Get alpha/ctrl modes working again

analysis/actuation_modes/alpha_response.py

@@ -9,35 +9,50 @@
 
 """
 
-from DBSpace.control import proc_dEEG
-import DBSpace as dbo
-from DBSpace.visualizations import EEG_Viz
-from DBSpace.control.TVB_DTI import DTI_support_model, plot_support_model
+#%%
 
+from dbspace.control import offline_segments
+from dbspace.viz.MM import EEG_Viz
+from dbspace.control.DTI_support import DTI_support_model, plot_support_model
 
-import scipy.stats as stats
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
-sns.set_context('paper')
-sns.set(font_scale=3)
-sns.set_style('white')
 
-import pickle
-import cmocean
+sns.set_context("paper")
+sns.set(font_scale=3)
+sns.set_style("white")
 
-pt_list = ['906','907','908']
+pt_list = ["906", "907", "908"]
 ## Basic initialization methods, need to suppress figures from these and clean these up
-eFrame = proc_dEEG.proc_dEEG(pts=pt_list,procsteps='conservative',condits=['OnT','OffT'])
+eFrame = offline_segments.network_action_dEEG(
+    pts=pt_list,
+    procsteps="conservative",
+    condits=["OnT", "OffT"],
+    config_file="../../assets/config/targeting_experiment.json",
+)
 eFrame.standard_pipeline()
 #%% Generate the control modes
-#eFrame.OnT_ctrl_modes(pt='POOL') #THIS HAS BEEN MOVED TO control_modes.py
+# eFrame.OnT_ctrl_modes(pt='POOL') #THIS HAS BEEN MOVED TO control_modes.py
 
 #%% Plot the median response
-eFrame.topo_median_response(do_condits=['OnT','OffT'],pt='POOL',band='Alpha',use_maya=True,scale_w_mad=False,avg_func=np.mean)
-#eFrame.topo_median_variability(do_condits=['OnT','OffT'],pt='POOL',band='Alpha',use_maya=False)
+eFrame.topo_median_response(
+    do_condits=["OnT", "OffT"],
+    pt="POOL",
+    band="Alpha",
+    use_maya=False,
+    scale_w_mad=False,
+    avg_func=np.mean,
+)
+# eFrame.topo_median_variability(do_condits=['OnT','OffT'],pt='POOL',band='Alpha',use_maya=False)
 
 #%% Plot individual patients
 for pp in pt_list:
-    eFrame.topo_median_response(do_condits=['OnT','OffT'],pt=pp,band='Alpha',use_maya=True,scale_w_mad=False)
-#eFrame.topo_median_variability(do_condits=['OnT','OffT'],pt='POOL',band='Alpha',use_maya=False)
+    eFrame.topo_median_response(
+        do_condits=["OnT", "OffT"],
+        pt=pp,
+        band="Alpha",
+        use_maya=False,
+        scale_w_mad=False,
+    )
+# eFrame.topo_median_variability(do_condits=['OnT','OffT'],pt='POOL',band='Alpha',use_maya=False)

analysis/actuation_modes/voltage_response.py

@@ -7,18 +7,12 @@
 Script to load and characterize Voltage-sweep data; Most likely just from 906
 """
 
+import matplotlib.pyplot as plt
+import numpy as np
 from dbspace.control.stream_buffers import streamEEG
 from dbspace.utils.structures import nestdict
-
-import itertools
-from sklearn.metrics import confusion_matrix
-
-import matplotlib.pyplot as plt
-import numpy as np 
-
-import pickle
-
 from dbspace.viz.MM import EEG_Viz
+from sklearn.metrics import confusion_matrix
 
 #%%
 #First, let's bring in the timeseries from DBS906 Voltage Sweep Experiment
@@ -61,6 +55,7 @@
 #Need to go into each channel and figure out which ones "GROW" and which ones don't
 from scipy.stats import linregress
 
+
 def isMonotonic(A): 
 
     return (all(A[i] <= A[i + 1] for i in range(len(A) - 1)) or
@@ -74,4 +69,4 @@ def isMonotonic(A):
     chann_mono[cc] = (voltage_diff > 0).all()
     chann_monoish[cc] = np.sum(voltage_diff > 0) > 4
 
-EEG_Viz.plot_3d_scalp(chann_monoish,unwrap=True)
+EEG_Viz.plot_3d_scalp(chann_monoish,unwrap=True)

scripts/pyvista_test.py

@@ -0,0 +1,6 @@
+from pyvista import examples
+mesh = examples.download_st_helens()
+warped = mesh.warp_by_scalar('Elevation')
+surf = warped.extract_surface().triangulate()
+surf = surf.decimate_pro(0.75)  # reduce the density of the mesh by 75%
+surf.plot(cmap='gist_earth')