use relative path to script

.gitignore

@@ -161,4 +161,15 @@ cython_debug/
 
 CryoREAD_Predict_Result/
 output_test/
-examples/
+examples/
+Tutorial5.0/
+test_outdir/
+test_cryoread_26363/
+job019_run_it050_class001_test/
+job052_run_it100_class003/
+22647_test_gmm/
+26363_test_gmm/
+CryoREAD/test_cryoread_22647/
+CryoREAD/test_cryoread_26363/
+CryoREAD/job019_run_it050_class001/
+CryoREAD/job052_run_it100_class003/

contour.py

@@ -0,0 +1,239 @@
+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
+
+# change to relative path to this file
+# CRYOREAD_PATH = "./CryoREAD/main.py"
+CURR_SCIPT_PATH = Path(__file__).absolute().parent
+CRYOREAD_PATH = CURR_SCIPT_PATH / "CryoREAD" / "main.py"
+
+
+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 gen_features(map_array):
+    non_zero_data = map_array[np.nonzero(map_array)]
+    data_normalized = (map_array - map_array.min()) * 2 / (map_array.max() - map_array.min()) - 1
+    local_grad_norm = rank.gradient(img_as_ubyte(data_normalized), ball(3))
+    local_grad_norm = local_grad_norm[np.nonzero(map_array)]
+    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))
+    features = np.hstack((non_zero_data_normalized, local_grad_norm))
+
+    return features
+
+
+def gmm_mask(input_map_path, output_folder, num_components=2, use_grad=False, n_init=1, plot_all=False):
+    print("input_map_path", input_map_path)
+    print("output_folder", output_folder)
+
+    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, resizing...")
+
+        # 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=False)
+        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=500, 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))
+        all_datas = []
+        for pred in np.unique(preds):
+            mask = np.zeros_like(map_data)
+            mask[np.nonzero(map_data)] = preds == pred
+            masked_map_data = map_data * mask
+            new_data_non_zero = masked_map_data[np.nonzero(masked_map_data)]
+            all_datas.append(new_data_non_zero.flatten())
+            mean = np.mean(new_data_non_zero)
+            ax.axvline(mean, linestyle="--", color="k", label=f"Mean_{pred}")
+        labels = [f"Component {i}" for i in range(num_components)]
+        ax.hist(all_datas, alpha=0.5, bins=256, density=True, log=True, label=labels, stacked=True)
+        ax.set_yscale("log")
+        ax.legend(loc="upper right")
+        ax.set_xlabel("Map Density Value")
+        ax.set_ylabel("Density (log scale)")
+        ax.set_title("Stacked Histogram by Component")
+        fig.tight_layout()
+        # print("Saving figure to", os.path.join(output_folder, "hist_by_component.png"))
+        fig.savefig(os.path.join(output_folder, Path(input_map_path).stem + "_hist_by_components.png"))
+
+    # choose ind that is closest to 0, and ind that has the highest mean
+    ind_noise = np.argmin(np.abs(g.means_[:, 0].flatten()))
+    # ind_max = np.argmax(g.covariances_[:, 0, 0].flatten())
+
+    print("Means: ", g.means_.shape, g.means_[:, 0], g.means_[:, 1])
+
+    # print("Variances: ", g.covariances_.shape, g.covariances_[ind_noise, 0, 0])
+    # print("Std: ", np.sqrt(g.covariances_[ind_noise, 0, 0]))
+
+    # generate a mask to keep only the component without the noise
+    mask = np.zeros_like(map_data)
+    mask[np.nonzero(map_data)] = preds != ind_noise
+
+    noise_comp = map_data[np.nonzero(map_data)][preds == ind_noise]
+    revised_contour = np.max(noise_comp)
+
+    prot_comp = map_data[np.nonzero(map_data)][preds != ind_noise]
+
+    print("Revised contour:", revised_contour)
+    print("Remaining mask region size in voxels:", np.count_nonzero(mask))
+
+    # use opening to remove small holes
+    mask = opening(mask.astype(bool), ball(3))
+    masked_map_data = map_data * mask
+    new_data_non_zero = masked_map_data[np.nonzero(masked_map_data)]
+
+    # calcualte new gradient norm
+    new_fit_data = gen_features(masked_map_data)
+    print("Fitting feature shape:", new_fit_data.shape)
+
+    # fit the GMM again on the new data
+    g2 = mixture.BayesianGaussianMixture(n_components=2, max_iter=500, n_init=n_init, tol=1e-2)
+    g2.fit(new_fit_data)
+
+    # predict the new data
+    new_preds = g2.predict(new_fit_data)
+    # ind_noise_second = np.argmin(np.abs(g2.means_[:, 0].flatten()))
+    ind_noise_second = np.argmin(g2.covariances_[:, 0, 0].flatten())
+    noise_comp_2 = masked_map_data[np.nonzero(masked_map_data)][new_preds == ind_noise_second]
+    prot_comp_2 = masked_map_data[np.nonzero(masked_map_data)][new_preds != ind_noise_second]
+    # revised_contour_2 = np.median(prot_comp_2)
+    revised_contour_2 = np.max(noise_comp_2)
+
+    print("Revised contour (high):", revised_contour_2)
+
+    # save the new data
+    save_mrc(input_map_path, masked_map_data, os.path.join(output_folder, Path(input_map_path).stem + "_mask.mrc"))
+
+    mask_percent = np.count_nonzero(masked_map_data > 1e-8) / np.count_nonzero(map_data > 1e-8)
+
+    # 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.axvline(revised_contour_2, label="Revised Contour (High)", linestyle="dashed")
+    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: {revised_contour}\n")
+        f.write(f"Revised contour (high): {revised_contour_2}\n")
+        f.write(f"Masked percentage: {mask_percent}\n")
+
+    # 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")
+    parser.add_argument("-n", "--num_components", type=int, default=2)
+    parser.add_argument("-r", "--resolution", type=float, default=None)
+    args = parser.parse_args()
+    revised_contour, mask_percent = gmm_mask(
+        input_map_path=args.input_map_path,
+        output_folder=args.output_folder,
+        num_components=args.num_components,
+        use_grad=True,
+        n_init=3,
+        plot_all=args.plot_all,
+    )

main.py

@@ -20,9 +20,8 @@
 
     logger.info("Input job folder path: ", args.F)
 
-    CRYOREAD_PATH = "./CryoREAD/main.py"
-
-    # mrc_files = glob(args.F + "/*.mrc")
+    CURR_SCIPT_PATH = Path(__file__).absolute().parent
+    CRYOREAD_PATH = CURR_SCIPT_PATH / "CryoREAD" / "main.py"
 
     mrc_files = args.F
     # check if files exists