added figure plotting scripts *_letters.py to plot using models named by letter
This commit is contained in:
nkoch1
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@@ -389,6 +389,21 @@ color_dict = {'Cb stellate': '#40A787', # cyan'#
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'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#873770', # magenta
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'STN': '#D03050' # pink
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}
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model_letter = {
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'Cb stellate': 'A',
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'RS Inhibitory': 'B',
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'FS': 'C',
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'RS Pyramidal': 'D',
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'RS Inhibitory': 'E',
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'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'F',
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'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'G',
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'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'H',
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'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'I',
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'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'J',
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'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'K',
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'STN':'L',
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}
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# plot setup
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marker_s_leg = 2
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543
Figures/AUC_correlation_letters.py
Normal file
543
Figures/AUC_correlation_letters.py
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@@ -0,0 +1,543 @@
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# -*- coding: utf-8 -*-
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"""
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Created on Sat Jul 3 19:52:04 2021
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@author: nils
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"""
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import pandas as pd
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import numpy as np
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import string
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import textwrap
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import json
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import matplotlib
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import matplotlib.lines as mlines
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from matplotlib import ticker
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from matplotlib.ticker import NullFormatter
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from Figures.plotstyle import boxplot_style
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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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#%% ##################### From https://stackoverflow.com/questions/52878845/swarmplot-with-hue-affecting-marker-beyond-color ##
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# to change marker types in seaborn swarmplot
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import seaborn as sns
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import matplotlib.pyplot as plt
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############## Begin hack ##############
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from matplotlib.axes._axes import Axes
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from matplotlib.markers import MarkerStyle
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from numpy import ndarray
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def GetColor2Marker(markers):
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colorslist = ['#40A787', # cyan'#
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'#F0D730', # yellow
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'#C02717', # red
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'#007030', # dark green
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'#AAB71B', # lightgreen
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'#008797', # light blue
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'#F78017', # orange
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'#478010', # green
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'#53379B', # purple
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'#2060A7', # blue
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'#873770', # magenta
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'#D03050' # pink
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]
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import matplotlib.colors
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palette = [matplotlib.colors.to_rgb(c) for c in colorslist]
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mkcolors = [(palette[i]) for i in range(len(markers))]
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return dict(zip(mkcolors,markers))
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def fixlegend(ax,markers,markersize=3,**kwargs):
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# Fix Legend
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legtitle = ax.get_legend().get_title().get_text()
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_,l = ax.get_legend_handles_labels()
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colorslist = ['#40A787', # cyan'#
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'#F0D730', # yellow
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'#C02717', # red
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'#007030', # dark green
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'#AAB71B', # lightgreen
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'#008797', # light blue
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'#F78017', # orange
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'#478010', # green
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'#53379B', # purple
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'#2060A7', # blue
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'#873770', # magenta
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'#D03050' # pink
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]
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import matplotlib.colors
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palette = [matplotlib.colors.to_rgb(c) for c in colorslist]
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mkcolors = [(palette[i]) for i in range(len(markers))]
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newHandles = [plt.Line2D([0],[0], ls="none", marker=m, color=c, mec="none", markersize=markersize,**kwargs) \
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for m,c in zip(markers, mkcolors)]
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ax.legend(newHandles,l)
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leg = ax.get_legend()
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leg.set_title(legtitle)
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old_scatter = Axes.scatter
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def new_scatter(self, *args, **kwargs):
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colors = kwargs.get("c", None)
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co2mk = kwargs.pop("co2mk",None)
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FinalCollection = old_scatter(self, *args, **kwargs)
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if co2mk is not None and isinstance(colors, ndarray):
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Color2Marker = GetColor2Marker(co2mk)
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paths=[]
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for col in colors:
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mk=Color2Marker[tuple(col)]
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marker_obj = MarkerStyle(mk)
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paths.append(marker_obj.get_path().transformed(marker_obj.get_transform()))
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FinalCollection.set_paths(paths)
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return FinalCollection
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Axes.scatter = new_scatter
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############## End hack. ##############
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########################################################################################################################
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#%% add gradient arrows
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import matplotlib.pyplot as plt
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import matplotlib.transforms
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import matplotlib.path
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from matplotlib.collections import LineCollection
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def gradientaxis(ax, start, end, cmap, n=100,lw=1):
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# Arrow shaft: LineCollection
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x = np.linspace(start[0],end[0],n)
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y = np.linspace(start[1],end[1],n)
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points = np.array([x,y]).T.reshape(-1,1,2)
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segments = np.concatenate([points[:-1],points[1:]], axis=1)
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lc = LineCollection(segments, cmap=cmap, linewidth=lw,zorder=15)
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lc.set_array(np.linspace(0,1,n))
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ax.add_collection(lc)
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return ax
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#%%
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#%%
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def boxplot_with_markers(ax,max_width, alteration='shift', msize=3):
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hlinewidth = 0.5
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model_names = ['RS pyramidal','RS inhibitory','FS',
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'RS pyramidal +$K_V1.1$','RS inhibitory +$K_V1.1$',
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'FS +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
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'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
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'STN $\Delta$$K_V1.1$']
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colorslist = ['#007030', # dark green
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'#F0D730', # yellow
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'#C02717', # red
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'#478010', # green
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'#AAB71B', # lightgreen
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'#F78017', # orange
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'#40A787', # cyan'#
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'#008797', # light blue
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'#2060A7', # blue
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'#D03050', # pink
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'#53379B', # purple
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'#873770', # magenta
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]
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import matplotlib.colors
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colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
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clr_dict = {}
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for m in range(len(model_names)):
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clr_dict[model_names[m]] = colors[m]
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print(colors)
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print(clr_dict)
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Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
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if alteration=='shift':
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i = 2 # Kd act
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ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor = 'k')
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df = pd.read_csv('./Figures/Data/AUC_shift_corr.csv')
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sns.swarmplot(y="corr", x="$\Delta V_{1/2}$", hue="model", data=df,
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palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
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order=['Na activation', 'Na inactivation', 'K activation', '$K_V1.1$ activation',
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'$K_V1.1$ inactivation', 'A activation', 'A inactivation'],
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hue_order=model_names, co2mk=Markers)
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lim = ax.get_xlim()
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ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
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ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
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ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
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ax.set_title("Shift ($\Delta V_{1/2}$)", y=1.05)
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ax.set_xticklabels(['Na \nactivation', 'Na \ninactivation', 'K \nactivation', '$K_V1.1$ \nactivation',
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'$K_V1.1$ \ninactivation', 'A \nactivation', 'A \ninactivation'])
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elif alteration=='slope':
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i = 3 # Kv1.1 act
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ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
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df = pd.read_csv('./Figures/Data/AUC_scale_corr.csv')
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# Add in points to show each observation
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sns.swarmplot(y="corr", x="Slope (k)", hue="model", data=df,
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palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
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order=['Na activation', 'Na inactivation', 'K activation', '$K_V1.1$ activation',
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'$K_V1.1$ inactivation', 'A activation', 'A inactivation'],
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hue_order=model_names, co2mk=Markers)
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lim = ax.get_xlim()
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ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
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ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
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ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
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ax.set_title("Slope (k)", y=1.05)
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ax.set_xticklabels(['Na \nactivation', 'Na \ninactivation', 'K \nactivation', '$K_V1.1$ \nactivation',
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'$K_V1.1$ \ninactivation', 'A \nactivation', 'A \ninactivation'])
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elif alteration=='g':
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i = 1 # Kd
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ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
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df = pd.read_csv('./Figures/Data/AUC_g_corr.csv')
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# Add in points to show each observation
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sns.swarmplot(y="corr", x="g", hue="model", data=df,
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palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
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order=['Na', 'K', '$K_V1.1$', 'A', 'Leak'],
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hue_order=model_names, co2mk=Markers)
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lim = ax.get_xlim()
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# ax.plot([lim[0], lim[1]], [0,0], ':k')
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ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
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ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
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ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
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# Tweak the visual presentation
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ax.set_title("Conductance (g)", y=1.05)
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ax.set_xticklabels(textwrap.fill(x.get_text(), max_width) for x in ax.get_xticklabels())
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else:
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print('Please chose "shift", "slope" or "g"')
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ax.get_legend().remove()
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ax.xaxis.grid(False)
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sns.despine(trim=True, bottom=True, ax=ax)
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ax.set(xlabel=None, ylabel=r'Kendall $\it{\tau}$')
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def model_legend(ax, marker_s_leg, pos, ncol):
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# colorslist = [ '#40A787', # cyan'#
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# '#F0D730', # yellow
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# '#C02717', # red
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# '#007030', # dark green
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# '#AAB71B', # lightgreen
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# '#008797', # light blue
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# '#F78017', # orange
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# '#478010', # green
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# '#53379B', # purple
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# '#2060A7', # blue
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# '#873770', # magenta
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# '#D03050' # pink
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# ]
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colorslist = ['#007030', # dark green
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'#F0D730', # yellow
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'#C02717', # red
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'#478010', # green
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'#AAB71B', # lightgreen
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'#F78017', # orange
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'#40A787', # cyan'#
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'#008797', # light blue
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'#2060A7', # blue
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'#D03050', # pink
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'#53379B', # purple
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'#873770', # magenta
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]
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import matplotlib.colors
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colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
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model_pos = {'Cb stellate':0, 'RS Inhibitory':1, 'FS':2, 'RS Pyramidal':3,
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'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4,
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'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':6,
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'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8,
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'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':9,
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'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10, 'STN':11}
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Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
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# RS_p = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal']], marker=Markers[model_pos['RS Pyramidal']], markersize=marker_s_leg, linestyle='None',
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# label='Model D')
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# RS_i = mlines.Line2D([], [], color=colors[model_pos['RS Inhibitory']], marker=Markers[model_pos['RS Inhibitory']], markersize=marker_s_leg, linestyle='None',
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# label='Model B')
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# FS = mlines.Line2D([], [], color=colors[model_pos['FS']], marker=Markers[model_pos['FS']], markersize=marker_s_leg, linestyle='None', label='Model C')
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# RS_p_Kv = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
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# label='Model H')
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# RS_i_Kv = mlines.Line2D([], [], color=colors[model_pos['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
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# label='Model E')
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# FS_Kv = mlines.Line2D([], [], color=colors[model_pos['Cb stellate']], marker=Markers[model_pos['Cb stellate']], markersize=marker_s_leg, linestyle='None', label='Model G')
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# Cb = mlines.Line2D([], [], color=colors[8], marker=Markers[8], markersize=marker_s_leg, linestyle='None',
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# label='Model A')
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# Cb_pl = mlines.Line2D([], [], color=colors[model_pos['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
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# label='Model F')
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# Cb_sw = mlines.Line2D([], [], color=colors[model_pos['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
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# label='Model J')
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# STN = mlines.Line2D([], [], color=colors[model_pos['STN']], marker=Markers[model_pos['STN']], markersize=marker_s_leg, linestyle='None', label='Model L')
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# STN_pl = mlines.Line2D([], [], color=colors[model_pos['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
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# label='Model I')
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# STN_sw = mlines.Line2D([], [], color=colors[model_pos['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], marker=Markers[model_pos['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg, linestyle='None',
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# label='Model K')
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RS_p = mlines.Line2D([], [], color='#007030', marker="^",
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markersize=marker_s_leg, linestyle='None',
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label='Model D')
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RS_i = mlines.Line2D([], [], color='#F0D730', marker="o",
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markersize=marker_s_leg, linestyle='None',
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label='Model B')
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FS = mlines.Line2D([], [], color='#C02717', marker="o", markersize=marker_s_leg,
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linestyle='None', label='Model C')
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RS_p_Kv = mlines.Line2D([], [], color='#478010',
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marker="D",
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markersize=marker_s_leg, linestyle='None',
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label='Model H')
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RS_i_Kv = mlines.Line2D([], [], color='#AAB71B',
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marker="^",
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markersize=marker_s_leg, linestyle='None',
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label='Model E')
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FS_Kv = mlines.Line2D([], [], color='#F78017',
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marker="D", markersize=marker_s_leg,
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linestyle='None', label='Model G')
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Cb = mlines.Line2D([], [], color='#40A787', marker="o",
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markersize=marker_s_leg, linestyle='None',
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label='Model A')
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Cb_pl = mlines.Line2D([], [], color='#008797',
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marker="^",
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markersize=marker_s_leg, linestyle='None',
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label='Model F')
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Cb_sw = mlines.Line2D([], [], color='#2060A7',
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marker="s",
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markersize=marker_s_leg, linestyle='None',
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label='Model J')
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STN = mlines.Line2D([], [], color='#D03050', marker="s", markersize=marker_s_leg,
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linestyle='None', label='Model L')
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STN_pl = mlines.Line2D([], [], color='#53379B',
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marker="D",
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markersize=marker_s_leg, linestyle='None',
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label='Model I')
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STN_sw = mlines.Line2D([], [], color='#873770',
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marker="s",
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markersize=marker_s_leg, linestyle='None',
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label='Model K')
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# ax.legend(handles=[RS_p, RS_i, FS, RS_p_Kv, RS_i_Kv, FS_Kv, Cb, Cb_pl, Cb_sw, STN, STN_pl, STN_sw], loc='center',
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# bbox_to_anchor=pos, ncol=ncol, frameon=False)
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ax.legend(handles=[Cb, RS_i, FS, RS_p, RS_i_Kv, Cb_pl, FS_Kv, RS_p_Kv, STN_pl, Cb_sw, STN_sw, STN], loc='center',
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bbox_to_anchor=pos, ncol=ncol, frameon=False)
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def plot_AUC_alt(ax, model='FS', color1='red', color2='dodgerblue', alteration='shift'):
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ax.spines["right"].set_visible(False)
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ax.spines["top"].set_visible(False)
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model_names = ['RS Pyramidal','RS Inhibitory','FS',
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'RS Pyramidal +$K_V1.1$','RS Inhibitory +$K_V1.1$',
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'FS +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
|
||||
'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
|
||||
'STN $\Delta$$K_V1.1$']
|
||||
|
||||
model_name_dict = {'RS Pyramidal': 'RS Pyramidal',
|
||||
'RS Inhibitory': 'RS Inhibitory',
|
||||
'FS': 'FS',
|
||||
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS Pyramidal +$K_V1.1$',
|
||||
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS Inhibitory +$K_V1.1$',
|
||||
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'FS +$K_V1.1$',
|
||||
'Cb stellate': 'Cb stellate',
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb stellate +$K_V1.1$',
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb stellate $\Delta$$K_V1.1$',
|
||||
'STN': 'STN',
|
||||
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN +$K_V1.1$',
|
||||
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN $\Delta$$K_V1.1$'}
|
||||
colorslist = ['#007030', # dark green
|
||||
'#F0D730', # yellow
|
||||
'#C02717', # red
|
||||
'#478010', # green
|
||||
'#AAB71B', # lightgreen
|
||||
'#F78017', # orange
|
||||
'#40A787', # cyan'#
|
||||
'#008797', # light blue
|
||||
'#2060A7', # blue
|
||||
'#D03050', # pink
|
||||
'#53379B', # purple
|
||||
'#873770', # magenta
|
||||
]
|
||||
|
||||
import matplotlib.colors
|
||||
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
|
||||
clr_dict = {}
|
||||
for m in range(len(model_names)):
|
||||
clr_dict[model_names[m]] = colors[m]
|
||||
if alteration=='shift':
|
||||
df = pd.read_csv('./Figures/Data/AUC_shift_ex.csv')
|
||||
df = df.sort_values('alteration')
|
||||
ax.set_xlabel('$\Delta$$V_{1/2}$')
|
||||
elif alteration=='slope':
|
||||
df = pd.read_csv('./Figures/Data/AUC_slope_ex.csv')
|
||||
ax.set_xscale("log")
|
||||
ax.set_xticks([0.5, 1, 2])
|
||||
ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
|
||||
ax.xaxis.set_minor_formatter(NullFormatter())
|
||||
ax.set_xlabel('$k$/$k_{WT}$')
|
||||
elif alteration=='g':
|
||||
df = pd.read_csv('./Figures/Data/AUC_g_ex.csv')
|
||||
ax.set_xscale("log")
|
||||
ax.set_xticks([0.5, 1, 2])
|
||||
ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
|
||||
ax.xaxis.set_minor_formatter(NullFormatter())
|
||||
ax.set_xlabel('$g$/$g_{WT}$')
|
||||
for mod in model_names:
|
||||
if mod == model_name_dict[model]:
|
||||
ax.plot(df['alteration'], df[mod], color=clr_dict[mod], alpha=1, zorder=10, linewidth=2)
|
||||
else:
|
||||
ax.plot(df['alteration'], df[mod], color=clr_dict[mod],alpha=0.5, zorder=1, linewidth=1)
|
||||
|
||||
if alteration=='shift':
|
||||
ax.set_ylabel('Normalized $\Delta$AUC', labelpad=4)
|
||||
else:
|
||||
ax.set_ylabel('Normalized $\Delta$AUC', labelpad=0)
|
||||
x = df['alteration']
|
||||
y = df[model_name_dict[model]]
|
||||
ax.set_xlim(x.min(), x.max())
|
||||
ax.set_ylim(df[model_names].min().min(), df[model_names].max().max())
|
||||
|
||||
# x axis color gradient
|
||||
cvals = [-2., 2]
|
||||
colors = ['lightgrey', 'k']
|
||||
norm = plt.Normalize(min(cvals), max(cvals))
|
||||
tuples = list(zip(map(norm, cvals), colors))
|
||||
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
|
||||
(xstart, xend) = ax.get_xlim()
|
||||
(ystart, yend) = ax.get_ylim()
|
||||
print(ystart, yend)
|
||||
start = (xstart, ystart * 1.0)
|
||||
end = (xend, ystart * 1.0)
|
||||
ax = gradientaxis(ax, start, end, cmap, n=200, lw=4)
|
||||
ax.spines['bottom'].set_visible(False)
|
||||
return ax
|
||||
|
||||
def plot_fI(ax, model='RS Pyramidal', type='shift', alt='m', color1='red', color2='dodgerblue'):
|
||||
model_save_name = {'RS Pyramidal': 'RS_pyr_posp',
|
||||
'RS Inhibitory': 'RS_inhib_posp',
|
||||
'FS': 'FS_posp',
|
||||
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS_pyr_Kv',
|
||||
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS_inhib_Kv',
|
||||
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'FS_Kv',
|
||||
'Cb stellate': 'Cb_stellate',
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb_stellate_Kv',
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb_stellate_Kv_only',
|
||||
'STN': 'STN',
|
||||
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN_Kv',
|
||||
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN_Kv_only'}
|
||||
cvals = [-2., 2]
|
||||
colors = [color1, color2]
|
||||
norm = plt.Normalize(min(cvals), max(cvals))
|
||||
tuples = list(zip(map(norm, cvals), colors))
|
||||
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
|
||||
colors = cmap(np.linspace(0, 1, 22))
|
||||
df = pd.read_csv('./Figures/Data/Model_fI/{}_fI.csv'.format(model_save_name[model]))
|
||||
df.drop(['Unnamed: 0'], axis=1)
|
||||
newdf = df.loc[df.index[(df['alt'] == alt) & (df['type'] == type)], :]
|
||||
newdf['mag'] = newdf['mag'].astype('float')
|
||||
newdf = newdf.sort_values('mag').reset_index()
|
||||
c = 0
|
||||
for i in newdf.index:
|
||||
ax.plot(json.loads(newdf.loc[i, 'I']), json.loads(newdf.loc[i, 'F']), color=colors[c])
|
||||
c += 1
|
||||
ax.set_ylabel('Frequency [Hz]')
|
||||
ax.set_xlabel('Current [nA]')
|
||||
if model == 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':
|
||||
ax.set_title("Model G", x=0.2, y=1.0)
|
||||
elif model == 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':
|
||||
ax.set_title("Model I", x=0.2, y=1.0)
|
||||
else:
|
||||
ax.set_title("", x=0.2, y=1.0)
|
||||
ax.spines["right"].set_visible(False)
|
||||
ax.spines["top"].set_visible(False)
|
||||
L = ax.get_ylim()
|
||||
ax.set_ylim([0, L[1]])
|
||||
return ax
|
||||
|
||||
|
||||
#%%
|
||||
boxplot_style()
|
||||
color_dict = {'Cb stellate': '#40A787', # cyan'#
|
||||
'RS Inhibitory': '#F0D730', # yellow
|
||||
'FS': '#C02717', # red
|
||||
'RS Pyramidal': '#007030', # dark green
|
||||
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#AAB71B', # lightgreen
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#008797', # light blue
|
||||
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#F78017', # orange
|
||||
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#478010', # green
|
||||
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#53379B', # purple
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#2060A7', # blue
|
||||
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#873770', # magenta
|
||||
'STN': '#D03050' # pink
|
||||
}
|
||||
model_letter = {
|
||||
'Cb stellate': 'A',
|
||||
'RS Inhibitory': 'B',
|
||||
'FS': 'C',
|
||||
'RS Pyramidal': 'D',
|
||||
'RS Inhibitory': 'E',
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'F',
|
||||
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'G',
|
||||
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'H',
|
||||
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'I',
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'J',
|
||||
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':'K',
|
||||
'STN':'L',
|
||||
}
|
||||
|
||||
|
||||
# plot setup
|
||||
marker_s_leg = 2
|
||||
max_width = 20
|
||||
pad_x = 0.85
|
||||
pad_y= 0.4
|
||||
pad_w = 1.1
|
||||
pad_h = 0.7
|
||||
|
||||
fig = plt.figure()
|
||||
gs = fig.add_gridspec(3, 7, wspace=1.2, hspace=1.)
|
||||
ax0 = fig.add_subplot(gs[0,2:7])
|
||||
ax0_ex = fig.add_subplot(gs[0,1])
|
||||
ax0_fI = fig.add_subplot(gs[0,0])
|
||||
ax1 = fig.add_subplot(gs[1,2:7])
|
||||
ax1_ex = fig.add_subplot(gs[1,1])
|
||||
ax1_fI = fig.add_subplot(gs[1,0])
|
||||
ax2 = fig.add_subplot(gs[2,2:7])
|
||||
ax2_ex = fig.add_subplot(gs[2,1])
|
||||
ax2_fI = fig.add_subplot(gs[2,0])
|
||||
|
||||
line_width = 1
|
||||
# plot fI examples
|
||||
ax0_fI = plot_fI(ax0_fI, model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='shift', alt='s', color1='lightgrey', color2='k')
|
||||
rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1 + pad_h, fill=False, lw=line_width,transform=ax0_fI.transAxes, color=color_dict['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
|
||||
rec = ax0_fI.add_patch(rec)
|
||||
rec.set_clip_on(False)
|
||||
|
||||
ax1_fI = plot_fI(ax1_fI, model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='slope', alt='u', color1='lightgrey', color2='k')
|
||||
rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1 + pad_h, fill=False, lw=line_width,transform=ax1_fI.transAxes, color=color_dict['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
|
||||
rec = ax1_fI.add_patch(rec)
|
||||
rec.set_clip_on(False)
|
||||
|
||||
ax2_fI = plot_fI(ax2_fI, model='STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='g', alt='Leak', color1='lightgrey', color2='k')
|
||||
rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1 + pad_h, fill=False, lw=line_width,transform=ax2_fI.transAxes, color=color_dict['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
|
||||
rec = ax2_fI.add_patch(rec)
|
||||
rec.set_clip_on(False)
|
||||
|
||||
# plot boxplots
|
||||
boxplot_with_markers(ax0,max_width, alteration='shift')
|
||||
boxplot_with_markers(ax1,max_width, alteration='slope')
|
||||
boxplot_with_markers(ax2,max_width, alteration='g')
|
||||
|
||||
# plot legend
|
||||
pos = (0.225, -0.9)
|
||||
ncol = 6
|
||||
model_legend(ax2, marker_s_leg, pos, ncol)
|
||||
|
||||
# plot examples
|
||||
plot_AUC_alt(ax0_ex,model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k', alteration='shift')
|
||||
plot_AUC_alt(ax1_ex,model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k',alteration='slope')
|
||||
plot_AUC_alt(ax2_ex, model='STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k', alteration='g')
|
||||
|
||||
# label subplots with letters
|
||||
ax0_fI.text(-0.875, 1.35, string.ascii_uppercase[0], transform=ax0_fI.transAxes, size=10, weight='bold')
|
||||
ax0_ex.text(-0.8, 1.35, string.ascii_uppercase[1], transform=ax0_ex.transAxes, size=10, weight='bold')
|
||||
ax0.text(-0.075, 1.35, string.ascii_uppercase[2], transform=ax0.transAxes, size=10, weight='bold')
|
||||
|
||||
ax1_fI.text(-0.875, 1.35, string.ascii_uppercase[3], transform=ax1_fI.transAxes,size=10, weight='bold')
|
||||
ax1_ex.text(-0.8, 1.35, string.ascii_uppercase[4], transform=ax1_ex.transAxes, size=10, weight='bold')
|
||||
ax1.text(-0.075, 1.35, string.ascii_uppercase[5], transform=ax1.transAxes, size=10, weight='bold')
|
||||
|
||||
ax2_fI.text(-0.875, 1.35, string.ascii_uppercase[6], transform=ax2_fI.transAxes,size=10, weight='bold')
|
||||
ax2_ex.text(-0.8, 1.35, string.ascii_uppercase[7], transform=ax2_ex.transAxes, size=10, weight='bold')
|
||||
ax2.text(-0.075, 1.35, string.ascii_uppercase[8], transform=ax2.transAxes, size=10, weight='bold')
|
||||
|
||||
#save
|
||||
fig.set_size_inches(cm2inch(20.75,12))
|
||||
fig.savefig('./Figures/AUC_correlation.pdf', dpi=fig.dpi) #pdf #eps
|
||||
# fig.savefig('./Figures/AUC_correlation.png', dpi=fig.dpi) #pdf #eps
|
||||
plt.show()
|
||||
322
Figures/ramp_firing_letters.py
Normal file
322
Figures/ramp_firing_letters.py
Normal file
@@ -0,0 +1,322 @@
|
||||
# # plot ramp protocol and responses of each model to ramp
|
||||
# import numpy as np
|
||||
# import pandas as pd
|
||||
# import matplotlib.pyplot as plt
|
||||
# import matplotlib.gridspec as gridspec
|
||||
# from matplotlib.transforms import Bbox
|
||||
# import string
|
||||
#
|
||||
# def cm2inch(*tupl):
|
||||
# inch = 2.54
|
||||
# if isinstance(tupl[0], tuple):
|
||||
# return tuple(i/inch for i in tupl[0])
|
||||
# else:
|
||||
# return tuple(i/inch for i in tupl)
|
||||
#
|
||||
# #### from https://gist.github.com/dmeliza/3251476 #####################################################################
|
||||
# from matplotlib.offsetbox import AnchoredOffsetbox
|
||||
# class AnchoredScaleBar(AnchoredOffsetbox):
|
||||
# def __init__(self, transform, sizex=0, sizey=0, labelx=None, labely=None, loc=4,
|
||||
# pad=0.1, borderpad=0.1, sep=2, prop=None, barcolor="black", barwidth=None,
|
||||
# **kwargs):
|
||||
# """
|
||||
# Draw a horizontal and/or vertical bar with the size in data coordinate
|
||||
# of the give axes. A label will be drawn underneath (center-aligned).
|
||||
# - transform : the coordinate frame (typically axes.transData)
|
||||
# - sizex,sizey : width of x,y bar, in data units. 0 to omit
|
||||
# - labelx,labely : labels for x,y bars; None to omit
|
||||
# - loc : position in containing axes
|
||||
# - pad, borderpad : padding, in fraction of the legend font size (or prop)
|
||||
# - sep : separation between labels and bars in points.
|
||||
# - **kwargs : additional arguments passed to base class constructor
|
||||
# """
|
||||
# from matplotlib.patches import Rectangle
|
||||
# from matplotlib.offsetbox import AuxTransformBox, VPacker, HPacker, TextArea, DrawingArea
|
||||
# bars = AuxTransformBox(transform)
|
||||
# if sizex:
|
||||
# bars.add_artist(Rectangle((0, 0), sizex, 0, ec=barcolor, lw=barwidth, fc="none"))
|
||||
# if sizey:
|
||||
# bars.add_artist(Rectangle((0, 0), 0, sizey, ec=barcolor, lw=barwidth, fc="none"))
|
||||
#
|
||||
# if sizex and labelx:
|
||||
# self.xlabel = TextArea(labelx)
|
||||
# bars = VPacker(children=[bars, self.xlabel], align="center", pad=0, sep=sep)
|
||||
# if sizey and labely:
|
||||
# self.ylabel = TextArea(labely)
|
||||
# bars = HPacker(children=[self.ylabel, bars], align="center", pad=0, sep=sep)
|
||||
#
|
||||
# AnchoredOffsetbox.__init__(self, loc, pad=pad, borderpad=borderpad,
|
||||
# child=bars, prop=prop, frameon=False, **kwargs)
|
||||
#
|
||||
#
|
||||
# def add_scalebar(ax, matchx=True, matchy=True, hidex=True, hidey=True, **kwargs):
|
||||
# """ Add scalebars to axes
|
||||
# Adds a set of scale bars to *ax*, matching the size to the ticks of the plot
|
||||
# and optionally hiding the x and y axes
|
||||
# - ax : the axis to attach ticks to
|
||||
# - matchx,matchy : if True, set size of scale bars to spacing between ticks
|
||||
# if False, size should be set using sizex and sizey params
|
||||
# - hidex,hidey : if True, hide x-axis and y-axis of parent
|
||||
# - **kwargs : additional arguments passed to AnchoredScaleBars
|
||||
# Returns created scalebar object
|
||||
# """
|
||||
#
|
||||
# def f(axis):
|
||||
# l = axis.get_majorticklocs()
|
||||
# return len(l) > 1 and (l[1] - l[0])
|
||||
#
|
||||
# if matchx:
|
||||
# kwargs['sizex'] = f(ax.xaxis)
|
||||
# kwargs['labelx'] = str(kwargs['sizex'])
|
||||
# if matchy:
|
||||
# kwargs['sizey'] = f(ax.yaxis)
|
||||
# kwargs['labely'] = str(kwargs['sizey'])
|
||||
#
|
||||
# sb = AnchoredScaleBar(ax.transData, **kwargs)
|
||||
# ax.add_artist(sb)
|
||||
#
|
||||
# if hidex: ax.xaxis.set_visible(False)
|
||||
# if hidey: ax.yaxis.set_visible(False)
|
||||
# if hidex and hidey: ax.set_frame_on(False)
|
||||
#
|
||||
# return sb
|
||||
# ########################################################################################################################
|
||||
#
|
||||
#
|
||||
# def plot_ramp_V(ax, model='RS Pyramidal'): # , stop=750
|
||||
# model_ramp = pd.read_csv('./Figures/Data/model_ramp.csv')
|
||||
# ax.plot(model_ramp['t'], model_ramp[model], 'k', linewidth=0.025)
|
||||
# ax.set_ylabel('V')
|
||||
# ax.set_xlabel('Time [s]')
|
||||
# ax.set_ylim(-80, 60)
|
||||
# ax.axis('off')
|
||||
# ax.set_title(model)
|
||||
#
|
||||
# #% plot setup
|
||||
# fig = plt.figure(figsize=cm2inch(17.6,17.6))
|
||||
#
|
||||
# gs0 = fig.add_gridspec(3, 2, wspace=0.1)
|
||||
# gs00 = gs0[:,0].subgridspec(7, 2, wspace=0.6, hspace=1)
|
||||
# gs01 = gs0[:,1].subgridspec(7, 2, wspace=0.6, hspace=1)
|
||||
#
|
||||
# ax1_ramp = fig.add_subplot(gs00[0,0:2])
|
||||
# ax2_ramp = fig.add_subplot(gs01[0,0:2])
|
||||
# ax3_ramp = fig.add_subplot(gs00[1,0:2])
|
||||
# ax4_ramp = fig.add_subplot(gs01[1,0:2])
|
||||
# ax5_ramp = fig.add_subplot(gs00[2, 0:2])
|
||||
# ax6_ramp = fig.add_subplot(gs01[2, 0:2])
|
||||
# ax7_ramp = fig.add_subplot(gs00[3,0:2])
|
||||
# ax8_ramp = fig.add_subplot(gs01[3,0:2])
|
||||
# ax9_ramp = fig.add_subplot(gs00[4,0:2])
|
||||
# ax10_ramp = fig.add_subplot(gs01[4,0:2])
|
||||
# ax11_ramp = fig.add_subplot(gs00[5,0:2])
|
||||
# ax12_ramp = fig.add_subplot(gs01[5,0:2])
|
||||
#
|
||||
# ramp_axs = [ax1_ramp, ax2_ramp, ax3_ramp, ax4_ramp, ax5_ramp,ax6_ramp, ax7_ramp, ax8_ramp,
|
||||
# ax9_ramp, ax10_ramp, ax11_ramp, ax12_ramp]
|
||||
#
|
||||
# # order of models
|
||||
# models = ['Cb stellate','RS Inhibitory','FS', 'RS Pyramidal','RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
# 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
# 'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
# 'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
# 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN']
|
||||
#
|
||||
# # plot ramps
|
||||
# for i in range(len(models)):
|
||||
# plot_ramp_V(ramp_axs[i], model=models[i])
|
||||
#
|
||||
# # add scalebar
|
||||
# plt.rcParams.update({'font.size': 6})
|
||||
#
|
||||
# add_scalebar(ax11_ramp, matchx=False, matchy=False, hidex=True, hidey=True, sizex=1000, sizey=50, labelx='1 s',
|
||||
# labely='50 mV', loc=3, pad=-2, borderpad=0, barwidth=1, bbox_to_anchor=Bbox.from_bounds(-0.05, 0.1, 1, 1),
|
||||
# bbox_transform=ax11_ramp.transAxes)
|
||||
# # add_scalebar(ax12_ramp, matchx=False, matchy=False, hidex=True, hidey=True, sizex=1000, sizey=25, labelx='1 s',
|
||||
# # labely='25 mV', loc=3, pad=-2, borderpad=0, barwidth=2, bbox_to_anchor=Bbox.from_bounds(-0.05, 0.1, 1, 1),
|
||||
# # bbox_transform=ax12_ramp.transAxes)
|
||||
#
|
||||
# # add subplot labels
|
||||
# for i in range(0,len(models)):
|
||||
# ramp_axs[i].text(-0.05, 1.08, string.ascii_uppercase[i], transform=ramp_axs[i].transAxes, size=10, weight='bold')
|
||||
#
|
||||
# fig.savefig('./Figures/ramp_firing.pdf', dpi=3000)
|
||||
# plt.show()
|
||||
|
||||
|
||||
|
||||
# plot ramp protocol and responses of each model to ramp
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.gridspec as gridspec
|
||||
from matplotlib.transforms import Bbox
|
||||
import string
|
||||
|
||||
def cm2inch(*tupl):
|
||||
inch = 2.54
|
||||
if isinstance(tupl[0], tuple):
|
||||
return tuple(i/inch for i in tupl[0])
|
||||
else:
|
||||
return tuple(i/inch for i in tupl)
|
||||
|
||||
#### from https://gist.github.com/dmeliza/3251476 #####################################################################
|
||||
from matplotlib.offsetbox import AnchoredOffsetbox
|
||||
class AnchoredScaleBar(AnchoredOffsetbox):
|
||||
def __init__(self, transform, sizex=0, sizey=0, labelx=None, labely=None, loc=4,
|
||||
pad=0.1, borderpad=0.1, sep=2, prop=None, barcolor="black", barwidth=None,
|
||||
**kwargs):
|
||||
"""
|
||||
Draw a horizontal and/or vertical bar with the size in data coordinate
|
||||
of the give axes. A label will be drawn underneath (center-aligned).
|
||||
- transform : the coordinate frame (typically axes.transData)
|
||||
- sizex,sizey : width of x,y bar, in data units. 0 to omit
|
||||
- labelx,labely : labels for x,y bars; None to omit
|
||||
- loc : position in containing axes
|
||||
- pad, borderpad : padding, in fraction of the legend font size (or prop)
|
||||
- sep : separation between labels and bars in points.
|
||||
- **kwargs : additional arguments passed to base class constructor
|
||||
"""
|
||||
from matplotlib.patches import Rectangle
|
||||
from matplotlib.offsetbox import AuxTransformBox, VPacker, HPacker, TextArea, DrawingArea
|
||||
bars = AuxTransformBox(transform)
|
||||
if sizex:
|
||||
bars.add_artist(Rectangle((0, 0), sizex, 0, ec=barcolor, lw=barwidth, fc="none"))
|
||||
if sizey:
|
||||
bars.add_artist(Rectangle((0, 0), 0, sizey, ec=barcolor, lw=barwidth, fc="none"))
|
||||
|
||||
if sizex and labelx:
|
||||
self.xlabel = TextArea(labelx)
|
||||
bars = VPacker(children=[bars, self.xlabel], align="center", pad=0, sep=sep)
|
||||
if sizey and labely:
|
||||
self.ylabel = TextArea(labely)
|
||||
bars = HPacker(children=[self.ylabel, bars], align="center", pad=0, sep=sep)
|
||||
|
||||
AnchoredOffsetbox.__init__(self, loc, pad=pad, borderpad=borderpad,
|
||||
child=bars, prop=prop, frameon=False, **kwargs)
|
||||
|
||||
|
||||
def add_scalebar(ax, matchx=True, matchy=True, hidex=True, hidey=True, **kwargs):
|
||||
""" Add scalebars to axes
|
||||
Adds a set of scale bars to *ax*, matching the size to the ticks of the plot
|
||||
and optionally hiding the x and y axes
|
||||
- ax : the axis to attach ticks to
|
||||
- matchx,matchy : if True, set size of scale bars to spacing between ticks
|
||||
if False, size should be set using sizex and sizey params
|
||||
- hidex,hidey : if True, hide x-axis and y-axis of parent
|
||||
- **kwargs : additional arguments passed to AnchoredScaleBars
|
||||
Returns created scalebar object
|
||||
"""
|
||||
|
||||
def f(axis):
|
||||
l = axis.get_majorticklocs()
|
||||
return len(l) > 1 and (l[1] - l[0])
|
||||
|
||||
if matchx:
|
||||
kwargs['sizex'] = f(ax.xaxis)
|
||||
kwargs['labelx'] = str(kwargs['sizex'])
|
||||
if matchy:
|
||||
kwargs['sizey'] = f(ax.yaxis)
|
||||
kwargs['labely'] = str(kwargs['sizey'])
|
||||
|
||||
sb = AnchoredScaleBar(ax.transData, **kwargs)
|
||||
ax.add_artist(sb)
|
||||
|
||||
if hidex: ax.xaxis.set_visible(False)
|
||||
if hidey: ax.yaxis.set_visible(False)
|
||||
if hidex and hidey: ax.set_frame_on(False)
|
||||
|
||||
return sb
|
||||
########################################################################################################################
|
||||
|
||||
|
||||
def plot_ramp_V(ax, model='RS Pyramidal'): # , stop=750
|
||||
model_ramp = pd.read_csv('./Figures/Data/model_ramp.csv')
|
||||
# ax.plot(model_ramp['t'], model_ramp[model], 'k', linewidth=0.0025)
|
||||
ax.plot(model_ramp['t'], model_ramp[model], 'k', linewidth=0.1)
|
||||
ax.set_ylabel('V')
|
||||
ax.set_xlabel('Time [s]')
|
||||
ax.set_ylim(-80, 60)
|
||||
ax.axis('off')
|
||||
ax.set_title(model, fontsize=8)
|
||||
|
||||
def plot_I_ramp(ax):
|
||||
dt = 0.01
|
||||
I_low = 0
|
||||
I_high = 0.001
|
||||
initial_period = 1000
|
||||
|
||||
sec = 4
|
||||
ramp_len = int(4 * 1000 * 1 / dt)
|
||||
stim_time = ramp_len * 2
|
||||
I_amp = np.array([0])
|
||||
I_amp = np.reshape(I_amp, (1, I_amp.shape[0]))
|
||||
I_ramp = np.zeros((stim_time, 1)) @ I_amp
|
||||
I_ramp[:, :] = np.ones((stim_time, 1)) @ I_amp
|
||||
stim_num_step = I_ramp.shape[1]
|
||||
start=0
|
||||
I_ramp[start:int(start + ramp_len), 0] = np.linspace(0, I_high, ramp_len)
|
||||
I_ramp[int(start + ramp_len):int(start + ramp_len * 2), 0] = np.linspace(I_high, 0, ramp_len)
|
||||
|
||||
t = np.arange(0, 4000 * 2, dt)
|
||||
ax.plot(t, I_ramp)
|
||||
ax.set_ylabel('I')
|
||||
ax.set_xlabel('Time [s]')
|
||||
ax.axis('off')
|
||||
ax.set_title('Ramp Current', fontsize=8, x=0.5, y=-0.5)
|
||||
return ax
|
||||
|
||||
#% plot setup
|
||||
fig = plt.figure(figsize=cm2inch(17.6,25))
|
||||
|
||||
gs0 = fig.add_gridspec(2, 1, wspace=0.)
|
||||
gs00 = gs0[:].subgridspec(13, 1, wspace=0.7, hspace=1.0)
|
||||
|
||||
ax1_ramp = fig.add_subplot(gs00[0])
|
||||
ax2_ramp = fig.add_subplot(gs00[1])
|
||||
ax3_ramp = fig.add_subplot(gs00[2])
|
||||
ax4_ramp = fig.add_subplot(gs00[3])
|
||||
ax5_ramp = fig.add_subplot(gs00[4])
|
||||
ax6_ramp = fig.add_subplot(gs00[5])
|
||||
ax7_ramp = fig.add_subplot(gs00[6])
|
||||
ax8_ramp = fig.add_subplot(gs00[7])
|
||||
ax9_ramp = fig.add_subplot(gs00[8])
|
||||
ax10_ramp = fig.add_subplot(gs00[9])
|
||||
ax11_ramp = fig.add_subplot(gs00[10])
|
||||
ax12_ramp = fig.add_subplot(gs00[11])
|
||||
ax13_I = fig.add_subplot(gs00[12])
|
||||
ramp_axs = [ax1_ramp, ax2_ramp, ax3_ramp, ax4_ramp, ax5_ramp,ax6_ramp, ax7_ramp, ax8_ramp,
|
||||
ax9_ramp, ax10_ramp, ax11_ramp, ax12_ramp]
|
||||
|
||||
# order of models
|
||||
models = ['Cb stellate','RS Inhibitory','FS', 'RS Pyramidal','RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN']
|
||||
|
||||
# plot ramps
|
||||
for i in range(len(models)):
|
||||
plot_ramp_V(ramp_axs[i], model=models[i])
|
||||
|
||||
# add scalebar
|
||||
plt.rcParams.update({'font.size': 6})
|
||||
|
||||
add_scalebar(ax12_ramp, matchx=False, matchy=False, hidex=True, hidey=True, sizex=1000, sizey=50, labelx='1 s',
|
||||
labely='50 mV', loc=3, pad=-2, borderpad=0, barwidth=1, bbox_to_anchor=Bbox.from_bounds(0.01, 0.05, 1, 1),
|
||||
bbox_transform=ax12_ramp.transAxes)
|
||||
|
||||
ax13_I = plot_I_ramp(ax13_I)
|
||||
add_scalebar(ax13_I, matchx=False, matchy=False, hidex=True, hidey=True, sizex=1000, sizey=0.0005, labelx='1 s',
|
||||
labely='0.5 $I_{max}$', loc=3, pad=-2, borderpad=0, barwidth=1,
|
||||
bbox_to_anchor=Bbox.from_bounds(0.0, -0.01, 1, 1), bbox_transform=ax13_I.transAxes)
|
||||
|
||||
# add subplot labels
|
||||
for i in range(0,len(models)):
|
||||
ramp_axs[i].text(-0.01, 1.1, string.ascii_uppercase[i], transform=ramp_axs[i].transAxes, size=10, weight='bold')
|
||||
|
||||
#save
|
||||
fig.set_size_inches(cm2inch(17.6,22))
|
||||
fig.savefig('./Figures/ramp_firing.png', dpi=fig.dpi)#pdf #eps
|
||||
plt.show()
|
||||
|
||||
Binary file not shown.
@@ -157,6 +157,8 @@ def boxplot_with_markers(ax,max_width, alteration='shift', msize=2.2):
|
||||
clr_dict = {}
|
||||
for m in range(len(model_names)):
|
||||
clr_dict[model_names[m]] = colors[m]
|
||||
print(colors)
|
||||
print(clr_dict)
|
||||
Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
|
||||
if alteration=='shift':
|
||||
i = 3 # Kv1.1 act
|
||||
@@ -217,27 +219,52 @@ def boxplot_with_markers(ax,max_width, alteration='shift', msize=2.2):
|
||||
|
||||
|
||||
def model_legend(ax, marker_s_leg, pos, ncol):
|
||||
colorslist = [ '#40A787', # cyan'#
|
||||
'#F0D730', # yellow
|
||||
'#C02717', # red
|
||||
'#007030', # dark green
|
||||
'#AAB71B', # lightgreen
|
||||
'#008797', # light blue
|
||||
'#F78017', # orange
|
||||
'#478010', # green
|
||||
'#53379B', # purple
|
||||
'#2060A7', # blue
|
||||
'#873770', # magenta
|
||||
'#D03050' # pink
|
||||
]
|
||||
import matplotlib.colors
|
||||
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
|
||||
# colorslist = [ '#40A787', # cyan'#
|
||||
# '#F0D730', # yellow
|
||||
# '#C02717', # red
|
||||
# '#007030', # dark green
|
||||
# '#AAB71B', # lightgreen
|
||||
# '#008797', # light blue
|
||||
# '#F78017', # orange
|
||||
# '#478010', # green
|
||||
# '#53379B', # purple
|
||||
# '#2060A7', # blue
|
||||
# '#873770', # magenta
|
||||
# '#D03050' # pink
|
||||
# ]
|
||||
# import matplotlib.colors
|
||||
# colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
|
||||
model_pos = {'Cb stellate':0, 'RS Inhibitory':1, 'FS':2, 'RS Pyramidal':3,
|
||||
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4,
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':6,
|
||||
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8,
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':9,
|
||||
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10, 'STN':11}
|
||||
|
||||
# model_pos = {'Cb stellate': 0, 'RS Inhibitory': 1, 'FS': 2, 'RS Pyramidal': 3,
|
||||
# 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 4,
|
||||
# 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 5,
|
||||
# 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 6,
|
||||
# 'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 7,
|
||||
# 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 8,
|
||||
# 'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 9,
|
||||
# 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 10, 'STN': 11}
|
||||
colorslist = ['#007030', # dark green
|
||||
'#F0D730', # yellow
|
||||
'#C02717', # red
|
||||
'#478010', # green
|
||||
'#AAB71B', # lightgreen
|
||||
'#F78017', # orange
|
||||
'#40A787', # cyan'#
|
||||
'#008797', # light blue
|
||||
'#2060A7', # blue
|
||||
'#D03050', # pink
|
||||
'#53379B', # purple
|
||||
'#873770', # magenta
|
||||
]
|
||||
|
||||
import matplotlib.colors
|
||||
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
|
||||
Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
|
||||
RS_p = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal']], marker=Markers[model_pos['RS Pyramidal']], markersize=marker_s_leg, linestyle='None',
|
||||
label='RS pyramidal')
|
||||
|
||||
648
Figures/rheobase_correlation_letters.py
Normal file
648
Figures/rheobase_correlation_letters.py
Normal file
@@ -0,0 +1,648 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Sat Jul 3 19:52:04 2021
|
||||
|
||||
@author: nils
|
||||
"""
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import string
|
||||
import textwrap
|
||||
import json
|
||||
import matplotlib
|
||||
import matplotlib.lines as mlines
|
||||
from matplotlib import ticker
|
||||
from matplotlib.ticker import NullFormatter
|
||||
from Figures.plotstyle import boxplot_style
|
||||
|
||||
|
||||
def cm2inch(*tupl):
|
||||
inch = 2.54
|
||||
if isinstance(tupl[0], tuple):
|
||||
return tuple(i/inch for i in tupl[0])
|
||||
else:
|
||||
return tuple(i/inch for i in tupl)
|
||||
#%% ##################### From https://stackoverflow.com/questions/52878845/swarmplot-with-hue-affecting-marker-beyond-color ##
|
||||
# to change marker types in seaborn swarmplot
|
||||
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
############## Begin hack ##############
|
||||
from matplotlib.axes._axes import Axes
|
||||
from matplotlib.markers import MarkerStyle
|
||||
from numpy import ndarray
|
||||
|
||||
def GetColor2Marker(markers):
|
||||
colorslist = ['#40A787', # cyan'#
|
||||
'#F0D730', # yellow
|
||||
'#C02717', # red
|
||||
'#007030', # dark green
|
||||
'#AAB71B', # lightgreen
|
||||
'#008797', # light blue
|
||||
'#F78017', # orange
|
||||
'#478010', # green
|
||||
'#53379B', # purple
|
||||
'#2060A7', # blue
|
||||
'#873770', # magenta
|
||||
'#D03050' # pink
|
||||
]
|
||||
import matplotlib.colors
|
||||
palette = [matplotlib.colors.to_rgb(c) for c in colorslist] #
|
||||
mkcolors = [(palette[i]) for i in range(len(markers))]
|
||||
return dict(zip(mkcolors,markers))
|
||||
|
||||
def fixlegend(ax,markers,markersize=3,**kwargs):
|
||||
# Fix Legend
|
||||
legtitle = ax.get_legend().get_title().get_text()
|
||||
_,l = ax.get_legend_handles_labels()
|
||||
colorslist = ['#40A787', # cyan'#
|
||||
'#F0D730', # yellow
|
||||
'#C02717', # red
|
||||
'#007030', # dark green
|
||||
'#AAB71B', # lightgreen
|
||||
'#008797', # light blue
|
||||
'#F78017', # orange
|
||||
'#478010', # green
|
||||
'#53379B', # purple
|
||||
'#2060A7', # blue
|
||||
'#873770', # magenta
|
||||
'#D03050' # pink
|
||||
]
|
||||
import matplotlib.colors
|
||||
palette = [matplotlib.colors.to_rgb(c) for c in colorslist]
|
||||
mkcolors = [(palette[i]) for i in range(len(markers))]
|
||||
newHandles = [plt.Line2D([0],[0], ls="none", marker=m, color=c, mec="none", markersize=markersize,**kwargs) \
|
||||
for m,c in zip(markers, mkcolors)]
|
||||
ax.legend(newHandles,l)
|
||||
leg = ax.get_legend()
|
||||
leg.set_title(legtitle)
|
||||
|
||||
old_scatter = Axes.scatter
|
||||
def new_scatter(self, *args, **kwargs):
|
||||
colors = kwargs.get("c", None)
|
||||
co2mk = kwargs.pop("co2mk",None)
|
||||
FinalCollection = old_scatter(self, *args, **kwargs)
|
||||
if co2mk is not None and isinstance(colors, ndarray):
|
||||
Color2Marker = GetColor2Marker(co2mk)
|
||||
paths=[]
|
||||
for col in colors:
|
||||
mk=Color2Marker[tuple(col)]
|
||||
marker_obj = MarkerStyle(mk)
|
||||
paths.append(marker_obj.get_path().transformed(marker_obj.get_transform()))
|
||||
FinalCollection.set_paths(paths)
|
||||
return FinalCollection
|
||||
Axes.scatter = new_scatter
|
||||
############## End hack. ##############
|
||||
########################################################################################################################
|
||||
#%% add gradient arrows
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.transforms
|
||||
import matplotlib.path
|
||||
from matplotlib.collections import LineCollection
|
||||
def rainbowarrow(ax, start, end, cmap, n=50,lw=3):
|
||||
# Arrow shaft: LineCollection
|
||||
x = np.linspace(start[0],end[0],n)
|
||||
y = np.linspace(start[1],end[1],n)
|
||||
points = np.array([x,y]).T.reshape(-1,1,2)
|
||||
segments = np.concatenate([points[:-1],points[1:]], axis=1)
|
||||
lc = LineCollection(segments, cmap=cmap, linewidth=lw)
|
||||
lc.set_array(np.linspace(0,1,n))
|
||||
ax.add_collection(lc)
|
||||
# Arrow head: Triangle
|
||||
tricoords = [(0,-0.02),(0.025,0),(0,0.02),(0,-0.02)]
|
||||
angle = np.arctan2(end[1]-start[1],end[0]-start[0])
|
||||
rot = matplotlib.transforms.Affine2D().rotate(angle)
|
||||
tricoords2 = rot.transform(tricoords)
|
||||
tri = matplotlib.path.Path(tricoords2, closed=True)
|
||||
ax.scatter(end[0],end[1], c=1, s=(4*lw)**2, marker=tri, cmap=cmap,vmin=0)
|
||||
ax.autoscale_view()
|
||||
return ax
|
||||
|
||||
def gradientaxis(ax, start, end, cmap, n=100,lw=1):
|
||||
# Arrow shaft: LineCollection
|
||||
x = np.linspace(start[0],end[0],n)
|
||||
y = np.linspace(start[1],end[1],n)
|
||||
points = np.array([x,y]).T.reshape(-1,1,2)
|
||||
segments = np.concatenate([points[:-1],points[1:]], axis=1)
|
||||
lc = LineCollection(segments, cmap=cmap, linewidth=lw,zorder=15)
|
||||
lc.set_array(np.linspace(0,1,n))
|
||||
ax.add_collection(lc)
|
||||
return ax
|
||||
#%%
|
||||
def boxplot_with_markers(ax,max_width, alteration='shift', msize=2.2):
|
||||
hlinewidth = 0.5
|
||||
model_names = ['RS pyramidal','RS inhibitory','FS',
|
||||
'RS pyramidal +$K_V1.1$','RS inhibitory +$K_V1.1$',
|
||||
'FS +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
|
||||
'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
|
||||
'STN $\Delta$$K_V1.1$']
|
||||
|
||||
colorslist = ['#007030', # dark green
|
||||
'#F0D730', # yellow
|
||||
'#C02717', # red
|
||||
'#478010', # green
|
||||
'#AAB71B', # lightgreen
|
||||
'#F78017', # orange
|
||||
'#40A787', # cyan'#
|
||||
'#008797', # light blue
|
||||
'#2060A7', # blue
|
||||
'#D03050', # pink
|
||||
'#53379B', # purple
|
||||
'#873770', # magenta
|
||||
]
|
||||
|
||||
import matplotlib.colors
|
||||
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
|
||||
clr_dict = {}
|
||||
for m in range(len(model_names)):
|
||||
clr_dict[model_names[m]] = colors[m]
|
||||
Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
|
||||
if alteration=='shift':
|
||||
i = 3 # Kv1.1 act
|
||||
ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
|
||||
df = pd.read_csv('./Figures/Data/rheo_shift_corr.csv')
|
||||
sns.swarmplot(y="corr", x="$\Delta V_{1/2}$", hue="model", data=df,
|
||||
palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
|
||||
order=['Na activation', 'Na inactivation', 'K activation', '$K_V1.1$ activation',
|
||||
'$K_V1.1$ inactivation', 'A activation', 'A inactivation'],
|
||||
hue_order=model_names, co2mk=Markers)
|
||||
lim = ax.get_xlim()
|
||||
ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
|
||||
ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
|
||||
ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
|
||||
ax.set_title("Shift ($\Delta V_{1/2}$)", y=1.05)
|
||||
ax.set_xticklabels(['Na \nactivation', 'Na \ninactivation', 'K \nactivation', '$K_V1.1$ \nactivation',
|
||||
'$K_V1.1$ \ninactivation', 'A \nactivation', 'A \ninactivation'])
|
||||
elif alteration=='slope':
|
||||
i = 4 # Kv1.1 inact
|
||||
ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
|
||||
df = pd.read_csv('./Figures/Data/rheo_scale_corr.csv')
|
||||
|
||||
# Add in points to show each observation
|
||||
sns.swarmplot(y="corr", x="Slope (k)", hue="model", data=df,
|
||||
palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
|
||||
order=['Na activation', 'Na inactivation', 'K activation', '$K_V1.1$ activation',
|
||||
'$K_V1.1$ inactivation', 'A activation', 'A inactivation'],
|
||||
hue_order=model_names, co2mk=Markers)
|
||||
lim = ax.get_xlim()
|
||||
ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
|
||||
ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
|
||||
ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
|
||||
ax.set_title("Slope (k)", y=1.05)
|
||||
ax.set_xticklabels(['Na \nactivation', 'Na \ninactivation', 'K \nactivation', '$K_V1.1$ \nactivation',
|
||||
'$K_V1.1$ \ninactivation', 'A \nactivation', 'A \ninactivation'])
|
||||
elif alteration=='g':
|
||||
i = 4 # Leak
|
||||
ax.axvspan(i - 0.4, i + 0.4, fill=False, edgecolor='k')
|
||||
df = pd.read_csv('./Figures/Data/rheo_g_corr.csv')
|
||||
|
||||
# Add in points to show each observation
|
||||
sns.swarmplot(y="corr", x="g", hue="model", data=df,
|
||||
palette=clr_dict, linewidth=0, orient='v', ax=ax, size=msize,
|
||||
order=['Na', 'K', '$K_V1.1$', 'A', 'Leak'],
|
||||
hue_order=model_names, co2mk=Markers)
|
||||
lim = ax.get_xlim()
|
||||
ax.plot([lim[0], lim[1]], [0, 0], ':r',linewidth=hlinewidth)
|
||||
ax.plot([lim[0], lim[1]], [1, 1], ':k',linewidth=hlinewidth)
|
||||
ax.plot([lim[0], lim[1]], [-1, -1], ':k',linewidth=hlinewidth)
|
||||
ax.set_title("Conductance (g)", y=1.05)
|
||||
ax.set_xticklabels(textwrap.fill(x.get_text(), max_width) for x in ax.get_xticklabels())
|
||||
else:
|
||||
print('Please chose "shift", "slope" or "g"')
|
||||
ax.get_legend().remove()
|
||||
ax.xaxis.grid(False)
|
||||
sns.despine(trim=True, bottom=True, ax=ax)
|
||||
ax.set(xlabel=None, ylabel=r'Kendall $\it{\tau}$')
|
||||
|
||||
|
||||
def model_legend(ax, marker_s_leg, pos, ncol):
|
||||
colorslist = [ '#40A787', # cyan'#
|
||||
'#F0D730', # yellow
|
||||
'#C02717', # red
|
||||
'#007030', # dark green
|
||||
'#AAB71B', # lightgreen
|
||||
'#008797', # light blue
|
||||
'#F78017', # orange
|
||||
'#478010', # green
|
||||
'#53379B', # purple
|
||||
'#2060A7', # blue
|
||||
'#873770', # magenta
|
||||
'#D03050' # pink
|
||||
]
|
||||
model_names = ['RS pyramidal', 'RS inhibitory', 'FS',
|
||||
'RS pyramidal +$K_V1.1$', 'RS inhibitory +$K_V1.1$',
|
||||
'FS +$K_V1.1$', 'Cb stellate', 'Cb stellate +$K_V1.1$',
|
||||
'Cb stellate $\Delta$$K_V1.1$', 'STN', 'STN +$K_V1.1$',
|
||||
'STN $\Delta$$K_V1.1$']
|
||||
|
||||
# colorslist = ['#007030', # dark green
|
||||
# '#F0D730', # yellow
|
||||
# '#C02717', # red
|
||||
# '#478010', # green
|
||||
# '#AAB71B', # lightgreen
|
||||
# '#F78017', # orange
|
||||
# '#40A787', # cyan'#
|
||||
# '#008797', # light blue
|
||||
# '#2060A7', # blue
|
||||
# '#D03050', # pink
|
||||
# '#53379B', # purple
|
||||
# '#873770', # magenta
|
||||
# ]
|
||||
|
||||
import matplotlib.colors
|
||||
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
|
||||
clr_dict = {}
|
||||
for m in range(len(model_names)):
|
||||
clr_dict[model_names[m]] = colors[m]
|
||||
|
||||
import matplotlib.colors
|
||||
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
|
||||
# model_pos = {'Cb stellate':0, 'RS Inhibitory':1, 'FS':2, 'RS Pyramidal':3,
|
||||
# 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4,
|
||||
# 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':6,
|
||||
# 'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8,
|
||||
# 'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':9,
|
||||
# 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10, 'STN':11}
|
||||
Markers = ["o", "o", "o", "^", "^", "^", "D", "D", "D", "s", "s", "s"]
|
||||
|
||||
model_pos = {'RS Pyramidal':0, 'RS Inhibitory':1, 'FS':2,
|
||||
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':3, 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':4,
|
||||
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':5, 'Cb stellate':6, 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':7,
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':8, 'STN':9, 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':10,
|
||||
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':11}
|
||||
|
||||
|
||||
|
||||
RS_p = mlines.Line2D([], [], color='#007030', marker="^",
|
||||
markersize=marker_s_leg, linestyle='None',
|
||||
label='Model D')
|
||||
RS_i = mlines.Line2D([], [], color='#F0D730', marker="o",
|
||||
markersize=marker_s_leg, linestyle='None',
|
||||
label='Model B')
|
||||
FS = mlines.Line2D([], [], color='#C02717', marker="o", markersize=marker_s_leg,
|
||||
linestyle='None', label='Model C')
|
||||
RS_p_Kv = mlines.Line2D([], [], color='#478010',
|
||||
marker="D",
|
||||
markersize=marker_s_leg, linestyle='None',
|
||||
label='Model H')
|
||||
RS_i_Kv = mlines.Line2D([], [], color='#AAB71B',
|
||||
marker="^",
|
||||
markersize=marker_s_leg, linestyle='None',
|
||||
label='Model E')
|
||||
FS_Kv = mlines.Line2D([], [], color='#F78017',
|
||||
marker="D", markersize=marker_s_leg,
|
||||
linestyle='None', label='Model G')
|
||||
Cb = mlines.Line2D([], [], color='#40A787', marker="o",
|
||||
markersize=marker_s_leg, linestyle='None',
|
||||
label='Model A')
|
||||
Cb_pl = mlines.Line2D([], [], color='#008797',
|
||||
marker="^",
|
||||
markersize=marker_s_leg, linestyle='None',
|
||||
label='Model F')
|
||||
Cb_sw = mlines.Line2D([], [], color='#2060A7',
|
||||
marker="s",
|
||||
markersize=marker_s_leg, linestyle='None',
|
||||
label='Model J')
|
||||
STN = mlines.Line2D([], [], color='#D03050', marker="s", markersize=marker_s_leg,
|
||||
linestyle='None', label='Model L')
|
||||
STN_pl = mlines.Line2D([], [], color='#53379B',
|
||||
marker="D",
|
||||
markersize=marker_s_leg, linestyle='None',
|
||||
label='Model I')
|
||||
STN_sw = mlines.Line2D([], [], color='#873770',
|
||||
marker="s",
|
||||
markersize=marker_s_leg, linestyle='None',
|
||||
label='Model K')
|
||||
#
|
||||
# RS_p = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal']], marker=Markers[model_pos['RS Pyramidal']],
|
||||
# markersize=marker_s_leg, linestyle='None',
|
||||
# label='Model D')
|
||||
# RS_i = mlines.Line2D([], [], color=colors[model_pos['RS Inhibitory']], marker=Markers[model_pos['RS Inhibitory']],
|
||||
# markersize=marker_s_leg, linestyle='None',
|
||||
# label='Model B')
|
||||
# FS = mlines.Line2D([], [], color=colors[model_pos['FS']], marker=Markers[model_pos['FS']], markersize=marker_s_leg,
|
||||
# linestyle='None', label='Model C')
|
||||
# RS_p_Kv = mlines.Line2D([], [], color=colors[model_pos['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# marker=Markers[model_pos['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# markersize=marker_s_leg, linestyle='None',
|
||||
# label='Model H')
|
||||
# RS_i_Kv = mlines.Line2D([], [], color=colors[model_pos['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# marker=Markers[model_pos['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# markersize=marker_s_leg, linestyle='None',
|
||||
# label='Model E')
|
||||
# FS_Kv = mlines.Line2D([], [], color=colors[model_pos['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# marker=Markers[model_pos['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']], markersize=marker_s_leg,
|
||||
# linestyle='None', label='Model G')
|
||||
# Cb = mlines.Line2D([], [], color=colors[model_pos['Cb stellate']], marker=Markers[model_pos['Cb stellate']],
|
||||
# markersize=marker_s_leg, linestyle='None',
|
||||
# label='Model A')
|
||||
# Cb_pl = mlines.Line2D([], [], color=colors[model_pos['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# marker=Markers[model_pos['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# markersize=marker_s_leg, linestyle='None',
|
||||
# label='Model F')
|
||||
# Cb_sw = mlines.Line2D([], [], color=colors[model_pos['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# marker=Markers[model_pos['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# markersize=marker_s_leg, linestyle='None',
|
||||
# label='Model J')
|
||||
# STN = mlines.Line2D([], [], color=colors[model_pos['STN']], marker=Markers[model_pos['STN']], markersize=marker_s_leg,
|
||||
# linestyle='None', label='Model L')
|
||||
# STN_pl = mlines.Line2D([], [], color=colors[model_pos['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# marker=Markers[model_pos['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# markersize=marker_s_leg, linestyle='None',
|
||||
# label='Model I')
|
||||
# STN_sw = mlines.Line2D([], [], color=colors[model_pos['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# marker=Markers[model_pos['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']],
|
||||
# markersize=marker_s_leg, linestyle='None',
|
||||
# label='Model K')
|
||||
# ax.legend(handles=[RS_p, RS_i, FS, RS_p_Kv, RS_i_Kv, FS_Kv, Cb, Cb_pl, Cb_sw, STN, STN_pl, STN_sw], loc='center',
|
||||
# bbox_to_anchor=pos, ncol=ncol, frameon=False)
|
||||
ax.legend(handles=[Cb, RS_i, FS, RS_p, RS_i_Kv, Cb_pl, FS_Kv, RS_p_Kv, STN_pl, Cb_sw, STN_sw, STN], loc='center',
|
||||
bbox_to_anchor=pos, ncol=ncol, frameon=False)
|
||||
|
||||
def plot_rheo_alt(ax, model='FS', color1='red', color2='dodgerblue', alteration='shift'):
|
||||
ax.spines["right"].set_visible(False)
|
||||
ax.spines["top"].set_visible(False)
|
||||
model_names = ['RS Pyramidal','RS Inhibitory','FS',
|
||||
'RS Pyramidal +$K_V1.1$','RS Inhibitory +$K_V1.1$',
|
||||
'FS +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
|
||||
'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
|
||||
'STN $\Delta$$K_V1.1$']
|
||||
|
||||
model_name_dict = {'RS Pyramidal': 'RS Pyramidal',
|
||||
'RS Inhibitory': 'RS Inhibitory',
|
||||
'FS': 'FS',
|
||||
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS Pyramidal +$K_V1.1$',
|
||||
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS Inhibitory +$K_V1.1$',
|
||||
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'FS +$K_V1.1$',
|
||||
'Cb stellate': 'Cb stellate',
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb stellate +$K_V1.1$',
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb stellate $\Delta$$K_V1.1$',
|
||||
'STN': 'STN',
|
||||
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN +$K_V1.1$',
|
||||
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN $\Delta$$K_V1.1$'}
|
||||
|
||||
colorslist = ['#007030', # dark green
|
||||
'#F0D730', # yellow
|
||||
'#C02717', # red
|
||||
'#478010', # green
|
||||
'#AAB71B', # lightgreen
|
||||
'#F78017', # orange
|
||||
'#40A787', # cyan'#
|
||||
'#008797', # light blue
|
||||
'#2060A7', # blue
|
||||
'#D03050', # pink
|
||||
'#53379B', # purple
|
||||
'#873770', # magenta
|
||||
]
|
||||
import matplotlib.colors
|
||||
colors = [matplotlib.colors.to_rgb(c) for c in colorslist]
|
||||
clr_dict = {}
|
||||
for m in range(len(model_names)):
|
||||
clr_dict[model_names[m]] = colors[m]
|
||||
if alteration=='shift':
|
||||
df = pd.read_csv('./Figures/Data/rheo_shift_ex.csv')
|
||||
df = df.sort_values('alteration')
|
||||
ax.set_xlabel('$\Delta$$V_{1/2}$')
|
||||
elif alteration=='slope':
|
||||
df = pd.read_csv('./Figures/Data/rheo_slope_ex.csv')
|
||||
ax.set_xscale("log")
|
||||
ax.set_xticks([0.5, 1, 2])
|
||||
ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
|
||||
ax.xaxis.set_minor_formatter(NullFormatter())
|
||||
ax.set_xlabel('$k$/$k_{WT}$')
|
||||
elif alteration=='g':
|
||||
df = pd.read_csv('./Figures/Data/rheo_g_ex.csv')
|
||||
ax.set_xscale("log")
|
||||
ax.set_xticks([0.5, 1, 2])
|
||||
ax.xaxis.set_major_formatter(ticker.ScalarFormatter())
|
||||
ax.xaxis.set_minor_formatter(NullFormatter())
|
||||
ax.set_xlabel('$g$/$g_{WT}$')
|
||||
for mod in model_names:
|
||||
if mod == model_name_dict[model]:
|
||||
ax.plot(df['alteration'], df[mod], color=clr_dict[mod], alpha=1, zorder=10, linewidth=2)
|
||||
else:
|
||||
ax.plot(df['alteration'], df[mod], color=clr_dict[mod], alpha=0.5, zorder=1, linewidth=1)
|
||||
|
||||
ax.set_ylabel('$\Delta$ Rheobase (nA)', labelpad=0)
|
||||
x = df['alteration']
|
||||
y = df[model_name_dict[model]]
|
||||
ax.set_xlim(x.min(), x.max())
|
||||
ax.set_ylim(df[model_names].min().min(), df[model_names].max().max())
|
||||
|
||||
# x axis color gradient
|
||||
cvals = [-2., 2]
|
||||
colors = ['lightgrey', 'k']
|
||||
norm = plt.Normalize(min(cvals), max(cvals))
|
||||
tuples = list(zip(map(norm, cvals), colors))
|
||||
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
|
||||
(xstart, xend) = ax.get_xlim()
|
||||
(ystart, yend) = ax.get_ylim()
|
||||
print(ystart, yend)
|
||||
start = (xstart, ystart*1.0)
|
||||
end = (xend, ystart*1.0)
|
||||
ax = gradientaxis(ax, start, end, cmap, n=200,lw=4)
|
||||
ax.spines['bottom'].set_visible(False)
|
||||
# ax.set_ylim(ystart, yend)
|
||||
|
||||
#xlabel tick colors
|
||||
# my_colors = ['lightgrey', 'grey', 'k']
|
||||
# for ticklabel, tickcolor in zip(ax.get_xticklabels(), my_colors):
|
||||
# ticklabel.set_color(tickcolor)
|
||||
return ax
|
||||
|
||||
def plot_fI(ax, model='RS Pyramidal', type='shift', alt='m', color1='red', color2='dodgerblue'):
|
||||
model_save_name = {'RS Pyramidal': 'RS_pyr_posp',
|
||||
'RS Inhibitory': 'RS_inhib_posp',
|
||||
'FS': 'FS_posp',
|
||||
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS_pyr_Kv',
|
||||
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'RS_inhib_Kv',
|
||||
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'FS_Kv',
|
||||
'Cb stellate': 'Cb_stellate',
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb_stellate_Kv',
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'Cb_stellate_Kv_only',
|
||||
'STN': 'STN',
|
||||
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN_Kv',
|
||||
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': 'STN_Kv_only'}
|
||||
cvals = [-2., 2]
|
||||
colors = [color1, color2]
|
||||
norm = plt.Normalize(min(cvals), max(cvals))
|
||||
tuples = list(zip(map(norm, cvals), colors))
|
||||
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
|
||||
colors = cmap(np.linspace(0, 1, 22))
|
||||
df = pd.read_csv('./Figures/Data/Model_fI/{}_fI.csv'.format(model_save_name[model]))
|
||||
df.drop(['Unnamed: 0'], axis=1)
|
||||
newdf = df.loc[df.index[(df['alt'] == alt) & (df['type'] == type)], :]
|
||||
newdf['mag'] = newdf['mag'].astype('float')
|
||||
newdf = newdf.sort_values('mag').reset_index()
|
||||
c = 0
|
||||
for i in newdf.index:
|
||||
ax.plot(json.loads(newdf.loc[i, 'I']), json.loads(newdf.loc[i, 'F']), color=colors[c])
|
||||
c += 1
|
||||
|
||||
# colors2 = [colors[10, :], 'k']
|
||||
# norm2 = plt.Normalize(min(cvals), max(cvals))
|
||||
# tuples2 = list(zip(map(norm2, cvals), colors2))
|
||||
# cmap2 = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples2)
|
||||
#
|
||||
# colors3 = [colors[11, :], 'lightgrey']
|
||||
# norm3 = plt.Normalize(min(cvals), max(cvals))
|
||||
# tuples3 = list(zip(map(norm3, cvals), colors3))
|
||||
# cmap3 = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples3)
|
||||
#
|
||||
# start = (1.1, json.loads(newdf.loc[10, 'F'])[-1])
|
||||
# end = (1.1, json.loads(newdf.loc[20, 'F'])[-1])#-json.loads(newdf.loc[20, 'F'])[-1]*0.1)
|
||||
# ax = rainbowarrow(ax, start, end, cmap2, n=50, lw=1)
|
||||
# ax.text(1.15, json.loads(newdf.loc[20, 'F'])[-1], '$+ \Delta V$', fontsize=4, color='k')
|
||||
#
|
||||
# start = (1.1, json.loads(newdf.loc[10, 'F'])[-1])
|
||||
# end = (1.1, json.loads(newdf.loc[0, 'F'])[-1])#-json.loads(newdf.loc[0, 'F'])[-1]*0.1)
|
||||
# ax = rainbowarrow(ax, start, end, cmap3, n=50, lw=1)
|
||||
# ax.text(1.15, json.loads(newdf.loc[0, 'F'])[-1], '$- \Delta V$', fontsize=4, color='lightgrey')
|
||||
|
||||
ax.set_ylabel('Frequency [Hz]')
|
||||
ax.set_xlabel('Current [nA]')
|
||||
if model == 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':
|
||||
ax.set_title("Model G", x=0.2, y=1.0)
|
||||
elif model == 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$':
|
||||
ax.set_title("Model F", x=0.2, y=1.0)
|
||||
elif model == 'Cb stellate':
|
||||
ax.set_title("Model A", x=0.2, y=1.0)
|
||||
else:
|
||||
ax.set_title("", x=0.2, y=1.0)
|
||||
# plot_rheo_alt(ax0_ex, model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k',
|
||||
# alteration='shift')
|
||||
# plot_rheo_alt(ax1_ex, model='Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k',
|
||||
# alteration='slope')
|
||||
# plot_rheo_alt(ax2_ex, model='Cb stellate', color1='lightgrey', color2='k', alteration='g')
|
||||
|
||||
# ax.set_title(model, x=0.2, y=1.025)
|
||||
ax.spines["right"].set_visible(False)
|
||||
ax.spines["top"].set_visible(False)
|
||||
L = ax.get_ylim()
|
||||
ax.set_ylim([0, L[1]])
|
||||
return ax
|
||||
|
||||
|
||||
#%%
|
||||
boxplot_style()
|
||||
color_dict = {'Cb stellate': '#40A787', # cyan'#
|
||||
'RS Inhibitory': '#F0D730', # yellow
|
||||
'FS': '#C02717', # red
|
||||
'RS Pyramidal': '#007030', # dark green
|
||||
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#AAB71B', # lightgreen
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#008797', # light blue
|
||||
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#F78017', # orange
|
||||
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#478010', # green
|
||||
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#53379B', # purple
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#2060A7', # blue
|
||||
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': '#873770', # magenta
|
||||
'STN': '#D03050' # pink
|
||||
}
|
||||
|
||||
# plot setup
|
||||
marker_s_leg = 2
|
||||
max_width = 20
|
||||
pad_x = 0.85
|
||||
pad_y= 0.4
|
||||
pad_w = 1.1
|
||||
pad_h = 0.7
|
||||
|
||||
fig = plt.figure()
|
||||
gs = fig.add_gridspec(3, 7, wspace=1.2, hspace=1.)
|
||||
ax0 = fig.add_subplot(gs[0,2:7])
|
||||
ax0_ex = fig.add_subplot(gs[0,1])
|
||||
ax0_fI = fig.add_subplot(gs[0,0])
|
||||
ax1 = fig.add_subplot(gs[1,2:7])
|
||||
ax1_ex = fig.add_subplot(gs[1,1])
|
||||
ax1_fI = fig.add_subplot(gs[1,0])
|
||||
ax2 = fig.add_subplot(gs[2,2:7])
|
||||
ax2_ex = fig.add_subplot(gs[2,1])
|
||||
ax2_fI = fig.add_subplot(gs[2,0])
|
||||
|
||||
line_width = 1
|
||||
# plot fI curves
|
||||
ax0_fI = plot_fI(ax0_fI, model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='shift', alt='s', color1='lightgrey', color2='k')
|
||||
rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1 + pad_h, fill=False, lw=line_width,transform=ax0_fI.transAxes, color=color_dict['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
|
||||
rec = ax0_fI.add_patch(rec)
|
||||
rec.set_clip_on(False)
|
||||
|
||||
ax1_fI = plot_fI(ax1_fI, model='Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='slope', alt='u', color1='lightgrey', color2='k')
|
||||
rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1 + pad_h, fill=False, lw=line_width,transform=ax1_fI.transAxes, color=color_dict['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'], alpha=1, zorder=-1)
|
||||
rec = ax1_fI.add_patch(rec)
|
||||
rec.set_clip_on(False)
|
||||
|
||||
ax2_fI = plot_fI(ax2_fI, model='Cb stellate', type='g', alt='Leak', color1='lightgrey', color2='k')
|
||||
rec = plt.Rectangle((-pad_x, -pad_y), 1 + pad_w, 1 + pad_h, fill=False, lw=line_width,transform=ax2_fI.transAxes, color=color_dict['Cb stellate'], alpha=1, zorder=-1)
|
||||
rec = ax2_fI.add_patch(rec)
|
||||
rec.set_clip_on(False)
|
||||
|
||||
|
||||
# plot boxplots
|
||||
boxplot_with_markers(ax0,max_width, alteration='shift')
|
||||
boxplot_with_markers(ax1,max_width, alteration='slope')
|
||||
boxplot_with_markers(ax2,max_width, alteration='g')
|
||||
|
||||
# plot legend
|
||||
pos = (0.225, -0.9)
|
||||
ncol = 6
|
||||
model_legend(ax2, marker_s_leg, pos, ncol)
|
||||
|
||||
# plot rheo across model for example alteration
|
||||
plot_rheo_alt(ax0_ex,model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k', alteration='shift')
|
||||
plot_rheo_alt(ax1_ex,model='Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', color1='lightgrey', color2='k',alteration='slope')
|
||||
plot_rheo_alt(ax2_ex, model='Cb stellate', color1='lightgrey', color2='k', alteration='g')
|
||||
|
||||
# label subplots with letters
|
||||
ax0_fI.text(-0.875, 1.35, string.ascii_uppercase[0], transform=ax0_fI.transAxes, size=10, weight='bold')
|
||||
ax0_ex.text(-0.8, 1.35, string.ascii_uppercase[1], transform=ax0_ex.transAxes, size=10, weight='bold')
|
||||
ax0.text(-0.075, 1.35, string.ascii_uppercase[2], transform=ax0.transAxes, size=10, weight='bold')
|
||||
|
||||
ax1_fI.text(-0.875, 1.35, string.ascii_uppercase[3], transform=ax1_fI.transAxes,size=10, weight='bold')
|
||||
ax1_ex.text(-0.8, 1.35, string.ascii_uppercase[4], transform=ax1_ex.transAxes, size=10, weight='bold')
|
||||
ax1.text(-0.075, 1.35, string.ascii_uppercase[5], transform=ax1.transAxes, size=10, weight='bold')
|
||||
|
||||
ax2_fI.text(-0.875, 1.35, string.ascii_uppercase[6], transform=ax2_fI.transAxes,size=10, weight='bold')
|
||||
ax2_ex.text(-0.8, 1.35, string.ascii_uppercase[7], transform=ax2_ex.transAxes, size=10, weight='bold')
|
||||
ax2.text(-0.075, 1.35, string.ascii_uppercase[8], transform=ax2.transAxes, size=10, weight='bold')
|
||||
|
||||
# save
|
||||
fig.set_size_inches(cm2inch(20.75,12))
|
||||
fig.savefig('./Figures/rheobase_correlation.pdf', dpi=fig.dpi)
|
||||
# fig.savefig('./Figures/rheobase_correlation.png', dpi=fig.dpi) #bbox_inches='tight', dpi=fig.dpi # eps # pdf
|
||||
plt.show()
|
||||
|
||||
|
||||
#%%
|
||||
# fig, axs = plt.subplots(1,2)
|
||||
# axs[0] = plot_fI(axs[0] , model='FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', type='shift', alt='s', color1='lightgrey', color2='k')
|
||||
# plt.show()
|
||||
#%%
|
||||
#
|
||||
#
|
||||
# cvals = [-2., 2]
|
||||
# colors = ['lightgrey', 'k']
|
||||
#
|
||||
# norm = plt.Normalize(min(cvals), max(cvals))
|
||||
# tuples = list(zip(map(norm, cvals), colors))
|
||||
# cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)
|
||||
# colors = cmap(np.linspace(0, 1, 22))
|
||||
#
|
||||
# colors2 = [colors[10,:], 'k']
|
||||
# norm2 = plt.Normalize(min(cvals), max(cvals))
|
||||
# tuples2 = list(zip(map(norm2, cvals), colors2))
|
||||
# cmap2 = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples2)
|
||||
#
|
||||
# colors3 = [colors[11,:], 'lightgrey']
|
||||
# norm3 = plt.Normalize(min(cvals), max(cvals))
|
||||
# tuples3 = list(zip(map(norm3, cvals), colors3))
|
||||
# cmap3 = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples3)
|
||||
#
|
||||
# fig, axs = plt.subplots(1,2)
|
||||
# start = (0,0)
|
||||
# end = (1,1)
|
||||
# axs[0] = rainbowarrow(axs[0], start, end, cmap2, n=50,lw=3)
|
||||
# start = (0,0)
|
||||
# end = (-1,-1)
|
||||
# axs[0] = rainbowarrow(axs[0], start, end, cmap3, n=50,lw=3)
|
||||
# plt.show()
|
||||
Binary file not shown.
259
Figures/simulation_model_comparison_letters.py
Normal file
259
Figures/simulation_model_comparison_letters.py
Normal file
@@ -0,0 +1,259 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import os
|
||||
import string
|
||||
from Figures.plotstyle import sim_style
|
||||
import seaborn as sns
|
||||
import scipy.stats as stats
|
||||
import matplotlib.lines as mlines
|
||||
|
||||
def cm2inch(*tupl):
|
||||
inch = 2.54
|
||||
if isinstance(tupl[0], tuple):
|
||||
return tuple(i/inch for i in tupl[0])
|
||||
else:
|
||||
return tuple(i/inch for i in tupl)
|
||||
|
||||
def Kendall_tau(df):
|
||||
tau = df.corr(method='kendall')
|
||||
p = pd.DataFrame(columns=df.columns, index=df.columns)
|
||||
for col in range((df.columns).shape[0]):
|
||||
for col2 in range((df.columns).shape[0]):
|
||||
if col != col2:
|
||||
_, p.loc[df.columns[col], df.columns[col2]] = stats.kendalltau(
|
||||
df[df.columns[col]], df[df.columns[col2]], nan_policy='omit')
|
||||
return tau, p
|
||||
|
||||
def correlation_plot(ax, df='AUC', title='', cbar=False):
|
||||
# cbar_ax = fig.add_axes([0.685, 0.44, .15, .01])
|
||||
cbar_ax = fig.add_axes([0.685, 0.48, .15, .01])
|
||||
cbar_ax.spines['left'].set_visible(False)
|
||||
cbar_ax.spines['bottom'].set_visible(False)
|
||||
cbar_ax.spines['right'].set_visible(False)
|
||||
cbar_ax.spines['top'].set_visible(False)
|
||||
cbar_ax.set_xticks([])
|
||||
cbar_ax.set_yticks([])
|
||||
if df == 'AUC':
|
||||
df = pd.read_csv(os.path.join('./Figures/Data/sim_mut_AUC.csv'), index_col='Unnamed: 0')
|
||||
elif df == 'rheo':
|
||||
df = pd.read_csv(os.path.join('./Figures/Data/sim_mut_rheo.csv'), index_col='Unnamed: 0')
|
||||
|
||||
# array for names
|
||||
cmap = sns.diverging_palette(220, 10, as_cmap=True)
|
||||
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
|
||||
'STN_Kv', 'STN_Kv_only']
|
||||
model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'Cb stellate',
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
|
||||
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
|
||||
# model_letter_names = ['Model H', 'Model E', 'Model G', 'Model A', 'Model F', 'Model J', 'Model L', 'Model I', 'Model K']
|
||||
model_letter_names = ['H', 'E', 'G', 'A', 'F', 'J', 'L', 'I', 'K']
|
||||
col_dict = {}
|
||||
for m in range(len(models)):
|
||||
col_dict[model_names[m]] = model_letter_names[m]
|
||||
df.rename(columns=col_dict, inplace=True)
|
||||
df = df[model_letter_names]
|
||||
|
||||
# calculate correlation matrix
|
||||
tau, p = Kendall_tau(df)
|
||||
# tau = tau.drop(columns='STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', index='RS pyramidal')
|
||||
|
||||
# mask to hide upper triangle of matrix
|
||||
mask = np.zeros_like(tau, dtype=bool)
|
||||
mask[np.triu_indices_from(mask)] = True
|
||||
np.fill_diagonal(mask, False)
|
||||
|
||||
# models and renaming of tau
|
||||
models = ['RS pyramidal', 'RS inhibitory', 'FS', 'Cb stellate',
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
|
||||
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
|
||||
model_names = ['RS pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
'RS inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
'Cb stellate', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
|
||||
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
|
||||
# model_letter_names = ['Model H', 'Model E', 'Model G', 'Model A', 'Model F', 'Model J', 'Model L', 'Model I', 'Model K']
|
||||
model_letter_names = ['H', 'E', 'G', 'A', 'F', 'J', 'L', 'I', 'K']
|
||||
col_dict = {}
|
||||
for m in range(len(models)):
|
||||
col_dict[model_names[m]] = model_letter_names[m]
|
||||
tau.rename(columns=col_dict, index=col_dict, inplace=True)
|
||||
tau = tau[model_letter_names]
|
||||
|
||||
# plotting with or without colorbar
|
||||
if cbar==False:
|
||||
res = sns.heatmap(tau, annot=False, mask=mask, center=0, vmax=1, vmin=-1, linewidths=.5, square=True, ax=ax,
|
||||
cbar=False, cmap=cmap, cbar_ax=cbar_ax, cbar_kws={"shrink": .52})
|
||||
else:
|
||||
res = sns.heatmap(tau, annot=False, mask=mask, center=0, vmax=1, vmin=-1, linewidths=.5, square=True, ax=ax,
|
||||
cbar=True, cmap=cmap, cbar_ax=cbar_ax,
|
||||
cbar_kws={"orientation": "horizontal",
|
||||
"ticks": [-1,-0.5, 0, 0.5, 1]} )
|
||||
cbar_ax.set_title(r'Kendall $\tau$', y=1.02, loc='center', fontsize=6)
|
||||
cbar_ax.tick_params(length=3)
|
||||
for tick in cbar_ax.xaxis.get_major_ticks():
|
||||
tick.label.set_fontsize(6)
|
||||
ax.set_title(title, fontsize=8)
|
||||
ax.set_xlabel("Model")
|
||||
ax.set_ylabel("Model")
|
||||
|
||||
def mutation_plot(ax, model='RS_pyramidal'):
|
||||
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
|
||||
'STN_Kv', 'STN_Kv_only']
|
||||
model_names = ['RS pyramidal', 'RS inhibitory', 'FS',
|
||||
'Cb stellate', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
|
||||
model_display_names = ['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'Cb stellate',
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN',
|
||||
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$']
|
||||
model_letter_names = ['Model H',
|
||||
'Model E',
|
||||
'Model G', 'Model A',
|
||||
'Model F',
|
||||
'Model J', 'Model L',
|
||||
'Model I',
|
||||
'Model K']
|
||||
col_dict = {}
|
||||
for m in range(len(models)):
|
||||
col_dict[models[m]] = model_display_names[m]
|
||||
|
||||
ax_dict = {}
|
||||
ax_dict['RS_pyramidal'] = (0, 0)
|
||||
ax_dict['RS_inhib'] = (0, 1)
|
||||
ax_dict['FS'] = (1, 0)
|
||||
ax_dict['Cb_stellate'] = (2, 0)
|
||||
ax_dict['Cb_stellate_Kv'] = (2, 1)
|
||||
ax_dict['Cb_stellate_Kv_only'] = (3, 0)
|
||||
ax_dict['STN'] = (3, 1)
|
||||
ax_dict['STN_Kv'] = (4, 0)
|
||||
ax_dict['STN_Kv_only'] = (4, 1)
|
||||
|
||||
ylim_dict = {}
|
||||
ylim_dict['RS_pyramidal'] = (-0.1, 0.3)
|
||||
ylim_dict['RS_inhib'] = (-0.6, 0.6)
|
||||
ylim_dict['FS'] = (-0.06, 0.08)
|
||||
ylim_dict['Cb_stellate'] = (-0.1, 0.4)
|
||||
ylim_dict['Cb_stellate_Kv'] = (-0.1, 0.5)
|
||||
ylim_dict['Cb_stellate_Kv_only'] = (-1, 0.8)
|
||||
ylim_dict['STN'] = (-0.01, 0.015)
|
||||
ylim_dict['STN_Kv'] = (-0.4, 0.6)
|
||||
ylim_dict['STN_Kv_only'] = (-0.03, 0.3)
|
||||
Marker_dict = {'Cb stellate': 'o', 'RS Inhibitory': 'o', 'FS': 'o', 'RS Pyramidal': "^",
|
||||
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "^",
|
||||
'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "^",
|
||||
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "D",
|
||||
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "D",
|
||||
'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "D",
|
||||
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "s",
|
||||
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$': "s", 'STN': "s"}
|
||||
|
||||
AUC = pd.read_csv(os.path.join('./Figures/Data/sim_mut_AUC.csv'), index_col='Unnamed: 0')
|
||||
rheo = pd.read_csv(os.path.join('./Figures/Data/sim_mut_rheo.csv'), index_col='Unnamed: 0')
|
||||
|
||||
mod = models.index(model)
|
||||
mut_names = AUC.index
|
||||
ax.plot(rheo.loc[mut_names, model_names[mod]]*1000, AUC.loc[mut_names, model_names[mod]]*100, linestyle='',
|
||||
markeredgecolor='grey', markerfacecolor='grey', marker=Marker_dict[model_display_names[mod]],
|
||||
markersize=2)
|
||||
|
||||
ax.plot(rheo.loc['wt', model_names[mod]], AUC.loc['wt', model_names[mod]]*100, 'sk')
|
||||
|
||||
mut_col = sns.color_palette("pastel")
|
||||
ax.plot(rheo.loc['V174F', model_names[mod]]*1000, AUC.loc['V174F', model_names[mod]]*100, linestyle='',
|
||||
markeredgecolor=mut_col[0], markerfacecolor=mut_col[0], marker=Marker_dict[model_display_names[mod]],markersize=4)
|
||||
ax.plot(rheo.loc['F414C', model_names[mod]]*1000, AUC.loc['F414C', model_names[mod]]*100, linestyle='',
|
||||
markeredgecolor=mut_col[1], markerfacecolor=mut_col[1], marker=Marker_dict[model_display_names[mod]],markersize=4)
|
||||
ax.plot(rheo.loc['E283K', model_names[mod]]*1000, AUC.loc['E283K', model_names[mod]]*100, linestyle='',
|
||||
markeredgecolor=mut_col[2], markerfacecolor=mut_col[2], marker=Marker_dict[model_display_names[mod]],markersize=4)
|
||||
ax.plot(rheo.loc['V404I', model_names[mod]]*1000, AUC.loc['V404I', model_names[mod]]*100, linestyle='',
|
||||
markeredgecolor=mut_col[3], markerfacecolor=mut_col[5], marker=Marker_dict[model_display_names[mod]],markersize=4)
|
||||
ax.set_title(model_letter_names[mod], pad=14)
|
||||
ax.set_xlabel('$\Delta$Rheobase [pA]')
|
||||
ax.set_ylabel('Normalized $\Delta$AUC (%)')
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['top'].set_visible(False)
|
||||
# ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0),useMathText=True)
|
||||
|
||||
xmin, xmax = ax.get_xlim()
|
||||
ymin, ymax = ax.get_ylim()
|
||||
ax.hlines(0, xmin, xmax, colors='lightgrey', linestyles='--')
|
||||
ax.vlines(0, ymin,ymax, colors='lightgrey', linestyles='--')
|
||||
return ax
|
||||
|
||||
def mutation_legend(ax, marker_s_leg, pos, ncol):
|
||||
colors = sns.color_palette("pastel")
|
||||
|
||||
Markers = ["o", "o", "o", "o"]
|
||||
V174F = mlines.Line2D([], [], color=colors[0], marker=Markers[0], markersize=marker_s_leg, linestyle='None',
|
||||
label='V174F')
|
||||
F414C = mlines.Line2D([], [], color=colors[1], marker=Markers[1], markersize=marker_s_leg, linestyle='None',
|
||||
label='F414C')
|
||||
E283K = mlines.Line2D([], [], color=colors[2], marker=Markers[2], markersize=marker_s_leg, linestyle='None', label='E283K')
|
||||
V404I = mlines.Line2D([], [], color=colors[5], marker=Markers[3], markersize=marker_s_leg, linestyle='None',
|
||||
label='V404I')
|
||||
WT = mlines.Line2D([], [], color='k', marker='s', markersize=marker_s_leg+2, linestyle='None', label='Wild type')
|
||||
|
||||
ax.legend(handles=[WT, V174F, F414C, E283K, V404I], loc='center', bbox_to_anchor=pos, ncol=ncol, frameon=False)
|
||||
|
||||
|
||||
|
||||
sim_style()
|
||||
# plot setup
|
||||
fig = plt.figure()
|
||||
gs0 = fig.add_gridspec(1, 6, wspace=-0.2)
|
||||
gsl = gs0[0:3].subgridspec(3, 3, wspace=0.9, hspace=0.8)
|
||||
gsr = gs0[4:6].subgridspec(7, 1, wspace=0.6, hspace=0.8)
|
||||
|
||||
ax00 = fig.add_subplot(gsl[0,0])
|
||||
ax01 = fig.add_subplot(gsl[0,1])
|
||||
ax02 = fig.add_subplot(gsl[0,2])
|
||||
ax10 = fig.add_subplot(gsl[1,0])
|
||||
ax11 = fig.add_subplot(gsl[1,1])
|
||||
ax12 = fig.add_subplot(gsl[1,2])
|
||||
ax20 = fig.add_subplot(gsl[2,0])
|
||||
ax21 = fig.add_subplot(gsl[2,1])
|
||||
ax22 = fig.add_subplot(gsl[2,2])
|
||||
|
||||
axr0 = fig.add_subplot(gsr[0:3,0])
|
||||
axr1 = fig.add_subplot(gsr[4:,0])
|
||||
|
||||
# plot mutations in each model
|
||||
ax00 = mutation_plot(ax00, model='RS_pyramidal')
|
||||
ax01 = mutation_plot(ax01, model='RS_inhib')
|
||||
ax02 = mutation_plot(ax02, model='FS')
|
||||
ax10 = mutation_plot(ax10, model='Cb_stellate')
|
||||
ax11 = mutation_plot(ax11, model='Cb_stellate_Kv')
|
||||
ax12 = mutation_plot(ax12, model='Cb_stellate_Kv_only')
|
||||
ax20 = mutation_plot(ax20, model='STN')
|
||||
ax21 = mutation_plot(ax21, model='STN_Kv')
|
||||
ax22 = mutation_plot(ax22, model='STN_Kv_only')
|
||||
|
||||
marker_s_leg = 4
|
||||
pos = (0.425, -0.7)
|
||||
ncol = 5
|
||||
mutation_legend(ax21, marker_s_leg, pos, ncol)
|
||||
|
||||
# plot correlation matrices
|
||||
correlation_plot(axr1,df = 'AUC', title='Normalized $\Delta$AUC', cbar=False)
|
||||
correlation_plot(axr0,df = 'rheo', title='$\Delta$Rheobase', cbar=True)
|
||||
|
||||
# add subplot labels
|
||||
axs = [ax00, ax01,ax02, ax10, ax11, ax12, ax20, ax21, ax22]
|
||||
j=0
|
||||
for i in range(0,9):
|
||||
# axs[i].text(-0.48, 1.175, string.ascii_uppercase[i], transform=axs[i].transAxes, size=10, weight='bold')
|
||||
axs[i].text(-0.625, 1.25, string.ascii_uppercase[i], transform=axs[i].transAxes, size=10, weight='bold')
|
||||
j +=1
|
||||
axr0.text(-0.77, 1.1, string.ascii_uppercase[j], transform=axr0.transAxes, size=10, weight='bold')
|
||||
axr1.text(-0.77, 1.1, string.ascii_uppercase[j+1], transform=axr1.transAxes, size=10, weight='bold')
|
||||
|
||||
# save
|
||||
fig.set_size_inches(cm2inch(22.2,15))
|
||||
fig.savefig('./Figures/simulation_model_comparison.pdf', dpi=fig.dpi) #eps
|
||||
# fig.savefig('./Figures/simulation_model_comparison.png', dpi=fig.dpi) #eps
|
||||
plt.show()
|
||||
|
||||
Reference in New Issue
Block a user