nikitinvv

import numpy as np
import matplotlib.pyplot as plt
PLANCK_CONSTANT = 4.135667696e-18  # [keV*s]
SPEED_OF_LIGHT = 299792458  # [m/s]
 
n = 1024 # 1d signal sizie
ns = 64+64
voxelsize = 2*10e-9 # object voxel size
energy = 33.35  # [keV] xray energy
wavelength = PLANCK_CONSTANT * SPEED_OF_LIGHT / energy

nikitinvv

import numpy
def gen_interlaced_theta_W():
    numproj = 1440#1800-360
    prime = 2
    nProj_per_rot = 360
    pst = 0
    pend = 360

    seq = []
    i = 0

nikitinvv

# syntax: python record_screen.py <output_file_name.avi> <fps> <monitor_id>
# example: python record_screen.py /local/test.avi 20 1

# on user2bmb@arcturus
# conda activate screenrecording
# (screenrecording) user2bmb@arcturus ~/vnikitin $python record_screen.py /local/test.avi 20 3
from mss import mss
from PIL import Image
import cv2
import time

nikitinvv

# real-time visualization of numpy arrays in ImageJ
######################################################################################
# Installation:

# 1. conda create -n pyimagej -c conda-forge pyimagej openjdk=8 
# 2. conda activate pyimagej
#######################################################################################

import imagej
import numpy as np

nikitinvv

# Storage performance test with reading/writing a stack of tiff files

# Necessary python packages: tiffile, threading, numpy

# Syntax:
# python test_storage.py <data size in each dimension> <number of threads> <path to store data>

# Example:
# python test_storage.py 1024 8 /data/tmp/tiff_stack/

nikitinvv

import numpy as np
import sys
import h5py
import cupy as  cp # subpixel shifts on gpu
#import numpy as  cp # subpixel shifts on cpu

## usage 
###  python merge_helical.py /data/helical/Coal_NaBr_075.h5 0.062480474851608875 3201
### where 0.062480474851608875 is shift in pixels between two projections
### 3201 - number of angles per 360 deg interval (<360 if only 1 rotation)

nikitinvv

# open numpy array in imagej
######################################################################################
# Installation:

# 1. conda create -n pyimagej -c conda-forge pyimagej openjdk=8 
# 2. conda activate pyimagej
#######################################################################################

import imagej
import numpy as np

nikitinvv

# Open a tiff or h5 file in ImageJ by loading it to memory in several threads
######################################################################################
# Installation:

# 1. conda create -n pyimagej -c conda-forge pyimagej openjdk=8 scyjava dxchange h5py ipython
# 2. conda activate pyimagej
#######################################################################################
# Test:

# (pyimagej) usertxm@txmthree ~/vnikitin $ ipython -i parallel_read_to_imagej.py 

nikitinvv

import tomopy as tp
import numpy as np
import time
#timing functions
def tic():
    global startTime_for_tictoc
    startTime_for_tictoc = time.time()

def toc():
    if 'startTime_for_tictoc' in globals():