def create_128channels_imec_prb(filename=None, bad_channels=None):
r1 = np.array([103, 101, 99, 97, 95, 93, 91, 89, 87, 70, 66, 84, 82, 80, 108, 110, 47, 45, 43, 41, 1,61, 57,
36, 34, 32, 30, 28, 26, 24, 22, 20])
r2 = np.array([106, 104, 115, 117, 119, 121, 123, 125, 127, 71, 67, 74, 76, 78, 114, 112,
49, 51, 53, 55, 2, 62, 58, 4, 6, 8, 10, 12, 14, 21, 19, 16])
r3 = np.array([102, 100, 98, 96, 94, 92, 90, 88, 86, 72, 68, 65, 85, 83, 81, 111, 46, 44, 42, 40, 38, 63, 59,
39, 37, 35, 33, 31, 29, 27, 25, 23])
r4 = np.array([109, 107, 105, 116, 118, 120, 122, 124, 126, 73, 69, 64, 75, 77, 79, 113,
48, 50, 52, 54, 56, 0, 60, 3, 5, 7, 9, 11, 13, 15, 18,-1])
all_electrodes_concat = np.concatenate((r1, r2, r3, r4))
all_electrodes = all_electrodes_concat.reshape((4, 32))
all_electrodes = np.flipud(all_electrodes.T)
if filename is not None:
prb_gen.generate_prb_file(filename=filename, all_electrodes_array=all_electrodes, channel_number=128)
electrode_coordinate_grid = list(itertools.product(np.arange(1, 32), np.arange(1, 5)))
electrode_coordinate_grid = [tuple(reversed(x)) for x in electrode_coordinate_grid]
electrode_amplifier_index_on_grid = all_electrodes_concat
electrode_amplifier_name_on_grid = np.array(["Int" + str(x) for x in electrode_amplifier_index_on_grid])
indices_arrays = [electrode_amplifier_index_on_grid.tolist(), electrode_amplifier_name_on_grid.tolist()]
indices_tuples = list(zip(*indices_arrays))
channel_position_indices = pd.MultiIndex.from_tuples(indices_tuples, names=['Numbers', 'Strings'])
channel_positions = pd.Series(electrode_coordinate_grid, index=channel_position_indices)
channel_positions.columns = ['Positions']
channel_positions = channel_positions.reset_index(level=None, drop=False, name='Positions')
if bad_channels is not None:
channel_positions = channel_positions[~channel_positions.Numbers.isin(bad_channels)]
return all_electrodes, channel_positions
def create_all_electrodes(filename=None, bad_channels=None):
r1 = np.array([
103, 101, 99, 97, 95, 93, 91, 89, 87, 70, 66, 84, 82, 80, 108, 110, 47,
45, 43, 41, 1, 61, 57, 36, 34, 32, 30, 28, 26, 24, 22, 20
])
r2 = np.array([
106, 104, 115, 117, 119, 121, 123, 125, 127, 71, 67, 74, 76, 78, 114,
112, 49, 51, 53, 55, 2, 62, 58, 4, 6, 8, 10, 12, 14, 21, 19, 16
])
r3 = np.array([
102, 100, 98, 96, 94, 92, 90, 88, 86, 72, 68, 65, 85, 83, 81, 111, 46,
44, 42, 40, 38, 63, 59, 39, 37, 35, 33, 31, 29, 27, 25, 23
])
r4 = np.array([
109, 107, 105, 116, 118, 120, 122, 124, 126, 73, 69, 64, 75, 77, 79,
113, 48, 50, 52, 54, 56, 0, 60, 3, 5, 7, 9, 11, 13, 15, 18, -1
])
all_electrodes_concat = np.concatenate((r1, r2, r3, r4))
all_electrodes = all_electrodes_concat.reshape((4, 32))
all_electrodes = np.flipud(all_electrodes.T)
if filename is not None:
prb_gen.generate_prb_file(filename=filename,
all_electrodes_array=all_electrodes,
channel_number=128)
return all_electrodes
def create_32channels_nn_prb(filename=None, bad_channels=None):
"""
This function produces a grid with the electrodes positions of
Neuronexus A1x32-Poly3-5mm-25s-177-CM32 silicone probe
Parameters
----------
filename -- the filename (with path) of the .prb file for klustakwik
bad_channels -- a list or numpy array with channels you may want to disregard
Returns
-------
all_electrodes --
channel_positions -- a Pandas Series with the electrodes positions (in
two dimensions)
"""
electrode_amplifier_index_on_grid = np.array([-1, 0, -1,
-1, -1, -1,
-1, 31, -1,
24, -1, 7,
-1, 1, -1,
21, -1, 10,
-1, 30, -1,
25, -1, 6,
-1, 15, -1,
20, -1, 11,
-1, 16, -1,
26, -1, 5,
-1, 14, -1,
19, -1, 12,
-1, 17, -1,
27, -1, 4,
-1, 8, -1,
18, -1, 13,
-1, 23, -1,
28, -1, 3,
-1, 9, -1,
29, -1, 2,
-1, 22, -1])
all_electrodes = electrode_amplifier_index_on_grid.reshape((23, 3))
if filename is not None:
prb_gen.generate_prb_file(filename=filename, all_electrodes_array=all_electrodes, channel_number=32,
steps_r=3, steps_c=3)
electrode_coordinate_grid = list(itertools.product(np.arange(1, 24), np.arange(1, 4)))
electrode_coordinate_grid = [tuple(reversed(x)) for x in electrode_coordinate_grid]
electrode_amplifier_name_on_grid = np.array(["Int" + str(x) for x in electrode_amplifier_index_on_grid])
indices_arrays = [electrode_amplifier_index_on_grid.tolist(), electrode_amplifier_name_on_grid.tolist()]
indices_tuples = list(zip(*indices_arrays))
channel_position_indices = pd.MultiIndex.from_tuples(indices_tuples, names=['Numbers', 'Strings'])
channel_positions = pd.Series(electrode_coordinate_grid, index=channel_position_indices)
channel_positions.columns = ['Positions']
channel_positions = channel_positions.reset_index(level=None, drop=False, name='Positions')
if bad_channels is not None:
channel_positions = channel_positions[~channel_positions.Numbers.isin(bad_channels)]
return all_electrodes, channel_positions
def create_128channels_imec_prb(filename=None, bad_channels=None):
r1 = np.array([
103, 101, 99, 97, 95, 93, 91, 89, 87, 70, 66, 84, 82, 80, 108, 110, 47,
45, 43, 41, 1, 61, 57, 36, 34, 32, 30, 28, 26, 24, 22, 20
])
r2 = np.array([
106, 104, 115, 117, 119, 121, 123, 125, 127, 71, 67, 74, 76, 78, 114,
112, 49, 51, 53, 55, 2, 62, 58, 4, 6, 8, 10, 12, 14, 21, 19, 16
])
r3 = np.array([
102, 100, 98, 96, 94, 92, 90, 88, 86, 72, 68, 65, 85, 83, 81, 111, 46,
44, 42, 40, 38, 63, 59, 39, 37, 35, 33, 31, 29, 27, 25, 23
])
r4 = np.array([
109, 107, 105, 116, 118, 120, 122, 124, 126, 73, 69, 64, 75, 77, 79,
113, 48, 50, 52, 54, 56, 0, 60, 3, 5, 7, 9, 11, 13, 15, 18, -1
])
all_electrodes_concat = np.concatenate((r1, r2, r3, r4))
all_electrodes = all_electrodes_concat.reshape((4, 32))
all_electrodes = np.flipud(all_electrodes.T)
if filename is not None:
prb_gen.generate_prb_file(filename=filename,
all_electrodes_array=all_electrodes,
channel_number=128)
electrode_coordinate_grid = list(
itertools.product(np.arange(1, 32), np.arange(1, 5)))
electrode_coordinate_grid = [
tuple(reversed(x)) for x in electrode_coordinate_grid
]
electrode_amplifier_index_on_grid = all_electrodes_concat
electrode_amplifier_name_on_grid = np.array(
["Int" + str(x) for x in electrode_amplifier_index_on_grid])
indices_arrays = [
electrode_amplifier_index_on_grid.tolist(),
electrode_amplifier_name_on_grid.tolist()
]
indices_tuples = list(zip(*indices_arrays))
channel_position_indices = pd.MultiIndex.from_tuples(
indices_tuples, names=['Numbers', 'Strings'])
channel_positions = pd.Series(electrode_coordinate_grid,
index=channel_position_indices)
channel_positions.columns = ['Positions']
channel_positions = channel_positions.reset_index(level=None,
drop=False,
name='Positions')
if bad_channels is not None:
channel_positions = channel_positions[~channel_positions.Numbers.
isin(bad_channels)]
return all_electrodes, channel_positions
def create_1440channels_neuroseeker_prb(base_info_folder,
connected_channels_filename,
prb_filename=None):
x_coords = np.squeeze(
np.load(join(base_info_folder,
'neuroseeker_channel_coordinates_x.npy')))
y_coords = np.squeeze(
np.load(join(base_info_folder,
'neuroseeker_channel_coordinates_y.npy')))
connected = np.squeeze(
np.load(join(base_info_folder, connected_channels_filename)))
bad_channels = np.squeeze(np.argwhere(connected == False).astype(np.int))
r1 = np.array([[i, i + 1] for i in np.arange(0, 1433, 8)])
r1 = r1.reshape(r1.size)
r2 = np.array([[i, i + 1] for i in np.arange(2, 1435, 8)])
r2 = r2.reshape(r1.size)
r3 = np.array([[i, i + 1] for i in np.arange(4, 1437, 8)])
r3 = r3.reshape(r1.size)
r4 = np.array([[i, i + 1] for i in np.arange(6, 1439, 8)])
r4 = r4.reshape(r1.size)
all_electrodes_concat = np.concatenate((r1, r2, r3, r4))
all_electrodes = all_electrodes_concat.reshape((4, 360))
if prb_filename is not None:
prb_gen.generate_prb_file(filename=prb_filename,
all_electrodes_array=all_electrodes)
electrode_coordinate_grid = [i for i in list(zip(x_coords, y_coords))]
electrode_amplifier_index_on_grid = np.arange(
len(electrode_coordinate_grid))
electrode_amplifier_name_on_grid = np.array(
["Int" + str(x) for x in electrode_amplifier_index_on_grid])
indices_arrays = [
electrode_amplifier_index_on_grid.tolist(),
electrode_amplifier_name_on_grid.tolist()
]
indices_tuples = list(zip(*indices_arrays))
channel_position_indices = pd.MultiIndex.from_tuples(
indices_tuples, names=['Numbers', 'Strings'])
channel_positions = pd.Series(electrode_coordinate_grid,
index=channel_position_indices)
channel_positions.columns = ['Positions']
channel_positions = channel_positions.reset_index(level=None,
drop=False,
name='Positions')
channel_positions = channel_positions[~channel_positions.Numbers.
isin(bad_channels)]
return all_electrodes, channel_positions
def create_128channels_imec_prb(filename=None, bad_channels=None):
r1 = np.array([103, 101, 99, 97, 95, 93, 91, 89, 87, 70, 66, 84, 82, 80, 108, 110, 47, 45, 43, 41, 1,61, 57,
36, 34, 32, 30, 28, 26, 24, 22, 20])
r2 = np.array([106, 104, 115, 117, 119, 121, 123, 125, 127, 71, 67, 74, 76, 78, 114, 112,
49, 51, 53, 55, 2, 62, 58, 4, 6, 8, 10, 12, 14, 21, 19, 16])
r3 = np.array([102, 100, 98, 96, 94, 92, 90, 88, 86, 72, 68, 65, 85, 83, 81, 111, 46, 44, 42, 40, 38, 63, 59,
39, 37, 35, 33, 31, 29, 27, 25, 23])
r4 = np.array([109, 107, 105, 116, 118, 120, 122, 124, 126, 73, 69, 64, 75, 77, 79, 113,
48, 50, 52, 54, 56, 0, 60, 3, 5, 7, 9, 11, 13, 15, 18,-1])
all_electrodes_concat = np.concatenate((r1, r2, r3, r4))
all_electrodes = all_electrodes_concat.reshape((4, 32))
all_electrodes = np.flipud(all_electrodes.T)
if filename is not None:
prb_gen.generate_prb_file(filename=filename, all_electrodes_array=all_electrodes, channel_number=128)
return all_electrodes
def create_32channels_nn_prb(filename=None, bad_channels=None):
"""
This function produces a grid with the electrodes positions of
Neuronexus A1x32-Poly3-5mm-25s-177-CM32 silicone probe
Parameters
----------
filename -- the filename (with path) of the .prb file for klustakwik
bad_channels -- a list or numpy array with channels you may want to disregard
Returns
-------
all_electrodes --
channel_positions -- a Pandas Series with the electrodes positions (in
two dimensions)
"""
electrode_amplifier_index_on_grid = np.array([-1, 0, -1,
-1, -1, -1,
-1, 31, -1,
24, -1, 7,
-1, 1, -1,
21, -1, 10,
-1, 30, -1,
25, -1, 6,
-1, 15, -1,
20, -1, 11,
-1, 16, -1,
26, -1, 5,
-1, 14, -1,
19, -1, 12,
-1, 17, -1,
27, -1, 4,
-1, 8, -1,
18, -1, 13,
-1, 23, -1,
28, -1, 3,
-1, 9, -1,
29, -1, 2,
-1, 22, -1])
all_electrodes = electrode_amplifier_index_on_grid.reshape((23, 3))
if filename is not None:
prb_gen.generate_prb_file(filename=filename, all_electrodes_array=all_electrodes, channel_number=32,
steps_r=3, steps_c=3)
electrode_coordinate_grid = list(itertools.product(np.arange(1, 24), np.arange(1, 4)))
electrode_coordinate_grid = [tuple(reversed(x)) for x in electrode_coordinate_grid]
electrode_amplifier_name_on_grid = np.array(["Int" + str(x) for x in electrode_amplifier_index_on_grid])
indices_arrays = [electrode_amplifier_index_on_grid.tolist(), electrode_amplifier_name_on_grid.tolist()]
indices_tuples = list(zip(*indices_arrays))
channel_position_indices = pd.MultiIndex.from_tuples(indices_tuples, names=['Numbers', 'Strings'])
channel_positions = pd.Series(electrode_coordinate_grid, index=channel_position_indices)
channel_positions.columns = ['Positions']
channel_positions = channel_positions.reset_index(level=None, drop=False, name='Positions')
if bad_channels is not None:
channel_positions = channel_positions[~channel_positions.Numbers.isin(bad_channels)]
return all_electrodes, channel_positions
def create_256channels_imec_prb(filename=None,
steps_r=2,
steps_c=2,
bad_channels=None):
r1 = np.array(
[163, 232, 236, 240, 249, 199, 193, 220, 36, 61, 1, 8, 12, 47, 41])
r2 = np.array(
[162, 212, 206, 244, 202, 198, 229, 222, 29, 25, 2, 5, 16, 46, 42])
r3 = np.array(
[217, 211, 207, 245, 251, 255, 228, 225, 35, 60, 55, 9, 17, 20, 95])
r4 = np.array(
[161, 233, 237, 241, 203, 197, 192, 223, 37, 58, 54, 6, 51, 45, 40])
r5 = np.array(
[216, 234, 205, 242, 201, 195, 218, 224, 28, 24, 3, 13, 50, 21, 39])
r6 = np.array(
[215, 210, 204, 246, 252, 231, 227, 32, 63, 59, 53, 14, 18, 22, 94])
r7 = np.array(
[160, 235, 238, 247, 200, 196, 219, 33, 27, 57, 7, 10, 48, 44, 38])
r8 = np.array(
[213, 209, 239, 248, 253, 194, 221, 30, 26, 0, 4, 11, 49, 43, 93])
r9 = np.array([
168, 159, 154, 181, 145, 140, 191, 131, 125, 120, 67, 111, 106, 77, 97
])
r10 = np.array([
172, 175, 153, 182, 185, 139, 167, 164, 90, 119, 114, 71, 105, 100, 81
])
r11 = np.array(
[169, 157, 179, 148, 143, 189, 134, 129, 123, 65, 68, 109, 75, 78, 84])
r12 = np.array([
173, 156, 151, 183, 142, 137, 166, 128, 122, 117, 69, 108, 103, 79, 85
])
r13 = np.array([
170, 176, 152, 147, 186, 138, 133, 127, 89, 118, 113, 72, 104, 99, 82
])
r14 = np.array([
171, 177, 180, 146, 187, 190, 132, 126, 121, 66, 112, 107, 76, 98, 86
])
r15 = np.array(
[174, 155, 150, 184, 141, 136, 165, 91, 88, 116, 70, 73, 102, 80, 83])
r16 = np.array([
158, 178, 149, 144, 188, 135, 130, 124, 64, 115, 110, 74, 101, 96, 87
])
r17 = np.array(
[214, 208, 243, 250, 254, 230, 226, 34, 62, 56, 52, 15, 19, 23, 92])
all_electrodes_concat = np.concatenate(
(r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15, r16,
r17))
all_electrodes = all_electrodes_concat.reshape((17, 15))
#all_electrodes = np.flipud(all_electrodes.T)
electrode_coordinate_grid = list(
itertools.product(np.arange(1, 18), np.arange(1, 16)))
electrode_coordinate_grid = [
tuple(reversed(x)) for x in electrode_coordinate_grid
]
electrode_amplifier_index_on_grid = all_electrodes_concat
electrode_amplifier_name_on_grid = np.array(
["Int" + str(x) for x in electrode_amplifier_index_on_grid])
indices_arrays = [
electrode_amplifier_index_on_grid.tolist(),
electrode_amplifier_name_on_grid.tolist()
]
indices_tuples = list(zip(*indices_arrays))
channel_position_indices = pd.MultiIndex.from_tuples(
indices_tuples, names=['Numbers', 'Strings'])
channel_positions = pd.Series(electrode_coordinate_grid,
index=channel_position_indices)
channel_positions.columns = ['Positions']
channel_positions = channel_positions.reset_index(level=None,
drop=False,
name='Positions')
if bad_channels is not None:
#channel_positions = channel_positions[~channel_positions.Numbers.isin(bad_channels)]
channel_positions[channel_positions.Numbers.isin(bad_channels)] = -1
bad_channels_mask = np.reshape(np.in1d(all_electrodes, bad_channels),
(32, 4))
min_one_val = np.empty_like(bad_channels)
min_one_val.fill(-1)
np.place(all_electrodes, bad_channels_mask, min_one_val)
if filename is not None:
prb_gen.generate_prb_file(filename=filename,
all_electrodes_array=all_electrodes,
steps_r=steps_r,
steps_c=steps_c)
return all_electrodes, channel_positions
all_true = number_of_juxta
precision = tp / all_pos
recall = tp / all_true
f_factor = 2 * (precision * recall) / (precision + recall)
print('Precision = {}, Recall = {}, F1 factor = {}'.format(
precision, recall, f_factor))
# ________________________________________
# ONE OFF FUNCTIONS
# ________________________________________
# Generate prb file ---------------
all_electrodes = np.array([0, 1, 2, 3]).reshape((2, 2))
prb_filename = join(base_folder, 'Analysis', 'KlustaKwik', 'tetrode.prb')
prb_gen.generate_prb_file(filename=prb_filename,
all_electrodes_array=all_electrodes,
steps_r=2,
steps_c=2)
# ---------------------------------
# SPIKEDETECT AND PCA---------------------
# filter all data
high_pass_freq = 500
filtered_data_type = np.float64
temp_unfiltered = extra_data
temp_unfiltered = temp_unfiltered.astype(filtered_data_type)
iir_params = {'order': 4, 'ftype': 'butter', 'padlen': 0}
extra_data_hp = filters.filter_data(temp_unfiltered,
sampling_freq,
l_freq=high_pass_freq,
h_freq=None,
method='iir',