Making Python's Matplotlib Graphics Look Like Graphics Created Using Originpro

I may have created a duplicate, but could not find exactly what I was looking for in any thread. I created graphics using OriginPro 8.5G and can not quite re-create them using matp

Solution 1:

I think the following is roughly the equivalent to the Origin figure. Unfortunately, not everything can be determined via rcParams, such as the limits and the minor tick locations. Also I changed scatter to plot, which makes it easier to get hollow markers.

from matplotlib import rcParams, cycler
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator


rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['Arial']
rcParams['font.size'] = 16
rcParams['axes.linewidth'] = 1.1
rcParams['axes.labelpad'] = 10.0
plot_color_cycle = cycler('color', ['000000', '0000FE', 'FE0000', '008001', 'FD8000', '8c564b', 
                                    'e377c2', '7f7f7f', 'bcbd22', '17becf'])
rcParams['axes.prop_cycle'] = plot_color_cycle
rcParams['axes.xmargin'] = 0
rcParams['axes.ymargin'] = 0
rcParams.update({"figure.figsize" : (6.4,4.8),
                 "figure.subplot.left" : 0.177, "figure.subplot.right" : 0.946,
                 "figure.subplot.bottom" : 0.156, "figure.subplot.top" : 0.965,
                 "axes.autolimit_mode" : "round_numbers",
                 "xtick.major.size"     : 7,
                 "xtick.minor.size"     : 3.5,
                 "xtick.major.width"    : 1.1,
                 "xtick.minor.width"    : 1.1,
                 "xtick.major.pad"      : 5,
                 "xtick.minor.visible" : True,
                 "ytick.major.size"     : 7,
                 "ytick.minor.size"     : 3.5,
                 "ytick.major.width"    : 1.1,
                 "ytick.minor.width"    : 1.1,
                 "ytick.major.pad"      : 5,
                 "ytick.minor.visible" : True,
                 "lines.markersize" : 10,
                 "lines.markerfacecolor" : "none",
                 "lines.markeredgewidth"  : 0.8})



for ion inrange(1, output_array_size_x):
    plt.plot(output_array[:,0], output_array[:,ion], marker='D', ls="none")
plt.xlim(0, 13)
plt.ylim(0, None)
plt.ylabel('Normalized signal intensity')
plt.xlabel('Excitation voltage [eV]')
plt.gca().xaxis.set_minor_locator(AutoMinorLocator(n=2))
plt.gca().yaxis.set_minor_locator(AutoMinorLocator(n=2))
plt.savefig("out.png", dpi=1000)
plt.show()