SYMBOL INDEX (561 symbols across 42 files)

FILE: data_retrieval/csi_extraction/csi_calibration.py
  function calibrate_phase (line 4) | def calibrate_phase(phases):
  function calibrate_amplitude (line 36) | def calibrate_amplitude(amplitudes, rssi):  # Basic statistical normaliz...

FILE: data_retrieval/csi_extraction/read_csi.py
  class csi_struct (line 11) | class csi_struct:
  function unpack_csi_struct (line 15) | def unpack_csi_struct(f, endianess='>'):  # Big-Endian as Default Value
  function read_csi (line 49) | def read_csi(csi_buf, num_tones, nc, nr, csi_len, endianess):
  function signbit_convert (line 103) | def signbit_convert(data, maxbit):
  function calc_frequency (line 109) | def calc_frequency(basefrequency, c, num_tones):
  function calc_phase_angle (line 119) | def calc_phase_angle(iq, unwrap=0):

FILE: data_retrieval/csi_extraction/read_log_file.py
  function read_log_file (line 6) | def read_log_file(filename, ignore_endian=0, endian="", check_tones=1):

FILE: data_retrieval/run_test_client.py
  function run_client (line 7) | def run_client(filename: str, host: str, port: int) -> None:
  function init_argparse (line 43) | def init_argparse() -> argparse.ArgumentParser:

FILE: data_retrieval/run_visualization_server.py
  class UI (line 15) | class UI(QtWidgets.QWidget):
    method __init__ (line 16) | def __init__(self, app: QtWidgets.QApplication, parent: QtWidgets.QWid...
    method update_plots (line 55) | def update_plots(self):
    method process_events (line 75) | def process_events(self):
  class UDPListener (line 79) | class UDPListener:
    method __init__ (line 82) | def __init__(self, save_data_path: str, sock: QtNetwork.QUdpSocket,
    method on_datagram_received (line 95) | def on_datagram_received(self):
    method get_csi_raw_data (line 113) | def get_csi_raw_data(self, csi_inf):
    method calc (line 145) | def calc(self, raw_peak_amplitudes, raw_phases, carriers_indexes, ante...
    method make_photo_and_save (line 162) | def make_photo_and_save(self):
    method save_csi_to_file (line 171) | def save_csi_to_file(self, raw_peak_amplitudes, raw_phases, carriers):
  function init_argparse (line 182) | def init_argparse() -> argparse.ArgumentParser:
  function run_app (line 199) | def run_app() -> None:

FILE: model/data_calibration.py
  function calibrate_single_phase (line 6) | def calibrate_single_phase(phases):
  function calibrate_phase (line 42) | def calibrate_phase(phases):
  function calibrate_amplitude (line 60) | def calibrate_amplitude(amplitudes, rssi=1):
  function calibrate_amplitude_custom (line 74) | def calibrate_amplitude_custom(amplitudes, min_val, max_val, rssi=1):
  function dwn_noise (line 79) | def dwn_noise(vals):
  function hampel (line 96) | def hampel(vals_orig, k=7, t0=3):

FILE: model/dataset.py
  function read_csi_data_from_csv (line 22) | def read_csi_data_from_csv(path_to_csv, is_five_hhz=False, antenna_pairs...
  function read_labels_from_csv (line 47) | def read_labels_from_csv(path_to_csv):
  function read_all_data_from_files (line 61) | def read_all_data_from_files(paths, is_five_hhz=True, antenna_pairs=4):
  function read_all_data (line 85) | def read_all_data(is_five_hhz=True, antenna_pairs=4):
  class CSIDataset (line 95) | class CSIDataset(Dataset):
    method __init__ (line 98) | def __init__(self, csv_files, window_size=32, step=1):
    method __getitem__ (line 147) | def __getitem__(self, idx):
    method __len__ (line 163) | def __len__(self):

FILE: model/label_activities.py
  class Activity (line 12) | class Activity(Enum):
  function set_activity (line 33) | def set_activity(activities, from_moment, to_moment, activity):
  function main (line 37) | def main():

FILE: model/label_human_boxes.py
  function detect_person_box (line 12) | def detect_person_box(path_to_the_image):
  function class_id_to_string (line 101) | def class_id_to_string(id):
  function main (line 105) | def main():

FILE: model/metrics.py
  function get_train_metric (line 9) | def get_train_metric(model, dl, criterion, BATCH_SIZE):

FILE: model/models/FCNBaseline.py
  class FCNBaseline (line 7) | class FCNBaseline(nn.Module):
    method __init__ (line 19) | def __init__(self, in_channels: int, num_pred_classes: int = 1) -> None:
    method forward (line 35) | def forward(self, x: torch.Tensor) -> torch.Tensor:  # type: ignore

FILE: model/models/InceptionTime.py
  class InceptionModel (line 8) | class InceptionModel(nn.Module):
    method __init__ (line 33) | def __init__(self, num_blocks: int, in_channels: int, out_channels: Un...
    method _expand_to_blocks (line 73) | def _expand_to_blocks(value: Union[int, bool, List[int], List[bool]],
    method forward (line 83) | def forward(self, x: torch.Tensor) -> torch.Tensor:  # type: ignore
  class InceptionBlock (line 88) | class InceptionBlock(nn.Module):
    method __init__ (line 93) | def __init__(self, in_channels: int, out_channels: int,
    method forward (line 123) | def forward(self, x: torch.Tensor) -> torch.Tensor:  # type: ignore
  class Conv1dSamePadding (line 134) | class Conv1dSamePadding(nn.Conv1d):
    method forward (line 140) | def forward(self, input):
  function conv1d_same_padding (line 145) | def conv1d_same_padding(input, weight, bias, stride, dilation, groups):
  class ConvBlock (line 158) | class ConvBlock(nn.Module):
    method __init__ (line 160) | def __init__(self, in_channels: int, out_channels: int, kernel_size: int,
    method forward (line 173) | def forward(self, x: torch.Tensor) -> torch.Tensor:  # type: ignore

FILE: model/models/LSTMClassifier.py
  class LSTMClassifier (line 8) | class LSTMClassifier(nn.Module):
    method __init__ (line 10) | def __init__(self, in_dim, hidden_dim, num_layers, dropout, bidirectio...
    method init_hidden (line 39) | def init_hidden(self, batch_size):
    method forward (line 50) | def forward(self, x):  # x is (batch_size, sequence_size, num_of_featu...

FILE: model/models/SimpleLSTMClassifier.py
  class SimpleLSTMClassifier (line 7) | class SimpleLSTMClassifier(nn.Module):
    method __init__ (line 10) | def __init__(self, input_dim, hidden_dim, num_layers_lstm, out_classes...
    method forward (line 26) | def forward(self, x):
    method init_hidden (line 35) | def init_hidden(self, batch_size):

FILE: model/models/utils.py
  class Conv1dSamePadding (line 6) | class Conv1dSamePadding(nn.Conv1d):
    method forward (line 11) | def forward(self, input):
  function conv1d_same_padding (line 16) | def conv1d_same_padding(input, weight, bias, stride, dilation, groups):
  class ConvBlock (line 29) | class ConvBlock(nn.Module):
    method __init__ (line 31) | def __init__(self, in_channels: int, out_channels: int, kernel_size: int,
    method forward (line 44) | def forward(self, x: torch.Tensor) -> torch.Tensor:  # type: ignore

FILE: model/notebooks/fastai_timeseries/exp/nb_ColorfulDim.py
  function splitAtFirstParenthesis (line 13) | def splitAtFirstParenthesis(s,showDetails,shapeData):
  class ActivationsHistogram (line 20) | class ActivationsHistogram(HookCallback):
    method __init__ (line 24) | def __init__(self, learn:Learner, do_remove:bool=True,
    method mkHist (line 46) | def mkHist(self, x, useClasses):
    method on_train_begin (line 55) | def on_train_begin(self, **kwargs):
    method on_epoch_begin (line 66) | def on_epoch_begin(self, **kwargs):
    method on_batch_begin (line 69) | def on_batch_begin(self, train, **kwargs):
    method hook (line 73) | def hook(self, m:nn.Module, i:Tensors, o:Tensors)->Rank0Tensor:
    method on_batch_end (line 79) | def on_batch_end(self, train, **kwargs):
    method on_epoch_end (line 90) | def on_epoch_end(self, **kwargs):
    method on_train_end (line 113) | def on_train_end(self, **kwargs):
    method get_color_value_from_map (line 119) | def get_color_value_from_map(idx:int, cmap='Reds', scale=1):
    method getHistImg (line 123) | def getHistImg(act,useClasses):
    method getMin (line 130) | def getMin(act,useClasses,zero_bin):
    method computeXY (line 137) | def computeXY(l,hscale,perc,hshift=0):
    method plotPerc (line 146) | def plotPerc(ax,l,hscale,perc,hshift=0,colorById=False,linewidth=1,add...
    method plotActsHist (line 159) | def plotActsHist(self, cols=10, toDisplay=None, hScale = .05,
  function telemetry (line 218) | def telemetry(learn:Learner, ah:Callable=None, modulesId:Union[bool, lis...
  function noop (line 227) | def noop(x): return x
  function get_layers (line 230) | def get_layers(model, cond=noop):
  function get_layers_idx (line 246) | def get_layers_idx(learn, cond=noop):

FILE: model/notebooks/fastai_timeseries/exp/nb_ImageDataAugmentation.py
  class CutMixCallback (line 8) | class CutMixCallback(LearnerCallback):
    method __init__ (line 10) | def __init__(self, learn:Learner, α:float=1., stack_y:bool=True, true_...
    method on_train_begin (line 14) | def on_train_begin(self, **kwargs):
    method on_batch_begin (line 17) | def on_batch_begin(self, last_input, last_target, train, **kwargs):
    method on_train_end (line 42) | def on_train_end(self, **kwargs):
  function rand_bbox (line 46) | def rand_bbox(last_input_size, λ):
  function cutmix (line 67) | def cutmix(learn:Learner, α:float=1., stack_x:bool=False, stack_y:bool=T...
  class RicapLoss (line 79) | class RicapLoss(nn.Module):
    method __init__ (line 82) | def __init__(self, crit, reduction='mean'):
    method forward (line 93) | def forward(self, output, target):
    method get_old (line 104) | def get_old(self):
  class RicapCallback (line 110) | class RicapCallback(LearnerCallback):
    method __init__ (line 112) | def __init__(self, learn:Learner, stack_y:bool=True):
    method on_train_begin (line 116) | def on_train_begin(self, **kwargs):
    method on_batch_begin (line 119) | def on_batch_begin(self, last_input, last_target, train, **kwargs):
    method on_train_end (line 148) | def on_train_end(self, **kwargs):
  function ricap (line 152) | def ricap(learn:Learner, stack_y:bool=True) -> Learner:

FILE: model/notebooks/fastai_timeseries/exp/nb_Initialization.py
  class ListContainer (line 13) | class ListContainer():
    method __init__ (line 14) | def __init__(self, items):
    method __getitem__ (line 16) | def __getitem__(self, idx):
    method __len__ (line 25) | def __len__(self): return len(self.items)
    method __iter__ (line 26) | def __iter__(self): return iter(self.items)
    method __setitem__ (line 27) | def __setitem__(self, i, o): self.items[i] = o
    method __delitem__ (line 28) | def __delitem__(self, i): del(self.items[i])
    method __repr__ (line 29) | def __repr__(self):
  class Hook (line 34) | class Hook():
    method __init__ (line 35) | def __init__(self, m, f):
    method remove (line 37) | def remove(self): self.hook.remove()
    method __del__ (line 38) | def __del__(self): self.remove()
  class Hooks (line 40) | class Hooks(ListContainer):
    method __init__ (line 41) | def __init__(self, ms, f): super().__init__([Hook(m, f) for m in ms])
    method __enter__ (line 42) | def __enter__(self, *args): return self
    method __exit__ (line 43) | def __exit__ (self, *args): self.remove()
    method __del__ (line 44) | def __del__(self): self.remove()
    method __delitem__ (line 45) | def __delitem__(self, i):
    method remove (line 48) | def remove(self):
  function noop (line 52) | def noop(x): return x
  function is_layer (line 54) | def is_layer(*args):
  function is_lin_layer (line 59) | def is_lin_layer(l):
  function is_conv_lin_layer (line 62) | def is_conv_lin_layer(l):
  function is_affine_layer (line 66) | def is_affine_layer(l):
  function is_conv_layer (line 69) | def is_conv_layer(l):
  function has_bias (line 73) | def has_bias(l):
  function has_weight (line 76) | def has_weight(l):
  function has_weight_or_bias (line 79) | def has_weight_or_bias(l):
  function find_modules (line 82) | def find_modules(m, cond=noop):
  function get_layers (line 87) | def get_layers(model, cond=noop):
  function append_stat (line 95) | def append_stat(hook, mod, inp, out):
  function zmuv_layer (line 103) | def zmuv_layer(model: Callable, m: Callable, xb: Tensor, max_attempts: i...
  function zmuv (line 119) | def zmuv(learn: Learner,  tol: float = 1e-5, exp_mean: float = 0.,
  function svd_orthonormal (line 151) | def svd_orthonormal(w):
  function orthogonal_weights_init (line 163) | def orthogonal_weights_init(m):
  function ortho_w_init (line 179) | def ortho_w_init(learn: Learner) -> Learner:
  function kaiming (line 187) | def kaiming(learn: Learner, normal: bool = True, a: float = 0,
  function activations (line 202) | def activations(learn, thr=.1, cond=noop) -> Learner:
  function layer_stats (line 285) | def layer_stats(hook, mod, inp, out, thr=.1):

FILE: model/notebooks/fastai_timeseries/exp/nb_NewDataAugmentation.py
  class RicapLoss (line 14) | class RicapLoss(nn.Module):
    method __init__ (line 17) | def __init__(self, crit, reduction='mean'):
    method forward (line 28) | def forward(self, output, target):
    method get_old (line 39) | def get_old(self):
  class RicapCallback (line 45) | class RicapCallback(LearnerCallback):
    method __init__ (line 50) | def __init__(self, learn:Learner, beta:float=.3, stack_y:bool=True):
    method on_train_begin (line 54) | def on_train_begin(self, **kwargs):
    method on_batch_begin (line 57) | def on_batch_begin(self, last_input, last_target, train, **kwargs):
    method on_train_end (line 97) | def on_train_end(self, **kwargs):
  function ricap (line 101) | def ricap(learn:Learner, beta:float=.3, stack_y:bool=True) -> Learner:
  class CutMixCallback (line 112) | class CutMixCallback(LearnerCallback):
    method __init__ (line 118) | def __init__(self, learn:Learner, alpha:float=1., stack_y:bool=True):
    method on_train_begin (line 122) | def on_train_begin(self, **kwargs):
    method on_batch_begin (line 125) | def on_batch_begin(self, last_input, last_target, train, **kwargs):
    method on_train_end (line 154) | def on_train_end(self, **kwargs):
  function rand_bbox (line 158) | def rand_bbox(last_input_size, λ):
  function cutmix (line 181) | def cutmix(learn:Learner, alpha:float=1., stack_x:bool=False, stack_y:bo...
  function get_x1_coords (line 190) | def get_x1_coords(x_size, n_patches, same_size=True):
  function get_x1_rand_coords (line 211) | def get_x1_rand_coords(x_size, n_patches, w, h, same_size=True):
  function get_x2_coords (line 226) | def get_x2_coords(x_size, bby1, bby2, bbx1, bbx2):
  class BlendLoss (line 236) | class BlendLoss(nn.Module):
    method __init__ (line 239) | def __init__(self, crit, reduction='mean'):
    method forward (line 250) | def forward(self, output, target):
    method get_old (line 262) | def get_old(self):
  class BlendCallback (line 269) | class BlendCallback(LearnerCallback):
    method __init__ (line 271) | def __init__(self, learn:Learner,
    method on_train_begin (line 298) | def on_train_begin(self, **kwargs):
    method on_batch_begin (line 301) | def on_batch_begin(self, last_input, last_target, train, **kwargs):
    method on_train_end (line 374) | def on_train_end(self, **kwargs):
  function blend (line 378) | def blend(learn:Learner, size:tuple=(.1, .1), alpha:float=1., fixed_prob...
  class TfmScheduler (line 392) | class TfmScheduler(LearnerCallback):
    method __init__ (line 394) | def __init__(self,
    method on_train_begin (line 431) | def on_train_begin(self, n_epochs: int, epoch: int, **kwargs: Any):
    method on_batch_begin (line 460) | def on_batch_begin(self, last_input, last_target, train, **kwargs):
    method on_train_end (line 475) | def on_train_end(self, **kwargs):
  class MyScheduler (line 479) | class MyScheduler():
    method __init__ (line 481) | def __init__(self, total_iters:int, sch_val:StartOptEnd, sch_iter:Opti...
    method restart (line 497) | def restart(self): self.n = 0
    method step (line 499) | def step(self)->Number:
  function cosine_annealing (line 507) | def cosine_annealing(start:Number, end:Number, pct:float, pct_start=.3, ...
  function inv_annealing_poly (line 514) | def inv_annealing_poly(start:Number, end:Number, pct:float, degree:Numbe...
  function inv_annealing_cos (line 518) | def inv_annealing_cos(start:Number, end:Number, pct:float, **kwargs)->Nu...
  function tuplify (line 523) | def tuplify(a):
  function get_fn (line 529) | def get_fn(a):
  function show_multi_img_tfms (line 537) | def show_multi_img_tfms(learn, rows=3, cols=3, figsize=(8, 8)):
  function show_single_img_tfms (line 559) | def show_single_img_tfms(learn, rows=3, cols=3, figsize=(8, 8)):
  function show_multi_img_tfms (line 568) | def show_multi_img_tfms(learn, rows=3, cols=3, figsize=(8, 8)):

FILE: model/notebooks/fastai_timeseries/exp/nb_Optimizers.py
  class RAdam (line 11) | class RAdam(Optimizer):
    method __init__ (line 13) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weig...
    method __setstate__ (line 18) | def __setstate__(self, state):
    method step (line 21) | def step(self, closure=None):
  class LAMB (line 85) | class LAMB(Optimizer):
    method __init__ (line 103) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6,
    method step (line 118) | def step(self, closure=None):
  class LARS (line 190) | class LARS(Optimizer):
    method __init__ (line 210) | def __init__(self, params, lr=1e-3, momentum=.9,
    method step (line 228) | def step(self, epoch=None, closure=None):
  class NovoGrad (line 287) | class NovoGrad(optim.Optimizer):
    method __init__ (line 288) | def __init__(self, params, grad_averaging=False, lr=0.01, betas=(0.9, ...
    method step (line 299) | def step(self, closure=None):
  class Lookahead (line 363) | class Lookahead(Optimizer):
    method __init__ (line 364) | def __init__(self, base_optimizer, alpha=0.5, k=6):
    method update_slow (line 380) | def update_slow(self, group):
    method sync_lookahead (line 392) | def sync_lookahead(self):
    method step (line 396) | def step(self, closure=None):
    method state_dict (line 406) | def state_dict(self):
    method load_state_dict (line 420) | def load_state_dict(self, state_dict):
  function LookaheadAdam (line 446) | def LookaheadAdam(params, alpha=0.5, k=6, *args, **kwargs):
  class AdaBound (line 456) | class AdaBound(Optimizer):
    method __init__ (line 475) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1, ...
    method __setstate__ (line 495) | def __setstate__(self, state):
    method step (line 500) | def step(self, closure=None):
  class AdaBoundW (line 570) | class AdaBoundW(Optimizer):
    method __init__ (line 589) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1, ...
    method __setstate__ (line 610) | def __setstate__(self, state):
    method step (line 615) | def step(self, closure=None):
  class RALAMB (line 692) | class RALAMB(Optimizer):
    method __init__ (line 694) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weig...
    method __setstate__ (line 699) | def __setstate__(self, state):
    method step (line 702) | def step(self, closure=None):
  class Ranger (line 796) | class Ranger(Optimizer):
    method __init__ (line 798) | def __init__(self, params, lr=1e-3, alpha=0.5, k=6, N_sma_threshhold=5...
    method __setstate__ (line 847) | def __setstate__(self, state):
    method step (line 851) | def step(self, closure=None):
  function Over9000 (line 938) | def Over9000(params, alpha=0.5, k=5, *args, **kwargs):

FILE: model/notebooks/fastai_timeseries/exp/nb_TSBasicData.py
  class TSItem (line 19) | class TSItem(ItemBase):
    method __init__ (line 22) | def __init__(self, item, *args, **kwargs):
    method __str__ (line 30) | def __str__(self):
    method clone (line 34) | def clone(self):
    method apply_tfms (line 37) | def apply_tfms(self, tfms=None, **kwargs):
    method reconstruct (line 43) | def reconstruct(self, item):
    method show (line 46) | def show(self, ax=None, title=None, **kwargs):
  class TimeSeriesItem (line 66) | class TimeSeriesItem(TSItem): pass
  class TSDataBunch (line 70) | class TSDataBunch(DataBunch):
    method scale (line 72) | def scale(self, scale_type='standardize', scale_by_channel=False, scal...
    method cw (line 179) | def cw(self)->None: return self._get_cw(self.train_dl)
    method dbtype (line 182) | def dbtype(self)->str: return '1D'
    method _get_cw (line 184) | def _get_cw(self, train_dl):
  function show_counts (line 193) | def show_counts(databunch):
  class TSPreProcessor (line 204) | class TSPreProcessor(PreProcessor):
    method __init__ (line 206) | def __init__(self, ds: ItemList): self.ds = ds
    method process (line 208) | def process(self, ds: ItemList):
  class TimeSeriesList (line 214) | class TimeSeriesList(ItemList):
    method __init__ (line 221) | def __init__(self, items, *args, mask=None, tfms=None, **kwargs):
    method get (line 227) | def get(self, i):
    method show_xys (line 233) | def show_xys(self, xs, ys, figsize=(10, 10), **kwargs):
    method show_xyzs (line 242) | def show_xyzs(self, xs, ys, zs, figsize=(10, 10), **kwargs):
    method from_array (line 269) | def from_array(cls, ts, **kwargs):
    method from_df (line 273) | def from_df(cls, df, path='.', cols=None, feat=None, processor=None, *...
  class TSList (line 293) | class TSList(TimeSeriesList): pass
  class MixedTimeSeriesList (line 297) | class MixedTimeSeriesList(ItemList):
    method __init__ (line 304) | def __init__(self, items, *args, mask=None, tfms=None, **kwargs):
    method get (line 310) | def get(self, i):
    method show_xys (line 316) | def show_xys(self, xs, ys, figsize=(10, 10), **kwargs):
    method show_xyzs (line 325) | def show_xyzs(self, xs, ys, zs, figsize=(10, 10), **kwargs):
    method from_array (line 352) | def from_array(cls, ts, processor=None, **kwargs):
    method from_df (line 356) | def from_df(cls, df, path='.', cols=None, feat=None, processor=None, *...
  function df2array (line 376) | def df2array(df, feat=None):

FILE: model/notebooks/fastai_timeseries/exp/nb_TSCallbacks.py
  class CutMixCallback (line 15) | class CutMixCallback(LearnerCallback):
    method __init__ (line 17) | def __init__(self, learn:Learner, alpha:float=1., alpha2:float=0., sta...
    method on_train_begin (line 28) | def on_train_begin(self, **kwargs):
    method on_batch_begin (line 31) | def on_batch_begin(self, last_input, last_target, train, **kwargs):
    method on_train_end (line 67) | def on_train_end(self, **kwargs):
  function rand_bbox (line 72) | def rand_bbox(last_input_size, λ):
  function cutmix (line 97) | def cutmix(learn:Learner, alpha:float=1., stack_y:bool=True) -> Learner:
  function cutout (line 102) | def cutout(learn:Learner, alpha:float=1., stack_y:bool=True, out:bool=Tr...
  function cutmixup (line 107) | def cutmixup(learn:Learner, alpha:float=1., alpha2:float=1., stack_y:boo...
  class RicapLoss (line 125) | class RicapLoss(nn.Module):
    method __init__ (line 128) | def __init__(self, crit, reduction='mean'):
    method forward (line 139) | def forward(self, output, target):
    method get_old (line 150) | def get_old(self):
  class RicapCallback (line 156) | class RicapCallback(LearnerCallback):
    method __init__ (line 161) | def __init__(self, learn:Learner, beta:float=.3, stack_y:bool=True):
    method on_train_begin (line 165) | def on_train_begin(self, **kwargs):
    method on_batch_begin (line 168) | def on_batch_begin(self, last_input, last_target, train, **kwargs):
    method on_train_end (line 208) | def on_train_end(self, **kwargs):
  function ricap (line 212) | def ricap(learn:Learner, beta:float=.3, stack_y:bool=True) -> Learner:
  function get_fn (line 221) | def get_fn(a):
  function show_tfms (line 227) | def show_tfms(learn, rows=3, cols=3, figsize=(8, 8)):
  function show_tfms_db (line 297) | def show_tfms_db(data, rows=3, cols=3, figsize=(8, 8)):
  class OverSamplingCallback (line 327) | class OverSamplingCallback(LearnerCallback):
    method __init__ (line 328) | def __init__(self,learn:Learner,weights:torch.Tensor=None):
    method on_train_begin (line 332) | def on_train_begin(self, **kwargs):
  function oversampling (line 342) | def oversampling(learn: Learner) -> Learner:
  class ReduceLROnPlateau (line 352) | class ReduceLROnPlateau(TrackerCallback):
    method __init__ (line 354) | def __init__(self, learn:Learner, monitor:str='valid_loss', mode:str='...
    method on_train_begin (line 360) | def on_train_begin(self, **kwargs:Any)->None:
    method on_epoch_end (line 365) | def on_epoch_end(self, epoch, **kwargs:Any)->None:
  function reduce_lr_on_plateau (line 378) | def reduce_lr_on_plateau(learn: Learner, monitor='valid_loss', mode='auto',
  class TfmScheduler (line 391) | class TfmScheduler(LearnerCallback):
    method __init__ (line 393) | def __init__(self,
    method on_train_begin (line 430) | def on_train_begin(self, n_epochs: int, epoch: int, **kwargs: Any):
    method on_batch_begin (line 459) | def on_batch_begin(self, last_input, last_target, train, **kwargs):
    method on_train_end (line 474) | def on_train_end(self, **kwargs):
  class MyScheduler (line 478) | class MyScheduler():
    method __init__ (line 480) | def __init__(self, total_iters:int, sch_val:StartOptEnd, sch_iter:Opti...
    method restart (line 496) | def restart(self): self.n = 0
    method step (line 498) | def step(self)->Number:
  function cosine_annealing (line 506) | def cosine_annealing(start:Number, end:Number, pct:float, pct_start=.3, ...
  function inv_annealing_poly (line 513) | def inv_annealing_poly(start:Number, end:Number, pct:float, degree:Numbe...
  function inv_annealing_cos (line 517) | def inv_annealing_cos(start:Number, end:Number, pct:float, **kwargs)->Nu...
  function tuplify (line 522) | def tuplify(a):
  function get_fn (line 528) | def get_fn(a):

FILE: model/notebooks/fastai_timeseries/exp/nb_TSCharts.py
  function splitAtFirstParenthesis (line 12) | def splitAtFirstParenthesis(s,showDetails,shapeData):
  class ActivationsHistogram (line 19) | class ActivationsHistogram(HookCallback):
    method __init__ (line 23) | def __init__(self, learn:Learner, do_remove:bool=True,
    method mkHist (line 44) | def mkHist(self, x, useClasses):
    method on_train_begin (line 53) | def on_train_begin(self, **kwargs):
    method on_epoch_begin (line 64) | def on_epoch_begin(self, **kwargs):
    method on_batch_begin (line 67) | def on_batch_begin(self, train, **kwargs):
    method hook (line 71) | def hook(self, m:nn.Module, i:Tensors, o:Tensors)->Rank0Tensor:
    method on_batch_end (line 77) | def on_batch_end(self, train, **kwargs):
    method on_epoch_end (line 88) | def on_epoch_end(self, **kwargs):
    method on_train_end (line 111) | def on_train_end(self, **kwargs):
    method get_color_value_from_map (line 116) | def get_color_value_from_map(idx:int, cmap='Reds', scale=1):
    method getHistImg (line 120) | def getHistImg(act,useClasses):
    method computeXY (line 127) | def computeXY(l,hscale,perc,hshift=0):
    method plotPerc (line 136) | def plotPerc(ax,l,hscale,perc,hshift=0,colorById=False,linewidth=1,add...
    method plotActsHist (line 149) | def plotActsHist(self, cols=3, figsize=(20,10), toDisplay=None, hScale...

FILE: model/notebooks/fastai_timeseries/exp/nb_TSDataAugmentation.py
  function shuffle_HLs (line 18) | def shuffle_HLs(ts, **kwargs):
  function get_diff (line 34) | def get_diff(a):
  class TSTransform (line 40) | class TSTransform():
    method __init__ (line 44) | def __init__(self, func:Callable, order:Optional[int]=None):
    method __call__ (line 53) | def __call__(self, *args:Any, p:float=1., is_random:bool=True, use_on_...
    method calc (line 58) | def calc(self, x:TSItem, *args:Any, **kwargs:Any)->Image:
    method name (line 64) | def name(self)->str: return self.__class__.__name__
    method __repr__ (line 66) | def __repr__(self)->str: return f'{self.name}({self.func.__name__})'
  class TSRandTransform (line 70) | class TSRandTransform():
    method __call__ (line 82) | def __call__(self, x:TSItem, *args, **kwargs) -> TSItem:
  function random_curve_generator (line 126) | def random_curve_generator(ts, magnitude=.1, order=4, noise=None):
  function random_cum_curve_generator (line 134) | def random_cum_curve_generator(ts, magnitude=.1, order=4, noise=None):
  function random_cum_noise_generator (line 142) | def random_cum_noise_generator(ts, magnitude=.1, noise=None):
  function _magnoise (line 151) | def _magnoise(x, magnitude=.1, add=True):
  function _timewarp (line 171) | def _timewarp(x, magnitude=.1, order=4):
  function _magwarp (line 188) | def _magwarp(x, magnitude=.1, order=4):
  function _magscale (line 201) | def _magscale(x, magnitude=.1):
  function _dimmagscale (line 215) | def _dimmagscale(x, magnitude=.1):
  function _timenoise (line 231) | def _timenoise(x, magnitude=.1):
  function _zoomin (line 247) | def _zoomin(x, magnitude=.2):
  function _zoomout (line 269) | def _zoomout(x, magnitude=.2):
  function _randomzoom (line 289) | def _randomzoom(x, magnitude=.2):
  function _randtimestep (line 297) | def _randtimestep(x, magnitude=.1):
  function _lookback (line 316) | def _lookback(x, magnitude=.2):
  function _dimout (line 333) | def _dimout(ts, magnitude=.2):
  function _cutout (line 353) | def _cutout(x, magnitude=.1):
  function _timestepout (line 373) | def _timestepout(x, magnitude=.1):
  function _timestepzero (line 390) | def _timestepzero(x, magnitude=.1):
  function _crop (line 408) | def _crop(x, magnitude=.1):
  function _randomcrop (line 428) | def _randomcrop(x, magnitude=.2):
  function _centercrop (line 450) | def _centercrop(x, magnitude=.2):
  function _maskout (line 472) | def _maskout(x, magnitude=.1):
  function TS_geometric_tfms (line 488) | def TS_geometric_tfms(**kwargs):
  function TS_erasing_tfms (line 505) | def TS_erasing_tfms(**kwargs):
  function TS_tfms (line 519) | def TS_tfms(**kwargs):
  function all_TS_tfms (line 523) | def all_TS_tfms(**kwargs):
  class RandAugment (line 529) | class RandAugment():
    method __init__ (line 530) | def __init__(self, tfms, N=1, **kwargs):
    method __call__ (line 542) | def __call__(self, x):
  function randaugment (line 549) | def randaugment(learn:Learner, tfms:list=TS_tfms(), N:int=1, **kwargs)->...

FILE: model/notebooks/fastai_timeseries/exp/nb_TSDatasets.py
  function decompress_from_url (line 24) | def decompress_from_url(url, target_dir=None, verbose=False):
  function get_UCR_univariate_list (line 67) | def get_UCR_univariate_list():
  function get_UCR_multivariate_list (line 107) | def get_UCR_multivariate_list():
  function get_UCR_univariate (line 120) | def get_UCR_univariate(sel_dataset, parent_dir='data/UCR', verbose=False...
  function get_UCR_multivariate (line 164) | def get_UCR_multivariate(sel_dataset, parent_dir='data/UCR', verbose=Fal...
  function get_UCR_data (line 222) | def get_UCR_data(dsid, parent_dir='data/UCR', verbose=False, check=True):
  function create_UCR_databunch (line 236) | def create_UCR_databunch(dsid, bs=64, scale_type='standardize', scale_by...
  function create_seq_optimized (line 247) | def create_seq_optimized(n_samples=1000, seq_len=100, channels=True, see...
  function get_translation_invariance_data (line 255) | def get_translation_invariance_data(n_samples, seq_len, seed):

FILE: model/notebooks/fastai_timeseries/exp/nb_TSImageData.py
  function GADF_encoder (line 35) | def GADF_encoder(ts, size=None, sample_range=None, overlapping=False,
  function GASF_encoder (line 50) | def GASF_encoder(ts, size=None, sample_range=None, overlapping=False,
  function MTF_encoder (line 65) | def MTF_encoder(ts,
  function RP_encoder (line 83) | def RP_encoder(ts,
  function Spectro_encoder (line 106) | def Spectro_encoder(ts,
  function Scalo_encoder (line 144) | def Scalo_encoder(ts,
  function AddCoordConv (line 164) | def AddCoordConv(arr, **kwargs):
  function get_plot_fig (line 180) | def get_plot_fig(ts, size, yrange=(-1, 1), dpi=72):
  function fig2img (line 192) | def fig2img(fig, size, return_img=True):
  function plot (line 200) | def plot(ts, size=None, yrange=(-1, 1), dpi=72, **kwargs):
  function norm (line 245) | def norm(tensor):
  function apply_cmap (line 249) | def apply_cmap(tensor, cmap=None, **kwargs):
  function ToImage (line 257) | def ToImage(tensor, size=None, cmap=None, **kwargs):
  function resize_tensor (line 271) | def resize_tensor(tensor, size):
  function add_dim (line 277) | def add_dim(tensor, **kwargs):
  function _repeat_ch (line 281) | def _repeat_ch(tensor, **kwargs):
  function _add_zero_ch (line 289) | def _add_zero_ch(tensor, **kwargs):
  class TS2Image (line 305) | class TS2Image():
    method __init__ (line 308) | def __init__(self,
    method __call__ (line 350) | def __call__(self, ts):
  function get_fill_between_fig (line 370) | def get_fill_between_fig(data, size=224, yrange=(-1, 1), dpi=72):
  function plot_fill_between (line 382) | def plot_fill_between(data, size=224, yrange=(-1, 1), dpi=DPI, return_im...
  function get_fill_between_plot (line 422) | def get_fill_between_plot(data, sel_TCs=None, sel_channels=None, size=No...
  class TS2ImageList (line 440) | class TS2ImageList(ItemList):
    method __init__ (line 444) | def __init__(self, items, *args, **kwargs):
    method get (line 451) | def get(self, i):
    method from_array (line 456) | def from_array(cls, ts, **kwargs):
    method from_df (line 460) | def from_df(cls, df, path='.', cols=0, processor=None,
    method from_csv (line 479) | def from_csv(cls, path, csv_name, header='infer', **kwargs) -> 'ItemLi...
    method reconstruct (line 485) | def reconstruct(self, t):
    method show_xys (line 488) | def show_xys(self, xs, ys, imgsize=4, figsize=None, **kwargs):
    method show_xyzs (line 498) | def show_xyzs(self, xs, ys, zs, imgsize=4, figsize=None, **kwargs):

FILE: model/notebooks/fastai_timeseries/exp/nb_TSTrain.py
  function run_UCR_test (line 18) | def run_UCR_test(iters, epochs, datasets, arch,
  function FlatCosAnnealScheduler (line 61) | def FlatCosAnnealScheduler(learn, lr:float=4e-3, epochs:int=1, moms:Floa...
  function fit_fc (line 81) | def fit_fc(learn:Learner, epochs:int, lr:float=4e-3,

FILE: model/notebooks/fastai_timeseries/exp/nb_TSUtilities.py
  function get_dpi (line 34) | def get_dpi():
  function get_elements (line 48) | def get_elements(arr, idx):
  function cloning (line 52) | def cloning(list1):
  function scale (line 58) | def scale(arr,
  function scale_data (line 146) | def scale_data(X_train,
  function cap_outliers (line 183) | def cap_outliers(y, lower=None, verbose=False):
  function get_y_range (line 196) | def get_y_range(y, problem_type):
  function get_stratified_train_val_test_idxs (line 207) | def get_stratified_train_val_test_idxs(y,
  function check_overlap (line 268) | def check_overlap(a, b):
  function leakage_finder (line 275) | def leakage_finder(train, val, test=None):
  function oversampled_idxs (line 289) | def oversampled_idxs(y, idx, seed=1, verbose=False):
  function split_data (line 299) | def split_data(X, y, train_idx, valid_idx, test_idx, train_add_idx=None):
  function count_classes (line 316) | def count_classes(y):
  function get_class_weights (line 320) | def get_class_weights(target):
  function get_weighted_sampler (line 328) | def get_weighted_sampler(target):
  function history_output (line 335) | def history_output(learn, max_lr, epochs, t0, t1):
  function model_summary (line 397) | def model_summary(model, data, find_all=False, print_mod=False):
  function get_batch (line 405) | def get_batch(dl, learn):
  function conv (line 412) | def conv(ni, nf, ks=3, stride=1, bias=False):
  function noopr (line 415) | def noopr(x, **kwargs):
  function ToTensor (line 420) | def ToTensor(arr, **kwargs):
  function ToArray (line 428) | def ToArray(arr):
  function To3dTensor (line 437) | def To3dTensor(arr):
  function To2dTensor (line 447) | def To2dTensor(arr):
  function To1dTensor (line 456) | def To1dTensor(arr):
  function To3dArray (line 465) | def To3dArray(arr):
  function To2dArray (line 474) | def To2dArray(arr):
  function To1dArray (line 482) | def To1dArray(arr):
  function ToDevice (line 490) | def ToDevice(ts, **kwargs):
  function mape (line 507) | def mape(pred, targ):
  class MAPE (line 514) | class MAPE(RegMetrics):
    method on_epoch_end (line 516) | def on_epoch_end(self, last_metrics, **kwargs):
  class BPR (line 522) | class BPR(CMScores):
    method __init__ (line 526) | def __init__(self, alpha=1, beta=1):
    method on_epoch_end (line 530) | def on_epoch_end(self, last_metrics, **kwargs):
  class FocalLoss (line 535) | class FocalLoss(nn.Module):
    method __init__ (line 536) | def __init__(self, α=.25, γ=2., weight=None):
    method forward (line 542) | def forward(self, inputs, targets, **kwargs):
  function get_model_hp (line 549) | def get_model_hp(tsmodel, kwargs=[{}]):
  function get_outcome_stats (line 560) | def get_outcome_stats (learn, y_outcome, problem_type, train, valid, tes...
  function get_last_pos (line 631) | def get_last_pos(arr, val, ndigits=5):
  function plot_weights (line 636) | def plot_weights(learn):
  function load_params (line 645) | def load_params(m, path):
  function noop (line 650) | def noop(x): return x
  function get_layers (line 652) | def get_layers(model, cond=noop):
  function count_params (line 656) | def count_params(model):
  function nb_auto_export (line 665) | def nb_auto_export():

FILE: model/notebooks/fastai_timeseries/exp/rocket_functions.py
  function generate_kernels (line 15) | def generate_kernels(input_length, num_kernels, kss=[7, 9, 11], pad=True...
  function apply_kernel (line 37) | def apply_kernel(X, weights, length, bias, dilation, padding):
  function apply_kernels (line 59) | def apply_kernels(X, kernels):

FILE: model/notebooks/torchtimeseries/models/FCN.py
  class FCN (line 16) | class FCN(nn.Module):
    method __init__ (line 17) | def __init__(self,c_in,c_out,layers=[128,256,128],kss=[7,5,3]):
    method forward (line 25) | def forward(self, x):

FILE: model/notebooks/torchtimeseries/models/InceptionTime.py
  function noop (line 11) | def noop(x):
  function shortcut (line 14) | def shortcut(c_in, c_out):
  class Inception (line 18) | class Inception(nn.Module):
    method __init__ (line 19) | def __init__(self, c_in, bottleneck=32, ks=40, nb_filters=32):
    method forward (line 37) | def forward(self, x):
  class InceptionBlock (line 49) | class InceptionBlock(nn.Module):
    method __init__ (line 50) | def __init__(self,c_in,bottleneck=32,ks=40,nb_filters=32,residual=True...
    method forward (line 73) | def forward(self, x):
  class InceptionTime (line 84) | class InceptionTime(nn.Module):
    method __init__ (line 85) | def __init__(self,c_in,c_out,bottleneck=32,ks=40,nb_filters=32,residua...
    method forward (line 92) | def forward(self, x):

FILE: model/notebooks/torchtimeseries/models/ROCKET.py
  class ROCKET (line 10) | class ROCKET(nn.Module):
    method __init__ (line 11) | def __init__(self, c_in, seq_len, n_kernels=10000, kss=[7, 9, 11]):
    method forward (line 37) | def forward(self, x):

FILE: model/notebooks/torchtimeseries/models/ResCNN.py
  class Block (line 14) | class Block(nn.Module):
    method __init__ (line 15) | def __init__(self, ni, nf, ks=[7, 5, 3], act_fn='relu'):
    method forward (line 25) | def forward(self, x):
  class ResCNN (line 36) | class ResCNN(nn.Module):
    method __init__ (line 37) | def __init__(self, c_in, c_out):
    method forward (line 47) | def forward(self, x):

FILE: model/notebooks/torchtimeseries/models/ResNet.py
  class ResBlock (line 17) | class ResBlock(nn.Module):
    method __init__ (line 18) | def __init__(self, ni, nf, ks=[7, 5, 3], act_fn='relu'):
    method forward (line 28) | def forward(self, x):
  class ResNet (line 38) | class ResNet(nn.Module):
    method __init__ (line 39) | def __init__(self,c_in, c_out):
    method forward (line 49) | def forward(self, x):

FILE: model/notebooks/torchtimeseries/models/layers.py
  function noop (line 16) | def noop(x): return x
  class LambdaPlus (line 18) | class LambdaPlus(Module):
    method __init__ (line 19) | def __init__(self, func, *args, **kwargs): self.func,self.args,self.kw...
    method forward (line 20) | def forward(self, x): return self.func(x, *self.args, **self.kwargs)
  function get_act_layer (line 23) | def get_act_layer(act_fn, **kwargs):
  function same_padding1d (line 35) | def same_padding1d(seq_len,ks,stride=1,dilation=1):
  class ZeroPad1d (line 45) | class ZeroPad1d(nn.ConstantPad1d):
    method __init__ (line 46) | def __init__(self, padding):
  class ConvSP1d (line 49) | class ConvSP1d(nn.Module):
    method __init__ (line 51) | def __init__(self,c_in,c_out,ks,stride=1,padding='same',dilation=1,bia...
    method forward (line 59) | def forward(self, x):
  function convlayer (line 63) | def convlayer(c_in,c_out,ks=3,padding='same',bias=True,stride=1,
  class Flatten (line 78) | class Flatten(nn.Module):
    method forward (line 79) | def forward(self, x): return x.view(x.size(0), -1)
  class Squeeze (line 81) | class Squeeze(nn.Module):
    method __init__ (line 82) | def __init__(self, dim=-1):
    method forward (line 85) | def forward(self, x): return x.squeeze(dim=self.dim)
  class Unsqueeze (line 87) | class Unsqueeze(nn.Module):
    method __init__ (line 88) | def __init__(self, dim=-1):
    method forward (line 91) | def forward(self, x): return x.unsqueeze(dim=self.dim)
  class YRange (line 93) | class YRange(nn.Module):
    method __init__ (line 94) | def __init__(self, y_range:tuple):
    method forward (line 97) | def forward(self, x):
  class Mult (line 103) | class Mult(nn.Module):
    method __init__ (line 104) | def __init__(self, mult, trainable=True):
    method forward (line 108) | def forward(self, x):
  class Exp (line 112) | class Exp(nn.Module):
    method __init__ (line 113) | def __init__(self, exp, trainable=True):
    method forward (line 117) | def forward(self, x):
  function get_cls (line 121) | def get_cls(model, c_in, seq_len, c_out, **kwargs):
  class FTSwishPlus (line 130) | class FTSwishPlus(nn.Module):
    method __init__ (line 131) | def __init__(self, threshold=-.25, sub=-.1, **kwargs):
    method forward (line 135) | def forward(self, x):
  class Swish (line 141) | class Swish(nn.Module):
    method __init__ (line 142) | def __init__(self, inplace=False):
    method forward (line 146) | def forward(self, x):
  class GeneralRelu (line 154) | class GeneralRelu(nn.Module):
    method __init__ (line 155) | def __init__(self, leak=None, sub=0., maxv=None, **kwargs):
    method forward (line 159) | def forward(self, x):
  class Mish (line 170) | class Mish(nn.Module):
    method __init__ (line 171) | def __init__(self):
    method forward (line 173) | def forward(self, x):
  function get_act_fn_norm (line 177) | def get_act_fn_norm(act_fn):
  class AFN (line 185) | class AFN(nn.Module):
    method __init__ (line 186) | def __init__(self, act_fn=F.relu, trainable=True, **kwargs):
    method forward (line 193) | def forward(self, x):

FILE: model/schedulers/CycleLR.py
  class CyclicLR (line 6) | class CyclicLR(_LRScheduler):
    method __init__ (line 8) | def __init__(self, optimizer, schedule, last_epoch=-1):
    method get_lr (line 13) | def get_lr(self):
  function cosine (line 17) | def cosine(t_max, eta_min=0):

FILE: model/train_lstm.py
  function load_data (line 40) | def load_data():
  function train (line 67) | def train():

FILE: router/recvCSI/csi_fun.c
  function is_big_endian (line 36) | bool is_big_endian() {
  function bit_convert (line 45) | int bit_convert(int data, int maxbit) {
  function fill_csi_matrix (line 53) | void fill_csi_matrix(u_int8_t* csi_addr, int nr, int nc, int num_tones, ...
  function open_csi_device (line 118) | int open_csi_device() {
  function close_csi_device (line 123) | void close_csi_device(int fd) {
  function read_csi_buf (line 128) | int read_csi_buf(unsigned char* buf_addr, int fd, int BUFSIZE) {
  function record_status (line 140) | void record_status(unsigned char* buf_addr, int cnt, csi_struct* csi_sta...
  function record_csi_payload (line 182) | void record_csi_payload(unsigned char* buf_addr, csi_struct* csi_status,...
  function process_csi (line 205) | void process_csi(unsigned char* data_buf, csi_struct* csi_status, COMPLE...

FILE: router/recvCSI/csi_fun.h
  type COMPLEX (line 27) | typedef struct {
  type csi_struct (line 32) | typedef struct {

FILE: router/recvCSI/main.c
  function sig_handler (line 54) | void sig_handler(int signo) {
  function main (line 59) | int main(int argc, char* argv[]) {

FILE: router/sendData/sendData.c
  function main (line 53) | int main(int argc, char* argv[]) {

FILE: router/sendData/sendData_con.c
  function main (line 54) | int main(int argc, char* argv[]) {