# positions = [0, 30, 60, 90, 120, 150, 180] positions = [90] colors = ['blue', 'green', 'orange', 'red', 'grey', 'magenta', 'black'] colors = ['blue'] B_T, R_xy = "$B\,(\mathrm{T})$", "$R_{\mathrm{xy}}\,(\Omega)$" # fig = go.Figure() fig=make_subplots(rows=1, cols=2, subplot_titles= ["{}deg
up+down (middle loops)".format(positions[0]), "{}deg
line subtract".format(positions[0])]) fig2=make_subplots(rows=1, cols=2, subplot_titles= ["{}deg
even+odd part (of averaged middle loops)".format(positions[0]), "{}deg
sym., antisym. part of Rxx (by subtraction)".format(positions[0])],specs = [list([{ 'secondary_y':True }]*2)]) for c, (idx, posit) in zip(colors, enumerate(positions)): x, y = fsRxx201016b_50K.resMRxx(ch=1, temp=50, pos=posit, geomFact=1) # xx, yy = fsRxx201016b_50K.selectMiddleLoops(x, y) # # # down xxd, yyd = xx[:int(len(xx)*0.66)], xx[:int(len(xx)*0.66)] #preselect imin = int( np.where( xxd == min(xxd) )[0] ) xxd, yyd = xx[:imin+1], yy[:imin+1] # up xxu, yyu = xx[imin:], yy[imin:] # interp (separately) xu, xd = np.linspace(-10,10,201), np.linspace(10,-10,201) # xu, xd = np.linspace(min(xx),max(xx),201), np.linspace(max(xx),min(xx),201) # print(xu, xd) yid = fsRxx201016b_50K.interpDataLoopLin(xxd, yyd, xd) yiu = fsRxx201016b_50K.interpDataLoopLin(xxu, yyu, xu) # concat xi, yi = np.concatenate([xd, xu]), np.concatenate([yid, yiu]) xi_mir, yi_mir = -xi, -yi # mirrored data i = np.where( xi > 5 ) i_mir = np.where( xi_mir > 5 ) xxx = np.concatenate([xi[i], xi_mir[i_mir]]) yyy = np.concatenate([yi[i], yi_mir[i_mir]]) p = np.polyfit(xxx, yyy, 1) yfit = np.polyval(p, xi) # print(p) xp, yp = xi, yi-np.mean(yi)# yi-yfit - np.mean(yi-yfit) # processed loops (selected middle loops and background subtractedef ) # xp, yp = xi, yi-yfit - np.mean(yi-yfit) # processed loops (selected middle loops and background subtractedef ) # x down processed xdp, ydp = xp[:int(len(xp)*0.66)], yp[:int(len(yp)*0.66)] #preselect imin_ = int( np.where( xdp == min(xdp))[0][0] ) xdp, ydp = xdp[:imin_+1], ydp[:imin_+1] # up processed xup, yup = xp[imin_:], yp[imin_:] # flipping of down sweep to be ordered ascending xdp, ydp= np.flip(xdp), np.flip(ydp) # xup, yup= xup[1:], yup[1:] xup, yup= xup[1:], yup[1:] # avg of the middle loops xavg = np.mean(np.stack([xdp, xup]), axis=0) yavg = np.mean(np.stack([ydp, yup]), axis=0) # fig.add_trace(go.Scatter(x=x, y=y-np.mean(y), marker = {'color' : 'black'}, # mode='lines+markers', name='raw'), row=1, col=1) # fig.add_trace(go.Scatter(x=xx, y=yy-np.mean(yy), marker = {'color' : 'black'}, # mode='lines+markers', name='raw middle loops'), row=1, col=1) fig.add_trace(go.Scatter(x=x, y=y-np.mean(y), marker = {'color' : 'black'}, mode='lines+markers', name='raw'), row=1, col=1) fig.add_trace(go.Scatter(x=xp, y=yp-np.mean(yp), marker = {'color' : 'red'}, mode='lines+markers', name='processed'), row=1, col=1) # fig.add_trace(go.Scatter(x=xdp, y=ydp, marker = {'color' : 'blue'}, # mode='lines+markers', name='down'), row=1, col=1) # fig.add_trace(go.Scatter(x=xup, y=yup, marker = {'color' : 'red'}, # mode='lines+markers', name='up'), row=1, col=1) fig.add_trace(go.Scatter(x=xavg, y=yavg, marker = {'color' : 'green'}, mode='lines+markers', name='avg'), row=1, col=1) fig.add_trace(go.Scatter(x=xavg, y=yavg, marker = {'color' : 'green'}, mode='lines+markers', name='avg'), row=1, col=2) p = np.polyfit(xavg, yavg, 1) yavgfit = np.polyval(p, xavg) fig.add_trace(go.Scatter(x=xavg, y=yavgfit, marker = {'color' : 'black'}, mode='lines', name='lin. fit'), row=1, col=2) xsub, ysub = xavg, yavg-yavgfit fig.add_trace(go.Scatter(x=xsub, y=ysub, marker = {'color' : 'orange'}, mode='lines+markers', name='subtracted'), row=1, col=2) xeven, yeven = xsub,(ysub+ysub[::-1])/2 xodd, yodd = xsub,(ysub-ysub[::-1])/2 fig2.add_trace(go.Scatter(x=xeven,y=yeven, marker = {'color' : 'red'}, mode='lines+markers', name='even'), row=1, col=1) fig2.add_trace(go.Scatter(x=xodd,y=yodd, marker = {'color' : 'blue'}, mode='lines+markers', name='odd'), row=1, col=1) # test if sum of even and odd part gives what it shoulh give # fig2.add_trace(go.Scatter(x=(xeven+xodd)/2,y=(yeven+yodd), marker = {'color' : 'black'}, # mode='lines+markers', name='sum'), row=1, col=1) # fig2.add_trace(go.Scatter(x=xsub, y=ysub, marker = {'color' : 'orange'}, # mode='lines+markers', name='subtracted'), row=1, col=1) # up and down concat xanti = np.concatenate([xup,xdp[::-1]]) yanti = np.concatenate([yup-yeven,ydp[::-1]-yeven[::-1]]) -p[0]*xanti fig2.add_trace(go.Scatter(x=xanti, y=yanti, marker = {'color' : 'blue'}, mode='lines+markers', name='antisymmetric'), row=1, col=2) # antisymmetric =raw-even xsym = np.concatenate([xdp,xup[::-1]]) ysym = np.concatenate([ydp-yodd,yup[::-1]-yodd[::-1]]) -p[0]*xsym fig2.add_trace(go.Scatter(x=xsym, y=ysym, marker = {'color' : 'red'}, mode='lines+markers', name='symmetric'), row=1, col=2) # symmetric =raw-odd # fig2.add_trace(go.Scatter(x=(xanti+xsym)/2,y=(-yanti+ysym), marker = {'color' : 'orange'}, # mode='lines+markers', name='sum'), row=1, col=2) # fig2.add_trace(go.Scatter(x=xsub, y=ysub, marker = {'color' : 'orange'}, # mode='lines+markers', name='subtracted'), row=1, col=2) # fig2.add_trace(go.Scatter(x=xavg, y=yavg, marker = {'color' : 'green'}, # mode='lines+markers', name='avg'), row=1, col=2) # fig2.add_trace(go.Scatter(x=xsub, y=ysub, marker = {'color' : 'green'}, # mode='lines+markers', name='sub'), row=1, col=2) # fig2.add_trace(go.Scatter(x=xp, y=yp, marker = {'color' : 'black'}, # mode='lines+markers', name='yp'), row=1, col=2) # fig2.add_trace(go.Scatter(x=(xanti+xsym)/2,y=(yanti+ysym), marker = {'color' : 'black'}, # mode='lines+markers', name='sum'), row=1, col=2) # fig2.add_trace(go.Scatter(x=xdp, y=ydp-yeven, marker = {'color' : 'blue'}, # mode='lines+markers', name='down-even'), row=1, col=2) # fig2.add_trace(go.Scatter(x=xup, y=yup-yeven, marker = {'color' : 'blue'}, # mode='lines+markers', name='up-even'), row=1, col=2) # fig2.add_trace(go.Scatter(x=xdp, y=ydp-yodd, marker = {'color' : 'red'}, # mode='lines+markers', name='down-odd'), row=1, col=2) # fig2.add_trace(go.Scatter(x=xup, y=yup-yodd, marker = {'color' : 'red'}, # mode='lines+markers', name='up-odd'), row=1, col=2) # fig2.add_trace(go.Scatter(x=xup, y=yup, marker = {'color' : 'green'}, # mode='lines+markers', name='up'), row=1, col=2) # fig2.add_trace(go.Scatter(x=xdp, y=ydp, marker = {'color' : 'magenta'}, # mode='lines+markers', name='down'), row=1, col=2) # xup, yup_sym, yup_asym = xup, (yup[::-1]+yup)/2, (yup-yup[::-1])/2 # xdp, ydp_sym, ydp_asym = xdp, (ydp[::-1]+ydp)/2, (ydp-ydp[::-1])/2 # labels fig2['layout']['xaxis']['title'] =fig2['layout']['xaxis2']['title'] = fig['layout']['xaxis2']['title'] = fig['layout']['xaxis']['title']="B (T)" fig['layout']['yaxis']['title']= "R_xx (Ohm)" fig2['layout']['yaxis']['title']= "R_xx (Ohm)" # fig.update_xaxes(showline=True, linewidth=0.5, linecolor='black', mirror=True, ticks="inside", tickwidth=0.5, tickcolor='black', ticklen=5) # fig.update_yaxes(showline=True, linewidth=0.5, linecolor='black', mirror=True, ticks="inside", tickwidth=0.5, tickcolor='black', ticklen=5) # fig.update_layout(title="Rxy at 150 K") fig.update_layout( width=1000, height=500) fig2.update_layout( width=1100, height=500) fig.show() fig2.show()