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Commit 5f9d8206 authored by Somnath, Suhas's avatar Somnath, Suhas
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Fixed bugs related to plot_utils, SVD, and kMeans changes

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jupyter_notebooks/Image_Cleaning_Atom_Finding.ipynb
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%% Cell type:markdown id: tags:
# Image cleaning and atom finding using pycroscopy
### Suhas Somnath, Chris R. Smith, Stephen Jesse
The Center for Nanophase Materials Science and The Institute for Functional Imaging for Materials <br>
Oak Ridge National Laboratory<br>
1/19/2017
%% Cell type:markdown id: tags:
## Configure the notebook first
%% Cell type:code id: tags:
``` python
!pip install -U numpy scipy skimage h5py matplotlib Ipython ipywidgets pycroscopy
# set up notebook to show plots within the notebook
% matplotlib notebook
# Import necessary libraries:
# General utilities:
import os
import sys
from time import time
from scipy.misc import imsave
# Computation:
import numpy as np
import h5py
from skimage import measure
from scipy.cluster.hierarchy import linkage, dendrogram
from scipy.spatial.distance import pdist
from sklearn.cluster import KMeans
# Visualization:
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from mpl_toolkits.axes_grid1 import make_axes_locatable
from IPython.display import display, HTML
import ipywidgets as widgets
from mpl_toolkits.axes_grid1 import ImageGrid
# Finally, pycroscopy itself
sys.path.append('..')
import pycroscopy as px
# Make Notebook take up most of page width
display(HTML(data="""
<style>
div#notebook-container { width: 95%; }
div#menubar-container { width: 65%; }
div#maintoolbar-container { width: 99%; }
</style>
"""))
```
%% Cell type:markdown id: tags:
## Load the image that will be cleaned:
%% Cell type:code id: tags:
``` python
image_path = px.io.uiGetFile('*.png *PNG *TIFF * TIF *tif *tiff *BMP *bmp','Images')
print('Working on: \n{}'.format(image_path))
folder_path, file_name = os.path.split(image_path)
base_name, _ = os.path.splitext(file_name)
```
%% Cell type:markdown id: tags:
## Make the image file pycroscopy compatible
Convert the source image file into a pycroscopy compatible hierarchical data format (HDF or .h5) file. This simple translation gives you access to the powerful data functions within pycroscopy
#### H5 files:
* are like smart containers that can store matrices with data, folders to organize these datasets, images, metadata like experimental parameters, links or shortcuts to datasets, etc.
* are readily compatible with high-performance computing facilities
* scale very efficiently from few kilobytes to several terabytes
* can be read and modified using any language including Python, Matlab, C/C++, Java, Fortran, Igor Pro, etc.
%% Cell type:code id: tags:
``` python
# Check if an HDF5 file with the chosen image already exists.
# Only translate if it does not.
h5_path = os.path.join(folder_path, base_name+'.h5')
need_translation = True
if os.path.exists(h5_path):
try:
h5_file = h5py.File(h5_path, 'r+')
h5_raw = h5_file['Measurement_000']['Channel_000']['Raw_Data']
need_translation = False
print('HDF5 file with Raw_Data found. No need to translate.')
except KeyError:
print('Raw Data not found.')
else:
print('No HDF5 file found.')
if need_translation:
# Initialize the Image Translator
tl = px.ImageTranslator()
# create an H5 file that has the image information in it and get the reference to the dataset
h5_raw = tl.translate(image_path)
# create a reference to the file
h5_file = h5_raw.file
print('HDF5 file is located at {}.'.format(h5_file.filename))
```
%% Cell type:markdown id: tags:
### Inspect the contents of this h5 data file
The file contents are stored in a tree structure, just like files on a contemporary computer.
The data is stored as a 2D matrix (position, spectroscopic value) regardless of the dimensionality of the data.
In the case of these 2D images, the data is stored as a N x 1 dataset
The main dataset is always accompanied by four ancillary datasets that explain the position and spectroscopic value of any given element in the dataset.
In the case of the 2d images, the positions will be arranged as row0-col0, row0-col1.... row0-colN, row1-col0....
The spectroscopic information is trivial since the data at any given pixel is just a scalar value
%% Cell type:code id: tags:
``` python
print('Datasets and datagroups within the file:')
px.io.hdf_utils.print_tree(h5_file)
print('\nThe main dataset:')
print(h5_file['/Measurement_000/Channel_000/Raw_Data'])
print('\nThe ancillary datasets:')
print(h5_file['/Measurement_000/Channel_000/Position_Indices'])
print(h5_file['/Measurement_000/Channel_000/Position_Values'])
print(h5_file['/Measurement_000/Channel_000/Spectroscopic_Indices'])
print(h5_file['/Measurement_000/Channel_000/Spectroscopic_Values'])
print('\nMetadata or attributes in a datagroup')
for key in h5_file['/Measurement_000'].attrs:
print('{} : {}'.format(key, h5_file['/Measurement_000'].attrs[key]))
```
%% Cell type:markdown id: tags:
## Initialize an object that will perform image windowing on the .h5 file
* Note that after you run this, the H5 file is opened. If you want to re-run this cell, close the H5 file first
%% Cell type:code id: tags:
``` python
# Initialize the windowing class
iw = px.ImageWindow(h5_raw, max_RAM_mb=1024*4)
# grab position indices from the H5 file
h5_pos = h5_raw.parent[h5_raw.attrs['Position_Indices']]
# determine the image size:
num_x = len(np.unique(h5_pos[:,0]))
num_y = len(np.unique(h5_pos[:,1]))
# extract figure data and reshape to proper numpy array
raw_image_mat = np.reshape(h5_raw[()], [num_x,num_y]);
```
%% Cell type:markdown id: tags:
## Visualize the source image:
Though the source file is actually grayscale image, we will visualize it using a color-scale
%% Cell type:code id: tags:
``` python
fig, axis = plt.subplots(figsize=(10,10))
img = axis.imshow(raw_image_mat,cmap=px.plot_utils.cmap_jet_white_center(), origin='lower');
divider = make_axes_locatable(axis)
cax = divider.append_axes("right", size="5%", pad=0.2)
plt.colorbar(img, cax=cax)
px.plot_utils.plot_map(axis, raw_image_mat, cmap=px.plot_utils.cmap_jet_white_center())
axis.set_title('Raw Image', fontsize=16);
```
%% Cell type:markdown id: tags:
## Extract the optimal window size from the image
%% Cell type:code id: tags:
``` python
num_peaks = 2
win_size , psf_width = iw.window_size_extract(num_peaks, save_plots=False, show_plots=True)
print('Window size = {}'.format(win_size))
```
%% Cell type:code id: tags:
``` python
# Uncomment this line if you need to manually specify a window size
# win_size = 8
# plot a single window
row_offset = int(0.5*(num_x-win_size))
col_offset = int(0.5*(num_y-win_size))
plt.figure()
plt.imshow(raw_image_mat[row_offset:row_offset+win_size,
col_offset:col_offset+win_size],
cmap=px.plot_utils.cmap_jet_white_center(),
origin='lower');
fig, axis = plt.subplots(figsize=(5, 5))
px.plot_utils.plot_map(axis, raw_image_mat[row_offset:row_offset+win_size,
col_offset:col_offset+win_size],
cmap=px.plot_utils.cmap_jet_white_center())
# the result should be about the size of a unit cell
# if it is the wrong size, just choose on manually by setting the win_size
plt.show()
axis.set_title('Example window', fontsize=18);
```
%% Cell type:markdown id: tags:
## Now break the image into a sequence of small windows
We do this by sliding a small window across the image. This artificially baloons the size of the data.
%% Cell type:code id: tags:
``` python
windowing_parms = {
'fft_mode': None, # Options are None, 'abs', 'data+abs', or 'complex'
'win_x': win_size,
'win_y': win_size,
'win_step_x': 1,
'win_step_y': 1,
}
win_parms_copy = windowing_parms.copy()
if windowing_parms['fft_mode'] is None:
win_parms_copy['fft_mode'] = 'data'
h5_wins_grp = px.hdf_utils.check_for_old(h5_raw, 'Windowing',
win_parms_copy)
if h5_wins_grp is None:
print('Windows either do not exist or were created with different parameters')
t0 = time()
h5_wins = iw.do_windowing(win_x=windowing_parms['win_x'],
win_y=windowing_parms['win_y'],
save_plots=False,
show_plots=False,
win_fft=windowing_parms['fft_mode'])
print( 'Windowing took {} seconds.'.format(round(time()-t0, 2)))
else:
print('Taking existing windows dataset')
h5_wins = h5_wins_grp['Image_Windows']
print('\nRaw data was of shape {} and the windows dataset is now of shape {}'.format(h5_raw.shape, h5_wins.shape))
print('Now each position (window) is descibed by a set of pixels')
```
%% Cell type:code id: tags:
``` python
# Peek at a few random windows
num_rand_wins = 9
rand_positions = np.random.randint(0, high=h5_wins.shape[0], size=num_rand_wins)
example_wins = np.zeros(shape=(windowing_parms['win_x'], windowing_parms['win_y'], num_rand_wins), dtype=np.float32)
for rand_ind, rand_pos in enumerate(rand_positions):
example_wins[:, :, rand_ind] = np.reshape(h5_wins[rand_pos], (windowing_parms['win_x'], windowing_parms['win_y']))
px.plot_utils.plot_map_stack(example_wins, heading='Example Windows', cmap=px.plot_utils.cmap_jet_white_center(),
title=['Window # ' + str(win_pos) for win_pos in rand_positions]);
title=['Window # ' + str(win_pos) for win_pos in rand_positions], fig_title_yoffset=0.93);
```
%% Cell type:markdown id: tags:
## Performing Singular Value Decompostion (SVD) on the windowed data
SVD decomposes data (arranged as position x value) into a sequence of orthogonal components arranged in descending order of variance. The first component contains the most significant trend in the data. The second component contains the next most significant trend orthogonal to all previous components (just the first component). Each component consists of the trend itself (eigenvector), the spatial variaion of this trend (eigenvalues), and the variance (statistical importance) of the component.
Since the data consists of the large sequence of small windows, SVD essentially compares every single window with every other window to find statistically significant trends in the image
%% Cell type:code id: tags:
``` python
# check to make sure number of components is correct:
num_comp = 1024
num_comp = min(num_comp,
min(h5_wins.shape)*len(h5_wins.dtype))
proc = px.SVD(h5_main, num_comps=num_comp)
proc = px.processing.SVD(h5_wins, num_components=num_comp)
# First check if SVD was already computed on this dataset:
if proc.duplicate_h5_groups is None:
print('SVD was either not performed or was performed with different parameters')
print('SVD not performed with these parameters')
h5_svd = proc.compute()
else:
print('Taking previous SVD results already present in file')
h5_svd = proc.duplicate_h5_groups[-1]
print('Taking existing results!')
h5_svd = proc.duplicate_h5_groups
h5_U = h5_svd['U']
h5_S = h5_svd['S']
h5_V = h5_svd['V']
# extract parameters of the SVD results
h5_pos = iw.hdf.file[h5_wins.attrs['Position_Indices']]
num_rows = len(np.unique(h5_pos[:, 0]))
num_cols = len(np.unique(h5_pos[:, 1]))
num_comp = h5_S.size
print("There are a total of {} components.".format(num_comp))
print('\nRaw data was of shape {} and the windows dataset is now of shape {}'.format(h5_raw.shape, h5_wins.shape))
print('Now each position (window) is descibed by a set of pixels')
plot_comps = 49
U_map_stack = np.reshape(h5_U[:, :plot_comps], [num_rows, num_cols, -1])
V_map_stack = np.reshape(h5_V, [num_comp, win_size, win_size])
V_map_stack = np.transpose(V_map_stack,(2,1,0))
```
%% Cell type:markdown id: tags:
## Visualize the SVD results
##### S (variance):
The plot below shows the variance or statistical significance of the SVD components. The first few components contain the most significant information while the last few components mainly contain noise.
Note also that the plot below is a log-log plot. The importance of each subsequent component drops exponentially.
%% Cell type:code id: tags:
``` python
fig_S, ax_S = px.plot_utils.plotScree(h5_S[()]);
fig_S, ax_S = px.plot_utils.plot_scree(h5_S[()]);
```