Write a program to detect redundant functions about aitom

[ijinjay]

👌got it.

[ijinjay]

Below is the list of the redundant functions. Function in the list that only exists in one file means it appears at least twice in that file, usually defined one more in the comments.

The results are organized as follow:

• function name
• files that contain the function

We need to make choices about which definition should be retained. 🤔

def align_vols__multiple_rotations(v1, m1, v2, m2, L):
aitom/align/fast/util.py:
aitom/tomominer/align/util.py:


def conv_block(x, nb_filter, nb0, nb1, nb2, border_mode='same', subsample=(1, 1, 1), bias=True, batch_norm=False):
aitom/classify/deep/supervised/cnn/autoencoding/auto_classifier_model.py:
aitom/classify/deep/unsupervised/autoencoder/seg_src.py:
aitom/classify/deep/unsupervised/autoencoder/seg_util.py:


def pickle_load(path):
aitom/classify/deep/supervised/cnn/autoencoding/utils.py:
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:
aitom/io/file.py:
aitom/tomominer/io/file.py:


def cub_img(v, view_dir=2):
aitom/classify/deep/supervised/cnn/autoencoding/utils.py:
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:
aitom/image/vol/util.py:


def save_png(m, name, normalize=True, verbose=False):
aitom/classify/deep/supervised/cnn/autoencoding/utils.py:
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:
aitom/image/io.py:


def get_shape(tensor):
aitom/classify/deep/supervised/cnn/openset/sphere_network.py:


def infer(input,embedding_size=512):
aitom/classify/deep/supervised/cnn/openset/sphere_network.py:


def prelu(x, name = 'prelu'):
aitom/classify/deep/supervised/cnn/openset/sphere_network.py:


def first_conv(input, num_output, name):
aitom/classify/deep/supervised/cnn/openset/sphere_network.py:


def block(input, name, num_output):
aitom/classify/deep/supervised/cnn/openset/sphere_network.py:



def get_normal_loss(input, label, num_output, lambda_value, m_value = 4):
aitom/classify/deep/supervised/cnn/openset/sphere_network.py:
aitom/classify/deep/supervised/cnn/openset/sphere_network.py:


def get_learning_rate_from_file(filename, epoch):
aitom/classify/deep/supervised/cnn/openset/train_double.py:
aitom/classify/deep/supervised/cnn/openset/utils.py:


def pickle_dump(o, path, protocol=-1):
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:
aitom/tomominer/io/file.py:
aitom/io/file.py:


def smooth(v, sigma):
aitom/filter/gaussian.py:
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:

def paste_to_whole_map__se(whole_map, vol, se):
aitom/image/vol/util.py:
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:



def grid_displacement_to_center(size, mid_co=None):
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:
aitom/image/vol/util.py:
aitom/tomominer/image/vol/util.py:


def grid_distance_sq_to_center(grid):
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:
aitom/image/vol/util.py:
aitom/tomominer/image/vol/util.py:


def cut_from_whole_map(whole_map, c, siz):
aitom/image/vol/util.py:
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:


def cut_from_whole_map__se(whole_map, se):
aitom/image/vol/util.py:
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:



def subvolume_center_start_end(c, map_siz, subvol_siz):
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:
aitom/image/vol/util.py:



def center_mass(v):
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:
aitom/tomominer/pose/normalize/util.py:


def pca(v, c, do_flip=False):
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:
aitom/tomominer/pose/normalize/util.py:


def rotation_matrix_zyz(ang):
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:
aitom/geometry/ang_loc.py:
aitom/tomominer/geometry/ang_loc.py:


def rotation_matrix_axis(dim, theta):
aitom/classify/deep/unsupervised/autoencoder/autoencoder_util.py:
aitom/geometry/ang_loc.py:
aitom/tomominer/geometry/ang_loc.py:



def rotation_matrix_zyz_normalized_angle(rm):
aitom/geometry/ang_loc.py:
aitom/tomominer/geometry/ang_loc.py:


def voronoi_weights_6d(phis):
aitom/geometry/volume/hypervolume/util.py:
aitom/tomominer/hypervolume/utils.py:



def distance_6d_sq__frobenius(p1, p2, weight=1.0):
aitom/geometry/volume/hypervolume/util.py:
aitom/tomominer/hypervolume/utils.py:


def dsp_img(v, new_figure=True):
aitom/image/util.py:
aitom/image/vol/util.py:


def roll(v, s0, s1, s2):
aitom/image/vol/util.py:
aitom/tomominer/image/vol/util.py:


def fft_mid_co(siz):
aitom/image/vol/util.py:
aitom/tomominer/image/vol/util.py:


def grid_distance_to_center(grid):
aitom/image/vol/util.py:
aitom/tomominer/image/vol/util.py:


def main():
aitom/pick/dog/particle_picking_dog__filter.py:
aitom/pick/plot/particle_location_display_imod.py:
aitom/structure/pdb/situs_pdb2vol__batch.py:
aitom/tomominer/average/genetic_algorithm/aligned_refine.py:
aitom/tomominer/pursuit/multi/run.py:


def read_mrc(mrc_path):
aitom/simulation/tomogram/single_bounding_sphere/boundingSphere.py:
aitom/simulation/tomogram/single_bounding_sphere/simulate.py:
aitom/tomominer/core/cython/core.pyx:
aitom/tomominer/io/file.py:


def ldivide(array, vector):
aitom/simulation/tomogram/single_bounding_sphere/boundingSphere.py:



def permute_dims(array, dims):
aitom/simulation/tomogram/single_bounding_sphere/boundingSphere.py:


def B_min_sphere(P, B):
aitom/simulation/tomogram/single_bounding_sphere/boundingSphere.py:


def convert(op):
aitom/structure/pdb/situs_pdb2vol.py:
aitom/structure/pdb/situs_pdb2vol__single.py:

[xulabs]

@ijinjay the code are merged. Please also refractor and add the program to aitom.prog.tools so that we can run use it in the future.