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NanoImagingPack

This is a package for simple image processing. It is oriented on the DIP-Image package, which is available for Matlab. The goal is to keep things simple and make it available for a broad community.

Installation

  1. Download Anaconda https://docs.anaconda.com/anaconda/install/
  2. Open an anaconda prompt
  3. create a new environment 
    conda create --name nanoimaging anaconda tifffile
  4. Activate 
    conda activate nanoimaging
  5. Install this feature branch of NanoImagingPack 
    pip install git+https://github.com/bionanoimaging/NanoImagingPack.git

Getting started

Start an ipython shell

ipython

Load and view a sample image

import NanoImagingPack as nip
import napari

img = nip.readim("erika")
viewer = napari.view_image(img)

The created image is of type "image".

Gain calibration from an inhomogenous stack

Perform a gain calibration using simulated data.

import NanoImagingPack as nip
import numpy as np

# define the input parameters
NPHOT = 100 # max number of photons in simulation
OFFSET = 100 # black level offset
READNOISE = 4 # read noise to simulate
STACK_SIZE = 30

img = nip.readim("MITO_SIM")[0] # load the first frame from the MITO_SIM sample

fg = np.tile(img,(STACK_SIZE,1,1)) # make a stack of frames
fg = nip.poisson(fg, NPhot=NPHOT) # simulate poissonian shot noise

fg = fg + np.random.normal(loc=OFFSET, scale=READNOISE, size=fg.shape) # add gaussian noise

bg = np.random.normal(loc=OFFSET, scale=READNOISE, size=fg.shape) # generate background stack

# perform a calibration and plot the results
nip.cal_readnoise(fg, bg, brightness_blurring=False)

Notes

  • the command nip.lookfor('some string') allows you to search for functions and methods
  • nip.view() or nip.vv() provides an image viewer
    • The default viewer is currently Napari, but this can be changed by nip.setDefault('IMG_VIEWER',myViewer) with myViewer being one of 'NIP_VIEW', 'VIEW5D','NAPARI','INFO'
    • in NIP_VIEW press 'h' to get help. In VIEW5d press "?"
  • graph() provides a simple viewer for 2D graphs -> you can add lists for multiple graphs

Features

  • multidimensional fft/ifft
  • image alignment
  • finding for SLM based SIM images
  • Controlling Hamamatsu LCOS SLM
  • Creating OTFs, PSFs etc.
  • Image manipulation (convolution, (simple) EdgeDamping, extracting, concattenating, Fourier-space extractions and padding)
  • helper functions such as ramps, xx, yy, zz, freq/realspace coord transforms

See "dependencies.txt" for help required dependencies