From 912189713e75880de0b0f25cd5ee15be0e880cd8 Mon Sep 17 00:00:00 2001 From: Han Zhu Date: Tue, 23 Jul 2024 15:52:03 -0400 Subject: [PATCH] Add GMM contouring --- .gitignore | 3 +- CryoREAD/data_processing/Resize_Map.py | 2 +- CryoREAD/data_processing/Unify_Map.py | 2 +- environment.yml | 3 + gmm_contour.py | 198 +++++++++++++++++++++++++ 5 files changed, 205 insertions(+), 3 deletions(-) create mode 100644 gmm_contour.py diff --git a/.gitignore b/.gitignore index 7415b93..d46dcf4 100644 --- a/.gitignore +++ b/.gitignore @@ -159,4 +159,5 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ -CryoREAD_Predict_Result/ \ No newline at end of file +CryoREAD_Predict_Result/ +output_test/ \ No newline at end of file diff --git a/CryoREAD/data_processing/Resize_Map.py b/CryoREAD/data_processing/Resize_Map.py index d68a45a..1a2818f 100644 --- a/CryoREAD/data_processing/Resize_Map.py +++ b/CryoREAD/data_processing/Resize_Map.py @@ -89,7 +89,7 @@ def Resize_Map(input_map_path,new_map_path): if __name__ == "__main__": args = argparse.ArgumentParser() args.add_argument("-i", "--input_map_path", type=str, default=None) - args.add_argument("-o", "--output_map_path", type=str, default=None) + args.add_argument("-o", "--output_folder", type=str, default=None) args = args.parse_args() Resize_Map(args.input_map_path,args.output_map_path) diff --git a/CryoREAD/data_processing/Unify_Map.py b/CryoREAD/data_processing/Unify_Map.py index 8416262..f7ed42a 100644 --- a/CryoREAD/data_processing/Unify_Map.py +++ b/CryoREAD/data_processing/Unify_Map.py @@ -47,7 +47,7 @@ def Unify_Map(input_map_path, new_map_path): args = argparse.ArgumentParser() args.add_argument("-i", "--input_map_path", type=str, default=None) - args.add_argument("-o", "--output_map_path", type=str, default=None) + args.add_argument("-o", "--output_folder", type=str, default=None) args = args.parse_args() new_map_path = Unify_Map(args.input_map_path, args.output_map_path) print(f"New map path is {new_map_path}") diff --git a/environment.yml b/environment.yml index 3b71572..7842e4a 100644 --- a/environment.yml +++ b/environment.yml @@ -11,6 +11,9 @@ dependencies: - python=3.10 - pytorch - pytorch-cuda=11.8 + - scikit-learn + - scikit-image + - matplotlib - pip: - biopython - numba diff --git a/gmm_contour.py b/gmm_contour.py new file mode 100644 index 0000000..ec5450a --- /dev/null +++ b/gmm_contour.py @@ -0,0 +1,198 @@ +from pathlib import Path + +import mrcfile +import numpy as np +from sklearn import mixture +import os + +from skimage.morphology import ball, opening +from skimage.filters import rank +from skimage.util import img_as_ubyte +from scipy.ndimage import zoom + +import matplotlib.pyplot as plt + + +def save_mrc(orig_map_path, data, out_path): + with mrcfile.open(orig_map_path, permissive=True) as orig_map: + with mrcfile.new(out_path, data=data.astype(np.float32), overwrite=True) as mrc: + mrc.voxel_size = orig_map.voxel_size + mrc.header.nxstart = orig_map.header.nxstart + mrc.header.nystart = orig_map.header.nystart + mrc.header.nzstart = orig_map.header.nzstart + mrc.header.origin = orig_map.header.origin + mrc.header.mapc = orig_map.header.mapc + mrc.header.mapr = orig_map.header.mapr + mrc.header.maps = orig_map.header.maps + mrc.update_header_stats() + mrc.update_header_from_data() + mrc.flush() + + +def gmm_mask(input_map_path, output_folder, num_components=3, use_grad=False, n_init=1, plot_all=False): + print("input_map_path", input_map_path) + print("output_folder", output_folder) + + # if os.path.exists(output_folder): + # # print("Output file already exists") + # raise ValueError("Output FOLD already exists") + # return None, None + + os.makedirs(output_folder, exist_ok=True) + + print("Opening map file") + + with mrcfile.open(input_map_path, permissive=True) as mrc: + map_data = mrc.data.copy() + + print("Input map shape:", map_data.shape) + + non_zero_data = map_data[np.nonzero(map_data)] + + data_normalized = (map_data - map_data.min()) * 2 / (map_data.max() - map_data.min()) - 1 + + print("Non-zero data shape", non_zero_data.shape) + + # Zooming to handling large maps + if len(non_zero_data) >= 5e6: + print("Map is too large") + + # resample + zoom_factor = (2e6 / len(non_zero_data)) ** (1 / 3) + print("Resample with zoom factor:", zoom_factor) + + map_data_zoomed = zoom(map_data, zoom_factor, order=3, mode="grid-constant", grid_mode=True) + data_normalized_zoomed = (map_data_zoomed - map_data_zoomed.min()) * 2 / ( + map_data_zoomed.max() - map_data_zoomed.min()) - 1 + non_zero_data_zoomed = map_data_zoomed[np.nonzero(map_data_zoomed)] + + print("Shape after resample:", data_normalized_zoomed.shape) + + print("Calculating gradient") + local_grad_norm_zoomed = rank.gradient(img_as_ubyte(data_normalized_zoomed), ball(3)) + local_grad_norm_zoomed = local_grad_norm_zoomed[np.nonzero(map_data_zoomed)] + local_grad_norm_zoomed = (local_grad_norm_zoomed - local_grad_norm_zoomed.min()) / ( + local_grad_norm_zoomed.max() - local_grad_norm_zoomed.min() + ) + + non_zero_data_normalized_zoomed = (non_zero_data_zoomed - non_zero_data_zoomed.min()) / ( + non_zero_data_zoomed.max() - non_zero_data_zoomed.min() + ) + + local_grad_norm_zoomed = np.reshape(local_grad_norm_zoomed, (-1, 1)) + non_zero_data_normalized_zoomed = np.reshape(non_zero_data_normalized_zoomed, (-1, 1)) + # print(non_zero_data_normalized_zoomed.shape, local_grad_norm_zoomed.shape) + data_zoomed = np.hstack((non_zero_data_normalized_zoomed, local_grad_norm_zoomed)) + + # calculate guassian gradient norm + local_grad_norm = rank.gradient(img_as_ubyte(data_normalized), ball(3)) + local_grad_norm = local_grad_norm[np.nonzero(map_data)] + + # min-max normalization + local_grad_norm = (local_grad_norm - local_grad_norm.min()) / (local_grad_norm.max() - local_grad_norm.min()) + non_zero_data_normalized = (non_zero_data - non_zero_data.min()) / (non_zero_data.max() - non_zero_data.min()) + + # stack the flattened data and gradient + local_grad_norm = np.reshape(local_grad_norm, (-1, 1)) + non_zero_data_normalized = np.reshape(non_zero_data_normalized, (-1, 1)) + data = np.hstack((non_zero_data_normalized, local_grad_norm)) + + print("Fitting GMM") + + # fit the GMM + g = mixture.BayesianGaussianMixture(n_components=num_components, max_iter=200, n_init=n_init, tol=1e-2) + + if use_grad: + data_to_fit = data_zoomed if len(non_zero_data) >= 5e6 else data + else: + data_to_fit = non_zero_data_normalized_zoomed if len(non_zero_data) >= 5e6 else non_zero_data_normalized + print("Fitting feature shape:", data_to_fit.shape) + g.fit(data_to_fit) + print("Predicting, feature shape:", data.shape) + preds = g.predict(data) + + if plot_all: + fig, ax = plt.subplots(1, 1, figsize=(10, 3)) + for pred in np.unique(preds): + mask = np.zeros_like(map_data) + mask[np.nonzero(map_data)] = preds == pred + new_data = map_data * mask + new_data_non_zero = new_data[np.nonzero(new_data)] + ax.hist(new_data_non_zero.flatten(), alpha=0.5, bins=256, density=False, log=True, label=f"Masked_{pred}") + # plot mean + # mean = g.means_[pred, 0] + # ax.axvline(mean, label=f"Mean_{pred}") + fig.tight_layout() + print("Saving figure to", os.path.join(output_folder, "hist_by_component.png")) + fig.savefig(os.path.join(output_folder, "hist_by_component.png")) + + # generate a mask to keep only the component with the largest variance + mask = np.zeros_like(map_data) + # mask[np.nonzero(masked_prot_data)] = (preds == np.argmax(g.means_[:, 0].flatten())) + + ind = np.argpartition(g.means_[:, 0].flatten(), -3)[-3:] + + print("ind", ind) + + # mask[np.nonzero(map_data)] = preds in ind + print( + "Means: ", + g.means_.shape, + g.means_[:, 0], + ) + print("Variances: ", g.covariances_.shape, g.covariances_[:, 0, 0]) + + mask[np.nonzero(map_data)] = (preds == ind[0]) | (preds == ind[1]) | (preds == ind[2]) + + print("Nonzero", np.count_nonzero(mask)) + + # use opening to remove small artifacts + mask = opening(mask.astype(bool)) + # gaussian_mask = gaussian(mask.astype(float), sigma=3, preserve_range=True) + # mask = np.clip(gaussian_mask + mask, 0, 1) + # mask[mask < 1] = mask[mask < 1] / np.max(mask[mask < 1]) + new_data = map_data * mask + new_data_non_zero = new_data[np.nonzero(new_data)] + + # save the new data + save_mrc(input_map_path, new_data, + os.path.join(output_folder, Path(input_map_path).stem + "_mask.mrc")) + + if use_grad == True: + # use 1 sigma cutoff from the masked data + # revised_contour = np.mean(new_data_non_zero) + np.std(new_data_non_zero) + # use median cutoff from the masked data, could be other percentile + revised_contour = np.percentile(new_data_non_zero, 50) + else: + revised_contour = np.min(new_data[new_data > 1e-8]) + + mask_percent = np.count_nonzero(new_data > 1e-6) / np.count_nonzero(map_data > 1e-6) + + # plot the histogram + fig, ax = plt.subplots(figsize=(10, 2)) + ax.hist(non_zero_data.flatten(), alpha=0.5, bins=256, density=False, log=True, label="Original") + ax.hist(new_data_non_zero.flatten(), alpha=0.5, bins=256, density=False, log=True, label="Masked") + ax.axvline(revised_contour, label="Revised Contour") + ax.legend() + plt.title(input_map_path) + plt.savefig(os.path.join(output_folder, Path(input_map_path).stem + "_hist_overall.png")) + + out_txt = os.path.join(output_folder, Path(input_map_path).stem + "_revised_contour.txt") + + with open(out_txt, "w") as f: + f.write(f"{revised_contour} {mask_percent}") + + # return revised contour level and mask percent + return revised_contour, mask_percent + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--input_map_path", type=str, default=None) + parser.add_argument("-o", "--output_folder", type=str, default=None) + parser.add_argument("-p", "--plot_all", action="store_true") + args = parser.parse_args() + revised_contour, mask_percent = gmm_mask(input_map_path=args.input_map_path, output_folder=args.output_folder, + num_components=5, use_grad=True, n_init=1, plot_all=args.plot_all)