Performed major refactoring of code

Since v0.2.4 following fixes are made.

• space file is updated after every iteration when isWidget=True
• To improve readability of import statements of global Variables, the global variables are arranged as logical dictionaries
• In order to refactor the code,
  • The save functions in the modules of File subfolder now take the root paths of various subfolders of PostProcessing from import statements from a new loadFilePaths module in the File subfolder.
  • The customROI flow of processing is merged with the original adaptiveROI flow using optional keyword argument list_counter.
  • The findEgdeCustomROI() method for customROI is merged with original findEdge() using optional keyword argument coordinates.

Faster ROI selection by blitting

Since v0.2.3 release, following fixes are made

• instead of rendering the figure everytime a event is generated, we blit the images by setting useblit = True flag. We observe drastic performance gain in the form of reduced latency while Rectangle Selection.
• also a warning associated with pickling numpy arrays while saving is cleared by adding dtype=object key:value to np.save() of KerrPy/File/processControls.py
• also another global variable image_shape = tuple([1066, 1344]) is added.

Figure labels rendered using matplotlib

Bug fix

Reordered mpl.rcParams[] assignment and fig, ax initialization

Single Widget for ROI selection

Since 0.2.1 release, following fixes are made

• hashed filename prefixes using sha1() from hashlib built-in module
  • To avoid the very file path length limit related OS Error, we use SHA-1 for hashing filenames
  • SHA-1, for secure hash algorithm, creates a unique 40 character hash of a string.
  • The prefix, made up of experiment indices, which was earlier directly added to filepath is now first hashed and then added. This creates unique prefix for a particular subspace. The original prefix is stored in a file named with the hash string for reference.
  • This method is inspired by the way hashes the commit objects within the .git directory.
• added global flags: mpl_stylesheet and usetex
  • Default value for usetex= False so it is not assumed LaTeX is installed on system.
• implemented single widget for ROI selection
  • Earlier implementation created new widget for every iteration as there was no way to retain the state information across iterations in an event based program structure
  • In the current release, we use function attributes for retaining the state of the system during the iteration over images. Other ways to retain the state are to
    • use global variables
    • use nonlocal variables
    • use classes
• process figures over a subspace
  • use processFiguresSubSpace.py
  • At prompt enter the space separated experiment indices and finally press Enter
• process velocity over a subspace
  • use processVelocitySubSpace.py
  • At prompt enter the space separated experiment indices and finally press Enter

KerrPy with CustomROI using Matplotlib Widget

Earlier version 0.1 required user to provide a center for the ROI. However in this update, the user can select a custom ROI selection using Matplotlib's Rectangle Selector widget

Bug Fix release

Minor bug fix

• added speckless_second flag to globalVariables.py

KerrPy with staticROI

In this first release, the user needs to specify a rough center of the bubble domain in the image coordinate system. There are two strategies for the bubble domain

• global ROI where the domain is searched across the whole of micrograph. This requires sufficient domain contrast as well as minimum topographical defects. Also the domain is captured if the histogram ''mass'' of the domain is sufficient to detect an optimum threshold for binarization.

• adaptive ROI where the domain is searched in a sequence of zoomed out ROIs to optimized the ratio of bubble to background histogram ''mass''. This strategy is useful when

  • the contrast is low
  • there are topographic defects present
  • other nucleation sites are present within the micrograph and need to be avoided for edge detection. In such case, the ROI can be restricted to certain part of the micrograph.