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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Authors: Robin Schirrmeister <[email protected]>\n",
"#\n",
"# License: BSD (3-clause)\n",
"\n",
"\n",
"import argparse\n",
"import logging\n",
"import os.path\n",
"import sys\n",
"\n",
"import numpy as np\n",
"import torch\n",
"from braindecode import EEGClassifier\n",
"from braindecode.datasets.moabb import MOABBDataset\n",
"from braindecode.datautil.preprocess import MNEPreproc, NumpyPreproc, preprocess\n",
"from braindecode.datautil.preprocess import exponential_moving_standardize\n",
"from braindecode.datautil.preprocess import exponential_moving_demean\n",
"from braindecode.datautil.windowers import create_windows_from_events\n",
"from braindecode.models import Deep4Net\n",
"from braindecode.models import ShallowFBCSPNet\n",
"from braindecode.models.util import to_dense_prediction_model, get_output_shape\n",
"from braindecode.training.losses import CroppedLoss\n",
"from braindecode.util import set_random_seeds\n",
"from braindecode.visualization.gradients import compute_amplitude_gradients\n",
"from skorch.callbacks import LRScheduler\n",
"from skorch.helper import predefined_split\n",
"from torch.utils.data import Subset\n",
"\n",
"log = logging.getLogger(__name__)\n",
"\n",
"\n",
"def load_preprocessed_data(subject_id, low_cut_hz, high_cut_hz, exponential_moving_fn,\n",
" only_C_sensors, do_common_average_reference):\n",
" log.info(\"Load dataset...\")\n",
" dataset = MOABBDataset(dataset_name=\"Schirrmeister2017\", subject_ids=[subject_id])\n",
" C_sensors = [\n",
" 'FC5', 'FC1', 'FC2', 'FC6', 'C3', 'Cz', 'C4', 'CP5',\n",
" 'CP1', 'CP2', 'CP6', 'FC3', 'FCz', 'FC4', 'C5', 'C1', 'C2', 'C6',\n",
" 'CP3', 'CPz', 'CP4', 'FFC5h', 'FFC3h', 'FFC4h', 'FFC6h', 'FCC5h',\n",
" 'FCC3h', 'FCC4h', 'FCC6h', 'CCP5h', 'CCP3h', 'CCP4h', 'CCP6h', 'CPP5h',\n",
" 'CPP3h', 'CPP4h', 'CPP6h', 'FFC1h', 'FFC2h', 'FCC1h', 'FCC2h', 'CCP1h',\n",
" 'CCP2h', 'CPP1h', 'CPP2h']\n",
" EEG_sensors = ['Fp1', 'Fp2', 'Fpz', 'F7', 'F3', 'Fz', 'F4', 'F8',\n",
" 'FC5', 'FC1', 'FC2', 'FC6', 'M1', 'T7', 'C3', 'Cz', 'C4', 'T8', 'M2',\n",
" 'CP5', 'CP1', 'CP2', 'CP6', 'P7', 'P3', 'Pz', 'P4', 'P8', 'POz', 'O1',\n",
" 'Oz', 'O2', 'AF7', 'AF3', 'AF4', 'AF8', 'F5', 'F1', 'F2', 'F6', 'FC3',\n",
" 'FCz', 'FC4', 'C5', 'C1', 'C2', 'C6', 'CP3', 'CPz', 'CP4', 'P5', 'P1',\n",
" 'P2', 'P6', 'PO5', 'PO3', 'PO4', 'PO6', 'FT7', 'FT8', 'TP7', 'TP8',\n",
" 'PO7', 'PO8', 'FT9', 'FT10', 'TPP9h', 'TPP10h', 'PO9', 'PO10', 'P9',\n",
" 'P10', 'AFF1', 'AFz', 'AFF2', 'FFC5h', 'FFC3h', 'FFC4h', 'FFC6h', 'FCC5h',\n",
" 'FCC3h', 'FCC4h', 'FCC6h', 'CCP5h', 'CCP3h', 'CCP4h', 'CCP6h', 'CPP5h',\n",
" 'CPP3h', 'CPP4h', 'CPP6h', 'PPO1', 'PPO2', 'I1', 'Iz', 'I2', 'AFp3h',\n",
" 'AFp4h', 'AFF5h', 'AFF6h', 'FFT7h', 'FFC1h', 'FFC2h', 'FFT8h', 'FTT9h',\n",
" 'FTT7h', 'FCC1h', 'FCC2h', 'FTT8h', 'FTT10h', 'TTP7h', 'CCP1h', 'CCP2h',\n",
" 'TTP8h', 'TPP7h', 'CPP1h', 'CPP2h', 'TPP8h', 'PPO9h', 'PPO5h', 'PPO6h',\n",
" 'PPO10h', 'POO9h', 'POO3h', 'POO4h', 'POO10h', 'OI1h', 'OI2h']\n",
" if only_C_sensors:\n",
" sensor_names = C_sensors\n",
" else:\n",
" sensor_names = EEG_sensors\n",
" # Parameters for exponential moving standardization\n",
" factor_new = 1e-3\n",
" init_block_size = 1000\n",
"\n",
" log.info(\"Preprocess dataset...\")\n",
"\n",
" moving_fn ={'standardize': exponential_moving_standardize,\n",
" 'demean': exponential_moving_demean}[exponential_moving_fn]\n",
" preprocessors = [\n",
" # keep only C sensors\n",
" MNEPreproc(fn='pick_channels', ch_names=sensor_names, ordered=True),\n",
" # convert from volt to microvolt, directly modifying the numpy array\n",
" NumpyPreproc(fn=lambda x: x * 1e6),\n",
" NumpyPreproc(fn=lambda x: np.clip(x, -800, 800)),\n",
" ]\n",
"\n",
" if do_common_average_reference:\n",
" preprocessors.append(MNEPreproc(fn='set_eeg_reference', ref_channels='average'),)\n",
" preprocessors.extend([\n",
" MNEPreproc(fn='resample', sfreq=250),\n",
" # bandpass filter\n",
" MNEPreproc(fn='filter', l_freq=low_cut_hz, h_freq=high_cut_hz),\n",
" # exponential moving standardization\n",
" NumpyPreproc(fn=moving_fn, factor_new=factor_new,\n",
" init_block_size=init_block_size),\n",
" ])\n",
"\n",
" # Transform the data\n",
" preprocess(dataset, preprocessors)\n",
" return dataset\n",
"\n",
"\n",
"def create_cropped_model(model_name, n_chans):\n",
" ######################################################################\n",
" # Now we create the model. To enable it to be used in cropped decoding\n",
" # efficiently, we manually set the length of the final convolution layer\n",
" # to some length that makes the receptive field of the ConvNet smaller\n",
" # than ``input_window_samples`` (see ``final_conv_length=30`` in the model\n",
" # definition).\n",
" #\n",
"\n",
" cuda = torch.cuda.is_available() # check if GPU is available, if True chooses to use it\n",
" device = 'cuda' if cuda else 'cpu'\n",
" if cuda:\n",
" torch.backends.cudnn.benchmark = True\n",
" seed = 20200220 # random seed to make results reproducible\n",
" # Set random seed to be able to reproduce results\n",
" set_random_seeds(seed=seed, cuda=cuda)\n",
"\n",
" n_classes = 4\n",
"\n",
" if model_name == 'shallow':\n",
" model = ShallowFBCSPNet(\n",
" n_chans,\n",
" n_classes,\n",
" input_window_samples=None, # no need to provide if final_conv_length given\n",
" final_conv_length=30,\n",
" )\n",
" else:\n",
" assert model_name == 'deep'\n",
" model = Deep4Net(\n",
" n_chans,\n",
" n_classes,\n",
" input_window_samples=None, # no need to provide if final_conv_length given\n",
" final_conv_length=2,\n",
" )\n",
"\n",
" # Send model to GPU\n",
" if cuda:\n",
" model.cuda()\n",
"\n",
" ######################################################################\n",
" # And now we transform model with strides to a model that outputs dense\n",
" # prediction, so we can use it to obtain predictions for all\n",
" # crops.\n",
" #\n",
"\n",
" to_dense_prediction_model(model)\n",
" return model\n",
"\n",
"\n",
"def cut_windows(dataset, input_window_samples, window_stride_samples):\n",
" ######################################################################\n",
" # Cut the data into windows\n",
" # -------------------------\n",
" #\n",
"\n",
" ######################################################################\n",
" # In contrast to trialwise decoding, we have to supply an explicit window size and window stride to the\n",
" # ``create_windows_from_events`` function.\n",
" #\n",
"\n",
" trial_start_offset_seconds = -0.5\n",
" # Extract sampling frequency, check that they are same in all datasets\n",
" sfreq = dataset.datasets[0].raw.info['sfreq']\n",
" assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n",
"\n",
" # Calculate the trial start offset in samples.\n",
" trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n",
"\n",
" # Create windows using braindecode function for this. It needs parameters to define how\n",
" # trials should be used.\n",
" windows_dataset = create_windows_from_events(\n",
" dataset,\n",
" trial_start_offset_samples=trial_start_offset_samples,\n",
" trial_stop_offset_samples=0,\n",
" window_size_samples=input_window_samples,\n",
" window_stride_samples=window_stride_samples,\n",
" drop_last_window=False,\n",
" preload=True,\n",
" mapping={'left_hand': 0, 'right_hand': 1, 'feet': 2, 'rest': 3},\n",
" )\n",
" return windows_dataset\n",
"\n",
"\n",
"def split_into_train_valid(windows_dataset, use_final_eval):\n",
" ######################################################################\n",
" # Split the dataset\n",
" # -----------------\n",
" #\n",
" # This code is the same as in trialwise decoding.\n",
" #\n",
"\n",
" splitted = windows_dataset.split('run')\n",
" if use_final_eval:\n",
" train_set = splitted['train']\n",
" valid_set = splitted['test']\n",
" else:\n",
" full_train_set = splitted['train']\n",
" n_split = int(np.round(0.8 * len(full_train_set)))\n",
" # ensure this is multiple of 2 (number of windows per trial)\n",
" n_windows_per_trial = 2 # here set by hand\n",
" n_split = n_split - (n_split % n_windows_per_trial)\n",
" valid_set = Subset(full_train_set, range(n_split, len(full_train_set)))\n",
" train_set = Subset(full_train_set, range(0, n_split))\n",
" return train_set, valid_set\n",
"\n",
"\n",
"def run_training(model, model_name, train_set, valid_set, device, n_epochs):\n",
" assert model_name in ['deep', 'shallow']\n",
" if model_name == 'shallow':\n",
" # These values we found good for shallow network:\n",
" lr = 0.0625 * 0.01\n",
" weight_decay = 0\n",
" else:\n",
" assert model_name == 'deep'\n",
" # For deep4 they should be:\n",
" lr = 1 * 0.01\n",
" weight_decay = 0.5 * 0.001\n",
"\n",
" batch_size = 64\n",
"\n",
" clf = EEGClassifier(\n",
" model,\n",
" cropped=True,\n",
" criterion=CroppedLoss,\n",
" criterion__loss_function=torch.nn.functional.nll_loss,\n",
" optimizer=torch.optim.AdamW,\n",
" train_split=predefined_split(valid_set),\n",
" optimizer__lr=lr,\n",
" optimizer__weight_decay=weight_decay,\n",
" iterator_train__shuffle=True,\n",
" batch_size=batch_size,\n",
" callbacks=[\n",
" \"accuracy\", (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n",
" ],\n",
" device=device,\n",
" )\n",
" # Model training for a specified number of epochs. `y` is None as it is already supplied\n",
" # in the dataset.\n",
" clf.fit(train_set, y=None, epochs=n_epochs)\n",
" return clf\n",
"\n",
"\n",
"def compute_and_store_amp_grads(model, train_set, filename):\n",
" amp_grads_per_filter = compute_amplitude_gradients(model, train_set, batch_size=64)\n",
" # average across compute windows\n",
" avg_amp_grads_per_filter = np.mean(amp_grads_per_filter, axis=1)\n",
" np.save(filename, avg_amp_grads_per_filter)\n",
"\n",
"\n",
"def run_exp(\n",
" seed,\n",
" subject_id,\n",
" low_cut_hz,\n",
" high_cut_hz,\n",
" exponential_moving_fn,\n",
" n_epochs,\n",
" model_name,\n",
" output_dir,\n",
" only_C_sensors,\n",
" do_common_average_reference,\n",
" use_final_eval,):\n",
" assert model_name in ['deep', 'shallow']\n",
" set_random_seeds(seed, True)\n",
" log.info(f\"Load and preprocess data for subject {subject_id}...\")\n",
" dataset = load_preprocessed_data(subject_id, low_cut_hz, high_cut_hz,\n",
" exponential_moving_fn=exponential_moving_fn,\n",
" only_C_sensors=only_C_sensors,\n",
" do_common_average_reference=do_common_average_reference,\n",
" )\n",
"\n",
" # Extract number of chans from dataset to create model\n",
" n_chans = dataset[0][0].shape[0]\n",
" log.info(\"Create cropped model...\")\n",
" model = create_cropped_model(model_name, n_chans)\n",
"\n",
" # Cut windows from the preprocessed data, using number of predictions\n",
" # per compute window to cut non-overlapping fully covering windows\n",
" # (except for overlap of last window to stay within trial bounds)\n",
" log.info(\"Cut windows from dataset ...\")\n",
" input_window_samples = 1000\n",
" # To know the models’ receptive field, we calculate the shape of model\n",
" # output for a dummy input.\n",
" n_preds_per_input = get_output_shape(model, n_chans, input_window_samples)[2]\n",
" windows_dataset = cut_windows(\n",
" dataset, input_window_samples, window_stride_samples=n_preds_per_input)\n",
"\n",
" # Split into train and valid, ignoring final evaluation for now\n",
" log.info(\"Split into train and valid...\")\n",
" train_set, valid_set = split_into_train_valid(windows_dataset, use_final_eval=use_final_eval)\n",
"\n",
" # Run actual training\n",
" log.info(\"Run training...\")\n",
" clf = run_training(model, model_name, train_set, valid_set, 'cuda', n_epochs)\n",
"\n",
" log.info(\"... Done.\")\n",
"\n",
" return clf, train_set, valid_set\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"!For realistic results, please increase n_epochs quite a lot, e.g., to 800!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"subject_id = 1\n",
"low_cut_hz = 4 # low cut frequency for filtering\n",
"high_cut_hz = None # high cut frequency for filtering\n",
"n_epochs = 20\n",
"model_name = 'deep'\n",
"output_dir = './results/'\n",
"\n",
"torch.backends.cudnn.benchmark = True # for faster runtime on GPU\n",
"\n",
"logging.basicConfig(format='%(asctime)s %(levelname)s : %(message)s',\n",
" level=logging.DEBUG, stream=sys.stdout)\n",
"seed = 0\n",
"exponential_moving_fn = 'demean'\n",
"\n",
"only_C_sensors = False\n",
"do_common_average_reference = True\n",
"use_final_eval = False\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-05-19 19:25:37,731 INFO : Load and preprocess data for subject 1...\n",
"2021-05-19 19:25:37,733 INFO : Load dataset...\n",
"2021-05-19 19:26:02,863 INFO : Preprocess dataset...\n",
"2021-05-19 19:26:45,065 INFO : Create cropped model...\n",
"2021-05-19 19:26:49,462 INFO : Cut windows from dataset ...\n",
"2021-05-19 19:26:51,654 INFO : Split into train and valid...\n",
"2021-05-19 19:26:51,659 INFO : Run training...\n",
" epoch train_accuracy train_loss valid_accuracy valid_loss lr dur\n",
"------- ---------------- ------------ ---------------- ------------ ------ ------\n",
" 1 \u001b[36m0.2500\u001b[0m \u001b[32m2.6032\u001b[0m \u001b[35m0.2500\u001b[0m \u001b[31m48.7524\u001b[0m 0.0100 1.6196\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/schirrmr/anaconda3/envs/invertible/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:156: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
" warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2 0.2500 \u001b[32m1.5394\u001b[0m 0.2500 \u001b[31m6.4878\u001b[0m 0.0099 1.2607\n",
" 3 \u001b[36m0.3008\u001b[0m \u001b[32m1.5002\u001b[0m \u001b[35m0.2812\u001b[0m \u001b[31m1.5140\u001b[0m 0.0097 1.3515\n",
" 4 \u001b[36m0.3281\u001b[0m \u001b[32m1.4509\u001b[0m 0.2812 1.7449 0.0094 1.3084\n",
" 5 0.2891 \u001b[32m1.4446\u001b[0m 0.2031 \u001b[31m1.4346\u001b[0m 0.0089 1.3199\n",
" 6 0.3242 \u001b[32m1.4359\u001b[0m \u001b[35m0.3281\u001b[0m 1.4649 0.0084 1.2724\n",
" 7 0.2852 1.4707 0.2500 \u001b[31m1.3890\u001b[0m 0.0077 1.0705\n",
" 8 \u001b[36m0.3320\u001b[0m \u001b[32m1.3798\u001b[0m 0.2812 \u001b[31m1.3718\u001b[0m 0.0070 1.1484\n",
" 9 0.2812 \u001b[32m1.3710\u001b[0m 0.2656 \u001b[31m1.3666\u001b[0m 0.0062 1.2024\n",
" 10 \u001b[36m0.3750\u001b[0m 1.3816 \u001b[35m0.3438\u001b[0m 1.3812 0.0054 1.2324\n",
" 11 0.3047 \u001b[32m1.3483\u001b[0m 0.2969 1.3668 0.0046 1.2142\n",
" 12 0.3242 \u001b[32m1.3432\u001b[0m 0.2500 1.3802 0.0038 1.0873\n",
" 13 0.3359 \u001b[32m1.3283\u001b[0m 0.2969 1.3799 0.0030 1.1816\n",
" 14 \u001b[36m0.4141\u001b[0m \u001b[32m1.3104\u001b[0m \u001b[35m0.3594\u001b[0m 1.3730 0.0023 1.1344\n",
" 15 0.3906 \u001b[32m1.2736\u001b[0m 0.3438 \u001b[31m1.3628\u001b[0m 0.0016 1.1260\n",
" 16 \u001b[36m0.4453\u001b[0m \u001b[32m1.2565\u001b[0m 0.3438 1.3657 0.0011 1.2551\n",
" 17 \u001b[36m0.4609\u001b[0m \u001b[32m1.2430\u001b[0m \u001b[35m0.3750\u001b[0m \u001b[31m1.3519\u001b[0m 0.0006 1.1797\n",
" 18 \u001b[36m0.4883\u001b[0m \u001b[32m1.2187\u001b[0m \u001b[35m0.3906\u001b[0m \u001b[31m1.3476\u001b[0m 0.0003 1.2408\n",
" 19 0.4844 1.2250 0.3906 \u001b[31m1.3423\u001b[0m 0.0001 1.1902\n",
" 20 \u001b[36m0.5117\u001b[0m 1.2208 \u001b[35m0.4062\u001b[0m \u001b[31m1.3353\u001b[0m 0.0000 1.1911\n",
"2021-05-19 19:27:31,779 INFO : ... Done.\n"
]
}
],
"source": [
"clf, train_set, valid_set = run_exp(\n",
" seed,\n",
" subject_id,\n",
" low_cut_hz,\n",
" high_cut_hz,\n",
" exponential_moving_fn,\n",
" n_epochs,\n",
" model_name,\n",
" output_dir,\n",
" only_C_sensors,\n",
" do_common_average_reference,\n",
" use_final_eval,)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Deep4Net(\n",
" (ensuredims): Ensure4d()\n",
" (dimshuffle): Expression(expression=transpose_time_to_spat) \n",
" (conv_time): Conv2d(1, 25, kernel_size=(10, 1), stride=(1, 1))\n",
" (conv_spat): Conv2d(25, 25, kernel_size=(1, 128), stride=(1, 1), bias=False)\n",
" (bnorm): BatchNorm2d(25, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv_nonlin): Expression(expression=elu) \n",
" (pool): MaxPool2d(kernel_size=(3, 1), stride=(1, 1), padding=0, dilation=(1, 1), ceil_mode=False)\n",
" (pool_nonlin): Expression(expression=identity) \n",
" (drop_2): Dropout(p=0.5, inplace=False)\n",
" (conv_2): Conv2d(25, 50, kernel_size=(10, 1), stride=(1, 1), dilation=(3, 1), bias=False)\n",
" (bnorm_2): BatchNorm2d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (nonlin_2): Expression(expression=elu) \n",
" (pool_2): MaxPool2d(kernel_size=(3, 1), stride=(1, 1), padding=0, dilation=(3, 1), ceil_mode=False)\n",
" (pool_nonlin_2): Expression(expression=identity) \n",
" (drop_3): Dropout(p=0.5, inplace=False)\n",
" (conv_3): Conv2d(50, 100, kernel_size=(10, 1), stride=(1, 1), dilation=(9, 1), bias=False)\n",
" (bnorm_3): BatchNorm2d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (nonlin_3): Expression(expression=elu) \n",
" (pool_3): MaxPool2d(kernel_size=(3, 1), stride=(1, 1), padding=0, dilation=(9, 1), ceil_mode=False)\n",
" (pool_nonlin_3): Expression(expression=identity) \n",
" (drop_4): Dropout(p=0.5, inplace=False)\n",
" (conv_4): Conv2d(100, 200, kernel_size=(10, 1), stride=(1, 1), dilation=(27, 1), bias=False)\n",
" (bnorm_4): BatchNorm2d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (nonlin_4): Expression(expression=elu) \n",
" (pool_4): MaxPool2d(kernel_size=(3, 1), stride=(1, 1), padding=0, dilation=(27, 1), ceil_mode=False)\n",
" (pool_nonlin_4): Expression(expression=identity) \n",
" (conv_classifier): Conv2d(200, 4, kernel_size=(2, 1), stride=(1, 1), dilation=(81, 1))\n",
" (softmax): LogSoftmax(dim=1)\n",
" (squeeze): Expression(expression=squeeze_final_output) \n",
")"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = clf.module\n",
"model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Sequential(\n",
" (ensuredims): Ensure4d()\n",
" (dimshuffle): Expression(expression=transpose_time_to_spat) \n",
" (conv_time): Conv2d(1, 25, kernel_size=(10, 1), stride=(1, 1))\n",
" (conv_spat): Conv2d(25, 25, kernel_size=(1, 128), stride=(1, 1), bias=False)\n",
" (bnorm): BatchNorm2d(25, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv_nonlin): Expression(expression=elu) \n",
" (pool): MaxPool2d(kernel_size=(3, 1), stride=(1, 1), padding=0, dilation=(1, 1), ceil_mode=False)\n",
" (pool_nonlin): Expression(expression=identity) \n",
" (drop_2): Dropout(p=0.5, inplace=False)\n",
" (conv_2): Conv2d(25, 50, kernel_size=(10, 1), stride=(1, 1), dilation=(3, 1), bias=False)\n",
")"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from torch import nn\n",
"# Let's construct a submodel for the layer we are interested in\n",
"wanted_layer = 'conv_2'\n",
"\n",
"submodel = nn.Sequential()\n",
"for name, module in model.named_children():\n",
" submodel.add_module(name, module)\n",
" if name == wanted_layer:\n",
" break\n",
"submodel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get all activations for this layer"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"trainloader = torch.utils.data.DataLoader(\n",
" train_set,\n",
" shuffle=False,\n",
" drop_last=False,\n",
" num_workers=2,\n",
" batch_size=32)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(512, 128, 1000)\n",
"(512, 50, 962, 1)\n"
]
}
],
"source": [
"# Could do on train set/valid set...\n",
"# Here we do on train set\n",
"# In future, should improve braindecode to have function\n",
"# Get non-overlapping activations (train_set) (but is that possible? maybe just assume defaults)\n",
"all_X = []\n",
"all_acts = []\n",
"with torch.no_grad():\n",
" for X,y,i in trainloader:\n",
" all_X.append(X)\n",
" act = submodel(X.cuda())\n",
" all_acts.append(act.cpu().detach().numpy())\n",
"all_X = np.concatenate(all_X)\n",
"all_acts = np.concatenate(all_acts)\n",
"print(all_X.shape)\n",
"print(all_acts.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate how many input timesteps the network needs to produce one activation (receptive field size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This only works because we have a densely predicting network where we have one output per timestep"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"39"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n_receptive_field = all_X.shape[2] - all_acts.shape[2] + 1\n",
"n_receptive_field"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Decide which unit to investigate"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"i_unit = 0"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"unit_acts = all_acts[:,i_unit].squeeze()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(512, 962)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unit_acts.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sort activations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we sort absolute activations but could also sort raw activations."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# sort 2d array, see https://stackoverflow.com/a/64338853/1469195\n",
"i_acts_sorted = np.stack(np.unravel_index(np.argsort(np.abs(unit_acts), axis=None), unit_acts.shape),axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"n_fields_to_collect = 100"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"all_most_activating_windows = []\n",
"for i_batch, i_act in i_acts_sorted[-n_fields_to_collect:]:\n",
" # Start of receptive field\n",
" i_start = i_act - n_receptive_field + 1\n",
" # One past end of receptive field\n",
" i_stop = i_act + 1\n",
" X_part = all_X[i_batch,:,i_start:i_stop]\n",
" all_most_activating_windows.append(X_part)\n",
"all_most_activating_windows = np.array(all_most_activating_windows)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### lowest absolute activation"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7.555001e-06"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unit_acts[i_acts_sorted[0][0], i_acts_sorted[0][1]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### highest absolute activation"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-37.544727"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unit_acts[i_acts_sorted[-1][0], i_acts_sorted[-1][1]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize most activating receptive fields"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-05-19 19:34:28,386 DEBUG : Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n"
]
}
],
"source": [
"import mne\n",
"from IPython.display import display\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import cm\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'png'\n",
"import matplotlib\n",
"matplotlib.style.use('seaborn')\n",
"import seaborn\n",
"seaborn.set_palette('colorblind')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Show distribution"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Activation Value')"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ZY5SdffY9WafyprZq8wWh2rB3uVxasWKFEhMTlZGRobi4OM2aNavWRdXGjTfeqIMHD+rw4cOKiIhQcnKy/vznP/t1n77SwOFQ44ZBatwwSOFNQ9SpbVMN6xyYfVd4PDpR7lFhaYWOFJboYMEJZRaV6Ydfjum7X44xjSYAXKSqDPsNGzZoyZIlWrNmjW655RY9/PDD6tevn4KCfHO6/JxFBQdr+vTpevDBB+V2uzVq1Ch17NjR7/u90AU3aKDQRg0U2ihYVzS/RLe2axmQ/XoHryl160hhiZzHy7Q9w6X0rCJ9n3FMeXzJAABLOUxlF8glxcTEaNSoUYqPj1ebNm0CXVet1OX0R3XqeloFZ/JXH40xKvcYuUorlFNcruzjZUrLdOnwsRJtPFSoXTnHfb5PADgfLRoFa8/kvmctt/Q0/ueff17rHQOB4nA4FBLkUJsmIWrTJETXtW2qvu1b+XWfHmNUXOZWfkmFjh4r0a6c40r9vzz94irVDmeRX/cNAOejRjfoATh5P0azRsFq1ihY7VqcvEwy/qbL/bIvjzEqqfAop7hMzqIyFRqHVqT9ojUH8/WLi7neAdQOYQ/UQw0cDjVpGKQrWzTWlS0aKywsVNFXNPf5fowxKi73KLu4TAfzT8hZVKYtRwu15mC+DjNREGAbhD1wEXM4HGoaEqSmIY11VcvGkqS7avm4aXUqPB4VlFQo01XqnYdh+e5sHcg/4dP9AKhajcJ+06ZNOnTokCoq/j0z2dixY/1WFAD7CG7QQG2bhKhtkxB1jQjVCEnP/brDeW/XGKMytzk5r0R2kb49XKjF/+tU1vGy8y8asJlqw/7ZZ59Venq6unTpEpDH7gCgJhwOhxoFO9SuxSVq1+ISDbq2rX7f75pqP1fdnc8nyt3KcJVqV85xbct0KfGnLC5p4IJXbdj/+OOPWrFihRo2bBiIegDAUo0bBqlD6ybq0LqJYjqFadqv6n4WosLjUc7xcu3LK1biT1n6cl8Og1vBEtWG/ekT0gAAai64QQNFhjZSZGgj9W3fSq/pulpvwxijY6UV+in7uJbtzNK/dmUr9wRfGFA71Yb9VVddpfHjxys6OlohISHe5VyzBwD/czgcanFJQ/Vu11K927XUHwd2qvFnjTFylbm1K/u4Pt+TrXe2HvFjpajPqg37srIyXXnlldqzZ08g6gEA+IjD4VDzRsHqdUUL9bqihV6IurZGnytze7Q75+RTE29sOuTnKhEI1Yb9K6+8Eog6AAD1REhQA90YEaobI0KrvGfh9Bsd3R6jXTnHNf/7I/pnWmal74e1qg17Y4w++eQTbdy4UQ6HQ3369NGYMWPOOb88AODiEdTAoRvCm+kvQzrrL0OqnubTGKMMV6nmf39Ub205HMAKUW3Yz549Wzt37tTIkSMlSUlJSTp48KB++9vf+r04AIB9OBwOXdb8Ek3vd42mV/GYpDFGO3OOa+rXe/XtkcIAV2hf1Yb9+vXrtXTpUgUHn3zrkCFDNHLkSMIeAOBzDodDXcKaadnYmypd7/YYfbM/V/cnpge4sgtbjUbQO/2UPafvAQBWCWrg0OCObeWccsdZ606Uu/XUl7u15KeswBdWz1Ub9n379tVDDz2kESNGyOFwaOnSperb9+z5eAEAsFLjhkF6O66L3o7rcsbyXdnH9esFWy2q6t+sPFauNuyfeeYZffzxx/rmm29kjFF0dLTuuuuuQNQGAMB56xzW9IwzAcYYLfnJqUdX7LKuqACrNuwbNGige+65R/fcc08g6gEAwK8cDodG3xCp0TecHCHWGKM/rT+oP2/82eLK/KfKsF+0aJHuv/9+zZo1q9Lr9NygBwCwA4fDod/efrV+e/vVkiRnUam6vbXJ4qp8q8qwb9SokSSpadOmASsGAACrRTRr5D3t//ftv+ipLy/8EWSrDPu7775b0slH7a655sznIffv3+/fqgAAqAfu7X6Z7u1+mXbnHNev3ju/m/yM8VFRddCgujc8/fTTNVoGAIBdXdf25E1+ib/pbnUpdVLlkX1eXp7y8vJUWlqq/fv3y/z/ryQul0vFxcUBKxAAgPqiz5Wt5JxyhyJmrba6lFqpMuyXL1+uRYsWKSsrSw899JB3eWhoqB588MGAFAcAQH1Ul8Cvl8/Z33///br//vv17rvv6pFHHglkTQAA1HvOKXcoYWm6Pt+TY3Up1ar2mv2poM/NzdUvv/zi/Q8AgIvd+yO6Wl1CjVQ7qM63336rKVOmKDc3Vw0aNFB5eblatmypTZvs9QwiAAB1cSFcw6/2yH727NlauHChrr32Wm3fvl0zZsxguFwAAE6zd3L1c8bU60fvJOnqq69WRUWFHA6H7rzzTm3dav2EAgAA1BfNG9VoElnLVBv2p+axj4iIUGpqqnbv3q3MzEy/FwYAwIWkJkf3Vqn2q8h9992nwsJCPf7443rqqafkcrk0bdq0QNQGAMAFoz4f3Vdb2dChQyVJ3bp10zfffOP3ggAAuFBNvPkKvbP1SKXrrHzOvtrT+NHR0Xr77beVkZHhs51+8cUXio2NVefOnbVjx44z1s2dO1cDBgzQoEGDtG7dOp/tEwAAf3sh6lqrS6hUtWH/zjvvyOVy6c4771RCQoKWL1+u0tLS89ppp06dNGfOHN18881nLN+3b5+Sk5OVnJys+fPna8aMGXK73ee1LwAALnbVhn3Hjh01ZcoUrV69Wvfdd5+++OIL3X777ee102uuuUYdOnQ4a3lKSopiY2MVEhKidu3aqX379kpLSzuvfQEAEEid21Y+NXy9f/ROOjmt7ZYtW7Rjxw7dcMMNfinG6XQqMjLS+zoiIkJOp9Mv+wIAwB8+vrOb1SWcpdob9D744AMlJSXp+PHjio+P16effqpLL7202g2PHz9eOTlnjxc8efJkRUdHV/oZU8nXHkcN7mho1aqJgoODqn1fbYWFhfp8mxcj+ugb9NF36KVv0MfKVdUXRwNHlev83ctqw3737t2aNm2aevbsWasNL1y4sNbFREZGnvEMv9PpVHh4eLWfy8/3/ZS7YWGhys52+Xy7Fxv66Bv00XfopW/Qx9ozHlNpz+ray9p8Qaj2NP7LL79c66Cvq6ioKCUnJ6usrEyHDx/WwYMH1a1b/TsdAgDAhaTKI/tnnnlGf/rTnzRq1KhKT6UvXry4zjv95ptv9Ic//EF5eXl6+OGHdf311+u9995Tx44dNWTIEMXExCgoKEjTp09XUJDvT88DAOBP/a5upVUH8s9YZuVz9g5T2YVySenp6eratau2bNlS6Qd79erl18Jqwx+nkjhF5Rv00Tfoo+/QS9+gj+e2fFeWHlz20xnLWl4SrN2Pnz2kbiBO41d5ZN+168k5ejMyMjR8+PAz1i1btqzWRQEAcLEY3LGt1SWcodpr9pXdaFeXm+8AALhYNAyq8ZPtAVHlkf2OHTuUlpam/Px8/eMf//AuLyoqUnl5eUCKAwAA56/KsHc6nUpPT9eJEyeUnp7uXd60aVO98sorASkOAACcvyrDPjo6WtHR0Vq/fr369q2/c/QCAIBzq/aiwk8//aSCggLv6/z8fM2fP9+vRQEAcKELsvBRu/9UbdgnJyerZcuW3tetWrXSihUr/FoUAAAXul5XtDjjtZXZX23YV/YYPtPOAgBwbk/edpXVJXhVG/ZXXXWV3n//fRlj5PF4tGDBAl155ZWBqA0AgAvWf1125qA3Fs5wW33YP/fcc1q1apW6deumHj16aM2aNfr9738fiNoAALhgNQ2pdq65gKm2koiICH3wwQcqLj45s1yTJk303Xff1Wg2OgAAYL0af+0oKipSUlKSlixZImOMvv76a3/WBQCArVh5g945w76iokKpqalavHixtm/froqKCr333nvq0aNHoOoDAADnqcpr9q+88oruuOMOffzxx4qLi9OaNWvUokULgh4AgDqwcorbKo/sP/roI910002aMGGCbr31VkmqdF57AABQv1UZ9uvXr9fy5cs1e/ZsFRYWKj4+nufrAQC4AFV5Gr958+YaO3asEhMT9dZbb6mwsFAlJSUaO3asPv7440DWCAAAzkONJtzt3Lmzfve732ndunUaO3asUlJS/F0XAADwkVo98d+wYUPFxMQoJibGX/UAAGAbV7W8RAcLSqwuo2ZH9gAAoPaahgRZXYIkwh4AAL8JqidPsRH2AAD4SZOG/z6yd1g4hh5hDwCAnzS/5N+3xhkL570j7AEA8JP7e1xmdQmSCHsAAPymGTfoAQCAQCDsAQDwk9aNG1pdgiTCHgAAv7m2TROrS5BE2AMAYHuEPQAAAcBz9gAAwG8IewAA/KR+DJZrUdjPmjVLgwcPVlxcnB599FEdO3bMu27u3LkaMGCABg0apHXr1llRHgAAPuG4mMfG79Onj1asWKHly5frqquu0ty5cyVJ+/btU3JyspKTkzV//nzNmDFDbrfbihIBALANS8K+b9++Cg4+OV5wjx49lJmZKUlKSUlRbGysQkJC1K5dO7Vv315paWlWlAgAgG1Yfs1+yZIl+tWvfiVJcjqdioyM9K6LiIiQ0+m0qjQAAGwhuPq31M348eOVk5Nz1vLJkycrOjpakvTOO+8oKChIw4YNkyQZc/aMQDW53tGqVRMFB/t+/OGwsFCfb/NiRB99gz76Dr30DfpYO44Gjip75u9e+i3sFy5ceM71S5cu1erVq7Vw4UJvoEdGRnpP6Usnj/TDw8Or3Vd+fvF51VqZsLBQZWe7fL7diw199A366Dv00jfoY+0Zj6m0Z3XtZW2+IFhyGn/t2rX629/+pnfeeUeNGzf2Lo+KilJycrLKysp0+PBhHTx4UN26dbOiRAAAbMNvR/bn8oc//EFlZWVKSEiQJHXv3l0vvviiOnbsqCFDhigmJkZBQUGaPn26goLqx/SAAABcqCwJ+2+++abKdRMnTtTEiRMDWA0AAPZm+d34AADAvwh7AABsjrAHAMDmCHsAAGyOsAcAIBAsnBOHsAcAwOYIewAAbI6wBwDA5gh7AABsjrAHACAQzp7YNWAIewAAbI6wBwAgADq2aWLZvgl7AAACoHFD6yKXsAcAwOYIewAAbI6wBwDA5gh7AABsjrAHAMDmCHsAAGyOsAcAwOYIewAAbI6wBwDA5gh7AABsjrAHAMDmCHsAAALAMMUtAADwF8IeAIAAKKnwWLZvwh4AgADoc2VLy/ZN2AMA4EfP39FBktTj0lDLaiDsAQDwo/oQtPWhBgAA4EeEPQAANkfYAwBgc5aE/euvv664uDgNHz5c//3f/y2n0+ldN3fuXA0YMECDBg3SunXrrCgPAABbsSTsH3zwQS1fvlzLli3THXfcobfeekuStG/fPiUnJys5OVnz58/XjBkz5Ha7rSgRAADbsCTsmzVr5v35xIkTcjgckqSUlBTFxsYqJCRE7dq1U/v27ZWWlmZFiQAA2EawVTv+y1/+oqSkJIWGhuqDDz6QJDmdTnXv3t37noiIiDNO8VelVasmCg4O8nmNYWHWPRNpJ/TRN+ij79BL36CPNdO02SWSpBYtmlTZM3/30m9hP378eOXk5Jy1fPLkyYqOjtYTTzyhJ554QnPnztXf//53TZo0SaaSWQJOHfWfS35+sU9qPl1YWKiys10+3+7Fhj76Bn30HXrpG/Sx5o4XlUiSCguLK+1ZXXtZmy8Ifgv7hQsX1uh9Q4cO1cMPP6xJkyYpMjJSmZmZ3nVOp1Ph4eF+qhAAgMCxcNI7a67ZHzx40PtzamqqOnQ4OZRgVFSUkpOTVVZWpsOHD+vgwYPq1q2bFSUCAOATNTlD7W+WXLP/85//rAMHDsjhcOjyyy/XjBkzJEkdO3bUkCFDFBMTo6CgIE2fPl1BQb6/Fg8AwMXEkrCfM2dOlesmTpyoiRMnBrAaAADsjRH0AACwOcIeAACbI+wBALA5wh4AAJsj7AEAsDnCHgAAmyPsAQCwOcIeAACbI+wBALA5wh4AAJsj7AEACIBKZnEPGMIeAAA/sn7OO8IeAADbI+wBALA5wh4AAJsj7AEAsDnCHgAAmyPsAQCwOcIeAACbI+wBALA5wh4AAJsj7AEAsDnCHgAAmyPsAQCwOcIeAIAAKCqrsGzfhD0AAH7UoXVjXRLcQNe1bWpZDcGW7Tj1oncAAA/7SURBVBkAgIvAwGvbat/kvmoYZN3xNUf2AAD4mZVBLxH2AADYHmEPAIDNEfYAANgcYQ8AgM1ZGvbvvfeerrvuOuXl5XmXzZ07VwMGDNCgQYO0bt06C6sDAMAeLHv0LiMjQxs3btRll13mXbZv3z4lJycrOTlZTqdTCQkJ+uqrrxQUFGRVmQAAXPAsO7J/5ZVX9Mwzz8jhcHiXpaSkKDY2ViEhIWrXrp3at2+vtLQ0q0oEAMAWLDmyT0lJUXh4uDp37nzGcqfTqe7du3tfR0REyOl0Vru9Vq2aKDjY90f/YWGhPt/mxYg++gZ99B166Rv00Xf83Uu/hf348eOVk5Nz1vLJkydr7ty5WrBgwVnrjDFnLTv9yL8q+fnFdSvyHMLCQpWd7fL5di829NE36KPv0EvfoI++U9de1uYLgt/CfuHChZUu3717t44cOaLhw4dLkjIzMzVy5Eh99tlnioyMVGZmpve9TqdT4eHh/ioRAICLQsCv2V933XXatGmTUlNTlZqaqsjISCUmJiosLExRUVFKTk5WWVmZDh8+rIMHD6pbt26BLhEAAFupVxPhdOzYUUOGDFFMTIyCgoI0ffr0Gt2J769rHVyP8g366Bv00XfopW/QR9/xdy8dprIL5QAAwDYYQQ8AAJsj7AEAsDnCHgAAmyPsAQCwOcIeAACbI+z/w9q1azVo0CANGDBA8+bNs7ocy0ydOlW9e/fW0KFDvcsKCgqUkJCggQMHKiEhQYWFhd51Vc1WmJ6erri4OA0YMEAvvfSSd5TEsrIyTZ48WQMGDNCYMWN05MgR72eWLl2qgQMHauDAgVq6dGkAflv/ycjI0Lhx4zRkyBDFxsZq0aJFkuhlbZWWlmr06NEaNmyYYmNj9eabb0qij3XldrsVHx+vhx9+WBJ9rKuoqCjFxcVp+PDhGjlypKR63EsDr4qKCtO/f39z6NAhU1paauLi4szevXutLssSW7ZsMenp6SY2Nta7bNasWWbu3LnGGGPmzp1rZs+ebYwxZu/evSYuLs6UlpaaQ4cOmf79+5uKigpjjDGjRo0yP/zwg/F4POaBBx4wq1evNsYY8/e//908//zzxhhjVqxYYR5//HFjjDH5+fkmKirK5Ofnm4KCAhMVFWUKCgoC9nv7mtPpNOnp6cYYY1wulxk4cKDZu3cvvawlj8djioqKjDHGlJWVmdGjR5sff/yRPtbRggULzJNPPmkmTJhgjOHvdl3169fP5ObmnrGsvvaSI/vTpKWlqX379mrXrp1CQkIUGxurlJQUq8uyxM0336wWLVqcsSwlJUXx8fGSpPj4eK1cudK7vLLZCrOyslRUVKSbbrpJDodD8fHx3n6mpqZqxIgRkqRBgwZp06ZNMsZo/fr16tOnj1q2bKkWLVqoT58+Z3wDvtCEh4frhhtukCQ1a9ZMHTp0kNPppJe15HA41LRpU0lSRUWFKioq5HA46GMdZGZmavXq1Ro9erR3GX30nfraS8L+NE6nU5GRkd7XNZ1172KRm5vrnasgPDxceXl5kqru238uj4yM9PbT6XTq0ksvlSQFBwcrNDRU+fn5tv4zOHLkiHbu3Knu3bvTyzpwu90aPny4brvtNt122230sY5mzpypZ555Rg0a/Puff/pYdw888IBGjhypTz75RFL97WW9Gi7XaqaOs+5d7Krq27n6WZfPXMiOHz+uSZMmadq0aWrWrFmV76OXVQsKCtKyZct07NgxPfroo9qzZ0+V76WPlVu1apVat26trl27avPmzdW+nz6e20cffaSIiAjl5uYqISFBHTp0qPK9VveSI/vTMOveubVp00ZZWVmSpKysLLVu3VpS1X37z+WZmZnefkZGRiojI0PSydOyLpdLLVu2tOWfQXl5uSZNmqS4uDgNHDhQEr08H82bN9ctt9yidevW0cda+uGHH5SamqqoqCg9+eST+vbbb/X000/TxzqKiIiQdPLv84ABA5SWllZve0nYn+bGG2/UwYMHdfjwYZWVlSk5OVlRUVFWl1VvREVFKSkpSZKUlJSk/v37e5dXNltheHi4mjZtqm3btskYc9ZnTt1B+tVXX+nWW2+Vw+FQ3759tX79ehUWFqqwsFDr169X3759rfmFfcAYo+eee04dOnRQQkKCdzm9rJ28vDwdO3ZMklRSUqKNGzeqQ4cO9LGWnnrqKa1du1apqal67bXXdOutt+rVV1+lj3VQXFysoqIi788bNmxQx44d628va37f4cVh9erVZuDAgaZ///7m7bfftrocyzzxxBOmT58+pkuXLub22283n376qcnLyzP33XefGTBggLnvvvtMfn6+9/1vv/226d+/vxk4cKD3TlJjjElLSzOxsbGmf//+ZsaMGcbj8RhjjCkpKTGPPfaYiY6ONqNGjTKHDh3yfuazzz4z0dHRJjo62ixevDhwv7QfbN261XTq1MkMHTrUDBs2zAwbNsysXr2aXtbSzp07zfDhw83QoUNNbGysmTNnjjHG0Mfz8O2333rvxqePtXfo0CETFxdn4uLiTExMjDcv6msvmfUOAACb4zQ+AAA2R9gDAGBzhD0AADZH2AMAYHOEPQAANkfYA+fpiy++UHx8vIYPH67BgwfrqaeeqtN2Vq5cqbS0tDp9NjExUZMmTapyvdvt1u23367/+Z//qdH2Nm/erPXr13tfO51OjRs3rk61nV7jgQMHvK9TUlI0a9as89rmf4qKijrnyHpVGTdunFatWuXTWoD6hOFygfOQlZWlGTNmaOnSpbr00ktljNGuXbtqvR23262VK1eqa9eu6tatm8/rXLt2rcLDw/X9998rJydHbdu2Pef7t2zZouLiYu9AHREREfrwww/Pq4alS5eqVatWuvrqqyVJ/fv39w4eAsC/CHvgPOTk5Cg4OFgtW7aUdHJ86uuvv967fu3atXrttdfkdrvVunVrvfjii2rfvr02b96smTNnqmfPntqxY4cmTJig1NRUbdy4UZ999pkSEhIUHx+vpUuX6p///KfcbreaNWumF154QR06dFBZWZleeuklbd68WREREecck1uSlixZorvvvlvbtm3TsmXL9MADD0iSXC6XZs6cqfT0dDkcDvXs2VN33XWXPv74Y3k8Hm3cuFGxsbGKiYnRqFGjtHnzZr311lsqLCzUtGnTJEn5+fkaPHiwVq1ape3bt+v1119XaWmp3G63HnnkEcXGxmrJkiVKT0/XSy+9pNdff11Tpkzxzr52am76efPm6V//+pekk6NZ/u53v1PTpk01Z84cHThwQC6XS4cPH9aVV16pN954Q40bNz7n7zxu3Dh17dpV27ZtU1ZWloYMGaKnn35akrRv3z5NnTpVFRUVuuaaa1RaWur9XFZWll566SX98ssvKi0tVWxsrB555BFt3rxZzz//vJYsWaLQ0FA9++yzatu2rXebQL3mk6GEgIuU2+02EydONL169TKPPfaYef/9901eXp4xxpicnBxzyy23mL179xpjjPn000/N6NGjjTEnRy/r3Lmz+eGHH7zbmjJlivnwww+9r7du3WoeeughU1paaow5ObrjXXfdZYwx5oMPPjAJCQmmrKzMFBcXmxEjRpjHHnus0hpzc3NNz549jcvlMt99950ZMmSId92zzz5rXnzxReN2u73vNcaYN9980/zxj3/0vu/w4cOmV69exhhjjh49avr06WPKy8u9tTz77LPGGGMKCgq8c3RnZ2eb22+/3TvP9r333mtSU1O921yyZIm35tWrV5vY2FjjcrmMx+MxzzzzjHce8DfffNMMGDDAFBYWGo/HYxISEswnn3xS6e/ar18/s3v3bu/+Hn/8ceN2u82xY8dMr169zIEDB4wxxowYMcIkJiYaY4z58ccfTefOnb21jR8/3mzZssUYY0xpaan5zW9+Y9avX2+MMeavf/2reeyxx8zSpUvNXXfd5e0BUN9xZA+chwYNGujtt9/Wnj17tHXrVq1cuVLvvfeeli9fru3bt6tz58669tprJUmjRo3SjBkzvONpt2/fXjfddFOV205NTdWuXbs0ZswYSSfH2T81PvzmzZsVHx+vhg0bqmHDhho2bJh++OGHSrezbNky9evXT82aNdN//dd/ye12a9u2berRo4dWrVqlxMRE73SnpybtOJfLLrtM11xzjdasWaP+/ftr6dKl3qP8vLw8TZs2TT///LOCgoJUWFioAwcOqEePHufc5qZNmxQTE+OdEfDOO+/UzJkzvev79u2r5s2bS5K6deumQ4cOVVunJA0ePFgNGjRQaGiorrnmGh06dEht27bVnj17NHz4cElSjx491KlTJ0knxzjfsmWLd1pS6eSMhfv371efPn00ceJEjR8/Xn/84x+VmJio4GD+CcWFgf9TAR/o1KmTOnXqpLFjxyomJkZbtmxRUFDQOaedbNKkyTm3aYzRqFGj9Pjjj1e6rqYSExOVl5fnndTJ5XJpyZIl1QbwuYwYMUJJSUlq166dXC6XevbsKUl64YUXFBUVpb/+9a9yOBwaNGjQGafIq2KMOWevGjVq5P05KCioRtus7HNut1tS1dOBejweORwOLV68WA0bNjxrvcvlUkZGhkJCQlRQUKDLLrusRnUAVuNufOA8OJ1O/fjjj97XmZmZysvL0xVXXKGbbrpJO3fu1P79+yWdvEGtS5cuVc5n36xZM7lcLu/rqKgoLVu2zDuVpdvtVnp6uiSpd+/eWrZsmSoqKlRSUqIVK1ZUus20tDS5XC6tX79eqampSk1N1YoVK/Tll1/qxIkT6tevn9577z3vl4dTR7T/Wct/GjRokLZu3aoFCxZoxIgR3uUul0uXX365HA6HNmzYoJ9//tm7rmnTplVu87bbbtPnn3+uoqIiGWO0ePFi3XbbbVXu/3w0a9ZMHTt21PLlyyWd7NGpO/hPnf2YN2+e9/0ZGRnKzs6WJE2dOlVjxozRrFmz9OSTT3rP0gD1HUf2wHmoqKjQnDlzdPToUV1yySXyeDyaPHmyunTpIkmaPXu2nn76aVVUVKh169b605/+VOW2hg0bpqlTp+rLL7/03qA3efJkTZw4UW63W+Xl5Ro8eLC6du2qO++8U7t371ZsbKwiIyN188036+jRo2dtc8mSJYqNjT3jSDYiIkLXX3+9vvrqK02dOlUzZ87U0KFDFRQUpF69eul3v/udoqOjtWzZMg0fPtx7g97pGjdurP79+ysxMVEpKSne5U899ZRmzJihv/3tb7ruuut03XXXedfdddddmjVrlhYsWKDf/va3Z2zv17/+tXbv3q27775bktS1a1dNnDixFn8StTN79mxNnTpVCxcu1A033KDu3bt717366qt65ZVXFBcXJ+nkl5SXX35ZycnJKi0t1UMPPSSHw6HBgwdr+vTpeu211/xWJ+ArzHoHAIDNcRofAACbI+wBALA5wh4AAJsj7AEAsDnCHgAAmyPsAQCwOcIeAACbI+wBALC5/wdehmxozfQrvQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"acts_sorted = np.array([unit_acts[i_b,i_t] for i_b,i_t in i_acts_sorted])\n",
"plt.plot(acts_sorted);\n",
"plt.xlabel('Sorted Activation Index')\n",
"plt.ylabel('Activation Value')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"tight_cap_positions = [\n",
" ['', '', '', '', 'Fp1', 'FPz', 'Fp2', '', '', '', ''],\n",
" ['', '', '', 'AFp3h', '', '', '', 'Afp4h', '', '', ''],\n",
" ['', 'AF7', '', 'AF3', '', 'AFz', '', 'AF4', '', 'AF8', ''],\n",
" ['', '', 'AFF5h', '', 'AFF1', '', 'AFF2', '', 'AFF6h', '', ''],\n",
" ['', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', ''],\n",
" ['FFT9h', 'FFT7h', 'FFC5h', 'FFC3h', 'FFC1h', '', 'FFC2h', 'FFC4h', 'FFC6h', 'FFT8h', 'FFT10h'],\n",
" ['FT9', 'FT7', 'FC5', 'FC3', 'FC1', 'FCz', 'FC2', 'FC4', 'FC6', 'FT8', 'FT10'],\n",
" ['FTT9h', 'FTT7h', 'FCC5h', 'FCC3h', 'FCC1h', '', 'FCC2h', 'FCC4h', 'FCC6h', 'FTT8h', 'FTT10h'],\n",
" ['M1', 'T7', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'T8', 'M2'],\n",
" ['', 'TTP7h', 'CCP5h', 'CCP3h', 'CCP1h', '', 'CCP2h', 'CCP4h', 'CCP6h', 'TTP8h', ''],\n",
" ['', 'TP7', 'CP5', 'CP3', 'CP1', 'CPz', 'CP2', 'CP4', 'CP6', 'TP8', ''],\n",
" ['TPP9h', 'TPP7h', 'CPP5h', 'CPP3h', 'CPP1h', '', 'CPP2h', 'CPP4h', 'CPP6h', 'TPP8h', 'TPP10h'],\n",
" ['P9', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8', 'P10'],\n",
" ['PPO9h', '', 'PPO5h', '', 'PPO1', '', 'PPO2', '', 'PPO6h', '', 'PPO10h'],\n",
" ['PO9', 'PO7', 'PO5', 'PO3', '', 'POz', '', 'PO4', 'PO6', 'PO8', 'PO10'],\n",
" ['POO9h', '', '', 'POO3h', '', '', '', 'POO4h', '', '', 'POO10h'],\n",
" ['', '', '', '', 'O1', 'Oz', 'O2', '', '', '', ''],\n",
" ['', '', '', '', 'OI1h', '', 'OI2h', '', '', '', ''],\n",
" ['', '', '', '', 'I1', 'Iz', 'I2', '', '', '', '']]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"def get_sensor_pos(sensor_name, sensor_map=tight_cap_positions):\n",
" sensor_pos = np.where(\n",
" np.char.lower(np.char.array(sensor_map)) == sensor_name.lower())\n",
" # unpack them: they are 1-dimensional arrays before\n",
" assert len(sensor_pos[0]) == 1, (\n",
" \"there should be a position for the sensor \"\n",
" \"{:s}\".format(sensor_name))\n",
" return sensor_pos[0][0], sensor_pos[1][0]\n",
"\n",
"def plot_head_signals_tight(signals, sensor_names=None, figsize=(12, 7),\n",
" plot_args=None, hspace=0.35,\n",
" sensor_map=tight_cap_positions,\n",
" tsplot=False, sharex=True, sharey=True):\n",
" assert sensor_names is None or len(signals) == len(sensor_names), (\"need \"\n",
" \"sensor names for all sensor matrices\")\n",
" assert sensor_names is not None\n",
" if plot_args is None:\n",
" plot_args = dict()\n",
" figure = plt.figure(figsize=figsize)\n",
" sensor_positions = [get_sensor_pos(name, sensor_map) for name in\n",
" sensor_names]\n",
" sensor_positions = np.array(sensor_positions) # sensors x 2(row and col)\n",
" maxima = np.max(sensor_positions, axis=0)\n",
" minima = np.min(sensor_positions, axis=0)\n",
" max_row = maxima[0]\n",
" max_col = maxima[1]\n",
" min_row = minima[0]\n",
" min_col = minima[1]\n",
" rows = max_row - min_row + 1\n",
" cols = max_col - min_col + 1\n",
" first_ax = None\n",
" for i in range(0, len(signals)):\n",
" sensor_name = sensor_names[i]\n",
" sensor_pos = sensor_positions[i]\n",
" assert np.all(sensor_pos == get_sensor_pos(sensor_name, sensor_map))\n",
" # Transform to flat sensor pos\n",
" row = sensor_pos[0]\n",
" col = sensor_pos[1]\n",
" subplot_ind = (\n",
" row - min_row) * cols + col - min_col + 1 # +1 as matlab uses based indexing\n",
" if first_ax is None:\n",
" ax = figure.add_subplot(rows, cols, subplot_ind)\n",
" first_ax = ax\n",
" elif sharex is True and sharey is True:\n",
" ax = figure.add_subplot(rows, cols, subplot_ind, sharey=first_ax,\n",
" sharex=first_ax)\n",
" elif sharex is True and sharey is False:\n",
" ax = figure.add_subplot(rows, cols, subplot_ind,\n",
" sharex=first_ax)\n",
" elif sharex is False and sharey is True:\n",
" ax = figure.add_subplot(rows, cols, subplot_ind, sharey=first_ax)\n",
" else:\n",
" ax = figure.add_subplot(rows, cols, subplot_ind)\n",
"\n",
" signal = signals[i]\n",
" if tsplot is False:\n",
" ax.plot(signal, **plot_args)\n",
" else:\n",
" seaborn.tsplot(signal.T, ax=ax, **plot_args)\n",
" ax.set_title(sensor_name)\n",
" ax.set_yticks([])\n",
" if len(signal) == 600:\n",
" ax.set_xticks([150, 300, 450])\n",
" ax.set_xticklabels([])\n",
" else:\n",
" ax.set_xticks([])\n",
"\n",
" ax.xaxis.grid(True)\n",
" # make line at zero\n",
" ax.axhline(y=0, ls=':', color=\"grey\")\n",
" figure.subplots_adjust(hspace=hspace)\n",
" return figure\n",
"\n",
"EEG_sensors = ['Fp1', 'Fp2', 'Fpz', 'F7', 'F3', 'Fz', 'F4', 'F8',\n",
" 'FC5', 'FC1', 'FC2', 'FC6', 'M1', 'T7', 'C3', 'Cz', 'C4', 'T8', 'M2',\n",
" 'CP5', 'CP1', 'CP2', 'CP6', 'P7', 'P3', 'Pz', 'P4', 'P8', 'POz', 'O1',\n",
" 'Oz', 'O2', 'AF7', 'AF3', 'AF4', 'AF8', 'F5', 'F1', 'F2', 'F6', 'FC3',\n",
" 'FCz', 'FC4', 'C5', 'C1', 'C2', 'C6', 'CP3', 'CPz', 'CP4', 'P5', 'P1',\n",
" 'P2', 'P6', 'PO5', 'PO3', 'PO4', 'PO6', 'FT7', 'FT8', 'TP7', 'TP8',\n",
" 'PO7', 'PO8', 'FT9', 'FT10', 'TPP9h', 'TPP10h', 'PO9', 'PO10', 'P9',\n",
" 'P10', 'AFF1', 'AFz', 'AFF2', 'FFC5h', 'FFC3h', 'FFC4h', 'FFC6h', 'FCC5h',\n",
" 'FCC3h', 'FCC4h', 'FCC6h', 'CCP5h', 'CCP3h', 'CCP4h', 'CCP6h', 'CPP5h',\n",
" 'CPP3h', 'CPP4h', 'CPP6h', 'PPO1', 'PPO2', 'I1', 'Iz', 'I2', 'AFp3h',\n",
" 'AFp4h', 'AFF5h', 'AFF6h', 'FFT7h', 'FFC1h', 'FFC2h', 'FFT8h', 'FTT9h',\n",
" 'FTT7h', 'FCC1h', 'FCC2h', 'FTT8h', 'FTT10h', 'TTP7h', 'CCP1h', 'CCP2h',\n",
" 'TTP8h', 'TPP7h', 'CPP1h', 'CPP2h', 'TPP8h', 'PPO9h', 'PPO5h', 'PPO6h',\n",
" 'PPO10h', 'POO9h', 'POO3h', 'POO4h', 'POO10h', 'OI1h', 'OI2h']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Let's look at first sensor"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(all_most_activating_windows[:,0].T);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Let's look at median for each sensor"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x1152 with 128 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plot_head_signals_tight(np.median(all_most_activating_windows, axis=0),\n",
" sensor_names=EEG_sensors, figsize=(16,16))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some cooridnates in case they are useful in future"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import math\n",
"\n",
"\n",
"CHANNEL_10_20_APPROX = (\n",
" \"angle\",\n",
" (\"Fpz\", (0.000, 4.000)),\n",
" (\"Fp1\", (-3.500, 3.500)),\n",
" (\"Fp2\", (3.500, 3.500)),\n",
" (\"AFp3h\", (-1.000, 3.500)),\n",
" (\"AFp4h\", (1.000, 3.500)),\n",
" (\"AF7\", (-4.000, 3.000)),\n",
" (\"AF3\", (-2.000, 3.000)),\n",
" (\"AFz\", (0.000, 3.000)),\n",
" (\"AF4\", (2.000, 3.000)),\n",
" (\"AF8\", (4.000, 3.000)),\n",
" (\"AFF5h\", (-2.500, 2.500)),\n",
" (\"AFF1\", (-0.500, 2.500)),\n",
" (\"AFF2\", (0.500, 2.500)),\n",
" (\"AFF6h\", (2.500, 2.500)),\n",
" (\"F7\", (-4.000, 2.000)),\n",
" (\"F5\", (-3.000, 2.000)),\n",
" (\"F3\", (-2.000, 2.000)),\n",
" (\"F1\", (-1.000, 2.000)),\n",
" (\"Fz\", (0.000, 2.000)),\n",
" (\"F2\", (1.000, 2.000)),\n",
" (\"F4\", (2.000, 2.000)),\n",
" (\"F6\", (3.000, 2.000)),\n",
" (\"F8\", (4.000, 2.000)),\n",
" (\"FFT7h\", (-3.500, 1.500)),\n",
" (\"FFC5h\", (-2.500, 1.500)),\n",
" (\"FFC3h\", (-1.500, 1.500)),\n",
" (\"FFC1h\", (-0.500, 1.500)),\n",
" (\"FFC2h\", (0.500, 1.500)),\n",
" (\"FFC4h\", (1.500, 1.500)),\n",
" (\"FFC6h\", (2.500, 1.500)),\n",
" (\"FFT8h\", (3.500, 1.500)),\n",
" (\"FT9\", (-5.000, 1.000)),\n",
" (\"FT7\", (-4.000, 1.000)),\n",
" (\"FC5\", (-3.000, 1.000)),\n",
" (\"FC3\", (-2.000, 1.000)),\n",
" (\"FC1\", (-1.000, 1.000)),\n",
" (\"FCz\", (0.000, 1.000)),\n",
" (\"FC2\", (1.000, 1.000)),\n",
" (\"FC4\", (2.000, 1.000)),\n",
" (\"FC6\", (3.000, 1.000)),\n",
" (\"FT8\", (4.000, 1.000)),\n",
" (\"FT10\", (5.000, 1.000)),\n",
" (\"FTT9h\", (-4.500, 0.500)),\n",
" (\"FTT7h\", (-3.500, 0.500)),\n",
" (\"FCC5h\", (-2.500, 0.500)),\n",
" (\"FCC3h\", (-1.500, 0.500)),\n",
" (\"FCC1h\", (-0.500, 0.500)),\n",
" (\"FCC2h\", (0.500, 0.500)),\n",
" (\"FCC4h\", (1.500, 0.500)),\n",
" (\"FCC6h\", (2.500, 0.500)),\n",
" (\"FTT8h\", (3.500, 0.500)),\n",
" (\"FTT10h\", (4.500, 0.500)),\n",
" (\"M1\", (-5.000, 0.000)),\n",
" # notsure if correct:\n",
" (\"T9\", (-4.500, 0.000)),\n",
" (\"T7\", (-4.000, 0.000)),\n",
" (\"C5\", (-3.000, 0.000)),\n",
" (\"C3\", (-2.000, 0.000)),\n",
" (\"C1\", (-1.000, 0.000)),\n",
" (\"Cz\", (0.000, 0.000)),\n",
" (\"C2\", (1.000, 0.000)),\n",
" (\"C4\", (2.000, 0.000)),\n",
" (\"C6\", (3.000, 0.000)),\n",
" (\"T8\", (4.000, 0.000)),\n",
" (\"T10\", (4.500, 0.000)),\n",
" (\"M2\", (5.000, 0.000)),\n",
" (\"TTP7h\", (-3.500, -0.500)),\n",
" (\"CCP5h\", (-2.500, -0.500)),\n",
" (\"CCP3h\", (-1.500, -0.500)),\n",
" (\"CCP1h\", (-0.500, -0.500)),\n",
" (\"CCP2h\", (0.500, -0.500)),\n",
" (\"CCP4h\", (1.500, -0.500)),\n",
" (\"CCP6h\", (2.500, -0.500)),\n",
" (\"TTP8h\", (3.500, -0.500)),\n",
" (\"TP7\", (-4.000, -1.000)),\n",
" (\"CP5\", (-3.000, -1.000)),\n",
" (\"CP3\", (-2.000, -1.000)),\n",
" (\"CP1\", (-1.000, -1.000)),\n",
" (\"CPz\", (0.000, -1.000)),\n",
" (\"CP2\", (1.000, -1.000)),\n",
" (\"CP4\", (2.000, -1.000)),\n",
" (\"CP6\", (3.000, -1.000)),\n",
" (\"TP8\", (4.000, -1.000)),\n",
" (\"TPP9h\", (-4.500, -1.500)),\n",
" (\"TPP7h\", (-3.500, -1.500)),\n",
" (\"CPP5h\", (-2.500, -1.500)),\n",
" (\"CPP3h\", (-1.500, -1.500)),\n",
" (\"CPP1h\", (-0.500, -1.500)),\n",
" (\"CPP2h\", (0.500, -1.500)),\n",
" (\"CPP4h\", (1.500, -1.500)),\n",
" (\"CPP6h\", (2.500, -1.500)),\n",
" (\"TPP8h\", (3.500, -1.500)),\n",
" (\"TPP10h\", (4.500, -1.500)),\n",
" (\"P9\", (-5.000, -2.000)),\n",
" (\"P7\", (-4.000, -2.000)),\n",
" (\"P5\", (-3.000, -2.000)),\n",
" (\"P3\", (-2.000, -2.000)),\n",
" (\"P1\", (-1.000, -2.000)),\n",
" (\"Pz\", (0.000, -2.000)),\n",
" (\"P2\", (1.000, -2.000)),\n",
" (\"P4\", (2.000, -2.000)),\n",
" (\"P6\", (3.000, -2.000)),\n",
" (\"P8\", (4.000, -2.000)),\n",
" (\"P10\", (5.000, -2.000)),\n",
" (\"PPO9h\", (-4.500, -2.500)),\n",
" (\"PPO5h\", (-3.000, -2.500)),\n",
" (\"PPO1\", (-0.650, -2.500)),\n",
" (\"PPO2\", (0.650, -2.500)),\n",
" (\"PPO6h\", (3.000, -2.500)),\n",
" (\"PPO10h\", (4.500, -2.500)),\n",
" (\"PO9\", (-5.000, -3.000)),\n",
" (\"PO7\", (-4.000, -3.000)),\n",
" (\"PO5\", (-3.000, -3.000)),\n",
" (\"PO3\", (-2.000, -3.000)),\n",
" (\"PO1\", (-1.000, -3.000)),\n",
" (\"POz\", (0.000, -3.000)),\n",
" (\"PO2\", (1.000, -3.000)),\n",
" (\"PO4\", (2.000, -3.000)),\n",
" (\"PO6\", (3.000, -3.000)),\n",
" (\"PO8\", (4.000, -3.000)),\n",
" (\"PO10\", (5.000, -3.000)),\n",
" (\"POO9h\", (-4.500, -3.250)),\n",
" (\"POO3h\", (-2.000, -3.250)),\n",
" (\"POO4h\", (2.000, -3.250)),\n",
" (\"POO10h\", (4.500, -3.250)),\n",
" (\"O1\", (-2.500, -3.750)),\n",
" (\"Oz\", (0.000, -3.750)),\n",
" (\"O2\", (2.500, -3.750)),\n",
" (\"OI1h\", (1.500, -4.250)),\n",
" (\"OI2h\", (-1.500, -4.250)),\n",
" (\"I1\", (1.000, -4.500)),\n",
" (\"Iz\", (0.000, -4.500)),\n",
" (\"I2\", (-1.000, -4.500)),\n",
")\n",
"\n",
"\n",
"def get_channelpos(channame, chan_pos_list):\n",
" if chan_pos_list[0] == \"angle\":\n",
" return get_channelpos_from_angle(channame, chan_pos_list[1:])\n",
" elif chan_pos_list[0] == \"cartesian\":\n",
" channame = channame.lower()\n",
" for name, coords in chan_pos_list[1:]:\n",
" if name.lower() == channame:\n",
" return coords[0], coords[1]\n",
" return None\n",
" else:\n",
" raise ValueError(\n",
" \"Unknown first element \"\n",
" \"{:s} (should be type of positions)\".format(chan_pos_list[0])\n",
" )\n",
"\n",
"\n",
"def get_channelpos_from_angle(channame, chan_pos_list=CHANNEL_10_20_APPROX):\n",
" \"\"\"Return the x/y position of a channel.\n",
"\n",
" This method calculates the stereographic projection of a channel\n",
" from ``CHANNEL_10_20``, suitable for a scalp plot.\n",
"\n",
" Parameters\n",
" ----------\n",
" channame : str\n",
" Name of the channel, the search is case insensitive.\n",
"\n",
" chan_pos_list=CHANNEL_10_20_APPROX,\n",
" interpolation='bilinear'\n",
"\n",
" Returns\n",
" -------\n",
" x, y : float or None\n",
" The projected point on the plane if the point is known,\n",
" otherwise ``None``\n",
"\n",
" Examples\n",
" --------\n",
"\n",
" >>> plot.get_channelpos_from_angle('C2')\n",
" (0.1720792096741632, 0.0)\n",
" >>> # the channels are case insensitive\n",
" >>> plot.get_channelpos_from_angle('c2')\n",
" (0.1720792096741632, 0.0)\n",
" >>> # lookup for an invalid channel\n",
" >>> plot.get_channelpos_from_angle('foo')\n",
" None\n",
"\n",
" \"\"\"\n",
" channame = channame.lower()\n",
" for i in chan_pos_list:\n",
" if i[0].lower() == channame:\n",
" # convert the 90/4th angular position into x, y, z\n",
" p = i[1]\n",
" x, y = _convert_2d_angle_to_2d_coord(*p)\n",
" return x, y\n",
" return None\n",
"\n",
"\n",
"def _convert_2d_angle_to_2d_coord(a, b):\n",
" # convert the 90/4th angular position into x, y, z\n",
" ea, eb = a * (90 / 4), b * (90 / 4)\n",
" ea = ea * math.pi / 180\n",
" eb = eb * math.pi / 180\n",
" x = math.sin(ea) * math.cos(eb)\n",
" y = math.sin(eb)\n",
" z = math.cos(ea) * math.cos(eb)\n",
" # Calculate the stereographic projection.\n",
" # Given a unit sphere with radius ``r = 1`` and center at\n",
" # the origin. Project the point ``p = (x, y, z)`` from the\n",
" # sphere's South pole (0, 0, -1) on a plane on the sphere's\n",
" # North pole (0, 0, 1).\n",
" #\n",
" # The formula is:\n",
" #\n",
" # P' = P * (2r / (r + z))\n",
" #\n",
" # We changed the values to move the point of projection\n",
" # further below the south pole\n",
" mu = 1 / (1.3 + z)\n",
" x *= mu\n",
" y *= mu\n",
" return x, y\n"
]
}
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