"""
import numpy as np
import matplotlib.pyplot as plt
import texplot
import plotfuncs as plf
from scipy import stats
data = np.load('/home/ycan/Documents/thesis/analysis_auxillary_files/'
'thesis_csiplotting.npz')
cells = data['cells']
csi = data['csi']
include = data['include']
colors = data['colors']
colorcategories = data['colorcategories']
fig = texplot.texfig(.9, .8)
axes = fig.subplots(len(colorcategories), 1, sharex=True)
csichange = csi[1, :] - csi[0, :]
changes = []
groups = []
bins = np.arange(-.2, .35+0.05, 0.05)
for i, color in enumerate(colorcategories):
group = [index for index, c in enumerate(colors) if c == color]
ax = axes[i]
change = csichange[group]
groups.append(group)
changes.append(change)
if i == 0:
csichange_on = np.copy(change)
elif i == 1:
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 14 08:50:54 2018
@author: ycan
"""
import numpy as np
import matplotlib.pyplot as plt
import iofuncs as iof
import plotfuncs as plf
import texplot
import scalebars
texplot.texfig(1.1, 1)
data = np.load('/home/ycan/Documents/thesis/'
'analysis_auxillary_files/thesis_csiplotting.npz')
cells = data['cells']
groups = data['groups']
bias = data['bias']
csi = data['csi']
colorcategories = data['colorcategories']
colorlabels = data['colorlabels']
toplot = [
['20180124', '02001'], # Increasing bias
['20180207', '03503'], # Decreasing bias
]
bias = data['bias']
groups = data['groups']
colorcategories = data['colorcategories']
colorlabels = data['colorlabels']
biaschange = bias[1, ] - bias[0, ]
csichange = csi[1, ] - csi[0, ]
scatterkwargs = {
'linewidths': .7,
# 'alpha':.8,
'edgecolor': 'k'
}
linekwargs = {'color': 'k', 'alpha': .5, 'linestyle': 'dashed', 'linewidth': 1}
fig = texplot.texfig(.85, aspect=1.85)
ax = plt.subplot2grid((4, 3), (0, 0), colspan=3, rowspan=2)
# Create an array for all the colors to use with plt.legend()
patches = []
for color, label in zip(colorcategories, colorlabels):
patches.append(mpatches.Patch(color=color, label=label))
ax.legend(handles=patches, fontsize='xx-small')
for group, color in zip(groups, colorcategories):
ax.scatter(csichange[group], biaschange[group], c=color, **scatterkwargs)
ax.axhline(0, **linekwargs)
ax.axvline(0, **linekwargs)
plf.subplottext('A', ax, x=-0.05)
import os import warnings import numpy as np import matplotlib.pyplot as plt import gaussfitter as gfit import iofuncs as iof import miscfuncs as msc import plotfuncs as plf import analysis_scripts as asc from matplotlib.patches import Rectangle from matplotlib.offsetbox import OffsetImage, AnnotationBbox import scalebars import texplot fig = texplot.texfig(1.2) spikecutoff=1000 ratingcutoff=4 staqualcutoff=0 inner_b=2 outer_b=4 exp_name = '20180207' stim_nr = 11 exp_dir = iof.exp_dir_fixer(exp_name) stim_nr = str(stim_nr) savefolder = 'surroundplots'
x = [np.nanmin(csi), np.nanmax(csi)]
scatterkwargs = {
'c': colors,
'alpha': .8,
'linewidths': .5,
'edgecolor': 'k',
's': 35
}
# Create an array for all the colors to use with plt.legend()
patches = []
for color, label in zip(colorcategories, colorlabels):
patches.append(mpatches.Patch(color=color, label=label))
fig = texplot.texfig(.90, aspect=1.85)
ax = plt.subplot2grid((5, 3), (0, 0), colspan=3, rowspan=3)
ax.plot(x, x, 'k--', alpha=.5, linewidth=.8)
plf.subplottext('A', ax, x=-0.05, y=1.03)
ax.scatter(csi[0, :], csi[1, :], **scatterkwargs)
# Mark the example cells with an asterisk
asterixes = [
(0.03443051, 0.19385925), # Example ON cell 20180207 03001
(0.03238909, 0.29553824)
] # Example OFF cell 20180118 23102
for asterix in asterixes:
ax.text(*asterix, '*', color='k')
ax.legend(handles=patches, fontsize='xx-small')
('20180124', '07401', 'offweird1'),
('20180207', '02001', 'offweird2'),
('20180124', '04001', 'offweird3'),
]
rows = 2
columns = 2
for i, (exp_name, clustertoplot, label) in enumerate(toplot):
if '20180124' in exp_name or '20180207' in exp_name:
stripeflicker = [6, 12]
elif '20180118' in exp_name:
stripeflicker = [7, 14]
fig = texplot.texfig(1, aspect=.8)
axes = [fig.add_subplot(rows, columns, i+1) for i in range(rows*columns)]
for j, stimnr in enumerate(stripeflicker):
exp_dir = iof.exp_dir_fixer(exp_name)
_, metadata = asc.read_spikesheet(exp_dir)
px_size = metadata['pixel_size(um)']
data = iof.load(exp_name, stimnr)
clusters = data['clusters']
stas = data['stas']
max_inds = data['max_inds']
filter_length = data['filter_length']
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 14 08:50:54 2018
@author: ycan
"""
import numpy as np
import matplotlib.pyplot as plt
import iofuncs as iof
import plotfuncs as plf
import texplot
import scalebars
texplot.texfig(1.2, .6)
data = np.load('/home/ycan/Documents/thesis/'
'analysis_auxillary_files/thesis_csiplotting.npz')
cells = data['cells']
groups = data['groups']
bias = data['bias']
csi = data['csi']
colorcategories = data['colorcategories']
toplot = [
['20180124', '02001'], # Increasing bias
['20180207', '03503'], # Decreasing bias
]
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 14 08:50:54 2018
@author: ycan
"""
import numpy as np
import matplotlib.pyplot as plt
import iofuncs as iof
import plotfuncs as plf
import texplot
import scalebars
texplot.texfig(1.2, 1)
data = np.load('/home/ycan/Documents/thesis/'
'analysis_auxillary_files/thesis_csiplotting.npz')
cells = data['cells']
groups = data['groups']
bias = data['bias']
csi = data['csi']
colorcategories = data['colorcategories']
colorlabels = data['colorlabels']
toplot = [
['20180124', '02001'], # Increasing bias
['20180207', '03503'], # Decreasing bias
]
@author: ycan
"""
import numpy as np
import matplotlib.pyplot as plt
import plotfuncs as plf
import texplot
data = np.load('/home/ycan/Documents/thesis/analysis_auxillary_files/'
'thesis_csiplotting.npz')
cells = data['cells']
csi = data['csi']
bias = data['bias']
groups = data['groups']
colorcategories = data['colorcategories']
#plt.figure(figsize=(6,6))
texplot.texfig(.8, 1)
ax3 = plt.subplot(111)
for color, group in zip(colorcategories, groups):
ax3.plot(bias[:, group], color=color, linewidth=.4)
#plt.axis('equal')
ax3.set_xticks([0, 1])
ax3.set_xticklabels(['Mesopic', 'Photopic'])
ax3.set_ylabel('Polarity Index')
plf.spineless(ax3)
texplot.savefig('polarityindexchange')
plt.show()