diff --git a/pycroscopy/io/translators/be_utils.py b/pycroscopy/io/translators/be_utils.py
index c394f72bf6909325e751afb6bdd2f2d0829532fa..4a3067456a3a686825e13151314949595286d773 100644
--- a/pycroscopy/io/translators/be_utils.py
+++ b/pycroscopy/io/translators/be_utils.py
@@ -8,12 +8,13 @@ Created on Tue Jan 05 07:55:56 2016
import numpy as np
from os import path
from warnings import warn
+import matplotlib.pyplot as plt
from ..microdata import MicroDataset,MicroDataGroup
-from ..beutils import getSliceForExcWfm,getActiveUDVSsteps,maxReadPixels
+from ..be_hdf_utils import getActiveUDVSsteps,maxReadPixels
from ..io_utils import getAvailableMem
from ..hdf_utils import getAuxData, getDataSet, getH5DsetRefs
from ..io_hdf5 import ioHDF5
-import matplotlib.pyplot as plt
+from ...viz.plot_utils import plot1DSpectrum, plot2DSpectrogram, plotHistgrams
from ...processing.procUtils import buildHistogram
import h5py
@@ -487,163 +488,7 @@ def reshapeMeanData(spec_inds, step_inds, mean_resp):
return (step_averaged_vec, mean_spectrogram)
###############################################################################
-
-def plot1DSpectrum(data_vec,freq,title, figure_path=None):
- """
- Plots the Step averaged BE response
-
- Parameters
- ------------
- data_vec : 1D numpy array
- Response of one BE pulse
- freq : 1D numpy array
- BE frequency that serves as the X axis of the plot
- title : String
- Plot group name
- figure_path : String / Unicode
- Absolute path of the file to write the figure to
-
- Returns
- ---------
- fig : Matplotlib.pyplot figure
- Figure handle
- ax : Matplotlib.pyplot axis
- Axis handle
- """
- if len(data_vec) != len(freq):
-# print '1D:',data_vec.shape, freq.shape
- warn('plot2DSpectrogram: Incompatible data sizes!!!!')
- return
- freq = freq*1E-3 # to kHz
- fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True);
- ax[0].plot(freq,np.abs(data_vec)*1E+3)
- ax[0].set_title('Amplitude (mV)')
- #ax[0].set_xlabel('Frequency (kHz)')
- ax[1].plot(freq,np.angle(data_vec)*180/np.pi)
- ax[1].set_title('Phase (deg)')
- ax[1].set_xlabel('Frequency (kHz)')
- fig.suptitle(title + ': mean UDVS, mean spatial response')
- if figure_path:
- plt.savefig(figure_path, format='png', dpi=300)
- return (fig,ax)
-
-###############################################################################
-
-def plot2DSpectrogram(mean_spectrogram,freq,title, figure_path=None):
- """
- Plots the position averaged spectrogram
-
- Parameters
- ------------
- mean_spectrogram : 2D numpy complex array
- Means spectrogram arranged as [frequency, UDVS step]
- freq : 1D numpy float array
- BE frequency that serves as the X axis of the plot
- title : String
- Plot group name
- figure_path : String / Unicode
- Absolute path of the file to write the figure to
-
- Returns
- ---------
- fig : Matplotlib.pyplot figure
- Figure handle
- ax : Matplotlib.pyplot axis
- Axis handle
- """
- if mean_spectrogram.shape[1] != len(freq):
- # print '2D:',mean_spectrogram.shape, freq.shape
- warn('plot2DSpectrogram: Incompatible data sizes!!!!')
- return
- freq = freq*1E-3 # to kHz
- fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True);
- #print mean_spectrogram.shape
- #print freq.shape
- ax[0].imshow(np.abs(mean_spectrogram),interpolation='nearest', extent=[freq[0],freq[-1],mean_spectrogram.shape[0],0])
- ax[0].set_title('Amplitude')
- #ax[0].set_xticks(freq)
- #ax[0].set_ylabel('UDVS Step')
- ax[0].axis('tight')
- ax[1].imshow(np.angle(mean_spectrogram),interpolation='nearest', extent=[freq[0],freq[-1],mean_spectrogram.shape[0],0])
- ax[1].set_title('Phase')
- ax[1].set_xlabel('Frequency (kHz)')
- #ax[0].set_ylabel('UDVS Step')
- ax[1].axis('tight')
- fig.suptitle(title)
- if figure_path:
- plt.savefig(figure_path, format='png', dpi=300)
- return (fig,ax)
-
-###############################################################################
-
-def plotHistgrams(p_hist,p_hbins,title,figure_path=None):
- """
- Plots the position averaged spectrogram
-
- Parameters
- ------------
- p_hist : 2D numpy array
- histogram data arranged as [physical quantity, frequency bin]
- p_hbins : 1D numpy array
- BE frequency that serves as the X axis of the plot
- title : String
- Plot group name
- figure_path : String / Unicode
- Absolute path of the file to write the figure to
-
- Returns
- ---------
- fig : Matplotlib.pyplot figure
- Figure handle
- """
-
- base_fig_size = 7
- h_fig = base_fig_size
- w_fig = base_fig_size*4
-
- fig = plt.figure(figsize=(w_fig,h_fig))
- fig.suptitle(title)
- iplot = 0
-
- p_Nx,p_Ny = np.amax(p_hbins,axis=1)+1
-
- p_hist = np.reshape(p_hist, (4,p_Ny,p_Nx))
-
- iplot +=1
- p_plot_title = 'Spectral BEHistogram Amp (log10 of counts)'
- p_plot = fig.add_subplot(1,4,iplot, title = p_plot_title)
- p_im = p_plot.imshow(np.rot90(np.log10(p_hist[0])),interpolation='nearest')
- p_plot.axis('tight')
- fig.colorbar(p_im,fraction=0.1)
-
- iplot +=1
- p_plot_title = 'Spectral BEHistogram Phase (log10 of counts)'
- p_plot = fig.add_subplot(1,4,iplot, title = p_plot_title)
- p_im = p_plot.imshow(np.rot90(np.log10(p_hist[1])),interpolation='nearest')
- p_plot.axis('tight')
- fig.colorbar(p_im,fraction=0.1)
-
- iplot +=1
- p_plot_title = 'Spectral BEHistogram Real (log10 of counts)'
- p_plot = fig.add_subplot(1,4,iplot, title = p_plot_title)
- p_im = p_plot.imshow(np.rot90(np.log10(p_hist[2])),interpolation='nearest')
- p_plot.axis('tight')
- fig.colorbar(p_im,fraction=0.1)
-
- iplot +=1
- p_plot_title = 'Spectral BEHistogram Imag (log10 of counts)'
- p_plot = fig.add_subplot(1,4,iplot, title = p_plot_title)
- p_im = p_plot.imshow(np.rot90(np.log10(p_hist[3])),interpolation='nearest')
- p_plot.axis('tight')
- fig.colorbar(p_im,fraction=0.1)
-
- if figure_path:
- plt.savefig(figure_path, format='png')
-
- return fig
-###############################################################################
-
def visualizePlotGroups(h5_filepath):
"""
Visualizes the plot groups present in the provided BE data file
diff --git a/pycroscopy/viz/plot_utils.py b/pycroscopy/viz/plot_utils.py
index 786785fa50f9731a3ca8054826f99c98eaa16aef..99328d8981733b093450888a61722385a13c0ac2 100644
--- a/pycroscopy/viz/plot_utils.py
+++ b/pycroscopy/viz/plot_utils.py
@@ -5,13 +5,14 @@ Created on Thu May 05 13:29:12 2016
@author: Suhas Somnath
"""
from __future__ import division # int/int = float
-from os import path
-from warnings import warn
import numpy as np
import matplotlib.pyplot as plt
-from ..io.hdf_utils import getDataSet, getAuxData, findH5group
-from ..analysis.be_sho_utils import getGoodLims
import h5py
+from scipy.cluster.hierarchy import linkage, dendrogram
+from scipy.spatial.distance import pdist
+from warnings import warn
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+from ..analysis.be_sho_utils import getGoodLims
###############################################################################
@@ -223,3 +224,675 @@ def plotVSsnapshots(resp_mat, title='', stdevs=2, save_path=None):
if save_path:
fig.savefig(save_path, format='png', dpi=300)
+
+def plotSpectrograms(eigenvectors, num_comps=4, title='Eigenvectors', xlabel='Step', stdevs=2,
+ show_colorbar=True):
+ '''
+ Plots the provided spectrograms from SVD V vector
+
+ Parameters:
+ -------------
+ eigenvectors : 3D numpy complex matrices
+ Eigenvectors rearranged as - [row, col, component]
+
+
+ xaxis : 1D real numpy array
+ The vector to plot against
+ num_comps : int
+ Number of components to plot
+ title : String
+ Title to plot above everything else
+ xlabel : String
+ Label for x axis
+ stdevs : int
+ Number of standard deviations to consider for plotting
+
+ Returns:
+ ---------
+ fig, axes
+ '''
+ import matplotlib.pyplot as plt
+ fig_h, fig_w = (4, 4 + show_colorbar * 1.00)
+ p_rows = int(np.ceil(np.sqrt(num_comps)))
+ p_cols = int(np.floor(num_comps / p_rows))
+ fig201, axes201 = plt.subplots(p_rows, p_cols, figsize=(p_cols * fig_w, p_rows * fig_h))
+ fig201.subplots_adjust(hspace=0.4, wspace=0.4)
+ fig201.canvas.set_window_title(title)
+
+ for index in xrange(num_comps):
+ cur_map = np.transpose(eigenvectors[index, :, :])
+ ax = axes201.flat[index]
+ (mean, std) = getGoodLims(cur_map)
+ ax.imshow(cur_map, cmap='jet',
+ vmin=mean - stdevs * std,
+ vmax=mean + stdevs * std)
+ ax.set_title('Eigenvector: %d' % (index + 1))
+ ax.set_aspect('auto')
+ ax.set_xlabel(xlabel)
+ ax.axis('tight')
+
+ return fig201, axes201
+
+
+###############################################################################
+
+def plotBEspectrograms(eigenvectors, num_comps=4, title='Eigenvectors', xlabel='UDVS Step', stdevs=2):
+ '''
+ Plots the provided spectrograms from SVD V vector
+
+ Parameters:
+ -------------
+ eigenvectors : 3D numpy complex matrices
+ Eigenvectors rearranged as - [row, col, component]
+
+
+ xaxis : 1D real numpy array
+ The vector to plot against
+ num_comps : int
+ Number of components to plot
+ title : String
+ Title to plot above everything else
+ xlabel : String
+ Label for x axis
+ stdevs : int
+ Number of standard deviations to consider for plotting
+
+ Returns:
+ ---------
+ fig, axes
+ '''
+ 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)
+
+ for index in xrange(num_comps):
+ cur_map = np.transpose(eigenvectors[index, :, :])
+ axes = [axes201.flat[index], axes201.flat[index + num_comps]]
+ funcs = [np.abs, np.angle]
+ labels = ['Amplitude', 'Phase']
+ for func, lab, ax in zip(funcs, labels, axes):
+ (amp_mean, amp_std) = getGoodLims(func(cur_map))
+ ax.imshow(func(cur_map), cmap='inferno',
+ vmin=amp_mean - stdevs * amp_std,
+ vmax=amp_mean + stdevs * amp_std)
+ ax.set_title('Eigenvector: %d - %s' % (index + 1, lab))
+ ax.set_aspect('auto')
+ ax.set_xlabel(xlabel)
+
+ return fig201, axes201
+
+
+###############################################################################
+
+def plotBEeigenvectors(eigenvectors, num_comps=4, xlabel=''):
+ '''
+ Plots the provided spectrograms from SVD V vector
+
+ Parameters:
+ -------------
+ eigenvectors : 3D numpy complex matrices
+ Eigenvectors rearranged as - [row, col, component]
+
+
+ xaxis : 1D real numpy array
+ The vector to plot against
+ num_comps : int
+ Number of components to plot
+ title : String
+ Title to plot above everything else
+ xlabel : String
+ Label for x axis
+ stdevs : int
+ Number of standard deviations to consider for plotting
+
+ Returns:
+ ---------
+ fig, axes
+ '''
+ funcs = [np.abs, np.angle]
+ labels = ['Amplitude', 'Phase']
+
+ fig201, axes201 = plt.subplots(len(funcs), num_comps, figsize=(num_comps * 4, 4 * len(funcs)))
+ fig201.subplots_adjust(hspace=0.4, wspace=0.4)
+ fig201.canvas.set_window_title("Eigenvectors")
+
+ for index in xrange(num_comps):
+ cur_map = eigenvectors[index, :]
+ # axes = [axes201.flat[index], axes201.flat[index+num_comps], axes201.flat[index+2*num_comps], axes201.flat[index+3*num_comps]]
+ axes = [axes201.flat[index], axes201.flat[index + num_comps]]
+ for func, lab, ax in zip(funcs, labels, axes):
+ ax.plot(func(cur_map))
+ ax.set_title('Eigenvector: %d - %s' % (index + 1, lab))
+ ax.set_xlabel(xlabel)
+ fig201.tight_layout()
+
+ return fig201, axes201
+
+
+###############################################################################
+
+def plotBELoops(xaxis, xlabel, amp_mat, phase_mat, num_comps, title=None):
+ '''
+ Plots the provided loops from the SHO. Replace / merge with function in BESHOUtils
+
+ Parameters:
+ -------------
+ xaxis : 1D real numpy array
+ The vector to plot against
+ xlabel : string
+ Label for x axis
+ amp_mat : 2D real numpy array
+ Amplitude matrix arranged as [points, component]
+ phase_mat : 2D real numpy array
+ Phase matrix arranged as [points, component]
+ num_comps : int
+ Number of components to plot
+ title : String
+ Title to plot above everything else
+
+ Returns:
+ ---------
+ fig, axes
+ '''
+ fig201, axes201 = plt.subplots(2, num_comps, figsize=(4 * num_comps, 6))
+ fig201.subplots_adjust(hspace=0.4, wspace=0.4)
+ fig201.canvas.set_window_title(title)
+
+ for index in xrange(num_comps):
+ axes = [axes201.flat[index], axes201.flat[index + num_comps]]
+ resp_vecs = [amp_mat[index, :], phase_mat[index, :]]
+ resp_titles = ['Amplitude', 'Phase']
+
+ for ax, resp, titl in zip(axes, resp_vecs, resp_titles):
+ ax.plot(xaxis, resp)
+ ax.set_title('%s %d' % (titl, index + 1))
+ ax.set_aspect('auto')
+ ax.set_xlabel(xlabel)
+
+ fig201.tight_layout()
+ return fig201, axes201
+
+
+###############################################################################
+
+def plotScree(S, title='Scree'):
+ '''
+ Plots the S or scree
+
+ Parameters:
+ -------------
+ S : 1D real numpy array
+ The S vector from SVD
+
+ Returns:
+ ---------
+ fig, axes
+ '''
+ fig203 = plt.figure(figsize=(6.5, 6))
+ axes203 = fig203.add_axes([0.1, 0.1, .8, .8]) # left, bottom, width, height (range 0 to 1)
+ axes203.loglog(np.arange(len(S)) + 1, S, 'b', marker='*')
+ axes203.set_xlabel('Principal Component')
+ axes203.set_ylabel('Variance')
+ axes203.set_title(title)
+ axes203.set_xlim(left=1, right=len(S))
+ axes203.set_ylim(bottom=np.min(S), top=np.max(S))
+ fig203.canvas.set_window_title("Scree")
+
+ return fig203, axes203
+
+
+###############################################################################
+
+def plotLoadingMaps(loadings, num_comps=4, stdevs=2, colormap='jet', show_colorbar=True):
+ '''
+ Plots the provided loading maps
+
+ Parameters:
+ -------------
+ loadings : 3D real numpy array
+ structured as [rows, cols, component]
+ num_comps : int
+ Number of components to plot
+ stdevs : int
+ Number of standard deviations to consider for plotting
+ colormap : string or object from matplotlib.colors (Optional. Default = jet or rainbow)
+ Colormap for the plots
+ show_colorbar : Boolean (Optional. Default = True)
+ Whether or not to show the color bar
+
+ Returns:
+ ---------
+ fig, axes
+ '''
+ fig_h, fig_w = (4, 4 + show_colorbar * 1.00)
+ p_rows = int(np.ceil(np.sqrt(num_comps)))
+ p_cols = int(np.floor(num_comps / p_rows))
+ fig202, axes202 = plt.subplots(p_cols, p_rows, figsize=(p_cols * fig_w, p_rows * fig_h))
+ fig202.subplots_adjust(hspace=0.4, wspace=0.4)
+ fig202.canvas.set_window_title("Loading Maps")
+
+ for index in xrange(num_comps):
+ cur_map = loadings[:, :, index]
+ (amp_mean, amp_std) = getGoodLims(cur_map)
+ if show_colorbar:
+ pcol0 = axes202.flat[index].pcolor(cur_map, vmin=amp_mean - stdevs * amp_std,
+ vmax=amp_mean + stdevs * amp_std)
+ fig202.colorbar(pcol0, ax=axes202.flat[index])
+ axes202.flat[index].axis('tight')
+ else:
+ axes202.flat[index].imshow(cur_map, cmap=colormap,
+ interpolation='none',
+ vmin=amp_mean - stdevs * amp_std,
+ vmax=amp_mean + stdevs * amp_std)
+
+ axes202.flat[index].set_title('Loading %d' % (index + 1))
+ axes202.flat[index].set_aspect('auto')
+ fig202.tight_layout()
+
+ return fig202, axes202
+
+
+###############################################################################
+
+def plotKMeansResults(label_mat, cluster_centroids, spec_val=None, cmap=plt.cm.jet,
+ spec_label='Spectroscopic Value', resp_label='Response'):
+ '''
+ label_mat : 2D int numpy array or h5py.Dataset object
+ Spatial map of cluster labels structured as [rows, cols]
+ cluster_centroids : 2D array or h5py.Dataset object
+ Centroids arranged as [cluster number, features]
+ spec_val : (Optional) 1D float numpy array or h5py.Dataset object
+ X axis to plot the centroids against
+ If no value is specified, the data is plotted against the index
+ cmap : plt.cm object (Optional. Default = plt.cm.jet)
+ Colormap to use for the labels map and the centroid.
+ Advised to pick a map where the centroid plots show clearly
+ spec_label : String (Optional. Default = 'Spectroscopic Value')
+ Label to use for X axis on cluster centroid plot
+ resp_label : String (Optional. Default = 'Response')
+ Label to use for Y axis on cluster centroid plot
+ '''
+
+ def __plotCentroids(centroids, ax, spec_val, spec_label, y_label, cmap, title=None):
+ num_clusters = centroids.shape[0]
+ for clust in xrange(num_clusters):
+ ax.plot(spec_val, centroids[clust],
+ label='Cluster {}'.format(clust),
+ color=cmap(int(255 * clust / (num_clusters - 1))))
+ ax.set_ylabel(y_label)
+ ax.legend(loc='best')
+ if title:
+ ax.set_title(title)
+ ax.set_xlabel(spec_label)
+
+ if type(spec_val) == type(None):
+ spec_val = np.arange(cluster_centroids.shape[1])
+
+ if cluster_centroids.dtype in [np.complex64, np.complex128, np.complex]:
+ fig = plt.figure(figsize=(12, 8))
+ ax_map = plt.subplot2grid((2, 10), (0, 0), colspan=6, rowspan=2)
+ ax_amp = plt.subplot2grid((2, 10), (0, 6), colspan=4)
+ ax_phase = plt.subplot2grid((2, 10), (1, 6), colspan=4)
+ axes = [ax_map, ax_amp, ax_phase]
+
+ __plotCentroids(np.abs(cluster_centroids), ax_amp, spec_val, spec_label,
+ resp_label + ' - Amplitude', cmap, 'Centroids')
+ __plotCentroids(np.angle(cluster_centroids), ax_phase, spec_val, spec_label,
+ resp_label + ' - Phase', cmap)
+ else:
+ fig, axes = plt.subplots(1, 2, figsize=(12, 6))
+ ax_map = axes[0]
+ __plotCentroids(cluster_centroids, axes[1], spec_val, spec_label,
+ resp_label, cmap, 'Centroids')
+
+ num_clusters = cluster_centroids.shape[0]
+ if isinstance(label_mat, h5py.Dataset):
+ '''
+ Reshape label_mat based on linked positions
+ '''
+ 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)
+ im = ax_map.imshow(label_mat, interpolation='none')
+ divider = make_axes_locatable(ax_map)
+ cax = divider.append_axes("right", size="5%", pad=0.05) # space for colorbar
+ fig.colorbar(im, cax=cax)
+ # pcol0 = ax_map.pcolor(label_mat, cmap=cmap)
+ # fig.colorbar(pcol0, ax=ax_map, ticks=np.arange(num_clusters))
+ ax_map.axis('tight')
+ ax_map.set_title('Cluster Label Map')
+
+ fig.tight_layout()
+ fig.suptitle('k-Means result')
+ fig.canvas.set_window_title('k-Means result')
+
+ return fig, axes
+
+
+###############################################################################
+
+def plotKMeansClusters(label_mat, cluster_centroids,
+ num_cluster=4):
+ '''
+ Plots the provided label mat and centroids
+ from KMeans clustering
+
+ Parameters:
+ -------------
+ label_mat : 2D int numpy array
+ structured as [rows, cols]
+ cluster_centroids: 2D real numpy array
+ structured as [cluster,features]
+ num_cluster : int
+ Number of centroids to plot
+
+ Returns:
+ ---------
+ fig
+ '''
+
+ if num_cluster < 5:
+
+ 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]
+
+ # Plot results
+ for ax, index in zip(axes_handles[0:num_cluster + 1], np.arange(num_cluster + 1)):
+ if index == 0:
+ im = ax.imshow(label_mat, interpolation='none')
+ ax.set_title('K-means Cluster Map')
+ divider = make_axes_locatable(ax)
+ cax = divider.append_axes("right", size="5%", pad=0.05) # space for colorbar
+ plt.colorbar(im, cax=cax)
+ else:
+ # ax.plot(Vdc_vec, cluster_centroids[index-1,:], 'g-')
+ ax.plot(cluster_centroids[index - 1, :], 'g-')
+ ax.set_xlabel('Voltage (V)')
+ ax.set_ylabel('Current (arb.)')
+ ax.set_title('K-means Centroid: %d' % (index))
+
+ fig501.subplots_adjust(hspace=0.60, wspace=0.60)
+
+ return fig501
+
+
+###############################################################################
+
+def plotClusterDendrograms(label_mat, e_vals, num_comp, num_cluster, mode='Full', last=None,
+ sort_type='distance', sort_mode=True):
+ '''
+ Creates and plots the dendrograms for the given label_mat and
+ eigenvalues
+
+ Parameters
+ -------------
+ label_mat : 2D real numpy array
+ structured as [rows, cols], from KMeans clustering
+ e_vals: 3D real numpy array of eigenvalues
+ structured as [component, rows, cols]
+ num_comps : int
+ Number of components used to make eigenvalues
+ num_cluster: int
+ Number of cluster used to make the label_mat
+ mode: str, optional
+ How should the dendrograms be created.
+ "Full" -- use all clusters when creating the dendrograms
+ "Truncated" -- stop showing clusters after 'last'
+ last: int, optional - should be provided when using "Truncated"
+ How many merged clusters should be shown when using
+ "Truncated" mode
+ sort_type: str, optional
+ What type of sorting should be used when plotting the
+ dendrograms. Options are:
+ count Default
+ Uses the count_sort from scipy.cluster.hierachy.dendrogram
+ distance
+ Uses the distance_sort from scipy.cluster.hierachy.dendrogram
+ sort_mode: str or bool, optional
+ For the chosen sort_type, which mode should be used.
+ Options:
+ False Default
+ 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
+
+ Returns
+ ---------
+ fig
+ '''
+ if mode == 'Truncated' and not last:
+ warn('Warning: Truncated dendrograms requested, but no last cluster given. Reverting to full dendrograms.')
+ mode = 'Full'
+
+ if mode == 'Full':
+ print 'Creating full dendrogram from clusters'
+ mode = None
+ elif mode == 'Truncated':
+ print 'Creating truncated dendrogram from clusters. Will stop at {}.'.format(last)
+ mode = 'lastp'
+ show_contracted = True
+ else:
+ raise ValueError('Error: Unknown mode requested for plotting dendrograms. mode={}'.format(mode))
+
+ c_sort = False
+ d_sort = False
+ if sort_type == 'count':
+ c_sort = sort_mode
+ if c_sort == 'descending':
+ c_sort = 'descendent'
+ elif sort_type == 'distance':
+ d_sort = sort_mode
+
+ centroid_mat = np.zeros([num_cluster, num_comp])
+ for k1 in xrange(num_cluster):
+ [i_x, i_y] = np.where(label_mat == k1)
+ u_stack = np.zeros([len(i_x), num_comp])
+ for k2 in xrange(len(i_x)):
+ u_stack[k2, :] = np.abs(e_vals[i_x[k2], i_y[k2], :num_comp])
+
+ centroid_mat[k1, :] = np.mean(u_stack, 0)
+
+
+ # Get the distrance between cluster means
+ distance_mat = pdist(centroid_mat)
+
+ # get hierachical pairings of clusters
+ linkage_pairing = linkage(distance_mat, 'weighted')
+ linkage_pairing[:, 3] = linkage_pairing[:, 3] / max(linkage_pairing[:, 3])
+
+ fig = plt.figure()
+ dendrogram(linkage_pairing, p=last, truncate_mode=mode, count_sort=c_sort, distance_sort=d_sort, leaf_rotation=90)
+
+ fig.axes[0].set_title('Dendrogram')
+ fig.axes[0].set_xlabel('Index or (cluster size)')
+ fig.axes[0].set_ylabel('Distance')
+
+ return fig
+
+
+def plot1DSpectrum(data_vec, freq, title, figure_path=None):
+ """
+ Plots the Step averaged BE response
+
+ Parameters
+ ------------
+ data_vec : 1D numpy array
+ Response of one BE pulse
+ freq : 1D numpy array
+ BE frequency that serves as the X axis of the plot
+ title : String
+ Plot group name
+ figure_path : String / Unicode
+ Absolute path of the file to write the figure to
+
+ Returns
+ ---------
+ fig : Matplotlib.pyplot figure
+ Figure handle
+ ax : Matplotlib.pyplot axis
+ Axis handle
+ """
+ if len(data_vec) != len(freq):
+ # print '1D:',data_vec.shape, freq.shape
+ warn('plot2DSpectrogram: Incompatible data sizes!!!!')
+ return
+ freq = freq * 1E-3 # to kHz
+ fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True);
+ ax[0].plot(freq, np.abs(data_vec) * 1E+3)
+ ax[0].set_title('Amplitude (mV)')
+ # ax[0].set_xlabel('Frequency (kHz)')
+ ax[1].plot(freq, np.angle(data_vec) * 180 / np.pi)
+ ax[1].set_title('Phase (deg)')
+ ax[1].set_xlabel('Frequency (kHz)')
+ fig.suptitle(title + ': mean UDVS, mean spatial response')
+ if figure_path:
+ plt.savefig(figure_path, format='png', dpi=300)
+ return (fig, ax)
+
+
+###############################################################################
+
+def plot2DSpectrogram(mean_spectrogram, freq, title, figure_path=None):
+ """
+ Plots the position averaged spectrogram
+
+ Parameters
+ ------------
+ mean_spectrogram : 2D numpy complex array
+ Means spectrogram arranged as [frequency, UDVS step]
+ freq : 1D numpy float array
+ BE frequency that serves as the X axis of the plot
+ title : String
+ Plot group name
+ figure_path : String / Unicode
+ Absolute path of the file to write the figure to
+
+ Returns
+ ---------
+ fig : Matplotlib.pyplot figure
+ Figure handle
+ ax : Matplotlib.pyplot axis
+ Axis handle
+ """
+ if mean_spectrogram.shape[1] != len(freq):
+ # print '2D:',mean_spectrogram.shape, freq.shape
+ warn('plot2DSpectrogram: Incompatible data sizes!!!!')
+ return
+ freq = freq * 1E-3 # to kHz
+ fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True);
+ # print mean_spectrogram.shape
+ # print freq.shape
+ ax[0].imshow(np.abs(mean_spectrogram), interpolation='nearest',
+ extent=[freq[0], freq[-1], mean_spectrogram.shape[0], 0])
+ ax[0].set_title('Amplitude')
+ # ax[0].set_xticks(freq)
+ # ax[0].set_ylabel('UDVS Step')
+ ax[0].axis('tight')
+ ax[1].imshow(np.angle(mean_spectrogram), interpolation='nearest',
+ extent=[freq[0], freq[-1], mean_spectrogram.shape[0], 0])
+ ax[1].set_title('Phase')
+ ax[1].set_xlabel('Frequency (kHz)')
+ # ax[0].set_ylabel('UDVS Step')
+ ax[1].axis('tight')
+ fig.suptitle(title)
+ if figure_path:
+ plt.savefig(figure_path, format='png', dpi=300)
+ return (fig, ax)
+
+
+###############################################################################
+
+def plotHistgrams(p_hist, p_hbins, title, figure_path=None):
+ """
+ Plots the position averaged spectrogram
+
+ Parameters
+ ------------
+ p_hist : 2D numpy array
+ histogram data arranged as [physical quantity, frequency bin]
+ p_hbins : 1D numpy array
+ BE frequency that serves as the X axis of the plot
+ title : String
+ Plot group name
+ figure_path : String / Unicode
+ Absolute path of the file to write the figure to
+
+ Returns
+ ---------
+ fig : Matplotlib.pyplot figure
+ Figure handle
+ """
+
+ base_fig_size = 7
+ h_fig = base_fig_size
+ w_fig = base_fig_size * 4
+
+ fig = plt.figure(figsize=(w_fig, h_fig))
+ fig.suptitle(title)
+ iplot = 0
+
+ p_Nx, p_Ny = np.amax(p_hbins, axis=1) + 1
+
+ p_hist = np.reshape(p_hist, (4, p_Ny, p_Nx))
+
+ iplot += 1
+ p_plot_title = 'Spectral BEHistogram Amp (log10 of counts)'
+ p_plot = fig.add_subplot(1, 4, iplot, title=p_plot_title)
+ p_im = p_plot.imshow(np.rot90(np.log10(p_hist[0])), interpolation='nearest')
+ p_plot.axis('tight')
+ fig.colorbar(p_im, fraction=0.1)
+
+ iplot += 1
+ p_plot_title = 'Spectral BEHistogram Phase (log10 of counts)'
+ p_plot = fig.add_subplot(1, 4, iplot, title=p_plot_title)
+ p_im = p_plot.imshow(np.rot90(np.log10(p_hist[1])), interpolation='nearest')
+ p_plot.axis('tight')
+ fig.colorbar(p_im, fraction=0.1)
+
+ iplot += 1
+ p_plot_title = 'Spectral BEHistogram Real (log10 of counts)'
+ p_plot = fig.add_subplot(1, 4, iplot, title=p_plot_title)
+ p_im = p_plot.imshow(np.rot90(np.log10(p_hist[2])), interpolation='nearest')
+ p_plot.axis('tight')
+ fig.colorbar(p_im, fraction=0.1)
+
+ iplot += 1
+ p_plot_title = 'Spectral BEHistogram Imag (log10 of counts)'
+ p_plot = fig.add_subplot(1, 4, iplot, title=p_plot_title)
+ p_im = p_plot.imshow(np.rot90(np.log10(p_hist[3])), interpolation='nearest')
+ p_plot.axis('tight')
+ fig.colorbar(p_im, fraction=0.1)
+
+ if figure_path:
+ plt.savefig(figure_path, format='png')
+
+ return fig
\ No newline at end of file