From 2d77708dcafc0bb395d98d8a0857b6ff6afbcd2c Mon Sep 17 00:00:00 2001
From: Maximilian Schmidt <max.schmidt@fz-juelich.de>
Date: Mon, 11 Jun 2018 09:23:57 +0900
Subject: [PATCH] Formatting in Fig2 script, delete deprecated 1D Data script
---
.../SchueckerSchmidt2017/Fig2_EE_example.py | 23 ++-
.../Fig2_EE_example_1D_data.py | 194 ------------------
.../SchueckerSchmidt2017/Fig2_EE_network.py | 18 +-
3 files changed, 30 insertions(+), 205 deletions(-)
delete mode 100644 figures/SchueckerSchmidt2017/Fig2_EE_example_1D_data.py
diff --git a/figures/SchueckerSchmidt2017/Fig2_EE_example.py b/figures/SchueckerSchmidt2017/Fig2_EE_example.py
index 5d8621d..51703ac 100644
--- a/figures/SchueckerSchmidt2017/Fig2_EE_example.py
+++ b/figures/SchueckerSchmidt2017/Fig2_EE_example.py
@@ -6,8 +6,6 @@ import pyx
from matplotlib import gridspec
from matplotlib.colors import ListedColormap
from plotcolors import myred, myblue
-import pyx
-import os
from Fig2_EE_network import network1D, network2D
"""
@@ -52,8 +50,6 @@ pl.rcParams['ytick.labelsize'] = scale * 8
pl.rcParams['xtick.labelsize'] = scale * 8
# use latex to generate the labels in plots
-# not needed anymore in newer versions
-# using this, font detection fails on adobe illustrator 2010-07-20
pl.rcParams['text.usetex'] = True
pl.rcParams['text.latex.preamble'] = [r"\usepackage{amsmath}"]
@@ -66,7 +62,6 @@ nrows = 2.
ncols = 2.
width = 4.61 # inches for 1.5 JoN columns
height = 5.67
-print(width, height)
pl.rcParams['figure.figsize'] = (width, height)
fig = pl.figure()
@@ -124,6 +119,8 @@ for label in ['A', 'B', 'C', 'D', 'E', 'F', 'G']:
"""
1D network
"""
+
+
ax = axes['A']
ax.yaxis.set_ticks_position('none')
ax.xaxis.set_ticks_position('none')
@@ -146,6 +143,14 @@ ax_inset.tick_params(axis='y', labelsize=4, pad=1)
x = np.arange(0, 150., 1.)
+
+"""
+Panel C (top): Transfer function
+for three cases:
+- normal network
+- noiseless neurons
+- neurons without refractory period
+"""
ax = axes['C']
ax.xaxis.set_ticks_position('none')
ax.spines['bottom'].set_color('none')
@@ -189,6 +194,10 @@ ax.set_xlim([-3, 70])
ax.set_ylim([-3, 70])
+"""
+Panel C (bottom): Transfer function
+for three levels of external stimulation.
+"""
ax = axes['C2']
colors = ['k', '0.3', '0.7']
markers = ['d', '+', '.']
@@ -413,7 +422,7 @@ y0 = 0.
x1 = 0.015
y1 = 0.015
-# vector fiels
+# vector fields
netp = network_params_base
net = network2D(netp)
range_vector = np.arange(0., 51., 10.)
@@ -440,7 +449,7 @@ nullcline_x0 = net.nullclines_x0(x0_vec)
ax.plot(nullcline_x0, x0_vec, '--', color='black', label='x0')
ax.plot(x0_vec, nullcline_x0, '--', color='black', label='x0')
-# set limes
+# set plot limits
axG.set_xlim((x0 * 1e2, x1 * 1e2))
axG.set_ylim((y0 * 1e2, y1 * 1e2))
ax.set_xlim([-5, 50])
diff --git a/figures/SchueckerSchmidt2017/Fig2_EE_example_1D_data.py b/figures/SchueckerSchmidt2017/Fig2_EE_example_1D_data.py
deleted file mode 100644
index 548f6fb..0000000
--- a/figures/SchueckerSchmidt2017/Fig2_EE_example_1D_data.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import os
-import pylab as pl
-import numpy as np
-from plotcolors import myred, myblue
-from matplotlib.colors import ListedColormap
-from multiarea_model.theory import Theory
-from multiarea_model.theory_helpers import nu0_fb
-from multiarea_model.default_params import single_neuron_dict
-from multiarea_model.multiarea_helpers import convert_syn_weight
-
-
-class network:
- def __init__(self, params, theory_spec):
- self.label = '1D'
- self.params = {'input_params': params['input_params'],
- 'neuron_params': {'single_neuron_dict': single_neuron_dict},
- 'connection_params': {'replace_cc': None,
- 'replace_cc_input_source': None}
- }
-
- self.add_DC_drive = np.zeros(1)
- self.structure = {'A': {'E'}}
- self.structure_vec = ['A-E']
- self.area_list = ['A']
- self.K_matrix = np.array([[params['K'], params['K']]])
- self.W_matrix = np.array([[params['W'], params['W']]])
- self.J_matrix = convert_syn_weight(self.W_matrix,
- self.params['neuron_params']['single_neuron_dict'])
- self.theory = Theory(self, theory_spec)
-
-
- def Phi(self, rate):
- mu, sigma = self.theory.mu_sigma(rate)
-# print(mu, sigma)
- NP = self.params['neuron_params']['single_neuron_dict']
- return list(map(lambda mu, sigma: nu0_fb(mu, sigma,
- 1.e-3*NP['tau_m'],
- 1.e-3*NP['tau_syn_ex'],
- 1.e-3*NP['t_ref'],
- NP['V_th'] - NP['E_L'],
- NP['V_reset'] - NP['E_L']),
- mu, sigma))
-
-
-"""
-space showing bifurcation
-"""
-
-rate_exts_array = np.arange(150., 170.1, 1.)
-
-network_params = {'K': 210.,
- 'W': 10.}
-theory_params = {'T': 20.,
- 'dt': 0.01}
-
-
-# for i, rate_ext in enumerate(rate_exts_array):
-# input_params = {'rate_ext': rate_ext}
-# network_params.update({'input_params': input_params})
-
-# net = network(network_params, theory_params)
-# r = net.theory.integrate_siegert()[1][:, -1]
-# print(r)
-
-# x = np.arange(0, 30., 0.02)
-# for i, rate_ext in enumerate([150., 160., 170.]):
-# input_params = {'rate_ext': rate_ext}
-# network_params.update({'input_params': input_params})
-# net = network(network_params, theory_params)
-# y = np.fromiter([net.Phi(xi) for xi in x], dtype=np.float)
-# pl.plot(x, y)
-
-# pl.savefig('Fig2_EE_example_1D_data.eps')
-
-
-fig = pl.figure()
-x = np.arange(0, 70., 1.)
-
-for rate_ext in [150., 160., 170.]:
- input_params = {'rate_ext': rate_ext}
- network_params.update({'input_params': input_params})
- net = network(network_params, theory_params)
- y = np.fromiter([net.Phi(x[i])[0] for i in range(len(x))], dtype=np.float)
- pl.plot(x, y)
-pl.plot(x, x, '--')
-pl.show()
-# for i, dic in enumerate(mfp.par_list(PS)):
-# print(dic)
-# para_dic, label = mfp.hashtag(dic)
-# mf = meanfield_multi(para_dic, label)
-# inits = np.arange(0, 50, 10)
-# Fps = []
-# for init in inits:
-# solution = mf.fsolve([init, init])
-# if solution['eps'] == 'The solution converged.':
-# Fps.append(mf.fsolve([init, init])['rates'][0])
-# Fps = np.unique(np.round(Fps, decimals=2))
-# Fps_array.append(Fps)
-
-# h5.add_to_h5('mf_data.h5', {'EE_example': {
-# 'Fps_array': Fps_array}}, 'a', overwrite_dataset=True)
-
-
-# rate_exts_array = np.arange(150., 170.1, 10.)
-# rate_exts = np.array([[x, x] for x in rate_exts_array])
-# PS = para.ParameterSpace({ # 'g': para.ParameterRange(np.arange(-3.5,-3.0,0.1)),
-# 'g': 1.,
-# 'rate': para.ParameterRange(rate_exts),
-# 'model': 'Brunel',
-# 'gamma': 1.,
-# 'rates_init_int': np.array([80, 80]),
-# 'W': 0.01,
-# 'K': 210.,
-# })
-# print(PS)
-# cmap = pl.get_cmap('Greys_r')
-
-
-# ################## rate instability ###############
-
-# x = np.arange(0, 150, 1.0)
-# x_long = np.arange(0, 10000, 10.0)
-# NUM_COLORS = len(mfp.par_list(PS))
-
-
-# for i, dic in enumerate(mfp.par_list(PS)):
-# para_dic, label = mfp.hashtag(dic)
-# mf = meanfield_multi(para_dic, label)
-# dic_refrac = copy.deepcopy(dic)
-# dic_refrac.update({'tau_refrac': 0})
-# para_dic_refrac, label_refrac = mfp.hashtag(dic_refrac)
-# mf_refrac = meanfield_multi(para_dic_refrac, label_refrac)
-# t = [mf.Phi(np.array([xval, xval]), return_leak=False) for xval in x]
-# t_noisefree = [mf.Phi_noisefree(np.array([xval, xval])) for xval in x]
-# t_refrac = [mf_refrac.Phi(np.array([xval, xval]),
-# return_leak=False) for xval in x]
-
-# t_long = [mf.Phi(np.array([xval, xval]), return_leak=False)
-# for xval in x_long]
-# h5.add_to_h5('mf_data.h5', {'EE_example': {label: {'t': t, 't_noisefree': t_noisefree,
-# 't_long': t_long, 't_refrac': t_refrac}}}, 'a', overwrite_dataset=True)
-
-# ########################## Stabilization ################
-
-# x = np.arange(0., 50., 0.1)
-# rate = 160.
-# start = 17.0
-# drate = 1.
-
-# dic = { # 'g': para.ParameterRange(np.arange(-3.5,-3.0,0.1)),
-# 'g': 1.,
-# 'rate': np.array([rate, rate]),
-# 'model': 'Brunel',
-# 'gamma': 1.,
-# 'W': 0.01,
-# 'K': 210.,
-# }
-
-# ######### base state ##########
-
-# para_dic, mf_label = mfp.hashtag(dic)
-# mf = meanfield_multi(para_dic, mf_label)
-# res_la_base = mf.fsolve(np.ones(2) * start)
-# res_in_base = mf.fsolve(np.ones(2) * start)
-# print('res', res_la_base)
-# t = [mf.Phi(np.array([xval, xval])) for xval in x]
-# savedic = {'res_la_base': res_la_base, 'res_in_base': res_in_base, 't': t}
-# h5.add_to_h5('mf_data.h5', {'EE_example': {
-# mf_label: savedic}}, 'a', overwrite_dataset=True)
-
-# ######## increased rate #######
-
-# dic.update({'rate': np.array([rate + drate, rate + drate])})
-# para_dic, mf_label = mfp.hashtag(dic)
-# mf_2 = meanfield_multi(para_dic, mf_label)
-# res_in_2 = mf_2.fsolve(np.ones(2) * start)
-# t_2 = [mf_2.Phi(np.array([xval, xval])) for xval in x]
-# savedic = {'res_in_2': res_in_2, 't_2': t_2}
-# h5.add_to_h5('mf_data.h5', {'EE_example': {
-# mf_label: savedic}}, 'a', overwrite_dataset=True)
-
-# ####### stabilized #######
-
-# matrix_prime, v, shift, delta = mft.stabilize(
-# mf, mf_2, fixed_point=res_la_base['rates'][0], method='least_squares')
-# print('1D K_prime', matrix_prime)
-# dic.update({'K_stable': matrix_prime})
-# para_dic, mf_label = mfp.hashtag(dic)
-# mf_s = meanfield_multi(para_dic, mf_label)
-# res_in_s = mf_s.fsolve(np.ones(2) * start)
-# t_s = [mf_s.Phi(np.array([xval, xval])) for xval in x]
-# savedic = {'res_in_s': res_in_s, 't_s': t_s}
-# h5.add_to_h5('mf_data.h5', {'EE_example': {
-# mf_label: savedic}}, 'a', overwrite_dataset=True)
diff --git a/figures/SchueckerSchmidt2017/Fig2_EE_network.py b/figures/SchueckerSchmidt2017/Fig2_EE_network.py
index de516c6..a0c3b4d 100644
--- a/figures/SchueckerSchmidt2017/Fig2_EE_network.py
+++ b/figures/SchueckerSchmidt2017/Fig2_EE_network.py
@@ -1,15 +1,18 @@
-import os
-import pylab as pl
import numpy as np
+
from scipy import optimize
-from plotcolors import myred, myblue
-from matplotlib.colors import ListedColormap
from multiarea_model.theory import Theory
from multiarea_model.theory_helpers import nu0_fb
from multiarea_model.default_params import single_neuron_dict, nested_update
from multiarea_model.multiarea_helpers import convert_syn_weight
from copy import copy
+"""
+Network class for the 1D case:
+1 excitatory population with recurrent connectivity and external
+stimulation.
+"""
+
class network1D:
def __init__(self, params):
@@ -66,6 +69,13 @@ class network1D:
return result_dic
+"""
+Network class for the 2D case:
+2 excitatory populations with recurrent connectivity and external
+stimulation.
+"""
+
+
class network2D:
def __init__(self, params):
self.label = '2D'
--
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