Update README.md

doc/README.md

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-The measurement parameters are extracted from the SSPFM measurement sheet, and each of the raw SSPFM measurement files is subsequently analyzed within the <code>multi_script</code> function. The <code>single_script</code> function analyzes a single raw SSPFM data file by following these steps: measurements are extracted from the file and calibrated if necessary. They are then segmented and processed into PFM data for each segment. The PFM phase signal is analyzed using the <code>phase_offset_determination</code> function of the <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/nanoloop/phase.py">utils/nanoloop/phase.py</a></code> script, both in On Field and Off Field conditions. During this analysis, a phase offset is determined, and the two main peaks are identified and recentered within the phase measurement range. For instance, if the phase value range extends from -180 to 180°, and the two peaks are spaced by 180°, a phase offset will be calculated to position them at -90 and 90°, respectively. This minimizes phase switching across all measurements. Ideally, the phase offsets determined in On and Off Field conditions should be close. The average offset corresponding to the entire measurement file is determined using the <code>mean_phase_offset</code> function of the <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/nanoloop/phase.py">utils/nanoloop/phase.py</a></code> script. The phase evolution with respect to the measurement file is generated using the <code>generate_graph_offset</code> function. It should be noted that the case of unipolar phase data (a single peak on the phase histogram) can be handled by the script functions. Typically, this script is used before the first step of processing SSPFM measurements in order to generate the file containing the phase offset value to apply to measurement before the treatment processing.
+The measurement parameters are extracted from the SSPFM measurement sheet, and each of the raw SSPFM measurement files is subsequently analyzed within the <code>multi_script</code> function. The <code>single_script</code> function analyzes a single raw SSPFM data file by following these steps: measurements are extracted from the file and calibrated if necessary. They are then segmented and processed into PFM data for each segment. The PFM phase signal is analyzed using the <code>phase_offset_determination</code> function of the <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/nanoloop/phase.py">utils/nanoloop/phase.py</a></code> script, both in On Field and Off Field conditions. During this analysis, a phase offset is determined, and the two main peaks are identified and recentered within the phase measurement range. For instance, if the phase value range extends from -180 to 180°, and the two peaks are spaced by 180°, a phase offset will be calculated to position them at -90 and 90°, respectively. This minimizes phase switching across all measurements. Ideally, the phase offsets determined in On and Off Field conditions should be close. The average offset corresponding to the entire measurement file is determined using the <code>mean_phase_offset</code> function of the <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/nanoloop/phase.py">utils/nanoloop/phase.py</a></code> script. The phase evolution with respect to the measurement file is generated using the <code>generate_graph_offset</code> function. It should be noted that the case of unipolar phase data (a single peak on the phase histogram) can be handled by the script functions. The list of phase offset values can then be saved using the <code>save_dict_to_txt</code> function from the script. Typically, this script is used before the first step of processing SSPFM measurements in order to generate the file containing the phase offset value to apply to measurement before the treatment processing.
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@@ -1676,7 +1676,7 @@ User parameters:
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-The measurement parameters are extracted from the SSPFM measurement sheet, and each of the raw SSPFM measurement files is subsequently analyzed within the <code>multi_script</code> function. The <code>single_script</code> function analyzes a single raw SSPFM data file by following these steps: measurements are extracted from the file and phase offset is applied if necessary. A list of phase offsets for each file can be applied with the <code>phase_file_path</code> parameter, containing the path of a file generated with the <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/gui/phase_offset_analyzer.py">gui/phase_offset_analyzer.py</a></code> script, otherwise, a value can be specified with the <code>offset</code> parameter. They are then segmented and processed into PFM data for each segment. For each mode (On and Off Field), the sign of the phase signal gradient with the writing voltage is determined using the <code>phase_bias_grad</code> function from the <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/nanoloop/phase.py">utils/nanoloop/phase.py</a></code> script. The two values are compared to determine if a phase inversion has occurred between the On and Off Field modes. Typically, this script is used before the second step of processing SSPFM measurements in order to generate the file containing the boolean value of the main electrostatic parameter to apply to the measurement before the treatment processing.
+The measurement parameters are extracted from the SSPFM measurement sheet, and each of the raw SSPFM measurement files is subsequently analyzed within the <code>multi_script</code> function. The <code>single_script</code> function analyzes a single raw SSPFM data file by following these steps: measurements are extracted from the file and phase offset is applied with <code>apply_phase_offset</code> of the script, if necessary. A list of phase offsets for each file can be applied with the <code>phase_file_path</code> parameter, containing the path of a file generated with the <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/gui/phase_offset_analyzer.py">gui/phase_offset_analyzer.py</a></code> script, otherwise, a value can be specified with the <code>offset</code> parameter. They are then segmented and processed into PFM data for each segment. For each mode (On and Off Field), the sign of the phase signal gradient with the writing voltage is determined using the <code>phase_bias_grad</code> function from the <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/nanoloop/phase.py">utils/nanoloop/phase.py</a></code> script. The two values are compared with the <code>revert_on_off</code> function from the script to determine if a phase inversion has occurred between the On and Off Field modes. Typically, this script is used before the second step of processing SSPFM measurements in order to generate the file containing the boolean value of the main electrostatic parameter, using the <code>save_dict_to_txt</code> function from the script, to apply to the measurement before the treatment processing.
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@@ -2158,7 +2158,7 @@ The entire assemblage of scripts under the <code><a href="https://github.com/CEA
 &#8226 <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/core/figure.py">figure.py</a></code>, which facilitates the generation of visual representations in a consistent style. This encompasses the creation of graphs, histograms, and mappings through the functions <code>plot_graph</code>, <code>plot_hist</code>, and <code>plot_map</code>. The <code>print_plots</code> function offers advanced control over the display and storage of visual representations. <br>
 &#8226 <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/core/fitting.py">fitting.py</a></code>, which is responsible for executing all fits (excluding hysteresis fitting) based on the <a href="https://lmfit.github.io/lmfit-py/fitting.html">minimize</a> function of <a href="https://pypi.org/project/lmfit/">lmfit</a> library. Fit methods like <code>least_sq</code>, <code>least_square</code> (prioritizing speed), or <code>nelder</code> (prioritizing convergence) can be selected with the <code>fit_method</code> setting. <a href="https://lmfit.github.io/lmfit-py/model.html#lmfit.model.Model">Model</a> and <a href="https://lmfit.github.io/lmfit-py/parameters.html#the-parameters-class">Parameters</a> objects of <a href="https://pypi.org/project/lmfit/">lmfit</a> are likewise employed, respectively, for the amalgamation of model functions (e.g., adding an affine or constant component that may correspond to noise) and for the management of parameter initialization prior to the fitting process (initial value, range of variation, etc.). It includes a parent class <code>CurveFit</code> and three subclasses, namely <code>GaussianPeakFit</code>, <code>ShoPeakFit</code>, and <code>ShoPhaseFit</code>, each built upon the parent class to execute Gaussian, Sho, and Sho phase (arctangent) fitting, respectively. The parent class incorporates a set of methods shared by the subclasses, including <code>eval</code> for evaluating the fitted peak at specified x-values, <code>fit</code>, <code>plot</code>, and more. The subclasses invoke the parent class during initialization and enable parameter initialization for fitting, with model-specific initial guesses. <br>
 &#8226 <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/core/iterable.py">iterable.py</a></code> for handling iterables. <br>
-&#8226 <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/core/multi_proc.py">multi_proc.py</a></code> for multi processing functions. If <code>multi_processing</code> setting is active, multi processing is used for <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/data_processing/datacube_to_nanoloop_s1.py">datacube_to_nanoloop_s1.py</a></code>, <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/data_processing/nanoloop_to_hyst_s2.py">nanoloop_to_hyst_s2.py</a></code> and <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/toolbox/phase_offset_analyzer.py">phase_offset_analyzer.py</a></code>. <br>
+&#8226 <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/core/multi_proc.py">multi_proc.py</a></code> for multi processing functions. If <code>multi_processing</code> setting is active, multi processing is used for <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/data_processing/datacube_to_nanoloop_s1.py">datacube_to_nanoloop_s1.py</a></code>, <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/data_processing/nanoloop_to_hyst_s2.py">nanoloop_to_hyst_s2.py</a></code>, <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/toolbox/phase_offset_analyzer.py">phase_offset_analyzer.py</a></code> and <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/toolbox/phase_inversion_analyzer.py">phase_inversion_analyzer.py</a></code>. <br>
 &#8226 <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/core/noise.py">noise.py</a></code> for noise management. The <code>filter_mean</code> function serves as a filtering mechanism for averaging, offering a choice of filter order to reduce measurement noise. The <code>noise</code> function is used to generate noise of a specific amplitude (widely employed for examples and tests to recreate the most realistic data) using three possible distribution models: <code>uniform</code>, <code>normal</code>, and <code>laplace</code>. Finally, the <code>butter_filter</code> function filters an input signal with a Butterworth filter type ('low', 'high', 'bandpass', or 'bandstop'), along with its associated cutoff frequency or frequencies, and its order.<br>
 &#8226 <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/core/path_management.py">path_management.py</a></code> for path management. The function <code>get_filenames_with_conditions</code> allows for the extraction of a list of file names from a directory, with the possibility of adding conditions on a prefix or a suffix. The function <code>gen_bruker_filenames</code> enables the generation of file names in the format used by Bruker, such as string.0_00000.ext. Finally, the function <code>sort_filenames</code> sorts a list of file names by automatically extracting any index included in the file name. <br>
 &#8226 <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/core/peak.py">peak.py</a></code> contains a set of functions for peak handling. <code>detect_peak</code> and <code>find_main_peaks</code> automatically identify peaks in an array of values, relying on the <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html#scipy.signal.find_peaks">find_peaks</a> function from the scipy.signal library. The function <code>plot_main_peaks</code> allow to plot an array of peaks. It also includes functions for guessing noise components (linear component with <code>guess_affine</code> and constant with <code>guess_bckgnd</code>) and determining peak width with <code>width_peak</code>. <br>