

    plot_args.update(fig_args)

    fig_h, fig_w = fig_mult
    p_rows = 2
    p_cols = 3

    fig_clean = plt.figure(figsize=(p_cols * fig_w, p_rows * fig_h))
1248
    axes_clean = ImageGrid(fig_clean, 111, nrows_ncols=(p_rows, p_cols), cbar_mode='each',
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
                           cbar_pad=plot_args['cbar_pad'], cbar_size=plot_args['cbar_size'],
                           axes_pad=(plot_args['hor_axis_pad']*fig_w, plot_args['vert_axis_pad']*fig_h))
    fig_clean.canvas.set_window_title(heading)
    fig_clean.suptitle(heading, fontsize=plot_args['sup_title_size'])

    '''
    Calculate the removed noise and the FFT's of the raw, clean, and noise
    '''
    removed_noise = raw_image - clean_image
    blackman_window_rows = scipy.signal.blackman(clean_image.shape[0])
    blackman_window_cols = scipy.signal.blackman(clean_image.shape[1])

    FFT_raw = np.abs(np.fft.fftshift(
        np.fft.fft2(blackman_window_rows[:, np.newaxis] * raw_image * blackman_window_cols[np.newaxis, :]),
        axes=(0, 1)))
    FFT_clean = np.abs(np.fft.fftshift(
        np.fft.fft2(blackman_window_rows[:, np.newaxis] * clean_image * blackman_window_cols[np.newaxis, :]),
        axes=(0, 1)))
    FFT_noise = np.abs(np.fft.fftshift(
        np.fft.fft2(blackman_window_rows[:, np.newaxis] * removed_noise * blackman_window_cols[np.newaxis, :]),
        axes=(0, 1)))

    '''
    Now find the mean and standard deviation of the images
    '''
    raw_mean = np.mean(raw_image)
    clean_mean = np.mean(clean_image)
    noise_mean = np.mean(removed_noise)

    raw_std = np.std(raw_image)
    clean_std = np.std(clean_image)
    noise_std = np.std(removed_noise)
    fft_clean_std = np.std(FFT_clean)

    '''
    Make lists of everything needed to plot
    '''
    plot_names = ['Original Image', 'Cleaned Image', 'Removed Noise',
                  'FFT Original Image', 'FFT Cleaned Image', 'FFT Removed Noise']
    plot_data = [raw_image, clean_image, removed_noise, FFT_raw, FFT_clean, FFT_noise]