alisterburt

from pathlib import Path
from typing import Tuple

import eulerangles
import imodmodel
import numpy as np
import pandas as pd
import starfile as starfile
from pydantic import BaseModel

alisterburt

import mrcfile
import numpy as np
import starfile
import napari

positions_xyz = np.loadtxt('picking/test_recon_noflipyz_noflipz.csv', delimiter=',') * 8
positions_xyz = np.c_[positions_xyz, np.ones(positions_xyz.shape[0])]  # add column of ones
positions_xyz = positions_xyz.reshape((-1, 1, 4, 1))
star = starfile.read('ImportTomo/job001/tomograms.star')
df = star['TS_01']

alisterburt

import pandas as pd
import starfile

STAR_FILE = '/path/to/rln30_file.star'

# starfile returns a pandas dataframe for a 'one-block' file
# if you have multiple blocks then
# you get a dictionary of {'block_name': pd.DataFrame)
df = starfile.read(STAR_FILE)

alisterburt

from pathlib import Path
from typing import Dict
from enum import Enum

import napari
import numpy as np
import mrcfile
import pandas as pd
import starfile
import eulerangles

alisterburt

from pathlib import Path

import mrcfile
import pandas as pd
import napari
import numpy as np
from magicgui import magicgui

OUTPUT_DIRECTORY = 'picking'
Path(OUTPUT_DIRECTORY).mkdir(exist_ok=True, parents=True)

alisterburt

from pathlib import Path

import mrcfile
import napari
import numpy as np
from magicgui import magicgui

OUTPUT_DIRECTORY = 'picking'
Path(OUTPUT_DIRECTORY).mkdir(exist_ok=True, parents=True)

alisterburt

import subprocess
from pathlib import Path

import starfile
import typer
from rich.console import Console
from rich.progress import track

prompt_kwargs = {'prompt': True, 'prompt_required': True}

alisterburt

m = dread('mdipoleSet_rawVesicle_1.omd');

sz = dpktomo.volume.slices.Slice();
sz.source = 'hiv.mrc';
sz.center=m.center;
sz.thickness=10;
sz.l = [200, 200];
sz.eulers = 'z';
sz.fetchData();

alisterburt

def area(a, num_classes):
    class_idx = einops.rearrange(torch.arange(num_classes), 'i -> i 1 1 1')
    a = a == class_idx
    area = einops.reduce(a, 'c ... -> c', reduction='sum')
    return area


def intersection(a, b, num_classes) -> float:
    class_idx = einops.rearrange(torch.arange(num_classes), 'i -> i 1 1 1')
    a = a == class_idx

alisterburt

import numpy as np
import einops
from pydantic import BaseModel


class Array(np.ndarray):
    def __class_getitem__(cls, t):
        return type('Array', (Array,), {'__dtype__': t})

    @classmethod