import matplotlib.pyplot as pl
import numpy as np
import os
import pyx
from helpers import original_data_path
from plotfuncs import create_fig
from matrix_plot import matrix_plot, rate_histogram_plot
from multiarea_model import MultiAreaModel
LOAD_ORIGINAL_DATA = True
scale = 1.
width = 7.0866
n_horz_panels = 3.
n_vert_panels = 3.
panel_factory = create_fig(
1, scale, width, n_horz_panels, n_vert_panels, voffset=0.25, hoffset=0.1, squeeze=0.1)
axes = {}
axes['A'] = panel_factory.new_panel(0, 1, r'A', label_position=-0.25)
axes['A2'] = panel_factory.new_empty_panel(0, 2, r'', label_position=-0.25)
axes['B'] = panel_factory.new_panel(1, 1, r'B', label_position=-0.25)
axes['B2'] = panel_factory.new_empty_panel(1, 2, r'', label_position=-0.25)
axes['C'] = panel_factory.new_panel(2, 1, r'C', label_position=-0.25)
axes['C2'] = panel_factory.new_empty_panel(2, 2, r'', label_position=-0.25)
# Simulation
if LOAD_ORIGINAL_DATA:
data = {}
data_labels = [('533d73357fbe99f6178029e6054b571b485f40f6'),
architecture_array = np.zeros(len(area_list))
log_density_array = np.zeros(len(area_list))
for i, area in enumerate(area_list):
architecture_array[i] = architecture_completed[area]
log_density_array[i] = np.log10(neuron_densities[area]['overall'])
# ################################################################################
scale = 1.0
width = 7.5
n_horz_panels = 3.
n_vert_panels = 1.
panel_factory = create_fig(1,
scale,
width,
n_horz_panels,
n_vert_panels,
hoffset=0.06,
voffset=0.19,
height_sup=.2)
axes = {}
axes['A'] = panel_factory.new_panel(0, 0, r'A', label_position=(-0.2, 1.2))
axes['B'] = panel_factory.new_panel(1, 0, r'B', label_position=(-0.2, 1.2))
axes['C'] = panel_factory.new_panel(2, 0, r'C', label_position=(-0.2, 1.2))
labels = ['A', 'B', 'C']
for label in labels:
axes[label].spines['right'].set_color('none')
axes[label].spines['top'].set_color('none')
axes[label].yaxis.set_ticks_position("left")
axes[label].xaxis.set_ticks_position("bottom")