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| from collections import namedtuple | |
| import numpy as np | |
| from scipy.interpolate import interp1d | |
| import torch | |
| import matplotlib.pyplot as plt | |
| # Mapping of nucleotides to float coordinates | |
| mapping_easy = { | |
| "A": np.array([0.5, -0.8660254037844386]), | |
| "T": np.array([0.5, 0.8660254037844386]), | |
| "G": np.array([0.8660254037844386, -0.5]), | |
| "C": np.array([0.8660254037844386, 0.5]), | |
| "N": np.array([0, 0]), | |
| } | |
| # coordinates for x+iy | |
| Coord = namedtuple("Coord", ["x", "y"]) | |
| # coordinates for a CGR encoding | |
| CGRCoords = namedtuple("CGRCoords", ["N", "x", "y"]) | |
| # coordinates for each nucleotide in the 2d-plane | |
| DEFAULT_COORDS = {"A": Coord(1, 1), "C": Coord(-1, 1), "G": Coord(-1, -1), "T": Coord(1, -1)} | |
| # Function to convert a DNA sequence to a list of coordinates | |
| def _dna_to_coordinates(dna_sequence: str, mapping: dict[str, np.ndarray]) -> np.ndarray: | |
| dna_sequence = dna_sequence.upper() | |
| coordinates = np.array([mapping.get(nucleotide, mapping["N"]) for nucleotide in dna_sequence]) | |
| return coordinates | |
| # Function to create the cumulative sum of a list of coordinates | |
| def _get_cumulative_coords(mapped_coords): | |
| cumulative_coords = np.cumsum(mapped_coords, axis=0) | |
| return cumulative_coords | |
| def generate_2d_sequence(seq): | |
| dna_sequence = seq.upper() | |
| mapped_coords = _dna_to_coordinates(dna_sequence, mapping_easy) | |
| cumulative_coords = _get_cumulative_coords(mapped_coords) | |
| # Scale the input data using standardization | |
| x_train = cumulative_coords[:, 0] | |
| y_train = cumulative_coords[:, 1] | |
| x_train_scaled = (x_train - x_train.mean()) / x_train.std() | |
| y_train_scaled = (y_train - y_train.mean()) / y_train.std() | |
| scaled_coords = np.column_stack((x_train_scaled, y_train_scaled)) | |
| # example["2D_Sequence"] = cumulative_coords.tolist() | |
| # example["2D_Sequence_Scaled"] = scaled_coords.tolist() | |
| # Interpolate the 2D sequences to have exactly 1000 pairs | |
| interpolated_coords = y_train_scaled # default to filter out bad examples | |
| if len(scaled_coords) != 1000: | |
| try: | |
| t = np.linspace(0, 1, len(scaled_coords)) | |
| t_new = np.linspace(0, 1, 1000) | |
| interp_func_x = interp1d(t, scaled_coords[:, 0], kind="linear") | |
| interp_func_y = interp1d(t, scaled_coords[:, 1], kind="linear") | |
| interpolated_coords = interp_func_x(t_new) | |
| except Exception as e: | |
| print(f"Interpolation error: {e}") | |
| tensor_2d_rep_y = torch.Tensor(interpolated_coords).reshape(1,1000) | |
| return y_train_scaled, x_train_scaled | |
| def generate_2d_sequence_small(seq): | |
| dna_sequence = seq.upper() | |
| mapped_coords = _dna_to_coordinates(dna_sequence, mapping_easy) | |
| cumulative_coords = _get_cumulative_coords(mapped_coords) | |
| # Scale the input data using standardization | |
| x_train = cumulative_coords[:, 0] | |
| y_train = cumulative_coords[:, 1] | |
| x_train_scaled = (x_train - x_train.mean()) / x_train.std() | |
| y_train_scaled = (y_train - y_train.mean()) / y_train.std() | |
| scaled_coords = np.column_stack((x_train_scaled, y_train_scaled)) | |
| # example["2D_Sequence"] = cumulative_coords.tolist() | |
| # example["2D_Sequence_Scaled"] = scaled_coords.tolist() | |
| # Interpolate the 2D sequences to have exactly 1000 pairs | |
| interpolated_coords = y_train_scaled # default to filter out bad examples | |
| if len(scaled_coords) != 1000: | |
| try: | |
| t = np.linspace(0, 1, len(scaled_coords)) | |
| t_new = np.linspace(0, 1, 400) | |
| interp_func_x = interp1d(t, scaled_coords[:, 0], kind="linear") | |
| interp_func_y = interp1d(t, scaled_coords[:, 1], kind="linear") | |
| interpolated_coords = interp_func_y(t_new) | |
| except Exception as e: | |
| print(f"Interpolation error: {e}") | |
| tensor_2d_rep_y = torch.Tensor(interpolated_coords).reshape(400) | |
| return tensor_2d_rep_y | |
| def plot_seq_full_label(df, filter): | |
| ncols = len(filter) | |
| unique_ids = df.label_id.unique() | |
| print(unique_ids) | |
| unique_ids_plot = [id for id in unique_ids if id in filter] | |
| print(unique_ids_plot) | |
| fig, axs = plt.subplots(ncols=ncols) | |
| for i, id in enumerate(unique_ids_plot): | |
| # data = (df[df['label_id'] == id].sample((n=3)))['seq'].values[0] | |
| # print(data) | |
| data = generate_2d_sequence_small(df[df['label_id'] == id].sample(n=1)['seq'].values[0]).numpy() | |
| # two_d = generate_2d_sequence(data)[0] | |
| axs[i].plot(data) | |
| return fig |