12. mpl¶
This is a rudimentary interface to matplotlib to use dataArrays easier. The standard way to use matplotlib is full available without using this module..
The intention is to allow fast/easy plotting (one command to plot) with some convenience function in relation to dataArrays and in a non blocking mode of matplotlib. E.g. to include automatically the value of an attribute (qq in example) in the legend:
fig[0].Plot(mydataArray, legend='sqr=$qq',sy=[2,3,-1],li=0)
# dataList
fig[0].Plot(mydataList , legend='sqr=$qq',sy=[2,3,-1],li=0)
With somehow shorter form to determine the marker (sy=symbol) and line (li) and allow plotting in one line. Matplotlib is quite slow (and looks for me ugly). For 2D plotting use xmgrace. For 3D plotting this will give some simple plot options (planned).
- The new methods introduced all start with a big Letter to allow still the access of the original methods.
- By indexing subplots can be accessed as figure[i] which is figure.axes[i].
- Same for axes with lines figure[0][i] is figure.axes[0].lines[i].
Example 1:
import jscatter as js
import numpy as np
i5=js.dL(js.examples.datapath+'/iqt_1hho.dat')
p=js.mplot()
p[0].Plot(i5,sy=[-1,0.4,-1],li=1,legend='Q= $q')
p[0].Yaxis(scale='l')
p[0].Title('intermediate scattering function')
p[0].Legend(x=1.13,y=1) # x,y in relative units of the plot
p[0].Yaxis(label='I(Q,t)/I(Q,0)',min=0.01)
p[0].Xaxis(label='Q / 1/nm',max=120)
Example 2 ( same as js.mpl.test() ):
import jscatter as js
import numpy as np
from matplotlib import pyplot
# use this
#fig=pyplot.figure(FigureClass=js.mpl.Figure)
# or
fig=js.mplot()
fig.Multi(2,1)
fig[0].SetView(0.1,0.25,0.8,0.9)
fig[1].SetView(0.1,0.09,0.8,0.23)
q=js.loglist(0.01,5,100)
aa=js.dL()
for pp in range(5):
aa.append(js.dA(np.c_[q,-pp*np.sin(q),0.2*np.cos(5*q)].T))
aa[-1].qq=pp
bb=js.dA(np.c_[q,q**2].T)
bb.qq=123
for pp in range(5):
fig[0].Plot(aa[pp].X,-1*aa[pp].Y,legend='some stufff',sy=[1,(pp+1)/10.],li=0)
fig[0].Plot(aa, legend='qq = $qq', sy=[-1, 0.4, -1, ''], li=0, markeredgewidth=1)
for pp in range(5):
fig[1].Plot(aa[-1].X/5+pp,pp*aa[-1].Y,legend='q=%.1f' %pp,sy=0,li=-1,markeredgewidth =1)
fig[1].Plot(bb,legend='sqr=$qq ',sy=2,li=2)
fig[0].Title('test')
fig[0].Legend(x=1.3,y=1)
fig[1].Legend(x=1.3,y=1)
fig[0].Yaxis(label='y-axis')
fig[1].Yaxis(label='something else')
fig[0].tick_params(labelbottom=False)
fig[1].Xaxis(label='x-axis')
12.1. Plot¶
mplot ([width, height]) |
Open matplotlib figure in interactive mode with mplot.jsFigure and mplot.jspaperAxes. |
surface (x, y, z[, xdim, levels, colorMap, …]) |
Surface plot of x,y,z, data |
scatter3d (x[, y, z, pointsize, color]) |
Scatter plot of x,y,z data points. |
contourImage (x[, y, z, levels, fontsize, …]) |
Image with contour lines of 3D dataArrays or image array. |
showColors () |
Get a list of the colormaps in matplotlib. |
test ([keepopen]) |
A small test for mpl module making a plot. |
12.2. Figures¶
-
class
jscatter.mpl.
jsFigure
(*args, **kwargs)[source] Create figure with Axes as jspaperAxes projection.
Examples
import jscatter as js import numpy as np i5=js.dL(js.examples.datapath+'/iqt_1hho.dat') p=js.mplot() p[0].Plot(i5,sy=[-1,0.4,-1],li=1,legend='Q= $q') p[0].Yaxis(scale='l') p[0].Title('intermediate scattering function') p[0].Legend(x=1.13,y=1) # x,y in relative units of the plot p[0].Yaxis(label='I(Q,t)/I(Q,0)',min=0.01) p[0].Xaxis(label='Q / 1/nm',max=120)
jsFigure.Multi (n, m) |
Creates multiple subplots on grid n,m. |
jsFigure.Addsubplot ([bbox]) |
Add a subplot in the foreground. |
jsFigure.Plot (*args, **kwargs) |
|
jsFigure.Clear () |
Clear content of all axes |
jsFigure.Save (filename[, format, dpi]) |
Save with filename |
jsFigure.is_open () |
Is the figure window still open. |
jsFigure.Exit () |
12.3. Axes¶
-
class
jscatter.mpl.
jspaperAxes
(*args, **kwargs)[source] An Axes that should look like typical paper layout.
jspaperAxes.Plot (*datasets, **kwargs) |
Plot dataArrays/dataList or array in matplotlib axes. |
jspaperAxes.Yaxis ([min, max, label, scale, …]) |
Set xaxis |
jspaperAxes.Xaxis ([min, max, label, scale, …]) |
Set xaxis |
jspaperAxes.Legend (**kwargs) |
Show/update legend. |
jspaperAxes.Title (title[, size]) |
set Axes title |
jspaperAxes.Subtitle (subtitle[, size]) |
Append subtitle to title |
jspaperAxes.SetView ([xmin, ymin, xmax, ymax]) |
This sets the bounding box of the axes. |
jspaperAxes.Clear () |
Clear content of this axes. |
jspaperAxes.Text (string, x, y, **kwargs) |
|
jspaperAxes.Arrow ([x1, y1, x2, y2, …]) |
Plot an arrow or line. |
This is a rudimentary interface to matplotlib to use dataArrays easier. The standard way to use matplotlib is full available without using this module..
The intention is to allow fast/easy plotting (one command to plot) with some convenience function in relation to dataArrays and in a non blocking mode of matplotlib. E.g. to include automatically the value of an attribute (qq in example) in the legend:
fig[0].Plot(mydataArray, legend='sqr=$qq',sy=[2,3,-1],li=0)
# dataList
fig[0].Plot(mydataList , legend='sqr=$qq',sy=[2,3,-1],li=0)
With somehow shorter form to determine the marker (sy=symbol) and line (li) and allow plotting in one line. Matplotlib is quite slow (and looks for me ugly). For 2D plotting use xmgrace. For 3D plotting this will give some simple plot options (planned).
- The new methods introduced all start with a big Letter to allow still the access of the original methods.
- By indexing subplots can be accessed as figure[i] which is figure.axes[i].
- Same for axes with lines figure[0][i] is figure.axes[0].lines[i].
Example 1:
import jscatter as js
import numpy as np
i5=js.dL(js.examples.datapath+'/iqt_1hho.dat')
p=js.mplot()
p[0].Plot(i5,sy=[-1,0.4,-1],li=1,legend='Q= $q')
p[0].Yaxis(scale='l')
p[0].Title('intermediate scattering function')
p[0].Legend(x=1.13,y=1) # x,y in relative units of the plot
p[0].Yaxis(label='I(Q,t)/I(Q,0)',min=0.01)
p[0].Xaxis(label='Q / 1/nm',max=120)
Example 2 ( same as js.mpl.test() ):
import jscatter as js
import numpy as np
from matplotlib import pyplot
# use this
#fig=pyplot.figure(FigureClass=js.mpl.Figure)
# or
fig=js.mplot()
fig.Multi(2,1)
fig[0].SetView(0.1,0.25,0.8,0.9)
fig[1].SetView(0.1,0.09,0.8,0.23)
q=js.loglist(0.01,5,100)
aa=js.dL()
for pp in range(5):
aa.append(js.dA(np.c_[q,-pp*np.sin(q),0.2*np.cos(5*q)].T))
aa[-1].qq=pp
bb=js.dA(np.c_[q,q**2].T)
bb.qq=123
for pp in range(5):
fig[0].Plot(aa[pp].X,-1*aa[pp].Y,legend='some stufff',sy=[1,(pp+1)/10.],li=0)
fig[0].Plot(aa, legend='qq = $qq', sy=[-1, 0.4, -1, ''], li=0, markeredgewidth=1)
for pp in range(5):
fig[1].Plot(aa[-1].X/5+pp,pp*aa[-1].Y,legend='q=%.1f' %pp,sy=0,li=-1,markeredgewidth =1)
fig[1].Plot(bb,legend='sqr=$qq ',sy=2,li=2)
fig[0].Title('test')
fig[0].Legend(x=1.3,y=1)
fig[1].Legend(x=1.3,y=1)
fig[0].Yaxis(label='y-axis')
fig[1].Yaxis(label='something else')
fig[0].tick_params(labelbottom=False)
fig[1].Xaxis(label='x-axis')
-
jscatter.mpl.
contourImage
(x, y=None, z=None, levels=8, fontsize=10, colorMap='jet', lineMap=None, title=None, axis=None)[source]¶ Image with contour lines of 3D dataArrays or image array.
Parameters: - x,y,z : arrays
x,y,z coordinates for z display in x,y location. If x is image array this is used. If x is dataArray we plot like x,y,z=x.X,x.Z,x.Y as dataArray use always .Y as value in X,Z coordinates.
- levels : in, sequence of values
Number of contour lines between min and max or sequence of specific values.
- colorMap : string
Get a colormap instance from name. Standard mpl colormap name (see showColors).
- lineMap : string
Label color Colormap name as in colorMap, otherwise as cs in in Axes.clabel * if None, the color of each label matches the color of the corresponding contour * if one string color, e.g., colors = ‘r’ or colors = ‘red’, all labels will be plotted in this color * if a tuple of matplotlib color args (string, float, rgb, etc), different labels will be plotted in different colors in the order specified
- fontsize : int, default 10
Size of line labels in pixel
- title : None, string
Title of the plot. May be set by fig.axes[0].set_title(‘title’)
- axis : None, ‘pixel’
If coordinates should be forced to pixel.
- Returns
- ——-
figure
Examples
Create log scale image for maskedArray (sasImage)
import jscatter as js import numpy as np # sets negative values to zero calibration = js.sas.sasImage(js.examples.datapath+'/calibration.tiff') fig=js.mpl.contourImage(calibration,title='Calibration lin scale') fig=js.mpl.contourImage(np.ma.log(calibration)) # # change labels and title ax=fig.axes[0] ax.set_xlabel('qx ') ax.set_ylabel('qy') ax.set_title(r'Calibration log scaled') js.mpl.pyplot.show(block=False) import jscatter as js import numpy as np x,z=np.mgrid[-5:5:0.25,-5:5:0.25] xyz=js.dA(np.c_[x.flatten(),z.flatten(),0.3*np.sin(x*z/np.pi).flatten()+0.01*np.random.randn(len(x.flatten())),0.01*np.ones_like(x).flatten() ].T) # set columns where to find X,Y,Z ) xyz.setColumnIndex(ix=0,iy=2,iz=1) # determine yourself what is x,y,z js.mpl.contourImage(xyz.X,xyz.Z,xyz.Y) # determined automatically from values in setColumnIndex above js.mpl.contourImage(xyz) # remove each 3rd point that we have missing points x,z=js.formel.randomPointsInCube(1500,0,2)*10-5 xyz=js.dA(np.c_[x.flatten(),z.flatten(),0.3*np.sin(x*z/np.pi).flatten()+0.01*np.random.randn(len(x.flatten()))].T) xyz.setColumnIndex(ix=0,iy=2,iz=1) js.mpl.contourImage(xyz)
-
jscatter.mpl.
gf
= 20¶ gracefactor to get same scaling as in grace set to 10
-
class
jscatter.mpl.
jsFigure
(*args, **kwargs)[source]¶ Bases:
matplotlib.figure.Figure
Create figure with Axes as jspaperAxes projection.
Examples
import jscatter as js import numpy as np i5=js.dL(js.examples.datapath+'/iqt_1hho.dat') p=js.mplot() p[0].Plot(i5,sy=[-1,0.4,-1],li=1,legend='Q= $q') p[0].Yaxis(scale='l') p[0].Title('intermediate scattering function') p[0].Legend(x=1.13,y=1) # x,y in relative units of the plot p[0].Yaxis(label='I(Q,t)/I(Q,0)',min=0.01) p[0].Xaxis(label='Q / 1/nm',max=120)
-
Addsubplot
(bbox=(0.2, 0.2, 0.6, 0.6), *args, **kwargs)[source]¶ Add a subplot in the foreground.
Parameters: - bbox : rect [left, bottom, width, height]
Bounding box position and size.
-
Multi
(n, m)[source]¶ Creates multiple subplots on grid n,m. with projection “jspaperAxes”.
Subplots can be accesses as fig[i]
-
Plot
(*args, **kwargs)¶
-
-
class
jscatter.mpl.
jspaperAxes
(*args, **kwargs)[source]¶ Bases:
matplotlib.axes._axes.Axes
An Axes that should look like typical paper layout.
-
Arrow
(x1=None, y1=None, x2=None, y2=None, linewidth=None, arrow=None)[source]¶ Plot an arrow or line.
Parameters: - x1,y1,x2,y2 : float
Start/end coordinates in box units [0..1].
- linewidth : float
Linewidth
- arrow : int or [‘-‘,’->’,’<-‘,’<->’]
Type of arrow. If int it selects from [‘-‘,’->’,’<-‘,’<->’]
-
Legend
(**kwargs)[source]¶ Show/update legend.
Parameters: - charsize, fontsize : int, default 12
Font size of labels
- labelspacing : int , default =12
Spacing of labels
- loc : int [0..10] default 1 ‘upper right’
Location specifier - ‘best’ 0, ‘upper right’ 1, ‘upper left’ 2, ‘lower left’ 3, ‘lower right’ 4,‘center left’ 6,
- x,y : float [0..1]
Determines if both given loc and sets position in axes coordinates. Sets bbox_to_anchor=(x,y)
- kwargs : kwargs of axes.legend
Any given kwarg overrides the previous
-
Plot
(*datasets, **kwargs)¶ Plot dataArrays/dataList or array in matplotlib axes.
Parameters are passed to matplotlib.axes.Axes.plot
Parameters: - datasets : dataArray/dataList or 1D arrays
- Datasets to plot.
- Can be several dataArray/dataList (with .X, .Y and .eY) or 1D arrays (a[1,:],b[2,:]), but dont mix it.
- If dataArray/dataList has .eY errors a errorbars are plotted.
- If format strings are found only the first is used. symbol, line override this.
- Only a single line for 1D arrays is allowed.
- symbol,sy : int, list of float
- [symbol,size,color,fillcolor,fillpattern] as [1,1,1,-1];
- single integer to chose symbol eg symbol=3; symbol=0 switches off
- negative increments from last
- symbol => see Line2D.filled_markers
- size => size in pixel
- color => int in sequence = wbgrcmyk
- fillcolor=None see color
- fillpattern=None 0 empty, 1 full, ….test it
- line,li : int, list of float or Line object
- [linestyle,linewidth,color] as [1,1,’‘];
- negative increments
- single integer to chose linestyle line=1; line=0 switches of
- linestyle int ‘-‘,’–’,’-.’,’:’
- linewidth float increasing thickness
- color see symbol color
- errorbar,er : int or list of float or Errorbar object
- [color,size] as [1,1]; no increment, no repeat
- color int see symbol color, non-integer syncs to symbol color
- size float default 1.0 ; smaller is 0.5
- legend,le : string
- determines legend for all datasets
- string replacement: attr name prepended by ‘$’ (eg. ‘$par’) is replaced by value str(par1.flatten()[0]) if possible. $(par) for not unique names
- errorbar,er : float
- errorbar thickness, zero is no errorbar
-
SetView
(xmin=None, ymin=None, xmax=None, ymax=None)[source]¶ This sets the bounding box of the axes.
Parameters: - xmin,xmax,ymin,ymax : float
view range
-
Xaxis
(min=None, max=None, label=None, scale=None, size=None, charsize=None, tick=None, ticklabel=None, **kwargs)[source]¶ Set xaxis
Parameters: - label : string
Label
- scale : ‘log’, ‘normal’
Scale
- min,max : float
Set min and max of scale
- size : int
Pixelsize of label
- kwargs : kwargs of axes.set_xscale
Any given kwarg overrides the previous
-
Yaxis
(min=None, max=None, label=None, scale=None, size=None, charsize=None, tick=None, ticklabel=None, **kwargs)[source]¶ Set xaxis
Parameters: - label : string
Label
- scale : ‘log’, ‘normal’
Scale
- min,max : float
Set min and max
- size : int
Pixelsize of label
- kwargs : kwargs of axes.set_xscale
Any given kwarg overrides the previous
-
name
= 'paper'¶
-
plot
(*datasets, **kwargs)[source]¶ Plot dataArrays/dataList or array in matplotlib axes.
Parameters are passed to matplotlib.axes.Axes.plot
Parameters: - datasets : dataArray/dataList or 1D arrays
- Datasets to plot.
- Can be several dataArray/dataList (with .X, .Y and .eY) or 1D arrays (a[1,:],b[2,:]), but dont mix it.
- If dataArray/dataList has .eY errors a errorbars are plotted.
- If format strings are found only the first is used. symbol, line override this.
- Only a single line for 1D arrays is allowed.
- symbol,sy : int, list of float
- [symbol,size,color,fillcolor,fillpattern] as [1,1,1,-1];
- single integer to chose symbol eg symbol=3; symbol=0 switches off
- negative increments from last
- symbol => see Line2D.filled_markers
- size => size in pixel
- color => int in sequence = wbgrcmyk
- fillcolor=None see color
- fillpattern=None 0 empty, 1 full, ….test it
- line,li : int, list of float or Line object
- [linestyle,linewidth,color] as [1,1,’‘];
- negative increments
- single integer to chose linestyle line=1; line=0 switches of
- linestyle int ‘-‘,’–’,’-.’,’:’
- linewidth float increasing thickness
- color see symbol color
- errorbar,er : int or list of float or Errorbar object
- [color,size] as [1,1]; no increment, no repeat
- color int see symbol color, non-integer syncs to symbol color
- size float default 1.0 ; smaller is 0.5
- legend,le : string
- determines legend for all datasets
- string replacement: attr name prepended by ‘$’ (eg. ‘$par’) is replaced by value str(par1.flatten()[0]) if possible. $(par) for not unique names
- errorbar,er : float
- errorbar thickness, zero is no errorbar
-
-
jscatter.mpl.
mplot
(width=None, height=None, **kwargs)[source]¶ Open matplotlib figure in interactive mode with mplot.jsFigure and mplot.jspaperAxes.
Parameters: - width,height : float
Size of plot in cm.
- kwargs :
Keyword args of matplotlib.pyplot.figure .
Returns: - matplotlib figure
Notes
- By indexing as the axes subplots can be accessed as figure[i] which is figure.axes[i].
- Same for axes with lines figure[0][i] is figure.axes[0].lines[i].
- Some methods with similar behaviour as in grace are defined (big letter commands)
- matplotlib methods are still available (small letters commands)
-
jscatter.mpl.
regrid
(x, y, z, xdim=None)[source]¶ Make a meshgrid from XYZ data columns.
Parameters: - x,y,z : array like
Array like data should be quadratic or rectangular.
- xdim : None, shape of first x dimension
If None the number of unique values in x is used as first dimension
Returns: - 2dim arrays for x,y,z
-
jscatter.mpl.
scatter3d
(x, y=None, z=None, pointsize=3, color='k')[source]¶ Scatter plot of x,y,z data points.
Parameters: - x,y,z : arrays
Data to plot. If x.shape is Nx3 these points are used.
- pointsize : float
Size of points
- color : string
Colors for points
Returns: - figure
Examples
# ellipsoid with grid build by mgrid import jscatter as js import numpy as np # cubic grid points ig=js.formel.randomPointsInCube(200) js.mpl.scatter3d(ig.T)
-
jscatter.mpl.
show
(**kwargs)[source]¶ Same as pyplot.show(**kwargs)
Parameters: - kwargs : args
Passed to pyplot.show added by block=False
-
jscatter.mpl.
showColors
()[source]¶ Get a list of the colormaps in matplotlib.
Ignore the ones that end with ‘_r’ because these are simply reversed versions of ones that don’t end with ‘_r’
- Colormaps Names
- Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Vega10, Vega10_r, Vega20, Vega20_r, Vega20b, Vega20b_r, Vega20c, Vega20c_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spectral, spectral_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, viridis, viridis_r, winter, winter_r
From https://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html
-
jscatter.mpl.
surface
(x, y, z, xdim=None, levels=8, colorMap='jet', lineMap=None, alpha=0.7)[source]¶ Surface plot of x,y,z, data
Parameters: - x,y,z : array
Data as array
- xdim : integer
First dimension of x
- levels : integer, array
Levels for contour lines as number of levels or array of specific values.
- colorMap : string
Color map name, see showColors.
- lineMap : string
- Color name for contour lines
b: blue g: green r: red c: cyan m: magenta y: yellow k: black w: white
- alpha : float [0,1], default 0.7
Transparency of surface
Returns: - figure
Examples
import jscatter as js import numpy as np R=8 N=50 qxy=np.mgrid[-R:R:N*1j, -R:R:N*1j].reshape(2,-1).T qxyz=np.c_[qxy,np.zeros(qxy.shape[0])] sclattice= js.lattice.scLattice(2.1, 5) ds=[[20,1,0,0],[5,0,1,0],[5,0,0,1]] ffs=js.sf.orientedLatticeStructureFactor(qxyz,sclattice,orientation=[1,0,0],domainsize=ds,rmsd=0.1,hklmax=2) fig=js.mpl.surface(qxyz[:,0],qxyz[:,1],ffs[3].array)
-
jscatter.mpl.
test
(keepopen=True)[source]¶ A small test for mpl module making a plot.
Examples
import jscatter as js import numpy as np from matplotlib import pyplot # use this #fig=pyplot.figure(FigureClass=js.mpl.Figure) # or fig=js.mplot() fig.Multi(2,1) fig[0].SetView(0.1,0.25,0.8,0.9) fig[1].SetView(0.1,0.09,0.8,0.23) q=js.loglist(0.01,5,100) aa=js.dL() for pp in range(5): aa.append(js.dA(np.c_[q,-pp*np.sin(q),0.2*np.cos(5*q)].T)) aa[-1].qq=pp bb=js.dA(np.c_[q,q**2].T) bb.qq=123 for pp in range(5): fig[0].Plot(aa[pp].X,-1*aa[pp].Y,legend='some stufff',sy=[1,(pp+1)/10.],li=0) fig[0].Plot(aa, legend='qq = $qq', sy=[-1, 0.4, -1, ''], li=0, markeredgewidth=1) for pp in range(5): fig[1].Plot(aa[-1].X/5+pp,pp*aa[-1].Y,legend='q=%.1f' %pp,sy=0,li=-1,markeredgewidth =1) fig[1].Plot(bb,legend='sqr=$qq ',sy=2,li=2) fig[0].Title('test') fig[0].Legend(x=1.3,y=1) fig[1].Legend(x=1.3,y=1) fig[0].Yaxis(label='y-axis') fig[1].Yaxis(label='something else') fig[0].tick_params(labelbottom=False) fig[1].Xaxis(label='x-axis')