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    <article id="content">
    <header>
    <h1 class="title">Module <code>dopes.data_analysis.data_processing</code></h1>
    </header>
    <section id="section-intro">
    </section>
    <section>
    </section>
    <section>
    </section>
    <section>
    <h2 class="section-title" id="header-functions">Functions</h2>
    <dl>
    <dt id="dopes.data_analysis.data_processing.finite_difference"><code class="name flex">
    <span>def <span class="ident">finite_difference</span></span>(<span>x, h, order, accuracy=2, kind='central')</span>
    </code></dt>
    <dd>
    <details class="source">
    <summary>
    <span>Expand source code</span>
    </summary>
    <pre><code class="python">def finite_difference(x,h,order,accuracy=2,kind=&#34;central&#34;):
        &#34;&#34;&#34; Function to calculate the coefficient to calculate a finite difference for derivative calculation
            
            args:
                - x (array): the 1D array for which to calculate the finite difference. The size of the array should be higher than the size of the stencil given by 2 * |_ (order + 1) / 2 _| - 1 + 2 * |_ accuracy / 2_|
                - h (scalar): the step size for an uniform grid spacing between each finite difference interval
                - order (integer): the order of the derivation
                - accuracy (integer): related to the number of points taken for the finite difference. see https://en.wikipedia.org/wiki/Finite_difference_coefficient
                - kind (string): the type of finite difference calculated. For now, only &#34;central&#34; finite difference is implemented.
        
            return:
                - results (numpy array): an array with the results of the calculation of the finite difference
                - x_results (numpy array): an array with the x value with the central value for each calculation of the finite difference
        &#34;&#34;&#34;  
        if kind==&#34;central&#34;:
            n_stencil=int(2*np.floor((order+1)/2)-1+2*np.floor(accuracy/2))
            
            stencil=range(-int(np.floor(n_stencil/2)),int(np.floor(n_stencil/2))+1)
            
        
        coeff=finite_difference_coeff(stencil,order)/h**order
        
        Nx=len(x)
        matrix_coeff=np.zeros((Nx-accuracy-order+1,Nx))
        for i in range(Nx-accuracy-order+1):
            matrix_coeff[i,i:n_stencil+i]=coeff[:,0]
            
        results=matrix_coeff @ x
        # x_results=x[int((n_stencil-1)/2):-int((n_stencil-1)/2)]
        return results</code></pre>
    </details>
    <div class="desc"><p>Function to calculate the coefficient to calculate a finite difference for derivative calculation</p>
    <p>args:
    - x (array): the 1D array for which to calculate the finite difference. The size of the array should be higher than the size of the stencil given by 2 * |<em> (order + 1) / 2 </em>| - 1 + 2 * |<em> accuracy / 2</em>|
    - h (scalar): the step size for an uniform grid spacing between each finite difference interval
    - order (integer): the order of the derivation
    - accuracy (integer): related to the number of points taken for the finite difference. see <a href="https://en.wikipedia.org/wiki/Finite_difference_coefficient">https://en.wikipedia.org/wiki/Finite_difference_coefficient</a>
    - kind (string): the type of finite difference calculated. For now, only "central" finite difference is implemented.</p>
    <p>return:
    - results (numpy array): an array with the results of the calculation of the finite difference
    - x_results (numpy array): an array with the x value with the central value for each calculation of the finite difference</p></div>
    </dd>
    <dt id="dopes.data_analysis.data_processing.finite_difference_coeff"><code class="name flex">
    <span>def <span class="ident">finite_difference_coeff</span></span>(<span>stencil, order)</span>
    </code></dt>
    <dd>
    <details class="source">
    <summary>
    <span>Expand source code</span>
    </summary>
    <pre><code class="python">def finite_difference_coeff(stencil,order):
        &#34;&#34;&#34; Function to calculate the coefficient to calculate a finite difference for derivative calculation
            https://en.wikipedia.org/wiki/Finite_difference_coefficient
            
            args:
                - stencil (list of integer): the node point for the calculation of each derivation point. The size of the stencil depends on the accuracy and the order of the derivation. For central difference, the length of the stencil is odd and symetric around 0.
                - order (integer): the order of the derivation
        
            return:
                - coeff (numpy array): a column array of the coefficients to be used to calculate the finite difference
        &#34;&#34;&#34;  
        N=len(stencil)
        stencil_matrix=np.zeros((N,N))
        for i in range(N):
            stencil_matrix[i]=np.array(stencil)**i
        delta=np.zeros((N,1))
        delta[order,0]= math.factorial(order)
    
        coeff=np.linalg.inv(stencil_matrix) @ delta
        return coeff</code></pre>
    </details>
    <div class="desc"><p>Function to calculate the coefficient to calculate a finite difference for derivative calculation
    <a href="https://en.wikipedia.org/wiki/Finite_difference_coefficient">https://en.wikipedia.org/wiki/Finite_difference_coefficient</a></p>
    <p>args:
    - stencil (list of integer): the node point for the calculation of each derivation point. The size of the stencil depends on the accuracy and the order of the derivation. For central difference, the length of the stencil is odd and symetric around 0.
    - order (integer): the order of the derivation</p>
    <p>return:
    - coeff (numpy array): a column array of the coefficients to be used to calculate the finite difference</p></div>
    </dd>
    <dt id="dopes.data_analysis.data_processing.fit_gaussian"><code class="name flex">
    <span>def <span class="ident">fit_gaussian</span></span>(<span>x, y, xmin=None, xmax=None, x0=0, A=1, W=1)</span>
    </code></dt>
    <dd>
    <details class="source">
    <summary>
    <span>Expand source code</span>
    </summary>
    <pre><code class="python">def fit_gaussian(x,y,xmin=None,xmax=None,x0=0,A=1,W=1):
        &#34;&#34;&#34; Function to fit the data with a Lorentzian function 
        
            args:
                - x (array_like) : a 1D input array.
                - y (array_like) : a 1D  input array with the same dimension of x.
                - xmin (scalar) : a scalar or array of values to set the minimum value of the data range on which the Gaussian is calculated. 
                - xmax (scalar) : a scalar or array of values to set the maximum value of the data range on which the Gaussian is calculated. .
                - x0 (scalar) : the starting position of the Gaussian function for the fit
                - A (scalar) : the starting amplitude of the Gaussian function for the fit
                - W (scalar) : the starting full width at half maximum (FWHM) of the Gaussian function for the fit
            return:
                - a tuple (parameter_dictionary, data_gaussian) with the a dictionary containing the fitting parameters (&#34;position&#34;, &#34;amplitude&#34; and &#34;width&#34;) and the corresponding Gaussian function evaluated in x.
        &#34;&#34;&#34;
        if (xmin is None) and (xmax is None):
            x_fit=x
            y_fit=y
        else:
            index=(x&gt;=xmin) &amp; (x&lt;=xmax) 
            x_fit=x[index]
            y_fit=y[index]
    
        p_gaussian=curve_fit(gaussian, x_fit, y_fit, p0=[x0,A,W])[0]
        return {&#34;position&#34;:p_gaussian[0],&#34;width&#34;:p_gaussian[1],&#34;amplitude&#34;:p_gaussian[2]}, gaussian(x,*p_gaussian)</code></pre>
    </details>
    <div class="desc"><p>Function to fit the data with a Lorentzian function </p>
    <p>args:
    - x (array_like) : a 1D input array.
    - y (array_like) : a 1D
    input array with the same dimension of x.
    - xmin (scalar) : a scalar or array of values to set the minimum value of the data range on which the Gaussian is calculated.
    - xmax (scalar) : a scalar or array of values to set the maximum value of the data range on which the Gaussian is calculated. .
    - x0 (scalar) : the starting position of the Gaussian function for the fit
    - A (scalar) : the starting amplitude of the Gaussian function for the fit
    - W (scalar) : the starting full width at half maximum (FWHM) of the Gaussian function for the fit
    return:
    - a tuple (parameter_dictionary, data_gaussian) with the a dictionary containing the fitting parameters ("position", "amplitude" and "width") and the corresponding Gaussian function evaluated in x.</p></div>
    </dd>
    <dt id="dopes.data_analysis.data_processing.fit_lorentzian"><code class="name flex">
    <span>def <span class="ident">fit_lorentzian</span></span>(<span>x, y, xmin=None, xmax=None, x0=520.7, A0=1, W0=3)</span>
    </code></dt>
    <dd>
    <details class="source">
    <summary>
    <span>Expand source code</span>
    </summary>
    <pre><code class="python">def fit_lorentzian(x,y,xmin=None,xmax=None,x0=520.7,A0=1,W0=3):
        &#34;&#34;&#34; Function to fit the data with a Lorentzian function 
        
            args:
                - x (array_like) : a 1D input array.
                - y (array_like) : a 1D  input array with the same dimension of x.
                - xmin (scalar) : a scalar or array of values to set the minimum value of the data range on which the Lorentzian is calculated. 
                - xmax (scalar) : a scalar or array of values to set the maximum value of the data range on which the Lorentzian is calculated. .
                - x0 (scalar) : the starting position of the Lorentzian function for the fit
                - A (scalar) : the starting amplitude of the Lorentzian function for the fit
                - W (scalar) : the starting full width at half maximum (FWHM) of the Lorentzian function for the fit
            return:
                - a tuple (parameter_dictionary, data_lorentzian) with the a dictionary containing the fitting parameters (&#34;position&#34;, &#34;amplitude&#34; and &#34;width&#34;) and the corresponding Lorentzian function evaluated in x.
        &#34;&#34;&#34;
        if (xmin is None) and (xmax is None):
            x_fit=x
            y_fit=y
        else:
            index=(x&gt;=xmin) &amp; (x&lt;=xmax) 
            x_fit=x[index]
            y_fit=y[index]
        p_lorentzian=curve_fit(lorentzian, x_fit, y_fit, p0=[x0,A0,W0])[0]
        return {&#34;position&#34;:p_lorentzian[0],&#34;amplitude&#34;:p_lorentzian[1],&#34;width&#34;:p_lorentzian[2]}, lorentzian(x,*p_lorentzian)</code></pre>
    </details>
    <div class="desc"><p>Function to fit the data with a Lorentzian function </p>
    <p>args:
    - x (array_like) : a 1D input array.
    - y (array_like) : a 1D
    input array with the same dimension of x.
    - xmin (scalar) : a scalar or array of values to set the minimum value of the data range on which the Lorentzian is calculated.
    - xmax (scalar) : a scalar or array of values to set the maximum value of the data range on which the Lorentzian is calculated. .
    - x0 (scalar) : the starting position of the Lorentzian function for the fit
    - A (scalar) : the starting amplitude of the Lorentzian function for the fit
    - W (scalar) : the starting full width at half maximum (FWHM) of the Lorentzian function for the fit
    return:
    - a tuple (parameter_dictionary, data_lorentzian) with the a dictionary containing the fitting parameters ("position", "amplitude" and "width") and the corresponding Lorentzian function evaluated in x.</p></div>
    </dd>
    <dt id="dopes.data_analysis.data_processing.fit_lorentzian_2peaks"><code class="name flex">
    <span>def <span class="ident">fit_lorentzian_2peaks</span></span>(<span>x, y, xmin=None, xmax=None, x0=(520, 520.7), A0=(1, 1), W0=(3, 3))</span>
    </code></dt>
    <dd>
    <details class="source">
    <summary>
    <span>Expand source code</span>
    </summary>
    <pre><code class="python">def fit_lorentzian_2peaks(x,y,xmin=None,xmax=None,x0=(520,520.7),A0=(1,1),W0=(3,3)):
        &#34;&#34;&#34; Function to fit the data with a Lorentzian function 
        
            args:
                - x (array_like) : a 1D input array.
                - y (array_like) : a 1D  input array with the same dimension of x.
                - xmin (scalar) : a scalar or array of values to set the minimum value of the data range on which the Lorentzian is calculated. 
                - xmax (scalar) : a scalar or array of values to set the maximum value of the data range on which the Lorentzian is calculated. .
                - x0 (tuple) : the two starting position of the double Lorentzian function for the fit
                - A (tuple) : the two starting amplitude of the double Lorentzian function for the fit
                - W (tuple) : the two starting full width at half maximum (FWHM) of the double Lorentzian function for the fit
            return:
                - a tuple (parameter_dictionary, data_lorentzian) with the a dictionary containing the fitting parameters (&#34;position&#34;, &#34;amplitude&#34; and &#34;width&#34;) and the corresponding double Lorentzian function evaluated in x.
            &#34;&#34;&#34;
        if (xmin is None) and (xmax is None):
            x_fit=x
            y_fit=y
        else:
            index=(x&gt;=xmin) &amp; (x&lt;=xmax) 
            x_fit=x[index]
            y_fit=y[index]
        p_lorentzian=curve_fit(lorentzian2, x_fit, y_fit, p0=np.reshape([x0,A0,W0],newshape=6))[0]
        return {&#34;position&#34;:p_lorentzian[:2],&#34;amplitude&#34;:p_lorentzian[2:4],&#34;width&#34;:p_lorentzian[4:]}, lorentzian(x,*p_lorentzian)</code></pre>
    </details>
    <div class="desc"><p>Function to fit the data with a Lorentzian function </p>
    <p>args:
    - x (array_like) : a 1D input array.
    - y (array_like) : a 1D
    input array with the same dimension of x.
    - xmin (scalar) : a scalar or array of values to set the minimum value of the data range on which the Lorentzian is calculated.
    - xmax (scalar) : a scalar or array of values to set the maximum value of the data range on which the Lorentzian is calculated. .
    - x0 (tuple) : the two starting position of the double Lorentzian function for the fit
    - A (tuple) : the two starting amplitude of the double Lorentzian function for the fit
    - W (tuple) : the two starting full width at half maximum (FWHM) of the double Lorentzian function for the fit
    return:
    - a tuple (parameter_dictionary, data_lorentzian) with the a dictionary containing the fitting parameters ("position", "amplitude" and "width") and the corresponding double Lorentzian function evaluated in x.</p></div>
    </dd>
    <dt id="dopes.data_analysis.data_processing.gaussian"><code class="name flex">
    <span>def <span class="ident">gaussian</span></span>(<span>x, x0, A, W)</span>
    </code></dt>
    <dd>
    <details class="source">
    <summary>
    <span>Expand source code</span>
    </summary>
    <pre><code class="python">def gaussian(x, x0, A, W):
        &#34;&#34;&#34; Faussian function 
        
            args:
                - x (array_like) : a 1D input array.
                - x0 (scalar) : the position of the Gaussian function
                - A (scalar) : the amplitude of the Lorentzian function
                - W (scalar) : the full width at half maximum (FWHM) of the Lorentzian function
            return:
                - an array of the same dimension od x
        &#34;&#34;&#34; 
        return A*np.exp(-np.power(x - x0, 2.) / (2 * np.power(W, 2.))) / (W * np.sqrt(2*np.pi))</code></pre>
    </details>
    <div class="desc"><p>Faussian function </p>
    <p>args:
    - x (array_like) : a 1D input array.
    - x0 (scalar) : the position of the Gaussian function
    - A (scalar) : the amplitude of the Lorentzian function
    - W (scalar) : the full width at half maximum (FWHM) of the Lorentzian function
    return:
    - an array of the same dimension od x</p></div>
    </dd>
    <dt id="dopes.data_analysis.data_processing.interpolate"><code class="name flex">
    <span>def <span class="ident">interpolate</span></span>(<span>x, y, x_interp, kind='cubic')</span>
    </code></dt>
    <dd>
    <details class="source">
    <summary>
    <span>Expand source code</span>
    </summary>
    <pre><code class="python">def interpolate(x,y,x_interp,kind=&#34;cubic&#34;):
        &#34;&#34;&#34; Function to interpolate an 1-D array
        
            args:
                - x (array_like) : a 1-D array of real values.
                - y (array_like) : a 1-D array of real values of the same dimension of x.
                - x_interp (array_like) : a 1-D array of real values of the any dimension but with all values include between the max and min value of x.
                - kind (string or integer) : Specifies the kind of interpolation as a string specifying the order of the spline interpolator to use. The string has to be one of ‘linear’, ‘nearest’, ‘nearest-up’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, or ‘next’. 
            return:
                - an array the same size as x_interp containing the interpolated result.
        &#34;&#34;&#34;    
        f=interp1d(x, y,kind=kind)    
        return f(x_interp)</code></pre>
    </details>
    <div class="desc"><p>Function to interpolate an 1-D array</p>
    <p>args:
    - x (array_like) : a 1-D array of real values.
    - y (array_like) : a 1-D array of real values of the same dimension of x.
    - x_interp (array_like) : a 1-D array of real values of the any dimension but with all values include between the max and min value of x.
    - kind (string or integer) : Specifies the kind of interpolation as a string specifying the order of the spline interpolator to use. The string has to be one of ‘linear’, ‘nearest’, ‘nearest-up’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, or ‘next’.
    return:
    - an array the same size as x_interp containing the interpolated result.</p></div>
    </dd>
    <dt id="dopes.data_analysis.data_processing.lorentzian"><code class="name flex">
    <span>def <span class="ident">lorentzian</span></span>(<span>x, x0, A, W)</span>
    </code></dt>
    <dd>
    <details class="source">
    <summary>
    <span>Expand source code</span>
    </summary>
    <pre><code class="python">def lorentzian(x,x0,A,W):
        &#34;&#34;&#34; Lorentzian function 
        
            args:
                - x (array_like) : a 1D input array.
                - x0 (scalar) : the position of the Lorentzian function
                - A (scalar) : the amplitude of the Lorentzian function
                - W (scalar) : the full width at half maximum (FWHM) of the Lorentzian function
            return:
                - an array of the same dimension as x
        &#34;&#34;&#34; 
        return A/(1+((x-x0)/(W/2))**2)</code></pre>
    </details>
    <div class="desc"><p>Lorentzian function </p>
    <p>args:
    - x (array_like) : a 1D input array.
    - x0 (scalar) : the position of the Lorentzian function
    - A (scalar) : the amplitude of the Lorentzian function
    - W (scalar) : the full width at half maximum (FWHM) of the Lorentzian function
    return:
    - an array of the same dimension as x</p></div>
    </dd>
    <dt id="dopes.data_analysis.data_processing.lorentzian2"><code class="name flex">
    <span>def <span class="ident">lorentzian2</span></span>(<span>x, x0, x01, A, A1, W, W1)</span>
    </code></dt>
    <dd>
    <details class="source">
    <summary>
    <span>Expand source code</span>
    </summary>
    <pre><code class="python">def lorentzian2(x,x0,x01,A,A1,W,W1):
        &#34;&#34;&#34; Function with the sum of two Lorentzian 
        
            args:
                - x (array_like) : a 1D input array.
                - x01 (scalar) : the position of the first Lorentzian function
                - A1 (scalar) : the amplitude of the first Lorentzian function
                - W1 (scalar) : the full width at half maximum (FWHM) of the first Lorentzian function
                - x02 (scalar) : the position of the second Lorentzian function
                - A2 (scalar) : the amplitude of the second Lorentzian function
                - W2 (scalar) : the full width at half maximum (FWHM) of the second Lorentzian function
            return:
                - an array of the same dimension as x
        &#34;&#34;&#34; 
        return A/(1+((x-x0)/(W/2))**2)+A1/(1+((x-x01)/(W1/2))**2)</code></pre>
    </details>
    <div class="desc"><p>Function with the sum of two Lorentzian </p>
    <p>args:
    - x (array_like) : a 1D input array.
    - x01 (scalar) : the position of the first Lorentzian function
    - A1 (scalar) : the amplitude of the first Lorentzian function
    - W1 (scalar) : the full width at half maximum (FWHM) of the first Lorentzian function
    - x02 (scalar) : the position of the second Lorentzian function
    - A2 (scalar) : the amplitude of the second Lorentzian function
    - W2 (scalar) : the full width at half maximum (FWHM) of the second Lorentzian function
    return:
    - an array of the same dimension as x</p></div>
    </dd>
    <dt id="dopes.data_analysis.data_processing.moving_median"><code class="name flex">
    <span>def <span class="ident">moving_median</span></span>(<span>x, window_length=3)</span>
    </code></dt>
    <dd>
    <details class="source">
    <summary>
    <span>Expand source code</span>
    </summary>
    <pre><code class="python">def moving_median(x,window_length=3):
        &#34;&#34;&#34; Function to perform a median filter on a array
        
            args:
                - x (array_like) : the input array.
                - window_length (scalar) : size of the median filter window.    
            return:
                - an array the same size as input containing the median filtered result.
        &#34;&#34;&#34;
        return signal.medfilt(x,window_length)</code></pre>
    </details>
    <div class="desc"><p>Function to perform a median filter on a array</p>
    <p>args:
    - x (array_like) : the input array.
    - window_length (scalar) : size of the median filter window.
    <br>
    return:
    - an array the same size as input containing the median filtered result.</p></div>
    </dd>
    <dt id="dopes.data_analysis.data_processing.remove_baseline"><code class="name flex">
    <span>def <span class="ident">remove_baseline</span></span>(<span>x, y, xmin_baseline, xmax_baseline, polyorder=2)</span>
    </code></dt>
    <dd>
    <details class="source">
    <summary>
    <span>Expand source code</span>
    </summary>
    <pre><code class="python">def remove_baseline(x,y,xmin_baseline,xmax_baseline,polyorder=2):
        &#34;&#34;&#34; Function to remove the baseline a 1-D array
        
            args:
                - x (array_like) : a 1D input array.
                - y (array_like) : a 1D  input array with the same dimension of x.
                - xmin_baseline (scalar or array) : a scalar or array of values to set the minimum value of the data ranges on which the baseline is calculated. Several windows can be specified by specifying an array of values
                - xmax_baseline (scalar or array) : a scalar or array of values to set the maximum value of the data ranges on which the baseline is calculated. Several windows can be specified by specifying an array of values.
                - polyorder (scalar) : the order of the polynome to calculate the baseline    
            return:
                - a tuple (corrected_data, baseline) with the corrected data and the baseline calculated.
        &#34;&#34;&#34;       
        if isinstance(xmin_baseline,(int,float)):
            xmin_baseline=[xmin_baseline]
            xmax_baseline=[xmax_baseline]
            
        index=[False]*len(x)
        for i in range(len(xmin_baseline)):
            index = index | ((x&gt;=xmin_baseline[i]) &amp; (x&lt;=xmax_baseline[i]) )
        p=np.polyfit(x[index],y[index],deg=polyorder)
        baseline=np.polyval(p,x)
        return y-baseline, baseline</code></pre>
    </details>
    <div class="desc"><p>Function to remove the baseline a 1-D array</p>
    <p>args:
    - x (array_like) : a 1D input array.
    - y (array_like) : a 1D
    input array with the same dimension of x.
    - xmin_baseline (scalar or array) : a scalar or array of values to set the minimum value of the data ranges on which the baseline is calculated. Several windows can be specified by specifying an array of values
    - xmax_baseline (scalar or array) : a scalar or array of values to set the maximum value of the data ranges on which the baseline is calculated. Several windows can be specified by specifying an array of values.
    - polyorder (scalar) : the order of the polynome to calculate the baseline
    <br>
    return:
    - a tuple (corrected_data, baseline) with the corrected data and the baseline calculated.</p></div>
    </dd>
    <dt id="dopes.data_analysis.data_processing.smooth"><code class="name flex">
    <span>def <span class="ident">smooth</span></span>(<span>x, window_length=11, polyorder=2)</span>
    </code></dt>
    <dd>
    <details class="source">
    <summary>
    <span>Expand source code</span>
    </summary>
    <pre><code class="python">def smooth(x, window_length=11, polyorder=2):
        &#34;&#34;&#34; Function to smooth an array by applying a Savitzky-Golay filter
        
            args:
                - x (array_like) : the data to be filtered.
                - window_length (scalar) : the length of the filter window.
                - polyorder (scalar) : the order of the polynomial used to fit the samples. polyorder must be less than window_length.   
            return:
                - an array the same size as input containing the filtered result.
        &#34;&#34;&#34;    
        return signal.savgol_filter(x,window_length,polyorder)</code></pre>
    </details>
    <div class="desc"><p>Function to smooth an array by applying a Savitzky-Golay filter</p>
    <p>args:
    - x (array_like) : the data to be filtered.
    - window_length (scalar) : the length of the filter window.
    - polyorder (scalar) : the order of the polynomial used to fit the samples. polyorder must be less than window_length. <br>
    return:
    - an array the same size as input containing the filtered result.</p></div>
    </dd>
    </dl>
    </section>
    <section>
    </section>
    </article>
    <nav id="sidebar">
    <div class="toc">
    <ul></ul>
    </div>
    <ul id="index">
    <li><h3>Super-module</h3>
    <ul>
    <li><code><a title="dopes.data_analysis" href="index.html">dopes.data_analysis</a></code></li>
    </ul>
    </li>
    <li><h3><a href="#header-functions">Functions</a></h3>
    <ul class="">
    <li><code><a title="dopes.data_analysis.data_processing.finite_difference" href="#dopes.data_analysis.data_processing.finite_difference">finite_difference</a></code></li>
    <li><code><a title="dopes.data_analysis.data_processing.finite_difference_coeff" href="#dopes.data_analysis.data_processing.finite_difference_coeff">finite_difference_coeff</a></code></li>
    <li><code><a title="dopes.data_analysis.data_processing.fit_gaussian" href="#dopes.data_analysis.data_processing.fit_gaussian">fit_gaussian</a></code></li>
    <li><code><a title="dopes.data_analysis.data_processing.fit_lorentzian" href="#dopes.data_analysis.data_processing.fit_lorentzian">fit_lorentzian</a></code></li>
    <li><code><a title="dopes.data_analysis.data_processing.fit_lorentzian_2peaks" href="#dopes.data_analysis.data_processing.fit_lorentzian_2peaks">fit_lorentzian_2peaks</a></code></li>
    <li><code><a title="dopes.data_analysis.data_processing.gaussian" href="#dopes.data_analysis.data_processing.gaussian">gaussian</a></code></li>
    <li><code><a title="dopes.data_analysis.data_processing.interpolate" href="#dopes.data_analysis.data_processing.interpolate">interpolate</a></code></li>
    <li><code><a title="dopes.data_analysis.data_processing.lorentzian" href="#dopes.data_analysis.data_processing.lorentzian">lorentzian</a></code></li>
    <li><code><a title="dopes.data_analysis.data_processing.lorentzian2" href="#dopes.data_analysis.data_processing.lorentzian2">lorentzian2</a></code></li>
    <li><code><a title="dopes.data_analysis.data_processing.moving_median" href="#dopes.data_analysis.data_processing.moving_median">moving_median</a></code></li>
    <li><code><a title="dopes.data_analysis.data_processing.remove_baseline" href="#dopes.data_analysis.data_processing.remove_baseline">remove_baseline</a></code></li>
    <li><code><a title="dopes.data_analysis.data_processing.smooth" href="#dopes.data_analysis.data_processing.smooth">smooth</a></code></li>
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