Scripts for visuals of brain with BrainRender

brainrender_regions.py

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+""" 
+    This tutorial shows how to create and render a brainrender scene with some brain regions
+"""
+import brainrender
+
+brainrender.SHADER_STYLE = "cartoon"
+from brainrender.scene import Scene
+
+import numpy as np
+import pandas as pd
+import pylab
+import matplotlib.pyplot as plt
+from scipy import stats
+from matplotlib.colors import ListedColormap
+from turbo_colormap import *
+import inspect
+import os
+import csv 
+import time
+import sys
+import glob
+import pandas as pd
+from pprint import pprint
+import scipy.cluster.hierarchy as hierarchy
+#from tvb.simulator.lab import *
+#from tvb.simulator.plot.tools import *
+# Input Simulation Pipeline
+#from SimulationPipeline import *
+#from useful_fns import *
+
+from brainrender.colors import *
+
+# Add the whole thalamus in gray (think they meant red)
+#scene.add_brain_regions(["TH"], alpha=0.15)
+# Alpha is liek transparency. 
+# Add VAL nucleus in wireframe style with the allen color
+#scene.add_brain_regions(["VAL"], use_original_color=True, wireframe=True)
+
+# Import the E and I densities
+df = pd.read_csv(r"C:\Users\Pok Him\Desktop\MouseBrainModelling\CortexDensitiesAlter.csv",delimiter=",")
+E_pop = df.excitatory.values
+I_pop = df.inhibitory.values
+E_mean = np.mean(E_pop)
+I_mean = np.mean(I_pop)
+
+# E_normalised is (when excluding region 7) -0.28 to 0.54
+E_normalised = (E_pop-E_mean)/E_mean
+# I_normalised is (when excluding region 7) -0.45 to 1.44
+I_normalised = (I_pop-I_mean)/I_mean
+
+"""
+Regime = "LCycle"
+G_value = 0.45
+B_e_value = 2.8
+File_start = r"D:\Simulations\2020_09_23\\"  + Regime + "_G[[]" + str(G_value) + "[]]_b_e[[]" + str(B_e_value) + "[]]"
+TseriesFile = glob.glob(File_start+"*Tseries*_.csv")[0]
+
+df = np.genfromtxt(TseriesFile,delimiter="\t")
+bold_time = df[0]
+bold_data = df[1:]
+
+Region0 = bold_data[0]
+print(Region0)
+Value = Region0[0]
+print(Value)
+"""
+
+# https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html
+# Transparency
+alpha = 0.15
+print(len(E_normalised))
+
+# For just normal colouring of brain regions. 
+# Create a scene_e
+scene_e = Scene(
+    #screenshot_kwargs=dict(folder="Docs/Media/clean_screenshots"),
+    title="Brain Regions",
+)
+
+for i in np.arange(len(E_normalised)):
+    acronym = df.acronym[i]
+    scene_e.add_brain_regions([acronym], alpha=0.5,use_original_color=True,wireframe=True)
+
+scene_e.render()
+
+'''
+Peace=colorMap(E_normalised, name=ListedColormap(turbo_colormap_data),vmin=min(E_normalised),vmax=max(E_normalised))
+
+# Create a scene_e
+scene_e = Scene(
+    #screenshot_kwargs=dict(folder="Docs/Media/clean_screenshots"),
+    title="Excitatory Neuron Densities",
+)
+
+for i in np.arange(len(E_normalised)):
+    colour_value = tuple(Peace[i])
+    acronym = df.acronym[i]
+    scene_e.add_brain_regions([acronym], alpha=alpha,color=colour_value)
+
+scene_e.render()
+
+# Create a scene_i
+scene_i = Scene(
+    #screenshot_kwargs=dict(folder="Docs/Media/clean_screenshots"),
+    title="Inhibitory Neuron Densities",
+)
+
+# Transparency
+alpha = 0.15
+print(len(I_normalised))
+Inhib = colorMap(I_normalised, name=ListedColormap(turbo_colormap_data),vmin=min(I_normalised),vmax=max(I_normalised))
+
+for i in np.arange(len(I_normalised)):
+    colour_value = tuple(Inhib[i])
+    acronym = df.acronym[i]
+    scene_i.add_brain_regions([acronym], alpha=alpha,color=colour_value)
+
+
+scene_i.render()
+
+# Create a scene
+scene = Scene(
+    #screenshot_kwargs=dict(folder="Docs/Media/clean_screenshots"),
+    title="Brain",
+)
+scene.render()
+'''

brainrender_tractography.py

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+""" 
+    This tutorial shows how download and rendered afferent mesoscale projection data
+    using the AllenBrainAtlas (ABA) and Scene classes
+"""
+import brainrender
+
+brainrender.SHADER_STYLE = "cartoon"
+from brainrender.scene import Scene
+
+import numpy as np
+import pandas as pd
+import pylab
+import matplotlib.pyplot as plt
+from scipy import stats
+from matplotlib.colors import ListedColormap
+from turbo_colormap import *
+import inspect
+import os
+import csv 
+import time
+import sys
+import glob
+import pandas as pd
+from pprint import pprint
+import scipy.cluster.hierarchy as hierarchy
+#from tvb.simulator.lab import *
+#from tvb.simulator.plot.tools import *
+# Input Simulation Pipeline
+#from SimulationPipeline import *
+#from useful_fns import *
+
+from brainrender.colors import *
+
+# Create a scene
+scene = Scene(title="tractography")
+
+#  Get data - using df just for the acronyms
+df = pd.read_csv(r"C:\Users\Pok Him\Desktop\MouseBrainModelling\CortexDensitiesAlter.csv",delimiter=",")
+
+name = df.acronym
+
+for i in np.arange(len(name)):
+    acronym = name[i]
+
+    # Get the center of mass of the region of interest
+    p0 = scene.atlas.get_region_CenterOfMass(acronym)
+
+    # Get projections to that point
+    tract = scene.atlas.get_projection_tracts_to_target(p0=p0)
+
+    # Add the brain regions and the projections to it
+    #scene.add_brain_regions([acronym], alpha=0.4, use_original_color=True)
+    scene.add_tractography(tract, color_by="target_region")
+
+scene.render()