bbarad

## Segmentation values and names are hardcoded (line 7). 
## All segmentations should be in a single "segmentations" folder.
## This should match what is used in the config.yml for surface morphometrics
from sys import argv
import mrcfile
import os
import numpy as np

component_values = {"vesicle":1,
                    "er":2,

bbarad

# Take two binary segmentations in mrc format and combine them, with the first segmentation having label 1 and the second segmentation having label 2.

import mrcfile
import numpy as np
import argparse

def combine_segmentations(seg1, seg2, clear_overlaps=True):
    """
    Combine two binary segmentations into one, with the first segmentation having label 1 and the second segmentation having label 2.

bbarad

## Convert all mitochondria STL files in current directory to SFF/HFF files
## Usage: python stl2sff.py FOLDERNAME
## Assumes there are multiple mitochondria per tomogram and up to one ER per tomogram.
## Performs both per-segment and global annotations based on assumption of MitoGFP MEF cells treated with thapsigargin (sometimes)
## Outputs HFF format sff files.
## Author: Benjamin Barad <[email protected]> with guidance from Paul Korir

import os
import sys
import glob

bbarad

# Author: Benjamin Barad
# A quick script to erode the masks generated by warp to more tightly surround frost and beads
# Warp's neural net masking can be used to quickly remove gold beads and hunks of frost from tomograms. 
# However, it tends to overmask and generate a nice big blob around each bead. This is desirable for
# limiting particle picking, but not for tomogram generation. This script erodes the mask to more tightly
# surround the frost and beads.
# Usage: 
# 1. Run warp and generate masks for your tomograms with boxnet3mask
# 2. Close warp and rename the warp "mask" folder to "mask_raw"
# 3. Run this script as follows:

bbarad

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Adapted by Benjamin Barad ([email protected]) from code by Digvijay Singh ([email protected])
# Run in an mdocs folder, makes an "Adjusted_mdoc" subfolder with corrected angles, which allows more flexibility for 
# which program is used for tilt series alignment while guaranteeing a flat tomogram. I use it with etomo and warp primarily.

# Instead of identifying the "0" tilt based on transmission, you do it based on a predetermined milling angle.
# This has benefits and disadvantages, and is particularly useful if knowing absolute orientation is useful for future
# analysis.

bbarad

# Adjust angles from imod alignment before warp import to allow flattening of tomograms
# Replaces imod's taSolution.log file with adjusted deltlts, while preserving the original file in taSolution.log.bak
# Author: Benjamin Barad 2022
# Usage:
# cd WARPTOPFOLDER/imod
# python adjust_angles.py * 
# Alternatively, instead of the wildcard individual tilt series folders can be specified.


import pandas as pd

bbarad

from autoscript_sdb_microscope_client import SdbMicroscopeClient
import time
ip_address = '192.168.0.1' # depends on system setup
open_time = 1.5 # seconds
microscope = SdbMicroscopeClient()
microscope.connect(ip_address)
gis_port = microscope.gas.get_gis_port("Pt dep")
print(gis_port.get_temperature())
gis_port.open()
time.sleep(open_time)

bbarad

"""
Script to convert eman2 segmentation hdf maps to imod model files at a user defined contour level.
Requires python>=3.5 as well as having Eman2 and IMOD in the path.

Usage: `python3 tomoseg_to_imod.py -i EXAMPLE.hdf -o EXAMPLE.mod -t 0.8`

Author: Benjamin Barad <[email protected]>
"""

import argparse

bbarad

import imageio
import mrcfile
import os
import sys

def make_photos(basename, working_directory="."):
    """
    Convert MRC file with stack of classes to series of scaled PNGs for web viewing.
    Args:
        basename (str): name of desired folder within class_images - usually the same name as the mrc file.

bbarad

import parse
import trace

filename_list = ["test_1.tpkl", "test_2.tpkl"]

for filename in filename_list:
  trace = parse.parse(filename)
  new_name = "{}.dat".format(filename[:-5])
  trace.write_dat(new_name)