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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:
 Band Excitation data procesing 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>
2/10/2017
Configure the notebook
Step1:  Set some basic parameters for computation
This notebook performs some functional fitting whose duration can be substantially decreased by using more memory and CPU cores. We have provided default values below but you may choose to change them if necessary.
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:  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.
Step4:  Get some basic parameters from the H5 file
This information will be vital for futher analysis and visualization of the data
Step5:  Visualize the raw data
Use the sliders below to visualize spatial maps (2D only for now), and spectrograms.
For simplicity, all the spectroscopic dimensions such as frequency, excitation bias, cycle, field, etc. have been collapsed to a single slider.
Step6:  Fit the Band Excitation (BE) spectra
Fit each of the acquired spectra to a simple harmonic oscillator (SHO) model to extract the following information regarding the response
Step7:  Visualize the SHO results
Here, we visualize the parameters for the SHO fits. BE-line (3D) data is visualized via simple spatial maps of the SHO parameters while more complex BEPS datasets (4+ dimensions) can be visualized using a simple interactive visualizer below. 
You can choose to visualize the guesses for SHO function or the final fit values from the first line of the cell below.
Use the sliders below to inspect the BE response at any given location.
Step8:  Fit loops to a function
This is applicable only to DC voltage spectroscopy datasets from BEPS. The PFM hysteresis loops in this dataset will be projected to maximize the loop area and then fitted to a function.
Note
Step9:  Prepare datasets for visualization
Step10:  Visualize Loop fits
Step11:  Save and close
Save the .h5 file that we are working on by closing it. <br>
Also, consider exporting this notebook as a notebook or an html file. <br> To do this, go to File >> Download as >> HTML
Finally consider saving this notebook if necessary
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Python Code:
!pip install -U numpy matplotlib Ipython ipywidgets pycroscopy
# Ensure python 3 compatibility
from __future__ import division, print_function, absolute_import
# Import necessary libraries:
# General utilities:
import sys
import os
# Computation:
import numpy as np
import h5py
# Visualization:
import matplotlib.pyplot as plt
from IPython.display import display
import ipywidgets as widgets
# Finally, pycroscopy itself
import pycroscopy as px
# set up notebook to show plots within the notebook
% matplotlib inline
Explanation: Band Excitation data procesing 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>
2/10/2017
Configure the notebook
End of explanation
max_mem         = 1024*8  # Maximum memory to use, in Mbs. Default = 1024
max_cores       = None    # Number of logical cores to use in fitting.  None uses all but 2 available cores.
Explanation: Set some basic parameters for computation
This notebook performs some functional fitting whose duration can be substantially decreased by using more memory and CPU cores. We have provided default values below but you may choose to change them if necessary.
End of explanation
input_file_path = px.io_utils.uiGetFile(caption='Select translated .h5 file or raw experiment data',
                                        filter='Parameters for raw BE data (*.txt *.mat *xls *.xlsx);; \
                                        Translated file (*.h5)')
(data_dir, data_name) = os.path.split(input_file_path)
if input_file_path.endswith('.h5'):
    # No translation here
    h5_path = input_file_path
    force = False # Set this to true to force patching of the datafile.
    tl = px.LabViewH5Patcher()
    hdf = tl.translate(h5_path, force_patch=force)
else:
    # Set the data to be translated
    data_path = input_file_path
    (junk, base_name) = os.path.split(data_dir)
    # Check if the data is in the new or old format.  Initialize the correct translator for the format.
    if base_name == 'newdataformat':
        (junk, base_name) = os.path.split(junk)
        translator = px.BEPSndfTranslator(max_mem_mb=max_mem)
    else:
        translator = px.BEodfTranslator(max_mem_mb=max_mem)
    if base_name.endswith('_d'):
        base_name = base_name[:-2]
    # Translate the data
    h5_path = translator.translate(data_path, show_plots=True, save_plots=False)
    hdf = px.ioHDF5(h5_path)
print('Working on:\n' + h5_path)
h5_main = px.hdf_utils.getDataSet(hdf.file, 'Raw_Data')[0]
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
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.
End of explanation
h5_pos_inds = px.hdf_utils.getAuxData(h5_main, auxDataName='Position_Indices')[-1]
pos_sort = px.hdf_utils.get_sort_order(np.transpose(h5_pos_inds))
pos_dims = px.hdf_utils.get_dimensionality(np.transpose(h5_pos_inds), pos_sort)
pos_labels = np.array(px.hdf_utils.get_attr(h5_pos_inds, 'labels'))[pos_sort]
print(pos_labels, pos_dims)
parm_dict = hdf.file['/Measurement_000'].attrs
is_ckpfm = hdf.file.attrs['data_type'] == 'cKPFMData'
if is_ckpfm:
    num_write_steps = parm_dict['VS_num_DC_write_steps']
    num_read_steps = parm_dict['VS_num_read_steps']
    num_fields = 2
Explanation: Get some basic parameters from the H5 file
This information will be vital for futher analysis and visualization of the data
End of explanation
px.be_viz_utils.jupyter_visualize_be_spectrograms(h5_main)
Explanation: Visualize the raw data
Use the sliders below to visualize spatial maps (2D only for now), and spectrograms.
For simplicity, all the spectroscopic dimensions such as frequency, excitation bias, cycle, field, etc. have been collapsed to a single slider.
End of explanation
sho_fit_points = 5 # The number of data points at each step to use when fitting
h5_sho_group = px.hdf_utils.findH5group(h5_main, 'SHO_Fit')
sho_fitter = px.BESHOmodel(h5_main, parallel=True)
if len(h5_sho_group) == 0:
    print('No SHO fit found. Doing SHO Fitting now')
    h5_sho_guess = sho_fitter.do_guess(strategy='complex_gaussian', processors=max_cores, options={'num_points':sho_fit_points})
    h5_sho_fit = sho_fitter.do_fit(processors=max_cores)
else:
    print('Taking previous SHO results already present in file')
    h5_sho_guess = h5_sho_group[-1]['Guess']
    try:
        h5_sho_fit = h5_sho_group[-1]['Fit']
    except KeyError:
        print('Previously computed guess found. Now computing fit')
        h5_sho_fit = sho_fitter.do_fit(processors=max_cores, h5_guess=h5_sho_guess)
Explanation: Fit the Band Excitation (BE) spectra
Fit each of the acquired spectra to a simple harmonic oscillator (SHO) model to extract the following information regarding the response:
* Oscillation amplitude
* Phase
* Resonance frequency
* Quality factor
By default, the cell below will take any previous result instead of re-computing the SHO fit
End of explanation
h5_sho_spec_inds = px.hdf_utils.getAuxData(h5_sho_fit, auxDataName='Spectroscopic_Indices')[0]
sho_spec_labels = px.io.hdf_utils.get_attr(h5_sho_spec_inds,'labels')
if is_ckpfm:
    # It turns out that the read voltage index starts from 1 instead of 0
    # Also the VDC indices are NOT repeating. They are just rising monotonically
    write_volt_index = np.argwhere(sho_spec_labels == 'write_bias')[0][0]
    read_volt_index = np.argwhere(sho_spec_labels == 'read_bias')[0][0]
    h5_sho_spec_inds[read_volt_index, :] -= 1
    h5_sho_spec_inds[write_volt_index, :] = np.tile(np.repeat(np.arange(num_write_steps), num_fields), num_read_steps)
(Nd_mat, success, nd_labels) = px.io.hdf_utils.reshape_to_Ndims(h5_sho_fit, get_labels=True)
print('Reshape Success: ' + str(success))
print(nd_labels)
print(Nd_mat.shape)
use_sho_guess = False
use_static_viz_func = False
if use_sho_guess:
    sho_dset = h5_sho_guess
else:
    sho_dset = h5_sho_fit
data_type = px.io.hdf_utils.get_attr(hdf.file, 'data_type')
    
if data_type == 'BELineData' or len(pos_dims) != 2:
    use_static_viz_func = True
    step_chan = None
else:
    vs_mode = px.io.hdf_utils.get_attr(h5_main.parent.parent, 'VS_mode')
    if vs_mode not in ['AC modulation mode with time reversal', 
                                                      'DC modulation mode']:
        use_static_viz_func = True
    else:
        if vs_mode == 'DC modulation mode':
            step_chan = 'DC_Offset'
        else:
            step_chan = 'AC_Amplitude'
if not use_static_viz_func:
    try:
        # use interactive visualization
        px.be_viz_utils.jupyter_visualize_beps_sho(sho_dset, step_chan)
    except:
        raise
        print('There was a problem with the interactive visualizer')
        use_static_viz_func = True
if use_static_viz_func:
    # show plots of SHO results vs. applied bias
    px.be_viz_utils.visualize_sho_results(sho_dset, show_plots=True, 
                                          save_plots=False)
Explanation: Visualize the SHO results
Here, we visualize the parameters for the SHO fits. BE-line (3D) data is visualized via simple spatial maps of the SHO parameters while more complex BEPS datasets (4+ dimensions) can be visualized using a simple interactive visualizer below. 
You can choose to visualize the guesses for SHO function or the final fit values from the first line of the cell below.
Use the sliders below to inspect the BE response at any given location.
End of explanation
# Do the Loop Fitting on the SHO Fit dataset
loop_success = False
h5_loop_group = px.hdf_utils.findH5group(h5_sho_fit, 'Loop_Fit')
if len(h5_loop_group) == 0:
    try:
        loop_fitter = px.BELoopModel(h5_sho_fit, parallel=True)
        print('No loop fits found. Fitting now....')
        h5_loop_guess = loop_fitter.do_guess(processors=max_cores, max_mem=max_mem)
        h5_loop_fit = loop_fitter.do_fit(processors=max_cores, max_mem=max_mem)
        loop_success = True
    except ValueError:
        print('Loop fitting is applicable only to DC spectroscopy datasets!')
else:
    loop_success = True
    print('Taking previously computed loop fits')
    h5_loop_guess = h5_loop_group[-1]['Guess']
    h5_loop_fit = h5_loop_group[-1]['Fit']
Explanation: Fit loops to a function
This is applicable only to DC voltage spectroscopy datasets from BEPS. The PFM hysteresis loops in this dataset will be projected to maximize the loop area and then fitted to a function.
Note: This computation generally takes a while for reasonably sized datasets.
End of explanation
# Prepare some variables for plotting loops fits and guesses
# Plot the Loop Guess and Fit Results
if loop_success:
    h5_projected_loops = h5_loop_guess.parent['Projected_Loops']
    h5_proj_spec_inds = px.hdf_utils.getAuxData(h5_projected_loops, 
                                                auxDataName='Spectroscopic_Indices')[-1]
    h5_proj_spec_vals = px.hdf_utils.getAuxData(h5_projected_loops, 
                                                auxDataName='Spectroscopic_Values')[-1]
    # reshape the vdc_vec into DC_step by Loop
    sort_order = px.hdf_utils.get_sort_order(h5_proj_spec_inds)
    dims = px.hdf_utils.get_dimensionality(h5_proj_spec_inds[()], 
                                           sort_order[::-1])
    vdc_vec = np.reshape(h5_proj_spec_vals[h5_proj_spec_vals.attrs['DC_Offset']], dims).T
    #Also reshape the projected loops to Positions-DC_Step-Loop
    # Also reshape the projected loops to Positions-DC_Step-Loop
    proj_nd, _ = px.hdf_utils.reshape_to_Ndims(h5_projected_loops)
    proj_3d = np.reshape(proj_nd, [h5_projected_loops.shape[0], 
                                   proj_nd.shape[2], -1])
Explanation: Prepare datasets for visualization
End of explanation
use_static_plots = False
if loop_success:
    if not use_static_plots:
        try:
            px.be_viz_utils.jupyter_visualize_beps_loops(h5_projected_loops, h5_loop_guess, h5_loop_fit)
        except:
            print('There was a problem with the interactive visualizer')
            use_static_plots = True
    if use_static_plots:
        for iloop in range(h5_loop_guess.shape[1]):
            fig, ax = px.be_viz_utils.plot_loop_guess_fit(vdc_vec[:, iloop], proj_3d[:, :, iloop], 
                                          h5_loop_guess[:, iloop], h5_loop_fit[:, iloop],
                                          title='Loop {} - All Positions'.format(iloop))
Explanation: Visualize Loop fits
End of explanation
# hdf.close()
Explanation: Save and close
Save the .h5 file that we are working on by closing it. <br>
Also, consider exporting this notebook as a notebook or an html file. <br> To do this, go to File >> Download as >> HTML
Finally consider saving this notebook if necessary
End of explanation
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