plot_utils.py 49.7 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
8
# TODO: All general plotting functions should support data with 1 or 2 spatial dimensions.

Somnath, Suhas's avatar
Somnath, Suhas committed
9
from __future__ import division  # int/int = float
10
from warnings import warn
11
import os
Chris Smith's avatar
merged    
Chris Smith committed
12
import h5py
13
import scipy
14
import matplotlib.pyplot as plt
15
from matplotlib.colors import LinearSegmentedColormap
16
from mpl_toolkits.axes_grid1 import ImageGrid
17
import numpy as np
18
from ..analysis.utils.be_loop import loop_fit_function
19
from ..io.hdf_utils import reshape_to_Ndims, get_formatted_labels
20

Somnath, Suhas's avatar
Somnath, Suhas committed
21

22
23
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
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
54

55
56
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
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)
84

Chris Smith's avatar
Chris Smith committed
85

Somnath, Suhas's avatar
Somnath, Suhas committed
86
87
88
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
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
147
148


Somnath, Suhas's avatar
Somnath, Suhas committed
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
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
168
169


170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
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)

197
198
199

def plot_loop_guess_fit(vdc, ds_proj_loops, ds_guess, ds_fit, title=''):
    """
200
201
202
203
    Plots the loop guess, fit, source projected loops for a single cycle

    Parameters
    ----------
204
    vdc - 1D float numpy array
205
206
        DC offset vector (unshifted)
    ds_proj_loops - 2D numpy array
207
        Projected loops arranged as [position, vdc]
208
209
210
211
212
213
214
215
216
217
218
219
220
    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
221
222
223
    """
    shift_ind = int(-1 * len(vdc) / 4)
    vdc_shifted = np.roll(vdc, shift_ind)
224
    loops_shifted = np.roll(ds_proj_loops, shift_ind, axis=1)
225
226
227
228
229

    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):
230
        ax.plot(vdc_shifted, loops_shifted[pos, :], 'k', label='Raw')
231
232
        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')
233
234
        ax.set_xlabel('V_DC (V)')
        ax.set_ylabel('PR (a.u.)')
235
        ax.set_title('Position ' + str(pos))
236
237
238
239
240
    ax.legend()
    fig.suptitle(title)
    fig.tight_layout()

    return fig, axes
Somnath, Suhas's avatar
Somnath, Suhas committed
241
242
243

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

244

245
def rainbow_plot(ax, ao_vec, ai_vec, num_steps=32, cmap=plt.cm.jet, **kwargs):
Somnath, Suhas's avatar
Somnath, Suhas committed
246
247
248
249
    """
    Plots the input against the output waveform (typically loops).
    The color of the curve changes as a function of time using the jet colorscheme

250
251
    Parameters
    ----------
Somnath, Suhas's avatar
Somnath, Suhas committed
252
253
254
255
256
257
258
259
    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
260
261
    cmap : matplotlib.colors.LinearSegmentedColormap object
        Colormap to be used
Somnath, Suhas's avatar
Somnath, Suhas committed
262
263
    """
    pts_per_step = int(len(ai_vec) / num_steps)
264
    for step in range(num_steps - 1):
Somnath, Suhas's avatar
Somnath, Suhas committed
265
266
        ax.plot(ao_vec[step * pts_per_step:(step + 1) * pts_per_step],
                ai_vec[step * pts_per_step:(step + 1) * pts_per_step],
267
                color=cmap(255 * step / num_steps), **kwargs)
Somnath, Suhas's avatar
Somnath, Suhas committed
268
269
270
    # plot the remainder:
    ax.plot(ao_vec[(num_steps - 1) * pts_per_step:],
            ai_vec[(num_steps - 1) * pts_per_step:],
271
            color=cmap(255 * num_steps / num_steps), **kwargs)
Somnath, Suhas's avatar
Somnath, Suhas committed
272
273
274
275
276
    """
    CS3=plt.contourf([[0,0],[0,0]], range(0,310),cmap=plt.cm.jet)
    fig.colorbar(CS3)"""


277
278
def plot_line_family(axis, x_axis, line_family, line_names=None, label_prefix='Line', label_suffix='', cmap=plt.cm.jet,
                     **kwargs):
279
280
281
282
283
    """
    Plots a family of lines with a sequence of colors

    Parameters
    ----------
284
    axis : axis handle
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
        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)]

308
    for line_ind in range(num_lines):
309
310
311
        axis.plot(x_axis, line_family[line_ind],
                  label=line_names[line_ind],
                  color=cmap(int(255 * line_ind / (num_lines - 1))), **kwargs)
312
313


314
def plot_map(axis, data, stdevs=2, **kwargs):
315
316
317
318
319
320
321
322
323
324
325
326
    """
    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
327

328
329
330
331
332
    Returns
    -------
    """
    data_mean = np.mean(data)
    data_std = np.std(data)
333
    origin = kwargs.pop('origin', 'lower')
334
335
336
    im = axis.imshow(data, interpolation='none',
                     vmin=data_mean - stdevs * data_std,
                     vmax=data_mean + stdevs * data_std,
337
                     origin=origin,
338
                     **kwargs)
339
340
    axis.set_aspect('auto')

341
    return im
342

343

344
345
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):
346
    # TODO: Allow multiple excitation waveforms
Somnath, Suhas's avatar
Somnath, Suhas committed
347
    """
348
    Plots loops from multiple datasets from up to 25 evenly spaced positions
Somnath, Suhas's avatar
Somnath, Suhas committed
349
350
351
352
353

    Parameters
    -----------
    excit_wfm : 1D numpy float array
        Excitation waveform in the time domain
354
355
356
357
358
359
    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
360
361
362
363
364
365
366
367
    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
368
    use_rainbow_plots : (optional) Boolean
Somnath, Suhas's avatar
Somnath, Suhas committed
369
370
371
372
373
374
375
376
377
378
379
380
381
382
        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
    """
383
    if type(datasets) in [h5py.Dataset, np.ndarray]:
384
385
386
        # can be numpy array or h5py.dataset
        num_pos = datasets.shape[0]
        num_points = datasets.shape[1]
Somnath, Suhas's avatar
Somnath, Suhas committed
387
        datasets = [datasets]
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
        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
425
426

    plots_on_side = min(abs(plots_on_side), 5)
427

Somnath, Suhas's avatar
Somnath, Suhas committed
428
429
430
431
432
433
434
    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))
435
    axes_lin = axes.flatten()
Somnath, Suhas's avatar
Somnath, Suhas committed
436

437
    cent_ind = int(0.5 * excit_wfm.size)
Somnath, Suhas's avatar
Somnath, Suhas committed
438
439
440
441
442
443
    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
444
        r_resp_ind = excit_wfm.size
Somnath, Suhas's avatar
Somnath, Suhas committed
445
446

    for count, posn in enumerate(chosen_pos):
447
448
        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
449
        else:
450
451
452
            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:
Somnath, Suhas's avatar
Somnath, Suhas committed
453
454
455
456
457
458
459
460
461
462
463
464
            # 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))
465
466
    if len(datasets) > 1:
        axes_lin[count].legend(dataset_names, loc='best')
Somnath, Suhas's avatar
Somnath, Suhas committed
467
468
469
470
    if title:
        fig.suptitle(title, fontsize=14)
    plt.tight_layout()
    return fig, axes
Chris Smith's avatar
merged    
Chris Smith committed
471

Somnath, Suhas's avatar
Somnath, Suhas committed
472
473
###############################################################################

474
475

def plot_complex_map_stack(map_stack, num_comps=4, title='Eigenvectors', xlabel='UDVS Step', stdevs=2):
Somnath, Suhas's avatar
Somnath, Suhas committed
476
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
477
478
479
480
    Plots the provided spectrograms from SVD V vector

    Parameters:
    -------------
481
    map_stack : 3D numpy complex matrices
Somnath, Suhas's avatar
Somnath, Suhas committed
482
483
484
485
486
487
488
489
490
491
492
493
494
        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

    Returns:
    ---------
    fig, axes
Somnath, Suhas's avatar
Somnath, Suhas committed
495
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
496
497
498
499
    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)

500
    for index in range(num_comps):
501
        cur_map = np.transpose(map_stack[index, :, :])
Somnath, Suhas's avatar
Somnath, Suhas committed
502
503
504
505
        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):
506
507
            amp_mean = np.mean(func(cur_map))
            amp_std = np.std(func(cur_map))
Somnath, Suhas's avatar
Somnath, Suhas committed
508
509
510
511
512
513
514
515
516
517
518
519
            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


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

520
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
521
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
522
523
524
525
    Plots the provided spectrograms from SVD V vector

    Parameters:
    -------------
526
527
528
    loop_stack : 3D numpy complex matrices
        Loops rearranged as - [component, points]
    x_axis : 1D real numpy array
Somnath, Suhas's avatar
Somnath, Suhas committed
529
530
531
532
533
        The vector to plot against
    num_comps : int
        Number of components to plot
    title : String
        Title to plot above everything else
534
    x_label : String
Somnath, Suhas's avatar
Somnath, Suhas committed
535
536
537
538
539
540
541
        Label for x axis
    stdevs : int
        Number of standard deviations to consider for plotting

    Returns:
    ---------
    fig, axes
Somnath, Suhas's avatar
Somnath, Suhas committed
542
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
543
544
545
546
547
    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)
548
    fig201.canvas.set_window_title(heading)
Somnath, Suhas's avatar
Somnath, Suhas committed
549

550
    for index in range(num_comps):
551
        cur_map = loop_stack[index, :]
Somnath, Suhas's avatar
Somnath, Suhas committed
552
553
        axes = [axes201.flat[index], axes201.flat[index + num_comps]]
        for func, lab, ax in zip(funcs, labels, axes):
554
555
556
            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
557
558
559
560
561
562
563
    fig201.tight_layout()

    return fig201, axes201

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


564
def plotScree(scree, title='Scree'):
Somnath, Suhas's avatar
Somnath, Suhas committed
565
    """
566
    Plots the scree or scree
Somnath, Suhas's avatar
Somnath, Suhas committed
567
568
569

    Parameters:
    -------------
570
571
    scree : 1D real numpy array
        The scree vector from SVD
Somnath, Suhas's avatar
Somnath, Suhas committed
572
573
574
575

    Returns:
    ---------
    fig, axes
Somnath, Suhas's avatar
Somnath, Suhas committed
576
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
577
578
    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)
579
    axes203.loglog(np.arange(len(scree)) + 1, scree, 'b', marker='*')
Somnath, Suhas's avatar
Somnath, Suhas committed
580
581
582
    axes203.set_xlabel('Principal Component')
    axes203.set_ylabel('Variance')
    axes203.set_title(title)
583
584
    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
585
586
587
588
589
    fig203.canvas.set_window_title("Scree")

    return fig203, axes203


590
591
592
# ###############################################################################


593
def plot_map_stack(map_stack, num_comps=9, stdevs=2, color_bar_mode=None, evenly_spaced=False,
594
                   title='Component', heading='Map Stack', fig_mult=(4, 4), **kwargs):
Somnath, Suhas's avatar
Somnath, Suhas committed
595
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
596
    Plots the provided stack of maps
Somnath, Suhas's avatar
Somnath, Suhas committed
597
598
599

    Parameters:
    -------------
Somnath, Suhas's avatar
Somnath, Suhas committed
600
    map_stack : 3D real numpy array
Somnath, Suhas's avatar
Somnath, Suhas committed
601
        structured as [rows, cols, component]
602
    num_comps : unsigned int
Somnath, Suhas's avatar
Somnath, Suhas committed
603
604
605
        Number of components to plot
    stdevs : int
        Number of standard deviations to consider for plotting
606
    color_bar_mode : String, Optional
607
608
609
610
611
        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.
612
613
614
615
616
    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)
Somnath, Suhas's avatar
Somnath, Suhas committed
617
618
619
620

    Returns:
    ---------
    fig, axes
Somnath, Suhas's avatar
Somnath, Suhas committed
621
    """
622
623
624
625
626
627
628
629
630
    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)

631
632
633
634
635
636
637
638
639
640
    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'
641
        title = [title + ' ' + str(x) for x in chosen_pos]
642

643
    fig_h, fig_w = fig_mult
644
645
    p_rows = int(np.floor(np.sqrt(num_comps)))
    p_cols = int(np.ceil(num_comps / p_rows))
646
647
    if p_rows*p_cols < num_comps:
        p_cols += 1
648
    fig202 = plt.figure(figsize=(p_cols * fig_w, p_rows * fig_h))
649
650
651
652
653
    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))
Somnath, Suhas's avatar
Somnath, Suhas committed
654
655
    fig202.canvas.set_window_title(heading)
    fig202.suptitle(heading, fontsize=16)
Somnath, Suhas's avatar
Somnath, Suhas committed
656

657
658
    for count, index, subtitle in zip(range(chosen_pos.size), chosen_pos, title):
        im = plot_map(axes202[count],
659
                      map_stack[:, :, index],
660
661
                      stdevs=stdevs, **kwargs)
        axes202[count].set_title(subtitle)
662
        if color_bar_mode is 'each':
663
            axes202.cbar_axes[count].colorbar(im)
664
665
666

    if color_bar_mode is 'single':
        axes202.cbar_axes[0].colorbar(im)
Somnath, Suhas's avatar
Somnath, Suhas committed
667
668
669

    return fig202, axes202

670

671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
def plot_cluster_h5_group(h5_group, y_spec_label, centroids_together=True):
    """
        Plots the cluster labels and mean response for each cluster

        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`
        """
691
    # TODO: The quantity and units for the main dataset itself are missing in most cases!
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
    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)
725
    # TODO: cleaner x and y axes labels instead of 0.0000125 etc.
726

727
728
729
730
731
732
733
    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
734
735

###############################################################################
736
737


738
739
740
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
741
    """
742
    Plot the cluster labels and mean response for each cluster in separate plots
Chris Smith's avatar
Chris Smith committed
743
744
745
746
747

    Parameters
    ----------
    label_mat : 2D ndarray or h5py.Dataset of ints
        Spatial map of cluster labels structured as [rows, cols]
748
    mean_response : 2D array or h5py.Dataset
Chris Smith's avatar
Chris Smith committed
749
750
        Mean value of each cluster over all samples 
        arranged as [cluster number, features]
751
    spec_val :  1D array or h5py.Dataset of floats, optional
Chris Smith's avatar
Chris Smith committed
752
753
754
755
756
757
758
759
760
761
762
763
        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
764
765
766
767
    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
768
769
770
771
772
773
774

    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
775
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
776

777
    def __plot_centroids(centroids, ax, spec_val, spec_label, y_label, cmap, title=None):
778
        plot_line_family(ax, spec_val, centroids, label_prefix='Cluster', cmap=cmap)
Somnath, Suhas's avatar
Somnath, Suhas committed
779
        ax.set_ylabel(y_label)
Chris Smith's avatar
Chris Smith committed
780
        # ax.legend(loc='best')
Somnath, Suhas's avatar
Somnath, Suhas committed
781
782
783
784
785
        if title:
            ax.set_title(title)
            ax.set_xlabel(spec_label)

    if type(spec_val) == type(None):
Chris Smith's avatar
Chris Smith committed
786
        spec_val = np.arange(mean_response.shape[1])
Somnath, Suhas's avatar
Somnath, Suhas committed
787

Chris Smith's avatar
Chris Smith committed
788
    if mean_response.dtype in [np.complex64, np.complex128, np.complex]:
Somnath, Suhas's avatar
Somnath, Suhas committed
789
        fig = plt.figure(figsize=(12, 8))
Chris Smith's avatar
Chris Smith committed
790
791
792
        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
793
794
        axes = [ax_map, ax_amp, ax_phase]

795
        __plot_centroids(np.abs(mean_response), ax_amp, spec_val, spec_label,
Chris Smith's avatar
Chris Smith committed
796
                        resp_label + ' - Amplitude', cmap, 'Mean Response')
797
        __plot_centroids(np.angle(mean_response), ax_phase, spec_val, spec_label,
Somnath, Suhas's avatar
Somnath, Suhas committed
798
                        resp_label + ' - Phase', cmap)
Chris Smith's avatar
Chris Smith committed
799
800
        plot_handles, plot_labels = ax_amp.get_legend_handles_labels()

Somnath, Suhas's avatar
Somnath, Suhas committed
801
    else:
Chris Smith's avatar
Chris Smith committed
802
803
804
805
        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]
806
        __plot_centroids(mean_response, ax_resp, spec_val, spec_label,
Chris Smith's avatar
Chris Smith committed
807
808
809
810
811
812
                        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
813
814

    if isinstance(label_mat, h5py.Dataset):
Somnath, Suhas's avatar
Somnath, Suhas committed
815
        """
Somnath, Suhas's avatar
Somnath, Suhas committed
816
        Reshape label_mat based on linked positions
Somnath, Suhas's avatar
Somnath, Suhas committed
817
        """
Somnath, Suhas's avatar
Somnath, Suhas committed
818
819
820
821
        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)
822

Chris Smith's avatar
Chris Smith committed
823
    # im = ax_map.imshow(label_mat, interpolation='none')
824
825
826
827
828
829
830
831
832
833
834
    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])

835
    """divider = make_axes_locatable(ax_map)
Somnath, Suhas's avatar
Somnath, Suhas committed
836
    cax = divider.append_axes("right", size="5%", pad=0.05)  # space for colorbar
837
838
839
840
841
    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
842
    ax_map.axis('tight')
843
    ax_map.set_aspect('auto')
Somnath, Suhas's avatar
Somnath, Suhas committed
844
845
846
    ax_map.set_title('Cluster Label Map')

    fig.tight_layout()
Chris Smith's avatar
Chris Smith committed
847
    fig.canvas.set_window_title('Cluster results')
Somnath, Suhas's avatar
Somnath, Suhas committed
848
849
850
851
852

    return fig, axes

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

853

854
855
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
856
    """
857
    Plots the provided labels mat and centroids from clustering
Somnath, Suhas's avatar
Somnath, Suhas committed
858

859
860
    Parameters
    ----------
Somnath, Suhas's avatar
Somnath, Suhas committed
861
862
863
864
    label_mat : 2D int numpy array
                structured as [rows, cols]
    cluster_centroids: 2D real numpy array
                       structured as [cluster,features]
865
866
    max_centroids : unsigned int
                    Number of centroids to plot
867
868
869
    spec_val :  array-like
        X axis to plot the centroids against
        If no value is specified, the data is plotted against the index
870
871
872
873
    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
874

875
876
    Returns
    -------
Somnath, Suhas's avatar
Somnath, Suhas committed
877
    fig
Somnath, Suhas's avatar
Somnath, Suhas committed
878
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
879

880
    if max_centroids < 5:
Somnath, Suhas's avatar
Somnath, Suhas committed
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908

        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]

909
    # First plot the labels map:
910
911
    pcol0 = fax1.pcolor(label_mat, cmap=discrete_cmap(cluster_centroids.shape[0],
                                                      base_cmap=plt.cm.jet))
912
    fig501.colorbar(pcol0, ax=fax1, ticks=np.arange(cluster_centroids.shape[0]))
913
914
    fax1.axis('tight')
    fax1.set_aspect('auto')
915
    fax1.set_title('Cluster Label Map')
916
    """im = fax1.imshow(label_mat, interpolation='none')
917
918
    divider = make_axes_locatable(fax1)
    cax = divider.append_axes("right", size="5%", pad=0.05)  # space for colorbar
919
920
921
922
    plt.colorbar(im, cax=cax)"""

    if spec_val is None and cluster_centroids.ndim == 2:
        spec_val = np.arange(cluster_centroids.shape[1])
923
924

    # Plot results
925
926
927
928
929
930
931
    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:
932
            plot_map(ax, cluster_centroids[index])
933
        ax.set_title('Centroid: %d' % index)
Somnath, Suhas's avatar
Somnath, Suhas committed
934
935

    fig501.subplots_adjust(hspace=0.60, wspace=0.60)
936
    fig501.tight_layout()
Somnath, Suhas's avatar
Somnath, Suhas committed
937
938
939
940
941
942

    return fig501


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

943
944
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
945
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
946
947
948
949
950
951
    Creates and plots the dendrograms for the given label_mat and
    eigenvalues

    Parameters
    -------------
    label_mat : 2D real numpy array
952
        structured as [rows, cols], from KMeans clustering
Somnath, Suhas's avatar
Somnath, Suhas committed
953
    e_vals: 3D real numpy array of eigenvalues
954
        structured as [component, rows, cols]
955
    num_comp : int
956
957
958
        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
959
    mode: str, optional
960
961
962
        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
963
    last: int, optional - should be provided when using "Truncated"
964
965
966
967
968
969
970
971
972
973
974
975
976
977
        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
978
979
980

    Returns
    ---------
981
982
    fig : matplotlib.pyplot Figure object
        Figure containing the dendrogram
Somnath, Suhas's avatar
Somnath, Suhas committed
983
    """
Somnath, Suhas's avatar
Somnath, Suhas committed
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
    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'
    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])
1007
    for k1 in range(num_cluster):
Somnath, Suhas's avatar
Somnath, Suhas committed
1008
1009
        [i_x, i_y] = np.where(label_mat == k1)
        u_stack = np.zeros([len(i_x), num_comp])
1010
        for k2 in range(len(i_x)):
Somnath, Suhas's avatar
Somnath, Suhas committed
1011
1012
1013
1014
            u_stack[k2, :] = np.abs(e_vals[i_x[k2], i_y[k2], :num_comp])

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

1015
    # Get the distrance between cluster means
1016
    distance_mat = scipy.spatial.distance.pdist(centroid_mat)
Somnath, Suhas's avatar
Somnath, Suhas committed
1017
1018

    # get hierachical pairings of clusters
1019
    linkage_pairing = scipy.cluster.hierarchy.linkage(distance_mat, 'weighted')
Somnath, Suhas's avatar
Somnath, Suhas committed
1020
1021
1022
    linkage_pairing[:, 3] = linkage_pairing[:, 3] / max(linkage_pairing[:, 3])

    fig = plt.figure()
1023
1024
1025
    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
1026
1027
1028
1029
1030
1031
1032
1033

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

    return fig


1034
def plot_1d_spectrum(data_vec, freq, title, figure_path=None):
Somnath, Suhas's avatar
Somnath, Suhas committed
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
    """
    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):
1057
1058
        warn('plot_1d_spectrum: Incompatible data sizes!!!!')
        print('1D:', data_vec.shape, freq.shape)
Somnath, Suhas's avatar
Somnath, Suhas committed
1059
        return
1060
1061
    freq *= 1E-3  # to kHz
    fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)
Somnath, Suhas's avatar
Somnath, Suhas committed
1062
1063
1064
1065
1066
1067
1068
1069
    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)
1070
    return fig, ax
Somnath, Suhas's avatar
Somnath, Suhas committed
1071
1072
1073
1074


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

1075
def plot_2d_spectrogram(mean_spectrogram, freq, title, cmap=None, figure_path=None, **kwargs):
Somnath, Suhas's avatar
Somnath, Suhas committed
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
    """
    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
1087
1088
    cmap : matplotlib.colors.LinearSegmentedColormap object
        color map. Default = plt.cm.jet
Somnath, Suhas's avatar
Somnath, Suhas committed
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
    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):
1100
1101
        warn('plot_2d_spectrogram: Incompatible data sizes!!!!')
        print('2D:', mean_spectrogram.shape, freq.shape)
Somnath, Suhas's avatar
Somnath, Suhas committed
1102
        return
1103
1104
1105
1106
1107
1108

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

1109
1110
    freq *= 1E-3  # to kHz
    fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)
Somnath, Suhas's avatar
Somnath, Suhas committed
1111
1112
    # print mean_spectrogram.shape
    # print freq.shape
1113
1114
    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
1115
1116
1117
1118
    ax[0].set_title('Amplitude')
    # ax[0].set_xticks(freq)
    # ax[0].set_ylabel('UDVS Step')
    ax[0].axis('tight')
1119
1120
    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
1121
1122
1123
1124
1125
1126
1127
    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)
1128
    return fig, ax
Somnath, Suhas's avatar
Somnath, Suhas committed
1129
1130
1131

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

1132
1133

def plot_histgrams(p_hist, p_hbins, title, figure_path=None):
Somnath, Suhas's avatar
Somnath, Suhas committed
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
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
    """
    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')

1197
1198
1199
    return fig


1200
def visualize_sho_results(h5_main, save_plots=True, show_plots=True):
1201
    """
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
    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
1217
    """
1218
1219
1220
1221

    def __plot_loops_maps(ac_vec, resp_mat, grp_name, win_title, spec_var_title, meas_var_title, save_plots,
                          folder_path, basename, num_rows, num_cols):
        plt_title = grp_name + '_' + win_title + '_Loops'
1222
        fig, ax = plot_loops(ac_vec, resp_mat, evenly_spaced=True, plots_on_side=5, use_rainbow_plots=False,
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
                             x_label=spec_var_title, y_label=meas_var_title, subtitles='Loop', title=plt_title)
        if save_plots:
            fig.savefig(os.path.join(folder_path, basename + '_' + plt_title + '.png'), format='png', dpi=300)

        plt_title = grp_name + '_' + win_title + '_Snaps'
        fig, axes = plot_map_stack(resp_mat.reshape(num_rows, num_cols, resp_mat.shape[1]),
                                   color_bar_mode="each", evenly_spaced=True, title='UDVS Step #',
                                   heading=plt_title, cmap=cmap_jet_white_center())
        if save_plots:
            fig.savefig(os.path.join(folder_path, basename + '_' + plt_title + '.png'), format='png', dpi=300)

1234
1235
1236
    plt_path = None

    print('Creating plots of SHO Results from {}.'.format(h5_main.name))
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249

    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
Chris Smith's avatar
Chris Smith committed
1250
1251

    chan_grp = h5_file['/'.join(sho_grp.name[1:].split('/')[:2])]
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263

    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:
1264
1265
        num_rows = len(np.unique(h5_pos[:, 0]))
        num_cols = len(np.unique(h5_pos[:, 1]))
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293

    try:
        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)
1294
            ac_vec = np.squeeze(h5_spec_vals[h5_spec_vals.attrs['AC_Amplitude']][0:center])
1295

1296
1297
            forw_resp = np.squeeze(amp_mat[:, slice(0, center)])
            rev_resp = np.squeeze(amp_mat[:, slice(center, None)])
1298
1299
1300
1301

            for win_title, resp_mat in zip(['Forward', 'Reverse'], [forw_resp, rev_resp]):
                __plot_loops_maps(ac_vec, resp_mat, grp_name, win_title, 'AC Amplitude', 'Amplitude', save_plots,
                                  folder_path, basename, num_rows, num_cols)
1302
1303
        else:
            # plot loops at a few locations
1304
            dc_vec = np.squeeze(h5_spec_vals[h5_spec_vals.attrs['DC_Offset']])
1305
1306
            if chan_grp.parent.attrs