How to use rampy - 10 common examples

To help you get started, we’ve selected a few rampy examples, based on popular ways it is used in public projects.

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github charlesll / rampy / rampy / baseline.py View on Github external
### Baseline is of the type y = a*exp(-log(2)*((x-b)/c)**2)
        # optional parameters
        p0_gauss = kwargs.get('p0_gaussian',[1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.gaussian,yafit[:,0],yafit[:,1],p0 = p0_gauss)

        baseline_fitted = rampy.gaussian(x,coeffs[0],coeffs[1],coeffs[2])

    elif method == 'exp':
        ### Baseline is of the type y = a*exp(b*(x-xo))
        # optional parameters
        p0_exp = kwargs.get('p0_exp',[1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.funexp,yafit[:,0],yafit[:,1],p0 = p0_exp)

        baseline_fitted = rampy.funexp(x,coeffs[0],coeffs[1],coeffs[2])

    elif method == 'log':
        ### Baseline is of the type y = a*exp(b*(x-xo))
        # optional parameters
        p0_log = kwargs.get('p0_log',[1.,1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.funlog,yafit[:,0],yafit[:,1],p0 = p0_log)

        baseline_fitted = rampy.funlog(x,coeffs[0],coeffs[1],coeffs[2],coeffs[3])

    elif method == 'rubberband':
        # code from this stack-exchange forum
        #https://dsp.stackexchange.com/questions/2725/how-to-perform-a-rubberband-correction-on-spectroscopic-data

        # Find the convex hull
        v = ConvexHull(np.array([x, y])).vertices
github charlesll / rampy / rampy / baseline.py View on Github external
elif method == 'gaussian':
        ### Baseline is of the type y = a*exp(-log(2)*((x-b)/c)**2)
        # optional parameters
        p0_gauss = kwargs.get('p0_gaussian',[1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.gaussian,yafit[:,0],yafit[:,1],p0 = p0_gauss)

        baseline_fitted = rampy.gaussian(x,coeffs[0],coeffs[1],coeffs[2])

    elif method == 'exp':
        ### Baseline is of the type y = a*exp(b*(x-xo))
        # optional parameters
        p0_exp = kwargs.get('p0_exp',[1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.funexp,yafit[:,0],yafit[:,1],p0 = p0_exp)

        baseline_fitted = rampy.funexp(x,coeffs[0],coeffs[1],coeffs[2])

    elif method == 'log':
        ### Baseline is of the type y = a*exp(b*(x-xo))
        # optional parameters
        p0_log = kwargs.get('p0_log',[1.,1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.funlog,yafit[:,0],yafit[:,1],p0 = p0_log)

        baseline_fitted = rampy.funlog(x,coeffs[0],coeffs[1],coeffs[2],coeffs[3])

    elif method == 'rubberband':
        # code from this stack-exchange forum
        #https://dsp.stackexchange.com/questions/2725/how-to-perform-a-rubberband-correction-on-spectroscopic-data
github charlesll / rampy / rampy / baseline.py View on Github external
elif method == 'exp':
        ### Baseline is of the type y = a*exp(b*(x-xo))
        # optional parameters
        p0_exp = kwargs.get('p0_exp',[1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.funexp,yafit[:,0],yafit[:,1],p0 = p0_exp)

        baseline_fitted = rampy.funexp(x,coeffs[0],coeffs[1],coeffs[2])

    elif method == 'log':
        ### Baseline is of the type y = a*exp(b*(x-xo))
        # optional parameters
        p0_log = kwargs.get('p0_log',[1.,1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.funlog,yafit[:,0],yafit[:,1],p0 = p0_log)

        baseline_fitted = rampy.funlog(x,coeffs[0],coeffs[1],coeffs[2],coeffs[3])

    elif method == 'rubberband':
        # code from this stack-exchange forum
        #https://dsp.stackexchange.com/questions/2725/how-to-perform-a-rubberband-correction-on-spectroscopic-data

        # Find the convex hull
        v = ConvexHull(np.array([x, y])).vertices

        # Rotate convex hull vertices until they start from the lowest one
        v = np.roll(v, -v.argmin())
        # Leave only the ascending part
        v = v[:v.argmax()]

        # Create baseline using linear interpolation between vertices
github charlesll / rampy / rampy / baseline.py View on Github external
### Baseline is of the type y = a*exp(b*(x-xo))
        # optional parameters
        p0_exp = kwargs.get('p0_exp',[1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.funexp,yafit[:,0],yafit[:,1],p0 = p0_exp)

        baseline_fitted = rampy.funexp(x,coeffs[0],coeffs[1],coeffs[2])

    elif method == 'log':
        ### Baseline is of the type y = a*exp(b*(x-xo))
        # optional parameters
        p0_log = kwargs.get('p0_log',[1.,1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.funlog,yafit[:,0],yafit[:,1],p0 = p0_log)

        baseline_fitted = rampy.funlog(x,coeffs[0],coeffs[1],coeffs[2],coeffs[3])

    elif method == 'rubberband':
        # code from this stack-exchange forum
        #https://dsp.stackexchange.com/questions/2725/how-to-perform-a-rubberband-correction-on-spectroscopic-data

        # Find the convex hull
        v = ConvexHull(np.array([x, y])).vertices

        # Rotate convex hull vertices until they start from the lowest one
        v = np.roll(v, -v.argmin())
        # Leave only the ascending part
        v = v[:v.argmax()]

        # Create baseline using linear interpolation between vertices
        baseline_fitted = np.interp(x, x[v], y[v])
github charlesll / rampy / rampy / baseline.py View on Github external
print('ERROR: Install gcvspline to use this mode (needs a working FORTRAN compiler).')

        # optional parameters
        splinesmooth = kwargs.get('s',2.0)

        # Spline baseline with mode 1 of gcvspl.f, see gcvspline documentation
        c, wk, ier = gcvspline(yafit[:,0],yafit[:,1],np.sqrt(np.abs(yafit[:,1])),splinesmooth,splmode = 1) # gcvspl with mode 1 and smooth factor

        baseline_fitted = splderivative(x,yafit[:,0],c)

    elif method == 'gaussian':
        ### Baseline is of the type y = a*exp(-log(2)*((x-b)/c)**2)
        # optional parameters
        p0_gauss = kwargs.get('p0_gaussian',[1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.gaussian,yafit[:,0],yafit[:,1],p0 = p0_gauss)

        baseline_fitted = rampy.gaussian(x,coeffs[0],coeffs[1],coeffs[2])

    elif method == 'exp':
        ### Baseline is of the type y = a*exp(b*(x-xo))
        # optional parameters
        p0_exp = kwargs.get('p0_exp',[1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.funexp,yafit[:,0],yafit[:,1],p0 = p0_exp)

        baseline_fitted = rampy.funexp(x,coeffs[0],coeffs[1],coeffs[2])

    elif method == 'log':
        ### Baseline is of the type y = a*exp(b*(x-xo))
        # optional parameters
        p0_log = kwargs.get('p0_log',[1.,1.,1.,1.])
github charlesll / rampy / rampy / baseline.py View on Github external
# optional parameters
        splinesmooth = kwargs.get('s',2.0)

        # Spline baseline with mode 1 of gcvspl.f, see gcvspline documentation
        c, wk, ier = gcvspline(yafit[:,0],yafit[:,1],np.sqrt(np.abs(yafit[:,1])),splinesmooth,splmode = 1) # gcvspl with mode 1 and smooth factor

        baseline_fitted = splderivative(x,yafit[:,0],c)

    elif method == 'gaussian':
        ### Baseline is of the type y = a*exp(-log(2)*((x-b)/c)**2)
        # optional parameters
        p0_gauss = kwargs.get('p0_gaussian',[1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.gaussian,yafit[:,0],yafit[:,1],p0 = p0_gauss)

        baseline_fitted = rampy.gaussian(x,coeffs[0],coeffs[1],coeffs[2])

    elif method == 'exp':
        ### Baseline is of the type y = a*exp(b*(x-xo))
        # optional parameters
        p0_exp = kwargs.get('p0_exp',[1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.funexp,yafit[:,0],yafit[:,1],p0 = p0_exp)

        baseline_fitted = rampy.funexp(x,coeffs[0],coeffs[1],coeffs[2])

    elif method == 'log':
        ### Baseline is of the type y = a*exp(b*(x-xo))
        # optional parameters
        p0_log = kwargs.get('p0_log',[1.,1.,1.,1.])
        ## fit of the baseline
        coeffs, pcov = curve_fit(rampy.funlog,yafit[:,0],yafit[:,1],p0 = p0_log)
github charlesll / rampy / rampy / rameau.py View on Github external
sp = np.genfromtxt(path_in+data_liste["Name"][i],delimiter=delim,skip_header=1)

        # constructing an interpolator: this will allow an output of all data with the same X axis
        f = scipy.interpolate.interp1d(sp[:,0], sp[:,1],fill_value="extrapolate")

        # temperature and excitation line correction (see Rameau help)
        x, y_all[:,i], sdf = rp.tlcorrection(x,f(x),23.0,laser,normalisation='intensity')

        # getting the roi
        roi = np.array(rois.loc[i]).reshape(int(len(rois.loc[i])/2),2)

        # calculating baseline
        if method == "LL2012": # spline

            try:
                c_hf, b_hf = rp.baseline(x,y_all[:,i],roi,"gcvspline",s=spline_coeff)
            except:
                break

            y_all_corr[:,i]=c_hf[:,0]
            y_all_base[:,i]=b_hf[:,0]

        elif method == "DG2017": # polynomial 3 following DG2017 method

            # getting portion of interrest
            x_lf = x[np.where(x<2000.)].reshape(-1)
            x_hf = x[np.where(x>2000.)].reshape(-1)

            y_lf = y_all[np.where(x<2000.),i].reshape(-1)
            y_hf = y_all[np.where(x>2000.),i].reshape(-1)

            c_lf, b_lf = rp.baseline(x_lf,y_lf,np.array([[0,200],[1240,1500]]),"poly",polynomial_order = poly_coeff)
github charlesll / rampy / rampy / rameau.py View on Github external
break

            y_all_corr[:,i]=c_hf[:,0]
            y_all_base[:,i]=b_hf[:,0]

        elif method == "DG2017": # polynomial 3 following DG2017 method

            # getting portion of interrest
            x_lf = x[np.where(x<2000.)].reshape(-1)
            x_hf = x[np.where(x>2000.)].reshape(-1)

            y_lf = y_all[np.where(x<2000.),i].reshape(-1)
            y_hf = y_all[np.where(x>2000.),i].reshape(-1)

            c_lf, b_lf = rp.baseline(x_lf,y_lf,np.array([[0,200],[1240,1500]]),"poly",polynomial_order = poly_coeff)
            c_hf, b_hf = rp.baseline(x_hf,y_hf,np.array([[2500,3100],[3750,3900]]),"poly",polynomial_order = poly_coeff)

            y_all_corr[:,i] = np.hstack((c_lf.reshape(-1),c_hf.reshape(-1)))
            y_all_base[:,i] = np.hstack((b_lf.reshape(-1),b_hf.reshape(-1)))

        else:
            raise TypeError('method should be set to LL2012 or DG2017')

        # Area / Integrated Intensity calculation
        S = np.trapz(y_all_corr[np.where((x>150)&(x<1250)),i],x[np.where((x>150)&(x<1250))])
        W = np.trapz(y_all_corr[np.where((x>3100)&(x<3750)),i],x[np.where((x>3100)&(x<3750))])

        # updating the Pandas dataframe rws
        rs[i] = S[0]
        rw[i] = W[0]
        rws[i] = W[0]/S[0]
github charlesll / rampy / rampy / rameau.py View on Github external
except:
                break

            y_all_corr[:,i]=c_hf[:,0]
            y_all_base[:,i]=b_hf[:,0]

        elif method == "DG2017": # polynomial 3 following DG2017 method

            # getting portion of interrest
            x_lf = x[np.where(x<2000.)].reshape(-1)
            x_hf = x[np.where(x>2000.)].reshape(-1)

            y_lf = y_all[np.where(x<2000.),i].reshape(-1)
            y_hf = y_all[np.where(x>2000.),i].reshape(-1)

            c_lf, b_lf = rp.baseline(x_lf,y_lf,np.array([[0,200],[1240,1500]]),"poly",polynomial_order = poly_coeff)
            c_hf, b_hf = rp.baseline(x_hf,y_hf,np.array([[2500,3100],[3750,3900]]),"poly",polynomial_order = poly_coeff)

            y_all_corr[:,i] = np.hstack((c_lf.reshape(-1),c_hf.reshape(-1)))
            y_all_base[:,i] = np.hstack((b_lf.reshape(-1),b_hf.reshape(-1)))

        else:
            raise TypeError('method should be set to LL2012 or DG2017')

        # Area / Integrated Intensity calculation
        S = np.trapz(y_all_corr[np.where((x>150)&(x<1250)),i],x[np.where((x>150)&(x<1250))])
        W = np.trapz(y_all_corr[np.where((x>3100)&(x<3750)),i],x[np.where((x>3100)&(x<3750))])

        # updating the Pandas dataframe rws
        rs[i] = S[0]
        rw[i] = W[0]
        rws[i] = W[0]/S[0]
github charlesll / rampy / rampy / rameau.py View on Github external
rs = np.ones(len(data_liste))

    record_std = np.zeros((len(data_liste),2))

    rois = data_liste.loc[:,"ROI1 lb":"ROI6 hb"]

    for i in range(len(data_liste)):

        # importing the spectra
        sp = np.genfromtxt(path_in+data_liste["Name"][i],delimiter=delim,skip_header=1)

        # constructing an interpolator: this will allow an output of all data with the same X axis
        f = scipy.interpolate.interp1d(sp[:,0], sp[:,1],fill_value="extrapolate")

        # temperature and excitation line correction (see Rameau help)
        x, y_all[:,i], sdf = rp.tlcorrection(x,f(x),23.0,laser,normalisation='intensity')

        # getting the roi
        roi = np.array(rois.loc[i]).reshape(int(len(rois.loc[i])/2),2)

        # calculating baseline
        if method == "LL2012": # spline

            try:
                c_hf, b_hf = rp.baseline(x,y_all[:,i],roi,"gcvspline",s=spline_coeff)
            except:
                break

            y_all_corr[:,i]=c_hf[:,0]
            y_all_base[:,i]=b_hf[:,0]

        elif method == "DG2017": # polynomial 3 following DG2017 method

rampy

A Python module containing functions to treat spectroscopic (XANES, Raman, IR...) data

GPL-2.0
Latest version published 5 months ago

Package Health Score

67 / 100
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