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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1:  G-Mode filtering and inspection using pycroscopy
Suhas Somnath and Stephen Jesse
The Center for Nanophase Materials Science and The Institute for Functional Imaging for Materials <br>
Oak Ridge National Laboratory<br>
5/05/2017
Configure the notebook
Step2:  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
Step3:  Open the .h5 file and extract some basic parameters
Step4:  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.
Step5:  Inspect the raw data
Step6:  Try different FFT filters on the data
Step7:  Apply selected filter to entire dataset
Step8:  Now break up the filtered lines into "pixels"
Also visualize loops from different pixels
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Python Code:
# 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
ui_file_window = False
try:
    from PyQt5 import QtWidgets
    
    def uiGetFile(filter='H5 file (*.h5)', caption='Select File'):
        
        Presents a File dialog used for selecting the .mat file
        and returns the absolute filepath of the selecte file\n
        Parameters
        ----------
        extension : String or list of strings
            file extensions to look for
        caption : (Optional) String
            Title for the file browser window
        Returns
        -------
        file_path : String
            Absolute path of the chosen file
        
        app = QtWidgets.QApplication([])
        path = QtWidgets.QFileDialog.getOpenFileName(caption=caption, filter=filter)[0]
        app.exit()
        del app
        return str(path)
    
    ui_file_window = True
except ImportError:
    print('***********************************************************')
    print('*                                                         *')
    print('*  You will need to specify the file path manually below  *')
    print('*                                                         *')
    print('***********************************************************')
Explanation: G-Mode filtering and inspection using pycroscopy
Suhas Somnath and Stephen Jesse
The Center for Nanophase Materials Science and The Institute for Functional Imaging for Materials <br>
Oak Ridge National Laboratory<br>
5/05/2017
Configure the notebook
End of explanation
if ui_file_window:
    input_file_path = uiGetFile(caption='Select translated .h5 file or raw experiment data',
                                filter='Parameters for raw G-Line data (*.txt);; \
                                        Translated file (*.h5)')
else:
    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(input_file_path)
else:
    h5_path = input_file_path
print('Working on:\n' + h5_path)
Explanation: 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
End of explanation
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'])]
Explanation: Open the .h5 file and extract some basic parameters
End of explanation
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]))
Explanation: 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.
End of explanation
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:')
Explanation: Inspect the raw data:
End of explanation
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)
Explanation: Try different FFT filters on the data
End of explanation
# 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:')
Explanation: Apply selected filter to entire dataset
End of explanation
# 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)
hdf.close()
Explanation: Now break up the filtered lines into "pixels"
Also visualize loops from different pixels
End of explanation
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