shahpnmlab

import numpy as np
import napari
from pathlib import Path
from scipy.interpolate import splprep, splev
from scipy import ndimage as ndi
from skimage import draw, filters, measure
from skimage.morphology import binary_closing, binary_dilation, binary_erosion
from napari.qt.threading import thread_worker
from magicgui import magicgui
from qtpy.QtWidgets import QVBoxLayout, QFileDialog

shahpnmlab

from pathlib import PureWindowsPath, Path
import mdocfile
import starfile
import pandas as pd
import typer

unbork = typer.Typer()


@unbork.command(no_args_is_help=True)

shahpnmlab

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from pathlib import Path
import starfile
import pandas as pd
from lxml import etree
import typer


def flatten(t):
    return [item for sublist in t for item in sublist]

shahpnmlab

import starfile
import pandas as pd
import numpy as np
import typer
from pathlib import Path
from typing import Optional

def process_star_file(input_path: str, output_path: Optional[str] = None) -> Optional[pd.DataFrame]:
    """
    Process a star file to average temporal samples and rename columns to Relion convention.

shahpnmlab

import typer
import xml.etree.ElementTree as ET
from pathlib import Path
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from rich.console import Console
from rich.table import Table
from collections import defaultdict

app = typer.Typer()

shahpnmlab

import torch_fourier_slice as tfs
import torch
import mrcfile
import numpy as np
from scipy.spatial.transform import Rotation as R

def process_mrc(input_path, output_projection, output_reconstruction, angles=(30, 20, 18)):
    # Load and convert MRC to tensor
    volume = torch.tensor(mrcfile.read(input_path))
    

shahpnmlab

#!/bin/bash
function ssh_manager {
    local HOSTS=("scan" "rc1" "rc2" "rc3" "rc4" "pinot")
    local HOST=""
    local CONTROL_PATH="$HOME/.ssh/control:%h:%p:%r"

    # Parse command line arguments
    if [[ $1 == "-h" || $1 == "--help" ]]; then
        echo "Usage: sshmgr [host]"
        echo "  host: The host to connect to (scan, rc1, rc2, rc3, rc4 or pinot). If not provided, you'll be prompted."

shahpnmlab

from pathlib import Path
import mdocfile
import shutil
import typer
import logging
from typing import Optional
# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'

shahpnmlab

from pathlib import Path
import imodmodel
import starfile
import pandas as pd
import numpy as np

# Create optics dataframe
df_optics = pd.DataFrame({
    'rlnOpticsGroup': [1],
    'rlnOpticsGroupName': ["OpticsGroup1"],

shahpnmlab

import numpy as np
import starfile

XDIM = 378
YDIM = 378
ZDIM = 228

df = starfile.read("gs07_ts_003_8.00Apx_emd_13474_clean.star")
xyz_headings = [f'rlnCoordinate{axis}' for axis in 'XYZ']
xyz_headings.append('rlnAutopickFigureOfMerit')