Configure the notebook¶
In [1]:
# Ensure python 3 compatibility
from __future__ import division, print_function, absolute_import, unicode_literals
# Import necessary libraries:
# General utilities:
from os import path
# Computation:
import numpy as np
import h5py
# Visualization:
import matplotlib.pyplot as plt
# Finally, pycroscopy itself
import pycroscopy as px
# set up notebook to show plots within the notebook
% matplotlib inline
Make the data pycroscopy compatible¶
Converting the raw data into a pycroscopy compatible hierarchical data format (HDF or .h5) file gives you access to the fast fitting algorithms and powerful analysis 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.
You can load either of the following:¶
- Any .mat or .txt parameter file from the original experiment
- A .h5 file generated from the raw data using pycroscopy - skips translation
You can select desired file type by choosing the second option in the pull down menu on the bottom right of the file window
In [2]:
"""input_file_path = px.io_utils.uiGetFile(caption='Select translated .h5 file or raw experiment data',
filter='Parameters for raw G-Line data (*.txt);; \
Translated file (*.h5)')"""
input_file_path = '/Volumes/IFgroup/SPM software development/Raw_Data/G_mode/GVS/2015_04_08_PZT_AuCu_nanocaps/GLine_8V_10kHz_256x256_0001/GLine_8V_10kHz_256x256.h5'
folder_path, _ = path.split(input_file_path)
if input_file_path.endswith('.txt'):
print('Translating raw data to h5. Please wait')
tran = px.GLineTranslator()
h5_path = tran.translate(file_path)
else:
h5_path = input_file_path
print('Working on:\n' + h5_path)
Open the .h5 file and extract some basic parameters¶
In [3]:
hdf = px.ioHDF5(h5_path)
h5_main = px.hdf_utils.getDataSet(hdf.file, 'Raw_Data')[-1]
parms_dict = h5_main.parent.parent.attrs
samp_rate = parms_dict['IO_rate_[Hz]']
ex_freq = parms_dict['BE_center_frequency_[Hz]']
h5_spec_vals = px.hdf_utils.getAuxData(h5_main, auxDataName='Spectroscopic_Values')[0]
pixel_ex_wfm = h5_spec_vals[0, :int(h5_spec_vals.shape[1]/parms_dict['grid_num_cols'])]
Inspect the contents of this h5 data file¶
The file contents are stored in a tree structure, just like files on a conventional computer. The data is stored as a 2D matrix (position, spectroscopic value) regardless of the dimensionality of the data. Thus, the positions will be arranged as row0-col0, row0-col1.... row0-colN, row1-col0.... and the data for each position is stored as it was chronologically collected
The main dataset is always accompanied by four ancillary datasets that explain the position and spectroscopic value of any given element in the dataset.
Note that G-mode data is acquired line-by-line rather than pixel-by-pixel.
In [4]:
print('Datasets and datagroups within the file:\n------------------------------------')
px.io.hdf_utils.print_tree(hdf.file)
print('\nThe main dataset:\n------------------------------------')
print(h5_main)
print('\nThe ancillary datasets:\n------------------------------------')
print(hdf.file['/Measurement_000/Channel_000/Position_Indices'])
print(hdf.file['/Measurement_000/Channel_000/Position_Values'])
print(hdf.file['/Measurement_000/Channel_000/Spectroscopic_Indices'])
print(hdf.file['/Measurement_000/Channel_000/Spectroscopic_Values'])
print('\nMetadata or attributes in a datagroup\n------------------------------------')
for key in hdf.file['/Measurement_000'].attrs:
print('{} : {}'.format(key, hdf.file['/Measurement_000'].attrs[key]))
Inspect the raw data:¶
In [8]:
row_ind = 40
raw_row = h5_main[row_ind].reshape(-1, pixel_ex_wfm.size)
fig, axes = px.plot_utils.plot_loops(pixel_ex_wfm, raw_row, x_label='Bias (V)', title='Raw Measurement',
plots_on_side=4, y_label='Deflection (a.u.)',
subtitles='Row: ' + str(row_ind) + ' Col:')
Try different FFT filters on the data¶
In [5]:
filter_parms = dict()
filter_parms['noise_threshold'] = 1E-4
filter_parms['comb_[Hz]'] = [ex_freq, 1E+3, 10]
# filter_parms['LPF_cutOff_[Hz]'] = -1
# Noise frequencies - 15.6 kHz ~ 14-17.5, 7.8-8.8, 45-49.9 ~ 48.9414 kHz
# filter_parms['band_filt_[Hz]'] = None # [[8.3E+3, 15.6E+3, 48.9414E+3], [1E+3, 0.5E+3, 0.1E+3]]
# filter_parms['phase_[rad]'] = 0
filter_parms['samp_rate_[Hz]'] = samp_rate
filter_parms['num_pix'] = 1
# Test filter on a single line:
row_ind = 40
filt_line, fig_filt, axes_filt = px.processing.gmode_utils.test_filter(h5_main[row_ind], filter_parms, samp_rate,
show_plots=True, use_rainbow_plots=False)
fig_filt.savefig(path.join(folder_path, 'FFT_filter_on_line_{}.png'.format(row_ind)), format='png', dpi=300)
filt_row = filt_line.reshape(-1, pixel_ex_wfm.size)
fig, axes = px.plot_utils.plot_loops(pixel_ex_wfm, filt_row, x_label='Bias (V)', title='FFT Filtering',
plots_on_side=4, y_label='Deflection (a.u.)',
subtitles='Row: ' + str(row_ind) + ' Col:')
# fig.savefig(path.join(folder_path, 'FFT_filtered_loops_on_line_{}.png'.format(row_ind)), format='png', dpi=300)
Apply selected filter to entire dataset¶
In [6]:
# h5_filt_grp = px.hdf_utils.findH5group(h5_main, 'FFT_Filtering')[-1]
h5_filt_grp = px.processing.gmode_utils.fft_filter_dataset(h5_main, filter_parms, write_filtered=True)
h5_filt = h5_filt_grp['Filtered_Data']
# Test to make sure the filter gave the same results
filt_row = h5_filt[row_ind].reshape(-1, pixel_ex_wfm.size)
fig, axes = px.plot_utils.plot_loops(pixel_ex_wfm, filt_row, x_label='Bias (V)', title='FFT Filtering',
plots_on_side=4, y_label='Deflection (a.u.)',
subtitles='Row: ' + str(row_ind) + ' Col:')
Now break up the filtered lines into "pixels"¶
Also visualize loops from different pixels
In [7]:
# h5_resh = h5_filt_grp['Filtered_Data-Reshape_000/Reshaped_Data']
h5_resh = px.processing.gmode_utils.reshape_from_lines_to_pixels(h5_filt, pixel_ex_wfm.size, 1)
fig, axes = px.plot_utils.plot_loops(pixel_ex_wfm, h5_resh, x_label='Bias (V)', title='FFT Filtering',
plots_on_side=5, y_label='Deflection (a.u.)')
# fig.savefig(path.join(folder_path, 'FFT_filtered_loops_on_line_{}.png'.format(row_ind)), format='png', dpi=300)
In [9]:
hdf.close()
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