SYMBOL INDEX (56 symbols across 10 files)

FILE: data_proc/data_utils.py
  function data_to_torch_X (line 35) | def data_to_torch_X(X):
  class SincleCellDataset (line 42) | class SincleCellDataset(data.Dataset):
    method __init__ (line 43) | def __init__(self,
    method __getitem__ (line 80) | def __getitem__(self, idx):
    method __len__ (line 99) | def __len__(self) -> int:
    method get_dim (line 102) | def get_dim(self) -> Dict[str, int]:
  function data_to_torch_X (line 106) | def data_to_torch_X(X):
  function anndata_to_sc_dataset (line 114) | def anndata_to_sc_dataset(adata:sc.AnnData,
  function adata_path_to_prot_chrom_starts (line 155) | def adata_path_to_prot_chrom_starts(adata, dataset_species, spec_pe_gene...
  function process_raw_anndata (line 173) | def process_raw_anndata(row, h5_folder_path, npz_folder_path, scp, skip,
  function get_species_to_pe (line 241) | def get_species_to_pe(EMBEDDING_DIR):
  function get_spec_chrom_csv (line 271) | def get_spec_chrom_csv(path="/dfs/project/cross-species/yanay/code/all_t...

FILE: data_proc/download_proc_czi_cxg.py
  function data_to_torch_X (line 29) | def data_to_torch_X(X):
  function istarmap (line 47) | def istarmap(self, func, iterable, chunksize=1):
  function process_row (line 90) | def process_row(row, num_genes, num_cells, paths, all_species, covar_col...

FILE: data_proc/gene_embeddings.py
  function load_gene_embeddings_adata (line 30) | def load_gene_embeddings_adata(adata: AnnData, species: list, embedding_...

FILE: data_proc/generate_reduced_chrom_files.py
  function padding_tensor (line 53) | def padding_tensor(sequences):

FILE: data_proc/preproc_many_dataset.py
  function data_to_torch_X (line 31) | def data_to_torch_X(X):
  class SincleCellDataset (line 38) | class SincleCellDataset(data.Dataset):
    method __init__ (line 39) | def __init__(self,
    method __getitem__ (line 76) | def __getitem__(self, idx):
    method __len__ (line 95) | def __len__(self) -> int:
    method get_dim (line 98) | def get_dim(self) -> Dict[str, int]:
  function data_to_torch_X (line 102) | def data_to_torch_X(X):
  function anndata_to_sc_dataset (line 110) | def anndata_to_sc_dataset(adata:sc.AnnData,
  function proc (line 151) | def proc(args):

FILE: eval_data.py
  class MultiDatasetSentences (line 17) | class MultiDatasetSentences(data.Dataset):
    method __init__ (line 18) | def __init__(self, sorted_dataset_names, shapes_dict, args,
    method __getitem__ (line 50) | def __getitem__(self, idx):
    method __len__ (line 73) | def __len__(self) -> int:
    method get_dim (line 76) | def get_dim(self) -> Dict[str, int]:
  class MultiDatasetSentenceCollator (line 80) | class MultiDatasetSentenceCollator(object):
    method __init__ (line 81) | def __init__(self, args):
    method __call__ (line 85) | def __call__(self, batch):
  function sample_cell_sentences (line 108) | def sample_cell_sentences(counts, batch_weights, dataset, args,

FILE: eval_single_anndata.py
  function main (line 81) | def main(args, accelerator):

FILE: evaluate.py
  class AnndataProcessor (line 33) | class AnndataProcessor:
    method __init__ (line 34) | def __init__(self, args, accelerator):
    method check_paths (line 64) | def check_paths(self):
    method preprocess_anndata (line 91) | def preprocess_anndata(self):
    method save_shapes_dict (line 108) | def save_shapes_dict(self, name, num_cells, num_genes, shapes_dict_path):
    method generate_idxs (line 114) | def generate_idxs(self):
    method run_evaluation (line 141) | def run_evaluation(self):
  function get_ESM2_embeddings (line 149) | def get_ESM2_embeddings(args):
  function padding_tensor (line 163) | def padding_tensor(sequences):
  function run_eval (line 183) | def run_eval(adata, name, pe_idx_path, chroms_path, starts_path, shapes_...

FILE: model.py
  function full_block (line 18) | def full_block(in_features, out_features, p_drop=0.1):
  class PositionalEncoding (line 27) | class PositionalEncoding(nn.Module):
    method __init__ (line 29) | def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = ...
    method forward (line 41) | def forward(self, x: Tensor) -> Tensor:
  class TransformerModel (line 50) | class TransformerModel(nn.Module):
    method __init__ (line 52) | def __init__(self, token_dim: int, d_model: int, nhead: int, d_hid: int,
    method forward (line 92) | def forward(self, src: Tensor, mask: Tensor):
    method predict (line 110) | def predict(self, cell_embedding, gene_embeddings):

FILE: utils.py
  function get_shapes_dict (line 16) | def get_shapes_dict(dataset_path):
  function figshare_download (line 72) | def figshare_download(url, save_path):