Merge branch 'develop'

Author: dbirman

.gitignore

@@ -58,10 +58,12 @@ sysinfo.txt
 # Crashlytics generated file
 crashlytics-build.properties
 
-Assets/Resources_moved/AllenCCF/*.obj
-Assets/Resources_moved/AllenCCF/*.obj.meta
+# AddressableAssets
+Assets/AddressableAssets/3D/*
+Assets/AddressableAssets/IBL/*
+Assets/AddressableAssets/Textures/*
+
 ServerData/*
-Assets/AddressableAssets/*
 Assets/Best HTTP/*
 
 
@@ -168,3 +170,4 @@ Assets/Unisave/Resources/UnisavePreferencesFile.asset
 Assets/Unisave/Resources/UnisavePreferencesFile.asset.meta
 
 Assets/Resources.meta
+**/HelloSGLX-win/*

Assets/AddressableAssets/3D.meta

@@ -0,0 +1,17 @@
+To define a new probe electrode layout, add a new CSV file.
+
+Imagine you are looking down onto the surface of the probe with the probe shanks pointing down. Start counting electrodes from the bottom-left shank. Go by shank (L->R), then row (B->T), then column (L->R). So elecrtodes 0,1 are the first row, 2,3 the next row, etc.
+
+Provide an x (right), y (up), and z (depth, usually 0) coordinate for the bottom left corner as well as the width, height (going up), and depth of each electrode.
+
+You can confirm that electrodes are correctly placed by turning on the Graphics setting for "Electrodes in scene" (off by default in WebGL). 
+
+
+
+To define a new probe selection layer, add a new TXT file. 
+
+The file should have a single line with a 0 or 1 for every electrode channel, indicating whether it is included in the selection. For Neuropixels probes the selection array should sum to 384.
+
+A script is included to convert IMRO files to selection layers.
+
+We include some sensible defaults for NP1/21/24 probes.

Assets/AddressableAssets/IBL.meta

@@ -0,0 +1,98 @@
+import pandas as pd
+import numpy as np
+
+n_electrodes = 960
+n_cols = 2
+
+columns = ['electrode','x','y','z','w','h','d','default','all','bank0','double_length']
+# neuropixels 1.0 1-shank
+electrodes = pd.DataFrame(np.zeros((n_electrodes, len(columns))), columns=columns)
+
+# assume for now that the tip length is 200 um (they say 175, but it looks more like 200)
+tip_length = 200
+
+pitch_row = 20
+pitch_col = 32
+
+# the first column starts to the left of the tip (since the tip is chisel-shaped)
+# I'm not sure if it's right, but the probes appear to be 60 um wide in the electrode region, we'll center at 30
+
+# odd rows start at 12um + 2um left of center
+# even rows start at 16um + 12 um left of center
+col_start = [-(12 + 2), -(16 + 12 +2)]
+
+width = 12
+height = 12
+depth = 24
+
+# only one shank
+count = 0
+
+# row
+for r in np.arange(n_electrodes/n_cols):
+    y_pos = tip_length + r * pitch_row
+    # column
+    for c in np.arange(n_cols):
+        x_pos = col_start[int(np.mod(r, 2))] + c * pitch_col
+
+        # also compute the selection layers
+        bank0 = count < 384
+        bank0odd_bank1even = 1 if (c==1 and count < 384) or (c==0 and count >= 384 and count < 768) else 0
+
+        # set a default selection layer and an all layer
+        default = bank0
+        all = True
+
+        electrodes.iloc[count] = [count, x_pos, y_pos, 0, width, height, depth, default, all, bank0, bank0odd_bank1even]
+        count += 1
+
+electrodes.to_csv('./neuropixels_1.csv', index=False)
+
+
+# neuropixels 2.0 4-shank // we're just going to generate the channel map for a single shank
+n_electrodes = 1280
+n_cols = 2
+
+columns = ['electrode','x','y','z','w','h','d','default','all','bank0','double_length','bank1','bank2','bank3','bank4']
+electrodes = pd.DataFrame(np.zeros((n_electrodes, len(columns))), columns=columns)
+
+tip_length = 200
+
+pitch_col = 32
+pitch_row = 15
+
+# the first row starts *above* the tip (since the tip is chisel-shaped)
+col_start = -30
+
+width = 12
+height = 12
+depth = 24
+
+count = 0
+
+# # shank
+# for s in np.arange(n_shanks):
+
+# row
+for r in np.arange(n_electrodes/n_cols):
+    y_pos = tip_length + r * pitch_row
+    # column
+    for c in np.arange(n_cols):
+        x_pos = col_start + c * pitch_col
+
+        # compute selection layers
+        bank0 = count < 96
+        double_length = 1 if (c==1 and count < 96) or (c==0 and count >= 96 and count < (96*2)) else 0
+        bank1 = count >= 96 and count < (96*2)
+        bank2 = count >= (96*2) and count < (96*3)
+        bank3 = count >= (96*3) and count < (96*4)
+        bank4 = count >= (96*4) and count < (96*5)
+
+        default = bank0
+        all = True
+
+        electrodes.iloc[count] = [count, x_pos, y_pos, 0, width, height, depth, default, all,
+                                  bank0, double_length, bank1, bank2, bank3, bank4]
+        count += 1
+
+electrodes.to_csv('./neuropixels_2.csv', index=False)

Assets/AddressableAssets/Probes.meta

@@ -0,0 +1,83 @@
+import pandas as pd
+import numpy as np
+
+# 128K
+
+# electrodes are in a l/c/r three column set, l/r are identical
+
+n_elec_lr = 10
+lr_start_y = 50
+lr_pitch_x = 25
+
+n_elec_c = 12
+c_start_y = 15
+c_start_y_high = 15+10+15+10+15
+
+pitch_y = 15+10
+
+
+columns = ['electrode','x','y','z','w','h','d','default','all']
+electrodes = pd.DataFrame(np.zeros((n_elec_lr*2 + n_elec_c, len(columns))), columns=columns)
+
+# electrode # ordering is a bit weird, it's 0/1 on center, then 2l 3r 4c, etc
+electrodes.iloc[0] = [0, -5, c_start_y, 0, 10, 10, 23, 1, 1]
+electrodes.iloc[1] = [1, -5, c_start_y+pitch_y, 0, 10, 10, 23, 1, 1]
+
+for ei in np.arange(0,30):
+    type = np.mod(ei,3)
+    row = np.floor(ei / 3)
+    
+    if type == 0:
+        # left
+        electrodes.iloc[ei+2] = [ei+2, -22.5, lr_start_y + row * pitch_y, 0, 10, 10, 23, 1, 1]
+
+    elif type == 1:
+        # right
+        electrodes.iloc[ei+2] = [ei+2, 12.5, lr_start_y + row * pitch_y, 0, 10, 10, 23, 1, 1]
+
+    elif type == 2:
+        # center
+        electrodes.iloc[ei+2] = [ei+2, -5, c_start_y_high + row * pitch_y, 0, 10, 10, 23, 1, 1]
+
+electrodes.to_csv('./ucla_128k.csv', index=False)
+
+
+# 256F
+
+# electrodes are in a l/c/r three column set, l/r are identical
+
+n_elec_lr = 42
+lr_start_y = 75
+lr_pitch_x = 20
+
+n_elec_c = 44
+c_start_y = 25
+c_start_y_high = 55
+
+pitch_y = 50
+
+
+columns = ['electrode','x','y','z','w','h','d','default','all']
+electrodes = pd.DataFrame(np.zeros((n_elec_lr*2 + n_elec_c, len(columns))), columns=columns)
+
+# electrode # ordering is a bit weird, it's 0/1 on center, then 2l 3r 4c, etc
+electrodes.iloc[0] = [0, -5, 30, 0, 10, 10, 23, 1, 1]
+electrodes.iloc[1] = [1, -5, 45, 0, 10, 10, 23, 1, 1]
+
+for ei in np.arange(0,126):
+    type = np.mod(ei,3)
+    row = np.floor(ei / 3)
+    
+    if type == 0:
+        # center
+        electrodes.iloc[ei+2] = [ei+2, -5, c_start_y_high + row * pitch_y, 0, 10, 10, 23, 1, 1]
+
+    elif type == 1:
+        # left
+        electrodes.iloc[ei+2] = [ei+2, -20, lr_start_y + row * pitch_y, 0, 10, 10, 23, 1, 1]
+
+    elif type == 2:
+        # right
+        electrodes.iloc[ei+2] = [ei+2, 10, lr_start_y + row * pitch_y, 0, 10, 10, 23, 1, 1]
+
+electrodes.to_csv('./ucla_256f.csv', index=False)