from imgseries import ImgSeries, series

# ----------------------------------------------------------------------------
# ======= WORKING WITH IMAGE SERIES (distinct, individual image files) =======
# ----------------------------------------------------------------------------

# EITHER:
images = ImgSeries(paths=['img1', 'img2'])  # implicitly, savepath is current directory
# OR:
images = series(paths=['img1', 'img2'])
# (the series() function is a bit more powerful than the ImgSeries class, as it
# allows the user to select caching options for speed improvements, see below)

# Image dimensions in x and y
images.nx, images.ny

# Access individual images in the series -------------------------------------

images.files[10]       # filo.File object of image number num=10
images.files[10].file  # actual pathlib.Path file object
images.read(10)        # read image number num=10 into numpy array
images.show(10)        # show image in a matplotlib graph

# Interactive views of image sequence ----------------------------------------

images.animate()       # see image series as a movie (start, end, skip options)
images.inspect()       # browse through image series with a slider (same options)
images.profile()         # object that also has inspect(), animate() methods etc.

# Define display options (only for grayscale imagee --------------------------
# (see details in notebook)
images.display.define('contrast')  # set vmin, vmax in imshow interactively
images.display.define('colormap')  # set cmap in imshow() interactively

# Manually: (equivalent: images.display.limits = 0, 255)
images.display.vmin, images.display.vmax = 0, 255
images.display.cmap = 'viridis'

images.save_display()  # save rotation and crop parameters in a json file
images.load_display()  # load rotation and crop parameters from json file

# Define global transform applied on all images (rotation + crop) ------------
# (see details in notebook)

# Note: the transforms to be considered and the order with which they are
# applied on the images can be modified by passing the argument
# transforms= in ImgSeries. For example:
# images = series(transforms=('rotation', 'crop', 'filter'))

# Interactive:
images.rotation.define()
images.crop.define()
images.filter.define()
images.threshold.define()

# Manual:
images.grayscale.apply = True
images.rotation.angle = -70
images.crop.zone = (2, 25, 400, 600)
images.filter.size = 10
images.subtraction.reference = range(5)  # avg first 5 images for subtraction
images.subtraction.relative = True       # (I - Iref) / I_ref instead of I - Iref
images.threshold.vmin = 220  # everything below vmin is False, rest True
images.threshold.vmax = 240  # everything below vmax is True, rest False
# Note: threshold.vmin can be combined with threshold.vmax for a bandpass

# Other transform parameters / methods:
images.save_transforms()  # save rotation, crop etc. parameters in a json file
images.load_transforms()  # load rotation, crop etc. parameters from json file
images.active_transforms  # see currently applied transforms on images
images.reset_transforms()  # reset all transforms

# Corrections can also be applied to image sequences, e.g. flicker and shaking
# (See also Flicker analysis class, below)
# Contrary to transforms, corrections can be
images.flicker.load()
images.save_corrections()
images.active_corrections  # see currently applied corrections on images
images.reset_corrections()  # reset all corrections

# Exporting images (with transforms and/or corrections)
images.export()  # see examples/Export.ipynb for examples

# Manage image timestamps ----------------------------------------------------
images.info  # see correspondence num / file info + automatically extracted image time
images.save_info()  # save above info in a csv file
images.load_info()  # Load info from previously saved csv data (overwrites images.files)
images.load_time('Time_File.txt')  # Keep images.files but update its time information with data from an external csv file.

# ----------------------------------------------------------------------------
# ===================== WORKING WITH A .TIFF IMAGE STACK =====================
# ----------------------------------------------------------------------------

images = ImgSeries(stack='ImgStack.tif')

# All methods/attributes above available, except those associated with timestamps

images = series(paths=['img1', 'img2'], cache=True)
images.inspect()  # inspection should be significantly faster

from imgseries import GreyLevel, GreyLevelResults

# Create analysis object -----------------------------------------------------
gl = GreyLevel(images)

# Prepare and run analysis ---------------------------------------------------
# NOTE: if no zones are defined, full image is taken as a default
gl.zones.define()  # interactively select zones on image
gl.zones.load()    # alternative to define() where zones are loaded from saved metadata

# other alternative to load image series and analysis parameters from saved files
gl.regenerate()

gl.run()    # run actual analysis (parallel computation available);
gl.results  # is an object containing data and metadata of the analysis
gl.results.save()

# Interactive views of results -----------------------------------------------
gl.show()      # show result of analysis on a given image (default: first one)
gl.animate()   # see results as a movie (start, end, skip options)
gl.inspect()   # browse through results with a slider (same options)

# Load analysis results afterwards (need save() to have been called) ---------
results = GreyLevelResults()
results.load()   # load analysis results (data + metadata)
results.data, results.metadata  # useful attributes

from imgseries import ContourTracking, ContourTrackingResults

# Create analysis object
ct = ContourTracking(images)

# Prepare and run analysis ---------------------------------------------------
ct.threshold.define()  # interactively select threshold level
ct.contours.define()   # interactively select contours at the above level
ct.contours.load()     # alternative to define() where contours are loaded from saved metadata

# other alternative to load image series and analysis parameters from saved files
ct.regenerate()

ct.run()      # run actual analysis (no parallel computation available)
ct.results    # is an object containing data and metadata of the analysis
ct.results.save()     # save results (data + metadata) to files (csv, json)

# Interactive views of results -----------------------------------------------
ct.show()      # show result of analysis on a given image (default: first one)
ct.animate()   # see results as a movie (start, end, skip options)
ct.inspect()   # browse through results with a slider (same options)

# Load analysis results afterwards (need save() to have been called) ---------
results = ContourTrackingResults()
results.load()   # load analysis results (data + metadata)
results.data, results.raw_contour_data, results.metadata  # useful attributes

from imgseries import Front1D, Front1DResults
f1d = Front1D(images)
f1d.run()

from imgseries import Flicker, FlickerResults
flick = Flicker(images)
flick.zones.define()
flick.run()

images.flicker.load()