182 lines
8.3 KiB
Python
182 lines
8.3 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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import string
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from plotstyle import scheme_style
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import pandas as pd
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from matplotlib import cm
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def cm2inch(*tupl):
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inch = 2.54
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if isinstance(tupl[0], tuple):
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return tuple(i / inch for i in tupl[0])
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else:
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return tuple(i / inch for i in tupl)
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def show_spines(ax, spines='lrtb'):
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""" Show and hide spines.
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From github.com/janscience/plottools.git spines.py
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Parameters
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----------
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ax: matplotlib figure, matplotlib axis, or list of matplotlib axes
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Axis on which spine and ticks visibility is manipulated.
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If figure, then apply manipulations on all axes of the figure.
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If list of axes, apply manipulations on each of the given axes.
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spines: string
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Specify which spines and ticks should be shown.
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All other ones or hidden.
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'l' is the left spine, 'r' the right spine,
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't' the top one and 'b' the bottom one.
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E.g. 'lb' shows the left and bottom spine, and hides the top
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and and right spines, as well as their tick marks and labels.
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'' shows no spines at all.
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'lrtb' shows all spines and tick marks.
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Examples
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--------
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```py
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import matplotlib.pyplot as plt
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import plottools.spines
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fig, (ax0, ax1, ax2) = plt.subplots(1, 3)
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ax0.show_spines('lb')
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ax1.show_spines('bt')
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ax2.show_spines('tr')
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```
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"""
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# collect spine visibility:
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xspines = []
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if 't' in spines:
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xspines.append('top')
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if 'b' in spines:
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xspines.append('bottom')
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yspines = []
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if 'l' in spines:
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yspines.append('left')
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if 'r' in spines:
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yspines.append('right')
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# collect axes:
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if isinstance(ax, (list, tuple, np.ndarray)):
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axs = ax
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elif hasattr(ax, 'get_axes'):
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# ax is figure:
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axs = ax.get_axes()
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else:
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axs = [ax]
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if not isinstance(axs, (list, tuple)):
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axs = [axs]
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for ax in axs:
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# hide spines:
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ax.spines['top'].set_visible('top' in xspines)
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ax.spines['bottom'].set_visible('bottom' in xspines)
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ax.spines['left'].set_visible('left' in yspines)
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ax.spines['right'].set_visible('right' in yspines)
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# ticks:
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if len(xspines) == 0:
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ax.xaxis.set_ticks_position('none')
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ax.xaxis.label.set_visible(False)
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ax.xaxis._orig_major_locator = ax.xaxis.get_major_locator()
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ax.xaxis.set_major_locator(ticker.NullLocator())
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else:
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if hasattr(ax.xaxis, '_orig_major_locator'):
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ax.xaxis.set_major_locator(ax.xaxis._orig_major_locator)
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delattr(ax.xaxis, '_orig_major_locator')
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elif isinstance(ax.xaxis.get_major_locator(), ticker.NullLocator):
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ax.xaxis.set_major_locator(ticker.AutoLocator())
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if len(xspines) == 1:
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ax.xaxis.set_ticks_position(xspines[0])
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ax.xaxis.set_label_position(xspines[0])
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else:
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ax.xaxis.set_ticks_position('both')
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ax.xaxis.set_label_position('bottom')
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if len(yspines) == 0:
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ax.yaxis.set_ticks_position('none')
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ax.yaxis.label.set_visible(False)
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ax.yaxis._orig_major_locator = ax.yaxis.get_major_locator()
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ax.yaxis.set_major_locator(ticker.NullLocator())
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else:
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if hasattr(ax.yaxis, '_orig_major_locator'):
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ax.yaxis.set_major_locator(ax.yaxis._orig_major_locator)
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delattr(ax.yaxis, '_orig_major_locator')
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elif isinstance(ax.yaxis.get_major_locator(), ticker.NullLocator):
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ax.yaxis.set_major_locator(ticker.AutoLocator())
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if len(yspines) == 1:
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ax.yaxis.set_ticks_position(yspines[0])
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ax.yaxis.set_label_position(yspines[0])
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else:
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ax.yaxis.set_ticks_position('both')
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ax.yaxis.set_label_position('left')
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def plot_g(ax, df, models, i, let_x, let_y, titlesize=10, letsize=12):
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# c = [cm.plasma(x) for x in np.linspace(0., 1., 9)]
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# c = [cm.turbo(x) for x in np.linspace(0., 1., 9)]
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c = [cm.gray(x) for x in np.linspace(0., 0.75, 9)]
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myorder = [0, 4, 1, 6, 2,7, 3,8]
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colors = [c[i] for i in myorder]
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df.plot.bar(y=models[i], rot=90, ax=ax, legend=False,
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ylabel='$\mathrm{g}_{\mathrm{max}}$ [$\mathrm{mS}/ \mathrm{cm}^2$]',
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color=colors)
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ax.set_title(models[i], fontsize=titlesize)
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show_spines(ax, spines='lb')
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ax.text(let_x, let_y, string.ascii_uppercase[i], transform=ax.transAxes, size=letsize, weight='bold')
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ax.set_yscale('log')
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ax.set_xlim(-0.5, 9)
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from matplotlib.ticker import ScalarFormatter
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for axis in [ax.yaxis]:
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axis.set_major_formatter(ScalarFormatter())
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return ax
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index = ['$\mathrm{g}_{\mathrm{Na}}$', '$\mathrm{g}_{\mathrm{Kd}}$', '$\mathrm{g}_{\mathrm{K_V1.1}}$',
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'$\mathrm{g}_{\mathrm{A}}$', '$\mathrm{g}_{\mathrm{M}}$', '$\mathrm{g}_{\mathrm{L}}$',
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'$\mathrm{g}_{\mathrm{T}}$', ' $\mathrm{g}_{\mathrm{Ca,K}}$', ' $\mathrm{g}_{\mathrm{Leak}}$']
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df = pd.DataFrame({'RS Pyramidal': [56, 6, 0, 0, 0.075, 0, 0, 0, 0.0205],
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'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [56, 5.4, 0.6, 0, 0.075, 0, 0, 0, 0.0205],
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'RS Inhibitory': [10, 2.1, 0, 0, 0.0098, 0, 0, 0, 0.0205],
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'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [10, 1.89, 0.21, 0, 0.0098, 0, 0, 0, 0.0205],
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'FS': [58, 3.9, 0, 0, 0.075, 0, 0, 0, 0.038],
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'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [58, 3.51, 0.39, 0, 0.075, 0, 0, 0, 0.038],
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'Cb stellate': [3.4, 9.0556, 0, 15.0159, 0, 0, 0.4545, 0, 0.07407],
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'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [3.4, 8.15, 0.90556, 15.0159, 0, 0, 0.4545, 0, 0.07407],
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'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [3.4, 9.0556, 1.50159, 0, 0, 0, 0.4545, 0, 0.07407],
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'STN': [49, 57, 0, 5, 0, 5, 5, 1, 0.035],
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'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [49, 56.43, 0.57, 5, 0, 5, 5, 1, 0.035],
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'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': [49, 57, 0.5, 0, 0, 5, 5, 1, 0.035]},
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index=index)
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#% with legend
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scheme_style()
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models = ['Cb stellate', 'RS Inhibitory', 'FS', 'RS Pyramidal', 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
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'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
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'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
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'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
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'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN']
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fig, axs = plt.subplots(4, 3, figsize=cm2inch(17.2, 20)) # , sharey=True)
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plt.subplots_adjust(hspace=1.5, wspace=1.0)
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let_x = -0.6
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let_y = 1.2
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titlesize = 9
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letsize = 10
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axs[0, 0] = plot_g(axs[0, 0], df, models, 0, let_x, let_y, titlesize=titlesize, letsize=letsize)
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axs[0, 1] = plot_g(axs[0, 1], df, models, 1, let_x, let_y, titlesize=titlesize, letsize=letsize)
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axs[0, 2] = plot_g(axs[0, 2], df, models, 2, let_x, let_y, titlesize=titlesize, letsize=letsize)
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axs[1, 0] = plot_g(axs[1, 0], df, models, 3, let_x, let_y, titlesize=titlesize, letsize=letsize)
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axs[1, 1] = plot_g(axs[1, 1], df, models, 4, let_x, let_y, titlesize=titlesize, letsize=letsize)
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axs[1, 2] = plot_g(axs[1, 2], df, models, 5, let_x, let_y, titlesize=titlesize, letsize=letsize)
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axs[2, 0] = plot_g(axs[2, 0], df, models, 6, let_x, let_y, titlesize=titlesize, letsize=letsize)
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axs[2, 1] = plot_g(axs[2, 1], df, models, 7, let_x, let_y, titlesize=titlesize, letsize=letsize)
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axs[2, 2] = plot_g(axs[2, 2], df, models, 8, let_x, let_y, titlesize=titlesize, letsize=letsize)
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axs[3, 0] = plot_g(axs[3, 0], df, models, 9, let_x, let_y, titlesize=titlesize, letsize=letsize)
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axs[3, 1] = plot_g(axs[3, 1], df, models, 10, let_x, let_y, titlesize=titlesize, letsize=letsize)
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axs[3, 2] = plot_g(axs[3, 2], df, models, 11, let_x, let_y, titlesize=titlesize, letsize=letsize)
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# save
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# fig.set_size_inches(cm2inch(21,20))
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fig.savefig('./Figures/model_g.jpg', dpi=300, bbox_inches='tight') # pdf # eps
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plt.show()
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