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Neuralpredictors #15

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Neuralpredictors #15

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7bbcf47
Update imports.
mohammadbashiri Feb 13, 2022
04be8c1
Merge branch 'master' of https://github.com/sinzlab/nnsysident into n…
mohammadbashiri Feb 13, 2022
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96 changes: 76 additions & 20 deletions nnsysident/models/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -119,7 +119,11 @@ def se2d_fullgaussian2d(
in_shapes_dict = {k: v[in_name] for k, v in session_shape_dict.items()}
input_channels = [v[in_name][1] for v in session_shape_dict.values()]

core_input_channels = list(input_channels.values())[0] if isinstance(input_channels, dict) else input_channels[0]
core_input_channels = (
list(input_channels.values())[0]
if isinstance(input_channels, dict)
else input_channels[0]
)

source_grids = None
grid_mean_predictor_type = None
Expand All @@ -133,7 +137,9 @@ def se2d_fullgaussian2d(
# real data
try:
if v.dataset.neurons.animal_ids[0] != 0:
source_grids[k] = v.dataset.neurons.cell_motor_coordinates[:, :input_dim]
source_grids[k] = v.dataset.neurons.cell_motor_coordinates[
:, :input_dim
]
# simulated data -> get random linear non-degenerate transform of true positions
else:
source_grid_true = v.dataset.neurons.center[:, :input_dim]
Expand All @@ -145,18 +151,30 @@ def se2d_fullgaussian2d(
det = np.linalg.det(matrix)
loops += 1
assert det > 5.0, "Did not find a non-degenerate matrix"
source_grids[k] = np.add((matrix @ source_grid_true.T).T, grid_bias)
source_grids[k] = np.add(
(matrix @ source_grid_true.T).T, grid_bias
)
except FileNotFoundError:
print("Dataset type is not recognized to be from Baylor College of Medicine.")
source_grids[k] = v.dataset.neurons.cell_motor_coordinates[:, :input_dim]
print(
"Dataset type is not recognized to be from Baylor College of Medicine."
)
source_grids[k] = v.dataset.neurons.cell_motor_coordinates[
:, :input_dim
]
elif grid_mean_predictor_type == "shared":
pass
else:
raise ValueError("Grid mean predictor type {} not understood.".format(grid_mean_predictor_type))
raise ValueError(
"Grid mean predictor type {} not understood.".format(
grid_mean_predictor_type
)
)

shared_match_ids = None
if share_features or share_grid:
shared_match_ids = {k: v.dataset.neurons.multi_match_id for k, v in dataloaders.items()}
shared_match_ids = {
k: v.dataset.neurons.multi_match_id for k, v in dataloaders.items()
}
all_multi_unit_ids = set(np.hstack(shared_match_ids.values()))

for match_id in shared_match_ids.values():
Expand Down Expand Up @@ -277,7 +295,11 @@ def se2d_pointpooled(
in_shapes_dict = {k: v[in_name] for k, v in session_shape_dict.items()}
input_channels = [v[in_name][1] for v in session_shape_dict.values()]

core_input_channels = list(input_channels.values())[0] if isinstance(input_channels, dict) else input_channels[0]
core_input_channels = (
list(input_channels.values())[0]
if isinstance(input_channels, dict)
else input_channels[0]
)

set_random_seed(seed)

Expand Down Expand Up @@ -376,7 +398,11 @@ def se2d_spatialxfeaturelinear(
in_shapes_dict = {k: v[in_name] for k, v in session_shape_dict.items()}
input_channels = [v[in_name][1] for v in session_shape_dict.values()]

core_input_channels = list(input_channels.values())[0] if isinstance(input_channels, dict) else input_channels[0]
core_input_channels = (
list(input_channels.values())[0]
if isinstance(input_channels, dict)
else input_channels[0]
)

set_random_seed(seed)

Expand Down Expand Up @@ -486,11 +512,17 @@ def se2d_fullSXF(
in_shapes_dict = {k: v[in_name] for k, v in session_shape_dict.items()}
input_channels = [v[in_name][1] for v in session_shape_dict.values()]

core_input_channels = list(input_channels.values())[0] if isinstance(input_channels, dict) else input_channels[0]
core_input_channels = (
list(input_channels.values())[0]
if isinstance(input_channels, dict)
else input_channels[0]
)

shared_match_ids = None
if share_features:
shared_match_ids = {k: v.dataset.neurons.multi_match_id for k, v in dataloaders.items()}
shared_match_ids = {
k: v.dataset.neurons.multi_match_id for k, v in dataloaders.items()
}
all_multi_unit_ids = set(np.hstack(shared_match_ids.values()))

for match_id in shared_match_ids.values():
Expand Down Expand Up @@ -624,7 +656,11 @@ def taskdriven_fullgaussian2d(
in_shapes_dict = {k: v[in_name] for k, v in session_shape_dict.items()}
input_channels = [v[in_name][1] for v in session_shape_dict.values()]

core_input_channels = list(input_channels.values())[0] if isinstance(input_channels, dict) else input_channels[0]
core_input_channels = (
list(input_channels.values())[0]
if isinstance(input_channels, dict)
else input_channels[0]
)

source_grids = None
grid_mean_predictor_type = None
Expand All @@ -638,7 +674,9 @@ def taskdriven_fullgaussian2d(
# real data
try:
if v.dataset.neurons.animal_ids[0] != 0:
source_grids[k] = v.dataset.neurons.cell_motor_coordinates[:, :input_dim]
source_grids[k] = v.dataset.neurons.cell_motor_coordinates[
:, :input_dim
]
# simulated data -> get random linear non-degenerate transform of true positions
else:
source_grid_true = v.dataset.neurons.center[:, :input_dim]
Expand All @@ -650,18 +688,30 @@ def taskdriven_fullgaussian2d(
det = np.linalg.det(matrix)
loops += 1
assert det > 5.0, "Did not find a non-degenerate matrix"
source_grids[k] = np.add((matrix @ source_grid_true.T).T, grid_bias)
source_grids[k] = np.add(
(matrix @ source_grid_true.T).T, grid_bias
)
except FileNotFoundError:
print("Dataset type is not recognized to be from Baylor College of Medicine.")
source_grids[k] = v.dataset.neurons.cell_motor_coordinates[:, :input_dim]
print(
"Dataset type is not recognized to be from Baylor College of Medicine."
)
source_grids[k] = v.dataset.neurons.cell_motor_coordinates[
:, :input_dim
]
elif grid_mean_predictor_type == "shared":
pass
else:
raise ValueError("Grid mean predictor type {} not understood.".format(grid_mean_predictor_type))
raise ValueError(
"Grid mean predictor type {} not understood.".format(
grid_mean_predictor_type
)
)

shared_match_ids = None
if share_features or share_grid:
shared_match_ids = {k: v.dataset.neurons.multi_match_id for k, v in dataloaders.items()}
shared_match_ids = {
k: v.dataset.neurons.multi_match_id for k, v in dataloaders.items()
}
all_multi_unit_ids = set(np.hstack(shared_match_ids.values()))

for match_id in shared_match_ids.values():
Expand Down Expand Up @@ -759,11 +809,17 @@ def taskdriven_fullSXF(
in_shapes_dict = {k: v[in_name] for k, v in session_shape_dict.items()}
input_channels = [v[in_name][1] for v in session_shape_dict.values()]

core_input_channels = list(input_channels.values())[0] if isinstance(input_channels, dict) else input_channels[0]
core_input_channels = (
list(input_channels.values())[0]
if isinstance(input_channels, dict)
else input_channels[0]
)

shared_match_ids = None
if share_features:
shared_match_ids = {k: v.dataset.neurons.multi_match_id for k, v in dataloaders.items()}
shared_match_ids = {
k: v.dataset.neurons.multi_match_id for k, v in dataloaders.items()
}
all_multi_unit_ids = set(np.hstack(shared_match_ids.values()))

for match_id in shared_match_ids.values():
Expand Down