diff --git a/Addons/FRCmetric/miplib-public/.gitignore b/Addons/FRCmetric/miplib-public/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..1996912bf37a11e5071097a70dc3e1f0ea8c0edd
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/.gitignore
@@ -0,0 +1,76 @@
+# Created by .ignore support plugin (hsz.mobi)
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Visual Studio Code
+.vscode/
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+.idea/
+
+# Exclude jupyter Examples (if not explicitely added)
+.ipynb_checkpoints/
+*.ipynb
+#scribbles/
+
+notebooks/
+
+# Ignore data thay may be saved in the examples directory.
+*.tif
+*.nd2
+*.tiff
diff --git a/Addons/FRCmetric/miplib-public/License.txt b/Addons/FRCmetric/miplib-public/License.txt
new file mode 100644
index 0000000000000000000000000000000000000000..532916e7cd7e3ee56928a15e0a4d91506d6d175b
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/License.txt
@@ -0,0 +1,54 @@
+
+
+Copyright (c) 2018, Sami Koho, Molecular Microscopy & Spectroscopy,
+Italian Institute of Technology. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+* Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above
+copyright notice, this list of conditions and the following
+disclaimer in the documentation and/or other materials provided
+with the distribution.
+
+* Neither the name of the Molecular Microscopy and Spectroscopy
+research line, nor the names of its contributors may be used to
+endorse or promote products derived from this software without
+specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY COPYRIGHT HOLDER AND CONTRIBUTORS ''AS
+IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
+TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL COPYRIGHT HOLDER
+OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+In addition to the terms of the license, we ask to acknowledge the use
+of the software in scientific articles by citing:
+
+Koho, S. et al. Fourier ring correlation simplifies image restoration in fluorescence
+microscopy. Nat. Commun. 10, 3103 (2019).
+
+Parts of the MIPLIB source code are based on previous BSD licensed
+open source projects:
+
+pyimagequalityranking:
+Copyright (c) 2015, Sami Koho, Laboratory of Biophysics, University of Turku.
+All rights reserved.
+
+supertomo:
+Copyright (c) 2014, Sami Koho, Laboratory of Biophysics, University of Turku.
+All rights reserved.
+
+iocbio-microscope:
+Copyright (c) 2009-2010, Laboratory of Systems Biology, Institute of
+Cybernetics at Tallinn University of Technology. All rights reserved
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/Readme.md b/Addons/FRCmetric/miplib-public/Readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..ab77e27ee66a37e796129d65a04669fbfd3a7d87
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/Readme.md
@@ -0,0 +1,76 @@
+# MIPLIB
+[](https://zenodo.org/badge/latestdoi/162555135)
+
+Microscope Image Processing Library (*MIPLIB*) is a Python based software library, created especially for processing and analysis of fluorescece microscopy images. It contains functions for example for:
+
+- image registration 2D/3D
+- image deconvolution and fusion (2D/3D), based on efficient CUDA GPU accelerated algorithms
+- Fourier Ring/Shell Correlation (FRC/FSC) based image resolution analysis -- and several blind image restoration methods based on FRC/FSC.
+- Image quality analysis
+- ...
+
+The library is distributed under a BSD open source license.
+
+## How do I install it?
+
+I would recommend going with the *Anaconda* Python distribution, as it removes all the hassle from installing the necessary packages. MIPLIB should work on all platforms (Windows, MacOS, Linux), however I do not actively test it on Windows.
+
+
+### Here's how to setup your machine for development:
+
+ 1. There are some C extensions in *miplib* that need to be compiled. Therefore, if you are on a *mac*, you will also need to install XCode command line tools. In order to do this, Open *Terminal* and write `xcode-select --install`. If you are on *Windows*, you will need the [C++ compiler](https://wiki.python.org/moin/WindowsCompilers)
+
+ 2. The Bioformats plugin that I leverage in MIPLIB to read microscopy image formats requires Java. Therefore, make sure that you have JRE installed if you want to use the bioformats reader. If you are on Windows, also make sure that the JAVA_HOME environment variable is set. You may also have to add the JAVA_HOME to your PATH. More info on that can be found here: [JPYPE](https://jpype.readthedocs.io/en/latest/install.html).
+
+3. Fork and clone the *MIBLIB* repository (`git clone git@github.com:<your_account>/miplib.git`). The code will be saved to a sub-directory called *miplib* of the current directory. Put the code somewhere where it can stay. You may need to generate an SSH key, if you have not used GitHub previously.
+
+4. Go to the *miplib* directory and create a new Python virtual environment `conda env create -f environment.yml`. Alternatively use `environment_nocuda.yml`, if you do not want to use GPU acceleration.
+
+5. Activate the created virtual environment by writing `conda activate miplib`
+
+6. Now, install the *miplib* package to the new environment by executing the following in the *miplib* directory `python setup.py develop`. This will only create a link to the source code, so don't delete the *miplib* directory afterwards.
+
+### And if you are not a developer
+
+If you just want to use the library, you can get everything running as follows:
+
+1. Download the *environment_client.yml* file and create a Python virtual environment `conda env create -f environment_client.yml`.
+
+2. Activate the created virtual environment by writing `conda activate miplib`
+
+## How do I use it?
+
+My preferred tool for explorative tasks is Jupyter Notebook/Lab. Please look for updates in the Examples/ folder (a work in progress). Let me know if you would be interested in some specific example to be included.
+
+There are also a number of command line scripts (entry points) in the bin/ directory that may be handy in different batch processing tasks. They are also a good place to start exploring the library.
+
+## Contribute?
+
+*MIPLIB* was born as a combination of several previously separate libraries. The code and structure, although working, might (does) not in all places make sense. Any suggestions for improvements, new features etc. are welcome.
+
+## Regarding Python versions
+
+I recenly migrated MIPLIB to Python 3, and have no intention to maintain backwards compatibility to Python 2.7. You can checkout an older version of the library, if you need to work on Python 2.7.
+
+## About GPU acceleration
+
+The deconvolution algorithms can be accelerated with a GPU. On MacOS the CUDA GPU acceleration currently does not work, because there are no NVIDIA drivers available for the latest OS versions. I recently re-factored the GPU acceleration functions, using the CuPy library. It would in principle be possible to use OpenCL backend, instead of CUDA, but I have not tried that (yet).
+
+## Publications
+
+Here are some works that have been made possible by the MIPLIB (and its predecessors):
+
+Koho, S. V. et al. Two-photon image-scanning microscopy with SPAD array and blind image reconstruction. Biomed. Opt. Express, BOE 11, 2905–2924 (2020)
+
+[Koho, S. *et al.* Fourier ring correlation simplifies image restoration in fluorescence microscopy. Nat. Commun. 10 3103 (2019).](https://doi.org/10.1038/s41467-019-11024-z)
+
+Koho, S., T. Deguchi, and P. E. E. Hänninen. 2015. “A Software Tool for Tomographic Axial Superresolution in STED Microscopy.” Journal of Microscopy 260 (2): 208–18.
+
+Koho, Sami, Elnaz Fazeli, John E. Eriksson, and Pekka E. Hänninen. 2016. “Image Quality Ranking Method for Microscopy.” Scientific Reports 6 (July): 28962.
+
+Prabhakar, Neeraj, Markus Peurla, Sami Koho, Takahiro Deguchi, Tuomas Näreoja, H-C Huan-Cheng Chang, Jessica M. J. M. Rosenholm, and Pekka E. P. E. Hänninen. 2017. “STED-TEM Correlative Microscopy Leveraging Nanodiamonds as Intracellular Dual-Contrast Markers.” Small 1701807 (December): 1701807.
+
+Deguchi, Takahiro, Sami Koho, Tuomas Näreoja, and Pekka Hänninen. 2014. “Axial Super-Resolution by Mirror-Reflected Stimulated Emission Depletion Microscopy.” Optical Review 21 (3): 389–94.
+
+Deguchi, Takahiro, Sami V. Koho, Tuomas Näreoja, Juha Peltonen, and Pekka Hänninen. 2015. “Tomographic STED Microscopy to Study Bone Resorption.” In Proceedings of the SPIE, 9330:93301M – 93301M – 6.
+
diff --git a/Addons/FRCmetric/miplib-public/bld.bat b/Addons/FRCmetric/miplib-public/bld.bat
new file mode 100644
index 0000000000000000000000000000000000000000..417479da0b7b78f6358e6f152809e35767a61f76
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/bld.bat
@@ -0,0 +1,2 @@
+"%PYTHON%" setup.py install --single-version-externally-managed --record=record.txt
+if errorlevel 1 exit 1
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/build.sh b/Addons/FRCmetric/miplib-public/build.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d38a3ce3fcb965f812036d0b8e68baeb28782842
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/build.sh
@@ -0,0 +1,2 @@
+#!/usr/bin/env bash
+$PYTHON setup.py install --single-version-externally-managed --record=record.txt # Python command to install the script.
diff --git a/Addons/FRCmetric/miplib-public/environment.yml b/Addons/FRCmetric/miplib-public/environment.yml
new file mode 100644
index 0000000000000000000000000000000000000000..3d081ca99b4db117cb61155b96d7063701a98308
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/environment.yml
@@ -0,0 +1,25 @@
+name: miplib
+
+dependencies:
+ - python=3.6
+ - numpy=1.14.5
+ - scipy
+ - h5py
+ - SimpleItk
+ - matplotlib
+ - pandas
+ - pims=0.4.1
+ - jpype1=0.7.5
+ - notebook
+ - scikit-image
+ - cudatoolkit=10.1
+ - pip
+ - pip:
+ - psf
+ - cupy-cuda101
+
+channels:
+ - anaconda
+ - conda-forge
+ - simpleitk
+
diff --git a/Addons/FRCmetric/miplib-public/environment_client.yml b/Addons/FRCmetric/miplib-public/environment_client.yml
new file mode 100644
index 0000000000000000000000000000000000000000..5b7c9a2f1d14a92b562ab1e98799508282b0f55f
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/environment_client.yml
@@ -0,0 +1,28 @@
+name: miplib
+
+dependencies:
+ - python=3.6
+ - numpy=1.14.5
+ - scipy
+ - h5py
+ - SimpleItk
+ - matplotlib
+ - pandas
+ - pims=0.4.1
+ - jpype1=0.7.5
+ - notebook
+ - scikit-image
+ - cudatoolkit=10.1
+ - pip
+ - pip:
+ - psf
+ - cupy-cuda101
+ - miplib >= 1.0.5
+
+
+channels:
+ - anaconda
+ - conda-forge
+ - simpleitk
+ - numba
+
diff --git a/Addons/FRCmetric/miplib-public/environment_nocuda.yml b/Addons/FRCmetric/miplib-public/environment_nocuda.yml
new file mode 100644
index 0000000000000000000000000000000000000000..8fba1874cdc6ffbd4b51e08746210921161a6013
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/environment_nocuda.yml
@@ -0,0 +1,23 @@
+name: miplib
+
+dependencies:
+ - python=3.6
+ - numpy=1.14.5
+ - scipy
+ - h5py
+ - SimpleItk
+ - matplotlib
+ - pandas
+ - pims=0.4.1
+ - jpype1=0.7.5
+ - notebook
+ - scikit-image
+ - pip
+ - pip:
+ - psf
+
+channels:
+ - anaconda
+ - conda-forge
+ - simpleitk
+
diff --git a/Addons/FRCmetric/miplib-public/meta.yaml b/Addons/FRCmetric/miplib-public/meta.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ab1303d43130f499793b8359c75904a2f52374bb
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/meta.yaml
@@ -0,0 +1,39 @@
+package:
+ name: miplib
+ version: "1.0"
+
+source:
+ git_rev: v1.0
+ git_url: https://sakoho81@github.com/sakoho81/miplib.git
+
+requirements:
+ build:
+ - python=3.6
+ - setuptools
+ - numpy
+
+ run:
+ - python=3.6
+ - pims
+ - pandas
+ - h5py
+ - matplotlib
+ - numba=0.39
+ - pyculib
+ - SimpleITK
+ - scipy
+ - numpy
+ - scikit-data
+ - cudatoolkit=7.5
+ - jpype1
+
+ test:
+ imports:
+ - pyculib
+ - miplib
+
+ about:
+ home: https://github.com/sakoho81/miplib
+ license: BSD
+ license_file: License.txt
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/__init__.py b/Addons/FRCmetric/miplib-public/miplib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/analysis/__init__.py b/Addons/FRCmetric/miplib-public/miplib/analysis/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/analysis/calculate.py b/Addons/FRCmetric/miplib-public/miplib/analysis/calculate.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b4e4402a53d56399ef6cc35e0ed73fc32543b87
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/analysis/calculate.py
@@ -0,0 +1,54 @@
+from miplib.processing.segmentation import masking
+from miplib.data.containers.image import Image
+import numpy as np
+
+
+def calculate_nearest_neighbor_distances(x_coords, y_coords):
+ """
+ Assumes an input of two arrays with coordinates of labels (e.g. centroids of blobs) and calculates
+ the distance between each label pair.
+
+ :param x_coords: the x coordinates
+ :param y_coords: the y coordinates
+ :return: Sorted list of distances (mean and std)
+ """
+
+ assert isinstance(x_coords, np.ndarray)
+ assert isinstance(y_coords, np.ndarray)
+
+ length = len(x_coords)
+
+ x_s = np.broadcast_to(x_coords, (length,) * 2)
+ y_s = np.broadcast_to(y_coords, (length,) * 2)
+
+ distances = np.sqrt((x_s - np.transpose(x_s)) ** 2 + (y_s - np.transpose(y_s)) ** 2)
+
+ distances = np.sort(distances, 0)
+
+ mean_distances = np.mean(distances, axis=1)
+ std_distances = np.std(distances, axis=1)
+
+ return mean_distances, std_distances
+
+
+def calculate_sbr(image, kernel_size=40, threshold=40):
+ """
+ Calculate the singal to background ratio of an image
+
+ :param image: an Image object
+ :param threshold: a threshold to separate the signal from the background. Gets values
+ between 0 and 100
+ :return: SBR float
+ """
+
+ assert isinstance(image, Image)
+
+ background_mask = masking.make_local_intensity_based_mask(
+ image, threshold, kernel_size=kernel_size, invert=True)
+ background_image = Image(image * background_mask, image.spacing)
+
+ background_level = np.mean(background_image[background_mask > 0])
+ signal_level = np.mean(image[background_mask == 0])
+
+ return signal_level/background_level
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/analysis/image_quality/__init__.py b/Addons/FRCmetric/miplib-public/miplib/analysis/image_quality/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/analysis/image_quality/filters.py b/Addons/FRCmetric/miplib-public/miplib/analysis/image_quality/filters.py
new file mode 100644
index 0000000000000000000000000000000000000000..e078883318dbab2607e9c750a04ba1cf744f03dc
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/analysis/image_quality/filters.py
@@ -0,0 +1,410 @@
+# coding=utf-8
+"""
+File: filters.py
+Author: Sami Koho (sami.koho@gmail.com)
+
+Description:
+This file contains the filters that are used for calculating the
+image quality parameters in the PyImageQuality software.
+- The LocalImageQuality class is used to run spatial domain
+ analysis. It calculates the Shannon entropy value at a
+ masked part of an image
+- The FrequencyQuality class is used to calculate statistical
+ quality parameters in the frequency domain. The calculations
+ are based on the analysis of the tail of the 1D power spect-
+ rum.
+- Brenner and Spectral domain autofocus metrics were impelemnted
+ as well, based on the two classes above.
+"""
+
+import argparse
+from math import floor
+
+import numpy as np
+from matplotlib import pyplot as plt
+from scipy import ndimage, fftpack, stats
+
+from . import utils
+from miplib.data.containers.image import Image
+from miplib.processing import image as imutils
+from miplib.data.iterators.fourier_ring_iterators import FourierRingIterator
+
+
+def get_common_options(parser):
+ """
+ Common command-line options for the image-quality filters
+ """
+ assert isinstance(parser, argparse.ArgumentParser)
+ group = parser.add_argument_group(
+ "Filters common", "Common options for the quality filters"
+ )
+ group.add_argument(
+ "--power-averaging",
+ dest="power_averaging",
+ choices=["radial", "additive"],
+ default="additive"
+ )
+ group.add_argument(
+ "--normalize-power",
+ dest="normalize_power",
+ action="store_true"
+
+ )
+ group.add_argument(
+ "--use-mask",
+ dest="use_mask",
+ action="store_true"
+ )
+ group.add_argument(
+ "--invert-mask",
+ dest="invert_mask",
+ action="store_true"
+ )
+ group.add_argument(
+ "--power-threshold",
+ dest="power_threshold",
+ type=float,
+ default=0.4
+ )
+ group.add_argument(
+ "--spatial-threshold",
+ dest="spatial_threshold",
+ type=int,
+ default=80
+ )
+
+ return parser
+
+
+class Filter(object):
+ """
+ A base class for a filter utilizing Image class object
+ """
+ def __init__(self, image, options, physical=False, verbal=False):
+
+ assert isinstance(image, Image)
+ self.options = options
+
+ self.data = image
+
+ self.spacing = self.data.spacing
+ self.dimensions = self.data.shape
+ self.physical = physical
+ self.verbal = verbal
+
+ def set_physical_coordinates(self):
+ self.physical = True
+
+ def set_pixel_coordinates(self):
+ self.physical = False
+
+
+class LocalImageQuality(Filter):
+ """
+ This is a filter for quantifying image quality, based on the calculation
+ of Shannon entropy at image neighborhoods that contain the highest amount
+ of detail.
+ """
+
+ def __init__(self, image, options, physical=False, verbal=False):
+
+ Filter.__init__(self, image, options, physical, verbal)
+
+ self.data_temp = None
+ self.kernel_size = []
+
+ def set_smoothing_kernel_size(self, size):
+
+ if isinstance(size, list):
+ assert len(size) == len(self.spacing)
+ sizes = size
+ elif isinstance(size, float) or isinstance(size, int):
+ sizes = [size, ] * len(self.spacing)
+ else:
+ print("Unknown size type")
+ return
+
+ if self.physical is True:
+ for i in range(len(sizes)):
+ self.kernel_size[i] = sizes[i]/self.spacing[i]
+ assert self.kernel_size[i] < self.dimensions[i]
+ else:
+ self.kernel_size = sizes
+ assert all(x < y for x, y in zip(self.kernel_size, self.dimensions)), \
+ "Kernel can not be larger than image"
+
+ def run_mean_smoothing(self, return_result=False):
+ """
+ Mean smoothing is used to create a mask for the entropy calculation
+ """
+
+ assert len(self.kernel_size) == len(self.dimensions)
+ self.data_temp = ndimage.uniform_filter(self.data[:], size=self.kernel_size)
+
+ if return_result:
+ return Image(self.data_temp, self.spacing)
+
+ def calculate_entropy(self):
+ """
+ Returns the Shannon entropy value of an image.
+ """
+ # Calculate histogram
+ histogram = ndimage.histogram(
+ self.data_temp,
+ self.data_temp.min(),
+ self.data_temp.max(), 50
+ )
+ # Exclude zeros
+ histogram = histogram[np.nonzero(histogram)]
+ # Normalize histogram bins to sum to one
+ histogram = histogram.astype(float)/histogram.sum()
+ return -np.sum(histogram*np.log2(histogram))
+
+ def find_sampling_positions(self):
+ """
+ Create a mask by finding pixel positions in the smoothed image
+ that have pixel values higher than 80% of the maximum value.
+ """
+ peaks = np.percentile(self.data_temp, self.options.spatial_threshold)
+ mask = np.where(self.data_temp >= peaks, 1, 0)
+ if self.options.invert_mask:
+ return np.invert(mask.astype(bool))
+ else:
+ return mask
+
+ def calculate_image_quality(self, kernel=None, show=False):
+ """
+ Calculate an estimate for image quality, based on the
+ Shannon entropy measure. options.use_mask switch can
+ be used to limit the entropy calculation to detailed
+ parts of the image.
+ """
+ if self.options.use_mask:
+ if kernel is not None:
+ self.set_smoothing_kernel_size(kernel)
+
+ assert len(self.kernel_size) != 0
+
+ if self.data_temp is None:
+ self.run_mean_smoothing()
+
+ positions = self.find_sampling_positions()
+ self.data_temp = self.data[:][np.nonzero(positions)]
+ if show:
+ Image(self.data[:]*positions, self.spacing).show()
+ else:
+ self.data_temp = self.data[:]
+
+ return self.calculate_entropy()
+
+
+class FrequencyQuality(Filter):
+ """
+ A filter for calculated image-quality related parameters in the frequency
+ domain. First a one-dimensional power spectrum is calculated for an image,
+ after which various types of statistics are calculated for the power
+ spectrum tail (frequencies > 40% of Nyquist)
+ """
+ def __init__(self, image, options, physical=False, verbal=False):
+
+ Filter.__init__(self, image, options, physical=physical, verbal=verbal)
+
+ # Additive form of power spectrum calculation requires a square shaped
+ # image
+ if self.options.power_averaging == "additive":
+ self.data = imutils.crop_to_largest_square(self.data)
+
+ self.simple_power = None
+ self.power = None
+ self.kernel_size = []
+
+ def set_image(self, image):
+ self.data = image
+
+ def calculate_power_spectrum(self):
+ """
+ A function that is used to calculate a centered 2D power spectrum.
+ Additionally the power spectrum can be normalized by image dimensions
+ and image intensity mean, if necessary.
+ """
+ self.power = np.abs(np.fft.fftshift(np.fft.fft2(self.data[:])))**2
+ if self.options.normalize_power:
+ dims = self.data[:].shape[0]*self.data[:].shape[1]
+ mean = np.mean(self.data[:])
+ self.power /= (dims*mean)
+
+ def calculate_radial_average(self, bin_size=2):
+ """
+ Convert a 2D centered power spectrum into 1D by averaging spectral
+ power at different radiuses from the zero frequency center
+ """
+ iterator = FourierRingIterator(self.power.shape, d_bin=bin_size)
+
+ average = np.zeros(iterator.nbins)
+
+ for idx, ring in enumerate(iterator):
+ subset = self.power[ring]
+ average[idx] = float(subset.sum())/subset.size
+
+ dx = self.data.spacing[0]
+ f_k = np.linspace(0, 1, iterator.nbins) * (1.0/(2*dx))
+
+ self.simple_power = [f_k, average]
+
+ if self.options.show_plots:
+ plt.plot(np.log10(self.simple_power[0]))
+ plt.ylabel("Average power")
+ plt.xlabel("Frequency")
+ plt.show()
+
+ def calculate_summed_power(self):
+ """
+ Calculate a 1D power spectrum fro 2D power spectrum, by summing all rows and
+ columns, and then summing negative and positive frequencies, to form a
+ N/2+1 long 1D array. This approach is significantly faster to calculate
+ than the radial average.
+ """
+
+ power_sum = np.zeros(self.power.shape[0])
+ for i in range(len(self.power.shape)):
+ power_sum += np.sum(self.power, axis=i)
+ zero = floor(float(power_sum.size)/2)
+ power_sum[zero+1:] = power_sum[zero+1:]+power_sum[:zero-1][::-1]
+ power_sum = power_sum[zero:]
+ dx = self.data.spacing[0]
+ f_k = np.linspace(0, 1, power_sum.size)*(1.0/(2*dx))
+
+ self.simple_power = [f_k, power_sum]
+
+ if self.options.show_plots:
+ plt.plot(self.simple_power[0], self.simple_power[1], linewidth=2, color="red")
+ plt.ylabel("Total power")
+ plt.yscale('log')
+ plt.xlabel('Frequency')
+ plt.show()
+
+ def analyze_power_spectrum(self):
+ """
+ Run the image quality analysis on the power spectrum
+ """
+ assert self.data is not None, "Please set an image to process"
+ self.calculate_power_spectrum()
+
+ # Choose a method to calculate 1D power spectrum
+ if self.options.power_averaging == "radial":
+ self.calculate_radial_average()
+ elif self.options.power_averaging == "additive":
+ self.calculate_summed_power()
+ else:
+ raise NotImplementedError
+
+ # Extract the power spectrum tail
+ hf_sum = self.simple_power[1][self.simple_power[0] > self.options.power_threshold*self.simple_power[0].max()]
+
+ # Calculate parameters
+ f_th = self.simple_power[0][self.simple_power[0] > self.options.power_threshold*self.simple_power[0].max()][-utils.analyze_accumulation(hf_sum, .2)]
+ mean = np.mean(hf_sum)
+ std = np.std(hf_sum)
+ entropy = utils.calculate_entropy(hf_sum)
+ nm_th = 1.0e9/f_th
+ pw_at_high_f = np.mean(self.simple_power[1][self.simple_power[0] > .9*self.simple_power[0].max()])
+ skew = stats.skew(np.log(hf_sum))
+ kurtosis = stats.kurtosis(hf_sum)
+ mean_bin = np.mean(hf_sum[0:5])
+
+ return [mean, std, entropy, nm_th, pw_at_high_f, skew, kurtosis, mean_bin]
+
+ def show_all(self):
+ """
+ A small utility to show a plot of the 2D and 1D power spectra
+ """
+ fig, subplots = plt.subplots(1, 2)
+ if self.power is not None:
+ subplots[0].imshow(np.log10(self.power))
+ if self.simple_power is not None:
+ #index = int(len(self.simple_power[0])*.4)
+ #subplots[1].plot(self.simple_power[0][index:], self.simple_power[1][index:], linewidth=1)
+ subplots[1].plot(self.simple_power[0], self.simple_power[1], linewidth=1)
+ subplots[1].set_yscale('log')
+ plt.show()
+
+ def get_power_spectrum(self):
+ """
+ Returns the calculated 1D power spectrum. Please make sure to create
+ power spectrum before calling this.
+ """
+ assert self.simple_power is not None
+ return self.simple_power
+
+
+class SpectralMoments(FrequencyQuality):
+ """
+ Our implementation of the Spectral Moments autofocus metric
+ Firestone, L. et al (1991). Comparison of autofocus methods for automated
+ microscopy. Cytometry, 12(3), 195–206.
+ http://doi.org/10.1002/cyto.990120302
+ """
+
+ def calculate_percent_spectrum(self):
+ self.simple_power[1] /= (self.simple_power[1].sum()/100)
+
+ def calculate_spectral_moments(self):
+ """
+ Run the image quality analysis on the power spectrum
+ """
+ assert self.data is not None, "Please set an image to process"
+ self.calculate_power_spectrum()
+
+ # Choose a method to calculate 1D power spectrum
+ if self.options.power_averaging == "radial":
+ self.calculate_radial_average()
+ elif self.options.power_averaging == "additive":
+ self.calculate_summed_power()
+ else:
+ raise NotImplementedError
+
+ self.calculate_percent_spectrum()
+
+ bin_index = np.arange(1, self.simple_power[1].shape[0]+1)
+
+ return (self.simple_power[1]*np.log10(bin_index)).sum()
+
+
+class BrennerImageQuality(Filter):
+ """
+ Our implementation of the Brenner autofocus metric
+ Brenner, J. F. et al (1976). An automated microscope for cytologic research
+ a preliminary evaluation. Journal of Histochemistry & Cytochemistry, 24(1),
+ 100–111. http://doi.org/10.1177/24.1.1254907
+ """
+ def __init__(self, image, options, physical=False, verbal=False):
+
+ Filter.__init__(self, image, options, physical, verbal)
+ # This is not really necessary. It was just added in order to compare
+ # with the frequency domain measures.
+ self.data = imutils.crop_to_largest_square(image)
+
+ def calculate_brenner_quality(self):
+ data = self.data
+ rows = data.shape[0]
+ columns = data.shape[1]-2
+ temp = np.zeros((rows, columns))
+
+ temp[:] = ((data[:, 0:-2] - data[:, 2:])**2)
+
+ return temp.sum()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/analysis/image_quality/image_quality_ranking.py b/Addons/FRCmetric/miplib-public/miplib/analysis/image_quality/image_quality_ranking.py
new file mode 100644
index 0000000000000000000000000000000000000000..c06140d45f0224ff993ee5e88acd19ce8ba35d41
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/analysis/image_quality/image_quality_ranking.py
@@ -0,0 +1,69 @@
+import os
+import pandas as pd
+
+from . import filters
+from miplib.data.io import read
+import miplib.analysis.resolution.fourier_ring_correlation as frc
+
+def evaluate_image_quality(image, options):
+ """
+ Calculate quality features sof a single image
+ """
+
+ # Run spatial domain analysis
+ task = filters.LocalImageQuality(image, options)
+ task.set_smoothing_kernel_size(100)
+ entropy = task.calculate_image_quality()
+
+ # Run frequency domain analysis
+ task2 = filters.FrequencyQuality(image, options)
+ results = task2.analyze_power_spectrum()
+
+ task3 = filters.SpectralMoments(image, options)
+ moments = task3.calculate_spectral_moments()
+
+ task4 = filters.BrennerImageQuality(image, options)
+ brenner = task4.calculate_brenner_quality()
+
+ # Save results
+ results.insert(0, moments)
+ results.insert(0, brenner)
+ results.insert(0, entropy)
+
+ return results
+
+
+def batch_evaluate_image_quality(path, options):
+ """
+ Batch calculate quality features for images in a directory
+ :param options: options for the quality ranking scripts, as in miplib/ui/image_quality_options.py
+ :parame path: directory that contains the images to be analyzed
+ """
+
+ df = pd.DataFrame(columns=["Filename", "tEntropy", "tBrenner", "fMoments", "fMean", "fSTD", "fEntropy",
+ "fTh", "fMaxPw", "Skew", "Kurtosis", "MeanBin", "Resolution"])
+
+ for idx, image_name in enumerate(os.listdir(path)):
+ if options.file_filter is None or options.file_filter in image_name:
+ real_path = os.path.join(path, image_name)
+ # Only process images
+ if not os.path.isfile(real_path) or not real_path.endswith((".jpg", ".tif", ".tiff", ".tif")):
+ continue
+ # ImageJ files have particular TIFF tags that can be processed correctly
+ # with the options.imagej switch
+ image = read.get_image(real_path, channel=options.rgb_channel)
+
+ # Only grayscale images are processed. If the input is an RGB image,
+ # a channel can be chosen for processing.
+ results = evaluate_image_quality(image, options)
+ results.insert(0, real_path)
+
+ # Add resolution value to the end
+ results.append(frc.calculate_single_image_frc(image, options).resolution["resolution"])
+
+ df.loc[idx] = results
+
+ print ("Done analyzing {}".format(image_name))
+
+ return df
+
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/analysis/image_quality/utils.py b/Addons/FRCmetric/miplib-public/miplib/analysis/image_quality/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..548015f528ad6ac7fd8ff5ab71be19fde94b3d4d
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/analysis/image_quality/utils.py
@@ -0,0 +1,45 @@
+"""
+File: utils.py
+Author: sami.koho@gmail.com
+
+Description:
+Various sorts of small utilities for the PyImageQuality software.
+Contains all kinds of code snippets that did not find a home in
+the main modules.
+"""
+
+import numpy
+from scipy import ndimage
+
+
+def analyze_accumulation(x, fraction):
+ """
+ Analyze the accumulation by starting from the end of the data.
+ """
+ assert 0.0 < fraction <= 1.0
+ final = fraction * x.sum()
+ index = 1
+ while x[-index:].sum() < final:
+ index += 1
+ return index
+
+
+def calculate_entropy(data):
+ """
+ Calculate the Shannon entropy for data
+ """
+ # Calculate histogram
+ histogram = ndimage.histogram(
+ data,
+ data.min(),
+ data.max(), 50)
+ # Exclude zeros
+ histogram = histogram[numpy.nonzero(histogram)]
+ # Normalize histogram bins to sum to one
+ histogram = histogram.astype(float) / histogram.sum()
+ return -numpy.sum(histogram * numpy.log2(histogram))
+
+
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/__init__.py b/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/analysis.py b/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9d97a03954c07548cba216fa1a52e88ee9232bb
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/analysis.py
@@ -0,0 +1,221 @@
+import numpy as np
+import scipy.ndimage as ndimage
+import scipy.optimize as optimize
+from scipy.interpolate import interp1d, UnivariateSpline
+from scipy.signal import medfilt, savgol_filter
+import miplib.processing.ndarray as arrayutils
+from miplib.data.containers.fourier_correlation_data import FourierCorrelationDataCollection, FourierCorrelationData
+import miplib.processing.converters as converters
+
+
+def fit_frc_curve(data_set, degree, fit_type='spline'):
+ """
+ Calculate a least squares curve fit to the FRC Data
+ :return: None. Will modify the frc argument in place
+ """
+ assert isinstance(data_set, FourierCorrelationData)
+
+ data = data_set.correlation["correlation"]
+
+ if fit_type == 'smooth-spline':
+ equation = UnivariateSpline(data_set.correlation["frequency"],
+ data)
+ equation.set_smoothing_factor(0.25)
+ # equation = interp1d(data_set.correlation["frequency"],
+ # data, kind='slinear')
+
+ elif fit_type == 'spline':
+ equation = interp1d(data_set.correlation["frequency"],
+ data, kind='slinear')
+
+ elif fit_type == 'polynomial':
+
+ coeff = np.polyfit(data_set.correlation["frequency"],
+ data,
+ degree,
+ w=1 - data_set.correlation["frequency"] ** 3)
+ equation = np.poly1d(coeff)
+ else:
+ raise AttributeError(fit_type)
+
+ data_set.correlation["curve-fit"] = equation(data_set.correlation["frequency"])
+
+ return equation
+
+
+def calculate_snr_threshold_value(points_x_bin, snr):
+ """
+ A function to calculate a SNR based resolution threshold, as described
+ in ...
+
+ :param points_x_bin: a 1D Array containing the numbers of points at each
+ FRC/FSC ring/shell
+ :param snr: the expected SNR value
+ :return:
+ """
+ nominator = snr + arrayutils.safe_divide(
+ 2.0 * np.sqrt(snr) + 1,
+ np.sqrt(points_x_bin)
+ )
+ denominator = snr + 1 + arrayutils.safe_divide(
+ 2.0 * np.sqrt(snr),
+ np.sqrt(points_x_bin)
+ )
+ return arrayutils.safe_divide(nominator, denominator)
+
+
+
+def calculate_resolution_threshold_curve(data_set, criterion, threshold, snr):
+ """
+ Calculate the two sigma curve. The FRC should be run first, as the results of the two sigma
+ depend on the number of points on the fourier rings.
+
+ :return: Adds the
+ """
+ assert isinstance(data_set, FourierCorrelationData)
+
+ points_x_bin = data_set.correlation["points-x-bin"]
+
+ if points_x_bin[-1] == 0:
+ points_x_bin[-1] = points_x_bin[-2]
+
+ if criterion == 'one-bit':
+ nominator = 0.5 + arrayutils.safe_divide(
+ 2.4142,
+ np.sqrt(points_x_bin)
+ )
+ denominator = 1.5 + arrayutils.safe_divide(
+ 1.4142,
+ np.sqrt(points_x_bin)
+ )
+ points = arrayutils.safe_divide(nominator, denominator)
+
+ elif criterion == 'half-bit':
+ nominator = 0.2071 + arrayutils.safe_divide(
+ 1.9102,
+ np.sqrt(points_x_bin)
+ )
+ denominator = 1.2071 + arrayutils.safe_divide(
+ 0.9102,
+ np.sqrt(points_x_bin)
+ )
+ points = arrayutils.safe_divide(nominator, denominator)
+
+ elif criterion == 'three-sigma':
+ points = arrayutils.safe_divide(np.full(points_x_bin.shape, 3.0), (np.sqrt(points_x_bin) + 3.0 - 1))
+
+
+ elif criterion == 'fixed':
+ points = threshold * np.ones(len(data_set.correlation["points-x-bin"]))
+ elif criterion == 'snr':
+ points = calculate_snr_threshold_value(points_x_bin, snr)
+
+ else:
+ raise AttributeError()
+
+ if criterion != 'fixed':
+ #coeff = np.polyfit(data_set.correlation["frequency"], points, 3)
+ #equation = np.poly1d(coeff)
+ equation = interp1d(data_set.correlation["frequency"], points, kind='slinear')
+ curve = equation(data_set.correlation["frequency"])
+ else:
+ curve = points
+ equation = None
+
+ data_set.resolution["threshold"] = curve
+ return equation
+
+
+class FourierCorrelationAnalysis(object):
+ def __init__(self, data, spacing, args):
+
+ assert isinstance(data, FourierCorrelationDataCollection)
+
+ self.data_collection = data
+ self.args = args
+ self.spacing = spacing
+
+ def execute(self, z_correction=1):
+ """
+ Calculate the spatial resolution as a cross-section of the FRC and Two-sigma curves.
+
+ :return: Returns the calculation results. They are also saved inside the class.
+ The return value is just for convenience.
+ """
+
+ criterion = self.args.resolution_threshold_criterion
+ threshold = self.args.resolution_threshold_value
+ snr = self.args.resolution_snr_value
+ tolerance = self.args.resolution_point_sigma
+ degree = self.args.frc_curve_fit_degree
+ fit_type = self.args.frc_curve_fit_type
+ verbose = self.args.verbose
+
+ def pdiff1(x):
+ return abs(frc_eq(x) - two_sigma_eq(x))
+
+ def pdiff2(x):
+ return abs(frc_eq(x) - threshold)
+
+ def first_guess(x, y, threshold):
+ #y_smooth = savgol_filter(y, 5, 2)
+ #return x[np.argmin(np.abs(y_smooth - threshold))]
+
+ difference = y - threshold
+
+ #return x[np.where(difference <= 0)[0][0] - 1]
+ return x[np.argmin(np.abs(y - threshold))]
+
+ for key, data_set in self.data_collection:
+
+ if verbose:
+ print("Calculating resolution point for dataset {}".format(key))
+ frc_eq = fit_frc_curve(data_set, degree, fit_type)
+ two_sigma_eq = calculate_resolution_threshold_curve(data_set, criterion, threshold, snr)
+
+ """
+ Todo: Make the first quess adaptive. For example find the data point at which FRC
+ value is closest to the mean of the threshold
+ """
+
+
+ # Find intersection
+ fit_start = first_guess(data_set.correlation["frequency"],
+ data_set.correlation["correlation"],
+ np.mean(data_set.resolution["threshold"])
+ )
+ if self.args.verbose:
+ print("Fit starts at {}".format(fit_start))
+ disp = 1
+ else:
+ disp = 0
+ root = optimize.fmin(pdiff2 if criterion == 'fixed' else pdiff1, fit_start, disp=disp)[0]
+ data_set.resolution["resolution-point"] = (frc_eq(root), root)
+ data_set.resolution["criterion"] = criterion
+
+ angle = converters.degrees_to_radians(int(key))
+ z_correction_multiplier = (1+(z_correction-1)*np.abs(np.sin(angle)))
+ resolution =z_correction_multiplier*(2*self.spacing / root)
+
+ data_set.resolution["resolution"] = resolution
+ data_set.resolution["spacing"] = self.spacing*z_correction_multiplier
+
+ self.data_collection[int(key)] = data_set
+
+
+ # # # Find intersection
+ # root, result = optimize.brentq(
+ # pdiff2 if criterion == 'fixed' else pdiff1,
+ # 0.0, 1.0, xtol=tolerance, full_output=True)
+ #
+ # # Save result, if intersection was found
+ # if result.converged is True:
+ # data_set.resolution["resolution-point"] = (frc_eq(root), root)
+ # data_set.resolution["criterion"] = criterion
+ # resolution = 2 * self.spacing / root
+ # data_set.resolution["resolution"] = resolution
+ # self.data_collection[int(key)] = data_set
+ # else:
+ # print "Could not find an intersection for the curves for the dataset %s." % key
+
+ return self.data_collection
diff --git a/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/fourier_ring_correlation.py b/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/fourier_ring_correlation.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7819ce515984208825c4c4adc8e4312de4ff6b6
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/fourier_ring_correlation.py
@@ -0,0 +1,280 @@
+"""
+Sami Koho 01/2017
+
+Image resolution measurement by Fourier Ring Correlation.
+
+"""
+
+import numpy as np
+import os
+import miplib.data.iterators.fourier_ring_iterators as iterators
+import miplib.processing.image as imops
+from miplib.data.containers.fourier_correlation_data import FourierCorrelationData, \
+ FourierCorrelationDataCollection
+from miplib.data.containers.image import Image
+from . import analysis as fsc_analysis
+from miplib.processing import windowing
+import miplib.data.io.read as imread
+
+def calculate_single_image_frc(image, args, average=True, trim=True, z_correction=1):
+ """
+ A simple utility to calculate a regular FRC with a single image input
+
+ :param image: the image as an Image object
+ :param args: the parameters for the FRC calculation. See *miplib.ui.frc_options*
+ for details
+ :return: returns the FRC result as a FourierCorrelationData object
+
+ """
+ assert isinstance(image, Image)
+
+ frc_data = FourierCorrelationDataCollection()
+
+ # Hamming Windowing
+ if not args.disable_hamming:
+ spacing = image.spacing
+ image = Image(windowing.apply_hamming_window(image), spacing)
+
+ # Split and make sure that the images are the same siz
+ image1, image2 = imops.checkerboard_split(image)
+ #image1, image2 = imops.reverse_checkerboard_split(image)
+ image1, image2 = imops.zero_pad_to_matching_shape(image1, image2)
+
+ # Run FRC
+ iterator = iterators.FourierRingIterator(image1.shape, args.d_bin)
+ frc_task = FRC(image1, image2, iterator)
+ frc_data[0] = frc_task.execute()
+
+ if average:
+ # Split and make sure that the images are the same size
+ image1, image2 = imops.reverse_checkerboard_split(image)
+ image1, image2 = imops.zero_pad_to_matching_shape(image1, image2)
+ iterator = iterators.FourierRingIterator(image1.shape, args.d_bin)
+ frc_task = FRC(image1, image2, iterator)
+
+ frc_data[0].correlation["correlation"] *= 0.5
+ frc_data[0].correlation["correlation"] += 0.5*frc_task.execute().correlation["correlation"]
+
+ freqs = frc_data[0].correlation["frequency"].copy()
+
+ def func(x, a, b, c, d):
+ return a * np.exp(c * (x - b)) + d
+
+ params = [0.95988146, 0.97979108, 13.90441896, 0.55146136]
+
+ # Analyze results
+ analyzer = fsc_analysis.FourierCorrelationAnalysis(frc_data, image1.spacing[0], args)
+
+ result = analyzer.execute(z_correction=z_correction)[0]
+ point = result.resolution["resolution-point"][1]
+
+ cut_off_correction = func(point, *params)
+ result.resolution["spacing"] /= cut_off_correction
+ result.resolution["resolution"] /= cut_off_correction
+
+ return result
+
+def calculate_two_image_frc(image1, image2, args, z_correction=1):
+ """
+ A simple utility to calculate a regular FRC with a two image input
+
+ :param image: the image as an Image object
+ :param args: the parameters for the FRC calculation. See *miplib.ui.frc_options*
+ for details
+ :return: returns the FRC result as a FourierCorrelationData object
+ """
+ assert isinstance(image1, Image)
+ assert isinstance(image2, Image)
+
+ assert image1.shape == image2.shape
+
+ frc_data = FourierCorrelationDataCollection()
+
+ spacing = image1.spacing
+
+ if not args.disable_hamming:
+
+ image1 = Image(windowing.apply_hamming_window(image1), spacing)
+ image2 = Image(windowing.apply_hamming_window(image2), spacing)
+
+ # Run FRC
+ iterator = iterators.FourierRingIterator(image1.shape, args.d_bin)
+ frc_task = FRC(image1, image2, iterator)
+ frc_data[0] = frc_task.execute()
+
+ # Analyze results
+ analyzer = fsc_analysis.FourierCorrelationAnalysis(frc_data, image1.spacing[0], args)
+
+ return analyzer.execute(z_correction=z_correction)[0]
+
+def calculate_single_image_sectioned_frc(image, args, rotation=45, orthogonal=True, trim=True):
+ """
+ A function utility to calculate a single image FRC on a Fourier ring section. The section
+ is defined by the section size d_angle (in args) and the section rotation.
+ :param image: the image as an Image object
+ :param args: the parameters for the FRC calculation. See *miplib.ui.frc_options*
+ for details
+ :param rotation: defines the orientation of the fourier ring section
+ :param orthogonal: if True, FRC is calculated from two sections, oriented at rotation
+ and rotation + 90 degrees
+ :return: returns the FRC result as a FourierCorrelationData object
+
+ """
+ assert isinstance(image, Image)
+
+ frc_data = FourierCorrelationDataCollection()
+
+ # Hamming Windowing
+ if not args.disable_hamming:
+ spacing = image.spacing
+ image = Image(windowing.apply_hamming_window(image), spacing)
+
+
+ # Run FRC
+ def frc_helper(image1, image2, args, rotation):
+ iterator = iterators.SectionedFourierRingIterator(image1.shape, args.d_bin, args.d_angle)
+ iterator.angle = rotation
+ frc_task = FRC(image1, image2, iterator)
+ return frc_task.execute()
+
+ image1, image2 = imops.checkerboard_split(image)
+ image1, image2 = imops.zero_pad_to_matching_shape(image1, image2)
+
+ image1_r, image2_r = imops.reverse_checkerboard_split(image)
+ image1_r, image2_r = imops.zero_pad_to_matching_shape(image1_r, image2_r)
+
+ pair_1 = frc_helper(image1, image2, args, rotation)
+ pair_2 = frc_helper(image1_r, image2_r, args, rotation)
+
+ pair_1.correlation["correlation"] * 0.5
+ pair_1.correlation["correlation"] += 0.5 * pair_2.correlation["correlation"]
+
+ if orthogonal:
+ pair_1_o = frc_helper(image1, image2, args, rotation+90)
+ pair_2_o = frc_helper(image1_r, image2_r, args, rotation+90)
+
+ pair_1_o.correlation["correlation"] * 0.5
+ pair_1_o.correlation["correlation"] += 0.5 * pair_2_o.correlation["correlation"]
+
+ pair_1.correlation["correlation"] += 0.5 * pair_1_o.correlation["correlation"]
+
+ frc_data[0] = pair_1
+
+ freqs = frc_data[0].correlation["frequency"].copy()
+
+ def func(x, a, b, c, d):
+ return a * np.exp(c * (x - b)) + d
+
+ params = [0.95988146, 0.97979108, 13.90441896, 0.55146136]
+
+ # Analyze results
+ analyzer = fsc_analysis.FourierCorrelationAnalysis(frc_data, image1.spacing[0], args)
+
+ result = analyzer.execute()[0]
+ point = result.resolution["resolution-point"][1]
+
+ log_correction = func(point, *params)
+ result.resolution["spacing"] /= log_correction
+ result.resolution["resolution"] /= log_correction
+
+ return result
+
+def batch_evaluate_frc(path, options):
+ """
+ Batch calculate FRC resolution for files placed in a directory
+ :param options: options for the FRC
+ :parame path: directory that contains the images to be analyzed
+ """
+ assert os.path.isdir(path)
+
+ measures = FourierCorrelationDataCollection()
+ image_names = []
+
+ for idx, image_name in enumerate(sorted(os.listdir(path))):
+
+ real_path = os.path.join(path, image_name)
+ # Only process images. The bioformats reader can actually do many more file formats
+ # but I was a little lazy here, as we usually have tiffs.
+ if not os.path.isfile(real_path) or not real_path.endswith((".tiff", ".tif")):
+ continue
+ # ImageJ files have particular TIFF tags that can be processed correctly
+ # with the options.imagej switch
+ image = imread.get_image(real_path)
+
+ # Only grayscale images are processed. If the input is an RGB image,
+ # a channel can be chosen for processing.
+ measures[idx] = calculate_single_image_frc(image, options)
+
+ image_names.append(image_name)
+
+ return measures, image_names
+
+
+
+
+class FRC(object):
+ """
+ A class for calcuating 2D Fourier ring correlation. Contains
+ methods to calculate the FRC as well as to plot the results.
+ """
+
+ def __init__(self, image1, image2, iterator):
+ assert isinstance(image1, Image)
+ assert isinstance(image2, Image)
+
+ if image1.shape != image2.shape or tuple(image1.spacing) != tuple(image2.spacing):
+ raise ValueError("The image dimensions do not match")
+ if image1.ndim != 2:
+ raise ValueError("Fourier ring correlation requires 2D images.")
+
+ self.pixel_size = image1.spacing[0]
+
+ # Expand to square
+ image1 = imops.zero_pad_to_cube(image1)
+ image2 = imops.zero_pad_to_cube(image2)
+
+ self.iterator = iterator
+ # Calculate power spectra for the input images.
+ self.fft_image1 = np.fft.fftshift(np.fft.fft2(image1))
+ self.fft_image2 = np.fft.fftshift(np.fft.fft2(image2))
+
+ # Get the Nyquist frequency
+ self.freq_nyq = int(np.floor(image1.shape[0] / 2.0))
+
+ def execute(self):
+ """
+ Calculate the FRC
+ :return: Returns the FRC results.
+
+ """
+ radii = self.iterator.radii
+ c1 = np.zeros(radii.shape, dtype=np.float32)
+ c2 = np.zeros(radii.shape, dtype=np.float32)
+ c3 = np.zeros(radii.shape, dtype=np.float32)
+ points = np.zeros(radii.shape, dtype=np.float32)
+
+ for ind_ring, idx in self.iterator:
+ subset1 = self.fft_image1[ind_ring]
+ subset2 = self.fft_image2[ind_ring]
+ c1[idx] = np.sum(subset1 * np.conjugate(subset2)).real
+ c2[idx] = np.sum(np.abs(subset1) ** 2)
+ c3[idx] = np.sum(np.abs(subset2) ** 2)
+
+ points[idx] = len(subset1)
+
+ # Calculate FRC
+ spatial_freq = radii.astype(np.float32) / self.freq_nyq
+ n_points = np.array(points)
+
+ with np.errstate(divide="ignore", invalid="ignore"):
+ frc = np.abs(c1) / np.sqrt(c2 * c3)
+ frc[frc == np.inf] = 0.0
+ frc = np.nan_to_num(frc)
+
+
+ data_set = FourierCorrelationData()
+ data_set.correlation["correlation"] = frc
+ data_set.correlation["frequency"] = spatial_freq
+ data_set.correlation["points-x-bin"] = n_points
+
+ return data_set
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/fourier_shell_correlation.py b/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/fourier_shell_correlation.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a2c9abec826ed6f6b8a8011406431d5fa3364e5
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/fourier_shell_correlation.py
@@ -0,0 +1,191 @@
+# coding=utf-8
+"""
+Sami Koho 01/2018
+
+Sectioned Fourier Shell Correlation for complex resolution analysis
+in 3D images.
+"""
+from scipy.ndimage.interpolation import rotate
+import numpy as np
+import miplib.data.iterators.fourier_shell_iterators as iterators
+
+import miplib.data.containers.fourier_correlation_data as containers
+import miplib.processing.ndarray as ndarray
+import miplib.processing.image as imops
+from miplib.data.containers.image import Image
+from miplib.processing import windowing
+from . import analysis as fsc_analysis
+from math import floor
+
+
+def calculate_fourier_plane_correlation(image1, image2, args, z_correction=1):
+ steps = np.arange(0, 360, args.d_angle)
+ data = containers.FourierCorrelationDataCollection()
+
+ for idx, step in enumerate(steps):
+ im1_rot = np.fft.fftshift(np.fft.fftn(rotate(image1, step, reshape=False)))
+ im2_rot = np.fft.fftshift(np.fft.fftn(rotate(image2, step, reshape=False)))
+
+ numerator = np.sum(im1_rot*np.conjugate(im2_rot), axis=(0,2))
+ denominator = np.sum(np.sqrt(np.abs(im1_rot)**2 * np.abs(im2_rot)**2), axis=(0,2))
+
+ correlation = ndarray.safe_divide(numerator, denominator)
+
+ zero = correlation.size / 2
+ correlation = correlation[zero:]
+
+ result = containers.FourierCorrelationData()
+ result.correlation["correlation"] = correlation
+ result.correlation["frequency"] = np.linspace(0, 1.0, num=correlation.size)
+ result.correlation["points-x-bin"] = np.ones(correlation.size)*(im2_rot.shape[2]*im2_rot.shape[0])
+
+ data[int(step)] = result
+
+ analyzer = fsc_analysis.FourierCorrelationAnalysis(data, image1.spacing[0], args)
+ return analyzer.execute(z_correction=z_correction)
+
+
+
+def calculate_one_image_sectioned_fsc(image, args, z_correction=1):
+ """ A function to calculate one-image sectioned FSC. I assume here that prior to calling the function,
+ the image is going to be in a correct shape, resampled to isotropic spacing and zero padded. If the image
+ dimensions are wrong (not a cube) the function will return an error.
+
+ :param image: a 3D image, with isotropic spacing and cubic shape
+ :type image: Image
+ :param options: options for the FSC calculation
+ :type options: argparse options
+ :param z_correction: correction, for anisotropic sampling. It is the ratio of axial vs. lateral spacing, defaults to 1
+ :type z_correction: float, optional
+ :return: the resolution measurement results organized by rotation angle
+ :rtype: FourierCorrelationDataCollection object
+ """
+ assert isinstance(image, Image)
+ assert all(s == image.shape[0] for s in image.shape)
+
+ image1, image2 = imops.checkerboard_split(image)
+
+ image1 = Image(windowing.apply_hamming_window(image1), image1.spacing)
+ image2 = Image(windowing.apply_hamming_window(image2), image2.spacing)
+
+ iterator = iterators.AxialExcludeSectionedFourierShellIterator(image1.shape, args.d_bin,
+ args.d_angle, args.d_extract_angle)
+ fsc_task = DirectionalFSC(image1, image2, iterator)
+
+ data = fsc_task.execute()
+
+ analyzer = fsc_analysis.FourierCorrelationAnalysis(data, image1.spacing[0], args)
+ result = analyzer.execute(z_correction=z_correction)
+
+ def func(x, a, b, c, d):
+ return a * np.exp(c * (x - b)) + d
+
+ params = [0.95988146, 0.97979108, 13.90441896, 0.55146136]
+
+ for angle, dataset in result:
+ point = dataset.resolution["resolution-point"][1]
+
+ cut_off_correction = func(point, *params)
+ dataset.resolution["spacing"] /= cut_off_correction
+ dataset.resolution["resolution"] /= cut_off_correction
+
+ return result
+
+def calculate_two_image_sectioned_fsc(image1, image2, args, z_correction=1):
+ assert isinstance(image1, Image)
+ assert isinstance(image2, Image)
+
+ image1 = Image(windowing.apply_hamming_window(image1), image1.spacing)
+ image2 = Image(windowing.apply_hamming_window(image2), image2.spacing)
+
+ iterator = iterators.AxialExcludeSectionedFourierShellIterator(image1.shape, args.d_bin, args.d_angle,
+ args.d_extract_angle)
+ fsc_task = DirectionalFSC(image1, image2, iterator)
+ data = fsc_task.execute()
+
+ analyzer = fsc_analysis.FourierCorrelationAnalysis(data, image1.spacing[0], args)
+ return analyzer.execute(z_correction=z_correction)
+
+
+
+
+class DirectionalFSC(object):
+ def __init__(self, image1, image2, iterator, normalize_power=False):
+ assert isinstance(image1, Image)
+ assert isinstance(image2, Image)
+
+ if image1.ndim != 3 or image1.shape[0] <= 1:
+ raise ValueError("You should provide a stack for FSC analysis")
+
+ if image1.shape != image2.shape:
+ raise ValueError("Image dimensions do not match")
+
+ # Create an Iterator
+ self.iterator = iterator
+
+ # FFT transforms of the input images
+ self.fft_image1 = np.fft.fftshift(np.fft.fftn(image1))
+ self.fft_image2 = np.fft.fftshift(np.fft.fftn(image2))
+
+ if normalize_power:
+ pixels = image1.shape[0]**3
+ self.fft_image1 /= (np.array(pixels * np.mean(image1)))
+ self.fft_image2 /= (np.array(pixels * np.mean(image2)))
+
+ self._result = None
+
+ self.pixel_size = image1.spacing[0]
+
+ @property
+ def result(self):
+ if self._result is None:
+ return self.execute()
+ else:
+ return self._result
+
+ def execute(self):
+ """
+ Calculate the FRC
+ :return: Returns the FRC results. They are also saved inside the class.
+ The return value is just for convenience.
+ """
+
+ data_structure = containers.FourierCorrelationDataCollection()
+ radii, angles = self.iterator.steps
+ freq_nyq = self.iterator.nyquist
+ shape = (angles.shape[0], radii.shape[0])
+ c1 = np.zeros(shape, dtype=np.float32)
+ c2 = np.zeros(shape, dtype=np.float32)
+ c3 = np.zeros(shape, dtype=np.float32)
+ points = np.zeros(shape, dtype=np.float32)
+
+ # Iterate through the sphere and calculate initial values
+ for ind_ring, shell_idx, rotation_idx in self.iterator:
+ subset1 = self.fft_image1[ind_ring]
+ subset2 = self.fft_image2[ind_ring]
+
+ c1[rotation_idx, shell_idx] = np.sum(subset1 * np.conjugate(subset2)).real
+ c2[rotation_idx, shell_idx] = np.sum(np.abs(subset1) ** 2)
+ c3[rotation_idx, shell_idx] = np.sum(np.abs(subset2) ** 2)
+
+ points[rotation_idx, shell_idx] = len(subset1)
+
+ # Finish up FRC calculation for every rotation angle and sav
+ # results to the data structure.
+ for i in range(angles.size):
+
+ # Calculate FRC for every orientation
+ spatial_freq = radii.astype(np.float32) / freq_nyq
+ n_points = np.array(points[i])
+ frc = ndarray.safe_divide(c1[i], np.sqrt(c2[i] * c3[i]))
+
+ result = containers.FourierCorrelationData()
+ result.correlation["correlation"] = frc
+ result.correlation["frequency"] = spatial_freq
+ result.correlation["points-x-bin"] = n_points
+
+ # Save result to the structure and move to next
+ # angle
+ data_structure[angles[i]] = result
+
+ return data_structure
diff --git a/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/test/__init__.py b/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/test/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/test/test_iterators.py b/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/test/test_iterators.py
new file mode 100644
index 0000000000000000000000000000000000000000..11474e17a3911882c95c7237b3ba2a5aca67f82d
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/analysis/resolution/test/test_iterators.py
@@ -0,0 +1,18 @@
+
+
+import numpy as np
+from mayavi import mlab
+
+from miplib.data.iterators.fourier_shell_iterators import SectionedFourierShellIterator
+from miplib.data.containers.image import Image
+
+dataset = Image(np.ones((255,255,255), dtype=np.uint8)*20, (0.05,0.05,0.05))
+
+iterator = SectionedFourierShellIterator(dataset.shape, 6, 20)
+
+section = iterator[(100, 106, 30, 60)]
+
+dataset[section] = 255
+
+mlab.contour3d(dataset)
+mlab.show()
diff --git a/Addons/FRCmetric/miplib-public/miplib/bin/__init__.py b/Addons/FRCmetric/miplib-public/miplib/bin/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/bin/correlatem.py b/Addons/FRCmetric/miplib-public/miplib/bin/correlatem.py
new file mode 100644
index 0000000000000000000000000000000000000000..4da1ed8140b7e1028cb8b99799d1f947a6449a4b
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/bin/correlatem.py
@@ -0,0 +1,181 @@
+#!/usr/bin/env python
+
+"""
+fusion_main.py
+
+Copyright (c) 2014 Sami Koho All rights reserved.
+
+This software may be modified and distributed under the terms
+of the BSD license. See the LICENSE file for details.
+"""
+
+import datetime
+import os
+import sys
+
+import SimpleITK as sitk
+
+from miplib.processing.registration import registration
+from miplib.ui.cli import miplib_entry_point_options
+from miplib.processing import itk as itkutils
+
+
+def main():
+ options = miplib_entry_point_options.get_correlate_tem_script_options(sys.argv[1:])
+
+ # SETUP
+ ##########################################################################
+
+ # Check that the STED image exists
+ options.sted_image_path = os.path.join(options.working_directory,
+ options.sted_image_path)
+ if not os.path.isfile(options.sted_image_path):
+ print('No such file: %s' % options.sted_image_path)
+ sys.exit(1)
+
+ # Check that the EM image exists
+ options.em_image_path = os.path.join(options.working_directory,
+ options.em_image_path)
+ if not os.path.isfile(options.em_image_path):
+ print('No such file: %s' % options.em_image_path)
+ sys.exit(1)
+
+
+
+ # Load input images
+ sted_image = sitk.ReadImage(options.sted_image_path)
+ em_image = sitk.ReadImage(options.em_image_path)
+
+
+ # PRE-PROCESSING
+ ##########################################################################
+ # Save originals for possible later use
+ sted_original = sted_image
+ em_original = em_image
+
+ if options.dilation_size != 0:
+ print('Degrading input images with Dilation filter')
+ sted_image = itkutils.grayscale_dilate_filter(
+ sted_image,
+ options.dilation_size
+ )
+ em_image = itkutils.grayscale_dilate_filter(
+ em_image,
+ options.dilation_size
+ )
+
+ if options.gaussian_variance != 0.0:
+ print('Degrading the EM image with Gaussian blur filter')
+
+ em_image = itkutils.gaussian_blurring_filter(
+ em_image,
+ options.gaussian_variance
+ )
+ if options.mean_kernel != 0:
+ sted_image = itkutils.mean_filter(
+ sted_image,
+ options.mean_kernel
+ )
+ em_image = itkutils.mean_filter(
+ em_image,
+ options.mean_kernel
+ )
+ #todo: convert the pixel type into a PixelID enum
+ if options.use_internal_type:
+
+ sted_image = itkutils.type_cast(
+ sted_image,
+ options.image_type
+ )
+ em_image = itkutils.type_cast(
+ em_image,
+ options.image_type
+ )
+ #
+ # if options.threshold > 0:
+ # sted_image = itkutils.threshold_image_filter(
+ # sted_image,
+ # options.threshold
+ # )
+ #
+ # em_image = itkutils.threshold_image_filter(
+ # em_image,
+ # options.threshold
+ # )
+
+ if options.normalize:
+ print('Normalizing images')
+
+ # Normalize
+ sted_image = itkutils.normalize_image_filter(sted_image)
+ em_image = itkutils.normalize_image_filter(em_image)
+
+ if options.rescale_to_full_range:
+ sted_image = itkutils.rescale_intensity(sted_image)
+ em_image = itkutils.rescale_intensity(em_image)
+
+
+ # REGISTRATION
+ ##########################################################################
+
+ if options.tfm_type == "rigid":
+ final_transform = registration.itk_registration_rigid_2d(sted_image, em_image, options)
+ elif options.tfm_type == "similarity":
+ final_transform = registration.itk_registration_similarity_2d(sted_image, em_image, options)
+ else:
+ raise ValueError(options.tfm_type)
+ em_image = itkutils.resample_image(
+ em_original,
+ final_transform,
+ reference=sted_image
+ )
+
+
+
+
+ # OUTPUT
+ ##########################################################################
+
+ while True:
+ keep = input("Do you want to keep the results (yes/no)? ")
+ if keep in ('y', 'Y', 'yes', 'YES'):
+ # Files are named according to current time (the date will be
+ # in the folder name)
+
+ # Output directory name will be automatically formatted according
+ # to current date and time; e.g. 2014-02-18_supertomo_output
+ output_dir = datetime.datetime.now().strftime("%Y-%m-%d") + '_clem_output'
+ output_dir = os.path.join(options.working_directory, output_dir)
+
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+
+ date_now = datetime.datetime.now().strftime("%H-%M-%S")
+ file_name = date_now + \
+ '-clem_registration-' + \
+ options.registration_method + \
+ '.tiff'
+ file_path = os.path.join(output_dir, file_name)
+ tfm_name = date_now + '_transform' + '.txt'
+ tfm_path = os.path.join(output_dir, tfm_name)
+ sitk.WriteTransform(final_transform, tfm_path)
+
+ rgb_image = itkutils.make_composite_rgb_image(sted_original, em_image)
+ sitk.WriteImage(rgb_image, file_path)
+ print("The image was saved to %s and the transform to %s in " \
+ "the output directory %s" % (file_name, tfm_name, output_dir))
+ break
+ elif keep in ('n', 'N', 'no', 'No'):
+ print("Exiting without saving results.")
+ break
+ else:
+ print("Unkown command. Please state yes or no")
+
+
+if __name__ == "__main__":
+ main()
+
+
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/bin/fuse.py b/Addons/FRCmetric/miplib-public/miplib/bin/fuse.py
new file mode 100644
index 0000000000000000000000000000000000000000..a372b07e040435ab8fac74ed90d039f1ec664421
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/bin/fuse.py
@@ -0,0 +1,83 @@
+
+"""
+fuse.py
+
+Copyright (c) 2016 Sami Koho All rights reserved.
+
+This software may be modified and distributed under the terms
+of the BSD license. See the LICENSE file for details.
+
+This is the main program file for the miplib fusion calculation
+"""
+
+import os
+import sys
+import time
+
+import miplib.data.containers.image_data as image_data
+import miplib.processing.fusion.fusion as fusion
+import miplib.processing.fusion.fusion_cuda as gpufusion
+import miplib.processing.to_string as genutils
+import miplib.ui.cli.miplib_entry_point_options as arguments
+import miplib.ui.utils as uiutils
+
+
+def main():
+
+ options = arguments.get_fusion_script_options(sys.argv[1:])
+ full_path = os.path.join(options.working_directory,
+ options.data_file)
+
+ if not os.path.isfile(full_path):
+ raise AttributeError("No such file: %s" % full_path)
+ elif not full_path.endswith(".hdf5"):
+ raise AttributeError("Not a HDF5 file")
+
+ data = image_data.ImageData(full_path)
+
+ if options.scale not in data.get_scales("registered"):
+ print("Images at the defined scale do not exist in the data structure." \
+ "The original images will be now resampled. This may take a long" \
+ "time depending on the image size and the number of views.")
+ data.create_rescaled_images("registered", options.scale)
+
+ if data.get_number_of_images("psf") != data.get_number_of_images("registered"):
+ print("Some PSFs are missing. They are going to be calculated from the " \
+ "original STED PSF (that is assumed to be at index 0).")
+ data.calculate_missing_psfs()
+
+ if not options.disable_cuda:
+ print("Trying to run the image fusion with GPU acceleration.")
+ task = gpufusion.MultiViewFusionRLCuda(data, options)
+ else:
+ task = fusion.MultiViewFusionRL(data, options)
+
+ # task = fusion.MultiViewFusionRL(data, options)
+ begin = time.time()
+ task.execute()
+ end = time.time()
+
+ if options.evaluate_results:
+ task.show_result()
+
+ print("Fusion complete.")
+ print("The fusion process with %i iterations " \
+ "took %s (H:M:S) to complete." % (options.max_nof_iterations,
+ genutils.format_time_string(
+ end-begin)))
+
+ if uiutils.get_user_input("Do you want to save the result to TIFF? "):
+ file_path = os.path.join(options.working_directory,
+ "fusion_result.tif")
+ task.save_to_tiff(file_path)
+
+ if uiutils.get_user_input("Do you want to save the result to the HDF data "
+ "structure? "):
+ task.save_to_hdf()
+
+ task.close()
+ data.close()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/Addons/FRCmetric/miplib-public/miplib/bin/import.py b/Addons/FRCmetric/miplib-public/miplib/bin/import.py
new file mode 100644
index 0000000000000000000000000000000000000000..03577e84aa0461de3436835d2fb45dc3b42bc5c1
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/bin/import.py
@@ -0,0 +1,166 @@
+"""
+A program to import data into our HDF5 archive format.
+All the images should be contained within a single directory
+that can be either provided as a command line parameter, or
+alternatively the program will try to use the current working
+directory as the source directory.
+
+The files should be named according to the following pattern:
+
+Original images:
+===============================================================================
+
+original_scale_<scale>_index_<index>_channel_<channel>_angle_<angle>.<suffix>
+
+<scale> is the image size, as a percentage of the raw microscope image
+<index> is the ordering index of the views. The regular STED image gets
+ index 0, the first rotation index 1 etc.
+<channel> the color channel index, should start from zero.
+<angle> is the estimated rotation angle
+<suffix> can be .tif/.tiff, .mhd/.mha
+
+
+Registered images:
+===============================================================================
+
+registered_scale_<scale>_index_<index>_channel_<channel>_angle_<angle>.<suffix>
+
+<scale> is the image size, as a percentage of the raw microscope image
+<index> is the ordering index of the views. The regular STED image gets
+ index 0, the first rotation index 1 etc.
+<channel> the color channel index, should start from zero.
+<angle> is the estimated rotation angle
+<suffix> can be .tif/.tiff, .mhd/.mha
+
+Transform files:
+===============================================================================
+transform_scale_<scale>_index_<index>_channel_<channel>_angle_<angle>.<suffix>
+
+The <scale>, <index> and <angle> parameters correspond to the registered
+image that the transform is coupled with.
+
+PSF images:
+===============================================================================
+
+psf_scale_<scale>_index_<index>_channel_<channel>_angle_<angle>.<suffix>
+
+<scale> is the image size, as a percentage of the raw microscope image
+<index> is the ordering index of the views. The regular STED image gets
+ index 0, the first rotation index 1 etc.
+<channel> the color channel index, should start from zero.
+<angle> is the estimated rotation angle
+<suffix> can be .tif/.tiff, .mhd/.mha
+
+
+"""
+import os
+import sys
+
+import numpy
+
+from miplib.data.containers import image_data
+from miplib.data.definitions import *
+from miplib.data.io import read
+from miplib.processing import itk as itkutils
+from ..ui.cli import miplib_entry_point_options
+
+
+def main():
+ options = miplib_entry_point_options.get_import_script_options(sys.argv[1:])
+ directory = options.data_dir_path
+
+ # Create a new HDF5 file. If a file exists, new data will be appended.
+ file_name = input("Give a name for the HDF5 file: ")
+ file_name += ".hdf5"
+ data_path = os.path.join(directory, file_name)
+ data = image_data.ImageData(data_path)
+
+ # Add image files that have been named according to the correct format
+ for image_name in os.listdir(directory):
+ full_path = os.path.join(directory, image_name)
+
+ if full_path.endswith((".tiff", ".tif", ".mhd", ".mha")):
+ images = read.get_image(full_path)
+ spacing = images.spacing
+ else:
+ continue
+
+ if options.normalize_inputs:
+ images = (images * (255.0/images.max())).astype(numpy.uint8)
+
+ if not all(x in image_name for x in params_c) or not any(x in image_name for x in image_types_c):
+ print("Unrecognized image name %s. Skipping it." % image_name)
+ continue
+
+ image_type = image_name.split("_scale")[0]
+ scale = image_name.split("scale_")[-1].split("_index")[0]
+ index = image_name.split("index_")[-1].split("_channel")[0]
+ channel = image_name.split("channel_")[-1].split("_angle")[0]
+ angle = image_name.split("angle_")[-1].split(".")[0]
+
+ assert all(x.isdigit() for x in (scale, index, channel, angle))
+ # data, angle, spacing, index, scale, channel, chunk_size=None
+
+ if image_type == "original":
+ data.add_original_image(images, scale, index, channel, angle, spacing)
+ elif image_type == "registered":
+ data.add_registered_image(images, scale, index, channel, angle, spacing)
+ elif image_type == "psf":
+ data.add_psf(images, scale, index, channel, angle, spacing)
+
+ # Calculate resampled images
+ if options.scales is not None:
+ for scale in options.scales:
+ print("Creating %s percent downsampled versions of the original images" % scale)
+ data.create_rescaled_images("original", scale)
+
+ # Add transforms for registered images.
+ for transform_name in os.listdir(directory):
+ if not transform_name.endswith(".txt"):
+ continue
+
+ if not all(x in transform_name for x in params_c) or not "transform" in transform_name:
+ print("Unrecognized transform name %s. Skipping it." % transform_name)
+ continue
+
+ scale = transform_name.split("scale_")[-1].split("_index")[0]
+ index = transform_name.split("index_")[-1].split("_channel")[0]
+ channel = transform_name.split("channel_")[-1].split("_angle")[0]
+ angle = transform_name.split("angle_")[-1].split(".")[0]
+
+ full_path = os.path.join(directory, transform_name)
+
+ # First calculate registered image if not in the data structure
+ if not data.check_if_exists("registered", index, channel, scale):
+ print("Resampling registered image for image nr. ", index)
+ data.set_active_image(0, channel, scale, "original")
+ reference = data.get_itk_image()
+ data.set_active_image(index, channel, scale, "original")
+ moving = data.get_itk_image()
+
+ transform = read.__itk_transform(full_path, return_itk=True)
+
+ registered = itkutils.resample_image(moving, transform, reference=reference)
+ registered = itkutils.convert_from_itk_image(registered)
+ spacing = registered.spacing
+
+ data.add_registered_image(registered, scale, index, channel, angle, spacing)
+
+ # The add it's transform
+ transform_type, params, fixed_params = read.__itk_transform(full_path)
+ data.add_transform(scale, index, channel, params, fixed_params, transform_type)
+
+ # Calculate missing PSFs
+ if options.calculate_psfs:
+ data.calculate_missing_psfs()
+
+ if options.copy_registration_result != -1:
+ from_scale = options.copy_registration_result[0]
+ to_scale = options.copy_registration_result[1]
+ data.copy_registration_result(from_scale, to_scale)
+
+ data.close()
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/bin/power.py b/Addons/FRCmetric/miplib-public/miplib/bin/power.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c7b30340a455fee690062d36b79f7db86a0cca7
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/bin/power.py
@@ -0,0 +1,73 @@
+#!/usr/bin/env python
+# -*- python -*-
+"""
+File: power.py
+Author: Sami Koho (sami.koho@gmail.com)
+
+Description:
+
+A utility script for extracting 1D power spectra of all images within
+a defined input directory. The spectra are saved in a single csv
+file, each column denoting a single image.
+"""
+import datetime
+import os
+import sys
+
+import numpy
+import pandas
+
+from miplib.analysis.image_quality import filters
+from miplib.data.io import read
+from miplib.processing import image as improc
+from miplib.ui.cli import miplib_entry_point_options
+
+
+def main():
+ options = miplib_entry_point_options.get_power_script_options(sys.argv[1:])
+ path = options.working_directory
+
+ assert os.path.isdir(path)
+
+ # Create output directory
+ output_dir = datetime.datetime.now().strftime("%Y-%m-%d")+'_PyIQ_output'
+ output_dir = os.path.join(options.working_directory, output_dir)
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+
+ # Create output file
+ date_now = datetime.datetime.now().strftime("%H-%M-%S")
+ file_name = date_now + '_PyIQ_power_spectra' + '.csv'
+ file_path = os.path.join(output_dir, file_name)
+
+ csv_data = pandas.DataFrame()
+
+ # Scan through images
+ for image_in in os.listdir(path):
+ if not image_in.endswith((".jpg", ".tif", ".tiff", ".png")):
+ continue
+ path_in = os.path.join(path, image_in)
+
+ # Get image
+ image = read.get_image(path_in, channel=options.rgb_channel)
+ image = improc.crop_to_rectangle(image)
+
+ for dim in image.shape:
+ if dim != options.image_size:
+ image = improc.resize(image, options.image_size)
+ break
+
+ task = filters.FrequencyQuality(image, options)
+ task.calculate_power_spectrum()
+ task.calculate_summed_power()
+
+ power_spectrum = task.get_power_spectrum()
+
+ csv_data[image_in] = power_spectrum[1]
+
+ csv_data.insert(0, "Power", numpy.linspace(0, 1, num=len(csv_data)))
+ csv_data.to_csv(file_path, index=False)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/Addons/FRCmetric/miplib-public/miplib/bin/pyimq.py b/Addons/FRCmetric/miplib-public/miplib/bin/pyimq.py
new file mode 100644
index 0000000000000000000000000000000000000000..12f79612000f6d0545644676a81d901f3a91b06a
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/bin/pyimq.py
@@ -0,0 +1,271 @@
+#!/usr/bin/env python
+# -*- python -*-
+
+"""
+File: pyimq.py
+Author: Sami Koho (sami.koho@gmail.com)
+
+Description:
+This is the main program of PyImageQualityRanking software. The
+purpose of the software is to sort large microscopy image datasets
+according to calculated image quality parameters. Possible
+applications can be: (1) finding the best images in a dataset, or
+(2) finding and discarding out-of-focus images before quantitative
+analysis, for example.
+
+The behavior of the program can be controlled by a rich command
+line options interface. Please run "python pyimq.py -h" for details.
+
+The program works in four main modes, that can be controlled by
+the --mode parameter:
+- file: A single file is analyzed and the results are
+ printed on the terminal screen
+- directory: All the images in a directory are analyzed and
+ the results are saved in a file
+- analyze: Variables are calculated from the analysis results.
+- plot: The analysis results are ordered according to a
+ selected image quality variable.
+License:
+The PyImageQuality software is licensed under BSD open-source license.
+
+Copyright (c) 2015, Sami Koho, Laboratory of Biophysics, University of Turku.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+* Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above
+copyright notice, this list of conditions and the following
+disclaimer in the documentation and/or other materials provided
+with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY COPYRIGHT HOLDER AND CONTRIBUTORS ''AS
+IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
+TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL COPYRIGHT HOLDER
+OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+In addition to the terms of the license, we ask to acknowledge the use
+of packages in scientific articles by citing the corresponding papers:
+
+**citation here**
+"""
+
+import csv
+import datetime
+import os
+import sys
+
+import pandas
+
+from miplib.analysis.image_quality import filters
+from miplib.data.io import read
+from miplib.ui.cli import miplib_entry_point_options
+from miplib.ui.plots.image import show_pics_from_disk
+
+
+def main():
+ """
+ The Main program of the PyImageQualityRanking software.
+ """
+ options = miplib_entry_point_options.get_quality_script_options(sys.argv[1:])
+ path = options.working_directory
+ file_path = None
+ csv_data = None
+
+ print("Mode option is %s" % options.mode)
+
+ if "file" in options.mode:
+ # In "file" mode a single file is analyzed and the various parameter
+ # values are printed on screen. This functionality is provided mainly
+ # for debugging purposes.
+ assert options.file is not None, "You have to specify a file with a " \
+ "--file option"
+ path = os.path.join(path, options.file)
+ assert os.path.isfile(path)
+ image = read.get_image(path, channel=options.rgb_channel)
+
+ print("The shape is %s" % str(image.shape))
+
+ task = filters.LocalImageQuality(image, options)
+ task.set_smoothing_kernel_size(100)
+ entropy = task.calculate_image_quality()
+ task2 = filters.FrequencyQuality(image, options)
+ finfo = task2.analyze_power_spectrum()
+
+ print("SPATIAL MEASURES:")
+ print("The entropy value of %s is %f" % (path, entropy))
+ print("ANALYSIS OF THE POWER SPECTRUM TAIL")
+ print("The mean is: %e" % finfo[0])
+ print("The std is: %e" % finfo[1])
+ print("The entropy is %e" % finfo[2])
+ print("The threshold frequency is %f Hz" % finfo[3])
+ print("Power at high frequencies %e" % finfo[4])
+ print("The skewness is %f" % finfo[5])
+ print("The kurtosis is %f" % finfo[6])
+
+ if "directory" in options.mode:
+ # In directory mode every image in a given directory is analyzed in a
+ # single run. The analysis results are saved into a csv file.
+
+ assert os.path.isdir(path), path
+
+ # Create output directory
+ output_dir = datetime.datetime.now().strftime("%Y-%m-%d")+'_PyIQ_output'
+ output_dir = os.path.join(options.working_directory, output_dir)
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+ # Create output file
+ date_now = datetime.datetime.now().strftime("%H-%M-%S")
+ file_name = date_now + '_PyIQ_out' + '.csv'
+ file_path = os.path.join(output_dir, file_name)
+ output_file = open(file_path, 'wt')
+ output_writer = csv.writer(
+ output_file, quoting=csv.QUOTE_NONNUMERIC, delimiter=",")
+ output_writer.writerow(
+ ("Filename", "tEntropy", "tBrenner", "fMoments", "fMean", "fSTD", "fEntropy",
+ "fTh", "fMaxPw", "Skew", "Kurtosis", "MeanBin"))
+
+ for image_name in os.listdir(path):
+ if options.file_filter is None or options.file_filter in image_name:
+ real_path = os.path.join(path, image_name)
+ # Only process images
+ if not os.path.isfile(real_path) or not real_path.endswith((".jpg", ".tif", ".tiff", ".png")):
+ continue
+ # ImageJ files have particular TIFF tags that can be processed correctly
+ # with the options.imagej switch
+ image = read.get_image(path, channel=options.rgb_channel)
+
+ # Only grayscale images are processed. If the input is an RGB image,
+ # a channel can be chosen for processing.
+
+ # Time series sometimes contain images of very different content: the start
+ # of the series may show nearly empty (black) images, whereas at the end
+ # of the series the whole field-of-view may be full of cells. Ranking such
+ # dataset in a single piece may be challenging. Therefore the beginning of
+ # the dataset can be separated from the end, by selecting a minimum value
+ # for average grayscale pixel value here.
+ if options.average_filter > 0 and image.average() < options.average_filter:
+ continue
+
+ # Run spatial domain analysis
+ task = filters.LocalImageQuality(image, options)
+ task.set_smoothing_kernel_size(100)
+ entropy = task.calculate_image_quality()
+ # Run frequency domain analysis
+ task2 = filters.FrequencyQuality(image, options)
+ results = task2.analyze_power_spectrum()
+
+ task3 = filters.SpectralMoments(image, options)
+ moments = task3.calculate_spectral_moments()
+
+ task4 = filters.BrennerImageQuality(image, options)
+ brenner = task4.calculate_brenner_quality()
+
+ # Save results
+ results.insert(0, moments)
+ results.insert(0, brenner)
+ results.insert(0, entropy)
+ results.insert(0, os.path.join(path, image_name))
+ output_writer.writerow(results)
+
+ print("Done analyzing %s" % image_name)
+
+ output_file.close()
+ print("The results were saved to %s" % file_path)
+
+ if "analyze" in options.mode:
+ # In analyze mode the previously created quality ranking variables are
+ # normalized to the highest value of every given variable. In addition
+ # some new parameters are calculated. The results are saved into a new
+ # csv file.
+ if file_path is None:
+ assert options.file is not None, "You have to specify a data file" \
+ "with the --file option"
+ path = os.path.join(options.working_directory, options.file)
+ print(path)
+ file_path = path
+ assert os.path.isfile(path), "Not a valid file %s" % path
+ assert path.endswith(".csv"), "Unknown suffix %s" % path.split(".")[-1]
+
+ csv_data = pandas.read_csv(file_path)
+ csv_data["cv"] = csv_data.fSTD/csv_data.fMean
+ csv_data["SpatEntNorm"] = csv_data.tEntropy/csv_data.tEntropy.max()
+ csv_data["SpectMean"] = csv_data.fMean/csv_data.fMean.max()
+ csv_data["SpectSTDNorm"] = csv_data.fSTD/csv_data.fSTD.max()
+ csv_data["InvSpectSTDNorm"] = 1- csv_data.SpectSTDNorm
+ csv_data["SpectEntNorm"] = csv_data.fEntropy/csv_data.fEntropy.max()
+ csv_data["SkewNorm"] = 1 - abs(csv_data.Skew)/abs(csv_data.Skew).max()
+ csv_data["KurtosisNorm"] = abs(csv_data.Kurtosis)/abs(csv_data.Kurtosis).max()
+ csv_data["SpectHighPowerNorm"] = csv_data.fMaxPw/csv_data.fMaxPw.max()
+ csv_data["MeanBinNorm"] = csv_data.MeanBin/csv_data.MeanBin.max()
+ csv_data["BrennerNorm"] = csv_data.tBrenner/csv_data.tBrenner.max()
+ csv_data["SpectMomentsNorm"] = csv_data.fMoments/csv_data.fMoments.max()
+
+ # Create output directory
+ output_dir = datetime.datetime.now().strftime("%Y-%m-%d")+'_PyIQ_output'
+ output_dir = os.path.join(options.working_directory, output_dir)
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+ date_now = datetime.datetime.now().strftime("%H-%M-%S")
+ file_name = date_now + '_PyIQ_analyze_out' + '.csv'
+ file_path = os.path.join(output_dir, file_name)
+
+ csv_data.to_csv(file_path)
+ print("The results were saved to %s" % file_path)
+
+ if "plot" in options.mode:
+ # With the plot option the dataset is sorted according to the desired ranking variable.
+ # The changes are saved to the original csv file. In addition a plot is created to show
+ # a subset of highest and lowest ranked images (the amount of images to show is
+ # controlled by the options.npics parameter
+ if csv_data is None:
+ file_path = os.path.join(options.working_directory, options.file)
+ assert os.path.isfile(file_path), "Not a valid file %s" % path
+ assert path.endswith(".csv"), "Unknown suffix %s" % path.split(".")[-1]
+ csv_data = pandas.read_csv(file_path)
+ if options.result == "average":
+ csv_data["Average"] = csv_data[["InvSpectSTDNorm", "SpatEntNorm"]].mean(axis=1)
+ csv_data.sort(columns="Average", ascending=False, inplace=True)
+ elif options.result == "fskew":
+ csv_data.sort(columns="SkewNorm", ascending=False, inplace=True)
+ elif options.result == "fentropy":
+ csv_data.sort(columns="SpectEntNorm", ascending=False, inplace=True)
+ elif options.result == "ientropy":
+ csv_data.sort(columns="SpatEntNorm", ascending=False, inplace=True)
+ elif options.result == "icv":
+ csv_data.sort(columns="SpatEntNorm", ascending=False, inplace=True)
+ elif options.result == "fstd":
+ csv_data.sort(columns="SpectSTDNorm", ascending=False, inplace=True)
+ elif options.result == "fkurtosis":
+ csv_data.sort(columns="KurtosisNorm", ascending=False, inplace=True)
+ elif options.result == "fpw":
+ csv_data.sort(columns="SpectHighPowerNorm", ascending=False, inplace=True)
+ elif options.result == "fmean":
+ csv_data.sort(columns="SpectHighPowerNorm", ascending=False, inplace=True)
+ elif options.result == "meanbin":
+ csv_data.sort(columns="MeanBinNorm", ascending=False, inplace=True)
+ else:
+ print("Unknown results sorting method %s" % options.result)
+ sys.exit()
+
+ best_pics = csv_data["Filename"].head(options.npics).as_matrix()
+ worst_pics = csv_data["Filename"].tail(options.npics).as_matrix()
+ show_pics_from_disk(best_pics, title="BEST PICS")
+ show_pics_from_disk(worst_pics, title="WORST PICS")
+
+ csv_data.to_csv(file_path, index=False)
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/bin/register.py b/Addons/FRCmetric/miplib-public/miplib/bin/register.py
new file mode 100644
index 0000000000000000000000000000000000000000..b953bbbb0751e7e17451bb00a5a137bf6e1de5b3
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/bin/register.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env python
+# -*- python -*-
+
+"""
+register.py
+
+Copyright (c) 2016 Sami Koho All rights reserved.
+
+This software may be modified and distributed under the terms
+of the BSD license. See the LICENSE file for details.
+
+This is the main program file for the miplib fusion calculation
+"""
+
+import os
+import sys
+
+import SimpleITK as sitk
+
+from miplib.data.containers import image_data
+from miplib.processing import itk as itkutils
+from miplib.processing.registration import registration_mv
+from miplib.ui import utils
+from miplib.ui.cli import miplib_entry_point_options
+
+
+def main():
+ options = miplib_entry_point_options.get_register_script_options(sys.argv[1:])
+
+ # Get Data
+ filename = sys.argv[1]
+ if not filename.endswith(".hdf5"):
+ raise ValueError("Please specify a HDF5 data file")
+ full_path = os.path.join(options.working_directory, filename)
+ if not os.path.exists(full_path):
+ raise ValueError("The specified file %s does not exist" % full_path)
+
+ data = image_data.ImageData(full_path)
+
+ # Check that requested image size exists. If not, create it.
+ if options.scale not in data.get_scales("original"):
+ print("Images at the defined scale do not exist in the data " \
+ "structure. The original images will be now resampled. " \
+ "This may take a long time depending on the image size " \
+ "and the number of views.")
+ data.create_rescaled_images("original", options.scale)
+
+ data.set_active_image(0, options.channel, options.scale, "original")
+ spacing = data.get_voxel_size()
+ data.add_registered_image(data[:], options.scale, 0, options.channel,
+ 0, spacing)
+
+ if options.evaluate_results:
+ fixed_image = data.get_itk_image()
+
+ # Setup registration. View number 0 is always the reference for now.
+ # The behavior can be easily changed if necessary.
+ task = registration_mv.RotatedMultiViewRegistration(data, options)
+ task.set_fixed_image(0)
+
+ # Iterate over the rotated views
+ for view in range(1, data.get_number_of_images("original")):
+ task.set_moving_image(view)
+ if data.check_if_exists("registered", view, options.channel,
+ options.scale):
+ if utils.get_user_input("There is a saved registration result for "
+ "the view %i. Do you want to skip it?" %
+ view):
+ continue
+
+ task.execute()
+
+ if options.evaluate_results:
+ moving_image = task.get_resampled_result()
+ sitk.Show(
+ itkutils.make_composite_rgb_image(fixed_image, moving_image))
+ if utils.get_user_input(
+ "Do you want to save the result (yes/no)? "):
+ task.save_result()
+ continue
+ else:
+ if utils.get_user_input(
+ "Skipping view %i. Do you want to continue "
+ "registration?" % view):
+ continue
+ else:
+ print("Exiting registration without saving results")
+ break
+ else:
+ task.save_result()
+ continue
+
+ data.close()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/Addons/FRCmetric/miplib-public/miplib/bin/resolution.py b/Addons/FRCmetric/miplib-public/miplib/bin/resolution.py
new file mode 100644
index 0000000000000000000000000000000000000000..252e6f4f502c80cf2d31f8f535f3c948098b1820
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/bin/resolution.py
@@ -0,0 +1,90 @@
+"""
+A convenience script to calculate FRC for images in a directory.
+I have a nicer version in a notebook -- will be updated.
+"""
+
+import datetime
+import os
+import sys
+
+import numpy as np
+import pandas
+import miplib.analysis.resolution.fourier_ring_correlation as frc
+from miplib.data.io import read as imread
+import miplib.ui.cli.miplib_entry_point_options as options
+import miplib.processing.to_string as strutils
+
+
+def main():
+
+ # Get input arguments
+ args = options.get_frc_script_options(sys.argv[1:])
+ path = args.directory
+
+ # Create output directory
+ output_dir = args.directory
+ date_now = datetime.datetime.now().strftime("%H-%M-%S")
+
+ filename = "{}_miplib_{}_frc_results.csv".format(date_now, args.frc_mode)
+ filename = os.path.join(output_dir, filename)
+
+ # Get image file names, sort in alphabetic order and complete.
+ files_list = list(i for i in os.listdir(path)
+ if i.endswith((".jpg", ".tif", ".tiff", ".png")))
+ files_list.sort()
+ print('Number of images to analyze: {}'.format(len(files_list)))
+
+ #df_main = pandas.DataFrame(0, index=np.arange(len(files_list)), columns=["Image", "Depth", "Kind", "Resolution"])
+ df_main = pandas.DataFrame(0, index=np.arange(len(files_list)), columns=["Image", "Resolution"])
+
+ if args.frc_mode == "two-image":
+
+ def pairwise(iterable):
+ a = iter(iterable)
+ return zip(a, a)
+
+ for idx, im1, im2 in enumerate(pairwise(files_list)):
+ # Read images
+ image1 = imread.get_image(os.path.join(path, im1))
+ image2 = imread.get_image(os.path.join(path, im2))
+
+ result = frc.calculate_two_image_frc(image1, image2, args)
+ title = strutils.common_start(im1, im2)
+
+ resolution = result.resolution['resolution']
+ df_main.iloc[idx] = title, resolution
+
+ elif args.frc_mode == "one-image":
+ for idx, im in enumerate(files_list):
+ image = imread.get_image(os.path.join(path, im))
+
+ print("Analyzing image {}".format(im))
+
+ result = frc.calculate_single_image_frc(image, args)
+
+ title = im.split('.')[0]
+
+ # I left these snippets here to show how one can add additional info
+ # to the dataframes in particular use cases.
+
+ #depth = title.split('um_')[0].split("_")[-1]
+
+ # kind = None
+ # for x in ("apr_ism", "apr_ism_bplus", "closed", "open", "static_ism", "ism_sim"):
+ # if x in title:
+ # kind = x
+ # if kind is None:
+ # raise RuntimeError("Unknown image: {}".format(title))
+ resolution = result.resolution['resolution']
+ #df_main.iloc[idx] = title, depth, kind, resolution
+ df_main.iloc[idx] = title, resolution
+
+ else:
+ raise NotImplementedError()
+
+ df_main.index = list(range(len(df_main)))
+ df_main.to_csv(filename)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/Addons/FRCmetric/miplib-public/miplib/bin/subjective.py b/Addons/FRCmetric/miplib-public/miplib/bin/subjective.py
new file mode 100644
index 0000000000000000000000000000000000000000..f309234ac30abc74c14849d62aa96ee3479b97be
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/bin/subjective.py
@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+# -*- python -*-
+
+"""
+File: subjective.py
+Author: Sami Koho (sami.koho@gmail.com)
+
+Description:
+
+A utility script for performing subjective image rankings. One image is
+displayed at a time, after which it is ranked on 1-5 scale, 5 being the
+best and 1 the worst. The script can be run multiple times to collect
+several ranking results in a single csv file. At every run the data
+is shuffled in order to not repeat the same image sequence twice.
+"""
+
+import os
+import sys
+
+import matplotlib.pyplot as plt
+import pandas
+
+import miplib.ui.cli.miplib_entry_point_options as script_options
+
+
+def main():
+
+ options = script_options.get_subjective_ranking_options(sys.argv[1:])
+ path = options.working_directory
+ index = 0
+ assert os.path.isdir(path), path
+
+ # Create or open a csv file
+ output_dir = path
+ file_name = "subjective_ranking_scores.csv"
+ file_path = os.path.join(output_dir, file_name)
+
+ if os.path.exists(file_path):
+ csv_data = pandas.read_csv(file_path)
+ # Append a new result column
+ for column in csv_data:
+ if "Result" in column:
+ index += 1
+ else:
+ csv_data = pandas.DataFrame()
+ file_names = []
+ # Get valid file names
+ for image_name in os.listdir(path):
+ real_path = os.path.join(path, image_name)
+ if not os.path.isfile(real_path) or not real_path.endswith((".jpg", ".tif", ".tiff", ".png")):
+ continue
+ file_names.append(image_name)
+ csv_data["Filename"] = file_names
+
+ result_name = "Result_" + str(index)
+ results = []
+
+ # Plot settings
+ plt.ion()
+ plt.axis('off')
+
+ # Shuffle the data frame so that the order of the displayed images is mixed every time.
+ csv_data = csv_data.sample(frac=1)
+ print("Images are graded on a scale 1-5, where 1 denotes a very bad image " \
+ "and 5 an excellent image")
+
+ for image_name in csv_data["Filename"]:
+ real_path = os.path.join(path, image_name)
+ image = plt.imread(real_path)
+
+ plt.imshow(image, cmap='hot', vmax=image.max(), vmin=image.min())
+
+ success = False
+ while not success:
+ input = input("Give grade: ")
+
+ if input.isdigit():
+ result = int(input)
+ else:
+ print("Please give a numeric grade 1-5.")
+ continue
+
+ if 1 <= result <= 5:
+ success = True
+ results.append(result)
+ else:
+ print("Please give a numeric grade 1-5.")
+
+ csv_data[result_name] = results
+ csv_data.to_csv(file_path, index=False)
+
+
+if __name__ == "__main__":
+ main()
+
+
+
+
+
+
+
+
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/__init__.py b/Addons/FRCmetric/miplib-public/miplib/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/adapters/array_detector_data.py b/Addons/FRCmetric/miplib-public/miplib/data/adapters/array_detector_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6b633125b22f9d169fcad4e0a4e8e614d08c4a5
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/adapters/array_detector_data.py
@@ -0,0 +1,72 @@
+"""
+Simple adapter wrappers that allow using ImageData and/or
+Image objects in funcitons that were written for ArrayDetectorData.
+"""
+
+from miplib.data.containers.image import Image
+
+
+class ImageDataAdapter(object):
+ def __init__(self, data, kind="original", scale=100):
+ self.data = data
+
+ self.kind = kind
+ self.scale = scale
+
+ self.data.set_active_image(0, 0, self.scale, self.kind)
+
+ @property
+ def ndetectors(self):
+ return self.data.series_count
+
+ @property
+ def ngates(self):
+ return self.data.channel_count
+
+ def __getitem__(self, item):
+ gate, detector = item
+
+ self.data.set_active_image(detector, gate, self.scale, self.kind)
+ spacing = self.data.get_voxel_size()
+
+ return Image(self.data[:], spacing)
+
+
+class ImageAdapter(object):
+ def __init__(self, data):
+ self.data = data
+
+ @property
+ def ndetectors(self):
+ return self.data.shape[1]
+
+ @property
+ def ngates(self):
+ return self.data.shape[0]
+
+ def __getitem__(self, item):
+ gate, detector = item
+
+ return self.data[gate, detector]
+
+
+class ArrayAdapter(object):
+ def __init__(self, data, spacing):
+ self.data = data
+ self.spacing = spacing
+
+ @property
+ def ndetectors(self):
+ return self.data.shape[1]
+
+ @property
+ def ngates(self):
+ return self.data.shape[0]
+
+ def __getitem__(self, item):
+ gate, detector = item
+
+ return Image(self.data[gate, detector], self.spacing)
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/containers/__init__.py b/Addons/FRCmetric/miplib-public/miplib/data/containers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/containers/array_detector_data.py b/Addons/FRCmetric/miplib-public/miplib/data/containers/array_detector_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..b218cd7c199d63db149557c1f234c5629817ed07
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/containers/array_detector_data.py
@@ -0,0 +1,90 @@
+from .image import Image
+
+
+class ArrayDetectorData(object):
+ """
+ A class to handle multi-dimensional data from an array detector.
+ The data consists of Images recorded with each pixel of the detector
+ array. In addition, each pixel can be split by laser gates into several
+ images.
+ """
+ def __init__(self, detectors, gates):
+
+ self._data_container = [[None] * detectors] * gates
+
+ self._nDetectors = detectors
+ self._nGates = gates
+
+ # Iterator helper variables
+ self._iteration_axis = 'detectors'
+ self.gate_idx = 0
+ self.detector_idx = 0
+
+ # region Properties
+
+ @property
+ def ndetectors(self):
+ return self._nDetectors
+
+ @property
+ def ngates(self):
+ return self._nGates
+
+ @property
+ def iteration_axis(self):
+ return self._iteration_axis
+
+ @iteration_axis.setter
+ def iteration_axis(self, value):
+ if value != 'detectors' and value != 'gates':
+ raise ValueError("Not a valid iteration axis. Please choose between "
+ "detectors or gates.")
+ else:
+ self._iteration_axis = value
+ # endregion
+
+ def __setitem__(self, key, value):
+ assert isinstance(key, tuple) and len(key) == 2
+ assert isinstance(value, Image)
+ gate = key[0]
+ detector = key[1]
+ self._data_container[gate][detector] = value
+
+ def __getitem__(self, item):
+ assert isinstance(item, tuple) and len(item) == 2
+ gate = item[0]
+ detector = item[1]
+ assert gate < self._nGates and detector < self._nDetectors
+ return self._data_container[gate][detector]
+
+ def __iter__(self):
+ return self
+
+ def __next__(self):
+ if self.gate_idx < self._nGates and self.detector_idx < self._nDetectors:
+ data = self._data_container[self.gate_idx][self.detector_idx]
+ if self._iteration_axis == 'detectors':
+ if self.detector_idx < (self._nDetectors - 1):
+ self.detector_idx += 1
+ else:
+ self.detector_idx = 0
+ self.gate_idx += 1
+ else:
+ if self.gate_idx < (self._nGates < 1):
+ self.gate_idx +=1
+ else:
+ self.gate_idx = 0
+ self.detector_idx += 1
+
+ return data
+
+ else:
+ self.gate_idx = 0
+ self.detector_idx = 0
+ raise StopIteration
+
+ def get_photosensor(self, photosensor):
+ data = ArrayDetectorData(self.ndetectors, 1)
+ for i in range(self.ndetectors):
+ data[0, i] = self._data_container[photosensor][i]
+ return data
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/containers/fourier_correlation_data.py b/Addons/FRCmetric/miplib-public/miplib/data/containers/fourier_correlation_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0aee9f0f012deaf0f59c8ee433ed3d40f326615
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/containers/fourier_correlation_data.py
@@ -0,0 +1,140 @@
+
+from miplib.data.core.dictionary import FixedDictionary
+
+import pandas as pd
+import numpy as np
+
+
+class FourierCorrelationDataCollection(object):
+ """
+ A container for the directional Fourier correlation data
+ """
+ def __init__(self):
+ self._data = dict()
+
+ self.iter_index = 0
+
+ def __setitem__(self, key, value):
+ assert isinstance(key, (int, np.integer))
+ assert isinstance(value, FourierCorrelationData)
+
+ self._data[str(key)] = value
+
+ def __getitem__(self, key):
+ return self._data[str(key)]
+
+ def __iter__(self):
+ return self
+
+ def __next__(self):
+ try:
+ item = list(self._data.items())[self.iter_index]
+ except IndexError:
+ self.iter_index = 0
+ raise StopIteration
+
+ self.iter_index += 1
+ return item
+
+ def __len__(self):
+ return len(self._data)
+
+ def clear(self):
+ self._data.clear()
+
+ def items(self):
+ return list(self._data.items())
+
+ def nitems(self):
+ return len(self._data)
+
+ def as_dataframe(self, include_results=False):
+ """
+ Convert a FourierCorrelationDataCollection object into a Pandas
+ dataframe. Only returns the raw Fourier correlation data,
+ not the processed results.
+
+ :return: A dataframe with columns: Angle (categorical), Correlation (Y),
+ Frequency (X) and nPoints (number of points in each bin)
+ """
+ df = pd.DataFrame(columns=['Correlation', 'Frequency', 'nPoints', 'Angle'])
+
+ for key, dataset in self._data.items():
+ df_temp = dataset.as_dataframe(include_results=include_results)
+
+ angle = np.full(len(df_temp), int(key), dtype=np.int64)
+ df_temp['Angle'] = angle
+
+ df = pd.concat([df, df_temp], ignore_index=True)
+
+ df['Angle'] = df['Angle'].astype('category')
+ return df
+
+
+class FourierCorrelationData(object):
+ """
+ A datatype for FRC data
+
+ """
+ #todo: the dictionary format here is a bit clumsy. Maybe change to a simpler structure
+
+ def __init__(self, data=None):
+
+ correlation_keys = "correlation frequency points-x-bin curve-fit " \
+ "curve-fit-coefficients"
+ resolution_keys = "threshold criterion resolution-point " \
+ "resolution-threshold-coefficients resolution spacing"
+
+ self.resolution = FixedDictionary(resolution_keys.split())
+ self.correlation = FixedDictionary(correlation_keys.split())
+
+ if data is not None:
+ assert isinstance(data, dict)
+
+ for key, value in data.items():
+ if key in self.resolution.keys:
+ self.resolution[key] = value
+ elif key in self.correlation.keys:
+ self.correlation[key] = value
+ else:
+ raise ValueError("Unknown key found in the initialization data")
+
+ def as_dataframe(self, include_results=False):
+ """
+ Convert a FourierCorrelationData object into a Pandas
+ dataframe. Only returns the raw Fourier correlation data,
+ not the processed results.
+
+ :return: A dataframe with columns: Correlation (Y), Frequency (X) and
+ nPoints (number of points in each bin)
+ """
+ if include_results is False:
+ to_df = {
+ 'Correlation': self.correlation["correlation"],
+ 'Frequency': self.correlation["frequency"],
+ 'nPoints': self.correlation["points-x-bin"],
+ }
+ else:
+ resolution = np.full(self.correlation["correlation"].shape,
+ self.resolution["resolution"],
+ dtype=np.float32)
+ resolution_point_x = np.full(self.correlation["correlation"].shape,
+ self.resolution["resolution-point"][0],
+ dtype=np.float32)
+ resolution_point_y = np.full(self.correlation["correlation"].shape,
+ self.resolution["resolution-point"][1],
+ dtype=np.float32)
+ threshold = self.resolution["threshold"],
+
+ to_df = {
+ 'Correlation': self.correlation["correlation"],
+ 'Frequency': self.correlation["frequency"],
+ 'nPoints': self.correlation["points-x-bin"],
+ 'Resolution': resolution,
+ 'Resolution_X': resolution_point_x,
+ 'Resolution_Y': resolution_point_y,
+ 'Threshold': threshold
+
+ }
+
+ return pd.DataFrame(to_df)
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/containers/image.py b/Addons/FRCmetric/miplib-public/miplib/data/containers/image.py
new file mode 100644
index 0000000000000000000000000000000000000000..45d6fde8e59203d73393fa4a32ba09742a766b27
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/containers/image.py
@@ -0,0 +1,101 @@
+"""
+File: image.py
+Author: Sami Koho (sami.koho@gmail.com)
+
+Description:
+This file contains a simple class for storing image data.
+"""
+
+import argparse
+
+import numpy
+
+
+class Image(numpy.ndarray):
+ """
+ A very simple extension to numpy.ndarray, to contain image data and
+ metadata.
+ """
+
+ # region Initialization
+
+ def __new__(cls, images, spacing, filename=None):
+ obj = numpy.asarray(images).view(cls)
+
+ obj.spacing = list(spacing)
+ obj.filename = filename
+
+ return obj
+
+ def __array__finalize__(self, obj):
+
+ self.spacing = getattr(obj, 'spacing')
+ self.filename = getattr(obj, 'filename', None)
+ # endregion
+
+ # region Properties
+
+ # @property
+ # def spacing(self): return self._spacing
+ #
+ # @spacing.setter
+ # def spacing(self, value):
+ # if len(value) != len(self.shape):
+ # raise ValueError("You should define spacing for every dimension")
+ # else:
+ # self._spacing = value
+
+ # endregion
+
+# region Command Line Arguments (refactor)
+def get_options(parser):
+ """
+ Command-line options for the image I/O
+ """
+ assert isinstance(parser, argparse.ArgumentParser)
+ group = parser.add_argument_group("Image I/O", "Options for image file I/O")
+ # Parameters for controlling how image files are handled
+ group.add_argument(
+ "--imagej",
+ help="Defines wheter the image are in ImageJ tiff format, "
+ "and thus contain the pixel size info etc in the TIFF tags. "
+ "By default true",
+ action="store_true"
+ )
+ group.add_argument(
+ "--rgb-channel",
+ help="Select which channel in an RGB image is to be used for quality"
+ " analysis",
+ dest="rgb_channel",
+ type=int,
+ choices=[0, 1, 2],
+ default=1
+ )
+ # File filtering for batch mode processing
+ parser.add_argument(
+ "--average-filter",
+ dest="average_filter",
+ type=int,
+ default=0,
+ help="Analyze only images with similar amount of detail, by selecting a "
+ "grayscale average pixel value threshold here"
+ )
+ parser.add_argument(
+ "--file-filter",
+ dest="file_filter",
+ default=None,
+ help="Define a common string in the files to be analysed"
+ )
+ return parser
+# endregion
+
+
+
+
+
+
+
+
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/containers/image_data.py b/Addons/FRCmetric/miplib-public/miplib/data/containers/image_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..03fbe634b446835dd57f73d9e4cbb885443d1d0b
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/containers/image_data.py
@@ -0,0 +1,698 @@
+import os
+
+import h5py
+import numpy
+import scipy.ndimage as ndimage
+
+import miplib.processing.itk as itkutils
+import miplib.ui.utils as uiutils
+from miplib.data.containers.image import Image
+from miplib.data.definitions import *
+import miplib.processing.ndarray as arrayutils
+
+
+class ImageData(object):
+ """
+ The data storage in miplib is based on a HDF5 file format. This
+ allows the efficient handing of large datasets
+ """
+
+ def __init__(self, path):
+
+ if not os.path.exists(os.path.dirname(path)):
+ os.mkdir(os.path.dirname(path))
+ assert path.endswith(".hdf5")
+
+ if os.path.exists(path):
+ self.data = h5py.File(path, mode="r+")
+ self.series_count = self.data.attrs["series_count"]
+ self.channel_count = self.data.attrs["channel_count"]
+ else:
+ self.data = h5py.File(path, mode="w")
+ self.series_count = 0
+ self.channel_count = 1
+
+ self.data.attrs["series_count"] = self.series_count
+ self.data.attrs["channel_count"] = self.channel_count
+
+ self.active_image = None
+
+ def add_original_image(self, data, scale, index, channel, angle, spacing, chunk_size=None):
+ """
+ Add a source image to the HDF5 file.
+
+ Parameters
+ ----------
+ :param data: Contains an image from a single rotation angle.
+ The data should be in numpy.ndarray format.
+ Multi-channel data is expected to be a 4D array
+ in which the color channel is the first
+ dimension.
+ :param scale Percentage from full size. It is possible to save
+ multiple versions of an image in different sizes.
+ :param index The image ordering index
+ :param channel The color channel to be associated with the image.
+ :param angle: Estimated rotation angle of the view, in
+ respect to the regular STED angle
+ :param spacing: Voxel size
+
+ :param chunk_size: A specific chunk size can be defined here in
+ order to optimize the data access, when
+ working with partial images.
+ :return:
+ """
+ assert isinstance(data, numpy.ndarray), "Invalid data format."
+
+ # if int(channel) > self.channel_count + 1:
+ # raise ValueError("Add the color channels in the correct order")
+
+ # Create a new image group, based on the ordering index. If the
+ # group exists, an attempt is made to add a new dataset.
+ group_name = "original/" + str(index)
+ if group_name not in self.data:
+ image_group = self.data.create_group(group_name)
+ self.series_count += 1
+ self.data.attrs["series_count"] = self.series_count
+ else:
+ image_group = self.data[group_name]
+
+ name = "channel_" + str(channel) + "_scale_" + str(scale)
+
+ # Don't overwrite an existing image.
+ if name in image_group:
+ return
+
+ # Zoom axial dimension for isotropic pixel size.
+ if data.ndim == 3 and spacing[0] != spacing[1]:
+ print("Image index %s needs to be resampled for isotropic spacing." \
+ "This will take a minute" % index)
+ z_zoom = spacing[0] / spacing[1]
+ data = ndimage.zoom(data, (z_zoom, 1, 1), order=3)
+ spacing = tuple(spacing[x] if x != 0 else spacing[x]/z_zoom for x in range(len(spacing)))
+
+ # Activate chunked storage of requested
+ if chunk_size is None:
+ image_group.create_dataset(name, data=data)
+ else:
+ image_group.create_dataset(name, data=data, chunks=chunk_size)
+
+ image_group[name].attrs["angle"] = angle
+ image_group[name].attrs["spacing"] = spacing
+ image_group[name].attrs["size"] = data.shape
+
+ # # The first image is the same in the registered group as well,
+ # # so a soft link will be created here.
+ # if int(index) == 0:
+ # reg_group_name = "registered/" + index
+ # reg_group = self.data.create_group(reg_group_name)
+ # reg_group[name] = image_group[name]
+
+ def add_registered_image(self, data, scale, index, channel, angle, spacing, chunk_size=None):
+ """
+ Add a registered/resampled image to the HDF5 file.
+
+ Parameters
+ ----------
+ :param data: Contains an image from a single rotation angle.
+ The data should be in numpy.ndarray format.
+ Multi-channel data is expected to be a 4D array
+ in which the color channel is the first
+ dimension.
+ :param scale Percentage from full size. It is possible to save
+ multiple versions of an image in different sizes.
+ :param index The image ordering index
+ :param channel The color channel to be associated with the image.
+ :param angle: Estimated rotation angle of the view, in
+ respect to the regular STED angle
+ :param spacing: Voxel size
+
+ :param chunk_size: A specific chunk size can be defined here in
+ order to optimize the data access, when
+ working with partial images.
+ :return:
+ """
+ assert isinstance(data, numpy.ndarray), "Invalid data format."
+
+ group_name = "registered/" + str(index)
+ if group_name not in self.data:
+ image_group = self.data.create_group(group_name)
+ else:
+ image_group = self.data[group_name]
+
+ # if channel > 0:
+ # if self.channel_count == 1:
+ # raise ValueError("Invalid channel count")
+
+ name = "channel_" + str(channel) + "_scale_" + str(scale)
+ if name in image_group:
+ if uiutils.get_user_input("The dataset %s already exists in image "
+ "group %s. Do you want to overwrite "
+ "it? " % (name, group_name)):
+ del image_group[name]
+ else:
+ return
+
+ if chunk_size is None:
+ image_group.create_dataset(name, data=data)
+ else:
+ image_group.create_dataset(name, data=data, chunks=chunk_size)
+
+ # Each image has its own attributes
+ image_group[name].attrs["angle"] = angle
+ image_group[name].attrs["spacing"] = spacing
+ image_group[name].attrs["size"] = data.shape
+
+ def add_psf(self, data, scale, index, channel, angle, spacing, chunk_size=None,
+ calculated=False):
+ """
+ Add a PSF image to the HDF5 file.
+
+ Parameters
+ ----------
+ :param data: Contains an image from a single rotation angle.
+ The data should be in numpy.ndarray format.
+ Multi-channel data is expected to be a 4D array
+ in which the color channel is the first
+ dimension.
+ :param scale Percentage from full size. It is possible to save
+ multiple versions of an image in different sizes.
+ :param index The image ordering index
+ :param channel The color channel to be associated with the image.
+ :param angle: Estimated rotation angle of the view, in
+ respect to the regular STED angle
+ :param spacing: Voxel size
+
+ :param chunk_size: A specific chunk size can be defined here in
+ order to optimize the data access, when
+ working with partial images.
+ :return:
+ """
+
+ assert isinstance(data, numpy.ndarray), "Invalid data format."
+
+ group_name = "psf/" + str(index)
+ if group_name not in self.data:
+ image_group = self.data.create_group(group_name)
+ else:
+ image_group = self.data[group_name]
+
+ name = "channel_" + str(channel) + "_scale_" + str(scale)
+ if name in image_group:
+ return
+
+ if chunk_size is None:
+ image_group.create_dataset(name, data=data)
+ else:
+ image_group.create_dataset(name, data=data, chunks=chunk_size)
+
+ image_group[name].attrs["angle"] = angle
+ image_group[name].attrs["spacing"] = spacing
+ image_group[name].attrs["size"] = data.shape
+ image_group[name].attrs["calculated"] = calculated
+
+ def add_transform(self, scale, index, channel, params, fixed_params, transform_type):
+ """
+ Adds a spatial transformation as an attribute to the corresponding registered
+ view. This means that the registered/resampled image has to be added first, otherwise
+ an error is raised.
+
+ Parameters
+ ----------
+ :param scale The scale, index and channel parameters identify the
+ :param index registered view, the transform is associated with.
+ :param channel
+
+ :param params The transform parameters. Usually what comes out of
+ transform.GetParameters()
+ :param fixed_params The transform fixed parameters (origin). Usually
+ what comes out of transform.GetFixedParameters()
+ :param transform_type
+ """
+ name = "registered/" + str(index) + "/channel_" + str(channel) + "_scale_" + str(scale)
+
+ if name not in self.data:
+ raise ValueError("Dataset %s does not exist" % name)
+
+ self.data[name].attrs["tfm_type"] = transform_type
+ self.data[name].attrs["tfm_params"] = params
+ self.data[name].attrs["tfm_fixed_params"] = fixed_params
+
+ def add_fused_image(self, data, channel, scale, spacing):
+ """
+ Add a fused image.
+
+ :param data: Contains an image from a single rotation angle.
+ The data should be in numpy.ndarray format.
+ Multi-channel data is expected to be a 4D array
+ in which the color channel is the first
+ dimension.
+ :param channel The color channel to be associated with the image.
+ :param scale Percentage from full size. It is possible to save
+ multiple versions of an image in different sizes.
+ :param spacing: Voxel size
+
+ """
+ assert isinstance(data, numpy.ndarray), "Invalid data format."
+
+ if "fused" not in self.data:
+ image_group = self.data.create_group("fused")
+ else:
+ image_group = self.data["fused"]
+
+ if channel > 0 and self.channel_count == 1:
+ raise ValueError("Invalid channel count")
+
+ name = "channel_" + str(channel) + "_scale_" + str(scale)
+ if name in image_group:
+ return
+
+ image_group.create_dataset(name, data=data)
+ image_group[name].attrs["spacing"] = spacing
+
+ def create_rescaled_images(self, type, scale, chunk_size=None):
+ """
+ Creates rescaled versions of images of a given type. The scaled images
+ are saved directly into the HDF5 file.
+
+ Parameters
+ ----------
+ type The image type
+ scale Scale is the percentage of the original image size.
+ chunk_size The same as with the other images. Can be used to define a
+ particular chunk size for data storage.
+ """
+ if scale in self.get_scales(type):
+ if uiutils.get_user_input(
+ "The scale %i already exists for the image type "
+ "%s. Do you want to recalculate?" % (scale, type)
+ ):
+ pass
+ else:
+ return
+ # Iterate over all the images
+ for index in range(self.get_number_of_images(type)):
+ group_name = type + "/" + str(index)
+ image_group = self.data[group_name]
+ # Iterate over channels
+ for channel in range(self.channel_count):
+ name_new = "channel_" + str(channel) + "_scale_" + str(scale)
+ name_ref = "channel_" + str(channel) + "_scale_100"
+ # Check if exists and delete if yes.
+ if name_new in image_group:
+ del image_group[name_new]
+ continue
+
+ # Zoom
+ spacing = tuple(100*x/scale for x in image_group[name_ref].attrs["spacing"])
+ z_factor = float(scale)/100
+ zoom = (z_factor, ) * self.get_number_of_dimensions()
+ data = ndimage.zoom(image_group[name_ref], zoom, order=3)
+
+ if chunk_size is None:
+ image_group.create_dataset(name_new, data=data)
+ else:
+ image_group.create_dataset(name_new, data=data, chunks=chunk_size)
+
+ image_group[name_new].attrs["angle"] = image_group[name_ref].attrs["angle"]
+ image_group[name_new].attrs["spacing"] = spacing
+ image_group[name_new].attrs["size"] = data.shape
+
+ def calculate_missing_psfs(self):
+ """
+ In case separate PSFs were not recorded for every view, the missing PSFs can be
+ calculated here before image fusion. This requires that the spatial transform
+ is available for every registered view.
+ """
+ max_scale = max(self.get_scales("registered"))
+ if max_scale < 100:
+ if uiutils.get_user_input("There is no registration result "
+ "available for the original images. The "
+ "largest available scale is %i. Do you "
+ "want to proceed with that? " %
+ max_scale):
+ pass
+ else:
+ raise ValueError("No suitable registration result available.")
+
+ for channel in range(self.channel_count):
+ self.set_active_image(0, channel, 100, "psf")
+ image_spacing = self.get_voxel_size()
+ psf_orig = self.data[self.active_image][:]
+
+ for index in range(1, self.get_number_of_images("registered")):
+ if not self.check_if_exists("psf", index, channel, 100):
+ self.set_active_image(index, channel, max_scale, "registered")
+ transform = self.get_transform()
+ psf_new = itkutils.rotate_psf(psf_orig,
+ transform,
+ image_spacing,
+ return_numpy=True)
+ self.add_psf(psf_new, 100, index, channel,
+ self.get_rotation_angle(), image_spacing,
+ calculated=True)
+
+ def copy_registration_result(self, from_scale, to_scale):
+ """
+ With this function it is possible
+ to migrate the registration results from one scale to another.
+
+ With very large images it is sometimes easier and faster to perform
+ image registration with downsampled versions of the original images.
+ The accuracy of the registration result is often very good, even with
+ 60 percent downsampled images.
+
+ Parameters
+ ----------
+ :item from_scale The scale for which there is an existing
+ registration result.
+ :item to_scale The scale for which the new registration results
+ should be calculated.
+
+ Returns
+ -------
+
+ """
+
+ # Check that the registration result for the specified scale
+ # exists.
+ assert from_scale in self.get_scales("registered")
+ print("Copying registration results from %i to %i percent scale" % (
+ from_scale, to_scale))
+ if to_scale not in self.get_scales("original"):
+ self.create_rescaled_images("original", to_scale)
+
+ for channel in range(self.channel_count):
+ print("Resampling view 0")
+ self.set_active_image(0, channel, to_scale, "original")
+ self.add_registered_image(self.data[self.active_image][:], to_scale,
+ 0, channel, 0, self.get_voxel_size())
+ self.set_active_image(0, channel, to_scale, "registered")
+ reference = self.get_itk_image()
+
+ for view in range(1, self.get_number_of_images("original")):
+ print("Resampling view %i" % view)
+ self.set_active_image(view, channel, from_scale, "registered")
+ transform = self.get_transform()
+ transform_params = self.get_transform_parameters()
+ self.set_active_image(view, channel, to_scale, "original")
+ image = self.get_itk_image()
+ angle = self.get_rotation_angle(radians=False)
+ spacing = self.get_voxel_size()
+ result = itkutils.convert_from_itk_image(
+ itkutils.resample_image(image, transform, reference=reference)
+ )[0]
+ self.add_registered_image(result, to_scale, view, channel, angle,
+ spacing)
+ self.add_transform(to_scale, view, channel, transform_params[0], transform_params[1], transform_params[2])
+
+ # def add_transform(self, scale, index, channel, params, fixed_params, transform_type):
+
+ def get_rotation_angle(self, radians=True):
+ """
+ Get rotation angle of the currently active image.
+
+ Parameters
+ ----------
+ radians Use radians instead of degrees
+
+ Returns
+ -------
+ Returns the rotation angle, as degrees or radians
+ """
+ if radians:
+ angle = numpy.pi * int(self.data[self.active_image].attrs["angle"]) / 180
+ return angle
+ else:
+ return int(self.data[self.active_image].attrs["angle"])
+
+ def get_voxel_size(self):
+ """
+ Get the voxel size of the currently active image.
+
+ Returns
+ -------
+ Voxel size as a three element tuple (assuming 3D image).
+ """
+ return list(self.data[self.active_image].attrs["spacing"])
+
+ def get_max(self):
+ return self.data[self.active_image][:].max()
+
+ def get_image_size(self):
+ """
+ Get dimensions of the currently active image
+
+ Returns
+ -------
+ Image dimensions as a tuple.
+ """
+ return self.data[self.active_image].attrs["size"]
+
+ def get_dtype(self):
+ """
+ Get the datatype of the currently acitve image
+
+ Returns
+ -------
+ The datatype as a numpy.dtype compatible parameter
+ """
+ return self.data[self.active_image].dtype
+
+ def get_number_of_images(self, image_type):
+ """
+ Get the number of images of a given type stored in the data structure
+
+ Parameters
+ ----------
+ :param image_type The image type
+
+ Returns
+ -------
+ The number of images of a given type.
+
+ """
+ assert image_type in image_types_c
+ return len(self.data[image_type])
+
+ def get_number_of_dimensions(self):
+ return self.data[self.active_image].ndim
+
+ def get_scales(self, image_type):
+ """
+ Get a list of the image sizes available for a given image type.
+
+ Parameters
+ ----------
+ :param image_type The image type
+
+ Returns
+ -------
+ Returns a list of the saved scales. Raises an error if the scales
+ have been saved inconsistently, i.e. all images of the same type
+ do not have the same scales available.
+ """
+
+ assert image_type in image_types_c
+ scales = []
+
+ def find_scale(name):
+ scales.append(int(name.split("_")[-1]))
+
+ for index in range(self.get_number_of_images(image_type)):
+ scales = []
+ group_name = image_type + "/" + str(index)
+ image_group = self.data[group_name]
+ image_group.visit(find_scale)
+
+ if index == 0:
+ scales_ref = scales
+ else:
+ if set(scales_ref) != set(scales):
+ raise ValueError("Database error. Resampled images have not been"
+ "saved consistently for image type %s" %
+ image_type)
+
+ return scales
+
+ def get_transform(self):
+ """
+ Get the spatial transformation of the current registered view. Requires a
+ registered view to be set as active.
+
+ Returns
+ -------
+ Return an ITK spatial transform.
+ """
+ assert "registered" in self.active_image
+ tfm_type = self.data[self.active_image].attrs["tfm_type"]
+ tfm_params = self.data[self.active_image].attrs["tfm_params"]
+ tfm_fixed_params = self.data[self.active_image].attrs["tfm_fixed_params"]
+ ndim = self.get_number_of_dimensions()
+
+ return itkutils.make_itk_transform(tfm_type,
+ ndim,
+ tfm_params,
+ tfm_fixed_params)
+
+ def get_transform_parameters(self):
+ assert "registered" in self.active_image
+ tfm_type = self.data[self.active_image].attrs["tfm_type"]
+ tfm_params = self.data[self.active_image].attrs["tfm_params"]
+ tfm_fixed_params = self.data[self.active_image].attrs["tfm_fixed_params"]
+
+ return tfm_params, tfm_fixed_params, tfm_type
+
+ def set_active_image(self, index, channel, scale, image_type):
+ """
+ Select which view is currently active.
+
+ :param index: View index, goes from 0 to number of views - 1
+ :param channel The currently active color channel. Goes from 0 to
+ number of channels - 1
+ :param scale Image size, as a percentage of the full size.
+ :param image_type Image type as a string, listed in image_types_c
+ """
+ if int(index) >= self.series_count:
+ print("Invalid index. There are only %i images in the file" % self.series_count)
+ return
+ elif image_type not in image_types_c:
+ print("Unkown image type.")
+ return
+ else:
+ if image_type == "fused":
+ self.active_image = image_type + "/channel_" + str(channel) + "_scale_" + str(scale)
+ else:
+ self.active_image = image_type + "/" + str(index) + "/channel_" + str(channel) + "_scale_" + str(scale)
+ if self.active_image not in self.data:
+ raise ValueError("No such image: %s" % self.active_image)
+
+ # def set_fused_block(self, block, start_index):
+ # assert isinstance(block, numpy.ndarray) and isinstance(start_index, numpy.ndarray)
+ # stop_index = start_index + block.shape
+ # self.data["fused"][start_index:stop_index] = block
+
+ def get_registered_block(self, block_size, block_pad, block_start_index):
+ """
+ When fusing large images, it is often necessary to divide the images
+ into several blocks in order to keep the memory requirements at bay.
+ For such use cases functionality was added here to read a partial image
+ of a given block size and start index directly from disk. Padding is
+ supported as well.
+
+ Parameters
+ ----------
+ :param block_size The size of the desired block
+ :param block_pad The amount of padding to be applied to the sides of the
+ block. This kind of partial overlap of adjacent blocks is
+ needed to avoid fusion artifacts at the block boundaries.
+
+ :param block_start_index
+ The index pointing to the beginning of the block.
+
+ Returns
+ -------
+ The padded image block as a 3D numpy array.
+
+ """
+ assert isinstance(block_size, numpy.ndarray)
+
+ assert "registered" in self.active_image, "You must specify a registered image"
+
+ image_size = self.data[self.active_image].shape
+
+ # Apply padding
+ end_index = block_start_index + block_size + block_pad
+ start_index = block_start_index - block_pad
+ # print "Getting a block from ", self.active_image
+ # print "The start index is %i %i %i" % tuple(start_index)
+ # print "The block size is %i %i %i" % tuple(block_size)
+
+ block_idx = arrayutils.start_to_stop_idx(start_index, end_index)
+
+ if (image_size >= end_index).all() and (start_index >= 0).all():
+ block = self.data[self.active_image][block_idx]
+ return block
+
+ else:
+ pad_block_size = block_size + 2 * block_pad
+ block = numpy.zeros(pad_block_size)
+
+ # If the start_index is close to the image boundaries, it is very
+ # probable that padding will introduce negative start_index values.
+ # In such case the first pixel index must be corrected.
+ if (start_index < 0).any():
+ block_start = numpy.negative(start_index.clip(max=0))
+ image_start = start_index + block_start
+ else:
+ block_start = (0, 0, 0)
+ image_start = start_index
+ # If the padded block is larger than the image size the
+ # block_size must be adjusted.
+ if not (image_size >= end_index).all():
+ block_crop = end_index - image_size
+ block_crop[block_crop < 0] = 0
+ block_end = pad_block_size - block_crop
+ else:
+ block_end = pad_block_size
+
+ end_index = start_index + block_end
+
+ block_read_idx = arrayutils.start_to_stop_idx(image_start, end_index)
+ block_write_idx = arrayutils.start_to_stop_idx(block_start, block_end)
+
+ block[block_write_idx] = self.data[self.active_image][block_read_idx]
+ return block
+
+ def get_itk_image(self):
+ """
+ Get the currently active image as a ITK image instead of a Numpy array.
+ """
+ return itkutils.convert_from_numpy(self.data[self.active_image][:],
+
+ self.data[self.active_image].attrs["spacing"])
+
+ def get_image(self):
+ """
+ Get the currently active image as an Image object instead of a Numpy array
+ """
+ return Image(self.data[self.active_image][:],
+ self.data[self.active_image].attrs["spacing"])
+
+ def get_active_image_index(self):
+ """
+ Get the image of the currently active image
+ """
+ return self.active_image.split('/')[1]
+
+ def close(self):
+ """
+ Close the file object.
+ """
+ self.data.attrs["series_count"] = self.series_count
+ self.data.attrs["channel_count"] = self.channel_count
+
+ self.data.close()
+
+ def check_if_exists(self, image_type, index, channel, scale):
+ """
+ Check if the specified image already exists in the data structure.
+
+ Parameters
+ ----------
+ :param image_type The parameters needed to identify an image in the
+ :param index data structure.
+ :param channel
+ :param scale
+
+ Returns
+ -------
+ True if Yes, False if No.
+ """
+ name = image_type + "/" + str(index) + "/channel_" + str(channel) + "_scale_" + str(scale)
+ return name in self.data
+
+ def __getitem__(self, item):
+ return self.data[self.active_image][item]
+
+ def __setitem__(self, key, value):
+ self.data[self.active_image][key] = value
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/containers/temp_data.py b/Addons/FRCmetric/miplib-public/miplib/data/containers/temp_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1cfcb1335938cde52ba7f6ac7d3f93e07354b87
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/containers/temp_data.py
@@ -0,0 +1,183 @@
+import datetime
+import os
+import tempfile
+
+import miplib.data.io.tiffile
+
+
+class TempData():
+
+ def __init__(self, directory=None):
+ if directory is None:
+ self.dir = tempfile.mkdtemp('-miplib.temp.data')
+ else:
+ date_now = datetime.datetime.now().strftime("%y_%m_%d_")
+ self.dir = '{}_supertomo_temp_data'.format(date_now)
+ if not os.path.exists(self.dir):
+ os.mkdir(self.dir)
+ self.data_file = None
+
+ def create_data_file(self, filename, col_names, append=False):
+ data_file_name = os.path.join(self.dir, filename)
+ self.data_file = RowFile(data_file_name,
+ titles=col_names,
+ append=append)
+
+ def write_comment(self, comment):
+ self.data_file.comment(comment)
+
+ def write_row(self, data):
+ self.data_file.write(data)
+
+ def save_image(self, data, filename):
+ image_path = os.path.join(self.dir, filename)
+ miplib.data.io.tiffile.imsave(image_path, data)
+
+ def close_data_file(self):
+ self.data_file.close()
+
+ def read_data_file(self):
+ self.close_data_file()
+ return self.data_file.read(with_titles=True)
+
+
+class RowFile:
+ """
+ Represents a row file.
+
+ The RowFile class is used for creating and reading row files.
+
+ The format of the row file is the following:
+ - row file may have a header line containg the titles of columns
+ - lines starting with ``#`` are ignored as comment lines
+ """
+
+ def __init__(self, filename, titles = None, append=False):
+ """
+ Parameters
+ ----------
+
+ filename : str
+ Path to a row file
+ titles : {None, list}
+ A list of column headers for writing mode.
+ append : bool
+ When True, new data will be appended to row file.
+ Otherwise, the row file will be overwritten.
+ """
+ self.filename = filename
+ dirname = os.path.dirname(self.filename)
+ if not os.path.exists(dirname) and dirname:
+ os.makedirs(dirname)
+ self.file = None
+ self.nof_cols = 0
+ self.append = append
+ self.extra_titles = ()
+ if titles is not None:
+ self.header(*titles)
+
+ self.data_sep = ', '
+
+ def __del__ (self):
+ if self.file is not None:
+ self.file.close()
+
+ def header(self, *titles):
+ """
+ Write titles of columns to file.
+ """
+ data = None
+ extra_titles = self.extra_titles
+ if self.file is None:
+ if os.path.isfile(self.filename) and self.append:
+ data_file = RowFile(self.filename)
+ data, data_titles = data_file.read(with_titles=True)
+ data_file.close()
+ if data_titles!=titles:
+ self.extra_titles = extra_titles = tuple([t for t in data_titles if t not in titles])
+ self.file = open(self.filename, 'w')
+ self.nof_cols = len(titles + extra_titles)
+ self.comment('@,@'.join(titles + extra_titles))
+ self.comment('To read data from this file, use ioc.microscope.data.RowFile(%r).read().' % (self.filename))
+
+ if data is not None:
+ for i in range(len(data[data_titles[0]])):
+ data_line = []
+ for t in titles + extra_titles:
+ if t in data_titles:
+ data_line.append(data[t][i])
+ else:
+ data_line.append(0)
+ self.write(*data_line)
+
+ def comment (self, msg):
+ """
+ Write a comment to file.
+ """
+ if self.file is not None:
+ self.file.write ('#%s\n' % msg)
+ self.file.flush ()
+
+ def write(self, *data):
+ """
+ Write a row of data to file.
+ """
+ if len (data) < self.nof_cols:
+ data = data + (0, ) * (self.nof_cols - len (data))
+ assert len(data) == self.nof_cols
+ self.file.write(', '.join(str(i).strip('[]') for i in data) + '\n')
+ self.file.flush()
+
+ def _get_titles (self, line):
+ if line.startswith('"'): # csv file header
+ self.data_sep = '\t'
+ return tuple([t[1:-1] for t in line.strip().split('\t')])
+ return tuple([t.strip() for t in line[1:].split('@,@')])
+
+ def read(self, with_titles = False):
+ """
+ Read data from a row file.
+
+ Parameters
+ ----------
+ with_titles : bool
+ When True, return also column titles.
+
+ Returns
+ -------
+ data : dict
+ A mapping of column values.
+ titles : tuple
+ Column titles.
+ """
+ f = open (self.filename, 'r')
+ titles = None
+ d = {}
+ for line in f.readlines():
+ if titles is None:
+ titles = self._get_titles(line)
+ for t in titles:
+ d[t] = []
+ continue
+ if line.startswith ('#'):
+ continue
+ data = line.strip().split(self.data_sep)
+ for i, t in enumerate (titles):
+ try:
+ v = float(data[i])
+ except (IndexError,ValueError):
+ v = 0.0
+ d[t].append(v)
+ f.close()
+ if with_titles:
+ return d, titles
+ return d
+
+ def close (self):
+ """
+ Close row file.
+ """
+ if self.file is not None:
+ print('Closing ',self.filename)
+ self.file.close ()
+ self.file = None
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/converters/__init__.py b/Addons/FRCmetric/miplib-public/miplib/data/converters/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/converters/conv_array_detector_data.py b/Addons/FRCmetric/miplib-public/miplib/data/converters/conv_array_detector_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e1f743ee619484b4d454cc22eef1e010712873e
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/converters/conv_array_detector_data.py
@@ -0,0 +1,103 @@
+import numpy as np
+from ..containers.array_detector_data import ArrayDetectorData
+from ..containers.image_data import ImageData
+from ..containers.image import Image
+
+
+def convert_to_image(data):
+ """
+ Convert ArrayDetectorData into an Image (a Numpy array)
+ :param data: data to convert
+ :return: an Image object (gate, channel, z, y, x)
+ """
+
+ assert isinstance(data, ArrayDetectorData)
+
+ gates = data.ngates
+ channels = data.ndetectors
+
+ dtype = data[0, 0].dtype
+ ndims = data[0, 0].ndim
+ shape = (1, ) * (3 - ndims) + data[0, 0].shape
+ spacing = (1,) * (3 - ndims) + tuple(data[0, 0].spacing)
+
+ im_shape = (gates, channels,) + shape
+ im_data = np.zeros(im_shape, dtype=dtype)
+
+ for gate_idx in range(gates):
+ for channel_idx in range(channels):
+ channel_im = data[gate_idx, channel_idx]
+ if ndims < 3:
+ new_shape = (1,) * (3 - ndims) + channel_im.shape
+ channel_im = np.reshape(channel_im, new_shape)
+ im_data[gate_idx, channel_idx] = channel_im
+
+ return Image(im_data, spacing)
+
+
+def convert_to_imagedata(data, path, data_type="original"):
+ """
+ A very simple converter to save ArrayDetectorData into the HDF5 data structure
+ used in MIPLIB for large image datasets.
+
+ :param data: an ArrayDetectorDAta object
+ :param path: full path to the new hdf5 file. Should end with .hdf5
+ :return: returns a handle to the new hdf5 file. The file is left open, so remember
+ to call close() method in order to ensure that all the data is written on the disk.
+ """
+ assert isinstance(data, ArrayDetectorData)
+
+ image_data = ImageData(path)
+
+ for gate_idx in range(data.ngates):
+ for det_idx in range(data.ndetectors):
+ temp = data[gate_idx, det_idx]
+ if data_type == "original":
+ image_data.add_original_image(temp, 100, det_idx, gate_idx, 0, temp.spacing)
+ elif data_type == "registered":
+ image_data.add_registered_image(temp, 100, det_idx, gate_idx, 0, temp.spacing)
+ elif data_type == "psf":
+ image_data.add_psf(temp, 100, det_idx, gate_idx, 0, temp.spacing)
+
+ return image_data
+
+def convert_to_numpy(data):
+ """
+ Convert ArrayDetectorData into a Numpy array.
+
+ :param data: the object to be converted
+ :type data: ArrayDetectorData
+
+ :return: the data array, organized as (gate, detector, z, y, x). If a two-dimensional
+ dataset is provided, the return shape is the same (len(z)=1).
+
+ """
+ assert isinstance(data, ArrayDetectorData)
+
+ # Get image shape
+ n_dim = data[0,0].ndim
+ if n_dim == 2:
+ image_shape = (1,) + data[0,0].shape
+ elif n_dim == 3:
+ image_shape = data[0,0].shape
+ else:
+ raise ValueError(f"Unsupported array shape ({data[0,0].shape})")
+
+ # Initialize new Numpy array
+ array_shape = (data.ngates, data.ndetectors) + image_shape
+ array = np.zeros(array_shape, dtype=data[0,0].dtype)
+
+ # Copy values
+ for gate_idx in range(data.ngates):
+ for det_idx in range(data.ndetectors):
+ if n_dim == 2:
+ array[gate_idx, det_idx, 0] = data[gate_idx, det_idx]
+ else:
+ array[gate_idx, det_idx] = data[gate_idx, det_idx]
+
+ image_spacing = data[0,0].spacing
+
+ return array, image_spacing
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/coordinates/__init__.py b/Addons/FRCmetric/miplib-public/miplib/data/coordinates/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/coordinates/polar.py b/Addons/FRCmetric/miplib-public/miplib/data/coordinates/polar.py
new file mode 100644
index 0000000000000000000000000000000000000000..c81067d19e6fabb5e52a4a9129ea1940ee29f803
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/coordinates/polar.py
@@ -0,0 +1,62 @@
+"""
+Sami Koho - IIT
+
+This file contains classes for generating different kinds of complex
+indexing structures (masks).
+"""
+
+import numpy as np
+
+
+def generate_polar_coordinate_grid(shape, spacing):
+ """
+ Generate a scaled polar coordinate grid axes.
+ :param shape: the size of the grid
+ :param spacing: the spacing between grid elements
+ :return: the generated coordinate axes (tuple)
+ """
+
+ axes = tuple(np.arange(-shape_n // 2, shape_n // 2, 1) * spacing_n
+ for shape_n, spacing_n in zip(shape, spacing))
+ return axes
+
+
+class SimplePolarIndexer(object):
+ """
+ Basic indexer for a polar/spherical coordinate system
+ """
+ def __init__(self, shape):
+ assert isinstance(shape, tuple) or \
+ isinstance(shape, list) or \
+ isinstance(shape, np.ndarray)
+ assert 1 < len(shape) < 4
+
+ # Create Fourier grid
+ axes = (np.arange(-np.floor(i / 2.0), np.ceil(i / 2.0)) for i in shape)
+
+ meshgrid = np.meshgrid(*axes)
+ self.r = np.sqrt(sum([axis**2 for axis in meshgrid]))
+
+ def __getitem__(self, item):
+ return self.r == item
+
+
+class PolarLowPassIndexer(SimplePolarIndexer):
+ """
+ Generates a low-pass mask in the polar coordinate system, i.e. points
+ closer than the specified distance will be selected.
+ """
+ def __getitem__(self, item):
+ return self.r < item
+
+
+class PolarHighPassIndexer(SimplePolarIndexer):
+ """
+ Generates a high-pass mask in the polar coordinate system, i.e. points
+ farther than the specified distance will be selected.
+ """
+ def __getitem__(self, item):
+ return self.r > item
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/core/__init__.py b/Addons/FRCmetric/miplib-public/miplib/data/core/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/core/dictionary.py b/Addons/FRCmetric/miplib-public/miplib/data/core/dictionary.py
new file mode 100644
index 0000000000000000000000000000000000000000..b387b929c33e7f1a5ee72a20760e86c6265fc77a
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/core/dictionary.py
@@ -0,0 +1,25 @@
+class FixedDictionary(object):
+ """
+ A dictionary with immutable keys. Is initialized at construction
+ with a list of key values.
+ """
+ def __init__(self, keys):
+ assert isinstance(keys, list) or isinstance(keys, tuple)
+ self._dictionary = dict.fromkeys(keys)
+
+ def __setitem__(self, key, value):
+ if key not in self._dictionary:
+ raise KeyError("The key {} is not defined".format(key))
+ else:
+ self._dictionary[key] = value
+
+ def __getitem__(self, key):
+ return self._dictionary[key]
+
+ @property
+ def keys(self):
+ return list(self._dictionary.keys())
+
+ @property
+ def contents(self):
+ return list(self._dictionary.keys()), list(self._dictionary.values())
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/core/tests/__init__.py b/Addons/FRCmetric/miplib-public/miplib/data/core/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/core/tests/test_fixedDictionary.py b/Addons/FRCmetric/miplib-public/miplib/data/core/tests/test_fixedDictionary.py
new file mode 100644
index 0000000000000000000000000000000000000000..0198e69b70bf083412545cde2ecd6f11721b0018
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/core/tests/test_fixedDictionary.py
@@ -0,0 +1,34 @@
+from unittest import TestCase
+
+from ..dictionary import FixedDictionary
+
+
+class TestFixedDictionary(TestCase):
+ def test_set_get_item(self):
+ dictionary = FixedDictionary(("key1", "key2", "key3"))
+
+ dictionary["key1"] = 23
+
+ self.assertEqual(dictionary["key1"], 23)
+
+ def test_set_wrong_item(self):
+ dictionary = FixedDictionary(("key1", "key2", "key3"))
+ with self.assertRaises(KeyError):
+ dictionary["key5"] = 25
+
+ def test_contents(self):
+ dictionary = FixedDictionary(("key1", "key2", "key3"))
+
+ dictionary["key2"] = 23
+ dictionary["key1"] = "temp"
+ dictionary["key3"] = (1, 2, 3)
+
+ keys, values = dictionary.contents
+
+ self.assertListEqual(keys, ['key3', 'key2', 'key1'])
+ self.assertListEqual(values, [(1, 2, 3), 23, 'temp'])
+
+
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/definitions.py b/Addons/FRCmetric/miplib-public/miplib/data/definitions.py
new file mode 100644
index 0000000000000000000000000000000000000000..941aea3c44debb8a09a9b7b04bf29fd301659eb3
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/definitions.py
@@ -0,0 +1,20 @@
+
+itk_transforms_c = {
+ 'sitkIdentity' : 0,
+ 'sitkTranslation' : 1,
+ 'sitkScale' : 2,
+ 'sitkScaleLogarithmic' : 3,
+ 'sitkEuler' : 4,
+ 'sitkSimilarity' : 5,
+ 'sitkQuaternionRigid' : 6,
+ 'sitkVersor' : 7,
+ 'sitkVersorRigid' : 8,
+ 'sitkScaleSkewVersor' : 9,
+ 'sitkAffine' : 10,
+ 'sitkComposite' : 11,
+ 'sitkDisplacementField' : 12,
+ 'sitkBSplineTransform' : 13
+}
+
+image_types_c = ("original", "registered", "fused", "psf")
+params_c = ("angle", "scale", "index", "channel")
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/io/__init__.py b/Addons/FRCmetric/miplib-public/miplib/data/io/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/io/array_detector_data.py b/Addons/FRCmetric/miplib-public/miplib/data/io/array_detector_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..cebb0370f1a21c56370552b98080ff3f6925ae79
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/io/array_detector_data.py
@@ -0,0 +1,112 @@
+import os
+import numpy as np
+import pims
+import itertools
+from scipy.io import loadmat
+
+from miplib.data.containers.array_detector_data import ArrayDetectorData
+from miplib.data.containers.image import Image
+from miplib.data.io import read as imread
+
+
+def read_carma_mat(filename):
+ """
+ A simple implementation for the carma file import in Python
+ :param filename: Path to the Carma .mat file
+ :return: Returns a 2D nested list of miplib Image objects. The first dimension
+ of the list corresponds to the Photosensors count and second, to the
+ Detectors count
+ """
+ assert filename.endswith(".mat")
+ #measurement = "meas_" + filename.split('/')[-1].split('.')[0]
+ data = loadmat(filename)
+
+ # Find measurement name (in case someone renamed the file)
+ for key in list(data.keys()):
+ if 'meas_' in key:
+ data = data[key]
+ break
+
+ # Get necessary metadata
+ spacing = list(data['PixelSize'][0][0][0][::-1])
+ shape = list(data['Size'][0][0][0][::-1])
+
+ detectors = int(data['DetectorsCount'][0][0][0])
+ photosensors = len(data["PhotosensorsTime"][0][0][0])
+
+ # Initialize data container
+ container = ArrayDetectorData(detectors, photosensors)
+
+ # Read images
+ for i in range(photosensors):
+ for j in range(detectors):
+ name = 'pixel_d{}_p{}'.format(j, i)
+ if shape[0] == 1:
+ container[i, j] = Image(np.transpose(data[name][0][0])[0], spacing[1:])
+ else:
+ container[i, j] = Image(np.transpose(data[name][0][0]), spacing)
+
+
+ return container
+
+def read_airyscan_data(image_path, time_points=1, detectors=32):
+ """ Read an Airyscan image.
+
+ Arguments:
+ image_path {string} -- Path to the file
+
+ Keyword Arguments:
+ time_points {int} -- Number of time points (if a time series) (default: {1})
+ detectors {int} -- Number of detectors (default: {32})
+
+ Returns:
+ ArrayDetectorData -- Returns the Airyscan image in the internal format for ISM
+ processing.
+ """
+ # Open file
+ data = pims.bioformats.BioformatsReader(image_path)
+
+ # Get metadata
+ spacing = [data.metadata.PixelsPhysicalSizeY(0), data.metadata.PixelsPhysicalSizeX(0)]
+
+ # Initialize data container
+ container = ArrayDetectorData(detectors, time_points)
+
+ # Split time points
+ data.bundle_axes = ['t', 'y', 'x']
+ data = np.stack(np.split(data[0], time_points))
+
+ # Save to data container
+ for i in range(time_points):
+ for j in range(detectors):
+ container[i, j] = Image(data[i, j, :, :], spacing)
+
+ return container
+
+
+def read_tiff_sequence(path, detectors=25, channels=1):
+ """
+ Construct ArrayDetectorData from a series of TIF images on disk. The images
+ should be named in a way that the detector channels are in a correct order
+ ((det_0, channel_0), (det_0, channel_1), (det_1, channel_0), (det_1, channel_1))
+ after a simple sort.
+
+ :param path: the directory that contains the images
+ :param detectors: number of pixels in the array detectors
+ :param channels: number of channels. Can denote photodetectors (pixel time split),
+ color channels, time-points etc.
+
+ :return: the ArrayDetectorData object that cotnains the imported data
+ """
+
+ files = sorted(filter(lambda x: x.endswith(".tif"), os.listdir(path)))
+ if len(files) != detectors * channels:
+ raise RuntimeError("The number of images does not match the data definition.")
+
+ data = ArrayDetectorData(detectors, channels)
+ steps = itertools.product(range(channels), range(detectors))
+ for idx, (channel, detector) in enumerate(steps):
+ image = imread.get_image(os.path.join(path, files[idx]), bioformats=False)
+ data[detector, channel] = image
+
+ return data
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/io/fourier_correlation_data_reader.py b/Addons/FRCmetric/miplib-public/miplib/data/io/fourier_correlation_data_reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ae43c108a63bc4d372f6de6e9bfecf7b1d6f447
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/io/fourier_correlation_data_reader.py
@@ -0,0 +1,95 @@
+import os
+
+import h5py
+
+from miplib.data.containers.fourier_correlation_data import FourierCorrelationDataCollection, FourierCorrelationData
+from miplib.data.containers.image import Image
+
+
+class FourierCorrelationDataReader(object):
+ """
+ A class for writing Fourier Correlation Data into a file.
+ """
+
+ # region Constructor and Destructor
+ def __init__(self, file_path):
+
+ # Create output dir, if it doesn't exist output dir if
+ if not os.path.isfile(file_path) or not file_path.endswith(".hdf5"):
+ raise ValueError("Not a valid filename: %s" % file_path)
+
+ self.data = h5py.File(file_path, mode="r")
+
+ def __del__(self):
+ self.close()
+
+ # endregion
+
+ def read_metadata(self):
+ """
+ Read a metadata dictionary from the HDF5 files header
+ """
+ return dict(self.data.attrs)
+
+ def read_images(self, index=None):
+ """
+ Read images from the data structure.
+ :returns images: a tuple of Image objects, or a single image
+ """
+
+ if "images" not in self.data:
+ raise ValueError("No images to read")
+
+ if index is not None:
+ image_name = "image_%i" % index
+ data_set = self.data["images"][image_name]
+ spacing = data_set.attrs["pixel_size"].split()[0:len(data_set.shape)]
+ return Image(data_set[:], spacing)
+
+ images = []
+ for data_set in self.data["images"]:
+ spacing = data_set.attrs["pixel_size"].split()[0:len(data_set.shape)]
+ images.append(Image(data_set[:], spacing))
+
+ return images
+
+ def read_data_set(self):
+ """
+ Read Fourier Correlation Data file (FRC, FSC etc) to the FourierCorrelationDataCollection
+ data structure.
+ :returns FourierCorrelationDataCollection
+ """
+ group_prefix = "data_set_"
+ data_sets = FourierCorrelationDataCollection()
+ for group_name in list(self.data.keys()):
+ if group_prefix in group_name:
+ angle = group_name.split("_")[-1]
+ data_set = FourierCorrelationData()
+ resolution_group = self.data[group_name]["resolution"]
+ correlation_group = self.data[group_name]["correlation"]
+
+ data_set.resolution["threshold"] = resolution_group["threshold"][:]
+ data_set.resolution["resolution-point"] = \
+ resolution_group.attrs["resolution-point"].split()[:-1]
+ data_set.resolution["criterion"] = resolution_group.attrs["criterion"]
+ data_set.resolution["resolution-threshold-coefficients"] = \
+ resolution_group["resolution-threshold-coefficients"][:]
+
+ data_set.correlation["correlation"] = correlation_group["correlation"][:]
+ data_set.correlation["frequency"] = correlation_group["frequency"][:]
+ data_set.correlation["points-x-bin"] = correlation_group["points-x-bin"][:]
+ data_set.correlation["curve-fit"] = correlation_group["curve-fit"][:]
+ data_set.correlation["curve-fit-coefficients"] = \
+ correlation_group["curve-fit-coefficients"][:]
+
+ data_sets[int(angle)] = data_set
+
+ return data_sets
+
+ def close(self):
+ """
+ A function to explicitly close the data file (will be called by the destructor, if not.
+ :return:
+ """
+ self.data.close()
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/io/fourier_correlation_data_writer.py b/Addons/FRCmetric/miplib-public/miplib/data/io/fourier_correlation_data_writer.py
new file mode 100644
index 0000000000000000000000000000000000000000..58b64d174985611cb7c4aa8911f3cece18fc30e1
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/io/fourier_correlation_data_writer.py
@@ -0,0 +1,110 @@
+import os
+
+import h5py
+
+import miplib.ui.utils as uiutils
+from miplib.data.containers.fourier_correlation_data import FourierCorrelationDataCollection
+from miplib.data.containers.image import Image
+
+
+class FourierCorrelationDataWriter(object):
+ """
+ A class for wrtiting Fourier Correlation Data into a file.
+ """
+ # region Constructor and Destructor
+ def __init__(self, output_dir, filename, append=False):
+
+ # Create output dir, if it doesn't exist output dir if
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+
+ output_path = os.path.join(output_dir, filename)
+
+ assert output_path.endswith(".hdf5")
+
+ if os.path.isfile(output_path):
+ self.data = h5py.File(output_path, mode="r+" if append else "w")
+ else:
+ self.data = h5py.File(output_path, mode="w")
+
+ def __del__(self):
+ self.close()
+ # endregion
+
+ def write_metadata(self, metadata):
+ """
+ Write a metadata dictionary to the HDF5 files header as a general description of
+ the dataset.
+ :param metadata: a dictionary with all the necessary data to be written in the file
+ """
+ assert isinstance(metadata, dict)
+
+ for key, value in metadata:
+ self.data.attrs[key] = value
+
+ def write_images(self, images):
+ """
+ Write images to the data structure
+ :param images: a tuple of Image objects, or a single image
+ """
+ if not isinstance(images, tuple):
+ images = tuple(images)
+ for image in images:
+ assert isinstance(image, Image)
+
+ self.data.create_group("images")
+
+ image_name_prefix = "image_"
+ for idx, image in enumerate(images):
+ image_name = image_name_prefix+str(idx)
+ self.data["images"].create_dataset(image_name, data=image)
+ if image.ndim == 2:
+ self.data["images"][image_name].attrs["pixel_size"] = "%d %d (yx)" % image.spacing
+ else:
+ self.data["images"][image_name].attrs["pixel_size"] = "%d %d %d (zyx)" % image.spacing
+
+ def write_data_set(self, data):
+ """
+ Write Fourier Correlation Data (FRC, FSC etc) to the data structure.
+ :param data:
+ :type data: FourierCorrelationDataCollection
+ """
+ assert isinstance(data, FourierCorrelationDataCollection)
+
+ group_prefix = "data_set_"
+
+ for angle, data_set in data:
+ group_name = group_prefix + angle
+ if group_name in self.data:
+ if not uiutils.get_user_input(
+ "The dataset %s already exists in the file structure. Do you want"
+ "to overwrite it?" % angle):
+ continue
+
+ # Create a group fot every dataset and sub-groups for the two dictionaries
+ # in the FourierCorrelationData structure.
+ data_set_group = self.data.create_group(group_name)
+ resolution_group = data_set_group.create_group("resolution")
+ correlation_group = data_set_group.create_group("correlation")
+
+ resolution_group.create_dataset("threshold", data=data_set.resolution["threshold"])
+ resolution_group.attrs["resolution"] = data_set.resolution["resolution"]
+ resolution_group.attrs["resolution-point"] = "%d %d (yx)" % data_set.resolution["resolution-point"]
+ resolution_group.attrs["criterion"] = data_set.resolution["criterion"]
+ resolution_group.create_dataset("resolution-threshold-coefficients",
+ data=data_set.resolution["resolution-threshold-coefficients"])
+
+ correlation_group.create_dataset("correlation", data=data_set.correlation["correlation"])
+ correlation_group.create_dataset("frequency", data=data_set.correlation["frequency"])
+ correlation_group.create_dataset("points-x-bin", data=data_set.correlation["points-x-bin"])
+ correlation_group.create_dataset("curve-fit", data=data_set.correlation["curve-fit"])
+ correlation_group.create_dataset("curve-fit-coefficients",
+ data=data_set.correlation["curve-fit-coefficients"])
+
+ def close(self):
+ """
+ A function to explicitly close the data file (will be called by the destructor, if not.
+ :return:
+ """
+ self.data.close()
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/io/read.py b/Addons/FRCmetric/miplib-public/miplib/data/io/read.py
new file mode 100644
index 0000000000000000000000000000000000000000..564bb636d504139fe1e71b05f40be1cc028d0de9
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/io/read.py
@@ -0,0 +1,186 @@
+import os
+
+import SimpleITK as sitk
+import pims
+import numpy as np
+
+import miplib.processing.itk as itkutils
+from . import tiffile
+from miplib.data.containers.image import Image
+from miplib.data.containers.array_detector_data import ArrayDetectorData
+
+scale_c = 1.0e6
+
+
+def get_image(filename, series=0, channel=0, return_type='image', bioformats=True):
+ """
+ A wrapper for the image read functions.
+ Parameters
+ :param series: The index of an image in a time series
+ :param channel: The color channel in a multi-channel image.
+ :param bioformats: Toggle to disable bioformats reader.
+ :param filename The full path to an image
+ :param return_type Return the image as miplib Image. sitk.Image.
+ the return type can be chosen with a string ('image, 'itk').
+
+ """
+ assert return_type in ('itk', 'image')
+
+ if filename.endswith(".mha"):
+ data = __itk_image(filename, return_type == 'itk')
+ else:
+ if bioformats:
+ data = __bioformats(filename, series, channel, return_type == 'itk')
+ else:
+ data = __tiff(filename, return_type == 'itk')
+
+
+ return data
+
+def __itk_image(filename, return_itk=True):
+ """
+ A function for reading image file types typical to ITK (mha & mhd). This is mostly
+ of a historical significance, because in the original miplib 1 such files were
+ used, mostly for convenience.
+
+ :param filename: Path to an ITK image
+ :param return_itk Toggle whether to convert the ITK image into Numpy format
+ :return: Image data as a Numpy array, voxel spacing tuple
+ """
+ assert filename.endswith((".mha", ".mhd"))
+ image = sitk.ReadImage(filename)
+ if return_itk:
+ return image
+ else:
+ return itkutils.convert_from_itk_image(image)
+
+
+def __tiff(filename, memmap=False, return_itk=False):
+ """
+ ImageJ has a bit peculiar way of saving image metadata, especially the tags
+ for voxel spacing, which is of main interest in miplib. This function reads
+ a 3D TIFF into a Numpy array and also calculates the voxel spacing parameters
+ from the TIFF tags. I will not guarantee that this will work with any other TIFF
+ files.
+
+ :param filename: Path to a TIFF.
+ :param memmap: Enables Memory mapping in case the TIFF file is too large to
+ be read in memory completely.
+ :param return_itk Converts the Image data into a sitk.Image. This can be used
+ when single images are needed, instead of using the HDF5
+ structure adopted in miplib.
+ :return: Image data either as a Numpy array, voxel spacing tuple or a
+ sitk.Image
+ """
+ assert filename.endswith((".tif", ".tiff"))
+ tags = {}
+ # Read images and tags
+ with tiffile.TiffFile(filename) as image:
+ # Get images
+ images = image.asarray(memmap=memmap)
+ # Get tags
+ page = image[0]
+ for tag in list(page.tags.values()):
+ tags[tag.name] = tag.value
+
+ # Figure out z-spacing, which in ImageJ is hidden in the "image_description"
+ # header (why, one might ask).
+ image_descriptor = tags["image_description"].split("\n")
+ z_spacing = None
+ for line in image_descriptor:
+ if "spacing" in line:
+ z_spacing = float(line.split("=")[-1])
+ break
+ assert z_spacing is not None
+
+ # Create a tuple for zxy-spacing. The order of the dimensions follows that of the
+ # image data
+ spacing = (z_spacing, scale_c/tags["x_resolution"][0], scale_c/tags[
+ "y_resolution"][0])
+
+ #print spacing
+ if return_itk:
+ return itkutils.convert_from_numpy(images, spacing)
+ else:
+ return images, spacing
+
+
+def __itk_transform(path, return_itk=False):
+ """
+ Prior to starting to use the HDF5 format data storage images and spatial
+ transforms were saved as separate image files on the hard drive. This
+ function can be used to read a spatial transform saved from ITK. It is
+ to transfer old files into the HDF5 format storage.
+
+ Parameters
+ ----------
+ path Path to the transform file (usually txt ended)
+
+ Returns Returns the transform type integer, parameters and fixed
+ parameters.
+ -------
+
+ """
+
+ if not os.path.isfile(path):
+ raise ValueError("Not a valid path: %s" % path)
+
+ transform = sitk.ReadTransform(path)
+
+ if return_itk:
+ return transform
+
+ else:
+ # #TODO: Check that this makes any sense. Also consult the ITK HDF implementation for ideas
+ # with open(path, 'r') as f:
+ # for line in f:
+ # if line.startswith('Transform:'):
+ # type_string = line.split(': ')[1].split('_')[0]
+ # if "VersorRigid" in type_string:
+ # transform_type = itk_transforms_c['sitkVersorRigid']
+ # break
+ # else:
+ # raise NotImplementedError("Unknown transform type: "
+ # "%s" % type_string)
+ transform_type = transform.GetName()
+ params = transform.GetParameters()
+ fixed_params = transform.GetFixedParameters()
+ return transform_type, params, fixed_params
+
+
+def __bioformats(filename, series=0, channel=0, return_itk = False):
+ """
+ Read an image using the Bioformats importer. Good for most microscopy formats.
+
+ :param filename:
+ :param series:
+ :param return_itk:
+ :return:
+ """
+ assert pims.bioformats.available(), "Please install jpype in order to use " \
+ "the bioformats reader."
+ image = pims.bioformats.BioformatsReader(filename, series=series)
+
+ # Get Pixel/Voxel size information
+ if 'z' not in image.axes:
+ spacing = (image.metadata.PixelsPhysicalSizeY(0),
+ image.metadata.PixelsPhysicalSizeX(0))
+ else:
+ spacing = (image.metadata.PixelsPhysicalSizeZ(0),
+ image.metadata.PixelsPhysicalSizeY(0),
+ image.metadata.PixelsPhysicalSizeX(0))
+
+ # Get color channel
+ if 'c' in image.sizes:
+ image.iter_axes = 'c'
+ assert len(image) > channel
+ image = image[channel]
+ else:
+ image = image[0]
+
+ if return_itk:
+ return itkutils.convert_from_numpy(image, spacing)
+ else:
+ return Image(image, spacing)
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/io/src/tifffile.c b/Addons/FRCmetric/miplib-public/miplib/data/io/src/tifffile.c
new file mode 100644
index 0000000000000000000000000000000000000000..5630058980b2f4f722bd1bdd794231d3f2a96897
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/io/src/tifffile.c
@@ -0,0 +1,975 @@
+/* tifffile.c
+
+A Python C extension module for decoding PackBits and LZW encoded TIFF data.
+
+Refer to the tifffile.py module for documentation and tests.
+
+:Author:
+ `Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/>`_
+
+:Organization:
+ Laboratory for Fluorescence Dynamics, University of California, Irvine
+
+:Version: 2015.08.17
+
+Requirements
+------------
+* `CPython 2.7 or 3.4 <http://www.python.org>`_
+* `Numpy 1.9.2 <http://www.numpy.org>`_
+* A Python distutils compatible C compiler (build)
+
+Install
+-------
+Use this Python distutils setup script to build the extension module::
+
+ # setup.py
+ # Usage: ``python setup.py build_ext --inplace``
+ from distutils.core import setup, Extension
+ import numpy
+ setup(name='_tifffile',
+ ext_modules=[Extension('_tifffile', ['tifffile.c'],
+ include_dirs=[numpy.get_include()])])
+
+License
+-------
+Copyright (c) 2008-2015, Christoph Gohlke
+Copyright (c) 2008-2015, The Regents of the University of California
+Produced at the Laboratory for Fluorescence Dynamics
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright
+ notice, this list of conditions and the following disclaimer.
+* Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in the
+ documentation and/or other materials provided with the distribution.
+* Neither the name of the copyright holders nor the names of any
+ contributors may be used to endorse or promote products derived
+ from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+*/
+
+#define _VERSION_ "2015.08.17"
+
+#define WIN32_LEAN_AND_MEAN
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+
+#include "Python.h"
+#include "string.h"
+#include "numpy/arrayobject.h"
+
+/* little endian by default */
+#ifndef MSB
+#define MSB 1
+#endif
+
+#if MSB
+#define LSB 0
+#define BOC '<'
+#else
+#define LSB 1
+#define BOC '>'
+#endif
+
+#define NO_ERROR 0
+#define VALUE_ERROR -1
+
+#if defined(_MSC_VER) && _MSC_VER < 1600
+typedef unsigned __int8 uint8_t;
+typedef unsigned __int16 uint16_t;
+typedef unsigned __int32 uint32_t;
+typedef unsigned __int64 uint64_t;
+#ifdef _WIN64
+typedef __int64 ssize_t;
+typedef signed __int64 intptr_t;
+typedef unsigned __int64 uintptr_t;
+#else
+typedef int ssize_t;
+typedef _W64 signed int intptr_t;
+typedef _W64 unsigned int uintptr_t;
+#endif
+#else
+/* non MS compilers */
+#include <stdint.h>
+#include <limits.h>
+#endif
+
+#ifndef SSIZE_MAX
+#ifdef _WIN64
+#define SSIZE_MAX (9223372036854775808L)
+#else
+#define SSIZE_MAX (2147483648)
+#endif
+#endif
+
+#define SWAP2BYTES(x) \
+ ((((x) >> 8) & 0x00FF) | (((x) & 0x00FF) << 8))
+
+#define SWAP4BYTES(x) \
+ ((((x) >> 24) & 0x00FF) | (((x)&0x00FF) << 24) | \
+ (((x) >> 8 ) & 0xFF00) | (((x)&0xFF00) << 8))
+
+#define SWAP8BYTES(x) \
+ ((((x) >> 56) & 0x00000000000000FF) | (((x) >> 40) & 0x000000000000FF00) | \
+ (((x) >> 24) & 0x0000000000FF0000) | (((x) >> 8) & 0x00000000FF000000) | \
+ (((x) << 8) & 0x000000FF00000000) | (((x) << 24) & 0x0000FF0000000000) | \
+ (((x) << 40) & 0x00FF000000000000) | (((x) << 56) & 0xFF00000000000000))
+
+struct BYTE_STRING {
+ unsigned int ref; /* reference count */
+ unsigned int len; /* length of string */
+ char *str; /* pointer to bytes */
+};
+
+typedef union {
+ uint8_t b[2];
+ uint16_t i;
+} u_uint16;
+
+typedef union {
+ uint8_t b[4];
+ uint32_t i;
+} u_uint32;
+
+typedef union {
+ uint8_t b[8];
+ uint64_t i;
+} u_uint64;
+
+/*****************************************************************************/
+/* C functions */
+
+/* Return mask for itemsize bits */
+unsigned char bitmask(const int itemsize) {
+ unsigned char result = 0;
+ unsigned char power = 1;
+ int i;
+ for (i = 0; i < itemsize; i++) {
+ result += power;
+ power *= 2;
+ }
+ return result << (8 - itemsize);
+}
+
+/** Unpack sequence of tigthly packed 1-32 bit integers.
+
+Native byte order will be returned.
+
+Input data array should be padded to the next 16, 32 or 64-bit boundary
+if itemsize not in (1, 2, 4, 8, 16, 24, 32, 64).
+
+*/
+int unpackbits(
+ unsigned char *data,
+ const ssize_t size, /** size of data in bytes */
+ const int itemsize, /** number of bits in integer */
+ ssize_t numitems, /** number of items to unpack */
+ unsigned char *result /** buffer to store unpacked items */
+ )
+{
+ ssize_t i, j, k, storagesize;
+ unsigned char value;
+ /* Input validation is done in wrapper function */
+ storagesize = (ssize_t)(ceil(itemsize / 8.0));
+ storagesize = storagesize < 3 ? storagesize : storagesize > 4 ? 8 : 4;
+ switch (itemsize) {
+ case 8:
+ case 16:
+ case 32:
+ case 64:
+ memcpy(result, data, numitems*storagesize);
+ return NO_ERROR;
+ case 1:
+ for (i = 0, j = 0; i < numitems/8; i++) {
+ value = data[i];
+ result[j++] = (value & (unsigned char)(128)) >> 7;
+ result[j++] = (value & (unsigned char)(64)) >> 6;
+ result[j++] = (value & (unsigned char)(32)) >> 5;
+ result[j++] = (value & (unsigned char)(16)) >> 4;
+ result[j++] = (value & (unsigned char)(8)) >> 3;
+ result[j++] = (value & (unsigned char)(4)) >> 2;
+ result[j++] = (value & (unsigned char)(2)) >> 1;
+ result[j++] = (value & (unsigned char)(1));
+ }
+ if (numitems % 8) {
+ value = data[i];
+ switch (numitems % 8) {
+ case 7: result[j+6] = (value & (unsigned char)(2)) >> 1;
+ case 6: result[j+5] = (value & (unsigned char)(4)) >> 2;
+ case 5: result[j+4] = (value & (unsigned char)(8)) >> 3;
+ case 4: result[j+3] = (value & (unsigned char)(16)) >> 4;
+ case 3: result[j+2] = (value & (unsigned char)(32)) >> 5;
+ case 2: result[j+1] = (value & (unsigned char)(64)) >> 6;
+ case 1: result[j] = (value & (unsigned char)(128)) >> 7;
+ }
+ }
+ return NO_ERROR;
+ case 2:
+ for (i = 0, j = 0; i < numitems/4; i++) {
+ value = data[i];
+ result[j++] = (value & (unsigned char)(192)) >> 6;
+ result[j++] = (value & (unsigned char)(48)) >> 4;
+ result[j++] = (value & (unsigned char)(12)) >> 2;
+ result[j++] = (value & (unsigned char)(3));
+ }
+ if (numitems % 4) {
+ value = data[i];
+ switch (numitems % 4) {
+ case 3: result[j+2] = (value & (unsigned char)(12)) >> 2;
+ case 2: result[j+1] = (value & (unsigned char)(48)) >> 4;
+ case 1: result[j] = (value & (unsigned char)(192)) >> 6;
+ }
+ }
+ return NO_ERROR;
+ case 4:
+ for (i = 0, j = 0; i < numitems/2; i++) {
+ value = data[i];
+ result[j++] = (value & (unsigned char)(240)) >> 4;
+ result[j++] = (value & (unsigned char)(15));
+ }
+ if (numitems % 2) {
+ value = data[i];
+ result[j] = (value & (unsigned char)(240)) >> 4;
+ }
+ return NO_ERROR;
+ case 24:
+ j = k = 0;
+ for (i = 0; i < numitems; i++) {
+ result[j++] = 0;
+ result[j++] = data[k++];
+ result[j++] = data[k++];
+ result[j++] = data[k++];
+ }
+ return NO_ERROR;
+ }
+ /* 3, 5, 6, 7 */
+ if (itemsize < 8) {
+ int shr = 16;
+ u_uint16 value, mask, tmp;
+ j = k = 0;
+ value.b[MSB] = data[j++];
+ value.b[LSB] = data[j++];
+ mask.b[MSB] = bitmask(itemsize);
+ mask.b[LSB] = 0;
+ for (i = 0; i < numitems; i++) {
+ shr -= itemsize;
+ tmp.i = (value.i & mask.i) >> shr;
+ result[k++] = tmp.b[LSB];
+ if (shr < itemsize) {
+ value.b[MSB] = value.b[LSB];
+ value.b[LSB] = data[j++];
+ mask.i <<= 8 - itemsize;
+ shr += 8;
+ } else {
+ mask.i >>= itemsize;
+ }
+ }
+ return NO_ERROR;
+ }
+ /* 9, 10, 11, 12, 13, 14, 15 */
+ if (itemsize < 16) {
+ int shr = 32;
+ u_uint32 value, mask, tmp;
+ mask.i = 0;
+ j = k = 0;
+#if MSB
+ for (i = 3; i >= 0; i--) {
+ value.b[i] = data[j++];
+ }
+ mask.b[3] = 0xFF;
+ mask.b[2] = bitmask(itemsize-8);
+ for (i = 0; i < numitems; i++) {
+ shr -= itemsize;
+ tmp.i = (value.i & mask.i) >> shr;
+ result[k++] = tmp.b[0]; /* swap bytes */
+ result[k++] = tmp.b[1];
+ if (shr < itemsize) {
+ value.b[3] = value.b[1];
+ value.b[2] = value.b[0];
+ value.b[1] = data[j++];
+ value.b[0] = data[j++];
+ mask.i <<= 16 - itemsize;
+ shr += 16;
+ } else {
+ mask.i >>= itemsize;
+ }
+ }
+#else
+ /* not implemented */
+#endif
+ return NO_ERROR;
+ }
+ /* 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31 */
+ if (itemsize < 32) {
+ int shr = 64;
+ u_uint64 value, mask, tmp;
+ mask.i = 0;
+ j = k = 0;
+#if MSB
+ for (i = 7; i >= 0; i--) {
+ value.b[i] = data[j++];
+ }
+ mask.b[7] = 0xFF;
+ mask.b[6] = 0xFF;
+ mask.b[5] = itemsize > 23 ? 0xFF : bitmask(itemsize-16);
+ mask.b[4] = itemsize < 24 ? 0x00 : bitmask(itemsize-24);
+ for (i = 0; i < numitems; i++) {
+ shr -= itemsize;
+ tmp.i = (value.i & mask.i) >> shr;
+ result[k++] = tmp.b[0]; /* swap bytes */
+ result[k++] = tmp.b[1];
+ result[k++] = tmp.b[2];
+ result[k++] = tmp.b[3];
+ if (shr < itemsize) {
+ value.b[7] = value.b[3];
+ value.b[6] = value.b[2];
+ value.b[5] = value.b[1];
+ value.b[4] = value.b[0];
+ value.b[3] = data[j++];
+ value.b[2] = data[j++];
+ value.b[1] = data[j++];
+ value.b[0] = data[j++];
+ mask.i <<= 32 - itemsize;
+ shr += 32;
+ } else {
+ mask.i >>= itemsize;
+ }
+ }
+#else
+ /* Not implemented */
+#endif
+ return NO_ERROR;
+ }
+ return VALUE_ERROR;
+}
+
+/*****************************************************************************/
+/* Python functions */
+
+/** Unpack tightly packed integers. */
+char py_unpackints_doc[] = "Unpack groups of bits into numpy array.";
+
+static PyObject*
+py_unpackints(PyObject *obj, PyObject *args, PyObject *kwds)
+{
+ PyObject *byteobj = NULL;
+ PyArrayObject *result = NULL;
+ PyArray_Descr *dtype = NULL;
+ char *encoded = NULL;
+ char *decoded = NULL;
+ Py_ssize_t encoded_len = 0;
+ Py_ssize_t decoded_len = 0;
+ Py_ssize_t runlen = 0;
+ Py_ssize_t i;
+ int storagesize, bytesize;
+ int itemsize = 0;
+ int skipbits = 0;
+ static char *kwlist[] = {"data", "dtype", "itemsize", "runlen", NULL};
+
+ if (!PyArg_ParseTupleAndKeywords(args, kwds, "OO&i|i", kwlist,
+ &byteobj, PyArray_DescrConverter, &dtype, &itemsize, &runlen))
+ return NULL;
+
+ Py_INCREF(byteobj);
+
+ if (((itemsize < 1) || (itemsize > 32)) && (itemsize != 64)) {
+ PyErr_Format(PyExc_ValueError, "itemsize out of range");
+ goto _fail;
+ }
+
+ if (!PyBytes_Check(byteobj)) {
+ PyErr_Format(PyExc_TypeError, "expected byte string as input");
+ goto _fail;
+ }
+
+ encoded = PyBytes_AS_STRING(byteobj);
+ encoded_len = PyBytes_GET_SIZE(byteobj);
+ bytesize = (int)ceil(itemsize / 8.0);
+ storagesize = bytesize < 3 ? bytesize : bytesize > 4 ? 8 : 4;
+ if ((encoded_len < bytesize) || (encoded_len > SSIZE_MAX / storagesize)) {
+ PyErr_Format(PyExc_ValueError, "data size out of range");
+ goto _fail;
+ }
+ if (dtype->elsize != storagesize) {
+ PyErr_Format(PyExc_TypeError, "dtype.elsize doesn't fit itemsize");
+ goto _fail;
+ }
+
+ if (runlen == 0) {
+ runlen = (Py_ssize_t)(((uint64_t)encoded_len*8) / (uint64_t)itemsize);
+ }
+ skipbits = (Py_ssize_t)(((uint64_t)runlen * (uint64_t)itemsize) % 8);
+ if (skipbits > 0) {
+ skipbits = 8 - skipbits;
+ }
+ decoded_len = (Py_ssize_t)((uint64_t)runlen * (((uint64_t)encoded_len*8) /
+ ((uint64_t)runlen*(uint64_t)itemsize + (uint64_t)skipbits)));
+
+ result = (PyArrayObject *)PyArray_SimpleNew(1, &decoded_len,
+ dtype->type_num);
+ if (result == NULL) {
+ PyErr_Format(PyExc_MemoryError, "unable to allocate output array");
+ goto _fail;
+ }
+ decoded = (char *)PyArray_DATA(result);
+
+ for (i = 0; i < decoded_len; i+=runlen) {
+ if (NO_ERROR !=
+ unpackbits((unsigned char *) encoded,
+ (ssize_t) encoded_len,
+ (int) itemsize,
+ (ssize_t) runlen,
+ (unsigned char *) decoded)) {
+ PyErr_Format(PyExc_ValueError, "unpackbits() failed");
+ goto _fail;
+ }
+ encoded += (Py_ssize_t)(((uint64_t)runlen * (uint64_t)itemsize +
+ (uint64_t)skipbits) / 8);
+ decoded += runlen * storagesize;
+ }
+
+ if ((dtype->byteorder != BOC) && (itemsize % 8 == 0)) {
+ switch (dtype->elsize) {
+ case 2: {
+ uint16_t *d = (uint16_t *)PyArray_DATA(result);
+ for (i = 0; i < PyArray_SIZE(result); i++) {
+ *d = SWAP2BYTES(*d); d++;
+ }
+ break; }
+ case 4: {
+ uint32_t *d = (uint32_t *)PyArray_DATA(result);
+ for (i = 0; i < PyArray_SIZE(result); i++) {
+ *d = SWAP4BYTES(*d); d++;
+ }
+ break; }
+ case 8: {
+ uint64_t *d = (uint64_t *)PyArray_DATA(result);
+ for (i = 0; i < PyArray_SIZE(result); i++) {
+ *d = SWAP8BYTES(*d); d++;
+ }
+ break; }
+ }
+ }
+ Py_DECREF(byteobj);
+ Py_DECREF(dtype);
+ return PyArray_Return(result);
+
+ _fail:
+ Py_XDECREF(byteobj);
+ Py_XDECREF(result);
+ Py_XDECREF(dtype);
+ return NULL;
+}
+
+
+/** Decode TIFF PackBits encoded string. */
+char py_decodepackbits_doc[] = "Return TIFF PackBits decoded string.";
+
+static PyObject *
+py_decodepackbits(PyObject *obj, PyObject *args)
+{
+ int n;
+ char e;
+ char *decoded = NULL;
+ char *encoded = NULL;
+ char *encoded_end = NULL;
+ char *encoded_pos = NULL;
+ unsigned int encoded_len;
+ unsigned int decoded_len;
+ PyObject *byteobj = NULL;
+ PyObject *result = NULL;
+
+ if (!PyArg_ParseTuple(args, "O", &byteobj))
+ return NULL;
+
+ if (!PyBytes_Check(byteobj)) {
+ PyErr_Format(PyExc_TypeError, "expected byte string as input");
+ goto _fail;
+ }
+
+ Py_INCREF(byteobj);
+ encoded = PyBytes_AS_STRING(byteobj);
+ encoded_len = (unsigned int)PyBytes_GET_SIZE(byteobj);
+
+ /* release GIL: byte/string objects are immutable */
+ Py_BEGIN_ALLOW_THREADS
+
+ /* determine size of decoded string */
+ encoded_pos = encoded;
+ encoded_end = encoded + encoded_len;
+ decoded_len = 0;
+ while (encoded_pos < encoded_end) {
+ n = (int)*encoded_pos++;
+ if (n >= 0) {
+ n++;
+ if (encoded_pos+n > encoded_end)
+ n = (int)(encoded_end - encoded_pos);
+ encoded_pos += n;
+ decoded_len += n;
+ } else if (n > -128) {
+ encoded_pos++;
+ decoded_len += 1-n;
+ }
+ }
+ Py_END_ALLOW_THREADS
+
+ result = PyBytes_FromStringAndSize(0, decoded_len);
+ if (result == NULL) {
+ PyErr_Format(PyExc_MemoryError, "failed to allocate decoded string");
+ goto _fail;
+ }
+ decoded = PyBytes_AS_STRING(result);
+
+ Py_BEGIN_ALLOW_THREADS
+
+ /* decode string */
+ encoded_end = encoded + encoded_len;
+ while (encoded < encoded_end) {
+ n = (int)*encoded++;
+ if (n >= 0) {
+ n++;
+ if (encoded+n > encoded_end)
+ n = (int)(encoded_end - encoded);
+ /* memmove(decoded, encoded, n); decoded += n; encoded += n; */
+ while (n--)
+ *decoded++ = *encoded++;
+ } else if (n > -128) {
+ n = 1 - n;
+ e = *encoded++;
+ /* memset(decoded, e, n); decoded += n; */
+ while (n--)
+ *decoded++ = e;
+ }
+ }
+ Py_END_ALLOW_THREADS
+
+ Py_DECREF(byteobj);
+ return result;
+
+ _fail:
+ Py_XDECREF(byteobj);
+ Py_XDECREF(result);
+ return NULL;
+}
+
+
+/** Decode TIFF LZW encoded string. */
+char py_decodelzw_doc[] = "Return TIFF LZW decoded string.";
+
+static PyObject *
+py_decodelzw(PyObject *obj, PyObject *args)
+{
+ PyThreadState *_save = NULL;
+ PyObject *byteobj = NULL;
+ PyObject *result = NULL;
+ int i, j;
+ unsigned int encoded_len = 0;
+ unsigned int decoded_len = 0;
+ unsigned int result_len = 0;
+ unsigned int table_len = 0;
+ unsigned int len;
+ unsigned int code, c, oldcode, mask, shr;
+ uint64_t bitcount, bitw;
+ char *encoded = NULL;
+ char *result_ptr = NULL;
+ char *table2 = NULL;
+ char *cptr;
+ struct BYTE_STRING *decoded = NULL;
+ struct BYTE_STRING *decoded_ptr = NULL;
+ struct BYTE_STRING *table[4096];
+ struct BYTE_STRING *newentry, *newresult, *t;
+ int little_endian = 0;
+
+ if (!PyArg_ParseTuple(args, "O", &byteobj))
+ return NULL;
+
+ if (!PyBytes_Check(byteobj)) {
+ PyErr_Format(PyExc_TypeError, "expected byte string as input");
+ goto _fail;
+ }
+
+ Py_INCREF(byteobj);
+ encoded = PyBytes_AS_STRING(byteobj);
+ encoded_len = (unsigned int)PyBytes_GET_SIZE(byteobj);
+ /*
+ if (encoded_len >= 512 * 1024 * 1024) {
+ PyErr_Format(PyExc_ValueError, "encoded data > 512 MB not supported");
+ goto _fail;
+ }
+ */
+ /* release GIL: byte/string objects are immutable */
+ _save = PyEval_SaveThread();
+
+ if ((*encoded != -128) || ((*(encoded+1) & 128))) {
+ PyEval_RestoreThread(_save);
+ PyErr_Format(PyExc_ValueError,
+ "strip must begin with CLEAR code");
+ goto _fail;
+ }
+ little_endian = (*(unsigned short *)encoded) & 128;
+
+ /* allocate buffer for codes and pointers */
+ decoded_len = 0;
+ len = (encoded_len + encoded_len/9) * sizeof(decoded);
+ decoded = PyMem_Malloc(len * sizeof(void *));
+ if (decoded == NULL) {
+ PyEval_RestoreThread(_save);
+ PyErr_Format(PyExc_MemoryError, "failed to allocate decoded");
+ goto _fail;
+ }
+ memset((void *)decoded, 0, len * sizeof(void *));
+ decoded_ptr = decoded;
+
+ /* cache strings of length 2 */
+ cptr = table2 = PyMem_Malloc(256*256*2 * sizeof(char));
+ if (table2 == NULL) {
+ PyEval_RestoreThread(_save);
+ PyErr_Format(PyExc_MemoryError, "failed to allocate table2");
+ goto _fail;
+ }
+ for (i = 0; i < 256; i++) {
+ for (j = 0; j < 256; j++) {
+ *cptr++ = (char)i;
+ *cptr++ = (char)j;
+ }
+ }
+
+ memset(table, 0, sizeof(table));
+ table_len = 258;
+ bitw = 9;
+ shr = 23;
+ mask = 4286578688u;
+ bitcount = 0;
+ result_len = 0;
+ code = 0;
+ oldcode = 0;
+
+ while ((unsigned int)((bitcount + bitw) / 8) <= encoded_len) {
+ /* read next code */
+ code = *((unsigned int *)((void *)(encoded + (bitcount / 8))));
+ if (little_endian)
+ code = SWAP4BYTES(code);
+ code <<= (unsigned int)(bitcount % 8);
+ code &= mask;
+ code >>= shr;
+ bitcount += bitw;
+
+ if (code == 257) /* end of information */
+ break;
+
+ if (code == 256) { /* clearcode */
+ /* initialize table and switch to 9 bit */
+ while (table_len > 258) {
+ t = table[--table_len];
+ t->ref--;
+ if (t->ref == 0) {
+ if (t->len > 2)
+ PyMem_Free(t->str);
+ PyMem_Free(t);
+ }
+ }
+ bitw = 9;
+ shr = 23;
+ mask = 4286578688u;
+
+ /* read next code, skip clearcodes */
+ /* TODO: bounds checking */
+ do {
+ code = *((unsigned int *)((void *)(encoded + (bitcount / 8))));
+ if (little_endian)
+ code = SWAP4BYTES(code);
+ code <<= bitcount % 8;
+ code &= mask;
+ code >>= shr;
+ bitcount += bitw;
+ } while (code == 256);
+
+ if (code == 257) /* end of information */
+ break;
+
+ /* decoded.append(table[code]) */
+ if (code < 256) {
+ result_len++;
+ *((int *)decoded_ptr++) = code;
+ } else {
+ newresult = table[code];
+ newresult->ref++;
+ result_len += newresult->len;
+ *(struct BYTE_STRING **)decoded_ptr++ = newresult;
+ }
+ } else {
+ if (code < table_len) {
+ /* code is in table */
+ /* newresult = table[code]; */
+ /* newentry = table[oldcode] + table[code][0] */
+ /* decoded.append(newresult); table.append(newentry) */
+ if (code < 256) {
+ c = code;
+ *((unsigned int *)decoded_ptr++) = code;
+ result_len++;
+ } else {
+ newresult = table[code];
+ newresult->ref++;
+ c = (unsigned int) *newresult->str;
+ *(struct BYTE_STRING **)decoded_ptr++ = newresult;
+ result_len += newresult->len;
+ }
+ newentry = PyMem_Malloc(sizeof(struct BYTE_STRING));
+ newentry->ref = 1;
+ if (oldcode < 256) {
+ newentry->len = 2;
+ newentry->str = table2 + (oldcode << 9) +
+ ((unsigned char)c << 1);
+ } else {
+ len = table[oldcode]->len;
+ newentry->len = len + 1;
+ newentry->str = PyMem_Malloc(newentry->len);
+ if (newentry->str == NULL)
+ break;
+ memmove(newentry->str, table[oldcode]->str, len);
+ newentry->str[len] = c;
+ }
+ table[table_len++] = newentry;
+ } else {
+ /* code is not in table */
+ /* newentry = newresult = table[oldcode] + table[oldcode][0] */
+ /* decoded.append(newresult); table.append(newentry) */
+ newresult = PyMem_Malloc(sizeof(struct BYTE_STRING));
+ newentry = newresult;
+ newentry->ref = 2;
+ if (oldcode < 256) {
+ newentry->len = 2;
+ newentry->str = table2 + 514*oldcode;
+ } else {
+ len = table[oldcode]->len;
+ newentry->len = len + 1;
+ newentry->str = PyMem_Malloc(newentry->len);
+ if (newentry->str == NULL)
+ break;
+ memmove(newentry->str, table[oldcode]->str, len);
+ newentry->str[len] = *table[oldcode]->str;
+ }
+ table[table_len++] = newentry;
+ *(struct BYTE_STRING **)decoded_ptr++ = newresult;
+ result_len += newresult->len;
+ }
+ }
+ oldcode = code;
+ /* increase bit-width if necessary */
+ switch (table_len) {
+ case 511:
+ bitw = 10;
+ shr = 22;
+ mask = 4290772992u;
+ break;
+ case 1023:
+ bitw = 11;
+ shr = 21;
+ mask = 4292870144u;
+ break;
+ case 2047:
+ bitw = 12;
+ shr = 20;
+ mask = 4293918720u;
+ }
+ }
+
+ PyEval_RestoreThread(_save);
+
+ if (code != 257) {
+ PyErr_WarnEx(NULL,
+ "py_decodelzw encountered unexpected end of stream", 1);
+ }
+
+ /* result = ''.join(decoded) */
+ decoded_len = (unsigned int)(decoded_ptr - decoded);
+ decoded_ptr = decoded;
+ result = PyBytes_FromStringAndSize(0, result_len);
+ if (result == NULL) {
+ PyErr_Format(PyExc_MemoryError, "failed to allocate decoded string");
+ goto _fail;
+ }
+ result_ptr = PyBytes_AS_STRING(result);
+
+ _save = PyEval_SaveThread();
+
+ while (decoded_len--) {
+ code = *((unsigned int *)decoded_ptr);
+ if (code < 256) {
+ *result_ptr++ = (char)code;
+ } else {
+ t = *((struct BYTE_STRING **)decoded_ptr);
+ memmove(result_ptr, t->str, t->len);
+ result_ptr += t->len;
+ if (--t->ref == 0) {
+ if (t->len > 2)
+ PyMem_Free(t->str);
+ PyMem_Free(t);
+ }
+ }
+ decoded_ptr++;
+ }
+ PyMem_Free(decoded);
+
+ while (table_len-- > 258) {
+ t = table[table_len];
+ if (t->len > 2)
+ PyMem_Free(t->str);
+ PyMem_Free(t);
+ }
+ PyMem_Free(table2);
+
+ PyEval_RestoreThread(_save);
+
+ Py_DECREF(byteobj);
+ return result;
+
+ _fail:
+ if (table2 != NULL)
+ PyMem_Free(table2);
+ if (decoded != NULL) {
+ /* Bug? are decoded_ptr and decoded_len correct? */
+ while (decoded_len--) {
+ code = *((unsigned int *) decoded_ptr);
+ if (code > 258) {
+ t = *((struct BYTE_STRING **) decoded_ptr);
+ if (--t->ref == 0) {
+ if (t->len > 2)
+ PyMem_Free(t->str);
+ PyMem_Free(t);
+ }
+ }
+ }
+ PyMem_Free(decoded);
+ }
+ while (table_len-- > 258) {
+ t = table[table_len];
+ if (t->len > 2)
+ PyMem_Free(t->str);
+ PyMem_Free(t);
+ }
+
+ Py_XDECREF(byteobj);
+ Py_XDECREF(result);
+
+ return NULL;
+}
+
+/*****************************************************************************/
+/* Create Python module */
+
+char module_doc[] =
+ "A Python C extension module for decoding PackBits and LZW encoded "
+ "TIFF data.\n\n"
+ "Refer to the tifffile.py module for documentation and tests.\n\n"
+ "Authors:\n Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/>\n"
+ " Laboratory for Fluorescence Dynamics, University of California, Irvine."
+ "\n\nVersion: %s\n";
+
+static PyMethodDef module_methods[] = {
+#if MSB
+ {"unpack_ints", (PyCFunction)py_unpackints, METH_VARARGS|METH_KEYWORDS,
+ py_unpackints_doc},
+#endif
+ {"decode_lzw", (PyCFunction)py_decodelzw, METH_VARARGS,
+ py_decodelzw_doc},
+ {"decode_packbits", (PyCFunction)py_decodepackbits, METH_VARARGS,
+ py_decodepackbits_doc},
+ {NULL, NULL, 0, NULL} /* Sentinel */
+};
+
+#if PY_MAJOR_VERSION >= 3
+
+struct module_state {
+ PyObject *error;
+};
+
+#define GETSTATE(m) ((struct module_state*)PyModule_GetState(m))
+
+static int module_traverse(PyObject *m, visitproc visit, void *arg) {
+ Py_VISIT(GETSTATE(m)->error);
+ return 0;
+}
+
+static int module_clear(PyObject *m) {
+ Py_CLEAR(GETSTATE(m)->error);
+ return 0;
+}
+
+static struct PyModuleDef moduledef = {
+ PyModuleDef_HEAD_INIT,
+ "_tifffile",
+ NULL,
+ sizeof(struct module_state),
+ module_methods,
+ NULL,
+ module_traverse,
+ module_clear,
+ NULL
+};
+
+#define INITERROR return NULL
+
+PyMODINIT_FUNC
+PyInit__tifffile(void)
+
+#else
+
+#define INITERROR return
+
+PyMODINIT_FUNC
+init_tifffile(void)
+
+#endif
+{
+ PyObject *module;
+
+ char *doc = (char *)PyMem_Malloc(sizeof(module_doc) + sizeof(_VERSION_));
+ PyOS_snprintf(doc, sizeof(module_doc) + sizeof(_VERSION_),
+ module_doc, _VERSION_);
+
+#if PY_MAJOR_VERSION >= 3
+ moduledef.m_doc = doc;
+ module = PyModule_Create(&moduledef);
+#else
+ module = Py_InitModule3("_tifffile", module_methods, doc);
+#endif
+
+ PyMem_Free(doc);
+
+ if (module == NULL)
+ INITERROR;
+
+ if (_import_array() < 0) {
+ Py_DECREF(module);
+ INITERROR;
+ }
+
+ {
+#if PY_MAJOR_VERSION < 3
+ PyObject *s = PyString_FromString(_VERSION_);
+#else
+ PyObject *s = PyUnicode_FromString(_VERSION_);
+#endif
+ PyObject *dict = PyModule_GetDict(module);
+ PyDict_SetItemString(dict, "__version__", s);
+ Py_DECREF(s);
+ }
+
+#if PY_MAJOR_VERSION >= 3
+ return module;
+#endif
+}
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/io/tests/__init__.py b/Addons/FRCmetric/miplib-public/miplib/data/io/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/io/tests/test_fourierCorrelationDataIO.py b/Addons/FRCmetric/miplib-public/miplib/data/io/tests/test_fourierCorrelationDataIO.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbf6e643e714d41956757f7acd506c8ec4b2a2c6
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/io/tests/test_fourierCorrelationDataIO.py
@@ -0,0 +1,7 @@
+from unittest import TestCase
+
+
+class TestFourierCorrelationDataIO(TestCase):
+
+
+ pass
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/io/tiffile.py b/Addons/FRCmetric/miplib-public/miplib/data/io/tiffile.py
new file mode 100644
index 0000000000000000000000000000000000000000..51488b3cf27ea25eedbc55289078bcf4a0e7d267
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/io/tiffile.py
@@ -0,0 +1,12099 @@
+# -*- coding: utf-8 -*-
+# tifffile.py
+
+# Copyright (c) 2008-2019, Christoph Gohlke
+# Copyright (c) 2008-2019, The Regents of the University of California
+# Produced at the Laboratory for Fluorescence Dynamics
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice,
+# this list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+
+"""Read and write TIFF(r) files.
+
+Tifffile is a Python library to
+
+(1) store numpy arrays in TIFF (Tagged Image File Format) files, and
+(2) read image and metadata from TIFF-like files used in bioimaging.
+
+Image and metadata can be read from TIFF, BigTIFF, OME-TIFF, STK, LSM, SGI,
+NIHImage, ImageJ, MicroManager, FluoView, ScanImage, SEQ, GEL, SVS, SCN, SIS,
+ZIF, QPI, NDPI, and GeoTIFF files.
+
+Numpy arrays can be written to TIFF, BigTIFF, and ImageJ hyperstack compatible
+files in multi-page, memory-mappable, tiled, predicted, or compressed form.
+
+Only a subset of the TIFF specification is supported, mainly uncompressed and
+losslessly compressed 1, 8, 16, 32 and 64-bit integer, 16, 32 and 64-bit float,
+grayscale and RGB(A) images.
+Specifically, reading slices of image data, CCITT and OJPEG compression,
+chroma subsampling without JPEG compression, or IPTC and XMP metadata are not
+implemented.
+
+TIFF(r), the Tagged Image File Format, is a trademark and under control of
+Adobe Systems Incorporated. BigTIFF allows for files greater than 4 GB.
+STK, LSM, FluoView, SGI, SEQ, GEL, and OME-TIFF, are custom extensions
+defined by Molecular Devices (Universal Imaging Corporation), Carl Zeiss
+MicroImaging, Olympus, Silicon Graphics International, Media Cybernetics,
+Molecular Dynamics, and the Open Microscopy Environment consortium
+respectively.
+
+For command line usage run ``python -m tifffile --help``
+
+:Author:
+ `Christoph Gohlke <https://www.lfd.uci.edu/~gohlke/>`_
+
+:Organization:
+ Laboratory for Fluorescence Dynamics, University of California, Irvine
+
+:License: 3-clause BSD
+
+:Version: 2019.7.2
+
+Requirements
+------------
+This release has been tested with the following requirements and dependencies
+(other versions may work):
+
+* `CPython 2.7.16, 3.5.4, 3.6.8, 3.7.3, 64-bit <https://www.python.org>`_
+* `Numpy 1.15.4 <https://www.numpy.org>`_
+* `Imagecodecs 2019.5.22 <https://pypi.org/project/imagecodecs/>`_
+ (optional; used for encoding and decoding LZW, JPEG, etc.)
+* `Matplotlib 3.1 <https://www.matplotlib.org>`_ (optional; used for plotting)
+* Python 2.7 requires 'futures', 'enum34', and 'pathlib'.
+
+Revisions
+---------
+2019.7.2
+ Pass 2868 tests.
+ Do not write SampleFormat tag for unsigned data types.
+ Write ByteCount tag values as SHORT or LONG if possible.
+ Allow to specify axes in FileSequence pattern via group names.
+ Add option to concurrently read FileSequence using threads.
+ Derive TiffSequence from FileSequence.
+ Use str(datetime.timedelta) to format Timer duration.
+ Use perf_counter for Timer if possible.
+2019.6.18
+ Fix reading planar RGB ImageJ files created by Bio-Formats.
+ Fix reading single-file, multi-image OME-TIFF without UUID.
+ Presume LSM stores uncompressed images contiguously per page.
+ Reformat some complex expressions.
+2019.5.30
+ Ignore invalid frames in OME-TIFF.
+ Set default subsampling to (2, 2) for RGB JPEG compression.
+ Fix reading and writing planar RGB JPEG compression.
+ Replace buffered_read with FileHandle.read_segments.
+ Include page or frame numbers in exceptions and warnings.
+ Add Timer class.
+2019.5.22
+ Add optional chroma subsampling for JPEG compression.
+ Enable writing PNG, JPEG, JPEGXR, and JPEG2000 compression (WIP).
+ Fix writing tiled images with WebP compression.
+ Improve handling GeoTIFF sparse files.
+2019.3.18
+ Fix regression decoding JPEG with RGB photometrics.
+ Fix reading OME-TIFF files with corrupted but unused pages.
+ Allow to load TiffFrame without specifying keyframe.
+ Calculate virtual TiffFrames for non-BigTIFF ScanImage files > 2GB.
+ Rename property is_chroma_subsampled to is_subsampled (breaking).
+ Make more attributes and methods private (WIP).
+2019.3.8
+ Fix MemoryError when RowsPerStrip > ImageLength.
+ Fix SyntaxWarning on Python 3.8.
+ Fail to decode JPEG to planar RGB (tentative).
+ Separate public from private test files (WIP).
+ Allow testing without data files or imagecodecs.
+2019.2.22
+ Use imagecodecs-lite as a fallback for imagecodecs.
+ Simplify reading numpy arrays from file.
+ Use TiffFrames when reading arrays from page sequences.
+ Support slices and iterators in TiffPageSeries sequence interface.
+ Auto-detect uniform series.
+ Use page hash to determine generic series.
+ Turn off page cache (tentative).
+ Pass through more parameters in imread.
+ Discontinue movie parameter in imread and TiffFile (breaking).
+ Discontinue bigsize parameter in imwrite (breaking).
+ Raise TiffFileError in case of issues with TIFF structure.
+ Return TiffFile.ome_metadata as XML (breaking).
+ Ignore OME series when last dimensions are not stored in TIFF pages.
+2019.2.10
+ Assemble IFDs in memory to speed-up writing on some slow media.
+ Handle discontinued arguments fastij, multifile_close, and pages.
+2019.1.30
+ Use black background in imshow.
+ Do not write datetime tag by default (breaking).
+ Fix OME-TIFF with SamplesPerPixel > 1.
+ Allow 64-bit IFD offsets for NDPI (files > 4GB still not supported).
+2019.1.4
+ Fix decoding deflate without imagecodecs.
+2019.1.1
+ Update copyright year.
+ Require imagecodecs >= 2018.12.16.
+ Do not use JPEG tables from keyframe.
+ Enable decoding large JPEG in NDPI.
+ Decode some old-style JPEG.
+ Reorder OME channel axis to match PlanarConfiguration storage.
+ Return tiled images as contiguous arrays.
+ Add decode_lzw proxy function for compatibility with old czifile module.
+ Use dedicated logger.
+2018.11.28
+ Make SubIFDs accessible as TiffPage.pages.
+ Make parsing of TiffSequence axes pattern optional (breaking).
+ Limit parsing of TiffSequence axes pattern to file names, not path names.
+ Do not interpolate in imshow if image dimensions <= 512, else use bilinear.
+ Use logging.warning instead of warnings.warn in many cases.
+ Fix numpy FutureWarning for out == 'memmap'.
+ Adjust ZSTD and WebP compression to libtiff-4.0.10 (WIP).
+ Decode old-style LZW with imagecodecs >= 2018.11.8.
+ Remove TiffFile.qptiff_metadata (QPI metadata are per page).
+ Do not use keyword arguments before variable positional arguments.
+ Make either all or none return statements in a function return expression.
+ Use pytest parametrize to generate tests.
+ Replace test classes with functions.
+2018.11.6
+ Rename imsave function to imwrite.
+ Readd Python implementations of packints, delta, and bitorder codecs.
+ Fix TiffFrame.compression AttributeError.
+2018.10.18
+ Rename tiffile package to tifffile.
+2018.10.10
+ Read ZIF, the Zoomable Image Format (WIP).
+ Decode YCbCr JPEG as RGB (tentative).
+ Improve restoration of incomplete tiles.
+ Allow to write grayscale with extrasamples without specifying planarconfig.
+ Enable decoding of PNG and JXR via imagecodecs.
+ Deprecate 32-bit platforms (too many memory errors during tests).
+2018.9.27
+ Read Olympus SIS (WIP).
+ Allow to write non-BigTIFF files up to ~4 GB (fix).
+ Fix parsing date and time fields in SEM metadata.
+ Detect some circular IFD references.
+ Enable WebP codecs via imagecodecs.
+ Add option to read TiffSequence from ZIP containers.
+ Remove TiffFile.isnative.
+ Move TIFF struct format constants out of TiffFile namespace.
+2018.8.31
+ Fix wrong TiffTag.valueoffset.
+ Towards reading Hamamatsu NDPI (WIP).
+ Enable PackBits compression of byte and bool arrays.
+ Fix parsing NULL terminated CZ_SEM strings.
+2018.8.24
+ Move tifffile.py and related modules into tiffile package.
+ Move usage examples to module docstring.
+ Enable multi-threading for compressed tiles and pages by default.
+ Add option to concurrently decode image tiles using threads.
+ Do not skip empty tiles (fix).
+ Read JPEG and J2K compressed strips and tiles.
+ Allow floating-point predictor on write.
+ Add option to specify subfiletype on write.
+ Depend on imagecodecs package instead of _tifffile, lzma, etc modules.
+ Remove reverse_bitorder, unpack_ints, and decode functions.
+ Use pytest instead of unittest.
+2018.6.20
+ Save RGBA with unassociated extrasample by default (breaking).
+ Add option to specify ExtraSamples values.
+2018.6.17 (included with 0.15.1)
+ Towards reading JPEG and other compressions via imagecodecs package (WIP).
+ Read SampleFormat VOID as UINT.
+ Add function to validate TIFF using 'jhove -m TIFF-hul'.
+ Save bool arrays as bilevel TIFF.
+ Accept pathlib.Path as filenames.
+ Move 'software' argument from TiffWriter __init__ to save.
+ Raise DOS limit to 16 TB.
+ Lazy load LZMA and ZSTD compressors and decompressors.
+ Add option to save IJMetadata tags.
+ Return correct number of pages for truncated series (fix).
+ Move EXIF tags to TIFF.TAG as per TIFF/EP standard.
+2018.2.18
+ Always save RowsPerStrip and Resolution tags as required by TIFF standard.
+ Do not use badly typed ImageDescription.
+ Coherce bad ASCII string tags to bytes.
+ Tuning of __str__ functions.
+ Fix reading 'undefined' tag values.
+ Read and write ZSTD compressed data.
+ Use hexdump to print byte strings.
+ Determine TIFF byte order from data dtype in imsave.
+ Add option to specify RowsPerStrip for compressed strips.
+ Allow memory-map of arrays with non-native byte order.
+ Attempt to handle ScanImage <= 5.1 files.
+ Restore TiffPageSeries.pages sequence interface.
+ Use numpy.frombuffer instead of fromstring to read from binary data.
+ Parse GeoTIFF metadata.
+ Add option to apply horizontal differencing before compression.
+ Towards reading PerkinElmer QPI (QPTIFF, no test files).
+ Do not index out of bounds data in tifffile.c unpackbits and decodelzw.
+2017.9.29
+ Many backward incompatible changes improving speed and resource usage:
+ Add detail argument to __str__ function. Remove info functions.
+ Fix potential issue correcting offsets of large LSM files with positions.
+ Remove TiffFile sequence interface; use TiffFile.pages instead.
+ Do not make tag values available as TiffPage attributes.
+ Use str (not bytes) type for tag and metadata strings (WIP).
+ Use documented standard tag and value names (WIP).
+ Use enums for some documented TIFF tag values.
+ Remove 'memmap' and 'tmpfile' options; use out='memmap' instead.
+ Add option to specify output in asarray functions.
+ Add option to concurrently decode pages using threads.
+ Add TiffPage.asrgb function (WIP).
+ Do not apply colormap in asarray.
+ Remove 'colormapped', 'rgbonly', and 'scale_mdgel' options from asarray.
+ Consolidate metadata in TiffFile _metadata functions.
+ Remove non-tag metadata properties from TiffPage.
+ Add function to convert LSM to tiled BIN files.
+ Align image data in file.
+ Make TiffPage.dtype a numpy.dtype.
+ Add 'ndim' and 'size' properties to TiffPage and TiffPageSeries.
+ Allow imsave to write non-BigTIFF files up to ~4 GB.
+ Only read one page for shaped series if possible.
+ Add memmap function to create memory-mapped array stored in TIFF file.
+ Add option to save empty arrays to TIFF files.
+ Add option to save truncated TIFF files.
+ Allow single tile images to be saved contiguously.
+ Add optional movie mode for files with uniform pages.
+ Lazy load pages.
+ Use lightweight TiffFrame for IFDs sharing properties with key TiffPage.
+ Move module constants to 'TIFF' namespace (speed up module import).
+ Remove 'fastij' option from TiffFile.
+ Remove 'pages' parameter from TiffFile.
+ Remove TIFFfile alias.
+ Deprecate Python 2.
+ Require enum34 and futures packages on Python 2.7.
+ Remove Record class and return all metadata as dict instead.
+ Add functions to parse STK, MetaSeries, ScanImage, SVS, Pilatus metadata.
+ Read tags from EXIF and GPS IFDs.
+ Use pformat for tag and metadata values.
+ Fix reading some UIC tags.
+ Do not modify input array in imshow (fix).
+ Fix Python implementation of unpack_ints.
+2017.5.23
+ Write correct number of SampleFormat values (fix).
+ Use Adobe deflate code to write ZIP compressed files.
+ Add option to pass tag values as packed binary data for writing.
+ Defer tag validation to attribute access.
+ Use property instead of lazyattr decorator for simple expressions.
+2017.3.17
+ Write IFDs and tag values on word boundaries.
+ Read ScanImage metadata.
+ Remove is_rgb and is_indexed attributes from TiffFile.
+ Create files used by doctests.
+2017.1.12 (included with scikit-image 0.14.x)
+ Read Zeiss SEM metadata.
+ Read OME-TIFF with invalid references to external files.
+ Rewrite C LZW decoder (5x faster).
+ Read corrupted LSM files missing EOI code in LZW stream.
+2017.1.1
+ ...
+
+Refer to the CHANGES file for older revisions.
+
+Notes
+-----
+The API is not stable yet and might change between revisions.
+
+Tested on little-endian platforms only.
+
+Python 2.7 and 32-bit versions are deprecated.
+
+Tifffile relies on the `imagecodecs <https://pypi.org/project/imagecodecs/>`_
+package for encoding and decoding LZW, JPEG, and other compressed images.
+The `imagecodecs-lite <https://pypi.org/project/imagecodecs-lite/>`_ package,
+which is easier to build, can be used for decoding LZW compressed images
+instead.
+
+Several TIFF-like formats do not strictly adhere to the TIFF6 specification,
+some of which allow file or data sizes to exceed the 4 GB limit:
+
+* *BigTIFF* is identified by version number 43 and uses different file
+ header, IFD, and tag structures with 64-bit offsets. It adds more data types.
+ Tifffile can read and write BigTIFF files.
+* *ImageJ* hyperstacks store all image data, which may exceed 4 GB,
+ contiguously after the first IFD. Files > 4 GB contain one IFD only.
+ The size (shape and dtype) of the up to 6-dimensional image data can be
+ determined from the ImageDescription tag of the first IFD, which is Latin-1
+ encoded. Tifffile can read and write ImageJ hyperstacks.
+* *OME-TIFF* stores up to 8-dimensional data in one or multiple TIFF of BigTIFF
+ files. The 8-bit UTF-8 encoded OME-XML metadata found in the ImageDescription
+ tag of the first IFD defines the position of TIFF IFDs in the high
+ dimensional data. Tifffile can read OME-TIFF files, except when the OME-XML
+ metadata is stored in a separate file.
+* *LSM* stores all IFDs below 4 GB but wraps around 32-bit StripOffsets.
+ The StripOffsets of each series and position require separate unwrapping.
+ The StripByteCounts tag contains the number of bytes for the uncompressed
+ data. Tifffile can read large LSM files.
+* *NDPI* uses some 64-bit offsets in the file header, IFD, and tag structures
+ and might require correcting 32-bit offsets found in tags.
+ JPEG compressed tiles with dimensions > 65536 are not readable with libjpeg.
+ Tifffile can read NDPI files < 4 GB and decompress large JPEG tiles using
+ the imagecodecs library on Windows.
+* *ScanImage* optionally allows corrupt non-BigTIFF files > 2 GB. The values
+ of StripOffsets and StripByteCounts can be recovered using the constant
+ differences of the offsets of IFD and tag values throughout the file.
+ Tifffile can read such files on Python 3 if the image data is stored
+ contiguously in each page.
+* *GeoTIFF* sparse files allow strip or tile offsets and byte counts to be 0.
+ Such segments are implicitly set to 0 or the NODATA value on reading.
+ Tifffile can read GeoTIFF sparse files.
+
+Other libraries for reading scientific TIFF files from Python:
+
+* `Python-bioformats <https://github.com/CellProfiler/python-bioformats>`_
+* `Imread <https://github.com/luispedro/imread>`_
+* `GDAL <https://github.com/OSGeo/gdal/tree/master/gdal/swig/python>`_
+* `OpenSlide-python <https://github.com/openslide/openslide-python>`_
+* `PyLibTiff <https://github.com/pearu/pylibtiff>`_
+* `SimpleITK <https://github.com/SimpleITK/SimpleITK>`_
+* `PyLSM <https://launchpad.net/pylsm>`_
+* `PyMca.TiffIO.py <https://github.com/vasole/pymca>`_ (same as fabio.TiffIO)
+* `BioImageXD.Readers <http://www.bioimagexd.net/>`_
+* `CellCognition <https://cellcognition-project.org/>`_
+* `pymimage <https://github.com/ardoi/pymimage>`_
+* `pytiff <https://github.com/FZJ-INM1-BDA/pytiff>`_
+* `ScanImageTiffReaderPython
+ <https://gitlab.com/vidriotech/scanimagetiffreader-python>`_
+* `bigtiff <https://pypi.org/project/bigtiff>`_
+
+Some libraries are using tifffile to write OME-TIFF files:
+
+* `Zeiss Apeer OME-TIFF library
+ <https://github.com/apeer-micro/apeer-ometiff-library>`_
+* `Allen Institute for Cell Science imageio
+ <https://pypi.org/project/aicsimageio>`_
+
+Acknowledgements
+----------------
+* Egor Zindy, University of Manchester, for lsm_scan_info specifics.
+* Wim Lewis for a bug fix and some LSM functions.
+* Hadrien Mary for help on reading MicroManager files.
+* Christian Kliche for help writing tiled and color-mapped files.
+
+References
+----------
+1) TIFF 6.0 Specification and Supplements. Adobe Systems Incorporated.
+ https://www.adobe.io/open/standards/TIFF.html
+2) TIFF File Format FAQ. https://www.awaresystems.be/imaging/tiff/faq.html
+3) MetaMorph Stack (STK) Image File Format.
+ http://mdc.custhelp.com/app/answers/detail/a_id/18862
+4) Image File Format Description LSM 5/7 Release 6.0 (ZEN 2010).
+ Carl Zeiss MicroImaging GmbH. BioSciences. May 10, 2011
+5) The OME-TIFF format.
+ https://docs.openmicroscopy.org/ome-model/5.6.4/ome-tiff/
+6) UltraQuant(r) Version 6.0 for Windows Start-Up Guide.
+ http://www.ultralum.com/images%20ultralum/pdf/UQStart%20Up%20Guide.pdf
+7) Micro-Manager File Formats.
+ https://micro-manager.org/wiki/Micro-Manager_File_Formats
+8) Tags for TIFF and Related Specifications. Digital Preservation.
+ https://www.loc.gov/preservation/digital/formats/content/tiff_tags.shtml
+9) ScanImage BigTiff Specification - ScanImage 2016.
+ http://scanimage.vidriotechnologies.com/display/SI2016/
+ ScanImage+BigTiff+Specification
+10) CIPA DC-008-2016: Exchangeable image file format for digital still cameras:
+ Exif Version 2.31.
+ http://www.cipa.jp/std/documents/e/DC-008-Translation-2016-E.pdf
+11) ZIF, the Zoomable Image File format. http://zif.photo/
+12) GeoTIFF File Format https://www.gdal.org/frmt_gtiff.html
+
+Examples
+--------
+Save a 3D numpy array to a multi-page, 16-bit grayscale TIFF file:
+
+>>> data = numpy.random.randint(0, 2**12, (4, 301, 219), 'uint16')
+>>> imwrite('temp.tif', data, photometric='minisblack')
+
+Read the whole image stack from the TIFF file as numpy array:
+
+>>> image_stack = imread('temp.tif')
+>>> image_stack.shape
+(4, 301, 219)
+>>> image_stack.dtype
+dtype('uint16')
+
+Read the image from first page in the TIFF file as numpy array:
+
+>>> image = imread('temp.tif', key=0)
+>>> image.shape
+(301, 219)
+
+Read images from a sequence of TIFF files as numpy array:
+
+>>> image_sequence = imread(['temp.tif', 'temp.tif'])
+>>> image_sequence.shape
+(2, 4, 301, 219)
+
+Save a numpy array to a single-page RGB TIFF file:
+
+>>> data = numpy.random.randint(0, 255, (256, 256, 3), 'uint8')
+>>> imwrite('temp.tif', data, photometric='rgb')
+
+Save a floating-point array and metadata, using zlib compression:
+
+>>> data = numpy.random.rand(2, 5, 3, 301, 219).astype('float32')
+>>> imwrite('temp.tif', data, compress=6, metadata={'axes': 'TZCYX'})
+
+Save a volume with xyz voxel size 2.6755x2.6755x3.9474 µm^3 to an ImageJ file:
+
+>>> volume = numpy.random.randn(57*256*256).astype('float32')
+>>> volume.shape = 1, 57, 1, 256, 256, 1 # dimensions in TZCYXS order
+>>> imwrite('temp.tif', volume, imagej=True, resolution=(1./2.6755, 1./2.6755),
+... metadata={'spacing': 3.947368, 'unit': 'um'})
+
+Get the shape and dtype of the images stored in the TIFF file:
+
+>>> tif = TiffFile('temp.tif')
+>>> len(tif.pages) # number of pages in the file
+57
+>>> page = tif.pages[0] # get shape and dtype of the image in the first page
+>>> page.shape
+(256, 256)
+>>> page.dtype
+dtype('float32')
+>>> page.axes
+'YX'
+>>> series = tif.series[0] # get shape and dtype of the first image series
+>>> series.shape
+(57, 256, 256)
+>>> series.dtype
+dtype('float32')
+>>> series.axes
+'ZYX'
+>>> tif.close()
+
+Read hyperstack and metadata from the ImageJ file:
+
+>>> with TiffFile('temp.tif') as tif:
+... imagej_hyperstack = tif.asarray()
+... imagej_metadata = tif.imagej_metadata
+>>> imagej_hyperstack.shape
+(57, 256, 256)
+>>> imagej_metadata['slices']
+57
+
+Read the "XResolution" tag from the first page in the TIFF file:
+
+>>> with TiffFile('temp.tif') as tif:
+... tag = tif.pages[0].tags['XResolution']
+>>> tag.value
+(2000, 5351)
+>>> tag.name
+'XResolution'
+>>> tag.code
+282
+>>> tag.count
+1
+>>> tag.dtype
+'2I'
+>>> tag.valueoffset
+360
+
+Read images from a selected range of pages:
+
+>>> image = imread('temp.tif', key=range(4, 40, 2))
+>>> image.shape
+(18, 256, 256)
+
+Create an empty TIFF file and write to the memory-mapped numpy array:
+
+>>> memmap_image = memmap('temp.tif', shape=(256, 256), dtype='float32')
+>>> memmap_image[255, 255] = 1.0
+>>> memmap_image.flush()
+>>> memmap_image.shape, memmap_image.dtype
+((256, 256), dtype('float32'))
+>>> del memmap_image
+
+Memory-map image data of the first page in the TIFF file:
+
+>>> memmap_image = memmap('temp.tif', page=0)
+>>> memmap_image[255, 255]
+1.0
+>>> del memmap_image
+
+Successively append images to a BigTIFF file, which can exceed 4 GB:
+
+>>> data = numpy.random.randint(0, 255, (5, 2, 3, 301, 219), 'uint8')
+>>> with TiffWriter('temp.tif', bigtiff=True) as tif:
+... for i in range(data.shape[0]):
+... tif.save(data[i], compress=6, photometric='minisblack')
+
+Iterate over pages and tags in the TIFF file and successively read images:
+
+>>> with TiffFile('temp.tif') as tif:
+... image_stack = tif.asarray()
+... for page in tif.pages:
+... for tag in page.tags.values():
+... tag_name, tag_value = tag.name, tag.value
+... image = page.asarray()
+
+Save two image series to a TIFF file:
+
+>>> data0 = numpy.random.randint(0, 255, (301, 219, 3), 'uint8')
+>>> data1 = numpy.random.randint(0, 255, (5, 301, 219), 'uint16')
+>>> with TiffWriter('temp.tif') as tif:
+... tif.save(data0, compress=6, photometric='rgb')
+... tif.save(data1, compress=6, photometric='minisblack', contiguous=False)
+
+Read the second image series from the TIFF file:
+
+>>> series1 = imread('temp.tif', series=1)
+>>> series1.shape
+(5, 301, 219)
+
+Read an image stack from a series of TIFF files with a file name pattern:
+
+>>> imwrite('temp_C001T001.tif', numpy.random.rand(64, 64))
+>>> imwrite('temp_C001T002.tif', numpy.random.rand(64, 64))
+>>> image_sequence = TiffSequence('temp_C001*.tif', pattern='axes')
+>>> image_sequence.shape
+(1, 2)
+>>> image_sequence.axes
+'CT'
+>>> data = image_sequence.asarray()
+>>> data.shape
+(1, 2, 64, 64)
+
+"""
+
+from __future__ import division, print_function
+
+__version__ = '2019.7.2'
+__docformat__ = 'restructuredtext en'
+__all__ = (
+ 'imwrite',
+ 'imread',
+ 'imshow',
+ 'memmap',
+ 'lsm2bin',
+ 'TiffFile',
+ 'TiffFileError',
+ 'TiffSequence',
+ 'TiffWriter',
+ 'TiffPage',
+ 'TiffPageSeries',
+ 'TiffFrame',
+ 'TiffTag',
+ 'TIFF',
+ # utility classes and functions used by oiffile, czifile, etc
+ 'FileHandle',
+ 'FileSequence',
+ 'Timer',
+ 'lazyattr',
+ 'natural_sorted',
+ 'stripnull',
+ 'transpose_axes',
+ 'squeeze_axes',
+ 'create_output',
+ 'repeat_nd',
+ 'format_size',
+ 'astype',
+ 'product',
+ 'xml2dict',
+ 'pformat',
+ 'str2bytes',
+ 'nullfunc',
+ 'update_kwargs',
+ 'parse_kwargs',
+ 'askopenfilename',
+ '_app_show',
+ # deprecated
+ 'imsave',
+ 'decode_lzw',
+ 'decodelzw',
+)
+
+import sys
+import os
+import io
+import re
+import glob
+import math
+import time
+import json
+import enum
+import struct
+import pathlib
+import logging
+import warnings
+import binascii
+import datetime
+import threading
+import collections
+
+try:
+ from collections.abc import Iterable
+except ImportError:
+ from collections import Iterable
+
+from concurrent.futures import ThreadPoolExecutor
+
+import numpy
+
+try:
+ import imagecodecs
+except ImportError:
+ import zlib
+
+ try:
+ import imagecodecs_lite as imagecodecs
+ except ImportError:
+ imagecodecs = None
+
+# delay import of mmap, pprint, fractions, xml, tkinter, lxml, matplotlib,
+# subprocess, multiprocessing, tempfile, zipfile, fnmatch
+
+log = logging.getLogger(__name__) # .addHandler(logging.NullHandler())
+
+
+def imread(files, **kwargs):
+ """Return image data from TIFF file(s) as numpy array.
+
+ Refer to the TiffFile and TiffSequence classes and their asarray
+ functions for documentation.
+
+ Parameters
+ ----------
+ files : str, binary stream, or sequence
+ File name, seekable binary stream, glob pattern, or sequence of
+ file names.
+ kwargs : dict
+ Parameters 'name', 'offset', 'size', 'multifile', and 'is_ome'
+ are passed to the TiffFile constructor.
+ The 'pattern' and 'ioworkers' parameters are passed to the
+ TiffSequence constructor.
+ Other parameters are passed to the asarray functions.
+ The first image series in the file is returned if no arguments are
+ provided.
+
+ """
+ kwargs_file = parse_kwargs(
+ kwargs,
+ 'is_ome',
+ 'multifile',
+ '_useframes',
+ 'name',
+ 'offset',
+ 'size',
+ # legacy
+ 'multifile_close',
+ 'fastij',
+ 'movie',
+ )
+ kwargs_seq = parse_kwargs(kwargs, 'pattern', 'ioworkers')
+
+ if kwargs.get('pages', None) is not None:
+ if kwargs.get('key', None) is not None:
+ raise TypeError(
+ "the 'pages' and 'key' arguments cannot be used together")
+ log.warning("imread: the 'pages' argument is deprecated")
+ kwargs['key'] = kwargs.pop('pages')
+
+ if isinstance(files, basestring) and any(i in files for i in '?*'):
+ files = glob.glob(files)
+ if not files:
+ raise ValueError('no files found')
+ if not hasattr(files, 'seek') and len(files) == 1:
+ files = files[0]
+
+ if isinstance(files, basestring) or hasattr(files, 'seek'):
+ with TiffFile(files, **kwargs_file) as tif:
+ return tif.asarray(**kwargs)
+ else:
+ with TiffSequence(files, **kwargs_seq) as imseq:
+ return imseq.asarray(**kwargs)
+
+
+def imwrite(file, data=None, shape=None, dtype=None, **kwargs):
+ """Write numpy array to TIFF file.
+
+ Refer to the TiffWriter class and its asarray function for documentation.
+
+ A BigTIFF file is created if the data size in bytes is larger than 4 GB
+ minus 32 MB (for metadata), and 'bigtiff' is not specified, and 'imagej'
+ or 'truncate' are not enabled.
+
+ Parameters
+ ----------
+ file : str or binary stream
+ File name or writable binary stream, such as an open file or BytesIO.
+ data : array_like
+ Input image. The last dimensions are assumed to be image depth,
+ height, width, and samples.
+ If None, an empty array of the specified shape and dtype is
+ saved to file.
+ Unless 'byteorder' is specified in 'kwargs', the TIFF file byte order
+ is determined from the data's dtype or the dtype argument.
+ shape : tuple
+ If 'data' is None, shape of an empty array to save to the file.
+ dtype : numpy.dtype
+ If 'data' is None, datatype of an empty array to save to the file.
+ kwargs : dict
+ Parameters 'append', 'byteorder', 'bigtiff', and 'imagej', are passed
+ to the TiffWriter constructor. Other parameters are passed to the
+ TiffWriter.save function.
+
+ Returns
+ -------
+ offset, bytecount : tuple or None
+ If the image data are written contiguously, return offset and bytecount
+ of image data in the file.
+
+ """
+ tifargs = parse_kwargs(kwargs, 'append', 'bigtiff', 'byteorder', 'imagej')
+ if data is None:
+ dtype = numpy.dtype(dtype)
+ size = product(shape) * dtype.itemsize
+ byteorder = dtype.byteorder
+ else:
+ try:
+ size = data.nbytes
+ byteorder = data.dtype.byteorder
+ except Exception:
+ size = 0
+ byteorder = None
+ bigsize = kwargs.pop('bigsize', 2**32 - 2**25)
+ if (
+ 'bigtiff' not in tifargs
+ and size > bigsize
+ and not (
+ tifargs.get('imagej', False) or tifargs.get('truncate', False)
+ )
+ ):
+ tifargs['bigtiff'] = True
+ if 'byteorder' not in tifargs:
+ tifargs['byteorder'] = byteorder
+
+ with TiffWriter(file, **tifargs) as tif:
+ return tif.save(data, shape, dtype, **kwargs)
+
+
+def memmap(filename, shape=None, dtype=None, page=None, series=0, mode='r+',
+ **kwargs):
+ """Return memory-mapped numpy array stored in TIFF file.
+
+ Memory-mapping requires data stored in native byte order, without tiling,
+ compression, predictors, etc.
+ If 'shape' and 'dtype' are provided, existing files will be overwritten or
+ appended to depending on the 'append' parameter.
+ Otherwise the image data of a specified page or series in an existing
+ file will be memory-mapped. By default, the image data of the first page
+ series is memory-mapped.
+ Call flush() to write any changes in the array to the file.
+ Raise ValueError if the image data in the file is not memory-mappable.
+
+ Parameters
+ ----------
+ filename : str
+ Name of the TIFF file which stores the array.
+ shape : tuple
+ Shape of the empty array.
+ dtype : numpy.dtype
+ Datatype of the empty array.
+ page : int
+ Index of the page which image data to memory-map.
+ series : int
+ Index of the page series which image data to memory-map.
+ mode : {'r+', 'r', 'c'}
+ The file open mode. Default is to open existing file for reading and
+ writing ('r+').
+ kwargs : dict
+ Additional parameters passed to imwrite() or TiffFile().
+
+ """
+ if shape is not None and dtype is not None:
+ # create a new, empty array
+ kwargs.update(
+ data=None,
+ shape=shape,
+ dtype=dtype,
+ returnoffset=True,
+ align=TIFF.ALLOCATIONGRANULARITY
+ )
+ result = imwrite(filename, **kwargs)
+ if result is None:
+ # TODO: fail before creating file or writing data
+ raise ValueError('image data are not memory-mappable')
+ offset = result[0]
+ else:
+ # use existing file
+ with TiffFile(filename, **kwargs) as tif:
+ if page is not None:
+ page = tif.pages[page]
+ if not page.is_memmappable:
+ raise ValueError('image data are not memory-mappable')
+ offset, _ = page.is_contiguous
+ shape = page.shape
+ dtype = page.dtype
+ else:
+ series = tif.series[series]
+ if series.offset is None:
+ raise ValueError('image data are not memory-mappable')
+ shape = series.shape
+ dtype = series.dtype
+ offset = series.offset
+ dtype = tif.byteorder + dtype.char
+ return numpy.memmap(filename, dtype, mode, offset, shape, 'C')
+
+
+class lazyattr(object):
+ """Attribute whose value is computed on first access."""
+
+ # TODO: help() doesn't work
+ __slots__ = ('func',)
+
+ def __init__(self, func):
+ self.func = func
+ # self.__name__ = func.__name__
+ # self.__doc__ = func.__doc__
+ # self.lock = threading.RLock()
+
+ def __get__(self, instance, owner):
+ # with self.lock:
+ if instance is None:
+ return self
+ try:
+ value = self.func(instance)
+ except AttributeError as exc:
+ raise RuntimeError(exc)
+ if value is NotImplemented:
+ return getattr(super(owner, instance), self.func.__name__)
+ setattr(instance, self.func.__name__, value)
+ return value
+
+
+class TiffFileError(Exception):
+ """Exception to indicate invalid TIFF structure."""
+
+
+class TiffWriter(object):
+ """Write numpy arrays to TIFF file.
+
+ TiffWriter instances must be closed using the 'close' method, which is
+ automatically called when using the 'with' context manager.
+
+ TiffWriter's main purpose is saving nD numpy array's as TIFF,
+ not to create any possible TIFF format. Specifically, JPEG compression,
+ SubIFDs, ExifIFD, or GPSIFD tags are not supported.
+
+ """
+
+ def __init__(self, file, bigtiff=False, byteorder=None, append=False,
+ imagej=False):
+ """Open a TIFF file for writing.
+
+ An empty TIFF file is created if the file does not exist, else the
+ file is overwritten with an empty TIFF file unless 'append'
+ is true. Use 'bigtiff=True' when creating files larger than 4 GB.
+
+ Parameters
+ ----------
+ file : str, binary stream, or FileHandle
+ File name or writable binary stream, such as an open file
+ or BytesIO.
+ bigtiff : bool
+ If True, the BigTIFF format is used.
+ byteorder : {'<', '>', '=', '|'}
+ The endianness of the data in the file.
+ By default, this is the system's native byte order.
+ append : bool
+ If True and 'file' is an existing standard TIFF file, image data
+ and tags are appended to the file.
+ Appending data may corrupt specifically formatted TIFF files
+ such as LSM, STK, ImageJ, or FluoView.
+ imagej : bool
+ If True, write an ImageJ hyperstack compatible file.
+ This format can handle data types uint8, uint16, or float32 and
+ data shapes up to 6 dimensions in TZCYXS order.
+ RGB images (S=3 or S=4) must be uint8.
+ ImageJ's default byte order is big-endian but this implementation
+ uses the system's native byte order by default.
+ ImageJ hyperstacks do not support BigTIFF or compression.
+ The ImageJ file format is undocumented.
+ When using compression, use ImageJ's Bio-Formats import function.
+
+ """
+ if append:
+ # determine if file is an existing TIFF file that can be extended
+ try:
+ with FileHandle(file, mode='rb', size=0) as fh:
+ pos = fh.tell()
+ try:
+ with TiffFile(fh) as tif:
+ if append != 'force' and not tif.is_appendable:
+ raise TiffFileError('cannot append to file'
+ ' containing metadata')
+ byteorder = tif.byteorder
+ bigtiff = tif.is_bigtiff
+ self._ifdoffset = tif.pages.next_page_offset
+ finally:
+ fh.seek(pos)
+ except (IOError, FileNotFoundError):
+ append = False
+
+ if byteorder in (None, '=', '|'):
+ byteorder = '<' if sys.byteorder == 'little' else '>'
+ elif byteorder not in ('<', '>'):
+ raise ValueError('invalid byteorder %s' % byteorder)
+ if imagej and bigtiff:
+ warnings.warn('writing incompatible BigTIFF ImageJ')
+
+ self._byteorder = byteorder
+ self._imagej = bool(imagej)
+ self._truncate = False
+ self._metadata = None
+ self._colormap = None
+
+ self._descriptionoffset = 0
+ self._descriptionlen = 0
+ self._descriptionlenoffset = 0
+ self._tags = None
+ self._shape = None # normalized shape of data in consecutive pages
+ self._datashape = None # shape of data in consecutive pages
+ self._datadtype = None # data type
+ self._dataoffset = None # offset to data
+ self._databytecounts = None # byte counts per plane
+ self._tagoffsets = None # strip or tile offset tag code
+
+ if bigtiff:
+ self._bigtiff = True
+ self._offsetsize = 8
+ self._tagsize = 20
+ self._tagnoformat = 'Q'
+ self._offsetformat = 'Q'
+ self._valueformat = '8s'
+ else:
+ self._bigtiff = False
+ self._offsetsize = 4
+ self._tagsize = 12
+ self._tagnoformat = 'H'
+ self._offsetformat = 'I'
+ self._valueformat = '4s'
+
+ if append:
+ self._fh = FileHandle(file, mode='r+b', size=0)
+ self._fh.seek(0, 2)
+ else:
+ self._fh = FileHandle(file, mode='wb', size=0)
+ self._fh.write({'<': b'II', '>': b'MM'}[byteorder])
+ if bigtiff:
+ self._fh.write(struct.pack(byteorder + 'HHH', 43, 8, 0))
+ else:
+ self._fh.write(struct.pack(byteorder + 'H', 42))
+ # first IFD
+ self._ifdoffset = self._fh.tell()
+ self._fh.write(struct.pack(byteorder + self._offsetformat, 0))
+
+ def save(self, data=None, shape=None, dtype=None, returnoffset=False,
+ photometric=None, planarconfig=None, extrasamples=None, tile=None,
+ contiguous=True, align=16, truncate=False, compress=0,
+ rowsperstrip=None, predictor=False, subsampling=None,
+ colormap=None, description=None, datetime=None, resolution=None,
+ subfiletype=0, software='tifffile.py', metadata={},
+ ijmetadata=None, extratags=()):
+ """Write numpy array and tags to TIFF file.
+
+ The data shape's last dimensions are assumed to be image depth,
+ height (length), width, and samples.
+ If a colormap is provided, the data's dtype must be uint8 or uint16
+ and the data values are indices into the last dimension of the
+ colormap.
+ If 'shape' and 'dtype' are specified, an empty array is saved.
+ This option cannot be used with compression or multiple tiles.
+ Image data are written uncompressed in one strip per plane by default.
+ Dimensions larger than 2 to 4 (depending on photometric mode, planar
+ configuration, and SGI mode) are flattened and saved as separate pages.
+ The SampleFormat and BitsPerSample tags are derived from the data type.
+
+ Parameters
+ ----------
+ data : numpy.ndarray or None
+ Input image array.
+ shape : tuple or None
+ Shape of the empty array to save. Used only if 'data' is None.
+ dtype : numpy.dtype or None
+ Datatype of the empty array to save. Used only if 'data' is None.
+ returnoffset : bool
+ If True and the image data in the file is memory-mappable, return
+ the offset and number of bytes of the image data in the file.
+ photometric : {'MINISBLACK', 'MINISWHITE', 'RGB', 'PALETTE', 'CFA'}
+ The color space of the image data.
+ By default, this setting is inferred from the data shape and the
+ value of colormap.
+ For CFA images, DNG tags must be specified in 'extratags'.
+ planarconfig : {'CONTIG', 'SEPARATE'}
+ Specifies if samples are stored interleaved or in separate planes.
+ By default, this setting is inferred from the data shape.
+ If this parameter is set, extra samples are used to store grayscale
+ images.
+ 'CONTIG': last dimension contains samples.
+ 'SEPARATE': third last dimension contains samples.
+ extrasamples : tuple of {'UNSPECIFIED', 'ASSOCALPHA', 'UNASSALPHA'}
+ Defines the interpretation of extra components in pixels.
+ 'UNSPECIFIED': no transparency information (default).
+ 'ASSOCALPHA': single, true transparency with pre-multiplied color.
+ 'UNASSALPHA': independent transparency masks.
+ tile : tuple of int
+ The shape ([depth,] length, width) of image tiles to write.
+ If None (default), image data are written in strips.
+ The tile length and width must be a multiple of 16.
+ If the tile depth is provided, the SGI ImageDepth and TileDepth
+ tags are used to save volume data.
+ Unless a single tile is used, tiles cannot be used to write
+ contiguous files.
+ Few software can read the SGI format, e.g. MeVisLab.
+ contiguous : bool
+ If True (default) and the data and parameters are compatible with
+ previous ones, if any, the image data are stored contiguously after
+ the previous one. In that case, 'photometric', 'planarconfig',
+ 'rowsperstrip', are ignored. Metadata such as 'description',
+ 'metadata', 'datetime', and 'extratags' are written to the first
+ page of a contiguous series only.
+ align : int
+ Byte boundary on which to align the image data in the file.
+ Default 16. Use mmap.ALLOCATIONGRANULARITY for memory-mapped data.
+ Following contiguous writes are not aligned.
+ truncate : bool
+ If True, only write the first page including shape metadata if
+ possible (uncompressed, contiguous, not tiled).
+ Other TIFF readers will only be able to read part of the data.
+ compress : int or str or (str, int)
+ If 0 (default), data are written uncompressed.
+ If 0-9, the level of ADOBE_DEFLATE compression.
+ If a str, one of TIFF.COMPESSORS, e.g. 'LZMA' or 'ZSTD'.
+ If a tuple, the first item is one of TIFF.COMPESSORS and the
+ second item is the compression level.
+ Compression cannot be used to write contiguous files.
+ Compressors may require certain data shapes, types or value ranges.
+ E.g. JPEG requires grayscale or RGB(A), uint8 or 12-bit uint16.
+ rowsperstrip : int
+ The number of rows per strip. By default, strips will be ~64 KB
+ if compression is enabled, else rowsperstrip is set to the image
+ length. Bilevel images are always stored in one strip per plane.
+ predictor : bool
+ If True, apply horizontal differencing or floating-point predictor
+ before compression.
+ subsampling : {(1, 1), (2, 1), (2, 2), (4, 1)}
+ The horizontal and vertical subsampling factors used for the
+ chrominance components of images. The default is (2, 2).
+ Currently applies to JPEG compression of RGB images only.
+ Images will be stored in YCbCr colorspace.
+ Segment widths must be a multiple of the horizontal factor.
+ Segment lengths and rowsperstrip must be a multiple of the vertical
+ factor.
+ colormap : numpy.ndarray
+ RGB color values for the corresponding data value.
+ Must be of shape (3, 2**(data.itemsize*8)) and dtype uint16.
+ description : str
+ The subject of the image. Must be 7-bit ASCII. Cannot be used with
+ the ImageJ format. Saved with the first page only.
+ datetime : datetime, str, or bool
+ Date and time of image creation in '%Y:%m:%d %H:%M:%S' format or
+ datetime object. Else if True, the current date and time is used.
+ Saved with the first page only.
+ resolution : (float, float[, str]) or ((int, int), (int, int)[, str])
+ X and Y resolutions in pixels per resolution unit as float or
+ rational numbers. A third, optional parameter specifies the
+ resolution unit, which must be None (default for ImageJ),
+ 'INCH' (default), or 'CENTIMETER'.
+ subfiletype : int
+ Bitfield to indicate the kind of data. Set bit 0 if the image
+ is a reduced-resolution version of another image. Set bit 1 if
+ the image is part of a multi-page image. Set bit 2 if the image
+ is transparency mask for another image (photometric must be
+ MASK, SamplesPerPixel and BitsPerSample must be 1).
+ software : str
+ Name of the software used to create the file. Must be 7-bit ASCII.
+ Saved with the first page only.
+ metadata : dict
+ Additional metadata to be saved along with shape information
+ in JSON or ImageJ formats in an ImageDescription tag.
+ If None, do not write a second ImageDescription tag.
+ Strings must be 7-bit ASCII. Saved with the first page only.
+ ijmetadata : dict
+ Additional metadata to be saved in application specific
+ IJMetadata and IJMetadataByteCounts tags. Refer to the
+ imagej_metadata_tag function for valid keys and values.
+ Saved with the first page only.
+ extratags : sequence of tuples
+ Additional tags as [(code, dtype, count, value, writeonce)].
+
+ code : int
+ The TIFF tag Id.
+ dtype : str
+ Data type of items in 'value' in Python struct format.
+ One of B, s, H, I, 2I, b, h, i, 2i, f, d, Q, or q.
+ count : int
+ Number of data values. Not used for string or byte string
+ values.
+ value : sequence
+ 'Count' values compatible with 'dtype'.
+ Byte strings must contain count values of dtype packed as
+ binary data.
+ writeonce : bool
+ If True, the tag is written to the first page only.
+
+ """
+ # TODO: refactor this function
+ fh = self._fh
+ byteorder = self._byteorder
+
+ if data is None:
+ if compress:
+ raise ValueError('cannot save compressed empty file')
+ datashape = shape
+ datadtype = numpy.dtype(dtype).newbyteorder(byteorder)
+ datadtypechar = datadtype.char
+ else:
+ data = numpy.asarray(data, byteorder + data.dtype.char, 'C')
+ if data.size == 0:
+ raise ValueError('cannot save empty array')
+ datashape = data.shape
+ datadtype = data.dtype
+ datadtypechar = data.dtype.char
+
+ returnoffset = returnoffset and datadtype.isnative
+ bilevel = datadtypechar == '?'
+ if bilevel:
+ index = -1 if datashape[-1] > 1 else -2
+ datasize = product(datashape[:index])
+ if datashape[index] % 8:
+ datasize *= datashape[index] // 8 + 1
+ else:
+ datasize *= datashape[index] // 8
+ else:
+ datasize = product(datashape) * datadtype.itemsize
+
+ # just append contiguous data if possible
+ self._truncate = bool(truncate)
+ if self._datashape:
+ if (
+ not contiguous
+ or self._datashape[1:] != datashape
+ or self._datadtype != datadtype
+ or (compress and self._tags)
+ or tile
+ or not numpy.array_equal(colormap, self._colormap)
+ ):
+ # incompatible shape, dtype, compression mode, or colormap
+ self._write_remaining_pages()
+ self._write_image_description()
+ self._truncate = False
+ self._descriptionoffset = 0
+ self._descriptionlenoffset = 0
+ self._datashape = None
+ self._colormap = None
+ if self._imagej:
+ raise ValueError(
+ 'ImageJ does not support non-contiguous data')
+ else:
+ # consecutive mode
+ self._datashape = (self._datashape[0] + 1,) + datashape
+ if not compress:
+ # write contiguous data, write IFDs/tags later
+ offset = fh.tell()
+ if data is None:
+ fh.write_empty(datasize)
+ else:
+ fh.write_array(data)
+ if returnoffset:
+ return offset, datasize
+ return None
+
+ input_shape = datashape
+ tagnoformat = self._tagnoformat
+ valueformat = self._valueformat
+ offsetformat = self._offsetformat
+ offsetsize = self._offsetsize
+ tagsize = self._tagsize
+
+ MINISBLACK = TIFF.PHOTOMETRIC.MINISBLACK
+ MINISWHITE = TIFF.PHOTOMETRIC.MINISWHITE
+ RGB = TIFF.PHOTOMETRIC.RGB
+ CFA = TIFF.PHOTOMETRIC.CFA
+ PALETTE = TIFF.PHOTOMETRIC.PALETTE
+ CONTIG = TIFF.PLANARCONFIG.CONTIG
+ SEPARATE = TIFF.PLANARCONFIG.SEPARATE
+
+ # parse input
+ if photometric is not None:
+ photometric = enumarg(TIFF.PHOTOMETRIC, photometric)
+ if planarconfig:
+ planarconfig = enumarg(TIFF.PLANARCONFIG, planarconfig)
+ if extrasamples is None:
+ extrasamples_ = None
+ else:
+ extrasamples_ = tuple(
+ enumarg(TIFF.EXTRASAMPLE, es) for es in sequence(extrasamples)
+ )
+ if not compress:
+ compress = False
+ compresstag = 1
+ # TODO: support predictors without compression
+ predictor = False
+ predictortag = 1
+ else:
+ if isinstance(compress, (tuple, list)):
+ compress, compresslevel = compress
+ elif isinstance(compress, int):
+ compress, compresslevel = 'ADOBE_DEFLATE', int(compress)
+ if not 0 <= compresslevel <= 9:
+ raise ValueError('invalid compression level %s' % compress)
+ else:
+ compresslevel = None
+ compress = compress.upper()
+ compresstag = enumarg(TIFF.COMPRESSION, compress)
+
+ if predictor:
+ if compresstag == 7:
+ predictor = False # disable predictor for lossy compression
+ elif datadtype.kind in 'iu':
+ predictortag = 2
+ predictor = TIFF.PREDICTORS[2]
+ elif datadtype.kind == 'f':
+ predictortag = 3
+ predictor = TIFF.PREDICTORS[3]
+ else:
+ raise ValueError('cannot apply predictor to %s' % datadtype)
+
+ # prepare ImageJ format
+ if self._imagej:
+ # if predictor or compress:
+ # warnings.warn(
+ # 'ImageJ cannot handle predictors or compression')
+ if description:
+ warnings.warn('not writing description to ImageJ file')
+ description = None
+ volume = False
+ if datadtypechar not in 'BHhf':
+ raise ValueError(
+ 'ImageJ does not support data type %s' % datadtypechar)
+ ijrgb = photometric == RGB if photometric else None
+ if datadtypechar not in 'B':
+ ijrgb = False
+ ijshape = imagej_shape(datashape, ijrgb)
+ if ijshape[-1] in (3, 4):
+ photometric = RGB
+ if datadtypechar not in 'B':
+ raise ValueError(
+ 'ImageJ does not support data type %s for RGB'
+ % datadtypechar)
+ elif photometric is None:
+ photometric = MINISBLACK
+ planarconfig = None
+ if planarconfig == SEPARATE:
+ raise ValueError('ImageJ does not support planar images')
+ planarconfig = CONTIG if ijrgb else None
+
+ # verify colormap and indices
+ if colormap is not None:
+ if datadtypechar not in 'BH':
+ raise ValueError('invalid data dtype for palette mode')
+ colormap = numpy.asarray(colormap, dtype=byteorder + 'H')
+ if colormap.shape != (3, 2**(datadtype.itemsize * 8)):
+ raise ValueError('invalid color map shape')
+ self._colormap = colormap
+
+ # verify tile shape
+ if tile:
+ tile = tuple(int(i) for i in tile[:3])
+ volume = len(tile) == 3
+ if (
+ len(tile) < 2
+ or tile[-1] % 16
+ or tile[-2] % 16
+ or any(i < 1 for i in tile)
+ ):
+ raise ValueError('invalid tile shape')
+ else:
+ tile = ()
+ volume = False
+
+ # normalize data shape to 5D or 6D, depending on volume:
+ # (pages, planar_samples, [depth,] height, width, contig_samples)
+ datashape = reshape_nd(datashape, 3 if photometric == RGB else 2)
+ shape = datashape
+ ndim = len(datashape)
+
+ samplesperpixel = 1
+ extrasamples = 0
+ if volume and ndim < 3:
+ volume = False
+ if colormap is not None:
+ photometric = PALETTE
+ planarconfig = None
+ if photometric is None:
+ photometric = MINISBLACK
+ if bilevel:
+ photometric = MINISWHITE
+ elif planarconfig == CONTIG:
+ if ndim > 2 and shape[-1] in (3, 4):
+ photometric = RGB
+ elif planarconfig == SEPARATE:
+ if volume and ndim > 3 and shape[-4] in (3, 4):
+ photometric = RGB
+ elif ndim > 2 and shape[-3] in (3, 4):
+ photometric = RGB
+ elif ndim > 2 and shape[-1] in (3, 4):
+ photometric = RGB
+ elif self._imagej:
+ photometric = MINISBLACK
+ elif volume and ndim > 3 and shape[-4] in (3, 4):
+ photometric = RGB
+ elif ndim > 2 and shape[-3] in (3, 4):
+ photometric = RGB
+ if planarconfig and len(shape) <= (3 if volume else 2):
+ planarconfig = None
+ if photometric not in (0, 1, 3, 4):
+ photometric = MINISBLACK
+ if photometric == RGB:
+ if len(shape) < 3:
+ raise ValueError('not a RGB(A) image')
+ if len(shape) < 4:
+ volume = False
+ if planarconfig is None:
+ if shape[-1] in (3, 4):
+ planarconfig = CONTIG
+ elif shape[-4 if volume else -3] in (3, 4):
+ planarconfig = SEPARATE
+ elif shape[-1] > shape[-4 if volume else -3]:
+ planarconfig = SEPARATE
+ else:
+ planarconfig = CONTIG
+ if planarconfig == CONTIG:
+ datashape = (-1, 1) + shape[(-4 if volume else -3):]
+ samplesperpixel = datashape[-1]
+ else:
+ datashape = (-1,) + shape[(-4 if volume else -3):] + (1,)
+ samplesperpixel = datashape[1]
+ if samplesperpixel > 3:
+ extrasamples = samplesperpixel - 3
+ elif photometric == CFA:
+ if len(shape) != 2:
+ raise ValueError('invalid CFA image')
+ volume = False
+ planarconfig = None
+ datashape = (-1, 1) + shape[-2:] + (1,)
+ if 50706 not in (et[0] for et in extratags):
+ raise ValueError('must specify DNG tags for CFA image')
+ elif planarconfig and len(shape) > (3 if volume else 2):
+ if planarconfig == CONTIG:
+ datashape = (-1, 1) + shape[(-4 if volume else -3):]
+ samplesperpixel = datashape[-1]
+ else:
+ datashape = (-1,) + shape[(-4 if volume else -3):] + (1,)
+ samplesperpixel = datashape[1]
+ extrasamples = samplesperpixel - 1
+ else:
+ planarconfig = None
+ while len(shape) > 2 and shape[-1] == 1:
+ shape = shape[:-1] # remove trailing 1s
+ if len(shape) < 3:
+ volume = False
+ if extrasamples_ is None:
+ datashape = (-1, 1) + shape[(-3 if volume else -2):] + (1,)
+ else:
+ datashape = (-1, 1) + shape[(-4 if volume else -3):]
+ samplesperpixel = datashape[-1]
+ extrasamples = samplesperpixel - 1
+
+ if subfiletype & 0b100:
+ # FILETYPE_MASK
+ if not (
+ bilevel
+ and samplesperpixel == 1
+ and photometric in (0, 1, 4)
+ ):
+ raise ValueError('invalid SubfileType MASK')
+ photometric = TIFF.PHOTOMETRIC.MASK
+
+ if bilevel:
+ bitspersample = 1
+ elif compresstag == 7 and datadtype == 'uint16':
+ bitspersample = 12 # use 12-bit JPEG compression
+ else:
+ bitspersample = datadtype.itemsize * 8
+
+ # normalize shape to 6D
+ assert len(datashape) in (5, 6)
+ if len(datashape) == 5:
+ datashape = datashape[:2] + (1,) + datashape[2:]
+ if datashape[0] == -1:
+ s0 = product(input_shape) // product(datashape[1:])
+ datashape = (s0,) + datashape[1:]
+ shape = datashape
+ if data is not None:
+ data = data.reshape(shape)
+
+ if photometric == PALETTE:
+ if (
+ samplesperpixel != 1
+ or extrasamples
+ or shape[1] != 1
+ or shape[-1] != 1
+ ):
+ raise ValueError('invalid data shape for palette mode')
+
+ if photometric == RGB and samplesperpixel == 2:
+ raise ValueError('not a RGB image (samplesperpixel=2)')
+
+ if bilevel:
+ if compresstag not in (1, 32773):
+ raise ValueError('cannot compress bilevel image')
+ if tile:
+ raise ValueError('cannot save tiled bilevel image')
+ if photometric not in (0, 1, 4):
+ raise ValueError('cannot save bilevel image as %s'
+ % str(photometric))
+ datashape = list(datashape)
+ if datashape[-2] % 8:
+ datashape[-2] = datashape[-2] // 8 + 1
+ else:
+ datashape[-2] = datashape[-2] // 8
+ datashape = tuple(datashape)
+ assert datasize == product(datashape)
+ if data is not None:
+ data = numpy.packbits(data, axis=-2)
+ assert datashape[-2] == data.shape[-2]
+
+ tags = [] # list of (code, ifdentry, ifdvalue, writeonce)
+
+ strip_or_tile = 'Tile' if tile else 'Strip'
+ tagbytecounts = TIFF.TAG_NAMES[strip_or_tile + 'ByteCounts']
+ tagoffsets = TIFF.TAG_NAMES[strip_or_tile + 'Offsets']
+ self._tagoffsets = tagoffsets
+
+ def pack(fmt, *val):
+ return struct.pack(byteorder + fmt, *val)
+
+ def addtag(code, dtype, count, value, writeonce=False):
+ # Compute ifdentry & ifdvalue bytes from code, dtype, count, value
+ # Append (code, ifdentry, ifdvalue, writeonce) to tags list
+ code = int(TIFF.TAG_NAMES.get(code, code))
+ try:
+ tifftype = TIFF.DATA_DTYPES[dtype]
+ except KeyError:
+ raise ValueError('unknown dtype %s' % dtype)
+ rawcount = count
+
+ if dtype == 's':
+ # strings; enforce 7-bit ASCII on unicode strings
+ value = bytestr(value, 'ascii') + b'\0'
+ count = rawcount = len(value)
+ rawcount = value.find(b'\0\0')
+ if rawcount < 0:
+ rawcount = count
+ else:
+ rawcount += 1 # length of string without buffer
+ value = (value,)
+ elif isinstance(value, bytes):
+ # packed binary data
+ dtsize = struct.calcsize(dtype)
+ if len(value) % dtsize:
+ raise ValueError('invalid packed binary data')
+ count = len(value) // dtsize
+ if len(dtype) > 1:
+ count *= int(dtype[:-1])
+ dtype = dtype[-1]
+ ifdentry = [pack('HH', code, tifftype),
+ pack(offsetformat, rawcount)]
+ ifdvalue = None
+ if struct.calcsize(dtype) * count <= offsetsize:
+ # value(s) can be written directly
+ if isinstance(value, bytes):
+ ifdentry.append(pack(valueformat, value))
+ elif count == 1:
+ if isinstance(value, (tuple, list, numpy.ndarray)):
+ value = value[0]
+ ifdentry.append(pack(valueformat, pack(dtype, value)))
+ else:
+ ifdentry.append(pack(valueformat,
+ pack(str(count) + dtype, *value)))
+ else:
+ # use offset to value(s)
+ ifdentry.append(pack(offsetformat, 0))
+ if isinstance(value, bytes):
+ ifdvalue = value
+ elif isinstance(value, numpy.ndarray):
+ assert value.size == count
+ assert value.dtype.char == dtype
+ ifdvalue = value.tostring()
+ elif isinstance(value, (tuple, list)):
+ ifdvalue = pack(str(count) + dtype, *value)
+ else:
+ ifdvalue = pack(dtype, value)
+ tags.append((code, b''.join(ifdentry), ifdvalue, writeonce))
+
+ def rational(arg, max_denominator=1000000):
+ """"Return nominator and denominator from float or two integers."""
+ from fractions import Fraction # delayed import
+
+ try:
+ f = Fraction.from_float(arg)
+ except TypeError:
+ f = Fraction(arg[0], arg[1])
+ f = f.limit_denominator(max_denominator)
+ return f.numerator, f.denominator
+
+ if description:
+ # user provided description
+ addtag('ImageDescription', 's', 0, description, writeonce=True)
+
+ # write shape and metadata to ImageDescription
+ self._metadata = {} if not metadata else metadata.copy()
+ if self._imagej:
+ description = imagej_description(
+ input_shape,
+ shape[-1] in (3, 4),
+ self._colormap is not None,
+ **self._metadata)
+ elif metadata or metadata == {}:
+ if self._truncate:
+ self._metadata.update(truncated=True)
+ description = json_description(input_shape, **self._metadata)
+ # elif metadata is None and self._truncate:
+ # raise ValueError('cannot truncate without writing metadata')
+ else:
+ description = None
+ if description:
+ # add 64 bytes buffer
+ # the image description might be updated later with the final shape
+ description = str2bytes(description, 'ascii')
+ description += b'\0' * 64
+ self._descriptionlen = len(description)
+ addtag('ImageDescription', 's', 0, description, writeonce=True)
+
+ if software:
+ addtag('Software', 's', 0, software, writeonce=True)
+ if datetime:
+ if isinstance(datetime, str):
+ if len(datetime) != 19 or datetime[16] != ':':
+ raise ValueError('invalid datetime string')
+ else:
+ try:
+ datetime = datetime.strftime('%Y:%m:%d %H:%M:%S')
+ except AttributeError:
+ datetime = self._now().strftime('%Y:%m:%d %H:%M:%S')
+ addtag('DateTime', 's', 0, datetime, writeonce=True)
+ addtag('Compression', 'H', 1, compresstag)
+ if predictor:
+ addtag('Predictor', 'H', 1, predictortag)
+ addtag('ImageWidth', 'I', 1, shape[-2])
+ addtag('ImageLength', 'I', 1, shape[-3])
+ if tile:
+ addtag('TileWidth', 'I', 1, tile[-1])
+ addtag('TileLength', 'I', 1, tile[-2])
+ if volume:
+ addtag('ImageDepth', 'I', 1, shape[-4])
+ addtag('TileDepth', 'I', 1, tile[0])
+ addtag('NewSubfileType', 'I', 1, subfiletype)
+ if not bilevel and not datadtype.kind == 'u':
+ sampleformat = {'u': 1, 'i': 2, 'f': 3, 'c': 6}[datadtype.kind]
+ addtag('SampleFormat', 'H', samplesperpixel,
+ (sampleformat,) * samplesperpixel)
+ if colormap is not None:
+ addtag('ColorMap', 'H', colormap.size, colormap)
+ addtag('SamplesPerPixel', 'H', 1, samplesperpixel)
+ if bilevel:
+ pass
+ elif planarconfig and samplesperpixel > 1:
+ addtag('PlanarConfiguration', 'H', 1, planarconfig.value)
+ addtag('BitsPerSample', 'H', samplesperpixel,
+ (bitspersample,) * samplesperpixel)
+ else:
+ addtag('BitsPerSample', 'H', 1, bitspersample)
+ if extrasamples:
+ if extrasamples_ is not None:
+ if extrasamples != len(extrasamples_):
+ raise ValueError('wrong number of extrasamples specified')
+ addtag('ExtraSamples', 'H', extrasamples, extrasamples_)
+ elif photometric == RGB and extrasamples == 1:
+ # Unassociated alpha channel
+ addtag('ExtraSamples', 'H', 1, 2)
+ else:
+ # Unspecified alpha channel
+ addtag('ExtraSamples', 'H', extrasamples, (0,) * extrasamples)
+
+ if compresstag == 7 and photometric == RGB and planarconfig == 1:
+ # JPEG compression with subsampling. Store as YCbCr
+ # TODO: use JPEGTables for multiple tiles or strips
+ if subsampling is None:
+ subsampling = (2, 2)
+ elif subsampling not in ((1, 1), (2, 1), (2, 2), (4, 1)):
+ raise ValueError('invalid subsampling factors')
+ maxsampling = max(subsampling) * 8
+ if tile and (tile[-1] % maxsampling or tile[-2] % maxsampling):
+ raise ValueError('tile shape not a multiple of %i'
+ % maxsampling)
+ if extrasamples > 1:
+ raise ValueError('JPEG subsampling requires RGB(A) images')
+ addtag('YCbCrSubSampling', 'H', 2, subsampling)
+ addtag('PhotometricInterpretation', 'H', 1, 6) # YCBCR
+ else:
+ if subsampling not in (None, (1, 1)):
+ log.warning('cannot apply subsampling')
+ subsampling = None
+ maxsampling = 1
+ addtag('PhotometricInterpretation', 'H', 1, photometric.value)
+ if compresstag == 7:
+ addtag('YCbCrSubSampling', 'H', 2, (1, 1))
+
+ if resolution is not None:
+ addtag('XResolution', '2I', 1, rational(resolution[0]))
+ addtag('YResolution', '2I', 1, rational(resolution[1]))
+ if len(resolution) > 2:
+ unit = resolution[2]
+ unit = 1 if unit is None else enumarg(TIFF.RESUNIT, unit)
+ elif self._imagej:
+ unit = 1
+ else:
+ unit = 2
+ addtag('ResolutionUnit', 'H', 1, unit)
+ elif not self._imagej:
+ addtag('XResolution', '2I', 1, (1, 1))
+ addtag('YResolution', '2I', 1, (1, 1))
+ addtag('ResolutionUnit', 'H', 1, 1)
+ if ijmetadata:
+ for t in imagej_metadata_tag(ijmetadata, byteorder):
+ addtag(*t)
+
+ def bytecount_format(bytecounts, compress=compress, size=offsetsize):
+ """Return bytecount format."""
+ if len(bytecounts) == 1:
+ return {4: 'I', 8: 'Q'}[size]
+ bytecount = bytecounts[0]
+ if compress:
+ bytecount = bytecount * 10
+ if bytecount < 2**16:
+ return 'H'
+ if bytecount < 2**32:
+ return 'I'
+ if size == 4:
+ return 'I'
+ return 'Q'
+
+ contiguous = not compress
+ if tile:
+ # one chunk per tile per plane
+ if len(tile) == 3:
+ tiles = (
+ (shape[2] + tile[0] - 1) // tile[0],
+ (shape[3] + tile[1] - 1) // tile[1],
+ (shape[4] + tile[2] - 1) // tile[2],
+ )
+ else:
+ tiles = (
+ (shape[3] + tile[0] - 1) // tile[0],
+ (shape[4] + tile[1] - 1) // tile[1],
+ )
+ numtiles = product(tiles) * shape[1]
+ databytecounts = [
+ product(tile) * shape[-1] * datadtype.itemsize] * numtiles
+ bytecountformat = bytecount_format(databytecounts)
+ addtag(tagbytecounts, bytecountformat, numtiles, databytecounts)
+ addtag(tagoffsets, offsetformat, numtiles, [0] * numtiles)
+ contiguous = contiguous and product(tiles) == 1
+ if not contiguous:
+ # allocate tile buffer
+ chunk = numpy.empty(tile + (shape[-1],), dtype=datadtype)
+ bytecountformat = bytecountformat * numtiles
+ elif contiguous and (bilevel or rowsperstrip is None):
+ # one strip per plane
+ if bilevel:
+ databytecounts = [product(datashape[2:])] * shape[1]
+ else:
+ databytecounts = [
+ product(datashape[2:]) * datadtype.itemsize] * shape[1]
+ bytecountformat = bytecount_format(databytecounts)
+ addtag(tagbytecounts, bytecountformat, shape[1], databytecounts)
+ addtag(tagoffsets, offsetformat, shape[1], [0] * shape[1])
+ addtag('RowsPerStrip', 'I', 1, shape[-3])
+ bytecountformat = bytecountformat * shape[1]
+ else:
+ # use rowsperstrip
+ rowsize = product(shape[-2:]) * datadtype.itemsize
+ if rowsperstrip is None:
+ # compress ~64 KB chunks by default
+ rowsperstrip = 65536 // rowsize if compress else shape[-3]
+ if rowsperstrip < 1:
+ rowsperstrip = maxsampling
+ elif rowsperstrip > shape[-3]:
+ rowsperstrip = shape[-3]
+ elif subsampling and rowsperstrip % maxsampling:
+ rowsperstrip = (math.ceil(rowsperstrip / maxsampling) *
+ maxsampling)
+ addtag('RowsPerStrip', 'I', 1, rowsperstrip)
+
+ numstrips1 = (shape[-3] + rowsperstrip - 1) // rowsperstrip
+ numstrips = numstrips1 * shape[1]
+ # TODO: save bilevel data with rowsperstrip
+ stripsize = rowsperstrip * rowsize
+ databytecounts = [stripsize] * numstrips
+ stripsize -= rowsize * (numstrips1 * rowsperstrip - shape[-3])
+ for i in range(numstrips1 - 1, numstrips, numstrips1):
+ databytecounts[i] = stripsize
+ bytecountformat = bytecount_format(databytecounts)
+ addtag(tagbytecounts, bytecountformat, numstrips, databytecounts)
+ addtag(tagoffsets, offsetformat, numstrips, [0] * numstrips)
+ bytecountformat = bytecountformat * numstrips
+
+ if data is None and not contiguous:
+ raise ValueError('cannot write non-contiguous empty file')
+
+ # add extra tags from user
+ for t in extratags:
+ addtag(*t)
+
+ # define compress function
+ if compress:
+ compressor = TIFF.COMPESSORS[compresstag]
+ if predictor:
+
+ def compress(data, compressor=compressor, level=compresslevel):
+ data = predictor(data, axis=-2)
+ return compressor(data, level)
+
+ elif subsampling:
+ # JPEG with subsampling. Store RGB as YCbCr
+ # TODO: use JPEGTables for multiple tiles or strips
+ def compress(data, compressor=compressor, level=compresslevel,
+ subsampling=subsampling):
+ return compressor(data, level, subsampling=subsampling,
+ colorspace=2, outcolorspace=3)
+
+ else:
+
+ def compress(data, compressor=compressor, level=compresslevel):
+ return compressor(data, level)
+
+ # TODO: check TIFFReadDirectoryCheckOrder warning in files containing
+ # multiple tags of same code
+ # the entries in an IFD must be sorted in ascending order by tag code
+ tags = sorted(tags, key=lambda x: x[0])
+
+ fhpos = fh.tell()
+ if (
+ not (self._bigtiff or self._imagej)
+ and fhpos + datasize > 2**32 - 1
+ ):
+ raise ValueError('data too large for standard TIFF file')
+
+ # if not compressed or multi-tiled, write the first IFD and then
+ # all data contiguously; else, write all IFDs and data interleaved
+ for pageindex in range(1 if contiguous else shape[0]):
+
+ ifdpos = fhpos
+ if ifdpos % 2:
+ # location of IFD must begin on a word boundary
+ fh.write(b'\0')
+ ifdpos += 1
+
+ # update pointer at ifdoffset
+ fh.seek(self._ifdoffset)
+ fh.write(pack(offsetformat, ifdpos))
+ fh.seek(ifdpos)
+
+ # create IFD in memory
+ if pageindex < 2:
+ ifd = io.BytesIO()
+ ifd.write(pack(tagnoformat, len(tags)))
+ tagoffset = ifd.tell()
+ ifd.write(b''.join(t[1] for t in tags))
+ ifdoffset = ifd.tell()
+ ifd.write(pack(offsetformat, 0)) # offset to next IFD
+ # write tag values and patch offsets in ifdentries
+ for tagindex, tag in enumerate(tags):
+ offset = tagoffset + tagindex * tagsize + offsetsize + 4
+ code = tag[0]
+ value = tag[2]
+ if value:
+ pos = ifd.tell()
+ if pos % 2:
+ # tag value is expected to begin on word boundary
+ ifd.write(b'\0')
+ pos += 1
+ ifd.seek(offset)
+ ifd.write(pack(offsetformat, ifdpos + pos))
+ ifd.seek(pos)
+ ifd.write(value)
+ if code == tagoffsets:
+ dataoffsetsoffset = offset, pos
+ elif code == tagbytecounts:
+ databytecountsoffset = offset, pos
+ elif code == 270 and value.endswith(b'\0\0\0\0'):
+ # image description buffer
+ self._descriptionoffset = ifdpos + pos
+ self._descriptionlenoffset = (
+ ifdpos + tagoffset + tagindex * tagsize + 4)
+ elif code == tagoffsets:
+ dataoffsetsoffset = offset, None
+ elif code == tagbytecounts:
+ databytecountsoffset = offset, None
+ ifdsize = ifd.tell()
+ if ifdsize % 2:
+ ifd.write(b'\0')
+ ifdsize += 1
+
+ # write IFD later when strip/tile bytecounts and offsets are known
+ fh.seek(ifdsize, 1)
+
+ # write image data
+ dataoffset = fh.tell()
+ skip = align - dataoffset % align
+ fh.seek(skip, 1)
+ dataoffset += skip
+ if contiguous:
+ if data is None:
+ fh.write_empty(datasize)
+ else:
+ fh.write_array(data)
+ elif tile:
+ # TODO: refactor this
+ # TODO: use multithreading and chunk buffer?
+ if data is None:
+ fh.write_empty(numtiles * databytecounts[0])
+ elif len(tile) == 3:
+ stripindex = 0
+ for plane in data[pageindex]:
+ for tz in range(tiles[0]):
+ for ty in range(tiles[1]):
+ for tx in range(tiles[2]):
+ c0 = min(tile[0], shape[2] - tz * tile[0])
+ c1 = min(tile[1], shape[3] - ty * tile[1])
+ c2 = min(tile[2], shape[4] - tx * tile[2])
+ chunk[c0:, c1:, c2:] = 0
+ chunk[:c0, :c1, :c2] = plane[
+ tz * tile[0]: tz * tile[0] + c0,
+ ty * tile[1]: ty * tile[1] + c1,
+ tx * tile[2]: tx * tile[2] + c2,
+ ]
+ if compress:
+ t = compress(chunk)
+ fh.write(t)
+ databytecounts[stripindex] = len(t)
+ stripindex += 1
+ else:
+ fh.write_array(chunk)
+ # fh.flush()
+ else:
+ stripindex = 0
+ for plane in data[pageindex]:
+ for ty in range(tiles[0]):
+ for tx in range(tiles[1]):
+ c1 = min(tile[0], shape[3] - ty * tile[0])
+ c2 = min(tile[1], shape[4] - tx * tile[1])
+ chunk[c1:, c2:] = 0
+ chunk[:c1, :c2] = plane[
+ 0,
+ ty * tile[0]: ty * tile[0] + c1,
+ tx * tile[1]: tx * tile[1] + c2,
+ ]
+ if compress:
+ t = compress(chunk)
+ fh.write(t)
+ databytecounts[stripindex] = len(t)
+ stripindex += 1
+ else:
+ fh.write_array(chunk)
+ # fh.flush()
+ elif compress:
+ # write one strip per rowsperstrip
+ assert data.shape[2] == 1 # not handling depth
+ numstrips = (shape[-3] + rowsperstrip - 1) // rowsperstrip
+ stripindex = 0
+ for plane in data[pageindex]:
+ for i in range(numstrips):
+ strip = plane[
+ 0,
+ i * rowsperstrip: (i + 1) * rowsperstrip
+ ]
+ strip = compress(strip)
+ fh.write(strip)
+ databytecounts[stripindex] = len(strip)
+ stripindex += 1
+ else:
+ fh.write_array(data[pageindex])
+
+ # update strip/tile offsets
+ offset, pos = dataoffsetsoffset
+ ifd.seek(offset)
+ if pos:
+ ifd.write(pack(offsetformat, ifdpos + pos))
+ ifd.seek(pos)
+ offset = dataoffset
+ for size in databytecounts:
+ ifd.write(pack(offsetformat, offset))
+ offset += size
+ else:
+ ifd.write(pack(offsetformat, dataoffset))
+
+ if compress:
+ # update strip/tile bytecounts
+ offset, pos = databytecountsoffset
+ ifd.seek(offset)
+ if pos:
+ ifd.write(pack(offsetformat, ifdpos + pos))
+ ifd.seek(pos)
+ ifd.write(pack(bytecountformat, *databytecounts))
+
+ fhpos = fh.tell()
+ fh.seek(ifdpos)
+ fh.write(iogetbuffer(ifd))
+ fh.flush()
+ fh.seek(fhpos)
+
+ self._ifdoffset = ifdpos + ifdoffset
+
+ # remove tags that should be written only once
+ if pageindex == 0:
+ tags = [tag for tag in tags if not tag[-1]]
+
+ self._shape = shape
+ self._datashape = (1,) + input_shape
+ self._datadtype = datadtype
+ self._dataoffset = dataoffset
+ self._databytecounts = databytecounts
+
+ if contiguous:
+ # write remaining IFDs/tags later
+ self._tags = tags
+ # return offset and size of image data
+ if returnoffset:
+ return dataoffset, sum(databytecounts)
+ return None
+
+ def _write_remaining_pages(self):
+ """Write outstanding IFDs and tags to file."""
+ if not self._tags or self._truncate:
+ return
+
+ pageno = self._shape[0] * self._datashape[0] - 1
+ if pageno < 1:
+ self._tags = None
+ self._datadtype = None
+ self._dataoffset = None
+ self._databytecounts = None
+ return
+
+ fh = self._fh
+ fhpos = fh.tell()
+ if fhpos % 2:
+ fh.write(b'\0')
+ fhpos += 1
+
+ pack = struct.pack
+ offsetformat = self._byteorder + self._offsetformat
+ offsetsize = self._offsetsize
+ tagnoformat = self._byteorder + self._tagnoformat
+ tagsize = self._tagsize
+ dataoffset = self._dataoffset
+ pagedatasize = sum(self._databytecounts)
+
+ # construct template IFD in memory
+ # need to patch offsets to next IFD and data before writing to file
+ ifd = io.BytesIO()
+ ifd.write(pack(tagnoformat, len(self._tags)))
+ tagoffset = ifd.tell()
+ ifd.write(b''.join(t[1] for t in self._tags))
+ ifdoffset = ifd.tell()
+ ifd.write(pack(offsetformat, 0)) # offset to next IFD
+ # tag values
+ for tagindex, tag in enumerate(self._tags):
+ offset = tagoffset + tagindex * tagsize + offsetsize + 4
+ code = tag[0]
+ value = tag[2]
+ if value:
+ pos = ifd.tell()
+ if pos % 2:
+ # tag value is expected to begin on word boundary
+ ifd.write(b'\0')
+ pos += 1
+ ifd.seek(offset)
+ try:
+ ifd.write(pack(offsetformat, fhpos + pos))
+ except Exception: # struct.error
+ if self._imagej:
+ warnings.warn('truncating ImageJ file')
+ self._truncate = True
+ return
+ raise ValueError('data too large for non-BigTIFF file')
+ ifd.seek(pos)
+ ifd.write(value)
+ if code == self._tagoffsets:
+ # save strip/tile offsets for later updates
+ dataoffsetsoffset = offset, pos
+ elif code == self._tagoffsets:
+ dataoffsetsoffset = offset, None
+
+ ifdsize = ifd.tell()
+ if ifdsize % 2:
+ ifd.write(b'\0')
+ ifdsize += 1
+
+ # check if all IFDs fit in file
+ if not self._bigtiff and fhpos + ifdsize * pageno > 2**32 - 32:
+ if self._imagej:
+ warnings.warn('truncating ImageJ file')
+ self._truncate = True
+ return
+ raise ValueError('data too large for non-BigTIFF file')
+
+ # assemble IFD chain in memory from IFD template
+ ifds = io.BytesIO(bytes(ifdsize * pageno))
+ ifdpos = fhpos
+ for _ in range(pageno):
+ # update strip/tile offsets in IFD
+ dataoffset += pagedatasize # offset to image data
+ offset, pos = dataoffsetsoffset
+ ifd.seek(offset)
+ if pos:
+ ifd.write(pack(offsetformat, ifdpos + pos))
+ ifd.seek(pos)
+ offset = dataoffset
+ for size in self._databytecounts:
+ ifd.write(pack(offsetformat, offset))
+ offset += size
+ else:
+ ifd.write(pack(offsetformat, dataoffset))
+ # update pointer at ifdoffset to point to next IFD in file
+ ifdpos += ifdsize
+ ifd.seek(ifdoffset)
+ ifd.write(pack(offsetformat, ifdpos))
+ # write IFD entry
+ ifds.write(iogetbuffer(ifd))
+
+ # terminate IFD chain
+ ifdoffset += ifdsize * (pageno - 1)
+ ifds.seek(ifdoffset)
+ ifds.write(pack(offsetformat, 0))
+ # write IFD chain to file
+ fh.write(iogetbuffer(ifds))
+ # update file to point to new IFD chain
+ pos = fh.tell()
+ fh.seek(self._ifdoffset)
+ fh.write(pack(offsetformat, fhpos))
+ fh.flush()
+ fh.seek(pos)
+
+ self._ifdoffset = fhpos + ifdoffset
+ self._tags = None
+ self._datadtype = None
+ self._dataoffset = None
+ self._databytecounts = None
+ # do not reset _shape or _datashape
+
+ def _write_image_description(self):
+ """Write metadata to ImageDescription tag."""
+ if (
+ not self._datashape
+ or self._datashape[0] == 1
+ or self._descriptionoffset <= 0
+ ):
+ return
+
+ colormapped = self._colormap is not None
+ if self._imagej:
+ isrgb = self._shape[-1] in (3, 4)
+ description = imagej_description(
+ self._datashape, isrgb, colormapped, **self._metadata)
+ else:
+ description = json_description(self._datashape, **self._metadata)
+
+ # rewrite description and its length to file
+ description = description.encode('utf-8')
+ description = description[:self._descriptionlen - 1]
+ pos = self._fh.tell()
+ self._fh.seek(self._descriptionoffset)
+ self._fh.write(description)
+ self._fh.seek(self._descriptionlenoffset)
+ self._fh.write(struct.pack(self._byteorder + self._offsetformat,
+ len(description) + 1))
+ self._fh.seek(pos)
+
+ self._descriptionoffset = 0
+ self._descriptionlenoffset = 0
+ self._descriptionlen = 0
+
+ def _now(self):
+ """Return current date and time."""
+ return datetime.datetime.now()
+
+ def close(self):
+ """Write remaining pages and close file handle."""
+ if not self._truncate:
+ self._write_remaining_pages()
+ self._write_image_description()
+ self._fh.close()
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ self.close()
+
+
+class TiffFile(object):
+ """Read image and metadata from TIFF file.
+
+ TiffFile instances must be closed using the 'close' method, which is
+ automatically called when using the 'with' context manager.
+
+ Attributes
+ ----------
+ pages : TiffPages
+ Sequence of TIFF pages in file.
+ series : list of TiffPageSeries
+ Sequences of closely related TIFF pages. These are computed
+ from OME, LSM, ImageJ, etc. metadata or based on similarity
+ of page properties such as shape, dtype, and compression.
+ is_flag : bool
+ If True, file is of a certain format.
+ Flags are: bigtiff, uniform, shaped, ome, imagej, stk, lsm, fluoview,
+ nih, vista, micromanager, metaseries, mdgel, mediacy, tvips, fei,
+ sem, scn, svs, scanimage, andor, epics, ndpi, pilatus, qpi.
+
+ All attributes are read-only.
+
+ """
+
+ def __init__(self, arg, name=None, offset=None, size=None,
+ multifile=True, _useframes=None, **kwargs):
+ """Initialize instance from file.
+
+ Parameters
+ ----------
+ arg : str or open file
+ Name of file or open file object.
+ The file objects are closed in TiffFile.close().
+ name : str
+ Optional name of file in case 'arg' is a file handle.
+ offset : int
+ Optional start position of embedded file. By default, this is
+ the current file position.
+ size : int
+ Optional size of embedded file. By default, this is the number
+ of bytes from the 'offset' to the end of the file.
+ multifile : bool
+ If True (default), series may include pages from multiple files.
+ Currently applies to OME-TIFF only.
+ kwargs : bool
+ 'is_ome': If False, disable processing of OME-XML metadata.
+
+ """
+ if kwargs:
+ for key in ('movie', 'fastij', 'multifile_close'):
+ if key in kwargs:
+ del kwargs[key]
+ log.warning("TiffFile: the '%s' argument is ignored" % key)
+ if 'pages' in kwargs:
+ raise TypeError(
+ "the TiffFile 'pages' argument is no longer supported.\n\n"
+ "Use TiffFile.asarray(keys=[...]) to read image data "
+ "from specific pages.\n")
+
+ for key, value in kwargs.items():
+ if key[:3] == 'is_' and key[3:] in TIFF.FILE_FLAGS:
+ if value is not None and not value:
+ setattr(self, key, bool(value))
+ else:
+ raise TypeError('unexpected keyword argument: %s' % key)
+
+ fh = FileHandle(arg, mode='rb', name=name, offset=offset, size=size)
+ self._fh = fh
+ self._multifile = bool(multifile)
+ self._files = {fh.name: self} # cache of TiffFiles
+ try:
+ fh.seek(0)
+ header = fh.read(4)
+ try:
+ byteorder = {b'II': '<', b'MM': '>'}[header[:2]]
+ except KeyError:
+ raise TiffFileError('not a TIFF file')
+
+ version = struct.unpack(byteorder + 'H', header[2:4])[0]
+ if version == 43:
+ # BigTiff
+ offsetsize, zero = struct.unpack(byteorder + 'HH', fh.read(4))
+ if zero != 0 or offsetsize != 8:
+ raise TiffFileError('invalid BigTIFF file')
+ if byteorder == '>':
+ self.tiff = TIFF.BIG_BE
+ else:
+ self.tiff = TIFF.BIG_LE
+ elif version == 42:
+ # Classic TIFF
+ if byteorder == '>':
+ self.tiff = TIFF.CLASSIC_BE
+ elif kwargs.get('is_ndpi', False):
+ # NDPI uses 64 bit IFD offsets
+ # TODO: fix offsets in NDPI tags if file size > 4 GB
+ self.tiff = TIFF.NDPI_LE
+ else:
+ self.tiff = TIFF.CLASSIC_LE
+ else:
+ raise TiffFileError('invalid TIFF file')
+
+ # file handle is at offset to offset to first page
+ self.pages = TiffPages(self)
+
+ if self.is_lsm and (
+ self.filehandle.size >= 2**32
+ or self.pages[0].compression != 1
+ or self.pages[1].compression != 1
+ ):
+ self._lsm_load_pages()
+ elif self.is_scanimage and (
+ not self.is_bigtiff and self.filehandle.size >= 2**31
+ ):
+ self.pages._load_virtual_frames()
+ elif _useframes:
+ self.pages.useframes = True
+
+ except Exception:
+ fh.close()
+ raise
+
+ @property
+ def byteorder(self):
+ return self.tiff.byteorder
+
+ @property
+ def is_bigtiff(self):
+ return self.tiff.version == 43
+
+ @property
+ def filehandle(self):
+ """Return file handle."""
+ return self._fh
+
+ @property
+ def filename(self):
+ """Return name of file handle."""
+ return self._fh.name
+
+ @lazyattr
+ def fstat(self):
+ """Return status of file handle as stat_result object."""
+ try:
+ return os.fstat(self._fh.fileno())
+ except Exception: # io.UnsupportedOperation
+ return None
+
+ def close(self):
+ """Close open file handle(s)."""
+ for tif in self._files.values():
+ tif.filehandle.close()
+ self._files = {}
+
+ def asarray(self, key=None, series=None, out=None, validate=True,
+ maxworkers=None):
+ """Return image data from selected TIFF page(s) as numpy array.
+
+ By default, the data from the first series is returned.
+
+ Parameters
+ ----------
+ key : int, slice, or sequence of indices
+ Defines which pages to return as array.
+ If None (default), data from a series (default 0) is returned.
+ If not None, data from the specified pages in the whole file
+ (if 'series' is None) or a specified series are returned as a
+ stacked array.
+ Requesting an array from multiple pages that are not compatible
+ wrt. shape, dtype, compression etc is undefined, i.e. may crash
+ or return incorrect values.
+ series : int or TiffPageSeries
+ Defines which series of pages to return as array.
+ out : numpy.ndarray, str, or file-like object
+ Buffer where image data will be saved.
+ If None (default), a new array will be created.
+ If numpy.ndarray, a writable array of compatible dtype and shape.
+ If 'memmap', directly memory-map the image data in the TIFF file
+ if possible; else create a memory-mapped array in a temporary file.
+ If str or open file, the file name or file object used to
+ create a memory-map to an array stored in a binary file on disk.
+ validate : bool
+ If True (default), validate various tags.
+ Passed to TiffPage.asarray().
+ maxworkers : int or None
+ Maximum number of threads to concurrently get data from multiple
+ pages or compressed segments.
+ If None (default), up to half the CPU cores are used.
+ If 1, multi-threading is disabled.
+ Reading data from file is limited to a single thread.
+ Using multiple threads can significantly speed up this function
+ if the bottleneck is decoding compressed data, e.g. in case of
+ large LZW compressed LSM files or JPEG compressed tiled slides.
+ If the bottleneck is I/O or pure Python code, using multiple
+ threads might be detrimental.
+
+ Returns
+ -------
+ numpy.ndarray
+ Image data from the specified pages.
+ See the TiffPage.asarray function for operations that are
+ applied (or not) to the raw data stored in the file.
+
+ """
+ if not self.pages:
+ return numpy.array([])
+ if key is None and series is None:
+ series = 0
+ if series is None:
+ pages = self.pages
+ else:
+ try:
+ series = self.series[series]
+ except (KeyError, TypeError):
+ pass
+ pages = series.pages
+
+ if key is None:
+ pass
+ elif series is None:
+ pages = self.pages._getlist(key)
+ elif isinstance(key, inttypes):
+ pages = [pages[key]]
+ elif isinstance(key, slice):
+ pages = pages[key]
+ elif isinstance(key, Iterable):
+ pages = [pages[k] for k in key]
+ else:
+ raise TypeError('key must be an int, slice, or sequence')
+
+ if not pages:
+ raise ValueError('no pages selected')
+
+ if key is None and series and series.offset:
+ typecode = self.byteorder + series.dtype.char
+ if (
+ pages[0].is_memmappable
+ and isinstance(out, str)
+ and out == 'memmap'
+ ):
+ # direct mapping
+ result = self.filehandle.memmap_array(
+ typecode, series.shape, series.offset)
+ else:
+ # read into output
+ if out is not None:
+ out = create_output(out, series.shape, series.dtype)
+ self.filehandle.seek(series.offset)
+ result = self.filehandle.read_array(
+ typecode, product(series.shape), out=out)
+ elif len(pages) == 1:
+ result = pages[0].asarray(out=out, validate=validate,
+ maxworkers=maxworkers)
+ else:
+ result = stack_pages(pages, out=out, maxworkers=maxworkers)
+
+ if result is None:
+ return None
+
+ if key is None:
+ try:
+ result.shape = series.shape
+ except ValueError:
+ try:
+ log.warning('TiffFile.asarray: failed to reshape %s to %s',
+ result.shape, series.shape)
+ # try series of expected shapes
+ result.shape = (-1,) + series.shape
+ except ValueError:
+ # revert to generic shape
+ result.shape = (-1,) + pages[0].shape
+ elif len(pages) == 1:
+ result.shape = pages[0].shape
+ else:
+ result.shape = (-1,) + pages[0].shape
+ return result
+
+ @lazyattr
+ def series(self):
+ """Return related pages as TiffPageSeries.
+
+ Side effect: after calling this function, TiffFile.pages might contain
+ TiffPage and TiffFrame instances.
+
+ """
+ if not self.pages:
+ return []
+
+ useframes = self.pages.useframes
+ keyframe = self.pages.keyframe.index
+ series = []
+ for name in (
+ 'lsm',
+ 'ome',
+ 'imagej',
+ 'shaped',
+ 'fluoview',
+ 'sis',
+ 'uniform',
+ 'mdgel',
+ ):
+ if getattr(self, 'is_' + name, False):
+ series = getattr(self, '_series_' + name)()
+ break
+ self.pages.useframes = useframes
+ self.pages.keyframe = keyframe
+ if not series:
+ series = self._series_generic()
+
+ # remove empty series, e.g. in MD Gel files
+ series = [s for s in series if product(s.shape) > 0]
+
+ for i, s in enumerate(series):
+ s.index = i
+ return series
+
+ def _series_generic(self):
+ """Return image series in file.
+
+ A series is a sequence of TiffPages with the same hash.
+
+ """
+ pages = self.pages
+ pages._clear(False)
+ pages.useframes = False
+ if pages.cache:
+ pages._load()
+
+ result = []
+ keys = []
+ series = {}
+ for page in pages:
+ if not page.shape or product(page.shape) == 0:
+ continue
+ key = page.hash
+ if key in series:
+ series[key].append(page)
+ else:
+ keys.append(key)
+ series[key] = [page]
+
+ for key in keys:
+ pages = series[key]
+ page = pages[0]
+ shape = page.shape
+ axes = page.axes
+ if len(pages) > 1:
+ shape = (len(pages),) + shape
+ axes = 'I' + axes
+ result.append(
+ TiffPageSeries(pages, shape, page.dtype, axes, kind='Generic')
+ )
+
+ self.is_uniform = len(result) == 1
+ return result
+
+ def _series_uniform(self):
+ """Return all images in file as single series."""
+ page = self.pages[0]
+ shape = page.shape
+ axes = page.axes
+ dtype = page.dtype
+ validate = not (page.is_scanimage or page.is_nih)
+ pages = self.pages._getlist(validate=validate)
+ lenpages = len(pages)
+ if lenpages > 1:
+ shape = (lenpages,) + shape
+ axes = 'I' + axes
+ if page.is_scanimage:
+ kind = 'ScanImage'
+ elif page.is_nih:
+ kind = 'NIHImage'
+ else:
+ kind = 'Uniform'
+ return [TiffPageSeries(pages, shape, dtype, axes, kind=kind)]
+
+ def _series_shaped(self):
+ """Return image series in "shaped" file."""
+ pages = self.pages
+ pages.useframes = True
+ lenpages = len(pages)
+
+ def append_series(series, pages, axes, shape, reshape, name,
+ truncated):
+ page = pages[0]
+ if not axes:
+ shape = page.shape
+ axes = page.axes
+ if len(pages) > 1:
+ shape = (len(pages),) + shape
+ axes = 'Q' + axes
+ size = product(shape)
+ resize = product(reshape)
+ if page.is_contiguous and resize > size and resize % size == 0:
+ if truncated is None:
+ truncated = True
+ axes = 'Q' + axes
+ shape = (resize // size,) + shape
+ try:
+ axes = reshape_axes(axes, shape, reshape)
+ shape = reshape
+ except ValueError as exc:
+ log.warning('Shaped series: %s: %s',
+ exc.__class__.__name__, exc)
+ series.append(
+ TiffPageSeries(pages, shape, page.dtype, axes,
+ name=name, kind='Shaped', truncated=truncated)
+ )
+
+ keyframe = axes = shape = reshape = name = None
+ series = []
+ index = 0
+ while True:
+ if index >= lenpages:
+ break
+ # new keyframe; start of new series
+ pages.keyframe = index
+ keyframe = pages.keyframe
+ if not keyframe.is_shaped:
+ log.warning(
+ 'Shaped series: invalid metadata or corrupted file')
+ return None
+ # read metadata
+ axes = None
+ shape = None
+ metadata = json_description_metadata(keyframe.is_shaped)
+ name = metadata.get('name', '')
+ reshape = metadata['shape']
+ truncated = metadata.get('truncated', None)
+ if 'axes' in metadata:
+ axes = metadata['axes']
+ if len(axes) == len(reshape):
+ shape = reshape
+ else:
+ axes = ''
+ log.warning('Shaped series: axes do not match shape')
+ # skip pages if possible
+ spages = [keyframe]
+ size = product(reshape)
+ npages, mod = divmod(size, product(keyframe.shape))
+ if mod:
+ log.warning(
+ 'Shaped series: series shape does not match page shape')
+ return None
+ if 1 < npages <= lenpages - index:
+ size *= keyframe._dtype.itemsize
+ if truncated:
+ npages = 1
+ elif (
+ keyframe.is_final
+ and keyframe.offset + size < pages[index + 1].offset
+ ):
+ truncated = False
+ else:
+ # need to read all pages for series
+ truncated = False
+ for j in range(index + 1, index + npages):
+ page = pages[j]
+ page.keyframe = keyframe
+ spages.append(page)
+ append_series(series, spages, axes, shape, reshape, name,
+ truncated)
+ index += npages
+
+ self.is_uniform = len(series) == 1
+
+ return series
+
+ def _series_imagej(self):
+ """Return image series in ImageJ file."""
+ # ImageJ's dimension order is always TZCYXS
+ # TODO: fix loading of color, composite, or palette images
+ pages = self.pages
+ pages.useframes = True
+ pages.keyframe = 0
+ page = pages[0]
+ ij = self.imagej_metadata
+
+ def is_virtual():
+ # ImageJ virtual hyperstacks store all image metadata in the first
+ # page and image data are stored contiguously before the second
+ # page, if any
+ if not page.is_final:
+ return False
+ images = ij.get('images', 0)
+ if images <= 1:
+ return False
+ offset, count = page.is_contiguous
+ if (
+ count != product(page.shape) * page.bitspersample // 8
+ or offset + count * images > self.filehandle.size
+ ):
+ raise ValueError()
+ # check that next page is stored after data
+ if len(pages) > 1 and offset + count * images > pages[1].offset:
+ return False
+ return True
+
+ try:
+ isvirtual = is_virtual()
+ except ValueError:
+ log.warning('ImageJ series: invalid metadata or corrupted file')
+ return None
+ if isvirtual:
+ # no need to read other pages
+ pages = [page]
+ else:
+ pages = pages[:]
+
+ images = ij.get('images', len(pages))
+ frames = ij.get('frames', 1)
+ slices = ij.get('slices', 1)
+ channels = ij.get('channels', 1)
+ mode = ij.get('mode', None)
+
+ shape = []
+ axes = []
+ if frames > 1:
+ shape.append(frames)
+ axes.append('T')
+ if slices > 1:
+ shape.append(slices)
+ axes.append('Z')
+ if channels > 1 and (page.photometric != 2 or mode != 'composite'):
+ shape.append(channels)
+ axes.append('C')
+
+ remain = images // (product(shape) if shape else 1)
+ if remain > 1:
+ shape.append(remain)
+ axes.append('I')
+
+ if page.axes[0] == 'S' and 'C' in axes:
+ # planar storage, S == C, saved by Bio-Formats
+ shape.extend(page.shape[1:])
+ axes.extend(page.axes[1:])
+ elif page.axes[0] == 'I':
+ # contiguous multiple images
+ shape.extend(page.shape[1:])
+ axes.extend(page.axes[1:])
+ elif page.axes[:2] == 'SI':
+ # color-mapped contiguous multiple images
+ shape = page.shape[0:1] + tuple(shape) + page.shape[2:]
+ axes = list(page.axes[0]) + axes + list(page.axes[2:])
+ else:
+ shape.extend(page.shape)
+ axes.extend(page.axes)
+
+ truncated = (
+ isvirtual
+ and len(self.pages) == 1
+ and page.is_contiguous[1] != (
+ product(shape) * page.bitspersample // 8)
+ )
+
+ self.is_uniform = True
+
+ return [
+ TiffPageSeries(pages, shape, page.dtype, axes,
+ kind='ImageJ', truncated=truncated)
+ ]
+
+ def _series_fluoview(self):
+ """Return image series in FluoView file."""
+ pages = self.pages._getlist(validate=False)
+
+ mm = self.fluoview_metadata
+ mmhd = list(reversed(mm['Dimensions']))
+ axes = ''.join(TIFF.MM_DIMENSIONS.get(i[0].upper(), 'Q')
+ for i in mmhd if i[1] > 1)
+ shape = tuple(int(i[1]) for i in mmhd if i[1] > 1)
+ self.is_uniform = True
+ return [
+ TiffPageSeries(pages, shape, pages[0].dtype, axes,
+ name=mm['ImageName'], kind='FluoView')
+ ]
+
+ def _series_mdgel(self):
+ """Return image series in MD Gel file."""
+ # only a single page, scaled according to metadata in second page
+ self.pages.useframes = False
+ self.pages.keyframe = 0
+ md = self.mdgel_metadata
+ if md['FileTag'] in (2, 128):
+ dtype = numpy.dtype('float32')
+ scale = md['ScalePixel']
+ scale = scale[0] / scale[1] # rational
+ if md['FileTag'] == 2:
+ # squary root data format
+ def transform(a):
+ return a.astype('float32')**2 * scale
+ else:
+ def transform(a):
+ return a.astype('float32') * scale
+ else:
+ transform = None
+ page = self.pages[0]
+ self.is_uniform = False
+ return [
+ TiffPageSeries([page], page.shape, dtype, page.axes,
+ transform=transform, kind='MDGel')
+ ]
+
+ def _series_sis(self):
+ """Return image series in Olympus SIS file."""
+ pages = self.pages._getlist(validate=False)
+ page = pages[0]
+ lenpages = len(pages)
+ md = self.sis_metadata
+ if 'shape' in md and 'axes' in md:
+ shape = md['shape'] + page.shape
+ axes = md['axes'] + page.axes
+ elif lenpages == 1:
+ shape = page.shape
+ axes = page.axes
+ else:
+ shape = (lenpages,) + page.shape
+ axes = 'I' + page.axes
+ self.is_uniform = True
+ return [
+ TiffPageSeries(pages, shape, page.dtype, axes, kind='SIS')
+ ]
+
+ def _series_ome(self):
+ """Return image series in OME-TIFF file(s)."""
+ from xml.etree import cElementTree as etree # delayed import
+
+ omexml = self.pages[0].description
+ try:
+ root = etree.fromstring(omexml)
+ except etree.ParseError as exc:
+ # TODO: test badly encoded OME-XML
+ log.warning('OME series: %s: %s', exc.__class__.__name__, exc)
+ try:
+ # might work on Python 2
+ omexml = omexml.decode('utf-8', 'ignore').encode('utf-8')
+ root = etree.fromstring(omexml)
+ except Exception:
+ return None
+
+ self.pages.cache = True
+ self.pages.useframes = True
+ self.pages.keyframe = 0
+ self.pages._load(keyframe=None)
+
+ root_uuid = root.attrib.get('UUID', None)
+ self._files = {root_uuid: self}
+ dirname = self._fh.dirname
+ modulo = {}
+ series = []
+ for element in root:
+ if element.tag.endswith('BinaryOnly'):
+ # TODO: load OME-XML from master or companion file
+ log.warning('OME series: not an ome-tiff master file')
+ break
+ if element.tag.endswith('StructuredAnnotations'):
+ for annot in element:
+ if not annot.attrib.get('Namespace',
+ '').endswith('modulo'):
+ continue
+ for value in annot:
+ for modul in value:
+ for along in modul:
+ if not along.tag[:-1].endswith('Along'):
+ continue
+ axis = along.tag[-1]
+ newaxis = along.attrib.get('Type', 'other')
+ newaxis = TIFF.AXES_LABELS[newaxis]
+ if 'Start' in along.attrib:
+ step = float(along.attrib.get('Step', 1))
+ start = float(along.attrib['Start'])
+ stop = float(along.attrib['End']) + step
+ labels = numpy.arange(start, stop, step)
+ else:
+ labels = [
+ label.text
+ for label in along
+ if label.tag.endswith('Label')
+ ]
+ modulo[axis] = (newaxis, labels)
+
+ if not element.tag.endswith('Image'):
+ continue
+
+ attr = element.attrib
+ name = attr.get('Name', None)
+
+ for pixels in element:
+ if not pixels.tag.endswith('Pixels'):
+ continue
+ attr = pixels.attrib
+ # dtype = attr.get('PixelType', None)
+ axes = ''.join(reversed(attr['DimensionOrder']))
+ shape = idxshape = [int(attr['Size' + ax]) for ax in axes]
+ size = product(shape[:-2])
+ ifds = None
+ spp = 1 # samples per pixel
+ for data in pixels:
+ if data.tag.endswith('Channel'):
+ attr = data.attrib
+ if ifds is None:
+ spp = int(attr.get('SamplesPerPixel', spp))
+ ifds = [None] * (size // spp)
+ if spp > 1:
+ # correct channel dimension for spp
+ idxshape = [
+ shape[i] // spp if ax == 'C' else shape[i]
+ for i, ax in enumerate(axes)]
+ elif int(attr.get('SamplesPerPixel', 1)) != spp:
+ raise ValueError('OME series: cannot handle '
+ 'differing SamplesPerPixel')
+ continue
+ if ifds is None:
+ ifds = [None] * (size // spp)
+ if not data.tag.endswith('TiffData'):
+ continue
+ attr = data.attrib
+ ifd = int(attr.get('IFD', 0))
+ num = int(attr.get('NumPlanes', 1 if 'IFD' in attr else 0))
+ num = int(attr.get('PlaneCount', num))
+ idx = [int(attr.get('First' + ax, 0)) for ax in axes[:-2]]
+ try:
+ idx = numpy.ravel_multi_index(idx, idxshape[:-2])
+ except ValueError:
+ # ImageJ produces invalid ome-xml when cropping
+ log.warning('OME series: invalid TiffData index')
+ continue
+ for uuid in data:
+ if not uuid.tag.endswith('UUID'):
+ continue
+ if root_uuid is None and uuid.text is not None:
+ # no global UUID, use this file
+ root_uuid = uuid.text
+ self._files[root_uuid] = self._files[None]
+ elif uuid.text not in self._files:
+ if not self._multifile:
+ # abort reading multifile OME series
+ # and fall back to generic series
+ return []
+ fname = uuid.attrib['FileName']
+ try:
+ tif = TiffFile(os.path.join(dirname, fname))
+ tif.pages.cache = True
+ tif.pages.useframes = True
+ tif.pages.keyframe = 0
+ tif.pages._load(keyframe=None)
+ except (IOError, FileNotFoundError, ValueError):
+ log.warning(
+ "OME series: failed to read '%s'", fname)
+ break
+ self._files[uuid.text] = tif
+ tif.close()
+ pages = self._files[uuid.text].pages
+ try:
+ for i in range(num if num else len(pages)):
+ ifds[idx + i] = pages[ifd + i]
+ except IndexError:
+ log.warning('OME series: index out of range')
+ # only process first UUID
+ break
+ else:
+ pages = self.pages
+ try:
+ for i in range(num if num else
+ min(len(pages), len(ifds))):
+ ifds[idx + i] = pages[ifd + i]
+ except IndexError:
+ log.warning('OME series: index out of range')
+
+ if all(i is None for i in ifds):
+ # skip images without data
+ continue
+
+ # find a keyframe
+ keyframe = None
+ for i in ifds:
+ # try find a TiffPage
+ if i and i == i.keyframe:
+ keyframe = i
+ break
+ if keyframe is None:
+ # reload a TiffPage from file
+ for i, keyframe in enumerate(ifds):
+ if keyframe:
+ keyframe.parent.pages.keyframe = keyframe.index
+ keyframe = keyframe.parent.pages[keyframe.index]
+ ifds[i] = keyframe
+ break
+
+ # move channel axis to match PlanarConfiguration storage
+ # TODO: is this a bug or a inconsistency in the OME spec?
+ if spp > 1:
+ if keyframe.planarconfig == 1 and axes[-1] != 'C':
+ i = axes.index('C')
+ axes = axes[:i] + axes[i + 1:] + axes[i: i + 1]
+ shape = shape[:i] + shape[i + 1:] + shape[i: i + 1]
+
+ # FIXME: this implementation assumes the last dimensions are
+ # stored in TIFF pages. Apparently that is not always the case.
+ # For now, verify that shapes of keyframe and series match
+ # If not, skip series.
+ if keyframe.shape != tuple(shape[-len(keyframe.shape):]):
+ log.warning('OME series: incompatible page shape %s; '
+ 'expected %s', keyframe.shape,
+ tuple(shape[-len(keyframe.shape):]))
+ del ifds
+ continue
+
+ # set a keyframe on all IFDs
+ for i in ifds:
+ if i is not None:
+ try:
+ i.keyframe = keyframe
+ except RuntimeError as exception:
+ log.warning('OME series: %s', str(exception))
+
+ series.append(
+ TiffPageSeries(ifds, shape, keyframe.dtype, axes,
+ parent=self, name=name, kind='OME')
+ )
+ del ifds
+
+ for serie in series:
+ shape = list(serie.shape)
+ for axis, (newaxis, labels) in modulo.items():
+ i = serie.axes.index(axis)
+ size = len(labels)
+ if shape[i] == size:
+ serie.axes = serie.axes.replace(axis, newaxis, 1)
+ else:
+ shape[i] //= size
+ shape.insert(i + 1, size)
+ serie.axes = serie.axes.replace(axis, axis + newaxis, 1)
+ serie.shape = tuple(shape)
+
+ # squeeze dimensions
+ for serie in series:
+ serie.shape, serie.axes = squeeze_axes(serie.shape, serie.axes)
+ self.is_uniform = len(series) == 1
+ return series
+
+ def _series_lsm(self):
+ """Return main and thumbnail series in LSM file."""
+ lsmi = self.lsm_metadata
+ axes = TIFF.CZ_LSMINFO_SCANTYPE[lsmi['ScanType']]
+ if self.pages[0].photometric == 2: # RGB; more than one channel
+ axes = axes.replace('C', '').replace('XY', 'XYC')
+ if lsmi.get('DimensionP', 0) > 1:
+ axes += 'P'
+ if lsmi.get('DimensionM', 0) > 1:
+ axes += 'M'
+ axes = axes[::-1]
+ shape = tuple(int(lsmi[TIFF.CZ_LSMINFO_DIMENSIONS[i]]) for i in axes)
+ name = lsmi.get('Name', '')
+ pages = self.pages._getlist(slice(0, None, 2), validate=False)
+ dtype = pages[0].dtype
+ series = [
+ TiffPageSeries(pages, shape, dtype, axes, name=name, kind='LSM')
+ ]
+
+ if self.pages[1].is_reduced:
+ pages = self.pages._getlist(slice(1, None, 2), validate=False)
+ dtype = pages[0].dtype
+ cp = 1
+ i = 0
+ while cp < len(pages) and i < len(shape) - 2:
+ cp *= shape[i]
+ i += 1
+ shape = shape[:i] + pages[0].shape
+ axes = axes[:i] + 'CYX'
+ series.append(
+ TiffPageSeries(pages, shape, dtype, axes, name=name,
+ kind='LSMreduced')
+ )
+
+ self.is_uniform = False
+ return series
+
+ def _lsm_load_pages(self):
+ """Load and fix all pages from LSM file."""
+ # cache all pages to preserve corrected values
+ pages = self.pages
+ pages.cache = True
+ pages.useframes = True
+ # use first and second page as keyframes
+ pages.keyframe = 1
+ pages.keyframe = 0
+ # load remaining pages as frames
+ pages._load(keyframe=None)
+ # fix offsets and bytecounts first
+ # TODO: fix multiple conversions between lists and tuples
+ self._lsm_fix_strip_offsets()
+ self._lsm_fix_strip_bytecounts()
+ # assign keyframes for data and thumbnail series
+ keyframe = pages[0]
+ for page in pages[::2]:
+ page.keyframe = keyframe
+ keyframe = pages[1]
+ for page in pages[1::2]:
+ page.keyframe = keyframe
+
+ def _lsm_fix_strip_offsets(self):
+ """Unwrap strip offsets for LSM files greater than 4 GB.
+
+ Each series and position require separate unwrapping (undocumented).
+
+ """
+ if self.filehandle.size < 2**32:
+ return
+
+ pages = self.pages
+ npages = len(pages)
+ series = self.series[0]
+ axes = series.axes
+
+ # find positions
+ positions = 1
+ for i in 0, 1:
+ if series.axes[i] in 'PM':
+ positions *= series.shape[i]
+
+ # make time axis first
+ if positions > 1:
+ ntimes = 0
+ for i in 1, 2:
+ if axes[i] == 'T':
+ ntimes = series.shape[i]
+ break
+ if ntimes:
+ div, mod = divmod(npages, 2 * positions * ntimes)
+ assert mod == 0
+ shape = (positions, ntimes, div, 2)
+ indices = numpy.arange(product(shape)).reshape(shape)
+ indices = numpy.moveaxis(indices, 1, 0)
+ else:
+ indices = numpy.arange(npages).reshape(-1, 2)
+
+ # images of reduced page might be stored first
+ if pages[0]._offsetscounts[0][0] > pages[1]._offsetscounts[0][0]:
+ indices = indices[..., ::-1]
+
+ # unwrap offsets
+ wrap = 0
+ previousoffset = 0
+ for i in indices.flat:
+ page = pages[int(i)]
+ dataoffsets = []
+ for currentoffset in page._offsetscounts[0]:
+ if currentoffset < previousoffset:
+ wrap += 2**32
+ dataoffsets.append(currentoffset + wrap)
+ previousoffset = currentoffset
+ page._offsetscounts = tuple(dataoffsets), page._offsetscounts[1]
+
+ def _lsm_fix_strip_bytecounts(self):
+ """Set databytecounts to size of compressed data.
+
+ The StripByteCounts tag in LSM files contains the number of bytes
+ for the uncompressed data.
+
+ """
+ pages = self.pages
+ if pages[0].compression == 1:
+ return
+ # sort pages by first strip offset
+ pages = sorted(pages, key=lambda p: p._offsetscounts[0][0])
+ npages = len(pages) - 1
+ for i, page in enumerate(pages):
+ if page.index % 2:
+ continue
+ offsets, bytecounts = page._offsetscounts
+ if i < npages:
+ lastoffset = pages[i + 1]._offsetscounts[0][0]
+ else:
+ # LZW compressed strips might be longer than uncompressed
+ lastoffset = min(offsets[-1] + 2 * bytecounts[-1],
+ self._fh.size)
+ bytecounts = list(bytecounts)
+ for j in range(len(bytecounts) - 1):
+ bytecounts[j] = offsets[j + 1] - offsets[j]
+ bytecounts[-1] = lastoffset - offsets[-1]
+ page._offsetscounts = offsets, tuple(bytecounts)
+
+ def __getattr__(self, name):
+ """Return 'is_flag' attributes from first page."""
+ if name[3:] in TIFF.FILE_FLAGS:
+ if not self.pages:
+ return False
+ value = bool(getattr(self.pages[0], name))
+ setattr(self, name, value)
+ return value
+ raise AttributeError("'%s' object has no attribute '%s'"
+ % (self.__class__.__name__, name))
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ self.close()
+
+ def __str__(self, detail=0, width=79):
+ """Return string containing information about file.
+
+ The detail parameter specifies the level of detail returned:
+
+ 0: file only.
+ 1: all series, first page of series and its tags.
+ 2: large tag values and file metadata.
+ 3: all pages.
+
+ """
+ info = [
+ "TiffFile '%s'",
+ format_size(self._fh.size),
+ ''
+ if byteorder_isnative(self.tiff.byteorder)
+ else {'<': 'little-endian',
+ '>': 'big-endian'}[self.tiff.byteorder]
+ ]
+ if self.is_bigtiff:
+ info.append('BigTiff')
+ info.append(' '.join(f.lower() for f in self.flags))
+ if len(self.pages) > 1:
+ info.append('%i Pages' % len(self.pages))
+ if len(self.series) > 1:
+ info.append('%i Series' % len(self.series))
+ if len(self._files) > 1:
+ info.append('%i Files' % (len(self._files)))
+ info = ' '.join(info)
+ info = info.replace(' ', ' ').replace(' ', ' ')
+ info = info % snipstr(self._fh.name, max(12, width + 2 - len(info)))
+ if detail <= 0:
+ return info
+ info = [info]
+ info.append('\n'.join(str(s) for s in self.series))
+ if detail >= 3:
+ info.extend(
+ (
+ TiffPage.__str__(p, detail=detail, width=width)
+ for p in self.pages
+ if p is not None
+ )
+ )
+ elif self.series:
+ info.extend(
+ (
+ TiffPage.__str__(s.pages[0], detail=detail, width=width)
+ for s in self.series
+ if s.pages[0] is not None
+ )
+ )
+ elif self.pages and self.pages[0]:
+ info.append(
+ TiffPage.__str__(self.pages[0], detail=detail, width=width)
+ )
+ if detail >= 2:
+ for name in sorted(self.flags):
+ if hasattr(self, name + '_metadata'):
+ m = getattr(self, name + '_metadata')
+ if m:
+ info.append(
+ '%s_METADATA\n%s'
+ % (name.upper(),
+ pformat(m, width=width, height=detail * 12))
+ )
+ return '\n\n'.join(info).replace('\n\n\n', '\n\n')
+
+ @lazyattr
+ def flags(self):
+ """Return set of file flags."""
+ return set(
+ name.lower()
+ for name in sorted(TIFF.FILE_FLAGS)
+ if getattr(self, 'is_' + name)
+ )
+
+ @lazyattr
+ def is_mdgel(self):
+ """File has MD Gel format."""
+ # TODO: this likely reads the second page from file
+ try:
+ ismdgel = self.pages[0].is_mdgel or self.pages[1].is_mdgel
+ if ismdgel:
+ self.is_uniform = False
+ return ismdgel
+ except IndexError:
+ return False
+
+ @lazyattr
+ def is_uniform(self):
+ """Return if file contains a uniform series of pages."""
+ # the hashes of IFDs 0, 7, and -1 are the same
+ pages = self.pages
+ page = pages[0]
+ if page.is_scanimage or page.is_nih:
+ return True
+ try:
+ useframes = pages.useframes
+ pages.useframes = False
+ h = page.hash
+ for i in (1, 7, -1):
+ if pages[i].aspage().hash != h:
+ return False
+ except IndexError:
+ return False
+ finally:
+ pages.useframes = useframes
+ return True
+
+ @property
+ def is_appendable(self):
+ """Return if pages can be appended to file without corrupting."""
+ # TODO: check other formats
+ return not (
+ self.is_lsm
+ or self.is_stk
+ or self.is_imagej
+ or self.is_fluoview
+ or self.is_micromanager
+ )
+
+ @lazyattr
+ def shaped_metadata(self):
+ """Return tifffile metadata from JSON descriptions as dicts."""
+ if not self.is_shaped:
+ return None
+ return tuple(
+ json_description_metadata(s.pages[0].is_shaped)
+ for s in self.series
+ if s.kind.lower() == 'shaped'
+ )
+
+ @property
+ def ome_metadata(self):
+ """Return OME XML."""
+ if not self.is_ome:
+ return None
+ # return xml2dict(self.pages[0].description)['OME']
+ return self.pages[0].description
+
+ @property
+ def lsm_metadata(self):
+ """Return LSM metadata from CZ_LSMINFO tag as dict."""
+ if not self.is_lsm:
+ return None
+ return self.pages[0].tags['CZ_LSMINFO'].value
+
+ @lazyattr
+ def stk_metadata(self):
+ """Return STK metadata from UIC tags as dict."""
+ if not self.is_stk:
+ return None
+ page = self.pages[0]
+ tags = page.tags
+ result = {}
+ result['NumberPlanes'] = tags['UIC2tag'].count
+ if page.description:
+ result['PlaneDescriptions'] = page.description.split('\0')
+ # result['plane_descriptions'] = stk_description_metadata(
+ # page.image_description)
+ if 'UIC1tag' in tags:
+ result.update(tags['UIC1tag'].value)
+ if 'UIC3tag' in tags:
+ result.update(tags['UIC3tag'].value) # wavelengths
+ if 'UIC4tag' in tags:
+ result.update(tags['UIC4tag'].value) # override uic1 tags
+ uic2tag = tags['UIC2tag'].value
+ result['ZDistance'] = uic2tag['ZDistance']
+ result['TimeCreated'] = uic2tag['TimeCreated']
+ result['TimeModified'] = uic2tag['TimeModified']
+ try:
+ result['DatetimeCreated'] = numpy.array(
+ [julian_datetime(*dt) for dt in
+ zip(uic2tag['DateCreated'], uic2tag['TimeCreated'])],
+ dtype='datetime64[ns]')
+ result['DatetimeModified'] = numpy.array(
+ [julian_datetime(*dt) for dt in
+ zip(uic2tag['DateModified'], uic2tag['TimeModified'])],
+ dtype='datetime64[ns]')
+ except ValueError as exc:
+ log.warning('STK metadata: %s: %s', exc.__class__.__name__, exc)
+ return result
+
+ @lazyattr
+ def imagej_metadata(self):
+ """Return consolidated ImageJ metadata as dict."""
+ if not self.is_imagej:
+ return None
+ page = self.pages[0]
+ result = imagej_description_metadata(page.is_imagej)
+ if 'IJMetadata' in page.tags:
+ try:
+ result.update(page.tags['IJMetadata'].value)
+ except Exception:
+ pass
+ return result
+
+ @lazyattr
+ def fluoview_metadata(self):
+ """Return consolidated FluoView metadata as dict."""
+ if not self.is_fluoview:
+ return None
+ result = {}
+ page = self.pages[0]
+ result.update(page.tags['MM_Header'].value)
+ # TODO: read stamps from all pages
+ result['Stamp'] = page.tags['MM_Stamp'].value
+ # skip parsing image description; not reliable
+ # try:
+ # t = fluoview_description_metadata(page.image_description)
+ # if t is not None:
+ # result['ImageDescription'] = t
+ # except Exception as exc:
+ # log.warning('FluoView metadata: '
+ # 'failed to parse image description (%s)', str(exc))
+ return result
+
+ @lazyattr
+ def nih_metadata(self):
+ """Return NIH Image metadata from NIHImageHeader tag as dict."""
+ if not self.is_nih:
+ return None
+ return self.pages[0].tags['NIHImageHeader'].value
+
+ @lazyattr
+ def fei_metadata(self):
+ """Return FEI metadata from SFEG or HELIOS tags as dict."""
+ if not self.is_fei:
+ return None
+ tags = self.pages[0].tags
+ if 'FEI_SFEG' in tags:
+ return tags['FEI_SFEG'].value
+ if 'FEI_HELIOS' in tags:
+ return tags['FEI_HELIOS'].value
+ return None
+
+ @property
+ def sem_metadata(self):
+ """Return SEM metadata from CZ_SEM tag as dict."""
+ if not self.is_sem:
+ return None
+ return self.pages[0].tags['CZ_SEM'].value
+
+ @lazyattr
+ def sis_metadata(self):
+ """Return Olympus SIS metadata from SIS and INI tags as dict."""
+ if not self.is_sis:
+ return None
+ tags = self.pages[0].tags
+ result = {}
+ try:
+ result.update(tags['OlympusINI'].value)
+ except Exception:
+ pass
+ try:
+ result.update(tags['OlympusSIS'].value)
+ except Exception:
+ pass
+ return result
+
+ @lazyattr
+ def mdgel_metadata(self):
+ """Return consolidated metadata from MD GEL tags as dict."""
+ for page in self.pages[:2]:
+ if 'MDFileTag' in page.tags:
+ tags = page.tags
+ break
+ else:
+ return None
+ result = {}
+ for code in range(33445, 33453):
+ name = TIFF.TAGS[code]
+ if name not in tags:
+ continue
+ result[name[2:]] = tags[name].value
+ return result
+
+ @property
+ def andor_metadata(self):
+ """Return Andor tags as dict."""
+ return self.pages[0].andor_tags
+
+ @property
+ def epics_metadata(self):
+ """Return EPICS areaDetector tags as dict."""
+ return self.pages[0].epics_tags
+
+ @property
+ def tvips_metadata(self):
+ """Return TVIPS tag as dict."""
+ if not self.is_tvips:
+ return None
+ return self.pages[0].tags['TVIPS'].value
+
+ @lazyattr
+ def metaseries_metadata(self):
+ """Return MetaSeries metadata from image description as dict."""
+ if not self.is_metaseries:
+ return None
+ return metaseries_description_metadata(self.pages[0].description)
+
+ @lazyattr
+ def pilatus_metadata(self):
+ """Return Pilatus metadata from image description as dict."""
+ if not self.is_pilatus:
+ return None
+ return pilatus_description_metadata(self.pages[0].description)
+
+ @lazyattr
+ def micromanager_metadata(self):
+ """Return consolidated MicroManager metadata as dict."""
+ if not self.is_micromanager:
+ return None
+ # from file header
+ result = read_micromanager_metadata(self._fh)
+ # from tag
+ result.update(self.pages[0].tags['MicroManagerMetadata'].value)
+ return result
+
+ @lazyattr
+ def scanimage_metadata(self):
+ """Return ScanImage non-varying frame and ROI metadata as dict."""
+ if not self.is_scanimage:
+ return None
+ result = {}
+ try:
+ framedata, roidata = read_scanimage_metadata(self._fh)
+ result['FrameData'] = framedata
+ result.update(roidata)
+ except ValueError:
+ pass
+ # TODO: scanimage_artist_metadata
+ try:
+ result['Description'] = scanimage_description_metadata(
+ self.pages[0].description)
+ except Exception as exc:
+ log.warning('ScanImage metadata: %s: %s',
+ exc.__class__.__name__, exc)
+ return result
+
+ @property
+ def geotiff_metadata(self):
+ """Return GeoTIFF metadata from first page as dict."""
+ if not self.is_geotiff:
+ return None
+ return self.pages[0].geotiff_tags
+
+
+class TiffPages(object):
+ """Sequence of TIFF image file directories (IFD chain).
+
+ Instances of TiffPages have a state (cache, keyframe, etc.) and are not
+ thread-safe.
+
+ """
+
+ def __init__(self, parent):
+ """Initialize instance and read first TiffPage from file.
+
+ If parent is a TiffFile, the file position must be at an offset to an
+ offset to a TiffPage. If parent is a TiffPage, page offsets are read
+ from the SubIFDs tag.
+
+ """
+ self.parent = None
+ self.pages = [] # cache of TiffPages, TiffFrames, or their offsets
+ self._indexed = False # True if offsets to all pages were read
+ self._cached = False # True if all pages were read into cache
+ self._tiffpage = TiffPage # class used for reading pages
+ self._keyframe = None # current page that is used as keyframe
+ self._cache = False # do not cache frames or pages (if not keyframe)
+ self._nextpageoffset = None
+
+ if isinstance(parent, TiffFile):
+ # read offset to first page from current file position
+ self.parent = parent
+ fh = parent.filehandle
+ self._nextpageoffset = fh.tell()
+ offset = struct.unpack(parent.tiff.ifdoffsetformat,
+ fh.read(parent.tiff.ifdoffsetsize))[0]
+ elif 'SubIFDs' not in parent.tags:
+ self._indexed = True
+ return
+ else:
+ # use offsets from SubIFDs tag
+ self.parent = parent.parent
+ fh = self.parent.filehandle
+ offsets = parent.tags['SubIFDs'].value
+ offset = offsets[0]
+
+ if offset == 0:
+ log.warning('TiffPages: file contains no pages')
+ self._indexed = True
+ return
+ if offset >= fh.size:
+ log.warning('TiffPages: invalid page offset (%i)', offset)
+ self._indexed = True
+ return
+
+ # read and cache first page
+ fh.seek(offset)
+ page = TiffPage(self.parent, index=0)
+ self.pages.append(page)
+ self._keyframe = page
+ if self._nextpageoffset is None:
+ # offsets from SubIFDs tag
+ self.pages.extend(offsets[1:])
+ self._indexed = True
+ self._cached = True
+
+ @property
+ def cache(self):
+ """Return if pages/frames are currently being cached."""
+ return self._cache
+
+ @cache.setter
+ def cache(self, value):
+ """Enable or disable caching of pages/frames. Clear cache if False."""
+ value = bool(value)
+ if self._cache and not value:
+ self._clear()
+ self._cache = value
+
+ @property
+ def useframes(self):
+ """Return if currently using TiffFrame (True) or TiffPage (False)."""
+ return self._tiffpage == TiffFrame and TiffFrame is not TiffPage
+
+ @useframes.setter
+ def useframes(self, value):
+ """Set to use TiffFrame (True) or TiffPage (False)."""
+ self._tiffpage = TiffFrame if value else TiffPage
+
+ @property
+ def keyframe(self):
+ """Return current keyframe."""
+ return self._keyframe
+
+ @keyframe.setter
+ def keyframe(self, index):
+ """Set current keyframe. Load TiffPage from file if necessary."""
+ index = int(index)
+ if index < 0:
+ index %= len(self)
+ if self._keyframe.index == index:
+ return
+ if index == 0:
+ self._keyframe = self.pages[0]
+ return
+ if self._indexed or index < len(self.pages):
+ page = self.pages[index]
+ if isinstance(page, TiffPage):
+ self._keyframe = page
+ return
+ if isinstance(page, TiffFrame):
+ # remove existing TiffFrame
+ self.pages[index] = page.offset
+ # load TiffPage from file
+ tiffpage = self._tiffpage
+ self._tiffpage = TiffPage
+ try:
+ self._keyframe = self._getitem(index)
+ finally:
+ self._tiffpage = tiffpage
+ # always cache keyframes
+ self.pages[index] = self._keyframe
+
+ @property
+ def next_page_offset(self):
+ """Return offset where offset to a new page can be stored."""
+ if not self._indexed:
+ self._seek(-1)
+ return self._nextpageoffset
+
+ def _load(self, keyframe=True):
+ """Read all remaining pages from file."""
+ if self._cached:
+ return
+ pages = self.pages
+ if not pages:
+ return
+ if not self._indexed:
+ self._seek(-1)
+ if not self._cache:
+ return
+ fh = self.parent.filehandle
+ if keyframe is not None:
+ keyframe = self._keyframe
+ for i, page in enumerate(pages):
+ if isinstance(page, inttypes):
+ fh.seek(page)
+ page = self._tiffpage(self.parent, index=i, keyframe=keyframe)
+ pages[i] = page
+ self._cached = True
+
+ def _load_virtual_frames(self):
+ """Calculate virtual TiffFrames."""
+ pages = self.pages
+ try:
+ if sys.version_info[0] == 2:
+ raise ValueError('not supported on Python 2')
+ if len(pages) > 1:
+ raise ValueError('pages already loaded')
+ page = pages[0]
+ bytecounts = page._offsetscounts[1]
+ if len(bytecounts) != 1:
+ raise ValueError('data not contiguous')
+ self._seek(4)
+ delta = pages[2] - pages[1]
+ if pages[3] - pages[2] != delta or pages[4] - pages[3] != delta:
+ raise ValueError('page offsets not equidistant')
+ page1 = self._getitem(1, validate=page.hash)
+ offsetoffset = page1._offsetscounts[0][0] - page1.offset
+ if offsetoffset < 0 or offsetoffset > delta:
+ raise ValueError('page offsets not equidistant')
+ pages = [page, page1]
+ filesize = self.parent.filehandle.size - delta
+ for index, offset in enumerate(range(page1.offset + delta,
+ filesize, delta)):
+ offsets = [offset + offsetoffset]
+ offset = offset if offset < 2**31 else None
+ pages.append(
+ TiffFrame(
+ parent=page.parent,
+ index=index + 2,
+ offset=None,
+ offsets=offsets,
+ bytecounts=bytecounts,
+ keyframe=page)
+ )
+ except Exception as exc:
+ log.warning(
+ 'TiffPages: failed to load virtual frames: %s', str(exc))
+ assert pages[1]
+ self.pages = pages
+ self._cache = True
+ self._cached = True
+ self._indexed = True
+
+ def _clear(self, fully=True):
+ """Delete all but first page from cache. Set keyframe to first page."""
+ pages = self.pages
+ if not pages:
+ return
+ self._keyframe = pages[0]
+ if fully:
+ # delete all but first TiffPage/TiffFrame
+ for i, page in enumerate(pages[1:]):
+ if not isinstance(page, inttypes) and page.offset is not None:
+ pages[i + 1] = page.offset
+ elif TiffFrame is not TiffPage:
+ # delete only TiffFrames
+ for i, page in enumerate(pages):
+ if isinstance(page, TiffFrame) and page.offset is not None:
+ pages[i] = page.offset
+ self._cached = False
+
+ def _seek(self, index, maxpages=None):
+ """Seek file to offset of page specified by index."""
+ pages = self.pages
+ lenpages = len(pages)
+ if lenpages == 0:
+ raise IndexError('index out of range')
+
+ fh = self.parent.filehandle
+ if fh.closed:
+ raise ValueError('seek of closed file')
+
+ if self._indexed or 0 <= index < lenpages:
+ page = pages[index]
+ offset = page if isinstance(page, inttypes) else page.offset
+ fh.seek(offset)
+ return
+
+ tiff = self.parent.tiff
+ offsetformat = tiff.ifdoffsetformat
+ offsetsize = tiff.ifdoffsetsize
+ tagnoformat = tiff.tagnoformat
+ tagnosize = tiff.tagnosize
+ tagsize = tiff.tagsize
+ unpack = struct.unpack
+
+ page = pages[-1]
+ offset = page if isinstance(page, inttypes) else page.offset
+
+ if maxpages is None:
+ maxpages = 2**22
+ while lenpages < maxpages:
+ # read offsets to pages from file until index is reached
+ fh.seek(offset)
+ # skip tags
+ try:
+ tagno = unpack(tagnoformat, fh.read(tagnosize))[0]
+ if tagno > 4096:
+ raise TiffFileError(
+ 'suspicious number of tags: %i' % tagno)
+ except Exception:
+ log.warning('TiffPages: corrupted tag list of page %i @ %i',
+ lenpages, offset)
+ del pages[-1]
+ lenpages -= 1
+ self._indexed = True
+ break
+ self._nextpageoffset = offset + tagnosize + tagno * tagsize
+ fh.seek(self._nextpageoffset)
+
+ # read offset to next page
+ offset = unpack(offsetformat, fh.read(offsetsize))[0]
+ if offset == 0:
+ self._indexed = True
+ break
+ if offset >= fh.size:
+ log.warning('TiffPages: invalid page offset (%i)', offset)
+ self._indexed = True
+ break
+
+ pages.append(offset)
+ lenpages += 1
+ if 0 <= index < lenpages:
+ break
+
+ # detect some circular references
+ if lenpages == 100:
+ for p in pages[:-1]:
+ if offset == (p if isinstance(p, inttypes) else p.offset):
+ raise TiffFileError('invalid circular IFD reference')
+
+ if index >= lenpages:
+ raise IndexError('index out of range')
+
+ page = pages[index]
+ fh.seek(page if isinstance(page, inttypes) else page.offset)
+
+ def _getlist(self, key=None, useframes=True, validate=True):
+ """Return specified pages as list of TiffPages or TiffFrames.
+
+ The first item is a TiffPage, and is used as a keyframe for
+ following TiffFrames.
+
+ """
+ getitem = self._getitem
+ _useframes = self.useframes
+
+ if key is None:
+ key = iter(range(len(self)))
+ elif isinstance(key, Iterable):
+ key = iter(key)
+ elif isinstance(key, slice):
+ start, stop, _ = key.indices(2**31 - 1)
+ if not self._indexed and max(stop, start) > len(self.pages):
+ self._seek(-1)
+ key = iter(range(*key.indices(len(self.pages))))
+ elif isinstance(key, inttypes):
+ # return single TiffPage
+ self.useframes = False
+ if key == 0:
+ return [self.pages[key]]
+ try:
+ return [getitem(key)]
+ finally:
+ self.useframes = _useframes
+ else:
+ raise TypeError('key must be an integer, slice, or iterable')
+
+ # use first page as keyframe
+ keyframe = self._keyframe
+ self.keyframe = next(key)
+ if validate:
+ validate = self._keyframe.hash
+ if useframes:
+ self.useframes = True
+ try:
+ pages = [getitem(i, validate) for i in key]
+ pages.insert(0, self._keyframe)
+ finally:
+ # restore state
+ self._keyframe = keyframe
+ if useframes:
+ self.useframes = _useframes
+
+ return pages
+
+ def _getitem(self, key, validate=False):
+ """Return specified page from cache or file."""
+ key = int(key)
+ pages = self.pages
+
+ if key < 0:
+ key %= len(self)
+ elif self._indexed and key >= len(pages):
+ raise IndexError(
+ 'index %i out of range(%i)' % (key, len(pages)))
+
+ if key < len(pages):
+ page = pages[key]
+ if self._cache:
+ if not isinstance(page, inttypes):
+ if validate and validate != page.hash:
+ raise RuntimeError('page hash mismatch')
+ return page
+ elif isinstance(page, (TiffPage, self._tiffpage)):
+ if validate and validate != page.hash:
+ raise RuntimeError('page hash mismatch')
+ return page
+
+ self._seek(key)
+ page = self._tiffpage(self.parent, index=key, keyframe=self._keyframe)
+ if validate and validate != page.hash:
+ raise RuntimeError('page hash mismatch')
+ if self._cache:
+ pages[key] = page
+ return page
+
+ def __getitem__(self, key):
+ """Return specified page(s)."""
+ pages = self.pages
+ getitem = self._getitem
+
+ if isinstance(key, inttypes):
+ if key == 0:
+ return pages[key]
+ return getitem(key)
+
+ if isinstance(key, slice):
+ start, stop, _ = key.indices(2**31 - 1)
+ if not self._indexed and max(stop, start) > len(pages):
+ self._seek(-1)
+ return [getitem(i) for i in range(*key.indices(len(pages)))]
+
+ if isinstance(key, Iterable):
+ return [getitem(k) for k in key]
+
+ raise TypeError('key must be an integer, slice, or iterable')
+
+ def __iter__(self):
+ """Return iterator over all pages."""
+ i = 0
+ while True:
+ try:
+ yield self._getitem(i)
+ i += 1
+ except IndexError:
+ break
+ if self._cache:
+ self._cached = True
+
+ def __bool__(self):
+ """Return True if file contains any pages."""
+ return len(self.pages) > 0
+
+ def __len__(self):
+ """Return number of pages in file."""
+ if not self._indexed:
+ self._seek(-1)
+ return len(self.pages)
+
+
+class TiffPage(object):
+ """TIFF image file directory (IFD).
+
+ Attributes
+ ----------
+ index : int
+ Index of page in file.
+ dtype : numpy.dtype or None
+ Data type (native byte order) of the image in IFD.
+ shape : tuple
+ Dimensions of the image in IFD.
+ axes : str
+ Axes label codes:
+ 'X' width, 'Y' height, 'S' sample, 'I' image series|page|plane,
+ 'Z' depth, 'C' color|em-wavelength|channel, 'E' ex-wavelength|lambda,
+ 'T' time, 'R' region|tile, 'A' angle, 'P' phase, 'H' lifetime,
+ 'L' exposure, 'V' event, 'Q' unknown, '_' missing
+ tags : dict
+ Dictionary of tags in IFD. {tag.name: TiffTag}
+ colormap : numpy.ndarray
+ Color look up table, if exists.
+
+ All attributes are read-only.
+
+ Notes
+ -----
+ The internal, normalized '_shape' attribute is 6 dimensional:
+
+ 0 : number planes/images (stk, ij).
+ 1 : planar samplesperpixel.
+ 2 : imagedepth Z (sgi).
+ 3 : imagelength Y.
+ 4 : imagewidth X.
+ 5 : contig samplesperpixel.
+
+ """
+
+ # default properties; will be updated from tags
+ subfiletype = 0
+ imagewidth = 0
+ imagelength = 0
+ imagedepth = 1
+ tilewidth = 0
+ tilelength = 0
+ tiledepth = 1
+ bitspersample = 1
+ samplesperpixel = 1
+ sampleformat = 1
+ rowsperstrip = 2**32 - 1
+ compression = 1
+ planarconfig = 1
+ fillorder = 1
+ photometric = 0
+ predictor = 1
+ extrasamples = 1
+ colormap = None
+ software = ''
+ description = ''
+ description1 = ''
+ nodata = 0
+
+ def __init__(self, parent, index, keyframe=None):
+ """Initialize instance from file.
+
+ The file handle position must be at offset to a valid IFD.
+
+ """
+ self.parent = parent
+ self.index = index
+ self.shape = ()
+ self._shape = ()
+ self.dtype = None
+ self._dtype = None
+ self.axes = ''
+ self.tags = tags = {}
+ self.dataoffsets = ()
+ self.databytecounts = ()
+
+ tiff = parent.tiff
+
+ # read TIFF IFD structure and its tags from file
+ fh = parent.filehandle
+ self.offset = fh.tell() # offset to this IFD
+ try:
+ tagno = struct.unpack(
+ tiff.tagnoformat, fh.read(tiff.tagnosize))[0]
+ if tagno > 4096:
+ raise TiffFileError('TiffPage %i: suspicious number of tags'
+ % self.index)
+ except Exception:
+ raise TiffFileError(
+ 'TiffPage %i: corrupted tag list at offset %i'
+ % (self.index, self.offset))
+
+ tagoffset = self.offset + tiff.tagnosize # fh.tell()
+ tagsize = tiff.tagsize
+ tagindex = -tagsize
+
+ data = fh.read(tagsize * tagno)
+
+ for _ in range(tagno):
+ tagindex += tagsize
+ try:
+ tag = TiffTag(parent, data[tagindex: tagindex + tagsize],
+ tagoffset + tagindex)
+ except TiffFileError as exc:
+ log.warning('TiffPage %i: %s: %s', self.index,
+ exc.__class__.__name__, exc)
+ continue
+ tagname = tag.name
+ if tagname not in tags:
+ name = tagname
+ tags[name] = tag
+ else:
+ # some files contain multiple tags with same code
+ # e.g. MicroManager files contain two ImageDescription tags
+ i = 1
+ while True:
+ name = '%s%i' % (tagname, i)
+ if name not in tags:
+ tags[name] = tag
+ break
+ name = TIFF.TAG_ATTRIBUTES.get(name, '')
+ if name:
+ if name[:3] in 'sof des' and not isinstance(tag.value, str):
+ pass # wrong string type for software, description
+ else:
+ setattr(self, name, tag.value)
+
+ if not tags:
+ return # found in FIBICS
+
+ if 'SubfileType' in tags and self.subfiletype == 0:
+ sft = tags['SubfileType'].value
+ if sft == 2:
+ self.subfiletype = 0b1 # reduced image
+ elif sft == 3:
+ self.subfiletype = 0b10 # multi-page
+
+ # consolidate private tags; remove them from self.tags
+ if self.is_andor:
+ self.andor_tags
+ elif self.is_epics:
+ self.epics_tags
+ # elif self.is_ndpi:
+ # self.ndpi_tags
+
+ if self.is_sis and 'GPSTag' in tags:
+ # TODO: can't change tag.name
+ tags['OlympusSIS2'] = tags['GPSTag']
+ del tags['GPSTag']
+
+ if self.is_lsm or (self.index and self.parent.is_lsm):
+ # correct non standard LSM bitspersample tags
+ tags['BitsPerSample']._fix_lsm_bitspersample(self)
+ if self.compression == 1 and self.predictor != 1:
+ # work around bug in LSM510 software
+ self.predictor = 1
+
+ if self.is_vista or (self.index and self.parent.is_vista):
+ # ISS Vista writes wrong ImageDepth tag
+ self.imagedepth = 1
+
+ if self.is_stk and 'UIC1tag' in tags and not tags['UIC1tag'].value:
+ # read UIC1tag now that plane count is known
+ uic1tag = tags['UIC1tag']
+ fh.seek(uic1tag.valueoffset)
+ tags['UIC1tag'].value = read_uic1tag(
+ fh, tiff.byteorder, uic1tag.dtype,
+ uic1tag.count, None, tags['UIC2tag'].count)
+
+ if 'IJMetadata' in tags:
+ # decode IJMetadata tag
+ try:
+ tags['IJMetadata'].value = imagej_metadata(
+ tags['IJMetadata'].value,
+ tags['IJMetadataByteCounts'].value,
+ tiff.byteorder)
+ except Exception as exc:
+ log.warning('TiffPage %i: %s: %s', self.index,
+ exc.__class__.__name__, exc)
+
+ if 'BitsPerSample' in tags:
+ tag = tags['BitsPerSample']
+ if tag.count == 1:
+ self.bitspersample = tag.value
+ else:
+ # LSM might list more items than samplesperpixel
+ value = tag.value[:self.samplesperpixel]
+ if any(v - value[0] for v in value):
+ self.bitspersample = value
+ else:
+ self.bitspersample = value[0]
+
+ if 'SampleFormat' in tags:
+ tag = tags['SampleFormat']
+ if tag.count == 1:
+ self.sampleformat = tag.value
+ else:
+ value = tag.value[:self.samplesperpixel]
+ if any(v - value[0] for v in value):
+ self.sampleformat = value
+ else:
+ self.sampleformat = value[0]
+
+ if 'TileWidth' in tags:
+ self.rowsperstrip = None
+ elif 'ImageLength' in tags:
+ if 'RowsPerStrip' not in tags or tags['RowsPerStrip'].count > 1:
+ self.rowsperstrip = self.imagelength
+ self.rowsperstrip = min(self.rowsperstrip, self.imagelength)
+ # self.stripsperimage = int(math.floor(
+ # float(self.imagelength + self.rowsperstrip - 1) /
+ # self.rowsperstrip))
+
+ # determine dtype
+ dtype = self.sampleformat, self.bitspersample
+ dtype = TIFF.SAMPLE_DTYPES.get(dtype, None)
+ if dtype is not None:
+ dtype = numpy.dtype(dtype)
+ self.dtype = self._dtype = dtype
+
+ # determine shape of data
+ imagelength = self.imagelength
+ imagewidth = self.imagewidth
+ imagedepth = self.imagedepth
+ samplesperpixel = self.samplesperpixel
+
+ if self.is_stk:
+ assert self.imagedepth == 1
+ uictag = tags['UIC2tag'].value
+ planes = tags['UIC2tag'].count
+ if self.planarconfig == 1:
+ self._shape = (
+ planes,
+ 1,
+ 1,
+ imagelength,
+ imagewidth,
+ samplesperpixel,
+ )
+ if samplesperpixel == 1:
+ self.shape = (planes, imagelength, imagewidth)
+ self.axes = 'YX'
+ else:
+ self.shape = (
+ planes,
+ imagelength,
+ imagewidth,
+ samplesperpixel,
+ )
+ self.axes = 'YXS'
+ else:
+ self._shape = (
+ planes,
+ samplesperpixel,
+ 1,
+ imagelength,
+ imagewidth,
+ 1,
+ )
+ if samplesperpixel == 1:
+ self.shape = (planes, imagelength, imagewidth)
+ self.axes = 'YX'
+ else:
+ self.shape = (
+ planes,
+ samplesperpixel,
+ imagelength,
+ imagewidth,
+ )
+ self.axes = 'SYX'
+ # detect type of series
+ if planes == 1:
+ self.shape = self.shape[1:]
+ elif numpy.all(uictag['ZDistance'] != 0):
+ self.axes = 'Z' + self.axes
+ elif numpy.all(numpy.diff(uictag['TimeCreated']) != 0):
+ self.axes = 'T' + self.axes
+ else:
+ self.axes = 'I' + self.axes
+ elif self.photometric == 2 or samplesperpixel > 1: # PHOTOMETRIC.RGB
+ if self.planarconfig == 1:
+ self._shape = (
+ 1,
+ 1,
+ imagedepth,
+ imagelength,
+ imagewidth,
+ samplesperpixel,
+ )
+ if imagedepth == 1:
+ self.shape = (imagelength, imagewidth, samplesperpixel)
+ self.axes = 'YXS'
+ else:
+ self.shape = (
+ imagedepth,
+ imagelength,
+ imagewidth,
+ samplesperpixel,
+ )
+ self.axes = 'ZYXS'
+ else:
+ self._shape = (
+ 1,
+ samplesperpixel,
+ imagedepth,
+ imagelength,
+ imagewidth,
+ 1,
+ )
+ if imagedepth == 1:
+ self.shape = (samplesperpixel, imagelength, imagewidth)
+ self.axes = 'SYX'
+ else:
+ self.shape = (
+ samplesperpixel,
+ imagedepth,
+ imagelength,
+ imagewidth,
+ )
+ self.axes = 'SZYX'
+ else:
+ self._shape = (1, 1, imagedepth, imagelength, imagewidth, 1)
+ if imagedepth == 1:
+ self.shape = (imagelength, imagewidth)
+ self.axes = 'YX'
+ else:
+ self.shape = (imagedepth, imagelength, imagewidth)
+ self.axes = 'ZYX'
+
+ # dataoffsets and databytecounts
+ if 'TileOffsets' in tags:
+ self.dataoffsets = tags['TileOffsets'].value
+ elif 'StripOffsets' in tags:
+ self.dataoffsets = tags['StripOffsets'].value
+ if 'TileByteCounts' in tags:
+ self.databytecounts = tags['TileByteCounts'].value
+ elif 'StripByteCounts' in tags:
+ self.databytecounts = tags['StripByteCounts'].value
+ else:
+ self.databytecounts = (
+ product(self.shape) * (self.bitspersample // 8),)
+ if self.compression != 1:
+ log.warning('TiffPage %i: ByteCounts tag is missing',
+ self.index)
+ # assert len(self.shape) == len(self.axes)
+
+ if 'GDAL_NODATA' in tags:
+ try:
+ pytype = type(dtype.type(0).item())
+ self.nodata = pytype(tags['GDAL_NODATA'].value)
+ except Exception:
+ pass
+
+ @lazyattr
+ def decode(self):
+ """Decode single tile or strip."""
+ raise NotImplementedError()
+ # TODO: retun function to decode single strips or tiles
+
+ def asarray(self, out=None, squeeze=True, lock=None, reopen=True,
+ maxsize=None, maxworkers=None, validate=True):
+ """Read image data from file and return as numpy array.
+
+ Raise ValueError if format is unsupported.
+
+ Parameters
+ ----------
+ out : numpy.ndarray, str, or file-like object
+ Buffer where image data will be saved.
+ If None (default), a new array will be created.
+ If numpy.ndarray, a writable array of compatible dtype and shape.
+ If 'memmap', directly memory-map the image data in the TIFF file
+ if possible; else create a memory-mapped array in a temporary file.
+ If str or open file, the file name or file object used to
+ create a memory-map to an array stored in a binary file on disk.
+ squeeze : bool
+ If True (default), all length-1 dimensions (except X and Y) are
+ squeezed out from the array.
+ If False, the shape of the returned array might be different from
+ the page.shape.
+ lock : {RLock, NullContext}
+ A reentrant lock used to synchronize seeks and reads from file.
+ If None (default), the lock of the parent's filehandle is used.
+ reopen : bool
+ If True (default) and the parent file handle is closed, the file
+ is temporarily re-opened and closed if no exception occurs.
+ maxsize: int
+ Maximum size of data before a ValueError is raised.
+ Can be used to catch DOS. Default: 16 TB.
+ maxworkers : int or None
+ Maximum number of threads to concurrently decode compressed
+ segments. If None (default), up to half the CPU cores are used.
+ See remarks in TiffFile.asarray.
+ validate : bool
+ If True (default), validate various parameters.
+ If None, only validate parameters and return None.
+
+ Returns
+ -------
+ numpy.ndarray
+ Numpy array of decompressed, depredicted, and unpacked image data
+ read from Strip/Tile Offsets/ByteCounts, formatted according to
+ shape and dtype metadata found in tags and parameters.
+ Photometric conversion, pre-multiplied alpha, orientation, and
+ colorimetry corrections are not applied. Specifically, CMYK images
+ are not converted to RGB, MinIsWhite images are not inverted,
+ and color palettes are not applied. An exception are YCbCr JPEG
+ compressed images, which will be converted to RGB.
+
+ """
+ # properties from TiffPage or TiffFrame
+ fh = self.parent.filehandle
+ byteorder = self.parent.tiff.byteorder
+ offsets, bytecounts = self._offsetscounts
+ self_ = self
+ self = self.keyframe # self or keyframe
+
+ if not self._shape or product(self._shape) == 0:
+ return None
+
+ tags = self.tags
+
+ if validate or validate is None:
+ if maxsize is None:
+ maxsize = 2**44
+ if maxsize and product(self._shape) > maxsize:
+ raise ValueError('TiffPage %i: data are too large %s'
+ % (self.index, str(self._shape)))
+ if self.dtype is None:
+ raise ValueError(
+ 'TiffPage %i: data type not supported: %s%i'
+ % (self.index, self.sampleformat, self.bitspersample))
+ if self.compression not in TIFF.DECOMPESSORS:
+ raise ValueError('TiffPage %i: cannot decompress %s'
+ % (self.index, self.compression.name))
+ if 'SampleFormat' in tags:
+ tag = tags['SampleFormat']
+ if (
+ tag.count != 1
+ and any(i - tag.value[0] for i in tag.value)
+ ):
+ raise ValueError(
+ 'TiffPage %i: sample formats do not match %s'
+ % (self.index, tag.value))
+ if self.is_subsampled and (self.compression not in (6, 7)
+ or self.planarconfig == 2):
+ raise NotImplementedError(
+ 'TiffPage %i: chroma subsampling not supported'
+ % self.index)
+ if validate is None:
+ return None
+
+ lock = fh.lock if lock is None else lock
+ with lock:
+ closed = fh.closed
+ if closed:
+ if reopen:
+ fh.open()
+ else:
+ raise IOError('TiffPage %i: file handle is closed'
+ % self.index)
+
+ dtype = self._dtype
+ shape = self._shape
+ imagewidth = self.imagewidth
+ imagelength = self.imagelength
+ imagedepth = self.imagedepth
+ bitspersample = self.bitspersample
+ typecode = byteorder + dtype.char
+ lsb2msb = self.fillorder == 2
+ istiled = self.is_tiled
+
+ if istiled:
+ tilewidth = self.tilewidth
+ tilelength = self.tilelength
+ tiledepth = self.tiledepth
+ tw = (imagewidth + tilewidth - 1) // tilewidth
+ tl = (imagelength + tilelength - 1) // tilelength
+ td = (imagedepth + tiledepth - 1) // tiledepth
+ tiledshape = (td, tl, tw)
+ tileshape = (tiledepth, tilelength, tilewidth, shape[-1])
+ runlen = tilewidth
+ else:
+ runlen = imagewidth
+
+ if self.planarconfig == 1:
+ runlen *= self.samplesperpixel
+
+ if isinstance(out, str) and out == 'memmap' and self.is_memmappable:
+ # direct memory map array in file
+ with lock:
+ result = fh.memmap_array(typecode, shape, offset=offsets[0])
+ elif self.is_contiguous:
+ # read contiguous bytes to array
+ if out is not None:
+ out = create_output(out, shape, dtype)
+ with lock:
+ fh.seek(offsets[0])
+ result = fh.read_array(typecode, product(shape), out=out)
+ if lsb2msb:
+ bitorder_decode(result, out=result)
+ else:
+ # decompress, unpack,... individual strips or tiles
+ result = create_output(out, shape, dtype)
+
+ decompress = TIFF.DECOMPESSORS[self.compression]
+
+ if self.compression in (6, 7): # COMPRESSION.JPEG
+ colorspace = None
+ outcolorspace = None
+ jpegtables = None
+ if lsb2msb:
+ log.warning('TiffPage %i: disabling LSB2MSB for JPEG',
+ self.index)
+ lsb2msb = False
+ if 'JPEGTables' in tags:
+ # load JPEGTables from TiffFrame
+ jpegtables = self_._gettags({347}, lock=lock)[0][1].value
+ # TODO: obtain table from OJPEG tags
+ # elif ('JPEGInterchangeFormat' in tags and
+ # 'JPEGInterchangeFormatLength' in tags and
+ # tags['JPEGInterchangeFormat'].value != offsets[0]):
+ # fh.seek(tags['JPEGInterchangeFormat'].value)
+ # fh.read(tags['JPEGInterchangeFormatLength'].value)
+ if 'ExtraSamples' in tags:
+ pass
+ elif self.photometric == 6:
+ # YCBCR -> RGB
+ outcolorspace = 'RGB'
+ elif self.photometric == 2:
+ if self.planarconfig == 1:
+ colorspace = outcolorspace = 'RGB'
+ else:
+ outcolorspace = TIFF.PHOTOMETRIC(self.photometric).name
+ if istiled:
+ heightwidth = tilelength, tilewidth
+ else:
+ heightwidth = imagelength, imagewidth
+
+ def decompress(data, bitspersample=bitspersample,
+ jpegtables=jpegtables, colorspace=colorspace,
+ outcolorspace=outcolorspace, shape=heightwidth,
+ out=None, _decompress=decompress):
+ return _decompress(data, bitspersample, jpegtables,
+ colorspace, outcolorspace, shape, out)
+
+ def unpack(data):
+ return data.reshape(-1)
+
+ elif bitspersample in (8, 16, 32, 64, 128):
+ if (bitspersample * runlen) % 8:
+ raise ValueError(
+ 'TiffPage %i: data and sample size mismatch'
+ % self.index)
+ if self.predictor == 3: # PREDICTOR.FLOATINGPOINT
+ # the floating-point horizontal differencing decoder
+ # needs the raw byte order
+ typecode = dtype.char
+
+ def unpack(data, typecode=typecode, out=None):
+ try:
+ # read only numpy array
+ return numpy.frombuffer(data, typecode)
+ except ValueError:
+ # strips may be missing EOI
+ # log.warning('TiffPage.asarray: ...')
+ bps = bitspersample // 8
+ xlen = (len(data) // bps) * bps
+ return numpy.frombuffer(data[:xlen], typecode)
+
+ elif isinstance(bitspersample, tuple):
+
+ def unpack(data, out=None):
+ return unpack_rgb(data, typecode, bitspersample)
+
+ else:
+
+ def unpack(data, out=None):
+ return packints_decode(data, typecode, bitspersample,
+ runlen)
+
+ # TODO: store decode function for future use
+ # TODO: unify tile and strip decoding
+ if istiled:
+ unpredict = TIFF.UNPREDICTORS[self.predictor]
+
+ def decode(tile, tileindex, tileshape=tileshape,
+ tiledshape=tiledshape, lsb2msb=lsb2msb,
+ decompress=decompress, unpack=unpack,
+ unpredict=unpredict, nodata=self.nodata,
+ out=result[0]):
+ return tile_decode(tile, tileindex, tileshape, tiledshape,
+ lsb2msb, decompress, unpack, unpredict,
+ nodata, out)
+
+ tileiter = fh.read_segments(offsets, bytecounts, lock)
+
+ if self.compression == 1 or len(offsets) < 3:
+ maxworkers = 1
+ elif maxworkers is None or maxworkers < 1:
+ import multiprocessing # noqa: delay import
+ maxworkers = max(multiprocessing.cpu_count() // 2, 1)
+
+ if maxworkers < 2:
+ for i, tile in enumerate(tileiter):
+ decode(tile, i)
+ else:
+ # decode first tile un-threaded to catch exceptions
+ decode(next(tileiter), 0)
+ with ThreadPoolExecutor(maxworkers) as executor:
+ executor.map(decode, tileiter, range(1, len(offsets)))
+
+ else:
+ stripsize = self.rowsperstrip * self.imagewidth
+ if self.planarconfig == 1:
+ stripsize *= self.samplesperpixel
+ outsize = stripsize * self.dtype.itemsize
+ result = result.reshape(-1)
+ index = 0
+ for strip in fh.read_segments(offsets, bytecounts, lock):
+ if strip is None:
+ result[index:index + stripsize] = self.nodata
+ index += stripsize
+ continue
+ if lsb2msb:
+ strip = bitorder_decode(strip, out=strip)
+ strip = decompress(strip, out=outsize)
+ strip = unpack(strip)
+ size = min(result.size, strip.size, stripsize,
+ result.size - index)
+ result[index:index + size] = strip[:size]
+ del strip
+ index += size
+
+ result.shape = self._shape
+
+ if self.predictor != 1 and not (istiled and not self.is_contiguous):
+ unpredict = TIFF.UNPREDICTORS[self.predictor]
+ result = unpredict(result, axis=-2, out=result)
+
+ if squeeze:
+ try:
+ result.shape = self.shape
+ except ValueError:
+ log.warning('TiffPage %i: failed to reshape %s to %s',
+ self.index, result.shape, self.shape)
+
+ if closed:
+ # TODO: file should remain open if an exception occurred above
+ fh.close()
+ return result
+
+ def asrgb(self, uint8=False, alpha=None, colormap=None,
+ dmin=None, dmax=None, **kwargs):
+ """Return image data as RGB(A).
+
+ Work in progress.
+
+ """
+ data = self.asarray(**kwargs)
+ self = self.keyframe # self or keyframe
+ photometric = self.photometric
+ PHOTOMETRIC = TIFF.PHOTOMETRIC
+
+ if photometric == PHOTOMETRIC.PALETTE:
+ colormap = self.colormap
+ if (
+ colormap.shape[1] < 2**self.bitspersample
+ or self.dtype.char not in 'BH'
+ ):
+ raise ValueError('TiffPage %i: cannot apply colormap'
+ % self.index)
+ if uint8:
+ if colormap.max() > 255:
+ colormap >>= 8
+ colormap = colormap.astype('uint8')
+ if 'S' in self.axes:
+ data = data[..., 0] if self.planarconfig == 1 else data[0]
+ data = apply_colormap(data, colormap)
+
+ elif photometric == PHOTOMETRIC.RGB:
+ if 'ExtraSamples' in self.tags:
+ if alpha is None:
+ alpha = TIFF.EXTRASAMPLE
+ extrasamples = self.extrasamples
+ if self.tags['ExtraSamples'].count == 1:
+ extrasamples = (extrasamples,)
+ for i, exs in enumerate(extrasamples):
+ if exs in alpha:
+ if self.planarconfig == 1:
+ data = data[..., [0, 1, 2, 3 + i]]
+ else:
+ data = data[:, [0, 1, 2, 3 + i]]
+ break
+ else:
+ if self.planarconfig == 1:
+ data = data[..., :3]
+ else:
+ data = data[:, :3]
+ # TODO: convert to uint8?
+
+ elif photometric == PHOTOMETRIC.MINISBLACK:
+ raise NotImplementedError()
+ elif photometric == PHOTOMETRIC.MINISWHITE:
+ raise NotImplementedError()
+ elif photometric == PHOTOMETRIC.SEPARATED:
+ raise NotImplementedError()
+ else:
+ raise NotImplementedError()
+ return data
+
+ def _gettags(self, codes=None, lock=None):
+ """Return list of (code, TiffTag)."""
+ tags = []
+ for tag in self.tags.values():
+ code = tag.code
+ if not codes or code in codes:
+ tags.append((code, tag))
+ return tags
+
+ def aspage(self):
+ """Return self."""
+ return self
+
+ @property
+ def keyframe(self):
+ """Return keyframe, self."""
+ return self
+
+ @keyframe.setter
+ def keyframe(self, index):
+ """Set keyframe, NOP."""
+ return
+
+ @lazyattr
+ def pages(self):
+ """Return sequence of sub-pages (SubIFDs)."""
+ if 'SubIFDs' not in self.tags:
+ return tuple()
+ return TiffPages(self)
+
+ @property
+ def hash(self):
+ """Return checksum to identify pages in same series."""
+ return hash(
+ self._shape
+ + (
+ self.tilewidth,
+ self.tilelength,
+ self.tiledepth,
+ self.bitspersample,
+ self.fillorder,
+ self.predictor,
+ self.extrasamples,
+ self.photometric,
+ self.compression,
+ self.planarconfig,
+ )
+ )
+
+ @lazyattr
+ def _offsetscounts(self):
+ """Return simplified offsets and bytecounts."""
+ if self.is_contiguous:
+ offset, bytecount = self.is_contiguous
+ return ((offset,), (bytecount,))
+ return self.dataoffsets, self.databytecounts
+
+ @lazyattr
+ def is_contiguous(self):
+ """Return offset and size of contiguous data, else None.
+
+ Excludes prediction and fill_order.
+
+ """
+ if self.compression != 1 or self.bitspersample not in (8, 16, 32, 64):
+ return None
+ if 'TileWidth' in self.tags:
+ if (
+ self.imagewidth != self.tilewidth
+ or self.imagelength % self.tilelength
+ or self.tilewidth % 16
+ or self.tilelength % 16
+ ):
+ return None
+ if (
+ 'ImageDepth' in self.tags
+ and 'TileDepth' in self.tags
+ and (self.imagelength != self.tilelength
+ or self.imagedepth % self.tiledepth)
+ ):
+ return None
+ offsets = self.dataoffsets
+ bytecounts = self.databytecounts
+ if len(offsets) == 1:
+ return offsets[0], bytecounts[0]
+ if self.is_stk or self.is_lsm:
+ return offsets[0], sum(bytecounts)
+ if all(
+ bytecounts[i] != 0 and offsets[i] + bytecounts[i] == offsets[i + 1]
+ for i in range(len(offsets) - 1)
+ ):
+ return offsets[0], sum(bytecounts)
+ return None
+
+ @lazyattr
+ def is_final(self):
+ """Return if page's image data are stored in final form.
+
+ Excludes byte-swapping.
+
+ """
+ return (
+ self.is_contiguous
+ and self.fillorder == 1
+ and self.predictor == 1
+ and not self.is_subsampled
+ )
+
+ @lazyattr
+ def is_memmappable(self):
+ """Return if page's image data in file can be memory-mapped."""
+ return (
+ self.parent.filehandle.is_file
+ and self.is_final
+ # and (self.bitspersample == 8 or self.parent.isnative)
+ # aligned?
+ and self.is_contiguous[0] % self.dtype.itemsize == 0
+ )
+
+ def __str__(self, detail=0, width=79):
+ """Return string containing information about page."""
+ if self.keyframe != self:
+ return TiffFrame.__str__(self, detail, width)
+ attr = ''
+ for name in ('memmappable', 'final', 'contiguous'):
+ attr = getattr(self, 'is_' + name)
+ if attr:
+ attr = name.upper()
+ break
+ info = ' '.join(
+ s.lower()
+ for s in (
+ 'x'.join(str(i) for i in self.shape),
+ '%s%s'
+ % (
+ TIFF.SAMPLEFORMAT(self.sampleformat).name,
+ self.bitspersample,
+ ),
+ ' '.join(
+ i
+ for i in (
+ TIFF.PHOTOMETRIC(self.photometric).name,
+ 'REDUCED' if self.is_reduced else '',
+ 'MASK' if self.is_mask else '',
+ 'TILED' if self.is_tiled else '',
+ self.compression.name if self.compression != 1 else '',
+ self.planarconfig.name
+ if self.planarconfig != 1
+ else '',
+ self.predictor.name if self.predictor != 1 else '',
+ self.fillorder.name if self.fillorder != 1 else '',
+ )
+ + tuple(f.upper() for f in self.flags)
+ + (attr,)
+ if i
+ ),
+ )
+ if s
+ )
+ info = 'TiffPage %i @%i %s' % (self.index, self.offset, info)
+ if detail <= 0:
+ return info
+ info = [info]
+ tags = self.tags
+ tlines = []
+ vlines = []
+ for tag in sorted(tags.values(), key=lambda x: x.code):
+ value = tag.__str__(width=width + 1)
+ tlines.append(value[:width].strip())
+ if detail > 1 and len(value) > width:
+ name = tag.name.upper()
+ if detail <= 2 and ('COUNTS' in name or 'OFFSETS' in name):
+ value = pformat(tag.value, width=width, height=detail * 4)
+ else:
+ value = pformat(tag.value, width=width, height=detail * 12)
+ vlines.append('%s\n%s' % (tag.name, value))
+ info.append('\n'.join(tlines))
+ if detail > 1:
+ info.append('\n\n'.join(vlines))
+ for name in ('ndpi',):
+ name = name + '_tags'
+ attr = getattr(self, name, False)
+ if attr:
+ info.append('%s\n%s' % (name.upper(), pformat(attr)))
+ if detail > 3:
+ try:
+ info.append(
+ 'DATA\n%s'
+ % pformat(self.asarray(), width=width, height=detail * 8)
+ )
+ except Exception:
+ pass
+ return '\n\n'.join(info)
+
+ @lazyattr
+ def flags(self):
+ """Return set of flags."""
+ return set(
+ name.lower()
+ for name in sorted(TIFF.FILE_FLAGS)
+ if getattr(self, 'is_' + name)
+ )
+
+ @property
+ def ndim(self):
+ """Return number of array dimensions."""
+ return len(self.shape)
+
+ @property
+ def size(self):
+ """Return number of elements in array."""
+ return product(self.shape)
+
+ @lazyattr
+ def andor_tags(self):
+ """Return consolidated metadata from Andor tags as dict.
+
+ Remove Andor tags from self.tags.
+
+ """
+ if not self.is_andor:
+ return None
+ tags = self.tags
+ result = {'Id': tags['AndorId'].value}
+ for tag in list(self.tags.values()):
+ code = tag.code
+ if not 4864 < code < 5031:
+ continue
+ value = tag.value
+ name = tag.name[5:] if len(tag.name) > 5 else tag.name
+ result[name] = value
+ del tags[tag.name]
+ return result
+
+ @lazyattr
+ def epics_tags(self):
+ """Return consolidated metadata from EPICS areaDetector tags as dict.
+
+ Remove areaDetector tags from self.tags.
+
+ """
+ if not self.is_epics:
+ return None
+ result = {}
+ tags = self.tags
+ for tag in list(self.tags.values()):
+ code = tag.code
+ if not 65000 <= code < 65500:
+ continue
+ value = tag.value
+ if code == 65000:
+ result['timeStamp'] = datetime.datetime.fromtimestamp(
+ float(value))
+ elif code == 65001:
+ result['uniqueID'] = int(value)
+ elif code == 65002:
+ result['epicsTSSec'] = int(value)
+ elif code == 65003:
+ result['epicsTSNsec'] = int(value)
+ else:
+ key, value = value.split(':', 1)
+ result[key] = astype(value)
+ del tags[tag.name]
+ return result
+
+ @lazyattr
+ def ndpi_tags(self):
+ """Return consolidated metadata from Hamamatsu NDPI as dict."""
+ if not self.is_ndpi:
+ return None
+ tags = self.tags
+ result = {}
+ for name in ('Make', 'Model', 'Software'):
+ result[name] = tags[name].value
+ for code, name in TIFF.NDPI_TAGS.items():
+ code = str(code)
+ if code in tags:
+ result[name] = tags[code].value
+ # del tags[code]
+ return result
+
+ @lazyattr
+ def geotiff_tags(self):
+ """Return consolidated metadata from GeoTIFF tags as dict."""
+ if not self.is_geotiff:
+ return None
+ tags = self.tags
+
+ gkd = tags['GeoKeyDirectoryTag'].value
+ if gkd[0] != 1:
+ log.warning('GeoTIFF tags: invalid GeoKeyDirectoryTag')
+ return {}
+
+ result = {
+ 'KeyDirectoryVersion': gkd[0],
+ 'KeyRevision': gkd[1],
+ 'KeyRevisionMinor': gkd[2],
+ # 'NumberOfKeys': gkd[3],
+ }
+ # deltags = ['GeoKeyDirectoryTag']
+ geokeys = TIFF.GEO_KEYS
+ geocodes = TIFF.GEO_CODES
+ for index in range(gkd[3]):
+ try:
+ keyid, tagid, count, offset = gkd[4 + index * 4: index * 4 + 8]
+ except Exception as exception:
+ log.warning('GeoTIFF tags: %s', str(exception))
+ continue
+ keyid = geokeys.get(keyid, keyid)
+ if tagid == 0:
+ value = offset
+ else:
+ tagname = TIFF.TAGS[tagid]
+ # deltags.append(tagname)
+ try:
+ value = tags[tagname].value[offset: offset + count]
+ except KeyError:
+ log.warning('GeoTIFF tags: %s not found', tagname)
+ continue
+ if tagid == 34737 and count > 1 and value[-1] == '|':
+ value = value[:-1]
+ value = value if count > 1 else value[0]
+ if keyid in geocodes:
+ try:
+ value = geocodes[keyid](value)
+ except Exception:
+ pass
+ result[keyid] = value
+
+ if 'IntergraphMatrixTag' in tags:
+ value = tags['IntergraphMatrixTag'].value
+ value = numpy.array(value)
+ if len(value) == 16:
+ value = value.reshape((4, 4)).tolist()
+ result['IntergraphMatrix'] = value
+ if 'ModelPixelScaleTag' in tags:
+ value = numpy.array(tags['ModelPixelScaleTag'].value).tolist()
+ result['ModelPixelScale'] = value
+ if 'ModelTiepointTag' in tags:
+ value = tags['ModelTiepointTag'].value
+ value = numpy.array(value).reshape((-1, 6)).squeeze().tolist()
+ result['ModelTiepoint'] = value
+ if 'ModelTransformationTag' in tags:
+ value = tags['ModelTransformationTag'].value
+ value = numpy.array(value).reshape((4, 4)).tolist()
+ result['ModelTransformation'] = value
+ # if 'ModelPixelScaleTag' in tags and 'ModelTiepointTag' in tags:
+ # sx, sy, sz = tags['ModelPixelScaleTag'].value
+ # tiepoints = tags['ModelTiepointTag'].value
+ # transforms = []
+ # for tp in range(0, len(tiepoints), 6):
+ # i, j, k, x, y, z = tiepoints[tp:tp+6]
+ # transforms.append([
+ # [sx, 0.0, 0.0, x - i * sx],
+ # [0.0, -sy, 0.0, y + j * sy],
+ # [0.0, 0.0, sz, z - k * sz],
+ # [0.0, 0.0, 0.0, 1.0]])
+ # if len(tiepoints) == 6:
+ # transforms = transforms[0]
+ # result['ModelTransformation'] = transforms
+
+ if 'RPCCoefficientTag' in tags:
+ rpcc = tags['RPCCoefficientTag'].value
+ result['RPCCoefficient'] = {
+ 'ERR_BIAS': rpcc[0],
+ 'ERR_RAND': rpcc[1],
+ 'LINE_OFF': rpcc[2],
+ 'SAMP_OFF': rpcc[3],
+ 'LAT_OFF': rpcc[4],
+ 'LONG_OFF': rpcc[5],
+ 'HEIGHT_OFF': rpcc[6],
+ 'LINE_SCALE': rpcc[7],
+ 'SAMP_SCALE': rpcc[8],
+ 'LAT_SCALE': rpcc[9],
+ 'LONG_SCALE': rpcc[10],
+ 'HEIGHT_SCALE': rpcc[11],
+ 'LINE_NUM_COEFF': rpcc[12:33],
+ 'LINE_DEN_COEFF ': rpcc[33:53],
+ 'SAMP_NUM_COEFF': rpcc[53:73],
+ 'SAMP_DEN_COEFF': rpcc[73:],
+ }
+
+ return result
+
+ @property
+ def is_reduced(self):
+ """Page is reduced image of another image."""
+ return self.subfiletype & 0b1
+
+ @property
+ def is_multipage(self):
+ """Page is part of multi-page image."""
+ return self.subfiletype & 0b10
+
+ @property
+ def is_mask(self):
+ """Page is transparency mask for another image."""
+ return self.subfiletype & 0b100
+
+ @property
+ def is_mrc(self):
+ """Page is part of Mixed Raster Content."""
+ return self.subfiletype & 0b1000
+
+ @property
+ def is_tiled(self):
+ """Page contains tiled image."""
+ return 'TileWidth' in self.tags
+
+ @property
+ def is_subsampled(self):
+ """Page contains chroma subsampled image."""
+ if 'YCbCrSubSampling' in self.tags:
+ return self.tags['YCbCrSubSampling'].value != (1, 1)
+ return (
+ self.compression == 7
+ and self.planarconfig == 1
+ and self.photometric in (2, 6)
+ )
+
+ @lazyattr
+ def is_imagej(self):
+ """Return ImageJ description if exists, else None."""
+ for description in (self.description, self.description1):
+ if not description:
+ return None
+ if description[:7] == 'ImageJ=':
+ return description
+ return None
+
+ @lazyattr
+ def is_shaped(self):
+ """Return description containing array shape if exists, else None."""
+ for description in (self.description, self.description1):
+ if not description:
+ return None
+ if description[:1] == '{' and '"shape":' in description:
+ return description
+ if description[:6] == 'shape=':
+ return description
+ return None
+
+ @property
+ def is_mdgel(self):
+ """Page contains MDFileTag tag."""
+ return 'MDFileTag' in self.tags
+
+ @property
+ def is_mediacy(self):
+ """Page contains Media Cybernetics Id tag."""
+ return (
+ 'MC_Id' in self.tags and self.tags['MC_Id'].value[:7] == b'MC TIFF'
+ )
+
+ @property
+ def is_stk(self):
+ """Page contains UIC2Tag tag."""
+ return 'UIC2tag' in self.tags
+
+ @property
+ def is_lsm(self):
+ """Page contains CZ_LSMINFO tag."""
+ return 'CZ_LSMINFO' in self.tags
+
+ @property
+ def is_fluoview(self):
+ """Page contains FluoView MM_STAMP tag."""
+ return 'MM_Stamp' in self.tags
+
+ @property
+ def is_nih(self):
+ """Page contains NIH image header."""
+ return 'NIHImageHeader' in self.tags
+
+ @property
+ def is_sgi(self):
+ """Page contains SGI image and tile depth tags."""
+ return 'ImageDepth' in self.tags and 'TileDepth' in self.tags
+
+ @property
+ def is_vista(self):
+ """Software tag is 'ISS Vista'."""
+ return self.software == 'ISS Vista'
+
+ @property
+ def is_metaseries(self):
+ """Page contains MDS MetaSeries metadata in ImageDescription tag."""
+ if self.index > 1 or self.software != 'MetaSeries':
+ return False
+ d = self.description
+ return d.startswith('<MetaData>') and d.endswith('</MetaData>')
+
+ @property
+ def is_ome(self):
+ """Page contains OME-XML in ImageDescription tag."""
+ if self.index > 1 or not self.description:
+ return False
+ d = self.description
+ return d[:14] == '<?xml version=' and d[-6:] == '</OME>'
+
+ @property
+ def is_scn(self):
+ """Page contains Leica SCN XML in ImageDescription tag."""
+ if self.index > 1 or not self.description:
+ return False
+ d = self.description
+ return d[:14] == '<?xml version=' and d[-6:] == '</scn>'
+
+ @property
+ def is_micromanager(self):
+ """Page contains Micro-Manager metadata."""
+ return 'MicroManagerMetadata' in self.tags
+
+ @property
+ def is_andor(self):
+ """Page contains Andor Technology tags."""
+ return 'AndorId' in self.tags
+
+ @property
+ def is_pilatus(self):
+ """Page contains Pilatus tags."""
+ return self.software[:8] == 'TVX TIFF' and self.description[:2] == '# '
+
+ @property
+ def is_epics(self):
+ """Page contains EPICS areaDetector tags."""
+ return (
+ self.description == 'EPICS areaDetector'
+ or self.software == 'EPICS areaDetector'
+ )
+
+ @property
+ def is_tvips(self):
+ """Page contains TVIPS metadata."""
+ return 'TVIPS' in self.tags
+
+ @property
+ def is_fei(self):
+ """Page contains SFEG or HELIOS metadata."""
+ return 'FEI_SFEG' in self.tags or 'FEI_HELIOS' in self.tags
+
+ @property
+ def is_sem(self):
+ """Page contains Zeiss SEM metadata."""
+ return 'CZ_SEM' in self.tags
+
+ @property
+ def is_svs(self):
+ """Page contains Aperio metadata."""
+ return self.description[:20] == 'Aperio Image Library'
+
+ @property
+ def is_scanimage(self):
+ """Page contains ScanImage metadata."""
+ return (
+ self.description[:12] == 'state.config'
+ or self.software[:22] == 'SI.LINE_FORMAT_VERSION'
+ or 'scanimage.SI' in self.description[-256:]
+ )
+
+ @property
+ def is_qpi(self):
+ """Page contains PerkinElmer tissue images metadata."""
+ # The ImageDescription tag contains XML with a top-level
+ # <PerkinElmer-QPI-ImageDescription> element
+ return self.software[:15] == 'PerkinElmer-QPI'
+
+ @property
+ def is_geotiff(self):
+ """Page contains GeoTIFF metadata."""
+ return 'GeoKeyDirectoryTag' in self.tags
+
+ @property
+ def is_sis(self):
+ """Page contains Olympus SIS metadata."""
+ return 'OlympusSIS' in self.tags or 'OlympusINI' in self.tags
+
+ @lazyattr # must not be property; tag 65420 is later removed
+ def is_ndpi(self):
+ """Page contains NDPI metadata."""
+ return '65420' in self.tags and 'Make' in self.tags
+
+
+class TiffFrame(object):
+ """Lightweight TIFF image file directory (IFD).
+
+ Only a limited number of tag values are read from file, e.g. StripOffsets,
+ and StripByteCounts. Other tag values are assumed to be identical with a
+ specified TiffPage instance, the keyframe.
+
+ TiffFrame is intended to reduce resource usage and speed up reading image
+ data from file, not for introspection of metadata.
+
+ Not compatible with Python 2.
+
+ """
+
+ __slots__ = 'index', 'parent', 'offset', '_offsetscounts', '_keyframe'
+
+ is_mdgel = False
+ pages = None
+ tags = {}
+
+ def __init__(self, parent, index, offset=None, keyframe=None,
+ offsets=None, bytecounts=None):
+ """Initialize TiffFrame from file or values.
+
+ The file handle position must be at the offset to a valid IFD.
+
+ """
+ self._keyframe = None
+ self.parent = parent
+ self.index = index
+ self.offset = offset
+
+ if offsets is not None:
+ # initialize "virtual frame" from offsets and bytecounts
+ self._offsetscounts = offsets, bytecounts
+ self._keyframe = keyframe
+ return
+
+ if offset is None:
+ self.offset = parent.filehandle.tell()
+ else:
+ parent.filehandle.seek(offset)
+
+ if keyframe is None:
+ tags = {273, 279, 324, 325}
+ elif keyframe.is_contiguous:
+ tags = {256, 273, 324}
+ else:
+ tags = {256, 273, 279, 324, 325}
+
+ dataoffsets = databytecounts = []
+
+ for code, tag in self._gettags(tags):
+ if code == 273 or code == 324:
+ dataoffsets = tag.value
+ elif code == 279 or code == 325:
+ databytecounts = tag.value
+ elif code == 256 and keyframe.imagewidth != tag.value:
+ raise RuntimeError(
+ 'TiffFrame %i: incompatible keyframe' % index)
+ # elif code == 270:
+ # tagname = tag.name
+ # if tagname not in tags:
+ # tags[tagname] = bytes2str(tag.value)
+ # elif 'ImageDescription1' not in tags:
+ # tags['ImageDescription1'] = bytes2str(tag.value)
+ # else:
+ # tags[tag.name] = tag.value
+
+ if not dataoffsets:
+ log.warning('TiffFrame %i: missing required tags', index)
+
+ self._offsetscounts = dataoffsets, databytecounts
+
+ if keyframe is not None:
+ self.keyframe = keyframe
+
+ def _gettags(self, codes=None, lock=None):
+ """Return list of (code, TiffTag) from file."""
+ fh = self.parent.filehandle
+ tiff = self.parent.tiff
+ unpack = struct.unpack
+ lock = NullContext() if lock is None else lock
+ tags = []
+
+ with lock:
+ fh.seek(self.offset)
+ try:
+ tagno = unpack(tiff.tagnoformat, fh.read(tiff.tagnosize))[0]
+ if tagno > 4096:
+ raise TiffFileError(
+ 'TiffFrame %i: suspicious number of tags' % self.index)
+ except Exception:
+ raise TiffFileError(
+ 'TiffFrame %i: corrupted page list at offset %i'
+ % (self.index, self.offset))
+
+ tagoffset = self.offset + tiff.tagnosize # fh.tell()
+ tagsize = tiff.tagsize
+ tagindex = -tagsize
+ codeformat = tiff.tagformat1[:2]
+ tagbytes = fh.read(tagsize * tagno)
+
+ for _ in range(tagno):
+ tagindex += tagsize
+ code = unpack(codeformat, tagbytes[tagindex: tagindex + 2])[0]
+ if codes and code not in codes:
+ continue
+ try:
+ tag = TiffTag(self.parent,
+ tagbytes[tagindex: tagindex + tagsize],
+ tagoffset + tagindex)
+ except TiffFileError as exc:
+ log.warning('TiffFrame %i: %s: %s',
+ self.index, exc.__class__.__name__, exc)
+ continue
+ tags.append((code, tag))
+
+ return tags
+
+ def aspage(self):
+ """Return TiffPage from file."""
+ if self.offset is None:
+ raise ValueError(
+ 'TiffFrame %i: cannot return virtual frame as page'
+ % self.index)
+ self.parent.filehandle.seek(self.offset)
+ return TiffPage(self.parent, index=self.index)
+
+ def asarray(self, *args, **kwargs):
+ """Read image data from file and return as numpy array."""
+ # TODO: fix TypeError on Python 2
+ # "TypeError: unbound method asarray() must be called with TiffPage
+ # instance as first argument (got TiffFrame instance instead)"
+ if self._keyframe is None:
+ raise RuntimeError('TiffFrame %i: keyframe not set' % self.index)
+ kwargs['validate'] = False
+ return TiffPage.asarray(self, *args, **kwargs)
+
+ def asrgb(self, *args, **kwargs):
+ """Read image data from file and return RGB image as numpy array."""
+ if self._keyframe is None:
+ raise RuntimeError('TiffFrame %i: keyframe not set' % self.index)
+ kwargs['validate'] = False
+ return TiffPage.asrgb(self, *args, **kwargs)
+
+ @property
+ def keyframe(self):
+ """Return keyframe."""
+ return self._keyframe
+
+ @keyframe.setter
+ def keyframe(self, keyframe):
+ """Set keyframe."""
+ if self._keyframe == keyframe:
+ return
+ if self._keyframe is not None:
+ raise RuntimeError(
+ 'TiffFrame %i: cannot reset keyframe' % self.index)
+ if len(self._offsetscounts[0]) != len(keyframe.dataoffsets):
+ raise RuntimeError(
+ 'TiffFrame %i: incompatible keyframe' % self.index)
+ if keyframe.is_tiled:
+ pass
+ if keyframe.is_contiguous:
+ self._offsetscounts = (
+ (self._offsetscounts[0][0], ),
+ (keyframe.is_contiguous[1], ),
+ )
+ self._keyframe = keyframe
+
+ @property
+ def is_contiguous(self):
+ """Return offset and size of contiguous data, else None."""
+ if self._keyframe is None:
+ raise RuntimeError('TiffFrame %i: keyframe not set' % self.index)
+ if self._keyframe.is_contiguous:
+ return self._offsetscounts[0][0], self._keyframe.is_contiguous[1]
+ return None
+
+ @property
+ def is_memmappable(self):
+ """Return if page's image data in file can be memory-mapped."""
+ if self._keyframe is None:
+ raise RuntimeError('TiffFrame %i: keyframe not set' % self.index)
+ return self._keyframe.is_memmappable
+
+ @property
+ def hash(self):
+ """Return checksum to identify pages in same series."""
+ if self._keyframe is None:
+ raise RuntimeError('TiffFrame %i: keyframe not set' % self.index)
+ return self._keyframe.hash
+
+ def __getattr__(self, name):
+ """Return attribute from keyframe."""
+ if name in TIFF.FRAME_ATTRS:
+ return getattr(self._keyframe, name)
+ # this error could be raised because an AttributeError was
+ # raised inside a @property function
+ raise AttributeError("'%s' object has no attribute '%s'"
+ % (self.__class__.__name__, name))
+
+ def __str__(self, detail=0, width=79):
+ """Return string containing information about frame."""
+ if self._keyframe is None:
+ info = ''
+ kf = None
+ else:
+ info = ' '.join(s for s in ('x'.join(str(i) for i in self.shape),
+ str(self.dtype)))
+ kf = TiffPage.__str__(self._keyframe, width=width - 11)
+ if detail > 3:
+ of, bc = self._offsetscounts
+ of = pformat(of, width=width - 9, height=detail - 3)
+ bc = pformat(bc, width=width - 13, height=detail - 3)
+ info = '\n Keyframe %s\n Offsets %s\n Bytecounts %s' % (kf, of, bc)
+ return 'TiffFrame %i @%s %s' % (self.index, self.offset, info)
+
+
+class TiffTag(object):
+ """TIFF tag structure.
+
+ Attributes
+ ----------
+ name : string
+ Name of tag.
+ code : int
+ Decimal code of tag.
+ dtype : str
+ Datatype of tag data. One of TIFF DATA_FORMATS.
+ count : int
+ Number of values.
+ value : various types
+ Tag data as Python object.
+ ImageSourceData : int
+ Location of value in file.
+
+ All attributes are read-only.
+
+ """
+
+ __slots__ = ('code', 'count', 'dtype', 'value', 'valueoffset')
+
+ def __init__(self, parent, tagheader, tagoffset):
+ """Initialize instance from tag header."""
+ fh = parent.filehandle
+ tiff = parent.tiff
+ byteorder = tiff.byteorder
+ offsetsize = tiff.offsetsize
+ unpack = struct.unpack
+
+ self.valueoffset = tagoffset + offsetsize + 4
+ code, type_ = unpack(tiff.tagformat1, tagheader[:4])
+ count, value = unpack(tiff.tagformat2, tagheader[4:])
+
+ try:
+ dtype = TIFF.DATA_FORMATS[type_]
+ except KeyError:
+ raise TiffFileError('unknown tag data type %i' % type_)
+
+ fmt = '%s%i%s' % (byteorder, count * int(dtype[0]), dtype[1])
+ size = struct.calcsize(fmt)
+ if size > offsetsize or code in TIFF.TAG_READERS:
+ self.valueoffset = offset = unpack(tiff.offsetformat, value)[0]
+ if offset < 8 or offset > fh.size - size:
+ raise TiffFileError('invalid tag value offset')
+ # if offset % 2:
+ # log.warning('TiffTag: value does not begin on word boundary')
+ fh.seek(offset)
+ if code in TIFF.TAG_READERS:
+ readfunc = TIFF.TAG_READERS[code]
+ value = readfunc(fh, byteorder, dtype, count, offsetsize)
+ elif type_ == 7 or (count > 1 and dtype[-1] == 'B'):
+ value = read_bytes(fh, byteorder, dtype, count, offsetsize)
+ elif code in TIFF.TAGS or dtype[-1] == 's':
+ value = unpack(fmt, fh.read(size))
+ else:
+ value = read_numpy(fh, byteorder, dtype, count, offsetsize)
+ elif dtype[-1] == 'B' or type_ == 7:
+ value = value[:size]
+ else:
+ value = unpack(fmt, value[:size])
+
+ process = (
+ code not in TIFF.TAG_READERS
+ and code not in TIFF.TAG_TUPLE
+ and type_ != 7
+ )
+ if process and dtype[-1] == 's' and isinstance(value[0], bytes):
+ # TIFF ASCII fields can contain multiple strings,
+ # each terminated with a NUL
+ value = value[0]
+ try:
+ value = bytes2str(stripascii(value).strip())
+ except UnicodeDecodeError:
+ # TODO: this doesn't work on Python 2
+ log.warning(
+ 'TiffTag %i: coercing invalid ASCII to bytes', code)
+ dtype = '1B'
+ else:
+ if code in TIFF.TAG_ENUM:
+ t = TIFF.TAG_ENUM[code]
+ try:
+ value = tuple(t(v) for v in value)
+ except ValueError as exc:
+ log.warning('TiffTag %i: %s', code, str(exc))
+ if process:
+ if len(value) == 1:
+ value = value[0]
+
+ self.code = code
+ self.dtype = dtype
+ self.count = count
+ self.value = value
+
+ @property
+ def name(self):
+ """Return name of tag from TIFF.TAGS registry."""
+ try:
+ return TIFF.TAGS[self.code]
+ except KeyError:
+ return str(self.code)
+
+ def _fix_lsm_bitspersample(self, parent):
+ """Correct LSM bitspersample tag.
+
+ Old LSM writers may use a separate region for two 16-bit values,
+ although they fit into the tag value element of the tag.
+
+ """
+ if self.code != 258 or self.count != 2:
+ return
+ # TODO: test this case; need example file
+ log.warning('TiffTag %i: correcting LSM bitspersample tag', self.code)
+ value = struct.pack('<HH', *self.value)
+ self.valueoffset = struct.unpack('<I', value)[0]
+ parent.filehandle.seek(self.valueoffset)
+ self.value = struct.unpack('<HH', parent.filehandle.read(4))
+
+ def __str__(self, detail=0, width=79):
+ """Return string containing information about tag."""
+ height = 1 if detail <= 0 else 8 * detail
+ tcode = '%i%s' % (self.count * int(self.dtype[0]), self.dtype[1])
+ if self.name == str(self.code):
+ codename = self.name
+ else:
+ codename = '%i %s' % (self.code, self.name)
+ line = 'TiffTag %s %s @%i ' % (codename, tcode, self.valueoffset)
+ line = line[:width]
+ if self.code in TIFF.TAG_ENUM:
+ if self.count == 1:
+ value = TIFF.TAG_ENUM[self.code](self.value).name
+ else:
+ value = pformat(tuple(v.name for v in self.value))
+ else:
+ value = pformat(self.value, width=width, height=height)
+ if detail <= 0:
+ line += value
+ line = line[:width]
+ else:
+ line += '\n' + value
+ return line
+
+
+class TiffPageSeries(object):
+ """Series of TIFF pages with compatible shape and data type.
+
+ Attributes
+ ----------
+ pages : list of TiffPage
+ Sequence of TiffPages in series.
+ dtype : numpy.dtype
+ Data type (native byte order) of the image array in series.
+ shape : tuple
+ Dimensions of the image array in series.
+ axes : str
+ Labels of axes in shape. See TiffPage.axes.
+ offset : int or None
+ Position of image data in file if memory-mappable, else None.
+
+ """
+
+ def __init__(self, pages, shape, dtype, axes, parent=None, name=None,
+ transform=None, kind=None, truncated=False):
+ """Initialize instance."""
+ self.index = 0
+ self._pages = pages # might contain only first of contiguous pages
+ self.shape = tuple(shape)
+ self.axes = ''.join(axes)
+ self.dtype = numpy.dtype(dtype)
+ self.kind = kind if kind else ''
+ self.name = name if name else ''
+ self.transform = transform
+ if parent:
+ self.parent = parent
+ elif pages:
+ self.parent = pages[0].parent
+ else:
+ self.parent = None
+ if not truncated and len(pages) == 1:
+ self._len = int(product(self.shape) // product(pages[0].shape))
+ else:
+ self._len = len(pages)
+
+ def asarray(self, out=None):
+ """Return image data from series of TIFF pages as numpy array."""
+ if self.parent:
+ result = self.parent.asarray(series=self, out=out)
+ if self.transform is not None:
+ result = self.transform(result)
+ return result
+ return None
+
+ @lazyattr
+ def offset(self):
+ """Return offset to series data in file, if any."""
+ if not self._pages:
+ return None
+
+ pos = 0
+ for page in self._pages:
+ if page is None:
+ return None
+ if not page.is_final:
+ return None
+ if not pos:
+ pos = page.is_contiguous[0] + page.is_contiguous[1]
+ continue
+ if pos != page.is_contiguous[0]:
+ return None
+ pos += page.is_contiguous[1]
+
+ page = self._pages[0]
+ offset = page.is_contiguous[0]
+ if (page.is_imagej or page.is_shaped) and len(self._pages) == 1:
+ # truncated files
+ return offset
+ if pos == offset + product(self.shape) * self.dtype.itemsize:
+ return offset
+ return None
+
+ @property
+ def ndim(self):
+ """Return number of array dimensions."""
+ return len(self.shape)
+
+ @property
+ def size(self):
+ """Return number of elements in array."""
+ return int(product(self.shape))
+
+ @property
+ def pages(self):
+ """Return sequence of all pages in series."""
+ # a workaround to keep the old interface working
+ return self
+
+ def _getitem(self, key):
+ """Return specified page of series from cache or file."""
+ key = int(key)
+ if key < 0:
+ key %= self._len
+ if len(self._pages) == 1 and 0 < key < self._len:
+ index = self._pages[0].index
+ return self.parent.pages._getitem(index + key)
+ return self._pages[key]
+
+ def __getitem__(self, key):
+ """Return specified page(s)."""
+ getitem = self._getitem
+ if isinstance(key, inttypes):
+ return getitem(key)
+ if isinstance(key, slice):
+ return [getitem(i) for i in range(*key.indices(self._len))]
+ if isinstance(key, Iterable):
+ return [getitem(k) for k in key]
+ raise TypeError('key must be an integer, slice, or iterable')
+
+ def __iter__(self):
+ """Return iterator over pages in series."""
+ if len(self._pages) == self._len:
+ for page in self._pages:
+ yield page
+ else:
+ pages = self.parent.pages
+ index = self._pages[0].index
+ for i in range(self._len):
+ yield pages[index + i]
+
+ def __len__(self):
+ """Return number of pages in series."""
+ return self._len
+
+ def __str__(self):
+ """Return string with information about series."""
+ s = ' '.join(
+ s
+ for s in (
+ snipstr("'%s'" % self.name, 20) if self.name else '',
+ 'x'.join(str(i) for i in self.shape),
+ str(self.dtype),
+ self.axes,
+ self.kind,
+ '%i Pages' % len(self.pages),
+ ('Offset=%i' % self.offset) if self.offset else '')
+ if s
+ )
+ return 'TiffPageSeries %i %s' % (self.index, s)
+
+
+class FileSequence(object):
+ """Series of files containing array data of compatible shape and data type.
+
+ Attributes
+ ----------
+ files : list
+ List of file names.
+ shape : tuple
+ Shape of file series. Excludes shape of individual arrays.
+ axes : str
+ Labels of axes in shape.
+
+ """
+
+ _patterns = {
+ 'axes': r"""
+ # matches Olympus OIF and Leica TIFF series
+ _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))
+ _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
+ _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
+ _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
+ _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
+ _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
+ _?(?:(q|l|p|a|c|t|x|y|z|ch|tp)(\d{1,4}))?
+ """
+ }
+
+ def __init__(self, fromfile, files, container=None, sort=None,
+ pattern=None):
+ """Initialize instance from multiple files.
+
+ Parameters
+ ----------
+ fromfile : function or class
+ Array read function or class with asarray function returning numpy
+ array from single file.
+ files : str, pathlib.Path, or sequence thereof
+ Glob filename pattern or sequence of file names. Default: \\*.
+ Binary streams are not supported.
+ container : str or container instance
+ Name or open instance of ZIP file in which files are stored.
+ sort : function
+ Sort function used to sort file names when 'files' is a pattern.
+ The default (None) is natural_sorted. Use sort=False to disable
+ sorting.
+ pattern : str
+ Regular expression pattern that matches axes and sequence indices
+ in file names. By default (None), no pattern matching is performed.
+ Axes can be specified by matching groups preceding the index groups
+ in the file name, be provided as group names for the index groups,
+ or be omitted. The predefined 'axes' pattern matches Olympus OIF
+ and Leica TIFF series.
+
+ """
+ if files is None:
+ files = '*'
+ if sort is None:
+ sort = natural_sorted
+ self._container = container
+ if container:
+ import fnmatch # noqa
+
+ if isinstance(container, basestring):
+ import zipfile # noqa
+
+ self._container = zipfile.ZipFile(container)
+ elif not hasattr(self._container, 'open'):
+ raise ValueError('invalid container')
+ if isinstance(files, basestring):
+ files = fnmatch.filter(self._container.namelist(), files)
+ if sort:
+ files = sort(files)
+ else:
+ if isinstance(files, pathlib.Path):
+ files = str(files)
+ if isinstance(files, basestring):
+ files = glob.glob(files)
+ if sort:
+ files = sort(files)
+ if not files:
+ raise ValueError('no files found')
+
+ files = list(files)
+ if not files:
+ raise ValueError('no files found')
+ if isinstance(files[0], pathlib.Path):
+ files = [str(pathlib.Path(f)) for f in files]
+ elif not isinstance(files[0], basestring):
+ raise ValueError('not a file name')
+
+ if hasattr(fromfile, 'asarray'):
+ # redefine fromfile to use asarray from fromfile class
+ if not callable(fromfile.asarray):
+ raise ValueError('invalid fromfile function')
+ _fromfile0 = fromfile
+
+ def fromfile(fname, **kwargs):
+ with _fromfile0(fname) as handle:
+ return handle.asarray(**kwargs)
+
+ elif not callable(fromfile):
+ raise ValueError('invalid fromfile function')
+
+ if container:
+ # redefine fromfile to read from container
+ _fromfile1 = fromfile
+
+ def fromfile(fname, **kwargs):
+ with self._container.open(fname) as handle1:
+ with io.BytesIO(handle1.read()) as handle2:
+ return _fromfile1(handle2, **kwargs)
+
+ axes = 'I'
+ shape = (len(files),)
+ indices = tuple((i,) for i in range(len(files)))
+ startindex = (0,)
+
+ pattern = self._patterns.get(pattern, pattern)
+ if pattern:
+ try:
+ axes, shape, indices, startindex = parse_filenames(files,
+ pattern)
+ except ValueError as exception:
+ log.warning(
+ 'FileSequence: failed to parse file names (%s)', exception)
+
+ if product(shape) != len(files):
+ log.warning(
+ 'FileSequence: files are missing. Missing data are zeroed')
+
+ self.fromfile = fromfile
+ self.files = files
+ self.pattern = pattern
+ self.axes = axes.upper()
+ self.shape = shape
+ self._indices = indices
+ self._startindex = startindex
+
+ def __str__(self):
+ """Return string with information about file series."""
+ return '\n'.join((
+ str(self._container) if self._container else self.files[0],
+ ' size: %i' % len(self.files),
+ ' shape: %s' % str(self.shape),
+ ' axes: %s' % self.axes,
+ ))
+
+ def __len__(self):
+ return len(self.files)
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ self.close()
+
+ def close(self):
+ if self._container:
+ self._container.close()
+ self._container = None
+
+ def asarray(self, file=None, ioworkers=1, out=None, **kwargs):
+ """Read image data from files and return as numpy array.
+
+ Raise IndexError or ValueError if array shapes do not match.
+
+ Parameters
+ ----------
+ file : int or None
+ Index or name of single file to read.
+ ioworkers : int or None
+ Maximum number of threads to execute the array read function
+ asynchronously. Default: 1.
+ If None, default to the number of processors multiplied by 5.
+ Using threads can significantly improve runtime when
+ reading many small files from a network share.
+ out : numpy.ndarray, str, or file-like object
+ Buffer where image data will be saved.
+ If None (default), a new array will be created.
+ If numpy.ndarray, a writable array of compatible dtype and shape.
+ If 'memmap', create a memory-mapped array in a temporary file.
+ If str or open file, the file name or file object used to
+ create a memory-map to an array stored in a binary file on disk.
+ kwargs : dict
+ Additional parameters passed to the array read function.
+
+ """
+ if file is not None:
+ if isinstance(file, int):
+ return self.fromfile(self.files[file], **kwargs)
+ return self.fromfile(file, **kwargs)
+
+ im = self.fromfile(self.files[0], **kwargs)
+ shape = self.shape + im.shape
+ result = create_output(out, shape, dtype=im.dtype)
+ result = result.reshape(-1, *im.shape)
+
+ def func(index, fname):
+ """Read single image from file into result."""
+ index = [i - j for i, j in zip(index, self._startindex)]
+ index = numpy.ravel_multi_index(index, self.shape)
+ im = self.fromfile(fname, **kwargs)
+ result[index] = im
+
+ if len(self.files) < 3:
+ ioworkers = 1
+ elif ioworkers is None or ioworkers < 1:
+ import multiprocessing # noqa: delay import
+ ioworkers = max(multiprocessing.cpu_count() * 5, 1)
+
+ if ioworkers < 2:
+ for index, fname in zip(self._indices, self.files):
+ func(index, fname)
+ else:
+ func(self._indices[0], self.files[0])
+ with ThreadPoolExecutor(ioworkers) as executor:
+ executor.map(func, self._indices[1:], self.files[1:])
+
+ result.shape = shape
+ return result
+
+
+class TiffSequence(FileSequence):
+ """Series of TIFF files."""
+
+ def __init__(self, files=None, container=None, sort=None, pattern=None,
+ imread=imread):
+ """Initialize instance from multiple TIFF files."""
+ super(TiffSequence, self).__init__(
+ imread, '*.tif' if files is None else files,
+ container=container, sort=sort, pattern=pattern)
+
+
+class FileHandle(object):
+ """Binary file handle.
+
+ A limited, special purpose file handle that can:
+
+ * handle embedded files (for CZI within CZI files)
+ * re-open closed files (for multi-file formats, such as OME-TIFF)
+ * read and write numpy arrays and records from file like objects
+
+ Only 'rb' and 'wb' modes are supported. Concurrently reading and writing
+ of the same stream is untested.
+
+ When initialized from another file handle, do not use it unless this
+ FileHandle is closed.
+
+ Attributes
+ ----------
+ name : str
+ Name of the file.
+ path : str
+ Absolute path to file.
+ size : int
+ Size of file in bytes.
+ is_file : bool
+ If True, file has a filno and can be memory-mapped.
+
+ All attributes are read-only.
+
+ """
+
+ __slots__ = ('_fh', '_file', '_mode', '_name', '_dir', '_lock',
+ '_offset', '_size', '_close', 'is_file')
+
+ def __init__(self, file, mode='rb', name=None, offset=None, size=None):
+ """Initialize file handle from file name or another file handle.
+
+ Parameters
+ ----------
+ file : str, pathlib.Path, binary stream, or FileHandle
+ File name or seekable binary stream, such as an open file
+ or BytesIO.
+ mode : str
+ File open mode in case 'file' is a file name. Must be 'rb' or 'wb'.
+ name : str
+ Optional name of file in case 'file' is a binary stream.
+ offset : int
+ Optional start position of embedded file. By default, this is
+ the current file position.
+ size : int
+ Optional size of embedded file. By default, this is the number
+ of bytes from the 'offset' to the end of the file.
+
+ """
+ self._file = file
+ self._fh = None
+ self._mode = mode
+ self._name = name
+ self._dir = ''
+ self._offset = offset
+ self._size = size
+ self._close = True
+ self.is_file = False
+ self._lock = NullContext()
+ self.open()
+
+ def open(self):
+ """Open or re-open file."""
+ if self._fh:
+ return # file is open
+
+ if isinstance(self._file, pathlib.Path):
+ self._file = str(self._file)
+ if isinstance(self._file, basestring):
+ # file name
+ self._file = os.path.realpath(self._file)
+ self._dir, self._name = os.path.split(self._file)
+ self._fh = open(self._file, self._mode)
+ self._close = True
+ if self._offset is None:
+ self._offset = 0
+ elif isinstance(self._file, FileHandle):
+ # FileHandle
+ self._fh = self._file._fh
+ if self._offset is None:
+ self._offset = 0
+ self._offset += self._file._offset
+ self._close = False
+ if not self._name:
+ if self._offset:
+ name, ext = os.path.splitext(self._file._name)
+ self._name = '%s@%i%s' % (name, self._offset, ext)
+ else:
+ self._name = self._file._name
+ if self._mode and self._mode != self._file._mode:
+ raise ValueError('FileHandle has wrong mode')
+ self._mode = self._file._mode
+ self._dir = self._file._dir
+ elif hasattr(self._file, 'seek'):
+ # binary stream: open file, BytesIO
+ try:
+ self._file.tell()
+ except Exception:
+ raise ValueError('binary stream is not seekable')
+ self._fh = self._file
+ if self._offset is None:
+ self._offset = self._file.tell()
+ self._close = False
+ if not self._name:
+ try:
+ self._dir, self._name = os.path.split(self._fh.name)
+ except AttributeError:
+ self._name = 'Unnamed binary stream'
+ try:
+ self._mode = self._fh.mode
+ except AttributeError:
+ pass
+ else:
+ raise ValueError('the first parameter must be a file name, '
+ 'seekable binary stream, or FileHandle')
+
+ if self._offset:
+ self._fh.seek(self._offset)
+
+ if self._size is None:
+ pos = self._fh.tell()
+ self._fh.seek(self._offset, 2)
+ self._size = self._fh.tell()
+ self._fh.seek(pos)
+
+ try:
+ self._fh.fileno()
+ self.is_file = True
+ except Exception:
+ self.is_file = False
+
+ def read(self, size=-1):
+ """Read 'size' bytes from file, or until EOF is reached."""
+ if size < 0 and self._offset:
+ size = self._size
+ return self._fh.read(size)
+
+ def readinto(self, b):
+ """Read up to len(b) bytes into b, and return number of bytes read."""
+ return self._fh.readinto(b)
+
+ def write(self, bytestring):
+ """Write bytestring to file."""
+ return self._fh.write(bytestring)
+
+ def flush(self):
+ """Flush write buffers if applicable."""
+ return self._fh.flush()
+
+ def memmap_array(self, dtype, shape, offset=0, mode='r', order='C'):
+ """Return numpy.memmap of data stored in file."""
+ if not self.is_file:
+ raise ValueError('cannot memory-map file without fileno')
+ return numpy.memmap(self._fh, dtype=dtype, mode=mode,
+ offset=self._offset + offset,
+ shape=shape, order=order)
+
+ def read_array(self, dtype, count=-1, out=None):
+ """Return numpy array from file in native byte order."""
+ fh = self._fh
+ dtype = numpy.dtype(dtype)
+
+ if count < 0:
+ size = self._size if out is None else out.nbytes
+ count = size // dtype.itemsize
+ else:
+ size = count * dtype.itemsize
+
+ result = numpy.empty(count, dtype) if out is None else out
+
+ if result.nbytes != size:
+ raise ValueError('size mismatch')
+
+ n = fh.readinto(result)
+ if n != size:
+ raise ValueError('failed to read %i bytes' % size)
+
+ if not result.dtype.isnative:
+ if not dtype.isnative:
+ result.byteswap(True)
+ result = result.newbyteorder()
+ elif result.dtype.isnative != dtype.isnative:
+ result.byteswap(True)
+
+ if out is not None:
+ if hasattr(out, 'flush'):
+ out.flush()
+
+ return result
+
+ def read_segments(self, offsets, bytecounts, lock=None, buffersize=None):
+ """Return iterator over segments read from file.
+
+ A reentrant lock can be used to synchronize seeks and reads up to
+ buffersize bytes.
+
+ """
+ length = len(offsets)
+ if length < 1:
+ return
+ if length == 1:
+ if bytecounts[0] > 0 and offsets[0] > 0:
+ if lock is None:
+ lock = self._lock
+ with lock:
+ self.seek(offsets[0])
+ yield self._fh.read(bytecounts[0])
+ else:
+ yield None
+ return
+
+ if lock is None:
+ lock = self._lock
+ if buffersize is None:
+ buffersize = 2**26 # 64 MB
+
+ seek = self.seek
+ read = self._fh.read
+ index = 0
+ while index < length:
+ segments = []
+ with lock:
+ size = 0
+ while size < buffersize and index < length:
+ offset = offsets[index]
+ bytecount = bytecounts[index]
+ if offset > 0 and bytecount > 0:
+ seek(offset)
+ segments.append(read(bytecount))
+ # buffer = bytearray(bytecount)
+ # n = fh.readinto(buffer)
+ # data.append(buffer[:n])
+ size += bytecount
+ else:
+ segments.append(None)
+ index += 1
+ for segment in segments:
+ yield segment
+
+ def read_record(self, dtype, shape=1, byteorder=None):
+ """Return numpy record from file."""
+ rec = numpy.rec
+ try:
+ record = rec.fromfile(self._fh, dtype, shape, byteorder=byteorder)
+ except Exception:
+ dtype = numpy.dtype(dtype)
+ if shape is None:
+ shape = self._size // dtype.itemsize
+ size = product(sequence(shape)) * dtype.itemsize
+ data = self._fh.read(size)
+ record = rec.fromstring(data, dtype, shape, byteorder=byteorder)
+ return record[0] if shape == 1 else record
+
+ def write_empty(self, size):
+ """Append size bytes to file. Position must be at end of file."""
+ if size < 1:
+ return
+ self._fh.seek(size - 1, 1)
+ self._fh.write(b'\x00')
+
+ def write_array(self, data):
+ """Write numpy array to binary file."""
+ try:
+ data.tofile(self._fh)
+ except Exception:
+ # BytesIO
+ self._fh.write(data.tostring())
+
+ def tell(self):
+ """Return file's current position."""
+ return self._fh.tell() - self._offset
+
+ def seek(self, offset, whence=0):
+ """Set file's current position."""
+ if self._offset:
+ if whence == 0:
+ self._fh.seek(self._offset + offset, whence)
+ return
+ if whence == 2 and self._size > 0:
+ self._fh.seek(self._offset + self._size + offset, 0)
+ return
+ self._fh.seek(offset, whence)
+
+ def close(self):
+ """Close file."""
+ if self._close and self._fh:
+ self._fh.close()
+ self._fh = None
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ self.close()
+
+ def __getattr__(self, name):
+ """Return attribute from underlying file object."""
+ if self._offset:
+ warnings.warn(
+ "FileHandle: '%s' not implemented for embedded files" % name)
+ return getattr(self._fh, name)
+
+ @property
+ def name(self):
+ return self._name
+
+ @property
+ def dirname(self):
+ return self._dir
+
+ @property
+ def path(self):
+ return os.path.join(self._dir, self._name)
+
+ @property
+ def size(self):
+ return self._size
+
+ @property
+ def closed(self):
+ return self._fh is None
+
+ @property
+ def lock(self):
+ return self._lock
+
+ @lock.setter
+ def lock(self, value):
+ self._lock = threading.RLock() if value else NullContext()
+
+
+class NullContext(object):
+ """Null context manager.
+
+ >>> with NullContext():
+ ... pass
+
+ """
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ pass
+
+
+class OpenFileCache(object):
+ """Keep files open."""
+
+ __slots__ = ('files', 'past', 'lock', 'size')
+
+ def __init__(self, size, lock=None):
+ """Initialize open file cache."""
+ self.past = [] # FIFO of opened files
+ self.files = {} # refcounts of opened files
+ self.lock = NullContext() if lock is None else lock
+ self.size = int(size)
+
+ def open(self, filehandle):
+ """Re-open file if necessary."""
+ with self.lock:
+ if filehandle in self.files:
+ self.files[filehandle] += 1
+ elif filehandle.closed:
+ filehandle.open()
+ self.files[filehandle] = 1
+ self.past.append(filehandle)
+
+ def close(self, filehandle):
+ """Close openend file if no longer used."""
+ with self.lock:
+ if filehandle in self.files:
+ self.files[filehandle] -= 1
+ # trim the file cache
+ index = 0
+ size = len(self.past)
+ while size > self.size and index < size:
+ filehandle = self.past[index]
+ if self.files[filehandle] == 0:
+ filehandle.close()
+ del self.files[filehandle]
+ del self.past[index]
+ size -= 1
+ else:
+ index += 1
+
+ def clear(self):
+ """Close all opened files if not in use."""
+ with self.lock:
+ for filehandle, refcount in list(self.files.items()):
+ if refcount == 0:
+ filehandle.close()
+ del self.files[filehandle]
+ del self.past[self.past.index(filehandle)]
+
+
+class Timer(object):
+ """Stopwatch for timing execution speed."""
+
+ __slots__ = ('started', 'stopped', 'duration')
+
+ try:
+ clock = time.perf_counter
+ except AttributeError:
+ clock = time.clock
+
+ def __init__(self, message='', end=' '):
+ """Initialize timer and print message."""
+ if message:
+ print_(message, end=end, flush=True)
+ self.duration = 0
+ self.started = self.stopped = Timer.clock()
+
+ def start(self, message='', end=' '):
+ """Start timer and return current time."""
+ if message:
+ print_(message, end=end, flush=True)
+ self.duration = 0
+ self.started = self.stopped = Timer.clock()
+ return self.started
+
+ def stop(self, message='', end=' '):
+ """Return duration of timer till start."""
+ self.stopped = Timer.clock()
+ if message:
+ print_(message, end=end, flush=True)
+ self.duration = self.stopped - self.started
+ return self.duration
+
+ def print(self, message='', end=None):
+ """Print duration from timer start till last stop or now."""
+ msg = str(self)
+ if message:
+ print_(message, end=' ')
+ print_(msg, end=end, flush=True)
+
+ def __str__(self):
+ """Return duration from timer start till last stop or now as string."""
+ if self.duration <= 0:
+ # not stopped
+ duration = Timer.clock() - self.started
+ else:
+ duration = self.duration
+ s = str(datetime.timedelta(seconds=duration))
+ i = 0
+ while i < len(s) and s[i:i + 2] in '0:0010203040506070809':
+ i += 1
+ return '%s s' % s[i:]
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ self.print()
+
+
+class LazyConst(object):
+ """Class whose attributes are computed on first access from its methods."""
+
+ def __init__(self, cls):
+ self._cls = cls
+ self.__doc__ = getattr(cls, '__doc__')
+
+ def __getattr__(self, name):
+ func = getattr(self._cls, name)
+ if not callable(func):
+ return func
+ try:
+ value = func()
+ except TypeError:
+ # Python 2 unbound method
+ value = func.__func__()
+ setattr(self, name, value)
+ return value
+
+
+@LazyConst
+class TIFF(object):
+ """Namespace for module constants."""
+
+ def CLASSIC_LE():
+ class ClassicTiffLe(object):
+ __slots__ = []
+ version = 42
+ byteorder = '<'
+ offsetsize = 4
+ offsetformat = '<I'
+ ifdoffsetsize = 4
+ ifdoffsetformat = '<I'
+ tagnosize = 2
+ tagnoformat = '<H'
+ tagsize = 12
+ tagformat1 = '<HH'
+ tagformat2 = '<I4s'
+
+ return ClassicTiffLe
+
+ def CLASSIC_BE():
+ class ClassicTiffBe(object):
+ __slots__ = []
+ version = 42
+ byteorder = '>'
+ offsetsize = 4
+ offsetformat = '>I'
+ ifdoffsetsize = 4
+ ifdoffsetformat = '>I'
+ tagnosize = 2
+ tagnoformat = '>H'
+ tagsize = 12
+ tagformat1 = '>HH'
+ tagformat2 = '>I4s'
+
+ return ClassicTiffBe
+
+ def BIG_LE():
+ class BigTiffLe(object):
+ __slots__ = []
+ version = 43
+ byteorder = '<'
+ offsetsize = 8
+ offsetformat = '<Q'
+ ifdoffsetsize = 8
+ ifdoffsetformat = '<Q'
+ tagnosize = 8
+ tagnoformat = '<Q'
+ tagsize = 20
+ tagformat1 = '<HH'
+ tagformat2 = '<Q8s'
+
+ return BigTiffLe
+
+ def BIG_BE():
+ class BigTiffBe(object):
+ __slots__ = []
+ version = 43
+ byteorder = '>'
+ offsetsize = 8
+ offsetformat = '>Q'
+ ifdoffsetsize = 8
+ ifdoffsetformat = '>Q'
+ tagnosize = 8
+ tagnoformat = '>Q'
+ tagsize = 20
+ tagformat1 = '>HH'
+ tagformat2 = '>Q8s'
+
+ return BigTiffBe
+
+ def NDPI_LE():
+ class NdpiTiffLe(object):
+ __slots__ = []
+ version = 42
+ byteorder = '<'
+ offsetsize = 4
+ offsetformat = '<I'
+ ifdoffsetsize = 8 # NDPI uses 8 bytes IFD offsets
+ ifdoffsetformat = '<Q'
+ tagnosize = 2
+ tagnoformat = '<H'
+ tagsize = 12
+ tagformat1 = '<HH'
+ tagformat2 = '<I4s'
+
+ return NdpiTiffLe
+
+ def TAGS():
+ # TIFF tag codes and names from TIFF6, TIFF/EP, EXIF, and other specs
+ return {
+ 11: 'ProcessingSoftware',
+ 254: 'NewSubfileType',
+ 255: 'SubfileType',
+ 256: 'ImageWidth',
+ 257: 'ImageLength',
+ 258: 'BitsPerSample',
+ 259: 'Compression',
+ 262: 'PhotometricInterpretation',
+ 263: 'Thresholding',
+ 264: 'CellWidth',
+ 265: 'CellLength',
+ 266: 'FillOrder',
+ 269: 'DocumentName',
+ 270: 'ImageDescription',
+ 271: 'Make',
+ 272: 'Model',
+ 273: 'StripOffsets',
+ 274: 'Orientation',
+ 277: 'SamplesPerPixel',
+ 278: 'RowsPerStrip',
+ 279: 'StripByteCounts',
+ 280: 'MinSampleValue',
+ 281: 'MaxSampleValue',
+ 282: 'XResolution',
+ 283: 'YResolution',
+ 284: 'PlanarConfiguration',
+ 285: 'PageName',
+ 286: 'XPosition',
+ 287: 'YPosition',
+ 288: 'FreeOffsets',
+ 289: 'FreeByteCounts',
+ 290: 'GrayResponseUnit',
+ 291: 'GrayResponseCurve',
+ 292: 'T4Options',
+ 293: 'T6Options',
+ 296: 'ResolutionUnit',
+ 297: 'PageNumber',
+ 300: 'ColorResponseUnit',
+ 301: 'TransferFunction',
+ 305: 'Software',
+ 306: 'DateTime',
+ 315: 'Artist',
+ 316: 'HostComputer',
+ 317: 'Predictor',
+ 318: 'WhitePoint',
+ 319: 'PrimaryChromaticities',
+ 320: 'ColorMap',
+ 321: 'HalftoneHints',
+ 322: 'TileWidth',
+ 323: 'TileLength',
+ 324: 'TileOffsets',
+ 325: 'TileByteCounts',
+ 326: 'BadFaxLines',
+ 327: 'CleanFaxData',
+ 328: 'ConsecutiveBadFaxLines',
+ 330: 'SubIFDs',
+ 332: 'InkSet',
+ 333: 'InkNames',
+ 334: 'NumberOfInks',
+ 336: 'DotRange',
+ 337: 'TargetPrinter',
+ 338: 'ExtraSamples',
+ 339: 'SampleFormat',
+ 340: 'SMinSampleValue',
+ 341: 'SMaxSampleValue',
+ 342: 'TransferRange',
+ 343: 'ClipPath',
+ 344: 'XClipPathUnits',
+ 345: 'YClipPathUnits',
+ 346: 'Indexed',
+ 347: 'JPEGTables',
+ 351: 'OPIProxy',
+ 400: 'GlobalParametersIFD',
+ 401: 'ProfileType',
+ 402: 'FaxProfile',
+ 403: 'CodingMethods',
+ 404: 'VersionYear',
+ 405: 'ModeNumber',
+ 433: 'Decode',
+ 434: 'DefaultImageColor',
+ 435: 'T82Options',
+ 437: 'JPEGTables_', # 347
+ 512: 'JPEGProc',
+ 513: 'JPEGInterchangeFormat',
+ 514: 'JPEGInterchangeFormatLength',
+ 515: 'JPEGRestartInterval',
+ 517: 'JPEGLosslessPredictors',
+ 518: 'JPEGPointTransforms',
+ 519: 'JPEGQTables',
+ 520: 'JPEGDCTables',
+ 521: 'JPEGACTables',
+ 529: 'YCbCrCoefficients',
+ 530: 'YCbCrSubSampling',
+ 531: 'YCbCrPositioning',
+ 532: 'ReferenceBlackWhite',
+ 559: 'StripRowCounts',
+ 700: 'XMP', # XMLPacket
+ 769: 'GDIGamma', # GDI+
+ 770: 'ICCProfileDescriptor', # GDI+
+ 771: 'SRGBRenderingIntent', # GDI+
+ 800: 'ImageTitle', # GDI+
+ 999: 'USPTO_Miscellaneous',
+ 4864: 'AndorId', # TODO: Andor Technology 4864 - 5030
+ 4869: 'AndorTemperature',
+ 4876: 'AndorExposureTime',
+ 4878: 'AndorKineticCycleTime',
+ 4879: 'AndorAccumulations',
+ 4881: 'AndorAcquisitionCycleTime',
+ 4882: 'AndorReadoutTime',
+ 4884: 'AndorPhotonCounting',
+ 4885: 'AndorEmDacLevel',
+ 4890: 'AndorFrames',
+ 4896: 'AndorHorizontalFlip',
+ 4897: 'AndorVerticalFlip',
+ 4898: 'AndorClockwise',
+ 4899: 'AndorCounterClockwise',
+ 4904: 'AndorVerticalClockVoltage',
+ 4905: 'AndorVerticalShiftSpeed',
+ 4907: 'AndorPreAmpSetting',
+ 4908: 'AndorCameraSerial',
+ 4911: 'AndorActualTemperature',
+ 4912: 'AndorBaselineClamp',
+ 4913: 'AndorPrescans',
+ 4914: 'AndorModel',
+ 4915: 'AndorChipSizeX',
+ 4916: 'AndorChipSizeY',
+ 4944: 'AndorBaselineOffset',
+ 4966: 'AndorSoftwareVersion',
+ 18246: 'Rating',
+ 18247: 'XP_DIP_XML',
+ 18248: 'StitchInfo',
+ 18249: 'RatingPercent',
+ 20481: 'ResolutionXUnit', # GDI+
+ 20482: 'ResolutionYUnit', # GDI+
+ 20483: 'ResolutionXLengthUnit', # GDI+
+ 20484: 'ResolutionYLengthUnit', # GDI+
+ 20485: 'PrintFlags', # GDI+
+ 20486: 'PrintFlagsVersion', # GDI+
+ 20487: 'PrintFlagsCrop', # GDI+
+ 20488: 'PrintFlagsBleedWidth', # GDI+
+ 20489: 'PrintFlagsBleedWidthScale', # GDI+
+ 20490: 'HalftoneLPI', # GDI+
+ 20491: 'HalftoneLPIUnit', # GDI+
+ 20492: 'HalftoneDegree', # GDI+
+ 20493: 'HalftoneShape', # GDI+
+ 20494: 'HalftoneMisc', # GDI+
+ 20495: 'HalftoneScreen', # GDI+
+ 20496: 'JPEGQuality', # GDI+
+ 20497: 'GridSize', # GDI+
+ 20498: 'ThumbnailFormat', # GDI+
+ 20499: 'ThumbnailWidth', # GDI+
+ 20500: 'ThumbnailHeight', # GDI+
+ 20501: 'ThumbnailColorDepth', # GDI+
+ 20502: 'ThumbnailPlanes', # GDI+
+ 20503: 'ThumbnailRawBytes', # GDI+
+ 20504: 'ThumbnailSize', # GDI+
+ 20505: 'ThumbnailCompressedSize', # GDI+
+ 20506: 'ColorTransferFunction', # GDI+
+ 20507: 'ThumbnailData',
+ 20512: 'ThumbnailImageWidth', # GDI+
+ 20513: 'ThumbnailImageHeight', # GDI+
+ 20514: 'ThumbnailBitsPerSample', # GDI+
+ 20515: 'ThumbnailCompression',
+ 20516: 'ThumbnailPhotometricInterp', # GDI+
+ 20517: 'ThumbnailImageDescription', # GDI+
+ 20518: 'ThumbnailEquipMake', # GDI+
+ 20519: 'ThumbnailEquipModel', # GDI+
+ 20520: 'ThumbnailStripOffsets', # GDI+
+ 20521: 'ThumbnailOrientation', # GDI+
+ 20522: 'ThumbnailSamplesPerPixel', # GDI+
+ 20523: 'ThumbnailRowsPerStrip', # GDI+
+ 20524: 'ThumbnailStripBytesCount', # GDI+
+ 20525: 'ThumbnailResolutionX',
+ 20526: 'ThumbnailResolutionY',
+ 20527: 'ThumbnailPlanarConfig', # GDI+
+ 20528: 'ThumbnailResolutionUnit',
+ 20529: 'ThumbnailTransferFunction',
+ 20530: 'ThumbnailSoftwareUsed', # GDI+
+ 20531: 'ThumbnailDateTime', # GDI+
+ 20532: 'ThumbnailArtist', # GDI+
+ 20533: 'ThumbnailWhitePoint', # GDI+
+ 20534: 'ThumbnailPrimaryChromaticities', # GDI+
+ 20535: 'ThumbnailYCbCrCoefficients', # GDI+
+ 20536: 'ThumbnailYCbCrSubsampling', # GDI+
+ 20537: 'ThumbnailYCbCrPositioning',
+ 20538: 'ThumbnailRefBlackWhite', # GDI+
+ 20539: 'ThumbnailCopyRight', # GDI+
+ 20545: 'InteroperabilityIndex',
+ 20546: 'InteroperabilityVersion',
+ 20624: 'LuminanceTable',
+ 20625: 'ChrominanceTable',
+ 20736: 'FrameDelay', # GDI+
+ 20737: 'LoopCount', # GDI+
+ 20738: 'GlobalPalette', # GDI+
+ 20739: 'IndexBackground', # GDI+
+ 20740: 'IndexTransparent', # GDI+
+ 20752: 'PixelUnit', # GDI+
+ 20753: 'PixelPerUnitX', # GDI+
+ 20754: 'PixelPerUnitY', # GDI+
+ 20755: 'PaletteHistogram', # GDI+
+ 28672: 'SonyRawFileType', # Sony ARW
+ 28722: 'VignettingCorrParams', # Sony ARW
+ 28725: 'ChromaticAberrationCorrParams', # Sony ARW
+ 28727: 'DistortionCorrParams', # Sony ARW
+ # Private tags >= 32768
+ 32781: 'ImageID',
+ 32931: 'WangTag1',
+ 32932: 'WangAnnotation',
+ 32933: 'WangTag3',
+ 32934: 'WangTag4',
+ 32953: 'ImageReferencePoints',
+ 32954: 'RegionXformTackPoint',
+ 32955: 'WarpQuadrilateral',
+ 32956: 'AffineTransformMat',
+ 32995: 'Matteing',
+ 32996: 'DataType', # use SampleFormat
+ 32997: 'ImageDepth',
+ 32998: 'TileDepth',
+ 33300: 'ImageFullWidth',
+ 33301: 'ImageFullLength',
+ 33302: 'TextureFormat',
+ 33303: 'TextureWrapModes',
+ 33304: 'FieldOfViewCotangent',
+ 33305: 'MatrixWorldToScreen',
+ 33306: 'MatrixWorldToCamera',
+ 33405: 'Model2',
+ 33421: 'CFARepeatPatternDim',
+ 33422: 'CFAPattern',
+ 33423: 'BatteryLevel',
+ 33424: 'KodakIFD',
+ 33434: 'ExposureTime',
+ 33437: 'FNumber',
+ 33432: 'Copyright',
+ 33445: 'MDFileTag',
+ 33446: 'MDScalePixel',
+ 33447: 'MDColorTable',
+ 33448: 'MDLabName',
+ 33449: 'MDSampleInfo',
+ 33450: 'MDPrepDate',
+ 33451: 'MDPrepTime',
+ 33452: 'MDFileUnits',
+ 33471: 'OlympusINI',
+ 33550: 'ModelPixelScaleTag',
+ 33560: 'OlympusSIS', # see also 33471 and 34853
+ 33589: 'AdventScale',
+ 33590: 'AdventRevision',
+ 33628: 'UIC1tag', # Metamorph Universal Imaging Corp STK
+ 33629: 'UIC2tag',
+ 33630: 'UIC3tag',
+ 33631: 'UIC4tag',
+ 33723: 'IPTCNAA',
+ 33858: 'ExtendedTagsOffset', # DEFF points IFD with private tags
+ 33918: 'IntergraphPacketData', # INGRPacketDataTag
+ 33919: 'IntergraphFlagRegisters', # INGRFlagRegisters
+ 33920: 'IntergraphMatrixTag', # IrasBTransformationMatrix
+ 33921: 'INGRReserved',
+ 33922: 'ModelTiepointTag',
+ 33923: 'LeicaMagic',
+ 34016: 'Site', # 34016..34032 ANSI IT8 TIFF/IT
+ 34017: 'ColorSequence',
+ 34018: 'IT8Header',
+ 34019: 'RasterPadding',
+ 34020: 'BitsPerRunLength',
+ 34021: 'BitsPerExtendedRunLength',
+ 34022: 'ColorTable',
+ 34023: 'ImageColorIndicator',
+ 34024: 'BackgroundColorIndicator',
+ 34025: 'ImageColorValue',
+ 34026: 'BackgroundColorValue',
+ 34027: 'PixelIntensityRange',
+ 34028: 'TransparencyIndicator',
+ 34029: 'ColorCharacterization',
+ 34030: 'HCUsage',
+ 34031: 'TrapIndicator',
+ 34032: 'CMYKEquivalent',
+ 34118: 'CZ_SEM', # Zeiss SEM
+ 34152: 'AFCP_IPTC',
+ 34232: 'PixelMagicJBIGOptions', # EXIF, also TI FrameCount
+ 34263: 'JPLCartoIFD',
+ 34122: 'IPLAB', # number of images
+ 34264: 'ModelTransformationTag',
+ 34306: 'WB_GRGBLevels', # Leaf MOS
+ 34310: 'LeafData',
+ 34361: 'MM_Header',
+ 34362: 'MM_Stamp',
+ 34363: 'MM_Unknown',
+ 34377: 'ImageResources', # Photoshop
+ 34386: 'MM_UserBlock',
+ 34412: 'CZ_LSMINFO',
+ 34665: 'ExifTag',
+ 34675: 'InterColorProfile', # ICCProfile
+ 34680: 'FEI_SFEG', #
+ 34682: 'FEI_HELIOS', #
+ 34683: 'FEI_TITAN', #
+ 34687: 'FXExtensions',
+ 34688: 'MultiProfiles',
+ 34689: 'SharedData',
+ 34690: 'T88Options',
+ 34710: 'MarCCD', # offset to MarCCD header
+ 34732: 'ImageLayer',
+ 34735: 'GeoKeyDirectoryTag',
+ 34736: 'GeoDoubleParamsTag',
+ 34737: 'GeoAsciiParamsTag',
+ 34750: 'JBIGOptions',
+ 34821: 'PIXTIFF', # ? Pixel Translations Inc
+ 34850: 'ExposureProgram',
+ 34852: 'SpectralSensitivity',
+ 34853: 'GPSTag', # GPSIFD also OlympusSIS2
+ 34855: 'ISOSpeedRatings',
+ 34856: 'OECF',
+ 34857: 'Interlace',
+ 34858: 'TimeZoneOffset',
+ 34859: 'SelfTimerMode',
+ 34864: 'SensitivityType',
+ 34865: 'StandardOutputSensitivity',
+ 34866: 'RecommendedExposureIndex',
+ 34867: 'ISOSpeed',
+ 34868: 'ISOSpeedLatitudeyyy',
+ 34869: 'ISOSpeedLatitudezzz',
+ 34908: 'HylaFAXFaxRecvParams',
+ 34909: 'HylaFAXFaxSubAddress',
+ 34910: 'HylaFAXFaxRecvTime',
+ 34911: 'FaxDcs',
+ 34929: 'FedexEDR',
+ 34954: 'LeafSubIFD',
+ 34959: 'Aphelion1',
+ 34960: 'Aphelion2',
+ 34961: 'AphelionInternal', # ADCIS
+ 36864: 'ExifVersion',
+ 36867: 'DateTimeOriginal',
+ 36868: 'DateTimeDigitized',
+ 36873: 'GooglePlusUploadCode',
+ 36880: 'OffsetTime',
+ 36881: 'OffsetTimeOriginal',
+ 36882: 'OffsetTimeDigitized',
+ # TODO: Pilatus/CHESS/TV6 36864..37120 conflicting with Exif tags
+ # 36864: 'TVX ?',
+ # 36865: 'TVX_NumExposure',
+ # 36866: 'TVX_NumBackground',
+ # 36867: 'TVX_ExposureTime',
+ # 36868: 'TVX_BackgroundTime',
+ # 36870: 'TVX ?',
+ # 36873: 'TVX_SubBpp',
+ # 36874: 'TVX_SubWide',
+ # 36875: 'TVX_SubHigh',
+ # 36876: 'TVX_BlackLevel',
+ # 36877: 'TVX_DarkCurrent',
+ # 36878: 'TVX_ReadNoise',
+ # 36879: 'TVX_DarkCurrentNoise',
+ # 36880: 'TVX_BeamMonitor',
+ # 37120: 'TVX_UserVariables', # A/D values
+ 37121: 'ComponentsConfiguration',
+ 37122: 'CompressedBitsPerPixel',
+ 37377: 'ShutterSpeedValue',
+ 37378: 'ApertureValue',
+ 37379: 'BrightnessValue',
+ 37380: 'ExposureBiasValue',
+ 37381: 'MaxApertureValue',
+ 37382: 'SubjectDistance',
+ 37383: 'MeteringMode',
+ 37384: 'LightSource',
+ 37385: 'Flash',
+ 37386: 'FocalLength',
+ 37387: 'FlashEnergy_', # 37387
+ 37388: 'SpatialFrequencyResponse_', # 37388
+ 37389: 'Noise',
+ 37390: 'FocalPlaneXResolution',
+ 37391: 'FocalPlaneYResolution',
+ 37392: 'FocalPlaneResolutionUnit',
+ 37393: 'ImageNumber',
+ 37394: 'SecurityClassification',
+ 37395: 'ImageHistory',
+ 37396: 'SubjectLocation',
+ 37397: 'ExposureIndex',
+ 37398: 'TIFFEPStandardID',
+ 37399: 'SensingMethod',
+ 37434: 'CIP3DataFile',
+ 37435: 'CIP3Sheet',
+ 37436: 'CIP3Side',
+ 37439: 'StoNits',
+ 37500: 'MakerNote',
+ 37510: 'UserComment',
+ 37520: 'SubsecTime',
+ 37521: 'SubsecTimeOriginal',
+ 37522: 'SubsecTimeDigitized',
+ 37679: 'MODIText', # Microsoft Office Document Imaging
+ 37680: 'MODIOLEPropertySetStorage',
+ 37681: 'MODIPositioning',
+ 37706: 'TVIPS', # offset to TemData structure
+ 37707: 'TVIPS1',
+ 37708: 'TVIPS2', # same TemData structure as undefined
+ 37724: 'ImageSourceData', # Photoshop
+ 37888: 'Temperature',
+ 37889: 'Humidity',
+ 37890: 'Pressure',
+ 37891: 'WaterDepth',
+ 37892: 'Acceleration',
+ 37893: 'CameraElevationAngle',
+ 40001: 'MC_IpWinScal', # Media Cybernetics
+ # 40001: 'RecipName', # MS FAX
+ 40002: 'RecipNumber',
+ 40003: 'SenderName',
+ 40004: 'Routing',
+ 40005: 'CallerId',
+ 40006: 'TSID',
+ 40007: 'CSID',
+ 40008: 'FaxTime',
+ 40100: 'MC_IdOld',
+ 40106: 'MC_Unknown',
+ 40965: 'InteroperabilityTag', # InteropOffset
+ 40091: 'XPTitle',
+ 40092: 'XPComment',
+ 40093: 'XPAuthor',
+ 40094: 'XPKeywords',
+ 40095: 'XPSubject',
+ 40960: 'FlashpixVersion',
+ 40961: 'ColorSpace',
+ 40962: 'PixelXDimension',
+ 40963: 'PixelYDimension',
+ 40964: 'RelatedSoundFile',
+ 40976: 'SamsungRawPointersOffset',
+ 40977: 'SamsungRawPointersLength',
+ 41217: 'SamsungRawByteOrder',
+ 41218: 'SamsungRawUnknown',
+ 41483: 'FlashEnergy',
+ 41484: 'SpatialFrequencyResponse',
+ 41485: 'Noise_', # 37389
+ 41486: 'FocalPlaneXResolution_', # 37390
+ 41487: 'FocalPlaneYResolution_', # 37391
+ 41488: 'FocalPlaneResolutionUnit_', # 37392
+ 41489: 'ImageNumber_', # 37393
+ 41490: 'SecurityClassification_', # 37394
+ 41491: 'ImageHistory_', # 37395
+ 41492: 'SubjectLocation_', # 37395
+ 41493: 'ExposureIndex_ ', # 37397
+ 41494: 'TIFF-EPStandardID',
+ 41495: 'SensingMethod_', # 37399
+ 41728: 'FileSource',
+ 41729: 'SceneType',
+ 41730: 'CFAPattern_', # 33422
+ 41985: 'CustomRendered',
+ 41986: 'ExposureMode',
+ 41987: 'WhiteBalance',
+ 41988: 'DigitalZoomRatio',
+ 41989: 'FocalLengthIn35mmFilm',
+ 41990: 'SceneCaptureType',
+ 41991: 'GainControl',
+ 41992: 'Contrast',
+ 41993: 'Saturation',
+ 41994: 'Sharpness',
+ 41995: 'DeviceSettingDescription',
+ 41996: 'SubjectDistanceRange',
+ 42016: 'ImageUniqueID',
+ 42032: 'CameraOwnerName',
+ 42033: 'BodySerialNumber',
+ 42034: 'LensSpecification',
+ 42035: 'LensMake',
+ 42036: 'LensModel',
+ 42037: 'LensSerialNumber',
+ 42112: 'GDAL_METADATA',
+ 42113: 'GDAL_NODATA',
+ 42240: 'Gamma',
+ 43314: 'NIHImageHeader',
+ 44992: 'ExpandSoftware',
+ 44993: 'ExpandLens',
+ 44994: 'ExpandFilm',
+ 44995: 'ExpandFilterLens',
+ 44996: 'ExpandScanner',
+ 44997: 'ExpandFlashLamp',
+ 48129: 'PixelFormat', # HDP and WDP
+ 48130: 'Transformation',
+ 48131: 'Uncompressed',
+ 48132: 'ImageType',
+ 48256: 'ImageWidth_', # 256
+ 48257: 'ImageHeight_',
+ 48258: 'WidthResolution',
+ 48259: 'HeightResolution',
+ 48320: 'ImageOffset',
+ 48321: 'ImageByteCount',
+ 48322: 'AlphaOffset',
+ 48323: 'AlphaByteCount',
+ 48324: 'ImageDataDiscard',
+ 48325: 'AlphaDataDiscard',
+ 50003: 'KodakAPP3',
+ 50215: 'OceScanjobDescription',
+ 50216: 'OceApplicationSelector',
+ 50217: 'OceIdentificationNumber',
+ 50218: 'OceImageLogicCharacteristics',
+ 50255: 'Annotations',
+ 50288: 'MC_Id', # Media Cybernetics
+ 50289: 'MC_XYPosition',
+ 50290: 'MC_ZPosition',
+ 50291: 'MC_XYCalibration',
+ 50292: 'MC_LensCharacteristics',
+ 50293: 'MC_ChannelName',
+ 50294: 'MC_ExcitationWavelength',
+ 50295: 'MC_TimeStamp',
+ 50296: 'MC_FrameProperties',
+ 50341: 'PrintImageMatching',
+ 50495: 'PCO_RAW', # TODO: PCO CamWare
+ 50547: 'OriginalFileName',
+ 50560: 'USPTO_OriginalContentType', # US Patent Office
+ 50561: 'USPTO_RotationCode',
+ 50648: 'CR2Unknown1',
+ 50649: 'CR2Unknown2',
+ 50656: 'CR2CFAPattern',
+ 50674: 'LercParameters', # ESGI 50674 .. 50677
+ 50706: 'DNGVersion', # DNG 50706 .. 51112
+ 50707: 'DNGBackwardVersion',
+ 50708: 'UniqueCameraModel',
+ 50709: 'LocalizedCameraModel',
+ 50710: 'CFAPlaneColor',
+ 50711: 'CFALayout',
+ 50712: 'LinearizationTable',
+ 50713: 'BlackLevelRepeatDim',
+ 50714: 'BlackLevel',
+ 50715: 'BlackLevelDeltaH',
+ 50716: 'BlackLevelDeltaV',
+ 50717: 'WhiteLevel',
+ 50718: 'DefaultScale',
+ 50719: 'DefaultCropOrigin',
+ 50720: 'DefaultCropSize',
+ 50721: 'ColorMatrix1',
+ 50722: 'ColorMatrix2',
+ 50723: 'CameraCalibration1',
+ 50724: 'CameraCalibration2',
+ 50725: 'ReductionMatrix1',
+ 50726: 'ReductionMatrix2',
+ 50727: 'AnalogBalance',
+ 50728: 'AsShotNeutral',
+ 50729: 'AsShotWhiteXY',
+ 50730: 'BaselineExposure',
+ 50731: 'BaselineNoise',
+ 50732: 'BaselineSharpness',
+ 50733: 'BayerGreenSplit',
+ 50734: 'LinearResponseLimit',
+ 50735: 'CameraSerialNumber',
+ 50736: 'LensInfo',
+ 50737: 'ChromaBlurRadius',
+ 50738: 'AntiAliasStrength',
+ 50739: 'ShadowScale',
+ 50740: 'DNGPrivateData',
+ 50741: 'MakerNoteSafety',
+ 50752: 'RawImageSegmentation',
+ 50778: 'CalibrationIlluminant1',
+ 50779: 'CalibrationIlluminant2',
+ 50780: 'BestQualityScale',
+ 50781: 'RawDataUniqueID',
+ 50784: 'AliasLayerMetadata',
+ 50827: 'OriginalRawFileName',
+ 50828: 'OriginalRawFileData',
+ 50829: 'ActiveArea',
+ 50830: 'MaskedAreas',
+ 50831: 'AsShotICCProfile',
+ 50832: 'AsShotPreProfileMatrix',
+ 50833: 'CurrentICCProfile',
+ 50834: 'CurrentPreProfileMatrix',
+ 50838: 'IJMetadataByteCounts',
+ 50839: 'IJMetadata',
+ 50844: 'RPCCoefficientTag',
+ 50879: 'ColorimetricReference',
+ 50885: 'SRawType',
+ 50898: 'PanasonicTitle',
+ 50899: 'PanasonicTitle2',
+ 50908: 'RSID', # DGIWG
+ 50909: 'GEO_METADATA', # DGIWG XML
+ 50931: 'CameraCalibrationSignature',
+ 50932: 'ProfileCalibrationSignature',
+ 50933: 'ProfileIFD',
+ 50934: 'AsShotProfileName',
+ 50935: 'NoiseReductionApplied',
+ 50936: 'ProfileName',
+ 50937: 'ProfileHueSatMapDims',
+ 50938: 'ProfileHueSatMapData1',
+ 50939: 'ProfileHueSatMapData2',
+ 50940: 'ProfileToneCurve',
+ 50941: 'ProfileEmbedPolicy',
+ 50942: 'ProfileCopyright',
+ 50964: 'ForwardMatrix1',
+ 50965: 'ForwardMatrix2',
+ 50966: 'PreviewApplicationName',
+ 50967: 'PreviewApplicationVersion',
+ 50968: 'PreviewSettingsName',
+ 50969: 'PreviewSettingsDigest',
+ 50970: 'PreviewColorSpace',
+ 50971: 'PreviewDateTime',
+ 50972: 'RawImageDigest',
+ 50973: 'OriginalRawFileDigest',
+ 50974: 'SubTileBlockSize',
+ 50975: 'RowInterleaveFactor',
+ 50981: 'ProfileLookTableDims',
+ 50982: 'ProfileLookTableData',
+ 51008: 'OpcodeList1',
+ 51009: 'OpcodeList2',
+ 51022: 'OpcodeList3',
+ 51023: 'FibicsXML', #
+ 51041: 'NoiseProfile',
+ 51043: 'TimeCodes',
+ 51044: 'FrameRate',
+ 51058: 'TStop',
+ 51081: 'ReelName',
+ 51089: 'OriginalDefaultFinalSize',
+ 51090: 'OriginalBestQualitySize',
+ 51091: 'OriginalDefaultCropSize',
+ 51105: 'CameraLabel',
+ 51107: 'ProfileHueSatMapEncoding',
+ 51108: 'ProfileLookTableEncoding',
+ 51109: 'BaselineExposureOffset',
+ 51110: 'DefaultBlackRender',
+ 51111: 'NewRawImageDigest',
+ 51112: 'RawToPreviewGain',
+ 51125: 'DefaultUserCrop',
+ 51123: 'MicroManagerMetadata',
+ 51159: 'ZIFmetadata', # Objective Pathology Services
+ 51160: 'ZIFannotations', # Objective Pathology Services
+ 59932: 'Padding',
+ 59933: 'OffsetSchema',
+ # Reusable Tags 65000-65535
+ # 65000: Dimap_Document XML
+ # 65000-65112: Photoshop Camera RAW EXIF tags
+ # 65000: 'OwnerName',
+ # 65001: 'SerialNumber',
+ # 65002: 'Lens',
+ # 65024: 'KDC_IFD',
+ # 65100: 'RawFile',
+ # 65101: 'Converter',
+ # 65102: 'WhiteBalance',
+ # 65105: 'Exposure',
+ # 65106: 'Shadows',
+ # 65107: 'Brightness',
+ # 65108: 'Contrast',
+ # 65109: 'Saturation',
+ # 65110: 'Sharpness',
+ # 65111: 'Smoothness',
+ # 65112: 'MoireFilter',
+ 65200: 'FlexXML',
+ }
+
+ def TAG_NAMES():
+ return {v: c for c, v in TIFF.TAGS.items()}
+
+ def TAG_READERS():
+ # Map TIFF tag codes to import functions
+ return {
+ 320: read_colormap,
+ # 700: read_bytes, # read_utf8,
+ # 34377: read_bytes,
+ 33723: read_bytes,
+ # 34675: read_bytes,
+ 33628: read_uic1tag, # Universal Imaging Corp STK
+ 33629: read_uic2tag,
+ 33630: read_uic3tag,
+ 33631: read_uic4tag,
+ 34118: read_cz_sem, # Carl Zeiss SEM
+ 34361: read_mm_header, # Olympus FluoView
+ 34362: read_mm_stamp,
+ 34363: read_numpy, # MM_Unknown
+ 34386: read_numpy, # MM_UserBlock
+ 34412: read_cz_lsminfo, # Carl Zeiss LSM
+ 34680: read_fei_metadata, # S-FEG
+ 34682: read_fei_metadata, # Helios NanoLab
+ 37706: read_tvips_header, # TVIPS EMMENU
+ 37724: read_bytes, # ImageSourceData
+ 33923: read_bytes, # read_leica_magic
+ 43314: read_nih_image_header,
+ # 40001: read_bytes,
+ 40100: read_bytes,
+ 50288: read_bytes,
+ 50296: read_bytes,
+ 50839: read_bytes,
+ 51123: read_json,
+ 33471: read_sis_ini,
+ 33560: read_sis,
+ 34665: read_exif_ifd,
+ 34853: read_gps_ifd, # conflicts with OlympusSIS
+ 40965: read_interoperability_ifd,
+ }
+
+ def TAG_TUPLE():
+ # Tags whose values must be stored as tuples
+ return frozenset((273, 279, 324, 325, 330, 530, 531, 34736))
+
+ def TAG_ATTRIBUTES():
+ # Map tag codes to TiffPage attribute names
+ return {
+ 'ImageWidth': 'imagewidth',
+ 'ImageLength': 'imagelength',
+ 'BitsPerSample': 'bitspersample',
+ 'Compression': 'compression',
+ 'PlanarConfiguration': 'planarconfig',
+ 'FillOrder': 'fillorder',
+ 'PhotometricInterpretation': 'photometric',
+ 'ColorMap': 'colormap',
+ 'ImageDescription': 'description',
+ 'ImageDescription1': 'description1',
+ 'SamplesPerPixel': 'samplesperpixel',
+ 'RowsPerStrip': 'rowsperstrip',
+ 'Software': 'software',
+ 'Predictor': 'predictor',
+ 'TileWidth': 'tilewidth',
+ 'TileLength': 'tilelength',
+ 'ExtraSamples': 'extrasamples',
+ 'SampleFormat': 'sampleformat',
+ 'ImageDepth': 'imagedepth',
+ 'TileDepth': 'tiledepth',
+ 'NewSubfileType': 'subfiletype',
+ }
+
+ def TAG_ENUM():
+ return {
+ # 254: TIFF.FILETYPE,
+ 255: TIFF.OFILETYPE,
+ 259: TIFF.COMPRESSION,
+ 262: TIFF.PHOTOMETRIC,
+ 263: TIFF.THRESHHOLD,
+ 266: TIFF.FILLORDER,
+ 274: TIFF.ORIENTATION,
+ 284: TIFF.PLANARCONFIG,
+ 290: TIFF.GRAYRESPONSEUNIT,
+ # 292: TIFF.GROUP3OPT,
+ # 293: TIFF.GROUP4OPT,
+ 296: TIFF.RESUNIT,
+ 300: TIFF.COLORRESPONSEUNIT,
+ 317: TIFF.PREDICTOR,
+ 338: TIFF.EXTRASAMPLE,
+ 339: TIFF.SAMPLEFORMAT,
+ # 512: TIFF.JPEGPROC,
+ # 531: TIFF.YCBCRPOSITION,
+ }
+
+ def FILETYPE():
+ class FILETYPE(enum.IntFlag):
+ # Python 3.6 only
+ UNDEFINED = 0
+ REDUCEDIMAGE = 1
+ PAGE = 2
+ MASK = 4
+
+ return FILETYPE
+
+ def OFILETYPE():
+ class OFILETYPE(enum.IntEnum):
+ UNDEFINED = 0
+ IMAGE = 1
+ REDUCEDIMAGE = 2
+ PAGE = 3
+
+ return OFILETYPE
+
+ def COMPRESSION():
+ class COMPRESSION(enum.IntEnum):
+ NONE = 1 # Uncompressed
+ CCITTRLE = 2 # CCITT 1D
+ CCITT_T4 = 3 # 'T4/Group 3 Fax',
+ CCITT_T6 = 4 # 'T6/Group 4 Fax',
+ LZW = 5
+ OJPEG = 6 # old-style JPEG
+ JPEG = 7
+ ADOBE_DEFLATE = 8
+ JBIG_BW = 9
+ JBIG_COLOR = 10
+ JPEG_99 = 99
+ KODAK_262 = 262
+ NEXT = 32766
+ SONY_ARW = 32767
+ PACKED_RAW = 32769
+ SAMSUNG_SRW = 32770
+ CCIRLEW = 32771
+ SAMSUNG_SRW2 = 32772
+ PACKBITS = 32773
+ THUNDERSCAN = 32809
+ IT8CTPAD = 32895
+ IT8LW = 32896
+ IT8MP = 32897
+ IT8BL = 32898
+ PIXARFILM = 32908
+ PIXARLOG = 32909
+ DEFLATE = 32946
+ DCS = 32947
+ APERIO_JP2000_YCBC = 33003 # Leica Aperio
+ APERIO_JP2000_RGB = 33005 # Leica Aperio
+ JBIG = 34661
+ SGILOG = 34676
+ SGILOG24 = 34677
+ JPEG2000 = 34712
+ NIKON_NEF = 34713
+ JBIG2 = 34715
+ MDI_BINARY = 34718 # Microsoft Document Imaging
+ MDI_PROGRESSIVE = 34719 # Microsoft Document Imaging
+ MDI_VECTOR = 34720 # Microsoft Document Imaging
+ LERC = 34887 # ESRI Lerc
+ JPEG_LOSSY = 34892
+ LZMA = 34925
+ ZSTD_DEPRECATED = 34926
+ WEBP_DEPRECATED = 34927
+ PNG = 34933 # Objective Pathology Services
+ JPEGXR = 34934 # Objective Pathology Services
+ ZSTD = 50000
+ WEBP = 50001
+ PIXTIFF = 50013
+ KODAK_DCR = 65000
+ PENTAX_PEF = 65535
+ # def __bool__(self): return self != 1 # Python 3.6+ only
+
+ return COMPRESSION
+
+ def PHOTOMETRIC():
+ class PHOTOMETRIC(enum.IntEnum):
+ MINISWHITE = 0
+ MINISBLACK = 1
+ RGB = 2
+ PALETTE = 3
+ MASK = 4
+ SEPARATED = 5 # CMYK
+ YCBCR = 6
+ CIELAB = 8
+ ICCLAB = 9
+ ITULAB = 10
+ CFA = 32803 # Color Filter Array
+ LOGL = 32844
+ LOGLUV = 32845
+ LINEAR_RAW = 34892
+
+ return PHOTOMETRIC
+
+ def THRESHHOLD():
+ class THRESHHOLD(enum.IntEnum):
+ BILEVEL = 1
+ HALFTONE = 2
+ ERRORDIFFUSE = 3
+
+ return THRESHHOLD
+
+ def FILLORDER():
+ class FILLORDER(enum.IntEnum):
+ MSB2LSB = 1
+ LSB2MSB = 2
+
+ return FILLORDER
+
+ def ORIENTATION():
+ class ORIENTATION(enum.IntEnum):
+ TOPLEFT = 1
+ TOPRIGHT = 2
+ BOTRIGHT = 3
+ BOTLEFT = 4
+ LEFTTOP = 5
+ RIGHTTOP = 6
+ RIGHTBOT = 7
+ LEFTBOT = 8
+
+ return ORIENTATION
+
+ def PLANARCONFIG():
+ class PLANARCONFIG(enum.IntEnum):
+ CONTIG = 1
+ SEPARATE = 2
+
+ return PLANARCONFIG
+
+ def GRAYRESPONSEUNIT():
+ class GRAYRESPONSEUNIT(enum.IntEnum):
+ _10S = 1
+ _100S = 2
+ _1000S = 3
+ _10000S = 4
+ _100000S = 5
+
+ return GRAYRESPONSEUNIT
+
+ def GROUP4OPT():
+ class GROUP4OPT(enum.IntEnum):
+ UNCOMPRESSED = 2
+
+ return GROUP4OPT
+
+ def RESUNIT():
+ class RESUNIT(enum.IntEnum):
+ NONE = 1
+ INCH = 2
+ CENTIMETER = 3
+ # def __bool__(self): return self != 1 # Python 3.6 only
+
+ return RESUNIT
+
+ def COLORRESPONSEUNIT():
+ class COLORRESPONSEUNIT(enum.IntEnum):
+ _10S = 1
+ _100S = 2
+ _1000S = 3
+ _10000S = 4
+ _100000S = 5
+
+ return COLORRESPONSEUNIT
+
+ def PREDICTOR():
+ class PREDICTOR(enum.IntEnum):
+ NONE = 1
+ HORIZONTAL = 2
+ FLOATINGPOINT = 3
+ # def __bool__(self): return self != 1 # Python 3.6 only
+
+ return PREDICTOR
+
+ def EXTRASAMPLE():
+ class EXTRASAMPLE(enum.IntEnum):
+ UNSPECIFIED = 0
+ ASSOCALPHA = 1
+ UNASSALPHA = 2
+
+ return EXTRASAMPLE
+
+ def SAMPLEFORMAT():
+ class SAMPLEFORMAT(enum.IntEnum):
+ UINT = 1
+ INT = 2
+ IEEEFP = 3
+ VOID = 4
+ COMPLEXINT = 5
+ COMPLEXIEEEFP = 6
+
+ return SAMPLEFORMAT
+
+ def DATATYPES():
+ class DATATYPES(enum.IntEnum):
+ NOTYPE = 0
+ BYTE = 1
+ ASCII = 2
+ SHORT = 3
+ LONG = 4
+ RATIONAL = 5
+ SBYTE = 6
+ UNDEFINED = 7
+ SSHORT = 8
+ SLONG = 9
+ SRATIONAL = 10
+ FLOAT = 11
+ DOUBLE = 12
+ IFD = 13
+ UNICODE = 14
+ COMPLEX = 15
+ LONG8 = 16
+ SLONG8 = 17
+ IFD8 = 18
+
+ return DATATYPES
+
+ def DATA_FORMATS():
+ # Map TIFF DATATYPES to Python struct formats
+ return {
+ 1: '1B', # BYTE 8-bit unsigned integer.
+ 2: '1s', # ASCII 8-bit byte that contains a 7-bit ASCII code;
+ # the last byte must be NULL (binary zero).
+ 3: '1H', # SHORT 16-bit (2-byte) unsigned integer
+ 4: '1I', # LONG 32-bit (4-byte) unsigned integer.
+ 5: '2I', # RATIONAL Two LONGs: the first represents the numerator
+ # of a fraction; the second, the denominator.
+ 6: '1b', # SBYTE An 8-bit signed (twos-complement) integer.
+ 7: '1B', # UNDEFINED An 8-bit byte that may contain anything,
+ # depending on the definition of the field.
+ 8: '1h', # SSHORT A 16-bit (2-byte) signed (twos-complement)
+ # integer.
+ 9: '1i', # SLONG A 32-bit (4-byte) signed (twos-complement)
+ # integer.
+ 10: '2i', # SRATIONAL Two SLONGs: the first represents the
+ # numerator of a fraction, the second the denominator.
+ 11: '1f', # FLOAT Single precision (4-byte) IEEE format.
+ 12: '1d', # DOUBLE Double precision (8-byte) IEEE format.
+ 13: '1I', # IFD unsigned 4 byte IFD offset.
+ # 14: '', # UNICODE
+ # 15: '', # COMPLEX
+ 16: '1Q', # LONG8 unsigned 8 byte integer (BigTiff)
+ 17: '1q', # SLONG8 signed 8 byte integer (BigTiff)
+ 18: '1Q', # IFD8 unsigned 8 byte IFD offset (BigTiff)
+ }
+
+ def DATA_DTYPES():
+ # Map numpy dtypes to TIFF DATATYPES
+ return {
+ 'B': 1,
+ 's': 2,
+ 'H': 3,
+ 'I': 4,
+ '2I': 5,
+ 'b': 6,
+ 'h': 8,
+ 'i': 9,
+ '2i': 10,
+ 'f': 11,
+ 'd': 12,
+ 'Q': 16,
+ 'q': 17,
+ }
+
+ def SAMPLE_DTYPES():
+ # Map TIFF SampleFormats and BitsPerSample to numpy dtype
+ return {
+ # UINT
+ (1, 1): '?', # bitmap
+ (1, 2): 'B',
+ (1, 3): 'B',
+ (1, 4): 'B',
+ (1, 5): 'B',
+ (1, 6): 'B',
+ (1, 7): 'B',
+ (1, 8): 'B',
+ (1, 9): 'H',
+ (1, 10): 'H',
+ (1, 11): 'H',
+ (1, 12): 'H',
+ (1, 13): 'H',
+ (1, 14): 'H',
+ (1, 15): 'H',
+ (1, 16): 'H',
+ (1, 17): 'I',
+ (1, 18): 'I',
+ (1, 19): 'I',
+ (1, 20): 'I',
+ (1, 21): 'I',
+ (1, 22): 'I',
+ (1, 23): 'I',
+ (1, 24): 'I',
+ (1, 25): 'I',
+ (1, 26): 'I',
+ (1, 27): 'I',
+ (1, 28): 'I',
+ (1, 29): 'I',
+ (1, 30): 'I',
+ (1, 31): 'I',
+ (1, 32): 'I',
+ (1, 64): 'Q',
+ # VOID : treat as UINT
+ (4, 1): '?', # bitmap
+ (4, 2): 'B',
+ (4, 3): 'B',
+ (4, 4): 'B',
+ (4, 5): 'B',
+ (4, 6): 'B',
+ (4, 7): 'B',
+ (4, 8): 'B',
+ (4, 9): 'H',
+ (4, 10): 'H',
+ (4, 11): 'H',
+ (4, 12): 'H',
+ (4, 13): 'H',
+ (4, 14): 'H',
+ (4, 15): 'H',
+ (4, 16): 'H',
+ (4, 17): 'I',
+ (4, 18): 'I',
+ (4, 19): 'I',
+ (4, 20): 'I',
+ (4, 21): 'I',
+ (4, 22): 'I',
+ (4, 23): 'I',
+ (4, 24): 'I',
+ (4, 25): 'I',
+ (4, 26): 'I',
+ (4, 27): 'I',
+ (4, 28): 'I',
+ (4, 29): 'I',
+ (4, 30): 'I',
+ (4, 31): 'I',
+ (4, 32): 'I',
+ (4, 64): 'Q',
+ # INT
+ (2, 8): 'b',
+ (2, 16): 'h',
+ (2, 32): 'i',
+ (2, 64): 'q',
+ # IEEEFP : 24 bit not supported by numpy
+ (3, 16): 'e',
+ # (3, 24): '', #
+ (3, 32): 'f',
+ (3, 64): 'd',
+ # COMPLEXIEEEFP
+ (6, 64): 'F',
+ (6, 128): 'D',
+ # RGB565
+ (1, (5, 6, 5)): 'B',
+ # COMPLEXINT : not supported by numpy
+ }
+
+ def PREDICTORS():
+ # Map PREDICTOR to predictor encode functions
+ if imagecodecs is None:
+ return {
+ None: identityfunc,
+ 1: identityfunc,
+ 2: delta_encode,
+ }
+ return {
+ None: imagecodecs.none_encode,
+ 1: imagecodecs.none_encode,
+ 2: imagecodecs.delta_encode,
+ 3: imagecodecs.floatpred_encode,
+ }
+
+ def UNPREDICTORS():
+ # Map PREDICTOR to predictor decode functions
+ if imagecodecs is None:
+ return {
+ None: identityfunc,
+ 1: identityfunc,
+ 2: delta_decode,
+ }
+ return {
+ None: imagecodecs.none_decode,
+ 1: imagecodecs.none_decode,
+ 2: imagecodecs.delta_decode,
+ 3: imagecodecs.floatpred_decode,
+ }
+
+ def COMPESSORS():
+ # Map COMPRESSION to compress functions
+ if hasattr(imagecodecs, 'zlib_encode'):
+ return {
+ None: imagecodecs.none_encode,
+ 1: imagecodecs.none_encode,
+ 7: imagecodecs.jpeg_encode,
+ 8: imagecodecs.zlib_encode,
+ 32946: imagecodecs.zlib_encode,
+ 32773: imagecodecs.packbits_encode,
+ 34712: imagecodecs.j2k_encode,
+ 34925: imagecodecs.lzma_encode,
+ 34933: imagecodecs.png_encode,
+ 34934: imagecodecs.jxr_encode,
+ 50000: imagecodecs.zstd_encode,
+ 50001: imagecodecs.webp_encode,
+ }
+
+ def zlib_encode(data, level=6, out=None):
+ """Compress Zlib DEFLATE."""
+ return zlib.compress(data, level)
+
+ if imagecodecs is None:
+ return {
+ None: identityfunc,
+ 1: identityfunc,
+ 8: zlib_encode,
+ 32946: zlib_encode,
+ # 34925: lzma.compress
+ }
+
+ return {
+ None: imagecodecs.none_encode,
+ 1: imagecodecs.none_encode,
+ 8: zlib_encode,
+ 32946: zlib_encode,
+ 32773: imagecodecs.packbits_encode,
+ }
+
+ def DECOMPESSORS():
+ # Map COMPRESSION to decompress functions
+ if hasattr(imagecodecs, 'zlib_decode'):
+ return {
+ None: imagecodecs.none_decode,
+ 1: imagecodecs.none_decode,
+ 5: imagecodecs.lzw_decode,
+ 6: imagecodecs.jpeg_decode,
+ 7: imagecodecs.jpeg_decode,
+ 8: imagecodecs.zlib_decode,
+ 32946: imagecodecs.zlib_decode,
+ 32773: imagecodecs.packbits_decode,
+ # 34892: imagecodecs.jpeg_decode, # DNG lossy
+ 34925: imagecodecs.lzma_decode,
+ 34926: imagecodecs.zstd_decode, # deprecated
+ 34927: imagecodecs.webp_decode, # deprecated
+ 33003: imagecodecs.j2k_decode,
+ 33005: imagecodecs.j2k_decode,
+ 34712: imagecodecs.j2k_decode,
+ 34933: imagecodecs.png_decode,
+ 34934: imagecodecs.jxr_decode,
+ 50000: imagecodecs.zstd_decode,
+ 50001: imagecodecs.webp_decode,
+ }
+
+ def zlib_decode(data, out=None):
+ """Decompress Zlib DEFLATE."""
+ return zlib.decompress(data)
+
+ if imagecodecs is None:
+ return {
+ None: identityfunc,
+ 1: identityfunc,
+ 8: zlib_decode,
+ 32946: zlib_decode,
+ # 34925: lzma.decompress
+ }
+
+ return {
+ None: imagecodecs.none_decode,
+ 1: imagecodecs.none_decode,
+ 5: imagecodecs.lzw_decode,
+ 8: zlib_decode,
+ 32946: zlib_decode,
+ 32773: imagecodecs.packbits_decode,
+ }
+
+ def FRAME_ATTRS():
+ # Attributes that a TiffFrame shares with its keyframe
+ return {
+ 'shape',
+ 'ndim',
+ 'size',
+ 'dtype',
+ 'axes',
+ 'is_final',
+ }
+
+ def FILE_FLAGS():
+ # TiffFile and TiffPage 'is_\*' attributes
+ exclude = {
+ 'reduced',
+ 'mask',
+ 'final',
+ 'memmappable',
+ 'contiguous',
+ 'tiled',
+ 'subsampled',
+ }
+ return set(
+ a[3:]
+ for a in dir(TiffPage)
+ if a[:3] == 'is_' and a[3:] not in exclude
+ )
+
+ def FILE_EXTENSIONS():
+ # TIFF file extensions
+ return (
+ 'tif', 'tiff', 'ome.tif', 'lsm', 'stk', 'qpi', 'pcoraw',
+ 'gel', 'seq', 'svs', 'zif', 'ndpi', 'bif', 'tf8', 'tf2', 'btf',
+ )
+
+ def FILEOPEN_FILTER():
+ # String for use in Windows File Open box
+ return [
+ ('%s files' % ext.upper(), '*.%s' % ext)
+ for ext in TIFF.FILE_EXTENSIONS
+ ] + [('allfiles', '*')]
+
+ def AXES_LABELS():
+ # TODO: is there a standard for character axes labels?
+ axes = {
+ 'X': 'width',
+ 'Y': 'height',
+ 'Z': 'depth',
+ 'S': 'sample', # rgb(a)
+ 'I': 'series', # general sequence, plane, page, IFD
+ 'T': 'time',
+ 'C': 'channel', # color, emission wavelength
+ 'A': 'angle',
+ 'P': 'phase', # formerly F # P is Position in LSM!
+ 'R': 'tile', # region, point, mosaic
+ 'H': 'lifetime', # histogram
+ 'E': 'lambda', # excitation wavelength
+ 'L': 'exposure', # lux
+ 'V': 'event',
+ 'Q': 'other',
+ 'M': 'mosaic', # LSM 6
+ }
+ axes.update(dict((v, k) for k, v in axes.items()))
+ return axes
+
+ def NDPI_TAGS():
+ # 65420 - 65458 Private Hamamatsu NDPI tags
+ tags = dict((code, str(code)) for code in range(65420, 65459))
+ tags.update({
+ 65420: 'FileFormat',
+ 65421: 'Magnification', # SourceLens
+ 65422: 'XOffsetFromSlideCentre',
+ 65423: 'YOffsetFromSlideCentre',
+ 65424: 'ZOffsetFromSlideCentre',
+ 65427: 'UserLabel',
+ 65428: 'AuthCode', # ?
+ 65442: 'ScannerSerialNumber',
+ 65449: 'Comments',
+ 65447: 'BlankLanes',
+ 65434: 'Fluorescence',
+ })
+ return tags
+
+ def EXIF_TAGS():
+ tags = {
+ # 65000 - 65112 Photoshop Camera RAW EXIF tags
+ 65000: 'OwnerName',
+ 65001: 'SerialNumber',
+ 65002: 'Lens',
+ 65100: 'RawFile',
+ 65101: 'Converter',
+ 65102: 'WhiteBalance',
+ 65105: 'Exposure',
+ 65106: 'Shadows',
+ 65107: 'Brightness',
+ 65108: 'Contrast',
+ 65109: 'Saturation',
+ 65110: 'Sharpness',
+ 65111: 'Smoothness',
+ 65112: 'MoireFilter',
+ }
+ tags.update(TIFF.TAGS)
+ return tags
+
+ def GPS_TAGS():
+ return {
+ 0: 'GPSVersionID',
+ 1: 'GPSLatitudeRef',
+ 2: 'GPSLatitude',
+ 3: 'GPSLongitudeRef',
+ 4: 'GPSLongitude',
+ 5: 'GPSAltitudeRef',
+ 6: 'GPSAltitude',
+ 7: 'GPSTimeStamp',
+ 8: 'GPSSatellites',
+ 9: 'GPSStatus',
+ 10: 'GPSMeasureMode',
+ 11: 'GPSDOP',
+ 12: 'GPSSpeedRef',
+ 13: 'GPSSpeed',
+ 14: 'GPSTrackRef',
+ 15: 'GPSTrack',
+ 16: 'GPSImgDirectionRef',
+ 17: 'GPSImgDirection',
+ 18: 'GPSMapDatum',
+ 19: 'GPSDestLatitudeRef',
+ 20: 'GPSDestLatitude',
+ 21: 'GPSDestLongitudeRef',
+ 22: 'GPSDestLongitude',
+ 23: 'GPSDestBearingRef',
+ 24: 'GPSDestBearing',
+ 25: 'GPSDestDistanceRef',
+ 26: 'GPSDestDistance',
+ 27: 'GPSProcessingMethod',
+ 28: 'GPSAreaInformation',
+ 29: 'GPSDateStamp',
+ 30: 'GPSDifferential',
+ 31: 'GPSHPositioningError',
+ }
+
+ def IOP_TAGS():
+ return {
+ 1: 'InteroperabilityIndex',
+ 2: 'InteroperabilityVersion',
+ 4096: 'RelatedImageFileFormat',
+ 4097: 'RelatedImageWidth',
+ 4098: 'RelatedImageLength',
+ }
+
+ def GEO_KEYS():
+ return {
+ 1024: 'GTModelTypeGeoKey',
+ 1025: 'GTRasterTypeGeoKey',
+ 1026: 'GTCitationGeoKey',
+ 2048: 'GeographicTypeGeoKey',
+ 2049: 'GeogCitationGeoKey',
+ 2050: 'GeogGeodeticDatumGeoKey',
+ 2051: 'GeogPrimeMeridianGeoKey',
+ 2052: 'GeogLinearUnitsGeoKey',
+ 2053: 'GeogLinearUnitSizeGeoKey',
+ 2054: 'GeogAngularUnitsGeoKey',
+ 2055: 'GeogAngularUnitsSizeGeoKey',
+ 2056: 'GeogEllipsoidGeoKey',
+ 2057: 'GeogSemiMajorAxisGeoKey',
+ 2058: 'GeogSemiMinorAxisGeoKey',
+ 2059: 'GeogInvFlatteningGeoKey',
+ 2060: 'GeogAzimuthUnitsGeoKey',
+ 2061: 'GeogPrimeMeridianLongGeoKey',
+ 2062: 'GeogTOWGS84GeoKey',
+ 3059: 'ProjLinearUnitsInterpCorrectGeoKey', # GDAL
+ 3072: 'ProjectedCSTypeGeoKey',
+ 3073: 'PCSCitationGeoKey',
+ 3074: 'ProjectionGeoKey',
+ 3075: 'ProjCoordTransGeoKey',
+ 3076: 'ProjLinearUnitsGeoKey',
+ 3077: 'ProjLinearUnitSizeGeoKey',
+ 3078: 'ProjStdParallel1GeoKey',
+ 3079: 'ProjStdParallel2GeoKey',
+ 3080: 'ProjNatOriginLongGeoKey',
+ 3081: 'ProjNatOriginLatGeoKey',
+ 3082: 'ProjFalseEastingGeoKey',
+ 3083: 'ProjFalseNorthingGeoKey',
+ 3084: 'ProjFalseOriginLongGeoKey',
+ 3085: 'ProjFalseOriginLatGeoKey',
+ 3086: 'ProjFalseOriginEastingGeoKey',
+ 3087: 'ProjFalseOriginNorthingGeoKey',
+ 3088: 'ProjCenterLongGeoKey',
+ 3089: 'ProjCenterLatGeoKey',
+ 3090: 'ProjCenterEastingGeoKey',
+ 3091: 'ProjFalseOriginNorthingGeoKey',
+ 3092: 'ProjScaleAtNatOriginGeoKey',
+ 3093: 'ProjScaleAtCenterGeoKey',
+ 3094: 'ProjAzimuthAngleGeoKey',
+ 3095: 'ProjStraightVertPoleLongGeoKey',
+ 3096: 'ProjRectifiedGridAngleGeoKey',
+ 4096: 'VerticalCSTypeGeoKey',
+ 4097: 'VerticalCitationGeoKey',
+ 4098: 'VerticalDatumGeoKey',
+ 4099: 'VerticalUnitsGeoKey',
+ }
+
+ def GEO_CODES():
+ try:
+ from .tifffile_geodb import GEO_CODES # delayed import
+ except (ImportError, ValueError):
+ try:
+ from tifffile_geodb import GEO_CODES # delayed import
+ except (ImportError, ValueError):
+ GEO_CODES = {}
+ return GEO_CODES
+
+ def CZ_LSMINFO():
+ return [
+ ('MagicNumber', 'u4'),
+ ('StructureSize', 'i4'),
+ ('DimensionX', 'i4'),
+ ('DimensionY', 'i4'),
+ ('DimensionZ', 'i4'),
+ ('DimensionChannels', 'i4'),
+ ('DimensionTime', 'i4'),
+ ('DataType', 'i4'), # DATATYPES
+ ('ThumbnailX', 'i4'),
+ ('ThumbnailY', 'i4'),
+ ('VoxelSizeX', 'f8'),
+ ('VoxelSizeY', 'f8'),
+ ('VoxelSizeZ', 'f8'),
+ ('OriginX', 'f8'),
+ ('OriginY', 'f8'),
+ ('OriginZ', 'f8'),
+ ('ScanType', 'u2'),
+ ('SpectralScan', 'u2'),
+ ('TypeOfData', 'u4'), # TYPEOFDATA
+ ('OffsetVectorOverlay', 'u4'),
+ ('OffsetInputLut', 'u4'),
+ ('OffsetOutputLut', 'u4'),
+ ('OffsetChannelColors', 'u4'),
+ ('TimeIntervall', 'f8'),
+ ('OffsetChannelDataTypes', 'u4'),
+ ('OffsetScanInformation', 'u4'), # SCANINFO
+ ('OffsetKsData', 'u4'),
+ ('OffsetTimeStamps', 'u4'),
+ ('OffsetEventList', 'u4'),
+ ('OffsetRoi', 'u4'),
+ ('OffsetBleachRoi', 'u4'),
+ ('OffsetNextRecording', 'u4'),
+ # LSM 2.0 ends here
+ ('DisplayAspectX', 'f8'),
+ ('DisplayAspectY', 'f8'),
+ ('DisplayAspectZ', 'f8'),
+ ('DisplayAspectTime', 'f8'),
+ ('OffsetMeanOfRoisOverlay', 'u4'),
+ ('OffsetTopoIsolineOverlay', 'u4'),
+ ('OffsetTopoProfileOverlay', 'u4'),
+ ('OffsetLinescanOverlay', 'u4'),
+ ('ToolbarFlags', 'u4'),
+ ('OffsetChannelWavelength', 'u4'),
+ ('OffsetChannelFactors', 'u4'),
+ ('ObjectiveSphereCorrection', 'f8'),
+ ('OffsetUnmixParameters', 'u4'),
+ # LSM 3.2, 4.0 end here
+ ('OffsetAcquisitionParameters', 'u4'),
+ ('OffsetCharacteristics', 'u4'),
+ ('OffsetPalette', 'u4'),
+ ('TimeDifferenceX', 'f8'),
+ ('TimeDifferenceY', 'f8'),
+ ('TimeDifferenceZ', 'f8'),
+ ('InternalUse1', 'u4'),
+ ('DimensionP', 'i4'),
+ ('DimensionM', 'i4'),
+ ('DimensionsReserved', '16i4'),
+ ('OffsetTilePositions', 'u4'),
+ ('', '9u4'), # Reserved
+ ('OffsetPositions', 'u4'),
+ # ('', '21u4'), # must be 0
+ ]
+
+ def CZ_LSMINFO_READERS():
+ # Import functions for CZ_LSMINFO sub-records
+ # TODO: read more CZ_LSMINFO sub-records
+ return {
+ 'ScanInformation': read_lsm_scaninfo,
+ 'TimeStamps': read_lsm_timestamps,
+ 'EventList': read_lsm_eventlist,
+ 'ChannelColors': read_lsm_channelcolors,
+ 'Positions': read_lsm_floatpairs,
+ 'TilePositions': read_lsm_floatpairs,
+ 'VectorOverlay': None,
+ 'InputLut': None,
+ 'OutputLut': None,
+ 'TimeIntervall': None,
+ 'ChannelDataTypes': None,
+ 'KsData': None,
+ 'Roi': None,
+ 'BleachRoi': None,
+ 'NextRecording': None,
+ 'MeanOfRoisOverlay': None,
+ 'TopoIsolineOverlay': None,
+ 'TopoProfileOverlay': None,
+ 'ChannelWavelength': None,
+ 'SphereCorrection': None,
+ 'ChannelFactors': None,
+ 'UnmixParameters': None,
+ 'AcquisitionParameters': None,
+ 'Characteristics': None,
+ }
+
+ def CZ_LSMINFO_SCANTYPE():
+ # Map CZ_LSMINFO.ScanType to dimension order
+ return {
+ 0: 'XYZCT', # 'Stack' normal x-y-z-scan
+ 1: 'XYZCT', # 'Z-Scan' x-z-plane Y=1
+ 2: 'XYZCT', # 'Line'
+ 3: 'XYTCZ', # 'Time Series Plane' time series x-y XYCTZ ? Z=1
+ 4: 'XYZTC', # 'Time Series z-Scan' time series x-z
+ 5: 'XYTCZ', # 'Time Series Mean-of-ROIs'
+ 6: 'XYZTC', # 'Time Series Stack' time series x-y-z
+ 7: 'XYCTZ', # Spline Scan
+ 8: 'XYCZT', # Spline Plane x-z
+ 9: 'XYTCZ', # Time Series Spline Plane x-z
+ 10: 'XYZCT', # 'Time Series Point' point mode
+ }
+
+ def CZ_LSMINFO_DIMENSIONS():
+ # Map dimension codes to CZ_LSMINFO attribute
+ return {
+ 'X': 'DimensionX',
+ 'Y': 'DimensionY',
+ 'Z': 'DimensionZ',
+ 'C': 'DimensionChannels',
+ 'T': 'DimensionTime',
+ 'P': 'DimensionP',
+ 'M': 'DimensionM',
+ }
+
+ def CZ_LSMINFO_DATATYPES():
+ # Description of CZ_LSMINFO.DataType
+ return {
+ 0: 'varying data types',
+ 1: '8 bit unsigned integer',
+ 2: '12 bit unsigned integer',
+ 5: '32 bit float',
+ }
+
+ def CZ_LSMINFO_TYPEOFDATA():
+ # Description of CZ_LSMINFO.TypeOfData
+ return {
+ 0: 'Original scan data',
+ 1: 'Calculated data',
+ 2: '3D reconstruction',
+ 3: 'Topography height map',
+ }
+
+ def CZ_LSMINFO_SCANINFO_ARRAYS():
+ return {
+ 0x20000000: 'Tracks',
+ 0x30000000: 'Lasers',
+ 0x60000000: 'DetectionChannels',
+ 0x80000000: 'IlluminationChannels',
+ 0xA0000000: 'BeamSplitters',
+ 0xC0000000: 'DataChannels',
+ 0x11000000: 'Timers',
+ 0x13000000: 'Markers',
+ }
+
+ def CZ_LSMINFO_SCANINFO_STRUCTS():
+ return {
+ # 0x10000000: 'Recording',
+ 0x40000000: 'Track',
+ 0x50000000: 'Laser',
+ 0x70000000: 'DetectionChannel',
+ 0x90000000: 'IlluminationChannel',
+ 0xB0000000: 'BeamSplitter',
+ 0xD0000000: 'DataChannel',
+ 0x12000000: 'Timer',
+ 0x14000000: 'Marker',
+ }
+
+ def CZ_LSMINFO_SCANINFO_ATTRIBUTES():
+ return {
+ # Recording
+ 0x10000001: 'Name',
+ 0x10000002: 'Description',
+ 0x10000003: 'Notes',
+ 0x10000004: 'Objective',
+ 0x10000005: 'ProcessingSummary',
+ 0x10000006: 'SpecialScanMode',
+ 0x10000007: 'ScanType',
+ 0x10000008: 'ScanMode',
+ 0x10000009: 'NumberOfStacks',
+ 0x1000000A: 'LinesPerPlane',
+ 0x1000000B: 'SamplesPerLine',
+ 0x1000000C: 'PlanesPerVolume',
+ 0x1000000D: 'ImagesWidth',
+ 0x1000000E: 'ImagesHeight',
+ 0x1000000F: 'ImagesNumberPlanes',
+ 0x10000010: 'ImagesNumberStacks',
+ 0x10000011: 'ImagesNumberChannels',
+ 0x10000012: 'LinscanXySize',
+ 0x10000013: 'ScanDirection',
+ 0x10000014: 'TimeSeries',
+ 0x10000015: 'OriginalScanData',
+ 0x10000016: 'ZoomX',
+ 0x10000017: 'ZoomY',
+ 0x10000018: 'ZoomZ',
+ 0x10000019: 'Sample0X',
+ 0x1000001A: 'Sample0Y',
+ 0x1000001B: 'Sample0Z',
+ 0x1000001C: 'SampleSpacing',
+ 0x1000001D: 'LineSpacing',
+ 0x1000001E: 'PlaneSpacing',
+ 0x1000001F: 'PlaneWidth',
+ 0x10000020: 'PlaneHeight',
+ 0x10000021: 'VolumeDepth',
+ 0x10000023: 'Nutation',
+ 0x10000034: 'Rotation',
+ 0x10000035: 'Precession',
+ 0x10000036: 'Sample0time',
+ 0x10000037: 'StartScanTriggerIn',
+ 0x10000038: 'StartScanTriggerOut',
+ 0x10000039: 'StartScanEvent',
+ 0x10000040: 'StartScanTime',
+ 0x10000041: 'StopScanTriggerIn',
+ 0x10000042: 'StopScanTriggerOut',
+ 0x10000043: 'StopScanEvent',
+ 0x10000044: 'StopScanTime',
+ 0x10000045: 'UseRois',
+ 0x10000046: 'UseReducedMemoryRois',
+ 0x10000047: 'User',
+ 0x10000048: 'UseBcCorrection',
+ 0x10000049: 'PositionBcCorrection1',
+ 0x10000050: 'PositionBcCorrection2',
+ 0x10000051: 'InterpolationY',
+ 0x10000052: 'CameraBinning',
+ 0x10000053: 'CameraSupersampling',
+ 0x10000054: 'CameraFrameWidth',
+ 0x10000055: 'CameraFrameHeight',
+ 0x10000056: 'CameraOffsetX',
+ 0x10000057: 'CameraOffsetY',
+ 0x10000059: 'RtBinning',
+ 0x1000005A: 'RtFrameWidth',
+ 0x1000005B: 'RtFrameHeight',
+ 0x1000005C: 'RtRegionWidth',
+ 0x1000005D: 'RtRegionHeight',
+ 0x1000005E: 'RtOffsetX',
+ 0x1000005F: 'RtOffsetY',
+ 0x10000060: 'RtZoom',
+ 0x10000061: 'RtLinePeriod',
+ 0x10000062: 'Prescan',
+ 0x10000063: 'ScanDirectionZ',
+ # Track
+ 0x40000001: 'MultiplexType', # 0 After Line; 1 After Frame
+ 0x40000002: 'MultiplexOrder',
+ 0x40000003: 'SamplingMode', # 0 Sample; 1 Line Avg; 2 Frame Avg
+ 0x40000004: 'SamplingMethod', # 1 Mean; 2 Sum
+ 0x40000005: 'SamplingNumber',
+ 0x40000006: 'Acquire',
+ 0x40000007: 'SampleObservationTime',
+ 0x4000000B: 'TimeBetweenStacks',
+ 0x4000000C: 'Name',
+ 0x4000000D: 'Collimator1Name',
+ 0x4000000E: 'Collimator1Position',
+ 0x4000000F: 'Collimator2Name',
+ 0x40000010: 'Collimator2Position',
+ 0x40000011: 'IsBleachTrack',
+ 0x40000012: 'IsBleachAfterScanNumber',
+ 0x40000013: 'BleachScanNumber',
+ 0x40000014: 'TriggerIn',
+ 0x40000015: 'TriggerOut',
+ 0x40000016: 'IsRatioTrack',
+ 0x40000017: 'BleachCount',
+ 0x40000018: 'SpiCenterWavelength',
+ 0x40000019: 'PixelTime',
+ 0x40000021: 'CondensorFrontlens',
+ 0x40000023: 'FieldStopValue',
+ 0x40000024: 'IdCondensorAperture',
+ 0x40000025: 'CondensorAperture',
+ 0x40000026: 'IdCondensorRevolver',
+ 0x40000027: 'CondensorFilter',
+ 0x40000028: 'IdTransmissionFilter1',
+ 0x40000029: 'IdTransmission1',
+ 0x40000030: 'IdTransmissionFilter2',
+ 0x40000031: 'IdTransmission2',
+ 0x40000032: 'RepeatBleach',
+ 0x40000033: 'EnableSpotBleachPos',
+ 0x40000034: 'SpotBleachPosx',
+ 0x40000035: 'SpotBleachPosy',
+ 0x40000036: 'SpotBleachPosz',
+ 0x40000037: 'IdTubelens',
+ 0x40000038: 'IdTubelensPosition',
+ 0x40000039: 'TransmittedLight',
+ 0x4000003A: 'ReflectedLight',
+ 0x4000003B: 'SimultanGrabAndBleach',
+ 0x4000003C: 'BleachPixelTime',
+ # Laser
+ 0x50000001: 'Name',
+ 0x50000002: 'Acquire',
+ 0x50000003: 'Power',
+ # DetectionChannel
+ 0x70000001: 'IntegrationMode',
+ 0x70000002: 'SpecialMode',
+ 0x70000003: 'DetectorGainFirst',
+ 0x70000004: 'DetectorGainLast',
+ 0x70000005: 'AmplifierGainFirst',
+ 0x70000006: 'AmplifierGainLast',
+ 0x70000007: 'AmplifierOffsFirst',
+ 0x70000008: 'AmplifierOffsLast',
+ 0x70000009: 'PinholeDiameter',
+ 0x7000000A: 'CountingTrigger',
+ 0x7000000B: 'Acquire',
+ 0x7000000C: 'PointDetectorName',
+ 0x7000000D: 'AmplifierName',
+ 0x7000000E: 'PinholeName',
+ 0x7000000F: 'FilterSetName',
+ 0x70000010: 'FilterName',
+ 0x70000013: 'IntegratorName',
+ 0x70000014: 'ChannelName',
+ 0x70000015: 'DetectorGainBc1',
+ 0x70000016: 'DetectorGainBc2',
+ 0x70000017: 'AmplifierGainBc1',
+ 0x70000018: 'AmplifierGainBc2',
+ 0x70000019: 'AmplifierOffsetBc1',
+ 0x70000020: 'AmplifierOffsetBc2',
+ 0x70000021: 'SpectralScanChannels',
+ 0x70000022: 'SpiWavelengthStart',
+ 0x70000023: 'SpiWavelengthStop',
+ 0x70000026: 'DyeName',
+ 0x70000027: 'DyeFolder',
+ # IlluminationChannel
+ 0x90000001: 'Name',
+ 0x90000002: 'Power',
+ 0x90000003: 'Wavelength',
+ 0x90000004: 'Aquire',
+ 0x90000005: 'DetchannelName',
+ 0x90000006: 'PowerBc1',
+ 0x90000007: 'PowerBc2',
+ # BeamSplitter
+ 0xB0000001: 'FilterSet',
+ 0xB0000002: 'Filter',
+ 0xB0000003: 'Name',
+ # DataChannel
+ 0xD0000001: 'Name',
+ 0xD0000003: 'Acquire',
+ 0xD0000004: 'Color',
+ 0xD0000005: 'SampleType',
+ 0xD0000006: 'BitsPerSample',
+ 0xD0000007: 'RatioType',
+ 0xD0000008: 'RatioTrack1',
+ 0xD0000009: 'RatioTrack2',
+ 0xD000000A: 'RatioChannel1',
+ 0xD000000B: 'RatioChannel2',
+ 0xD000000C: 'RatioConst1',
+ 0xD000000D: 'RatioConst2',
+ 0xD000000E: 'RatioConst3',
+ 0xD000000F: 'RatioConst4',
+ 0xD0000010: 'RatioConst5',
+ 0xD0000011: 'RatioConst6',
+ 0xD0000012: 'RatioFirstImages1',
+ 0xD0000013: 'RatioFirstImages2',
+ 0xD0000014: 'DyeName',
+ 0xD0000015: 'DyeFolder',
+ 0xD0000016: 'Spectrum',
+ 0xD0000017: 'Acquire',
+ # Timer
+ 0x12000001: 'Name',
+ 0x12000002: 'Description',
+ 0x12000003: 'Interval',
+ 0x12000004: 'TriggerIn',
+ 0x12000005: 'TriggerOut',
+ 0x12000006: 'ActivationTime',
+ 0x12000007: 'ActivationNumber',
+ # Marker
+ 0x14000001: 'Name',
+ 0x14000002: 'Description',
+ 0x14000003: 'TriggerIn',
+ 0x14000004: 'TriggerOut',
+ }
+
+ def NIH_IMAGE_HEADER():
+ return [
+ ('FileID', 'a8'),
+ ('nLines', 'i2'),
+ ('PixelsPerLine', 'i2'),
+ ('Version', 'i2'),
+ ('OldLutMode', 'i2'),
+ ('OldnColors', 'i2'),
+ ('Colors', 'u1', (3, 32)),
+ ('OldColorStart', 'i2'),
+ ('ColorWidth', 'i2'),
+ ('ExtraColors', 'u2', (6, 3)),
+ ('nExtraColors', 'i2'),
+ ('ForegroundIndex', 'i2'),
+ ('BackgroundIndex', 'i2'),
+ ('XScale', 'f8'),
+ ('Unused2', 'i2'),
+ ('Unused3', 'i2'),
+ ('UnitsID', 'i2'), # NIH_UNITS_TYPE
+ ('p1', [('x', 'i2'), ('y', 'i2')]),
+ ('p2', [('x', 'i2'), ('y', 'i2')]),
+ ('CurveFitType', 'i2'), # NIH_CURVEFIT_TYPE
+ ('nCoefficients', 'i2'),
+ ('Coeff', 'f8', 6),
+ ('UMsize', 'u1'),
+ ('UM', 'a15'),
+ ('UnusedBoolean', 'u1'),
+ ('BinaryPic', 'b1'),
+ ('SliceStart', 'i2'),
+ ('SliceEnd', 'i2'),
+ ('ScaleMagnification', 'f4'),
+ ('nSlices', 'i2'),
+ ('SliceSpacing', 'f4'),
+ ('CurrentSlice', 'i2'),
+ ('FrameInterval', 'f4'),
+ ('PixelAspectRatio', 'f4'),
+ ('ColorStart', 'i2'),
+ ('ColorEnd', 'i2'),
+ ('nColors', 'i2'),
+ ('Fill1', '3u2'),
+ ('Fill2', '3u2'),
+ ('Table', 'u1'), # NIH_COLORTABLE_TYPE
+ ('LutMode', 'u1'), # NIH_LUTMODE_TYPE
+ ('InvertedTable', 'b1'),
+ ('ZeroClip', 'b1'),
+ ('XUnitSize', 'u1'),
+ ('XUnit', 'a11'),
+ ('StackType', 'i2'), # NIH_STACKTYPE_TYPE
+ # ('UnusedBytes', 'u1', 200)
+ ]
+
+ def NIH_COLORTABLE_TYPE():
+ return (
+ 'CustomTable',
+ 'AppleDefault',
+ 'Pseudo20',
+ 'Pseudo32',
+ 'Rainbow',
+ 'Fire1',
+ 'Fire2',
+ 'Ice',
+ 'Grays',
+ 'Spectrum',
+ )
+
+ def NIH_LUTMODE_TYPE():
+ return (
+ 'PseudoColor',
+ 'OldAppleDefault',
+ 'OldSpectrum',
+ 'GrayScale',
+ 'ColorLut',
+ 'CustomGrayscale',
+ )
+
+ def NIH_CURVEFIT_TYPE():
+ return (
+ 'StraightLine',
+ 'Poly2',
+ 'Poly3',
+ 'Poly4',
+ 'Poly5',
+ 'ExpoFit',
+ 'PowerFit',
+ 'LogFit',
+ 'RodbardFit',
+ 'SpareFit1',
+ 'Uncalibrated',
+ 'UncalibratedOD',
+ )
+
+ def NIH_UNITS_TYPE():
+ return (
+ 'Nanometers',
+ 'Micrometers',
+ 'Millimeters',
+ 'Centimeters',
+ 'Meters',
+ 'Kilometers',
+ 'Inches',
+ 'Feet',
+ 'Miles',
+ 'Pixels',
+ 'OtherUnits',
+ )
+
+ def TVIPS_HEADER_V1():
+ # TVIPS TemData structure from EMMENU Help file
+ return [
+ ('Version', 'i4'),
+ ('CommentV1', 'a80'),
+ ('HighTension', 'i4'),
+ ('SphericalAberration', 'i4'),
+ ('IlluminationAperture', 'i4'),
+ ('Magnification', 'i4'),
+ ('PostMagnification', 'i4'),
+ ('FocalLength', 'i4'),
+ ('Defocus', 'i4'),
+ ('Astigmatism', 'i4'),
+ ('AstigmatismDirection', 'i4'),
+ ('BiprismVoltage', 'i4'),
+ ('SpecimenTiltAngle', 'i4'),
+ ('SpecimenTiltDirection', 'i4'),
+ ('IlluminationTiltDirection', 'i4'),
+ ('IlluminationTiltAngle', 'i4'),
+ ('ImageMode', 'i4'),
+ ('EnergySpread', 'i4'),
+ ('ChromaticAberration', 'i4'),
+ ('ShutterType', 'i4'),
+ ('DefocusSpread', 'i4'),
+ ('CcdNumber', 'i4'),
+ ('CcdSize', 'i4'),
+ ('OffsetXV1', 'i4'),
+ ('OffsetYV1', 'i4'),
+ ('PhysicalPixelSize', 'i4'),
+ ('Binning', 'i4'),
+ ('ReadoutSpeed', 'i4'),
+ ('GainV1', 'i4'),
+ ('SensitivityV1', 'i4'),
+ ('ExposureTimeV1', 'i4'),
+ ('FlatCorrected', 'i4'),
+ ('DeadPxCorrected', 'i4'),
+ ('ImageMean', 'i4'),
+ ('ImageStd', 'i4'),
+ ('DisplacementX', 'i4'),
+ ('DisplacementY', 'i4'),
+ ('DateV1', 'i4'),
+ ('TimeV1', 'i4'),
+ ('ImageMin', 'i4'),
+ ('ImageMax', 'i4'),
+ ('ImageStatisticsQuality', 'i4'),
+ ]
+
+ def TVIPS_HEADER_V2():
+ return [
+ ('ImageName', 'V160'), # utf16
+ ('ImageFolder', 'V160'),
+ ('ImageSizeX', 'i4'),
+ ('ImageSizeY', 'i4'),
+ ('ImageSizeZ', 'i4'),
+ ('ImageSizeE', 'i4'),
+ ('ImageDataType', 'i4'),
+ ('Date', 'i4'),
+ ('Time', 'i4'),
+ ('Comment', 'V1024'),
+ ('ImageHistory', 'V1024'),
+ ('Scaling', '16f4'),
+ ('ImageStatistics', '16c16'),
+ ('ImageType', 'i4'),
+ ('ImageDisplaType', 'i4'),
+ ('PixelSizeX', 'f4'), # distance between two px in x, [nm]
+ ('PixelSizeY', 'f4'), # distance between two px in y, [nm]
+ ('ImageDistanceZ', 'f4'),
+ ('ImageDistanceE', 'f4'),
+ ('ImageMisc', '32f4'),
+ ('TemType', 'V160'),
+ ('TemHighTension', 'f4'),
+ ('TemAberrations', '32f4'),
+ ('TemEnergy', '32f4'),
+ ('TemMode', 'i4'),
+ ('TemMagnification', 'f4'),
+ ('TemMagnificationCorrection', 'f4'),
+ ('PostMagnification', 'f4'),
+ ('TemStageType', 'i4'),
+ ('TemStagePosition', '5f4'), # x, y, z, a, b
+ ('TemImageShift', '2f4'),
+ ('TemBeamShift', '2f4'),
+ ('TemBeamTilt', '2f4'),
+ ('TilingParameters', '7f4'), # 0: tiling? 1:x 2:y 3: max x
+ # 4: max y 5: overlap x 6: overlap y
+ ('TemIllumination', '3f4'), # 0: spotsize 1: intensity
+ ('TemShutter', 'i4'),
+ ('TemMisc', '32f4'),
+ ('CameraType', 'V160'),
+ ('PhysicalPixelSizeX', 'f4'),
+ ('PhysicalPixelSizeY', 'f4'),
+ ('OffsetX', 'i4'),
+ ('OffsetY', 'i4'),
+ ('BinningX', 'i4'),
+ ('BinningY', 'i4'),
+ ('ExposureTime', 'f4'),
+ ('Gain', 'f4'),
+ ('ReadoutRate', 'f4'),
+ ('FlatfieldDescription', 'V160'),
+ ('Sensitivity', 'f4'),
+ ('Dose', 'f4'),
+ ('CamMisc', '32f4'),
+ ('FeiMicroscopeInformation', 'V1024'),
+ ('FeiSpecimenInformation', 'V1024'),
+ ('Magic', 'u4'),
+ ]
+
+ def MM_HEADER():
+ # Olympus FluoView MM_Header
+ MM_DIMENSION = [
+ ('Name', 'a16'),
+ ('Size', 'i4'),
+ ('Origin', 'f8'),
+ ('Resolution', 'f8'),
+ ('Unit', 'a64'),
+ ]
+ return [
+ ('HeaderFlag', 'i2'),
+ ('ImageType', 'u1'),
+ ('ImageName', 'a257'),
+ ('OffsetData', 'u4'),
+ ('PaletteSize', 'i4'),
+ ('OffsetPalette0', 'u4'),
+ ('OffsetPalette1', 'u4'),
+ ('CommentSize', 'i4'),
+ ('OffsetComment', 'u4'),
+ ('Dimensions', MM_DIMENSION, 10),
+ ('OffsetPosition', 'u4'),
+ ('MapType', 'i2'),
+ ('MapMin', 'f8'),
+ ('MapMax', 'f8'),
+ ('MinValue', 'f8'),
+ ('MaxValue', 'f8'),
+ ('OffsetMap', 'u4'),
+ ('Gamma', 'f8'),
+ ('Offset', 'f8'),
+ ('GrayChannel', MM_DIMENSION),
+ ('OffsetThumbnail', 'u4'),
+ ('VoiceField', 'i4'),
+ ('OffsetVoiceField', 'u4'),
+ ]
+
+ def MM_DIMENSIONS():
+ # Map FluoView MM_Header.Dimensions to axes characters
+ return {
+ 'X': 'X',
+ 'Y': 'Y',
+ 'Z': 'Z',
+ 'T': 'T',
+ 'CH': 'C',
+ 'WAVELENGTH': 'C',
+ 'TIME': 'T',
+ 'XY': 'R',
+ 'EVENT': 'V',
+ 'EXPOSURE': 'L',
+ }
+
+ def UIC_TAGS():
+ # Map Universal Imaging Corporation MetaMorph internal tag ids to
+ # name and type
+ from fractions import Fraction # delayed import
+
+ return [
+ ('AutoScale', int),
+ ('MinScale', int),
+ ('MaxScale', int),
+ ('SpatialCalibration', int),
+ ('XCalibration', Fraction),
+ ('YCalibration', Fraction),
+ ('CalibrationUnits', str),
+ ('Name', str),
+ ('ThreshState', int),
+ ('ThreshStateRed', int),
+ ('tagid_10', None), # undefined
+ ('ThreshStateGreen', int),
+ ('ThreshStateBlue', int),
+ ('ThreshStateLo', int),
+ ('ThreshStateHi', int),
+ ('Zoom', int),
+ ('CreateTime', julian_datetime),
+ ('LastSavedTime', julian_datetime),
+ ('currentBuffer', int),
+ ('grayFit', None),
+ ('grayPointCount', None),
+ ('grayX', Fraction),
+ ('grayY', Fraction),
+ ('grayMin', Fraction),
+ ('grayMax', Fraction),
+ ('grayUnitName', str),
+ ('StandardLUT', int),
+ ('wavelength', int),
+ ('StagePosition', '(%i,2,2)u4'), # N xy positions as fract
+ ('CameraChipOffset', '(%i,2,2)u4'), # N xy offsets as fract
+ ('OverlayMask', None),
+ ('OverlayCompress', None),
+ ('Overlay', None),
+ ('SpecialOverlayMask', None),
+ ('SpecialOverlayCompress', None),
+ ('SpecialOverlay', None),
+ ('ImageProperty', read_uic_image_property),
+ ('StageLabel', '%ip'), # N str
+ ('AutoScaleLoInfo', Fraction),
+ ('AutoScaleHiInfo', Fraction),
+ ('AbsoluteZ', '(%i,2)u4'), # N fractions
+ ('AbsoluteZValid', '(%i,)u4'), # N long
+ ('Gamma', 'I'), # 'I' uses offset
+ ('GammaRed', 'I'),
+ ('GammaGreen', 'I'),
+ ('GammaBlue', 'I'),
+ ('CameraBin', '2I'),
+ ('NewLUT', int),
+ ('ImagePropertyEx', None),
+ ('PlaneProperty', int),
+ ('UserLutTable', '(256,3)u1'),
+ ('RedAutoScaleInfo', int),
+ ('RedAutoScaleLoInfo', Fraction),
+ ('RedAutoScaleHiInfo', Fraction),
+ ('RedMinScaleInfo', int),
+ ('RedMaxScaleInfo', int),
+ ('GreenAutoScaleInfo', int),
+ ('GreenAutoScaleLoInfo', Fraction),
+ ('GreenAutoScaleHiInfo', Fraction),
+ ('GreenMinScaleInfo', int),
+ ('GreenMaxScaleInfo', int),
+ ('BlueAutoScaleInfo', int),
+ ('BlueAutoScaleLoInfo', Fraction),
+ ('BlueAutoScaleHiInfo', Fraction),
+ ('BlueMinScaleInfo', int),
+ ('BlueMaxScaleInfo', int),
+ # ('OverlayPlaneColor', read_uic_overlay_plane_color),
+ ]
+
+ def PILATUS_HEADER():
+ # PILATUS CBF Header Specification, Version 1.4
+ # Map key to [value_indices], type
+ return {
+ 'Detector': ([slice(1, None)], str),
+ 'Pixel_size': ([1, 4], float),
+ 'Silicon': ([3], float),
+ 'Exposure_time': ([1], float),
+ 'Exposure_period': ([1], float),
+ 'Tau': ([1], float),
+ 'Count_cutoff': ([1], int),
+ 'Threshold_setting': ([1], float),
+ 'Gain_setting': ([1, 2], str),
+ 'N_excluded_pixels': ([1], int),
+ 'Excluded_pixels': ([1], str),
+ 'Flat_field': ([1], str),
+ 'Trim_file': ([1], str),
+ 'Image_path': ([1], str),
+ # optional
+ 'Wavelength': ([1], float),
+ 'Energy_range': ([1, 2], float),
+ 'Detector_distance': ([1], float),
+ 'Detector_Voffset': ([1], float),
+ 'Beam_xy': ([1, 2], float),
+ 'Flux': ([1], str),
+ 'Filter_transmission': ([1], float),
+ 'Start_angle': ([1], float),
+ 'Angle_increment': ([1], float),
+ 'Detector_2theta': ([1], float),
+ 'Polarization': ([1], float),
+ 'Alpha': ([1], float),
+ 'Kappa': ([1], float),
+ 'Phi': ([1], float),
+ 'Phi_increment': ([1], float),
+ 'Chi': ([1], float),
+ 'Chi_increment': ([1], float),
+ 'Oscillation_axis': ([slice(1, None)], str),
+ 'N_oscillations': ([1], int),
+ 'Start_position': ([1], float),
+ 'Position_increment': ([1], float),
+ 'Shutter_time': ([1], float),
+ 'Omega': ([1], float),
+ 'Omega_increment': ([1], float),
+ }
+
+ def ALLOCATIONGRANULARITY():
+ # alignment for writing contiguous data to TIFF
+ import mmap # delayed import
+
+ return mmap.ALLOCATIONGRANULARITY
+
+
+def read_tags(fh, byteorder, offsetsize, tagnames, customtags=None,
+ maxifds=None):
+ """Read tags from chain of IFDs and return as list of dicts.
+
+ The file handle position must be at a valid IFD header.
+
+ """
+ if offsetsize == 4:
+ offsetformat = byteorder + 'I'
+ tagnosize = 2
+ tagnoformat = byteorder + 'H'
+ tagsize = 12
+ tagformat1 = byteorder + 'HH'
+ tagformat2 = byteorder + 'I4s'
+ elif offsetsize == 8:
+ offsetformat = byteorder + 'Q'
+ tagnosize = 8
+ tagnoformat = byteorder + 'Q'
+ tagsize = 20
+ tagformat1 = byteorder + 'HH'
+ tagformat2 = byteorder + 'Q8s'
+ else:
+ raise ValueError('invalid offset size')
+
+ if customtags is None:
+ customtags = {}
+ if maxifds is None:
+ maxifds = 2**32
+
+ result = []
+ unpack = struct.unpack
+ offset = fh.tell()
+ while len(result) < maxifds:
+ # loop over IFDs
+ try:
+ tagno = unpack(tagnoformat, fh.read(tagnosize))[0]
+ if tagno > 4096:
+ raise TiffFileError('suspicious number of tags')
+ except Exception:
+ log.warning('read_tags: corrupted tag list at offset %i', offset)
+ break
+
+ tags = {}
+ data = fh.read(tagsize * tagno)
+ pos = fh.tell()
+ index = 0
+ for _ in range(tagno):
+ code, type_ = unpack(tagformat1, data[index:index + 4])
+ count, value = unpack(tagformat2, data[index + 4: index + tagsize])
+ index += tagsize
+ name = tagnames.get(code, str(code))
+ try:
+ dtype = TIFF.DATA_FORMATS[type_]
+ except KeyError:
+ raise TiffFileError('unknown tag data type %i' % type_)
+
+ fmt = '%s%i%s' % (byteorder, count * int(dtype[0]), dtype[1])
+ size = struct.calcsize(fmt)
+ if size > offsetsize or code in customtags:
+ offset = unpack(offsetformat, value)[0]
+ if offset < 8 or offset > fh.size - size:
+ raise TiffFileError('invalid tag value offset %i' % offset)
+ fh.seek(offset)
+ if code in customtags:
+ readfunc = customtags[code][1]
+ value = readfunc(fh, byteorder, dtype, count, offsetsize)
+ elif type_ == 7 or (count > 1 and dtype[-1] == 'B'):
+ value = read_bytes(fh, byteorder, dtype, count, offsetsize)
+ elif code in tagnames or dtype[-1] == 's':
+ value = unpack(fmt, fh.read(size))
+ else:
+ value = read_numpy(fh, byteorder, dtype, count, offsetsize)
+ elif dtype[-1] == 'B' or type_ == 7:
+ value = value[:size]
+ else:
+ value = unpack(fmt, value[:size])
+
+ if code not in customtags and code not in TIFF.TAG_TUPLE:
+ if len(value) == 1:
+ value = value[0]
+ if type_ != 7 and dtype[-1] == 's' and isinstance(value, bytes):
+ # TIFF ASCII fields can contain multiple strings,
+ # each terminated with a NUL
+ try:
+ value = bytes2str(stripascii(value).strip())
+ except UnicodeDecodeError:
+ log.warning(
+ 'read_tags: coercing invalid ASCII to bytes (tag %i)',
+ code)
+
+ tags[name] = value
+
+ result.append(tags)
+ # read offset to next page
+ fh.seek(pos)
+ offset = unpack(offsetformat, fh.read(offsetsize))[0]
+ if offset == 0:
+ break
+ if offset >= fh.size:
+ log.warning('read_tags: invalid page offset (%i)', offset)
+ break
+ fh.seek(offset)
+
+ if result and maxifds == 1:
+ result = result[0]
+ return result
+
+
+def read_exif_ifd(fh, byteorder, dtype, count, offsetsize):
+ """Read EXIF tags from file and return as dict."""
+ exif = read_tags(fh, byteorder, offsetsize, TIFF.EXIF_TAGS, maxifds=1)
+ for name in ('ExifVersion', 'FlashpixVersion'):
+ try:
+ exif[name] = bytes2str(exif[name])
+ except Exception:
+ pass
+ if 'UserComment' in exif:
+ idcode = exif['UserComment'][:8]
+ try:
+ if idcode == b'ASCII\x00\x00\x00':
+ exif['UserComment'] = bytes2str(exif['UserComment'][8:])
+ elif idcode == b'UNICODE\x00':
+ exif['UserComment'] = exif['UserComment'][8:].decode('utf-16')
+ except Exception:
+ pass
+ return exif
+
+
+def read_gps_ifd(fh, byteorder, dtype, count, offsetsize):
+ """Read GPS tags from file and return as dict."""
+ return read_tags(fh, byteorder, offsetsize, TIFF.GPS_TAGS, maxifds=1)
+
+
+def read_interoperability_ifd(fh, byteorder, dtype, count, offsetsize):
+ """Read Interoperability tags from file and return as dict."""
+ tag_names = {1: 'InteroperabilityIndex'}
+ return read_tags(fh, byteorder, offsetsize, tag_names, maxifds=1)
+
+
+def read_bytes(fh, byteorder, dtype, count, offsetsize):
+ """Read tag data from file and return as byte string."""
+ dtype = 'B' if dtype[-1] == 's' else byteorder + dtype[-1]
+ count *= numpy.dtype(dtype).itemsize
+ data = fh.read(count)
+ if len(data) != count:
+ log.warning('read_bytes: failed to read all bytes (%i < %i)',
+ len(data), count)
+ return data
+
+
+def read_utf8(fh, byteorder, dtype, count, offsetsize):
+ """Read tag data from file and return as unicode string."""
+ return fh.read(count).decode('utf-8')
+
+
+def read_numpy(fh, byteorder, dtype, count, offsetsize):
+ """Read tag data from file and return as numpy array."""
+ dtype = 'b' if dtype[-1] == 's' else byteorder + dtype[-1]
+ return fh.read_array(dtype, count)
+
+
+def read_colormap(fh, byteorder, dtype, count, offsetsize):
+ """Read ColorMap data from file and return as numpy array."""
+ cmap = fh.read_array(byteorder + dtype[-1], count)
+ cmap.shape = (3, -1)
+ return cmap
+
+
+def read_json(fh, byteorder, dtype, count, offsetsize):
+ """Read JSON tag data from file and return as object."""
+ data = fh.read(count)
+ try:
+ return json.loads(unicode(stripnull(data), 'utf-8'))
+ except ValueError:
+ log.warning('read_json: invalid JSON')
+
+
+def read_mm_header(fh, byteorder, dtype, count, offsetsize):
+ """Read FluoView mm_header tag from file and return as dict."""
+ mmh = fh.read_record(TIFF.MM_HEADER, byteorder=byteorder)
+ mmh = recarray2dict(mmh)
+ mmh['Dimensions'] = [
+ (bytes2str(d[0]).strip(), d[1], d[2], d[3], bytes2str(d[4]).strip())
+ for d in mmh['Dimensions']]
+ d = mmh['GrayChannel']
+ mmh['GrayChannel'] = (
+ bytes2str(d[0]).strip(), d[1], d[2], d[3], bytes2str(d[4]).strip())
+ return mmh
+
+
+def read_mm_stamp(fh, byteorder, dtype, count, offsetsize):
+ """Read FluoView mm_stamp tag from file and return as numpy.ndarray."""
+ return fh.read_array(byteorder + 'f8', 8)
+
+
+def read_uic1tag(fh, byteorder, dtype, count, offsetsize, planecount=None):
+ """Read MetaMorph STK UIC1Tag from file and return as dict.
+
+ Return empty dictionary if planecount is unknown.
+
+ """
+ assert dtype in ('2I', '1I') and byteorder == '<'
+ result = {}
+ if dtype == '2I':
+ # pre MetaMorph 2.5 (not tested)
+ values = fh.read_array('<u4', 2 * count).reshape(count, 2)
+ result = {'ZDistance': values[:, 0] / values[:, 1]}
+ elif planecount:
+ for _ in range(count):
+ tagid = struct.unpack('<I', fh.read(4))[0]
+ if tagid in (28, 29, 37, 40, 41):
+ # silently skip unexpected tags
+ fh.read(4)
+ continue
+ name, value = read_uic_tag(fh, tagid, planecount, offset=True)
+ result[name] = value
+ return result
+
+
+def read_uic2tag(fh, byteorder, dtype, planecount, offsetsize):
+ """Read MetaMorph STK UIC2Tag from file and return as dict."""
+ assert dtype == '2I' and byteorder == '<'
+ values = fh.read_array('<u4', 6 * planecount).reshape(planecount, 6)
+ return {
+ 'ZDistance': values[:, 0] / values[:, 1],
+ 'DateCreated': values[:, 2], # julian days
+ 'TimeCreated': values[:, 3], # milliseconds
+ 'DateModified': values[:, 4], # julian days
+ 'TimeModified': values[:, 5], # milliseconds
+ }
+
+
+def read_uic3tag(fh, byteorder, dtype, planecount, offsetsize):
+ """Read MetaMorph STK UIC3Tag from file and return as dict."""
+ assert dtype == '2I' and byteorder == '<'
+ values = fh.read_array('<u4', 2 * planecount).reshape(planecount, 2)
+ return {'Wavelengths': values[:, 0] / values[:, 1]}
+
+
+def read_uic4tag(fh, byteorder, dtype, planecount, offsetsize):
+ """Read MetaMorph STK UIC4Tag from file and return as dict."""
+ assert dtype == '1I' and byteorder == '<'
+ result = {}
+ while True:
+ tagid = struct.unpack('<H', fh.read(2))[0]
+ if tagid == 0:
+ break
+ name, value = read_uic_tag(fh, tagid, planecount, offset=False)
+ result[name] = value
+ return result
+
+
+def read_uic_tag(fh, tagid, planecount, offset):
+ """Read a single UIC tag value from file and return tag name and value.
+
+ UIC1Tags use an offset.
+
+ """
+
+ def read_int(count=1):
+ value = struct.unpack('<%iI' % count, fh.read(4 * count))
+ return value[0] if count == 1 else value
+
+ try:
+ name, dtype = TIFF.UIC_TAGS[tagid]
+ except IndexError:
+ # unknown tag
+ return '_TagId%i' % tagid, read_int()
+
+ Fraction = TIFF.UIC_TAGS[4][1]
+
+ if offset:
+ pos = fh.tell()
+ if dtype not in (int, None):
+ off = read_int()
+ if off < 8:
+ if dtype is str:
+ return name, ''
+ log.warning("read_uic_tag: invalid offset for tag '%s' (%i)",
+ name, off)
+ return name, off
+ fh.seek(off)
+
+ if dtype is None:
+ # skip
+ name = '_' + name
+ value = read_int()
+ elif dtype is int:
+ # int
+ value = read_int()
+ elif dtype is Fraction:
+ # fraction
+ value = read_int(2)
+ value = value[0] / value[1]
+ elif dtype is julian_datetime:
+ # datetime
+ value = julian_datetime(*read_int(2))
+ elif dtype is read_uic_image_property:
+ # ImagePropertyEx
+ value = read_uic_image_property(fh)
+ elif dtype is str:
+ # pascal string
+ size = read_int()
+ if 0 <= size < 2**10:
+ value = struct.unpack('%is' % size, fh.read(size))[0][:-1]
+ value = bytes2str(stripnull(value))
+ elif offset:
+ value = ''
+ log.warning("read_uic_tag: corrupt string in tag '%s'", name)
+ else:
+ raise ValueError('read_uic_tag: invalid string size %i' % size)
+ elif dtype == '%ip':
+ # sequence of pascal strings
+ value = []
+ for _ in range(planecount):
+ size = read_int()
+ if 0 <= size < 2**10:
+ string = struct.unpack('%is' % size, fh.read(size))[0][:-1]
+ string = bytes2str(stripnull(string))
+ value.append(string)
+ elif offset:
+ log.warning("read_uic_tag: corrupt string in tag '%s'", name)
+ else:
+ raise ValueError('read_uic_tag: invalid string size: %i' %
+ size)
+ else:
+ # struct or numpy type
+ dtype = '<' + dtype
+ if '%i' in dtype:
+ dtype = dtype % planecount
+ if '(' in dtype:
+ # numpy type
+ value = fh.read_array(dtype, 1)[0]
+ if value.shape[-1] == 2:
+ # assume fractions
+ value = value[..., 0] / value[..., 1]
+ else:
+ # struct format
+ value = struct.unpack(dtype, fh.read(struct.calcsize(dtype)))
+ if len(value) == 1:
+ value = value[0]
+
+ if offset:
+ fh.seek(pos + 4)
+
+ return name, value
+
+
+def read_uic_image_property(fh):
+ """Read UIC ImagePropertyEx tag from file and return as dict."""
+ # TODO: test this
+ size = struct.unpack('B', fh.read(1))[0]
+ name = struct.unpack('%is' % size, fh.read(size))[0][:-1]
+ flags, prop = struct.unpack('<IB', fh.read(5))
+ if prop == 1:
+ value = struct.unpack('II', fh.read(8))
+ value = value[0] / value[1]
+ else:
+ size = struct.unpack('B', fh.read(1))[0]
+ value = struct.unpack('%is' % size, fh.read(size))[0]
+ return dict(name=name, flags=flags, value=value)
+
+
+def read_cz_lsminfo(fh, byteorder, dtype, count, offsetsize):
+ """Read CZ_LSMINFO tag from file and return as dict."""
+ assert byteorder == '<'
+ magic_number, structure_size = struct.unpack('<II', fh.read(8))
+ if magic_number not in (50350412, 67127628):
+ raise ValueError('invalid CZ_LSMINFO structure')
+ fh.seek(-8, 1)
+
+ if structure_size < numpy.dtype(TIFF.CZ_LSMINFO).itemsize:
+ # adjust structure according to structure_size
+ lsminfo = []
+ size = 0
+ for name, dtype in TIFF.CZ_LSMINFO:
+ size += numpy.dtype(dtype).itemsize
+ if size > structure_size:
+ break
+ lsminfo.append((name, dtype))
+ else:
+ lsminfo = TIFF.CZ_LSMINFO
+
+ lsminfo = fh.read_record(lsminfo, byteorder=byteorder)
+ lsminfo = recarray2dict(lsminfo)
+
+ # read LSM info subrecords at offsets
+ for name, reader in TIFF.CZ_LSMINFO_READERS.items():
+ if reader is None:
+ continue
+ offset = lsminfo.get('Offset' + name, 0)
+ if offset < 8:
+ continue
+ fh.seek(offset)
+ try:
+ lsminfo[name] = reader(fh)
+ except ValueError:
+ pass
+ return lsminfo
+
+
+def read_lsm_floatpairs(fh):
+ """Read LSM sequence of float pairs from file and return as list."""
+ size = struct.unpack('<i', fh.read(4))[0]
+ return fh.read_array('<2f8', count=size)
+
+
+def read_lsm_positions(fh):
+ """Read LSM positions from file and return as list."""
+ size = struct.unpack('<I', fh.read(4))[0]
+ return fh.read_array('<2f8', count=size)
+
+
+def read_lsm_timestamps(fh):
+ """Read LSM time stamps from file and return as list."""
+ size, count = struct.unpack('<ii', fh.read(8))
+ if size != (8 + 8 * count):
+ log.warning('read_lsm_timestamps: invalid LSM TimeStamps block')
+ return []
+ # return struct.unpack('<%dd' % count, fh.read(8*count))
+ return fh.read_array('<f8', count=count)
+
+
+def read_lsm_eventlist(fh):
+ """Read LSM events from file and return as list of (time, type, text)."""
+ count = struct.unpack('<II', fh.read(8))[1]
+ events = []
+ while count > 0:
+ esize, etime, etype = struct.unpack('<IdI', fh.read(16))
+ etext = bytes2str(stripnull(fh.read(esize - 16)))
+ events.append((etime, etype, etext))
+ count -= 1
+ return events
+
+
+def read_lsm_channelcolors(fh):
+ """Read LSM ChannelColors structure from file and return as dict."""
+ result = {'Mono': False, 'Colors': [], 'ColorNames': []}
+ pos = fh.tell()
+ (size, ncolors, nnames,
+ coffset, noffset, mono) = struct.unpack('<IIIIII', fh.read(24))
+ if ncolors != nnames:
+ log.warning(
+ 'read_lsm_channelcolors: invalid LSM ChannelColors structure')
+ return result
+ result['Mono'] = bool(mono)
+ # Colors
+ fh.seek(pos + coffset)
+ colors = fh.read_array('uint8', count=ncolors * 4).reshape((ncolors, 4))
+ result['Colors'] = colors.tolist()
+ # ColorNames
+ fh.seek(pos + noffset)
+ buffer = fh.read(size - noffset)
+ names = []
+ while len(buffer) > 4:
+ size = struct.unpack('<I', buffer[:4])[0]
+ names.append(bytes2str(buffer[4:3 + size]))
+ buffer = buffer[4 + size:]
+ result['ColorNames'] = names
+ return result
+
+
+def read_lsm_scaninfo(fh):
+ """Read LSM ScanInfo structure from file and return as dict."""
+ block = {}
+ blocks = [block]
+ unpack = struct.unpack
+ if struct.unpack('<I', fh.read(4))[0] != 0x10000000:
+ # not a Recording sub block
+ log.warning('read_lsm_scaninfo: invalid LSM ScanInfo structure')
+ return block
+ fh.read(8)
+ while True:
+ entry, dtype, size = unpack('<III', fh.read(12))
+ if dtype == 2:
+ # ascii
+ value = bytes2str(stripnull(fh.read(size)))
+ elif dtype == 4:
+ # long
+ value = unpack('<i', fh.read(4))[0]
+ elif dtype == 5:
+ # rational
+ value = unpack('<d', fh.read(8))[0]
+ else:
+ value = 0
+ if entry in TIFF.CZ_LSMINFO_SCANINFO_ARRAYS:
+ blocks.append(block)
+ name = TIFF.CZ_LSMINFO_SCANINFO_ARRAYS[entry]
+ newobj = []
+ block[name] = newobj
+ block = newobj
+ elif entry in TIFF.CZ_LSMINFO_SCANINFO_STRUCTS:
+ blocks.append(block)
+ newobj = {}
+ block.append(newobj)
+ block = newobj
+ elif entry in TIFF.CZ_LSMINFO_SCANINFO_ATTRIBUTES:
+ name = TIFF.CZ_LSMINFO_SCANINFO_ATTRIBUTES[entry]
+ block[name] = value
+ elif entry == 0xFFFFFFFF:
+ # end sub block
+ block = blocks.pop()
+ else:
+ # unknown entry
+ block['Entry0x%x' % entry] = value
+ if not blocks:
+ break
+ return block
+
+
+def read_sis(fh, byteorder, dtype, count, offsetsize):
+ """Read OlympusSIS structure and return as dict.
+
+ No specification is avaliable. Only few fields are known.
+
+ """
+ result = {}
+
+ (magic, _, minute, hour, day, month, year, _, name, tagcount
+ ) = struct.unpack('<4s6shhhhh6s32sh', fh.read(60))
+
+ if magic != b'SIS0':
+ raise ValueError('invalid OlympusSIS structure')
+
+ result['name'] = bytes2str(stripnull(name))
+ try:
+ result['datetime'] = datetime.datetime(1900 + year, month + 1, day,
+ hour, minute)
+ except ValueError:
+ pass
+
+ data = fh.read(8 * tagcount)
+ for i in range(0, tagcount * 8, 8):
+ tagtype, count, offset = struct.unpack('<hhI', data[i: i + 8])
+ fh.seek(offset)
+ if tagtype == 1:
+ # general data
+ (_, lenexp, xcal, ycal, _, mag, _, camname, pictype,
+ ) = struct.unpack('<10shdd8sd2s34s32s', fh.read(112)) # 220
+ m = math.pow(10, lenexp)
+ result['pixelsizex'] = xcal * m
+ result['pixelsizey'] = ycal * m
+ result['magnification'] = mag
+ result['cameraname'] = bytes2str(stripnull(camname))
+ result['picturetype'] = bytes2str(stripnull(pictype))
+ elif tagtype == 10:
+ # channel data
+ continue
+ # TODO: does not seem to work?
+ # (length, _, exptime, emv, _, camname, _, mictype,
+ # ) = struct.unpack('<h22sId4s32s48s32s', fh.read(152)) # 720
+ # result['exposuretime'] = exptime
+ # result['emvoltage'] = emv
+ # result['cameraname2'] = bytes2str(stripnull(camname))
+ # result['microscopename'] = bytes2str(stripnull(mictype))
+
+ return result
+
+
+def read_sis_ini(fh, byteorder, dtype, count, offsetsize):
+ """Read OlympusSIS INI string and return as dict."""
+ inistr = fh.read(count)
+ inistr = bytes2str(stripnull(inistr))
+ try:
+ return olympusini_metadata(inistr)
+ except Exception as exc:
+ log.warning('olympusini_metadata: %s: %s', exc.__class__.__name__, exc)
+ return {}
+
+
+def read_tvips_header(fh, byteorder, dtype, count, offsetsize):
+ """Read TVIPS EM-MENU headers and return as dict."""
+ result = {}
+ header = fh.read_record(TIFF.TVIPS_HEADER_V1, byteorder=byteorder)
+ for name, typestr in TIFF.TVIPS_HEADER_V1:
+ result[name] = header[name].tolist()
+ if header['Version'] == 2:
+ header = fh.read_record(TIFF.TVIPS_HEADER_V2, byteorder=byteorder)
+ if header['Magic'] != int(0xAAAAAAAA):
+ log.warning('read_tvips_header: invalid TVIPS v2 magic number')
+ return {}
+ # decode utf16 strings
+ for name, typestr in TIFF.TVIPS_HEADER_V2:
+ if typestr.startswith('V'):
+ s = header[name].tostring().decode('utf16', errors='ignore')
+ result[name] = stripnull(s, null='\0')
+ else:
+ result[name] = header[name].tolist()
+ # convert nm to m
+ for axis in 'XY':
+ header['PhysicalPixelSize' + axis] /= 1e9
+ header['PixelSize' + axis] /= 1e9
+ elif header.version != 1:
+ log.warning('read_tvips_header: unknown TVIPS header version')
+ return {}
+ return result
+
+
+def read_fei_metadata(fh, byteorder, dtype, count, offsetsize):
+ """Read FEI SFEG/HELIOS headers and return as dict."""
+ result = {}
+ section = {}
+ data = bytes2str(stripnull(fh.read(count)))
+ for line in data.splitlines():
+ line = line.strip()
+ if line.startswith('['):
+ section = {}
+ result[line[1:-1]] = section
+ continue
+ try:
+ key, value = line.split('=')
+ except ValueError:
+ continue
+ section[key] = astype(value)
+ return result
+
+
+def read_cz_sem(fh, byteorder, dtype, count, offsetsize):
+ """Read Zeiss SEM tag and return as dict.
+
+ See https://sourceforge.net/p/gwyddion/mailman/message/29275000/ for
+ unnamed values.
+
+ """
+ result = {'': ()}
+ key = None
+ data = bytes2str(stripnull(fh.read(count)))
+ for line in data.splitlines():
+ if line.isupper():
+ key = line.lower()
+ elif key:
+ try:
+ name, value = line.split('=')
+ except ValueError:
+ try:
+ name, value = line.split(':', 1)
+ except Exception:
+ continue
+ value = value.strip()
+ unit = ''
+ try:
+ v, u = value.split()
+ number = astype(v, (int, float))
+ if number != v:
+ value = number
+ unit = u
+ except Exception:
+ number = astype(value, (int, float))
+ if number != value:
+ value = number
+ if value in ('No', 'Off'):
+ value = False
+ elif value in ('Yes', 'On'):
+ value = True
+ result[key] = (name.strip(), value)
+ if unit:
+ result[key] += (unit,)
+ key = None
+ else:
+ result[''] += (astype(line, (int, float)),)
+ return result
+
+
+def read_nih_image_header(fh, byteorder, dtype, count, offsetsize):
+ """Read NIH_IMAGE_HEADER tag from file and return as dict."""
+ a = fh.read_record(TIFF.NIH_IMAGE_HEADER, byteorder=byteorder)
+ a = a.newbyteorder(byteorder)
+ a = recarray2dict(a)
+ a['XUnit'] = a['XUnit'][:a['XUnitSize']]
+ a['UM'] = a['UM'][:a['UMsize']]
+ return a
+
+
+def read_scanimage_metadata(fh):
+ """Read ScanImage BigTIFF v3 static and ROI metadata from open file.
+
+ Return non-varying frame data as dict and ROI group data as JSON.
+
+ The settings can be used to read image data and metadata without parsing
+ the TIFF file.
+
+ Raise ValueError if file does not contain valid ScanImage v3 metadata.
+
+ """
+ fh.seek(0)
+ try:
+ byteorder, version = struct.unpack('<2sH', fh.read(4))
+ if byteorder != b'II' or version != 43:
+ raise Exception
+ fh.seek(16)
+ magic, version, size0, size1 = struct.unpack('<IIII', fh.read(16))
+ if magic != 117637889 or version != 3:
+ raise Exception
+ except Exception:
+ raise ValueError('not a ScanImage BigTIFF v3 file')
+
+ frame_data = matlabstr2py(bytes2str(fh.read(size0)[:-1]))
+ roi_data = read_json(fh, '<', None, size1, None) if size1 > 1 else {}
+ return frame_data, roi_data
+
+
+def read_micromanager_metadata(fh):
+ """Read MicroManager non-TIFF settings from open file and return as dict.
+
+ The settings can be used to read image data without parsing the TIFF file.
+
+ Raise ValueError if the file does not contain valid MicroManager metadata.
+
+ """
+ fh.seek(0)
+ try:
+ byteorder = {b'II': '<', b'MM': '>'}[fh.read(2)]
+ except IndexError:
+ raise ValueError('not a MicroManager TIFF file')
+
+ result = {}
+ fh.seek(8)
+ (
+ index_header,
+ index_offset,
+ display_header,
+ display_offset,
+ comments_header,
+ comments_offset,
+ summary_header,
+ summary_length
+ ) = struct.unpack(byteorder + 'IIIIIIII', fh.read(32))
+
+ if summary_header != 2355492:
+ raise ValueError('invalid MicroManager summary header')
+ result['Summary'] = read_json(fh, byteorder, None, summary_length, None)
+
+ if index_header != 54773648:
+ raise ValueError('invalid MicroManager index header')
+ fh.seek(index_offset)
+ header, count = struct.unpack(byteorder + 'II', fh.read(8))
+ if header != 3453623:
+ raise ValueError('invalid MicroManager index header')
+ data = struct.unpack(byteorder + 'IIIII' * count, fh.read(20 * count))
+ result['IndexMap'] = {
+ 'Channel': data[::5],
+ 'Slice': data[1::5],
+ 'Frame': data[2::5],
+ 'Position': data[3::5],
+ 'Offset': data[4::5],
+ }
+
+ if display_header != 483765892:
+ raise ValueError('invalid MicroManager display header')
+ fh.seek(display_offset)
+ header, count = struct.unpack(byteorder + 'II', fh.read(8))
+ if header != 347834724:
+ raise ValueError('invalid MicroManager display header')
+ result['DisplaySettings'] = read_json(fh, byteorder, None, count, None)
+
+ if comments_header != 99384722:
+ raise ValueError('invalid MicroManager comments header')
+ fh.seek(comments_offset)
+ header, count = struct.unpack(byteorder + 'II', fh.read(8))
+ if header != 84720485:
+ raise ValueError('invalid MicroManager comments header')
+ result['Comments'] = read_json(fh, byteorder, None, count, None)
+
+ return result
+
+
+def read_metaseries_catalog(fh):
+ """Read MetaSeries non-TIFF hint catalog from file.
+
+ Raise ValueError if the file does not contain a valid hint catalog.
+
+ """
+ # TODO: implement read_metaseries_catalog
+ raise NotImplementedError()
+
+
+def imagej_metadata_tag(metadata, byteorder):
+ """Return IJMetadata and IJMetadataByteCounts tags from metadata dict.
+
+ The tags can be passed to the TiffWriter.save function as extratags.
+
+ The metadata dict may contain the following keys and values:
+
+ Info : str
+ Human-readable information as string.
+ Labels : sequence of str
+ Human-readable labels for each channel.
+ Ranges : sequence of doubles
+ Lower and upper values for each channel.
+ LUTs : sequence of (3, 256) uint8 ndarrays
+ Color palettes for each channel.
+ Plot : bytes
+ Undocumented ImageJ internal format.
+ ROI: bytes
+ Undocumented ImageJ internal region of interest format.
+ Overlays : bytes
+ Undocumented ImageJ internal format.
+
+ """
+ header = [{'>': b'IJIJ', '<': b'JIJI'}[byteorder]]
+ bytecounts = [0]
+ body = []
+
+ def _string(data, byteorder):
+ return data.encode('utf-16' + {'>': 'be', '<': 'le'}[byteorder])
+
+ def _doubles(data, byteorder):
+ return struct.pack(byteorder + ('d' * len(data)), *data)
+
+ def _ndarray(data, byteorder):
+ return data.tobytes()
+
+ def _bytes(data, byteorder):
+ return data
+
+ metadata_types = (
+ ('Info', b'info', 1, _string),
+ ('Labels', b'labl', None, _string),
+ ('Ranges', b'rang', 1, _doubles),
+ ('LUTs', b'luts', None, _ndarray),
+ ('Plot', b'plot', 1, _bytes),
+ ('ROI', b'roi ', 1, _bytes),
+ ('Overlays', b'over', None, _bytes),
+ )
+
+ for key, mtype, count, func in metadata_types:
+ if key.lower() in metadata:
+ key = key.lower()
+ elif key not in metadata:
+ continue
+ if byteorder == '<':
+ mtype = mtype[::-1]
+ values = metadata[key]
+ if count is None:
+ count = len(values)
+ else:
+ values = [values]
+ header.append(mtype + struct.pack(byteorder + 'I', count))
+ for value in values:
+ data = func(value, byteorder)
+ body.append(data)
+ bytecounts.append(len(data))
+
+ if not body:
+ return ()
+ body = b''.join(body)
+ header = b''.join(header)
+ data = header + body
+ bytecounts[0] = len(header)
+ bytecounts = struct.pack(byteorder + ('I' * len(bytecounts)), *bytecounts)
+ return (
+ (50839, 'B', len(data), data, True),
+ (50838, 'I', len(bytecounts) // 4, bytecounts, True)
+ )
+
+
+def imagej_metadata(data, bytecounts, byteorder):
+ """Return IJMetadata tag value as dict.
+
+ The 'Info' string can have multiple formats, e.g. OIF or ScanImage,
+ that might be parsed into dicts using the matlabstr2py or
+ oiffile.SettingsFile functions.
+
+ """
+
+ def _string(data, byteorder):
+ return data.decode('utf-16' + {'>': 'be', '<': 'le'}[byteorder])
+
+ def _doubles(data, byteorder):
+ return struct.unpack(byteorder + ('d' * (len(data) // 8)), data)
+
+ def _lut(data, byteorder):
+ return numpy.frombuffer(data, 'uint8').reshape(-1, 256)
+
+ def _bytes(data, byteorder):
+ return data
+
+ # big-endian
+ metadata_types = {
+ b'info': ('Info', _string),
+ b'labl': ('Labels', _string),
+ b'rang': ('Ranges', _doubles),
+ b'luts': ('LUTs', _lut),
+ b'plot': ('Plots', _bytes),
+ b'roi ': ('ROI', _bytes),
+ b'over': ('Overlays', _bytes),
+ }
+ # little-endian
+ metadata_types.update({k[::-1]: v for k, v in metadata_types.items()})
+
+ if not bytecounts:
+ raise ValueError('no ImageJ metadata')
+
+ if not data[:4] in (b'IJIJ', b'JIJI'):
+ raise ValueError('invalid ImageJ metadata')
+
+ header_size = bytecounts[0]
+ if header_size < 12 or header_size > 804:
+ raise ValueError('invalid ImageJ metadata header size')
+
+ ntypes = (header_size - 4) // 8
+ header = struct.unpack(byteorder + '4sI' * ntypes, data[4: 4 + ntypes * 8])
+ pos = 4 + ntypes * 8
+ counter = 0
+ result = {}
+ for mtype, count in zip(header[::2], header[1::2]):
+ values = []
+ name, func = metadata_types.get(mtype, (bytes2str(mtype), read_bytes))
+ for _ in range(count):
+ counter += 1
+ pos1 = pos + bytecounts[counter]
+ values.append(func(data[pos:pos1], byteorder))
+ pos = pos1
+ result[name.strip()] = values[0] if count == 1 else values
+ return result
+
+
+def imagej_description_metadata(description):
+ """Return metatata from ImageJ image description as dict.
+
+ Raise ValueError if not a valid ImageJ description.
+
+ >>> description = 'ImageJ=1.11a\\nimages=510\\nhyperstack=true\\n'
+ >>> imagej_description_metadata(description) # doctest: +SKIP
+ {'ImageJ': '1.11a', 'images': 510, 'hyperstack': True}
+
+ """
+
+ def _bool(val):
+ return {'true': True, 'false': False}[val.lower()]
+
+ result = {}
+ for line in description.splitlines():
+ try:
+ key, val = line.split('=')
+ except Exception:
+ continue
+ key = key.strip()
+ val = val.strip()
+ for dtype in (int, float, _bool):
+ try:
+ val = dtype(val)
+ break
+ except Exception:
+ pass
+ result[key] = val
+
+ if 'ImageJ' not in result:
+ raise ValueError('not a ImageJ image description')
+ return result
+
+
+def imagej_description(shape, rgb=None, colormaped=False, version=None,
+ hyperstack=None, mode=None, loop=None, **kwargs):
+ """Return ImageJ image description from data shape.
+
+ ImageJ can handle up to 6 dimensions in order TZCYXS.
+
+ >>> imagej_description((51, 5, 2, 196, 171)) # doctest: +SKIP
+ ImageJ=1.11a
+ images=510
+ channels=2
+ slices=5
+ frames=51
+ hyperstack=true
+ mode=grayscale
+ loop=false
+
+ """
+ if colormaped:
+ raise NotImplementedError('ImageJ colormapping not supported')
+ if version is None:
+ version = '1.11a'
+ shape = imagej_shape(shape, rgb=rgb)
+ rgb = shape[-1] in (3, 4)
+
+ result = ['ImageJ=%s' % version]
+ append = []
+ result.append('images=%i' % product(shape[:-3]))
+ if hyperstack is None:
+ hyperstack = True
+ append.append('hyperstack=true')
+ else:
+ append.append('hyperstack=%s' % bool(hyperstack))
+ if shape[2] > 1:
+ result.append('channels=%i' % shape[2])
+ if mode is None and not rgb:
+ mode = 'grayscale'
+ if hyperstack and mode:
+ append.append('mode=%s' % mode)
+ if shape[1] > 1:
+ result.append('slices=%i' % shape[1])
+ if shape[0] > 1:
+ result.append('frames=%i' % shape[0])
+ if loop is None:
+ append.append('loop=false')
+ if loop is not None:
+ append.append('loop=%s' % bool(loop))
+ for key, value in kwargs.items():
+ append.append('%s=%s' % (key.lower(), value))
+
+ return '\n'.join(result + append + [''])
+
+
+def imagej_shape(shape, rgb=None):
+ """Return shape normalized to 6D ImageJ hyperstack TZCYXS.
+
+ Raise ValueError if not a valid ImageJ hyperstack shape.
+
+ >>> imagej_shape((2, 3, 4, 5, 3), False)
+ (2, 3, 4, 5, 3, 1)
+
+ """
+ shape = tuple(int(i) for i in shape)
+ ndim = len(shape)
+ if 1 > ndim > 6:
+ raise ValueError('invalid ImageJ hyperstack: not 2 to 6 dimensional')
+ if rgb is None:
+ rgb = shape[-1] in (3, 4) and ndim > 2
+ if rgb and shape[-1] not in (3, 4):
+ raise ValueError('invalid ImageJ hyperstack: not a RGB image')
+ if not rgb and ndim == 6 and shape[-1] != 1:
+ raise ValueError('invalid ImageJ hyperstack: not a non-RGB image')
+ if rgb or shape[-1] == 1:
+ return (1, ) * (6 - ndim) + shape
+ return (1, ) * (5 - ndim) + shape + (1,)
+
+
+def json_description(shape, **metadata):
+ """Return JSON image description from data shape and other metadata.
+
+ Return UTF-8 encoded JSON.
+
+ >>> json_description((256, 256, 3), axes='YXS') # doctest: +SKIP
+ b'{"shape": [256, 256, 3], "axes": "YXS"}'
+
+ """
+ metadata.update(shape=shape)
+ return json.dumps(metadata) # .encode('utf-8')
+
+
+def json_description_metadata(description):
+ """Return metatata from JSON formated image description as dict.
+
+ Raise ValuError if description is of unknown format.
+
+ >>> description = '{"shape": [256, 256, 3], "axes": "YXS"}'
+ >>> json_description_metadata(description) # doctest: +SKIP
+ {'shape': [256, 256, 3], 'axes': 'YXS'}
+ >>> json_description_metadata('shape=(256, 256, 3)')
+ {'shape': (256, 256, 3)}
+
+ """
+ if description[:6] == 'shape=':
+ # old-style 'shaped' description; not JSON
+ shape = tuple(int(i) for i in description[7:-1].split(','))
+ return dict(shape=shape)
+ if description[:1] == '{' and description[-1:] == '}':
+ # JSON description
+ return json.loads(description)
+ raise ValueError('invalid JSON image description', description)
+
+
+def fluoview_description_metadata(description, ignoresections=None):
+ """Return metatata from FluoView image description as dict.
+
+ The FluoView image description format is unspecified. Expect failures.
+
+ >>> descr = ('[Intensity Mapping]\\nMap Ch0: Range=00000 to 02047\\n'
+ ... '[Intensity Mapping End]')
+ >>> fluoview_description_metadata(descr)
+ {'Intensity Mapping': {'Map Ch0: Range': '00000 to 02047'}}
+
+ """
+ if not description.startswith('['):
+ raise ValueError('invalid FluoView image description')
+ if ignoresections is None:
+ ignoresections = {'Region Info (Fields)', 'Protocol Description'}
+
+ result = {}
+ sections = [result]
+ comment = False
+ for line in description.splitlines():
+ if not comment:
+ line = line.strip()
+ if not line:
+ continue
+ if line[0] == '[':
+ if line[-5:] == ' End]':
+ # close section
+ del sections[-1]
+ section = sections[-1]
+ name = line[1:-5]
+ if comment:
+ section[name] = '\n'.join(section[name])
+ if name[:4] == 'LUT ':
+ a = numpy.array(section[name], dtype='uint8')
+ a.shape = -1, 3
+ section[name] = a
+ continue
+ # new section
+ comment = False
+ name = line[1:-1]
+ if name[:4] == 'LUT ':
+ section = []
+ elif name in ignoresections:
+ section = []
+ comment = True
+ else:
+ section = {}
+ sections.append(section)
+ result[name] = section
+ continue
+ # add entry
+ if comment:
+ section.append(line)
+ continue
+ line = line.split('=', 1)
+ if len(line) == 1:
+ section[line[0].strip()] = None
+ continue
+ key, value = line
+ if key[:4] == 'RGB ':
+ section.extend(int(rgb) for rgb in value.split())
+ else:
+ section[key.strip()] = astype(value.strip())
+ return result
+
+
+def pilatus_description_metadata(description):
+ """Return metatata from Pilatus image description as dict.
+
+ Return metadata from Pilatus pixel array detectors by Dectris, created
+ by camserver or TVX software.
+
+ >>> pilatus_description_metadata('# Pixel_size 172e-6 m x 172e-6 m')
+ {'Pixel_size': (0.000172, 0.000172)}
+
+ """
+ result = {}
+ if not description.startswith('# '):
+ return result
+ for c in '#:=,()':
+ description = description.replace(c, ' ')
+ for line in description.split('\n'):
+ if line[:2] != ' ':
+ continue
+ line = line.split()
+ name = line[0]
+ if line[0] not in TIFF.PILATUS_HEADER:
+ try:
+ result['DateTime'] = datetime.datetime.strptime(
+ ' '.join(line), '%Y-%m-%dT%H %M %S.%f')
+ except Exception:
+ result[name] = ' '.join(line[1:])
+ continue
+ indices, dtype = TIFF.PILATUS_HEADER[line[0]]
+ if isinstance(indices[0], slice):
+ # assumes one slice
+ values = line[indices[0]]
+ else:
+ values = [line[i] for i in indices]
+ if dtype is float and values[0] == 'not':
+ values = ['NaN']
+ values = tuple(dtype(v) for v in values)
+ if dtype == str:
+ values = ' '.join(values)
+ elif len(values) == 1:
+ values = values[0]
+ result[name] = values
+ return result
+
+
+def svs_description_metadata(description):
+ """Return metatata from Aperio image description as dict.
+
+ The Aperio image description format is unspecified. Expect failures.
+
+ >>> svs_description_metadata('Aperio Image Library v1.0')
+ {'Aperio Image Library': 'v1.0'}
+
+ """
+ if not description.startswith('Aperio Image Library '):
+ raise ValueError('invalid Aperio image description')
+ result = {}
+ lines = description.split('\n')
+ key, value = lines[0].strip().rsplit(None, 1) # 'Aperio Image Library'
+ result[key.strip()] = value.strip()
+ if len(lines) == 1:
+ return result
+ items = lines[1].split('|')
+ result[''] = items[0].strip() # TODO: parse this?
+ for item in items[1:]:
+ key, value = item.split(' = ')
+ result[key.strip()] = astype(value.strip())
+ return result
+
+
+def stk_description_metadata(description):
+ """Return metadata from MetaMorph image description as list of dict.
+
+ The MetaMorph image description format is unspecified. Expect failures.
+
+ """
+ description = description.strip()
+ if not description:
+ return []
+ try:
+ description = bytes2str(description)
+ except UnicodeDecodeError as exc:
+ log.warning('stk_description_metadata: %s: %s',
+ exc.__class__.__name__, exc)
+ return []
+ result = []
+ for plane in description.split('\x00'):
+ d = {}
+ for line in plane.split('\r\n'):
+ line = line.split(':', 1)
+ if len(line) > 1:
+ name, value = line
+ d[name.strip()] = astype(value.strip())
+ else:
+ value = line[0].strip()
+ if value:
+ if '' in d:
+ d[''].append(value)
+ else:
+ d[''] = [value]
+ result.append(d)
+ return result
+
+
+def metaseries_description_metadata(description):
+ """Return metatata from MetaSeries image description as dict."""
+ if not description.startswith('<MetaData>'):
+ raise ValueError('invalid MetaSeries image description')
+
+ from xml.etree import cElementTree as etree # delayed import
+
+ root = etree.fromstring(description)
+ types = {
+ 'float': float,
+ 'int': int,
+ 'bool': lambda x: asbool(x, 'on', 'off'),
+ }
+
+ def parse(root, result):
+ # recursive
+ for child in root:
+ attrib = child.attrib
+ if not attrib:
+ result[child.tag] = parse(child, {})
+ continue
+ if 'id' in attrib:
+ i = attrib['id']
+ t = attrib['type']
+ v = attrib['value']
+ if t in types:
+ result[i] = types[t](v)
+ else:
+ result[i] = v
+ return result
+
+ adict = parse(root, {})
+ if 'Description' in adict:
+ adict['Description'] = adict['Description'].replace(' ', '\n')
+ return adict
+
+
+def scanimage_description_metadata(description):
+ """Return metatata from ScanImage image description as dict."""
+ return matlabstr2py(description)
+
+
+def scanimage_artist_metadata(artist):
+ """Return metatata from ScanImage artist tag as dict."""
+ try:
+ return json.loads(artist)
+ except ValueError as exc:
+ log.warning('scanimage_artist_metadata: %s: %s',
+ exc.__class__.__name__, exc)
+
+
+def olympusini_metadata(inistr):
+ """Return OlympusSIS metadata from INI string.
+
+ No documentation is available.
+
+ """
+
+ def keyindex(key):
+ # split key into name and index
+ index = 0
+ i = len(key.rstrip('0123456789'))
+ if i < len(key):
+ index = int(key[i:]) - 1
+ key = key[:i]
+ return key, index
+
+ result = {}
+ bands = []
+ zpos = None
+ tpos = None
+ for line in inistr.splitlines():
+ line = line.strip()
+ if line == '' or line[0] == ';':
+ continue
+ if line[0] == '[' and line[-1] == ']':
+ section_name = line[1:-1]
+ result[section_name] = section = {}
+ if section_name == 'Dimension':
+ result['axes'] = axes = []
+ result['shape'] = shape = []
+ elif section_name == 'ASD':
+ result[section_name] = []
+ elif section_name == 'Z':
+ if 'Dimension' in result:
+ result[section_name]['ZPos'] = zpos = []
+ elif section_name == 'Time':
+ if 'Dimension' in result:
+ result[section_name]['TimePos'] = tpos = []
+ elif section_name == 'Band':
+ nbands = result['Dimension']['Band']
+ bands = [{'LUT': []} for i in range(nbands)]
+ result[section_name] = bands
+ iband = 0
+ else:
+ key, value = line.split('=')
+ if value.strip() == '':
+ value = None
+ elif ',' in value:
+ value = tuple(astype(v) for v in value.split(','))
+ else:
+ value = astype(value)
+
+ if section_name == 'Dimension':
+ section[key] = value
+ axes.append(key)
+ shape.append(value)
+ elif section_name == 'ASD':
+ if key == 'Count':
+ result['ASD'] = [{}] * value
+ else:
+ key, index = keyindex(key)
+ result['ASD'][index][key] = value
+ elif section_name == 'Band':
+ if key[:3] == 'LUT':
+ lut = bands[iband]['LUT']
+ value = struct.pack('<I', value)
+ lut.append(
+ [ord(value[0:1]), ord(value[1:2]), ord(value[2:3])])
+ else:
+ key, iband = keyindex(key)
+ bands[iband][key] = value
+ elif key[:4] == 'ZPos' and zpos is not None:
+ zpos.append(value)
+ elif key[:7] == 'TimePos' and tpos is not None:
+ tpos.append(value)
+ else:
+ section[key] = value
+
+ if 'axes' in result:
+ sisaxes = {'Band': 'C'}
+ axes = []
+ shape = []
+ for i, x in zip(result['shape'], result['axes']):
+ if i > 1:
+ axes.append(sisaxes.get(x, x[0].upper()))
+ shape.append(i)
+ result['axes'] = ''.join(axes)
+ result['shape'] = tuple(shape)
+ try:
+ result['Z']['ZPos'] = numpy.array(
+ result['Z']['ZPos'][:result['Dimension']['Z']], 'float64')
+ except Exception:
+ pass
+ try:
+ result['Time']['TimePos'] = numpy.array(
+ result['Time']['TimePos'][:result['Dimension']['Time']], 'int32')
+ except Exception:
+ pass
+ for band in bands:
+ band['LUT'] = numpy.array(band['LUT'], 'uint8')
+ return result
+
+
+def tile_decode(tile, tileindex, tileshape, tiledshape,
+ lsb2msb, decompress, unpack, unpredict, nodata, out):
+ """Decode tile segment bytes into 5D output array."""
+ _, imagedepth, imagelength, imagewidth, _ = out.shape
+ tileddepth, tiledlength, tiledwidth = tiledshape
+ tiledepth, tilelength, tilewidth, samples = tileshape
+ tilesize = tiledepth * tilelength * tilewidth * samples
+ pl = tileindex // (tiledwidth * tiledlength * tileddepth)
+ td = (tileindex // (tiledwidth * tiledlength)) % tileddepth * tiledepth
+ tl = (tileindex // tiledwidth) % tiledlength * tilelength
+ tw = tileindex % tiledwidth * tilewidth
+
+ if tile is None:
+ out[pl,
+ td: td + tiledepth,
+ tl: tl + tilelength,
+ tw: tw + tilewidth] = nodata
+ return
+
+ if lsb2msb:
+ tile = bitorder_decode(tile, out=tile)
+ tile = decompress(tile)
+ tile = unpack(tile)
+ # decompression / unpacking might return too many bytes
+ tile = tile[:tilesize]
+ try:
+ # complete tile according to TIFF specification
+ tile.shape = tileshape
+ except ValueError:
+ # tile fills remaining space; found in some JPEG compressed slides
+ s = (
+ min(imagedepth - td, tiledepth),
+ min(imagelength - tl, tilelength),
+ min(imagewidth - tw, tilewidth),
+ samples,
+ )
+ try:
+ tile.shape = s
+ except ValueError:
+ # incomplete tile; see gdal issue #1179
+ log.warning('tile_decode: incomplete tile %s %s',
+ tile.shape, tileshape)
+ t = numpy.zeros(tilesize, tile.dtype)
+ s = min(tile.size, tilesize)
+ t[:s] = tile[:s]
+ tile = t.reshape(tileshape)
+ tile = unpredict(tile, axis=-2, out=tile)
+ out[pl,
+ td: td + tiledepth,
+ tl: tl + tilelength,
+ tw: tw + tilewidth] = tile[:imagedepth - td,
+ :imagelength - tl,
+ :imagewidth - tw]
+
+
+def unpack_rgb(data, dtype=None, bitspersample=None, rescale=True):
+ """Return array from byte string containing packed samples.
+
+ Use to unpack RGB565 or RGB555 to RGB888 format.
+
+ Parameters
+ ----------
+ data : byte str
+ The data to be decoded. Samples in each pixel are stored consecutively.
+ Pixels are aligned to 8, 16, or 32 bit boundaries.
+ dtype : numpy.dtype
+ The sample data type. The byteorder applies also to the data stream.
+ bitspersample : tuple
+ Number of bits for each sample in a pixel.
+ rescale : bool
+ Upscale samples to the number of bits in dtype.
+
+ Returns
+ -------
+ numpy.ndarray
+ Flattened array of unpacked samples of native dtype.
+
+ Examples
+ --------
+ >>> data = struct.pack('BBBB', 0x21, 0x08, 0xff, 0xff)
+ >>> print(unpack_rgb(data, '<B', (5, 6, 5), False))
+ [ 1 1 1 31 63 31]
+ >>> print(unpack_rgb(data, '<B', (5, 6, 5)))
+ [ 8 4 8 255 255 255]
+ >>> print(unpack_rgb(data, '<B', (5, 5, 5)))
+ [ 16 8 8 255 255 255]
+
+ """
+ if bitspersample is None:
+ bitspersample = (5, 6, 5)
+ if dtype is None:
+ dtype = '<B'
+ dtype = numpy.dtype(dtype)
+ bits = int(numpy.sum(bitspersample))
+ if not (
+ bits <= 32 and all(i <= dtype.itemsize * 8 for i in bitspersample)
+ ):
+ raise ValueError('sample size not supported: %s' % str(bitspersample))
+ dt = next(i for i in 'BHI' if numpy.dtype(i).itemsize * 8 >= bits)
+ data = numpy.frombuffer(data, dtype.byteorder + dt)
+ result = numpy.empty((data.size, len(bitspersample)), dtype.char)
+ for i, bps in enumerate(bitspersample):
+ t = data >> int(numpy.sum(bitspersample[i + 1:]))
+ t &= int('0b' + '1' * bps, 2)
+ if rescale:
+ o = ((dtype.itemsize * 8) // bps + 1) * bps
+ if o > data.dtype.itemsize * 8:
+ t = t.astype('I')
+ t *= (2**o - 1) // (2**bps - 1)
+ t //= 2**(o - (dtype.itemsize * 8))
+ result[:, i] = t
+ return result.reshape(-1)
+
+
+def delta_encode(data, axis=-1, out=None):
+ """Encode Delta."""
+ if isinstance(data, (bytes, bytearray)):
+ data = numpy.frombuffer(data, dtype='u1')
+ diff = numpy.diff(data, axis=0)
+ return numpy.insert(diff, 0, data[0]).tobytes()
+
+ dtype = data.dtype
+ if dtype.kind == 'f':
+ data = data.view('u%i' % dtype.itemsize)
+
+ diff = numpy.diff(data, axis=axis)
+ key = [slice(None)] * data.ndim
+ key[axis] = 0
+ diff = numpy.insert(diff, 0, data[tuple(key)], axis=axis)
+
+ if dtype.kind == 'f':
+ return diff.view(dtype)
+ return diff
+
+
+def delta_decode(data, axis=-1, out=None):
+ """Decode Delta."""
+ if out is not None and not out.flags.writeable:
+ out = None
+ if isinstance(data, (bytes, bytearray)):
+ data = numpy.frombuffer(data, dtype='u1')
+ return numpy.cumsum(data, axis=0, dtype='u1', out=out).tobytes()
+ if data.dtype.kind == 'f':
+ view = data.view('u%i' % data.dtype.itemsize)
+ view = numpy.cumsum(view, axis=axis, dtype=view.dtype)
+ return view.view(data.dtype)
+ return numpy.cumsum(data, axis=axis, dtype=data.dtype, out=out)
+
+
+def bitorder_decode(data, out=None, _bitorder=[]):
+ """Reverse bits in each byte of byte string or numpy array.
+
+ Decode data where pixels with lower column values are stored in the
+ lower-order bits of the bytes (TIFF FillOrder is LSB2MSB).
+
+ Parameters
+ ----------
+ data : byte string or ndarray
+ The data to be bit reversed. If byte string, a new bit-reversed byte
+ string is returned. Numpy arrays are bit-reversed in-place.
+
+ Examples
+ --------
+ >>> bitorder_decode(b'\\x01\\x64')
+ b'\\x80&'
+ >>> data = numpy.array([1, 666], dtype='uint16')
+ >>> bitorder_decode(data)
+ >>> data
+ array([ 128, 16473], dtype=uint16)
+
+ """
+ if not _bitorder:
+ _bitorder.append(
+ b'\x00\x80@\xc0 \xa0`\xe0\x10\x90P\xd00\xb0p\xf0\x08\x88H\xc8('
+ b'\xa8h\xe8\x18\x98X\xd88\xb8x\xf8\x04\x84D\xc4$\xa4d\xe4\x14'
+ b'\x94T\xd44\xb4t\xf4\x0c\x8cL\xcc,\xacl\xec\x1c\x9c\\\xdc<\xbc|'
+ b'\xfc\x02\x82B\xc2"\xa2b\xe2\x12\x92R\xd22\xb2r\xf2\n\x8aJ\xca*'
+ b'\xaaj\xea\x1a\x9aZ\xda:\xbaz\xfa\x06\x86F\xc6&\xa6f\xe6\x16'
+ b'\x96V\xd66\xb6v\xf6\x0e\x8eN\xce.\xaen\xee\x1e\x9e^\xde>\xbe~'
+ b'\xfe\x01\x81A\xc1!\xa1a\xe1\x11\x91Q\xd11\xb1q\xf1\t\x89I\xc9)'
+ b'\xa9i\xe9\x19\x99Y\xd99\xb9y\xf9\x05\x85E\xc5%\xa5e\xe5\x15'
+ b'\x95U\xd55\xb5u\xf5\r\x8dM\xcd-\xadm\xed\x1d\x9d]\xdd=\xbd}'
+ b'\xfd\x03\x83C\xc3#\xa3c\xe3\x13\x93S\xd33\xb3s\xf3\x0b\x8bK'
+ b'\xcb+\xabk\xeb\x1b\x9b[\xdb;\xbb{\xfb\x07\x87G\xc7\'\xa7g\xe7'
+ b'\x17\x97W\xd77\xb7w\xf7\x0f\x8fO\xcf/\xafo\xef\x1f\x9f_'
+ b'\xdf?\xbf\x7f\xff'
+ )
+ _bitorder.append(numpy.frombuffer(_bitorder[0], dtype='uint8'))
+ try:
+ view = data.view('uint8')
+ numpy.take(_bitorder[1], view, out=view)
+ return data
+ except AttributeError:
+ return data.translate(_bitorder[0])
+ except ValueError:
+ raise NotImplementedError('slices of arrays not supported')
+ return None
+
+
+def packints_decode(data, dtype, numbits, runlen=0, out=None):
+ """Decompress byte string to array of integers.
+
+ This implementation only handles itemsizes 1, 8, 16, 32, and 64 bits.
+ Install the imagecodecs package for decoding other integer sizes.
+
+ Parameters
+ ----------
+ data : byte str
+ Data to decompress.
+ dtype : numpy.dtype or str
+ A numpy boolean or integer type.
+ numbits : int
+ Number of bits per integer.
+ runlen : int
+ Number of consecutive integers, after which to start at next byte.
+
+ Examples
+ --------
+ >>> packints_decode(b'a', 'B', 1)
+ array([0, 1, 1, 0, 0, 0, 0, 1], dtype=uint8)
+
+ """
+ if numbits == 1: # bitarray
+ data = numpy.frombuffer(data, '|B')
+ data = numpy.unpackbits(data)
+ if runlen % 8:
+ data = data.reshape(-1, runlen + (8 - runlen % 8))
+ data = data[:, :runlen].reshape(-1)
+ return data.astype(dtype)
+ if numbits in (8, 16, 32, 64):
+ return numpy.frombuffer(data, dtype)
+ raise NotImplementedError(
+ 'unpacking %s-bit integers to %s not supported'
+ % (numbits, numpy.dtype(dtype)))
+
+
+if imagecodecs is not None:
+ bitorder_decode = imagecodecs.bitorder_decode # noqa
+ packints_decode = imagecodecs.packints_decode # noqa
+
+
+def apply_colormap(image, colormap, contig=True):
+ """Return palette-colored image.
+
+ The image values are used to index the colormap on axis 1. The returned
+ image is of shape image.shape+colormap.shape[0] and dtype colormap.dtype.
+
+ Parameters
+ ----------
+ image : numpy.ndarray
+ Indexes into the colormap.
+ colormap : numpy.ndarray
+ RGB lookup table aka palette of shape (3, 2**bits_per_sample).
+ contig : bool
+ If True, return a contiguous array.
+
+ Examples
+ --------
+ >>> image = numpy.arange(256, dtype='uint8')
+ >>> colormap = numpy.vstack([image, image, image]).astype('uint16') * 256
+ >>> apply_colormap(image, colormap)[-1]
+ array([65280, 65280, 65280], dtype=uint16)
+
+ """
+ image = numpy.take(colormap, image, axis=1)
+ image = numpy.rollaxis(image, 0, image.ndim)
+ if contig:
+ image = numpy.ascontiguousarray(image)
+ return image
+
+
+def parse_filenames(files, pattern):
+ """Return shape and axes from sequence of file names matching pattern.
+
+ >>> parse_filenames(['c1001.ext', 'c2002.ext'],
+ ... r'([^\\d])(\\d)(?P<t>\\d+)\\.ext')
+ ('ct', (2, 2), [(1, 1), (2, 2)], (1, 1))
+
+ """
+ if not pattern:
+ raise ValueError('invalid pattern')
+ pattern = re.compile(pattern, re.IGNORECASE | re.VERBOSE)
+
+ def parse(fname, pattern=pattern):
+ """Return axes and indices from file name."""
+ fname = os.path.split(fname)[-1]
+ axes = []
+ indices = []
+ groupindex = {v: k for k, v in pattern.groupindex.items()}
+ match = pattern.search(fname)
+ if not match:
+ raise ValueError('pattern does not match file name')
+ ax = None
+ for i, m in enumerate(match.groups()):
+ if m is None:
+ continue
+ if m[0].isalpha():
+ if ax is not None:
+ raise ValueError('invalid pattern')
+ ax = m
+ elif m[0].isdigit():
+ if i + 1 in groupindex:
+ ax = groupindex[i + 1]
+ else:
+ ax = 'Q' if ax is None else ax
+ axes.append(ax[0])
+ indices.append(int(m))
+ ax = None
+ return ''.join(axes), tuple(indices)
+
+ axes = None
+ indices = []
+ for fname in files:
+ ax, idx = parse(fname)
+ if axes is None:
+ axes = ax
+ elif axes != ax:
+ raise ValueError('axes do not match within image sequence')
+ indices.append(idx)
+ shape = tuple(numpy.max(indices, axis=0))
+ startindex = tuple(numpy.min(indices, axis=0))
+ shape = tuple(i - j + 1 for i, j in zip(shape, startindex))
+ # if product(shape) != len(files):
+ # raise VaueError('files are missing')
+ return axes, shape, indices, startindex
+
+
+def reorient(image, orientation):
+ """Return reoriented view of image array.
+
+ Parameters
+ ----------
+ image : numpy.ndarray
+ Non-squeezed output of asarray() functions.
+ Axes -3 and -2 must be image length and width respectively.
+ orientation : int or str
+ One of TIFF.ORIENTATION names or values.
+
+ """
+ orient = TIFF.ORIENTATION
+ orientation = enumarg(orient, orientation)
+
+ if orientation == orient.TOPLEFT:
+ return image
+ if orientation == orient.TOPRIGHT:
+ return image[..., ::-1, :]
+ if orientation == orient.BOTLEFT:
+ return image[..., ::-1, :, :]
+ if orientation == orient.BOTRIGHT:
+ return image[..., ::-1, ::-1, :]
+ if orientation == orient.LEFTTOP:
+ return numpy.swapaxes(image, -3, -2)
+ if orientation == orient.RIGHTTOP:
+ return numpy.swapaxes(image, -3, -2)[..., ::-1, :]
+ if orientation == orient.RIGHTBOT:
+ return numpy.swapaxes(image, -3, -2)[..., ::-1, :, :]
+ if orientation == orient.LEFTBOT:
+ return numpy.swapaxes(image, -3, -2)[..., ::-1, ::-1, :]
+ return image
+
+
+def repeat_nd(a, repeats):
+ """Return read-only view into input array with elements repeated.
+
+ Zoom nD image by integer factors using nearest neighbor interpolation
+ (box filter).
+
+ Parameters
+ ----------
+ a : array_like
+ Input array.
+ repeats : sequence of int
+ The number of repetitions to apply along each dimension of input array.
+
+ Examples
+ --------
+ >>> repeat_nd([[1, 2], [3, 4]], (2, 2))
+ array([[1, 1, 2, 2],
+ [1, 1, 2, 2],
+ [3, 3, 4, 4],
+ [3, 3, 4, 4]])
+
+ """
+ a = numpy.asarray(a)
+ reshape = []
+ shape = []
+ strides = []
+ for i, j, k in zip(a.strides, a.shape, repeats):
+ shape.extend((j, k))
+ strides.extend((i, 0))
+ reshape.append(j * k)
+ return numpy.lib.stride_tricks.as_strided(
+ a, shape, strides, writeable=False).reshape(reshape)
+
+
+def reshape_nd(data_or_shape, ndim):
+ """Return image array or shape with at least ndim dimensions.
+
+ Prepend 1s to image shape as necessary.
+
+ >>> reshape_nd(numpy.empty(0), 1).shape
+ (0,)
+ >>> reshape_nd(numpy.empty(1), 2).shape
+ (1, 1)
+ >>> reshape_nd(numpy.empty((2, 3)), 3).shape
+ (1, 2, 3)
+ >>> reshape_nd(numpy.empty((3, 4, 5)), 3).shape
+ (3, 4, 5)
+ >>> reshape_nd((2, 3), 3)
+ (1, 2, 3)
+
+ """
+ is_shape = isinstance(data_or_shape, tuple)
+ shape = data_or_shape if is_shape else data_or_shape.shape
+ if len(shape) >= ndim:
+ return data_or_shape
+ shape = (1,) * (ndim - len(shape)) + shape
+ return shape if is_shape else data_or_shape.reshape(shape)
+
+
+def squeeze_axes(shape, axes, skip=None):
+ """Return shape and axes with single-dimensional entries removed.
+
+ Remove unused dimensions unless their axes are listed in 'skip'.
+
+ >>> squeeze_axes((5, 1, 2, 1, 1), 'TZYXC')
+ ((5, 2, 1), 'TYX')
+
+ """
+ if len(shape) != len(axes):
+ raise ValueError('dimensions of axes and shape do not match')
+ if skip is None:
+ skip = 'XY'
+ shape, axes = zip(*(i for i in zip(shape, axes)
+ if i[0] > 1 or i[1] in skip))
+ return tuple(shape), ''.join(axes)
+
+
+def transpose_axes(image, axes, asaxes=None):
+ """Return image with its axes permuted to match specified axes.
+
+ A view is returned if possible.
+
+ >>> transpose_axes(numpy.zeros((2, 3, 4, 5)), 'TYXC', asaxes='CTZYX').shape
+ (5, 2, 1, 3, 4)
+
+ """
+ for ax in axes:
+ if ax not in asaxes:
+ raise ValueError('unknown axis %s' % ax)
+ # add missing axes to image
+ if asaxes is None:
+ asaxes = 'CTZYX'
+ shape = image.shape
+ for ax in reversed(asaxes):
+ if ax not in axes:
+ axes = ax + axes
+ shape = (1,) + shape
+ image = image.reshape(shape)
+ # transpose axes
+ image = image.transpose([axes.index(ax) for ax in asaxes])
+ return image
+
+
+def reshape_axes(axes, shape, newshape, unknown=None):
+ """Return axes matching new shape.
+
+ By default, unknown dimensions are labelled 'Q'.
+
+ >>> reshape_axes('YXS', (219, 301, 1), (219, 301))
+ 'YX'
+ >>> reshape_axes('IYX', (12, 219, 301), (3, 4, 219, 1, 301, 1))
+ 'QQYQXQ'
+
+ """
+ shape = tuple(shape)
+ newshape = tuple(newshape)
+ if len(axes) != len(shape):
+ raise ValueError('axes do not match shape')
+
+ size = product(shape)
+ newsize = product(newshape)
+ if size != newsize:
+ raise ValueError('cannot reshape %s to %s' % (shape, newshape))
+ if not axes or not newshape:
+ return ''
+
+ lendiff = max(0, len(shape) - len(newshape))
+ if lendiff:
+ newshape = newshape + (1,) * lendiff
+
+ i = len(shape) - 1
+ prodns = 1
+ prods = 1
+ result = []
+ for ns in newshape[:: -1]:
+ prodns *= ns
+ while i > 0 and shape[i] == 1 and ns != 1:
+ i -= 1
+ if ns == shape[i] and prodns == prods * shape[i]:
+ prods *= shape[i]
+ result.append(axes[i])
+ i -= 1
+ elif unknown:
+ result.append(unknown)
+ else:
+ unknown = 'Q'
+ result.append(unknown)
+
+ return ''.join(reversed(result[lendiff:]))
+
+
+def stack_pages(pages, out=None, maxworkers=None, **kwargs):
+ """Read data from sequence of TiffPage and stack them vertically.
+
+ Additional parameters are passsed to the TiffPage.asarray function.
+
+ """
+ npages = len(pages)
+ if npages == 0:
+ raise ValueError('no pages')
+
+ if npages == 1:
+ kwargs['maxworkers'] = maxworkers
+ return pages[0].asarray(out=out, **kwargs)
+
+ page0 = next(p for p in pages if p is not None).keyframe
+ shape = (npages,) + page0.shape
+ dtype = page0.dtype
+ out = create_output(out, shape, dtype)
+
+ if maxworkers is None or maxworkers < 1:
+ import multiprocessing # noqa: delay import
+ maxworkers = max(multiprocessing.cpu_count() // 2, 1)
+
+ if maxworkers == 1:
+ kwargs['maxworkers'] = 1
+ elif npages < 3:
+ kwargs['maxworkers'] = maxworkers
+ maxworkers = 1
+ elif page0.compression > 1 and len(page0.dataoffsets) > 2:
+ kwargs['maxworkers'] = min(maxworkers, len(page0.dataoffsets))
+ maxworkers = max(maxworkers - kwargs['maxworkers'], 1)
+ else:
+ kwargs['maxworkers'] = 1
+
+ page0.parent.filehandle.lock = maxworkers > 1
+
+ filecache = OpenFileCache(size=max(4, maxworkers),
+ lock=page0.parent.filehandle.lock)
+
+ def func(page, index, out=out, filecache=filecache, validate=0,
+ kwargs=kwargs):
+ """Read, decode, and copy page data."""
+ if page is not None:
+ filecache.open(page.parent.filehandle)
+ out[index] = page.asarray(lock=filecache.lock, reopen=False,
+ validate=False, **kwargs)
+ filecache.close(page.parent.filehandle)
+
+ if maxworkers < 2:
+ for i, page in enumerate(pages):
+ func(page, i)
+ else:
+ # TODO: add exception handling
+ # read first page un-threaded to catch exceptions
+ func(page0, 0, validate=True)
+ with ThreadPoolExecutor(maxworkers) as executor:
+ executor.map(func, pages[1:], range(1, npages))
+
+ filecache.clear()
+ page0.parent.filehandle.lock = None
+ return out
+
+
+def create_output(out, shape, dtype, mode='w+', suffix=None):
+ """Return numpy array where image data of shape and dtype can be copied.
+
+ The 'out' parameter may have the following values or types:
+
+ None
+ An empty array of shape and dtype is created and returned.
+ numpy.ndarray
+ An existing writable array of compatible dtype and shape. A view of
+ the same array is returned after verification.
+ 'memmap' or 'memmap:tempdir'
+ A memory-map to an array stored in a temporary binary file on disk
+ is created and returned.
+ str or open file
+ The file name or file object used to create a memory-map to an array
+ stored in a binary file on disk. The created memory-mapped array is
+ returned.
+
+ """
+ if out is None:
+ return numpy.zeros(shape, dtype)
+ if isinstance(out, str) and out[:6] == 'memmap':
+ import tempfile # noqa: delay import
+
+ tempdir = out[7:] if len(out) > 7 else None
+ if suffix is None:
+ suffix = '.memmap'
+ with tempfile.NamedTemporaryFile(dir=tempdir, suffix=suffix) as fh:
+ return numpy.memmap(fh, shape=shape, dtype=dtype, mode=mode)
+ if isinstance(out, numpy.ndarray):
+ if product(shape) != product(out.shape):
+ raise ValueError('incompatible output shape')
+ if not numpy.can_cast(dtype, out.dtype):
+ raise ValueError('incompatible output dtype')
+ return out.reshape(shape)
+ if isinstance(out, pathlib.Path):
+ out = str(out)
+ return numpy.memmap(out, shape=shape, dtype=dtype, mode=mode)
+
+
+def matlabstr2py(string):
+ """Return Python object from Matlab string representation.
+
+ Return str, bool, int, float, list (Matlab arrays or cells), or
+ dict (Matlab structures) types.
+
+ Use to access ScanImage metadata.
+
+ >>> matlabstr2py('1')
+ 1
+ >>> matlabstr2py("['x y z' true false; 1 2.0 -3e4; NaN Inf @class]")
+ [['x y z', True, False], [1, 2.0, -30000.0], [nan, inf, '@class']]
+ >>> d = matlabstr2py("SI.hChannels.channelType = {'stripe' 'stripe'}\\n"
+ ... "SI.hChannels.channelsActive = 2")
+ >>> d['SI.hChannels.channelType']
+ ['stripe', 'stripe']
+
+ """
+ # TODO: handle invalid input
+ # TODO: review unboxing of multidimensional arrays
+
+ def lex(s):
+ # return sequence of tokens from matlab string representation
+ tokens = ['[']
+ while True:
+ t, i = next_token(s)
+ if t is None:
+ break
+ if t == ';':
+ tokens.extend((']', '['))
+ elif t == '[':
+ tokens.extend(('[', '['))
+ elif t == ']':
+ tokens.extend((']', ']'))
+ else:
+ tokens.append(t)
+ s = s[i:]
+ tokens.append(']')
+ return tokens
+
+ def next_token(s):
+ # return next token in matlab string
+ length = len(s)
+ if length == 0:
+ return None, 0
+ i = 0
+ while i < length and s[i] == ' ':
+ i += 1
+ if i == length:
+ return None, i
+ if s[i] in '{[;]}':
+ return s[i], i + 1
+ if s[i] == "'":
+ j = i + 1
+ while j < length and s[j] != "'":
+ j += 1
+ return s[i: j + 1], j + 1
+ if s[i] == '<':
+ j = i + 1
+ while j < length and s[j] != '>':
+ j += 1
+ return s[i: j + 1], j + 1
+ j = i
+ while j < length and not s[j] in ' {[;]}':
+ j += 1
+ return s[i:j], j
+
+ def value(s, fail=False):
+ # return Python value of token
+ s = s.strip()
+ if not s:
+ return s
+ if len(s) == 1:
+ try:
+ return int(s)
+ except Exception:
+ if fail:
+ raise ValueError()
+ return s
+ if s[0] == "'":
+ if fail and s[-1] != "'" or "'" in s[1:-1]:
+ raise ValueError()
+ return s[1:-1]
+ if s[0] == '<':
+ if fail and s[-1] != '>' or '<' in s[1:-1]:
+ raise ValueError()
+ return s
+ if fail and any(i in s for i in " ';[]{}"):
+ raise ValueError()
+ if s[0] == '@':
+ return s
+ if s in ('true', 'True'):
+ return True
+ if s in ('false', 'False'):
+ return False
+ if s[:6] == 'zeros(':
+ return numpy.zeros([int(i) for i in s[6:-1].split(',')]).tolist()
+ if s[:5] == 'ones(':
+ return numpy.ones([int(i) for i in s[5:-1].split(',')]).tolist()
+ if '.' in s or 'e' in s:
+ try:
+ return float(s)
+ except Exception:
+ pass
+ try:
+ return int(s)
+ except Exception:
+ pass
+ try:
+ return float(s) # nan, inf
+ except Exception:
+ if fail:
+ raise ValueError()
+ return s
+
+ def parse(s):
+ # return Python value from string representation of Matlab value
+ s = s.strip()
+ try:
+ return value(s, fail=True)
+ except ValueError:
+ pass
+ result = add2 = []
+ levels = [add2]
+ for t in lex(s):
+ if t in '[{':
+ add2 = []
+ levels.append(add2)
+ elif t in ']}':
+ x = levels.pop()
+ if len(x) == 1 and isinstance(x[0], (list, str)):
+ x = x[0]
+ add2 = levels[-1]
+ add2.append(x)
+ else:
+ add2.append(value(t))
+ if len(result) == 1 and isinstance(result[0], (list, str)):
+ result = result[0]
+ return result
+
+ if '\r' in string or '\n' in string:
+ # structure
+ d = {}
+ for line in string.splitlines():
+ line = line.strip()
+ if not line or line[0] == '%':
+ continue
+ k, v = line.split('=', 1)
+ k = k.strip()
+ if any(c in k for c in " ';[]{}<>"):
+ continue
+ d[k] = parse(v)
+ return d
+ return parse(string)
+
+
+def stripnull(string, null=b'\x00'):
+ """Return string truncated at first null character.
+
+ Clean NULL terminated C strings. For unicode strings use null='\\0'.
+
+ >>> stripnull(b'string\\x00')
+ b'string'
+ >>> stripnull('string\\x00', null='\\0')
+ 'string'
+
+ """
+ i = string.find(null)
+ return string if (i < 0) else string[:i]
+
+
+def stripascii(string):
+ """Return string truncated at last byte that is 7-bit ASCII.
+
+ Clean NULL separated and terminated TIFF strings.
+
+ >>> stripascii(b'string\\x00string\\n\\x01\\x00')
+ b'string\\x00string\\n'
+ >>> stripascii(b'\\x00')
+ b''
+
+ """
+ # TODO: pythonize this
+ i = len(string)
+ while i:
+ i -= 1
+ if 8 < byte2int(string[i]) < 127:
+ break
+ else:
+ i = -1
+ return string[: i + 1]
+
+
+def asbool(value, true=(b'true', u'true'), false=(b'false', u'false')):
+ """Return string as bool if possible, else raise TypeError.
+
+ >>> asbool(b' False ')
+ False
+
+ """
+ value = value.strip().lower()
+ if value in true: # might raise UnicodeWarning/BytesWarning
+ return True
+ if value in false:
+ return False
+ raise TypeError()
+
+
+def astype(value, types=None):
+ """Return argument as one of types if possible.
+
+ >>> astype('42')
+ 42
+ >>> astype('3.14')
+ 3.14
+ >>> astype('True')
+ True
+ >>> astype(b'Neee-Wom')
+ 'Neee-Wom'
+
+ """
+ if types is None:
+ types = int, float, asbool, bytes2str
+ for typ in types:
+ try:
+ return typ(value)
+ except (ValueError, AttributeError, TypeError, UnicodeEncodeError):
+ pass
+ return value
+
+
+def format_size(size, threshold=1536):
+ """Return file size as string from byte size.
+
+ >>> format_size(1234)
+ '1234 B'
+ >>> format_size(12345678901)
+ '11.50 GiB'
+
+ """
+ if size < threshold:
+ return "%i B" % size
+ for unit in ('KiB', 'MiB', 'GiB', 'TiB', 'PiB'):
+ size /= 1024.0
+ if size < threshold:
+ return "%.2f %s" % (size, unit)
+ return 'ginormous'
+
+
+def identityfunc(arg, *args, **kwargs):
+ """Single argument identity function.
+
+ >>> identityfunc('arg')
+ 'arg'
+
+ """
+ return arg
+
+
+def nullfunc(*args, **kwargs):
+ """Null function.
+
+ >>> nullfunc('arg', kwarg='kwarg')
+
+ """
+ return
+
+
+def sequence(value):
+ """Return tuple containing value if value is not a tuple or list.
+
+ >>> sequence(1)
+ (1,)
+ >>> sequence([1])
+ [1]
+ >>> sequence('ab')
+ ('ab',)
+
+ """
+ return value if isinstance(value, (tuple, list)) else (value,)
+
+
+def product(iterable):
+ """Return product of sequence of numbers.
+
+ Equivalent of functools.reduce(operator.mul, iterable, 1).
+ Multiplying numpy integers might overflow.
+
+ >>> product([2**8, 2**30])
+ 274877906944
+ >>> product([])
+ 1
+
+ """
+ prod = 1
+ for i in iterable:
+ prod *= i
+ return prod
+
+
+def natural_sorted(iterable):
+ """Return human sorted list of strings.
+
+ E.g. for sorting file names.
+
+ >>> natural_sorted(['f1', 'f2', 'f10'])
+ ['f1', 'f2', 'f10']
+
+ """
+
+ def sortkey(x):
+ return [(int(c) if c.isdigit() else c) for c in re.split(numbers, x)]
+
+ numbers = re.compile(r'(\d+)')
+ return sorted(iterable, key=sortkey)
+
+
+def excel_datetime(timestamp, epoch=None):
+ """Return datetime object from timestamp in Excel serial format.
+
+ Convert LSM time stamps.
+
+ >>> excel_datetime(40237.029999999795)
+ datetime.datetime(2010, 2, 28, 0, 43, 11, 999982)
+
+ """
+ if epoch is None:
+ epoch = datetime.datetime.fromordinal(693594)
+ return epoch + datetime.timedelta(timestamp)
+
+
+def julian_datetime(julianday, milisecond=0):
+ """Return datetime from days since 1/1/4713 BC and ms since midnight.
+
+ Convert Julian dates according to MetaMorph.
+
+ >>> julian_datetime(2451576, 54362783)
+ datetime.datetime(2000, 2, 2, 15, 6, 2, 783)
+
+ """
+ if julianday <= 1721423:
+ # no datetime before year 1
+ return None
+
+ a = julianday + 1
+ if a > 2299160:
+ alpha = math.trunc((a - 1867216.25) / 36524.25)
+ a += 1 + alpha - alpha // 4
+ b = a + (1524 if a > 1721423 else 1158)
+ c = math.trunc((b - 122.1) / 365.25)
+ d = math.trunc(365.25 * c)
+ e = math.trunc((b - d) / 30.6001)
+
+ day = b - d - math.trunc(30.6001 * e)
+ month = e - (1 if e < 13.5 else 13)
+ year = c - (4716 if month > 2.5 else 4715)
+
+ hour, milisecond = divmod(milisecond, 1000 * 60 * 60)
+ minute, milisecond = divmod(milisecond, 1000 * 60)
+ second, milisecond = divmod(milisecond, 1000)
+
+ return datetime.datetime(year, month, day,
+ hour, minute, second, milisecond)
+
+
+def byteorder_isnative(byteorder):
+ """Return if byteorder matches the system's byteorder.
+
+ >>> byteorder_isnative('=')
+ True
+
+ """
+ if byteorder in ('=', sys.byteorder):
+ return True
+ keys = {'big': '>', 'little': '<'}
+ return keys.get(byteorder, byteorder) == keys[sys.byteorder]
+
+
+def recarray2dict(recarray):
+ """Return numpy.recarray as dict."""
+ # TODO: subarrays
+ result = {}
+ for descr, value in zip(recarray.dtype.descr, recarray):
+ name, dtype = descr[:2]
+ if dtype[1] == 'S':
+ value = bytes2str(stripnull(value))
+ elif value.ndim < 2:
+ value = value.tolist()
+ result[name] = value
+ return result
+
+
+def xml2dict(xml, sanitize=True, prefix=None):
+ """Return XML as dict.
+
+ >>> xml2dict('<?xml version="1.0" ?><root attr="name"><key>1</key></root>')
+ {'root': {'key': 1, 'attr': 'name'}}
+
+ """
+ from xml.etree import cElementTree as etree # delayed import
+
+ at = tx = ''
+ if prefix:
+ at, tx = prefix
+
+ def astype(value):
+ # return value as int, float, bool, or str
+ for t in (int, float, asbool):
+ try:
+ return t(value)
+ except Exception:
+ pass
+ return value
+
+ def etree2dict(t):
+ # adapted from https://stackoverflow.com/a/10077069/453463
+ key = t.tag
+ if sanitize:
+ key = key.rsplit('}', 1)[-1]
+ d = {key: {} if t.attrib else None}
+ children = list(t)
+ if children:
+ dd = collections.defaultdict(list)
+ for dc in map(etree2dict, children):
+ for k, v in dc.items():
+ dd[k].append(astype(v))
+ d = {key: {k: astype(v[0]) if len(v) == 1 else astype(v)
+ for k, v in dd.items()}}
+ if t.attrib:
+ d[key].update((at + k, astype(v)) for k, v in t.attrib.items())
+ if t.text:
+ text = t.text.strip()
+ if children or t.attrib:
+ if text:
+ d[key][tx + 'value'] = astype(text)
+ else:
+ d[key] = astype(text)
+ return d
+
+ return etree2dict(etree.fromstring(xml))
+
+
+def hexdump(bytestr, width=75, height=24, snipat=-2, modulo=2, ellipsis=None):
+ """Return hexdump representation of byte string.
+
+ >>> hexdump(binascii.unhexlify('49492a00080000000e00fe0004000100'))
+ '49 49 2a 00 08 00 00 00 0e 00 fe 00 04 00 01 00 II*.............'
+
+ """
+ size = len(bytestr)
+ if size < 1 or width < 2 or height < 1:
+ return ''
+ if height == 1:
+ addr = b''
+ bytesperline = min(modulo * (((width - len(addr)) // 4) // modulo),
+ size)
+ if bytesperline < 1:
+ return ''
+ nlines = 1
+ else:
+ addr = b'%%0%ix: ' % len(b'%x' % size)
+ bytesperline = min(modulo * (((width - len(addr % 1)) // 4) // modulo),
+ size)
+ if bytesperline < 1:
+ return ''
+ width = 3 * bytesperline + len(addr % 1)
+ nlines = (size - 1) // bytesperline + 1
+
+ if snipat is None or snipat == 1:
+ snipat = height
+ elif 0 < abs(snipat) < 1:
+ snipat = int(math.floor(height * snipat))
+ if snipat < 0:
+ snipat += height
+
+ if height == 1 or nlines == 1:
+ blocks = [(0, bytestr[:bytesperline])]
+ addr = b''
+ height = 1
+ width = 3 * bytesperline
+ elif height is None or nlines <= height:
+ blocks = [(0, bytestr)]
+ elif snipat <= 0:
+ start = bytesperline * (nlines - height)
+ blocks = [(start, bytestr[start:])] # (start, None)
+ elif snipat >= height or height < 3:
+ end = bytesperline * height
+ blocks = [(0, bytestr[:end])] # (end, None)
+ else:
+ end1 = bytesperline * snipat
+ end2 = bytesperline * (height - snipat - 1)
+ blocks = [
+ (0, bytestr[:end1]),
+ (size - end1 - end2, None),
+ (size - end2, bytestr[size - end2:]),
+ ]
+
+ ellipsis = b'...' if ellipsis is None else str2bytes(ellipsis)
+ result = []
+ for start, bytestr in blocks:
+ if bytestr is None:
+ result.append(ellipsis) # 'skip %i bytes' % start)
+ continue
+ hexstr = binascii.hexlify(bytestr)
+ strstr = re.sub(br'[^\x20-\x7f]', b'.', bytestr)
+ for i in range(0, len(bytestr), bytesperline):
+ h = hexstr[2 * i: 2 * i + bytesperline * 2]
+ r = (addr % (i + start)) if height > 1 else addr
+ r += b' '.join(h[i: i + 2] for i in range(0, 2 * bytesperline, 2))
+ r += b' ' * (width - len(r))
+ r += strstr[i: i + bytesperline]
+ result.append(r)
+ result = b'\n'.join(result)
+ if sys.version_info[0] > 2:
+ result = result.decode('ascii')
+ return result
+
+
+def isprintable(string):
+ """Return if all characters in string are printable.
+
+ >>> isprintable('abc')
+ True
+ >>> isprintable(b'\01')
+ False
+
+ """
+ string = string.strip()
+ if not string:
+ return True
+ if sys.version_info[0] > 2:
+ try:
+ return string.isprintable()
+ except Exception:
+ pass
+ try:
+ return string.decode('utf-8').isprintable()
+ except Exception:
+ pass
+ else:
+ if string.isalnum():
+ return True
+ printable = ('0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRST'
+ 'UVWXYZ!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ \t\n\r\x0b\x0c')
+ return all(c in printable for c in string)
+
+
+def clean_whitespace(string, compact=False):
+ """Return string with compressed whitespace."""
+ for a, b in (
+ ('\r\n', '\n'),
+ ('\r', '\n'),
+ ('\n\n', '\n'),
+ ('\t', ' '),
+ (' ', ' ')
+ ):
+ string = string.replace(a, b)
+ if compact:
+ for a, b in (
+ ('\n', ' '),
+ ('[ ', '['),
+ (' ', ' '),
+ (' ', ' '),
+ (' ', ' ')
+ ):
+ string = string.replace(a, b)
+ return string.strip()
+
+
+def pformat_xml(xml):
+ """Return pretty formatted XML."""
+ try:
+ from lxml import etree # delayed import
+
+ if not isinstance(xml, bytes):
+ xml = xml.encode('utf-8')
+ xml = etree.parse(io.BytesIO(xml))
+ xml = etree.tostring(xml, pretty_print=True, xml_declaration=True,
+ encoding=xml.docinfo.encoding)
+ xml = bytes2str(xml)
+ except Exception:
+ if isinstance(xml, bytes):
+ xml = bytes2str(xml)
+ xml = xml.replace('><', '>\n<')
+ return xml.replace(' ', ' ').replace('\t', ' ')
+
+
+def pformat(arg, width=79, height=24, compact=True):
+ """Return pretty formatted representation of object as string.
+
+ Whitespace might be altered.
+
+ """
+ if height is None or height < 1:
+ height = 1024
+ if width is None or width < 1:
+ width = 256
+
+ npopt = numpy.get_printoptions()
+ numpy.set_printoptions(threshold=100, linewidth=width)
+
+ if isinstance(arg, basestring):
+ if arg[:5].lower() in ('<?xml', b'<?xml'):
+ if isinstance(arg, bytes):
+ arg = bytes2str(arg)
+ if height == 1:
+ arg = arg[: 4 * width]
+ else:
+ arg = pformat_xml(arg)
+ elif isinstance(arg, bytes):
+ if isprintable(arg):
+ arg = bytes2str(arg)
+ arg = clean_whitespace(arg)
+ else:
+ numpy.set_printoptions(**npopt)
+ return hexdump(arg, width=width, height=height, modulo=1)
+ arg = arg.rstrip()
+ elif isinstance(arg, numpy.record):
+ arg = arg.pprint()
+ else:
+ import pprint # delayed import
+
+ compact = {} if sys.version_info[0] == 2 else dict(compact=compact)
+ arg = pprint.pformat(arg, width=width, **compact)
+
+ numpy.set_printoptions(**npopt)
+
+ if height == 1:
+ arg = clean_whitespace(arg, compact=True)
+ return arg[:width]
+
+ argl = list(arg.splitlines())
+ if len(argl) > height:
+ arg = '\n'.join(argl[:height // 2] + ['...'] + argl[-height // 2:])
+ return arg
+
+
+def snipstr(string, width=79, snipat=None, ellipsis='...'):
+ """Return string cut to specified length.
+
+ >>> snipstr('abcdefghijklmnop', 8)
+ 'abc...op'
+
+ """
+ if snipat is None:
+ snipat = 0.5
+ if ellipsis is None:
+ if isinstance(string, bytes):
+ ellipsis = b'...'
+ else:
+ ellipsis = u'\u2026' # does not print on win-py3.5
+ esize = len(ellipsis)
+
+ splitlines = string.splitlines()
+ # TODO: finish and test multiline snip
+
+ result = []
+ for line in splitlines:
+ if line is None:
+ result.append(ellipsis)
+ continue
+ linelen = len(line)
+ if linelen <= width:
+ result.append(string)
+ continue
+
+ split = snipat
+ if split is None or split == 1:
+ split = linelen
+ elif 0 < abs(split) < 1:
+ split = int(math.floor(linelen * split))
+ if split < 0:
+ split += linelen
+ if split < 0:
+ split = 0
+
+ if esize == 0 or width < esize + 1:
+ if split <= 0:
+ result.append(string[-width:])
+ else:
+ result.append(string[:width])
+ elif split <= 0:
+ result.append(ellipsis + string[esize - width:])
+ elif split >= linelen or width < esize + 4:
+ result.append(string[:width - esize] + ellipsis)
+ else:
+ splitlen = linelen - width + esize
+ end1 = split - splitlen // 2
+ end2 = end1 + splitlen
+ result.append(string[:end1] + ellipsis + string[end2:])
+
+ if isinstance(string, bytes):
+ return b'\n'.join(result)
+ return '\n'.join(result)
+
+
+def enumarg(enum, arg):
+ """Return enum member from its name or value.
+
+ >>> enumarg(TIFF.PHOTOMETRIC, 2)
+ <PHOTOMETRIC.RGB: 2>
+ >>> enumarg(TIFF.PHOTOMETRIC, 'RGB')
+ <PHOTOMETRIC.RGB: 2>
+
+ """
+ try:
+ return enum(arg)
+ except Exception:
+ try:
+ return enum[arg.upper()]
+ except Exception:
+ raise ValueError('invalid argument %s' % arg)
+
+
+def parse_kwargs(kwargs, *keys, **keyvalues):
+ """Return dict with keys from keys|keyvals and values from kwargs|keyvals.
+
+ Existing keys are deleted from kwargs.
+
+ >>> kwargs = {'one': 1, 'two': 2, 'four': 4}
+ >>> kwargs2 = parse_kwargs(kwargs, 'two', 'three', four=None, five=5)
+ >>> kwargs == {'one': 1}
+ True
+ >>> kwargs2 == {'two': 2, 'four': 4, 'five': 5}
+ True
+
+ """
+ result = {}
+ for key in keys:
+ if key in kwargs:
+ result[key] = kwargs[key]
+ del kwargs[key]
+ for key, value in keyvalues.items():
+ if key in kwargs:
+ result[key] = kwargs[key]
+ del kwargs[key]
+ else:
+ result[key] = value
+ return result
+
+
+def update_kwargs(kwargs, **keyvalues):
+ """Update dict with keys and values if keys do not already exist.
+
+ >>> kwargs = {'one': 1, }
+ >>> update_kwargs(kwargs, one=None, two=2)
+ >>> kwargs == {'one': 1, 'two': 2}
+ True
+
+ """
+ for key, value in keyvalues.items():
+ if key not in kwargs:
+ kwargs[key] = value
+
+
+def validate_jhove(filename, jhove=None, ignore=None):
+ """Validate TIFF file using jhove -m TIFF-hul.
+
+ Raise ValueError if jhove outputs an error message unless the message
+ contains one of the strings in 'ignore'.
+
+ JHOVE does not support bigtiff or more than 50 IFDs.
+
+ See `JHOVE TIFF-hul Module <http://jhove.sourceforge.net/tiff-hul.html>`_
+
+ """
+ import subprocess # noqa: delayed import
+
+ if ignore is None:
+ ignore = ['More than 50 IFDs']
+ if jhove is None:
+ jhove = 'jhove'
+ out = subprocess.check_output([jhove, filename, '-m', 'TIFF-hul'])
+ if b'ErrorMessage: ' in out:
+ for line in out.splitlines():
+ line = line.strip()
+ if line.startswith(b'ErrorMessage: '):
+ error = line[14:].decode('utf-8')
+ for i in ignore:
+ if i in error:
+ break
+ else:
+ raise ValueError(error)
+ break
+
+
+def lsm2bin(lsmfile, binfile=None, tile=None, verbose=True):
+ """Convert [MP]TZCYX LSM file to series of BIN files.
+
+ One BIN file containing 'ZCYX' data are created for each position, time,
+ and tile. The position, time, and tile indices are encoded at the end
+ of the filenames.
+
+ """
+ verbose = print_ if verbose else nullfunc
+
+ if tile is None:
+ tile = (256, 256)
+
+ if binfile is None:
+ binfile = lsmfile
+ elif binfile.lower() == 'none':
+ binfile = None
+ if binfile:
+ binfile += '_(z%ic%iy%ix%i)_m%%ip%%it%%03iy%%ix%%i.bin'
+
+ verbose('\nOpening LSM file... ', end='', flush=True)
+ timer = Timer()
+
+ with TiffFile(lsmfile) as lsm:
+ if not lsm.is_lsm:
+ verbose('\n', lsm, flush=True)
+ raise ValueError('not a LSM file')
+ series = lsm.series[0] # first series contains the image data
+ shape = series.shape
+ axes = series.axes
+ dtype = series.dtype
+ size = product(shape) * dtype.itemsize
+
+ verbose(timer)
+ # verbose(lsm, flush=True)
+ verbose('Image\n axes: %s\n shape: %s\n dtype: %s\n size: %s'
+ % (axes, shape, dtype, format_size(size)), flush=True)
+ if not series.axes.endswith('TZCYX'):
+ raise ValueError('not a *TZCYX LSM file')
+
+ verbose('Copying image from LSM to BIN files', end='', flush=True)
+ timer.start()
+ tiles = shape[-2] // tile[-2], shape[-1] // tile[-1]
+ if binfile:
+ binfile = binfile % (shape[-4], shape[-3], tile[0], tile[1])
+ shape = (1,) * (7 - len(shape)) + shape
+ # cache for ZCYX stacks and output files
+ data = numpy.empty(shape[3:], dtype=dtype)
+ out = numpy.empty((shape[-4], shape[-3], tile[0], tile[1]),
+ dtype=dtype)
+ # iterate over Tiff pages containing data
+ pages = iter(series.pages)
+ for m in range(shape[0]): # mosaic axis
+ for p in range(shape[1]): # position axis
+ for t in range(shape[2]): # time axis
+ for z in range(shape[3]): # z slices
+ data[z] = next(pages).asarray()
+ for y in range(tiles[0]): # tile y
+ for x in range(tiles[1]): # tile x
+ out[:] = data[
+ ...,
+ y * tile[0]: (y + 1) * tile[0],
+ x * tile[1]: (x + 1) * tile[1]
+ ]
+ if binfile:
+ out.tofile(binfile % (m, p, t, y, x))
+ verbose('.', end='', flush=True)
+ verbose(timer, flush=True)
+
+
+def imshow(data, photometric=None, planarconfig=None, bitspersample=None,
+ interpolation=None, cmap=None, vmin=None, vmax=None,
+ figure=None, title=None, dpi=96, subplot=None, maxdim=None,
+ **kwargs):
+ """Plot n-dimensional images using matplotlib.pyplot.
+
+ Return figure, subplot and plot axis.
+ Requires pyplot already imported C{from matplotlib import pyplot}.
+
+ Parameters
+ ----------
+ data : nd array
+ The image data.
+ photometric : {'MINISWHITE', 'MINISBLACK', 'RGB', or 'PALETTE'}
+ The color space of the image data.
+ planarconfig : {'CONTIG' or 'SEPARATE'}
+ Defines how components of each pixel are stored.
+ bitspersample : int
+ Number of bits per channel in integer RGB images.
+ interpolation : str
+ The image interpolation method used in matplotlib.imshow. By default,
+ 'nearest' will be used for image dimensions <= 512, else 'bilinear'.
+ cmap : str or matplotlib.colors.Colormap
+ The colormap maps non-RGBA scalar data to colors.
+ vmin, vmax : scalar
+ Data range covered by the colormap. By default, the complete
+ range of the data is covered.
+ figure : matplotlib.figure.Figure
+ Matplotlib figure to use for plotting.
+ title : str
+ Window and subplot title.
+ subplot : int
+ A matplotlib.pyplot.subplot axis.
+ maxdim : int
+ Maximum image width and length.
+ kwargs : dict
+ Additional arguments for matplotlib.pyplot.imshow.
+
+ """
+ # TODO: rewrite detection of isrgb, iscontig
+ # TODO: use planarconfig
+ if photometric is None:
+ photometric = 'RGB'
+ if maxdim is None:
+ maxdim = 2**16
+ isrgb = photometric in ('RGB', 'YCBCR') # 'PALETTE', 'YCBCR'
+
+ if data.dtype == 'float16':
+ data = data.astype('float32')
+
+ if data.dtype.kind == 'b':
+ isrgb = False
+
+ if isrgb and not (
+ data.shape[-1] in (3, 4)
+ or (data.ndim > 2 and data.shape[-3] in (3, 4))
+ ):
+ isrgb = False
+ photometric = 'MINISBLACK'
+
+ data = data.squeeze()
+ if photometric in ('MINISWHITE', 'MINISBLACK', None):
+ data = reshape_nd(data, 2)
+ else:
+ data = reshape_nd(data, 3)
+
+ dims = data.ndim
+ if dims < 2:
+ raise ValueError('not an image')
+ if dims == 2:
+ dims = 0
+ isrgb = False
+ else:
+ if isrgb and data.shape[-3] in (3, 4):
+ data = numpy.swapaxes(data, -3, -2)
+ data = numpy.swapaxes(data, -2, -1)
+ elif not isrgb and (
+ data.shape[-1] < data.shape[-2] // 8
+ and data.shape[-1] < data.shape[-3] // 8
+ ):
+ data = numpy.swapaxes(data, -3, -1)
+ data = numpy.swapaxes(data, -2, -1)
+ isrgb = isrgb and data.shape[-1] in (3, 4)
+ dims -= 3 if isrgb else 2
+
+ if interpolation is None:
+ threshold = 512
+ elif isinstance(interpolation, int):
+ threshold = interpolation
+ else:
+ threshold = 0
+
+ if isrgb:
+ data = data[..., :maxdim, :maxdim, :maxdim]
+ if threshold:
+ if data.shape[-2] > threshold or data.shape[-3] > threshold:
+ interpolation = 'bilinear'
+ else:
+ interpolation = 'nearest'
+ else:
+ data = data[..., :maxdim, :maxdim]
+ if threshold:
+ if data.shape[-1] > threshold or data.shape[-2] > threshold:
+ interpolation = 'bilinear'
+ else:
+ interpolation = 'nearest'
+
+ if photometric == 'PALETTE' and isrgb:
+ datamax = data.max()
+ if datamax > 255:
+ data = data >> 8 # possible precision loss
+ data = data.astype('B')
+ elif data.dtype.kind in 'ui':
+ if not (isrgb and data.dtype.itemsize <= 1) or bitspersample is None:
+ try:
+ bitspersample = int(math.ceil(math.log(data.max(), 2)))
+ except Exception:
+ bitspersample = data.dtype.itemsize * 8
+ elif not isinstance(bitspersample, inttypes):
+ # bitspersample can be tuple, e.g. (5, 6, 5)
+ bitspersample = data.dtype.itemsize * 8
+ datamax = 2**bitspersample
+ if isrgb:
+ if bitspersample < 8:
+ data = data << (8 - bitspersample)
+ elif bitspersample > 8:
+ data = data >> (bitspersample - 8) # precision loss
+ data = data.astype('B')
+ elif data.dtype.kind == 'f':
+ datamax = data.max()
+ if isrgb and datamax > 1.0:
+ if data.dtype.char == 'd':
+ data = data.astype('f')
+ data /= datamax
+ else:
+ data = data / datamax
+ elif data.dtype.kind == 'b':
+ datamax = 1
+ elif data.dtype.kind == 'c':
+ data = numpy.absolute(data)
+ datamax = data.max()
+
+ if isrgb:
+ vmin = 0
+ else:
+ if vmax is None:
+ vmax = datamax
+ if vmin is None:
+ if data.dtype.kind == 'i':
+ dtmin = numpy.iinfo(data.dtype).min
+ vmin = numpy.min(data)
+ if vmin == dtmin:
+ vmin = numpy.min(data[data > dtmin])
+ elif data.dtype.kind == 'f':
+ dtmin = numpy.finfo(data.dtype).min
+ vmin = numpy.min(data)
+ if vmin == dtmin:
+ vmin = numpy.min(data[data > dtmin])
+ else:
+ vmin = 0
+
+ pyplot = sys.modules['matplotlib.pyplot']
+
+ if figure is None:
+ pyplot.rc('font', family='sans-serif', weight='normal', size=8)
+ figure = pyplot.figure(dpi=dpi, figsize=(10.3, 6.3), frameon=True,
+ facecolor='1.0', edgecolor='w')
+ try:
+ figure.canvas.manager.window.title(title)
+ except Exception:
+ pass
+ size = len(title.splitlines()) if title else 1
+ pyplot.subplots_adjust(
+ bottom=0.03 * (dims + 2),
+ top=0.98 - size * 0.03,
+ left=0.1,
+ right=0.95,
+ hspace=0.05,
+ wspace=0.0)
+ if subplot is None:
+ subplot = 111
+ subplot = pyplot.subplot(subplot)
+ subplot.set_facecolor((0, 0, 0))
+
+ if title:
+ try:
+ title = unicode(title, 'Windows-1252')
+ except TypeError:
+ pass
+ pyplot.title(title, size=11)
+
+ if cmap is None:
+ if data.dtype.char == '?':
+ cmap = 'gray'
+ elif data.dtype.kind in 'buf' or vmin == 0:
+ cmap = 'viridis'
+ else:
+ cmap = 'coolwarm'
+ if photometric == 'MINISWHITE':
+ cmap += '_r'
+
+ image = pyplot.imshow(numpy.atleast_2d(data[(0,) * dims].squeeze()),
+ vmin=vmin, vmax=vmax, cmap=cmap,
+ interpolation=interpolation, **kwargs)
+
+ if not isrgb:
+ pyplot.colorbar() # panchor=(0.55, 0.5), fraction=0.05
+
+ def format_coord(x, y):
+ # callback function to format coordinate display in toolbar
+ x = int(x + 0.5)
+ y = int(y + 0.5)
+ try:
+ if dims:
+ return '%s @ %s [%4i, %4i]' % (
+ curaxdat[1][y, x], current, y, x)
+ return '%s @ [%4i, %4i]' % (data[y, x], y, x)
+ except IndexError:
+ return ''
+
+ def none(event):
+ return ''
+
+ subplot.format_coord = format_coord
+ image.get_cursor_data = none
+ image.format_cursor_data = none
+
+ if dims:
+ current = list((0,) * dims)
+ curaxdat = [0, data[tuple(current)].squeeze()]
+ sliders = [pyplot.Slider(
+ pyplot.axes([0.125, 0.03 * (axis + 1), 0.725, 0.025]),
+ 'Dimension %i' % axis, 0, data.shape[axis] - 1, 0, facecolor='0.5',
+ valfmt='%%.0f [%i]' % data.shape[axis]) for axis in range(dims)]
+ for slider in sliders:
+ slider.drawon = False
+
+ def set_image(current, sliders=sliders, data=data):
+ # change image and redraw canvas
+ curaxdat[1] = data[tuple(current)].squeeze()
+ image.set_data(curaxdat[1])
+ for ctrl, index in zip(sliders, current):
+ ctrl.eventson = False
+ ctrl.set_val(index)
+ ctrl.eventson = True
+ figure.canvas.draw()
+
+ def on_changed(index, axis, data=data, current=current):
+ # callback function for slider change event
+ index = int(round(index))
+ curaxdat[0] = axis
+ if index == current[axis]:
+ return
+ if index >= data.shape[axis]:
+ index = 0
+ elif index < 0:
+ index = data.shape[axis] - 1
+ current[axis] = index
+ set_image(current)
+
+ def on_keypressed(event, data=data, current=current):
+ # callback function for key press event
+ key = event.key
+ axis = curaxdat[0]
+ if str(key) in '0123456789':
+ on_changed(key, axis)
+ elif key == 'right':
+ on_changed(current[axis] + 1, axis)
+ elif key == 'left':
+ on_changed(current[axis] - 1, axis)
+ elif key == 'up':
+ curaxdat[0] = 0 if axis == len(data.shape) - 1 else axis + 1
+ elif key == 'down':
+ curaxdat[0] = len(data.shape) - 1 if axis == 0 else axis - 1
+ elif key == 'end':
+ on_changed(data.shape[axis] - 1, axis)
+ elif key == 'home':
+ on_changed(0, axis)
+
+ figure.canvas.mpl_connect('key_press_event', on_keypressed)
+ for axis, ctrl in enumerate(sliders):
+ ctrl.on_changed(lambda k, a=axis: on_changed(k, a))
+
+ return figure, subplot, image
+
+
+def _app_show():
+ """Block the GUI. For use as skimage plugin."""
+ pyplot = sys.modules['matplotlib.pyplot']
+ pyplot.show()
+
+
+def askopenfilename(**kwargs):
+ """Return file name(s) from Tkinter's file open dialog."""
+ try:
+ from Tkinter import Tk
+ import tkFileDialog as filedialog
+ except ImportError:
+ from tkinter import Tk, filedialog
+ root = Tk()
+ root.withdraw()
+ root.update()
+ filenames = filedialog.askopenfilename(**kwargs)
+ root.destroy()
+ return filenames
+
+
+def main(argv=None):
+ """Tifffile command line usage main function."""
+ if argv is None:
+ argv = sys.argv
+
+ log.setLevel(logging.INFO)
+
+ import optparse # TODO: use argparse
+
+ parser = optparse.OptionParser(
+ usage='usage: %prog [options] path',
+ description='Display image data in TIFF files.',
+ version='%%prog %s' % __version__, prog='tifffile')
+ opt = parser.add_option
+ opt('-p', '--page', dest='page', type='int', default=-1,
+ help='display single page')
+ opt('-s', '--series', dest='series', type='int', default=-1,
+ help='display series of pages of same shape')
+ opt('--nomultifile', dest='nomultifile', action='store_true',
+ default=False, help='do not read OME series from multiple files')
+ opt('--noplots', dest='noplots', type='int', default=10,
+ help='maximum number of plots')
+ opt('--interpol', dest='interpol', metavar='INTERPOL', default=None,
+ help='image interpolation method')
+ opt('--dpi', dest='dpi', type='int', default=96,
+ help='plot resolution')
+ opt('--vmin', dest='vmin', type='int', default=None,
+ help='minimum value for colormapping')
+ opt('--vmax', dest='vmax', type='int', default=None,
+ help='maximum value for colormapping')
+ opt('--debug', dest='debug', action='store_true', default=False,
+ help='raise exception on failures')
+ opt('--doctest', dest='doctest', action='store_true', default=False,
+ help='runs the docstring examples')
+ opt('-v', '--detail', dest='detail', type='int', default=2)
+ opt('-q', '--quiet', dest='quiet', action='store_true')
+
+ settings, path = parser.parse_args()
+ path = ' '.join(path)
+
+ if settings.doctest:
+ import doctest
+ if sys.version_info < (3, 6):
+ print('Doctests work with Python >=3.6 only')
+ return 0
+ doctest.testmod(optionflags=doctest.ELLIPSIS)
+ return 0
+ if not path:
+ path = askopenfilename(title='Select a TIFF file',
+ filetypes=TIFF.FILEOPEN_FILTER)
+ if not path:
+ parser.error('No file specified')
+
+ if any(i in path for i in '?*'):
+ path = glob.glob(path)
+ if not path:
+ print('No files match the pattern')
+ return 0
+ # TODO: handle image sequences
+ path = path[0]
+
+ if not settings.quiet:
+ print_('\nReading TIFF header:', end=' ', flush=True)
+ timer = Timer()
+ try:
+ tif = TiffFile(path, multifile=not settings.nomultifile)
+ except Exception as exc:
+ if settings.debug:
+ raise
+ print('\n\n%s: %s' % (exc.__class__.__name__, exc))
+ sys.exit(0)
+
+ if not settings.quiet:
+ print(timer)
+
+ if tif.is_ome:
+ settings.norgb = True
+
+ images = []
+ if settings.noplots > 0:
+ if not settings.quiet:
+ print_('Reading image data: ', end=' ', flush=True)
+
+ def notnone(x):
+ return next(i for i in x if i is not None)
+
+ timer.start()
+ try:
+ if settings.page >= 0:
+ images = [(tif.asarray(key=settings.page),
+ tif[settings.page], None)]
+ elif settings.series >= 0:
+ images = [(tif.asarray(series=settings.series),
+ notnone(tif.series[settings.series]._pages),
+ tif.series[settings.series])]
+ else:
+ for i, s in enumerate(tif.series[:settings.noplots]):
+ try:
+ images.append((tif.asarray(series=i),
+ notnone(s._pages),
+ tif.series[i]))
+ except Exception as exc:
+ images.append((None, notnone(s.pages), None))
+ if settings.debug:
+ raise
+ print('\nSeries %i failed with %s: %s... '
+ % (i, exc.__class__.__name__, exc), end='')
+ except Exception as exc:
+ if settings.debug:
+ raise
+ print('%s: %s' % (exc.__class__.__name__, exc))
+
+ if not settings.quiet:
+ print(timer)
+
+ if not settings.quiet:
+ print_('Generating report:', end=' ', flush=True)
+ timer.start()
+ info = TiffFile.__str__(tif, detail=int(settings.detail))
+ print(timer)
+ print()
+ print(info)
+ print()
+ tif.close()
+
+ if images and settings.noplots > 0:
+ try:
+ import matplotlib
+ matplotlib.use('TkAgg')
+ from matplotlib import pyplot
+
+ except ImportError as exc:
+ log.warning('tifffile.main: %s: %s', exc.__class__.__name__, exc)
+ else:
+ for img, page, series in images:
+ if img is None:
+ continue
+ vmin, vmax = settings.vmin, settings.vmax
+ if page.keyframe.nodata:
+ try:
+ vmin = numpy.min(img[img > page.keyframe.nodata])
+ except ValueError:
+ pass
+ if tif.is_stk:
+ try:
+ vmin = tif.stk_metadata['MinScale']
+ vmax = tif.stk_metadata['MaxScale']
+ except KeyError:
+ pass
+ else:
+ if vmax <= vmin:
+ vmin, vmax = settings.vmin, settings.vmax
+ if series:
+ title = '%s\n%s\n%s' % (str(tif), str(page), str(series))
+ else:
+ title = '%s\n %s' % (str(tif), str(page))
+ photometric = 'MINISBLACK'
+ if page.photometric not in (3,):
+ photometric = TIFF.PHOTOMETRIC(page.photometric).name
+ imshow(img, title=title, vmin=vmin, vmax=vmax,
+ bitspersample=page.bitspersample,
+ photometric=photometric,
+ interpolation=settings.interpol,
+ dpi=settings.dpi)
+ pyplot.show()
+ return 0
+
+
+if sys.version_info[0] == 2:
+ inttypes = int, long, numpy.integer # noqa
+
+ def print_(*args, **kwargs):
+ """Print function with flush support."""
+ flush = kwargs.pop('flush', False)
+ print(*args, **kwargs)
+ if flush:
+ sys.stdout.flush()
+
+ def bytes2str(b, encoding=None, errors=None):
+ """Return string from bytes."""
+ return b
+
+ def str2bytes(s, encoding=None):
+ """Return bytes from string."""
+ return s
+
+ def bytestr(s, encoding='cp1252'):
+ """Return byte string from unicode string, else pass through."""
+ return s.encode(encoding) if isinstance(s, unicode) else s
+
+ def byte2int(b):
+ """Return value of byte as int."""
+ return ord(b)
+
+ def iogetbuffer(bio):
+ """Return contents of BytesIO buffer."""
+ return bio.getvalue()
+
+ class FileNotFoundError(IOError):
+ """FileNotFoundError exception for Python 2."""
+
+ TiffFrame = TiffPage # noqa
+else:
+ inttypes = int, numpy.integer
+ basestring = str, bytes
+ unicode = str
+ print_ = print
+
+ def bytes2str(b, encoding=None, errors='strict'):
+ """Return unicode string from encoded bytes."""
+ if encoding is not None:
+ return b.decode(encoding, errors)
+ try:
+ return b.decode('utf-8', errors)
+ except UnicodeDecodeError:
+ return b.decode('cp1252', errors)
+
+ def str2bytes(s, encoding='cp1252'):
+ """Return bytes from unicode string."""
+ return s.encode(encoding)
+
+ def bytestr(s, encoding='cp1252'):
+ """Return byte string from unicode string, else pass through."""
+ return s.encode(encoding) if isinstance(s, str) else s
+
+ def byte2int(b):
+ """Return value of byte as int."""
+ return b
+
+ def iogetbuffer(bio):
+ """Return view over BytesIO buffer."""
+ return bio.getbuffer()
+
+
+# deprecated
+
+def decodelzw(encoded):
+ """Decompress LZW encoded byte string."""
+ warnings.warn(
+ 'The decodelzw function was removed from the tifffile package.\n'
+ 'Use the lzw_decode function from the imagecodecs package instead.')
+ return imagecodecs.lzw_decode(encoded)
+
+
+decode_lzw = decodelzw
+imsave = imwrite
+
+if __name__ == '__main__':
+ sys.exit(main())
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/io/write.py b/Addons/FRCmetric/miplib-public/miplib/data/io/write.py
new file mode 100644
index 0000000000000000000000000000000000000000..8384f50a5772db3ac06002b7565a5b45e7bb323d
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/io/write.py
@@ -0,0 +1,78 @@
+import SimpleITK as sitk
+import pims
+import miplib.processing.itk as itkutils
+from miplib.data.containers.image import Image
+from miplib.data.io import tiffile
+
+
+def image(path, image):
+ """
+ A wrapper for the various image writing functions. The consumers
+ should only call this function
+
+ :param path: A full path to the image.
+ :param image: An image as :type image: numpy.ndarray or sitk.image.
+
+ :return:
+ """
+
+ assert isinstance(image, Image)
+
+ if path.endswith(('.tiff', '.tif')):
+ __tiff(path, image, image.spacing)
+ else:
+ __itk_image(path, image)
+
+
+def __itk_image(path, image):
+ """
+ A writer for ITK supported image formats.
+
+ :param path: A full path to the image.
+ :param image: An image as :type image: numpy.ndarray.
+ :param spacing: Pixel size ZXY, as a :type spacing: list.
+ """
+ assert isinstance(image, Image)
+
+ image = itkutils.convert_to_itk_image(image)
+ sitk.WriteImage(image, path)
+
+
+def __imagej_tiff(path, image, spacing):
+ """
+ Write a TIFF in ImageJ mode. May improve compatibility with
+ older imageJ. I would recommend using the other one instead.
+ :param path: A full path to the image.
+ :param image: An image as :type image: numpy.ndarray.
+ :param spacing: Pixel size ZXY, as a :type spacing: list.
+ """
+ tiffile.imsave(path,
+ image,
+ imagej=True,
+ resolution=list(1.0/x for x in spacing))
+
+
+def __tiff(path, image, spacing):
+ """
+ Write a TIFF. Will be automatically converted into BigTIFF, if the
+ file is too big for regulare TIFF definition.
+
+ :param path: A full path to the image.
+ :param image: An image as Numpy.ndarray.
+ :param spacing: Pixel size ZXY, as a list.
+ """
+
+ if image.ndim >= 3:
+ image_description = "images={} slices={} unit=micron spacing={}".format(image.shape[0],
+ image.shape[0],
+ spacing[0])
+ tiffile.imsave(path,
+ image,
+ resolution=(1.0/spacing[1], 1.0/spacing[2]),
+ metadata={'description': image_description})
+ else:
+ tiffile.imsave(path,
+ image,
+ imagej=True,
+ resolution=(1.0 / spacing[0], 1.0 / spacing[1]))
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/iterators/__init__.py b/Addons/FRCmetric/miplib-public/miplib/data/iterators/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/iterators/fourier_ring_iterators.py b/Addons/FRCmetric/miplib-public/miplib/data/iterators/fourier_ring_iterators.py
new file mode 100644
index 0000000000000000000000000000000000000000..91acc9879858a9c574416bdedf1218fa34643764
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/iterators/fourier_ring_iterators.py
@@ -0,0 +1,142 @@
+# coding=utf-8
+from math import floor
+
+import numpy as np
+import miplib.processing.converters as converters
+
+
+class FourierRingIterator(object):
+ """
+ A Fourier ring iterator class for 2D images. Calculates a 2D polar coordinate
+ centered at the geometric center of the data shape.
+ """
+ def __init__(self, shape, d_bin):
+ """
+ :param shape: the volume shape
+ :param d_bin: thickness of the ring in pixels
+ """
+
+ assert len(shape) == 2
+
+ # Get bin size
+ self.d_bin = d_bin
+ self.ring_start = 0
+ self._nbins = int(floor(shape[0] / (2 * self.d_bin)))
+ # Create Fourier grid
+ axes = (np.arange(-np.floor(i / 2.0), np.ceil(i / 2.0)) for i in shape)
+ y, x = np.meshgrid(*axes)
+ self.meshgrid = (y, x)
+
+ # Create OP vector array
+ self.r = np.sqrt(x ** 2 + y ** 2)
+ # Current ring index
+ self.current_ring = self.ring_start
+
+ self.freq_nyq = int(np.floor(shape[0] / 2.0))
+ self._radii = np.arange(0, self.freq_nyq, self.d_bin)
+
+ @property
+ def radii(self): return self._radii
+
+ @property
+ def nbins(self): return self._nbins
+
+ def get_points_on_ring(self, ring_start, ring_stop):
+
+ arr_inf = self.r >= ring_start
+ arr_sup = self.r < ring_stop
+
+ return arr_inf*arr_sup
+
+ def __iter__(self):
+ return self
+
+ def __next__(self):
+ if self.current_ring < self._nbins:
+ ring = self.get_points_on_ring(self.current_ring * self.d_bin,
+ (self.current_ring + 1) * self.d_bin)
+ else:
+ raise StopIteration
+
+ self.current_ring += 1
+ return np.where(ring), self.current_ring-1
+
+
+class SectionedFourierRingIterator(FourierRingIterator):
+ """
+ An iterator for 2D images. Includes the option use only a specific rotated section of
+ the fourier ring for FRC calculation.
+ """
+ def __init__(self, shape, d_bin, d_angle):
+ """
+ :param shape: Shape of the data
+ :param d_bin: The radius increment size (pixels)
+ :param d_angle: The angle increment size (degrees)
+ """
+
+ FourierRingIterator.__init__(self, shape, d_bin)
+
+ self.d_angle = converters.degrees_to_radians(d_angle)
+
+ y, x = self.meshgrid
+
+ # Create inclination and azimuth angle arrays
+ self.phi = np.arctan2(y, x) + np.pi
+
+ self.phi += self.d_angle/2
+ self.phi[self.phi >= 2*np.pi] -= 2*np.pi
+
+ self._angle = 0
+
+ self.angle_sector = self.get_angle_sector(0, d_bin)
+
+ @property
+ def angle(self):
+ return self._angle
+
+ @angle.setter
+ def angle(self, value):
+ angle = converters.degrees_to_radians(value)
+ self._angle = angle
+ self.angle_sector = self.get_angle_sector(angle, angle + self.d_angle)
+
+ def get_angle_sector(self, phi_min, phi_max):
+ """
+ Use this to extract
+ a section from a sphere that is defined by start and stop angles.
+
+ :param phi_min: the angle at which to start the section, in radians
+ :param phi_max: the angle at which to stop the section, in radians
+ :return:
+
+ """
+ arr_inf = self.phi >= phi_min
+ arr_sup = self.phi < phi_max
+
+ arr_inf_neg = self.phi >= phi_min + np.pi
+ arr_sup_neg = self.phi < phi_max + np.pi
+
+ return arr_inf * arr_sup + arr_inf_neg * arr_sup_neg
+
+ def __getitem__(self, limits):
+ """
+ Get a single conical section of a 2D ring.
+
+ :param limits: a list of parameters (ring_start, ring_stop, angle_min, angle_ma)
+ that are required to define a single section of a fourier ring.
+ """
+ (ring_start, ring_stop, angle_min, angle_max) = limits
+ ring = self.get_points_on_ring(ring_start, ring_stop)
+ cone = self.get_angle_sector(angle_min, angle_max)
+
+ return np.where(ring*cone)
+
+ def __next__(self):
+ if self.current_ring < self._nbins:
+ ring = self.get_points_on_ring(self.current_ring * self.d_bin,
+ (self.current_ring + 1) * self.d_bin)
+ else:
+ raise StopIteration
+
+ self.current_ring += 1
+ return np.where(ring*self.angle_sector), self.current_ring-1
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/iterators/fourier_shell_iterators.py b/Addons/FRCmetric/miplib-public/miplib/data/iterators/fourier_shell_iterators.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ff0e084da3b8b157165d83c19a1adaef0c943b0
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/iterators/fourier_shell_iterators.py
@@ -0,0 +1,391 @@
+# coding=utf-8
+from math import floor
+
+import numpy as np
+
+import miplib.processing.converters as converters
+import miplib.processing.ndarray as nputils
+import miplib.processing.itk as itkutils
+
+
+class FourierShellIterator(object):
+ """
+ A Simple Fourier Shell Iterator. Basically the same as a Fourier Ring Iterator,
+ but for 3D.
+ """
+
+ def __init__(self, shape, d_bin):
+ self.d_bin = d_bin
+
+ # Create Fourier grid
+ axes = (np.arange(-np.floor(i / 2.0), np.ceil(i / 2.0)) for i in shape)
+ z, y, x = np.meshgrid(*axes)
+ self.meshgrid = (z, y, x)
+
+ # Create OP vector array
+ self.r = np.sqrt(x ** 2 + y ** 2 + z ** 2)
+
+ self.shell_start = 0
+ self.shell_stop = int(floor(shape[0] / (2 * self.d_bin))) - 1
+
+ self.current_shell = self.shell_start
+
+ self.freq_nyq = int(np.floor(shape[0] / 2.0))
+
+ self.radii = np.arange(0, self.freq_nyq, self.d_bin)
+
+ @property
+ def steps(self):
+ return self.radii
+
+ @property
+ def nyquist(self):
+ return self.freq_nyq
+
+ def get_points_on_shell(self, shell_start, shell_stop):
+
+ arr_inf = self.r >= shell_start
+ arr_sup = self.r < shell_stop
+
+ return arr_inf*arr_sup
+
+ def __getitem__(self, limits):
+ """
+ Get a points on a Fourier shell specified by the start and stop coordinates
+
+ :param shell_start: The start of the shell (0 ... Nyquist)
+ :param shell_stop: The end of the shell
+
+ :return: Returns the coordinates of the points that are located on
+ the specified shell
+ """
+ (shell_start, shell_stop) = limits
+ shell = self.get_points_on_shell(shell_start, shell_stop)
+ return np.where(shell)
+
+ def __iter__(self):
+ return self
+
+ def __next__(self):
+
+ shell_idx = self.current_shell
+
+ if shell_idx <= self.shell_stop:
+ shell = self.get_points_on_shell(self.current_shell * self.d_bin,
+ (self.current_shell + 1) * self.d_bin)
+ else:
+ raise StopIteration
+
+ self.current_shell += 1
+
+ return np.where(shell), shell_idx
+
+
+class SectionedFourierShellIterator(FourierShellIterator):
+ """
+ A sectioned Fourier Shell iterator. Allows dividing a shell into sections, to access
+ anisotropic features in the Fourier transform.
+ """
+ def __init__(self, shape, d_bin, d_angle):
+ """
+ :param shape: Shape of the data
+ :param d_bin: The radius increment size (pixels)
+ :param d_angle: The angle increment size (degrees)
+ """
+
+ FourierShellIterator.__init__(self, shape, d_bin)
+
+ self.d_angle = converters.degrees_to_radians(d_angle)
+
+ z, y, x = self.meshgrid
+
+ # Create inclination and azimuth angle arrays
+ self.phi = np.arctan2(y, z) + np.pi
+
+ self.phi += self.d_angle/2
+ self.phi[self.phi >= 2*np.pi] -= 2*np.pi
+
+ self.rotation_start = 0
+ self.rotation_stop = 360 / d_angle - 1
+ self.current_rotation = self.rotation_start
+
+ self.angles = np.arange(0, 360, d_angle, dtype=int)
+
+ @property
+ def steps(self):
+ return self.radii, self.angles
+
+ def get_angle_sector(self, phi_min, phi_max):
+ """
+ Assuming a classical spherical coordinate system the azimutahl
+ angle is the angle between the x- and y- axes. Use this to extract
+ a section from a sphere that is defined by start and stop azimuth
+ angles.
+
+ :param phi_min: the angle at which to start the section, in radians
+ :param phi_max: the angle at which to stop the section, in radians
+ :return:
+
+ """
+ arr_inf = self.phi >= phi_min
+ arr_sup = self.phi < phi_max
+
+ arr_inf_neg = self.phi >= phi_min + np.pi
+ arr_sup_neg = self.phi < phi_max + np.pi
+
+ return arr_inf * arr_sup + arr_inf_neg * arr_sup_neg
+
+ def __getitem__(self, limits):
+ """
+ Get a single section of a 3D shell.
+
+ :param shell_start: The start of the shell (0 ... Nyquist)
+ :param shell_stop: The end of the shell
+ :param angle_min: The start of the section (degrees 0-360)
+ :param angle_max: The end of the section
+ :return: Returns the coordinates of the points that are located inside
+ the portion of a shell that intersects with the points on the
+ cone.
+ """
+ (shell_start, shell_stop, angle_min, angle_max) = limits
+ angle_min = converters.degrees_to_radians(angle_min)
+ angle_max = converters.degrees_to_radians(angle_max)
+
+ shell = self.get_points_on_shell(shell_start, shell_stop)
+ cone = self.get_angle_sector(angle_min, angle_max)
+
+ return np.where(shell*cone)
+
+ def __next__(self):
+
+ rotation_idx = self.current_rotation
+ shell_idx = self.current_shell
+
+ if rotation_idx <= self.rotation_stop and shell_idx <= self.shell_stop:
+ shell = self.get_points_on_shell(self.current_shell * self.d_bin,
+ (self.current_shell + 1) * self.d_bin)
+
+ cone = self.get_angle_sector(self.current_rotation * self.d_angle,
+ (self.current_rotation + 1) * self.d_angle)
+ else:
+ raise StopIteration
+
+ if rotation_idx >= self.rotation_stop:
+ self.current_rotation = 0
+ self.current_shell += 1
+ else:
+ self.current_rotation += 1
+
+ return np.where(shell*cone), shell_idx, rotation_idx
+
+
+class HollowSectionedFourierShellIterator(SectionedFourierShellIterator):
+ """
+ A sectioned Fourier shell iterator with the added possibility to remove
+ a central section of the cone, to better deal with interpolation artefacts etc.
+ """
+
+ def __init__(self, shape, d_bin, d_angle, d_extract_angle=5):
+
+ SectionedFourierShellIterator.__init__(self, shape, d_bin, d_angle)
+
+ self.d_extract_angle = converters.degrees_to_radians(d_extract_angle)
+
+ def get_angle_sector(self, phi_min, phi_max):
+ """
+ Assuming a classical spherical coordinate system the azimutahl
+ angle is the angle between the x- and y- axes. Use this to extract
+ a section from a sphere that is defined by start and stop azimuth
+ angles.
+
+ In the hollow implementation a small slice in the center of the section is
+ removed to avoid the effect of resampling when calculating the resolution
+ along the lowest resolution axis (z), on images with very isotropic resolution
+ (e.g. STED).
+
+ :param phi_min: the angle at which to start the section, in radians
+ :param phi_max: the angle at which to stop the section, in radians
+ :return:
+
+ """
+ # Calculate angular sector
+ arr_inf = self.phi >= phi_min
+ arr_sup = self.phi < phi_max
+
+ arr_inf_neg = self.phi >= phi_min + np.pi
+ arr_sup_neg = self.phi < phi_max + np.pi
+
+ full_section = arr_inf * arr_sup + arr_inf_neg * arr_sup_neg
+
+ # Calculate part of the section to exclude
+ sector_center = phi_min + (phi_max-phi_min)/2
+ phi_min_ext = sector_center - self.d_extract_angle
+ phi_max_ext = sector_center + self.d_extract_angle
+
+ arr_inf_ext = self.phi >= phi_min_ext
+ arr_sup_ext = self.phi < phi_max_ext
+
+ arr_inf_neg_ext = self.phi >= phi_min_ext + np.pi
+ arr_sup_neg_ext = self.phi < phi_max_ext + np.pi
+
+ extract_section = arr_inf_ext * arr_sup_ext + arr_inf_neg_ext * arr_sup_neg_ext
+
+ return np.logical_xor(full_section, extract_section)
+
+
+class AxialExcludeSectionedFourierShellIterator(HollowSectionedFourierShellIterator):
+ """
+ A sectioned Fourier shell iterator with the added possibility to remove
+ a central section of the cone, to better deal with interpolation artefacts etc.
+ """
+
+ def __init__(self, shape, d_bin, d_angle, d_extract_angle=5):
+
+ HollowSectionedFourierShellIterator.__init__(self, shape, d_bin, d_angle)
+
+
+ def get_angle_sector(self, phi_min, phi_max):
+ """
+ Assuming a classical spherical coordinate system the azimutahl
+ angle is the angle between the x- and y- axes. Use this to extract
+ a section from a sphere that is defined by start and stop azimuth
+ angles.
+
+ In the hollow implementation a small slice in the center of the section is
+ removed to avoid the effect of resampling when calculating the resolution
+ along the lowest resolution axis (z), on images with very isotropic resolution
+ (e.g. STED).
+
+ :param phi_min: the angle at which to start the section, in radians
+ :param phi_max: the angle at which to stop the section, in radians
+ :return:
+
+ """
+ # Calculate angular sector
+ arr_inf = self.phi >= phi_min
+ arr_sup = self.phi < phi_max
+
+ arr_inf_neg = self.phi >= phi_min + np.pi
+ arr_sup_neg = self.phi < phi_max + np.pi
+
+ full_section = arr_inf * arr_sup + arr_inf_neg * arr_sup_neg
+
+ axis_pos = converters.degrees_to_radians(90) + self.d_angle/2
+ axis_neg = converters.degrees_to_radians(270) + self.d_angle/2
+
+ if phi_min <= axis_pos <= phi_max:
+ phi_min_ext = axis_pos - self.d_extract_angle
+ phi_max_ext = axis_pos + self.d_extract_angle
+
+ elif phi_min <= axis_neg <= phi_max:
+
+ # Calculate part of the section to exclude
+ phi_min_ext = axis_neg - self.d_extract_angle
+ phi_max_ext = axis_neg + self.d_extract_angle
+
+ else:
+ return full_section
+
+ arr_inf_ext = self.phi >= phi_min_ext
+ arr_sup_ext = self.phi < phi_max_ext
+
+ arr_inf_neg_ext = self.phi >= phi_min_ext + np.pi
+ arr_sup_neg_ext = self.phi < phi_max_ext + np.pi
+
+ extract_section = arr_inf_ext * arr_sup_ext + arr_inf_neg_ext * arr_sup_neg_ext
+
+ return np.logical_xor(full_section, extract_section)
+
+
+class RotatingFourierShellIterator(FourierShellIterator):
+ """
+ A 3D Fourier Ring Iterator -- not a Fourier Shell Iterator, but rather
+ single planes are extracted from a 3D shape by rotating the XY plane,
+ as in:
+
+ Nieuwenhuizen, Rpj, K. A. Lidke, and Mark Bates. 2013.
+ “Measuring Image Resolution in Optical Nanoscopy.” Nature
+ advance on (April). https://doi.org/10.1038/nmeth.2448.
+
+ Here the shell iteration is still in 3D (for compatilibility with the others
+ which doesn't make much sense in terms of calculation effort, but it should make
+ it possible to
+
+ """
+
+ def __init__(self, shape, d_bin, d_angle):
+ """
+ :param shape: Shape of the data
+ :param d_bin: The radius increment size (pixels)
+ :param d_angle: The angle increment size (degrees)
+ """
+
+ assert len(shape) == 3, "This iterator assumes a 3D shape"
+
+ FourierShellIterator.__init__(self, shape, d_bin)
+
+ plane = nputils.expand_to_shape(np.ones((1, shape[1], shape[2])), shape)
+
+ self.plane = itkutils.convert_from_numpy(
+ plane,
+ (1, 1, 1))
+
+ self.rotated_plane = plane > 0
+
+ self.rotation_start = 0
+ self.rotation_stop = 360 / d_angle - 1
+ self.current_rotation = self.rotation_start
+
+ self.angles = np.arange(0, 360, d_angle, dtype=int)
+
+ @property
+ def steps(self):
+ return self.radii, self.angles
+
+ def __getitem__(self, limits):
+ """
+ Get a single section of a 3D shell.
+
+ :param shell_start: The start of the shell (0 ... Nyquist)
+ :param shell_stop: The end of the shell
+ :param angle:
+ """
+ (shell_start, shell_stop, angle) = limits
+ rotated_plane = itkutils.convert_from_itk_image(
+ itkutils.rotate_image(self.plane, angle))
+
+ points_on_plane = rotated_plane > 0
+ points_on_shell = self.get_points_on_shell(shell_start, shell_stop)
+
+ return np.where(points_on_plane * points_on_shell)
+
+ def __next__(self):
+
+ rotation_idx = self.current_rotation + 1
+ shell_idx = self.current_shell
+
+ if shell_idx <= self.shell_stop:
+ shell = self.get_points_on_shell(self.current_shell * self.d_bin,
+ (self.current_shell + 1) * self.d_bin)
+ self.current_shell += 1
+
+ elif rotation_idx <= self.rotation_stop:
+
+ rotated_plane = itkutils.convert_from_itk_image(
+ itkutils.rotate_image(self.plane, self.angles[rotation_idx],
+ interpolation='linear'))
+
+ self.rotated_plane = rotated_plane > 0
+ self.current_shell = 0
+ shell_idx = 0
+ self.current_rotation += 1
+
+ shell = self.get_points_on_shell(self.current_shell * self.d_bin,
+ (self.current_shell + 1) * self.d_bin)
+
+ else:
+ raise StopIteration
+
+ return np.where(shell * self.rotated_plane), shell_idx, self.current_rotation
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/messages/__init__.py b/Addons/FRCmetric/miplib-public/miplib/data/messages/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/data/messages/image_writer_wrappers.py b/Addons/FRCmetric/miplib-public/miplib/data/messages/image_writer_wrappers.py
new file mode 100644
index 0000000000000000000000000000000000000000..8863e3d81c09582b6bd8f99d626c41d7cdd3791e
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/data/messages/image_writer_wrappers.py
@@ -0,0 +1,43 @@
+import os
+
+from multiprocessing import Queue
+
+from miplib.data.containers.image import Image
+from miplib.data.io import write as imwrite
+
+
+class ImageWriterBase(object):
+ def write(self, image):
+ pass
+
+
+class QueuedImageWriter(ImageWriterBase):
+ def __init__(self, queue):
+ assert isinstance(queue, Queue)
+
+ self.queue = queue
+
+ def write(self, image):
+ assert isinstance(image, Image)
+
+ self.queue.put(image)
+
+
+class TiffImageWriter(ImageWriterBase):
+ def __init__(self, directory):
+ self.index = 0
+ self.dir = directory
+
+ def __get_full_path(self):
+ filename = "result_{}.tif".format(self.index)
+ return os.path.join(self.dir, filename)
+
+ def write(self, image):
+ assert isinstance(image, Image)
+
+ imwrite.image(self.__get_full_path(), image)
+
+ self.index += 1
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/__init__.py b/Addons/FRCmetric/miplib-public/miplib/processing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/converters.py b/Addons/FRCmetric/miplib-public/miplib/processing/converters.py
new file mode 100644
index 0000000000000000000000000000000000000000..703df4070875dec58a6dd49b0318e6563e43f103
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/converters.py
@@ -0,0 +1,15 @@
+from math import pi
+
+
+def degrees_to_radians(angle):
+ if angle == 0:
+ return 0
+ else:
+ return angle*pi/180
+
+
+def radians_to_degrees(angle):
+ if angle == 0:
+ return 0
+ else:
+ return angle*180.0/pi
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/__init__.py b/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/deconvolve.py b/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/deconvolve.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c9ce76770ad8c8d0c1d494414cb77de494cf1b3
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/deconvolve.py
@@ -0,0 +1,499 @@
+"""
+fusion.py
+
+Copyright (C) 2014, 2016 Sami Koho
+All rights reserved.
+
+This software may be modified and distributed under the terms
+of the BSD license. See the LICENSE file for details.
+
+This file contains the miplib multi-view image fusion algorithms.
+They have been inteded for use with computers that do not support
+hardware GPU acceleration. THe accelerated versions of the same functions
+can be found in fusion_cuda.py. The fftconvolve function that is
+used in this file, can take advantage of MKL optimizations available
+in the Anaconda Accelerate package.
+
+"""
+
+import itertools
+import os
+import shutil
+import sys
+import tempfile
+import time
+import pandas
+
+import numpy
+import miplib.processing.ops_ext as ops_ext
+from scipy.ndimage.interpolation import zoom
+from scipy.ndimage.filters import uniform_filter
+from scipy.signal import fftconvolve, medfilt
+
+import miplib.processing.to_string as ops_output
+import miplib.processing.ndarray
+import miplib.psf.psfgen as psfgen
+from miplib.data.containers import temp_data
+from miplib.data.containers.image import Image
+from miplib.data.messages.image_writer_wrappers import ImageWriterBase
+from miplib.processing.segmentation import masking
+from numpy.fft import fftn, fftshift
+
+import miplib.analysis.resolution.fourier_ring_correlation as frc
+
+class DeconvolutionRL(object):
+ """
+ The Richardson-Lucy fusion is a result of simultaneous deblurring of
+ several 3D volumes.
+ """
+
+ def __init__(self, image, psf, writer, options):
+ """
+ :param image: a MyImage object
+
+ :param options: command line options that control the behavior
+ of the fusion algorithm
+ """
+ assert isinstance(image, Image)
+ assert isinstance(psf, Image)
+ if options.save_intermediate_results:
+ assert issubclass(writer.__class__, ImageWriterBase)
+
+ self.image = image
+ self.psf = psf
+ self.options = options
+ self.writer = writer
+
+ self.image_size = numpy.array(self.image.shape)
+ self.image_spacing = self.image.spacing
+ self.psf_spacing = self.psf.spacing
+ self.imdims = image.ndim
+
+ self.__get_psfs()
+
+ if options.verbose:
+ print("The original image size is %s" % (self.image_size,))
+
+ self.iteration_count = 0
+
+ # Setup blocks
+ self.num_blocks = options.num_blocks
+ self.block_size, self.image_size = self.__calculate_block_and_image_size()
+ self.memmap_directory = tempfile.mkdtemp()
+
+ # Memmap the estimates to reduce memory requirements. This will slow
+ # down the fusion process considerably..
+ if self.options.memmap_estimates:
+ estimate_new_f = os.path.join(self.memmap_directory, "estimate_new.dat")
+ self.estimate_new = Image(numpy.memmap(estimate_new_f, dtype='float32',
+ mode='w+',
+ shape=tuple(self.image_size)), self.image_spacing)
+
+ estimate_f = os.path.join(self.memmap_directory, "estimate.dat")
+ self.estimate = Image(numpy.memmap(estimate_f, dtype=numpy.float32,
+ mode='w+',
+ shape=tuple(self.image_size)), self.image_spacing)
+ else:
+ self.estimate = Image(numpy.zeros(tuple(self.image_size),
+ dtype=numpy.float32), self.image_spacing)
+ self.estimate_new = Image(numpy.zeros(tuple(self.image_size),
+ dtype=numpy.float32), self.image_spacing)
+
+ if not self.options.disable_tau1:
+ prev_estimate_f = os.path.join(self.memmap_directory, "prev_estimate.dat")
+ self.prev_estimate = Image(numpy.memmap(prev_estimate_f, dtype=numpy.float32,
+ mode='w+',
+ shape=tuple(self.image_size)), self.image_spacing)
+
+ padded_block_size = tuple(i + 2 * self.options.block_pad for i in self.block_size)
+ if options.verbose:
+ print("The deconvolution will be run with %i blocks" % self.num_blocks)
+ print("The internal block size is %s" % (padded_block_size,))
+
+ # Create temporary directory and data file.
+ self.column_headers = ('t', 'tau1', 'leak', 'e',
+ 's', 'u', 'n', 'uesu')
+ self._progress_parameters = numpy.empty((self.options.max_nof_iterations, len(self.column_headers)),
+ dtype=numpy.float32)
+
+ # Get initial resolution (in case you are using the FRC based stopping.)
+ if self.options.rl_frc_stop > 0:
+ self.resolution = frc.calculate_single_image_frc(self.image,
+ self.options).resolution["resolution"]
+
+ # Enable automatic background correction with --rl-auto-background
+ if self.options.rl_auto_background:
+ background_mask = masking.make_local_intensity_based_mask(
+ image, threshold=30, kernel_size=60, invert=True)
+ masked_image = Image(image * background_mask, image.spacing)
+ self.options.rl_background = numpy.mean(masked_image[masked_image > 0])
+
+ @property
+ def progress_parameters(self):
+ return pandas.DataFrame(data=self._progress_parameters, columns=self.column_headers)
+
+ def compute_estimate(self):
+ """
+ Calculates a single RL deconvolution estimate. There is no reason to call this
+ function -- it is used internally by the class during fusion process.
+ """
+
+ if self.options.verbose:
+ print('Beginning the computation of the %i. estimate' % self.iteration_count)
+
+ self.estimate_new[:] = numpy.float32(0)
+
+ # Iterate over blocks
+ block_nr = 1
+ iterables = (range(0, m, n) for m, n in zip(self.image_size, self.block_size))
+ pad = self.options.block_pad
+ cache_idx = tuple(slice(pad, pad + block) for block in self.block_size)
+
+ for idx in itertools.product(*iterables):
+
+ estimate_idx = tuple(slice(j, j+k) for j, k in zip(idx, self.block_size))
+
+ index = numpy.array(idx, dtype=int)
+
+ if self.options.block_pad > 0:
+ estimate_block = self.get_padded_block(
+ self.estimate, index.copy())
+ image_block = self.get_padded_block(self.image, index.copy())
+ else:
+ estimate_block = self.estimate[estimate_idx]
+ image_block = self.image[estimate_idx]
+
+ # print "The current block is %i" % block_nr
+ block_nr += 1
+
+ # Execute: cache = convolve(PSF, estimate), non-normalized
+ cache = fftconvolve(estimate_block, self.psf, mode='same')
+
+ if self.options.rl_background != 0:
+ cache += self.options.rl_background
+
+ # ops_ext.inverse_division_inplace(cache, image_block)
+ with numpy.errstate(divide="ignore"):
+ cache = image_block.astype(numpy.float32) / cache
+ cache[cache == numpy.inf] = 0.0
+ cache = numpy.nan_to_num(cache)
+
+ # Execute: cache = convolve(PSF(-), cache), inverse of non-normalized
+ # Convolution with virtual PSFs is performed here as well, if
+ # necessary
+ cache = fftconvolve(cache, self.adj_psf, mode='same')
+
+ self.estimate_new[estimate_idx] = cache[cache_idx]
+
+ if self.options.tv_lambda > 0 and self.iteration_count > 0:
+ if self.estimate.ndim == 2:
+ spacing = list(self.image_spacing)
+ spacing.insert(0,1)
+ dv_est = ops_ext.div_unit_grad(numpy.expand_dims(self.estimate, 0),
+ spacing)[0]
+ else:
+ dv_est = ops_ext.div_unit_grad(self.estimate, self.image_spacing)
+ with numpy.errstate(divide="ignore"):
+ self.estimate_new /= (1.0 - self.options.tv_lambda * dv_est)
+ self.estimate_new[self.estimate_new == numpy.inf] = 0.0
+ self.estimate_new[:] = numpy.nan_to_num(self.estimate_new)
+
+ return ops_ext.update_estimate_poisson(self.estimate,
+ self.estimate_new,
+ self.options.convergence_epsilon)
+
+ def execute(self):
+ """
+ This is the main fusion function
+ """
+
+ save_intermediate_results = self.options.save_intermediate_results
+
+ first_estimate = self.options.first_estimate
+
+ if first_estimate == 'image':
+ self.estimate[:] = self.image[:].astype(numpy.float32)
+ elif first_estimate == 'blurred':
+ self.estimate[:] = uniform_filter(self.image, 3).astype(numpy.float32)
+ elif first_estimate == 'image_mean':
+ self.estimate[:] = numpy.float32(numpy.mean(self.image[:]))
+ elif first_estimate == 'constant':
+ self.estimate[:] = numpy.float32(self.options.estimate_constant)
+ else:
+ raise NotImplementedError(repr(first_estimate))
+
+ self.iteration_count = 0
+ max_count = self.options.max_nof_iterations
+ initial_photon_count = self.image[:].sum()
+
+ bar = ops_output.ProgressBar(0,
+ max_count,
+ totalWidth=40,
+ show_percentage=False)
+
+ self._progress_parameters = numpy.zeros((self.options.max_nof_iterations, len(self.column_headers)),
+ dtype=numpy.float32)
+
+
+ # duofrc_prev = 0
+ # The Fusion calculation starts here
+ # ====================================================================
+ try:
+ while True:
+
+ if (
+ self.options.update_blind_psf > 0 and
+ self.iteration_count > 0 and
+ (self.iteration_count+1) % self.options.update_blind_psf == 0
+ ):
+ self.psf = psfgen.generate_frc_based_psf(Image(self.estimate, self.image_spacing), self.options)
+ self.__get_psfs()
+ self.image = self.estimate.copy()
+
+ info_map = {}
+ ittime = time.time()
+
+ self.prev_estimate[:] = self.estimate.copy()
+
+ e, s, u, n = self.compute_estimate()
+
+ self.iteration_count += 1
+ photon_leak = 1.0 - (e + s + u) / initial_photon_count
+ u_esu = u / (e + s + u)
+
+ tau1 = abs(self.estimate - self.prev_estimate).sum() / abs(
+ self.prev_estimate).sum()
+ info_map['TAU1=%s'] = tau1
+
+ t = time.time() - ittime
+ leak = 100 * photon_leak
+
+ if self.options.verbose:
+ # Update UI
+ info_map['E/S/U/N=%s/%s/%s/%s'] = int(e), int(s), int(u), int(n)
+ info_map['LEAK=%s%%'] = leak
+ info_map['U/ESU=%s'] = u_esu
+ info_map['TIME=%ss'] = t
+
+ bar.updateComment(' ' + ', '.join([k % (ops_output.tostr(info_map[k])) for k in sorted(info_map)]))
+ bar(self.iteration_count)
+ print()
+
+ # Save parameters to file
+ self._progress_parameters[self.iteration_count - 1] = (t, tau1, leak, e, s, u, n, u_esu)
+
+
+ # Save intermediate image
+ if save_intermediate_results:
+ # self.temp_data.save_image(
+ # self.estimate,
+ # 'result_%s.tif' % self.iteration_count
+ # )
+ self.writer.write(Image(self.estimate, self.image_spacing))
+
+ # Check if it's time to stop:
+ if int(u) == 0 and int(n) == 0:
+ stop_message = 'The number of non converging photons reached to zero.'
+ break
+ elif self.iteration_count >= max_count:
+ stop_message = 'The number of iterations reached to maximal count: %s' % max_count
+ break
+ elif not self.options.disable_tau1 and tau1 <= self.options.stop_tau:
+ stop_message = 'Desired tau-threshold achieved'
+ break
+ elif self.options.rl_frc_stop > 0:
+ resolution_new = frc.calculate_single_image_frc(
+ Image(self.estimate, self.image_spacing), self.options).resolution["resolution"]
+ frc_diff = numpy.abs(self.resolution - resolution_new)
+ if frc_diff <= self.options.rl_frc_stop:
+ print('Desired FRC diff reached after {} iterations'.format(
+ self.iteration_count))
+ break
+ else:
+ self.resolution = resolution_new
+
+ # elif self.iteration_count >= 4 and abs(frc_diff) <= .0001:
+ # stop_message = 'FRC stop condition reached'
+ # break
+ else:
+ continue
+
+ except KeyboardInterrupt:
+ stop_message = 'Iteration was interrupted by user.'
+
+ # if self.num_blocks > 1:
+ # self.estimate = self.estimate[0:real_size[0], 0:real_size[1], 0:real_size[2]]
+ if self.options.verbose:
+ print()
+ bar.updateComment(' ' + stop_message)
+ bar(self.iteration_count)
+ print()
+
+ def __get_psfs(self):
+ """
+ Reads the PSFs from the HDF5 data structure and zooms to the same pixel
+ size with the registered images, of selected scale and channel.
+ """
+ psf_orig = self.psf[:]
+
+ # Zoom to the same voxel size
+ zoom_factors = tuple(x / y for x, y in zip(self.psf_spacing, self.image_spacing))
+ psf_new = zoom(psf_orig, zoom_factors).astype(numpy.float32)
+
+ psf_new /= psf_new.sum()
+
+ # Save the zoomed and rotated PSF, as well as its mirrored version
+ self.psf = psf_new
+ if self.imdims == 3:
+ self.adj_psf = psf_new[::-1, ::-1, ::-1]
+ else:
+ self.adj_psf = psf_new[::-1, ::-1]
+
+ def get_result(self):
+ """
+ Show fusion result. This is a temporary solution for now
+ calling Fiji through ITK. An internal viewer would be
+ preferable.
+ """
+
+ return Image(self.estimate, self.image_spacing)
+
+ def __calculate_block_and_image_size(self):
+ """
+ Calculate the block size and the internal image size for a given
+ number of blocks. 1,2,4 or 8 blocks are currently supported.
+
+ """
+ block_size = self.image_size
+ image_size = self.image_size
+
+ if self.num_blocks == 1:
+ return block_size, image_size
+ elif self.num_blocks == 2:
+ multiplier3 = numpy.array([2, 1, 1])
+ multiplier2 = numpy.array([2, 1])
+ elif self.num_blocks == 4:
+ multiplier3 = numpy.array([4, 1, 1])
+ multiplier2 = numpy.array([2, 2])
+ elif self.num_blocks == 8:
+ multiplier3 = numpy.array([4, 2, 1])
+ multiplier2 = numpy.array([4, 2])
+ elif self.num_blocks == 12:
+ multiplier3 = numpy.array([4, 2, 2])
+ multiplier2 = numpy.array([4, 3])
+ elif self.num_blocks == 24:
+ multiplier3 = numpy.array([4, 3, 2])
+ multiplier2 = numpy.array([6, 4])
+ elif self.num_blocks == 48:
+ multiplier3 = numpy.array([4, 4, 3])
+ multiplier2 = numpy.array([8, 6])
+ elif self.num_blocks == 64:
+ multiplier3 = numpy.array([4, 4, 4])
+ multiplier2 = numpy.array([8, 8])
+ elif self.num_blocks == 96:
+ multiplier3 = numpy.array([6, 4, 4])
+ multiplier2 = numpy.array([12, 8])
+ elif self.num_blocks == 144:
+ multiplier3 = numpy.array([4, 6, 6])
+ multiplier2 = numpy.array([12, 12])
+ else:
+ raise NotImplementedError
+
+ if self.imdims == 2:
+ block_size = numpy.ceil(self.image_size.astype(numpy.float16) / multiplier2).astype(numpy.int64)
+ image_size += (multiplier2 * block_size - image_size)
+ else:
+ block_size = numpy.ceil(self.image_size.astype(numpy.float16) / multiplier3).astype(numpy.int64)
+ image_size += (multiplier3 * block_size - image_size)
+
+ return block_size, image_size
+
+ def get_padded_block(self, image, block_start_index):
+ """
+ Get a padded block from the self.estimate
+
+ Parameters
+ ----------
+ :param image: a numpy.ndarray or or its subclass
+ :param block_start_index The real block start index, not considering the padding
+
+ Returns
+ -------
+ Returns the padded estimate block as a numpy array.
+
+ """
+
+ block_pad = self.options.block_pad
+ image_size = self.image_size
+ ndims = self.imdims
+
+ # Apply padding
+ end_index = block_start_index + self.block_size + block_pad
+ start_index = block_start_index - block_pad
+
+ idx = tuple(slice(start, stop) for start, stop in zip(start_index, end_index))
+
+ # If the padded block fits within the image boundaries, nothing special
+ # is needed to extract it. Normal numpy slicing notation is used.
+ if (image_size >= end_index).all() and (start_index >= 0).all():
+ return image[idx]
+
+ else:
+ block_size = tuple(i + 2 * block_pad for i in self.block_size)
+ # Block outside the image boundaries will be filled with zeros.
+ block = numpy.zeros(block_size)
+ # If the start_index is close to the image boundaries, it is very
+ # probable that padding will introduce negative start_index values.
+ # In such case the first pixel index must be corrected.
+ if (start_index < 0).any():
+ block_start = numpy.negative(start_index.clip(max=0))
+ image_start = start_index + block_start
+ else:
+ block_start = (0,) * ndims
+ image_start = start_index
+
+ # If the padded block is larger than the image size the
+ # block_size must be adjusted.
+ if not (image_size >= end_index).all():
+ block_crop = end_index - image_size
+ block_crop[block_crop < 0] = 0
+ block_end = block_size - block_crop
+ else:
+ block_end = block_size
+
+ end_index = start_index + block_end
+
+ block_idx = tuple(slice(start, stop) for start, stop in zip(block_start, block_end))
+ image_idx = tuple(slice(start, stop) for start, stop in zip(image_start, end_index))
+
+ block[block_idx] = image[image_idx]
+
+ return block
+
+ def get_8bit_result(self, denoise=False):
+ """
+ Returns the current estimate (the fusion result) as an 8-bit uint, rescaled
+ to the full 0-255 range.
+ """
+ if denoise:
+ image = medfilt(self.estimate)
+ else:
+ image = self.estimate
+
+ image *= (255.0 / image.max())
+ image[image < 0] = 0
+ return Image(image.astype(numpy.uint8), self.image_spacing)
+
+ # def get_saved_data(self):
+ # return pandas.DataFrame(columns=self.column_headers, data=self._progress_parameters)
+
+ def close(self):
+ if self.options.memmap_estimates:
+ del self.estimate
+ del self.estimate_new
+ if not self.options.disable_tau1:
+ del self.prev_estimate
+
+ shutil.rmtree(self.memmap_directory)
+
+ #self.temp_data.close_data_file()
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/deconvolve_cuda.py b/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/deconvolve_cuda.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5f95397190a105df68f828cd1f9aa1f87353696
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/deconvolve_cuda.py
@@ -0,0 +1,144 @@
+# coding=utf-8
+"""
+deconvolve.py
+
+Copyright (C) 2016 Sami Koho
+All rights reserved.
+
+This software may be modified and distributed under the terms
+of the BSD license. See the LICENSE file for details.
+
+This file contains the GPU accelerated miplib deconvolution
+algorithms. The MultiViewFusionRLCuda class implements all the same methods
+as the MultiViewFusionRL class, for non-accelerated iterative image fusion.
+
+"""
+
+import itertools
+
+import numpy as np
+import miplib.processing.ops_ext as ops_ext
+import cupy as cp
+from cupyx.scipy import fftpack
+from . import deconvolve
+import miplib.processing.ndarray as ops_array
+
+
+class DeconvolutionRLCuda(deconvolve.DeconvolutionRL):
+ """
+ This class implements GPU accelerated versions of the iterative image
+ fusion algorithms discussed in:
+
+ Koho, S., Deguchi, T., & Hanninen, P. E. (2015). A software tool for
+ tomographic axial superresolution in STED microscopy. Journal of Microscopy,
+ 260(2), 208–218. http://doi.org/10.1111/jmi.12287
+
+ MultiViewFusionRLCuda is inherits most of its functionality from
+ MultiViewFusionRL (see deconvolve.py).
+ """
+ def __init__(self, image, psf, writer, options):
+ """
+ :param image: the image as a Image object
+ :param psf: the psf as an Image object
+
+ :param options: command line options that control the behavior
+ of the fusion algorithm
+ """
+ deconvolve.DeconvolutionRL.__init__(self, image, psf, writer, options)
+ self._fft_plan = fftpack.get_fft_plan(cp.zeros(self.block_size, dtype=cp.complex64))
+ self.__get_fourier_psfs()
+
+ def compute_estimate(self):
+ """
+ Calculates a single RL fusion estimate. There is no reason to call this
+ function -- it is used internally by the class during fusion process.
+ """
+ self.estimate_new[:] = np.zeros(self.image_size, dtype=np.float32)
+
+ # Iterate over blocks
+ iterables = (range(0, m, n) for m, n in zip(self.image_size, self.block_size))
+ pad = self.options.block_pad
+ block_idx = tuple(slice(pad, pad + block) for block in self.block_size)
+
+ for pos in itertools.product(*iterables):
+
+ estimate_idx = tuple(slice(j, j + k) for j, k in zip(pos, self.block_size))
+ index = np.array(pos, dtype=int)
+
+ if self.options.block_pad > 0:
+ h_estimate_block = self.get_padded_block(self.estimate, index.copy()).astype(np.complex64)
+ else:
+ h_estimate_block = self.estimate[estimate_idx].astype(np.complex64)
+
+ # # Execute: cache = convolve(PSF, estimate), non-normalized
+ h_estimate_block_new = self._fft_convolve(h_estimate_block, self.psf_fft)
+
+ # Execute: cache = data/cache. Add background bias if requested.
+ h_image_block = self.get_padded_block(self.image, index.copy()).astype(np.float32)
+ if self.options.rl_background != 0:
+ h_image_block += self.options.rl_background
+ ops_ext.inverse_division_inplace(h_estimate_block_new, h_image_block)
+
+ # Execute correlation with PSF
+ h_estimate_block_new = self._fft_convolve(h_estimate_block_new, self.adj_psf_fft).real
+
+ # Get new weights
+ self.estimate_new[estimate_idx] = h_estimate_block_new[block_idx]
+
+ # TV Regularization (doesn't seem to do anything miraculous).
+ if self.options.tv_lambda > 0 and self.iteration_count > 0:
+ dv_est = ops_ext.div_unit_grad(self.estimate, self.image_spacing)
+ self.estimate_new = ops_array.safe_divide(self.estimate_new,
+ (1.0 - self.options.rltv_lambda * dv_est))
+
+ # Update estimate inplace. Get convergence statistics.
+ return ops_ext.update_estimate_poisson(self.estimate,
+ self.estimate_new,
+ self.options.convergence_epsilon)
+
+ def _fft_convolve(self, h_data, h_kernel):
+ """
+ Calculate a convolution on GPU, using FFTs.
+
+ :param h_data: a Numpy array with the data to convolve
+ :param h_kernel: a Numpy array with the convolution kernel. The kernel
+ should already be in Fourier domain (To avoid repeating the transform at
+ every iteration.)
+ """
+ #todo: See whether to add back streams. I removed them on Cupy refactor.
+
+ d_data = cp.asarray(h_data)
+ d_data = fftpack.fftn(d_data, overwrite_x=True, plan=self._fft_plan)
+
+ d_kernel = cp.asarray(h_kernel)
+ d_data *= d_kernel
+
+ d_data = fftpack.ifftn(d_data, overwrite_x=True, plan=self._fft_plan)
+ return cp.asnumpy(d_data)
+
+
+ def __get_fourier_psfs(self):
+ """
+ Pre-calculates the PSFs during image fusion process.
+ """
+ psf = self.psf[:]
+ if self.imdims == 3:
+ adj_psf = psf[::-1, ::-1, ::-1]
+ else:
+ adj_psf = psf[::-1, ::-1]
+
+ padded_block_size = tuple(self.block_size + 2*self.options.block_pad)
+
+ psf_fft = ops_array.expand_to_shape(psf, padded_block_size).astype(np.complex64)
+ adj_psf_fft = ops_array.expand_to_shape(adj_psf, padded_block_size).astype(np.complex64)
+ psf_fft = np.fft.fftshift(psf_fft)
+ adj_psf_fft = np.fft.fftshift(adj_psf_fft)
+
+ self.psf_fft = np.fft.fftn(psf_fft)
+ self.adj_psf_fft = np.fft.fftn(adj_psf_fft)
+
+
+
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/wiener.py b/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/wiener.py
new file mode 100644
index 0000000000000000000000000000000000000000..7116bfcffa31bf880b997a336be817f0847d3862
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/wiener.py
@@ -0,0 +1,43 @@
+import numpy as np
+
+from numpy.fft import fftn, ifftn, fftshift
+
+from miplib.data.containers.image import Image
+import miplib.processing.image as imops
+import miplib.processing.ndarray as arrayops
+
+#todo: Speed up with CUDA/Multithreading. Functions are ready in the ufuncs.py
+
+def wiener_deconvolution(image, psf, snr=30, add_pad=0):
+ assert isinstance(image, Image)
+ assert isinstance(psf, Image)
+
+ image_s = Image(image.copy(), image.spacing)
+ orig_shape = image.shape
+
+ if image.ndim != psf.ndim:
+ raise ValueError("Image and psf dimensions do not match")
+
+ if psf.spacing != image.spacing:
+ psf = imops.zoom_to_spacing(psf, image.spacing)
+
+ if add_pad != 0:
+ new_shape = list(i + 2*add_pad for i in image_s.shape)
+ image_s = imops.zero_pad_to_shape(image_s, new_shape)
+
+ if psf.shape != image_s.shape:
+ psf = imops.zero_pad_to_shape(psf, image_s.shape)
+
+ psf /= psf.max()
+
+ psf_f = fftn(fftshift(psf))
+
+ wiener = arrayops.safe_divide(np.abs(psf_f)**2/(np.abs(psf_f)**2 + snr), psf_f)
+
+ image_s = fftn(image_s)
+
+ image_s = Image(np.abs(ifftn(image_s * wiener).real), image.spacing)
+
+ return imops.remove_zero_padding(image_s, orig_shape)
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/wiener_cuda.py b/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/wiener_cuda.py
new file mode 100644
index 0000000000000000000000000000000000000000..59a6278144b078f6faecee4578add44432dfe81a
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/deconvolution/wiener_cuda.py
@@ -0,0 +1,60 @@
+import numpy as np
+import cupy as cp
+from cupyx.scipy.fftpack import fftn, ifftn, get_fft_plan
+from numpy.fft import fftshift
+
+from miplib.data.containers.image import Image
+import miplib.processing.image as imops
+import miplib.processing.ndarray as arrayops
+
+
+def wiener_deconvolution(image, psf, snr=30, add_pad=0):
+ """ A GPU accelerated implementation of a linear Wiener filter. Some effort is made
+ to allow processing even relatively large images, but some kind of block-based processing
+ (as in the RL implementation) may be required in some cases."""
+ assert isinstance(image, Image)
+ assert isinstance(psf, Image)
+
+ image_s = Image(image.copy(), image.spacing)
+ orig_shape = image.shape
+
+ if image.ndim != psf.ndim:
+ raise ValueError("Image and psf dimensions do not match")
+
+ if psf.spacing != image.spacing:
+ psf = imops.zoom_to_spacing(psf, image.spacing)
+
+ if add_pad != 0:
+ new_shape = list(i + 2 * add_pad for i in image_s.shape)
+ image_s = imops.zero_pad_to_shape(image_s, new_shape)
+
+ if psf.shape != image_s.shape:
+ psf = imops.zero_pad_to_shape(psf, image_s.shape)
+
+ psf /= psf.max()
+ psf = fftshift(psf)
+
+ psf_dev = cp.asarray(psf.astype(np.complex64))
+ with get_fft_plan(psf_dev):
+ psf_dev = fftn(psf_dev, overwrite_x=True)
+
+ below = cp.asnumpy(psf_dev)
+ psf_abs = cp.abs(psf_dev) ** 2
+ psf_abs /= (psf_abs + snr)
+ above = cp.asnumpy(psf_abs)
+ psf_abs = None
+ psf_dev = None
+
+ image_dev = cp.asarray(image_s.astype(np.complex64))
+ with get_fft_plan(image_dev):
+ image_dev = fftn(image_dev, overwrite_x=True)
+
+ wiener_dev = cp.asarray(arrayops.safe_divide(above, below))
+
+ image_dev *= wiener_dev
+
+ result = cp.asnumpy(cp.abs(ifftn(image_dev, overwrite_x=True)).real)
+ result = Image(result, image.spacing)
+
+ return imops.remove_zero_padding(result, orig_shape)
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/fftutils.py b/Addons/FRCmetric/miplib-public/miplib/processing/fftutils.py
new file mode 100644
index 0000000000000000000000000000000000000000..856410ceb47db90dec2ead0139b8257146499766
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/fftutils.py
@@ -0,0 +1,161 @@
+import numpy as np
+from math import floor
+from miplib.data.containers.image import Image
+from miplib.data.coordinates import polar as indexers
+from miplib.processing import windowing, ndarray
+
+
+def fft(array, interpolation=1.0, window='tukey', *kwargs):
+ """ A n-dimensional Forward Discrete Fourier transform with some extra bells and whistles
+ added on top of the standard Numpy method.
+
+ :param array: the image to be transformed
+ :type array: np.ndarray
+ :param interpolation: Add "interpolation" to the FFT by zero-padding prior to transform.
+ This is expressed as a multiple of the image size.
+ :type interpolation: float
+ :param window: a window function to apply. 'tukey' or 'hamming'
+ :type window: str or None
+ :return: the complex Fourier transform of the input array
+ """
+
+ # Apply a Window if requested
+ if window is None:
+ pass
+ elif window == 'tukey':
+ array = windowing.apply_tukey_window(array, *kwargs)
+ elif window == 'hamming':
+ array = windowing.apply_hamming_window(array)
+
+ # Add extra padding
+ if interpolation > 1.0:
+ new_shape = tuple(int(interpolation * i) for i in array.shape)
+ array = ndarray.expand_to_shape(array, new_shape)
+
+ # Transform forward
+ array = np.fft.fftshift(np.fft.fftn(array))
+
+ return array
+
+
+def ifft(array_f, interpolation=1.0):
+ """ A n-dimensional Inverse Discrete Fourier transform with some extra bells and whistles
+ added on top of the standard Numpy method. Assumes a FFT shifted Fourier domain image.
+
+ :param array_f: the image to be transformed
+ :type array_f: np.ndarray
+ :param interpolation: add interpolation, by defining a value > 1.0. Corresponds to
+ enlargement of the result image.
+ :type interpolation: float
+ :return: returns the iFFTd array
+ """
+
+ # Add padding
+ if interpolation > 1.0:
+ new_shape = tuple(int(interpolation * i) for i in array_f.shape)
+ array_f = ndarray.expand_to_shape(array_f, new_shape)
+
+ # Transform back
+ iarray_f = np.fft.ifftn(np.fft.fftshift(array_f))
+
+ return iarray_f
+
+
+def ideal_fft_filter(image, threshold, kind='low'):
+ """
+ An ideal high/low pass frequency domain noise filter.
+ :param image: an Image object
+ :param threshold: threshold value [0,1], where 1 corresponds
+ to the maximum frequency.
+ :param kind: filter type 'low' for low-pass, 'high' for high pass
+ :return: returns the filtered Image.
+ """
+ assert isinstance(image, Image)
+
+ spacing = image.spacing
+
+ fft_image = np.fft.fftshift(np.fft.fftn(image))
+
+ if kind == 'low':
+ indexer = indexers.PolarLowPassIndexer(image.shape)
+ elif kind == 'high':
+ indexer = indexers.PolarHighPassIndexer(image.shape)
+ else:
+ raise ValueError("Unknown filter kind: {}".format(kind))
+
+ r_max = floor(min(image.shape) / 2)
+
+ fft_image *= indexer[threshold*r_max]
+
+ return Image(np.abs(np.fft.ifftn(fft_image).real), spacing)
+
+
+def butterworth_fft_filter(image, threshold, n=3):
+ """Create low-pass 2D Butterworth filter.
+ :Parameters:
+ size : tuple
+ size of the filter
+ cutoff : float
+ relative cutoff frequency of the filter (0 - 1.0)
+ n : int, optional
+ order of the filter, the higher n is the sharper
+ the transition is.
+ :Returns:
+ numpy.ndarray
+ filter kernel in 2D centered
+ """
+ if not 0 < threshold <= 1.0:
+ raise ValueError('Cutoff frequency must be between 0 and 1.0')
+
+ if not isinstance(n, int):
+ raise ValueError('n must be an integer >= 1')
+
+ assert isinstance(image, Image)
+
+ spacing = image.spacing
+
+ # Create Fourier grid
+ r = indexers.SimplePolarIndexer(image.shape).r
+
+ threshold *= image.shape[0]
+
+ butter = 1.0 / (1.0 + (r / threshold) ** (2 * n)) # The filter
+
+ fft_image = np.fft.fftshift(np.fft.fftn(image))
+ fft_image *= butter
+
+ return Image(np.abs(np.fft.ifftn(fft_image).real), spacing)
+
+
+def gaussian_fft_filter(image, threshold):
+ """
+ Create low-pass 2D Gaussian filter.
+ :Parameters:
+ size : tuple
+ size of the filter
+ cutoff : float
+ relative cutoff frequency of the filter (0 - 1.0)
+ n : int, optional
+ order of the filter, the higher n is the sharper
+ the transition is.
+ :Returns:
+ numpy.ndarray: filter kernel in 2D centered
+ """
+ if not 0 < threshold <= 1.0:
+ raise ValueError('Cutoff frequency must be between 0 and 1.0')
+
+ assert isinstance(image, Image)
+
+ spacing = image.spacing
+
+ # Create Fourier grid
+ r = indexers.SimplePolarIndexer(image.shape).r
+
+ r /= image.shape[0]
+
+ gauss = np.exp(-(r**2/(2*(threshold**2))))
+ fft_image = np.fft.fftshift(np.fft.fftn(image))
+ fft_image *= gauss
+
+ return Image(np.abs(np.fft.ifftn(fft_image).real), spacing)
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/fusion/__init__.py b/Addons/FRCmetric/miplib-public/miplib/processing/fusion/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/fusion/fusion.py b/Addons/FRCmetric/miplib-public/miplib/processing/fusion/fusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd885b8f67507ba92918a7c8bd8f0ba40487fec3
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/fusion/fusion.py
@@ -0,0 +1,591 @@
+"""
+fusion.py
+
+Copyright (C) 2014, 2016 Sami Koho
+All rights reserved.
+
+This software may be modified and distributed under the terms
+of the BSD license. See the LICENSE file for details.
+
+This file contains the miplib multi-view image fusion algorithms.
+They have been inteded for use with computers that do not support
+hardware GPU acceleration. THe accelerated versions of the same functions
+can be found in fusion_cuda.py. The fftconvolve function that is
+used in this file, can take advantage of MKL optimizations available
+in the Anaconda Accelerate package.
+
+"""
+import itertools
+import os
+import shutil
+import tempfile
+import time
+
+import numpy as np
+import pandas
+
+import miplib.processing.ops_ext as ops_ext
+from scipy.ndimage.interpolation import zoom
+from scipy.signal import fftconvolve, medfilt
+
+import miplib.processing.ndarray as ops_array
+import miplib.processing.to_string as ops_output
+from miplib.data.containers import image_data, image
+from miplib.data.containers.image import Image
+from . import utils as fusion_utils
+from miplib.utils.generic import isiterable
+
+class MultiViewFusionRL(object):
+ """
+ The Richardson-Lucy fusion is a result of simultaneous deblurring of
+ several 3D volumes.
+ """
+
+ def __init__(self, data, writer, options):
+ """
+ :param data: a ImageData object
+
+ :param options: command line options that control the behavior
+ of the fusion algorithm
+ """
+ assert isinstance(data, image_data.ImageData)
+
+ self.data = data
+ self.options = options
+ self.writer = writer
+
+
+ # Select views to fuse
+ if self.options.fuse_views == -1:
+ self.views = range(self.data.get_number_of_images("registered"))
+ else:
+ self.views = self.options.fuse_views
+
+ self.n_views = len(self.views)
+
+ # Get weights
+ self.weights = np.zeros(self.n_views, dtype=np.float32)
+
+ for idx, view in enumerate(self.views):
+ self.data.set_active_image(view, self.options.channel,
+ self.options.scale, "registered")
+
+ self.weights[idx] = self.data.get_max()
+
+ self.weights /= self.weights.sum()
+
+ # Get background correction
+ background = self.options.fusion_background
+ if not isiterable(background) and background:
+ self.background = np.full(self.n_views, background)
+ elif isiterable(background):
+ if len(background) == self.n_views:
+ self.background = np.asarray(background)
+ elif len(background) == self.data.get_number_of_images("registered"):
+ self.background = np.asarray(background)[self.views]
+ else:
+ raise ValueError("Invalid background definition length.")
+ else:
+ self.background = np.zeros(self.n_views)
+
+ # Get image size
+ self.data.set_active_image(0, self.options.channel, self.options.scale,
+ "registered")
+ self.image_size = self.data.get_image_size()
+ self.imdims = len(self.image_size)
+
+ print("The original image size is {}".format(tuple(self.image_size)))
+
+ self.voxel_size = self.data.get_voxel_size()
+ self.iteration_count = 0
+
+ # Setup blocks
+ self.num_blocks = options.num_blocks
+ self.block_size, self.image_size = self.__calculate_block_and_image_size()
+ self.memmap_directory = tempfile.mkdtemp()
+
+ # Memmap the estimates to reduce memory requirements. This will slow
+ # down the fusion process considerably..
+ if self.options.memmap_estimates:
+ estimate_new_f = os.path.join(self.memmap_directory, "estimate_new.dat")
+ self.estimate_new = Image(np.memmap(estimate_new_f, dtype='float32',
+ mode='w+',
+ shape=tuple(self.image_size)), self.voxel_size)
+
+ estimate_f = os.path.join(self.memmap_directory, "estimate.dat")
+ self.estimate = Image(np.memmap(estimate_f, dtype=np.float32,
+ mode='w+',
+ shape=tuple(self.image_size)), self.voxel_size)
+ else:
+ self.estimate = Image(np.zeros(tuple(self.image_size),
+ dtype=np.float32), self.voxel_size)
+ self.estimate_new = Image(np.zeros(tuple(self.image_size),
+ dtype=np.float32), self.voxel_size)
+
+ if not self.options.disable_tau1:
+ prev_estimate_f = os.path.join(self.memmap_directory, "prev_estimate.dat")
+ self.prev_estimate = np.memmap(prev_estimate_f, dtype=np.float32,
+ mode='w+',
+ shape=tuple(self.image_size))
+ # Setup PSFs
+ self.psfs = []
+ self.adj_psfs = []
+ self.__get_psfs()
+ if "opt" in self.options.fusion_method:
+ self.virtual_psfs = []
+ self.__compute_virtual_psfs()
+ else:
+ pass
+
+ print("The fusion will be run with %i blocks" % self.num_blocks)
+ padded_block_size = tuple(i + 2 * self.options.block_pad for i in self.block_size)
+ print("The internal block size is %s" % (padded_block_size,))
+
+ self.column_headers = ('t', 'tau1', 'leak', 'e',
+ 's', 'u', 'n', 'uesu')
+ self._progress_parameters = np.empty((self.options.max_nof_iterations, len(self.column_headers)),
+ dtype=np.float32)
+
+ @property
+ def progress_parameters(self):
+ return pandas.DataFrame(data=self._progress_parameters, columns=self.column_headers)
+
+ def compute_estimate(self):
+ """
+ Calculates a single RL fusion estimate. There is no reason to call this
+ function -- it is used internally by the class during fusion process.
+ """
+
+ print('Beginning the computation of the %i. estimate' % self.iteration_count)
+
+ if "multiplicative" in self.options.fusion_method:
+ self.estimate_new[:] = np.float32(1.0)
+ else:
+ self.estimate_new[:] = np.float32(0)
+
+ # Iterate over views
+ for idx, view in enumerate(self.views):
+
+ # Get PSFs for view
+ psf = self.psfs[idx]
+ adj_psf = self.adj_psfs[idx]
+
+ self.data.set_active_image(view, self.options.channel,
+ self.options.scale, "registered")
+
+ weighting = self.weights[idx]
+ background = self.background[idx]
+
+ iterables = (range(0, m, n) for m, n in zip(self.image_size, self.block_size))
+ pad = self.options.block_pad
+ block_idx = tuple(slice(pad, pad + block) for block in self.block_size)
+
+ # Iterate over blocks
+ for pos in itertools.product(*iterables):
+
+ estimate_idx = tuple(slice(j, j + k) for j, k in zip(pos, self.block_size))
+ index = np.array(pos, dtype=int)
+ if self.options.block_pad > 0:
+ estimate_block = self.get_padded_block(self.estimate, index.copy())
+ else:
+ estimate_block = self.estimate[estimate_idx]
+
+ # Execute: cache = convolve(PSF, estimate), non-normalized
+ estimate_block_new = fftconvolve(estimate_block, psf, mode='same')
+
+ # Apply weighting
+ estimate_block_new *= weighting
+
+ # Add background bias
+ estimate_block_new += background
+
+ # Execute: cache = data/cache
+ image_block = self.data.get_registered_block(self.block_size,
+ self.options.block_pad,
+ index.copy())
+
+ estimate_block_new = ops_array.safe_divide(image_block, estimate_block_new)
+
+ # Execute: cache = convolve(PSF(-), cache), inverse of non-normalized
+ # Convolution with virtual PSFs is performed here as well, if
+ # necessary
+ estimate_block_new = fftconvolve(estimate_block_new, adj_psf, mode='same')
+
+ self._write_estimate_block(estimate_block_new, estimate_idx, block_idx)
+
+ # Divide with the number of projections
+ if "summative" in self.options.fusion_method:
+ self.estimate_new *= (1.0 / self.n_views)
+ else:
+ self.estimate_new[:] = ops_array.nroot(self.estimate_new,
+ self.n_views)
+
+ # TV Regularization (doesn't seem to do anything miraculous).
+ if self.options.tv_lambda > 0 and self.iteration_count > 0:
+ dv_est = ops_ext.div_unit_grad(self.estimate, self.voxel_size)
+ self.estimate_new = ops_array.safe_divide(self.estimate, (1.0 - self.options.rltv_lambda * dv_est))
+
+ return ops_ext.update_estimate_poisson(self.estimate,
+ self.estimate_new,
+ self.options.convergence_epsilon)
+
+ def _write_estimate_block(self, block, estimate_idx, block_idx):
+ """ Update the contribution from a single view to the new estimate
+ :param block: the data block to save
+ :param estimate_idx: the position of the block inside the image
+ :param block_idx: the position of valid data inside the block (excluding padding)
+ :return: Nothing
+ """
+
+ if self.options.block_pad == 0:
+ if "multiplicative" in self.options.fusion_method:
+ self.estimate_new[estimate_idx] *= block
+ else:
+ self.estimate_new[estimate_idx] += block
+ else:
+ if "multiplicative" in self.options.fusion_method:
+ self.estimate_new[estimate_idx] *= block[block_idx]
+
+ else:
+ # print "The block size is ", self.block_size
+ self.estimate_new[estimate_idx] += block[block_idx]
+
+ def execute(self):
+ """
+ This is the main fusion function
+ """
+
+ print("Preparing image fusion.")
+
+ save_intermediate_results = self.options.save_intermediate_results
+
+ first_estimate = self.options.first_estimate
+
+ self.data.set_active_image(0,
+ self.options.channel,
+ self.options.scale,
+ "registered")
+
+ if first_estimate == 'first_image':
+ self.estimate[:] = self.data[:].astype(np.float32)
+ elif first_estimate == 'first_image_mean':
+ self.estimate[:] = np.float32(np.mean(self.data[:]))
+ elif first_estimate == 'sum_of_originals':
+ self.estimate[:] = fusion_utils.sum_of_all(self.data,
+ self.options.channel,
+ self.options.scale)
+ elif first_estimate == 'sum_of_registered':
+ self.estimate[:] = fusion_utils.sum_of_all(self.data,
+ self.options.channel,
+ self.options.scale,
+ "registered")
+ elif first_estimate == 'simple_fusion':
+ self.estimate[:] = fusion_utils.simple_fusion(self.data,
+ self.options.channel,
+ self.options.scale)
+ elif first_estimate == 'average_af_all':
+ self.estimate[:] = fusion_utils.average_of_all(self.data,
+ self.options.channel,
+ self.options.scale,
+ "registered")
+ elif first_estimate == 'constant':
+ self.estimate[:] = np.float32(self.options.estimate_constant)
+ else:
+ raise NotImplementedError(repr(first_estimate))
+
+ self.iteration_count = 0
+ max_count = self.options.max_nof_iterations
+ initial_photon_count = self.data[:].sum()
+
+ bar = ops_output.ProgressBar(0,
+ max_count,
+ totalWidth=40,
+ show_percentage=False)
+
+ self._progress_parameters = np.zeros((self.options.max_nof_iterations,
+ len(self.column_headers)),
+ dtype=np.float32)
+
+ # The Fusion calculation starts here
+ # ====================================================================
+ try:
+ while True:
+
+ info_map = {}
+ ittime = time.time()
+
+ if not self.options.disable_tau1:
+ self.prev_estimate[:] = self.estimate.copy()
+
+ e, s, u, n = self.compute_estimate()
+
+ self.iteration_count += 1
+ photon_leak = 1.0 - (e + s + u) / initial_photon_count
+ u_esu = u / (e + s + u)
+
+ if not self.options.disable_tau1:
+ tau1 = abs(self.estimate - self.prev_estimate).sum() / abs(
+ self.prev_estimate).sum()
+ info_map['TAU1=%s'] = tau1
+
+ t = time.time() - ittime
+ leak = 100 * photon_leak
+
+ # Update UI
+ info_map['E/S/U/N=%s/%s/%s/%s'] = int(e), int(s), int(u), int(n)
+ info_map['LEAK=%s%%'] = leak
+ info_map['U/ESU=%s'] = u_esu
+ info_map['TIME=%ss'] = t
+ bar.updateComment(' ' + ', '.join([k % (ops_output.tostr(info_map[k])) for k in sorted(info_map)]))
+ bar(self.iteration_count)
+ print()
+
+ # Save parameters to file
+ self._progress_parameters[self.iteration_count - 1] = (t, tau1, leak, e, s, u, n, u_esu)
+
+ # Save intermediate image
+ if save_intermediate_results:
+ self.writer.write(Image(self.estimate, self.voxel_size))
+
+ # Check if it's time to stop:
+ if int(u) == 0 and int(n) == 0:
+ stop_message = 'The number of non converging photons reached to zero.'
+ break
+ elif self.iteration_count >= max_count:
+ stop_message = 'The number of iterations reached to maximal count: %s' % max_count
+ break
+ elif not self.options.disable_tau1 and tau1 <= self.options.rltv_stop_tau:
+ stop_message = 'Desired tau-threshold achieved'
+ break
+ else:
+ continue
+
+ except KeyboardInterrupt:
+ stop_message = 'Iteration was interrupted by user.'
+
+ # if self.num_blocks > 1:
+ # self.estimate = self.estimate[0:real_size[0], 0:real_size[1], 0:real_size[2]]
+
+ print()
+ bar.updateComment(' ' + stop_message)
+ bar(self.iteration_count)
+ print()
+
+ # region Prepare PSFs
+ def __get_psfs(self):
+ """
+ Reads the PSFs from the HDF5 data structure and zooms to the same pixel
+ size with the registered images, of selected scale and channel.
+ """
+ self.data.set_active_image(0, self.options.channel,
+ self.options.scale, "registered")
+ image_spacing = self.data.get_voxel_size()
+
+ for i in self.views:
+ self.data.set_active_image(i, 0, 100, "psf")
+ psf_orig = self.data[:]
+ psf_spacing = self.data.get_voxel_size()
+
+ # Zoom to the same voxel size
+ zoom_factors = tuple(x / y for x, y in zip(psf_spacing, image_spacing))
+ psf_new = zoom(psf_orig, zoom_factors).astype(np.float32)
+
+ psf_new /= psf_new.sum()
+
+ # Save the zoomed and rotated PSF, as well as its mirrored version
+ self.psfs.append(psf_new)
+
+ if self.imdims == 3:
+ self.adj_psfs.append(psf_new[::-1, ::-1, ::-1])
+ else:
+ self.adj_psfs.append(psf_new[::-1, ::-1])
+
+ def __compute_virtual_psfs(self):
+ """
+ Implements a Virtual PSF calculation routine, as described in "Efficient
+ Bayesian-based multiview deconvolution" by Preibich et al in Nature
+ Methods 11/6 (2014)
+ """
+
+ print("Caclulating Virtual PSFs")
+
+ for i in range(self.n_views):
+ virtual_psf = np.ones(self.psfs[0].shape, dtype=self.psfs[0].dtype)
+ for j in range(self.n_views):
+ if j == i:
+ pass
+ else:
+ cache = fftconvolve(
+ fftconvolve(
+ self.adj_psfs[i],
+ self.psfs[j],
+ mode='same'
+ ),
+ self.adj_psfs[j],
+ mode='same'
+
+ )
+
+ virtual_psf *= cache.real
+
+ virtual_psf *= self.adj_psfs[i]
+ virtual_psf /= virtual_psf.sum()
+ self.adj_psfs[i] = virtual_psf
+ # self.virtual_psfs.append(virtual_psf)
+
+ # endregion
+
+
+ def __calculate_block_and_image_size(self):
+ """
+ Calculate the block size and the internal image size for a given
+ number of blocks. 1,2,4 or 8 blocks are currently supported.
+
+ """
+ block_size = self.image_size
+ image_size = self.image_size
+
+ if self.num_blocks == 1:
+ return block_size, image_size
+ elif self.num_blocks == 2:
+ multiplier3 = np.array([1, 1, 2])
+ multiplier2 = np.array([2, 1])
+ elif self.num_blocks == 4:
+ multiplier3 = np.array([1, 2, 2])
+ multiplier2 = np.array([2, 2])
+ elif self.num_blocks == 8:
+ multiplier3 = np.array([4, 2, 1])
+ multiplier2 = np.array([4, 2])
+ elif self.num_blocks == 12:
+ multiplier3 = np.array([4, 2, 2])
+ multiplier2 = np.array([4, 3])
+ elif self.num_blocks == 24:
+ multiplier3 = np.array([4, 3, 2])
+ multiplier2 = np.array([6, 4])
+ elif self.num_blocks == 48:
+ multiplier3 = np.array([4, 4, 3])
+ multiplier2 = np.array([8, 6])
+ elif self.num_blocks == 64:
+ multiplier3 = np.array([4, 4, 4])
+ multiplier2 = np.array([8, 8])
+ elif self.num_blocks == 96:
+ multiplier3 = np.array([6, 4, 4])
+ multiplier2 = np.array([12, 8])
+ elif self.num_blocks == 144:
+ multiplier3 = np.array([4, 6, 6])
+ multiplier2 = np.array([12, 12])
+ else:
+ raise NotImplementedError
+
+ if self.imdims == 2:
+ block_size = np.ceil(self.image_size.astype(np.float16) / multiplier2).astype(np.int64)
+ image_size += (multiplier2 * block_size - image_size)
+ else:
+ block_size = np.ceil(self.image_size.astype(np.float16) / multiplier3).astype(np.int64)
+ image_size += (multiplier3 * block_size - image_size)
+
+ return block_size, image_size
+
+ def get_padded_block(self, image, block_start_index):
+ """
+ Get a padded block from the self.estimate
+
+ Parameters
+ ----------
+ :param image: a np.ndarray or or its subclass
+ :param block_start_index The real block start index, not considering the padding
+
+ Returns
+ -------
+ Returns the padded estimate block as a np array.
+
+ """
+
+ block_pad = self.options.block_pad
+ image_size = self.image_size
+ ndims = self.imdims
+
+ # Apply padding
+ end_index = block_start_index + self.block_size + block_pad
+ start_index = block_start_index - block_pad
+
+ idx = tuple(slice(start, stop) for start, stop in zip(start_index, end_index))
+
+ # If the padded block fits within the image boundaries, nothing special
+ # is needed to extract it. Normal np slicing notation is used.
+ if (image_size >= end_index).all() and (start_index >= 0).all():
+ return image[idx]
+
+ else:
+ block_size = tuple(i + 2 * block_pad for i in self.block_size)
+ # Block outside the image boundaries will be filled with zeros.
+ block = np.zeros(block_size)
+ # If the start_index is close to the image boundaries, it is very
+ # probable that padding will introduce negative start_index values.
+ # In such case the first pixel index must be corrected.
+ if (start_index < 0).any():
+ block_start = np.negative(start_index.clip(max=0))
+ image_start = start_index + block_start
+ else:
+ block_start = (0,) * ndims
+ image_start = start_index
+
+ # If the padded block is larger than the image size the
+ # block_size must be adjusted.
+ if not (image_size >= end_index).all():
+ block_crop = end_index - image_size
+ block_crop[block_crop < 0] = 0
+ block_end = block_size - block_crop
+ else:
+ block_end = block_size
+
+ end_index = start_index + block_end
+
+ block_idx = tuple(slice(start, stop) for start, stop in zip(block_start, block_end))
+ image_idx = tuple(slice(start, stop) for start, stop in zip(image_start, end_index))
+
+ block[block_idx] = image[image_idx]
+
+ return block
+
+ # region Get Output
+ def get_result(self, cast_to_8bit=False):
+ """
+ Show fusion result. This is a temporary solution for now
+ calling Fiji through ITK. An internal viewer would be
+ preferable.
+ """
+ if cast_to_8bit:
+ result = self.estimate.copy()
+ result *= (255.0 / result.max())
+ result[result < 0] = 0
+ return Image(result, self.voxel_size)
+
+ return Image(self.estimate, self.voxel_size)
+
+ def save_to_hdf(self):
+ """
+ Save result to the miplib data structure.
+
+ """
+ self.data.set_active_image(0,
+ self.options.channel,
+ self.options.scale,
+ "registered")
+ spacing = self.data.get_voxel_size()
+
+ self.data.add_fused_image(self.estimate,
+ self.options.channel,
+ self.options.scale,
+ spacing)
+
+ # endregion
+
+ def close(self):
+ if self.options.memmap_estimates:
+ del self.estimate
+ del self.estimate_new
+ if not self.options.disable_tau1:
+ del self.prev_estimate
+
+ shutil.rmtree(self.memmap_directory)
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/fusion/fusion_cuda.py b/Addons/FRCmetric/miplib-public/miplib/processing/fusion/fusion_cuda.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7fd61c7cf75ca3645513fffac81ad0bd3c33e21
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/fusion/fusion_cuda.py
@@ -0,0 +1,200 @@
+# coding=utf-8
+"""
+fusion.py
+
+Copyright (C) 2016 Sami Koho
+All rights reserved.
+
+This software may be modified and distributed under the terms
+of the BSD license. See the LICENSE file for details.
+
+This file contains the GPU accelerated miplib multi-view image fusion
+algorithms. The MultiViewFusionRLCuda class implements all the same methods
+as the MultiViewFusionRL class, for non-accelerated iterative image fusion.
+
+"""
+
+import itertools
+import os
+
+import numpy
+import miplib.processing.ops_ext as ops_ext
+import cupy as cp
+from cupyx.scipy import fftpack
+
+import miplib.processing.fusion.fusion as fusion
+import miplib.processing.ndarray as ops_array
+
+
+class MultiViewFusionRLCuda(fusion.MultiViewFusionRL):
+ """
+ This class implements GPU accelerated versions of the iterative image
+ fusion algorithms discussed in:
+
+ Koho, S., Deguchi, T., & Hanninen, P. E. (2015). A software tool for
+ tomographic axial superresolution in STED microscopy. Journal of Microscopy,
+ 260(2), 208–218. http://doi.org/10.1111/jmi.12287
+
+ MultiViewFusionRLCuda is inherits most of its functionality from
+ MultiViewFusionRL (see fusion.py).
+ """
+ def __init__(self, data, writer , options):
+ """
+ :param data: a ImageData object
+
+ :param options: command line options that control the behavior
+ of the fusion algorithm
+ :param writer: a writer object that can save intermediate results
+ """
+ fusion.MultiViewFusionRL.__init__(self, data, writer, options)
+
+ padded_block_size = self.block_size + 2*self.options.block_pad
+
+ self._fft_plan = fftpack.get_fft_plan(cp.zeros(padded_block_size, dtype=cp.complex64))
+ self.__get_fourier_psfs()
+
+ def compute_estimate(self):
+ """
+ Calculates a single RL fusion estimate. There is no reason to call this
+ function -- it is used internally by the class during fusion process.
+ """
+ print(f'Beginning the computation of the {self.iteration_count + 1}. estimate')
+
+ if "multiplicative" in self.options.fusion_method:
+ self.estimate_new[:] = numpy.ones(self.image_size, dtype=numpy.float32)
+ else:
+ self.estimate_new[:] = numpy.zeros(self.image_size, dtype=numpy.float32)
+
+
+ # Iterate over views
+ for idx, view in enumerate(self.views):
+
+ psf_fft = self.psfs_fft[idx]
+ adj_psf_fft = self.adj_psfs_fft[idx]
+
+ self.data.set_active_image(view, self.options.channel,
+ self.options.scale, "registered")
+
+ weighting = self.weights[idx]
+ background = self.background[idx]
+
+ iterables = (range(0, m, n) for m, n in zip(self.image_size, self.block_size))
+ pad = self.options.block_pad
+ block_idx = tuple(slice(pad, pad + block) for block in self.block_size)
+
+ for pos in itertools.product(*iterables):
+
+ estimate_idx = tuple(slice(j, j + k) for j, k in zip(pos, self.block_size))
+ index = numpy.array(pos, dtype=int)
+
+ if self.options.block_pad > 0:
+ h_estimate_block = self.get_padded_block(
+ self.estimate, index.copy()).astype(numpy.complex64)
+ else:
+ h_estimate_block = self.estimate[estimate_idx].astype(numpy.complex64)
+
+ # Convolve estimate block with the PSF
+ h_estimate_block_new = self._fft_convolve(h_estimate_block, psf_fft)
+
+ # Apply weighting
+ h_estimate_block_new *= weighting
+
+ # Apply background
+ h_estimate_block_new += background
+
+ # Divide image block with the convolution result
+ h_image_block = self.data.get_registered_block(self.block_size,
+ self.options.block_pad,
+ index.copy()).astype(numpy.float32)
+
+ #h_estimate_block_new = ops_array.safe_divide(h_image_block, h_estimate_block_new)
+ ops_ext.inverse_division_inplace(h_estimate_block_new,
+ h_image_block)
+
+ # Correlate with adj PSF
+ h_estimate_block_new = self._fft_convolve(h_estimate_block_new, adj_psf_fft).real
+
+ # Update the contribution from a single view to the new estimate
+ self._write_estimate_block(h_estimate_block_new, estimate_idx, block_idx)
+
+ # Divide with the number of projections
+ if "summative" in self.options.fusion_method:
+ # self.estimate_new[:] = self.float_vmult(self.estimate_new,
+ # self.scaler)
+ self.estimate_new *= (1.0 / self.n_views)
+ else:
+ self.estimate_new[self.estimate_new < 0] = 0
+ self.estimate_new[:] = ops_array.nroot(self.estimate_new,
+ self.n_views)
+
+ # TV Regularization (doesn't seem to do anything miraculous).
+ if self.options.tv_lambda > 0 and self.iteration_count > 0:
+ dv_est = ops_ext.div_unit_grad(self.estimate, self.voxel_size)
+ self.estimate_new = ops_array.safe_divide(self.estimate, (1.0 - self.options.rltv_lambda * dv_est))
+
+ # Update estimate inplace. Get convergence statistics.
+ return ops_ext.update_estimate_poisson(self.estimate,
+ self.estimate_new,
+ self.options.convergence_epsilon)
+
+ def _fft_convolve(self, h_data, h_kernel):
+ """
+ Calculate a convolution on GPU, using FFTs.
+
+ :param h_data: a Numpy array with the data to convolve
+ :param h_kernel: a Numpy array with the convolution kernel. The kernel
+ should already be in Fourier domain (To avoid repeating the transform at
+ every iteration.)
+ """
+ #todo: See whether to add back streams. I removed them on Cupy refactor.
+
+ d_data = cp.asarray(h_data)
+ d_data = fftpack.fftn(d_data, overwrite_x=True, plan=self._fft_plan)
+
+ d_kernel = cp.asarray(h_kernel)
+ d_data *= d_kernel
+
+ d_data = fftpack.ifftn(d_data, overwrite_x=True, plan=self._fft_plan)
+ return cp.asnumpy(d_data)
+
+ def __get_fourier_psfs(self):
+ """
+ Pre-calculates the PSFs during image fusion process.
+ """
+ print("Pre-calculating PSFs")
+
+ padded_block_size = tuple(self.block_size + 2*self.options.block_pad)
+
+ memmap_shape = (self.n_views,) + padded_block_size
+
+ if self.options.disable_fft_psf_memmap:
+ self.psfs_fft = numpy.zeros(memmap_shape, dtype=numpy.complex64)
+ self.adj_psfs_fft = numpy.zeros(memmap_shape, dtype=numpy.complex64)
+ else:
+ psfs_fft_f = os.path.join(self.memmap_directory, "psf_fft_f.dat")
+ self.psfs_fft = numpy.memmap(psfs_fft_f, dtype='complex64', mode='w+', shape=memmap_shape)
+ adj_psfs_fft_f = os.path.join(self.memmap_directory, "adj_psf_fft_f.dat")
+ self.adj_psfs_fft = numpy.memmap(adj_psfs_fft_f, dtype='complex64', mode='w+', shape=memmap_shape)
+
+ for idx in range(self.n_views):
+ self.psfs_fft[idx] = ops_array.expand_to_shape(
+ self.psfs[idx], padded_block_size).astype(numpy.complex64)
+ self.adj_psfs_fft[idx] = ops_array.expand_to_shape(
+ self.adj_psfs[idx], padded_block_size).astype(numpy.complex64)
+ self.psfs_fft[idx] = numpy.fft.fftshift(self.psfs_fft[idx])
+ self.adj_psfs_fft[idx] = numpy.fft.fftshift(self.adj_psfs_fft[idx])
+
+ self.psfs_fft[idx] = cp.asnumpy(fftpack.fftn(cp.asarray(self.psfs_fft[idx]), plan=self._fft_plan))
+ self.adj_psfs_fft[idx] = cp.asnumpy(fftpack.fftn(cp.asarray(self.adj_psfs_fft[idx]), plan=self._fft_plan))
+
+ def close(self):
+ if not self.options.disable_fft_psf_memmap:
+ del self.psfs_fft
+ del self.adj_psfs_fft
+ fusion.MultiViewFusionRL.close(self)
+
+
+
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/fusion/fusion_linear.py b/Addons/FRCmetric/miplib-public/miplib/processing/fusion/fusion_linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..7237ea947e7972deb62731f56bd145a2823462c8
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/fusion/fusion_linear.py
@@ -0,0 +1,20 @@
+import numpy as np
+import miplib.processing.deconvolution.wiener_cuda as wiener
+from miplib.data.containers.image_data import ImageData
+
+def wiener_fusion(data, options, gate=0, scale=100, views=None):
+ assert isinstance(data, ImageData)
+
+ if views is None:
+ views = list(range(data.get_number_of_images("registered")))
+
+ result = np.zeros(data.get_image_size(), dtype=np.float32)
+ for idx in views:
+ data.set_active_image(idx, gate, scale, "registered")
+ image = data.get_image()
+ data.set_active_image(idx, gate, scale, "psf")
+ psf = data.get_image()
+
+ result += wiener.wiener_deconvolution(image, psf,
+ snr=options.wiener_snr, add_pad=options.block_pad)
+ return result
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/fusion/utils.py b/Addons/FRCmetric/miplib-public/miplib/processing/fusion/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..29f2424b8dc7d6a2f22ed6d8b87ae9d4a5944be8
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/fusion/utils.py
@@ -0,0 +1,47 @@
+import numpy as np
+
+from miplib.data.containers.image_data import ImageData
+from miplib.data.containers.image import Image
+
+
+def sum_of_all(data_structure, channel=0, scale=100, image_type="original"):
+ assert isinstance(data_structure, ImageData)
+
+ n_views = data_structure.get_number_of_images(image_type)
+ data_structure.set_active_image(0, channel, scale, image_type)
+ result = np.zeros(data_structure.get_image_size(), dtype=np.float32)
+ pixel_size = data_structure.get_voxel_size()
+
+ for i in range(n_views):
+ data_structure.set_active_image(i, channel, scale, image_type)
+ result += data_structure[:]
+
+ return Image(result, pixel_size)
+
+
+def average_of_all(data_structure, channel=0, scale=100, image_type="original"):
+ assert isinstance(data_structure, ImageData)
+ n_views = data_structure.get_number_of_images(image_type)
+ data_structure.set_active_image(0, channel, scale, image_type)
+ pixel_size = data_structure.get_voxel_size()
+
+ result = sum_of_all(data_structure, channel, scale, image_type)
+
+ return Image(result/n_views, pixel_size)
+
+
+def simple_fusion(data_structure, channel=0, scale=100):
+ assert isinstance(data_structure, ImageData)
+ image_type = "registered"
+
+ n_views = data_structure.get_number_of_images(image_type)
+ data_structure.set_active_image(0, channel, scale, image_type)
+ pixel_size = data_structure.get_voxel_size()
+
+ result = data_structure[:]
+
+ for i in range(1, n_views):
+ data_structure.set_active_image(i, channel, scale, image_type)
+ result = (result - (result - data_structure[:]).clip(min=0)).clip(min=0).astype(np.float32)
+
+ return Image(result, pixel_size)
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/image.py b/Addons/FRCmetric/miplib-public/miplib/processing/image.py
new file mode 100644
index 0000000000000000000000000000000000000000..76e398aefff97d744472149b6dd3afdd966499cb
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/image.py
@@ -0,0 +1,457 @@
+import numpy as np
+from scipy.ndimage import interpolation
+
+from . import ndarray
+from miplib.data.containers.image import Image
+
+
+def zoom_to_isotropic_spacing(image, order=3):
+ """
+ Resize an Image to isotropic pixel spacing.
+
+ :param image: a Image object
+ :param order: the spline interpolation type
+ :return: a isotropically spaced Image
+ """
+ assert isinstance(image, Image)
+
+ spacing = image.spacing
+ old_shape = image.shape
+ min_spacing = min(spacing)
+ zoom = tuple(pixel_spacing / min_spacing for pixel_spacing in spacing)
+ new_shape = tuple(int(pixels * dim_zoom) for (pixels, dim_zoom) in zip(old_shape, zoom))
+
+ if new_shape == old_shape:
+ return image
+ else:
+ return resize(image, new_shape, order)
+
+def zoom_to_spacing(image, spacing, order=3, verbose=False):
+
+ assert isinstance(image, Image)
+ assert image.ndim == len(spacing)
+
+ zoom = tuple(i/j for i, j in zip(image.spacing, spacing))
+ if verbose:
+ print("The zoom is ", zoom)
+
+ array = interpolation.zoom(image, zoom, order=order)
+
+ return Image(array, spacing)
+
+
+def resize(image, size, order=3, verbose=False): # type: (Image, tuple) -> Image
+ """
+ Resize the image, using interpolation.
+
+ :param order: The interpolation type defined as order of the b-spline
+ :param image: The MyImage object.
+ :param size: A tuple of new image dimensions.
+
+ """
+ assert isinstance(size, tuple)
+ assert isinstance(image, Image)
+
+ zoom = [float(a) / b for a, b in zip(size, image.shape)]
+ if verbose:
+ print("The zoom is %s" % zoom)
+
+ array = interpolation.zoom(image, tuple(zoom), order=order)
+ spacing = tuple(i / j for i, j in zip(image.spacing, zoom))
+
+ return Image(array, spacing)
+
+
+def apply_hanning(image): # type: (Image) -> Image
+ """
+ Apply Hanning window to the image.
+
+ :return:
+ """
+
+ windows = (np.hanning(i) for i in image.shape)
+
+ result = Image(image.astype('float64'), image.spacing)
+ for window in windows:
+ result *= window
+
+ return result
+
+
+def zero_pad_to_shape(image, shape):
+ """
+ Apply zero padding to cast an Image into the given shape. The zero padding
+ will be applied evenly on all sides of the image.
+
+ :param image: an Image object
+ :param shape: a shape tuple
+ :return: the zero padded Image
+ """
+ assert isinstance(image, Image)
+
+ if image.shape == shape:
+ return image
+ else:
+ return Image(ndarray.expand_to_shape(image, shape), image.spacing)
+
+
+def zero_pad_to_matching_shape(image1, image2):
+ """
+ Apply zero padding to make the size of two Images match.
+ :param image1: an Image object
+ :param image2: an Image object
+ :return: zero padded image1 and image2
+ """
+
+ assert isinstance(image1, Image)
+ assert isinstance(image2, Image)
+
+ shape = tuple(max(x, y) for x, y in zip(image1.shape, image2.shape))
+
+ if any(map(lambda x, y: x != y, image1.shape, shape)):
+ image1 = zero_pad_to_shape(image1, shape)
+ if any(map(lambda x, y: x != y, image2.shape, shape)):
+ image2 = zero_pad_to_shape(image2, shape)
+
+ return image1, image2
+
+
+def remove_zero_padding(image, shape):
+ """
+
+ :param image: The zero padded image
+ :param shape: The original image size (before padding)
+ :return:
+ """
+
+ assert isinstance(image, Image)
+ assert len(shape) == image.ndim
+
+ return Image(ndarray.contract_to_shape(image, shape), image.spacing)
+
+
+def checkerboard_split(image, disable_3d_sum = False):
+ """
+ Splits an image in two, by using a checkerboard pattern.
+
+ :param image: a miplib Image
+ :return: two miplib Images
+ """
+ assert isinstance(image, Image)
+
+ # Make an index chess board structure
+ shape = image.shape
+ odd_index = list(np.arange(1, shape[i], 2) for i in range(len(shape)))
+ even_index = list(np.arange(0, shape[i], 2) for i in range(len(shape)))
+
+ # Create the two pseudo images
+ if image.ndim == 2:
+ image1 = image[odd_index[0], :][:, odd_index[1]]
+ image2 = image[even_index[0], :][:, even_index[1]]
+ else:
+ if disable_3d_sum:
+ image1 = image[odd_index[0], :, :][:, odd_index[1], :][:, :, odd_index[2]]
+ image2 = image[even_index[0], :, :][:, even_index[1], :][:, :, even_index[2]]
+
+ else:
+ image1 = image.astype(np.uint32)[even_index[0], :, :][:, odd_index[1], :][:, :, odd_index[2]] + \
+ image.astype(np.uint32)[odd_index[0], :, :][:, odd_index[1], :][:, :, odd_index[2]]
+
+ image2 = image.astype(np.uint32)[even_index[0], :, :][:, even_index[1], :][:, :, even_index[2]] + \
+ image.astype(np.uint32)[odd_index[0], :, :][:, even_index[1], :][:, :, even_index[2]]
+
+ # image1.spacing = tuple(i * np.sqrt(2) for i in image.spacing)
+ image1.spacing = image.spacing
+ image2.spacing = image1.spacing
+
+ return image1, image2
+
+
+def reverse_checkerboard_split(image, disable_3d_sum = False):
+ """
+ Splits an image in two, by using a checkerboard pattern.
+
+ :param image: a miplib Image
+ :return: two miplib Images
+ """
+ assert isinstance(image, Image)
+
+ # Make an index chess board structure
+ shape = image.shape
+ odd_index = list(np.arange(1, shape[i], 2) for i in range(len(shape)))
+ even_index = list(np.arange(0, shape[i], 2) for i in range(len(shape)))
+
+ # Create the two pseudo images
+ if image.ndim == 2:
+ image1 = image[odd_index[0], :][:, even_index[1]]
+ image2 = image[even_index[0], :][:, odd_index[1]]
+ else:
+ if disable_3d_sum:
+ image1 = image[odd_index[0], :, :][:, odd_index[1], :][:, :, even_index[2]]
+ image2 = image[even_index[0], :, :][:, even_index[1], :][:, :, odd_index[2]]
+
+ else:
+ image1 = image.astype(np.uint32)[even_index[0], :, :][:, odd_index[1], :][:, :, even_index[2]] + \
+ image.astype(np.uint32)[odd_index[0], :, :][:, even_index[1], :][:, :, odd_index[2]]
+
+ image2 = image.astype(np.uint32)[even_index[0], :, :][:, even_index[1], :][:, :, odd_index[2]] + \
+ image.astype(np.uint32)[odd_index[0], :, :][:, odd_index[1], :][:, :, even_index[2]]
+
+ #image1.spacing = tuple(i * np.sqrt(2) for i in image.spacing)
+ image1.spacing = image.spacing
+ image2.spacing = image1.spacing
+
+ return image1, image2
+
+def summed_checkerboard_split(image):
+ """
+ Splits an image in two, by using a checkerboard pattern and diagonal pixels
+ in each 4 pixel group (2D) case and orthogonal diagonal groups (never adjacent)
+ in 3D case.
+
+ :param image: a miplib Image
+ :return: two miplib Images
+ """
+ assert isinstance(image, Image)
+
+ # Make an index chess board structure
+ shape = image.shape
+ odd_index = list(np.arange(1, shape[i], 2) for i in range(len(shape)))
+ even_index = list(np.arange(0, shape[i], 2) for i in range(len(shape)))
+
+ # Create the two pseudo images
+ if image.ndim == 2:
+ image1 = image[odd_index[0], :][:, odd_index[1]] + image[even_index[0], :][:, even_index[1]]
+ image2 = image[odd_index[0], :][:, even_index[1]] + image[even_index[0], :][:, odd_index[1]]
+
+ image1.spacing = tuple(i * 2 for i in image.spacing)
+ image2.spacing = image1.spacing
+ else:
+ image1 = image.astype(np.uint32)[even_index[0], :, :][:, odd_index[1], :][:, :, odd_index[2]] + \
+ image.astype(np.uint32)[odd_index[0], :, :][:, odd_index[1], :][:, :, odd_index[2]] + \
+ image.astype(np.uint32)[even_index[0], :, :][:, even_index[1], :][:, :, even_index[2]] + \
+ image.astype(np.uint32)[odd_index[0], :, :][:, even_index[1], :][:, :, even_index[2]]
+
+ image2 = image.astype(np.uint32)[even_index[0], :, :][:, odd_index[1], :][:, :, even_index[2]] + \
+ image.astype(np.uint32)[odd_index[0], :, :][:, odd_index[1], :][:, :, even_index[2]] + \
+ image.astype(np.uint32)[even_index[0], :, :][:, even_index[1], :][:, :, odd_index[2]] +\
+ image.astype(np.uint32)[odd_index[0], :, :][:, even_index[1], :][:, :, odd_index[2]]
+
+ image1.spacing = tuple(i * 2 for i in image.spacing)
+ image2.spacing = image1.spacing
+
+
+ return image1, image2
+
+
+def zero_pad_to_cube(image):
+ """
+ Apply zero padding to cast an image into a cube shape (to match the number
+ of pixels in all dimensions)
+ :param image: an Image object
+ :return: zero padded input image, or the original, if already a cube
+ """
+ assert isinstance(image, Image)
+
+ original_shape = image.shape
+ nmax = max(original_shape)
+ square_shape = (nmax,) * image.ndim
+ if square_shape != original_shape:
+ return zero_pad_to_shape(image, square_shape)
+ else:
+ return image
+
+
+def crop_to_largest_square(image, physical_dims=False):
+ """
+ Crops an image into a largest square shape that fits inside the image area in all
+ dimensions. The cropping can bone either in physical units or in pixels (typically pixels)
+ :param image: an image Object
+ :return: the cropped image
+ """
+ assert isinstance(image, Image)
+
+ if physical_dims:
+ shape_real = list(x*y for x, y in zip(image.shape, image.spacing))
+ min_shape_real = (min(*shape_real), ) * image.ndim
+ min_shape_px = list(x / y for x, y in zip(min_shape_real, image.spacing))
+ else:
+ min_shape_px = (min(*image.shape),) * image.ndim
+
+ return remove_zero_padding(image, min_shape_px)
+
+
+def crop_to_shape(image, shape, offset):
+ """
+ Crop image to shape.
+
+ :param image: An N-dimensional Image to be cropped
+ :type image: Image
+ :param shape: The new, cropped size; should be greater or equal than
+ the original
+ :type shape: tuple
+ :return: Returns the cropped image as an Image object
+ """
+ assert isinstance(image, Image)
+ assert image.ndim == len(shape) == len(offset)
+ assert all((v + x <= y for v, x, y in zip(offset, shape, image.shape)))
+
+ crop_idx = tuple(slice(start, size + start) for start, size in zip(offset, shape))
+
+ return Image(image[crop_idx], image.spacing)
+
+
+def noisy(image, noise_type):
+ """
+ Parameters
+ ----------
+ image :
+ Input image data. Will be converted to float.
+ noise_type : str
+ One of the following strings, selecting the type of noise to add:
+
+ 'gauss' Gaussian-distributed additive noise.
+ 'poisson' Poisson-distributed noise generated from the data.
+ 's&p' Replaces random pixels with 0 or 1.
+ 'speckle' Multiplicative noise using out = image + n*image,where
+ n is uniform noise with specified mean & variance.
+ """
+ assert isinstance(image, Image)
+ assert image.ndim < 4
+ spacing = image.spacing
+
+ if noise_type == "gauss":
+ mean = 0
+ var = 0.1
+ sigma = var ** 0.5
+ gauss = np.random.normal(mean, sigma, image.shape)
+ gauss = gauss.reshape(image.shape)
+ return Image(image + gauss, spacing)
+ elif noise_type == "s&p":
+ s_vs_p = 0.5
+ amount = 0.004
+ out = np.copy(image)
+ # Salt mode
+ num_salt = np.ceil(amount * image.size * s_vs_p)
+ coords = [np.random.randint(0, i - 1, int(num_salt))
+ for i in image.shape]
+ out[coords] = 1
+
+ # Pepper mode
+ num_pepper = np.ceil(amount * image.size * (1. - s_vs_p))
+ coords = [np.random.randint(0, i - 1, int(num_pepper))
+ for i in image.shape]
+ out[coords] = 0
+ return Image(out, spacing)
+ elif noise_type == "poisson":
+ vals = 2 ** np.ceil(np.log2(len(np.unique(image))))
+ return Image(np.random.poisson(image * vals) / float(vals), spacing)
+ elif noise_type == "speckle":
+ gauss = np.random.standard_normal(image.shape).reshape(image.shape)
+ return Image(image + image * gauss, spacing)
+
+
+def enhance_contrast(image, percent_saturated=0.3, out_type=np.uint8):
+ """
+ Performs historgram stretching (not equalization), with a given percentage
+ :param percent_saturated of pixels saturated in the output.
+
+ :param image: an Image object
+ :param percent_saturated: Percentage value of saturated pixels. Defaults to 0.3
+ :param out_type: The type of the output image. The default is 8-bit uint
+ :return: an Image with intensity values rescaled to the whole dynamic range
+ """
+
+ assert isinstance(image, Image)
+
+ percent_saturated /= 100
+
+ spacing = image.spacing
+
+ if out_type == np.uint8:
+ out_max = 255
+ out_min = 0
+ else:
+ raise ValueError("Not supported output type {}".format(out_type))
+
+ # Get Input Image Min/Max from histogram
+ histogram, bin_edges = np.histogram(image, bins=250, density=True)
+ cumulative = np.cumsum(histogram * np.diff(bin_edges))
+
+ in_max = bin_edges[1:][cumulative >= 1.0 - percent_saturated].min()
+
+ to_zero = cumulative <= percent_saturated
+ if not np.any(to_zero):
+ in_min = image.min()
+ else:
+ in_min = bin_edges[1:][to_zero].max()
+
+ # Trim and rescale
+ image = np.clip(image, in_min, in_max)
+ image *= (out_max-out_min)/image.max()
+ return Image(image.astype(out_type), spacing)
+
+
+def rescale_to_8_bit(image):
+ """
+ Converts an Image into 8-bit (typically for saving)
+ :param image: an Image object
+ :return: a 8-bit version of the Image
+ """
+ assert isinstance(image, Image)
+ return Image((image*(255.0/image.max())).astype(np.uint8), image.spacing)
+
+
+
+
+def flip_image(image):
+
+ assert isinstance(image, Image)
+
+ indexer = (np.s_[::-1],) * image.ndim
+
+ return Image(image[indexer], image.spacing)
+
+
+def translate_image(image, shift):
+ """
+ Apply a circular shift to an image
+
+ :param image: An Image object
+ :param shift: The shift as a single numeric value
+ :return: returns the translated image.
+ """
+ fft_image = np.fft.fftshift(np.fft.fft2(image))
+
+ shape = fft_image.shape
+ axes = (np.arange(-np.floor(i / 2.0), np.ceil(i / 2.0)) for i in shape)
+ axes = (i / (2 * i.max()) for i in axes)
+ y, x = np.meshgrid(*axes)
+
+ xx = np.zeros(fft_image.shape, dtype=np.complex64)
+ xx.real[:] = np.cos(2 * np.pi * shift * x)
+ xx.imag[:] = np.sin(-2 * np.pi * shift * x)
+
+ yy = np.zeros(fft_image.shape, dtype=np.complex64)
+ yy.real[:] = np.cos(2 * np.pi * shift * y)
+ yy.imag[:] = np.sin(-2 * np.pi * shift * y)
+
+ multiplier = xx * yy
+
+ result = np.abs(np.fft.ifftn(fft_image * multiplier).real)
+
+ return Image(result, image.spacing)
+
+def maximum_projection(image, axis=0):
+ """ Generate a maximum projection image along an axis
+
+ :param image: an image
+ :type image: Image
+ :param axis: the axis on which the projeciton is to be calculated, defaults to 0
+ :type axis: int, optional
+ :return: a maximum projection image, with one dimension less thatn the input image
+ :rtype: Image
+ """
+ assert isinstance(image, Image)
+ spacing = (image.spacing[s] for s in filter(lambda x : x != axis, range(image.ndim)))
+ return Image(np.amax(image, axis=axis), spacing)
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/ism/__init__.py b/Addons/FRCmetric/miplib-public/miplib/processing/ism/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/ism/helpers.py b/Addons/FRCmetric/miplib-public/miplib/processing/ism/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6035d82d02a21df436c0b269b3f7c448586cc54
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/ism/helpers.py
@@ -0,0 +1,93 @@
+import itertools
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+plt.style.use("seaborn-paper")
+
+
+def make_template_image(data, imagesz=250):
+ """
+ Makes the "fingerprint" map of the array detector images
+ :param data: ArrayDetecorData object with all the iamges
+ :param imagesz: integer Size of the images in pixels
+ :return: returns a tuple of images (one for each channel)
+ """
+
+ blocksz = int(imagesz/np.sqrt(data.ndetectors))
+
+ # First calculate the total photon count for images from each detector
+ # and photosensor
+ pixels = np.zeros(data.ndetectors*data.ngates)
+
+ data.iteration_axis = 'detectors'
+ for idx, image in enumerate(data):
+ pixels[idx] = image.sum()
+
+ # Then generate a template image for each photosensor
+ container = []
+ for gate in range(data.ngates):
+ image = np.zeros((imagesz, imagesz))
+ idx = 0
+ for x, y in itertools.product(range(0, imagesz, blocksz), range(0, imagesz, blocksz)):
+ pixel_index = gate*data.ndetectors + idx
+ image[x:x + blocksz, y:y + blocksz] = pixels[pixel_index]
+ idx += 1
+
+ container.append(image)
+
+ return container
+
+
+def make_psf_plot(data, size=(5,5)):
+ """
+ Makes a 5x5 matrix plot of Point-Spread-Functions (PSFs) that are used in the
+ blind multi-image APR-ISM image fusion
+ :param data: ArrayDetectorData or a compatible adapter with the 25 PSF images
+ :param size: Size of the plot
+ :return: Returns the figure
+ """
+
+ fig, axs = plt.subplots(5, 5, figsize=size)
+
+ for idx, ax in enumerate(axs.flatten()):
+ ax.imshow(data[0,idx])
+
+ ax.get_xaxis().set_visible(False)
+ ax.get_yaxis().set_visible(False)
+
+ plt.tight_layout(pad=-0.3, w_pad=-0.3, h_pad=-0.3)
+
+ return fig
+
+
+def calculate_theoretical_shifts_xy(pitch, magnification, alpha=0.5, width=5):
+ """ Calculate theoretical ISM shift matrix, based on detector pixel pitch
+ and
+
+ Arguments:
+ pitch {float} -- Distance between two detector elements.
+
+ magnification {float} -- Total magnification from object plane to the
+ detector plane.
+
+ Keyword Arguments:
+ width {int} -- Width of the detector (number of pixels) (default: {5})
+ alpha {float} -- the reassignment factor (default: {0.5})
+
+ Returns:
+ [list(float, float)] -- Returns a list of the y and x coordinates of
+ the image offsets
+ """
+
+ pitch_pt = pitch*alpha/magnification
+
+ radius = width//2
+ axis = np.linspace(-pitch_pt*radius, pitch_pt*radius, width)
+ y_pt, x_pt = np.meshgrid(axis,axis)
+
+ x_pts = list(x_pt.ravel())
+ y_pts = list(y_pt.ravel())[::-1]
+
+ return y_pts, x_pts
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/ism/reconstruction.py b/Addons/FRCmetric/miplib-public/miplib/processing/ism/reconstruction.py
new file mode 100644
index 0000000000000000000000000000000000000000..36d9b813a445cd111ef9893c17da52da82862994
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/ism/reconstruction.py
@@ -0,0 +1,210 @@
+from math import floor
+
+import numpy as np
+import SimpleITK as sitk
+
+from miplib.data.containers.array_detector_data import ArrayDetectorData
+from miplib.data.containers.image import Image
+from miplib.processing import itk
+from miplib.processing.registration import registration, stack
+from miplib.processing.windowing import apply_hamming_window
+
+import miplib.processing.ism.helpers as ismutils
+from miplib.processing import transform as tfm
+
+
+def find_image_shifts(data, options, photosensor=0, fixed_idx=12):
+ """
+ Register all images in an ISM ArrayDetectorData dataset. The central image (pixel 12)
+ is used as reference and other images are aligned with it. This function used an
+ iterative algorithm (ITK) that can be customized with various command line options.
+
+
+ :param options: Various options that can be used on fine tune the image registration. Look into
+ supertomo_options.py file
+ :param data: ArrayDetectorData object with all the individual images
+ :param photosensor: The photosensor number (from 0 upwards) that is to be processed
+ :param fixed_idx: The index of the reference image. Defaults to 12 (IIT SPAD array)
+ :return: a three element tuple: x offset, y offset, transforms. The x and y offsets are expressed
+ in physical units (um). The transforms are sitk.TranslationTransform objects that can be used
+ to resample the images into a common coordinate system.
+ """
+ assert photosensor < data.ngates
+
+ fixed_image = itk.convert_to_itk_image(data[photosensor, fixed_idx])
+ transforms = []
+ shifts = np.zeros((data.ndetectors, fixed_image.GetDimension()), dtype=np.float)
+
+ for idx in range(data.ndetectors):
+ image = data[photosensor, idx]
+ moving_image = itk.convert_to_itk_image(image)
+ transform = registration.itk_registration_rigid_2d(fixed_image, moving_image, options)
+ shifts_ = transform.GetParameters()
+ shifts[idx] = shifts_[::-1]
+ transforms.append(transform)
+
+ return shifts, transforms
+
+
+def find_static_image_shifts(pitch, wavelength, fov, na, alpha=0.5, width=5, rotation=0):
+ """
+ Generate spatial transforms for ISM image reconstruction, based on theoretical values.
+ :param pitch: the detector pixel spacing
+ :param wavelength: the wavelength to be used in the calculations. Can be e.g. the average
+ of the excitation and emission wavelengths
+ :param fov: the size of the SPAD field of view in Airy units
+ :param na: the objective numerical aperture
+ :param alpha: the reassignment factor ]0, 1]
+ :param width: the number of detectors along one dimension of the SPAD.
+ :return: a list of ITK transforms that can be used to resample the images.
+ """
+ assert 0 < alpha <= 1
+
+ d_airy = 1.22 * wavelength / na
+ d_detector_sp = fov*d_airy
+ d_detector_ip = pitch*width
+
+ magnification = d_detector_ip/d_detector_sp
+
+ x,y = ismutils.calculate_theoretical_shifts_xy(pitch, magnification, alpha=alpha)
+ if rotation != 0:
+ x,y = tfm.rotate_xy_points_lists(y, x, rotation)
+
+ return x, y, tfm.make_translation_transforms_from_xy(y, x)
+
+
+def find_image_shifts_frequency_domain(data, photosensor=0):
+ """
+ Register all image in an ISM ArrayDetectorDAta dataset, with a single step frequency domain
+ phase correlation based method. This might be slightly faster than the iterative method
+ above (depending on the sampling strategy in the latter mainly), but usually does not
+ work quite as well.
+
+ :param data: ArrayDetectorData object with all the individual images
+ :param photosensor: The photosensor number (from 0 upwards) that is to be processed
+ :return: a three element tuple: x offset, y offset, transforms. The x and y offsets are expressed
+ in physical units (um). The transforms are sitk.TranslationTransform objects that can be used
+ to resample the images into a common coordinate system.
+ """
+ assert photosensor < data.ngates
+
+ spacing = data[0,0].spacing
+ fixed_image = Image(apply_hamming_window(data[photosensor, int(floor(data.ndetectors / 2))]), spacing)
+ transforms = []
+ shifts = np.zeros((data.ndetectors, fixed_image.ndim), dtype=np.float)
+
+ for idx in range(data.ndetectors):
+ moving_image = Image(apply_hamming_window(data[photosensor, idx]), spacing)
+ shifts_ = registration.phase_correlation_registration(fixed_image, moving_image,
+ verbose=False, resample=False)
+ tfm = sitk.TranslationTransform(len(shifts_))
+ tfm.SetParameters(shifts_[::-1])
+ transforms.append(tfm)
+
+ shifts[idx] = shifts_
+
+ return shifts, transforms
+
+
+def shift_and_sum(data, transforms, photosensor=0, detectors=None, supersampling=1.0):
+ """
+ Adaptive ISM pixel reassignment. Please use one of the functions above to figure out
+ the shifts first, if you haven't already.
+
+ :param supersampling: Insert a number != 1, if you want to rescale the result image to
+ a different size. This might make sense, if you the original sampling has been sampled
+ sparsely
+ :param data: ArrayDetectorData object with all the individual images
+ :param transforms: ITK spatial transformation that are to be used for the resampling
+ :param photosensor: The photosensor index, if more than one
+ :param detectors: a list of detectors to be included in the reconstruction. If None given (default),
+ all the images will be used
+ :return: reconstruction result Image
+ """
+ assert isinstance(transforms, list) and len(transforms) == data.ndetectors
+
+ if supersampling != 1.0:
+ new_shape = list(int(i*supersampling) for i in data[photosensor, 0].shape)
+ new_spacing = list(i/supersampling for i in data[photosensor, 0].spacing)
+ output = Image(np.zeros(new_shape, dtype=np.float64), new_spacing)
+ else:
+ output = Image(np.zeros(data[photosensor, 0].shape, dtype=np.float64), data[photosensor, 0].spacing)
+
+ if detectors is None:
+ detectors = list(range(data.ndetectors))
+
+ for i in detectors:
+ image = itk.resample_image(
+ itk.convert_to_itk_image(data[photosensor, i]),
+ transforms[i],
+ reference=itk.convert_to_itk_image(output))
+
+ output += itk.convert_from_itk_image(image)
+
+ return output
+
+
+def shift(data, transforms):
+ """
+ Resamples all the images in an ArrayDetectorData structure with the supplied transforms,
+ and saves the result in a new ArrayDetectorData structure
+
+ :param data: ArrayDetectorData object with images
+ :param transforms: A list of transforms (Simple ITK), one for each image
+ :return: ArrayDetectorDAta object with shifted images
+ """
+
+ assert isinstance(transforms, list) and len(transforms) == data.ndetectors
+
+ shifted = ArrayDetectorData(data.ndetectors, data.ngates)
+
+ for gate in range(data.ngates):
+ for i in range(data.ndetectors):
+ image = itk.resample_image(
+ itk.convert_to_itk_image(data[gate, i]),
+ transforms[i])
+
+ shifted[gate, i] = itk.convert_from_itk_image(image)
+
+ return shifted
+
+
+def sum(data, photosensor=0, detectors=None):
+ """
+ Sums all the images in a ArrayDetectorData structure
+
+ :param detectors: A subset of detectors to be summed. If left empty, all the images
+ will be summed
+ :param photosensor: The photosensor index.
+ :param data: ArrayDetectorData object with images
+ :return: result Image
+ """
+
+ if detectors is None:
+ detectors = list(range(data.ndetectors))
+
+ result = np.zeros(data[0,0].shape, dtype=np.float64)
+
+ for i in detectors:
+ result += data[photosensor, i]
+
+ return Image(result, data[0, 0].spacing)
+
+
+def drift_correct_ism_stack(data):
+ """
+ Correct for xy-drift in 3D ISM datasets.
+
+ :param data: the data
+ :return: the drift corrected data
+ """
+ sum_image = sum(data)
+ shifts = stack.register_stack_slices(sum_image)
+
+ result = ArrayDetectorData(data.ndetectors, data.ngates)
+
+ for g_idx in range(data.ngates):
+ for c_idx in range(data.ndetectors):
+ result[g_idx, c_idx] = stack.shift_stack_slices(data[g_idx, c_idx], shifts)
+
+ return result
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/itk.py b/Addons/FRCmetric/miplib-public/miplib/processing/itk.py
new file mode 100644
index 0000000000000000000000000000000000000000..161f86231e8653570b02c3e9c83d8edeed0b8d73
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/itk.py
@@ -0,0 +1,493 @@
+"""
+itkutils.py
+
+Copyright (C) 2014 Sami Koho
+All rights reserved.
+
+This software may be modified and distributed under the terms
+of the BSD license. See the LICENSE file for details.
+
+This file contains several utilities & filters for simplified
+usage of ITK (www.itk.org) modules in Python. Most of the ITK classes
+have been implemented in similar manner, so it should be rather
+easy to include additional filters.
+
+"""
+import SimpleITK as sitk
+import numpy
+import scipy
+
+from miplib.data.containers.image import Image
+from miplib.processing import converters
+
+
+def convert_from_itk_image(image):
+ """
+ A simple conversion function from ITK:Image to a Numpy array. Please notice
+ that the pixel size information gets lost in the conversion. If you want
+ to conserve image information, rather use ImageStack class method in
+ iocbio.io.image_stack module
+ """
+ assert isinstance(image, sitk.Image)
+ array = sitk.GetArrayFromImage(image)
+ # In ITK the order of the dimensions differs from Numpy. The array conversion
+ # re-orders the dimensions, but of course the same has to be done to the spacing
+ # information.
+ spacing = image.GetSpacing()[::-1]
+
+ return Image(array, spacing)
+
+
+def convert_to_itk_image(image):
+ assert isinstance(image, Image)
+ return convert_from_numpy(image, image.spacing)
+
+
+def convert_from_numpy(array, spacing):
+ assert isinstance(array, numpy.ndarray)
+ image = sitk.GetImageFromArray(array)
+ image.SetSpacing(spacing[::-1])
+
+ return image
+
+
+def make_itk_transform(type, dims, parameters, fixed_parameters):
+ """
+ A function that can be used to construct a ITK spatial transform from
+ known transform parameters.
+ :param type: A string that exactly matches the ITK transform
+ :param dims: Number of dimensions
+ type, eg "VerorRigid3DTransform"
+ :param parameters: The transform parameters tuple
+ :param fixed_parameters: The transform fixed parameters tuple
+ :return: Returns an initialized ITK spatial transform.
+ """
+ if type == "AffineTransform":
+ transform = sitk.AffineTransform(dims)
+ else:
+ raise NotImplementedError()
+
+ transform.SetParameters(parameters)
+ transform.SetFixedParameters(fixed_parameters)
+
+ return transform
+
+def get_itk_transform_parameters(transform):
+
+ tfm_type = transform.GetName()
+ params = transform.GetParameters()
+ fixed_params = transform.GetFixedParameters()
+
+ return tfm_type, params, fixed_params
+
+
+def resample_image(image, transform, reference=None, interpolation="linear"):
+ """
+ Resampling filter for manipulating data volumes. This function can be
+ used to transform an image module or to perform up or down sampling
+ for example.
+
+ image = input image object itk::Image
+ transform = desired transform itk::Transform
+ image_type = pixel type of the image data
+ reference = a reference image, which can be used in resizing
+ applications, when different dimensions and or
+ spacing are desired to the output image
+ """
+ assert isinstance(image, sitk.Image)
+ if reference is None:
+ reference = image
+
+ if interpolation == "nearest":
+ interpolator = sitk.sitkNearestNeighbor
+ elif interpolation == "linear":
+ interpolator = sitk.sitkLinear
+ elif interpolation == "Bspline":
+ interpolator = sitk.sitkBSpline
+ else:
+ raise ValueError("Unknown interpolation type.")
+
+ resampler = sitk.ResampleImageFilter()
+ resampler.SetTransform(transform)
+
+ resampler.SetInterpolator(interpolator)
+ resampler.SetOutputPixelType(reference.GetPixelID())
+ resampler.SetSize(reference.GetSize())
+ resampler.SetOutputOrigin(reference.GetOrigin())
+ resampler.SetOutputSpacing(reference.GetSpacing())
+ resampler.SetOutputDirection(reference.GetDirection())
+ resampler.SetDefaultPixelValue(0)
+
+ return resampler.Execute(image)
+
+
+def rotate_image(image, angle, axis=0, interpolation="linear"):
+ """
+ Rotate an image around the selected axis
+
+ :param interpolation:
+ :param image: a SimpleITK image
+ :param angle: rotation angle in degrees
+ :param axis: rotation axis
+ :return:
+ """
+
+ assert isinstance(image, sitk.Image)
+
+ radians = converters.degrees_to_radians(angle)
+
+ if image.GetDimension() == 3:
+ transform = sitk.Euler3DTransform()
+ rotation = [0.0, 0.0, 0.0]
+ rotation[axis] = radians
+ transform.SetRotation(*rotation)
+ elif image.GetDimension() == 2:
+ transform = sitk.Euler2DTransform()
+ transform.SetAngle(radians)
+ else:
+ raise ValueError(image)
+
+ transform.SetCenter(calculate_center_of_image(image))
+
+ return resample_image(image, transform, interpolation=interpolation)
+
+
+def rotate_psf(psf, transform, spacing=None, return_numpy=False):
+ """
+ In case, only one point-spread-function (PSF) is to be used in the image
+ fusion, it needs to be rotated with the transform of the moving_image.
+ The transform is generated during the registration process.
+
+ psf = A Numpy array, containing PSF data
+ transform = itk::VersorRigid3DTransform object
+ return_numpy = it is possible to either return the result as an
+ itk:Image, or a ImageStack.
+
+ """
+ #assert isinstance(transform, sitk.VersorRigid3DTransform)
+
+ if isinstance(psf, numpy.ndarray):
+ image = convert_from_numpy(psf, spacing)
+ else:
+ image = psf
+
+ assert isinstance(image, sitk.Image)
+
+ if isinstance(transform, sitk.AffineTransform):
+ #print "Hep"
+
+ array = numpy.array(transform.GetMatrix()).reshape(3, 3)
+ rotation = scipy.linalg.polar(array, "right")[0]
+ matrix = tuple(rotation.ravel())
+ transform.SetMatrix(matrix)
+ transform.SetTranslation((0.0, 0.0, 0.0))
+
+ else:
+ # We don't want to translate, but only rotate
+ parameters = transform.GetParameters()
+ parameters = tuple(0.0 if i in range(3, 6) else parameters[i] for i in range(len(parameters)))
+ transform.SetParameters(parameters)
+
+ # Find and set center of rotation This assumes that the PSF is in
+ # the centre of the volume, which should be expected, as otherwise it
+ # will cause translation of details in the final image.
+ imdims = image.GetSize()
+ imspacing = image.GetSpacing()
+
+ center = list(map(
+ lambda size, spacing: spacing * size / 2, imdims, imspacing
+ ))
+
+ transform.SetFixedParameters(center)
+
+ # Rotate
+ image = resample_image(image, transform)
+
+ if return_numpy:
+ return convert_from_itk_image(image)
+ else:
+ return image
+
+
+def resample_to_isotropic(itk_image):
+ """
+ This function can be used to rescale or upsample a confocal stack,
+ which generally has a larger spacing in the z direction.
+
+ :param itk_image: an ITK:Image object
+ :return: returns a new ITK:Image object with rescaled
+ axial dimension
+ """
+ assert isinstance(itk_image, sitk.Image)
+
+ method = sitk.ResampleImageFilter()
+ transform = sitk.Transform()
+ transform.SetIdentity()
+
+ method.SetInterpolator(sitk.sitkBSpline)
+ method.SetDefaultPixelValue(0)
+
+ # Set output spacing
+ spacing = itk_image.GetSpacing()
+
+ if len(spacing) != 3:
+ print("The function resample_to_isotropic(itk_image, image_type) is" \
+ "intended for processing 3D images. The input image has %d " \
+ "dimensions" % len(spacing))
+ return
+
+ scaling = spacing[2]/spacing[0]
+
+ spacing[:] = spacing[0]
+
+ method.SetOutputSpacing(spacing)
+ method.SetOutputDirection(itk_image.GetDirection())
+ method.SetOutputOrigin(itk_image.GetOrigin())
+
+ # Set Output Image Size
+ region = itk_image.GetLargestPossibleRegion()
+ size = region.GetSize()
+ size[2] = int(size[2]*scaling)
+ method.SetSize(size)
+
+ transform.SetIdentity()
+ method.SetTransform(transform)
+
+ return method.Execute(itk_image)
+
+
+def rescale_intensity(image):
+ """
+ A filter to scale the intensities of the input image to the full range
+ allowed by the pixel type
+
+ Inputs:
+ image = an itk.Image() object
+ input_type = pixel type string of the input image. Must be an ITK
+ recognized pixel type
+ output_type = same as above, for the output image
+ """
+ assert isinstance(image, sitk.Image)
+ method = sitk.RescaleIntensityImageFilter()
+ image_type = image.GetPixelIDTypeAsString()
+ if image_type == '8-bit unsigned integer':
+ method.SetOutputMinimum(0)
+ method.SetOutputMaximum(255)
+ else:
+ print("The rescale intensity filter has not been implemented for ", image_type)
+ return image
+
+ # TODO: Add pixel type check that is needed to check the bounds of re-scaling
+ return method.Execute(image)
+
+
+def gaussian_blurring_filter(image, variance):
+ """
+ Gaussian blur filter
+ """
+
+ filter = sitk.DiscreteGaussianImageFilter()
+ filter.SetUseImageSpacing(False)
+ filter.SetVariance(variance)
+
+ return filter.Execute(image)
+
+
+def grayscale_dilate_filter(image, kernel_radius):
+ """
+ Grayscale dilation filter
+ """
+
+ method = sitk.GrayscaleDilateImageFilter()
+ kernel = method.GetKernel()
+ kernel.SetKernelRadius(kernel_radius)
+ kernel = kernel.Ball(kernel.GetRadius())
+ method.SetKernel(kernel)
+
+ return method.Execute(image)
+
+
+def mean_filter(image, kernel_radius):
+ """
+ Uniform Mean filter for itk.Image objects
+ """
+ method = sitk.MeanImageFilter()
+ method.SetRadius(kernel_radius)
+
+ return method.Execute(image)
+
+
+def median_filter(image, kernel_radius):
+ """
+ Median filter for itk.Image objects
+
+ :param image: an itk.Image object
+ :param kernel_radius: median kernel radius
+ :return: filtered image
+ """
+ method = sitk.MedianImageFilter()
+ kernel = [kernel_radius,] * image.GetDimension()
+ method.SetRadius(kernel)
+
+ return method.Execute(image)
+
+
+def normalize_image_filter(image):
+ """
+ Normalizes the pixel values in an image to Mean of zero and Variance
+ of one. A floating point image_type is expected. For integer pixel
+ type, casting to a float is recommended before using this.
+ """
+
+ method = sitk.NormalizeImageFilter()
+ return method.Execute(image)
+
+
+def threshold_image_filter(image, threshold, th_value=0,
+ th_method="below"):
+ """
+ Thresholds an image by setting pixel values above or below "threshold"
+ to "th_value". The result is not a binary image, but a thresholded
+ grayscale image.
+ """
+
+ method = sitk.ThresholdImageFilter()
+ if th_method is "above":
+ method.SetLower(threshold)
+ elif th_method is "below":
+ method.SetUpper(threshold)
+
+ method.SetOutsideValue(th_value)
+
+ return method.Execute(image)
+
+
+def get_image_statistics(image):
+ """
+ A utility to calculate basic image statistics (Mean and Variance here)
+
+ :param image: an ITK:Image object
+ naming convention as in ITK
+ :return: returns the image mean and variance in a tuple
+ """
+ method = sitk.StatisticsImageFilter()
+ method.Execute(image)
+ mean = method.GetMean()
+ variance = method.GetVariance()
+ max = method.GetMaximum()
+ min = method.GetMinimum()
+
+ return mean, variance, min, max
+
+
+def type_cast(image, output_type):
+ """
+ A utility for changing the image pixel container type
+
+ :param image: An ITK:Image
+ :param output_type: output image type as ITK PixelID
+ :return: returns the image with new pixel type
+ """
+ assert isinstance(image, sitk.Image)
+
+ method = sitk.CastImageFilter()
+ method.SetOutputPixelType(output_type)
+
+ return method.Execute(image)
+
+
+def calculate_center_of_image(image, center_of_mass=False):
+ """
+ Center of an image can be defined either geometrically or statistically,
+ as a Center-of-Gravity measure.
+
+ This was originally Based on itk::ImageMomentsCalculator
+ http://www.itk.org/Doxygen/html/classitk_1_1ImageMomentsCalculator.html
+
+ However that filter is not currently implemented in SimpleITK and therefore
+ a Numpy approach is used.
+ """
+ assert isinstance(image, sitk.Image)
+
+ imdims = image.GetSize()
+ imspacing = image.GetSpacing()
+
+ if center_of_mass:
+ np_image, spacing = convert_from_itk_image(image)
+ center = scipy.ndimage.center_of_mass(np_image)
+ center *= numpy.array(spacing)
+ else:
+ center = list(map(
+ lambda size, spacing: spacing * size / 2,
+ imdims, imspacing
+ ))
+ return center
+
+
+def make_composite_rgb_image(red, green, blue=None, return_numpy=False):
+ """
+ A utitity to combine two or threegrayscale images into a single RGB image.
+ If only two images are provided, an empty image is placed in the blue
+ channel.
+
+ :param red: Red channel image. All the images should be sitk.Image
+ objects
+ :param green Green channel image
+ :param blue: Blue channel image.
+ :return: Returns a RGB composite image.
+ """
+ assert isinstance(red, sitk.Image) and isinstance(green, sitk.Image)
+ red = sitk.Cast(red, sitk.sitkUInt8)
+ green = sitk.Cast(green, sitk.sitkUInt8)
+ if blue is not None:
+ assert isinstance(blue, sitk.Image)
+ return sitk.Compose(red, green, blue)
+ else:
+ blue = sitk.Image(red.GetSize(), sitk.sitkUInt8)
+ blue.CopyInformation(red)
+ if return_numpy:
+ import numpy as np
+ images = (convert_from_itk_image(red)[0],
+ convert_from_itk_image(green)[0],
+ convert_from_itk_image(blue)[0])
+
+ spacing = convert_from_itk_image(red)[1]
+ return np.concatenate(
+ [aux[..., np.newaxis] for aux in images], axis=-1), spacing
+ else:
+ return sitk.Compose(red, green, blue)
+
+
+def make_translation_transforms_from_offsets(offsets):
+ """
+ Makes translation transforms from offsets.
+ :param offsets: a Numpy array or similar with each row defining an offset
+ in n dimensions
+ :return: returns the itk transforms
+ """
+ ndims = len(offsets[0])
+ transforms = []
+
+ for offset in offsets:
+ tfm = sitk.TranslationTransform(ndims)
+ tfm.SetParameters(offset)
+ transforms.append(tfm)
+
+ return transforms
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/ndarray.py b/Addons/FRCmetric/miplib-public/miplib/processing/ndarray.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebe8f5b8c5a7b4538659e3353ea77296ec019cc1
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/ndarray.py
@@ -0,0 +1,272 @@
+import numpy as np
+from functools import reduce
+
+def nroot(array, n):
+ """
+
+ :param array: A n dimensional numpy array by default. Of course this works
+ with single numbers and whatever the interpreter can understand
+ :param n: The root - a number
+ :return:
+ """
+ return array ** (1.0 / n)
+
+
+def normalize(array):
+ """
+ Normalizes a numpy array by dividing each element with the array.sum()
+
+ :param array: a numpy.array
+ :return:
+ """
+ return array / array.sum()
+
+
+def float2dtype(float_type):
+ """Return numpy float dtype object from float type label.
+ """
+ if float_type == 'single' or float_type is None:
+ return np.float32
+ if float_type == 'double':
+ return np.float64
+ raise NotImplementedError (repr(float_type))
+
+
+def contract_to_shape(data, shape):
+ """
+ Remove padding from input data array. The function
+ expects the padding to be symmetric on all sides
+ """
+ assert all(x <= y for x,y in zip(shape, data.shape))
+
+ if any(x != y for x,y in zip(shape, data.shape)):
+
+ slices = []
+ for s1, s2 in zip(data.shape, shape):
+ slices.append(slice((s1 - s2) // 2, (s1 + s2) // 2))
+
+ image = data[tuple(slices)]
+ else:
+ image = data
+
+ return image
+
+
+def expand_to_shape(data, shape, dtype=None, background=None):
+ """
+ Expand data to given shape by zero-padding.
+ """
+ if dtype is None:
+ dtype = data.dtype
+
+ start_index = np.array(shape) - data.shape
+ data_start = np.negative(start_index.clip(max=0))
+ data = cast_to_dtype(data, dtype, rescale=False)
+ if data.ndim == 3:
+ data = data[data_start[0]:, data_start[1]:, data_start[2]:]
+ else:
+ data = data[data_start[0]:, data_start[1]:]
+
+ if background is None:
+ background = 0
+
+ if tuple(shape) != data.shape:
+ expanded_data = np.zeros(shape, dtype=dtype) + background
+ slices = []
+ rhs_slices = []
+ for s1, s2 in zip(shape, data.shape):
+ a, b = (s1 - s2 + 1) // 2, (s1 + s2 + 1) // 2
+ c, d = 0, s2
+ while a < 0:
+ a += 1
+ b -= 1
+ c += 1
+ d -= 1
+ slices.append(slice(a, b))
+ rhs_slices.append(slice(c, d))
+ try:
+ expanded_data[tuple(slices)] = data[tuple(rhs_slices)]
+ except ValueError:
+ print(data.shape, shape)
+ raise
+ return expanded_data
+ else:
+ return data
+
+
+def mul_seq(seq):
+ return reduce(lambda x, y: x * y, seq, 1)
+
+
+def float2dtype(float_type):
+ """Return numpy float dtype object from float type label.
+ """
+ if float_type == 'single' or float_type is None:
+ return np.float32
+ if float_type == 'double':
+ return np.float64
+ raise NotImplementedError(repr(float_type))
+
+
+def cast_to_dtype(data, dtype, rescale=True, remove_outliers=False):
+ """
+ A function for casting a numpy array into a new data type.
+ The .astype() property of Numpy sometimes produces satisfactory
+ results, but if the data type to cast into has a more limited
+ dynamic range than the original data type, problems may occur.
+
+ :param data: a np.array object
+ :param dtype: data type string, as in Python
+ :param rescale: switch to enable rescaling pixel
+ values to the new dynamic range.
+ This should always be enabled when
+ scaling to a more limited range,
+ e.g. from float to int
+ :param remove_outliers: sometimes deconvolution/fusion generates
+ bright artifacts, which interfere with
+ the rescaling calculation. You can remove them
+ with this switch
+ :return: Returns the input data, cast into the new datatype
+ """
+ if data.dtype == dtype:
+ return data
+
+ if 'int' in str(dtype):
+ data_info = np.iinfo(dtype)
+ data_max = data_info.max
+ data_min = data_info.min
+ elif 'float' in str(dtype):
+ data_info = np.finfo(dtype)
+ data_max = data_info.max
+ data_min = data_info.min
+ else:
+ data_max = data.max()
+ data_min = data.min()
+ print("Warning casting into unknown data type. Detail clipping" \
+ "may occur")
+
+ # In case of unsigned integers, numbers below zero need to be clipped
+ if 'uint' in str(dtype):
+ data_max = 255
+ data_min = 0
+
+ if remove_outliers:
+ data = data.clip(0, np.percentile(data, 99.99))
+
+ if rescale is True:
+ return rescale_to_min_max(data, data_min, data_max).astype(dtype)
+ else:
+ return data.clip(data_min, data_max).astype(dtype)
+
+
+def rescale_to_min_max(data, data_min, data_max):
+ """
+ A function to rescale data intensities to range, define by
+ data_min and data_max input parameters.
+
+ :param data: Input data (Numpy array)
+ :param data_min: Minimum pixel value. Can be any type of a number
+ (preferably of the same type with the data.dtype)
+ :param data_max: Maximum pixel value
+ :return: Return the rescaled array
+ """
+ # Return array with max value in the original data scaled to correct
+ # range
+ if abs(data.max()) > abs(data.min()) or data_min == 0:
+ return data_max / data.max() * data
+ else:
+ return data_min / data.min() * data
+
+def safe_divide(numerator, denominator):
+ """
+ Division of numpy arrays that can handle division by zero. NaN results are
+ coerced to zero. Also suppresses the division by zero warning.
+ :param numerator:
+ :param denominator:
+ :return:
+ """
+ with np.errstate(divide="ignore", invalid="ignore"):
+ result = numerator / denominator
+ result[result == np.inf] = 0.0
+ return np.nan_to_num(result)
+
+
+def start_to_stop_idx(start, stop):
+ """
+ Generate n-dimensional indexing strucure for a numpy array,
+ consisting of a start-to-stop slice in each dimension
+ :param start: start indexes
+ :param stop: stop indexes
+ :return:
+ """
+ return tuple(slice(a, b) for a, b in zip(start, stop))
+
+
+def start_to_offset_idx(start, offset):
+ """
+ Generate n-dimensional indexing structure for a numpy array,
+ based on start indexes and offsets
+ :param start: list of indexes to start the slicing from
+ :param offset: list of slice lengths
+ :return:
+ """
+ stop = start + offset
+ return tuple(slice(a, b) for a, b in zip(start, stop))
+
+
+def reverse_array(array):
+
+ temp = array.copy()
+ for i in range(temp.ndim):
+ temp = np.flip(temp, i)
+
+ return temp
+
+
+def first_order_derivative_2d(array):
+ """
+ Calculates the first order (a[i]-a[i+1]) derivative of a 2D array
+ :param array: a 2D numeric array
+ :type array: np.ndarray
+ """
+ d1 = np.vstack([np.zeros((1, array.shape[1])), np.diff(array, axis=0)])
+ d2 = np.hstack([np.zeros((array.shape[0], 1)), np.diff(array, axis=1)])
+ return d1 ** 2 + d2 ** 2
+
+
+def get_rounded_kernel(diameter):
+ """
+ Makes a rounded kernel of a desired size for filtering operations
+ :param size:
+ :return:
+ """
+ dd = np.linspace(-1, 1, diameter)
+ xx1, yy1 = np.meshgrid(dd, dd)
+ rr = np.sqrt(xx1 ** 2 + yy1 ** 2)
+
+ kernel = np.zeros((diameter,)*2)
+ kernel[rr < 1] = 1
+
+ return kernel
+
+
+def center_of_mass(xx, yy, array, threshold=0.0):
+ """
+ A small utility calculate the center of mass on a meshgrid
+ :param xx: the x coordinates of the meshgrid
+ :param yy: the y coordinates of the meshgrid
+ :param array: an array with numeric values
+ :param threshold: a threshold value that can be used to exclude certain
+ array elements from the calculation.
+ :return: the x,y coordinates of the center of mass
+ """
+
+ if threshold > 0.0:
+ array = array.copy()
+ array[array < threshold] = 0
+
+ xsum = (xx * array).sum()
+ ysum = (yy * array).sum()
+ mass = array.sum()
+
+ return xsum / mass, ysum / mass
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/registration/__init__.py b/Addons/FRCmetric/miplib-public/miplib/processing/registration/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/registration/registration.py b/Addons/FRCmetric/miplib-public/miplib/processing/registration/registration.py
new file mode 100644
index 0000000000000000000000000000000000000000..50c43cf74baa4c58b563442796f51da42eca097f
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/registration/registration.py
@@ -0,0 +1,520 @@
+"""
+registration.py
+
+Copyright (C) 2014 Sami Koho
+All rights reserved.
+
+This software may be modified and distributed under the terms
+of the BSD license. See the LICENSE file for details.
+
+This file contains functions for registration of microscope image
+volumes. The methods are based on Insight Toolkit (www.itk.org),
+the installation of which is required in order to run the contained
+functions
+
+Currently (04/2014) rigid body registration of three-dimensional volumes
+has been implemented. Several metrics 1. least-squares 2. viola-wells mutual
+information 3. mattes mutual information are supported implemented
+
+"""
+
+
+import SimpleITK as sitk
+import matplotlib.pyplot as plt
+from skimage.feature import register_translation
+from scipy.ndimage import fourier_shift
+
+import miplib.processing.itk as ops_itk
+import miplib.ui.plots.image as show
+import miplib.processing.image as imops
+from miplib.data.containers.image import Image
+
+import numpy as np
+
+# region OBSERVERS
+
+# PLOTS
+# =============================================================================
+# Plotting functions for showing the registration progress.
+
+
+def start_plot():
+ global metric_values
+
+ metric_values = []
+
+
+def end_plot(fixed, moving, transform):
+ global metric_values
+ plt.subplots(1, 2, figsize=(10, 8))
+
+ # Plot metric values
+ plt.subplot(1, 2, 1)
+ plt.plot(metric_values, 'r')
+ plt.title("Metric values")
+ plt.xlabel('Iteration Number', fontsize=12)
+ plt.ylabel('Metric Value', fontsize=12)
+
+ # Plot image overlay
+ resampled = ops_itk.resample_image(moving, transform, reference=fixed)
+
+ if fixed.GetDimension() == 3:
+ fixed = sitk.MaximumProjection(fixed, 2)[:, :, 0]
+ resampled = sitk.MaximumProjection(resampled, 2)[:, :, 0]
+
+ fixed = sitk.Cast(fixed, sitk.sitkUInt8)
+ resampled = sitk.Cast(resampled, sitk.sitkUInt8)
+ fixed = sitk.RescaleIntensity(fixed, 0, 255)
+ resampled = sitk.RescaleIntensity(resampled, 0, 255)
+
+ plt.subplot(1, 2, 2)
+ plt.title("Overlay")
+ show.display_2d_image_overlay(fixed, resampled)
+
+
+ del metric_values
+
+
+def plot_values(registration_method):
+ global metric_values
+
+ metric_values.append(registration_method.GetMetricValue())
+ # clear the output area (wait=True, to reduce flickering), and plot current data
+ # plot the similarity metric values
+
+
+# endregion
+
+# region RIGID SPATIAL DOMAIN REGISTRATION METHODS
+
+#todo: Make a single method for n-dimensions. Too complicated now
+
+def itk_registration_rigid_3d(fixed_image, moving_image, options):
+ """
+ A Python implementation for a Rigid Body 3D registration, utilizing
+ ITK (www.itk.org) library functions.
+
+ :param fixed_image: The reference image. Must be an instance of
+ sitk.Image class.
+ :param moving_image: The image for which the spatial transform will
+ be calculated. Same requirements as above.
+ :param options: Options provided by the user via CLI or the
+ included GUI. See image_quality_options.py.
+ :return:
+ The final transform as a sitk.Euler2DTransform
+ """
+ print('Setting up registration job')
+
+ assert isinstance(fixed_image, sitk.Image)
+ assert isinstance(moving_image, sitk.Image)
+
+ fixed_image = sitk.Cast(fixed_image, sitk.sitkFloat32)
+ moving_image = sitk.Cast(moving_image, sitk.sitkFloat32)
+
+ # REGISTRATION COMPONENTS SETUP
+ # ========================================================================
+
+ registration = sitk.ImageRegistrationMethod()
+
+ # OPTIMIZER
+ registration.SetOptimizerAsRegularStepGradientDescent(
+ options.learning_rate,
+ options.min_step_length,
+ options.registration_max_iterations,
+ relaxationFactor=options.relaxation_factor,
+ estimateLearningRate=registration.EachIteration
+ )
+
+ registration.SetOptimizerScalesFromJacobian()
+
+ # translation_scale = 1.0/options.translation_scale
+ # registration.SetOptimizerScales([1.0, translation_scale, translation_scale])
+
+ # INTERPOLATOR
+ registration.SetInterpolator(sitk.sitkLinear)
+
+ # METRIC
+ if options.registration_method == 'mattes':
+ registration.SetMetricAsMattesMutualInformation(
+ numberOfHistogramBins=options.mattes_histogram_bins
+ )
+ elif options.registration_method == 'correlation':
+ registration.SetMetricAsCorrelation()
+
+ elif options.registration_method == 'mean-squared-difference':
+ registration.SetMetricAsMeanSquares()
+ else:
+ raise ValueError("Unknown metric: %s" % options.registration_method)
+
+ registration.SetMetricSamplingStrategy(registration.RANDOM)
+ registration.SetMetricSamplingPercentage(options.sampling_percentage)
+
+ if options.reg_translate_only:
+ tx = sitk.TranslationTransform(3)
+ else:
+
+ tx = sitk.Euler3DTransform()
+
+ transform = sitk.CenteredTransformInitializer(
+ fixed_image,
+ moving_image,
+ tx,
+ sitk.CenteredTransformInitializerFilter.MOMENTS
+ )
+ registration.SetInitialTransform(transform)
+
+ tx.SetCenter(ops_itk.calculate_center_of_image(moving_image))
+
+ registration.SetInitialTransform(tx)
+
+ if options.reg_enable_observers:
+ # OBSERVERS
+ registration.AddCommand(sitk.sitkStartEvent, start_plot)
+ registration.AddCommand(sitk.sitkIterationEvent, lambda: plot_values(registration))
+
+ # START
+ # ========================================================================
+
+ print("Starting registration")
+ final_transform = registration.Execute(fixed_image, moving_image)
+
+ print(('Final metric value: {0}'.format(registration.GetMetricValue())))
+ print(('Optimizer\'s stopping condition, {0}'.format(registration.GetOptimizerStopConditionDescription())))
+
+ if options.reg_enable_observers:
+ end_plot(fixed_image, moving_image, final_transform)
+
+ return final_transform
+
+
+def itk_registration_rigid_2d(fixed_image, moving_image, options):
+ """
+ A Python implementation for a Rigid Body 2D registration, utilizing
+ ITK (www.itk.org) library functions.
+
+ :param fixed_image: The reference image. Must be an instance of
+ sitk.Image class.
+ :param moving_image: The image for which the spatial transform will
+ be calculated. Same requirements as above.
+ :param options: Options provided by the user via CLI or the
+ included GUI. See image_quality_options.py.
+ :return:
+ The final transform as a sitk.Euler2DTransform
+ """
+ if options.verbose:
+ print('Setting up registration job')
+
+ assert isinstance(fixed_image, sitk.Image)
+ assert isinstance(moving_image, sitk.Image)
+
+ fixed_image = sitk.Cast(fixed_image, sitk.sitkFloat32)
+ moving_image = sitk.Cast(moving_image, sitk.sitkFloat32)
+
+ # REGISTRATION COMPONENTS SETUP
+ # ========================================================================
+
+ registration = sitk.ImageRegistrationMethod()
+
+ # OPTIMIZER
+ registration.SetOptimizerAsRegularStepGradientDescent(
+ options.learning_rate,
+ options.min_step_length,
+ options.registration_max_iterations,
+ relaxationFactor=options.relaxation_factor,
+ estimateLearningRate=registration.EachIteration
+ )
+
+ translation_scale = 1.0/options.translation_scale
+ registration.SetOptimizerScales([1.0, translation_scale, translation_scale])
+
+ # INTERPOLATOR
+ registration.SetInterpolator(sitk.sitkLinear)
+
+ # METRIC
+ if options.registration_method == 'mattes':
+ registration.SetMetricAsMattesMutualInformation(
+ numberOfHistogramBins=options.mattes_histogram_bins
+ )
+ elif options.registration_method == 'correlation':
+ registration.SetMetricAsCorrelation()
+
+ elif options.registration_method == 'mean-squared-difference':
+ registration.SetMetricAsMeanSquares()
+ else:
+ raise ValueError("Unknown metric: %s" % options.registration_method)
+
+ registration.SetMetricSamplingStrategy(registration.RANDOM)
+ registration.SetMetricSamplingPercentage(options.sampling_percentage)
+
+ if options.reg_translate_only:
+ tx = sitk.TranslationTransform(2)
+ else:
+
+ tx = sitk.Euler2DTransform()
+ tx.SetAngle(options.set_rotation)
+ if options.initializer:
+ if options.verbose:
+ print('Calculating initial registration parameters')
+ transform = sitk.CenteredTransformInitializer(
+ fixed_image,
+ moving_image,
+ tx,
+ sitk.CenteredTransformInitializerFilter.GEOMETRY
+ )
+ registration.SetInitialTransform(transform)
+
+ else:
+ tx.SetTranslation([options.y_offset, options.x_offset])
+
+ tx.SetCenter(ops_itk.calculate_center_of_image(moving_image))
+ registration.SetInitialTransform(tx)
+
+ if options.reg_enable_observers:
+ # OBSERVERS
+ registration.AddCommand(sitk.sitkStartEvent, start_plot)
+ registration.AddCommand(sitk.sitkIterationEvent, lambda: plot_values(registration))
+
+ # START
+ # ========================================================================
+ if options.verbose:
+ print("Starting registration")
+ final_transform = registration.Execute(fixed_image, moving_image)
+
+ if options.verbose:
+ print(('Final metric value: {0}'.format(registration.GetMetricValue())))
+ print(('Optimizer\'s stopping condition, {0}'.format(registration.GetOptimizerStopConditionDescription())))
+
+ if options.reg_enable_observers:
+ end_plot(fixed_image, moving_image, final_transform)
+
+ return final_transform
+
+# endregion
+
+# region DEFORMABLE SPATILA DOMAIN REGISTRATION METHDOS
+
+def itk_registration_similarity_2d(fixed_image, moving_image, options):
+ """
+ A Python implementation for a Rigid Body 2D registration, utilizing
+ ITK (www.itk.org) library functions.
+
+ :param fixed_image: The reference image. Must be an instance of
+ sitk.Image class.
+ :param moving_image: The image that is to be registered. Must be
+ an instance of sitk.Image class.
+ The image for which the spatial transform will
+ be calculated. Same requirements as above.
+ :param options: Options provided by the user via CLI
+
+ :return: The final transform as a sitk.Similarity2DTransform
+ """
+ print('Setting up registration job')
+
+ assert isinstance(fixed_image, sitk.Image)
+ assert isinstance(moving_image, sitk.Image)
+
+ fixed_image = sitk.Cast(fixed_image, sitk.sitkFloat32)
+ moving_image = sitk.Cast(moving_image, sitk.sitkFloat32)
+
+ # REGISTRATION COMPONENTS SETUP
+ # ========================================================================
+
+ registration = sitk.ImageRegistrationMethod()
+
+ # OPTIMIZER
+ registration.SetOptimizerAsRegularStepGradientDescent(
+ options.max_step_length,
+ options.min_step_length,
+ options.registration_max_iterations,
+ relaxationFactor=options.relaxation_factor
+ )
+ translation_scale = 1.0 / options.translation_scale
+ scaling_scale = 1.0 / options.scaling_scale
+
+ registration.SetOptimizerScales([scaling_scale, 1.0, translation_scale, translation_scale])
+
+ # INTERPOLATOR
+ registration.SetInterpolator(sitk.sitkLinear)
+
+ # METRIC
+ if options.registration_method == 'mattes':
+ registration.SetMetricAsMattesMutualInformation(
+ numberOfHistogramBins=options.mattes_histogram_bins
+ )
+ registration.SetMetricSamplingStrategy(registration.RANDOM)
+ registration.SetMetricSamplingPercentage(options.mattes_sampling_percentage)
+
+ elif options.registration_method == 'correlation':
+ registration.SetMetricAsCorrelation()
+
+ elif options.registration_method == 'mean-squared-difference':
+ registration.SetMetricAsMeanSquares()
+ else:
+ raise ValueError("Unknown metric: %s" % options.registration_method)
+
+ print('Calculating initial registration parameters')
+ tx = sitk.Similarity2DTransform()
+ tx.SetAngle(options.set_rotation)
+ tx.SetScale(options.set_scale)
+
+ if options.initializer:
+ transform = sitk.CenteredTransformInitializer(
+ fixed_image,
+ moving_image,
+ tx,
+ sitk.CenteredTransformInitializerFilter.GEOMETRY
+ )
+ registration.SetInitialTransform(transform)
+ else:
+ tx.SetTranslation([options.y_offset, options.x_offset])
+ tx.SetCenter(ops_itk.calculate_center_of_image(moving_image))
+ registration.SetInitialTransform(tx)
+
+ # OBSERVERS
+
+ registration.AddCommand(sitk.sitkStartEvent, start_plot)
+ #registration.AddCommand(sitk.sitkEndEvent, end_plot)
+ registration.AddCommand(sitk.sitkIterationEvent, lambda: plot_values(registration))
+
+ # START
+ # ========================================================================
+
+ print("Starting registration")
+ final_transform = registration.Execute(fixed_image, moving_image)
+
+ print(('Final metric value: {0}'.format(registration.GetMetricValue())))
+ print(('Optimizer\'s stopping condition, {0}'.format(registration.GetOptimizerStopConditionDescription())))
+
+ end_plot(fixed_image, moving_image, final_transform)
+
+ return final_transform
+
+
+def itk_registration_affine_2d(fixed_image, moving_image, options):
+ """
+ A Python implementation for a Rigid Body 2D registration, utilizing
+ ITK (www.itk.org) library functions.
+
+ :param fixed_image: The reference image. Must be an instance of
+ sitk.Image class.
+ :param moving_image: The image that is to be registered. Must be
+ an instance of sitk.Image class.
+ The image for which the spatial transform will
+ be calculated. Same requirements as above.
+ :param options: Options provided by the user via CLI
+
+ :return: The final transform as a sitk.Similarity2DTransform
+ """
+ print('Setting up registration job')
+
+ assert isinstance(fixed_image, sitk.Image)
+ assert isinstance(moving_image, sitk.Image)
+
+ fixed_image = sitk.Cast(fixed_image, sitk.sitkFloat32)
+ moving_image = sitk.Cast(moving_image, sitk.sitkFloat32)
+
+ # REGISTRATION COMPONENTS SETUP
+ # ========================================================================
+
+ registration = sitk.ImageRegistrationMethod()
+
+ # OPTIMIZER
+ registration.SetOptimizerAsRegularStepGradientDescent(
+ options.max_step_length,
+ options.min_step_length,
+ options.registration_max_iterations,
+ relaxationFactor=options.relaxation_factor
+ )
+ translation_scale = 1.0 / options.translation_scale
+ scaling_scale = 1.0 / options.scaling_scale
+
+ registration.SetOptimizerScales([scaling_scale, 1.0, translation_scale, translation_scale])
+
+ # INTERPOLATOR
+ registration.SetInterpolator(sitk.sitkLinear)
+
+ # METRIC
+ if options.registration_method == 'mattes':
+ registration.SetMetricAsMattesMutualInformation(
+ numberOfHistogramBins=options.mattes_histogram_bins
+ )
+ registration.SetMetricSamplingStrategy(registration.RANDOM)
+ registration.SetMetricSamplingPercentage(options.mattes_sampling_percentage)
+
+ elif options.registration_method == 'correlation':
+ registration.SetMetricAsCorrelation()
+
+ elif options.registration_method == 'mean-squared-difference':
+ registration.SetMetricAsMeanSquares()
+ else:
+ raise ValueError("Unknown metric: %s" % options.registration_method)
+
+ print('Calculating initial registration parameters')
+ tx = sitk.AffineTransform()
+
+
+ if options.initializer:
+ transform = sitk.CenteredTransformInitializer(
+ fixed_image,
+ moving_image,
+ tx,
+ sitk.CenteredTransformInitializerFilter.MOMENTS
+ )
+ registration.SetInitialTransform(transform)
+ else:
+ tx.SetTranslation([options.y_offset, options.x_offset])
+ tx.SetCenter(ops_itk.calculate_center_of_image(moving_image))
+ registration.SetInitialTransform(tx)
+
+ # OBSERVERS
+
+ registration.AddCommand(sitk.sitkStartEvent, start_plot)
+ #registration.AddCommand(sitk.sitkEndEvent, end_plot)
+ registration.AddCommand(sitk.sitkIterationEvent, lambda: plot_values(registration))
+
+ # START
+ # ========================================================================
+
+ print("Starting registration")
+ final_transform = registration.Execute(fixed_image, moving_image)
+
+ print(('Final metric value: {0}'.format(registration.GetMetricValue())))
+ print(('Optimizer\'s stopping condition, {0}'.format(registration.GetOptimizerStopConditionDescription())))
+
+ end_plot(fixed_image, moving_image, final_transform)
+
+ return final_transform
+
+# endregion
+
+# region RIGID FREQUENCY DOMAIN REGISTRATION METHODS
+
+
+def phase_correlation_registration(fixed_image, moving_image, subpixel=100, verbose=False, resample=True):
+ """
+ A simple Phase Correlation based image registration method.
+ :param verbose: enable print functions
+ :param subpixel: resampling factor; registration will be perfromed to
+ 1/subpixel accuracy
+ :param fixed_image: the reference image as MIPLIB Image object
+ :param moving_image: the moving image as MIPLIB Image object
+ :return: returns the SimpleITK transform
+ """
+ assert isinstance(fixed_image, Image)
+ assert isinstance(moving_image, Image)
+
+ shift, error, diffphase = register_translation(fixed_image, moving_image, subpixel)
+
+ scaled_shifts = list(-offset * spacing for offset, spacing in zip(shift, fixed_image.spacing))
+ if verbose:
+ print(("Detected offset (y, x): {}".format(scaled_shifts)))
+
+ if resample:
+ resampled = np.abs(np.fft.ifftn(fourier_shift(np.fft.fftn(moving_image),
+ shift)).real)
+
+ return Image(resampled, fixed_image.spacing)
+ else:
+ return scaled_shifts
+
+# endregion
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/registration/registration_mv.py b/Addons/FRCmetric/miplib-public/miplib/processing/registration/registration_mv.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a71e10aba5fa9dc285780c830ae48e0ca0669b2
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/registration/registration_mv.py
@@ -0,0 +1,334 @@
+import SimpleITK as sitk
+import numpy
+
+import miplib.processing.itk as ops_itk
+from miplib.data.containers import image_data
+from . import registration
+
+
+# todo: This class has way too many responsibilities. Need to refactor at some point.
+class RotatedMultiViewRegistration(object):
+ """
+ A class for multiview image registration. The method is based on
+ functions inside the Insight Toolkit (www.itk.org), as in the original
+ *miplib*. In *miplib* SimpleITK was used instead of Python
+ wrapped ITK.
+
+ The registration was updated to support multiple views
+ and the new HDF5 data storage implementation. It was also implemented
+ as a class.
+ """
+
+ def __init__(self, data, options):
+ """
+ :param data: a ImageData object
+
+ :param options: command line options that control the behavior
+ of the registration algorithm
+ """
+ assert isinstance(data, image_data.ImageData)
+
+ # Parameters
+ self.data = data
+ self.options = options
+
+ # Fixed and moving image
+ self.fixed_index = 0
+ self.moving_index = 1
+
+ # Results
+ self.final_transform = None
+
+ # REGISTRATION COMPONENTS SETUP
+ # ========================================================================
+
+ self.registration = sitk.ImageRegistrationMethod()
+
+ # OPTIMIZER
+
+ self.registration.SetOptimizerAsRegularStepGradientDescent(
+ options.learning_rate,
+ options.min_step_length,
+ options.registration_max_iterations,
+ relaxationFactor=options.relaxation_factor,
+ estimateLearningRate=self.registration.EachIteration
+ )
+ # translation_scale = 1.0 / options.translation_scale
+ # self.registration.SetOptimizerScales([10, 10, 10,
+ # .1, .1,.1])
+
+ self.registration.SetOptimizerScalesFromJacobian()
+
+ # INTERPOLATOR
+ self.registration.SetInterpolator(sitk.sitkLinear)
+
+ # METRIC
+ if options.registration_method == 'mattes':
+ self.registration.SetMetricAsMattesMutualInformation(
+ numberOfHistogramBins=options.mattes_histogram_bins
+ )
+
+ elif options.registration_method == 'correlation':
+ self.registration.SetMetricAsCorrelation()
+
+ elif options.registration_method == 'mean-squared-difference':
+ self.registration.SetMetricAsMeanSquares()
+ else:
+ raise ValueError("Unknown metric: %s" % options.registration_method)
+
+ self.registration.SetMetricSamplingStrategy(self.registration.RANDOM)
+ self.registration.SetMetricSamplingPercentage(
+ options.sampling_percentage)
+
+ def execute(self):
+ """
+ Run image registration. All the views are registered one by one. The
+ image
+ at index 0 is used as a reference.
+ """
+
+ # Get reference image.
+ self.data.set_active_image(self.fixed_index,
+ self.options.channel,
+ self.options.scale,
+ "original")
+ fixed_image = self.data.get_itk_image()
+
+ # Get moving image
+ self.data.set_active_image(self.moving_index,
+ self.options.channel,
+ self.options.scale,
+ "original")
+ moving_image = self.data.get_itk_image()
+
+ # INITIALIZATION
+ # --------------
+ # Start by rotating the moving image with the known rotation angle.
+ print('Initializing registration')
+ manual_transform = sitk.Euler3DTransform()
+
+ # Rotate around the physical center of the image.
+ rotation_center = moving_image.TransformContinuousIndexToPhysicalPoint(
+ [(index - 1) / 2.0 for index in moving_image.GetSize()])
+ manual_transform.SetCenter(rotation_center)
+
+ # Rotation
+ initial_rotation = self.data.get_rotation_angle(radians=True)
+ if self.options.rot_axis == 0:
+ manual_transform.SetRotation(initial_rotation, 0, 0)
+ elif self.options.rot_axis == 1:
+ manual_transform.SetRotation(0, initial_rotation, 0)
+ else:
+ manual_transform.SetRotation(0, 0, initial_rotation)
+
+ # Translation
+ manual_transform.SetTranslation([self.options.y_offset,
+ self.options.x_offset,
+ self.options.z_offset])
+
+ modified_moving_image = ops_itk.resample_image(moving_image,
+ manual_transform)
+
+ # 2. Run Automatic initialization
+
+ transform = sitk.CenteredTransformInitializer(
+ fixed_image,
+ modified_moving_image,
+ sitk.AffineTransform(3),
+ sitk.CenteredTransformInitializerFilter.MOMENTS
+ )
+
+ # print "The initial transform is:"
+ # print transform
+
+ # Set initial transform
+ self.registration.SetInitialTransform(transform)
+
+ # SPATIAL MASK
+ # =====================================================================
+ # The registration metric works more reliably when it knows where
+ # non-zero
+ # voxels are located.
+ thd = self.options.mask_threshold
+ fixed_mask = sitk.BinaryDilate(
+ sitk.BinaryThreshold(fixed_image, 0, thd, 0, 1))
+ moving_mask = sitk.BinaryDilate(
+ sitk.BinaryThreshold(modified_moving_image, 0, thd, 0, 1))
+
+ self.registration.SetMetricFixedMask(fixed_mask)
+ self.registration.SetMetricMovingMask(moving_mask)
+
+ # START
+ # ======================================================================
+
+ if self.options.reg_enable_observers:
+ # OBSERVERS
+ self.registration.AddCommand(sitk.sitkStartEvent, registration.start_plot)
+ self.registration.AddCommand(sitk.sitkIterationEvent, lambda: registration.plot_values(self.registration))
+
+ print("Starting registration of views " \
+ "%i (fixed) & %i (moving)" % (self.fixed_index, self.moving_index))
+
+ result = self.registration.Execute(
+ sitk.Cast(fixed_image, sitk.sitkFloat32),
+ sitk.Cast(modified_moving_image, sitk.sitkFloat32))
+
+ result = sitk.AffineTransform(result)
+ # RESULTS
+ # =====================================================================
+ # Combine two partial transforms into one.
+ # self.final_transform = sitk.Transform(manual_transform)
+ # self.final_transform.AddTransform(result)
+
+ # The two resulting transforms are combined into one here, because
+ # it is easier to save a single transform into a HDF5 file.
+
+ A0 = numpy.asarray(manual_transform.GetMatrix()).reshape(3, 3)
+ c0 = numpy.asarray(manual_transform.GetCenter())
+ t0 = numpy.asarray(manual_transform.GetTranslation())
+
+ A1 = numpy.asarray(result.GetMatrix()).reshape(3, 3)
+ c1 = numpy.asarray(result.GetCenter())
+ t1 = numpy.asarray(result.GetTranslation())
+
+ combined_mat = numpy.dot(A0, A1)
+ combined_center = c1
+ combined_translation = numpy.dot(A0, t1 + c1 - c0) + t0 + c0 - c1
+ self.final_transform = sitk.AffineTransform(combined_mat.flatten(),
+ combined_translation,
+ combined_center)
+
+ # Print final metric value and stopping condition
+ print((
+ 'Final metric value: {0}'.format(self.registration.GetMetricValue())))
+ print((
+ 'Optimizer\'s stopping condition, {0}'.format(
+ self.registration.GetOptimizerStopConditionDescription())))
+ print(self.final_transform)
+
+ if self.options.reg_enable_observers:
+ registration.end_plot(fixed_image, moving_image, self.final_transform)
+
+ def set_moving_image(self, index):
+ """
+ Parameters
+ ----------
+ :param index The moving image index from 0 to views number - 1
+
+ """
+ self.moving_index = index
+
+ def set_fixed_image(self, index):
+ """
+ Parameters
+ ----------
+ :param index The fixed image index from 0 to views number - 1. Should
+ be zero in most cases.
+
+ """
+ self.fixed_index = index
+
+ def get_final_transform(self):
+ """"
+ Returns
+ -------
+
+ Get the final transform as an ITK transform
+ """
+ return self.final_transform
+
+ def get_resampled_result(self):
+ """
+
+ Returns
+ -------
+
+ Get the registration result as a resampled image.
+ """
+
+ self.data.set_active_image(self.fixed_index,
+ self.options.channel,
+ self.options.scale,
+ "original")
+
+ fixed_image = self.data.get_itk_image()
+
+ self.data.set_active_image(self.moving_index,
+ self.options.channel,
+ self.options.scale,
+ "original")
+
+ moving_image = self.data.get_itk_image()
+
+ return ops_itk.resample_image(moving_image, self.final_transform,
+ fixed_image)
+
+ def save_result(self):
+ scale = self.options.scale
+ channel = self.options.channel
+ view = self.moving_index
+
+ # Add registered image
+ self.data.set_active_image(view, channel, scale, "original")
+ angle = self.data.get_rotation_angle(radians=False)
+ spacing = self.data.get_voxel_size()
+ registered_image = ops_itk.convert_from_itk_image(
+ self.get_resampled_result())
+ self.data.add_registered_image(registered_image, scale, view, channel,
+ angle, spacing)
+ # Add transform
+ transform = self.get_final_transform()
+ tfm_params = ops_itk.get_itk_transform_parameters(transform)
+ self.data.add_transform(scale, view, channel, tfm_params[1],
+ tfm_params[2], tfm_params[0])
+
+ def add_observers(self, start, update):
+ """
+
+ Parameters
+ ----------
+ start Observer to add for the registration start
+ update Observer to add for registration progress updates.
+
+ Returns
+ -------
+
+ """
+ self.registration.AddCommand(sitk.sitkStartEvent, start)
+ self.registration.AddCommand(sitk.sitkIterationEvent,
+ lambda: update(self.registration))
+
+
+class MultiViewRegistrationISM(RotatedMultiViewRegistration):
+
+ def execute(self):
+ # Get reference image.
+ self.data.set_active_image(self.fixed_index,
+ self.options.channel,
+ self.options.scale,
+ "original")
+ fixed_image = self.data.get_itk_image()
+
+ for idx in range(self.data.get_number_of_images("original")):
+ print("Registering view {}".format(idx))
+ self.moving_index = idx
+
+ # Get moving image
+ self.data.set_active_image(self.moving_index,
+ self.options.channel,
+ self.options.scale,
+ "original")
+ moving_image = self.data.get_itk_image()
+
+ # Register
+ if moving_image.GetDimension() == 2:
+ self.final_transform = registration.itk_registration_rigid_2d(fixed_image,
+ moving_image,
+ self.options)
+ else:
+ self.final_transform = registration.itk_registration_rigid_3d(fixed_image,
+ moving_image,
+ self.options)
+ self.save_result()
+
+ print("All views registered and saved to the data structure")
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/registration/stack.py b/Addons/FRCmetric/miplib-public/miplib/processing/registration/stack.py
new file mode 100644
index 0000000000000000000000000000000000000000..20677f426833c7c45fcd21f9787fbb532faf86d1
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/registration/stack.py
@@ -0,0 +1,75 @@
+import numpy as np
+
+from miplib.data.containers.image import Image
+from miplib.processing.registration import registration
+from scipy.ndimage import fourier_shift
+
+
+def register_stack_slices(stack):
+ """
+ An utility to register slices in an image stack. The registration is performed
+ by iterating over adjacent layers (0->1, 1->2,...). The shift obtained for each
+ layer, is with respect to the image at idx=0.
+
+ :param stack {Image}: a 3D image stack
+ :return: the image shifts with respect to index 0 (in pixels).
+
+ """
+ assert isinstance(stack, Image)
+ assert stack.ndim == 3
+
+ shifts = np.zeros((stack.shape[0], 2), dtype=np.float)
+
+ for f_idx, m_idx in zip(range(0, stack.shape[0] - 1), range(1, stack.shape[0])):
+ fixed = Image(stack[f_idx], stack.spacing[1:])
+ moving = Image(stack[m_idx], stack.spacing[1:])
+
+ offset = registration.phase_correlation_registration(fixed, moving, resample=False)
+ shifts[m_idx] = shifts[f_idx] + np.asarray(offset)
+
+ return shifts
+
+
+def register_stack_slices_with_reference(stack, fixed):
+ """
+ Same as above, but the fixed image is provided by the user.
+
+ :param stack {Image}: a 3D image stack
+ :param fixed: the image that is to be used as a
+ reference for the registration
+ :return: the image shifts with respect to the fixed (in pixels).
+ """
+ assert isinstance(stack, Image)
+ assert stack.ndim == 3
+
+ shifts = np.zeros((stack.shape[0], 2), dtype=np.float)
+
+ for i in range(stack.shape[0]):
+ moving = Image(stack[i], stack.spacing[1:])
+ shifts[i] = registration.phase_correlation_registration(fixed, moving, resample=False)
+
+ return shifts
+
+
+def shift_stack_slices(stack, shifts):
+ """
+ Shift stack slices
+ :param stack {Image}: a 3D stack
+ :param shifts {np.ndarray}: a (stack_depth, 2) array with y,x shift defined on each
+ row, corresponding to the relative of an image at postition i with respect to the first
+ image in the stack (i=0)
+ :returns {Image}: the resampled image with all the slices aligned.
+ """
+
+ assert isinstance(stack, Image)
+ assert stack.ndim == 3
+
+ if stack.shape[0] != shifts.shape[0]:
+ raise ValueError("The shift array does not match the stack depth.")
+
+ resampled = Image(np.zeros_like(stack), spacing=stack.spacing)
+
+ for idx, (image, shift) in enumerate(zip(stack, shifts)):
+ resampled[idx] = np.abs(np.fft.ifftn(fourier_shift(np.fft.fftn(image), shift)).real)
+
+ return resampled
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/segmentation/__init__.py b/Addons/FRCmetric/miplib-public/miplib/processing/segmentation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/segmentation/masking.py b/Addons/FRCmetric/miplib-public/miplib/processing/segmentation/masking.py
new file mode 100644
index 0000000000000000000000000000000000000000..b51af60f4a7f858d872d8fded9bee9dca85144f8
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/segmentation/masking.py
@@ -0,0 +1,18 @@
+import numpy as np
+from scipy import ndimage
+
+
+from miplib.data.containers.image import Image
+
+
+def make_local_intensity_based_mask(image, threshold, kernel_size=40, invert=False):
+ assert isinstance(image, Image)
+
+ blurred_image = ndimage.uniform_filter(image, size=kernel_size)
+
+ peaks = np.percentile(blurred_image, threshold)
+ mask = np.where(blurred_image >= peaks, 1, 0)
+ if invert:
+ return np.invert(mask.astype(bool))
+ else:
+ return mask
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/src/ops_ext.c b/Addons/FRCmetric/miplib-public/miplib/processing/src/ops_ext.c
new file mode 100644
index 0000000000000000000000000000000000000000..329e9acd882c53b5257c1953ef0d188532b3da72
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/src/ops_ext.c
@@ -0,0 +1,1141 @@
+
+#define _VERSION_ "2018.08.13"
+
+#include <Python.h>
+//#define PY_ARRAY_UNIQUE_SYMBOL PyArray_API
+#include "numpy/arrayobject.h"
+
+#include <math.h>
+
+#ifndef M_PI
+#define M_PI 3.1415926535897932384626433832795
+#endif
+
+void sincos(double x, double *sn, double *cs)
+{
+ *sn = sin(x);
+ *cs = cos(x);
+}
+
+#ifndef PyMODINIT_FUNC /* declarations for DLL import/export */
+#define PyMODINIT_FUNC void
+#endif
+
+static PyObject *update_estimate_poisson(PyObject *self, PyObject *args)
+{
+ PyObject* a = NULL;
+ PyObject* b = NULL;
+ npy_intp sz = 0, i;
+ double tmp, tmp2;
+ npy_float32* a_data_sp = NULL;
+ npy_float32* b_data_sp = NULL;
+ npy_complex64* b_data_csp = NULL;
+ npy_float64* a_data_dp = NULL;
+ npy_float64* b_data_dp = NULL;
+ npy_complex128* b_data_cdp = NULL;
+ double c, c0, c1, c2;
+ double unstable = 0.0, stable = 0.0, negative = 0.0, exact = 0.0;
+ if (!PyArg_ParseTuple(args, "OOd", &a, &b, &c))
+ return NULL;
+ if (c<0 || c>0.5)
+ {
+ PyErr_SetString(PyExc_TypeError,"third argument must be non-negative and less than 0.5");
+ return NULL;
+ }
+ if (!(PyArray_Check(a) && PyArray_Check(b)))
+ {
+ PyErr_SetString(PyExc_TypeError,"first two arguments must be array objects");
+ return NULL;
+ }
+ sz = PyArray_SIZE(a);
+ if (sz != PyArray_SIZE(b))
+ {
+ PyErr_SetString(PyExc_TypeError,"array argument sizes must be equal");
+ return NULL;
+ }
+ c0 = -c;
+ c1 = 1.0+c;
+ c2 = 1.0-c;
+ if ((PyArray_TYPE(a) == PyArray_FLOAT32) && (PyArray_TYPE(b) == PyArray_FLOAT32))
+ {
+ a_data_sp = (npy_float32*)PyArray_DATA(a);
+ b_data_sp = (npy_float32*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp = b_data_sp[i];
+ tmp2 = (a_data_sp[i] *= (tmp>0?tmp:0.0));
+ if (tmp==0.0 || tmp==1.0)
+ exact += tmp2;
+ else if (((tmp>c0) && (tmp<c)) || ((tmp<c1) && (tmp>c2)))
+ stable += tmp2;
+ else
+ unstable += tmp2;
+ if (tmp2<0)
+ negative += tmp2;
+ }
+ }
+ else if ((PyArray_TYPE(a) == PyArray_FLOAT32) && (PyArray_TYPE(b) == PyArray_COMPLEX64))
+ {
+ a_data_sp = (npy_float32*)PyArray_DATA(a);
+ b_data_csp = (npy_complex64*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp = b_data_csp[i].real;
+ tmp2 = (a_data_sp[i] *= (tmp>0?tmp:0.0));
+ if (tmp==0.0 || tmp==1.0)
+ exact += tmp2;
+ else if (((tmp>c0) && (tmp<c)) || ((tmp<c1) && (tmp>c2)))
+ stable += tmp2;
+ else
+ unstable += tmp2;
+ if (tmp2<0)
+ negative += tmp2;
+ }
+ }
+ else if ((PyArray_TYPE(a) == PyArray_FLOAT64) && (PyArray_TYPE(b) == PyArray_FLOAT64))
+ {
+ a_data_dp = (npy_float64*)PyArray_DATA(a);
+ b_data_dp = (npy_float64*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp = b_data_dp[i];
+ tmp2 = (a_data_dp[i] *= (tmp>0?tmp:0.0));
+ if (tmp==0.0 || tmp==1.0)
+ exact += tmp2;
+ else if (((tmp>c0) && (tmp<c)) || ((tmp<c1) && (tmp>c2)))
+ stable += tmp2;
+ else
+ unstable += tmp2;
+ if (tmp2<0)
+ negative += tmp2;
+ }
+ }
+ else if ((PyArray_TYPE(a) == PyArray_FLOAT64) && (PyArray_TYPE(b) == PyArray_COMPLEX128))
+ {
+ a_data_dp = (npy_float64*)PyArray_DATA(a);
+ b_data_cdp = (npy_complex128*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp = (b_data_cdp[i]).real;
+ tmp2 = (a_data_dp[i] *= (tmp>0?tmp:0.0));
+ if (tmp==0.0 || tmp==1.0)
+ exact += tmp2;
+ else if (((tmp>c0) && (tmp<c)) || ((tmp<c1) && (tmp>c2)))
+ stable += tmp2;
+ else
+ unstable += tmp2;
+ if (tmp2<0)
+ negative += tmp2;
+ }
+ }
+ else
+ {
+ PyErr_SetString(PyExc_TypeError,"array argument types must be either float32 or float64");
+ return NULL;
+ }
+ return Py_BuildValue("dddd", exact, stable, unstable, negative);
+}
+
+static PyObject *update_estimate_gauss(PyObject *self, PyObject *args)
+{
+ PyObject* a = NULL;
+ PyObject* b = NULL;
+ npy_intp sz = 0, i;
+ double tmp, tmp2;
+ npy_float32* a_data_sp = NULL;
+ npy_float32* b_data_sp = NULL;
+ npy_complex64* b_data_csp = NULL;
+ npy_float64* a_data_dp = NULL;
+ npy_float64* b_data_dp = NULL;
+ npy_complex128* b_data_cdp = NULL;
+ double c, c0, c1, c2, alpha;
+ double unstable = 0.0, stable = 0.0, negative=0.0, exact=0.0;
+ if (!PyArg_ParseTuple(args, "OOdd", &a, &b, &c, &alpha))
+ return NULL;
+ if (c<0 || c>0.5)
+ {
+ PyErr_SetString(PyExc_TypeError,"third argument must be non-negative and less than 0.5");
+ return NULL;
+ }
+ if (!(PyArray_Check(a) && PyArray_Check(b)))
+ {
+ PyErr_SetString(PyExc_TypeError,"first two arguments must be array objects");
+ return NULL;
+ }
+ sz = PyArray_SIZE(a);
+ if (sz != PyArray_SIZE(b))
+ {
+ PyErr_SetString(PyExc_TypeError,"array argument sizes must be equal");
+ return NULL;
+ }
+ c0 = -c;
+ c1 = 1.0+c;
+ c2 = 1.0-c;
+ if ((PyArray_TYPE(a) == PyArray_FLOAT32) && (PyArray_TYPE(b) == PyArray_FLOAT32))
+ {
+ a_data_sp = (npy_float32*)PyArray_DATA(a);
+ b_data_sp = (npy_float32*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp = a_data_sp[i];
+ tmp2 = (a_data_sp[i] += alpha * b_data_sp[i]);
+ if (tmp==0.0)
+ tmp = 2.0; // force unstable
+ else
+ tmp = a_data_sp[i] / tmp;
+ if (tmp==0.0 || tmp==1.0)
+ exact += tmp2;
+ else if (((tmp>c0) && (tmp<c)) || ((tmp<c1) && (tmp>c2)))
+ stable += tmp2;
+ else
+ unstable += tmp2;
+ if (tmp2<0)
+ negative += tmp2;
+ }
+ }
+ else if ((PyArray_TYPE(a) == PyArray_FLOAT32) && (PyArray_TYPE(b) == PyArray_COMPLEX64))
+ {
+ a_data_sp = (npy_float32*)PyArray_DATA(a);
+ b_data_csp = (npy_complex64*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp = a_data_sp[i];
+ tmp2 = (a_data_sp[i] += alpha * b_data_csp[i].real);
+ if (tmp==0.0)
+ tmp = 2.0; // force unstable
+ else
+ tmp = a_data_sp[i] / tmp;
+ if (tmp==0.0 || tmp==1.0)
+ exact += tmp2;
+ else if (((tmp>c0) && (tmp<c)) || ((tmp<c1) && (tmp>c2)))
+ stable += tmp2;
+ else
+ unstable += tmp2;
+ if (tmp2<0)
+ negative += tmp2;
+ }
+ }
+ else if ((PyArray_TYPE(a) == PyArray_FLOAT64) && (PyArray_TYPE(b) == PyArray_FLOAT64))
+ {
+ a_data_dp = (npy_float64*)PyArray_DATA(a);
+ b_data_dp = (npy_float64*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp = a_data_dp[i];
+ tmp2 = (a_data_dp[i] += alpha * b_data_dp[i]);
+ if (tmp==0.0)
+ tmp = 2.0; // force unstable
+ else
+ tmp = a_data_dp[i] / tmp;
+ if (tmp==0.0 || tmp==1.0)
+ exact += tmp2;
+ else if (((tmp>c0) && (tmp<c)) || ((tmp<c1) && (tmp>c2)))
+ stable += tmp2;
+ else
+ unstable += tmp2;
+ if (tmp2<0)
+ negative += tmp2;
+ }
+ }
+ else if ((PyArray_TYPE(a) == PyArray_FLOAT64) && (PyArray_TYPE(b) == PyArray_COMPLEX128))
+ {
+ a_data_dp = (npy_float64*)PyArray_DATA(a);
+ b_data_cdp = (npy_complex128*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp = a_data_dp[i];
+ tmp2 = (a_data_dp[i] += alpha * b_data_cdp[i].real);
+ if (tmp==0.0)
+ tmp = 2.0; // force unstable
+ else
+ tmp = a_data_dp[i] / tmp;
+ if (tmp==0.0 || tmp==1.0)
+ exact += tmp2;
+ else if (((tmp>c0) && (tmp<c)) || ((tmp<c1) && (tmp>c2)))
+ stable += tmp2;
+ else
+ unstable += tmp2;
+ if (tmp2<0)
+ negative += tmp2;
+ }
+ }
+ else
+ {
+ PyErr_SetString(PyExc_TypeError,"array argument types must be either float32 or float64");
+ return NULL;
+ }
+ return Py_BuildValue("dddd", exact, stable, unstable, negative);
+}
+
+
+static PyObject *poisson_hist_factor_estimate(PyObject *self, PyObject *args)
+{
+ PyObject* a = NULL;
+ PyObject* b = NULL;
+ npy_intp sz = 0, i;
+ npy_float32 tmp;
+ npy_float32* a_data = NULL;
+ npy_float32* b_data = NULL;
+ double c;
+ double unstable = 0.0, stable = 0.0;
+ if (!PyArg_ParseTuple(args, "OOd", &a, &b, &c))
+ return NULL;
+ if (c<0 || c>0.5)
+ {
+ PyErr_SetString(PyExc_TypeError,"third argument must be non-negative and less than 0.5");
+ return NULL;
+ }
+ if (!(PyArray_Check(a) && PyArray_Check(b)))
+ {
+ PyErr_SetString(PyExc_TypeError,"first two arguments must be array objects");
+ return NULL;
+ }
+ sz = PyArray_SIZE(a);
+ if (sz != PyArray_SIZE(b))
+ {
+ PyErr_SetString(PyExc_TypeError,"array argument sizes must be equal");
+ return NULL;
+ }
+ if (! ((PyArray_TYPE(a) == PyArray_FLOAT32) && (PyArray_TYPE(b) == PyArray_FLOAT32)))
+ {
+ PyErr_SetString(PyExc_TypeError,"array argument types must be float32");
+ return NULL;
+ }
+ a_data = (npy_float32*)PyArray_DATA(a);
+ b_data = (npy_float32*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp = a_data[i];
+ if (((tmp>-c) && (tmp<c)) || ((tmp<1.0+c) && (tmp>1.0-c)))
+ stable += tmp * b_data[i];
+ else
+ {
+ unstable += tmp * b_data[i];
+ }
+ }
+ return Py_BuildValue("dd",stable, unstable);
+}
+
+// kldiv(f,f0)=E(f0-f+f*log(f/f0)) f0,f>=level
+static PyObject* kullback_leibler_divergence(PyObject *self, PyObject *args)
+{
+ PyObject* a = NULL;
+ PyObject* b = NULL;
+ npy_float64 f,f0, level = 1.0;
+ npy_intp sz = 0, i, count=0;
+ if (!PyArg_ParseTuple(args, "OO|f", &a, &b, &level))
+ return NULL;
+ if (!(PyArray_Check(a) && PyArray_Check(b)))
+ {
+ PyErr_SetString(PyExc_TypeError,"arguments must be array objects");
+ return NULL;
+ }
+ sz = PyArray_SIZE(a);
+
+ if (sz != PyArray_SIZE(b))
+ {
+ PyErr_SetString(PyExc_TypeError,"argument sizes must be equal");
+ return NULL;
+ }
+ if (PyArray_TYPE(a) != PyArray_TYPE(b))
+ {
+ PyErr_SetString(PyExc_TypeError,"argument types must be same");
+ return NULL;
+ }
+ level = (level<0? 0.0 : level);
+ switch(PyArray_TYPE(a))
+ {
+ case PyArray_FLOAT64:
+ {
+ npy_float64 result=0.0;
+ for (i=0; i<sz; ++i)
+ {
+ f = *((npy_float64*)PyArray_DATA(a) + i);
+ f0 = *((npy_float64*)PyArray_DATA(b) + i);
+ if (f0<=level || f<level)
+ continue;
+ if (f==0.0)
+ result += f0;
+ else
+ result += f0 - f + f*log(f/f0);
+ count ++;
+ }
+ return Py_BuildValue("f", result/count);
+ }
+ break;
+ case PyArray_FLOAT32:
+ {
+ npy_float64 result=0.0;
+ for (i=0; i<sz; ++i)
+ {
+ f = *((npy_float32*)PyArray_DATA(a) + i);
+ f0 = *((npy_float32*)PyArray_DATA(b) + i);
+ if (f0<=level || f<level)
+ continue;
+ if (f==0.0)
+ result += f0;
+ else
+ result += f0 - f + f*log(f/f0);
+ count ++;
+ }
+ return Py_BuildValue("f", result/count);
+ }
+ break;
+ default:
+ PyErr_SetString(PyExc_TypeError,"argument types must be float64");
+ return NULL;
+ }
+}
+
+static PyObject *zero_if_zero_inplace(PyObject *self, PyObject *args)
+{
+ PyObject* a = NULL;
+ PyObject* b = NULL;
+ npy_intp sz = 0, i;
+ npy_complex64* tmp = NULL;
+ npy_complex64* a_data = NULL;
+ npy_float32* b_data = NULL;
+ if (!PyArg_ParseTuple(args, "OO", &a, &b))
+ return NULL;
+ if (!(PyArray_Check(a) && PyArray_Check(b)))
+ {
+ PyErr_SetString(PyExc_TypeError,"arguments must be array objects");
+ return NULL;
+ }
+ sz = PyArray_SIZE(a);
+
+ if (sz != PyArray_SIZE(b))
+ {
+ PyErr_SetString(PyExc_TypeError,"argument sizes must be equal");
+ return NULL;
+ }
+ if (! ((PyArray_TYPE(a) == PyArray_COMPLEX64) && (PyArray_TYPE(b) == PyArray_FLOAT32)))
+ {
+ PyErr_SetString(PyExc_TypeError,"argument types must be complex64 and float32");
+ return NULL;
+ }
+ a_data = (npy_complex64*)PyArray_DATA(a);
+ b_data = (npy_float32*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ if (b_data[i]==0)
+ {
+ tmp = a_data + i;
+ tmp->real = tmp->imag = 0.0;
+ }
+ }
+ return Py_BuildValue("");
+}
+
+static PyObject *inverse_division_inplace(PyObject *self, PyObject *args)
+{
+ PyObject* a = NULL;
+ PyObject* b = NULL;
+ npy_intp sz = 0, i;
+ npy_complex64* tmp_sp = NULL;
+ npy_float32 tmp2_sp;
+ npy_complex64* a_data_sp = NULL;
+ npy_float32* b_data_sp = NULL;
+ npy_complex128* tmp_dp = NULL;
+ npy_float64 tmp2_dp;
+ npy_complex128* a_data_dp = NULL;
+ npy_float64* b_data_dp = NULL;
+ if (!PyArg_ParseTuple(args, "OO", &a, &b))
+ return NULL;
+ if (!(PyArray_Check(a) && PyArray_Check(b)))
+ {
+ PyErr_SetString(PyExc_TypeError,"arguments must be array objects");
+ return NULL;
+ }
+ sz = PyArray_SIZE(a);
+
+ if (sz != PyArray_SIZE(b))
+ {
+ PyErr_SetString(PyExc_TypeError,"argument sizes must be equal");
+ return NULL;
+ }
+ if ((PyArray_TYPE(a) == PyArray_COMPLEX64) && (PyArray_TYPE(b) == PyArray_FLOAT32))
+ {
+ a_data_sp = (npy_complex64*)PyArray_DATA(a);
+ b_data_sp = (npy_float32*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp_sp = a_data_sp + i;
+ if (tmp_sp->real==0.0 || (b_data_sp[i]==0.0))
+ {
+ tmp_sp->real = tmp_sp->imag = 0.0;
+ }
+ else
+ {
+ tmp2_sp = b_data_sp[i] / (tmp_sp->real * tmp_sp->real + tmp_sp->imag * tmp_sp->imag);
+ tmp_sp->real *= tmp2_sp;
+ tmp_sp->imag *= -tmp2_sp;
+ }
+ }
+ }
+ else if ((PyArray_TYPE(a) == PyArray_COMPLEX128) && (PyArray_TYPE(b) == PyArray_FLOAT64))
+ {
+ a_data_dp = (npy_complex128*)PyArray_DATA(a);
+ b_data_dp = (npy_float64*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp_dp = a_data_dp + i;
+ if (tmp_dp->real==0.0 || (b_data_dp[i]==0.0))
+ {
+ tmp_dp->real = tmp_dp->imag = 0.0;
+ }
+ else
+ {
+ tmp2_dp = b_data_dp[i] / (tmp_dp->real * tmp_dp->real + tmp_dp->imag * tmp_dp->imag);
+ tmp_dp->real *= tmp2_dp;
+ tmp_dp->imag *= -tmp2_dp;
+ }
+ }
+ }
+ else
+ {
+ PyErr_SetString(PyExc_TypeError,"argument types must be complex64 and float32");
+ return NULL;
+ }
+ return Py_BuildValue("");
+}
+
+static PyObject *inverse_subtraction_inplace(PyObject *self, PyObject *args)
+{
+ PyObject* a = NULL;
+ PyObject* b = NULL;
+ npy_intp sz = 0, i;
+ npy_complex64* tmp_sp = NULL;
+ npy_complex64* a_data_sp = NULL;
+ npy_float32* b_data_sp = NULL;
+ npy_complex128* tmp_dp = NULL;
+ npy_complex128* a_data_dp = NULL;
+ npy_float64* b_data_dp = NULL;
+ double c;
+ if (!PyArg_ParseTuple(args, "OOd", &a, &b, &c))
+ return NULL;
+ if (!(PyArray_Check(a) && PyArray_Check(b)))
+ {
+ PyErr_SetString(PyExc_TypeError,"arguments must be array objects");
+ return NULL;
+ }
+ sz = PyArray_SIZE(a);
+
+ if (sz != PyArray_SIZE(b))
+ {
+ PyErr_SetString(PyExc_TypeError,"argument sizes must be equal");
+ return NULL;
+ }
+ if ((PyArray_TYPE(a) == PyArray_COMPLEX64) && (PyArray_TYPE(b) == PyArray_FLOAT32))
+ {
+ a_data_sp = (npy_complex64*)PyArray_DATA(a);
+ b_data_sp = (npy_float32*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp_sp = a_data_sp + i;
+ tmp_sp->real = b_data_sp[i] - tmp_sp->real * c;
+ }
+ }
+ else if ((PyArray_TYPE(a) == PyArray_COMPLEX128) && (PyArray_TYPE(b) == PyArray_FLOAT64))
+ {
+ a_data_dp = (npy_complex128*)PyArray_DATA(a);
+ b_data_dp = (npy_float64*)PyArray_DATA(b);
+ for (i=0; i<sz; ++i)
+ {
+ tmp_dp = a_data_dp + i;
+ tmp_dp->real = b_data_dp[i] - tmp_dp->real * c;
+ }
+ }
+ else
+ {
+ PyErr_SetString(PyExc_TypeError,"argument types must be complex64 and float32");
+ return NULL;
+ }
+ return Py_BuildValue("");
+}
+
+static
+double m(double a, double b)
+{
+ if (a<0 && b<0)
+ {
+ if (a >= b) return a;
+ return b;
+ }
+ if (a>0 && b>0)
+ {
+ if (a < b) return a;
+ return b;
+ }
+ return 0.0;
+}
+
+static
+__inline double hypot3(double a, double b, double c)
+{
+ return sqrt(a*a + b*b + c*c);
+}
+
+#define FLOAT32_EPS 0.0 //1e-8
+#define FLOAT64_EPS 0.0 //1e-16
+
+static PyObject *div_unit_grad(PyObject *self, PyObject *args)
+{
+ PyObject* f = NULL;
+ npy_intp Nx, Ny, Nz;
+ int i, j, k, im1, im2, ip1, jm1, jm2, jp1, km1, km2, kp1;
+ npy_float64* f_data_dp = NULL;
+ npy_float64* r_data_dp = NULL;
+ npy_float32* f_data_sp = NULL;
+ npy_float32* r_data_sp = NULL;
+ double hx, hy, hz;
+ double hx2, hy2, hz2;
+ PyArrayObject* r = NULL;
+ double fip, fim, fjp, fjm, fkp, fkm, fijk;
+ double fimkm, fipkm, fjmkm, fjpkm, fimjm, fipjm, fimkp, fjmkp, fimjp;
+ double aim, bjm, ckm, aijk, bijk, cijk;
+ double Dxpf, Dxmf, Dypf, Dymf, Dzpf, Dzmf;
+ double Dxma, Dymb, Dzmc;
+ if (!PyArg_ParseTuple(args, "O(ddd)", &f, &hx, &hy, &hz))
+ return NULL;
+ hx2 = 2*hx; hy2 = 2*hy; hz2 = 2*hz;
+ if (!PyArray_Check(f))
+ {
+ PyErr_SetString(PyExc_TypeError,"first argument must be array");
+ return NULL;
+ }
+ if (PyArray_NDIM(f) != 3)
+ {
+ PyErr_SetString(PyExc_TypeError,"array argument must have rank 3");
+ return NULL;
+ }
+ Nx = PyArray_DIM(f, 0);
+ Ny = PyArray_DIM(f, 1);
+ Nz = PyArray_DIM(f, 2);
+ r = (PyArrayObject*)PyArray_SimpleNew(3, PyArray_DIMS(f), PyArray_TYPE(f));
+
+ if (PyArray_TYPE(f) == PyArray_FLOAT32)
+ {
+ f_data_sp = (npy_float32*)PyArray_DATA(f);
+ r_data_sp = (npy_float32*)PyArray_DATA(r);
+ for (i=0; i<Nx; ++i)
+ {
+ im1 = (i?i-1:0);
+ im2 = (im1?im1-1:0);
+ ip1 = (i+1==Nx?i:i+1);
+ for (j=0; j<Ny; ++j)
+ {
+ jm1 = (j?j-1:0);
+ jm2 = (jm1?jm1-1:0);
+ jp1 = (j+1==Ny?j:j+1);
+ for (k=0; k<Nz; ++k)
+ {
+ km1 = (k?k-1:0);
+ km2 = (km1?km1-1:0);
+ kp1 = (k+1==Nz?k:k+1);
+
+ fimjm = *((npy_float32*)PyArray_GETPTR3(f, im1, jm1, k));
+ fim = *((npy_float32*)PyArray_GETPTR3(f, im1, j, k));
+ fimkm = *((npy_float32*)PyArray_GETPTR3(f, im1, j, km1));
+ fimkp = *((npy_float32*)PyArray_GETPTR3(f, im1, j, kp1));
+ fimjp = *((npy_float32*)PyArray_GETPTR3(f, im1, jp1, k));
+
+ fjmkm = *((npy_float32*)PyArray_GETPTR3(f, i, jm1, km1));
+ fjm = *((npy_float32*)PyArray_GETPTR3(f, i, jm1, k));
+ fjmkp = *((npy_float32*)PyArray_GETPTR3(f, i, jm1, kp1));
+
+ fkm = *((npy_float32*)PyArray_GETPTR3(f, i, j, km1));
+ fijk = *((npy_float32*)PyArray_GETPTR3(f, i, j, k));
+ fkp = *((npy_float32*)PyArray_GETPTR3(f, i, j, kp1));
+
+ fjpkm = *((npy_float32*)PyArray_GETPTR3(f, i, jp1, km1));
+ fjp = *((npy_float32*)PyArray_GETPTR3(f, i, jp1, k));
+
+ fipjm = *((npy_float32*)PyArray_GETPTR3(f, ip1, jm1, k));
+ fipkm = *((npy_float32*)PyArray_GETPTR3(f, ip1, j, km1));
+ fip = *((npy_float32*)PyArray_GETPTR3(f, ip1, j, k));
+
+ Dxpf = (fip - fijk) / hx;
+ Dxmf = (fijk - fim) / hx;
+ Dypf = (fjp - fijk) / hy;
+ Dymf = (fijk - fjm) / hy;
+ Dzpf = (fkp - fijk) / hz;
+ Dzmf = (fijk - fkm) / hz;
+ aijk = hypot3(Dxpf, m(Dypf, Dymf), m(Dzpf, Dzmf));
+ bijk = hypot3(Dypf, m(Dxpf, Dxmf), m(Dzpf, Dzmf));
+ cijk = hypot3(Dzpf, m(Dypf, Dymf), m(Dxpf, Dxmf));
+
+ aijk = (aijk>FLOAT32_EPS?Dxpf / aijk:0.0);
+ bijk = (bijk>FLOAT32_EPS?Dypf / bijk: 0.0);
+ cijk = (cijk>FLOAT32_EPS?Dzpf / cijk:0.0);
+
+
+ Dxpf = (fijk - fim) / hx;
+ Dypf = (fimjp - fim) / hy;
+ Dymf = (fim - fimjm) / hy;
+ Dzpf = (fimkp - fim) / hz;
+ Dzmf = (fim - fimkm) / hz;
+ aim = hypot3(Dxpf, m(Dypf, Dymf), m(Dzpf, Dzmf));
+
+ aim = (aim>FLOAT32_EPS?Dxpf/aim:0.0);
+
+
+ Dxpf = (fipjm - fjm) / hx;
+ Dxmf = (fjm - fimjm) / hx;
+ Dypf = (fijk - fjm) / hy;
+ Dzpf = (fjmkp - fjm) / hz;
+ Dzmf = (fjm - fjmkm) / hz;
+ bjm = hypot3(Dypf, m(Dxpf, Dxmf), m(Dzpf, Dzmf));
+
+ bjm = (bjm>FLOAT32_EPS?Dypf/bjm:0.0);
+
+
+ Dxpf = (fipkm - fkm) / hx;
+ Dxmf = (fjm - fimkm) / hx;
+ Dypf = (fjpkm - fkm) / hy;
+ Dymf = (fkm - fjmkm) / hy;
+ Dzpf = (fijk - fkm) / hz;
+ ckm = hypot3(Dzpf, m(Dypf, Dymf), m(Dxpf, Dxmf));
+
+ ckm = (ckm>FLOAT32_EPS?Dzpf/ckm:0.0);
+
+ Dxma = (aijk - aim) / hx;
+ Dymb = (bijk - bjm) / hy;
+ Dzmc = (cijk - ckm) / hz;
+
+ //*((npy_float32*)PyArray_GETPTR3(r, i, j, k)) = Dxma/hx + Dymb/hy + Dzmc/hz;
+ *((npy_float32*)PyArray_GETPTR3(r, i, j, k)) = Dxma + Dymb + Dzmc;
+ }
+ }
+ }
+ }
+ else if (PyArray_TYPE(f) == PyArray_FLOAT64)
+ {
+ f_data_dp = (npy_float64*)PyArray_DATA(f);
+ r_data_dp = (npy_float64*)PyArray_DATA(r);
+ for (i=0; i<Nx; ++i)
+ {
+ im1 = (i?i-1:0);
+ im2 = (im1?im1-1:0);
+ ip1 = (i+1==Nx?i:i+1);
+ for (j=0; j<Ny; ++j)
+ {
+ jm1 = (j?j-1:0);
+ jm2 = (jm1?jm1-1:0);
+ jp1 = (j+1==Ny?j:j+1);
+ for (k=0; k<Nz; ++k)
+ {
+ km1 = (k?k-1:0);
+ km2 = (km1?km1-1:0);
+ kp1 = (k+1==Nz?k:k+1);
+
+ fimjm = *((npy_float64*)PyArray_GETPTR3(f, im1, jm1, k));
+ fim = *((npy_float64*)PyArray_GETPTR3(f, im1, j, k));
+ fimkm = *((npy_float64*)PyArray_GETPTR3(f, im1, j, km1));
+ fimkp = *((npy_float64*)PyArray_GETPTR3(f, im1, j, kp1));
+ fimjp = *((npy_float64*)PyArray_GETPTR3(f, im1, jp1, k));
+
+ fjmkm = *((npy_float64*)PyArray_GETPTR3(f, i, jm1, km1));
+ fjm = *((npy_float64*)PyArray_GETPTR3(f, i, jm1, k));
+ fjmkp = *((npy_float64*)PyArray_GETPTR3(f, i, jm1, kp1));
+
+ fkm = *((npy_float64*)PyArray_GETPTR3(f, i, j, km1));
+ fijk = *((npy_float64*)PyArray_GETPTR3(f, i, j, k));
+ fkp = *((npy_float64*)PyArray_GETPTR3(f, i, j, kp1));
+
+ fjpkm = *((npy_float64*)PyArray_GETPTR3(f, i, jp1, km1));
+ fjp = *((npy_float64*)PyArray_GETPTR3(f, i, jp1, k));
+
+ fipjm = *((npy_float64*)PyArray_GETPTR3(f, ip1, jm1, k));
+ fipkm = *((npy_float64*)PyArray_GETPTR3(f, ip1, j, km1));
+ fip = *((npy_float64*)PyArray_GETPTR3(f, ip1, j, k));
+
+ Dxpf = (fip - fijk) / hx;
+ Dxmf = (fijk - fim) / hx;
+ Dypf = (fjp - fijk) / hy;
+ Dymf = (fijk - fjm) / hy;
+ Dzpf = (fkp - fijk) / hz;
+ Dzmf = (fijk - fkm) / hz;
+ aijk = hypot3(Dxpf, m(Dypf, Dymf), m(Dzpf, Dzmf));
+ aijk = (aijk>FLOAT64_EPS?Dxpf / aijk:0.0);
+ bijk = hypot3(Dypf, m(Dxpf, Dxmf), m(Dzpf, Dzmf));
+ bijk = (bijk>FLOAT64_EPS?Dypf / bijk: 0.0);
+ cijk = hypot3(Dzpf, m(Dypf, Dymf), m(Dxpf, Dxmf));
+ cijk = (cijk>FLOAT64_EPS?Dzpf/cijk:0.0);
+
+ Dxpf = (fijk - fim) / hx;
+ Dypf = (fimjp - fim) / hy;
+ Dymf = (fim - fimjm) / hy;
+ Dzpf = (fimkp - fim) / hz;
+ Dzmf = (fim - fimkm) / hz;
+ aim = hypot3(Dxpf, m(Dypf, Dymf), m(Dzpf, Dzmf));
+ aim = (aim>FLOAT64_EPS?Dxpf/aim:0.0);
+
+ Dxpf = (fipjm - fjm) / hx;
+ Dxmf = (fjm - fimjm) / hx;
+ Dypf = (fijk - fjm) / hy;
+ Dzpf = (fjmkp - fjm) / hz;
+ Dzmf = (fjm - fjmkm) / hz;
+ bjm = hypot3(Dypf, m(Dxpf, Dxmf), m(Dzpf, Dzmf));
+ bjm = (bjm>FLOAT64_EPS?Dypf/bjm:0.0);
+
+
+
+ Dxpf = (fipkm - fkm) / hx;
+ Dxmf = (fjm - fimkm) / hx;
+ Dypf = (fjpkm - fkm) / hy;
+ Dymf = (fkm - fjmkm) / hy;
+ Dzpf = (fijk - fkm) / hz;
+ ckm = hypot3(Dzpf, m(Dypf, Dymf), m(Dxpf, Dxmf));
+ ckm = (ckm>FLOAT64_EPS?Dzpf/ckm:0.0);
+
+ Dxma = (aijk - aim) / hx;
+ Dymb = (bijk - bjm) / hy;
+ Dzmc = (cijk - ckm) / hz;
+
+ //*((npy_float64*)PyArray_GETPTR3(r, i, j, k)) = Dxma/hx + Dymb/hy + Dzmc/hz;
+ *((npy_float64*)PyArray_GETPTR3(r, i, j, k)) = Dxma + Dymb + Dzmc;
+ }
+ }
+ }
+ }
+ else
+ {
+ PyErr_SetString(PyExc_TypeError,"array argument type must be float64");
+ return NULL;
+ }
+ return Py_BuildValue("N", r);
+}
+
+static PyObject *div_unit_grad1(PyObject *self, PyObject *args)
+{
+ PyObject* f = NULL;
+ npy_intp Nx;
+ int i, im1, im2, ip1;
+ npy_float64* f_data_dp = NULL;
+ npy_float64* r_data_dp = NULL;
+ double hx;
+ double hx2;
+ PyArrayObject* r = NULL;
+ double fip, fim, fijk;
+ double aim, aijk;
+ double Dxpf, Dxmf;
+ double Dxma;
+ if (!PyArg_ParseTuple(args, "Od", &f, &hx))
+ return NULL;
+ hx2 = 2*hx;
+ if (!PyArray_Check(f))
+ {
+ PyErr_SetString(PyExc_TypeError,"first argument must be array");
+ return NULL;
+ }
+ if (PyArray_NDIM(f) != 1)
+ {
+ PyErr_SetString(PyExc_TypeError,"array argument must have rank 1");
+ return NULL;
+ }
+ Nx = PyArray_DIM(f, 0);
+ r = (PyArrayObject*)PyArray_SimpleNew(1, PyArray_DIMS(f), PyArray_TYPE(f));
+
+ if (PyArray_TYPE(f) == PyArray_FLOAT64)
+ {
+ f_data_dp = (npy_float64*)PyArray_DATA(f);
+ r_data_dp = (npy_float64*)PyArray_DATA(r);
+ for (i=0; i<Nx; ++i)
+ {
+ im1 = (i?i-1:0);
+ ip1 = (i+1==Nx?i:i+1);
+ fim = *((npy_float64*)PyArray_GETPTR1(f, im1));
+ fijk = *((npy_float64*)PyArray_GETPTR1(f, i));
+ fip = *((npy_float64*)PyArray_GETPTR1(f, ip1));
+ Dxpf = (fip - fijk) / hx;
+ //if (Dxpf==0.0) aijk = 0.0;
+ //else if (Dxpf<0.0) aijk = -1.0;
+ //else aijk = 1.0;
+ aijk = sqrt(Dxpf*Dxpf);
+ aijk = (aijk>FLOAT64_EPS?Dxpf / aijk:0.0);
+ //aijk = abs(Dxpf);
+ //aijk = (aijk>FLOAT64_EPS?Dxpf / aijk:0.0);
+ Dxpf = (fijk - fim) / hx;
+ //if (Dxpf==0.0) aim = 0.0;
+ //else if (Dxpf<0.0) aim = -1.0;
+ //else aim = 1.0;
+ //aim = abs(Dxpf);
+ //aim = (aim>FLOAT64_EPS?Dxpf/aim:0.0);
+ aim = sqrt(Dxpf*Dxpf);
+ aim = (aim>FLOAT64_EPS?Dxpf/aim:0.0);
+ Dxma = (aijk - aim) / hx;
+ *((npy_float64*)PyArray_GETPTR1(r, i)) = Dxma;
+ }
+ }
+ else
+ {
+ PyErr_SetString(PyExc_TypeError,"array argument type must be float64");
+ return NULL;
+ }
+ return Py_BuildValue("N", r);
+}
+
+static PyObject *fourier_sphere(PyObject *self, PyObject *args)
+{
+ /*
+ Computes Fourier Transform of an ellipsoid:
+
+ fourier_sphere((Nx,Ny,Nz), (Dx, Dy, Dz), pcount) -> array
+
+ Nx, Ny, Nz - defines the shape of an output array
+ Dx, Dy, Dz - defines the diameters of the ellipsoid in index units
+ pcount - defines periodicity parameter of the algorithm, the higher
+ pcount is, the more accurate is the result but the more time it
+ takes to compute the result. pcount should be around 1000.
+
+ References:
+ http://dx.doi.org/10.3247/SL2Math07.002
+ */
+ npy_intp dims[] = {0, 0, 0};
+ double r[] = {1.0, 1.0, 1.0};
+ double a, b;
+ double ir, jr, kr;
+ double sn, cs;
+ int i,j,k,n1,n2,n3,nx,ny,nz;
+ int n1_end, n2_end, n3_end;
+ int dn1, dn2, dn3;
+ int total, count, pcount;
+ PyObject* result = NULL;
+ double eps, inveps;
+ clock_t start_clock = clock();
+ double eta;
+ PyObject* write_func = NULL;
+ int verbose;
+ if (!PyArg_ParseTuple(args, "(iii)(ddd)dO", &nx, &ny, &nz, r, r+1, r+2, &eps, &write_func))
+ return NULL;
+ if (write_func == Py_None)
+ verbose = 0;
+ else if (PyCallable_Check(write_func))
+ verbose = 1;
+ else
+ {
+ PyErr_SetString(PyExc_TypeError,"eighth argument must be None or callable object");
+ return NULL;
+ }
+ dims[0] = nx;
+ dims[1] = ny;
+ dims[2] = nz;
+ dn1 = nx;
+ dn2 = ny;
+ dn3 = nz;
+
+#define min2(a,b) ((a>b)?(b):(a))
+#define min3(a,b,c) min2((a), min2((b),(c)))
+#define CALL_WRITE(ARGS) \
+ if (verbose && PyObject_CallFunctionObjArgs ARGS == NULL) return NULL;
+
+ r[0] /= dims[0];
+ r[1] /= dims[1];
+ r[2] /= dims[2];
+
+ if (eps>1.0) // eps specifies peridicity count
+ {
+ eps = 2e-4 * pow(eps,-2.0/3.0) / min3(r[0]*r[0],r[1]*r[1],r[2]*r[2]);
+ CALL_WRITE((write_func,PyUnicode_FromString("fourier_sphere: using eps = %.5e\n\n"), PyFloat_FromDouble(eps), NULL));
+ }
+ result = PyArray_SimpleNew(3, dims, PyArray_FLOAT64);
+ r[0] *= M_PI;
+ r[1] *= M_PI;
+ r[2] *= M_PI;
+ total = (dn1/2+1) * (dn2/2+1) * (dn3/2+1);
+ count = 0;
+ pcount = 0;
+ inveps = 3.0/eps;
+ nx = 1 + sqrt(inveps)/(dn1*r[0]);
+ nx *= dn1;
+
+ for (i=0; i<dn1/2+1; ++i)
+ {
+ for (j=0; j<dn2/2+1; ++j)
+ {
+ for (k=0; k<dn3/2+1; ++k)
+ {
+ a = 0.0;
+ pcount = 0;
+ n1_end = i + nx;
+ for (n1=i-nx; n1<=n1_end; n1 += dn1)
+ {
+ ir = n1*r[0];
+ ir *= ir;
+ if (ir>inveps) continue;
+ ny = 1 + sqrt(inveps-ir)/(dn2*r[1]);
+ ny *= dn2;
+ n2_end = j + ny;
+ for (n2=j-ny; n2<=n2_end; n2 += dn2)
+ {
+ jr = n2*r[1];
+ jr = jr*jr + ir;
+ if (jr>inveps) continue;
+ nz = (1 + sqrt(inveps-jr)/(dn3*r[2]));
+ nz *= dn3;
+ n3_end = k + nz;
+ for (n3=k-nz; n3<=n3_end; n3 += dn3)
+ {
+ kr = n3*r[2];
+ kr = kr*kr + jr;
+ if (kr>inveps) continue;
+ if (kr==0.0)
+ a += 1.0;
+ else
+ {
+ b = sqrt(kr);
+ sincos(b, &sn, &cs);
+ a += 3.0 * (sn/b - cs)/kr;
+ }
+ pcount++;
+ }
+ }
+ }
+ *((npy_float64*)PyArray_GETPTR3(result, i, j, k)) = a;
+ if (i)
+ *((npy_float64*)PyArray_GETPTR3(result, dn1-i, j, k)) = a;
+ if (j)
+ *((npy_float64*)PyArray_GETPTR3(result, i, dn2-j, k)) = a;
+ if (k)
+ *((npy_float64*)PyArray_GETPTR3(result, i, j, dn3-k)) = a;
+ if (i && j)
+ *((npy_float64*)PyArray_GETPTR3(result, dn1-i, dn2-j, k)) = a;
+ if (i && k)
+ *((npy_float64*)PyArray_GETPTR3(result, dn1-i, j, dn3-k)) = a;
+ if (j && k)
+ *((npy_float64*)PyArray_GETPTR3(result, i, dn2-j, dn3-k)) = a;
+ if (i && j && k)
+ *((npy_float64*)PyArray_GETPTR3(result, dn1-i, dn2-j, dn3-k)) = a;
+ count ++;
+ }
+ eta = (clock() - start_clock) * (total/(count+0.0)-1.0) / CLOCKS_PER_SEC;
+ CALL_WRITE((write_func,
+ PyUnicode_FromString("\rfourier_sphere: %6.2f%% done (%d), ETA:%4.1fs"),
+ PyFloat_FromDouble((count*100.0)/total),
+ PyLong_FromLong(pcount),
+ PyFloat_FromDouble(eta),
+ NULL));
+ }
+ }
+ *((npy_float64*)PyArray_GETPTR3(result, 0, 0, 0)) = 1.0; // normalize sum(sphere) to 1.0
+ CALL_WRITE((write_func, PyUnicode_FromString("\n"), NULL));
+ return Py_BuildValue("N", result);
+}
+
+char module_doc[] =
+ "Python C extension module Richardson Lucy deconvolution algorithm.\n";
+
+
+static PyMethodDef module_methods[] = {
+ {"inverse_division_inplace", inverse_division_inplace, METH_VARARGS, "inverse_division_inplace(a,b) == `a = b/a if a!=0 else 0`"},
+ {"inverse_subtraction_inplace", inverse_subtraction_inplace, METH_VARARGS, "inverse_subtraction_inplace(a,b,c) == `a = b-c*a`"},
+ {"update_estimate_poisson", update_estimate_poisson, METH_VARARGS, "update_estimate_poisson(a,b,epsilon) -> e,s,u,n == `a *= b, s,u are photon counts`"},
+ {"update_estimate_gauss", update_estimate_gauss, METH_VARARGS, "update_estimate_gauss(a,b,epsilon, alpha) -> e,s,u,n == `a += alpha * b, s,u are photon counts`"},
+ {"div_unit_grad", div_unit_grad, METH_VARARGS, "div_unit_grad(f, (hx,hy,hz)) == `div(grad f/|grad f|)`"},
+ {"div_unit_grad1", div_unit_grad1, METH_VARARGS, "div_unit_grad1(f, hx) == `div(grad f/|grad f|)`"},
+ {"fourier_sphere", fourier_sphere, METH_VARARGS, "fourier_sphere((Nx, Ny, Nz), (Dx, Dy, Dz), eps or pcount)"},
+ {"kullback_leibler_divergence", kullback_leibler_divergence, METH_VARARGS, "kullback_leibler_divergence(f, f0) -> float"},
+ // {"zero_if_zero_inplace", zero_if_zero_inplace, METH_VARARGS, "zero_if_zero_inplace(a,b) == `a = a if b!=0 else 0`"},
+ //{"poisson_hist_factor_estimate", poisson_hist_factor_estimate, METH_VARARGS, "poisson_hist_factor_estimate(a,b,c) -> (stable,unstable)"},
+
+ {NULL} /* Sentinel */
+};
+
+//PyMODINIT_FUNC
+//initops_ext(void)
+//{
+// PyObject* m = NULL;
+// import_array();
+// if (PyErr_Occurred())
+// {PyErr_SetString(PyExc_ImportError, "can't initialize module ops_ext (failed to import numpy)"); return;}
+// m = Py_InitModule3("ops_ext", module_methods, "Provides operations in C.");
+//}
+
+#if PY_MAJOR_VERSION >= 3
+
+struct module_state {
+ PyObject *error;
+};
+
+#define GETSTATE(m) ((struct module_state*)PyModule_GetState(m))
+
+static int module_traverse(PyObject *m, visitproc visit, void *arg) {
+ Py_VISIT(GETSTATE(m)->error);
+ return 0;
+}
+
+static int module_clear(PyObject *m) {
+ Py_CLEAR(GETSTATE(m)->error);
+ return 0;
+}
+
+static struct PyModuleDef moduledef = {
+ PyModuleDef_HEAD_INIT,
+ "ops_ext",
+ NULL,
+ sizeof(struct module_state),
+ module_methods,
+ NULL,
+ module_traverse,
+ module_clear,
+ NULL
+};
+
+#define INITERROR return NULL
+
+PyMODINIT_FUNC
+PyInit_ops_ext(void)
+
+#else
+
+#define INITERROR return
+
+PyMODINIT_FUNC
+initops_ext(void)
+#endif
+{
+ PyObject *module;
+
+ char *doc = (char *)PyMem_Malloc(sizeof(module_doc) + sizeof(_VERSION_));
+ PyOS_snprintf(doc, sizeof(module_doc) + sizeof(_VERSION_),
+ module_doc, _VERSION_);
+
+#if PY_MAJOR_VERSION >= 3
+ moduledef.m_doc = doc;
+ module = PyModule_Create(&moduledef);
+#else
+ module = Py_InitModule3("ops_ext", module_methods, doc);
+#endif
+
+ PyMem_Free(doc);
+
+ if (module == NULL)
+ INITERROR;
+
+ if (_import_array() < 0) {
+ Py_DECREF(module);
+ INITERROR;
+ }
+
+ {
+#if PY_MAJOR_VERSION < 3
+ PyObject *s = PyUnicode_FromString(_VERSION_);
+#else
+ PyObject *s = PyUnicode_FromString(_VERSION_);
+#endif
+ PyObject *dict = PyModule_GetDict(module);
+ PyDict_SetItemString(dict, "__version__", s);
+ Py_DECREF(s);
+ }
+
+// if (bessel_init() != 0) {
+// PyErr_Format(PyExc_ValueError, "bessel_init function failed");
+// Py_DECREF(module);
+// INITERROR;
+// }
+
+#if PY_MAJOR_VERSION >= 3
+ return module;
+#endif
+}
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/to_string.py b/Addons/FRCmetric/miplib-public/miplib/processing/to_string.py
new file mode 100644
index 0000000000000000000000000000000000000000..46c88e4ba107dfc0cb141edd1bbd164e47e30269
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/to_string.py
@@ -0,0 +1,350 @@
+import hashlib
+import sys
+import time
+
+import numpy
+
+VERBOSE = False
+
+
+def concatenate_to_csv(values):
+ assert isinstance(values, tuple) or \
+ isinstance(values, list)
+
+ return ",".join('%.6f' % s for s in values)
+
+
+def argument_string(obj):
+ if isinstance(obj, str):
+ return repr(obj)
+ if isinstance(obj, (int, float, complex)):
+ return str(obj)
+ if isinstance(obj, tuple):
+ if len(obj) < 2: return '(%s,)' % (', '.join(map(argument_string, obj)))
+ if len(obj) < 5: return '(%s)' % (', '.join(map(argument_string, obj)))
+ return '<%s-tuple>' % (len(obj))
+ if isinstance(obj, list):
+ if len(obj) < 5: return '[%s]' % (', '.join(map(argument_string, obj)))
+ return '<%s-list>' % (len(obj))
+ if isinstance(obj, numpy.ndarray):
+ return '<%s %s-array>' % (obj.dtype, obj.shape)
+ if obj is None:
+ return str(obj)
+ return '<' + str(type(obj))[8:-2] + '>'
+
+
+def time_it(func):
+ """Decorator: print how long calling given function took.
+
+ Notes
+ -----
+ ``iocbio.utils.VERBOSE`` must be True for this decorator to be
+ effective.
+ """
+ if not VERBOSE:
+ return func
+
+ def new_func(*args, **kws):
+ t = time.time()
+ r = func(*args, **kws)
+ dt = time.time() - t
+ print('Calling %s(%s) -> %s took %s seconds' % \
+ (func.__name__, ', '.join(map(argument_string, args)), argument_string(r), dt))
+ return r
+
+ return new_func
+
+
+def format_time_string(seconds):
+ m, s = divmod(seconds, 60)
+ h, m = divmod(m, 60)
+ return "%d:%02d:%02d" % (h, m, s)
+
+
+class ProgressBar:
+ """Creates a text-based progress bar.
+
+ Call the object with the ``print`` command to see the progress bar,
+ which looks something like this::
+
+ [=======> 22% ]
+
+ You may specify the progress bar's width, min and max values on
+ init. For example::
+
+ bar = ProgressBar(N)
+ for i in range(N):
+ print bar(i)
+ print bar(N)
+
+ References
+ ----------
+ http://code.activestate.com/recipes/168639/
+
+ See also
+ --------
+ __init__, updateComment
+ """
+
+ def __init__(self, minValue=0, maxValue=100, totalWidth=80, prefix='',
+ show_percentage=True):
+ self.show_percentage = show_percentage
+ self.progBar = self.progBar_last = "[]" # This holds the progress bar string
+ self.min = minValue
+ self.max = maxValue
+ self.span = maxValue - minValue or 1
+ self.width = totalWidth
+ self.amount = 0 # When amount == max, we are 100% done
+ self.start_time = self.current_time = self.prev_time = time.time()
+ self.starting_amount = None
+ self.updateAmount(0) # Build progress bar string
+ self.prefix = prefix
+ self.comment = self.comment_last = ''
+
+ def updateComment(self, comment):
+ self.comment = comment
+
+ def updateAmount(self, newAmount=0):
+ """ Update the progress bar with the new amount (with min and max
+ values set at initialization; if it is over or under, it takes the
+ min or max value as a default. """
+ if newAmount and self.starting_amount is None:
+ self.starting_amount = newAmount
+ self.starting_time = time.time()
+ if newAmount < self.min: newAmount = self.min
+ if newAmount > self.max: newAmount = self.max
+ self.prev_amount = self.amount
+ self.amount = newAmount
+
+ # Figure out the new percent done, round to an integer
+ diffFromMin = float(self.amount - self.min)
+ percentDone = (diffFromMin / float(self.span)) * 100.0
+ percentDone = int(round(percentDone))
+
+ # Figure out how many hash bars the percentage should be
+ allFull = self.width - 2
+ numHashes = (percentDone / 100.0) * allFull
+ numHashes = int(round(numHashes))
+
+ # Build a progress bar with an arrow of equal signs; special cases for
+ # empty and full
+
+ if numHashes == 0:
+ self.progBar = "[>%s]" % (' ' * (allFull - 1))
+ elif numHashes == allFull:
+ self.progBar = "[%s]" % ('=' * allFull)
+ else:
+ self.progBar = "[%s>%s]" % ('=' * (numHashes - 1),
+ ' ' * (allFull - numHashes))
+
+ if self.show_percentage:
+ # figure out where to put the percentage, roughly centered
+ percentPlace = (len(self.progBar) / 2) - len(str(percentDone))
+ percentString = str(percentDone) + "%"
+ else:
+ percentPlace = int((len(self.progBar) / 2) - len(str(percentDone)))
+ percentString = '%s/%s' % (self.amount, self.span)
+ # slice the percentage into the bar
+ self.progBar = ''.join([self.progBar[0:percentPlace], percentString,
+ self.progBar[percentPlace + len(percentString):]])
+ if self.starting_amount is not None:
+ amount_diff = self.amount - self.starting_amount
+ if amount_diff:
+ self.prev_time = self.current_time
+ self.current_time = time.time()
+ elapsed = self.current_time - self.starting_time
+ eta = elapsed * (self.max - self.amount) / float(amount_diff)
+ self.progBar += ' ETA:' + time_to_str(eta)
+
+ def __str__(self):
+ return str(self.progBar)
+
+ def __call__(self, value):
+ """ Updates the amount, and writes to stdout. Prints a carriage return
+ first, so it will overwrite the current line in stdout."""
+ self.updateAmount(value)
+ if self.progBar_last == self.progBar and self.comment == self.comment_last:
+ return
+ print('\r', end=' ')
+ sys.stdout.write(self.prefix + str(self) + str(self.comment) + ' ')
+ sys.stdout.flush()
+ self.progBar_last = self.progBar
+ self.comment_last = self.comment
+
+
+class Holder:
+ """ Holds pairs ``(name, value)`` as instance attributes.
+
+ The set of Holder pairs is extendable by
+
+ ::
+
+ <Holder instance>.<name> = <value>
+
+ and the values are accessible as
+
+ ::
+
+ value = <Holder instance>.<name>
+ """
+
+ def __init__(self, descr):
+ self._descr = descr
+ self._counter = 0
+
+ def __str__(self):
+ return self._descr % (self.__dict__)
+
+ def __repr__(self):
+ return '%s(%r)' % (self.__class__.__name__, str(self))
+
+ def __getattr__(self, name):
+ raise AttributeError('%r instance has no attribute %r' % (self, name))
+
+ def __setattr__(self, name, obj):
+ if name not in self.__dict__ and '_counter' in self.__dict__:
+ self._counter += 1
+ self.__dict__[name] = obj
+
+ def iterNameValue(self):
+ for k, v in self.__dict__.items():
+ if k.startswith('_'):
+ continue
+ yield k, v
+
+ def copy(self, **kws):
+ r = self.__class__(self._descr + ' - a copy')
+ for name, value in self.iterNameValue():
+ setattr(r, name, value)
+ for name, value in list(kws.items()):
+ setattr(r, name, value)
+ return r
+
+
+options = Holder('Options')
+
+alphabet = '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
+
+
+def getalpha(r):
+ if r >= len(alphabet):
+ return '_' + nary(r - len(alphabet), len(alphabet))
+ return alphabet[r]
+
+
+def nary(number, base=64):
+ if isinstance(number, str):
+ number = eval(number)
+ n = number
+ s = ''
+ while n:
+ n1 = n // base
+ r = n - n1 * base
+ n = n1
+ s = getalpha(r) + s
+ return s
+
+
+def encode(string):
+ """ Return encoded string.
+ """
+ return nary('0x' + hashlib.md5(string).hexdigest())
+
+
+def fix_exp_str(s):
+ return s.replace('e+00', '').replace('e+0', 'E').replace('e+', 'E').replace('e-0', 'E-').replace('e-', 'E-')
+
+
+def float_to_str(x):
+ if abs(x) >= 1000: return fix_exp_str('%.1e' % x)
+ if abs(x) >= 100: return '%.0f' % x
+ if abs(x) >= 10: return '%.1f' % x
+ if abs(x) >= 1: return '%.2f' % x
+ if abs(x) >= .1: return '%.3f' % x
+ if abs(x) <= 1e-6: return fix_exp_str('%.1e' % x)
+ if not x: return '0'
+ return fix_exp_str('%.2e' % x)
+
+
+def tostr(x):
+ """ Return pretty string representation of x.
+
+ Parameters
+ ----------
+ x : {tuple, float, :numpy:.float32, :numpy:.float64}
+ """
+ if isinstance(x, tuple):
+ return tuple(map(tostr, x))
+ if isinstance(x, (float, numpy.float32, numpy.float64)):
+ return float_to_str(x)
+ return str(x)
+
+
+def time_to_str(s):
+ """ Return human readable time string from seconds.
+
+ Examples
+ --------
+ >>> from miplib.processing.to_string import time_to_str
+ >>> print time_to_str(123000000)
+ 3Y10M24d10h40m
+ >>> print time_to_str(1230000)
+ 14d5h40m
+ >>> print time_to_str(1230)
+ 20m30.0s
+ >>> print time_to_str(0.123)
+ 123ms
+ >>> print time_to_str(0.000123)
+ 123us
+ >>> print time_to_str(0.000000123)
+ 123ns
+
+ """
+ seconds_in_year = 31556925.9747 # a standard SI year
+ orig_s = s
+ years = int(s / (seconds_in_year))
+ r = []
+ if years:
+ r.append('%sY' % (years))
+ s -= years * (seconds_in_year)
+ months = int(s / (seconds_in_year / 12.0))
+ if months:
+ r.append('%sM' % (months))
+ s -= months * (seconds_in_year / 12.0)
+ days = int(s / (60 * 60 * 24))
+ if days:
+ r.append('%sd' % (days))
+ s -= days * 60 * 60 * 24
+ hours = int(s / (60 * 60))
+ if hours:
+ r.append('%sh' % (hours))
+ s -= hours * 60 * 60
+ minutes = int(s / 60)
+ if minutes:
+ r.append('%sm' % (minutes))
+ s -= minutes * 60
+ seconds = int(s)
+ if seconds:
+ r.append('%.1fs' % (s))
+ s -= seconds
+ elif not r:
+ mseconds = int(s * 1000)
+ if mseconds:
+ r.append('%sms' % (mseconds))
+ s -= mseconds / 1000
+ elif not r:
+ useconds = int(s * 1000000)
+ if useconds:
+ r.append('%sus' % (useconds))
+ s -= useconds / 1000000
+ elif not r:
+ nseconds = int(s * 1000000000)
+ if nseconds:
+ r.append('%sns' % (nseconds))
+ s -= nseconds / 1000000000
+ if not r:
+ return '0'
+ return ''.join(r)
+
+
+time2str = time_to_str
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/transform.py b/Addons/FRCmetric/miplib-public/miplib/processing/transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcc138e1e9b6212ded9cc721e58f57afeebc2436
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/transform.py
@@ -0,0 +1,48 @@
+import SimpleITK as sitk
+import math
+
+def make_translation_transforms_from_xy(xs, ys):
+ """
+ Makes ITK translation transforms from x,y coordinate pairs.
+ :param xs: a list, tuple or other iterable of x coordinates
+ :param ys: a list, tuple or other iterable of y coordinates
+ :return: a list of transforms
+ """
+ assert len(xs) == len(ys)
+ transforms = []
+
+ for x, y in zip(xs, ys):
+ tfm = sitk.TranslationTransform(2)
+ tfm.SetParameters((x, y))
+
+ transforms.append(tfm)
+
+ return transforms
+
+
+def rotate_xy_points_lists(xs, ys, radians):
+ """
+ Rotate two lists of XY coordinates by an angle on XY plane
+ :param xs: the x coordinates
+ :param ys: the y coordinates
+ :param radians: an angle in radians
+ :return: return the xs and ys roated by radians.
+ """
+
+ def rotate_origin_only(x, y, radians):
+ """Only rotate a point around the origin (0, 0)."""
+ xx = x * math.cos(radians) + y * math.sin(radians)
+ yy = -x * math.sin(radians) + y * math.cos(radians)
+
+ return xx, yy
+
+ def get_point_pairs(xs, ys, radians):
+ for x, y in zip(xs, ys):
+ yield rotate_origin_only(x, y, radians)
+
+ points = list(get_point_pairs(xs, ys, radians))
+
+ xs_rot = list(i[0] for i in points)
+ ys_rot = list(i[1] for i in points)
+
+ return xs_rot, ys_rot
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/ufuncs.py b/Addons/FRCmetric/miplib-public/miplib/processing/ufuncs.py
new file mode 100644
index 0000000000000000000000000000000000000000..aaffb824d0ddfefa6cecd7970f70f6bbb7cb656f
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/ufuncs.py
@@ -0,0 +1,115 @@
+from numba import vectorize
+
+
+@vectorize(['complex64(complex64, complex64)'], target='cuda')
+def cuda_complex_div(a, b):
+ """
+ Implements array division on GPU
+
+ Parameters
+ ----------
+ :param a Two Numpy arrays of the same shape, dtype=numpy.complex64
+ :param b
+
+ Returns
+ -------
+
+ a/b
+
+ """
+
+ return a / b
+
+
+@vectorize(['complex64(complex64, complex64)'], target='parallel')
+def complex_div(a, b):
+ """
+ Implements array division on GPU
+
+ Parameters
+ ----------
+ :param a Two Numpy arrays of the same shape, dtype=numpy.complex64
+ :param b
+
+ Returns
+ -------
+
+ a/b
+
+ """
+
+ return a / b
+
+
+@vectorize(['complex64(complex64)'], target='parallel')
+def complex_squared(a):
+ """
+ Implements array division on GPU
+
+ Parameters
+ ----------
+ :param a Two Numpy arrays of the same shape, dtype=numpy.complex64
+
+ Returns
+ -------
+
+ a**2
+
+ """
+
+ return a**2
+
+@vectorize(['complex64(complex64)'], target='cuda')
+def complex_squared_cuda(a):
+ """
+ Implements array division on GPU
+
+ Parameters
+ ----------
+ :param a Two Numpy arrays of the same shape, dtype=numpy.complex64
+
+ Returns
+ -------
+
+ a**2
+
+ """
+
+ return a**2
+
+
+@vectorize(['complex64(complex64)'], target='parallel')
+def complex_mul(a,b):
+ """
+ Implements array division on GPU
+
+ Parameters
+ ----------
+ :param a Two Numpy arrays of the same shape, dtype=numpy.complex64
+
+ Returns
+ -------
+
+ a**2
+
+ """
+
+ return a*b
+
+@vectorize(['complex64(complex64)'], target='cuda')
+def complex_mul_cuda(a,b):
+ """
+ Implements array division on GPU
+
+ Parameters
+ ----------
+ :param a Two Numpy arrays of the same shape, dtype=numpy.complex64
+
+ Returns
+ -------
+
+ a**2
+
+ """
+
+ return a*b
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/processing/windowing.py b/Addons/FRCmetric/miplib-public/miplib/processing/windowing.py
new file mode 100644
index 0000000000000000000000000000000000000000..20a929a4fb3267dba304abfcb71333404ae31eea
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/processing/windowing.py
@@ -0,0 +1,52 @@
+
+import numpy as np
+#from scipy.signal import tukey
+
+
+def _nd_window(data, filter_function, **kwargs):
+ """
+ Performs on N-dimensional spatial-domain data.
+ This is done to mitigate boundary effects in the FFT.
+
+ Parameters
+ ----------
+ data : ndarray
+ Input data to be windowed, modified in place.
+ filter_function : 1D window generation function
+ Function should accept one argument: the window length.
+ Example: scipy.signal.hamming
+ """
+ result = data.copy().astype(np.float64)
+ for axis, axis_size in enumerate(data.shape):
+ # set up shape for numpy broadcasting
+ filter_shape = [1, ] * data.ndim
+ filter_shape[axis] = axis_size
+ window = filter_function(axis_size, **kwargs).reshape(filter_shape)
+ # scale the window intensities to maintain array intensity
+ np.power(window, (1.0/data.ndim), out=window)
+ result *= window
+ return result
+
+
+def apply_hamming_window(data):
+ """
+ Apply Hamming window to data
+
+ :param data (np.ndarray): An N-dimensional Numpy array to be used in Windowing
+ :return:
+ """
+ assert issubclass(data.__class__, np.ndarray)
+
+ return _nd_window(data, np.hamming)
+
+
+def apply_tukey_window(data, alpha=0.25, sym=True):
+ """
+ Apply Tukey window to data
+
+ :param data (np.ndarray): An N-dimensional Numpy array to be used in Windowing
+ :return:
+ """
+ assert issubclass(data.__class__, np.ndarray)
+
+ return _nd_window(data, tukey, alpha=alpha, sym=sym)
diff --git a/Addons/FRCmetric/miplib-public/miplib/psf/__init__.py b/Addons/FRCmetric/miplib-public/miplib/psf/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/psf/psfgen.py b/Addons/FRCmetric/miplib-public/miplib/psf/psfgen.py
new file mode 100644
index 0000000000000000000000000000000000000000..1127b24492b8237f19c8c35587ec7f120a8251a2
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/psf/psfgen.py
@@ -0,0 +1,54 @@
+import math
+from psf import psf, _psf
+
+from miplib.analysis.resolution import fourier_ring_correlation as frc
+from miplib.data.containers.image import Image
+
+
+class PsfFromFwhm(object):
+
+ def __init__(self, fwhm, shape=(128, 128), dims=(4., 4.)):
+ assert isinstance(fwhm, list)
+
+ if len(fwhm) == 1:
+ print ("Only one resolution value given. Assuming the same"
+ " resolution for the axial direction.")
+ fwhm = [fwhm, ] * 2
+
+ self.shape = int(shape[0]), int(shape[1])
+ self.dims = psf.Dimensions(px=shape, um=(float(dims[0]), float(dims[1])))
+
+ self.spacing = list(x/y for x, y in zip(self.dims.um, self.dims.px))
+ self.sigma_px = list(x/(2*math.sqrt(2*math.log(2))*y) for x, y in zip(fwhm, self.spacing))
+
+ self.data = _psf.gaussian2d(self.dims.px, self.sigma_px)
+
+ def xy(self):
+ """Return a z slice of the PSF with rotational symmetries applied."""
+ data = psf.mirror_symmetry(_psf.zr2zxy(self.data))
+ spacing = (self.spacing[1], self.spacing[1])
+
+ center = self.shape[0] - 1
+ return Image(data[center], spacing)
+
+ def volume(self):
+ """Return a 3D volume of the PSF with all symmetries applied.
+
+ The shape of the returned array is
+ (2*self.shape[0]-1, 2*self.shape[1]-1, 2*self.shape[1]-1)
+
+ """
+ data = psf.mirror_symmetry(_psf.zr2zxy(self.data))
+ spacing = (self.spacing[0], self.spacing[1], self.spacing[1])
+
+ return Image(data, spacing)
+
+
+def generate_frc_based_psf(image, args):
+ fwhm = [frc.calculate_single_image_frc(image, args).resolution["resolution"], ] * 2
+ psf_generator = PsfFromFwhm(fwhm)
+
+ if image.ndim == 2:
+ return psf_generator.xy()
+ else:
+ return psf_generator.volume()
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/__init__.py b/Addons/FRCmetric/miplib-public/miplib/ui/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/cli/__init__.py b/Addons/FRCmetric/miplib-public/miplib/ui/cli/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/cli/argparse_helpers.py b/Addons/FRCmetric/miplib-public/miplib/ui/cli/argparse_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..34a4e4961a26fc56fef3bfbd2e159c71f6d5e977
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/cli/argparse_helpers.py
@@ -0,0 +1,109 @@
+import os
+import argparse
+
+from itertools import chain
+
+def parse_range_list(rngs):
+ """ This parser type was created to enable the input of numeric ranges,
+ such as "2, 5, 7-11, 26". It returns a sorted list of integers.
+
+ Arguments:
+ rngs {string} -- A string containing comma delimited list of ranges,
+ e.g. "2, 5, 7-11, 26". A range should consist of a start and end (3-6)
+ or a single integer number.
+
+ Raises:
+ ValueError: Raised if a bad range, e.g. 7-11-3 is given.
+
+ Returns:
+ tuple -- A tuple with the selected indices. The above range for example,
+ "2, 5, 7-11, 26" will generate a tuple(2, 5, 7, 8, 9, 10, 11, 26)
+ """
+ def parse_range(rng):
+ parts = rng.split('-')
+ if 1 > len(parts) > 2:
+ raise ValueError("Bad range: '%s'" % (rng,))
+ parts = [int(i) for i in parts]
+ start = parts[0]
+ end = start if len(parts) == 1 else parts[1]
+ if start > end:
+ end, start = start, end
+ return list(range(start, end + 1))
+
+ return sorted(set(chain(*[parse_range(rng) for rng in rngs.split(',')])))
+
+
+def parseFromToString(string):
+ return list(int(i) for i in string.split("to"))
+
+
+def ensure_positive(number):
+ """ Check that a positive number is inserted
+
+ Arguments:
+ number {string} -- a string of a number, may be float or an int
+
+ Raises:
+ argparse.ArgumentTypeError: raises an error if argument is not a number
+ or if the number is negative
+
+ Returns:
+ float -- returns the number as as a float
+ """
+ try:
+ number = float(number)
+ except ValueError:
+ msg = "You must enter a number"
+ raise argparse.ArgumentTypeError(msg)
+ if number <= 0:
+ raise argparse.ArgumentTypeError("The value should be greater than zero")
+
+ return number
+
+ import os
+import argparse
+
+def parse_int_tuple(string):
+ """ Converts a string of comma separated integer digits into a tuple of ints
+
+ Arguments:
+ string {string} -- The input string
+
+ Returns:
+ tuple -- A tuple of integers
+ """
+
+ return tuple(int(i) for i in string.split(','))
+
+
+def parse_float_tuple(string):
+ """ Converts a string of comma separated floating point numbers
+ (. for decimal) into a tuple of floating point numbers.
+
+ Arguments:
+ string {string} -- The input string
+
+ Returns:
+ tuple -- The tuple of floats.
+ """
+ return tuple(float(i) for i in string.split(','))
+
+def parse_is_dir(dirname):
+ """ Checks if a path is an actual directory
+
+
+ Arguments:
+ dirname {string} -- A path to a directory
+
+ Raises:
+ argparse.ArgumentTypeError: Raises a parse error if the directory does
+ not exist.
+
+ Returns:
+ string -- Returns the directory, if it exists.
+ """
+ if not os.path.isdir(dirname):
+ msg = "{0} is not a directory".format(dirname)
+ raise argparse.ArgumentTypeError(msg)
+ else:
+ return dirname
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/cli/deconvolution_options.py b/Addons/FRCmetric/miplib-public/miplib/ui/cli/deconvolution_options.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0fc3177e9e4ace78526d933b21766e6e0486d9b
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/cli/deconvolution_options.py
@@ -0,0 +1,133 @@
+import argparse
+
+
+def get_deconvolution_options_group(parser):
+ assert isinstance(parser, argparse.ArgumentParser)
+ group = parser.add_argument_group("Deconvolution", "Options for controlling the deconvolution algorithm")
+
+ group.add_argument(
+ '--max-nof-iterations',
+ type=int,
+ default=20,
+ help='Specify maximum number of iterations.'
+ )
+ group.add_argument(
+ '--update-blind-psf',
+ type=int,
+ default=0,
+ help='Updates the blind psf during deconvolution, at specified iteration intervals.'
+ )
+ group.add_argument(
+ '--convergence-epsilon',
+ dest='convergence_epsilon',
+ type=float,
+ default=0.05,
+ help='Specify small positive number that determines '
+ 'the window for convergence criteria.'
+ )
+
+ group.add_argument(
+ '--wiener-nsr',
+ type=float,
+ default=100.0
+ )
+
+ group.add_argument(
+ '--first-estimate',
+ choices=['image',
+ 'blurred',
+ 'image_mean',
+ 'constant'],
+ default='image',
+ help='Specify first estimate for iteration.'
+ )
+
+ group.add_argument(
+ '--estimate-constant',
+ dest='estimate_constant',
+ type=float,
+ default=1.0
+ )
+
+ group.add_argument(
+ '--save-intermediate-results',
+ action='store_true',
+ help='Save intermediate results.'
+ )
+
+ group.add_argument(
+ '--output-cast',
+ dest='output_cast',
+ action='store_true',
+ help='By default the fusion output is returned as a 32-bit image'
+ 'This switch can be used to enable 8-bit unsigned output'
+ )
+
+ group.add_argument(
+ '--blocks',
+ dest='num_blocks',
+ type=int,
+ default=1,
+ help="Define the number of blocks you want to break the images into"
+ "for the image fusion. This argument defaults to 1, which means"
+ "that the entire image will be used -- you should define a larger"
+ "number to optimize memory consumption"
+ )
+ group.add_argument(
+ '--stop-tau',
+ type=float,
+ default=0.0001,
+ help='Specify parameter for tau-stopping criteria.'
+ )
+ group.add_argument(
+ '--tv-lambda',
+ type=float,
+ default=0,
+ help="Enable Total Variation regularization by selecting value > 0"
+ )
+
+ group.add_argument(
+ '--pad',
+ dest='block_pad',
+ type=int,
+ default=0,
+ help='The amount of padding to apply to a fusion block.'
+ )
+
+ group.add_argument(
+ '--memmap-estimates',
+ action='store_true'
+ )
+
+ group.add_argument(
+ '--disable-tau1',
+ action='store_true'
+ )
+
+ group.add_argument(
+ '--disable-fft-psf-memmap',
+ action='store_true'
+ )
+
+ group.add_argument(
+ '--rl-background',
+ type=float,
+ default=0.0,
+ help="Background correction term for RL"
+ )
+ group.add_argument(
+ '--rl-auto-background',
+ action="store_true"
+ )
+ group.add_argument(
+ '--rl-frc-stop',
+ type=float,
+ default=0.0,
+ help= "Set a stopping condition for the deconvolution based on FRC"
+ )
+ return parser
+
+
+
+
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/cli/frc_options.py b/Addons/FRCmetric/miplib-public/miplib/ui/cli/frc_options.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7d7a1591c2160168022024d64de4b0d992edb48
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/cli/frc_options.py
@@ -0,0 +1,81 @@
+import argparse
+
+def get_frc_options_group(parser):
+ assert isinstance(parser, argparse.ArgumentParser)
+
+ group = parser.add_argument_group("Fourier ring correlation analysis", "Options for FRC analysis")
+
+ group.add_argument('--bin-delta', dest='d_bin', type=int, default=1,
+ help='Set thickness of the ring for FRC calculation')
+
+ group.add_argument('--square', dest='resol_square', action='store_true',
+ help='Enable analysis only in the resolution square')
+
+ group.add_argument('--frc-curve-fit-degree',
+ dest='frc_curve_fit_degree',
+ type=int,
+ default=8)
+
+ group.add_argument('--resolution-threshold-curve-fit-degree',
+ dest='resolution_threshold_curve_fit_degree',
+ type=int,
+ default=3)
+
+ group.add_argument('--resolution-threshold-criterion',
+ dest='resolution_threshold_criterion',
+ choices=['one-bit', 'half-bit', 'fixed', 'three-sigma', 'snr'],
+ default='fixed')
+
+ group.add_argument('--resolution-threshold-value',
+ type=float,
+ default=1.0/7,
+ help="The resolution threshold value to be used when fixed threshold" \
+ "is applied")
+
+ group.add_argument('--resolution-point-sigma',
+ type=float,
+ default=0.01,
+ help="The maximum difference between the value of the FRC and threshold"
+ "curves at the intersection. ")
+
+ group.add_argument('--resolution-snr-value',
+ type=float,
+ default=0.25,
+ help="The target SNR value for the resolution measurement.")
+
+ group.add_argument('--angle-delta',
+ dest='d_angle',
+ type=int,
+ default=20,
+ help="The size of angle increment in directional FSC analysis."
+ )
+
+ group.add_argument('--extract-angle-delta',
+ dest='d_extract_angle',
+ type=float,
+ default=5.0,
+ help="The size of the angle when using hollow sphere iterator."
+ )
+
+ group.add_argument('--enable-hollow-iterator',
+ dest='hollow_iterator',
+ action='store_true',
+ help="Enable hollow iterator"
+ )
+
+ group.add_argument('--curve-fit-min',
+ dest='min_filter',
+ action='store_true',
+ help="Enable min filtering for Correlation curve fitting. This will help"
+ "with saturation artefacts, but doesn't behave nicely with very few"
+ "data points."
+ )
+
+ group.add_argument('--disable-hamming',
+ action='store_true')
+
+ group.add_argument('--frc-curve-fit-type',
+ choices=['smooth-spline', 'spline', 'polynomial'],
+ default='spline')
+
+ return parser
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/cli/fusion_options.py b/Addons/FRCmetric/miplib-public/miplib/ui/cli/fusion_options.py
new file mode 100644
index 0000000000000000000000000000000000000000..faa69ca6fa0029b97acb20d7a3ec1935fc40526c
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/cli/fusion_options.py
@@ -0,0 +1,129 @@
+"""
+Options for multi-view image fusion
+"""
+import argparse
+from miplib.ui.cli.argparse_helpers import parse_range_list
+
+def get_fusion_options_group(parser):
+ assert isinstance(parser, argparse.ArgumentParser)
+ group = parser.add_argument_group("Fusion", "Options for image fusion")
+
+ group.add_argument(
+ '--disable-cuda',
+ action='store_true'
+ )
+ group.add_argument(
+ '--max-nof-iterations',
+ type=int,
+ default=100,
+ help='Specify maximum number of iterations.'
+ )
+ group.add_argument(
+ '--convergence-epsilon',
+ dest='convergence_epsilon',
+ type=float,
+ default=0.05,
+ help='Specify small positive number that determines '
+ 'the window for convergence criteria.'
+ )
+
+ group.add_argument(
+ '--first-estimate',
+ choices=['first_image',
+ 'first_image_mean',
+ 'sum_of_originals',
+ 'sum_of_registered',
+ 'average_of_all',
+ 'simple_fusion',
+ 'constant'],
+ default='first_image_mean',
+ help='Specify first estimate for iteration.'
+ )
+
+ group.add_argument(
+ '--estimate-constant',
+ dest='estimate_constant',
+ type=float,
+ default=1.0
+ )
+
+ group.add_argument(
+ '--wiener-snr',
+ type=float,
+ default=100.0
+ )
+
+ group.add_argument(
+ '--save-intermediate-results',
+ action='store_true',
+ help='Save intermediate results.'
+ )
+
+ group.add_argument(
+ '--output-cast',
+ dest='output_cast',
+ action='store_true',
+ help='By default the fusion output is returned as a 32-bit image'
+ 'This switch can be used to enable 8-bit unsigned output'
+ )
+
+ group.add_argument(
+ '--fusion-method',
+ dest='fusion_method',
+ choices=['multiplicative', 'multiplicative-opt', 'summative',
+ 'summative-opt'],
+ default='summative'
+ )
+
+ group.add_argument(
+ '--blocks',
+ dest='num_blocks',
+ type=int,
+ default=1,
+ help="Define the number of blocks you want to break the images into"
+ "for the image fusion. This argument defaults to 1, which means"
+ "that the entire image will be used -- you should define a larger"
+ "number to optimize memory consumption"
+ )
+ group.add_argument(
+ '--rltv-stop-tau',
+ type=float,
+ default=0.002,
+ help='Specify parameter for tau-stopping criteria.'
+ )
+ group.add_argument(
+ '--tv-lambda',
+ type=float,
+ default=0,
+ help="Enable Total Variation regularization by selecting value > 0"
+ )
+
+ group.add_argument(
+ '--pad',
+ dest='block_pad',
+ type=int,
+ default=0,
+ help='The amount of padding to apply to a fusion block.'
+ )
+ group.add_argument(
+ '--fuse-views',
+ dest='fuse_views',
+ type=parse_range_list,
+ default=-1
+ )
+ group.add_argument(
+ '--memmap-estimates',
+ action='store_true'
+ )
+
+ group.add_argument(
+ '--disable-tau1',
+ action='store_true'
+ )
+
+ group.add_argument(
+ '--disable-fft-psf-memmap',
+ action='store_true'
+ )
+ return parser
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/cli/ism_options.py b/Addons/FRCmetric/miplib-public/miplib/ui/cli/ism_options.py
new file mode 100644
index 0000000000000000000000000000000000000000..66463960270489ddbb6f939078c1e9f4221ded19
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/cli/ism_options.py
@@ -0,0 +1,42 @@
+import argparse
+
+
+def get_ism_reconstruction_options_group(parser):
+ assert isinstance(parser, argparse.ArgumentParser)
+ group = parser.add_argument_group(
+ "ISM reconstruction",
+ "Options for controlling the ISM reconstruction")
+
+ group.add_argument(
+ '--ism-spad-pitch',
+ type=float,
+ default=75,
+ help="The pixel pitch (horizontal/vertical) in microns on the SPAD array"
+ )
+ group.add_argument(
+ '--ism-spad-fov-au',
+ type=float,
+ default=1.5,
+ help='Define the size of the SPAD field of view in Airy units'
+ )
+ group.add_argument(
+ '--ism-wavelength',
+ type=float,
+ default=.550,
+ help='Define the wavelength to be used for theoretical shifts calculation.'
+ )
+ group.add_argument(
+ '--ism-na',
+ type=float,
+ default=1.4,
+ help='The objective numerical aperture.'
+ )
+ group.add_argument(
+ '--ism-alpha',
+ type=float,
+ default=0.5,
+ help="The ISM reassignment factor."
+ )
+
+ return parser
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/cli/miplib_entry_point_options.py b/Addons/FRCmetric/miplib-public/miplib/ui/cli/miplib_entry_point_options.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1e0c7933b01a323ce885c34041fbf3a7f61aeb7
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/cli/miplib_entry_point_options.py
@@ -0,0 +1,574 @@
+"""
+File: miplib_entry_point_options.py
+
+In this file the command line argument interface is configured
+for the various *miplib* entry points, that can be found in the
+/bin directory.
+"""
+
+import argparse
+
+import miplib.analysis.image_quality.filters as filters
+import miplib.ui.cli.argparse_helpers as helpers
+from miplib.ui.cli.deconvolution_options import get_deconvolution_options_group
+from miplib.ui.cli.frc_options import get_frc_options_group
+from miplib.ui.cli.fusion_options import get_fusion_options_group
+from miplib.ui.cli.psf_estimation_options import get_psf_estimation_options_group
+from miplib.ui.cli.registration_options import get_registration_options_group
+from miplib.ui.cli.ism_options import get_ism_reconstruction_options_group
+
+
+
+# region Fourier Ring Correlation scripts
+
+def get_frc_script_options(arguments):
+ """ Command line options for the Fourier ring correlation script
+
+ Arguments:
+ arguments {tuple} -- Command line parameters as a tuple of strings,
+ typically obtained as sys.argv[1:]. But one can of course just use
+ string.split(" "), if using in a notebook for example.
+
+ Returns:
+ [Namespace object] -- Simple class used by default by parse_args()
+ to create an object holding attributes and return it.
+ """
+ parser = argparse.ArgumentParser(description='Fourier ring correlation analysis',
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+ parser.add_argument('directory')
+ parser.add_argument('--debug',
+ action='store_true')
+ parser.add_argument('--frc-mode', choices=["two-image", "one-image"], default="one-image")
+ parser.add_argument('--outdir', dest='pathout',
+ help='Select output folder where to save the log file'
+ + ' and the plots')
+ parser = get_common_options_group(parser)
+ parser = get_frc_options_group(parser)
+ return parser.parse_args(arguments)
+
+
+# endregion
+
+# region Deconvolution scripts
+def get_deconvolve_script_options(arguments):
+ parser = argparse.ArgumentParser(
+ description="Command line arguments for the"
+ "image Deconvolution script"
+ )
+ parser.add_argument('image')
+ parser.add_argument('psf')
+ parser = get_common_options_group(parser)
+ parser = get_deconvolution_options_group(parser)
+ parser = get_psf_estimation_options_group(parser)
+ parser = get_frc_options_group(parser)
+ return parser.parse_args(arguments)
+
+
+# endregion
+
+# region Image Scanning Microscopy reconstruction scripts
+
+def get_ism_script_options(arguments):
+
+ """ Command line options for the ISM reconstruction script
+
+ Arguments:
+ arguments {tuple} -- Command line parameters as a tuple of strings,
+ typically obtained as sys.argv[1:]. But one can of course just use
+ string.split(" "), if using in a notebook for example.
+
+ Returns:
+ [Namespace object] -- Simple class used by default by parse_args()
+ to create an object holding attributes and return it.
+ """
+ parser = argparse.ArgumentParser(
+ description="Command line arguments for the"
+ "ISM image reconstruction script"
+ )
+ parser.add_argument('directory', type=helpers.parse_is_dir)
+ parser.add_argument('ism_mode',
+ choices=["adaptive", "static", "wiener", "rl", "all"],
+ default="reassign",
+ help="Indicate the reassignment approach"
+ )
+ parser = get_common_options_group(parser)
+ parser = get_registration_options_group(parser)
+ parser = get_deconvolution_options_group(parser)
+ parser = get_psf_estimation_options_group(parser)
+ parser = get_frc_options_group(parser)
+ parser = get_ism_reconstruction_options_group(parser)
+ return parser.parse_args(arguments)
+
+
+# endregion
+
+# region Multi-View Reconstruction scripts
+
+def get_import_script_options(arguments):
+ """ Import script is used in *miplib* to import data to the internal
+ HDF5 file structure.
+
+
+ Arguments:
+ arguments {tuple} -- Command line parameters as a tuple of strings,
+ typically obtained as sys.argv[1:]. But one can of course just use
+ string.split(" "), if using in a notebook for example.
+
+ Returns:
+ [Namespace object] -- Simple class used by default by parse_args()
+ to create an object holding attributes and return it.
+ """
+ parser = argparse.ArgumentParser(
+ description="Command line arguments for the"
+ "miplib data import script."
+ )
+ parser.add_argument('data_dir_path')
+ parser.add_argument(
+ '--scales',
+ type=helpers.parse_int_tuple,
+ action='store'
+ )
+ parser.add_argument(
+ '--calculate-psfs',
+ dest='calculate_psfs',
+ action='store_true'
+ )
+
+ parser.add_argument(
+ '--copy-registration-result',
+ dest='copy_registration_result',
+ type=helpers.parseFromToString,
+ default=-1,
+ )
+ parser.add_argument(
+ '--normalize-inputs',
+ action='store_true'
+ )
+
+ return parser.parse_args(arguments)
+
+
+def get_register_script_options(arguments):
+ """ Command line options for the multi-view image registration script
+
+
+ Arguments:
+ arguments {tuple} -- Command line parameters as a tuple of strings,
+ typically obtained as sys.argv[1:]. But one can of course just use
+ string.split(" "), if using in a notebook for example.
+
+ Returns:
+ [Namespace object] -- Simple class used by default by parse_args()
+ to create an object holding attributes and return it.
+ """
+ parser = argparse.ArgumentParser(
+ description="Command line arguments for the "
+ "miplib image registration script"
+ )
+ parser.add_argument(
+ 'data_file',
+ help="Give a path to a HDF5 file that contains the images")
+
+ parser = get_common_options_group(parser)
+ parser = get_registration_options_group(parser)
+
+ return parser.parse_args(arguments)
+
+
+def get_fusion_script_options(arguments):
+ """ Command line options for the multi-view image fusion script
+
+
+ Arguments:
+ arguments {tuple} -- Command line parameters as a tuple of strings,
+ typically obtained as sys.argv[1:]. But one can of course just use
+ string.split(" "), if using in a notebook for example.
+
+ Returns:
+ [Namespace object] -- Simple class used by default by parse_args()
+ to create an object holding attributes and return it.
+ """
+
+ parser = argparse.ArgumentParser(
+ description="Command line arguments for the"
+ "miplib image fusion script"
+ )
+ parser.add_argument(
+ 'data_file',
+ help="Give a path to a HDF5 file that contains the images")
+ parser = get_common_options_group(parser)
+ parser = get_fusion_options_group(parser)
+
+ return parser.parse_args(arguments)
+
+
+# endregion
+
+# region Correlative Microscopy scripts
+
+def get_tem_correlation_options(parser):
+ assert isinstance(parser, argparse.ArgumentParser)
+
+ group = parser.add_argument_group("TEM Correlation",
+ "Options for STED-TEM correlation")
+
+ # Image file path prefix
+
+ group.add_argument(
+ '--emfile', '--em',
+ dest='em_image_path',
+ metavar='PATH',
+ default=None,
+ help='Specify PATH to Electro microscope Image'
+ )
+ # STED image path
+ group.add_argument(
+ '--stedfile', '--st',
+ dest='sted_image_path',
+ metavar='PATH',
+ default=None,
+ help='Specify PATH to STED Image'
+ )
+ group.add_argument(
+ '--register',
+ action='store_true'
+ )
+ group.add_argument(
+ '--transform',
+ action='store_true'
+ )
+ group.add_argument(
+ '--transform-path', '-t',
+ dest='transform_path',
+ metavar='PATH',
+ help='Specify PATH to transform file'
+ )
+ group.add_argument(
+ '--tfm-type',
+ dest='tfm_type',
+ choices=['rigid', 'similarity'],
+ default='rigid',
+ help='Define the spatial transform type to be used with registration'
+ )
+
+ return parser
+
+
+def get_correlate_tem_script_options(arguments):
+ """ This script is used to correlate fluoresence microscope (STED) and
+ TEM images
+
+
+ Arguments:
+ arguments {tuple} -- Command line parameters as a tuple of strings,
+ typically obtained as sys.argv[1:]. But one can of course just use
+ string.split(" "), if using in a notebook for example.
+
+ Returns:
+ [Namespace object] -- Simple class used by default by parse_args()
+ to create an object holding attributes and return it.
+ """
+ parser = argparse.ArgumentParser(
+ description="Command line arguments for the "
+ "miplib correlative STED-TEM image registration script"
+ )
+ parser = get_common_options_group(parser)
+ parser = get_tem_correlation_options(parser)
+ parser = get_registration_options_group(parser)
+
+ return parser.parse_args(arguments)
+
+
+def get_transform_script_options(arguments):
+ """ A utility script that can be used to apply a saved spatial transform
+ to an image.
+
+
+ Arguments:
+ arguments {tuple} -- Command line parameters as a tuple of strings,
+ typically obtained as sys.argv[1:]. But one can of course just use
+ string.split(" "), if using in a notebook for example.
+
+ Returns:
+ [Namespace object] -- Simple class used by default by parse_args()
+ to create an object holding attributes and return it.
+ """
+ parser = argparse.ArgumentParser(
+ description="Command line arguments for the"
+ " miplib image transform script"
+ )
+ parser = get_common_options_group(parser)
+
+ parser.add_argument('moving_image')
+ parser.add_argument('fixed_image')
+ parser.add_argument('transform')
+ parser.add_argument('--hdf', action='store_true')
+
+ return parser.parse_args(arguments)
+
+
+# endregion
+
+# region Image Quality Ranking
+
+def get_quality_script_options(arguments):
+ """ Command line options for the image quality ranking script
+
+
+ Arguments:
+ arguments {tuple} -- Command line parameters as a tuple of strings,
+ typically obtained as sys.argv[1:]. But one can of course just use
+ string.split(" "), if using in a notebook for example.
+
+ Returns:
+ [Namespace object] -- Simple class used by default by parse_args()
+ to create an object holding attributes and return it.
+ """
+
+ parser = argparse.ArgumentParser(
+ description="Command line arguments for the "
+ "image quality ranking software"
+ )
+
+ parser.add_argument(
+ "--file",
+ help="Defines a path to the image files",
+ default=None
+ )
+ parser.add_argument('--debug',
+ action='store_true')
+ parser.add_argument(
+ "--file-filter",
+ dest="file_filter",
+ default=None,
+ help="Define a common string in the files to be analysed"
+ )
+ parser.add_argument(
+ "--rgb-channel",
+ help="Select which channel in an RGB image is to be used for quality"
+ " analysis",
+ dest="rgb_channel",
+ type=int,
+ choices=[0, 1, 2],
+ default=1
+ )
+ # File filtering for batch mode processing
+ parser.add_argument(
+ "--average-filter",
+ dest="average_filter",
+ type=int,
+ default=0,
+ help="Analyze only images with similar amount of detail, by selecting a "
+ "grayscale average pixel value threshold here"
+ )
+ parser.add_argument(
+ "--working-directory",
+ dest="working_directory",
+ help="Defines the location of the working directory",
+ default="/home/sami/Pictures/Quality"
+ )
+ parser.add_argument(
+ "--mode",
+ choices=["file", "directory", "analyze", "plot"],
+ action="append",
+ help="The argument containing the functionality of the main program"
+ "You can concatenate actions by defining multiple modes in a"
+ "single command, e.g. --mode=directory --mode=analyze"
+ )
+ # Parameters for controlling the way plot functionality works.
+ parser.add_argument(
+ "--result",
+ default="average",
+ choices=["average", "fskew", "ientropy", "fentropy", "fstd",
+ "fkurtosis", "fpw", "fmean", "icv", "meanbin"],
+ help="Tell how you want the results to be calculated."
+ )
+ parser.add_argument(
+ "--npics",
+ type=int,
+ default=9,
+ help="Define how many images are shown in the plots"
+ )
+
+ parser = filters.get_common_options(parser)
+ parser = get_common_options_group(parser)
+ parser = get_frc_options_group(parser)
+ return parser.parse_args(arguments)
+
+
+def get_power_script_options(arguments):
+ """
+ Command line arguments for the power.py script that is used to calculate
+ 1D power spectra of images within a directory.
+ """
+ parser = argparse.ArgumentParser(
+ description="Command line options for the power.py script that can be"
+ "used to save the power spectra of images within a "
+ "directory"
+ )
+ parser.add_argument(
+ "--working-directory",
+ dest="working_directory",
+ help="Defines the location of the working directory",
+ default="/home/sami/Pictures/Quality"
+ )
+ parser.add_argument(
+ "--image-size",
+ dest="image_size",
+ type=int,
+ default=512
+ )
+ parser = filters.get_common_options(parser)
+ return parser.parse_args(arguments)
+
+
+def get_subjective_ranking_options(arguments):
+ """
+ Command line arguments for the subjective.py script that can be used
+ to obtain subjective opinion scores for image quality.
+ """
+ parser = argparse.ArgumentParser(
+ description="Command line arguments for the "
+ "subjective image quality ranking"
+ "script."
+ )
+ parser.add_argument(
+ "--working-directory",
+ dest="working_directory",
+ help="Defines the location of the working directory",
+ default="/home/sami/Pictures/Quality"
+ )
+
+ return parser.parse_args(arguments)
+
+
+# endregion
+
+# region Common options
+def get_common_options_group(parser):
+ """ Common options for all the above scripts
+
+ Arguments:
+ parser {argparse.ArgumentParser} -- An argument parser to which
+ the common options group is to be added.
+
+ Returns:
+ [argparse.ArgumentParser] -- The parser instance augmented with
+ the new options group.
+ """
+ assert isinstance(parser, argparse.ArgumentParser)
+ group = parser.add_argument_group("Common",
+ "Common Options for miplib scripts")
+ group.add_argument(
+ '--verbose',
+ action='store_true'
+ )
+ group.add_argument(
+ '--dir',
+ dest='working_directory',
+ default='/home/sami/Data',
+ help='Path to image files'
+ )
+ group.add_argument(
+ '--show-plots',
+ dest='show_plots',
+ action='store_true',
+ help='Show summary plots of registration/fusion variables'
+ )
+ group.add_argument(
+ '--show-image',
+ dest='show_image',
+ action='store_true',
+ help='Show a 3D image of the fusion/registration result upon '
+ 'completion'
+ )
+ group.add_argument(
+ '--scale',
+ type=int,
+ default=100,
+ help="Define the size of images to use. By default the full size "
+ "originals"
+ "will be used, but it is possible to select resampled images as "
+ "well"
+ )
+
+ group.add_argument(
+ '--channel',
+ type=int,
+ default=0,
+ help="Select the active color channel."
+ )
+
+ group.add_argument(
+ '--jupyter',
+ action='store_true',
+ help='A switch to enable certain functions that only work when using'
+ 'Jupyter notebook to run the code.'
+ )
+ group.add_argument(
+ '--test-drive',
+ dest="test_drive",
+ action='store_true',
+ help="Enable certain sections of code that are used for debugging or "
+ "tuning parameter values with new images"
+ )
+
+ group.add_argument(
+ '--evaluate',
+ dest='evaluate_results',
+ action='store_true',
+ help='Indicate whether you want to evaluate the registration/fusion '
+ 'results by eye before they are saved'
+ 'to the data structure.'
+ )
+
+ group.add_argument(
+ '--temp-dir',
+ help="Specify a custom directory for Temp data. By default it will"
+ "be saved into an automatically generated directory in the "
+ "system's temp file directory (/temp on *nix)",
+ default=None
+ )
+
+ group.add_argument(
+ '--carma-gate-idx',
+ type=int,
+ default=0,
+ help='Carma files contain several images from various detector/laser gate'
+ 'combinations. Some scripts only work with single images, so one can'
+ 'specify a certain image in the file structure with the --carma-gate-idx'
+ 'and --carma-det-idx keywords.'
+ )
+
+ group.add_argument(
+ '--carma-det-idx',
+ type=int,
+ default=0,
+ help='Carma files contain several images from various detector/laser gate'
+ 'combinations. Some scripts only work with single images, so one can'
+ 'specify a certain image in the file structure with the --carma-gate-idx'
+ 'and --carma-det-idx keywords.'
+ )
+
+ group.add_argument(
+ '--plot-size',
+ type=helpers.parse_float_tuple,
+ default=(2.5, 2.5),
+ help='Size of the generated plots (in)'
+ )
+ group.add_argument(
+ '--save-plots',
+ action='store_true',
+ help='Save some extra plots that a script may generate'
+ )
+
+ group.add_argument(
+ '--enhance-contrast-on-save',
+ action='store_true',
+ help='Enhance contrast of the output images, by allowing a small percentage '
+ 'of the pixels to saturate.'
+ )
+
+ return parser
+# endregion
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/cli/psf_estimation_options.py b/Addons/FRCmetric/miplib-public/miplib/ui/cli/psf_estimation_options.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfa2f209299ddd33635105e4b3aa1ddcdc014aef
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/cli/psf_estimation_options.py
@@ -0,0 +1,69 @@
+import argparse
+import psf
+import miplib.ui.cli.argparse_helpers as helpers
+
+def parse_psf_type(args):
+ if args == "confocal" or args == "sted":
+ return psf.GAUSSIAN | psf.CONFOCAL
+ elif args == "widefield":
+ return psf.GAUSSIAN | psf.WIDEFIELD
+ else:
+ raise argparse.ArgumentTypeError("Unknown PSF type")
+
+
+def get_psf_estimation_options_group(parser):
+ assert isinstance(parser, argparse.ArgumentParser)
+ group = parser.add_argument_group(
+ "PSF estimation",
+ "Options for controlling the PSF estimation algorithm")
+
+ group.add_argument(
+ '--psf-type',
+ type=parse_psf_type,
+ default=psf.GAUSSIAN | psf.CONFOCAL
+ )
+
+ group.add_argument(
+ '--psf-shape',
+ type=helpers.parse_int_tuple,
+ default=(256,256)
+
+ )
+ group.add_argument(
+ '--psf-size',
+ type=helpers.parse_float_tuple,
+ default=(4., 4.)
+ )
+ group.add_argument(
+ '--ex-wl',
+ type=float,
+ default=488
+ )
+ group.add_argument(
+ '--em-wl',
+ type=float,
+ default=550
+ )
+ group.add_argument(
+ '--na',
+ type=float,
+ default=1.4
+ )
+ group.add_argument(
+ '--refractive-index',
+ type=float,
+ default=1.414
+ )
+ group.add_argument(
+ '--magnification',
+ type=float,
+ default=1.0
+ )
+ group.add_argument(
+ '--pinhole-radius',
+ type=float,
+ default=None
+
+ )
+
+ return parser
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/cli/registration_options.py b/Addons/FRCmetric/miplib-public/miplib/ui/cli/registration_options.py
new file mode 100644
index 0000000000000000000000000000000000000000..1669ccff0fcd693aa6c7806c5af23a8fcc30ae27
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/cli/registration_options.py
@@ -0,0 +1,240 @@
+import argparse
+
+def get_registration_options_group(parser):
+
+ assert isinstance(parser, argparse.ArgumentParser)
+ group = parser.add_argument_group("Registration",
+ "Options for image registration")
+ group.add_argument(
+ '--initializer-off',
+ dest='initializer',
+ action='store_false'
+ )
+ group.add_argument(
+ '--reg-method',
+ dest='registration_method',
+ choices=['mattes', 'mean-squared-difference', 'viola-wells',
+ 'correlation'],
+ default='correlation',
+ help='Specify registration method'
+ )
+ group.add_argument(
+ '--two-step',
+ dest='two_step_registration',
+ action='store_true',
+ help='Select if you want to do a two phase registration, '
+ 'the first being with a degraded image and the second'
+ 'with the high-resolution original'
+ )
+ group.add_argument(
+ '--normalize',
+ action='store_true',
+ help='Choose this option if you want to normalize the intensity values'
+ 'before registration. Some registration methods work better with'
+ 'normalized intensities.'
+ )
+
+ group.add_argument(
+ '--gaussian',
+ dest='gaussian_variance',
+ type=float,
+ default=0.0,
+ help='Define variance for Gaussian blur'
+ )
+ group.add_argument(
+ '--dilation',
+ dest='dilation_size',
+ type=int,
+ default=0,
+ help='Define size for Grayscale dilation'
+ )
+ group.add_argument(
+ '--mean',
+ dest='mean_kernel',
+ type=int,
+ default=0,
+ help='In case you would like to use a mean filter to smoothen the '
+ 'images'
+ 'before registration, define a kernel here'
+ )
+ group.add_argument(
+ '--median',
+ dest='median_size',
+ type=int,
+ default=0,
+ help='Enable median filtering before registering by a non-zero kernel '
+ 'size'
+ )
+
+ # Mattes mutual information metric specific options
+ group.add_argument(
+ '--mattes-histogram-bins',
+ dest='mattes_histogram_bins',
+ type=int,
+ default=15,
+ help='Specify the number of histogram bins for Mattes '
+ 'Mutual Information sampling'
+ )
+ group.add_argument(
+ '--sampling-percentage',
+ dest='sampling_percentage',
+ type=float,
+ default=0.1,
+ help='Specify the number of samples to take from each '
+ 'histogram bin'
+ )
+
+ # Viola Wells mutual information specific parameters
+ group.add_argument(
+ '--vw-fixed-sd',
+ dest='vw_fixed_sd',
+ type=float,
+ default=0.4,
+ help='Specify the fixed image SD value in Viola-Wells mutual '
+ 'information registration'
+ )
+ group.add_argument(
+ '--vw-moving-sd',
+ dest='vw_moving_sd',
+ type=float,
+ default=0.4,
+ help='Specify the fixed image SD value in Viola-Wells mutual '
+ 'information registration'
+ )
+ group.add_argument(
+ '--vw-samples-multiplier',
+ dest='vw_samples_multiplier',
+ type=float,
+ default=0.2,
+ help='Specify the amount of spatial samples to be used in '
+ 'mutual information calculations. The amount is given'
+ 'as a proportion of the total number of pixels in the'
+ 'fixed image.'
+ )
+
+ # Initializer options
+ group.add_argument(
+ '--set-rot-axis',
+ dest='rot_axis',
+ type=int,
+ default=0,
+ help='Specify the axis for initial rotation of the '
+ 'moving image'
+ )
+ group.add_argument(
+ '--set-rotation',
+ dest='set_rotation',
+ type=float,
+ default=1.0,
+ help='Specify an estimate for initial rotation angle'
+ )
+ group.add_argument(
+ '--set-scale',
+ dest='set_scale',
+ type=float,
+ default=1.0,
+ help='Specify the initial scale for similarity transform'
+ )
+ # Optimizer options
+ group.add_argument(
+ '--set-translation-scale',
+ dest='translation_scale',
+ type=float,
+ default=1.0,
+ help='A scaling parameter to adjust optimizer behavior'
+ 'effect on rotation and translation. By default'
+ 'the translation scale is 1000 times that of rotation'
+ )
+ group.add_argument(
+ '--set-scaling-scale',
+ dest='scaling_scale',
+ type=float,
+ default=10.0
+ )
+ group.add_argument(
+ '--max-step',
+ dest='max_step_length',
+ type=float,
+ default=0.2,
+ help='Specify an estimate for initial rotation angle'
+ )
+ group.add_argument(
+ '--min-step',
+ dest='min_step_length',
+ type=float,
+ default=0.001,
+ help='Specify an estimate for initial rotation angle'
+ )
+ group.add_argument(
+ '--x-offset',
+ dest='x_offset',
+ type=float,
+ default=0.0
+ )
+ group.add_argument(
+ '--y-offset',
+ dest='y_offset',
+ type=float,
+ default=0.0
+ )
+ group.add_argument(
+ '--z-offset',
+ dest='z_offset',
+ type=float,
+ default=0.0
+ )
+
+ group.add_argument(
+ '--reg-max-iterations',
+ dest='registration_max_iterations',
+ type=int,
+ default=300,
+ help='Specify an estimate for initial rotation angle'
+ )
+ group.add_argument(
+ '--reg-relax-factor',
+ dest='relaxation_factor',
+ type=float,
+ default=0.7,
+ help='Defines how quickly optmizer shortens the step size'
+ )
+ group.add_argument(
+ '--reg-print-prog',
+ dest='print_registration_progress',
+ action='store_true'
+ )
+ group.add_argument(
+ '--reg-enable-observers',
+ action='store_true'
+ )
+
+ group.add_argument(
+ '--reg-translate-only',
+ action='store_true'
+ )
+ group.add_argument(
+ '--use-internal-type',
+ dest='use_internal_type',
+ action='store_true'
+ )
+ group.add_argument(
+ '--disable-init-moments',
+ dest='moments',
+ action='store_false'
+ )
+ group.add_argument(
+ '--mask-threshold',
+ dest='mask_threshold',
+ type=int,
+ default=30,
+ help='Intensity threshold for the registration spatial mask. It '
+ 'defaults to 30, which works with most images.'
+ )
+ group.add_argument(
+ '--learning-rate',
+ dest='learning_rate',
+ type=float,
+ default=.7
+ )
+
+ return parser
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/cli/resolution_options.py b/Addons/FRCmetric/miplib-public/miplib/ui/cli/resolution_options.py
new file mode 100644
index 0000000000000000000000000000000000000000..106f7d7c715dcc28ca46766200a5602baf8059c2
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/cli/resolution_options.py
@@ -0,0 +1,87 @@
+import argparse
+
+
+def get_fsc_script_options(arguments):
+ parser = argparse.ArgumentParser("Command line arguments for the 3D FSC script")
+
+ parser.add_argument("halfmap1",
+ type=str,
+ help="First half map of 3D reconstruction. MRC format. Can be masked or unmasked.",
+ metavar="HALFMAP1.MRC")
+
+ parser.add_argument("halfmap2",
+ type=str,
+ help="Second half map of 3D reconstruction. MRC format. Can be masked or unmasked.",
+ metavar="HALFMAP2.MRC")
+
+ parser.add_argument("fullmap",
+ type=str,
+ help="Full map of 3D reconstruction. MRC format. Can be masked or unmasked, "
+ "can be sharpened or unsharpened. ",
+ metavar="FULLMAP.MRC")
+
+ parser.add_argument('--dir',
+ dest='working_directory',
+ default='/home/sami/Data',
+ help='Path to image files')
+
+ parser.add_argument("--apix",
+ type=float,
+ default=1.0,
+ help="Angstrom per pixel of 3D map.",
+ metavar="FLOAT")
+
+ parser.add_argument("--mask",
+ type=str,
+ help="If given, it would be used to mask the half maps during 3DFSC generation and analysis.",
+ metavar="MASK.MRC")
+
+ parser.add_argument("--dthetaInDegrees",
+ type=float,
+ default=20.0,
+ help="Angle of cone to be used for 3D FSC sampling in degrees. Default is 20 degrees.",
+ metavar="FLOAT")
+
+ parser.add_argument("--histogram",
+ type=str,
+ default="histogram",
+ help="Name of output histogram graph. No file extension required - it will automatically be "
+ "given a .pdf extension. No paths please.",
+ metavar="FILENAME")
+
+ parser.add_argument("--FSCCutoff",
+ type=float,
+ default=0.143,
+ help="FSC cutoff criterion. 0.143 is default.",
+ metavar="FLOAT")
+
+ parser.add_argument("--ThresholdForSphericity",
+ type=float,
+ default=0.5,
+ help="Threshold value for 3DFSC volume for calculating sphericity. 0.5 is default.",
+ metavar="FLOAT")
+
+ parser.add_argument("--HighPassFilter",
+ type=float,
+ default=200.0,
+ help="High pass filter for thresholding in Angstrom. Prevents small dips in directional "
+ "FSCs at low spatial frequency due to noise from messing up the thresholding step. "
+ "Decrease if you see a huge wedge missing from your thresholded 3DFSC volume. "
+ "200 Angstroms is default.",
+ metavar="FLOAT")
+
+ parser.add_argument("--Skip3DFSCGeneration",
+ action="store_true",
+ help="Allows for skipping of 3DFSC generation to directly run the analysis on a previously "
+ "generated set of results.",
+ metavar="True or False")
+
+ parser.add_argument("--numThresholdsForSphericityCalcs",
+ type=int,
+ default=0,
+ help="calculate sphericities at different threshold cutoffs to determine sphericity deviation "
+ "across spatial frequencies. This can be useful to evaluate possible effects of "
+ "overfitting or improperly assigned orientations.",
+ metavar="INT")
+
+ return parser.parse_args(arguments)
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/plots/__init__.py b/Addons/FRCmetric/miplib-public/miplib/ui/plots/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/plots/frc.py b/Addons/FRCmetric/miplib-public/miplib/ui/plots/frc.py
new file mode 100644
index 0000000000000000000000000000000000000000..55b543f81af23e77045d74482a8ab2e67baa6443
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/plots/frc.py
@@ -0,0 +1,571 @@
+import os
+
+import matplotlib.pyplot as plt
+
+#plt.style.use("seaborn-colorblind")
+plt.style.use("seaborn-v0_8")
+
+import numpy as np
+from skimage import draw
+import miplib.processing.ndarray as arrayops
+
+from miplib.data.containers.fourier_correlation_data import FourierCorrelationData, FourierCorrelationDataCollection
+from miplib.processing.converters import degrees_to_radians
+
+
+def plot_resolution_curves(data_to_plot, x_idx=0, size=(2, 2), disable_ax_labels=False):
+ """
+ Make a figure with all FRC curves plotted into a single subplot
+
+ :param data_to_plot: a FourierCorrelationDataCollection object with the FRC results
+ :param x_idx: if the data contains FRC results with different dynamic range (pixel size), indicate
+ the index of the dataset here with the maximum range
+ :param size: size of the plot
+ :param disable_ax_labels: disable y- and x-axis labels (Correlation, Frequency), in case you want to add them
+ later for instance
+ :return: returns the matplotlib.pyplot.Figure object that you can use to make further modificaitons
+ to the plot
+ """
+
+ assert isinstance(data_to_plot, FourierCorrelationDataCollection)
+
+ fig, ax = plt.subplots(figsize=size)
+
+ resolution_curves_subplot(ax, data_to_plot, x_idx, disable_ax_labels)
+
+ return fig
+
+
+def resolution_curves_subplot(ax, data_to_plot, x_idx=0, disable_ax_labels=False, line_style='-'):
+ """
+ Does the actual owrk fo the plot_resolution_curves function, but requires an axis as an input. It is useful eg.
+ when one desires to have several subplots.
+
+ :param ax: plot axis (as in matplotlib subplot instance)
+ :param data_to_plot: a FourierCorrelationDataCollection object with the FRC results
+ :param x_idx: if the data contains FRC results with different dynamic range (pixel size), indicate
+ the index of the dataset here with the maximum range
+ :param size: size of the plot
+ :param disable_ax_labels: disable y- and x-axis labels (Correlation, Frequency), in case you want to add them
+ later for instance
+ :return: returns the matplotlib.pyplot.Figure object that you can use to make further modificaitons
+ to the plot
+ """
+ assert isinstance(data_to_plot, FourierCorrelationDataCollection)
+
+ angles = list()
+ datasets = list()
+
+ # Sort datasets by angle.
+ for dataset in data_to_plot:
+ angles.append((int(dataset[0])))
+ datasets.append(dataset[1])
+
+ angles, datasets = list(zip(*sorted(zip(angles, datasets))))
+
+ # plot threshold
+ dataset = datasets[int(x_idx)]
+
+ y = dataset.resolution["threshold"]
+ x = dataset.correlation["frequency"]
+ if x[-1] < 1.0:
+ x = np.append(x, 1.0)
+ y = np.append(y, y[-1])
+
+ x_axis = arrayops.safe_divide(x, 2 * dataset.resolution["spacing"])
+
+ ax.plot(x_axis, y, linestyle='--', color='#b5b5b3')
+
+ if not disable_ax_labels:
+ xlabel = r'Frequency ($\mathrm{\mu m}^{-1}$)'
+ ylabel = 'Correlation'
+ ax.set_xlabel(xlabel)
+ ax.set_ylabel(ylabel)
+
+ for idx, dataset in enumerate(datasets):
+ ax.set_ylim([0, 1.2])
+
+ # Plot calculated FRC values as xy scatter.
+ y = dataset.correlation["curve-fit"]
+ x = dataset.correlation["frequency"]
+ x_axis = arrayops.safe_divide(x, 2 * dataset.resolution["spacing"])
+
+ ax.plot(x_axis, y, linestyle=line_style)
+
+ return ax
+
+
+def power_spectrum_plot_with_contour(image, data, size=(2.5, 2.5)):
+ def draw_contour_3d(data, image, spacing):
+ angles = list()
+ radii = list()
+
+ for dataset in data:
+ angles.append(degrees_to_radians(float(dataset[0])))
+ radii.append(image.shape[0] * (spacing / dataset[1].resolution["resolution"]))
+
+ angles, radii = zip(*sorted(zip(angles, radii)))
+ angles = list(angles)
+ radii = list(radii)
+ angles.append(angles[0])
+ radii.append(radii[0])
+
+ center = list(i / 2 for i in image.shape)
+ xs = list(radius * np.cos(angle) + center[1] for radius, angle in zip(radii, angles))
+ ys = list(radius * np.sin(angle) + center[0] for radius, angle in zip(radii, angles))
+ image[draw.polygon_perimeter(ys, xs)] = 255
+
+ return image
+
+ def draw_contour_2d(data, image, spacing):
+ radius = image.shape[0] * (spacing / data.resolution["resolution"])
+
+ center = tuple(x//2 for x in image.shape)
+ image[draw.circle_perimeter(*center, radius)] = 255
+
+ def get_ps_image(image):
+ fft_image = np.abs(np.fft.fftshift(np.fft.fftn(image))).real
+ if image.ndim == 3:
+ max_proj = np.sum(fft_image, axis=1)
+ else:
+ max_proj = fft_image
+ return max_proj
+
+ ps = 20*np.log10(get_ps_image(image))
+ spacing = image.spacing[0]
+
+ if image.ndim == 3:
+ ps = draw_contour_3d(data, ps, spacing)
+ else:
+ ps = draw_contour_2d(data, ps, spacing)
+
+
+ fig = plt.figure(figsize=size)
+ ax = plt.subplot(111)
+ ax.imshow(ps)
+
+ return ax
+
+
+
+
+def fsc_polar_plot(ax, data):
+ """ Generate a polar plot from SFSC data. This is mainly used to overlay several plots
+ with ...
+
+ :param ax: pyplot ax instance that is to be used for the plotting
+ :param data: FourierCorrelationDataCollection instance that includes the data to plot.
+ :return: returns the same ax instance for further modifications
+ """
+
+ angles = list()
+ radii = list()
+
+ for dataset in data:
+ angles.append(degrees_to_radians(float(dataset[0])))
+ radii.append(dataset[1].resolution["resolution"])
+
+ angles, radii = zip(*sorted(zip(angles, radii)))
+ angles = list(angles)
+ radii = list(radii)
+ angles.append(angles[0])
+ radii.append(radii[0])
+
+ ax.plot(angles, radii)
+
+ ax.set_rlabel_position(-80) # get radial labels away from plotted line
+
+ return ax
+
+
+class FourierDataPlotter(object):
+ # todo: consider making this plotter class disappear. It is often easier to just use the functions as above.
+ """
+ An attempt of sorts to make a class to handle the various types of FRC/FSC plots. I'm not quite sure
+ how much sense that makes. It might be better to just have individual funcitons, more Python.
+ """
+
+ def __init__(self, data, path=None):
+ assert isinstance(data, FourierCorrelationDataCollection)
+
+ self.data = data
+
+ if len(self.data) < 3:
+ self._columns = len(self.data)
+ else:
+ self._columns = 3
+
+ if len(self.data) % self._columns == 0:
+ self._rows = int(len(self.data) / self._columns)
+ else:
+ self._rows = int(len(self.data) / self._columns + 1)
+
+ if path is not None:
+ assert os.path.isdir(path)
+ self.path = path
+
+ def plot_all(self, save_fig=False, custom_titles=None, show=True):
+ """
+ Plot all curves in the FourierCorrelationDataCollection data object
+ that was supplied to the constructor.
+
+ """
+ axescolor = '#f6f6f6'
+
+ size = (6, self._rows * 2) if save_fig else (12, self._rows * 4)
+
+ fig, plots = plt.subplots(self._rows, self._columns,
+ figsize=size)
+ # rect = fig.patch
+ # rect.set_facecolor('white')
+
+ fig.tight_layout(pad=0.4, w_pad=2, h_pad=6)
+
+ angles = list()
+ datasets = list()
+
+ # Sort datasets by angle.
+ for dataset in self.data:
+ angles.append((int(dataset[0])))
+ datasets.append(dataset[1])
+
+ angles, datasets = list(zip(*sorted(zip(angles, datasets))))
+
+ if custom_titles is None:
+ titles = list("FRC @ angle %i" % angle for angle in angles)
+ else:
+ assert len(custom_titles) == len(angles)
+ titles = custom_titles
+
+ # Make subplots
+ for title, dataset, plot in zip(titles, datasets, plots.flatten()):
+ self.__make_frc_subplot(plot, dataset, title)
+
+ if save_fig:
+ file_name = os.path.join(self.path, "all_frc_curves.eps")
+ plt.savefig(file_name, dpi=1200)
+
+ if show:
+ plt.show()
+
+ def plot_all_to_files(self, custom_titles=None, size=(3.3, 3), header=False):
+ assert self.path is not None
+
+ fig, plot = plt.subplots(1, 1, figsize=size, tight_layout=True)
+ # plot.set(aspect='equal')
+
+ angles = list()
+ datasets = list()
+
+ # Sort datasets by angle.
+ for dataset in self.data:
+ angles.append((int(dataset[0])))
+ datasets.append(dataset[1])
+
+ angles, datasets = list(zip(*sorted(zip(angles, datasets))))
+
+ if custom_titles is None:
+ titles = list("FRC @ angle %i" % angle for angle in angles)
+ else:
+ assert len(custom_titles) == len(angles)
+ titles = custom_titles
+
+ # Make subplots
+ for title, dataset in zip(titles, datasets):
+ self.__make_printable_frc_subplot(plot, dataset, title=(title if header else None))
+ file_name = os.path.join(self.path, "{}.eps".format(title))
+ plt.savefig(file_name, dpi=1200)
+ plt.cla()
+
+ def plot_one(self, angle):
+
+ plt.figure(figsize=(5, 4))
+ ax = plt.subplot(111)
+
+ self.__make_frc_subplot(ax, self.data[int(angle)], "FRC at angle %s" % str(angle))
+
+ plt.show()
+
+ def plot_one_to_file(self, angle, filename, title=None, size=(2, 2), coerce_ticks=True, legend=False):
+ fig, ax = plt.subplots(1, 1, figsize=size)
+
+ self.__make_printable_frc_subplot(ax, self.data[int(angle)], title, coerce_ticks=coerce_ticks)
+ file_name = os.path.join(self.path, "{}.eps".format(filename))
+ if legend:
+ fig.legend(('FRC', 'curve-fit', 'threshold', 'resolution-point'), bbox_to_anchor=(1.10, 1.0), loc=2,
+ borderaxespad=0.)
+
+ plt.savefig(file_name, dpi=1200, bbox_inches='tight', pad_inches=0, transparent=True)
+
+ def plot_polar(self):
+ """
+ Show the resolution as a 2D polar plot in which the resolution values are plotted
+ as a function of rotatino angle.
+ """
+
+ angles = list()
+ radii = list()
+
+ for dataset in self.data:
+ angles.append(degrees_to_radians(float(dataset[0])))
+ radii.append(dataset[1].resolution["resolution"])
+
+ angles, radii = list(zip(*sorted(zip(angles, radii))))
+ angles = list(angles)
+ radii = list(radii)
+ angles.append(angles[0])
+ radii.append(radii[0])
+
+ radii_norm = list(i / max(radii) for i in radii)
+ fig = plt.figure(figsize=(4, 4))
+ ax = plt.subplot(111, projection="polar")
+ ax.plot(angles, radii_norm, color='#61a2da')
+ ax.set_rmax(1.2)
+ r_ticks = np.linspace(0.1, 1.0, 5)
+ r_ticks_scale = r_ticks * max(radii)
+
+ x_labels = ['%.2f' % n for n in r_ticks_scale]
+
+ ax.set_rticks(r_ticks)
+ ax.set_yticklabels(x_labels)
+ ax.set_rlabel_position(-80) # get radial labels away from plotted line
+ # ax.grid(True)
+
+ # ax.set_title("The image resolution as a function of rotation angle")
+
+ # ax.set_xlabel("XY")
+ # ax.set_ylabel("Z")
+
+ return ax
+
+ def plot_polar_to_file(self, filename, size=(2, 2)):
+ """
+ Show the resolution as a 2D polar plot in which the resolution values are plotted
+ as a function of rotatino angle.
+ """
+
+ angles = list()
+ radii = list()
+
+ for dataset in self.data:
+ angles.append(degrees_to_radians(float(dataset[0])))
+ radii.append(dataset[1].resolution["resolution"])
+
+ angles, radii = list(zip(*sorted(zip(angles, radii))))
+ angles = list(angles)
+ radii = list(radii)
+ angles.append(angles[0])
+ radii.append(radii[0])
+
+ radii_norm = list(i / max(radii) for i in radii)
+ plt.figure(figsize=size)
+ ax = plt.subplot(111, projection="polar")
+ ax.plot(angles, radii_norm, color='#61a2da')
+ ax.set_rmax(1.2)
+ r_ticks = np.linspace(0.1, 1.0, 5)
+ r_ticks_scale = r_ticks * max(radii)
+
+ print(r_ticks_scale)
+ print(max(radii))
+
+ x_labels = ['%.2f' % n for n in r_ticks_scale]
+
+ print(x_labels)
+ ax.set_rticks(r_ticks)
+ ax.set_yticklabels(x_labels)
+ ax.set_rlabel_position(-80) # get radial labels away from plotted line
+ # ax.grid(True)
+
+ # ax.set_title("The image resolution as a function of rotation angle")
+
+ # ax.set_xlabel("XY")
+ # ax.set_ylabel("Z")
+
+ file_name = os.path.join(self.path, "{}.eps".format(filename))
+
+ plt.savefig(file_name, dpi=1200, bbox_inches='tight', pad_inches=0, transparent=True)
+
+ @staticmethod
+ def __make_frc_subplot(ax, frc, title):
+ """
+ Creates a plot of the FRC curves in the curve_list. Single or multiple vurves can
+ be plotted.
+ """
+ assert isinstance(frc, FourierCorrelationData)
+
+ # # Font setting
+ # font0 = FontProperties()
+ # font1 = font0.copy()
+ # font1.set_size('medium')
+ # font = font1.copy()
+ # font.set_family('sans')
+ # rc('text', usetex=True)
+
+ # Enable grid
+ gridLineWidth = 0.2
+ # ax.yaxis.grid(True, linewidth=gridLineWidth, linestyle='-', color='0.05')
+
+ # Axis labelling
+ xlabel = 'Frequency (1/um)'
+ ylabel = 'Correlation'
+ # ax.set_xlabel(xlabel, fontsize=12, position=(0.5, -0.2))
+ # ax.set_ylabel(ylabel, fontsize=12, position=(0.5, 0.5))
+ ax.set_xlabel(xlabel)
+ ax.set_ylabel(ylabel)
+
+ ax.set_ylim([0, 1.2])
+
+ # Title
+ ax.set_title(title)
+
+ # Plot calculated FRC values as xy scatter.
+ y = frc.correlation["correlation"]
+ x = frc.correlation["frequency"]
+ x_axis = arrayops.safe_divide(x, 2 * frc.resolution["spacing"])
+
+ ax.plot(x_axis, y, '^', markersize=6, color='#b5b5b3',
+ label='FRC')
+
+ # Plot polynomial fit as a line plot over the FRC scatter
+ y = frc.correlation["curve-fit"]
+ ax.plot(x_axis, y, linewidth=3, color='#61a2da',
+ label='Least-squares fit')
+
+ # Plot the resolution threshold curve
+ y = frc.resolution["threshold"]
+ res_crit = frc.resolution["criterion"]
+ if res_crit == 'one-bit':
+ label = 'One-bit curve'
+ elif res_crit == 'half-bit':
+ label = 'Half-bit curve'
+ elif res_crit == 'fixed':
+ label = 'y = %f' % y[0]
+ else:
+ label = "Threshold"
+
+ if x[-1] < 1.0:
+ x = np.append(x, 1.0)
+ y = np.append(y, y[-1])
+
+ x_axis = arrayops.safe_divide(x, 2 * frc.resolution["spacing"])
+
+ ax.plot(x_axis, y, color='#d77186',
+ label=label, lw=2, linestyle='--')
+
+ # Plot resolution point
+ y0 = frc.resolution["resolution-point"][0]
+ x0 = frc.resolution["resolution-point"][1] / (2 * frc.resolution["spacing"])
+
+ ax.plot(x0, y0, 'ro', markersize=8, label='Resolution point', color='#D75725')
+
+ verts = [(x0, 0), (x0, y0)]
+ xs, ys = list(zip(*verts))
+
+ ax.plot(xs, ys, 'x--', lw=3, color='#D75725', ms=10)
+ # ax.text(x0, y0 + 0.10, 'RESOL-FREQ', fontsize=12)
+
+ resolution = "The resolution is {} um.".format(
+ frc.resolution["resolution"])
+ ax.text(0.5, -0.3, resolution, ha="center", fontsize=12)
+
+ # x_axis = arrayops.safe_divide(np.linspace(0.0, 1.0, num=len(ax.get_xticklabels())),
+ # 2*frc.resolution["spacing"])
+
+ # x_labels = map(lambda n: '%.1f' % n, x_axis)
+
+ # ax.set_xticklabels(x_labels)
+
+ # Add legend
+ # ax.legend()
+
+ @staticmethod
+ def __make_printable_frc_subplot(ax, frc, title=None, coerce_ticks=True):
+ """
+ Creates a plot of the FRC curves in the curve_list. Single or multiple vurves can
+ be plotted.
+ """
+ assert isinstance(frc, FourierCorrelationData)
+
+ # # Font setting
+ # font0 = FontProperties()
+ # font1 = font0.copy()
+ # font1.set_size('medium')
+ # font = font1.copy()
+ # font.set_family('sans')
+ # rc('text', usetex=True)
+
+ # Enable grid
+ gridLineWidth = 0.2
+ # ax.yaxis.grid(True, linewidth=gridLineWidth, linestyle='-', color='0.05')
+
+ # Marker setup
+ colorArray = ['blue', 'green', 'red', 'orange', 'brown', 'black', 'violet', 'pink']
+ marker_array = ['^', 's', 'o', 'd', '1', 'v', '*', 'p']
+
+ # Axis labelling
+ xlabel = 'Frequency'
+ ylabel = 'Correlation'
+ # ax.set_xlabel(xlabel, fontsize=12, position=(0.5, -0.2))
+ # ax.set_ylabel(ylabel, fontsize=12, position=(0.5, 0.5))
+ ax.set_xlabel(xlabel)
+ ax.set_ylabel(ylabel)
+
+ ax.set_ylim([0, 1.2])
+
+ # Title
+ if title is not None:
+ ax.set_title(title)
+
+ # Plot calculated FRC values as xy scatter.
+ y = frc.correlation["correlation"]
+ x_raw = frc.correlation["frequency"]
+ x = arrayops.safe_divide(x_raw, 2 * frc.resolution["spacing"])
+ ax.plot(x, y, marker_array[0], color='#b5b5b3',
+ label='FRC')
+
+ # Plot polynomial fit as a line plot over the FRC scatter
+ y = frc.correlation["curve-fit"]
+ ax.plot(x, y, color='#61a2da',
+ label='Least-squares fit')
+
+ # Plot the resolution threshold curve
+ y = frc.resolution["threshold"]
+ res_crit = frc.resolution["criterion"]
+ if res_crit == 'one-bit':
+ label = 'One-bit curve'
+ elif res_crit == 'half-bit':
+ label = 'Half-bit curve'
+ elif res_crit == 'fixed':
+ label = 'y = %f' % y[0]
+ else:
+ label = "Threshold"
+
+ if x_raw[-1] < 1.0:
+ x_th = arrayops.safe_divide(np.append(x_raw, 1.0), 2 * frc.resolution["spacing"])
+ y = np.append(y, y[-1])
+ else:
+ x_th = x
+
+ ax.plot(x_th, y, color='#d77186',
+ label=label, linestyle='--')
+
+ # Plot resolution point
+ y0 = frc.resolution["resolution-point"][0]
+ x0 = frc.resolution["resolution-point"][1] / (2 * frc.resolution["spacing"])
+
+ ax.plot(x0, y0, 'ro', label='Resolution point', color='#D75725')
+
+ verts = [(x0, 0), (x0, y0)]
+ xs, ys = list(zip(*verts))
+
+ ax.plot(xs, ys, 'x--', color='#D75725', ms=10)
+
+ # x_axis = arrayops.safe_divide(np.linspace(0.0, 1.0, num=len(ax.get_xticklabels())),
+ # 2 * frc.resolution["spacing"])
+ #
+ # if coerce_ticks is True:
+ # x_labels = map(lambda n: '%d' % n, x_axis)
+ # else:
+ # x_labels = map(lambda n: '%.1f' % n, x_axis)
+ #
+ # ax.set_xticklabels(x_labels)
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/plots/image.py b/Addons/FRCmetric/miplib-public/miplib/ui/plots/image.py
new file mode 100644
index 0000000000000000000000000000000000000000..d488f4eecc2027f36f19abb9c95431516faaae96
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/plots/image.py
@@ -0,0 +1,247 @@
+import os
+#import subprocess
+
+import SimpleITK as sitk
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import numpy as np
+
+# import miplib.data.io.tiffile as tiffile
+#
+# vaa3d_bin = "/home/sami/bin/Vaa3D_Ubuntu_64bit_v3.200/vaa3d"
+#
+# def evaluate_3d_image(data):
+# """
+# A utility function that can be used to display the registration
+# and/or fusion results in Vaa3D volume viewer. The function returns
+# a Boolean value based on whether the user wants to save the data
+# into the data storage or not.
+#
+# Parameters
+# ----------
+# data A 3D data volume as a numpy.ndarray. The order of the
+# dimensions should be ZXYC. C can be omitted if one.
+#
+# """
+# assert os.path.exists(vaa3d_bin)
+#
+# filename = "temp.tif"
+# tiffile.imsave(filename, data)
+#
+# subprocess.call([vaa3d_bin, "-i", filename])
+#
+# os.remove(filename)
+
+
+# callback invoked by the interact ipython method for scrolling through the data stacks of
+# the two images (moving and fixed)
+def display_3d_slices(fixed_image_z, moving_image_z, fixed_npa, moving_npa):
+ # create a figure with two subplots and the specified size
+ plt.subplots(1, 2, figsize=(10, 8))
+
+ # draw the fixed data in the first subplot
+ plt.subplot(1, 2, 1)
+ plt.imshow(fixed_npa[fixed_image_z, :, :], cmap='gray')
+ plt.title('fixed data')
+ plt.axis('off')
+
+ # draw the moving data in the second subplot
+ plt.subplot(1, 2, 2)
+ plt.imshow(moving_npa[moving_image_z, :, :], cmap='gray')
+ plt.title('moving data')
+ plt.axis('off')
+
+# callback invoked by the ipython interact method for scrolling and modifying the alpha blending
+# of an data stack of two images that occupy the same physical space.
+
+
+def display_3d_slice_with_alpha(image_z, alpha, fixed, moving):
+ img = (1.0 - alpha) * fixed[:, :, image_z] + alpha * moving[:, :, image_z]
+ plt.imshow(sitk.GetArrayFromImage(img), cmap='gray')
+ plt.axis('off')
+
+
+def create_axial_views_plot(image, x_idx, y_idx, z_idx):
+
+ assert issubclass(image.__class__, np.ndarray)
+ assert image.ndim == 3
+
+ xy = image[z_idx, :, :]
+ xz = image[:, y_idx, :]
+ yz = image[:, :, x_idx]
+
+ yz = np.transpose(yz)
+
+ width_ratio = xy.shape[1]/yz.shape[1]
+ height_ratio = xy.shape[0]/xz.shape[0]
+
+ fig = plt.figure(figsize=(8, 8))
+ gs = gridspec.GridSpec(2, 2,
+ width_ratios=[width_ratio, 1],
+ height_ratios=[height_ratio, 1])
+
+ ax0 = plt.subplot(gs[0, 0])
+ ax0.imshow(xy, cmap="hot")
+ ax0.set_title("XY")
+ ax0.axis('off')
+
+ ax1 = plt.subplot(gs[0, 1])
+ ax1.imshow(yz, cmap="hot")
+ ax1.set_title("YZ")
+ ax1.axis('off')
+
+ ax2 = plt.subplot(gs[1,0])
+ ax2.imshow(xz, cmap="hot")
+ ax2.set_title("XZ")
+ ax2.axis('off')
+
+ #fig.delaxes(axes[1, 1])
+ return fig
+
+
+def display_2d_images(image1,
+ image2,
+ image1_title='image1',
+ image2_title='image2',
+ vertical=False):
+ """
+ A function that can be used to display two SimpleITK images side by side.
+ It is also possible to select paired landmarks from the two images, by
+ enabling the landmarks argument.
+
+ Parameters
+
+ image1 A numpy.ndarray or its subclass
+ image2 A numpy.ndarray or its subclass
+
+ """
+ assert issubclass(type(image1), np.ndarray)
+ assert issubclass(type(image2), np.ndarray)
+
+ assert image1.ndim == 2 and image2.ndim == 2
+
+ if vertical:
+ fig, (ax1, ax2) = plt.subplots(
+ 2, 1, figsize=(13, 10),
+ gridspec_kw = {'height_ratios':[3, 1], 'width_ratios': [1, 1]}
+ )
+ else:
+ fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 10))
+
+ # draw the fixed data in the first subplot
+ ax1.imshow(image1, cmap="hot")
+ ax1.set_title(image1_title)
+ ax1.axis('off')
+
+ # draw the moving data in the second subplot
+ ax2.imshow(image2, cmap="hot")
+ ax2.set_title(image2_title)
+ ax2.axis('off')
+
+ plt.show()
+
+
+def display_2d_image(image):
+ """
+ A function that can be used to display two SimpleITK images side by side.
+ It is also possible to select paired landmarks from the two images, by
+ enabling the landmarks argument.
+
+ Parameters
+
+ data a Numpy array or a SimpleITk data object
+
+ """
+
+ if isinstance(image, sitk.Image):
+ image = sitk.GetArrayFromImage(image)
+
+ assert image.ndim == 2
+
+ plt.imshow(image, cmap="rainbow")
+ plt.axis('off')
+ plt.show()
+
+
+def display_2d_slices_with_alpha(alpha, fixed, moving):
+ img = (1.0 - alpha) * fixed + alpha * moving
+ plt.imshow(sitk.GetArrayFromImage(img), cmap='gray')
+ plt.axis('off')
+
+
+def display_2d_image_overlay(image1, image2, image3=None):
+ '''
+ Overlays 2-3 images into a single RGB plot. This was intended for use in
+ evaluating registration results.
+ Parameters
+ ----------
+ image1 A 2D numpy.array or sitk.Image that
+ image2 A 2D numpy.array or sitk.Image that
+ image3 A 2D numpy.array or sitk.Image that
+
+ Returns Nothing
+ -------
+
+ '''
+ if isinstance(image1, sitk.Image):
+ image1 = sitk.GetArrayFromImage(image1)
+ if isinstance(image2, sitk.Image):
+ image2 = sitk.GetArrayFromImage(image2)
+
+ if image1.shape != image2.shape:
+ raise ValueError("The dimensions of the images to be overlaid should match")
+
+ if image3 is None:
+ image3 = np.zeros(image1.shape, dtype=np.uint8)
+
+ rgb_image = np.concatenate([aux[..., np.newaxis] for aux in (image1, image2, image3)], axis=-1)
+
+ plt.imshow(rgb_image)
+ plt.axis('off')
+ plt.show()
+
+
+def show_pics_from_disk(filenames, title="Image collage"):
+ """
+ A utility for creating a collage of images, to be shown
+ in a single plot. The images are loaded from disk according
+ to the provided filenames:
+ :param filenames: A list containing the data filenames
+ :param title: Name of the plot
+ :return: Nothing
+ """
+ if len(filenames) > 1:
+ if 4 < len(filenames) <= 9:
+ fig, subplots = plt.subplots(3, 3)
+ elif 9 < len(filenames) <= 16:
+ fig, subplots = plt.subplots(4, 4)
+ elif 16 < len(filenames) <= 25:
+ fig, subplots = plt.subplots(5, 5)
+ elif 25 < len(filenames) <= 36:
+ fig, subplots = plt.subplots(6, 6)
+ else:
+ fig, subplots = plt.subplots(2, 2)
+
+ # fig.title(title)
+ i = 0
+ j = 0
+ k = 0
+ while k < len(filenames):
+ j = 0
+ while j < subplots.shape[1] and k < len(filenames):
+ print(filenames[i + j])
+ subplots[i, j].imshow(plt.imread(filenames[k]), cmap='hot')
+ subplots[i, j].set_title(os.path.basename(filenames[k]))
+ subplots[i, j].axis("off")
+ k += 1
+ j += 1
+ i += 1
+ plt.subplots_adjust(wspace=-0.5, hspace=0.2)
+ plt.suptitle(title, size=16)
+ plt.show()
+
+ else:
+ plt.imshow(plt.imread(filenames))
+ plt.axis("off")
+ plt.show()
+
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/plots/scatter.py b/Addons/FRCmetric/miplib-public/miplib/ui/plots/scatter.py
new file mode 100644
index 0000000000000000000000000000000000000000..9562fdefd90be5887db95c5e1b3e055f01c8abd6
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/plots/scatter.py
@@ -0,0 +1,22 @@
+import matplotlib.pyplot as plt
+#plt.style.use("seaborn-colorblind")
+plt.style.use("seaborn-v0_8")
+
+def xy_scatter_plot_with_labels(x, y, labels, size=(3,3),
+ x_title=r"X-offset ($\mathrm{\mu m}$)",
+ y_title=r"Y-offset ($\mathrm{\mu m}$)"):
+
+ assert len(x) == len(y) == len(labels)
+
+ fig, ax = plt.subplots(figsize=size)
+ ax.scatter(x, y)
+
+ if x_title is not None:
+ ax.set_xlabel(x_title)
+ if y_title is not None:
+ ax.set_ylabel(y_title)
+
+ for i, txt in enumerate(labels):
+ ax.annotate(txt, (x[i], y[i]))
+
+ return fig
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/plots/stats.py b/Addons/FRCmetric/miplib-public/miplib/ui/plots/stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..121ec4474e8703bea9f2f97214f54747f1b32cb3
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/plots/stats.py
@@ -0,0 +1,23 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def plot_histogram(data, bins=50, figsize=(2,2)):
+ """
+ Calculate histogram for data
+
+ :param data: some numpy.ndarray related datatype
+ :param figsize: size of the plot (x,y)
+ :param bins: the number of histogram bins
+ :return: returns the Figure
+ """
+ assert issubclass(np.ndarray, data)
+
+ fig, ax = plt.subplots(1,1, figsize=figsize)
+
+ hist, bins = np.histogram(data.astype(np.uint16), bins=bins)
+ width = 0.7 * (bins[1] - bins[0])
+ center = (bins[:-1] + bins[1:]) / 2
+ ax.bar(center, hist, align='center', width=width)
+
+ return fig
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/plots/utils.py b/Addons/FRCmetric/miplib-public/miplib/ui/plots/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a157c49485109939c36acd03ee2a3492dc3bca6b
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/plots/utils.py
@@ -0,0 +1,14 @@
+from matplotlib import pyplot as plt
+
+def save_figure(figure, path, dpi=1200):
+ """
+ A really simple utility to save a figure to file.
+ :param figure: a matplotlib.pyplot.Figure instance
+ :param path: a full path to the file. Make sure that the directory exists. The file type
+ will be decided according to the filename suffix.
+ :param dpi: dpi value for the plot
+ :return: nothing
+ """
+ assert isinstance(figure, plt.Figure)
+
+ figure.savefig(path, dpi=dpi, bbox_inches='tight', pad_inches=0, transparent=True)
diff --git a/Addons/FRCmetric/miplib-public/miplib/ui/utils.py b/Addons/FRCmetric/miplib-public/miplib/ui/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6fee324c93b76e60e34ecb7e3c628021c80a8db
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/ui/utils.py
@@ -0,0 +1,79 @@
+"""
+Various utilities that are used to convert command line parameters into
+data types that the progrma understands.
+"""
+import os
+
+file_extensions = ['.tif', '.lsm', 'tiff', '.raw', '.data']
+
+
+def get_user_input(message):
+ """
+ A method to ask question. The answer needs to be yes or no.
+
+ Parameters
+ ----------
+ :param message string, the question
+
+ Returns
+ -------
+
+ Return a boolean: True for Yes, False for No
+ """
+ while True:
+ answer = input(message)
+ if answer in ('y', 'Y', 'yes', 'YES'):
+ return True
+ elif answer in ('n', 'N', 'no', 'No'):
+ return False
+ else:
+ print("Unkown command. Please state yes or no")
+
+
+def get_path_dir(path, suffix):
+ """ Return a directory name with suffix that will be used to save data
+ related to given path.
+ """
+ if os.path.isfile(path):
+ path_dir = path + '.' + suffix
+ elif os.path.isdir(path):
+ path_dir = os.path.join(path, suffix)
+ elif os.path.exists(path):
+ raise ValueError('Not a file or directory: %r' % path)
+ else:
+ base, ext = os.path.splitext(path)
+ if ext in file_extensions:
+ path_dir = path + '.' + suffix
+ else:
+ path_dir = os.path.join(path, suffix)
+ return path_dir
+
+
+def get_full_path(path, prefix):
+ """
+ :param path: Path to a file (string)
+ :param prefix: Path prefix, if applicable. Used in cases in
+ which the path argument is not an absolute
+ path
+ :return: Returns the absolute path, if the file is found,
+ None otherwise
+ """
+ if not os.path.isfile(path):
+ path = os.path.join(prefix, path)
+ if not os.path.isfile(path):
+ raise ValueError('Not a valid file %s' % path)
+ return path
+
+
+def get_filename_and_extension(path):
+ """
+ Returns a filename and the file extension. The filename cna
+ be either a simlpe filename or a full path.
+
+ :param path:
+ :return:
+ """
+ filename = path.split('.')[:-1]
+ extension = path.split('.')[-1]
+
+ return filename, extension
diff --git a/Addons/FRCmetric/miplib-public/miplib/utils/__init__.py b/Addons/FRCmetric/miplib-public/miplib/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Addons/FRCmetric/miplib-public/miplib/utils/generic.py b/Addons/FRCmetric/miplib-public/miplib/utils/generic.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2108dfbc268c81879e3ff11f383c4fba376c525
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/utils/generic.py
@@ -0,0 +1,13 @@
+def isiterable(something):
+ """ Check if a variable is iterable
+
+ :param something: some variable that you are interested in
+ :type something: any
+ :return: True/False
+ :rtype: boolean
+ """
+ try:
+ iter(something)
+ return True
+ except TypeError:
+ return False
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/miplib/utils/numeric.py b/Addons/FRCmetric/miplib-public/miplib/utils/numeric.py
new file mode 100644
index 0000000000000000000000000000000000000000..3afaa4d3a2e1926d413fc70ace44a1a311b7f72d
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/utils/numeric.py
@@ -0,0 +1,9 @@
+import numpy as np
+
+
+def find_next_power_of_2(number):
+ """ A simple utility to find the closest power of two
+ :arg number: a non-zero numeric value
+ """
+ power = np.ceil(np.log2(number))
+ return 2**power
diff --git a/Addons/FRCmetric/miplib-public/miplib/utils/string.py b/Addons/FRCmetric/miplib-public/miplib/utils/string.py
new file mode 100644
index 0000000000000000000000000000000000000000..8196dca10141dd62e48d1335d006e5c4cf6c2b83
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/miplib/utils/string.py
@@ -0,0 +1,43 @@
+def common_start(sa, sb):
+ """ returns the longest common substring from the beginning of sa and sb """
+ def _iter():
+ for a, b in zip(sa, sb):
+ if a == b:
+ yield a
+ else:
+ return
+
+ return ''.join(_iter())
+
+def common_string(strings):
+ """
+ Find the longest common string.
+
+ :param strings:
+ :return:
+ """
+ prefix1 = strings[0]
+ prefix2 = strings[1]
+
+ if prefix1.find('/') != -1:
+ prefix1 = prefix1.split('/')
+ prefix1 = prefix1[len(prefix1) - 1]
+
+ if prefix2.find('/') != -1:
+ prefix2 = prefix2.split('/')
+ prefix2 = prefix2[len(prefix2) - 1]
+
+ strings = [prefix1, prefix2]
+ prefix = prefix1
+
+ for s in strings:
+ if len(s) < len(prefix):
+ prefix = prefix[:len(s)]
+ if not prefix:
+ return ''
+ for i in range(len(prefix)):
+ if prefix[i] != s[i]:
+ prefix = prefix[:i]
+ break
+
+ return prefix
diff --git a/Addons/FRCmetric/miplib-public/notebooks/FRCcode.ipynb b/Addons/FRCmetric/miplib-public/notebooks/FRCcode.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9a1d8111dafec978bc0bcc1e844af7b65cfaccc8
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/notebooks/FRCcode.ipynb
@@ -0,0 +1,342 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "V31NttDshBfL"
+ },
+ "source": [
+ "# Deconvolution with PSF estimated from FRC\n",
+ "\n",
+ "Notebook adapted from the original version to compute FRC on ULM images."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#!git clone https://github.com/sakoho81/miplib"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "S7uF2fnChDfM",
+ "outputId": "48bea80c-8f91-451b-b626-8926cc444c38"
+ },
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Cloning into 'miplib'...\n",
+ "remote: Enumerating objects: 2107, done.\u001b[K\n",
+ "remote: Counting objects: 100% (159/159), done.\u001b[K\n",
+ "remote: Compressing objects: 100% (101/101), done.\u001b[K\n",
+ "remote: Total 2107 (delta 80), reused 111 (delta 58), pack-reused 1948 (from 1)\u001b[K\n",
+ "Receiving objects: 100% (2107/2107), 80.93 MiB | 15.46 MiB/s, done.\n",
+ "Resolving deltas: 100% (1368/1368), done.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "cd /content/miplib"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ZLzgi9Lchb9Z",
+ "outputId": "df073414-645c-47ca-9079-770436ea21b3"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "/content/miplib\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install -r requirements.txt"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ie1hoFQthSO1",
+ "outputId": "594b315a-c351-4906-ee56-f38f388bf361"
+ },
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: scikit-image in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 1)) (0.25.2)\n",
+ "Collecting pims (from -r requirements.txt (line 2))\n",
+ " Downloading pims-0.7.tar.gz (87 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.8/87.8 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 3)) (2.2.2)\n",
+ "Requirement already satisfied: h5py in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 4)) (3.12.1)\n",
+ "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 5)) (3.10.0)\n",
+ "Requirement already satisfied: numba in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 6)) (0.61.0)\n",
+ "Collecting SimpleITK (from -r requirements.txt (line 7))\n",
+ " Downloading SimpleITK-2.4.1-cp311-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.9 kB)\n",
+ "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 8)) (1.13.1)\n",
+ "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 9)) (1.26.4)\n",
+ "Collecting jpype1 (from -r requirements.txt (line 10))\n",
+ " Downloading jpype1-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB)\n",
+ "Collecting psf (from -r requirements.txt (line 11))\n",
+ " Downloading psf-2025.1.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.0 kB)\n",
+ "Requirement already satisfied: networkx>=3.0 in /usr/local/lib/python3.11/dist-packages (from scikit-image->-r requirements.txt (line 1)) (3.4.2)\n",
+ "Requirement already satisfied: pillow>=10.1 in /usr/local/lib/python3.11/dist-packages (from scikit-image->-r requirements.txt (line 1)) (11.1.0)\n",
+ "Requirement already satisfied: imageio!=2.35.0,>=2.33 in /usr/local/lib/python3.11/dist-packages (from scikit-image->-r requirements.txt (line 1)) (2.37.0)\n",
+ "Requirement already satisfied: tifffile>=2022.8.12 in /usr/local/lib/python3.11/dist-packages (from scikit-image->-r requirements.txt (line 1)) (2025.2.18)\n",
+ "Requirement already satisfied: packaging>=21 in /usr/local/lib/python3.11/dist-packages (from scikit-image->-r requirements.txt (line 1)) (24.2)\n",
+ "Requirement already satisfied: lazy-loader>=0.4 in /usr/local/lib/python3.11/dist-packages (from scikit-image->-r requirements.txt (line 1)) (0.4)\n",
+ "Collecting slicerator>=0.9.8 (from pims->-r requirements.txt (line 2))\n",
+ " Downloading slicerator-1.1.0-py3-none-any.whl.metadata (1.9 kB)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->-r requirements.txt (line 3)) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->-r requirements.txt (line 3)) (2025.1)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->-r requirements.txt (line 3)) (2025.1)\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->-r requirements.txt (line 5)) (1.3.1)\n",
+ "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->-r requirements.txt (line 5)) (0.12.1)\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->-r requirements.txt (line 5)) (4.56.0)\n",
+ "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->-r requirements.txt (line 5)) (1.4.8)\n",
+ "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->-r requirements.txt (line 5)) (3.2.1)\n",
+ "Requirement already satisfied: llvmlite<0.45,>=0.44.0dev0 in /usr/local/lib/python3.11/dist-packages (from numba->-r requirements.txt (line 6)) (0.44.0)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->-r requirements.txt (line 3)) (1.17.0)\n",
+ "Downloading SimpleITK-2.4.1-cp311-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (52.3 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.3/52.3 MB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading jpype1-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (494 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m494.1/494.1 kB\u001b[0m \u001b[31m38.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading psf-2025.1.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (64 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.8/64.8 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading slicerator-1.1.0-py3-none-any.whl (10 kB)\n",
+ "Building wheels for collected packages: pims\n",
+ " Building wheel for pims (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for pims: filename=PIMS-0.7-py3-none-any.whl size=84591 sha256=5dd1f5684fe8ca3912e24a69da20f72b4fbefebc2723338ca6777b61af82e9d0\n",
+ " Stored in directory: /root/.cache/pip/wheels/19/dc/d2/e872d34a5e460ff64d2f916938044498fc123855a68318b9d5\n",
+ "Successfully built pims\n",
+ "Installing collected packages: slicerator, SimpleITK, psf, jpype1, pims\n",
+ "Successfully installed SimpleITK-2.4.1 jpype1-1.5.2 pims-0.7 psf-2025.1.1 slicerator-1.1.0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "lGUJ_Ki8hBfM"
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "\n",
+ "import os\n",
+ "import numpy as np\n",
+ "import pandas\n",
+ "\n",
+ "import miplib.ui.plots.image as showim\n",
+ "#from miplib.psf import psfgen\n",
+ "#from miplib.processing.deconvolution import deconvolve\n",
+ "from miplib.data.messages import image_writer_wrappers as imwrap\n",
+ "import miplib.data.io.read as imread\n",
+ "import miplib.processing.image as imops\n",
+ "from miplib.data.containers.image import Image\n",
+ "\n",
+ "import miplib.analysis.resolution.fourier_ring_correlation as frc\n",
+ "from miplib.data.containers.fourier_correlation_data import FourierCorrelationDataCollection\n",
+ "\n",
+ "import miplib.ui.plots.frc as frcplots\n",
+ "\n",
+ "import miplib.ui.cli.miplib_entry_point_options as options\n",
+ "import urllib.request as dl\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nd3zKZ9shBfN"
+ },
+ "source": [
+ "## Setup deconvolution\n",
+ "\n",
+ "Setup deconvolution parameters. Most of the times the default values should be fine, but you can of course change anything you like."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "jbwyxHAbhBfN",
+ "outputId": "765e8515-a094-44bb-f82d-b247cca671f1"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Namespace(image='image', psf='psf', verbose=False, working_directory='/home/sami/Data', show_plots=False, show_image=False, scale=100, channel=0, jupyter=False, test_drive=False, evaluate_results=False, temp_dir=None, carma_gate_idx=0, carma_det_idx=0, plot_size=(2.5, 2.5), save_plots=False, enhance_contrast_on_save=False, max_nof_iterations=50, update_blind_psf=0, convergence_epsilon=0.05, wiener_nsr=100.0, first_estimate='image', estimate_constant=1.0, save_intermediate_results=False, output_cast=False, num_blocks=1, stop_tau=0.0001, tv_lambda=0.0, block_pad=0, memmap_estimates=False, disable_tau1=False, disable_fft_psf_memmap=False, rl_background=0.0, rl_auto_background=False, rl_frc_stop=0.0, psf_type=<PsfType.GAUSSIAN|CONFOCAL: 132>, psf_shape=(256, 256), psf_size=(4.0, 4.0), ex_wl=488, em_wl=550, na=1.4, refractive_index=1.414, magnification=1.0, pinhole_radius=None, d_bin=1, resol_square=False, frc_curve_fit_degree=8, resolution_threshold_curve_fit_degree=3, resolution_threshold_criterion='fixed', resolution_threshold_value=0.14285714285714285, resolution_point_sigma=0.01, resolution_snr_value=0.25, d_angle=20, d_extract_angle=5.0, hollow_iterator=False, min_filter=False, disable_hamming=False, frc_curve_fit_type='smooth-spline')\n"
+ ]
+ }
+ ],
+ "source": [
+ "n_iterations = 50\n",
+ "args_list = (\"image psf\"\n",
+ " \" --max-nof-iterations={} --first-estimate=image \"\n",
+ " \" --blocks=1 --pad=0 --resolution-threshold-criterion=fixed \"\n",
+ " \" --tv-lambda=0 --bin-delta=1 --frc-curve-fit-type=smooth-spline\").format(n_iterations).split()\n",
+ "\n",
+ "args = options.get_deconvolve_script_options(args_list)\n",
+ "\n",
+ "print (args)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZWYEb6T5hBfO"
+ },
+ "source": [
+ "## Load ULM image\n",
+ "\n",
+ "The image is in tif format in order to not alter the software code too much.\n",
+ "The image should be crated using the associate Matlab code."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "id": "TheJKkYShBfO"
+ },
+ "outputs": [],
+ "source": [
+ "# Image\n",
+ "data_dir = os.getcwd()\n",
+ "\n",
+ "filename = \"SRPCA_MatOutTours10.tif\"\n",
+ "filename = \"RSULM_MatOutTours10.tif\"\n",
+ "filename = \"SRPCA_MatOutPala10.tif\"\n",
+ "filename = \"RSULM_MatOutPala10.tif\"\n",
+ "full_path = os.path.join(data_dir, filename)\n",
+ "image = imread.get_image(full_path, channel=0)\n",
+ "\n",
+ "#Size of the pixels in the super-resolved image in micrometer\n",
+ "#spacing = [6.25, 5.010] # WKY Tours data\n",
+ "spacing= [5.0, 5.07310] # Pala supplementary data\n",
+ "image = Image(image - image.min(), spacing)\n",
+ "\n",
+ "lambdaULM = 98.56 # micrometer\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jzoWweO9hBfO"
+ },
+ "source": [
+ "## Calculate resolution\n",
+ "\n",
+ "Here I estimate the resolution of the image with the single-image FRC method."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 162
+ },
+ "id": "KOV2XZ1RhBfO",
+ "outputId": "2b47060b-d421-4741-b5a8-1061e1e8eee4"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 500x400 with 1 Axes>"
+ ],
+ "image/png": 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+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "import miplib.analysis.resolution.fourier_ring_correlation as frc\n",
+ "frc_results = FourierCorrelationDataCollection()\n",
+ "\n",
+ "frc_results[0] = frc.calculate_single_image_frc(image, args)\n",
+ "plotter = frcplots.FourierDataPlotter(frc_results)\n",
+ "plotter.plot_one(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print(frc_results[0].resolution)\n",
+ "fwhm =frc_results[0].resolution['resolution']\n",
+ "#print(fwhm)\n",
+ "print(fwhm/lambdaULM)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "slJEWFitqOge",
+ "outputId": "04019534-a72c-457a-b785-200f0d31bc2f"
+ },
+ "execution_count": 29,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "<miplib.data.core.dictionary.FixedDictionary object at 0x7fad4e93cb50>\n",
+ "0.2613008373159072\n"
+ ]
+ }
+ ]
+ }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ },
+ "orig_nbformat": 2,
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "npconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": 3,
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ }
+}
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/notebooks/Notebooks/FRC Based RL deconvolution.ipynb b/Addons/FRCmetric/miplib-public/notebooks/Notebooks/FRC Based RL deconvolution.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6164138da3faf3fa7480add0915b4ffeba527fdd
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/notebooks/Notebooks/FRC Based RL deconvolution.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Deconvolution with PSF estimated from FRC \n","\n","In this example I run FRC based blind Richardson Lucy deconvolution on a single 2D confocal microscope image. The image can be downloaded from [here](https://doi.org/10.6084/m9.figshare.8158928.v1). "]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":"%matplotlib inline\n\nimport os\nimport numpy as np\nimport pandas\n\nimport miplib.ui.plots.image as showim\nfrom miplib.psf import psfgen\nfrom miplib.processing.deconvolution import deconvolve\nfrom miplib.data.messages import image_writer_wrappers as imwrap\nimport miplib.data.io.read as imread\nimport miplib.processing.image as imops\nfrom miplib.data.containers.image import Image\n\nimport miplib.analysis.resolution.fourier_ring_correlation as frc\nfrom miplib.data.containers.fourier_correlation_data import FourierCorrelationDataCollection\n\nimport miplib.ui.plots.frc as frcplots\n\nimport miplib.ui.cli.miplib_entry_point_options as options\nimport urllib.request as dl\n"},{"cell_type":"markdown","metadata":{},"source":["## Setup deconvolution\n","\n","Setup deconvolution parameters. Most of the times the default values should be fine, but you can of course change anything you like. "]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"Namespace(block_pad=0, carma_det_idx=0, carma_gate_idx=0, channel=0, convergence_epsilon=0.05, d_angle=20, d_bin=1, d_extract_angle=5.0, disable_fft_psf_memmap=False, disable_hamming=False, disable_tau1=False, em_wl=550, estimate_constant=1.0, evaluate_results=False, ex_wl=488, first_estimate='image', frc_curve_fit_degree=8, frc_curve_fit_type='smooth-spline', hollow_iterator=False, image='image', jupyter=False, magnification=1.0, max_nof_iterations=50, memmap_estimates=False, min_filter=False, na=1.4, num_blocks=1, output_cast=False, pinhole_radius=None, plot_size=(2.5, 2.5), psf='psf', psf_shape=(256, 256), psf_size=(4.0, 4.0), psf_type=132, refractive_index=1.414, resol_square=False, resolution_point_sigma=0.01, resolution_snr_value=0.25, resolution_threshold_criterion='fixed', resolution_threshold_curve_fit_degree=3, resolution_threshold_value=0.14285714285714285, rl_auto_background=False, rl_background=0.0, save_intermediate_results=False, save_plots=False, scale=100, show_image=False, show_plots=False, stop_tau=0.0001, temp_dir=None, test_drive=False, tv_lambda=0.0, update_blind_psf=0, verbose=False, wiener_nsr=100.0, working_directory='/home/sami/Data')\n"}],"source":"n_iterations = 50\nargs_list = (\"image psf\" \n \" --max-nof-iterations={} --first-estimate=image \" \n \" --blocks=1 --pad=0 --resolution-threshold-criterion=fixed \"\n \" --tv-lambda=0 --bin-delta=1 --frc-curve-fit-type=smooth-spline\").format(n_iterations).split()\n \nargs = options.get_deconvolve_script_options(args_list)\n\nprint (args)"},{"cell_type":"markdown","metadata":{},"source":["## Load an image\n","\n","The image is from a Nikon A1 confocal system, of a Tubulin stained HeLa cell. There is a zero offset in the image, which I correct here, to make the devonvolution behave nicely. "]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"The image dimensions are (1389, 1389) and spacing [0.028800001013760037, 0.028800001013760037] um.\n"}],"source":"# Image\ndata_dir = os.getcwd()\nfilename = \"Vimentin_029nm.tif\"\nfull_path = os.path.join(data_dir, filename)\n\n# Automatically dowload the file from figshare, if necessary.\nif not os.path.exists(full_path):\n dl.urlretrieve(\"https://ndownloader.figshare.com/files/15202565\", full_path)\n\nimage = imread.get_image(full_path, channel=0)\n\nspacing = image.spacing\nprint (\"The image dimensions are {} and spacing {} um.\".format(image.shape, image.spacing))\n\nimage = Image(image - image.min(), image.spacing)\nimage_copy = Image(image.copy(), image.spacing)\n\n"},{"cell_type":"markdown","metadata":{},"source":["## Calculate resolution\n","\n","Here I estimate the resolution of the image with the single-image FRC method. "]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZcAAAExCAYAAACwI+/+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dd3xc1Zn/8c93NOrFHXA3xQkB02VK8DoEUoAlJNmUDakk2bD7y5LeN7spbHY3v2Q3ZdNJsiEk2dRfCkkIpEGwwRSDwWBssDC2mrvVpZnRzDy/P+4daTQayTIaaVSe9+s1L8+t89zx6D73nHPvOTIznHPOuUKKFDsA55xzM48nF+eccwXnycU551zBeXJxzjlXcJ5cnHPOFZwnF+eccwXnycU5NyEk3Snp74odhysOTy7OFZik3ZL6JHVnvZZIWiXJsubtlvThPNu/VtLmcJ29kn4nad0ziCPzedHCHNnkkXS2pAcl9Yb/nl3smNyx8eTi3MR4iZnVZL1as5bNNbMa4JXAv0h6YWaBpPcCXwD+HTgeWAF8FXjpJMZeVJLKgF8B3wfmAd8FfhXOd9OEJxfnisTMNgPbgLMBJM0BbgD+0cx+bmY9ZtZvZr82sw/k24ekv5a0RVKnpCZJn8hafFf4b3tYCrooz/bnS9okqT0sJX05+yQelnz+QdJOSW2SviJJ4bISSf8l6ZCkpyVdP1pJSdJbJG0P93O7pJUjfDWXAFHgC2YWN7P/BgRcOuKX6aYcTy7OFYmkC4E1QEM46yKgAvjFMeymB3gjMBf4a+D/SHpZuGx9+O/csPS0Kc/2KeA9wMLw8y8D3p6zzlXAWuAs4NXAi8P5bwOuIEiO5wIvYwRhTP8E/A2wCNgA/HCE1U8HttrQvqm2hvPdNOHJxbmJ8cuwNNAu6Zc5yw5J6gM2EVR5ZZYvAA6ZWXKsH2Jmd5rZo2aWNrOtBCfs5x3D9g+a2b1mljSz3cA38mz/aTNrN7NG4A7CkhZBovmimTWbWRvw6VE+6u+B/zCz7eHx/Ttw9gillxqgI2deB1A71uNyxefJxbmJ8TIzmxu+cq/oFxKcQN9PUAVUGs4/DCw8lgZ4SRdIukPSQUkdwD+E+x/r9s+S9BtJ+yR1Epz0c7ffl/W+N4wdYAnQlLUs+32ulcAXMwkXOEJQ1bU0z7rdQF3OvDqga9SDcVOKJxfnisDMUmb2X0CMwWqoTeH0iNVLefwvcAuw3MzmAF8nOGkDjKXL868BO4DVZlZHUHWl0TcZsBdYljW9fJR1m4C/z0q4c82s0szuybPuNuDMTNtO6MxwvpsmPLk4V1yfBj4oqcLMOoCPAV+R9DJJVZJKJV0h6TMjbF8LHDGzmKTzgddmLTsIpIGTRvn8WqAT6JZ0KvB/jiH2nwDvkrRU0lzgQ6Os+3XgI5JOh+DmBUmvGmHdOwnagt4pqVzS9eH8Px9DbK7IPLk4V1y/BdoIGscxs88B7wX+mSA5NAHXM9guk+vtwA2SuggS008yC8ysF/g34O6wOurCPNu/nyAhdQHfBH58DLF/E/g9QWP7FuBWIEmQGIYws18A/xf4UVj99hjBzQDDmFmCoPT2RqAdeAtBNWPiGGJzRSYfLMw5VwiSrgC+bmYj3WLsZhEvuTjnnhFJlZKulBSVtBT4OMd2G7WbwTy5FJGk/5F0QNJjIyx/naSt4eseSWdNdozOjULAJwmq9bYA2wmq5pzzarFikrSe4LbLm81sTZ7lzwW2m1lbWOXwCTO7YLLjdM65YzXtOrSbSczsLkmrRlmefZvmvQy97dM556YsrxabPt4K/K7YQTjn3Fh4yWUakPR8guQyYrfrkq4DrgOorq4+79RTT52k6Jxzs8WDDz54yMwWjWVdTy5TnKQzgW8BV5jZ4ZHWM7MbgRsB6uvrbfPmzZMUoXNutpC0Z6zrerXYFCZpBfBz4A1m9mSx43HOubHykksRSfohQceFCyU1EzwnUApgZl8nuK1zAfDVsJulpJnVFyda55wbO08uRWRm1xxl+d8BPga5c27a8Wox55xzBefJxTnnXMF5cnHOOVdwnlycc84VnCcX55xzBefJxTnnXMF5cnHOOVdwnlycc84VnCcX55xzBefJxTnnXMF5cnHOOVdwnlycc84VnCcX55xzBefJxTnnXMF5cnHOOVdwnlycc84VnCcX55xzBefJxTnnXMF5cnHOOVdwnlycc84VnCcX55xzBefJxTnnXMF5cnG09yaLHYJzbobx5DLL3flkBx/8xW62tvQUOxTn3AziyWUW+8P2Nm6+7wDJtPGNDfvY25EodkjOuRnCk8ssdsGqWuZXRQHo60/zpTtb6U2kihyVc24m8OQyi9VVRnnHJYspKxEA+zr7+caGfSTTVuTInHPTnSeXIpL0P5IOSHpshOWS9N+SGiRtlXRuoWNYuaCCay86fmD60dZevrlxHylPMM65cYgWO4BZ7ibgy8DNIyy/Algdvi4Avhb+WzCxWIxU6yOcNaeKRzpqAXhgTzdHDm3lnMoWaqqriEajnHdePeXlFYX8aOfcDObJpYjM7C5Jq0ZZ5aXAzWZmwL2S5kpabGZ7CxVDQ8NOOjraOaWknZ6K42iIzQPgqZ4quhOLuTC1l9JImrvu+gvl5eX09vZSVVUFQG9vLxUVFcRiMWpqali79nxPQM45wJPLVLcUaMqabg7nFSS5xGIxmpoaAZDgrKoDpEw8HZ8LwP7+Gu7sXMGFtS3U0k9/fz8A3d3dA/vo7e0FoLOzk7vu+gupVIqqqioikQiRSMRLPM7NUp5cpjblmZe3MUTSdcB1ACtWrBjTzhsadhIUijL7gHOr91MeSbGjbwEAHaly/ti+ijOqDnJyRTvKF1EoX/LJLfF4FZtzs4Mnl6mtGVieNb0MaM23opndCNwIUF9ff9TW+OxSSzYJ1lQdoiaS4KGe40kTIUWEh3uP5+n4HM6oOsjxpb2jJpls/f3DSzyZhNPX18dFFz2Xuro5Y9uZc27a8OQytd0CXC/pRwQN+R2Fam/JLbXkWlXRydxonAe6T6AjFZQyOlIVbOxaztySGCdVtLOsrIuySPqYPzs74dx990aqq6vp7e31dhvnZhBPLkUk6YfAJcBCSc3Ax4FSADP7OnArcCXQAPQCby7UZ7e3tx11nbnROJfOaWRH33ye7JtPKrxzvT1VwUM9J7Cl53jmR/tYVNrHnJI4tSUJKiNJypQaKNmYQRrRbxGSFiGRLiFuJfRbhHi6hISVkOguIZGuJdFewi8anySpUkrLyimLRigtEdVlJSyojrKotpRVCypYtaCcugr/6To3lWm0q1c3PdXX19vmzZvHtY+NG++is7NzYLovXcL23gXsjs8hPabHo4wIhiEsb9PR+Jy4oJxzltdw0Um1LKguLfj+nXPDSXrQzOrHtK4nl5mnEMllJD3xFPfs6uTep7vYfTie/+6CSSTBuctruOL0eZy00KvTnJtInlxmuYlMLtk6Y0m27+2jsS1OU1ucwz1JDnfFSKSHlmwipIkqTWn4KoukKFOKcqUojaQoz5pXphRlkaACLmUihYinS+hNl9KZKqc9VcmRZAX5OhC4cFUtrzh3gZdknJsgx5JcvOLaPWN1FVEuOLGWC06sHTLfzEgbpNKGBPdt2jikim28EukI+/qr2R2fw4H+6oH59+7uYktzN69du4h1J9ehsd7S5pwrOC+5zECTVXJ5pnLbc8YjFq1jW/d8nu4pHzL//JU1XHvR8VSUevd5zhWKl1zclLZu3foh07fddivp9LHf0gxQkezkvIpOVkareDy1igPdwZAB9+/pZn9XP+++dAlzKv1n7txk88s6V3SXX34lV155FXV1dc94HwujvbxgbiPrTxncx54jcf7ttib2d/ogaM5NNq8Wm4GmerXYWDzTqrPa2lriC07nhw+1kflpL6iO8pEXL2O+N/Q7Ny5+t9gsNxOSS7ZnkmiORBbyl0PzSVnQqL94ThkfftEyaitKJiJE52aFY0kuXi3mprx169YTiRzbT3V++hAX1rQQCZ/E2duR4Et3tpJM+cWUc5PBWzrdtHD55VcOvH/ssUdpbNxz1G0Wl/WwtmYv93cvwYCGgzF+/OBBXnf+cRMYqXMOvOTipqGx9IuWsby8izVVBwam//REB5t2Fe6ZG+dcfl5ycdNO9q3MYynFPKuijSPJSloSwcOe3733ACcvquC42rIJjdO52cxLLm5aG0spRoL6mr3UlsQBSKSMb9+9j7TfzOLchPHk4qa1devWc+WVVx21wb9Uxvk1e1HYwL/zYJzbHzs0GSE6Nyt5cnEzwlgexJwXjfPsyiMD0794pI2DXf2TEZ5zs44nFzejrFu3ftQE85zKw9SF1WNJEz/avH+yQnNuVvHk4mac0RJMiYzzqvcNTG9p7mNrU/tkhebcrOHJxc1ImbaYfElmQWmMFWUdA9M3bWxm490bicdjkxmiczOaJxc3o61bt54VK1YOm39G1SFKCHpibk+W8/B+Y+fOnZMdnnMzlicXN+Plu125siTJqZWHB6Z39C1gT1Ozl16cKxBPLm7GG6kNZnVlG+VKAtCbLmVXXy07dmxn06Z7PMk4N06eXNyskGmDya4ii8qG3Jq8o28+jc2ttLUd8Soy58bJk4ubVXKryE6qaB8ovfSlS3k6NgeA5uYmL704Nw6eXNyskltFFpUNaXt5MjaftEE6nfbSi3Pj4MnFzTqZKrJLL30BkUiEkyo6KMtqe8l0cOmlF+eeOU8ubtZqaNhJOp2mRMYpFYMPUj7RNx/z0otz4+LJxc1a2e0vJ1e0E8k895Kq4GCyctg6zrmx8+TiZq3s9pfySIpV5YODiD3ZNx+AM888qyixOTfdeXIpMkmXS3pCUoOkD+dZvkLSHZK2SNoq6cp8+3HPTPYtys+qaoOwS/59/dV0p0p5+OEtxQ3QuWnKk0sRSSoBvgJcAZwGXCPptJzV/hn4iZmdA7wG+OrkRjk7tLe3URNJcEJpTzhH7IrNobu7m9bWFn+w0rlj5MmluM4HGsxsl5klgB8BL81Zx4DMvbNzgNZJjG/WyJRgXn3x6oF5u+NzSJl45JGH/cFK546RJ5fiWgo0ZU03h/OyfQJ4vaRm4FbgHfl2JOk6SZslbT548OBExDorrKxNUhUJBhBLWJSWRA0WDofstyY7N3aeXIpLeeblDux+DXCTmS0DrgS+J2nY/5uZ3Whm9WZWv2jRogkIdXZ4dOvDnFg+eFvyU7G5A+/T6TQbN27wBOPcGHhyKa5mYHnW9DKGV3u9FfgJgJltAiqAhZMS3SwTi8Xo7u7mxIoOFOb4w8kqOpJlA+vE43E2bLjLE4xzR+HJpbgeAFZLOlFSGUGD/S056zQClwFIeg5BcvF6rwnQ0LATSVREUiwt6xqYn116AUgkEmzb9thkh+fctOLJpYjMLAlcD9wObCe4K2ybpBskXR2u9j7gbZIeAX4IXGuZRgBXUO3tbQPtKydlPbHflKgjZUNrMPft2+elF+dGIT9PzTz19fW2efPmYocxrT366KPcuDVCTzqoErugppXl5V1D1lm0aBFr115QjPCcKwpJD5pZ/VjW9ZKLc3l0dLSxqrxjYHp3fM6wdQ4ePOilF+dG4MnFuTzWrVvP6QvTZG7e299fRV8qOmy9bdu2TXJkzk0PnlycG8Hll6xjeU06nBJ7EsOHSt6/f9/kBuXcNOHJxblRnFgx2M6yJ1ZHbhOlmXnVmHN5eHJxbhTXvOA8yqPBnWJd6XLaUpVDlkvybmGcy8OTi3OjKC+NsHZl7cD07tjQqjEz48iRw7mbOTfrDW+hdMdMUjnwCmAVWd+pmd1QrJhc4Vx8ch0bnwrGetlv83nRi+uJlojHHnuUxsY9zJ+/oMgROjf1eMmlMH5F0JtxEujJerkZYPVxFcyrCq4ZehJpHt/XS0dHB42NewDv0NK5fLzkUhjLzOzyYgfhJkZE4oJVNdz2ePDU/n1Pd9Ge3jGw3MzYuXMna9acUawQnZtyvORSGPdI8jPLDHbBqsF2l4cau2nvGiyYmhlNTY1eenEuiyeXwlgHPBgOV7xV0qOSthY7KFc4K+aXc3xdKQDxlLEvUT1kuZmxY8eOfJs6Nyt5tVhhXFHsANzEksQFq2q5ZesRIOjMcll595B1/IFK5wZ5yaUAzGwPMBd4SfiaG85zM0h21djeRDX96aF/PqlUyqvGnAt5cikASe8CfgAcF76+LynvcMRu+lo8p4z5ZcEQyGkitPbXDFluZj7Oi3MhTy6F8VbgAjP7mJl9DLgQeFuRY3IT4LI1Jwy8b4zXDlvu47w4F/DkUhgCUlnTqXCem2Gyn9Y/0F9NPF0ybB1v2HfOG/QL5TvAfZJ+EU6/DPh2EeNxE2RhTSnHlSc4EC/DEC2JGk6q6BiyzoED+4sUnXNTh5dcCsDMPge8GTgCtAFvNrMvFDcqN1EuO2PpwPvempVEIkP/jJLJpFeNuVnPk8s4SKoL/50P7Aa+D3wP2BPOczPQeSsGn3F5cn+MeGpoDWjmiX3nZjOvFhuf/wWuAh4kM2RhQOH0ScUIyk2s+dWlnLSwgl2HYqQRLfFqVlV0Dlnn0KGDRYrOuanBk8s4mNlV4b8nFjsWN7nOW1HDrkNB1Vdi7smsOO4ITU2NWDiaWG5VmXOzjf8FFICkP41lnps5zlsx+IzL43t7ebqxZSCxAHR3d9PZ2ZFvU+dmBU8u4yCpImxbWShpnqT54WsVsKS40bmJdFxtKSvmlQOQTENLvHLYOg8/vGWyw3JuyvDkMj5/T9Decmr4b+b1K+ArRYzLTYLzVg6WXloSwx+o7O7u9rvG3KzlyWUczOyLYXvL+83sJDM7MXydZWZfLnZ8bmLVZ1WN7UtUk7Shd41J8rvG3KzlDfoFYGZfkrQGOA2oyJp/c/GichNt8ZwyFtdF2duZJEWEfYnqIT0lmxnt7W1FjNC54vHkUgCSPg5cQpBcbiXogn8j4MllhltRFWNvZ/Bn1JKoHUgu5eXlrFv3V5SXV4y2uXMzlleLFcYrgcuAfWb2ZuAsoPxoG0m6PBxgrEHSh0dY59WSHpe0TdL/FjZsN14nRAZLJnv7q0mFVWPxeNyrxNys5smlMPrMLA0kw6f2D3CUBygllRA0+l9BUOK5RtJpOeusBj4CXGxmpwPvnojg3TN39aUXcVxtMEJl0kqYf8ragWXNzU3eoO9mLU8uhbFZ0lzgmwR3iz0E3H+Ubc4HGsxsl5klgB8BL81Z523AV8ysDcDMDhQ2bDdekoY883LnttaB994NjJvNPLkUgJm93czazezrwAuBN4XVY6NZCjRlTTeH87I9C3iWpLsl3Svp8sJF7QolO7k09VaQDp+lNDMvvbhZyxv0x0HSuaMtM7OHRts8zzzLmY4CqwluFlgGbJC0xsza83zedcB1ACtWrDhK5K6QVi0oZ25lCe19KRJWwuFkJYtK+4DB0suaNWcUOUrnJpcnl/H5r1GWGXDpKMubgeVZ08uA1jzr3Gtm/cDTkp4gSDYPDPswsxuBGwHq6+tzk5SbQBGJM5ZUsOGpHgBaEjVDkovfjuxmI08u42Bmzx/H5g8AqyWdCLQArwFem7POL4FrgJskLSSoJts1js90E2QRbUAZAHsTNZxVdRAJampqWLdufXGDc64IvM2lACRVSfpnSTeG06slXTXaNmaWBK4Hbge2Az8xs22SbpB0dbja7cBhSY8DdwAfMLPDE3ck7pmqTR0mqmCk6550GR2p4E5078DSzVbK7snVPTOSfkxwl9gbzWyNpEpgk5mdXYx46uvrbfPmzcX46Fnt63ft5f49wUOUp1cd4jmVwXVATU0N69dfUsTInCsMSQ+aWf1Y1vWSS2GcbGafAfoBzKyP/A32bgY7e/ngXWOt8cH3Xnpxs5Enl8JIhKUVA5B0MhAvbkhusp25tIqIgpqAtlQFvanBJk3vft/NNp5cCuPjwG3Ackk/AP4EfLC4IbnJVlVWwgnliYHp1v6hpRd/3sXNJp5cxkmSgB3A3wDXAj8E6s3sziKG5Yrk0jOXDbxvTQwmF+9+3802nlzGyYI7In5pZofN7Ldm9hszO1TsuFxxnL2seuD9wf4qEungT8yfd3GzjT/nUhj3SlprZsMebnSzy/zqUlbOL2fPkTiGOOH0dVx4Yl2xw3Ju0nnJpTCeD2yS9JSkrZIelbS12EG54jg3666xLU09RYzEueLxkkthXFHsANzUcc7yan7xSPCMy8ONnWy4+ynOOuMMtm3bxrnnnusDiLlZwZPLOEmKAL81szXFjsVNDUvnlrGoppSD3f30W4SdBxP03HM36XTaO7F0s4ZXi41TOEjYI5K8K2IHBHeGnbF4cCDS1kQt6XQagKamRr8l2c0KnlwKYzGwTdKfJN2SeRU7KFc8CxnsAq41UYNljfHityS72cCrxQrjk8UOwE0dsViM1JE9lOkkEhYlZlHakhXMLw1KLE1NjaxevdrbXtyM5iWXAjCzvxA8SFkbvraH89ws1NCwE2EsLhu8U6wl62l9L7242cCTSwFIejVwP/Aq4NXAfZJeWdyoXLFkHpZcUtY9MG9v1tP6AEeO+MgJbmbzarHC+Ciw1swOAEhaBPwR+FlRo3JFkRkc7I67NnBfV5o0ETpT5XSlSqkt6Qdg/vwFxQzRuQnnJZfCiGQSS+gw/t3Oes9f/1csq8rqyDKr9OJdwbiZzk+AhXGbpNslXSvpWuC3wK1FjslNAZedNXiH+t5ELQCRSIS1a88vVkjOTQpPLuMg6RRJF5vZB4BvAGcCZwGbgBuLGpybEs5aVo3CYeMOJSuIpUu8Qd/NCp5cxucLQBeAmf3czN5rZu8hKLV8oaiRuSmhriLKyQsyD1SKvYkazIzm5iZ/mNLNaJ5cxmeVmQ3roNLMNgOrJj8cNxUtLR+8ayzT7pJOp9mxY0exQnJuwnlyGZ/RnoKrnLQo3JS2SION9/v7q0haUE924MD+YoXk3ITz5DI+D0h6W+5MSW8FHixCPG4KuvL5F7N0bhkAaSLsTwQDiqVSKa8aczOWP+cyPu8GfiHpdQwmk3qgDHh50aJyU845y2toaT8CQEuihqXl3QMN+95LspuJvOQyDma238yeS9C32O7w9Ukzu8jM9hUzNje1nLt8cPjjvf01pA1v2HczmpdcCsDM7gDuKHYcbuo6vsqojPTTly6l30o4lKziuNJeL724GctLLs5NgqeeamBJ6fC7xszMn9Z3M5InF+cmQXt725COLDNjvNTV1Q30RebcTOLJpcgkXS7pCUkNkj48ynqvlGSS6iczPlcY69at5w1XP5/K0uA25N50KZ3pSu8Gxs1YnlyKSFIJ8BXgCuA04BpJp+VZrxZ4J3Df5EboCikaEStrkgPTzfEqNm7c4A36bkby5FJc5wMNZrbLzBLAj4CX5lnvX4HPAH4WmsZisRjzUoOdZ7cmaojH42zYcJcnGDfjeHIprqVAU9Z0czhvgKRzgOVm9pvJDMwVXkPDTo6PdhMhDUBHqoKeVCmJRIIdO7YXOTrnCsuTS3EpzzwbWChFgM8D7zvqjqTrJG2WtPngwYMFDNEVSnt7G1GlOa60d2Be5q6x1tZWL724GcWTS3E1A8uzppcBrVnTtcAa4E5Ju4ELgVvyNeqb2Y1mVm9m9YsWLZrAkN0ztW7deq688irWn7Z4YF5L1i3JXnpxM4knl+J6AFgt6URJZcBrgFsyC82sw8wWmtkqM1sF3AtcHfa67Karw0+SKaAeSlYST5cAXnpxM4snlyIysyRwPXA7sB34iZltk3SDpKuLG52bCLFYjHRfJ/OjmSQi9oYdWfogYm4m8e5fiszMbiVnSGQz+9gI614yGTG5idPQsBNJLC3r5kgyGJWhNVHDqopOAH9a380YXnJxbhK1t7dhZiwp6xqYt7+/mqSJFStW+tP6bsbwkotzkyg7eWz91W72dvaTIsKB/mrKmptYvXo15eWjjUHn3PTgJRfniuSMJYNJpCVR420ubkbx5OJckcxLDg5z3JqoIZX28V3czOHJxbkiiMViJI80URnpB6DfSjjQX+WlFzdjeHJxrgiCu8ZgWVbDfkui1sd3cTOGN+g7N8lisRjNzUGXckvLutgZmw/AAZvPiy+vpySSr1cg56YXL7k4N8kaGnZiFjyhvyAao0JB1VhPIs1jzR3FDM25gvHk4twkyzzrAgRVY+WDI1TesW1vscJyrqC8Wsy5SZb7oOTypnY+f2fQk/UTh6Gvr4/KyspihOZcwXjJxbkis/YmyhWMUBm3KL+44wG/HdlNe55cnCuiWCxGa0vTkLvGnu6p8O733bTnycW5Iso07i8tz7olOV5LS4t3v++mN08uzhVRpnF/YbRvoGosZlEO9Vf4w5RuWvPk4lwRrVu3nhUrVhIRLCkbvGusKV7rXcG4ac2Ti3NFlP1A5fLyzoH5zYlaUmnvCsZNX55cnCuihoadpNNpABZF+wYeqIxblP2JSu8Kxk1bnlycK6Ls5CHB8qyG/SOlS3zwMDdteXJxrojWrVtPXV3dwHR21djONpFIposRlnPj5k/oO1dk2aUTM2Pbr/awv6uffouwtaWH+pW1RYzOuWfGSy7OTSGSuODEwWRy3+6uUdZ2bury5OLcFHPBqsHk8khzL72JVBGjce6Z8eTi3BSzeE4Zy+eWApBMGw/s6T7KFs5NPZ5cnJtiYrEYi9L7B6Y3PtU5ytrOTU2eXJybYrZte4ylJUcQwZgvTx2MsbcjUeSonDs2nlycm0JisRj79++jPJJicelgddjdXnpx04wnF+emkG3bHht4v6picMjju3d1kk5bMUJy7hnx5FJEki6X9ISkBkkfzrP8vZIel7RV0p8krSxGnG5yZEotGSeU9gz0lNzRl+Kxvb3FCs25Y+bJpUgklQBfAa4ATgOukXRazmpbgHozOxP4GfCZyY3STabcAcIighVZT+x71ZibTjy5FM/5QIOZ7TKzBPAj4KXZK5jZHWaWuVy9F1g2yTG6SXTw4IFh81aVD1aNbWnqoTvuz7y46cGTS/EsBZqyppvDeSN5K/C7CY3IFVVlZeWweXOiCRaUBneKJdPmtyW7acP7Fise5ZmXt8VW0uuBeuB5I+5Mug64DmDFihWFiIjKJJQAAB/kSURBVM9NstwekO+66066u7s5ta6Huw+XAXDnkx286DlziSjfz8e5qcNLLsXTDCzPml4GtOauJOkFwEeBq80sPtLOzOxGM6s3s/pFixYVPFg3uTo6OujuDm5FXmQHqSwNksmBrn4e94Z9Nw14cimeB4DVkk6UVAa8BrglewVJ5wDfIEgswyvk3Yz1yCNbBt5HZZxYMdiB5Z+f6Mi3iXNTiieXIjGzJHA9cDuwHfiJmW2TdIOkq8PVPgvUAD+V9LCkW0bYnZtBskstGctLBq8tHmnp4VB3/2SH5dwx8TaXIjKzW4Fbc+Z9LOv9CyY9KFd02aWWjNqSfhaXx9gbr8AMbt92iJPST3PuuedSXl5RhCidG52XXJybYnp787epnFR2aOD9XQ1d7D/czs6dOycrLOeOiZdcnJtiLr/8yoH3sViMO+/8M+l0mlOf/DXt1Reyre4c+tNiV3wuFc1NrF69eqD00rPtXmJPPcqCq99WrPCdAzy5ODel7dixnXQ6DUB84XJe+JdP03vG+1nS/gTnNv6WikQnu35cy8Ir30TlKWey96sfYsm7v1jkqJ3z5OLclBWLxWhtHbw7PXbCag4+9xpecccNpBUhakG/Y/R1cfiX3wBLs+wDX6P69AuLFLFzg7zNxbkpKuhrbOhztZWH92DSYGLJSPWDIvQ1bJ28AJ0bhZdcHDve+x8jLjvhVZcz96JzAGjftIV9P71txHVP/dxHBt4//bnvEG/el3e9OReezeJXXwFArGkvuz9/04j7XPWea6lYvhiAvT/5HR33Ppx3vfJlJ3Die988MD0TjilSVwnnnzIwfdIfH4W9fwEboX+xVD/tf/hfSpf91ZQ9ppn4/zRdj2miecnFuSnK8nUGNHInDQCkutomJhjnjpEs7y/YTWf19fW2efPmYofhxuGxxx6lqamR3L/PlT/+F0riPSNuV1I3n9U33jvR4blZStKDZlY/lnW95OLcFNTe3jYssQB0PPti0pER/mxLSpn7wtdOcGTOjY23uTg3BWV6SM4twcQXrkBmpCMRIuEtygBJRYmkU0RXPqco8TqXy5OLc1NYdgmmYt9Ojrv7h+x7/lspP9TI/K2/H1jvvpNfRevcZ/M3X/sIZdW1fjuyKzqvFnNuClu3bj0rVqxEEuWHmtj/vDfSt+w02s++fMh6dz/79Tx9/FpuPed9dO54qEjROjfIk4tzU1ym9NKx5lJiJ6zOu04JQRXZ9nn1/Lz6eWzadA/xeGwyw3RuCK8Wc26Kyx2h8rHHHqWxcc+QeWdUHeTh3uMBePBgKTXxPno3bmDdur8a1mtyLBZjy5aHvEdlN6G85OLcNBKLxWhubho2/+SKdo4vzdyiLO7vXkxnXzJ8yn+ohoadtLUd8R6V3YTy5OLcNNLQsHOggb9z9YUDLwnqq/dRquDp/b50KZu6ltDc0jqkeiw7OTU27qGz00e1dBPDk4tz00QmMWSSy6GLXj3wksQpK47ngtr9ZPojO5SsYkv3cWzfPlh6yU5OAA8/PHxgsmKJxWLeVjSDeHJxbprITQzZzIyWlhZOKO1iTdXgoGK74nP541Mx4vHYsOQE0N3dPWVKL15dN7MSrCcX56aJkZ7az/XsiiMsL+scmH68dyH/u3HXiMlpKpResqvrmpub6OzsKPpJdiJP9CPteyYlWE8uzk0T69at58orr+LKK6+irq5uxPUkqK/Zl9XADxtaS7n9yR7S6eHJpbu7e9QT6HhOsmPdNjvxmRkPP7yl6CfZiTzR59t3boId6/c9VUs7nlycm4bWrVvPqkdvZeGmn7Bw00+GLJPEi15wGZ945ZmcenzlwPxtPQt4sOcEcvOLpFFPoNknwlgsxt13b+SeezaO6WQ2lhN0bnWdmdHd3Q2M/SSbOcEWqsQz1hsfxvJ9dHR0cPvtt9HZ2TGwfr4kkptgx5rUpmppx5OLc9NQLBYjsuWP1O28l7qdQ3tBNjN27NhOeTTCO5+/hFOPLx9Ytjs+h790rqA3FR2yfnt7/q76c6+mn3hiOx0d7bS3t+c9mWVfRY/1SryhYefAUM65MscyUsLInKw3bryLtrYjAyWe7G2OdmWfb/njj28bEtM999w97K67TZvuOer3AfDII1tIpZI8/PAWGhp20tHRPrDvzPFt2HAXjY17hiSXfNWDubE+09LOZPCHKJ2bhhoado76x9va2sqppz6HivIKLlt0mL72FHvicwA4nKzkjx2rOLd6H0vLuikrKx32oGbGjh3bB06E6XSalpaWgWWNjXtYsWIFdXXBfmOxGBs3biCRiLNx4wYWLlw45GS5Y8d2env7hjy8OdJzOxmZGxUAdu7cyZo1Zwz7Hjo62gemMyWezDZPPrmT7n544kCMxnt3UbPgBDr6UnT2JYkl0/SnjPauHmKxWn7TuofammpIJ+nuEGWR4ylTmrJIinKl6N30OJXqZ139Gex5+ina2o7QlpWTGxv30N7extq15w8c38GDBwdi6u7upqdn6HAJ2ceXK51Oc889d5NOpweOPbuUcsopq9m4ccPAd5xOp9mxYwdnnXX2iN/nZPLk4tw0E4vFaGpq5MRR1smczLu7e+joaKe+GmpLEmzrXYghElbCvd1LWVzaxbk1B7nnno2cd149ZrBly0OcfvrpPProo0NO3PncffdG5syZw3nn1fPEE9tJJILBzOLx+JCTZvZJNDvJPPHE9hFLLblyT96Z7yEjZaItWcGRZAVtyQq6UmV0HS4jRQRYDp1A4+E8e44AFdALe3v7wnm1w1cL88JPG1uoUJTKkhXURhLUliSoKwn+TXd0DkmCW7Y8OGQXxzp+Vua7aWpq5MiRwwOJqrFxD319fQPfd0ZLSzOnnnrqlOh5wQcLm4F8sLCZLdP9y0k3v3dg3q43fm7YetFolGQyOWTeof5K7uteTF+6dHA90jy78jDrT6qk48ghEok4kUhkzCd9gNLSUvr7+4/pOE444QT27cs/dO9oysrKqKysIhap5om9XRxJVnAkWUlHqhxDx7y/QhJGdaSf+RVpls2vItHWMpB8SiNj+z6XLl1Ka2vrMSeiwe2XDSu9dHR0cO+9m1h7/oXsajf+tLWJi09byrrVC45p38cyWJiXXJybZkZqH8lYunQpLS0tJJOpYcsWlvbxwjm7ebR3EU/H5wKQJMK2vkXsfDzJqZVVnFiRoPQYEgtwzIkFGDWx5CbGeLokTCIVHOms5Eiygn4rAWqO+jmlSlFbkqAm0k9FJDnwiipNCUaJjAhGGkgj0iaSFiFhJSSshHg6ePWmS+lNl9KXjo6YxAzRnS6juxcae9PA4Hj1FUpSWxIfKOVkkk5FJImydjdSNdlY7du3l+7ubszSSBEWrnwOv39gBwcSx/Hz3+6n3yJAJfFtB485uRwLTy7OTROHb/kmFSefMdA+siOr5JJRsW8n3Y/9GdZcSuZJ/VxlkTTn1exnZXknD/UcT2cqaPBPWJStvcfxeO9CVpZ3cFJFO3OiiQk7npGkTBzui9KWrOFwMkgkPemyMW1bWxJnfjTG/GiMOSUxakv6KY8MT7LjkTboS0fpSZfSnSqjM1UWVMGlyunNKhHmilmUWDLKwWT1kPlRpaiJ9FMZ6aeqJElVpJ+qMAGWKk2ZUpRFUpRgQ5IQgBmkEP0WoS9dSl8qSm86SndHGe2pcjpS5SR3HQGOGxZPY1eE9q5e5tZWFeJrGcaTi3PTRMXJZ9D6hXex5N1fzDsYWMW+nRz/l5vZ/7w3jml/C0v7eMGc3TTG63i8bwG94Qk8SYSn4vN4Kj6PupI4S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xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n </g>\n <defs>\n <clipPath id=\"pf9c67a46a5\">\n <rect height=\"217.44\" width=\"279\" x=\"116.954174\" y=\"22.318125\"/>\n </clipPath>\n </defs>\n</svg>\n","text/plain":"<Figure size 360x288 with 1 Axes>"},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":"frc_results = FourierCorrelationDataCollection()\n\nfrc_results[0] = frc.calculate_single_image_frc(image, args)\n\nplotter = frcplots.FourierDataPlotter(frc_results)\nplotter.plot_one(0)"},{"cell_type":"markdown","metadata":{},"source":["## Generate PSF\n","\n","Based on the FRC measurement, a PSF is genrated with a simple Gaussian model, using the FRC resolution figure as the FWHM value. "]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"data":{"image/png":"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psf_generator.xy()\n\nshowim.display_2d_images(imops.enhance_contrast(image_copy, percent_saturated=0.3), \n psf,\n image1_title=\"Original\",\n image2_title=\"PSF from FRC\"\n \n )"},{"cell_type":"markdown","metadata":{},"source":["## Run deconvolution\n","\n","Having generated the PSF, deconvolution is now run for 50 iterations. Specifying the TiffImageWriter here is actually not necessary, as the *save-intermediate-images* option is not used (by enabling it you can save each intermediate deconvolution result to the specified directory)."]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":"/Users/sami/miniconda3/envs/miplib/lib/python3.6/site-packages/scipy/ndimage/interpolation.py:611: UserWarning: From scipy 0.13.0, the output shape of zoom() is calculated with round() instead of int() - for these inputs the size of the returned array has changed.\n \"the returned array has changed.\", UserWarning)\n"}],"source":"\ntemp_dir = os.path.join(data_dir, \"Temp\")\nif not os.path.exists(temp_dir):\n os.mkdir(temp_dir)\n \nwriter = imwrap.TiffImageWriter(data_dir)\n\ntask = deconvolve.DeconvolutionRL(image, psf, writer, args)\ntask.execute()"},{"cell_type":"markdown","metadata":{},"source":["## Results\n","\n","Get and show result"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"image/png":"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</clipPath>\n </defs>\n</svg>\n","text/plain":"<Figure size 1008x720 with 2 Axes>"},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":"rl_result = task.get_result()\n\nshowim.display_2d_images(imops.enhance_contrast(image, percent_saturated=0.3), \n imops.enhance_contrast(rl_result, percent_saturated=0.3),\n image1_title=\"Original\",\n image2_title=\"Deconvolved, after {} iterations.\".format(n_iterations))\n\n\n"},{"cell_type":"markdown","metadata":{},"source":["## Evaluate the deconvolution quality\n","\n","Here for the sake of simplicity I only analyze the last deconvolution result with FRC. It is of coruse possible to do that for each intermediate estimate, to get the progress curves that were shown in the (Koho S. et al. Nat. Communications 2019) paper"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"image/png":"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diff --git a/Addons/FRCmetric/miplib-public/notebooks/Notebooks/FRC based frequency domain filtering.ipynb b/Addons/FRCmetric/miplib-public/notebooks/Notebooks/FRC based frequency domain filtering.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6383a314ee5ae255bcd2a1e938f73769f000fd85
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@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Fourier Space Filtering Based on FRC\n","\n","Here I show hot to do frequency domain denoising based on FRC"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":"%matplotlib inline\n\nimport os\nimport numpy as np\n\nimport miplib.ui.plots.image as showim\nimport miplib.data.io.read as imread\nimport miplib.processing.image as imops\nfrom miplib.data.containers.image import Image\n\nimport miplib.analysis.resolution.fourier_ring_correlation as frc\nfrom miplib.data.containers.fourier_correlation_data import FourierCorrelationDataCollection\n\nimport miplib.ui.plots.frc as frcplots\nfrom miplib.processing.fft_filters import fft_filter, butterworth_fft_filter, gaussian_fft_filter\nfrom miplib.ui.cli import miplib_entry_point_options as options\n\nimport urllib.request as dl\n"},{"cell_type":"markdown","metadata":{},"source":["## Load an image\n","\n","The image is from a Nikon A1 confocal system, of a Tubulin stained HeLa cell. You can find the image [here](https://doi.org/10.6084/m9.figshare.8159180.v1)"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"The image dimensions are (512, 512) and spacing [0.05179004745018662, 0.05179004745018662] um.\n"}],"source":"# Image\ndata_dir = os.getcwd()\nfilename = \"FRC_GaAsP_AU04_.nd2\"\nfull_path = os.path.join(data_dir, filename)\n\n# Automatically dowload the file from figshare, if necessary.\nif not os.path.exists(full_path):\n dl.urlretrieve(\"https://ndownloader.figshare.com/files/15203147\", full_path)\n\nimage = imread.get_image(full_path)\n\nimage_copy = image.copy()\nspacing = image.spacing\nprint (\"The image dimensions are {} and spacing {} um.\".format(image.shape, image.spacing))\n\nimage = Image(image - image.min(), image.spacing)"},{"cell_type":"markdown","metadata":{},"source":["## Setup\n","\n","I expose some options for the FRC here. None of them you typically have to touch, but naturally can, e.g. to adjust the binning or the threshold."]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"Namespace(carma_det_idx=0, carma_gate_idx=0, channel=0, d_angle=20, d_bin=1, d_extract_angle=5.0, debug=False, directory='None', disable_hamming=False, evaluate_results=False, frc_curve_fit_degree=8, frc_curve_fit_type='smooth-spline', frc_mode='one-image', hollow_iterator=False, jupyter=False, min_filter=False, pathout=None, plot_size=(2.5, 2.5), resol_square=False, resolution_point_sigma=0.01, resolution_snr_value=0.25, resolution_threshold_criterion='fixed', resolution_threshold_curve_fit_degree=3, resolution_threshold_value=0.14285714285714285, save_plots=False, scale=100, show_image=False, show_plots=False, temp_dir=None, test_drive=False, verbose=False, working_directory='/home/sami/Data')\n"}],"source":"args_list = (\"None --bin-delta=1 --frc-curve-fit-type=smooth-spline \" \n \" --resolution-threshold-criterion=fixed\").split()\n \nargs = options.get_frc_script_options(args_list)\n\nprint (args)\n"},{"cell_type":"markdown","metadata":{},"source":["## Calculate resolution\n","\n","Here I estimate the resolution of the image with the single-image FRC method. "]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"image/png":"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If you are using two-image FRC, the threshold point is the same as the FRC cut-off point, which you can get directly from the FRC result with the *resolution-point* key. In the case of one-image FRC, the point in the full-size image has to be calculated, e.g. as in here."]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"data":{"image/png":"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resolution\nfrc_resolution = frc_results[0].resolution['resolution']\nthreshold_point = 2 * image.spacing[0] / frc_resolution\n\n\n# Run the filter\nideal_result = fft_filter(image, \n threshold_point)\n\nshowim.display_2d_images(image, \n ideal_result,\n image1_title=\"Original\",\n image2_title=\"FFT low-pass filtered\")\n"},{"cell_type":"markdown","metadata":{},"source":["## Butterworth\n","\n","The second type of a low-pass filter is a Butterworth filter. It also work well, but is not able to remove all the noise."]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"data":{"image/png":"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butterworth_fft_filter(image, \n threshold_point, n=3)\n\nshowim.display_2d_images(image, \n butterworth_result,\n image1_title=\"Original\",\n image2_title=\"FFT low-pass filtered\")\n"},{"cell_type":"markdown","metadata":{},"source":["## Gaussian\n","\n","\n","The Gaussian filter works ok as well. It does seem to blur the details a little bit, and is not able to remove all the noise -- but that was expected."]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"image/png":"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diff --git a/Addons/FRCmetric/miplib-public/notebooks/Notebooks/Image_quality_ranking.ipynb b/Addons/FRCmetric/miplib-public/notebooks/Notebooks/Image_quality_ranking.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7273c5c8d8acffb3912b460e5b3de1314116d7b2
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@@ -0,0 +1,686 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Image quality ranking example\n",
+ "\n",
+ "This is a simple example for using the image quality ranking functionality in MIPLIB. The principle is the same as in the [PyImageQualityRanking](https://github.com/sakoho81/pyimagequalityranking) package (1), but MIPLIB also contains some additional toys, e.g. Fourier Ring Correlation analysis (2) which makes it possible to expand the image quality calculation a little bit.\n",
+ "\n",
+ "As with the PyImageQualityRanking package you can alternatively use this functionality through a command line script. You can read more about that [here](https://github.com/sakoho81/pyimagequalityranking/wiki).\n",
+ "\n",
+ "---\n",
+ "(1) Koho, Sami, Elnaz Fazeli, John E. Eriksson, and Pekka E. Hänninen. 2016. “Image Quality Ranking Method for Microscopy.” Scientific Reports 6 (July): 28962.\n",
+ "\n",
+ "(2) Koho, S. et al. Fourier ring correlation simplifies image restoration in fluorescence microscopy. Nat. Commun. 10 3103 (2019)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "import miplib.ui.cli.miplib_entry_point_options as opts\n",
+ "\n",
+ "from miplib.data.io import read\n",
+ "import miplib.processing.image as imops\n",
+ "from miplib.analysis.image_quality import image_quality_ranking as imq\n",
+ "import miplib.data.iterators.fourier_ring_iterators as iterators\n",
+ "\n",
+ "# These are just needed to download the data from Figshare if not already available\n",
+ "import urllib.request as dl\n",
+ "import zipfile"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup\n",
+ "\n",
+ "Here I use the same command line options interface that is used in the *pyimq.main* script. Please refer to the Wiki, or ```pyimq.main --help``` for more details. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "args_list = (\"--use-mask --normalize-power --bin-delta=1 \" \n",
+ " \" --resolution-threshold-criterion=fixed --frc-curve-fit-type=smooth-spline \").split()\n",
+ "\n",
+ "options = opts.get_quality_script_options(args_list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check that the data directory exists, and if not, download the data from Figshare"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = os.path.join(os.getcwd(), \"Image_Quality_Ranking_Data\")\n",
+ "\n",
+ "if not os.path.exists(path):\n",
+ " os.mkdir(path)\n",
+ " zip_path = os.path.join(path, \"images.zip\")\n",
+ " dl.urlretrieve(\"https://ndownloader.figshare.com/files/20749572\", zip_path)\n",
+ " with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
+ " zip_ref.extractall(path)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Run\n",
+ "\n",
+ "Now we are ready to run the script. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done analyzing Detector_1_STED_100perc.tif\n",
+ "Done analyzing Detector_1_STED_50perc.tif\n",
+ "Done analyzing Detector_1_STED_60perc.tif\n",
+ "Done analyzing Detector_1_STED_70perc.tif\n",
+ "Done analyzing Detector_1_STED_80perc.tif\n",
+ "Done analyzing Detector_1_STED_90perc.tif\n",
+ "Done analyzing Detector_2_STED_100perc.tif\n",
+ "Done analyzing Detector_2_STED_50perc.tif\n",
+ "Done analyzing Detector_2_STED_60perc.tif\n",
+ "Done analyzing Detector_2_STED_70perc.tif\n",
+ "Done analyzing Detector_2_STED_80perc.tif\n",
+ "Done analyzing Detector_2_STED_90perc.tif\n"
+ ]
+ }
+ ],
+ "source": [
+ "df = imq.batch_evaluate_image_quality(path, options)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Evaluate\n",
+ "\n",
+ "The data is saved into a Pandas DataFrame. Let's first check what it looks like. There are only a few images in the dataset, so the default print will work. I would recommend *df.describe()* for anyting more involved."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Filename</th>\n",
+ " <th>tEntropy</th>\n",
+ " <th>tBrenner</th>\n",
+ " <th>fMoments</th>\n",
+ " <th>fMean</th>\n",
+ " <th>fSTD</th>\n",
+ " <th>fEntropy</th>\n",
+ " <th>fTh</th>\n",
+ " <th>fMaxPw</th>\n",
+ " <th>Skew</th>\n",
+ " <th>Kurtosis</th>\n",
+ " <th>MeanBin</th>\n",
+ " <th>Resolution</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_100perc.tif</td>\n",
+ " <td>4.285611</td>\n",
+ " <td>43180262.0</td>\n",
+ " <td>49.344954</td>\n",
+ " <td>10328.350177</td>\n",
+ " <td>198.187984</td>\n",
+ " <td>5.094598</td>\n",
+ " <td>3.441091e+07</td>\n",
+ " <td>10247.090423</td>\n",
+ " <td>-0.039543</td>\n",
+ " <td>-0.272079</td>\n",
+ " <td>10530.424537</td>\n",
+ " <td>0.119985</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_50perc.tif</td>\n",
+ " <td>4.730817</td>\n",
+ " <td>54573467.0</td>\n",
+ " <td>36.565536</td>\n",
+ " <td>10099.634840</td>\n",
+ " <td>193.568316</td>\n",
+ " <td>5.062354</td>\n",
+ " <td>3.441091e+07</td>\n",
+ " <td>10045.522567</td>\n",
+ " <td>0.016194</td>\n",
+ " <td>-0.171923</td>\n",
+ " <td>10003.404877</td>\n",
+ " <td>0.164919</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_60perc.tif</td>\n",
+ " <td>4.795810</td>\n",
+ " <td>54982596.0</td>\n",
+ " <td>37.439599</td>\n",
+ " <td>10120.469787</td>\n",
+ " <td>181.443549</td>\n",
+ " <td>5.195893</td>\n",
+ " <td>3.441091e+07</td>\n",
+ " <td>10114.066394</td>\n",
+ " <td>0.070219</td>\n",
+ " <td>-0.291461</td>\n",
+ " <td>10289.242332</td>\n",
+ " <td>0.158282</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_70perc.tif</td>\n",
+ " <td>4.603168</td>\n",
+ " <td>48760263.0</td>\n",
+ " <td>42.567657</td>\n",
+ " <td>10274.365860</td>\n",
+ " <td>213.621519</td>\n",
+ " <td>5.085422</td>\n",
+ " <td>3.441091e+07</td>\n",
+ " <td>10226.728254</td>\n",
+ " <td>-0.115382</td>\n",
+ " <td>-0.338652</td>\n",
+ " <td>10392.621445</td>\n",
+ " <td>0.139546</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_80perc.tif</td>\n",
+ " <td>4.580109</td>\n",
+ " <td>50641291.0</td>\n",
+ " <td>41.937592</td>\n",
+ " <td>10184.757844</td>\n",
+ " <td>182.145878</td>\n",
+ " <td>5.039310</td>\n",
+ " <td>3.441091e+07</td>\n",
+ " <td>10189.336215</td>\n",
+ " <td>0.138788</td>\n",
+ " <td>-0.254724</td>\n",
+ " <td>10402.966292</td>\n",
+ " <td>0.135971</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_90perc.tif</td>\n",
+ " <td>4.367668</td>\n",
+ " <td>45722663.0</td>\n",
+ " <td>46.243127</td>\n",
+ " <td>10275.311030</td>\n",
+ " <td>178.514408</td>\n",
+ " <td>5.127605</td>\n",
+ " <td>3.441091e+07</td>\n",
+ " <td>10208.036749</td>\n",
+ " <td>-0.066109</td>\n",
+ " <td>-0.215196</td>\n",
+ " <td>10439.145446</td>\n",
+ " <td>0.127693</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_100perc.tif</td>\n",
+ " <td>3.215201</td>\n",
+ " <td>28964592.0</td>\n",
+ " <td>78.310588</td>\n",
+ " <td>60353.834168</td>\n",
+ " <td>1082.283004</td>\n",
+ " <td>5.223152</td>\n",
+ " <td>3.441091e+07</td>\n",
+ " <td>60205.023895</td>\n",
+ " <td>-0.024671</td>\n",
+ " <td>-0.454378</td>\n",
+ " <td>59927.386978</td>\n",
+ " <td>0.149342</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_50perc.tif</td>\n",
+ " <td>3.605043</td>\n",
+ " <td>34375844.0</td>\n",
+ " <td>59.983093</td>\n",
+ " <td>55074.975164</td>\n",
+ " <td>989.126724</td>\n",
+ " <td>5.047844</td>\n",
+ " <td>3.441091e+07</td>\n",
+ " <td>54802.091433</td>\n",
+ " <td>-0.036091</td>\n",
+ " <td>-0.196196</td>\n",
+ " <td>53827.973087</td>\n",
+ " <td>0.189043</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_60perc.tif</td>\n",
+ " <td>3.578477</td>\n",
+ " <td>34131927.0</td>\n",
+ " <td>61.658737</td>\n",
+ " <td>55671.798799</td>\n",
+ " <td>955.466877</td>\n",
+ " <td>4.962645</td>\n",
+ " <td>3.441091e+07</td>\n",
+ " <td>55324.291588</td>\n",
+ " <td>0.210412</td>\n",
+ " <td>0.311979</td>\n",
+ " <td>56439.283505</td>\n",
+ " <td>0.172909</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_70perc.tif</td>\n",
+ " <td>3.624614</td>\n",
+ " <td>34195793.0</td>\n",
+ " <td>60.299502</td>\n",
+ " <td>54478.888632</td>\n",
+ " <td>983.583022</td>\n",
+ " <td>5.041294</td>\n",
+ " <td>3.441091e+07</td>\n",
+ " <td>54339.532782</td>\n",
+ " <td>0.245611</td>\n",
+ " <td>0.202735</td>\n",
+ " <td>55003.158593</td>\n",
+ " <td>0.157665</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_80perc.tif</td>\n",
+ " <td>3.470891</td>\n",
+ " <td>32429913.0</td>\n",
+ " <td>67.517521</td>\n",
+ " <td>57222.310447</td>\n",
+ " <td>1066.324564</td>\n",
+ " <td>5.012315</td>\n",
+ " <td>3.441091e+07</td>\n",
+ " <td>57215.815934</td>\n",
+ " <td>0.200798</td>\n",
+ " <td>0.015310</td>\n",
+ " <td>57867.984847</td>\n",
+ " <td>0.150416</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_90perc.tif</td>\n",
+ " <td>3.357066</td>\n",
+ " <td>30412258.0</td>\n",
+ " <td>73.278296</td>\n",
+ " <td>59317.666371</td>\n",
+ " <td>1001.853657</td>\n",
+ " <td>5.035076</td>\n",
+ " <td>3.441091e+07</td>\n",
+ " <td>59551.647057</td>\n",
+ " <td>-0.393376</td>\n",
+ " <td>-0.213147</td>\n",
+ " <td>59907.150371</td>\n",
+ " <td>0.147366</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Filename tEntropy \\\n",
+ "0 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_100perc.tif 4.285611 \n",
+ "1 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_50perc.tif 4.730817 \n",
+ "2 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_60perc.tif 4.795810 \n",
+ "3 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_70perc.tif 4.603168 \n",
+ "4 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_80perc.tif 4.580109 \n",
+ "5 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_90perc.tif 4.367668 \n",
+ "6 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_100perc.tif 3.215201 \n",
+ "7 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_50perc.tif 3.605043 \n",
+ "8 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_60perc.tif 3.578477 \n",
+ "9 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_70perc.tif 3.624614 \n",
+ "10 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_80perc.tif 3.470891 \n",
+ "11 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_90perc.tif 3.357066 \n",
+ "\n",
+ " tBrenner fMoments fMean fSTD fEntropy fTh \\\n",
+ "0 43180262.0 49.344954 10328.350177 198.187984 5.094598 3.441091e+07 \n",
+ "1 54573467.0 36.565536 10099.634840 193.568316 5.062354 3.441091e+07 \n",
+ "2 54982596.0 37.439599 10120.469787 181.443549 5.195893 3.441091e+07 \n",
+ "3 48760263.0 42.567657 10274.365860 213.621519 5.085422 3.441091e+07 \n",
+ "4 50641291.0 41.937592 10184.757844 182.145878 5.039310 3.441091e+07 \n",
+ "5 45722663.0 46.243127 10275.311030 178.514408 5.127605 3.441091e+07 \n",
+ "6 28964592.0 78.310588 60353.834168 1082.283004 5.223152 3.441091e+07 \n",
+ "7 34375844.0 59.983093 55074.975164 989.126724 5.047844 3.441091e+07 \n",
+ "8 34131927.0 61.658737 55671.798799 955.466877 4.962645 3.441091e+07 \n",
+ "9 34195793.0 60.299502 54478.888632 983.583022 5.041294 3.441091e+07 \n",
+ "10 32429913.0 67.517521 57222.310447 1066.324564 5.012315 3.441091e+07 \n",
+ "11 30412258.0 73.278296 59317.666371 1001.853657 5.035076 3.441091e+07 \n",
+ "\n",
+ " fMaxPw Skew Kurtosis MeanBin Resolution \n",
+ "0 10247.090423 -0.039543 -0.272079 10530.424537 0.119985 \n",
+ "1 10045.522567 0.016194 -0.171923 10003.404877 0.164919 \n",
+ "2 10114.066394 0.070219 -0.291461 10289.242332 0.158282 \n",
+ "3 10226.728254 -0.115382 -0.338652 10392.621445 0.139546 \n",
+ "4 10189.336215 0.138788 -0.254724 10402.966292 0.135971 \n",
+ "5 10208.036749 -0.066109 -0.215196 10439.145446 0.127693 \n",
+ "6 60205.023895 -0.024671 -0.454378 59927.386978 0.149342 \n",
+ "7 54802.091433 -0.036091 -0.196196 53827.973087 0.189043 \n",
+ "8 55324.291588 0.210412 0.311979 56439.283505 0.172909 \n",
+ "9 54339.532782 0.245611 0.202735 55003.158593 0.157665 \n",
+ "10 57215.815934 0.200798 0.015310 57867.984847 0.150416 \n",
+ "11 59551.647057 -0.393376 -0.213147 59907.150371 0.147366 "
+ ]
+ },
+ "execution_count": 86,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The *batch_evaluate_image_quality()* function does not normalize the numerical values to [0, 1], so you have to do that separately, if thats what you want. Here's an example for getting that done."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_norm = df.copy()\n",
+ "df_norm.iloc[:, 1:] = df.iloc[:, 1:].subtract(df.iloc[:, 1:].min(), axis=1)\n",
+ "df_norm.iloc[:, 1:] = df_norm.iloc[:, 1:].divide(df_norm.iloc[:, 1:].max(), axis=1)\n",
+ "\n",
+ "## The resolution has to be flipped around to make sense. Do the same, e.g. tp get the invfSTD measure \n",
+ "df_norm[\"Resolution\"] = 1 - df_norm[\"Resolution\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Utilize\n",
+ "\n",
+ "As a simple example here, I take a couple of parameters (Resolution and Spatial entropy) that are well known to correlate with image quality. Then I create a third parameter, by simply taking an average of the two. The images are clearly divided in two groups. On this normalized scale each paramter gets its maximum (best) value at 1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000028EB7F55C50>,\n",
+ " <matplotlib.axes._subplots.AxesSubplot object at 0x0000028EB7F90160>,\n",
+ " <matplotlib.axes._subplots.AxesSubplot object at 0x0000028EB7FBD5C0>]], dtype=object)"
+ ]
+ },
+ "execution_count": 88,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x216 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_norm[\"Average\"] = (df_norm[\"tEntropy\"] + df_norm[\"Resolution\"]) / 2\n",
+ "\n",
+ "df_res = df_norm.sort_values(by=['Average']).reset_index(drop=True)\n",
+ "df_res = df_res.loc[:, [\"Average\", \"Resolution\", \"tEntropy\"]]\n",
+ " \n",
+ "df_res.plot(subplots=True, layout=(1,3), figsize=(9,3))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So let's look, what does that mean. As it turns out, the ranking separates the two detectors that the data was acquired with. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Filename</th>\n",
+ " <th>Average</th>\n",
+ " <th>Resolution</th>\n",
+ " <th>tEntropy</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_50perc.tif</td>\n",
+ " <td>0.123320</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.246641</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_60perc.tif</td>\n",
+ " <td>0.231730</td>\n",
+ " <td>0.233628</td>\n",
+ " <td>0.229833</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_100perc.tif</td>\n",
+ " <td>0.287446</td>\n",
+ " <td>0.574893</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_90perc.tif</td>\n",
+ " <td>0.346634</td>\n",
+ " <td>0.603514</td>\n",
+ " <td>0.089754</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_70perc.tif</td>\n",
+ " <td>0.356695</td>\n",
+ " <td>0.454367</td>\n",
+ " <td>0.259022</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_80perc.tif</td>\n",
+ " <td>0.360555</td>\n",
+ " <td>0.559342</td>\n",
+ " <td>0.161767</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_50perc.tif</td>\n",
+ " <td>0.654102</td>\n",
+ " <td>0.349322</td>\n",
+ " <td>0.958881</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_60perc.tif</td>\n",
+ " <td>0.722721</td>\n",
+ " <td>0.445443</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_70perc.tif</td>\n",
+ " <td>0.797436</td>\n",
+ " <td>0.716750</td>\n",
+ " <td>0.878121</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_90perc.tif</td>\n",
+ " <td>0.808761</td>\n",
+ " <td>0.888393</td>\n",
+ " <td>0.729129</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_80perc.tif</td>\n",
+ " <td>0.816022</td>\n",
+ " <td>0.768512</td>\n",
+ " <td>0.863533</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_100perc.tif</td>\n",
+ " <td>0.838607</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.677213</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Filename Average \\\n",
+ "0 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_50perc.tif 0.123320 \n",
+ "1 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_60perc.tif 0.231730 \n",
+ "2 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_100perc.tif 0.287446 \n",
+ "3 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_90perc.tif 0.346634 \n",
+ "4 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_70perc.tif 0.356695 \n",
+ "5 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_2_STED_80perc.tif 0.360555 \n",
+ "6 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_50perc.tif 0.654102 \n",
+ "7 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_60perc.tif 0.722721 \n",
+ "8 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_70perc.tif 0.797436 \n",
+ "9 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_90perc.tif 0.808761 \n",
+ "10 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_80perc.tif 0.816022 \n",
+ "11 c:\\Temp\\Image_Quality_Ranking_Data\\Detector_1_STED_100perc.tif 0.838607 \n",
+ "\n",
+ " Resolution tEntropy \n",
+ "0 0.000000 0.246641 \n",
+ "1 0.233628 0.229833 \n",
+ "2 0.574893 0.000000 \n",
+ "3 0.603514 0.089754 \n",
+ "4 0.454367 0.259022 \n",
+ "5 0.559342 0.161767 \n",
+ "6 0.349322 0.958881 \n",
+ "7 0.445443 1.000000 \n",
+ "8 0.716750 0.878121 \n",
+ "9 0.888393 0.729129 \n",
+ "10 0.768512 0.863533 \n",
+ "11 1.000000 0.677213 "
+ ]
+ },
+ "execution_count": 89,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.set_option('display.max_colwidth', -1)\n",
+ "\n",
+ "df_check = df_norm.sort_values(by=['Average']).reset_index(drop=True)\n",
+ "df_check = df_check.loc[:, [\"Filename\", \"Average\", \"Resolution\", \"tEntropy\"]]\n",
+ "\n",
+ "df_check"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Addons/FRCmetric/miplib-public/notebooks/Notebooks/One Image Sectioned FSC and 3D Wiener filtering.ipynb b/Addons/FRCmetric/miplib-public/notebooks/Notebooks/One Image Sectioned FSC and 3D Wiener filtering.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..947a713d86bdd552ca0d2384e72337824d86132f
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/notebooks/Notebooks/One Image Sectioned FSC and 3D Wiener filtering.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{},"source":["# One-image Sectioned FSC and Wiener filtering\n","\n","Ideally for FRC/FSC analysis one would have two independent observations of the region-of-interest. Oftentimes, one only has one image to work with. Here I show hot to estimate the resolution from a single 3D image (stack), and furthermore, how to leverage the measured resolution values to do blind Wiener filtering on the same image."]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":"%matplotlib inline\n\nimport os\nimport numpy as np\nimport miplib.ui.cli.miplib_entry_point_options as options\nimport miplib.ui.plots.image as implots\nimport miplib.ui.plots.frc as frcplots\nfrom miplib.data.io import read\nimport miplib.processing.image as imops\nimport miplib.analysis.resolution.fourier_shell_correlation as fsc\nfrom miplib.data.containers.image import Image\n\nimport urllib.request as dl\n\n"},{"cell_type":"markdown","metadata":{},"source":["## Data\n","\n","A single 3D stack of a pollen sample was acquired with a Nikon A1 confocal microscope.The image is resampled to isotropic spacing and then padded/cropped into a cubic shape, in order to make it compatible witht the SFSC calculation. The image can be downloaded from [Figshare](https://doi.org/10.6084/m9.figshare.8159165.v1)."]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"data":{"image/png":"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It is assume that the image file is in the same directory with\n# the notebook.\ndata_dir = os.getcwd()\nimage_name = \"40x_TAGoff_z_galvo.nd2\"\nfull_path = os.path.join(data_dir, image_name)\n\n# Automatically dowload the file from figshare, if necessary.\nif not os.path.exists(full_path):\n dl.urlretrieve(\"https://ndownloader.figshare.com/files/15203144\", full_path)\n\nimage = read.get_image(full_path, channel=0)\n\n# The FSC z-correction is based on the difference of the sampling density in XY versus Z.\nz_correction = image.spacing[0]/image.spacing[1]\n\n# Pre-process the image for FSC\n# 1. Zoom to isotropic spacing, 2. zero-pad to a cube and 3. crop some useless black borders\nimage = imops.zoom_to_isotropic_spacing(image, order=0)\nimage = imops.zero_pad_to_cube(image)\nimage = imops.remove_zero_padding(image, ([500,]*3))\n\nimplots.display_2d_images(imops.maximum_projection(image, axis=0),\n imops.maximum_projection(image, axis=1), \n image1_title='XY', image2_title='XZ')\n"},{"cell_type":"markdown","metadata":{},"source":["## Sectioned FSC\n","\n","Here I setup and run the SFSC analysis on the image shown above. The Fourier sphere is divided into 24 wedges (sections), with 15 degrees angular increments. "]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"Namespace(carma_det_idx=0, carma_gate_idx=0, channel=0, d_angle=15, d_bin=10, d_extract_angle=0.1, debug=False, directory=None, disable_hamming=False, evaluate_results=False, frc_curve_fit_degree=8, frc_curve_fit_type='spline', frc_mode='one-image', hollow_iterator=True, jupyter=False, min_filter=False, pathout=None, plot_size=(2.5, 2.5), resol_square=False, resolution_point_sigma=0.01, resolution_snr_value=0.5, resolution_threshold_criterion='snr', resolution_threshold_curve_fit_degree=3, resolution_threshold_value=0.14285714285714285, save_plots=False, scale=100, show_image=False, show_plots=False, temp_dir=None, test_drive=False, verbose=False, working_directory='/home/sami/Data')\n"}],"source":"# Get script options\nargs_list = [None, '--bin-delta=10', '--resolution-threshold-criterion=snr', '--resolution-snr-value=0.5',\n '--angle-delta=15', '--enable-hollow-iterator', '--extract-angle-delta=.1', \n '--resolution-point-sigma=0.01', '--frc-curve-fit-type=spline']\nargs = options.get_frc_script_options(args_list)\n\nprint (args)"},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":"%%capture\n\nresult = fsc.calculate_one_image_sectioned_fsc(image, args, z_correction=z_correction)"},{"cell_type":"markdown","metadata":{},"source":["## Results\n","\n","The SFSC results are shown in a polar plot. The plot describes the calculated resolution values at different rotation angles, with respect to the XY plane. The XY plane is at 0$^\\circ$-180$^\\circ$ axis, and the XZ plane at the 90$^\\circ$-270$^\\circ$ axis. The resolution is given in micrometers."]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"data":{"text/plain":"<matplotlib.axes._subplots.PolarAxesSubplot at 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then used in a regular Wiener filter."]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"A 3D PSF was generated with FWHM (Z: 3.8875587137876315 um, XY: 0.5986743538754852 um)\n"}],"source":"import miplib.psf.psfgen as psfgen\n\nfwhm = [result[90].resolution[\"resolution\"], result[0].resolution[\"resolution\"]]\n\npsf_generator = psfgen.PsfFromFwhm(fwhm)\n\npsf = psf_generator.volume()\n\nprint (r\"A 3D PSF was generated with FWHM (Z: {} um, XY: {} um)\".format(*fwhm))"},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":"/Users/sami/miniconda3/envs/miplib/lib/python3.6/site-packages/scipy/ndimage/interpolation.py:611: UserWarning: From scipy 0.13.0, the output shape of zoom() is calculated with round() instead of int() - for these inputs the size of the returned array has changed.\n \"the returned array has changed.\", UserWarning)\n"}],"source":"from miplib.processing.deconvolution import wiener\n\n# Load the image again from the disk (to get a bit smaller image to work with). \nimage = read.get_image(os.path.join(data_dir, image_name), channel=0)\n\ndeconvolved = wiener.wiener_deconvolution(image, psf, snr=200, add_pad=100)\n"},{"cell_type":"markdown","metadata":{},"source":["## Results\n","\n","The images before and after the Wiener filtering (deconvolution) are compared."]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"image/png":"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6G+MAYYRyido63GOBwO8khFwnTUIpghNrk/mSzCTkUunTCU/wDUznGeEHJ6hcb00t/SKXE1t1PS2kSUpTm4+wA3eudgDP8PS6wENy07hk7oyDN+0aaNaM5CO1+SQ2TSOmaz7TBjwtSrtkKEBXRsGbXveLJIsq2JKx7ZikgwUFjIHwcTsC3A7Ic4FEgAUgYmVmJgiYPpzUWOo+k61uCxfjPcw0Psc9J7v4zYhECNvlxupxciMZsbfkanff5VKMzxyOMuLWEIjnQ9zLOHd87lEG055kynxY+b7q95ovQLBWl4txfUAQQgZrw3OsY1vo+DpJUyrHmQol/rPOte1hprmGs0zPnUDhwey2Xum7JmaBh/umr7mkVrWSiBPWsA7rgeIh5uw8hJ/dZuCOvuq5Lb6gZfB0I1MYKj9t1K56ksDqZLKNDnufXnMsPnug5F9sXZvvLEOg/ZqApRrFdADeeMY8TtgPWBzyQSGCH3t3xofXFN53yAelyjwZ17g1XlZqdyeX8y3Om3wkeA46MuqBNHYj7H6AEFujKfcs1DalvRfZ7pjjXKelj1V5DitmU9qmT/Aoo+TFySS7xE78oUnZDZhMPYG68eOefqsalSRgjTMhKOjnvQxckXCwK3o346Q5cHA7QwkepGlhJyKA2fgirGKY9tOEoeE3MTuhWF1IzgpxxQDtsVqdGEQYgB/cRnBAuKgK5RO1B5bTtBCCQxB7mSZdaMt6LzIsOG9KqB7Hp/tncGCvBJy52sKCy+EP/vObE4IktX1ok9sRxzUeGMvx4ej5hZ5xctKJU4Sb+0suGy1WqvxuQwGcO+RVr0WasYAFbT72ZUPMeGYM1pAxoHVCL4uMuuBNBh1ZTvAdYW+YquHgbfaO99dT3k7S8gjA/FD4oG+HbJkTytix6G36Tj1P3MxWd5vUmAyW8lySxfEWEEZs0787FvgoU2JggVw94ViZifRpsTPgtJCvFwKqEtUmYweQ+6AU7Srr3HAYXlvSzDPqywfGcP92krf1nUQVtZU1brXRs8xpLsYPPFdtzPBv4DAZG0KwiCFAiMpx5YpXCJnv3pHpMefgTfnddOjEEZ5CTZzZml3n8s4ujz+30WcON1fxjudqHqm78A5m+qjx2qsS5VaAjqXzDPifS3LPxUydvKxzrrzmcALWqMev4QbaoWR8FGqFKTO/1fFRpjXfU0MczL/Oz4lk5vs0XrZSmltrX3O7m5PGx9B65fGPDCVrLAabcykdQrBgq+a+46jVLYYxvQgOXdjiQAg7tlRLqGyRrzLc5mohY6Wh4W2BYOkgSCB2HxeKFWq8cPpXnb+tXgQVlpATfszLQhiLfKk2mzRhDMzUr9o64Tn/h5Dt5lEbaRfddZvuw+udDM+A50/SrFMfa+oaZq+9LTPuHWYoZ4/rUsmn0SIwGQemneTyGSpVwD8s82SzBvPD65oTxvRpCzmZrj9tfZypw1YIX2gPwQN+mKfDEIw1FybhsmBDGNWaXPMwAsbGVw0PLTOldX/34U7VBU9pt82wCqt3gYJYlTZzHvjDjCMMFnrGOMFwsTW7UHtkFetU2+NJbTI92a96MvALR8LCnxiReJ/vZSik6mUmXf4QD/VGDIjKmvxIHZhh5kp3sJzMaEf6Xt3q6tbYdXLpiYm5IjKZWju4bxCWjwUkaYMQQSjYgno3l4WLY1YuwKetFYoVBQgnfFKTT6uMzSQ89yjTpJH7X6YtsnFfid+wg+N3NX+uKvQg3hqWom9gQrhyahoWJ5cZHqYAt2x8cea5WtJ4U1VRILiwPG0tQSMLPUvIyEK1foaegIm5YmXXBA0XsVHP15UBDiFBM6vyvN10V7r4wKl1xtpvMnIiFh7V2oLxHWpMhpLxuHMGlZUUOKwxT+bFGlc4gJvLIRVfpsetvqNImDfr6LBj9QoITUILtHeuyvQBrB6/WsNzpYyEImwcnGRqdSfTqgwu+jXPci0QFra6IGQaXeVW2KJGszlG5smxcycZmz64qqtQaz6dRNzkMsF44k7m2IJnXtXisuVQ42NmJseSPQ7PLjN2s3mx39NzrqSwe7/OUHgs/JFgcZgExvb5EW7nNaOt428WTvU8X8/HysWXLS/Gg+A4KIffk2E1oFzNfDWvkCQv6b7pAsZw/Jj1xJolXGFhjgCvNJRMhQy4dC7CQrrSDUKu5hOq8K4CbK3nauiBNWWbus/Phvmhtb2ZsW3AcDlHAu86vOTkLbhbZnqYEzFoKy+PaYVY64NtoVrRV3wZ5+7bRlr1gEzLttidWOSiVDPleSc+oV0LT5/v4gv+AF82Iu1hrjIs42SUbNr4sDzb8SucasyjHqTCgvl3AKBDzHysA4jal2N/FtokY6qWMeIda8byBUH3y2/1wvWo/Rt+YIMgq4tpoq0xyZR+3b+3nm703wpuLjbownEnKOmDsWs81Vq4xhUtFFa5vD5c3n1Z50O4wXFqxnbNOHDVWnaEgS1Kw578/zt46GnPcO+qZ+zFzK0z87XiMMy21Fhr8iKsDWsxF1ukP/o0Luqz7tO18RWuOcsXxVLDescZGzaqh8S6srvQ8zDuLACtlGq4p65xpdEKt3MX0LuraHim5moqfyeXeWIuTg79VuOFi/FrPbxhn/MiPIfk8mu5eG6BVQCDXRWOYBHRQDWrbCAID2CdWcPMaRszdHUN2O6LZUg1AdqNGA1bbp21jNqBLO9yY16G31YXsT+YwjFjMw/lSe4nGUL9UP1VLV0XHCYEZt4+AGFigcMczJHPrmIwgyAwHLNdZ2w7N66cqee3OZfLeHGcnGw362P3GBiTqVKvSUkUeY1le67VY6rWVo0n2vJbZSo4WEv4wF6JLUr65zdCDMmwfOrRmbT11mt+JyRgGKqAteXukI4TeQ41WCB527GTg+YfErOOWSMwqGSg/VrzQ7BhSDkcU0MPNTzi7/Cc8RXdf1L6gt7ol/lbQab0ZWFccw+WBd6JW/MzyXTzCbCwjlXZu11VOnhBPIPBtnAyxgKiuuw+ag6iAol2kV1y5HssvusBuUh8OehuIvXCWKACx4NMLb/9jPdaETcysjxPuxe+zxgWFHZnjzUP3tUWtUsG4T7Rc4w9F5t0bJRNAPQHQy/Uh5lwm+nuJ8+nEpYJt56/cGnnUKaVExaqDk+R7XZoAJee+VSChSmJ/TGeldtchh0BDsy2PqrgrTjG4l7ovukRYwNaxQCp8UYnQ1dpCdN6LOc240AfC5nbGZ4kdeOmOXgL3NfEYMVHNXIO9d+H9Rhnm0wVjwUURgFzMP+ZdujT7QxDNdTAlV19nnVIkOu5TAUgn+9myCMrAdPL0wy/SocOc1Z6WWXILuZt+uBzPRrUhlsy6I7v1TPEa9+Ze+s0C1lL1Jx0ABjOLK3awFbvnCaCUb3gjGuXtoYXksvleHPn15pp1/rN/ykbs2tY4bVgmLvmxgJuX1XImwl9rgPMYsXlkMRV/RgOz/dpMNe5zs2RZOLDcr/CWZNUyXxYpl4OtXDNua+sl8sPq2tr4V3HrAy6LG3qFnpvPa+Cpc5pLgRnC9VrbQU/5zJX2Olr7sDzlGf82fAaBntWNUY/N0//nzsDu87DdIicqOWvlRY8jkNDNrq4vObmOYcPKavzVcOBtpbrfOsFTjAaOYuay/zL83UNGeMsbVOIX7xgg2cBsEh8YiNz5r81zDojW849FqIyhpmBjSe2FBmD5My6A16tNVtndoER4rbQnAwhsRQ9g/C35ZAMgcpn/gwD49KvrTyEJPPiz1UsxNcprTtQH8wP4txmaOhkmriCOZgX800ul53Zm+Ee87qdqYVdwxO8HNTWi5M2VVBz326c18Xegd1DK0Vf9GMlbi/MrrxDaQ612IurdArT22V32Ibf5rwO02HdcEMIjvEW+nMf1eI1cwKLjR6PWYVIFSw2JsyT8PHqinbJFHcRDHUTVK1MqPFvhLlh5s/4Mo9Uj7F6RsBVadkhSIewzMe28A8y5RXuLzK8GObFvO9nugZViJv+kunuPYwKJ00Za5l+2lsVetaGHO58VQAbQnM2uwbcV2pvbbDMsFKj/9Zc9ZVI1jxzVsNVMM5ZuU5yzVnyc1bdJvObGYDbbWpSygTv5IYTI7Srygg817irywJtVc+tafRcTSoBUxWuTtx5neuc7P0Y/3abqydTk4yV2axoPb9qhTqJyFUThnV+wLbMsLqBvQom726M+qjrNifw5hKTc4zsdQEPXltvArrKG5jrl2dJevs+6+nLsFc8XAX3Vc9e5aE9rS3jWxnMbe65aoMZ9+tRBliny1w+AKx6LXXtfNXNRszDa/A0mXWVLPgojEkCKeUhALEpXjVqMiw1Z/6tAWFgngVAW3QQvPtEI3oPuAPuyTTmjNBlUfZLf44PVZctmQp5C6C7mV4wMGMCFxrXsUsy7cTIqnKwdnfJjRccWHlDBEoMi9zCCrwdqG1lDJ67lemFsuSqVg7rbGFoPG1zWYFZGCfDA/L51U6KGD4LUysBj896uqSSC68E/CRTiwsjwztAHTZyso04oel/LmzCZ2L40GMNB0Tf7fmAC/DNOkITFb8ej+/0yx/eV425Vjhp73yJBYbHi9pA33PK5nDmeVuWq/J7MuQI/10UwPobzsNMjxv1/VpmWb0n1tN5CMNdt0knU0FLArwKeNfCp8BgWJIpf7JRaRJH4aHosysganKHgffUnrMYIBDH5rAqWESXsNWkwypNcNi9NVL8rLUqSRDg43I1QHL5AB0YCEZdZhw0TTsnrWh3WtrbgrHCsvbFLaIPFA/zqFqUg+phLjOnCdTwLNMSI6uMGmZwxDsErcG5Z/jrFs/oO2EJ7lWX1GdIOOzgvuiPTDqHJNky9uWQx1wSD2HKof/JdBddDUNZkfjUOOignpHyRJ+Zqw/c4jczPPhalue8bo6TbjJeFQ+Mda2sbPndsDLmafnsMaC/O7m8HihPj189GCuPZKyF7zHWnLKC5rxhw5ufWJcbmc6V8TGOoMOay7IhxLxqOMzeOfSOEjCPW74kI/Rqb9OhDytJcMB3nyViDz0pIYt6WDKuuU336h5ayBhZaOMH+o1F8IUWd7ytXtW85wJJtprQkjBW3ahCf3MWzpybVRM/ydSqYs6uPcWiIR6+zOWC/RoewJq8nUEUDhmYCUlcVgv0z7sqrAgNr0tNCNZadAv+udAKLhsWCpfn4PWo8fvksnVOuMbuI3hG8M+dyVHjdfbW6sXc5kJeyeWEc3V3/b2utWnHc+S7vQwLHicWa8iitqtzqeG/Khh8rkOdg+ftNZ/jj3o5DOKxr6q7ZTzDU61sx6wdEnMiz3P0wUBPC+VUQ6YqvGX5jWfofy50NzdWna/HrzAuPOgDNaae1HHLTabvkEqGxnOZGlsN2aUGwtalrRmlHrfHZavD4Ylt+e9Qy1r/rcE2pf2c9rpq7LnkgdvYcqonQ/Gc51DDAydpREsCjb6wFFhMu89PMk0YWKPTr61WtzNB4vmgqRGAyXQLvK0Eu48IYfqEbuwi2mKwUIKpDLddSMZ9khFmsSUD3Ti5zDP3M2rXGRcrspbFGefAcqz7rGnNEfCsXyWfTN11w8xnGLoqRAsV6BR+cinaMlP6hwZ8HkoVOOaTuXBfPe2tvpy1erKeG9e2fMbY4hQ+18E7YWo4LbjtDeO52HKf42XGqDy6Kf8X5XMy1p9QiOfkM2CcR5jDCeP4aFjDQbjO4x0k2Xk5uXhYGgBgDTdcJfV91T6q9exj+ghkbzJNGnq8ajnMje/+Cajb/a+JF2u82lfVhnVOtzLcJJ696uQoGMCWV00sMUes6KrtLcDdb7XSqqatXkGdJ8/MJTohdMcT5yxhezbRXFLmUOfhPrxezI9xEXJY215D73x0spH7tjLpo1YFYEi4xJOLt4PM0dFZxtkseEPVOqp4sGdpOqvJ0DmLt17Gm3+bs5ivWg/GrBa7Bbx3xM0loT2H+n9uvBpmmrO8q0U+R6NXWaPwmHF9r3+udcI+C6bCUY2DpyX752TGnGFpOIEVWreXsKhuhDs1gdpaMLBL/dG2TmSjZ7DGHKNECNtyw+JyuMAaCitukFWKAAAgAElEQVTH8SH6h2CuyiJbu9W425Hu0SfPHaZZsAjhOr9kIDqCnbkhNLFCbT3aGkY78/ycS+p4XbUu6uacWnLkJOSc50HCMJmGCjZ63m6a8WRFRBvmaHxZCSXTA3VcbE9ixgxrt9Xt3C+4qPFi5oSgJsxkD6LWgFsRJo1GoOFtxqH6y0wVgEMyxMaTETNlPhVGrwmX1w86T3kO2vNvhO18YRnaOmQuxK5rnsKhPxSTN234P7iqFqmF9RxfEluFZm9nKiyrBc9n8AWPkeRdpZ2UWGmcaID7vpWpHODP6wHc5i3DcjuX5RHPwbOG1Z4ta7rc9lc4+ZU0F+UvHVE7QtY67TVE1Q30K3LOM7UWXLOb/uxRxqtXFpm+XuVcY/h1KcBKEmaT6auHYGC+76rduZ6tGopXOCVTgnKCjvEgLl4jw1ick+zXTTFfu0BmHOaNQMBVvK7ngYfx6R+i4Tv3+A+eEBQ7uSyYaL/tMO5kxKlZTxJzvFIqaWuD68v40AgX8F7LVADs9N+Y6zLTubFO4JCrzpWwx365h6DzuLfS6Ph62mt9gGWbQROspefAd7+GB7rcZrzuZzfTOSaDrsG7n0lGratr0T+WJsAtgOAFhCY0a3xD8/X1TPx5Pgv9p8+7gqcqhGT6KiaMIdOHYTzPoC1wC7zmB9ab+8/0tueZ8vFFBr/TdiV4d3JZvgDXk34f+kVQXuv3djMNpZhn+Y78sJA+zPRN7k8y1tu0GY3pV6stM5Vnu0kWfvMvE02m7rA7xOpw4sDxN2siXMK5g3oQ6o8ynwSJ2rhg3FrNMShrc0rVQIgtEsPPvG3pMXZNMNQLAU3ChQVAEJyVZ6Pn5zwN4LXl4C27c+2SqfXDXIGDq8Zx7WbVLc32Vpjj3UxDTfRBrHitZ4HD47jKgfke9/ndy9SSiJ5BcdEnSslnhFhpemzauQSJZ5yU5jcfkMNa2mqqiS9bjN7oFP1uHiCfwTNn5VnobZuxC8yxfdPtkfoGHmjQli9zxjqzAeAzS5Jpbf1+n3uNg9MH6wD/4ukyj2oZG29RH8zZ1QnksRB09FNpxF7CXBlscvnEQowMZI7PhcGz8vr7QgCbHqqMqJ6cL3tLxxmn4nluDmFNXCa73yZ0D3Inl/e7Ow5mxgZwa1/GceLHrrwBrckxH+eIu5EMxDiZiOtbrQSQZ6Ta3UX73tD3KjC4fJYGuDjq/eHGeCFrEsIaNvq9XnZ7uKz0ENpzceF1phYXTIIQd5+0Z+fm/Uyz9qwRYZulfrfFYCFG+IE14HVU7wl+u/yec50vePKrm6wsXB961SExJM2gHY+LcGVdb6l/C2P6q8KtGiqEYdZ6lnUzvaf041ATdEMIx/CAX9bC/ANuTHMpz28yxeFJxssHVpmeCwNueHZP/220sfYWdMzDuOFzNWj2Mk3Ibspnzk6pxpj78pGb0PGN3rcPyacAwPBDww4zeQez8Y8yvKE2Fq6WY4RlaO9Q8FmS5VHyOgJkk+lbbJkwbqpd9g8zJRaesUvsMIJdJ2pqrWWiPngTrV0yJnWRwdj80RcaEOLF1bBbafd2VZ4FBgs1LDLe6JyMN/TiwkFEdv+5f5qpC3Su+4s0t2k/06RgtQgc6sAFvKbfIEC72ghd3vZd38wMLpcZB57TxlbmWcZbgetbfZ9kOq+dNAI7yzS0sZMpbdTYmq0qBN2e/luwoHiAfSV46H+j+7uak9cCIfREz9dk2EUak30gHETP1jAV62C6QHg5zAIubNEyF74TCtvNNCxh78O0XE918xuka1jAbnIyFeKV1wg/8GZl6NECC9cdeDYZ/GKZAf05pEJCnGeBk/AhfLSTwX+E93YzxS34cdj0xQ4La4KRRciKda2hQNYRnnZYpiq3jdrfyDA8Hc4BVofmPEd4YXkjeZ0Y0k5GfM1JpWuZLjidsvAIieQyw5tA0idUGXaRES81k9KG8YktWdtaC9OnF5+FJi7KGMBqpuC19Y6tMb+F+kHAX0tjcl4zD1GCcAto4+F2BrNdz7DEIPb6WnfGPO+/Oe7K/IjFey3py/iIvkMoKCsLnWQwGBYEzxMf9PgwURXq1QInbgfugAnLsTJ/Ms1nIKBhStz4OYHKnO3B7WSKi8MMw4E4JYJ8mWZ4mIFsSbFewJtMhUEyhAnXYRp/gVvWiDXn3GHmj/D0msED4Gc3QwhcZEpDq/K8z/qFRuFJ6JPYPoIXGfA401zD4/KM1+BaedbwRN9PM5S4FQw0tinPOjfDmDW8YAX9foZMsCFhuge3u5mGlVgXjBAEsfNGNq72MpKiC/0nX7VM49PH6j8pnpbNdC6SF0zUx15yYbF4kR3esPa15nQGnmedNEum9ZoIZ7tpGz1nd9kVB/VZx589B9+vLnONKx+r3YG+s4i4obR1Esh4Bpdks20dOibsuCJ4Y2EJXxBH8068q2qmubxbkTkYLtrAtMeZnuVgN89vhFirj1v6zRUJvC3GzxFKsTUCDBao4ARB6LU2TUW/VxzQ5yZNMdrdBwaHWpLpLkCEBOtRFd1pph5NpTlK5NgI4d2MrgDy2A5r1Aw/NOF4rxNUbLlHMUJvhIjqoUXJcM1rzgA4oB/oG3r0VWPf9I0Srs+DM2/useKYCw/NyRKuyndc4Mb0vFKbulXb//mMoVf7Nn/y3WHYZNTGb65ou3O7C+o5gjYQNa7s2CzEstWzFjJ3Mg6QP0iLG9YdOGv9VmEhAH6aIfxJpvnMg7lDZoDf+9MRMvWNtRUPbPFGy9tF9xkPzLPigN8rUzJfx9MsKObqe+nL9Z8RXFftRqPOdlV+n4Onxu7dv5+1K7664r8v4LZbjsKfY3yXvVnhQyOEGuwhIWwq7Vhgup66zhM4wYXXxPfqwTIWyhgFjm0zBiETLOC5eL8PvYfmwDN9cRbIXE2zcVt5wQKgzpuDh7wDjTkbzrOMZOxVeKVGez3Tvtbe1gOPPBdgrqEB8/lVx4Canrn8bFWiDq147o8yEoN1znOwJVNYHKJIpobF8orPi5pUgKh8QQhYE9F/CGZO63H/nQzriYSbCdsJNo9vDYYmNqK9oQXrxFrNRIj244IQEGROGHAhjG0NQVAkqeocsGgYf63vHg+4beV4Ln6Gdms9j5KBWUmeOp6YTI9/TKYxSFsbJG7mMsle13WmiVDiuqtM9+hvShvWld+NW/dfvRTfg/5QqsYBbd3G3lcyaIjdja4o4nngBc8r/V6Ty4xnD+9heQYcbzMSVbbSGPuu2izTPAd4q3pNWFmrTOnBBoE9rwqzKzKSIajqWTUIqZXa1/PKzZvVGLOytGKHlzDSCBl4LVHEycA/RlU9/jK9n6MMJWZByFG+9Gue4rdkSisoz5NM35NpL8VtmZeVCp7QWm1JNJsP3H4BYiGOOYaCMT3AHEAuX6vufv0/t7U0mYZKuNgx5fsmLMazlTdHHFUref+852TYCL9QKF8PJ+KC0XB/SZJhJcCc3vyAgPcbhRFUJ+qXk8nmBAZ98ZxxR4bcFhwCFAVImIByoye5vDbeJGKrAFygnObehQgcy0xLy04zzu2ocWtvorAFuRJs4Alc1LMbWH8ECjTBWjLXKhjNzA63gDczky9XsFQBWT0G4DS+7EEsMqocoAnfQzCS3K4le8y3KiiU0Jnuobz9m9tBi6y1hX6t+PDcVqU9+RvWc51BeweZVjFwWTFgdACfZRKwnGZqLNT5YHTh+WKsODwFTqphRD9sGkKwwsdzsq5+nuvT+x6SZHnQqywQdHYvHLwn0G+TnYQGSYeaKXUA3gkVEhBUNJAIoQ1IwvU5TFtQgvoQBBlYYEEjw4QkWmq4hfvEXS3EYMSN5oaV81yatU8SyRUIBO8/3vHlpMx5f474opMgzH2TaUbdyUESCCQmnYCK8EQ2GUGdTDcRcM7BeZoFxil0zMFhE4dTEEKs41F/DiEIbDXUwe8kstgUATN+qPnfED7A3SJj88k2I3G8yEiMrTUOF/ghGWxDAmHlShlowlahM+Qk5lBiDnNdy3T+TgghJKkYudXhPupr8Wz/TpXCjuZqAbYjuB0S29c4O7lc7cE6kDBDuXjezNEJ6OsZr5m6UJ/bDvOHfUwnzy7SNnVcZNC/k6SsF3RO5Q9yw3XdrBvnZrtixF4G87f3DA0xP3AOPsArcwN/wLbUvfTvbHZhPSwj0j+T2CfpSqKY+bvyynsULIsukixvJq8zaTqimgEhcqFOb2Q6mYtMY1/L3g5AQKRLU5IhnB338eJTMoars5txVCcIssB3OdO1DIJAm1rgg8RFpvDZhUcwelffaenD2W0I4iSXFQ19oJ0Zo+4Kczbdu+WorkCZIRjtKtfM8PWMHUowkROrDzNVAFaEVr7QA+MdZDAceHOJFuuAgPHOJZdbYf3AnMzRYaU6b0qOHLZityd9wrDMk00A60xL76CTufh+MgSgq2pMc/R/3sekhAlL2fQ+dyQrFh/GBEnSZzIYvHqLplm+owR2OxwwvPHMvFhrhK8NHvIQFjQWINA6FTKuygCv4Nk7VZNBZ8lUGEGrFnZUfEB/LkOr+ON+DV9YwVLRgIDnPuNjna8zrbY5zqAN8zyXaXqh33jeMg05AL0gSzGUrEQWxATRMvyxcwetARLq0ZvJ5eSeE3O2thwusAXmvmrYg3FJTGGlQMjJOEsgueyyQmTE3EzQXCw0FmNNfmEJkrGusBtuEwbEbjfNsa258zkMk8MLMOONTEsQ66YO5s06HaTt5ze8jnPVOCtj4d56jgjMGrO1gGS+h/rzDk2HrFyxkExpi/uOWSeXY+BYV3On7QHncaYJMpjJhxp5XOiM+ZNUPtJz0LCFaTLcYZeNIqSNS5+OxxiLjA0znmsyPWOF+dsiZx3r2lzFc9sMfNe8EJ/BKTTI+Ft9N14Zx5sxyAeAc5dLzuWvDHvUFp5e6BkqdMAFb3LG47A8Az7zC7RDgtDhUL9M2UpqLlxVY9EOl0HDDql6XSvv7txNLhxumLuqkEumL/ZjEmTT5zKYbutJeNFrctDC2RnO2rfhtGtmYqzjGw4WwM/V6oJN5vsxU2XmecNtgeSYLv3w+yYjPl7PAF7rPwtOKVl9ZZEvZ5mZM2c2w1CONfoNE8Dsc4cdl17pXhVytt6rkiQO6MtKwArDZ3Uv0gSUM+3M39Um9A9cCFYnKxGgyWVcc9ko8X0ErmG3MVJpw3DUM37xPkh0Of/AfBxOJHZrWgVOr4PXtcZOuZy4I15cq0mMi+QyXzIWgtC5E/MObZkDFTSugLCit8GwLN+j/ips9rZ8v8JR+6/9IPAf5vKLTJ8mV+qLT+tFO2jwI3wdJK8nI/TgOkoX3ydDyyEIIEpCHN42m4yY4SqXd9Fh4REHog+7MS5Sx8zn4jlMfhCLq2xBiDtnweADTxxuWWSq8RxD5DO4wNUAhmTEKi/0DHEl5klfm4wFt/tFfI0YOOEPwh1YXeCcdsuMEMEiY5MPDIegZzMLYRrXO6M8cVGBsSY/LtSn3U7jDfcMWsG99eYP4wzlQN+303bJgWdwZnphntAIrj5CDcG1SAsHQHsU6O+qn1WmYRfHXO3SRr8zVxiM++CGPu1q+5Ac8Gz3mF2YrCnr43wNYSTmy/OEd2B0K4XrmYbMuFgnaMkW61b/PQafHR5go4tDXawlOK012szdaw8fOmzhGDd97Kid+R14OEzK1jl9wHfkJW5merSDPbHz8rt3WiKUyQdA964esYzis/kIOXGRZHkneZ3GuBoQkHfj8Ew9KY24MvecHEwuM5+txa3agGjihraSvPuJSZpxzCj+jUTLWvdxZ3Bd/B3icxKE+Cbzh+gQVnu6D5E5E+840ypjh9qO7oMzLE9rVxjWRIGF55hsLdsjpukkhN1oEkubTGOpbMs+TyNS4CRck0wZlVO5WDNi32YaYovsvgLfrBtxSpJMxGsfZQgnC2Bi5MlYW1tjuxrDL+klnkyyBSaFXp1QgwbAseOCvhaZrjc7OE8yhAtMj8D26YOOC3tnGuMRHiApjvdBP8TQa+IX4UouwUaBYd8TDi20kqnlezdT/jfdoyAd0+YztATPOPfEvInZPs7Uy4YuzSPJMLqoukHJ1nAksW9ki5VeMtYJ+vN6cwG7jb4qa+BdyuRc5WLLPnrev03ybdSvos3tYtnVqpoymcZnsDp80V+NySajpMcubY21HKQxE/ddjsUi1MoA3Id6glzK77j3Pj85GYy11nNJY2YQ/9HLCDMQiaW9yHhtkOHhYjxfzMEv2USI2SVyDNz4xg2ttd2sGXNyrTK1nI7/eextRmzWVhXCh1K926UtOMMFtQKmQsMhAtrUmORKzxg+1toHx5xm+tLWRaZvM/dLUL1eWJ/gAReVBJfp3TB6HMI1yRBmDvcg6B06wJih32SEifyyU9qzDtCf49XrTMOECHFwAJ2jsPnd8yfOS0yVU+5MOx/kcjwfYb0t3033T/Tfp/2ZzzYZa8Tcat29LU7i7euMc40Pyz1o1N4496FL1yZjkFq4c/ncZ+D27k3oxOWB3Kt5JYcFgfNY93deTC5Y7BrzSaauwNx3JuSdbzWuRDtbkVinjn3NbTllIbwTy66S4z/+T3y5xl2vujyvuRhTvV/jTY4L1niuYXDyzzFwtCQ4cX9zMHJ5c8jc+qXM3fkAj+/kYjIYyXTBc5QgOklY438QtOun7VKaFiBO5xMQiuxK8xGLVflyVaHn+dqLeJRp/LrGdIF1bs1dh2+lDx0c6HcLHmDxVmlCT5tMa6L9rENKhocYqd+1l/I8cDmRZUVQ6cT9rzLdau3cgePU4BZ42X2HcjIv0S8Wb3QfnnE9tGPZwFDx53uWLTVWbLqvhiXC28qLPurOWPPwHH1UXpgzDKHF+qamTZKFLbZl+Q+T1tgPGoXLJzbZYnVGG61UY1jO4PqoTgCkCPuh4DQCgBf3xTADA0RroWNmsUtEv964AeK9q2+lz7jEwGOLif435bMJhsWzd+JrlWmsLBkWnLPHaGcTP3mBpJ09THiBOXJtM85WuJnpmrOzLWkC0uELxrNFtc7I0sPYwO5NKcyDOQA/gopkT7UynQQzoyWX8efvWO4W0nhJyZSpq/BbqR2hLCpeUu6bPqB78Mua8KZn7zwEj+DPSoM5m6553sLFOysdB42eY91NJ/U6yHSdnWdILr9/EzyfZJzX/DRhTPiv8gzv3sNjoB00A/685l4Xj2dB53tee6pq6q7fq56veK+ycFPaz+GW9WUdTEMLkOnOvYAmJjOUBYrLjGD+ZabhBmeFt/pvK3BT/tdXotTQBovq/hGMdpHOMhUCRuBZpok1EGgrh3Hpk7G8U41rqT5wpZivXbBkCPnqkifTBfUCO968zlRAeHcfczhNi//tZxpqSUYYBgsnfU7vZliTHp+ddczFZ0Xzm10yVwPsZ0qc3DtOc5ERANQdX4VTx9+Aa5XpLkV7YFZclTkokeKP/r0rza6zx6s7E1EQzL2G/QzbSdoGo2o9V8WPdUZ/0NKxnrP1XMvrTGtXbUevv23SFLfPeSHP4nnwLHBG92sIjP91V6ZLWR2OS6aWp3kBmVGFbbW4uapRZGua8BqfPXb1KnmGq4YimPteRljEYUfjIhkyB6X7kQHLQtOgCpQqoOcsOE8G7bbKeEN1Bd4Lyb3q3idDC9cFr8qgWkf7updcjntZyCOoiashgKpLEj0DHFXAcaFRHcivW67tqppxbAEYXnBqt5Y+DjPd3898GfNRpotua+dO/+yYOsKVZ7FMeCM24/vUumQoTQiyuu38dq/3eav3ieIw/pgn3g243Mv01D0EAG67Y9woWQQq8VHi6A4VGAbmA+M6LEZi1C6plciNmX5sCSbDsDGDOiyEQFjqnsMqWPmmTRsT7pN7wIkQsgBHcGw7/G/r96ThiIPUHRZwXom2zA+63qovG3KeG/8tX6qwMzzJ8Lzos3qHyWWP+CDNOLEMoyKljjPH9+Z1e7XVAzD8vl/DO9vS9iBpB9RD8GQY9zIVPGytxTVnEXb1LJlrMuRYEn4P14XaYOnBQAiw/YwqD373Ocgu66JPXGLces5y9Y42E46RY7fuKFPmd7wJIkMAspBnetbVHi4lIvuajkvH3pgHiojMOwvEwjmsYiIG78cZQnuRkWGmwoVMM7gxc3CflwLUHVvAWA/Fh5C9pTmZekFkn0keIbiO0xTFuZ7zQfisNeOBW3ZekbHfZlRcAGcyysGYz6Y/R8mSK042GbvcEObQgdeFqg36T0bVDKV3jzvsjONzn28VuKCldZ8f23xZd9Z6X9+Z4zpTPrXb7vcIcrnUimqmar0ng5ftDa/1O89Uy5MyN65KK8CwyLRCKpnyTrWQ59oBC7wOn23KM1QzQffX0nas0ieXK2kQtNAHsFiOVTmYDHmDjHPoFt46VV+sI8+8lJY4Xd7uB9QDmLdsUm6GgHicaa2xy9m4WMhtQV76RPkMASdDOFGuQ0LnqlI2Jss41cWgHGiRaS0mfdka924xnjWhb9WOywS6ynjbCjARBjnX7+n3cMsJFblEiZIzSrPAu90xC/2V2uAKM38YxUKEeUM4MPqePjOu66ddv2lcIMhQxMBykra+0Am4RphyCBOHwOOhrDKtq3UM1QrJdbzJsFStrFmHWxnEv8wQmPRxVPBFbTfWDmuJsKO0jPmCbyw8LPBqmOBq76SFfqhZ9RECHKoEvFikfLYFBh4uMgQ5yglPCOOnvnwVnrXQYVzXOiMwTHMWrj4iYJ2hgCiVhb6x/hHaFkimyZX6WZS/TaZ1x1Eflg8OA+6XsZxgcwml5RK87/p+toJv1NbHHlzoN+Cdq0k+UHsnWnfTogHnSTugnhtODIF8gOMZv3UDBgFhNaa6n2YJ1ZjzNtMXZDqp59iQ/x/q3lrPA6NPJIP416XNOtOaP1xdx5e92CZYflvpj+tRBuPVfvwceLDrhTDivi1PKwmfJkaJF4rMsb56GpnXoiZvsbpI5jkpejvT5BqfbQVzROSi9MkZCRt9JtSBcHZsloPi2WoLflkn5u45bPXf7rbDYfsZoYhqMTpBRrgGLxDaYCxww/pXT8s0c5ppuVUyEkdc72Xwj3MBlBI6bsxpfeDAfAa+atiEsBdw1pgusLjKxOEShxT4jTVzOM88vcx0hyCenU9kox/ooo7jLdc15GcZAR7wwowznuEsCuaOwicu71ABdAP8jHeYRuMULLAOy0wrMWp/4MMxZQwmaOWqnZA7d7tycNwYYqDsiEN9mBgnnuHO1KtmU72ALFYyhKdLXZyAMVHXmG6N78IsT8o9M6LDF+s8vT/3u9TzVHy4ZM/bw1O+4/6QEHSM96rxnayxdWa4IQiEmu8flu9eo7n5HGS8bHStPog3AhPKZI6ho/k70+8EB9l3tgc/l+RPMhiFzSrJoBXcPAQP41Rc3c44B8J5ABsBbB22gEAh2T1HODopA0MmQwijvJ7os2nICUDmZHqpJWqVLh0+meM15ugt4VYQc5fb1OMCGNfj15ivw3jJNIRWDQqHO2jPnBnDnq1xU+GtMFYFaxlTS9UcBoHGr8KReda8U2P2f9Frbk6+WDvmtKCRByfuxiYIX2RfIXC7js7U1iQGfZ9kiiCe5W9b2kbft2pbLWms4vSxOPDbsLGwTnZgWWzLs+AAwcDzCECY0K4Hh5tUIoTJKHFydQLWE+Mj6BnzVO1pUzPAtrixCr0WLl9jDgjj0zRBBg4OMqxjBNjtDMFxpM8VlqSVzIHTe70Pyqc+SFPmyzQX7Z0MK9V4dMzczLfQ71YKbJElfm54sEBhVmLZrhPf13f6fZQpjmkP/mr5HnTrxA3wfiZTSwqFZzwmU7oknMbvCHcnqlCEnNVt2lpkWPvQsy0506f5woJuWf4nY+MKl/mVPqFll5riyZ6onXmRccDtvn6zt2zhXK1TaJbPvuB1l/kCE+vk5Cs4X6bJkirc3UfKPC0T6WujP/djqzmRhcxD1WXZpFkyb2WKnBrb9EYMhE/VrnPazc9U7ZtyjzGwcvz6FwjP2s/WtfuyAMZN8WIdZwipmmCrCzHn4sG8HgfhBtFUnCSXLS4z7FrP1E0Etmwg6tOME/vowxUAMAOhiGptgxsLgxsZr9+yi+/qglUf0y4pr/WhpnyT5nmxieBRpnH7ZYZXhjBBSVXL0qEc5oaSMo7X6i+ZHkiEcvKpdMzN50k7jOE66mWmdax+xVDUjvlHuPGYNfQDP9hjrMo4pd/qDTmMxm/QLPzkcU1LdXMQ86v0zlVdfnsyXOZB+ltnnjd5nuc8h3Uuw+A+7ib5cS57r6aBikeeg9//Iod2ZeZelXlW1J4bB2YhG9ZJq7LggQt1gsa4yEAyLtQmLQBNNvdCbSemd6bagoQVSPNe8Oi32p7kleO0Z6UPqg3YTrzMSEZAzBfqh/l4H/7zaVnYD2f65lmYwucdOJZORYGTAJuMhAZwGHe31Ia98wcZgtcJRXBD0mip+8bJblpi4UZGEsolbCQzEDQkQMBXFdjJSGRBcNCGLVkIkcSk366djPKph/0eSsq49jrt6juJWtaA/n2wO3DydupkbHbBmn6QQYv7/bNfBgoMXl+y9D5jIsKHK4iYO3ziRCBC2P2TeCOpR+yYzQPGz5zAQqGwtquMszxInl50XPsFCFiZZ/oduFa5zD+M73NYzKc+A8ZvlffF86wd8/KLDEwD0Dg8RDtkCIduQY/EuT/IEIQ+jxi+tUfuuUA7j9R+lZGwq/OuwthJx2Scl+1iCNpDj9DJYYpAZjIIXgfhk+mB7IsM97hqBxYOorwmxFkQ2nKt2VMLMZeaXJVh5bdFxqLa1d8pfyyOkW531MJgk0FcKCQflGLhusm07Oi6+gGfz6YRTDI0I0xxN00hOMvLBbP5Ne1UcoBLhD54IiGL4IcozjIOD8KqBb4nepY6ZeaKN8H6YO0iuKmEoYzsIOOtD9cyXMzrmYYBeCV7+n/izT4giucQzAhJcHSQacUBguFexmvAUHzbDOVJn7czmDHs/AoAACAASURBVBq83ezjoygfZnoAjWkFhkaAQDMWaFbuJDMJN5hvKJ1inYA75f8ig5lRflTWuC8UpqsL1mW+wI9whw/t0VowWSihNFzVYNp1aMDKBQVzI40nwBU4t6Vpg9F48JuBmCdCFC/HuLdx5Zh2rTIDZodKPf6ccqJ/6NLrPRePN9436QIZJkHYug6VP0p8/GaI4wyCdskHdbpO1FykHX/4OKPkrVpFtraXmR6JV90uu1vA7In7LRbJYGofbWki8vGYPtW/Vi1wItV5GvPD9Ptqn0wtC1xK3CxbXraimIPfCpIMgYGy4CwGlAsMtJep5UO8D+v0SZmfCQYiwcJD0CfDokPQW0kj2MAvY0DEbIWl2gZFca/P5XGaZ/KTJC9mWDYIdfC5k0ZLXivokKMPbyX5adqafZhphUe1UoEXxkHY2vuiooi24BIGdrkbz23Vjpp6+AFFyjw+7G1YHwQnuMdapYQK69M0D9+BFyxthEPK/IHT7j3lhwd9LM8T3qe0Ek/FnjD8jNcC7mwpVqOJC6Fqa9pH3PqqAs4WdBWY0HhNGjIP2lueATvzmvvNl+WX8cIbjTwuuKmGZ9RukWTnXnJhsx3GrMmaer9+Jm5ma9lZ42hQW6SOk/kAGZBpYUafFpA1rmaN5uSPiZN+GN+JijpnVxfMxa08z7tpAsWlOYflu+NTxMIdp/XFdmLigz5zwXFV47TGJdeZX0uXKGHhOP7v3XbJqA93zB18OJ7tdSOOnCQ/13FzR+OxfRj38GGG0gR+dnveSdvSXWPBnjebcMCZY9HJVIkY1yg60xnrY/x5ruDfa1Fpe19tGOeOYALeueoR1mKrtt5Zl1yOv27SaPAd3YcvzTc1rpxczlngUpuGzIvGg3FDe3imCkXzZa2GSHnGl3cqer6mTz57Hf2sZVTtYy5GzG9OnM8dl+DLOR7jJOoHbz+6v+xtl9eT19EaSG+7NoQTklHkbS3nOCnWMZoIi2pH7axB0b4Wjjb9iT0+1u/JsEKwcK2t/d40ngWGZLib1sLVnXIC5Tzj5aGMf6hnQaatw2RoRKwjFggGZDwI5yJDEHE+61mf/wcZLjECF/iIESfDWr8u2JjjNs1FtsW5m+mh7Vg6xEptJfBHcT5CxfWu6WOs1Qfn9cJ8mzRr+V9LEx7P93vX0izddzI2l5ynCbDHmZasoXBhfHsltvyIYx4IDgQ+fUB/0PGNPt6djnfozOdr13gyFjUbCLAk2RQAv2BFrjPyMsDO2nnzhOPyDjVVume+OxnWNNYY+ZBkCAgLTpfgEWaxK20+8ndogTk7DAFs/EZt+an6Py/9chFTXWZaBnkq3CRj7Z7RHG0pU/9sS9oW9kp91Q1nZ2rruSFsXT2CB06/lo+Mg8dHVIEkITISz2mdZLHVA864+jASNPecFYcVBVGjOU4yBA9CB+3iuA3I84Eu9LvO9MQpCGI/483HCFPvtWdhIDKERvQdOBw75nK2lf4Yn7i5rSwsBBMXuCS8wEE2ED/XSv1gaTkrTHKmWivM4X6HBwKklA1GYG5UNEAgxC3ZQEO7Tf/vIzZRJPSHBQ380M7NTDc93Bf+biT5bJrAvpVWtfP1fu+vJPnXk7zZ79fT5BiDmCohEkrqEDSECqCdZKzHmfqo3gLXacY5Dh9knPVsmuSPOWOwcHobSmqVaSkU61436lgpc3F4EDTG2R3eOMXFZp5tRqmnBS1X9QowKsyvfqZ6FTbM+I1NN/ZYTM9Oqq7L73gwL/ffyM08yKBBl9xCs57/OlNvIBnyBlq4kSYrbK0aXmjlrNyv8W9guiX417l8YNKBngdGh3+Zkz1nH4Q2CVnMuQq+bEFXFw4BtNZ3XAQmZaRZu2/SSuveybTUyv3hQiFQ7eobPpCCFcLYlUARWCflvgu1scIssGkHwqug9ILY3QQOu3OGieQDMcmq8bGMIfgavphTliTHwDPxS86r9RzYBIRFwvq4nGqTYe3azWcuLn2EucHn30yrx33UPy/TBN7DJN/qff83vd331Q9CHRxt+m8ukzvKZSFOWAVc8g6+Gkarwtv0TyjLW/xZP4cs+N2KigvPwGV10AYVHzU0wlgWCjV04rBCSls+76stVqrfS5gMwfiw9EFbl6+aXu2tMidvpkHgIAMs7Ob4kT6qYOWCXj0etFeVh8Oup2rPGtU3BIHrGlJ1iLLC7EqNOrdkGmJx+WnFW712LRjrVX+HcQHU1uAcMrzIjhVVZk9GPNH1nGhFx8n48+leJxk7tWAUJm+L2fMh2cZYZgTmRz2y3cJkGj9y0s+xYC8y+LAiA466AYf5OBbo09+O1Y8VkuOaSbM8fpypdneoxxcxRcOaTAUCbhjCFzipQUZhIHhe7vCfJnklyZeT/NUkR8/2yf1y+3/zpeTr329jfPm/Sv7nNOsZ4YHgrXNF+FEb7VjdTX22Z0d8PBl0jEACT6wdtMgachiS49L05xxIXX+YkRDFzQzrjzn4JLSqqKFNhDDjusQNHDDHvUyVdt0sxC495mRPz4YKBgnzAU7j2hfGBHybDGFc9ynUOvHksjBzHXeNCfOZ747dchle4E/mE472ts2fXgevzToj/FBxwvypYb6vZ7zXgGvS9ih5fZGmrS/KTVx04ii4jHTish6Xw6wyYlnJqLAg1uNDgUCEqyOI53A5bpQMFz4ZJ219kOnrb8iAO0taM6a1FM6xr2tpAsFx1APNk1I8Kjb8nTkRb8X6jMZgHgv9EXcmhgeRYe1gQfvIQIicxSY2/DiDadk5h4WxVZ9YBAgxBIDLoRznoj9wzL13+jpQcfJ+7/tXk3wtyd/9YrL31ST/UZow/nqSr/RO/pMG8P6byRd/mHwno1RtN6PaxrmGZR/v/QwXkhj+RRpd0GbZ+yJMQFKlnlAGLmBKSuigHQQAORHqh4lP8/5A13QTTybW+iDj1DnWB2uLyhDGY60WGXXTDqMdZ9AEfEuJIQKVkwYZL5oDtGsByrsYEaLOWaAMCBtW3q2xbuia3FP6fR9uBL+5TI6QGzKENWGO0B1wUI4LT3MRP6ayymvquL6rT8gVbXKZNnxanA9s4n4yTixcZVS78BtyA2/Vc+LsjY/K3rDQnMyrACdjUVkcCOdehvtPEuQ8Y6MGiTHuI1BcYgaiuS7Ul2Eg3kNSBKIGbgfxr2cwQz0NzEX7DvrTjxHpJA1jI9icoETQIVwhQDM6c8QSqAlN5u7ECgXmTqrZjYOACedQm4pyIaFyoD64SOZtMt2kAsE7NsvYRxkW2HmSV9OE8stp1tq1/vdX08JRn/9Pk3wsyW8k+RufSN550M4c3E/y3H+WfO47yWeeZOeF5OO/PUJYJGQhYntb5CGIK7OWPifaRsQzGXFTW/8oP052s2HCCWYIjmRsJrGh4AQi8EJLfoMGp8250gEYMYpOO6wojcOMkAz1xos0I8RWHfOHZ/2byypdz4vxw3wdvjRNgi9KxFA0jutC48YLY/Hf3ibjmtc2aouBBl/xnbVwQvEi49RC5ujPCGXzi+uck+E1wR/wkfHBmIvSzvsSKPuknJHwGWWFwGL8fVSOepS8zgNYVz7r1VrQi+zsIoOCbDYI4CogUBCEUZ8X6sMWtxWB63TXmdYSghhcQWtLx4EQMvRDRpvFhRFhFggFuPnvLDPuC/0iLM7UttYKk+DCEt1Jqy6g9pSKAAQiAsEbDhC8tsIR1ngnKD9XRKDcqjBIhhtHeAPrGaZiHmdqv+iwn/UxgOm4t38lyb+X5K9/MsmvpGntD5N8+CD5xb+T/O5328O//U+Tz95LXv7l5Jf+OPc2yae+kby7Sb6XJpx+mnHGxiajhA7hCt1iCUF33sgEHdn932RY/3hFrCP0UxU5tcqs60mHkZKqZwQXtO3jGQmXXcvgF3ALgz7S2kKr0NQmUwuN+Vrx4i6vMhQqCsdjI0Cc7K0XeNjN4EELNKxgW8j8+WhT/36he44X4114zq7YsICvVRDILVuuwGhcbGf6YF70RS07eQvLK2C2x8a8jJPHGcYkVVOsj5XFtQyjbvlS8ro3RQCszWq71CAcAeDkgwFG2zl2CwAuLAd4NCEIddG2d2d50wG74Oqee4coEJTcB1ZnsSGkU7VBmCIcUSTepAF+PB8QS9UIh/EQOzvL2GjBXJgrVgGCllcbsT5oYfCUTAn+iX5jvT7MdOfcRcbxhBD+Tsa5yAhjhDmCBGHrcsOjjNAIpXZYdD+T5KtJ/u2/lia1fz1t98d/0Bv+4LvJz/aBf/nLyTe/lyz+uBUbf6MB8sX3k99ajwqPd4X7494dygjBuZfBCEcZlhPP4CZzNjPrRfyYMARx12RYUQdqz3wpM7QC3C39ILw5tJ2NGKwP+POOMpdg2ZICx97EBT2w/tRiO7zArjXChsCKd2BlSx/cW6kNPAJfWoDWWCx9mu9s7IBXz9cWKnO34cW88dYQvh5/pX7sRR9m7JBdlD/yUcCLJ0NlBQrP9c4I8LqjkDlBexhWiwwLGzwR7v1o6/47ufw6ES6XVyHsQIDjUTWgvdDnG/oNIeXTybhYDASdE3jRPbtXnJ6GEPNiE5/dlP+UwDkZx9wMO2eysvBYXE5SMu6pnsUyYpMD1zojKVrPx8X9Bn4H/O+nCRaHDWxtJ6PCwRl+yg45vY14ILi/r89O9gArCgqL9wP9Bo5QPDczrQx5MUOI5ttpxca300zm307yOxknv9xP8ge/1xad+q4vpe0keX6Unt1PE/J2d1GkrCPlTcznkeawLs+DO+674uZGBr5Zc9YZS6vG/6xMq4WNsHmkftaZJs5gSOh8C/4yTWhx2bCwxbbI9HjLdUZizetNCd5cn/SL5U15W8W7x/c6WI74sy3hmpivcIA3ywqeYX3NJ7ZkWYsKA0ln8gh13t5Q5fY2eOxBI59Q1Cg5X8hWxrNM8IY0+GUBgeOOISx8MlkyLCcmjtYwEzAJT+Z+RvzkRPcRjtEkWPA7AtQWONaJBa+JwNlqiITvzMcLTVa8VmXwHEIGWGpJUzIEoT0LhDjzT8ZB4xzwg+WC1ex3wFlBLDNORHPtIzF7Sn9gLiw+iHzT21NwD9Pi5SSD2U50j/VcpjE0BAMMJJ988NRLvb+7Sb6Y5D9MWo3bSx2Iv5S2uF9NE87fSfKVf7V1cpzkN5N86rBN9nNtkL/d53KUVqeMYkBgIQCTYTCgIJYZm0EifFCFAHMeZShwKiCoxWbONljAKcruXl+/e2mx9KOM8Er61Hj2qD+36L+TY/G2d4fb9md+s8eJcrbBAp0eZlqn7Cqpu+oTQbPUGBYcfj2YjRbwZeHsq8aj6XNVnqtHUFZesoFHaAS+t3HG8yflHmNiocIzwFYNURsd0L+fQXnu6xnm5v0VbuOa6oV+Q+nCPwvH3BAU20yP0UvGQSxMnHpGiMYLaBcFbertmwgesrnrTAXoQ032o2PpMj3vGMuby4QRfcblqmVJTgq5zY0MAYtWREhikSdTwelEHovOfc6kfS9DeKMtb+rZ6mVgPCI0KGNCu7+Tcd4wzIyr6n4g4LczhNFRH9veBmPa+kG4JC1ZV2s62QyRjINyXuptPpNkDz/8O/2hf5qx4J96rQ2Qbzah/Nrt5I0kf/ekWcdvJnm+wfOrHb8v9H4Z6yBNsCyT/KLgR/FEz/nNNXZ708EBbwf6I+4HvbEO9Hfc8fl2fwZ4UOx3Mz37GqX5Xi5v8XYZmD1D11tDwxhKwGhDwYleePpmphb1IuNcagwDNm94owcVQu9l8IvL54DHSmBfbbksYGkLn5juk6mw9ny9nmt9r5asx3EVE/N6mMvea7X+65nPjGEBbE/WCszK0nMzbODcYy/TXtawvJm8jsAj608MbJlp+RodE9NhAhCFBatPxIIIHPCPAPHCWGgDMAghnnQrI0ZGVp3+64UgRSs6pkd1hd0NlME60woNby3dUz9YMGvdhxmIbxP3BF9oRpfVbTNOiSPGyLbt9M83M0I6t3t7EkFY2ySIXKZH/AtcmYCIhT6XcXg7x1G63M8JyINMrUiSkp/ua/MrHZbHZ8m9L6WVuKE13kiTZN97N3ntc8n//Vby5o+Snz5O/kGS15L8n/35ryaH/7DFPf8o7T/bqIllPuxzoOaaKgd7b8uMeCbWImuGQjxL8vGMBB3rhxfxXB8fRueZW71vqkKcFzlNC9+QCyGxDAxHfQ1dCUE8GFrlMKsIhXy+lvEOPaxfwiKU9lG1wTbkbYZxVT1N6A6BhrG2SaO3DzOSzniu9iCT6cE69WQ4hPa5nkl5Bh62PEHm+FlODgQOFxzwPNY/8dvTjCQ0F3KAOUV9JmMrPZ4s8Xxw59I9HyMAvMCAMnfi3CEm8PxRHbLjL85aUumAAHKIA+Lb6rsR5sQeNboA5SQXySrH5ZiM64kRamgm6l7p1ydUAQdtEJT0c6DvTpQwN5JlBPdxq25mWBPJIDIWmaQSZx1w4BBzvSYYISKYEeXDQT4OD4DnuTg2a/JsRoioJmhJzD1OEya7GQdwYx1ez3hN+jLjnIvn0up9n8+o+z1ME2AcWfm5DOvib3U8/Nwn00IT/0uSfyvJHyT5V5J8Ksl/m2TvnQb4NzqS3uiT/9kO6PeS/fvJ+kGT1awFL9E87etxJ6NMjIPvNxnVLiQfKeRHIR9nMBs7EElwsn7g8kmahX4zTcg+6dNgPPByp+OGQ5E49vNOhnJDIWDBWdjw0lVoDAPA9e4wM3yDRwAtW6FyrjLCnfFIomOh8Tv9QHeE7lyh4t2ptAEe+mQM5pYMeeBKCVx28x+4gI8RqK60wJo3r5Jg9drbYkX22HAjaU2FEoqYRCXGDP0DuxPeNqqAndi4TwZ0zqqGSUiOLg+6QHaiBneCc4URxp/IKPhH4CIoyDbSl4uvk+mWXCaejGoCazoW+SDDKvERj+4TrU71AmMiKKkXxQoBBsehEJJcDquwENT3onH5I37HohC7RdAS2jkXLE4smqg5RhJBnUxfT8SmCwgSq4g5c54s+MVahFDuCIe3M5TdsxnlUDAv8L+Y5rIS4jjrz99JE+i30s6ouJ4moL+ettPuM0mO8I05VPlfppnR/yRN8j9KE9LrJL+f5A974/8xyY/SFv+7ye9ftLK319IqK/6sz/fjGUz5owwBZaWdDieWEYKQ7De1ycRanxPOjvu8OMAJJvyF/v+raYnGRxn0zbm+72YoSrbqnvZ7VKhc9LYulUIQLzJeHQTdUWt9M4OmERqEHRByGClYxTauWPfTTN1zDJezjNK9ZKpEbBXDZ9685CodC2N71sk0H4TBwH+X3tKH326OjLJhaEXiC76E31FgjI+xeJrBm+CA9g43uIyR7/TP8zZQWQ+H0Vw65zbnSTvtjZsw4lEGc+9kuDtYAS4JgtldD2hrt1rOmXl+re8uX8Fdr0XuRmi1NtBoEPdGfbu+F+FJlt0XIRBKihzjJbSABrVARDl5t9Q19ZOMODDu6nGG1YOw9WEj9Zg+GP4wY3cPlvQijVmvd1gI4T7bP6/ShNj7vf31JJ/s7e/076/0Ma9lGuumjOwLGZUfX+/j/PtpgulxmkD7S0muX082D5LFl9O4+1GS/zzJP0/yg7QNIveS/G8dYb/fn/snfSLPN8C+/afJ7yb544wSOwTgg4x6WyzDZ/r8sHAvMkLS1PCC81VGiOpJmpL6SQZtrDLKxNjc8bE0nfHFtNA44RFCJs90uG533DDWsuOQ3XWUOkE/0BgMz27LTbkHrN5EdVE+u9Z8q/7sMbHe5FPZsGAhzn+8MbyuTZoihr8wpMAvgtaWaA1VusyT+ZAHOVe/9IMyxWvGoEFw2lCr+ZhbGTIL65cwH2FK+KUqSHv024wwlq185sR3y72lfqf9R8JX8MKrO3eTCxMEzO1E3Vxstl5ULlhTYEE7Y4xA2pa2NSlIIgVNjoWaTA/WcdzH/Tgj7cvCC2EWPetYuuOtMAJaznMFZqxPl48dZCRvmDfKzAevULVggsaq8sKiHJcZGz4QzDX5Yzz8XO/rdkYMfC9NJr6QkZA7TZOJr6adP/GbmR4edLuP91L//EKadXqaxuQ/SPKV231y7yb5r5P8Vpr1+18k+e/TpParSf6HNDPw7zeA3/qwMc+1e8n/+naD792Oo3+QkdDhrGK8jy+kWfFvdph/0OfGYVUkzd5Kq6j7f4XvV9KSKS4N3HSw3uzfn+/9v5xW9fF27+u5/uy3Ms7teDPD4uJAI9ZimabMeOcgFSPEER2PhSbWGbWzPn85GfwFfTr5bB7jECMuh6nMh04OQl++Dw8kQwAxPvdrWHGujftkLvClQwyvpkWxDPf9jLM4lqWteZBxkTlVLnnOPgPjqiShc1zkTjblec81aTT0VobMYH2SwaPkt4BpedwtZDonhoL16TCEd9thffiq+8iJvdCv43Sbfp+4JghYqC1xsP2MPeTEZyFirGvgwpJwPKeGVxblO7/Z+iYOxTM1Y+oNK1gPzLuGZNDEuNRbtXHGmBADcKCJHX+nmoTxsfKdBHk2YyPEc2mC8iyjvOvZ/vnL/f4n+7M/n2F1Pk6zSk/ThNhLve2nMpJYn0iyWCS3dpI7n0g+eNAE9PJxsvjFNCn1b/SBvpGWrHuQJsF+Jc06/knyZ28n18+Sm4vkmxfJ4aPGePc7DD/ucz9Oi3psMzL/xPk3acrl/Y7LFzN2pD2TJtgPe/u9Puf7fd6vpTH4BxkKlNDNcZIfJvk7GYc+fT/N0/h4Wkn1e/2ZJ2nnPJO0ebbjigQsNMwZ3yRICb9QVcNrokhiEl9lYwkJKmgPJU74jjgqiT1i0S/0PuFFJ5vgibq9GIEDXeKB7uk3b05y7oIwAv+XGgeZQnyYtUThXMv0vXh+LZo3WpE3ImFLCMvxWsKgDiUiu+h/qXbIH3sGhE2tMCzkmTf8eJ6pjLFFjUxzwh5rfHmQvM62YczoJ3qAjkgugBCEr4UoF9+dIUVgwiSEJLyYWKs2+x0TZfEQ4iAF62BX/TkWzdyAA0JKhiWBm7oofbHQuJbE1ti1gxWL+7RROxJujzJcUMaGMGEiYCbWBKGdZqrp72Z6NsMjjbObZvF9LE1w7qdZxs+kWZG7aaGFu0l+TbB+ovf9uReT3Q+acHg+7VCgr3U4X0zyS4fJap18fpGcXSTX/15a+cPnk7yfXL+V7K2SncfJzg97J19KMxt/mGZa7qaZpP8oefxG8uFPGu7eS/KPLxru/7B//2zavpI/ShO2b2TQyY3e1Z0Mhtztv5P/eNBx+laalXiUFvdFif/lNGv53QyPJP3eMx2nD/o0XkgTtgi1J2nC/c2M9+592PH8Rlo450/S5vNSn88XMmryXZ54vcNoIwcrDCFIOBF3Htcda3supsnuS/IXnH2xm8sH11OGhTBEaDp5yAYIhB55DJdPetcpeMa48E5AhywJd2CtAoeNJWQE4Qbv5LXygW+35Xngg++djCPOj1DGePJ9J/QsjMkroDDsOXsHY9SPrXQS0Om42bmbXHiAGn/h8vmevtyWZ+ZM/eXM89XEN2DbDNfqzwuZVFfIoQdCDr6wNBkHF8hwum9cMM+PUEzVmJ4vGrZWRSxKPwiCRUZoAxcTNxIGddjHIaGXM3B23J//appAJon3apqQ+NKiN1gmj/84ufZraebZSfLud3rN7vUkN5OzHyd7X+hAPJ8Wfvh30yTRm2kS6nNJvtn6O/u/kr3DPpGX0qTA15L8syTvJBcfNoF7O83SXPbH/n6mGzbO0mT4W1orn129SrOeXxQ+PptmiD/f8ec67lczzlbm94Mk303b1f3NNEV1nGb1Lnvfx2k1zn+zgZ+TPpVln/7bGd7Hon+/mxEi+Wwf77h/RxjT16OM11oddXigK0JbPoKU5zAC8JjIH/AKsRtqn0zDCih4/55MaWqjz3VDFgabadxhTocxKv9Dv94QYYvaoU3gcLmY+yIhR/lohIsaFqnyzRvF5kIP3DvLwL/DuQ49JoPOHuq7Q6PAXcMtVVZ+VPbG5awkwLE4dIBJ733dTBIthcuM5ebyHA5BqYLWGWK0FtrHSIZpIQa0Povm2DPuAcSCq2TXgRgv7peTB9Q7PlFbrG80NXP3K8xJGrjUz4lJnwOA0OfAdodxDtLccCwBar75j8eAlfxMb/OVNEv5nYyStOMknzhMHp0le+dJfilZ/cs0ybibvPtHyZ1byeJGsrmfLF5Mlss0qf6TvkZ/luzc68A9n2YOfpiPpM3yq+3W4zeS1TtJ/s0k/1Ny8dPkO2dtLg8zjGViv1gaq7QDhd7r97+TkRj92Yxa6A8y3s7NzjysMNaVmur7/fln0qxtElhYwliBr2TEnfHQvpomVJ9LU2Z/mhHW+YOMjT6bjEoUe08/01G37u04EIkySpJKtzLCKdBRMgQWXizJyscZJXcYNeDvIoP/bmfESEk0JlM+xaOD9gmHESqA79bqFxp3QgqL1OVrfjnqRabloTWpj/WIjHHodCG4uM/RouBoqX6wlK1gFmm0kwyB61Clk4KOSafAg3AGByggW9msLbzukstkyCPWhZLOndvJBZ0kl0uGalgADcF2WmtKt0mmWsAC1JpjpWfpw8KXDDfJL4Sxt0fyG7BS/uYKBOZmjQhSailehYHvS7VHwzohs1J/Ec48f7uJXDUhysHhyXRHly3s4zQhcah7x2my8zMZccaX0iy2nb+X5Pf6D7+TZjJ+v/9fplHF8x3Zv5lm+X67d/i3+wC/1Qf9QZr5+jP9999pk9z8aRN8WMBHGQfqv5W2hn+YJkTeTBNwJMbYQcVh3gdpoYTjNGP+lY6/dztov5cmPB9lJEjwKtghx3o8zGCEB/373YxD/OuYz3e47nZ0/bUk/13H6zrNisVCTZ/HsqPyB2lr/qjD7sQNZWYPNS5eHEnG9/raPcg409mWKLQ7lxCGjv1i17rjtvIfcNnjswCGDyqf29L2yYH07z6x5FEMGG7gz2dIMD8bgNC8Kx6Az3xTvVQnCrmAcS5BX3Hjzuk2FQAAIABJREFUPuif32ykwpf13I1laZ9yP5nKgSRtpx7ahviUtYUtPhaKuCrtvH2zujnJVLi5POeZjNIafkuGJsJq4hXvZPvrPnS7Hmv1xbhGmGsSDzTGvu57J1LUH31dpFkFp+oLywFrxrChNLiwDrDgOJicnYHE6K73vjhTAW37UhrTscvuRprA+vW06MA7SX7+XnL9UXL708nD95L9H2VkvF7NeDkY5sT1NAlxllEyASf9szRJ9cU03/6tNKn1vTSp8+kk/yJ5/2QkrK5lbMa4vp/c3zQhtO5d/CjjdVI/zbDINmmx1u+m6QJc0tc6+C9kWGpHaQLMDMPmmIv+7I8zSvxgAvIBb6THznsfuMmLtDwk9dnvp4XJ38t4GcLDtLj2qsN/2lF0pnX7MC2WT507YZMbafqM0AZMuez38Q59sDtwQ5sk3CkJSwavms5rEs5hNFcXuI6ZeLWtcyebETam7er+k3O6lpGAXQgOxrXxRy4Hi5pE+UKfGR+5gdfKSwyYi/vBinVMGLw4LAKumSuezkbjEFZEBlIi6rwRzwADhp/j5pZDeAW7SZaHPWRRKypABO65s5y43UYUsVqEsYW0g/6VeNCY3s6IMARRKIqDDCLa1zO4MA7Iu16Qtt72COEyh3WGgAOZhCtwxbz99iTjUG0IkxgviUvmxHjbtOw+GX/ghnhJziCAzzISJ1jCn+99fCzNYvuF/veZNMHydroQvJbsb5LNO8m1l9OyTJ9OE6a/kCZMX02T3sR6P5EmZT6e9laPb6ZZx+y1/UFvs5Pmh+OXnyUX32qPvN3n+e2MDRhvb5pl/OO0GC/4+LMMAn6cJgRP0qzn1zrunktTMgcdB1/qYL6UkWClTv1THfzjvtZf6Lj69Y6bv56xNfpxxzWbj7DSbqUpAmq999McgX/UUfKdjLpWrNOzjC3UdzOSp8+l0cELvc830l6YsuzLgIXHVnoELDSOIfIoY9u8NykdZ7y4Fn6C8YGPMMVS/SeDhxD8Ps+B9hZe9IGhYQPD4cBkqizWGRtZEIyEIaM2hP6cRFtmGhp1pcdraaTLPLHWL/Rnw859w3c24JIRXnHohLaM7Q05CFXvTmRzF20oRvBLDhDIGAnIuGV6DJkBVpmWhPDZ2soBcCaFNew6RCZrq5GFdQw3mbpYyVQhnGWUYXkMb1HGhQA+9v+fZbxGfqM5eruo4z2UEhEb5lwAMrXs7sI6cDyMOeEGWjui/Z5Lk2NYHjAfCSHCLbjgJ2mCd9vbUl2w7b9zONHHMtztR2nCeedekofJ4m+kSclfS/K/pwViX0p7zfNP06Qk+3pvJPnHaeb2D/t9grSvpEnTX+3f/0VD7ON/nrz7/eTGs8nvnDQYHqcJoJ95Pnnj4QhjHGWcSXGQES1BuPxpmrBdpwnWTZJ/pw/7G/2Z59Oq5h530L6QtteE0/ReadPOZ9Jislh9X02zyu+kWbwvdpigbSolDtOYnXDF3f590fs76P/POuo4mP7ZDEvpIk2QvtDX/+cz6lH/n7S3qPwoTSFRKcP5HJS8wStUBdj1dnzZLy9IRh6BWDo8XCscaA+tQqPQKziBN3f0DO7+XKKNaiVXKSAf4H0MINoiuLF4sdDp3946vIjRdZGhFNnJh8dLXJYx6ANes+B1bNxC20lKjFCEKUqIfnYylT3eGYlRWWUnsLGLd3mUvB4tBJYwAtNJAyQ5gsyDGHiHPRYZB5NgCUIcuxnbMpPpRg+QZK2PgEYrpcPg2kPcBTQa5T24et71l4ydXg6RAD9hCWJ0LpUhGVITdGhOx92I+b2fsSEDbfphRlLEp39h0e/1dp/pfT+bJkxeyNjCTDXKL/6V5PnHyc4nkvxSmiQk8PlWmmS4lsb130gzr77cB/9akv8jyX+ZtmuOmMFvZ+xXPurtfjcfmQmrneSNJ8nBSRMyF2ku6lGSxw8b7XBoz6J388kOwif6XJ/tf++nWT3Ppgn2VzKOGt1LE6Cfvpe88mzykwetv99LU0hfyIgnc/bG19KcgbMkX7idnD9uY1IK92xGadxFxoaXT3ZUPey4Rnm+k6ZESLp9PU3XPexzdL7jV9ME9t20Ko0bSf7yreRjT1rs+jzj+I51mvPykw6DS6hgbAwRTjk86LAjLD+ewbMIY4yNvYwjCM47TBwiBv8hUJLBY2y35swGDC3+w3PJMFzAI1u9HV7A0MH7BD5bs1iNyCBfKBAEtQU7BmMtKEjGGRd4rvAofwj/i0zLYJ2Ed46NMcHDRm0c63ao8kJ9ONbNbyiwjwRyzaRi+eLKLUoHDIB1aunPZ4CvMS8EJNYsi1SLur3dEy2zyqiHZhHs1lBhgYA1TLgNVgDUagIXYzuBtsyw1G0BszW1Kg2IezfTBCPCndjVh30OL2dsccZlfC0jCUQC7zRtP8Vemnv+uLd9qT/znR8kzzxK9k77zZ+kmXOc8n4tTege9t++lbbP+Vtpgvd7aZLwgw7w/ST/cZoA3smI6TxO8onk8bdbXfLyIvmHfS7vpwm0dzp+vpJhDX47Tbj+JM3SXacpFhT/RcfJrQxh/UsdZ68eJq+ukx8/Sr71oAmgz6fVEa/ShPXNjDeJPJMmELEgf/q4rdsL+8nNTYP1JE0wb/r4tztMfyttz8pLGbvcPtVhJS7+bkY44Tc67CdpFYHf62O/lqHYX+rFzDfeTvb2kz/phHbccfMw04OyOLsFA4EKCDaSsMWXLcwfZHpuhQ2a845PwjAcKnUn4xAkVwxd6DO0TGjxXPcw1ICNsTi9DB6gagiDCmEFT9uYsYHnXA68zMFSZ2rH88TY8VypXkBIemwMq2QacuH+MlOZlkxDowhax7GRH7bGHXFABqEAXLONXP3ocCEPuNZDmOvUPgIMnTKBw4wNEK7IcObyVkaQfC9D8BsokHArjTkdc+G/A/p8J5E2VzLkWFKNVfMsGrHOi1AGbmQyYmBYLlUzOnYEsSYjRkX/wEyt9ccyFCBu0U//P77eLDjy7Drz+2UmgAQSawGFAgqofe2qrurqld1kL2x2kyIpURQ1lEYzsjSWhx7FmONlRqMXhx9M2xO2XyZinvxge8IaO2bsCUnWxpAUI4mi2Nx6YbOb1V1d+4JCoQAU9h1IZKYfzvlwTiZbRkQFCpn//93vd8/5znIxW1oJE1g1iQL2ZQyYrmEgNSz/uS6YvQfdz2Ni3gSGkpuYmDdOiGhjmNj0lD/3Cubk24bRHAcI3qgTQ5tHUNyAHzRCCykSqTj3Y2DzDvDCCHy4bpKiPANqGNjq0Gl48y54OaJmXuqH2W3YqMLAILy/GQa4CR+7297lGX+vB1s/iz4+x7y+BeBozeoaTN/LVe0wRm10e5crPkSHCSWjnXCNGyM2cR8GRD/2oZSb2peLsNSA/kH7ojQHP9qy+t7FFJhpghoQWOhig+yzK21wv6+LUUISxdssANRalv1BSY2y65gCRQYIPr2Rvs/gKK1P+1lgiX8uDlyZ9VTXNs3UQZFw3ZMwo32auV4Z4VtVfF0cmumP7DAggQp++sbwHECWpeOPo1L1nXBN45Btaa32K62FLLRqHHJq3VY7WpPkvA++kaVQgYwGI1MTKqDVUpqthDoFi4RAlTlp2YHyBEMzHaBDQUCaB0KdlrpVpRmYM50iyVn1V1MZGgCpMNkyq5+cQlQHSFbhcjsE2uX0vPyfs2olFzVJMpmi2CC0gJNe9kEMJAZ9PAcwXvUecZPDUec81oFSHbp2gG3o7sB2nXajCL4PMKl3GNtB81Z57ZtQHMaQrezP/CPgB5hBrwuTptdhdh16e6B7JwIklPj7IqFCPz0GjenQBnawDJw6iPsxwHtA+A4riGIQ+GDb6OoRYGQXPqoHmMrLQprCtI/hiH+uDG9yCez2rmievu3ztIr5Ghe8zvvethlM2h8fhLVNOF6EG43I2nba18mclz3K3lnIUe//8a9A31UoiGs4DGNLsLhjgD6D2U7bCEn8FmH7kOSnfdLtfcyBH4OEP/0mEXggMJDErYNOANwgUqyWiUO+g2a3ToGigKSW3s/eVdAsFWYhRZ4dWdrPXhTCA9Ek2vdZWs+0Y+ag857OPwLgfmLtqX1qq7T1DNKSvFtpWGiWfj+OP8+ALUzTQVGj2WFBGnvGvVIvlqC+nF6WZKtByrxyVk00uJlayIgvy6N+dMLVCQNaduTP0mxTI1s6KdUpS9e7xCItpHIK2ISobfkEE68EQSvoR14QAvkcAim+LEvUMoIIVMsYCGm8FIUmtVz5JWSF7/D2SKXpxSz1n/ZyX8Gkpj4MJ+cxwHkauLVjwDFcMUnyTg3aN6HrlzDwPYxZ054neJaqN+I6pmMfhuKv+d8/xnbqCoZ88+xZDVdWoaNhxdxyMJ72Pt/BQOYJ/FYSV1ke1a2vs4Rku+z9lrHsNW/qpPfzEaExHPbvCnUDWrDf1wi+8CPgdaw9isJbxbjZBa+j18d3EZOqTxPS3TkikdEZTCtZ9zl75NzzzYatpROYAtGFKQuXvI1zPrcXrducA8r/FRSuEE7SJ2xx3J6KvfIWAajzvg7WCIlxlVi3Asr9xHpUKoMVAlAFEuJkZcUf83aVMM1BxjEJC7LjCnBlBCc9I8EtB3vkYBjtM7VZdEEGJ3laCphbtWRp1HJPy0KhBLGcx0VlQ9iicoBZw8dMEnGmI7oIb50seJZpFvJaQV9CnMZGbdf/5XEm/JRhMwfHQdBABTx0ujUoQj/i1v62kGm9J5UqG+TUmI2W/5MalXmbj/vRiSpDycc9m4E0O4KL9lhO741hCx5CypA024ktaIXh5ug8nfCZpJelOTvo577JZ1rjIpCQ4alIgLTG+AQhzX0a4yO7fw5D4X7gt7FsaR8QbhW3Yf2mtWG0Yr7AMvgAjBZhom6AUTgLK9d8QRyF2j0DvtGzVt7s+1bOyVNw+SZcPASzk3CgAisb9qx49CWrmncwiVSC9tPAq0XYrEPXKVi5aWOyjkmDV7CD5rK377yP1Tv+e4zmwJJHxLVICxjY3cA23W1C8pNBTuvkSS9Pl7luYI4mf+ZtEf/Zj7m0jWPnVZEIaT7m5Z0gNJst7/8NzJ1ONhJ5ShwmMgX27fcPsqh51xq9OWV9XvL669i5KL9riOAgAYdMAZ2YNP4Z7EBY8HFbwjSnqzTvgwph272EKTnygZaboaS3eW+LUqxCZJvT4aXw4FbpWDlJWvehfnTA1NM7GXta7VCiRmW0lhdSK65UUxnS6lvLz5RJ/jsHZen/WarPGJXb2irASQHKd+cJnLXP81i11qu+F1U4NKf208m2wU+fDBosgeNWqlyDIqdpdVz/z0YzUhlZ+sziPTSnCiz+Lc/kgArl8dXk6Rn5foq/Vbt2CCOUJF61SYtS9VdpdmZXYILy5crIOUbzIsgg3Y5hbGs717GNtko6PD4N/F1Mmr2NIdLv+GcA/0PwmhMb1pZH/u4jgBMOxufg+jVXlcasklLRQPzBNZh437O5jVmnzuJgfNY6qEPqwNHIv1Alwr2nfQzOA7W6g8rTNj5v+FhMY6AnH+U6Bii6cm8LA20dXso5MeZ9GSSi23K+Bm3Iv3PJAPaY17GDgQ3+7l9j/XqSZn/yx7zMK/7sI/99nzD0SWuc8n4MY4B6BRtT9UkGsytgO/HzREKOJeyU3QqgPJ/qHyJycGQ3tV5irVzE/KF1eK8DXyRoijJxeTfEHl7yuq5hS+o0Edp9grigVhTOxTQnEOtafwsrBFjaV9k/WXtMe36byD7Xy8fTDDnZDsRBvE0ctjmh0QZx8ULGlN70/z2gS39n+kVlCcfq6Ttp7h/XVmnIeDsExsX0veYiHxrCsHaagX0LN+oJhKqpMMUCZK5WP+JhMkezTTOoanKyRFtseUYclIxr4hAl8mugxOWqbpUjMV8GQKWpzKqN1CMtGKm48lsUn6VFUE7vi66pYupO5sekzok+GSBUyzaCrthJfytvsXhoGTjKRMrMQYwt+PoI9P+32O77FCZ6ghHKIlj/WQdceI3SmduMfAH6d6FyM6iiKrC74KA0Z2ByH+hYhXub0NOApaqp3+Jjp1ahsmmsxTZwZd546ZvY+9eWTcK6RgB/zZsziFED59thow5bHxq9MmDVU8WirC9h950OY/SBPB4WfWzk/jeASXxTXs+av3MLo2imMa76CYyaeHcmAjMOYZTHAGar7Pe5evJnoHTL5uZtr0+GsTn//TjGaa8Qt0c/8t+XsXV2maDc7hDraZowGq6vGn+8t0AvAL8HfBVKZRhbhM1q0G13iWhNqeFydzyL3YL1C97Xn/8vofNNWxYjGF3z4lE4vGx9fsXLUfsHvRk9RFa8cUJdXiEOonWfi3yfptayfIyz65toANEikgr1uVR2Pa8yZVsR9VEkqFNo5pEh9qbeFXhnQ7h+Mpbp+VZblyRf0SEC9EzRZn/qzBXLRiU6Up+30ywEai/mMSikMrJLXQfYjSE6ARRRos7ohWwM02SoUuUtVqM1YVqsxfRd5lsKBC0AAU6qQz+thjZavpMVVJyMrNtSbzRI6qMOCi2AvHDEdUGz29pBbIHKJ1QD3CBUHPk4FghDnRaoyqumzxsYCKo8GZR2MI7vk4cwR9qnsd2yAFw6AuXHYegC3LlpoXiVOejZhtdPwEeLFO5Azy7ccan2ipd9BjhShJlGHC4fYWC3CXwCS0x/BAOmPm/3iD/3yP9dx0DvtH++SODNGQzM1usGpH3AMyOwsG7gqDH6LnauvE8YR7Yx7UBhyUWMa+4jsrQpbHmckIDOEVHebQRtccLbNev9f+Tzsn3LAFT8YBVr2xoG4jPYoTWKSavdmGJSxvo0Shy64/5ZL36HIEEDKDhj8Ch2mhWwk+QSdkrdg80tq/eej+ct7OBZ9jUy4u3Z520c975OAWNvekRjESpnoNAG8w9s/iaIEHOF2H+RuEll2cvbwQ6zPyHoKIFT1esVvyuJfpUQcMShbvlYt2qt2bAu11LhQwYyiBgFSfoC3iy0KQBEWFWkWZDK3hy0fC6wFRapfcInaReth4AEQwmN2WCZBbfdVI6EW2GpDrNiqiMzETIy1khub7JmbmMLQRZeAXMrFyTQFXmvZ7MlM0+GJjDzN/kU00KQw/puei+HQbca/bIRQIOmzmZraoNmyVvgLLU1T6422Ca2GJR8Rs+3RjNKKlcIpQJNdG+apP8c0SdD4y62OQ742B/2Z159BgPj1/vgxraH+jXgw5tQvGmnxDywbxv2fQau/zhI0M9B21/Yxi1jG/IBcL9h6vyPsY09iwHaJIb3ExhALPpYThGRaTOEL/UCBmq7BHApi9YwxmEW/L176zYWMxgQf87H8zohEVe9LcuEVqYABgGfEg2VMbBdwAD0CYLiuHgI6is2tkM+FOuEyt8LjFyA0VnDxLo/pz6vEof5fSJxvWci5ZG35zahIjdSOXf9/8d87q8CJ4eA87B4HTqrUBByH4L2eVjctnV2mzjcFLUnranL+7oG/NwYjKxCZw+MFmBzF9oP2mBV9kFhDc7WzNVO4CiabdHb1kMkB/sQO4C1/2QYlMC0Ruxx+QBr/2V3MbnFZi+jVpDKlEb2HBGNkDVPAbb6UEvltxrrtHdFo2TPLWgGTzkhtGrvEMbNjBX6EZa0GvczfmUwFghrfHQg5XZJiKind0tD2I0hEKeiDF+yhAp8JBm2pplUp1v5YDVI0k2+SoVURpaixRH1pEGoE9KpytNk1dIzWUpXwIlc1STVKLJOErEGVtFyavfHTXorj5TdgXTYnMAWuxYVXrbc39RW0TPKzzGOScgj2EZ87gtYhEJlO6WT247MQt0En8RdI0gbwK8Aw9D9GThyGSbmYkHeYy/tMe9jIKi4+zWvX67KZe/H32D0hNT6Ga96ggBO+Z+PYxv8lJc5RRhVq/7/t7yuGQx87qbxVYpMjdtDTJJ+lwgbfx5T318FzhyCnsehvwT9n7ICu+agez8U2qGxCcNFKDZg3yAUNqF9F0olONdneaELO8YIHfG6j2Bg/DJ2wcmi9++qj8cdTAuYJTxChjAeXNndypjbWwfQvw3v3YDj7VDohJ1HUNoCrsPEWuRzHiCkxJs2hdSIaMx/judnPgXta9jJNgXtv4X58s15Y0dhfh6ODsLpPri2HtnuRE889H56Cus9Nz0FUogPzWDXRlwppT2o/ZY5dlGDol4kQet5xRBI0JK3lbw3MuVYTM/VW+rNUrJ+sveV3he4it6QdiqBUsJV1o4hBLd6S1m5vkZLGXJZlbeGNGVRuHpXdK9wRnVAoixk5cu5VXOkjE4GGb7E+4j4znSA3mk11GXg1UmWXU1UJoRqL5DP3wlgRVN8nKScJVHlvBC4fpwhQqCvcjJfBQGwcsGB5r5LCpYvrvrTTmwI/S2KRO+dIpKtdWJMxXHFE0skufj3oWsQ1u/Bvj4jIbvGofA/QeEtqK0bsar0U38C3ISj7VBdMaDcwvasnOvLhOR7AwOZdYzq7MM27TEigOABoXKuYFK90lVqU9/ANrTCgOXudhcD4j4M4LuIPBBrxL11nRgPK15zlFhHL2Fg2AlUPuMVLGCnoE6IGjAFDzbcul2A9mNQewgdY5ij7/PYwfYIDpShqwJdG9b2HUyAvYMBUBkDKvHcSkaUhRWprLtExN3beMKoHQeOOkxWYfiCecWUqlbeTe+GLhq9SwgNclk76EvhTBEaD6GwD7gKO8tQuuNf+sZcuQ/72u3lH86EprJDcyDLhNd5DNMWKj53eJ/keic+X6DaGmmn6DgISVnygwKGMg+b94ooRknfOapOgor2W1vL+1lLVd3b6bMMytrrpL8l1GlPV2nOp10koiJFsWbpWOPUalNqFV43+Ph2Z+FV+7EOlNqdspBPrirUaSVeV6S9DHytdIAaI1DMeSlyJySJ5glS43SK6PqizNdI/e8nsmPlJEG5nlZVQiR+Bv6sQtRpPjA60rPqRw5zlOoktSQfTKqLljGrE4mESoSr3QCR5vEoBs4HgCMvYYj0i15w988Dl80ixxHgIsy9DbvfhPq6FfQjzAPjQ2xne2TKg4ng0TaIvAa6UmrSP1vCKIAvH4VDq3Dg78GhJTi7Yt8d9/cVjFEk+Nt91ro9umaHuHLpDqHGXid80XcwjUD5LmRUa/PyXyAMxr+OgfPI16Drx1BQ/PWvE5E1Q5gv2HvQdxjaD3vHlqH4GQy4n/QCzxNuIfuhfB8ea4dC1dqWqbNVDJg6CD/zDh+vSS8uu4Pdwajih5i0fcPXwimgYxGm6jA4CKVNm+JsnJbwIs+IfVi4/DGgx7linrYJLI0S12yfAm7DYhXm6nBj3cr9EDNq3vf26J66I0SEqAKQ3iVSonYRyf21VtoxyV3Ss7ITap1LSpQdRcCsfQQh5GSblDRGCTISmiBwSBTKDs0YkveqDgmBYfaY0o8oIe1R7X2tR2kMmcPWgQuBEVkgzYcMhJub6Nocp5F5a/2/mr4vigvLV6ZkLwlxovmn1XdObki583+b77J+6329I8uy3lXnBNgy9MgFTrxStsq2UgrqS3ZnU1gpLe/kCcqSs/pRTd9BuFvJHUwHhPwmlYlN7wov1tNzAmZJFecxKXQUIpPNfeDAf43JUtPYzjsJ3LICt/zZBS/wl1Jn/E6gUQz41JZ5Isn6vdSOCz4+37uH7TxZ0zDsUxrIbiJxvkKX3ydc1qa9jhFMlZc/sXhzzed97NxQ88UN72CuWTNe7/PAkX4YHQPehMIgJuG+RvBOL2PId5twYh6zr3bkJ3XDKxAfsEWI7L9mDX6sbF9dSvOnYe4kDJolHx4l6lGkotzCpCnpZwNLpDdRtT4/WLC5PuXlHBkxemieuLm6AztnO4HRc4TP3xVCdD/mlb0Lsxv2cb+PvfjsC8RFsZ0+BJeJa6ae9r9144lAV66FGgc9P8xPJ26vE0mFajRfq6T+a6r0W1kOpVWPpu+0VkSR1NL/dfBJyMlpfnPMQzGVp3a2XvekultvU1F/M86pDFE0+XPtf70vPCymZ3VpQu5XbhtgkXrS9FRB5joyyLalF/W9Cm8lyWWxlfRcTc9migNCahUXq1NHJ19utL7ravk+t1UqgE5bOZbnk1eSQTYA5rogJkNlSlpvwxavDgideJ2EpVehveqvJL12bOGVMf/TESwBz2NezxlMUq6UsdXzn38KZv9v6D6KyTj72WOAazUTea5j/mRXsR3zFrYL/wrm7xkO3fRxkNfBIYJWmMP2+otE2G7XOqzdhO+uGqiKf/sukXpQXgqjhOSyRqSQVMR2HQMEqaAynHVjwCcpS9TSBQxXOwm+9uwwpjqUMPDVInqX4Eq6vNI5aFyFgou5xXUoTGOoeYLw86tgYPwzGBqetkYcWoe+OlyqQLUagRUPfJw+8rma8/k/Qkh4ugB1h4j8q3uVM9jhc4cIfvyxN+U76xE+LWBbxbjyu8DQnPHHe+rCIyz2+wYwBg8e2PMbhJq87P/+kNCMJK3qWrD9WHI/pUTtI6RhSf8HiQOz6G3P9h9xokrgL08s0XTyjoBmLliSr5wJdC1V3vuZspQ0rPWn9CpFArSz5py520xrdtCcJ6NA0CsZe5TOAMIon70wMkjLqCepWRTpMOHpIltL5pP1/p4nhtzDlABdSH6IZoDFv89STv7JBja5x6gSnbKdxD7KnRGnSmqLThsIKbT1xFU7JLlK8tYJ2vp3q7N4JZVdSs/noBG9I2OD2qoQXiXBqRLqumxuq+n5DZoXTC/h96noN439UBkaH2Ai819830CWU9i0zwGvw+wOtBUjcmEAs3Z9F0PUPwDuW13Pe5sErGOpL8rj0IEJXiWv6Spm9LmW+nGNYAcGMa51CGueXJgUcSjp5AQhqT3yzxT5NeJlLhPAPu7tkNrX4WXQCY0fA1/C0LqK3a30nHdu1SuYAu5ZuZtTMD1jBr69At/FLIV/hOWE/izhFP0tK6NWtfff2LCxO42BqYIlclCDU9F7ASgKUtCY6TAaIYLY0LIQAAAgAElEQVQv6l7d+wR3fp7w5tFzSrYv6XLifS/sXxOIPgBvXIPx9rikFZ8TKTpaHocJKqTi436NWJ/yLpkmrtqqeJWiMuYJl8jsnlalObBMkrYUkSzcbKVntH8h8mlIApUmLuGtVfrVeFVTmRk3IHCNlrZqvQoc1Zf8O2vA+Z3eVJ4k3lZ3O3020/Lsx9G4pfS7NJi8LESy17BtLy5GYKusUa0/GuzsEqPTT3xwNgaI0NdJk/nZfLLJE0Hif36mlWTfT7OaoBzGmszMTeUJxsvaJEJTt4iTWpZTUhsE2pKWd7GDqjP1UzxadteTIWiHsKx3ERTHM3j2yxqMKMP5WWzXDv6sv30eOAbdz8DiD6D3BGwshh66D5OQ3WG1rwuubNrXvZg0d8b7I6PFPZo1APz3I2/jJCalqv89qR87BPct7xzNdxFbR0cxI1KPl6krjmSE0UWjN31cTnpdUqErQHEBes4RKdKGsUx0EgMbmKYwaRTFGtbnGvCgbsmB1qrQv4WpAXLb+ANMXLyJ6e4FKK7ASMmk5GX/WiHK8hrowg6QEsEva71PENnPlH+kjXCtk5vfWQwAewiPlw0icf4KJh0PYufOPWCgBh2fhAffMze6q+vmujhVt7JvEpfbDrF32TePfJwfEob0J7zuEeJM2yIOF8239om8qxSVKkFERsjsCqi9KKlyK/1fLm/SoLVnywS+ZA8M4ZPUf90UI3zQns5GefHyusigy8vIgWfSiFulVhk1BcK7hECmd6WRtzoU6H0dMtmmlp0UsqFPP21AYQQamc+QFCeJjb/lO/1UaOaLdQpkb4t6+i7Hb+dyNNjK66C/tXFJZel3ziOhNupwUNBJttq28keK7NPCyt/pYNomDHgytujiTp3kkuh1EkMzpwyGH/3YxhC9+Zj/voBN/HODML1g7e3FNh5fAv4ZJk69UIQf1g04RGBe9kLfwXbvFvAfgHW4vmxfTRLXw3+N2KxvELc+Q1jSZfiRNHXU/xZ/qGxgFzEgLgEny/D2dnDKoxh2aqPXMXriXfaoXe5jkrPG6z4eek0EeLyGAf3juhJkCBrXfOHOQOGQd6IdppetvneIQ/lnx2B6yv6+DzzTjknIz2OS8XNYMolVeDAD469A4ztQKMJsPSScQ5hn4ZsYgN30+RzF1oACK3J+4UvelpqPm2wS/cQBmdexJERJpas+Du0+LnVsreigueHzOJ/GSxzxtLdFcyttTPOnMOElQoNUudJ6Bv2zwfS9/I2VSuUwkZdE+1paYquUKek172HS/4Ul2R01Y0XGCwhJVcZJ/ej5WsvfaqN+Mr5pDlox5uN+RE9Km5f221o+RAqJ3CZ5lmQOXlRxqQzfgEBsSbQS42XwyZJt9mbQCZZ9jms0++ZloNNpoTpy6LLAUw3MJ4raKMDOkkp2w1HC7mw4VF/kEaETUf0R/5d56HyYqP1SWWRxVd8F2FXC8iyPEHl5yDg6TmTFOuTlFLGNvbtpPGUf0HcBJ5Mx94YhYKFhCFjEUO8DTOT5S4wIlPXsNrAOq3Ur67oXVSby/PaNQGE97rVb8LbO+3PDxA3IRYxfzuNfIYw7+zE6e56IZAM7uF4gAtVmCRpIGol41cNen5zllwhf1icOwcQM9Lu7S2HLCpqqwb0VmKvC1LaVJW60HwOjjdUIiLgHHKlD4bA3Ruh2CzgFjyZg3z1LwvT2XCT+P3QK/njB+q2UFHhb57z/D/3fEHYAym6hkP4ywVOqnAFCixjC1tKklydvNmlW97x8AX6Pv6NQ8ykv910vW65u+Jye8DIUFq1EQIp+PEnQA+uE54z2yElCm9H8bvvcSeJW37RnBML7iRwfW9iBLLsBNEuJWbKUn7G8WLRXta+FL1mCzm5niiRUegJRgpJgxQPvpDKyTSpjlw6abKSDwMNWTjljZO6XpOksAGqs2oDSAfiGYtTbaA6DVgBF9tvN7m3Q7FqSK8j0AgRgkzokABQ4qqGiFsTz6nn5HYsGUOdUp3wWs+FO4K/FKdc0AbCCUDJnpQEVUEvNUXvVRlEQW+kdWW4bXqdUtx5MRa3777MYIPdjPv69wMg5WJ+DI9rhCxgYT3ph/Ri63vbvCpg0PAR8B/ghrP/E3KmKvfAn27b5rhP0ybOfha77cHXFVGOI3L7X/e8ebBPLyj/mzTnq/XoCA+h+PCl+EcoHYPwI7M6ZJrKA5Vf4PS+nz8foDGEQfNnHagxr52sYSE8TN1qcBz5c8fmtQ8c63K1Z1PgsBghd3nZlnHvf62t4O750FO4sm2Tb9wlovAkFuXnswIfXYHgijNSz8+YCt1m3fq8v2Dzu+rxVfV51sYooihIGpCcJH+ucbrYfA1TdijLu46uIyjl/Xmv4NJHaVPO3430WU/MDIgDjivdXhtspr0e01EV/p43QDv3yF7oxxesJ7OKAM5g21YUZFteI/bVFhPpvEMZbRVlqL2lv5UCpavpb+zdLxNAc0SoaQtqmjIE5Jad+hEuaR9UhAU34kiV51V9Oz6jubLiECG6Tfz0051ImfdbqICCaI9Mj2TV4wMe4NOoSMgT4CGzUOFk49Yw+k7uWxHB1qExzQqA86PmkycEX8rAg1d/Kz0gKblVdcjmZ3lBYs9qruiWRS4oXlyneuZKeGaQ5GEBuNrvpbx00KrtE8+Br8TewTaZQ1U8Sl5UeqMD6Qzg4CA/moa8NyyazgSUieAxbyS8+YVl01jAd+i6GrJNWUHUJyhW4tRb+s0PYZnsIPP7AGrb/LEzM7tnAuINt3jZsQz+G4VWX/92JSb9nsMVzEDj8Ceh6AIVx2HkIO3Mm4Ysjxps8gH3+LAaY8kq7T+TBwH+7EMyz3rVur/Ogz1/hIOys2pwuYNF04p0lQDyOAXwHJqHfX/bouU/CrTdhsAK1FSgOwvQVa2vvl6F6zco83A4r1bgCaouIIBTgSTOYJFwaN4jr6EsY2A37fBcx4OojuM1eTKpX0Iay/S0Q+ZAPeN1bxJn8yPsp55JXMFvup4k8HWOEEe8IkStEe/aYz1MRU7qeIHKNv0LksZCE+h/731cwGeFDb4O0BYh9IQO+BBftIwku2SYjVX03lSPvjLZUhkA0ezK0emCV03ey7UBI8NAMsqVUhuIH9F1r1J0OVWgO6sp8sWiJrFWrr7IvqQ36XEKfImFLZfiGwmd1esj9RBZ1uXJllUCLSoVngluDmk8bfa6JEvhmCVdWVA2AfpOebfDTp9nHqQV1miNwsmufJk1GLak1Ok0F0hostaODCIHuI8h+RSxBhMDqYFPdu9hmuIAZYbRhBr2srk/6obfiid3l6Pp5H+hrGrwZ+7yIiYJXvIArcH3RU2FWzUPiEQaEm4Q0sVaDQ2VYmYxwYFEPy4TEX8U28mMY8PyMl9fAQKIb6FqGqR3oewyWHhg47LcuMIMxAVLLvWss+FjLyKeDawQ7GLp9XL7nYz7t9c75kGysGui8Q9yi8rNEMqMH3vanvJ5BQtp/f9JzeVRtvpaXbFyuAcvXPIlPD0xvwVDREvELHDsxqnkYA+ojRJCM3BoX/Tkd7Dk3RRED0Gd9jF4gXB9/g8j0p8PoBHYQ/UIRag3TSBRGLhfBu4RtEkziXvIyh4hw/HHsQBvFhIB/2A8ntuHnsMPrcyNw+pNwcQXOPQ3dT8KBB3DgIJzshoercV3Woo/xHCF5yjMi03NZS5ZwI6cBSdKi7gTk0BwsJjqzNbxZe1aCDsQay+5nsiPIja9OBGllsBUnvpnK1mEgwVECov6JS1e9woXWcG4ZLwXWwk/1b50IowaakwtlK6F40KwqkAYnczQahN1UuOiK9lS2OinPjZysJBsDs4tZK31QTu9Jeimm8gWcAlktlGr6TtK4OCm1W77KAmp5EWhhabB1UkoazuMirJRW0ebtOIAtoBOE98J5f3+sB0OvHmy3DmM8RgMD3YcYkhzHEtQPAL9LXBvyI9jZCKPRTW+TchJc9L7LQHdlx0DvR5hPrYxuZzFwyhz9lldzAKMU9nlfRyuwvm6AWXoQ1xpd9Tmc8fGU54HsAdqEAqA5DOBEMYgim8E27uf8+8eBPyXWxyK2Nl7GzqTLXseYl/V4O9x3L4nnivCmg9oa8PhxmFoyYN/ydn4Ck/rGz0FPxQ63HS9rmkgyJduHuGOtY2lSW8Thp0MbjJ5qw8DyMZ+HC9gt2m+t27qY9fYdIzSLWsPeO3wUugZM2r+Jrak+Inp8x9t32MueIRIiSbosY9Jw5Rh0z0PhOBSWoNzmFV7wRk5jA+dWv9GaAfJPsIO+iP29S1xinulI8bw5ihU+3uCu/SXBSRqT9pDKgGaglqvbPkIrzmkw9VtYlFMnCFs2iaRYrZniMh5mmlKC6RbNQqr6krX7LFhmzWA3PS+ht+afl0bhGwJfNaRBcMqZfO5IA6mGapAEVpIMNVlykxKXqpNuO72rxmkCRH9kEFQnBNyKEy/TTAlo4+/y05OpgZL6oDLaiDwLqi8PvurV+OggytqBNqEmuEQsVi04ndafxNb+HAbK7dLPXsWQpwsTx7YwP9nrWEhwB7ZrfwcTs7sxg94r8OiaAYpAeBgDqkz9XCM8Tu5jLlGHvO/3iQPqFAbAY/7954nouyEv472qnRWnvA7lO17AQFLr5n4aw6NEpF8diwr/kFB7p/13FTt7jmAAcNPHcz8mFZ7z+g55m3oxZ4k2L68APKxbv04A13wRKCBne8kAfb8/e9vLXAF2Z+DeUtzscsPrGsRokR7sXRniZok1p407ikmkJQx8D2PS7UXgwggMnIOuE1Dy6I8jRbhfs/fmMRA9Rbienj8Ki/egvmzS6dNEUM+Y/79C3Bsofl7eHWcxKflxoPKSTXThZWtoeQ74ZR/8EqZqTMDKGpRPwNWHYT+W58YSofWJ7tNhmmlFuQIKT7ITgPZMFto6sDkrEJ5L2l9aF5K4u/wZcdvSbuWEIGCU0CSNF4La0vuiQBW01JqzQjgkQTJTq8IXGTAzJaK+SVAU/mlPKoAt06ylApZcSDdC6wtJeFkCzZZHubJ00UzQa7DFrchwlgnsGuG5oQGWH6PcrTLvm/klGfcy2Ga3GIUnamC0COTPrFM60xyaYKn2+cDRBGQjgP7Js0IqTPanlluM8uIK4F70//djamt7Oxa2O+Afui5W+y4UD2FWG0VobQC/hfnO3sH0em/M2lxILQPYJtLhUPAiOon0ihOYELROJAtSZsgKwTHK+CinBIADRVhtGNd42/v9LX//Es3J61/BAOQTR+H4r8Bjj8G54/BMBcrT8OJZ6JyPvBeiwpT3twdTrd8iclwMYT7b19OYL/l3497v20QUYieR2kObVjkkav7cin9/DTto8Dl9G6MU3vNx+YHX0+fvnPL/S+IawgB4CgPwCkb3HKnA4CGM6N3EQvRetgdmJ6wtt/2rWxjo92KH6unl0FgU+XXffw94/ePAz5WhoxYeHbtEbueDT3n+j9PeyF2MEzmMneDvAe2weBO6+i3KcWnWDsP3vLz3vM/ThCfMAM05wUXZ6UDTnHbS7DAAIRiJG241qmV3VOGPfgpERKikSz1TT/9EkWquJcxV0+cCcLU11yOhsULQCxIchYGiOCXstGJI9j5TG7cJx4KmA6THKQuplhstDRY/JMlSg5YLkorbIEBbvn0lgszXjzovcMwALo8EScWSpLNq0E0kpc+ScE4aX0jPy7gnn0BSfaTf2ZC5i9/64N/pMOkjEh/pEJITulQmiJNRJ6EMT0eBX9wP5Q27c27vqpEvYZaZEjAHRUWMKMxrFhO73sB262/D9X8DQyfg7auRlLwPWzx3McC/g+H8Q0Ki0o0fa0RASCfhBdKOgVqfNYUBmqPOFhr2/Rv+7DUMKG77P43lAe/vcA+Wqm0Ju/LiEnDK+1iBrVsG3kozIY8MBVLcxlNPEp4KPRi27BDXHu1ikrLCfyHW8/MY0PVjt3Rf3YkbVEYJD4p+TDU/TfjhdmLGOx1eZ/0ZcYBb3tYT/vzjxJp8wb8bwheArOCjwLdh4n5oUvNEoEbF2ybXsqK354BLNpcJrnQIW3uLTi2IU76JaWBVYHUaKg0oKTF0tsYdxjibGWvL9LSN3xRhL3nDX1F/JQ2uE0KTJE+BlWw9wgAJXdkzQcKU3M8gDGxFmpMHSUhTuQI+4Ymez5ghzVsYpqRDEuykvQpAcwqFCoETeT9rCoV1HekZ0Zk5QVEOXoFm7buV3ih1u9sbqSMagAYRuinOVZXVsUnLAA0Bxu3+fT7hGjQPoAa0RnO2uVbXNwG3OpMT/qiTUom0GKQCqfz29F0+qWRUyoOlhSL1Sfy1+qZUiZpsUTDiosXv6dQcxtb7PuDrReAAdBUxPfnTXuE5bFf+mjf8m1gQxF0o9GOk4DwG2g+AfwfVHehZg1rNVPw7hF9xG36BKRH9dcv/FTBglm9oHQPzF/3zT2Ob7wzw2H7oPg/bU3CkB6Z3rMxbRP6Bj6xLexnPHgde/TIcfx56/jnwL4F/VIRf/W241A1nbsdVzZOwvwue6YDTCwZ8N4jD/oKXO4sdDie9rj8iaHcZ3o758Ny3oeMkBipPYcaw107B1gJs7FjbX+2Bt3dM6mv3Pl8h1t2YfzZHBEJWMY+E2z5uY0SegoqP9xHskPhVYF8Rhp7A7kDUydUHbEHtThifFQk4QbinbXndD4k8FVtVa9tHBLU05f/asHXwMLWn28fm/H4rYHcGSrexw33IJ/Hv+QD9wJ75P7dtHi77UvsPRJrUevot+k50QKvk2ENoswq3lsCkvStBRgeYtG0JTtKOBfDCIs2RAFRCkahS4UwWvFSupN7sUABxeLSld6R1y76U32mk99RGjYu49CzlZ7DXe8r/Ik2vNJjc3mh5WLwMqXP6f3b+FtAKVDNdkIEOYgKyy4lAWMY7GX5a+WUNlDhcRTZJstXJI/5KKhMEyPfQbCxo8+/bUrmiarpS2SVMQlkjeGdJ5HlCNcCiayBuuugGuhowtgSFi4TD7yEvREfytzHycdPL6MF26Bx75OHKI+tTTxvUa7bQLlTgXtWKe7EMV2v2zH1CS9D8LGGbeRXbxP2EuidpQCHNjXsuUezCjxrhN3sH27gj2H4ewqTH5y9gKPgU8LUO2KrBZgMWvw99R+H6XavwtzCR9qo1aqAdbq5EzmYZbnT10NMYYE0TqTs3/bcOy6fxYJJ2+Ld1k9AlYe9bsOdH+2HsNPy/kzbkL2H0x/tejrxJtJnKhDt4A3P5fp3Qztp9bk8QN3y8AnQoxE0+couY6vIjWLwDleNwZwnGjsP0ko25sqzJc6PgdUvK68fGH9wISUh9Y/7urP//NsF5n7wExRKUNjC+5+9gh/x54C+8c3ILWTBGpUrYF2YJoUbGKHlLCIAq6ftx/0yBOisYsG0TPLccAYQVWRDUHhXGSDAUBkjClTCVjfXZi0Jl4M8osAl+GlSFScIKfSfvEbVRdUBI+ApIy+VmDMweGJpLaQXCoDopUi+T52pc9hkWEEqyFUBmdw5ZKuXmkd1PGkQeHE2G+JwdbOG0EwmxxQ0JRHfSRGigdBrJsCCxn/ScLnmUdTb3r05IEJKk9X1rhqo8iHIM14nX723XIaAFMkq4yj2NGXZeqkDhk/7iRUwyvoQZ8UrYytfOfBsYhtnL0L0OD9agt26RauWnoMetNl1LUG1AR9UWfg/wvZq1URb4ewRn1ocBlPDis97OU/73Yz1wdweOjMGfL1j7LwPfbYSr4zRxeM14H4eAL34W+AKWd3gA6K/B6Beh8mvQdxF4A3rckfhPrJ+LfwQzM7C1Ai9U4FTVJFXREFpDk8SBUvXilzCAPIgB6aK3ZapukvaA92/4KBS34IMa1Lfhg9nI73zdyx/D8PIstg7FSa/43weIfM73iHzBRwka61kfw9F/DItvmDsj/xl2ejWwwwfoOmeTc2DEfo/W4cgAHN8MP3WB0g4mddd8eWxh5gNJm1p7kuIHvLpXMPB+HiiPEvHT9wgfx//d1hAV+PbbsLMA/8ofkb/zHKGmK4e2Diu59SnPjQSuZR/bbOgTKG2xpyTscaw5/iFTp9kQR3q2TvCwZSLsuUFz3gkJTrkueXOpvnyYtKXnhCGiWrJ/sSTxHUxwWUn1V/jp/M7ZG0vlitYQddIJlIax5EKSUiUlQrMDtE5HUgU1mo1+arD8CjNRDgHY2eKYXd1UXzWV1UYsBtWjn0oqqyO9m/lkgbQmXIeB2qkEJgJRtUHURrbsqg3rqTy54ekAO0LcfiB1axSTqD4DFC9hAPwcJh3rWoouTBf+ANsB9zAdvB26Z4F+uyl6ZD/wlOUV6liHYhF21ptvaZBRaJdISi5ufpVQh495P36mB+7v2MLqAvoPQGMFbq/a2LyT5mTb+/jA61slbn/+FLD/hPdDA3oNOHITuvaxlwVj4ib8e0zvXoeZGzamuxio/6BuT04TV0sVvE8XidTPuxjgPIZJdK8Arw/C720aMB3CQPcesLsM0zXr9xB2+Fzz79/3+VrABNkZInBDRsurRFY63UIjGussBoJfKMOf1eCV/cB96Cr7QL3tg/9lTMQ/7fP83/jvBeA1O3jbgLODcGzTwHfHhmjP57eKAeUEphno/kN9N+bD2u/968EOssIUDGpziHT+LHZqLVkHv7ltrpBzhM+xBJU54mo0+eBKAhUPm6kEgZvsQdIYJdxsEkKe9n7WdAWMirLVMxDh2dmNLGvO4nSFQ1k6bRWyhE/SbqVpqa056Ct7VMl414Wt0YxzmSVoS59DCHFKdqR5E1aVSvCNanowi9lSRzRoQnOBKcSEqRMSz0nf67O2/5+yBbRZHRBgS+LOnhp6T9y2Bk+RUlI5JV1JxSINbHbhk8SbDYUFwiBdJaK1trDNKiNG9urQpi5gC/cQBnyfB/b1YwTtIoZgv0BYbNqwzascmEeAv4T5SVirQ23Tvjp1GrhmblPFYWAGdmohJa1h3O9fEb6xfURimHYM1AYxqf0gMLdj1Y09Bf1FYBn69kN51c4MHTCTPm63iMNY0Wi9GM4MfCZN9A+B3wb+HLhyFZbehfab9vBb7Bkqy+9YPeeLsFmDcyNwcN20ab8mbo8u2MHpAAw0ThFr6yrQvRkHj1TlAa9KkYv4vE1ga+oBIXVpLTyGgXoHxkVvEIErWsO6g28LT4JUM5Ds34BC0St+HOvIF7ywP2TPwLL+R9Axjh3QT0D3u/CwCu2bhtOyQTQI7xlJdBIoHvq/qk0b73ubjmMgepgAybY6dNageAAa12Hjr6G0DBNL8O+3ra4bhJFUwCqhS1KxUqZACC87xI0q8jPPTgGte1FCkLRkgWVXmiNprMIEgXV2i62lugTGilOQY4K8OQo0MwES5IQ/W+mzHP0LP31zkLLAyVU088OZCskankBbXHYGeWFkaQi+0crbagBUqABOkmU2pkGI9bkBbel9NShbRSUpt9Ia2UG7nJ7RZGRDYOaIPu6k1SKqp3cUfy/VXYa4rIY1sMW/S9ySMUK4VonfhKBBRP+OE5voFPCqP/8kUDmBicnHMapCvM+oFzCLrcgXMR65DpvrcZ39NqbWsosh6fv2bvsmtB+GyWVr609SX5aIoJcODDRXaL5U9fhT0N0gMtgXgSG44u5oC5jFXm5Oj4ggCHGLFeCzFQzJbhOhdN/CkGENQ91/6xPwu/7MD6B9FEZeskFbW4Cu34LKOzBYNB78LiaFdmHgv4xlXVN48QbhilbEsF6HhdbUPq9uHyZ5f9vH8x7B2y5jdQ34/w9jh+ssAWrKETyOHbaSuL/jbfRLWujfhNoCtC9houoPsQV63ido1Djm9Q9M06FolQ0Aj5atXvkYKzvcJLZOezEpWGpy2b8fwNbaVOq3JOwjmBbx+CHr7P31yBvyN96/7/g4KAG9VH4FxUDkZ4HQUmSAFUhJIu3zsrLxvEZQDXI5k5QoUJObnIQdCOBSdrVsrMu2KgGpPD+070V/qN5WZwNRI8I9aci7hEak/guzhFt6J3uDZMFThlsdXDpccq51jV2px70ssuVRQEr6XGAtUV8NUPISDXaWoPMgtoK4Phfg6nTMARg7hH+0JFdxytnVRs/mycg0CmlQtTAkfWtRCcDUTy0SjcEyAfrDhEVXGclk8MvRUfsx3D0KnH8FE2VexFwuft8f8tSR3MNW7wUMFQ4C922Mrm4a9m4AJ+ehdIBAm38ALML0DRgvw4c1K3Ydm/C7xC0WB71fxzFceIRJpXs7pw9zmr0MP3po/ZeEfItw9C/x01ntLgHHhMxHsdSWEifuYrrwQx+8P7CG7Ny0UFFWMfQpQ9dhDG2/BOtvG2ebE/gfwwDvnPfxGBZoo3UrY84ZIlLwDhFAdAqTpC/4nClYRsYxBalJaJDz/hnCu0FrZQcDwZMYOC9618f7obrtQRi3CUm5CvwR1JahWAW+CFM/goFBXyzv+Hqoh0ZT9TLv+v9f9Lm8Rdz88QJx8IIdDNIGlMVv1MtZWoGH63YQlzDHilv+/0WaUyIo4c0izdRkEePTRR1Jjc+URM7dkIUuAa144hq27ASy+T3FDghPhEu9hGSbJczsT5ztQXLtFJUgyqWt5Tm1UX9n21ErNgg3MgOgvpLK0n7ZSH9LAMyBL8KawnDC3xrNPnMCM6XN00vQ7NObP1M+U0m1KkOStUA4N0aGwdyG7B1RSeVkzwlJ7MMECApIJc3kE731x21iewl4lPNA5Yua0G0JmhRdx7RFSNwj/uyyt/cEtqD/xRcxKfF5//e0F/4Oca/9NzGE+IE35le8wv8CVrbjRoorGO135izmGqBcyJPekAH402sRm//I+7+BAfC73iZt8k9h0tTFpwjfvVswv2Z4uoWdE71EXt3zGDgsE0E4U5gxq5tIIqNxPIpJ12cxdbgXAzCN5wWg8B/5l895w/8J8N/b35f/Saiw3/I6tEal8fRi4LDk7bvizyv8eAvDe0Ux7nhVNwg/51FM4lRAzwkMnDsximc6jcVhwlCM/EgAACAASURBVPJ/FfhPfKzP9LMXR7zphkmlMb3oz4wQyfokUUuIKLTDraodMveJqLghwt7xyPv3fkz5XoTfspcz7+0+7OWc9nI+TfhdK7VqlbguC4KKuIrtuek01iXC0Ef6TOVIopVRfCt9BrF/IPZldmkV7bZKc5ZHAW4plaO/BaAlIh90FqSEG2qvntXhtd3yXH4ml5HbKMNgLT33cT+a12wMzN4Z6pvKqeF+yKQP1SFJgEXixMwuGyq4kiqQt4PoC0nBWZrdofn0kKSauWh1IqsGOvkgiPKPMwbKcpknrJrqU1SVgF+EvhIqSZVYJxgFATypHJWvyRf1sYJh7klsE56dwmiKX8XEvSqRReYskaNQPqFTmF79NnAOyh/CXMM2wiHg6aOYyLfI3u2aV7dh/+Nw6ydBPck3dwKT/mScqmLUtWiLoy6Z8kte5rxJUOIBpU3UMXD9kJCWRFtUiCi+9zFp8q7/VqDETSKLXi8mrQ8AHYpiaSfE3ln/dxP2fQhHi/CTRszZI2/XeZ830QYawjFvew/NNItyPaz42NwkHFp2/bN9qZ0Nf+cUdvYpYbw0uYP+ewZTet7dhrGDcGs58hYfxwR+CSF/4+9vE4BfADrbYb0KI8/C4pQ9c7xi4FyoG88tQ+ptQu3XhQA6eJex5Tbp43GfSLE5RaTTVqrSbX+2zfs/QBgFReVon2ospbaLcsj5biTlFtO72rfSYsXv7x1EBHUgukKRfjkBmKgF0YpZo9+kOXGZ3mkjIoBl7INmKVrYUsbWQYXQqCV1S7OGAGnhkDT0SipTz+XkYmpL5rCFjdLISt3uZSGQEn0hEVwPCtG1ULOoLcpBJ5wcwXPgSK480wmZgxHoiR9utPzTdxrY7pby87tyW8vt0cAJRAWo/UR0oiZXfskauIPYRlaOAi2IfmyT9mKL9UkMKD6DSSKDX8MQeh4TI08T/MEahrJ3MeT8a2/827D4Z9DVBd+etI0mK/fqMow8RyQd3gf724B7lr/hurfjMob7cn+VKtjmr3b4fJRWoToL5Tsw/zZUdiwP8CPMWeJDbMOC0cPL/v8jPpeLhGvhXeyMue3d2/HnR/25lzGp9BwRwFKsQrsmfolwfnbj58oP4VHD5sLzKPGiz9EDL1u0uhwG5ENewgCp4c/VCL75FpFzYgE7pG5gQHAwze+Iz7u8AtoIiX3BPx/AAlVOAtOr0bY2LKiiHzvMpolw5gdpvK4AV+omlbc/glt1G9+Fqs3FBLY2lXdYvLDWohIaiV5RMIvybZQxWmMXeP04jF+A8SEYn7a1u+xtl+T/gHCJXCS0wczJSpASP5ztRQ1v6yBBNcr4J+OdQFPUV3aFzcZMaeI5diEbEgWSmeoQvsgDoo9IoCVPjBzeLAzaJbTqVZopMAmhomVoKUdeFwLgTIXon8rLB4w0CNFTpW4Pnc6eAmp0Mf1WAwSsSqMn1ULSr7iVrGqo0SK3MyGv8nXyqS3qgE4uPS/fP4gTLKslIsdljV0hnLYlxUrq6yKAe41QR/L7EJKG+GzxyDJgHMIW9EEsdXEv8NxxGNzFRJKv+pf7MLRS4oQfeGP+jVeg7G23oOuAuUHl3Aib3obRZ7yBEzCxDP0l+MOtcFtbJCRB9fkQpoZ2Ears549C1yq0NeD6kknME3Vr4nPY+aCsa1oXihrbIQJEStim3gd7F9k+T3gxFL3765gk3T4Cg+v2TgELlmEL+AqGtrvsZTPqugdrG/bsLnZATGGgo4N8LM2XIoM/wPojNVvBFDVvx6f8uwkMyH5CHB7yANj0sfwkJk0vYofJR8TVTTIWKUXnho+R6ISbPt7XfQ5kEJWh8DQGyAcxaXqpbkugox226zaHMgQpWmzI+9LpbX/gc6R8ynJr3IcFfnb6fFy8BHwdszS/DuVXoLsHLm7D9oKVfdv7sonVvUHkya6mekX9iyPNmdbaCe1JVzllQJL2kmlQ7UlJo1nKFV6IetS+lHAmvOkl1rsOEB1YmRuWFFxLZQhXJMRJ+pWHVcaGbAtTcIrsUDpwVJeEOgmE4uilFUgrVxv30m9qMDOfoheyxwUtg6PB3KbZeTpbGzUh6mQ+PbKHROZZWq2fWSVSW7I3RU5WJCOkfFgl8WYpW7+1kLUJ1S+pPx3YguwnNtwgsQgb2GLvxSJQLx2HviWonAP+PiZiPUVkaTnuBUxg4vQtzEm5E9ux7j4wPWkSzqY/uojn6wUGb8PdSag14OAITC8aizHn1XRigNXApNyT3q8T/vksZoTbWIb9Q3DdU3e+j23mVQzAjhPGj1lvqpLWSMo+SGTe+hRGU5z2NlwomvdGGwbsg0Bv2VT6NvxOtqPEdRpXiZ3+IvC/wcr9yMG7SlyRdJoI2ujDpM1pb8M9wrA0iSkoU4S9oc+HuQ8DhjFfA5JWB4gE96exIAy5TSpL3AHsUKsSKrhycIxja/Cm1/ehz8Wcv/cjQsOc9rmQS/p9LNfG3a0QBCRdH8ekcg1V1fst6WyYiGocwQ6CV7BDpL0fW6Rf8EE8gZ00ncBXYf/LMD4HpycDNJ72+heIvSNAWiLypkhSbXj/tDYUyZelWY2jwDjzwqJAslbenv7uJIJlMqU54H+LQ9bBlQ3PolsliQoDhDnCn5xkTAdDlowbqTy9L1/sLBlDHCoZK3dSGaJ71L8a7mVBGnCdHBLHNQES5YX62b1Mrh2yaArtMxjLUFdPdYiHUsdVb24P6b32lk4LsDtSeTpxs9paIS5DFS+MPyvXN/HeFSJFpIBcbnId2KIvYpvjBHEP9AhupNnv1vWXvZDXMElYkQolzAVq1v//FpHA4IY/dwTuTdvryxiY7GD7qB+oOafYBWytx4aXd0HZ/72EbaY3CVA76c16yft3a8P6MuPjcIu4VeItIkPcM4RKpjiWfh8rbXxpDSNFz7F7yMoqbMOJU9DdDrPL1vVuQkOhA0PSo15ZBXOkXoZ7c3CmHd6q2xjj4z+DSapDGGjeAv5TzOW5QvCjaxi/O+HvLRKui31EaLA26rBPy7jP+xuY1ClnlH1EEvwJwqi44mMribyPuEX7aeyQOORL4Szm3XHM+zGJgbIiO+/vWFnX/bP9l6AwAyfGYM6Txe8n8kS/43Og/BWDwM9jcsC+fwCF38QSS3/ZG/HpV2D4Neh6Dh7vh6t3raIGdLwBZzzHx/eww6pKGE41Z2tEoIg02xwsI5Bp+FhoX0rAKxI8rUA2g6Q0a1GnohMhgF2f5eyQkoqFR4ocVCyDIvqyn3EOuxYwC7+EO/pcgS4QgSHSlHJcg7BMmoUE0RrBKqitqq8AlHrhG61GtixpQkSjCfSyO4garudyo7LkLdCWOiHKQKekPhNoyjiXuWF9ny2XdR8s5djV95pkncaSeMXBZX4oG+k6ifBbSU9KozmGAd4gcZo+iwnCR4H95zB18EVsR7dhgQADGDDfx2b/I+JWylEv/M+xDOxFYBl6t00q+mNs89WxzbtGSEl3vN9T2EK7hIGF+lHCJKgxIntYCVOp24jLP//CP7/k47JIHJbHMWnsoT8r8N9Wn4GvjsBAF1Sq0FfBvERkXalB2RfI4ox1dYkw+NS3oetJTFcewXJtft8aUXvfwPsv65YM7zYR9DGJYbh8cLcxAJPk1UPkThA4S+KVEbeKMUoFIhHRQeJW8EmCkugieGGptCrrcX9vkQi+mfY2HCBSm0rae8/fe4SBqDSfd/3/B7F+nzkO80vQvwD9FWjMhwFth+aLZEYJO85vAieL0PN1jCM6hp0KL5+AQ/uwRfYk5n9YhTO/Bod+CGtQ+A3gFnTfs3G56+XOEDyrIlUrRB57CVW7hP1IY7VGuBBKIxXtIIpAQoRsQ12ERtqGrbN8m49AUgKj5rxKeOJIKpebWauba/bkEJYIqLMHhbAm26uypJzd7lrfFW4VCG3i41gEAXZpn/shC2ir6eFsaMtiugajlP5W5Zma0ImmAVPcu9QSDUyrZTJ7Y8glTgZBcT3ifvSujCuKHFsjJGgdIAJgudHpuiFZYc8SdqWThFtbL7b5D2KgNYRJHxdx/85XoKuXvbSSDGH6fxnTF2ewHVfDUOMVbHWtYzry9zExexauLsKPt6391/z1XWzz9vtvuepJsp/0ol8vwtWGCec9aT7kivgREaE3TXicPPQyZnwsl/ydNUy9lpQw6WPS4+Nz2tvwmEhUIfhHGEp+GhrftQlcWLe23iJCoD/08R+vY6h53Cf3j61hxSX4oGGH4Pexg0GxK27HpEFQF5KURG1cJjadwORHRLIgXSm1n+AID/sYt/t3p4m8Tl3+bA6iaveuPyDuS9S4Kk3sKpFBVd4gCz4XMlZuEHcsDozB+ir0laG/gGWKe2Bucbc3wyf+bWy5LfvcSGN7EWg/CfwGtlBfHYNhMfhP+Sz8OhHcfByKvwU7/84W9HkYuQLP7MJfb1j/5BKow0zSq/ZhNvbXfMyH0nsQIcYS3LIULWpy18dxgBB6BlK9ik3IQCoAVBmyF+WoOEnBsmGJZhVgZnAUcGZJPdMKrcZACDBXH/T/XG45vZPZg3aCWSi1wTckDWVXjt30gr4XaOdB1Cml8EWdQKIA1AGpCSpTA1Fs+Z39Ekl15Y5BALNONGW1kgqTDwO9rwnQYSJ1QQtrlgj0GMAW+Dlsk572Ph0EvnIcCkvBUQ9JBH0GE60KGCBXsLUvUWbGC/g/MD36PcKB+rvWwA9qtpGvEWrNDPZZHyaV1jEQPYCxHMcwgHy/AV8cgzdX7ZlhDMR0yGwTi13VKnjiAabRTmMLvgeTSLPBoxPbZFsYoO3zdnVsQs852HkApSe9wV+3RhYKUOiGh0vN19h/z/sxCrAK3YcwdaMMfAiLk/DNhg3XDAZgi5ix7pi3S4aSAQKYFa7+gMi+tYHh/asYiLVh0vAABuo5FucQsXFOejn7/PMG4bUyTHiPiHM9QgSOzPnamCKCpeQVtI0B9HT6XClNykBtFQ7shwePoM+Rb+cWlLbtYoDL3u4jPr87RA7mJ4CzZ32CLuAJ8VehfBzjLa5ji/IcppJ1YupZG4x0w767lmfkgBX+49UIelFuaIg03p0+PhK4tLd9WuklXNf0rqRZUUbt6dmCfydpVw4Gy4RkLGM+BN5kjVouuMpvIw5YB0V2KlD5rcDZ6m0GQYvkcGs9u4/I8igBNEvW4sRb6RUdchJQS33wjTaC95V4LUBtlVg1oKro497RBs5cjE7F7NMs2kJS88e50UBwvpKIJaFDMx2SjQI6METGi5IYIiTu7GstI8A+bDGMYpLklwnj2ClsEQ2WYfArdvXO0GmC97znlQ5jaA6BduPY7pz2Z7/l3z0LvG0h0is1A4ZNb9MEti82CHW7im36c9h+WyYkpK+MwLen41B9FwOVojdtMI2rjAqXfPxnMArkhDVnT3KsYnv7PuE3+5p//gK2tZ88BA9uwr4RL6jhv3/ig7gOOzMGppOE98dPvMz9wOg5H/zvQuNO5P+9jEl8twmgHMLCgE8QtJd8i7cxTrULkwVFOyxinK4uTegmUgL3Y5y6DJIV4Nlzxl8v+ue3Cd6ymwCEzAXqRokBwsjdjS2HJR9rRZ8poFHBNb+EKUuS2hc2/BA+aGC8BWw4GG9jh9JN4rCuAv8UON8O/EPgdypwvAo9Pw/lu1hqvRXvzS/4qJ7HVvtXMX2lAwpfhaHvWEVfg53ftUNfN61sEoYv5XaRB4zsNvJkkpAkoJLPtVwHs9AnW1COMRDIysBaJHKUC7hVhvBGgCvXV2FVdonTnpf3lTAu26GyFCzpWODeoBngSeVDCJ6Z85bQlykK0aq53FIFSy4kfquYChGQSZ2rp+/E50o1lpie+RGIlJvZOVqGuWzIy7RCnUi5qXLq6e9Wv0e1XSekOFSBbPbQaG/5fDW1YwBb6MPY5nndPzuMhcMOlGBQpuz32bsKovEeFCQhNwgL0iCGiiMYStwhsqc8BG7Dgx/C0qb9eZlI76hxuYttGyUqv+Jtu42BoVTDMeCv1sPNSDRFO7YNVzGaoIb5Ez/0MXiASVnPE6G4MxjgX8c2nly+LnnbHnlZRzBQWV9xlzwtgDp2T9sbMP0TmJqxPAk/xjatpHbNaw9wYBK6HrFnMesifI5nMQl3A4OTyz4nBR9OuVHJAHscA605DGA3iBwQ7d72Xuws/HngXD8MbEPfL0LhGeg7Yh0+Ng7fm7FyxE0rMq7qY7NMRG+CUVuT2EE6S4QFi2/d8fbhZSwCv3EIrq7YATNBSIM3sBzXpU3YbQT9NIEdkHXC06UNOyA7Po+l+7xShfNKz/TLBKlzGfjvvAX/I8El78Li70PXEPzpVVsk34Y7b9o6/IgQdh4ROUFEH/YSXPsWzXt3nNjv8sMt+zvaizuEbUZzqYP2JLZ1Mi8sCipH+2W7UplI/ynhTO2Rh5SAWngirKsTTgxa0rVUbqvxUe/KxVPh3uKVs6tcpiqyVxre71Kne1kIENXRTB/oJJCxLUunuXH6TB2QY7a4YmgOwMinReaGZXgTfyzJO6sX+mknjI6S+jTAWSJvEB5V0giUFWqEZj/lA9jieAx33h+EzSVofx74CFZ2TVrpOgf8BAqfI67QeB/bkUewDDYVQtT9NnDP8k70nIf5O9auy6ltuhFhnMhQNk/c6tyGgfIBYsEU/Nkz3s8pgmO7jIFmhbhZAsKm2I5Jkn/jn1d8zq4TyW0eYZtmEtuI5/zZIz1QKkC5YdGEc5sw9DI8+AD67lpB1Zq9N+nt+5C4EeMlYrN0AgcKFrY96fXLLegeca7J0V9CwfP+3E0MIA4TfslbhNS8DwPCYQyeDgMn91vuANqh+CIs/gl0rbEXc734DrxahAMNg7ULRJDk4wSP3IYdiquEZvHI/38K23jPEHSL6JIdb2vXSlAfDUwDcQqe+XXoKcD3G+Z4ogRC84TKu9/bc7YfczVZBn7pCKzdhI7Peo1PA1/zlvxTbDV9H3PW7ANuQFcbNN6wxbdmrx2/Bh+sW/tmfd5loJRUe9T7JX5Xwk2DuHuhRFAU8msWJshnueD/P+KtO+6trvuYCx+yYJa5a9UtibcnvVNqqVOCm3CtneZDRO+JNpG0Ds2ateqSJ5ewT1K1KI/MJmQbW+apd3DKQidGPn0y+azP9Hc5fdbuk7GYKsi/Ff0i0T67rwhstXkk6kMAu4Be6oV+VL78DjUwMqRow8r6qxh55eXpIMItPf1Ak/vNIa/vHNDYhN5+uHoD9nnje4XiEEkOHkFjCgpHMA3xoRf4DrACtxZhtuYubFPwUcPqlQVbSW3ABEUZmGSRlxSm8d7xcTvvfXjg/dJmqGHK6Ls009k1gid+hwD1NiJnRS8GMlcIo9MhzNuhC5NaZnfgQQ3uN+DxdhisQ2EZ+r7EXqz3+9PGh88Rh+G41/cEtsmfxFzshrZtLBS6jrdt0vuYObwNzHj50Ms47P2ZxcA8b9oxbF0UMal4sB26nwYWYGUa2lZg9qaNUcd+9kLYu8aBA6YZLa7H9UziQ4sY0MvYuU7k8hggfODv+tx+BWNrn/V2r2F7IEvwnansaeBkGWq7Ru/oIJphL2qeNsLeMXrYO/0vzlvmpq4nMDefCqYrzGKOd5/yN/9nLMZwxjrMq1CYgh8uBlr9PuzWrQ9bPs6iZBTNJ1dBhcR3YodJicipLAOdpEa5ntcJMO4m7nJc9Fa+5+O34c+JOpHkqjB3NXfL25Sl54wl0uZFi4iCEthmYa+RyhKgCqcy1SluWNgle1tmFcSJt+KjDos9T6+Ku71JVBeQkR7OSK4kJ9mgtuDPq6G5Q6o0WyelqqhTOlGlzef6JZVLDZdEpfIkSWfjnSZAUUFynakS4K2+DhA3TwiUGqlPTvFy9mXYvm1Ltx3YXYfyJixuQ9fzGJItQ+FlWHkTlusWBcV92KnB5SUDtO96G77lnZzCsKuCqeieToIy4R8r9UvzUMJA+QgGmh8RAQtK27hOLMgbhNfJJwgr/UNsUy15PervO/7+mpe3igHLmL83j0mrEqRGgcpRKMhh+dveqWX4qBoJ1mex5EhVb38JO/AOej3iIScxj4OG1zvv8/Ip7IDQu7MYqCtr25Z/B6EWPubPfQKT0Mc/gU2E+90uzzQDaWkBVpbtMoD6ArTPwaP1cOdqIwyT+4lcGD2Yl46EjF4fL7mHFYCT7XCtbs9/gvCBbweeabe813I5PNIDXTswWbM+3/I+bmNSeg+R5GrY2z/+Scxud+YRVDqwO7L+2Ht2x1eCxI7PeU8eEn45HcACHLkdvoTbMHYL7nu7x4m0kRVfD4PYAd1PeAzc8rWx5uOwD1urjxG+vApz7yd4YBnOX8D2hny+RddkGlXavAxoAmPhR3t6V14Poh30vIQRHQoNmm8ZyvyxQFb4kg2JrZ4XmSlQZjt93xpLoQMOUqSeHmgltPU7S7UQ06rTJYNF9h0upTL0W0Ch00sDId/FenpWgC4/ZfkYS1KSZJwjakS5iJqopPezZbQDWxTiyKtEvlX5sB7CVKf96zC17hLeIQOfpQYMnoP1d6G6DR3dsHMDKmPQ7U7QizXbSIOYZ8EQ4UZVxaSgDTy5OgFMuxgwSYoYxRZ2L3G7cS/B2SkfwS4hIY5gW3AUa0NbKv9hKlsAftPbdAY7eGREVQDNGAYkL/TAuztwogyNmmv4EgFKhJf8CTg8GwEdh7yeZ73fY16n3NfaiCxkpyqwUzWKQ+6Jyh0hI+9r/ruLCKWWQVRa2QBxUzLA6Bewk27CCu0eg+V52Fe2q/9EXa1iQC41+qG/P4+B0j0i0VGPt++6t0MCxLj366GXc79ucycJ+jq2Lo5jgS8d2Bo4cs4KLl80DvsowVnfxQ4xRRNOYRTQSaD/H2OC8OODPosTwP9KOKc9BP4XbHW8DfxrTGf043z2N6H7KNy7a406zZ6IemUl1q4EswLh4jeMrelXvV8bRNoWRQ/WsHXY5+Mk4UeJjCrYWhkmUgVUsbUozVU9yQLZIOFGJ0oUAhDlxgbNOXIEnhButQJ+4WAxvSeck6NAxjk9l13eJJzuEBJyjhxUkEx2Zij1EFc4QZw+kjq1x7JkSuqUTrVhH1TxzgJnuaSo85KK5SAtkFWjMhetgVJEj9QehfOKJ1bZGigZ9TLpru9zKGY/cTruEl4IIvWPeRsOABPrnjpyzBpzddMAZnbOyilXrJDpGiysQq0Gk/XwBviet/Wm/xMfnl2CNjHOVxTLIBEIMU5EBRaJWzoeEZJYkUhEIze5q17eE4QKeYPYUPv92Q3Ce+A2cS3R7TQe+3ANZgeecXGwsAnDLxHcxs95QX/XGjT/gdWzhG22z4xB26rN3zK2ydsx0Jnx8l8H/h837PVhYDdMuN0NEGHG85garQ2939fIIa9z2Oex0+sZkYh2jD1poc8JvK5PQvtEaGVV4HQ7VNphoGZ/zxOJlyThvUQE4uR+NQjuW4es+ijDVRnLY72vZrTNYaB7Dmbr8NczBvy3sN/D3r/7Xvakl/Np4PRXMeeJI8DQcV8xxzDmej/wfwF/Br8/BXeuwMY7MPoyvPWvYPzrVnr3KDAH0zfh4C9D4YqpdMPw7nsmrU54u8ew9ayxf4qQaNswGur73vdlbC3PE5eVdhAh31Uvp4xJxzoQy9j62ySSQvUTNIi04xV+mlpVbISAVFq4+NquVIawLEcRSnATHolLhtgr2Z9Ykq9kkWzrkrAHAcYFAruEWw2I5EIZbMWJ6EH5yuUTBZr5EInkjZZ/kr4lOatTAsd80uj0yoeBJGfxcQJ8ifht6bcOCJ064ovkvlUibvRtEFSGBnMQ2yyOuXsGExllxs6yF3lyYAiKu9BzAoqub9W24sCZ+f/oetPgyLPryu+XmchMILEvha1QQO3VtfRS3V29sJtsUiIptVZLImcoaaSxpRhZo/FYofDYEeMIO2jFRHgsO8KKsOyY+SBpFomiRhxJM5RIiou49N7N7q7qqq4FtaFQAAr7DiSQiUT6w70H96HEQUQFUJn/5a3n3Xfuuff570V/z01/hvLNtmODTXXeIZyMZ4gDPof9/+NE8pMjGHBL4n/Mn9+EAWe7P2cOAwadIqFQbPH6y5jFJQ5/0MukfpwiEgRliBQT48DiAvSXLX3m2G1z7OVbgVGovQ3ZV63gzTVoqVrD3wGOr5l1fDQPd3bDodMPnDoNH87vHUJNOwas3dgidhSb4FUinPe5IfjrVbO6G7CN+QngZBHWa/bcw8ChLPQVoHIRstchk8OIWeexVregWIDcICzOQu8Q9A5bp0xvxBhZIeRfijCTwV0ggiFkBdaxhayP0HKXiZOtK8BIEWaq1i+LGKiV/e9p9gdYiNJaJk4JeRLoXsHyVNwCzs5B9hPessPAn8Frfw031iws83vAXwP5DzyxyXew0f/TwBtwIAtf/K4NmH8NtT8PyaJoiicJSksBLv3szwWhoCNRVtIrdxIcb4tf94SPvxNex1VvB/HzspC1Q5JDTzggAIb9dINAVcC8TVjC6g+S61MLVrvDlMpM5W9KAZHGTshwFF0qgE+dgBD4KEpQ3+3lQ06FzuLfpA1OAVI/rck9AlbYTzmkeTFENaghVWGBpbYMMvPTqJYiAbAKg35YDbLFfk+myiLxftXrI0qik+gUNdyaf76ATQINiAK2gvfuYOZyMzYD3VtUuQm5GmQzlrlsiVg9d7DJ04JtEuWNv4CBzXP+3Y9iE+xlzGN+DuNRjxJRhT/ZDjvbZox+pgtulK0f3vHrTni9q0RQh3hzWf6zRIrFHsJKfxKbdM8TocYN/t1dDBwLxKkYeb+uULXmaMkCJ2H1uh3uuVqB3SuQ34QP183Ce8LLMgW8sxtUQhcGwn2u+x0CfigPBxuNg57FFp2TfbC2YbuNj3lfFQ9aUpxV4EgeOh3k79aC31sCeptgaQtaPgmZWXtJeQzyZXNOtgP1RciuQFMNHqwaMHd8ElraYWoqeM9uH0OHsQRKy/U43ktBFAcw8B7C0cjveAAAIABJREFUFj2IZE2SjYlKu1GN1CaXiVOt5byaI8aUZO4thA38ceW5PIKZ7qtAXwPwP2NL9k3IjcGXMEN5yTv629gEef4Q8N9jdMZRYB167+yd5ZR9Gh68av2uOTPq463Lx0MDsWjsEGlZpwjN9ikfdwqbPmNv4gVsUXuCCNBaw4waWZe9yfNTy1LKDgV94Z+nYczaHYu7FeUp/lZxENpdp6quVAGiOYTXTZZ5SoOkys80uCQNQhPGaWFP6Y5cM3xeHIuE3rJ2BXBaKVK5Rjn5rp5cqwcrFDAFZ/i7p1RvEJE1CtbQqiKuVw0rXlCLQ2Nyrd6Tav4U1aNrtJo2E8CusopeUUOK9+nzej8J3N+GQUkAtrBRU7X4c0bsATOb0YmKji5gE+w0xnue97+7gZHj0LoIfXkY2DXutKFqp0u3DBrH2bcD3WXIVy17Wg6YKwfdIqffVa/nvaTNhrBBp63yHLYIdBKBIOKo5YCYICiCcWzSN2BcZQ5T9h3wf6JH3qtD25TnVqhBZxZW6zBatWuGgKEW2K7Y8x/1Zw8R0YF1Iln81C50V23Cag1c2bA6tgJnzsPONBQ2oXgGCjOQfwnyYzbpi9jC1oJHM47A3CK062XdUB6HpbqnYG6H+jZkh6G+bGC6DnTchNWpSO7UgUGceMb366FvVr7iVmyB2cScWK97O9d9mNzz9r9NbP/HvO5tGNBKPSKg1qQe9P6Rzj4DPFJ20BvBCv5zj2EhTf8HZiHfhN+btFV6AWZvQvN9zCDOAgNz0PUWATPfgObPQfsHVuE/h9HrZlynErIOL/c01u/icO94+79O5Fx5AvP1PuljsZNYuJYxA+R17OcDDMhFdUn73kQYYfJ/aIGQ1V1MrhPoyj8hB57yqHQSmewEtimWCXsEshCgqoAS7Zz0vSLyigTVWk+uyRJ5ojfZn4wtC5ZcSCuCxO/pjwBXvpofFCEndUWqbhA3Iz4aYqsp5YPeK0BNeWQ9T3ywvtOKp0QlJA2QTa4VuEuYnfLicvxJaqPVs0Jwhx3+7HYMPLPYhDmVhclpaPtpIo/lbWAIKtftOW0YdzyEcYjdmF42Cxx7GprPQ6YIpQbgKNTGLPdt93lgAKqTUPiM3zRhlS4vw2g9DuW8gc2/dW/LQ/5ZmRDajxC5crWozmEANYMZUxUiYcxRjMeseZmvEBrNGjbRhongmZMtUK7YFFYWNIH0t+qRI/mxosnjOiqWPS2HKUo+QdBQAp5hf9dpgqYpAQefBqasP54owugEDMgbWYZ8BjZumnXeU4euZpMfXl60yb60CAd/jThipAOK29C0AfkscA6yFWuEpfHQlmYwh2XHMNQW984EYAHb5XRifq+nvb1WiWCRO8TJz/e87W/7Pa8Tc2UTW/gfJSztWwQ1od3jFAbURzEwy1s1TEt/GAv3Owqc/GXM1l4C/i/gWzB4x8Tmb0LzZ/zmI17Bn/4Y8C+wNFl/gMHsA7i2Af8KeA2+sGzjTtLLaSJNy67XWZLTBwT9JhXFmI+pYeK8x25C/vis3zNPWJ4ThDUp56qs3Cp/N3CsTuyk68n3MuzSOAcJAkR5plSC/pYlnWKinqf31h76W0adDDJhZIb9P1J7pTEZ4LI3OeZSK1c/cjCpQk3JdQ0P3ScArbPfIhbXs5tc94M4ZD1b3JwANyXm0x/lO1WFtZVJG03BDw87KLXVUMfK4lY5dojkOe2YtfUxINcNbZ+CpS9Bk/ZMPwS1L0P+PNyfjmxxmjTHMLXAR4DcNDaKW6B8F/Lr5hBc34SSO5cKHV7QaWAC6ndtsIpu0MKpHA13CKdSlUgcXsCs2SomjZO8THTFYczi6iBE9UrcIoVHDpu3DYRVLe3naMWeOYVZrtpRaGHY8PJt16ws417+PiKB/k1/fje2gNzHre8uWC/b/RWgPAUDWdshZE9D97o/qOxBO31QeMYrdwR4BL7ynpVp3d+Xm8eQYZg9s2t7A/K93jhrULtibdlWgvWqL/aeeiy7bBrmyV1rvyuE6kHh4LKStdC1en2u+rUeW7SXCyJDSBRVd6fi9064vu/XKbOanJfTfv0JoKMT+/n7QPuz3lOfYy/ur/Nta5dRTIBxHJPMHAaO/lPgq95zu1D/EC5tGDbX4CvftDEmrfWqN6GolB0io5uMsDX/Xlx7CzZfO/xa5VdpJ84dVDTqPOHvEF3pCry9v+vEri5LUA4yvrSQQwC1/Fei8jTvBYraXUu1sZvcL1zp8/rrR1aw8EYWLwT9kVIgD/PWqp/esefUS4M3UmDWtiBdiWA/XUHymX4EoFol0+/S9HnthNXdkNynBpT+uEhYaumKp9VOzrptImdrSl+kIJ1a/dJEp15W/V3HJtBhDIxL57DRfwmaTjigHgNeg+xhq2jT4t7Bwcxgk3AEG4gDRcx83rB/6xVoGrREOutA+xZsLAMrFi5bvwyZDXvYPWzwbxBH7PRjwHk+aSNtbZswwOjAuDtZJaOYUX8Cm9BdwEuPw/ZMyPyu+fUnsa23qI9e4Be9bZTprgWbxhIufB1THVQJR6x0mDvAC1mYqRsebGHW0Z6GugUaKzDg4uhiHY5mob3u+Sjqdk/zkr+sCViF/AWgHTa+A4WnYfwitO/A4KrV5YU+WN2AppwX/DJ7q1peXNi4VTSbg+xBGzgtZcjUIVeFhSUDmI4u6O2yBeFq3dpeC0sJu0Y7Fkl5l739ThG5i+VEHbdXMUpEwy0RB+tuJp9liIMSNLaXMIrjYB74MWwSHdeBYpexY8R3ofo165A2L+QP+0teOkME/Y4Dx+H66za5LgPfgdlZK98j2AL5uI8LAXA7BqI1H1ND7I+i05iUdO8MkSNFBt8L2K7hHWyBn8HmhHBGVIlwQ/pvGWKpQ84FT3sc88PAB2EZCzBTsFbiQmGBjLl279OUxhB2NBPWdBrVl2JQC8EiPIy1exZyM3w+nxQiJafTqDkBWJH93G4KtFqpasnvavIc/eSJo342ks/lsVRDyRtaT/5fIsz9In83ob74aAiOpk4kPpGzCoIxEyemAdSDzdsD2OC5gCcpF8q6Ju3dceichI1tyC1ALms8773dkOYtYNgxCnTVoLEM2UFsjnjwQesRO2VksQ7tj1sinnwnbK3C6m4EuT6LJZs/5PW+jg0S7VYmMKtDE3iIsFSOEUf4Nfg1Z71NBtdgwb38N7EJLonW4/5/5Qxa9GdtYvTGLHEk/Cw2MTeICbVCKFZ2MHBtJfj1EqG/vlrxyVy2c/Q6hqCyAk3HoZiFlhI0H8XMM3esbsxDoQ+4FDzq7j1ocj3VAyyJfxdQ6PfCLGMI0Aqrd6G4DpUtyG1DeRvykgAM2PvzGVscuh63itUXDIx77BFMex+ItrnrbSI6qUQchHqF4ISVH+SBj5EdDC8HCItQ4djSYM968aXL3sWAcqQB+KfYT8cdP7JGS+ZrkCtA/jfh9DdtO/Lcb8HwAWxZeIk9ImT2/7Ejxd4GpuDdbxv3KyDUDix1kG8TlJNAashuJ4ctROOEYksyN8nYZrFFfdzrtURka9si+OGsXy8aQ7SDZLMp8MoQhMArGWSpCiN1xGWT62QkQuCiAF/KLNVFPjXRnqJNpEeXEauEU/8lWjiLB4akJjlJJdcfKqxulqdS18tbnNIPtYeelX4uZ5QqnwaIiHgXQZ82YongoAXmqdWcckKiLxQYooMOISgOcT5aSNRgWkBGsMEk3m5nE/ItUJmAyrh59Qsdlo8lV4bpdeOCNwnHqMDpKGYtDwDTqzBRhkODkD9q3PMUHjY7YxO6umrlFA+5iX2u3AzzRLarLQwIJLU6igGh0hg2+z0VDIeue/2H8XP4qmYMKe9IE/uzbskKGcbA+QNsm35qEEY2YKpu7X/R37fhzxj0tn0KOFeEOzX7XtGBx7qs3VSH4Txk8zBXg948LC27XnQE2ysOY+RqnxVibAl6H4el96DpjHXW7e/C4P8IH37XAOseZoktALllaJJ3twwsQXEH6hXfYbRZ5GXzDtQqkG2GfBFub1j/9DVC2RUdI3ko7FqfaLu9QgS2iFY6hgH3LHFYrByCAljNjXe8rz5K5P+osT/xPYRkrELsgJq3oflnfBCc+RgxYz7rvX7O3pB5AfobvFe6/S2HMXZ/Hpq34M6Kkd1fgHfr1ldZIuxejrN5Qhct9cOAd89tv77Hr6v7mFnwOmQw3n0Xs+FfxxarbWL+aAzC/hgGSR4V7CUVRGqVy5oWOGaJRE8Cyxo2FpUCtEIYC8I4qSKEQaIzRcOmCovd5F2iLhqTZwkfi8l1AnjVM9cGnxdHnIKnJrtWgRTFBcQpXVEncu6WiENQBXpaicRtaoVSuLQqpnfBfh2gviPpGAG5BOJZIqZc0XdaSNRpDUT+WYG1Vtqs//8Isbr1YJK0axgefHcjEv68sguHyjC6Cn2DsLgaDo5lDAyeIhxVzcRZfweB0TVom7E6aHDnS7BcDQF9FlMnHSe2YlVsCslC3vbnvoDZOl3YxOjHrPsFTFnRigHvY94On2iBrh54bS1CfEf9vjH//yRmgWlSPVeCu1WzcK6vWVIh0d3i2j+CWTttWD2uYBnV+ssGAQ0/bidgkDGFSlMWmp6zGy5PWH+u74Y+s6QGFMc1YZbxDeBb3n7MQHs7dHkEQu+PQu81K0/7IejZskRHS5swswuTK5bDolSDat3Ao3QIms/Ze7I+OBem7dX9QLEKq1Vrz9qupWH9oGYAfcrHnPTF45j1u25FYxmzmqU3fwxbW+r+XcXHioBmmjh89VHMFyD+WKAoFYLCxzvAMr0VfhbLWfGGN9i7fucv+R3nfUT9TxgJ1QblfwHzo3BtBf491P4IJupGQS1iu0UZLAoSOkMoKgaIXaEMtGGsv5uJ5Ds7GBUmfDgD/B5xlJUS9Mjw0o5XgCwAS7nfbkLNBIFLokolRpC/SeApvBAYp0os4ZyMPBlqAuRU0pburmXAajFQPTLJPaJadc8+GrX0UKSeLE6Z1mrkVBwtVYQKrRVeK1BaEAjlRKqUSN8hnkhlEKOV6gbV0KJCBOYCaVndokNIKp5qn5WMSOVPgVrZoSRZ+RjhODiKpTDuJxaEg/6sISyUuutpaJ0KHe8pzIJSmR7LwmQdTpagmIe+GuSbTQerxDjZKgxmbTI0YGBW8PePJu1UAT6OWR192KTsL0FmxLb43U3QcwiKrXCiEdqPQ/O0Wa2HvK9aK7C0BmeyMNwJb5dtEilHsdKAthA7pnnXHR/Jw9Kun8tH7Ai0OOaICXEEqJShswSZz2B74EkMLacxLdQb9lnfYbNkB7LwVh0ebyG4kwFrhPq4tfs3iACaZmDwEBFPfdM5/jxMLkDbSevX9U0DLlmyHW3G25XLUJJJ4+Gg5Wlo64PCBrScshdlNs1BdxD4Zs24+KyXo0o4dLe8vTYIZ+kNQhUxg7XdAx+jHyFyhNz0exSJOErkeoAwJiS/agZ+SslFasDymzC7BAdf8IY9h8klvoeRx694B5yB8r+E/Ddtlf2yV+Q1GL1nYDvpZez0dzVhC7ScwCVswfg+Nl5fwowXzaMPva2lOtBca8ISVV0lToJRGgEFVYjOKhKRmGleCYGaxtlO8nmGGLfCLPkzstg8WCMCMyAckuluXJgk7BKvrc+FU8IacdipdDj1w6nMWcKqTz/f45AlldKDxeeKHIdYDeQAFBDrZ4vYJucI0p3kWWlFVGCtMHKkpauP3p+uUPi7tXIWCHDf/QH3pO97mKxPt4IQgSLd/pz/Jg/5XTtW50QrjK/Ftv5MF3SVg2+/OmVW8UEMOx54OU5jwFyr20B4rwpDQ7CxBLmKgeNdzMo50AXf2IzdhmRPk9hEPYFxyU8C/Y9b1rKuRedMP+mdMICNigZYuAg7a1CYhqY+GN2AoyULBb5bdx1oHVbLkdd3BrOftL26i103QkRQ1XYjEc5drA5P+XcQ+QMGgSMlqFahSZKBRmy/qthjpXhbhfKSUR9fq1sbtFX8NJHL7M2ozLopXG7U7BEL3h+PSEtVB9Ygvw0bK9BdAn4WeNd00OJjF4C2bbhVNuDsPeeVPgRcMSdidsMz7bmUYHw3qCQFHE1g1ISUCAppXyHmyioGtuJWLxLKCTmWl/zfmLfbuxiAzxKh1hDnIbRi4/cR4OwGFF7AVscmLMIoJ43DK8D/B7P/JzTP+RNOWGnyHpq0cN0KNQH8KbxRN+tcc6zDy3qEkCrKcXvVy3bHu7fsY+WB10MLpoJFer3u7/vvB9i7pP3uJpLalwnLXBRFlVCw1DDwS3XZMhw3CGtWhoV27FvJb4G3jAldL+O0TpwkJPCEMEilfJKqI/WvifZoYj8rIGXbTnJdHdchi8QWMGvFUaPLgtXqIa+mCiu9ngpUJVYNFTy1ckeIJCWpJrjO/mNeBKoQNIYqImIdYhvfSjj25ESS+kIN1ERY9YpEE0jvYJboEKEqGNyF3mdg4zoU8nBw21QCRWC7DG1D5oTKPAr3Z2wgfR/DmCFvk5RX3QbO9kBtyh0+DlYN+AkQZStDEbOMdrF58hGMDTz1y9D+9yD/O9gsacQcMEcwNOwjth+bUKqbQyzzEnAMDo5DbROyA9BVgVrN2mUVm/j3iXPjlrAJ8Thh/UkaLWvvLpZW0tPnchjbEPe1w3AV2htgfBv6z2FbhnbvqEv+8G4v+037PF+F4V2rexEPhizD5Ba0zkN1GnJ1YMCysuWxBe/jQP408AjM/mvj+C9t2OtKZ7yg3dbeTcuGuSN5WNy1tu8GMtO2Y2HVypJdNl3zdg2y2wbGcqi1Y4vvDmbZbXh/CXyniQRSx4kt9W1sl6VgEjn4Gn285P05sorvJt2pMZ1nT6VHEyENfFRp6E55e35jA5aX4OQp4F0o3oedMZi8A+2Pwupvw5XvwOAo/EEdvgVjfwTv1OMw9AfELlnc8cmk6zow2uEIsYAf9TL2enFGifl6iMivveb1u+Lv6COiR3sIOZmc93LMF4mDY9OYgVQ8oB15Sjto1y7AhMCvcvL/QvKslLIV7qnfKsmztWsVGyAmQFglR6SMz1StpvdmccoCwrRXbmDYvzqk8pPUyhW4Cfn1chVE+lhRgA2EskINliOOFNez9UwBuxxvWm3TvKhVgl+Vw07hkAJpLSDy+Gs1lRxLOttGzBos4tao780KLqi+vmXW0IDqtgvvbsHgHAx2w9ymAVgzcd5ZHRucIyVoq8LNTT8V2bmR5Q3PfevtfwWbZGXCg/+k16fwGcxdL/X5m35DxU51XrkHLR+H2T+H5iPsnSxZvgz165D7e1gAxHFLlK9TLIb7LOevUkk6fvEOBr7nsGirJW8r38HvRSGeJLi/g32wsOCBL4PQfopIefcYtlpdgPKbkJ01R97eSaAH4c40vHQOTsxD81lgEdZqEUDS3gU8ByOz0FmGx/NQ3zWHCI9C8y2YWrOdSglrl+ZO9ryT+R+CzDxcXjfQ6cZVNED2CPaiGdsZLW1bG8mSGvLxOUCAjUKEde7ftLePtvh3CWXCsP++4c/Au7OZOFGlQER4Kd9D1ptnktAwK6ebkmI90+mD9hUim+avdkF9DN65797YAtyrwYO3YeQZKE/CP6/b7uqPYHnRlDz3rCn3fC7HsAXmALYgi20a8jK97t+XiBNmNrAFqANbpO9gAH8aK0o7cSxYo1/XQChzOgmw1DgtE/i0TiQbE14Is7TTFR6JBhElkkv+yfKWwSRQFaDqmTLsJG1LOWHdA2FcpsYfxMIqwYKwQdZxBsj0QT3d1ov7EOmePki/VdnUgk4rKb0eyfd6lt6DF7SVyBKn7Z62IXqnPJ/6kd5YVIF4ZHlSxdtVCS9n6nWFEH+LDy8R56ntYvzWGeCRHrg9b5bffeDwINQ9RPiRIX9wFyy8H8J5ZbeStOswNvAAnhi0AlVm4qwy5bPdwqxrcVo3MXB/FDj8MobKlwlz6ctAM9RnrGxzmHWSA9raYWwFDhexPW2fV2zaOqc2FcfQ9xOnkqzZI/cyd93x8rcm358iItFGiLwNg8BBeXSPwsLb0H0cxm7ZmDlZNIDrPE+soles8ksT0PlJ75Cb/tIt74gpmP1/ofcI1O5Cbgj4LX/GN72xngb+ygqz+m1rAyW1KQG9p7zw3kkXt21cNHo/Z07Dd67tHRayN+46vC2eJpyXt/yZi/6OZSLxzpT/E7d609tlLmnbA4TFh/dZq/fDMhFo0+HVUyCCAivyGH5exZiYP/CuPYWN4Y/gkuNfxyQbp7CsbeexnckFL1jVH/J7Jvd7A1vfL2GL1FFsXB0iQscHvSz9Xo8tf1SOSPW6ShzSmyWSXEFwt7+FBQ7+7Dl48wo81wPvzJufRqzWVQJHNPYgtMWyguX4E8UgAEzxKcWvrYe+F6crCx9CkCB8yvF38S59D4Q6I5d8nnLIsD+SOMUtOQJzRfi8LEjJPoT6eqhUCDLXJVCXNVUhskBJkqZQxzKxkqQOQa04UhLI8k5lKiSfqSzi26Q11DZOK4xUHvob9lvsihpaJ5yA2uXvYgNGK1Yv0LQJB1+E7CJ0PAWz12zCHAFybr7P3rV3dj9usrUNIhnMAO63OgL9q6bI6NqInMi9/q6+IlyvhcPjHb//SeDsOeDXveCPYcdOlOGVW5an+RKxw9BZZ1e2bWD0ySN72H/ftQpni7C4EmqOS9iE2sW4YJXtEAZAYPN5ErM8bwL/oGjGlvr/uLyxbp2VSsAQdByw5Dx9j1mkYm0atmY9RPwlYNz02Rltuc9jK9PnMHnIVWi+CpU5yP86ZmY+DfwZtiIoacT3rI6bM1b3znPQkTHKozZvdVYkxc2VcNauAVueZc7Tk+w5iM7ELfsyrR3CrL8LmLW3i4HyprffCnHwgGRrWvhaCED/pN877P3eSCwiirKUtZzx5yxa8zJLJIKHcHxl/V2Z70P+NhaEJ+5h18fAn2Em+bfML/J1r4eoKlEyI9h4H/GySU7Z5GUe9L87MSD+JEax4WVYI5zzM16+F719f7gFaq4FXNoMA62Kja9VjLrQotdJSNQ0b0SPiiboZn/eiCyhfBBYpjSG+GD9FtjD36UTRNsKHwe9jNIeQ9C3krhJhKCyCJBVniYif88ukBmCemrG68G1/8JnsH+1SQsufii9Nn25uF05/H7QO/SjwqermQBWmeZ0zy5mEUgmpRVIZdX/VW6Bb2oJSfZ1BrNsft6vaT6HeVkumOU1jQ3KO5jfpK0HlubDaL2P5UyuT9nfM1jH5TErbfoG9LfA6Lrlgri4bgP3trfdNjYZpvyeXzrtBX/WG+NLcHvb3i+DbwrjKTuI5PVqt+davFK3rMCrf+z5lU/D+DUr3zm/5w7w0SH4zoQ9q4ptRVPgaiT8DEf9HYf82v4u74CjGIItYGcI3jMrbs7bVpb8Z/PAL8LCv7Hnjtnl4EUu4Y62p/2lN7HkIAV/9kfwrE9+81WgH67fsucpEnEReC7Lnkd06d9BZ49J55oHoTJlbf0fiGxszwIfLcLqtv1fIc16/Sve3tNetDH/veBtsezdprDpCzGMmCRORF4G/oHf9yxhYYtj/rZff8XbRVaXdn8CsFRtgH/XD/wTIgz+EnGqyRUM6HzDtLcD2vQ2y3nZpDK64L9/wq8/XILbm1a/thexwXPUbqpfMbXP6D1790VsjNzBRDAXnvbGaMQmh4/v2d+F3h6YnLcufRVbJG8QO8wCEczVSIxJ7bL1UyMOJ1D7SDmRYo0MO12j79JdeLqTTnf3kiA+fJ2epeeku32S71ROlW0LyDV5cqFUKyzeQ8gtJYQCN0QDpBmXdH+dsHrFkYiflkxNZL0caakOWpWU8FpcTSn5rS2cuGA5GhWqqxVK/I9WpwpGachZ2ESoNupYB+5gebJGslA4S+yxpuDepuGMFpVJv46KAclA0VI35kuWPKi9DQ5WYHkXBnugfh9a3WPWUYWJCpx6HHIzNj4z3j7PYoN3B2ibh5ZZ46p5A+rlSIf57aQPpG5RysdxfPtbgSNLwC8Aj0Px69B2GDgO7Xdgum7gcNfrf3PV6jiELRJd3j8KbpBF8iz7pYR9TnSvXofisN9wyTqrpQp/UzcjfZzQ4R7uAO5DqROmVsxgm/Q+Gcd2Lh1yzz8AjllU7+4aNKvzXgb+CCv0BMxOx9h6hNj59T2BUTWvQqnH/i5csPJlfS884fV9ClsQKjWLouz6NBSv2dmBp7OW5KmMqSD6CPmkqLJtzII7SeTzmMFA6Zy3aw+xuB0kdpeNXt5l4oyPM5j1egxbHJSHuI1QJohrfZYIrV8hTrG+RmjjR/3ZU17nViJ/RHtSFs2po8Rc6vFntlYtXPvWLvQ+4QPjrL0gcxboh+4J+H7N7hHv/DywPAWlVn+JMidmoDkLk/fto3Fs/I/5b4kAUpWU+Fft2iGkZOLhhUHCHWGLQFPOfVnOso6FR9nkO+GhrOlqcl3qJBRoQ1jBFfaHvKeMQZbgxnM98PnqQ18KgHeSG9MCp6JnFSRLSExIrpOjRyuHVhk9TxIXOQTl0c0/9Byt3CLE5TWFyN4vrqdCHC0lyYm8p5LxqFEl4clhA+ekN/yjzQSP6TqehQkDSkmQ8sBOxcpz0JfQvCryaai/Cd/fNSfGjU0bXIub0FP1/L/AzEykxsQ77U1CdvfxHsjJXB2ETA4qazbp7hMOhoP46T0YD3cem2TH8GCJCjbKwYjpr8FoLfJbSF/Zg9HUG/68b2BZ2b6KTegTxILc6488U4TLO9C3AkWZ0gftxoVvwHrdHEV3iGONmrFAkdJHgVl4Z8VAYxMDCtFJx4a8/Y8Cr0BmzUBjo2Iaa94kkOsANHdDSwu0t8LOqvXrBNA5DXfWoLfoA+kce8c/r81ae76BUVFPtED+LBSUFKURGLCQ7Pt1u306GUOzXgQlUOpn75xUxrxN+4ijiIaJHCAvE5Fpj2O63Q6sX6b82kUvRt2fIdCZJVQImq9T7N+yr2D0V97L3OD9Xfaxo6RASmRUt2bkno9uyZp8AAAgAElEQVSdPBHc0YS10wWvZ2XXdnmMe5s+R5Dky9ZmN+9ZmY8QqW4HHEHrM5B5kjiKvABtXbA2FbTfRe9aacfF8RaJhWiFoBK0S0j53VRTLOzKEzt25YGRgSEBgXwMUlfIcG1K3lFM3qX3pD8PKzqEs8JWLX5yHOb64fOyKtMVQjc3ETyyAFicjV6kn1TaBgGmP0h6ohVBfFnF/6+BlWoCVXnJ2ORlFshLJSELX++G2MqJ0049rvI/KWHLj2Jb/+eA9o9jM+TLwCG4+I5ZXOMYKAn0DxZhuWZa3rY2e/DSgiWyyYyYNj+zaVzvgV0YLEK2x7ZmxUFoz0HzhoHnspdbGtYhoH0TxtagtxnKt+DLayZJ0o7kMGZBHSQG1hHgK16nQ9ghnZUJKNyGXDvwlh011Ya99zw2+BcxVuCs98/XMCDZ9vJ8yq9fwDjFEnC2HWY3rCxdB4iwtjF7SNMtA7pjxA5nGwOpx4DcmHXQnapNPI2ZvUNml4wSavpV6+TSjC1QxSHv/AteqCnvmLpXYAFyz8INJ8BvY4vL5RqcWYHlcWhuBmagUHFO86dg+wZ0HSFOPKgAV2HjltFTpU0Dtvv+ui0iEdAuBiQjybhMLdk8hjst2GJ2AbMYj/VBcQO6PgY99+K4qdPeBu1E7goIq/80EeWqvMFT3mdygsmAEYfb4OVbI5yMeDlFh0hp0E4YQguE7C1PBEh1/Ia/UJP6FLai/yjwdZhesbKXMdbsAXBwF6rbpouvXzGHKo9ii+4H0HEYjqzCUwXIV2zhmsHm/Sr7KdQaISiRxbz90PcyKKXm0PdSdckQTA1MtZvy5gijhJECeC18TYRTT+WQ8kJGbZH9QCzDMNUt5xrg8y3+pQhnVSblXGVtpqCZrsQZIlmIwFT3txBeUAgr+mGQr/szRI1AgLnCI0WJyHLQd9tJZRUlpFVQjaL49aK/R4NL/PEpDNg6gI4mjPA6A7wFras2KLSgVP3abBYadz3U+oCt+rewKLy9ExvfhKWK6xvPeWVnYGHKJuISsfVtIc6F+9DLexiYWzOvtLb897H1QlvhEYwymajZ+4/4vd/DJt09L8rqMrQ8BS1FuLNswPBN9iecn/F3dmJgMu11lcPiFGEN7GzbYD5yGjbuwNYKFE/4hYcgc90ka9eIQ1gPYgvfwR8HnoLae1avGSLpThlbkM432ll3HCeSD2exgfaS988hL+RPEome3QzquWH1m/Sxs4wxKUexZE88BZlGaFyAlRsu8zrtHdGJka2PQWHBnF/bXo9Wgps9R3CV0m3LsK9h7dNNOKJ+jFArHHA5znYVtu7ZPb2PQ9scFI5B42IsVGs+JtqIIBJZwX2ElnmR2MFWidOtRdMp54YAapOQ5bUQObXnva+mvbxbmN672d+3CeTfhPwExuc/4u1/0PvpGixv23wWR3rW264/D5yEzBxsTENBmZrk1HEO4tJqpCZVoFmGoD9lOG4ndYWgWKSakopLtKWUYgLE9Ec4Unvot6xrWbUVInWBPk/pjVRal0neI0NS4oJc8p5c0SkLgXH6wpSO6CCsToGxwFt0AOznVLQClJPPJJeTV1HAq5wSUkQUCPqi+tDztF2RVS3iXJE46iRxPjn2d5yi4ApEsvGj2Jz/mS7o+Dg24v4Ztte+AIU1CxzoIKiT1iF7efZ5yOWBE5BpgJ0FaPske2myKuPQkofcICa7KAPnLP9xNmcBVYvYoCtiE/AWkcmtzT+75v9Oe7s86vV+3ut2uWaTZZb9Z/DdJTTX72GSt/Fle/agt+9tf5ecagV/111/Vzu21f1cEXZqBi4HgJZz0N6NWZpt8MYG3BmDuxNw8BLkWqD/EJyvwPFnYGAcnu6C/mPY7JyDlZv2boUTD2NZF3oxrjLfiIHsLoYSXdhpF58mMr9dYO+0Iu2761+0o+OaMCtLW9C6t/UF3xpMXw/JYRboO+bPfBXqO5DJw4cLcGII5lZt7GW8rcrE0XzbhPNzlf0nQSxgOHUIeHbQMtf1/LoX7mN22HPpDuRq0DADGZcJFZ6B9XGjHRZ93K2yP+tiiz973N/TSnCjdWyBUBCJyiPfjYwcWcePEyfOyAJv97HwU97EF4kczXPAwDZkrmEEdhNmBdwAnoTKLbPaZwl6ocXH3ewkNB+CwgkvgMIUf5w959QXJm0K1gmRSIX9UXuqp3boCtGGyBgp/Gjw71NVhbBJ7SPM2QNJQlQgulUxFQ+LHgT+uredOLVIi50AOHX+7QkcmrB8yPpQICbzXMCmSJaUCkh5Yl1L8ltONhWuntyrhhBYahVJo2GyyT1a8dSIjckzd5K/5e3U/cp0BuHEk5B7gLCwe7DAh6NlyC5j5N73/cYOWPj30NkCTQ2QPQPNmh277B2XXr8OmRaorcDf3IBH+u2Fq9PQ1I8hzKy/uI6NzjU7daPPnXt3vC0WsPF5hEj43ooNgn7sUOcJ4MdaoFaJftMuRGHM2h6LwzxJpMxsYz94j2B4pqAXBYR8CrPMTgBDNWgvWlRcRuT/NnAaPrwSScY1Hvp+HkMQnzXNi1DZcCflz1n9myYsx8QuBixz2Nh5vAXLN/1fYx60017wZa/MaQwd+4E/8YrvYKj3feuL8UUre5WQPtYww/q4OwJadmG57Al+tA89ZN9lVu35+VloaoPKqvVRk1dLUnQFL2z7mJNl9AJxAG0nFm1ZXoPiz3mhhvxdr5vTN98FmU9hK+EjMPa2rTlD2A7pNvEjEGnCAPMIAcabmPGw7U2VRrlNESlT5WuRUSV7ASIdprsv9oBc8/MCSTRdu1fwrwhTfBsaJvef7vOSl3W6Dmez/gJNWOnW5oG3YOEa/I3XZ4GgChQIk9IAAsZUSlYnrGRZqHLii7ZU4JkwSZSEMDH9LWWFAFo7IFnJeq4MW7E4Ej54NoM9wxD2Z9NswgyYz+shauzsD/inCa8fNYBoC0XMpFyuGkirjABWIC+g1ntlzWortU78CIxFRehePUeOPb2vj+DX9FzFrmtVKhPOo08AP99uFitd/uGP+8MWoDSEnRl5ArPWXsBG/IvsZSbPbEBlwQZ6BVhfs4Q+c5j2mKq/qI6ZIUeBFRvMtZtx9M1pTG4mB6Qkgve8aMf886e8IfJ5qFQjoXkVs2KOYdvNKS/yCDGRrmHAsoEBt3jbk8ThktvE2iGJ4BCQPQ7T16Cl1Qv7UaDFJvmJWWis27tODrIH1nuBHJ2QOwo8gI0/h0IByjfhwDOQnzQL7dkStFeh8JtYXPanrQ/4Xah9BbI73g/K2bHgFf9bQsIxbWW6P254p59hbCFaB46sWMRe7ilo+ygMrXujiYBtw1a9+ZjIuW2jcxr8WQsE1ax80wqTXcIYjyLwM94M/S9C8X/H0HPey/1XNg4ynbD6ADI3nNp6AB2fhveuh5F0h9AiQ5xE3YEBgDTK8uOueznlc1EyojmMehLlVSeYH+2Qe/zfOhHMcsm7dN3HzaY3UcMmTF+GxWXo7rGKr05YGRb8/RuYlG0VG2ctdbhVh94RwmQ8hlkBC1Cahz+shupGAWIPAyLs52FFX2r3rVDolMbRPXIwk9yT+q70o928cEeUx8M4JSe76BRppxvZn3ZCOzVRFypXrjXJ9qZVXaAs63TAG1HWsL5LZXGpOS5wTs1yvUMVV0CJIuYEyFoFJR1a9+9KBN8lUK8RgnQ5DwXoSk4iR57kehViAVCjHMYG6JFts1T5mF/UAfwaJon4Z9iElxTgExhCZojEN4OQWzCLK4tZMwoMGCxgs/XT2CgfwLZ2yzC7YGB818u8TKTsbcIAVUm6D3pdnz1nqSFL/8QvnrQcwq27sbMfwwbyi9iEOIQZINruzRFypxPEeXFlzDqWo2IAw486cOBpGLsMQ6esoPXrkFGjPgJchJ4CtD+GrYpPYrPvNQyRlrBZ+aipGJYuQWseNu7DgRK8XzXHZ+EIxkv2YN443y9mxzDzP4NN3O9i+3khUjuG6g3AVTgwb2Ntkjgy6AniPMEt3Lknyc8WNijvw9I1aOokog2qpqop5E09cxtjXfqItKVLhPplmcgJ/Kj/Lv2CX9zo9WsnTmW9Bpu7Rm/Vp0w6ySUr2wcYeD5g/zZdmvwdYhe1ShwMKoDRnO706w95f4tbdSEJWpPuERneNC9r7HdQPuZ1bfZyvXAKupfMj7JWh7aXoXjL2udDbG7OEOlv27G5lz2Aac0Xsbn3un2x9DZ8iwh+kUNO9Jt2g8IiCAMx720h9RIEbqXys1TX/bASTGqLFLd0XSo+gLCU9QzpzMURq/20y8gk/1JlRq7klIVoB0k70kIJFIX6qVoh5x23/FDDqBO1TRK1oGfqGm15tNLJwdeKDW5tj7aJVVDWcNrQ+tEWRt5vvUcdteOfuyCCBgxsTmCJCYtdxAR5FYuQ+xBDrnZs/76AAfI1DHEPAFOw+j4Uz0C229QToxvBy57Wi65go9Jjj+uLVp4JwjCb9nquE5Om3ct91D8vz/oZlQ9g9aqfULxr/XDFn9GK8cuFpL3uEQnmL/mzB72tB/2zOWxSthGSQ1lWO1O+bVswj3Bm0Nvm00TS3mYMNFcJT2g/tuJcw2b+rEU9NmHReoU6jM1bH/f/MKabftIL+sPY8fVN2OyeIwi8Nwln0g3Cc9Njlc7fiAx0z/mt/f51Sx6aH8UaTbO9ib1M/03aGl6wxqzctK5v3I3sX7MYGDUR+RikkxcYP4KNr97jGNg/g6l3JPb+EizdMhpnBWg76u16BlgwOgUiw5kM65Q7zXjTrBE+MamkNC+10Msay3j/igIQeExiQCmmSQFIK4Ry5yAW8FLD1qsy0LYAd+r2jC6Pbsrm4GbFxl+3V3kOG5OT/vdhodwpL9CLwO9AUyt8oRyG4jrBfadaYhmESlcg/5cWKlnNojO2CJwQ7sjo0/9FLyjAKsUb4VE9+Uz4KAta/jJZ1jIitXvKPHS/FotccxI6nVIK2tan4CxTXGgvqzYN9NCPrlVjpGGKKjQEES4HhZIE6R4Bu8BfDZrKT3R/nmhkzSWdoqEkXlJUpELtJzEf0aE8BhZNwK9iJkAJ6+UnibN4Xgb+M/APsdV8BxgxxUT9CjAPNzaiXAXMWdO2C5M3gDFYuWRC+CvbwUeq3rvY1vQIse3MkDiFsDWgYRD4KBRL0FGG7m47l441s6plBd8jDi+dxjDuQ4zbzGKLwbY/vxuTwd3z9j/o5ZDmdYYIp+3ow1DgKJFJ6gC2R36J8HodxnjgS97QY8BHoLnFg28WrMIdH4P+cSJjzlFMIvGHWGzvNQyof9Lv+bZV4OJfwMYVs8xKBSh/F/JjwEXI/DL0fBwOj0NTBQ6fN4da5ow3+scxIJ+A0THonsNQYwIDROlqX4Pcz8LaFeg8DqVFa5NWL0qeCLzYxhbSQSxf/BM90PLTPhC+7I3fgnlu/8LGWtM25H4Y2h54O57EQvgumtN0c96a7673UTeRdEhWVpXI76v5IqDVmqg1J+/VHMbmk5JZKdBIc7rBiz1PzO+qXy/VzSAxt/q9m9q3TfrJLpSqNoVWfBwpUZBkd4PbUND2rR/4HZhchK+WbXM6TRymKsntOpFdMrU4Nf/VJjLo6sk1+jtPLGYQ/i4xAUrDsPPQvcIuCLCV8ir1o0nRtf3QfVpEhLXqwz1AltUqIFOhtpK/RX63ELk/a8k9qqSANw0QSX/nCX4ZwrqVyS+uSytM6lxMPZrikfVbHLGAHUKGlD5Psh+R+kOEDKv/l7EJqhF5F9tWfhublN/DtlZXiSOpsxiIzwGjJnTPNMC9BWujWSJ728qaS4ZehuYp2Fi3slwkBh3Y4D6AAabaTuL9Y8CBPiwF5aC9U2HVlVFYXrPi5Qlq9ajX+wFxaq7a9gFWztNe1scxC7pEUOkTWLlPYYeO5s5CcRbyJ70MdQyNnvF2WcNEzD+BmUG9GKBeIA6wULBHGZtdnpUOknpN+P1q7/v+udDjll17YCzyLVSXfUz0EpKTi94AEvbOY8j5pPXt2ALkq/a44SpkjhFZciRD+FGY/X3PJ+EE7hrGVjVh4+c94rTtY16NJ05juwWXzdRuQLYCs6PQ/D9Yv3EBo8PWsWQQBWzRqsP4V6C9Ge6v2PsGsTFbJ84rXfDxIT+zaD1lZhvwLpgjolG7/Z55QllVJ7TssjQVIbdLpMaUf0bOsgI2xle8e/u96VcqdlbjvaRs97z8K37vIeD4TxHauyowDq+v2lo1Sli60rALlxTUlVIKipZMg8dkGcu4bCbyg4iGkJRO+JU654Q/KR7pnRIIpFRvKr9Nf1J2QD4JLQZ6Z67RATmVuEHwr/JKp6ujzHOtnqqQrFZZx6XkN8kzRJiLn6kSJxBI/qGVS3RDGlSigBL9DbHiCNghoqcGiURIu9iglDOvBPw2cKwI/HfYqPsMcV75f/aXfxGbJAph+0sMIZ/FgPoONrp27LrBOjRUjDrNYwM/j9E7b9yCcpW9nB6z/sgaYYm2EHmRaxhd+gxw7Ag2aeUmX8bjfCF3yji7bmLH8gQeTYcZ/CsYzjR5cbexfn6ATZR3iIV1B5tcTd7OwyVThOS3IX+ACPs6C7wLlf8Ela9D/hImJ7iEURmdGCjd8DbtxJyif4kpLcpEUIG2FR2YlfhX1tnfec+CBFiGYo934CF7Xn0imIxXvQ1nF+HSCozMQ26AmK3KCD8H/LfAZehohPc2zPpfAIq3oWUCe+ghWPqm5VBu7oGWdbhfs3EjXlSe9BNebPH+jwFDhzBL923rs6wjXvMxbAV+zG/6o2H4ykoc/d0BtT90v+JKKAHKREY6qQGv+njRHDpoTbZH220RtKOkvgKoHuwnT7A1rf7ZQcKaVZco6VCDj6lVDPR3MeCf9HIe8DIMt1ta2rtrttM6gIXINxBRsYdLPiY+DfwpfGPSch/dI4CtTPiKhC94ndT+294OimMQGBeJtKbifWXMydATqEr9IHpUsQvSUUtaKyM0jfATBrUTBurDdKzwVQEsLcl7NnGVBX6hvH16WQq0eqC4FgGlrpeZD/sderpPhdLqUU+uEZ+jxpBFqIGgOSrgFp0hxZWAWKZ/KjXRPOwghNwb/rw2v3cAeOSXgN/wBx7GlueKt5Lc2H+Lef0XCXNeno8hTCY3CzRC+b5NFInQy/7VBWwA54iIU02uaX+UJsSk/32COLetRXvDBYxr06h4BvgSZA9DqQPuLVr7iK9z9RZlzJp739uv3/sz1VnKiq4BLxahr26nRX9YdV10zhyRzW3YkWwuV7k+6RKoOmTf9YplsYYoYjP4Kb/+j4ntwyS2IrR74bawleQr9t3STDiDBkoYSvxj9kTo2VXIdEBzo6lZ5EwZwBanygLk+2D1JhRlZqqxT8LoO9YXU94nn+rzhjkHXIOmf04k8e2C9kXIFaC1BueG4N1Vq4qiyab8FSeArgVslfsVbKc1BBsfGGfOU9hAHcKONhnygn8PaIHsOGSq0JGHlV3bsLlSck8vfJc4mXnciz1JSDqVfvqcl01AoUCkYR+PBwgZnLLGbRM7KO1C8TLUfIwMYw5LGTwP/L5BL0uuH65P2PVniSRM4oV/pIgZQK9i8+51+NOKURXCGAWxNBCWrSxK0QlSMQiDZPkqPkLALupV1nHqVxKOCPBllEAwBrKUc8n1ED64PEExFojdSso8pD4t3S/DN9fhgSEpgKax1akmT2CsiZtSFil/rIoITFPgzhKrhrjh1LOpSumfzH4tDNWk8QUkalBJTnLsPytPjql6cp+CQp7Ccsq2PIHtMw9hI/3LmNX7NWAYFr7o567Ne6EmiCw83f6CRWwWfg7yM9A0HgL6u17n+8QRQo1ehgKRRa6TOH3htr16z2lUAUaaiRyhw17WOS/n8+zFgefuwSNd0FK295/1d1cwxqCX4MhuEjTOmLfTM9j8qNUsf0N/i50w0vmLdlFzDrNu/63f+C68VXML7QixgjZgYbRLWKDMZcyqvwO1y/CtKZibgfUN6FklzvjxPenCFXMQrW179r8qtPwEsYX4CLZSjQJrJj082A4jHZDfgJbjbln3Q/EktqCOY87ZXhss3S9D76vm9HtxyDvkNzAOYh1bnF8FjsOH71kDNQ+YRnl2NTLj3SaSxg8Cp077eHoO2yp9whq28IYPigNElqFOf8DX7D28ZZ3+3iqM7sbZczLy/5MXbdzf/YBQQx3G5sRPELkvZJEVsEV+0K974EXYwSWH3p2iz7sI7bKoSC3e7dZ8SrJHA+HsVChyTw56MjBUgmyz+Toe64ObG/BflWBsC0pvQq4K734PRiu2KbqP/YhSl5MOwqiCWCjSuAZdK/ph56Hv9C/llwW4EJptgaw+aySoEhmr4qtT0G1Ini9Zrn4EwrvJvfLhNeEqCz1YNMF28nA9LJ/c3Ep4LdUoqTkuz69Mfa1SAkPxMPoMwqupLYbiwgW4aXlqhBxHUTlV9q9Ovclneo9WNzVuDuMdf7KPUMhLJ/w1IuRqA4rzNgiaGwntzxPY5JnFRmQXZkl9AfgOFGtWhkV/TCeGnYPYpC0QiWk2CHnZK0Tu2Iq392fzMLELw8cw8MkS/GavV3oYs64K0DRjet2ZXTvE9Pt1q+Jpv11b1S3CUSf8lGG7illSg1lLYH4T6LwMuWPYwvMqJiHot4fUN1w5tgzN572yv4zxp89glMXjmHm+BnenrR0OAX3yULV5g/UDl+DBrB0BVffqnsxjDsISZlndw5JlFICbMLoNvc9aXzTlsEWql/D6VjEK4WuY9/KGtV3TAyzxfQ5bbK9j1vujUL8Bmeets3p7oXkXJmfMEu0m8kKIYruDqbcuzcPBcxi4Dnnj/7aPsap3/gahGvkTzIdxD0PGV+ALu1bsu37JFSLg4wFhiUEcf6Yd1lWvunJeN2JMUaMPlfNetCcxSu004TT7GJGjQ5swzfdOgqNV7IHm07R3sYB8qwzrVShsQ+4gFH2gFVZgvBpHnOX64MNV899mCM5ZuSRS7lZ+oQ4vjyxdYYqoAv3tG6m9hF0y7MQICBPkCE1BUrsDzQ39LS10KobYSe6T2Cj1eaXsg4BbY0YHa+RK8Hnln5D5nwZ3qBPSFWHroQdCgHGq1JBlmlrAipbR32mkilauhzWBokjU+NIxP+yp3MaMDnmWa+xP0dlIrHwZDPwGgI/+LxgyPI1Nkm9jk/y83zQH24tu3SthQDeW66KCjW5xyxlMinXPTsd4327fK/c0e9QnbxLH+Ijv2iaSw+T872bgvV2zaG7Mw40K9C9CYc5vniDyJc/bTdm85fsdxzSh6svrmLHaivm6GjBrpOS3Hse85Ie8TN3AeD2cg4Uj7pB6BluQ5jDa4iXoeQ/amjw15pMYj/ybBXj5F6D2gc2k34XK+5CrQNcO3Ns1XH+7AqfKkM9hO48XrU27TkL/LPT/AgzOeCE+jo1g7cPHMKS6Z6eK3H4fura9DN/BGu4mkUj3PrYqfhtLLPE8Zmkfxhps0AfJP8YOVe3C6CiPtKzNGxiezZsEbs7H2N/60HgSGBqBAytQuw1jq7B+G9o/xBawCQusWZyH3TumsLCkzf6ee3Dx63B914C+zp7/kqz3VZv3TZ//7vSx1EDkavmo33vWis2jXtWsN2PJqy+xyWFrcl4ACqegZwmG6vCxT0LvHVtHRonUoHJj9BA+kjbMgp334dGAnTae+yQRErsC6/NWpk1sbfziqnXVInFqe42YCwrKUFCfOFcBcY5YmPIELZkaYRsETyy86fb6C8+Eadr1a5cu4EyFD1okhF+p4SiwT5kDWfpaLFR21aWGB4bIVBeoytkmYluFrBEEtlYHrUpqCIGqdqx6eXqtHIOZh+5J5SlV4vQSrV47ybNKyXO0FRFpr+93CN7rIMGnatU8gO1cTzyL9dIJb5n7xOmLd4FPQ/6Ka5TPEE6hF7ER+gnvga9jMi2PsihNwaW6lWfa33nC33sbm7gzBKd7nnC+TGCDRfU64K887EUoFbEFZN4baBuz9qZgdQyKFRiruCaUyEnwGgbwt/32GhEyXcQMU3nU54En+uDahrECq0DzKjSfhPL33LFXxTjSVCH/Kxj98x7QVoOlDwxR5uy6HMAAzE4Y8BcxrrPtE9gH/f7c5zDH4PPYtuG3MTPuTeBHvH9OYCvLe3YMVHURBkQ7dFj/zV6G5mcxmeK73sej2J7+TQyx3sYQ4ecxumobQ8O/wtCrz689A9kW6KlDecPacQhbE6pEqsnOFWgswu2ate0w5hRkC/gpaHkL2jv9pJQa5qh9E0PSOxapuGp/7h2pVPWxModN7mWvosLji9hiewabL3esmff09ie8i6TZnfd+P4plHyxkzV+Q6YLV+7BTh7ZTwCzkVkJzPevP15zqw8bsHQJcDhFgnctiK8bLGP0zAg1joZy4iI3/DSLZe4oVqpcAT3EG2gkIpCv+TyAoY3HTf2vnnU2+kypDYc7CPXHcivoTcMoPBUHH6lmpcy8VQoiaEJhXkntIrge3kAW6agTdoM9kgcpBlYKfHqgVJQ09VKHrD11XIlYWeUBTL6Uqn8ruCuzXJqc6aTWEuGpt6VqJzFXLhGxH0uIBzELo+xUi0GARs2JE1jViSKlV6iA2Uy5giPa0//8uhpaXsVn6GtAC39u0ea1Vuc3fP+yvHMKwodnb4nVsoqx5eRV4M0dE2xaAxhpsjJsGmTWYnoSWZ+2hxQFriI0Nq7ckNp3+3nH2L25T2CSuYzh6H9sw1ID7G1auXqDjiF9wO6igvdNZP0XIVn4Rc4A96g9qxPbAOmpl1a5rfhQyn4biO9AuLZTC6LowIJ5NGu8j3pH/jkhvNoOhyjuwuWLt1bAK2RPsmW3NHd6A/5Awax4lDm+7RxwbM0boqO9gnv95Il3cBka/LMDSMowUodgAWzVbn7fxHDvnLX/2uFfrMcxhwwUM1WagsgwN5zFe+wN/7iJcvmlj/6tebPlEjmPgJZXlSWwBV46TFfbWPLq931uJxEIX/DWDPhrqacAAACAASURBVIaGi3CoZot/D/5gn3TrZfu8d8favOm4STnvYfNvi3DE9xE73+OEcgfgUJHQa06yR0hvLdrOaNqfdZ84cFhOaO2SldUu5cLljBP9oB2wuGLt9mWwyZdUIdRXO9icgIhyFLhXCXlv6t8ScIuOFdAWCSM05YwhQFu4lnLYD/vncs3w+YbkQ5LKp8S4zH49JHXopXRFGv2SKi9kXWvrrKCMhuT6bPKZgFyhobtEak9VUo2bSZ6jz+vY3O0jeGXxRgewAfs4ZoyN7GCz6V2/SaTdW5j5eBcD5XMYcj2PTdRjQPs/gtYrdtTyO2vmJRwFlmByLE7IFjfvAUxM+KsgTi2eIjJaDXjZx71dLhOUwg2vwyVgcMPO4jvag5klV6yhZyetrrcJcfrbGB0hulkc9oaXp4/gDOVs7MG25gu7kFuG/DLwv/p7zvvFI/7QDsJpcAfzPF3GUOQSxvn+CDYqf84L9vexE70LGODe8ueNYOcPncQQ5dewvewfY2D6Oma9ygn2PJSaoPgsdqr2M9bJo69aTt7WFch/kdAvnfB3HbYOqv8+ZEawAfgvMS7+p73sygtwkr2QbBah+QWr5/SOLaxveJVOloBJOwllpgyf7IPcSWw1zmGL0zbkfpWI8Bhj77jmD+fh933cKDXAY8QZfeKuH3iTtxGZ8j5FRJEf9Ef3EzLfl/PQUbK2Yhgy/dDjYX8XH0D/ANyeNrskg5XzZg2uL4SkddqfedKb6wEROSiDqBl4poQZLKPYgNaX/XDvum0I7ntzdhMJ5yWllY9IuCFutj35bJNQTYj2yxGpGSCOd9OOOr1PaiLFVwjA5X9KcU74KEtd74NQUuWS61VmWdKNRJyFjKQG9lvae4Ccvhhix6e5svsDrklXAq0+nQQBXk2+F6ciU14rasqpaBUTR1wnIutaiPwUItBTCYk0l5LODRLbAnFOWvkU5TOC7YAHn8cmg0b+f8QAN48h1mcxBJSp/QjwzEnIdxPpxLNmQqxiKPaXMO3WZROhFT1COCraiANJ2r0eSu5SxiyDXmIxOoZRjDtev2GvbxvQVGXPnFqahp4eb9eq1fe011fbX1luO5hFt4lh066X8wlv5+GS1btQhsZ2qK/D1jchP4QBy3UCrJq9gv/BG7oNm3E7mNLifNZQ6ukj0DBnL1rDclV0Ywvfut+/4A01g5l1pzDgrWNyOOkdD2HgDiEzGPZu+WtTWlRw2qDfK/40RqdsYgvodSzdZbuX+baPg78kkjq0eieWsVDuLHs5KaqbcQJ1B9A/hC0a43BQgTN1Iiv/UWzgLxDm2inv3LvwRs2aYwYbfgLfEz4+jhDpLJ/0ZitgLo0b/vlx4pSPceJopoO7kPuUP6AM5buQH4D6jAeFrFi3LPk7pUMe8+b1jR85Iu2oJGMt2NTo8O8HqpA5CPX7JkvcS+S8A18asymFf3yTyN3yAFtMyj502og8NMKPVmzu9BIyV9EYCrGWGiQVEQgIa36NcEH/1+5f/LHwRUarooVllIrekJMx5ZohWIAMwYWLIq4n1+5x4c3weX0oGkAXC0BldcpMr7H/R5UpYfNBQKjoFxVcK4YcfqIgJJFROaQu0Gd6thq6kbB4Uws+4//aCS+tnIQKUGkmKMqzGO4Wath+7i42+j+L7ac+ill0Z/1hLwE/2QXjZcgvQNtZbPa+DDt/bSPjfeDX2uBPt+neMgtJOSrq2GrdTmiRtR2bTeopnedZzMBUgNkMserfxAB0Anvt4i4seoBFJ3BzE9aqka7zFSLHcQeGDQsYdXmTvdQae84Tcda9NbshNwK370PXIOS7iEp9hMiGX8Jm7jLwfdh4BXITkL3t7Xi/DqdqJq+4iBVUq0E/BlR/Ah9uQe99GJuEsXHoz2Lbhz8G/m8sWOe0V/x5H0zn/f8/4hU5BjxjEYVdwHADBt6PEOdnNXkdRn3g3CE059/zZzwgSMxhItztVXt37QMoZS2abr3iwRPnoP4KZH4CQ8OXibDgOeIU2iV/9rS32WvACfjKrH11DaPUrns1JzHQl6Naiqc57wYw8JrEc2f4vxfa7bBWJf7vvAl5n0j5AX/gvJ2UUtiA+7v2TvkaJggO90MiAnUN2zRmiVOhN4j8Kc3A9oRd25aHhfdhYgLuj3nkqj9Hi0u7d0ueCOcXfuxi/ThCJG1SoEzqvBOlIR9SilWStmknrsg7YZO4XjlGISS8snyFiT+IHRCeyToXHoliEU2Rhk7Lkhb9shepl3LI+N9byc16qQojglumeOqB1P1qHPEnKgTs90xqtUst7tQhmGqKa9igS1Ls7mkjy8TioS2N3pPDQKiAzYdj2CAeAErD2Mjr8xeVCC9KixfiBX/pG2UrwBBQFHv2VcgesNH8JPBr2wYgS3Bz2z6q+TsVjfs0Zs00EhGE4p86/fcpbAv6TTyhEHEEe6e37SqRPUvW0xWCg97CsO9ZDA8+IJI3Lfj9cxgWHiYGeKd/v1yH1jXIznpaxS7MFAND83f9RUveCVcxUL5pGuZC3gv9j7yyPTUrxAlM5fDCUbi2ZOjzXbv3at1OGRnFJuBG2U7Q4AkMKH+JCNuSh0kZ9P4SS/W5je16Vuz3wjqUWrzzGzD+51cw5DmJ8Q092KL7lP/bwCz8i4TnquaN6JxR9ueszpe34dygHWSwPg5NPwO1v4CsQrmO+7NWsPH1vv8e8nLvwOobcGs2xnUN85c+g9kK2mHdwkbdR4lMgAew8XTH+7HDx8McJjmDkMsf6fEOfwCjM9C9CBltxbbgQc2aaQUzAgr+7msYaLrbYk/atU74O5RYaULdnYW2DNRX9uVt4qoPGQWhHPb7HiMiC9uTrtWOedfHRK93o+KkhE3yT4na0MIlOZss3VQlob/bCCNQjn/tZiGgIHUa6n7hjDZuuq6e/NN74QcDc5YkUk9WK0RgiG5ISWmB227yT5q8lHPWj1YuFUQav1RSJ1G3uBUR7uKttYKowhKF671HCctRom5VGGzQpFrlw/gR6S1m1TS3YqN+A9v+jmA9veSFPIRNyN7PwtZV4wt6ClAeh3wR+HFYfQs6CvCvfL85C7NXrX1WsQlyFbMe7nudJGE+TCTVlnLuJGa53sCs+ElCuir6Z5FQgMn59z7w3Ah8e8We9bcYV37Rq7MO/FIffGkjjr6qY4N3x8sy4s+/j+28S12QbYLVBSjKzL6Bjf5RQnO0C/xvXtlHIb+JR0gQ+9qvYovfe17Y/7hk4Cgt3l3YqkQkehbfNteg7Q5kXsJm6FPegALKt7xcH3rD3CBOF/kFKEkA3uMNdRj4nP+9SOyzxwjh+D0i0qKArVDKnvNjhGb9MvT9DLAJlVk3WB5AVh416dQKGPhewZD1HDZgX7R37b5p/TCOCT4G/fIl78NZb48hgvtssibjZSK3Q4v3nZxrb2GALQVD/6Alf89U7fPsMfti4Z5p58vYjyzOrDfJISKxUN2bf4kwlPIEC3MYpyXrUGyDTKMF94z6s+4RGwRFzp/Gxrmc8hX/uwMD4DPebP3EgiTDQQaIuOIqUQ/NLWGJQFoRecIWgaoAWkahOOn/n6w3Da47zc77frgXFyAWAsROYuHW3Mlmd7P37pluTc/0jJRZNBrtsiRbseLISdlyJMv6IFUyqXLFVa5slURy5JTjVMmLbMtWpNHMaPZRL9P7wm42m/sCgiAAEvu+XNx8eM6D8weDKhbAu7z/d33e8z7nOeddJZUYFhvYQLQEz9horLTz3FSHDVoryryW/VMegq86SqvowYTkXpdJjR/3PbBGgqzj4+2IM4gXrWOv26I+r+jBLDoOIa3uejLhvD2Sft+DB8ldW7bSiQa+TPK0A/H3yTXo7UQ5LKyoeAZZTS+iEbZp2dIA/C70vQ7T87BZhZ1fjJI/hKl5+KuqZv/3YfoltXGAvCjEC8bJxd+J9nyJzF/k3fkSmvDzZGatn2T7Ja9nEMg/2Cgv/wyynjZm1WdvRPXfRuAblCeLi7KsjkY5p8kTkY9dpi4G++KS0TrYWIeGz5DnLK8WT5ABBDYfRGEPRAW6ESX05SMwNSm0eAWhyqcRF301KrgIryymw/Nq1PEEsKMPXZXSCvxMA1yvatL9jjr4O98HbkDnSURrfAr4LVTYKEJ5S1t+GvhD8gbXHaheJxFArpFg/DEp9vWqXY8OXkZm6CZwGRraoakFJqZhbQmaPh2D9BFaGK+TKV0HkEX+Z0A73H4F/lU8ztKu8L1xO5phV8FQzJdZBEwfxXesEByM7ztc+gKpPBifUoBNZRd8vAy9JXVy8xCMTGXc0XrMp0G0hsZJh/w+cl7ORfP3xzOPoE1lDzDQqevCWlfzrj9b8j4dNkc9Z8nUouam90Y3PYnmaxM6XT5B5j1/gu0RvXbY2TI2n+z2O/R5S/tLRiAXFWLGLINnFU1lOzBtSNoALbPdEvb/DeA2Cg3O3jiMcVUkEfzqjsKLtl7LhQ+3kLKzovXrxthT6COAEd/g6PKLFd1R+JzB3OomO+qdBMdCbXeWyXZzNOaKitZ6M7lb2XNr3XMvYiAebQf+CVqsX0QWSx8ZdvU+mnk9e5FJdlX/3lyW+dI0BlevKXNKD4q0+iFwDZr6oWUDyjWY2VR9bLh5oyhuHG0ItFfQwvsVtI7Xor7GhzEylcVzaNJfrOq9y2iyjkWTDhT66YNo1ijbT/sbiFadYuviEzrRAt4PlBcjjP0xaDgMtW9C3SxaSQ+SV0L0otUzF111M/qzLR76acS7vxPvzaJVeDze241MvbtQnVIbnZWLqHv9IrQNRqUPVtWBY/H861BejZwdd9AGYLf6h1HYJJpQ3yI1Tz1RRi+pQWxFyLBGRmPsQeg0RuYnLZNn9iG2AnOqi5pzd4Cue+gI1x31eIg8AnahY8yC2nj7JX30r2PcWqJLLN00q9BJ3nK9BzlqH4vmHY/q70Jrop/MZe3m7gI2NlXwQCNU5xVIND2WSfuuo3nzZHTjKDlHR8mjvf0/NnysMLSGd3o5o0OvIiCeLHSn1+hBUl+9GF10OPpwP9rbp9GcHkBzfTdarh3k7T9t8R0rVBdJAPXpvWgpN5CndVvOfu/+YBPThv6xr6yBpGjttLNzD7bz2UWVhylVg3Y94dSD7YBoC9a7YY2Ua7jyRRMftu8ELtzf98NNyRrYrftrJJ2GjoN3SGZRGmcgdz3d6DW20xL+vJUcRU31gfjMp4DBF6Kn/yaaJf1kis1Hfg0e3YT2P0JL4Q0UfjcGJ5+BpgPACFxf14r4K7TQ53WLbnkGSnslYdrbD/XzmkQPoEm5F03sTjRRnyat1Q7yhpS+qO8n0UK9ixbMKjlh9iJAraLJCBlg8nLU3ukz98brU/FdH7vGEabsAk6VoKsW3PQhqHgX/RjqGhCXOx2VGUSW5YHoQ+eEbEMcM6ScZdczMHpLD/9vopwb0cDP9sO/m4cNWBrRhrAS9T0WxXc1q8u5BfxfyNo8qw68eldFLQMH6uIzrWjVfxQds4FMt71kzPgBMgZ3Eh2dlhAX9ESMaWd0fjvpzLwTbe8kd7ggIUsbUDkOXQ8jhJuM8mtk0pKnyARHQbW8e0Msji202ei6nqjqnvj4KJq/exCm74nHPBjVaUHzZxStrZbC596P6u9D8/BGVcM1tyawPRbfe6gPVhd14LlCWonzaN1a4VhFwzmFAHCUjBK/R+ZMcVPH4rvvo/nniPpylN0e3dWNALchXu9CoL0X+VS+0Acbi3ByECpzkZ8pyqtEPSZJPbR10851s0wCsA1PG3jGP4OsA0v8fq3w3aJKojHqWpS+OeahyDUX/24kDdYdhFPP4uYitWCrsuiB9IPNU9lkt7Vr893k+/3BHT4++DhkiZqB3fWw1WvL2I0uykXut3qtUKgnZSk+fpurWUWAeIYw6F4g72azZGoZzcq2X4gS/wRN8/3AM9A0Aq9/ALtvwp8FCff9+O7b6pSGA1A3AVNTChxgA360pmdejbZMk5K1SCrGITSJPYmaPEjx/yfRnrFKZm47UtKFkT45r0XZDQiHuknL5tPxnAF0BN5NSguPIIu8E0VoeUI1NccEuItm/CZ5n307QoKH0Io4GA28Ev+snngVIcrirVw1Z4Evd0LPsgTSnfP6zmGovJwBNLVoQ/tDJFF4msyzHBKVzp3QNa9NpeFxkis2h/tuTILHSVPyDhq/98nbEeqIS+IQGBN/d5GpTs+QV0V9SEbxOffjitq+dg7dRh4gv3g3HGzOcfnzJDE8DPeGM0jCEs8OcrFORXNsFa7FsOwBPtGvOxzvoJzVYzU15zx50hyPR3v/aSW1zi3A0fje0AGojqiKVitZQmwKrSeq7rifWZLOPxj/nyKvzfJGsoPU1j9IpjBdJ8OdF2PaOOaohGi9C8AD7fDxqsB4L3KCMa82HophvojWySiJWcYFB5zY0PNagzztr5Lcbq3wuq15Y1iRSy5SD6Z//VqFxCj7t2xVu2+3/HatEanXQgKlk5H4wbCddig69OpIotu7h3/WC6+bx6mxHbzdmKJm2Ly09XtuZE/hWd6diM9ZFldP6hLNC7WSCosvEInDgeafR+d+SFBdQB73Y9/VlRY8jWyvt1CSgfM6kv/f8fIr8d3vKQfvyiI0DwBj0PwcVK/BlTVNsh40ge+Qm4kjhCxx2xv1ayUpms6oVguabI8jK+EAsFjLizVH0N/OEfBJ0nHSimz8k/GaPdBVUmLdhOq5Ey2aVuDsHHTPQsN+tMJsplm4fAutUFuOLYiDdTRMfXT6VAzKGdJ0+/6ygPUj4H+Kh7+hvumswO4d0L8fmo+Tu3W//k1/B5qW49n/VOU1noSG8ySBuhnjY+/kQHTU34bq70HpOJkp/Qzp9v8fydDO19BKL8X7h2PcP4y29SEaJrLsbeXfXIfyoRiEYeCLsP42rNegwaS9Lfl/AWtXdTgbI7MWmtLrJ51Dw2Tu5xlSj/7t+by5Yz021FfIjG1T8b3DsJUEqBTl2MeyGt8bm8mgKlt0zoG0szA/BhAV0cvWHrQVVrwr/l4ufNfOyKvRdLfhOHkPoGXkV6LMbjRFRtF0m13V5/YSMtB5rZN58h5B78MOKDM/XQwwK4Kqnf0bpPysaM2aZijK3exjofCaf4yNVkIVdcm1wv/vp383IW8MqRYquVboPHMia2SkXFHPZ89h0ZJ1Q70DFANLbGEXqQ6Lvr17+IRcV+jENfJiw7rC+8VGQgJ5C8klWzZrHeMvdEakktUTPQg0LsSHPg/s6USQdxL4Lmxcgpt3NLP/KfLCvUd6+I8DY9AentC6HmASSvPQVQf1tcxley36d1+03cL3AwhM9wFN+3SnWHtDtKEiXfEBZPXsQBRELaoxEd8fJunZHsQF2lkxGE3uJJ2hT8d3QBO/E+HMYxVduNkFdDbC2rgeVjqGVlSJvFuwE62gGbTTXYvBtCD262gX+RzQ8UX1ZR0CyWtQ/W9jPnwcjf8tdEr53Sh7OBp0Jyr8ITT9PPDryAn7YjR+J+nynif4CzIQ4waakG9D6UjU9yjaZL5DJgPujmftREhgsez+KGuOreT1vElyaTOkKduNzLu7MRA35PBrKMoVruv1Szd1YLhDSpQ3ohmWf62TIdDmVR1A+HZUz8D7UIynDwnXyGi9K/F4KyL2RPc+Fe/vBXpKcLemg40DUypk/ouGaFYpuuJWNHE3SScux7OOkVdbeb9bIxMV2tfzFqlFdtnN8ftElNEZ7bFu+SZa6wtsv6bqfJR9i+Rvvbn5pG1cs3FnDPEzi0ajaQdjTKlQ5g5SE+33irEXReD3qb+Z7TEeBvwaUO4JCxmS110mHXT2FBYfUpRqFCmO+zllk9Y29e2M2ySt4o3C93w0M3jbKihG2pWijibnO8jkQ/bM2kquIpycIlVHvwS0LUOlATlnniY1ZM+hWfpgP5oCLwDfgIXzKXv4czRDXwEOQ/X/hVI9cBfercKdTTmlhielvVyvKcHQIKIhrW2042MCLaDrJJa0Aw074fYI7FiC0roAvrlZF6c2INCtAfva4Z1V4cpZtH3Uk0684zEOkT6Cpij/5CDMz8k4dQhsC6lEmd3Uc3Z3w9g8tH8eSneQGe7oHHsxylGh30DAZvf8MRKUfxgdcOOSVs8xZBWfg9LH8LUF6N0Mav45REvYU9WIUKcRWaZfQqvRGqcno/LjMY6OqPO5HmTOLSAk64lyHkOr3J21h7z+wpl7LqBNYoWMKHoP8UxnSdH7ECn0HUQOgyXJy5rroHpe48ggAvlytHEYzo+ruxy09EoUtZfMCjsdzbAi4GYU5X1lL5pXjvacR/tmV4xpd7z36WZoW9ewHIohOYCAdg8C79GaNNV35+FzB+T4u7moIbtB6t4XSI1yPWnF1mLYT5KXqk7Gd80nQyoWDHqbhWErRfkPob45GN811WeaY5P0kS6i+ex+ME3SQTpBbfCZZi2e3GuFMil81oczSAlu8W/jozHJFEk7Scta8WGDF7YrNEzVlsvwVfMXkLywAdTWa9Hcnyd/ipIPP7gIyj61FmUiBnc7BSqkR92OvKIVXSIvKYWUZxWleuXCe1YvmFM+gybuEeCLFagcRuj3X6JFcfgI9J2B8s/DwCu6/K7pBT350vdgTwkWalrw34qHvAC8BKUqTM4qq9cScoLcRZOvghZFC5rs3k07o65LZI77RqC/k61Iu7pl3cxRfgzqTMZXoHcVFqq5GEZW1f6raMFcJnky0yKXEGC/i/LYvA/0ziXV0UtefmL95gDQcwjWRrRZ9FyG0hPkee9INODx+H0UocRttIoejIf/EuJX9iLu2IP8zeiIvwv8sdrVYX74adLssBr/NWQafgmB5uskJ+WEUP8L2uEuo8lnMt1u9nfQxjGGwPhoNPqXSVOvhTRlxlDI9yIKbDEHYLN1L0Kaz5G3yCwRFzSqU5v7VYeSSct5Pas6CrV3YGNcjtcZBIoOruiOpvSRB5InEDhBCli+Q2aDnUAguDeaUkXGy83oqmHgeBVu1tJCMw24FL8Ho+sW5pVInh1AIxxYgfF10SLjZECRDwZOXDWC5rrDoe2st5N5Fj17CM2r/uiWhfjsfhK0fHquRr0uRtuPk7L2uWhDJ9qzTcU5QddM/G4jrWhb1U2FZ5kjtvFYFA0YDyFx0lPPYAzpx6qSwO7vOVdGXeH7Rf3xFiDvga8W5Wy2eItCaL9XRHd/zvyu+U6b56YpymwPu/Z6tKluHtNroAjwRe7ZHbNWeL9GUii20O0EWok29JEi9aPArk1obURA8Gud0PwZqL0Gda3AdSj9Nox9GzrG4PZ7cOBXYO6sQLlUE+A8iM5Yr8L0XZW9QkYPLqMBN7XqMNEb0Z7LaJ1fjPqe6pO8rOEYtD4NKxciPNkDMBUN2INkVZuZjGgdAWoFYdZGPLsRAa+xqg8ZfA/0wfCiFsJdtFiPkGklPGH3xfZeLiuVZxNQt6rX5sag0SYLaCUcIqMQ/gHyhYKI0ZYYnL9E58/bKNHQNZSXoh2adqATyw+izc8B/x3plTJJuYyAsjmeezy+Y02gg0x60GbxLPDX8OEHMLEOH8/A/iPRGf8oOustcrVWSdNqV3TGZYSKkGqK55E5NkzelbU36rQPodYwKanb0BhOz0B5M9+6RiaPHyFVACPkdUw29KukNTtNMkSV+PsnydvKjz0ElXGNvU+0A0BXPdQ2VeYPSAfvMQR6Tq2xuw9tRMvAg3Dpgpp/ibQCIa+rMoaYx94kfZzdJOWyTtLtu6Mdc9E+U/WgeTuMgHZ3DGd9lOUES8eizFMleLOmNl0n12B9fG41hsOW6woJiI7cs4/KQWf2jzWQfL7xqUKm8XSuKlO1pmyKYgIHmRQ5Z2NltfD5DaBcFyoLSEvUuwAkheBovWJsuTW/RvqiqW9OZpMMFHEHWJNnuVoxgdEGeXR3A4o643q2N8wN9+66RMay15MU4cPx2oP9aOZ9EnjkN/SNupMIMlfQuftfwtl1OPGLwGvQeByuhwv8g6hArIqmElycz2PMFfL2jzI6rQ+QaXgb0QS7hijG48DmYmxiC0AXbNyQ7pfPEQklELh0weJHKZV1JNYcsn6/iLDj0ajLPOlFHozntdZgcD1uPwGONcJ3qloYbfFvGHjgl2HyR9DcLU01k1C3Bvw9aLS+aQytgof0PjfRCnIFnRHKSWXOk1zsq2S41zDa5Magdk4KFV4j79qzHvAL0ZFraOXtjvKq8ft1DeHiCDQ8Fx301+qgj5bVN0eBzZGIzmyN7/01WsHPxgDtQRO9K579KEKCK8gE7UEm6M9Hxx9CRyMHwbyCNp7HETXSGGM4A00rsFZTMMZ8TRulpdSOUbHvw/LM9ajKlajyfpKuc6j77eiSI1H1j8ZV1ZH4zr4YghubGR3XWnhvCe0vnYSluChwoAbVC9CzD1ZmVd4C6V+qi+YvRR27SBxpIpPqGdzuksd865Z7oy0LZJ4aU5T1MX3OR9d3xpTwaeHJA/D9ae3lzoZoNcd1UtM8S9IKpkdMdzSQaT4hT+s2CudJFYT7rng6h+0JjIp8cdHQLOqd/Tk7CM0slLvgqwZQA59piSJ3YiC089ygaOQ3IW7C3IBhB509mUXz3+BsWsSftY6vSk46l+fvmbfeUWi00xS4DZ0IDF3uPeDRGnICHQdOTqGQrrfQwfEeTPxzBeDfq8HuOTRdhmDXeRXQTObQ/BCYhY5ltdsqKuPHCgLeNwuDuU46TZeRZXPkaMihQoVeaUHo+TqasZ8A/lid37AEzZuavKtoknkCldDiXkb4YK6+G22KfUBzBUqNsGNN9bxXVZ+1kIF3i0D/e1H+IlTvaSG2O/fCXyEE+AJ643Y0bggBVRMZlrUDbWLfJcOj52Jgd0Sbz7KlLap7GJZfh+FRqVa6u6LzfpcUqM7B4sfQ8Cm2ROy1H8HFZVid1TxrfBTxv6OwNqXFeZpMQtfcj9DjTfJun91shW+zgOgRm38hyWMJmVsvspUMaEsw3EWmb171mQAAIABJREFUVrtLOjU2ofaessLdqAn/j9XUnFdJFUU/Sc3Naipseemn4lG2TndFFR1UtAvtJ6OkEtEgcIeM8qtH/bO3D0qLmgPfAH68pLwlBrSep+HqDehsho1VuDULewdhai7TF4zEnOuMf0fRQaI7nn0X+WnNXxtTOmIq1EgVlCPnRtESG0BraQdadsdIO6iXvJdhbkb74DUy95MdeqNRvn28pl0doWfO2PWw6tHGoK1nx2jAdiqDQh/7b/PRtn4tHbY/zjhY9LdVC7/LjQHIfhBkDlIDrPlfA2FR5lEqfNefqSd5TJdtzsUeTDe2SKIbWIuVXCC9tj4CuXNa2O6ttAB+lfTJOKr3DKFM+GlkFX0e2PsHJKH5BPCO7mdfvhd6szIsXIHb5+F/IzN8r7N1597YTZ3SneRkAC2c22gg30NHyCYy9r816jeAMGCXX3gamQKOgfYudC066ZTe27Gkl0dJr/sQokROkRc3Px/1GgBeKMFcDabWZek6IKQMHKzovj5PKFO3reENmpgL8cFeBHJltKouoVVgbd2BGLCfVN+wEO04DtX3oNSNzPh70a7bUdHnYoCuwfS/0SMuRXtuT8LhPdGYHcgi7YeGerbibse+Azs74cpy+uDKZ6Ec/G35AOwYFVY+ArRUYvyX0WawEJPlKZLoXECWsUnT3yR3/Rp5Yd13yEibx0gO6DyakI8Dryq9Z/k0XB3VxzzPHYXpOT1MSr9Okc62MTJybSd5MemO6CtzqWNozxwYhPITsH4N+uPcPAj0NeoSl7oOaJ7TsNUDl2rwbDPUlaCjCnUr0LQEd1ahswSVmsB4ApkvVvLYMLpHXt/UiT63C8XXeC9rY7s+ty3a7HYMRF/sJgOmTEVacdkGPNkP/fNbOZm2/L5l5Pwj6uOoVJ8inJrBEjePwQaZZH+NjN7zZni/IqJoNFq4sEkqJTyW9ms5lmCF5MSbCt9vpLAeHalXlGe4ouYTbYlawFwMBLHjyJZvEbAdgeeAEAvR3cn2PPrHFTPdYaeE6+GoPkgKw9+zhb9MHhG82/kosww8PIosn88BO38zWnACWchVYAoqA9D5gkqZu6NZdhGdFS+TxNu7utW5tab1a07sdKvuvRtDE8vKlFm0oI6hBddHZOfqR6vpTTSLXySzsDjUaT9wE2p3oK4T2pvg9rKeZ2u7DRnVI2S4qy2BnTUV1YSsFh8DR4Gdm8IdWzPzCKcO7VIdzlXhYYu970bFT0TBQxrQsZehtSMKuY5yRVyKfuuE5csBop3RJgvDz6Kj/58AfwFNe5RQvi7a8CJB5RwitUqLZCLpswrlPrucuRV6CflhB5LF9UDbFdg3AKVlFCEzjxQzTsH5S2izfQxtGo7QeBOt8q9He3+IPKP15P1ID5Obj/tgCO0OA6hioUtrHoeOVqg8AK1LskBvrWovuEKqgSxRm0Br5CR52YrDeXujOq1RFWdHmwUW56D5WhhCa+mGuF4NWrAebi7r7xOd0LsMl9dhqQodYUlVDkL7nAoeXVa576H17+jAoaifeWLHA9xD+3NrdEEluvFUtO0Gmc/7OHnz+YGYMlYf1qN15YPKp1EqmeZOuLus1zsHoTSnSM7X17Vnf4AA3XSQKVO7MwySG2SyQlMXZtjs9LNF683HVq5P8ZA0xHrUdZlMI1zEROOmZb5LpH+gRETqNRYKvJ/vgEy1aW7EtMD9gmd7Ku3gc9i1lRR2ELp8W8oNJH2xWXivKJvzZgEaZDv7HH5N4XM+DqwWvu/OenwZGu4glcTg0Sjh36KL1L4YteyDha9J5/UueSHdTuCP0Ex8CRiHV1dT0/tINwz2A9PwejV58Z3xuwfh6gx5SesTT5Bc6RACZLunHyB3vyX9XplTfoCWXqk7etC6tz7zdFS3O1pyMv4+NShO8MKqAPsaWlS98f3X4rOOEBsAqoswUYVTjcqn2whbSQPWPoRyNwLUFmjdGY07idLTvYYCZvYBc9DwaBT+ecQSDQC/j1bLmD7DU8B5aJ1Tkw9Es2tr0PR8fGcYrbRD8ez/GhouwkCrFmUtHlnZHZPkYHTMj0cnDcVkeDGet0aSqM+TOS8ux/uj8b6tgc+hXe9ajJvPoSvRkT+hNvBejN+HMa0uAaeUpGlrg2+FxRn9177MYZJ67405M4UArhpVGUJj2IZOhDUyys0h1lU0z8ZJlcFb5D2+3atwtSZQrDTAu6twphN2LkOpCRaXYHRSN8V0dsC78+qWTyOws1qoN8pzYNnleIYdYUskfeLDR0+8/j65jhuizdOk48+BJetkwJpP7k0d0DQfaqtVaDwE3xjTFPwP5MHHp/QGMuG98clGm/XCdkya/rMBaeAt4tIqeXOJhQY1Umizdt8z/H1rxy1KKOLbJhGpZ9S29AK2W7wbhUqYyK4VHmJzvlp4vyhf82smxn1ULioxVskw6E62x4R7V/JvC7rNx/pzRW7GUjhnfaonc7l07kOA2vtdOPKfgG5Y/gi+/TYc+RPgD6HhCzByLknzReBP0WpoAt6F5TtwsBFaY1eaW5IG+V5VX7kddVlEOHWJ5Knq0Gm2rolcdY+ghV5CK+9RNIMdfN+khOIzE1pgG6twuBFWq7lZNcaEmEI4MEpkyZqTDravTcdQOzROHoDZGfk4PyaDFd9Flvf+Tri9ECHVq1BuAo7D3KisWRaQFfpOfNkavJmozOfRSj5NBoy0xYD9P/G9h+J7H0DtQ9jRDH09sOtvaBE27UOUxjlyhbyMyMPvopwZq9D5GAw+CJWn0Be/SHo2+0g5wzFSnO3Ndh2ZYkaJdmTGLaHGX0Tm3RQ6JbwYY/RefP4sumaqhYxL70T8/zUy842fuQRrkVSogvaXCrKuPkJz1XLoNgTAjrJeJNVFR0i6zGqfOrS/d0d3L5BOwoPtkkrO1TJHfucq7Ast5sQyLK1vP9UOz6f+2M5oy8p8DN+B9tVfIWNlVtFBagxll5ve1CZ7AW0U9cg6NmNky7MPMT9WX5mebwYeflpUChu6928TaGyFq7d1WrjMtrPuVmCacawoQHBUoffU+0/3RQlvETSNYbZs69ielN57t08ytcJ3l+M1c/lFPXU9YSFbSVGUYZgOKJEJglwxF2hr2nywj8duvDlcA+0q2+VvlpLYynYEnmPvi8DvsF5r780D+Xuuaw+aZC3xWgu5qw6gNdI/BuVTRGjROuxtENLtHoeL/xLq5qFlClbnBYidyPsyRIZFjchZdLkKA92wvKQkQh/N5x15VkhtkpTwNJmpbRlo2REvWBF/Mhr2fBTyGlthk9NXYWVCKUNvz8QOXtX4XCAzenWRfJ1jJVaBlhrcWM2xPQ/cmlF/3kULeDz6yVruW8twYh+UylD2TruuPLfVW7CxFCHCU9Gff5vM9zmECPZnYeseIIuxG9Gq3odQpw5YgLrfRhvTB9Fxn0XCl3m0Idpbav3TZjR+BlnDNtFOI/PvGtowLPZ+hOTLBqKsPQgpymj3ao/+fz/aYA6hG6HNA+SK/c3o7CfI9JpepV+Iz3tntratX88qd8L0lKbTLTTVrkeTHTzUwZYxvaXLbUaGxSLaz5yI6l6Mm5PRLUa3PYQs731Ayyr090HXBgw0we6aQGB6DsrLSZk4lNn011AM8Z0oyxbgCdIaPhTPdLzAUEyDnUDL34FdK1Bagn11cKmaQDQSv8fi+8aKp6OLP0kC3uoI1JYVbFO3Ds0/B2+9q777DjIqbpM+rHnSqV+kSa2AWCaxz3Metst6t2g/0uFXPLETr/vUu1koqxh/YeO0yF372U6CVm4Kp54t3aLnz+a2zfQSKVGrFAqE5EzsELSV2sv2O67q2X7biGkO/7SQO42VHQ5SsXUA2wn2Yv2LO5DF2Ctord5AdOUldOPuVjayt+9CbVwz4jZaiPvnobsZBn5dXo3521qIMwgsFmF+Evb1AXNwtwqd7TAQ3FwP2uk9Kdbiq1W0YA4Cu34Oxl6H1t0IPI6Q7uP98fccrJ2H8i55urtIy6DnMWiehPVq9k8/W2mFmYznXEST6QLpB7iMNkzH/dvh6vHfT06qyVkFqizWZGVXYqA3qtDQCMsfwsJsCOSX0a4zQspIhkkSc6jwdy8Z5UYM4stolc+TN20OFyo2RGbm+yHJ436aTJd6PD7n2N/bqOP2I0v77eiMa8iK7iV1iqtop6oiuuG5+OzfJSV3M2jjXEI89IMIkG15vIY2p6/HAFj693CUcSbaMAotPXKuvYmA5zAClc+i+XqXvFPyLnnsXYrmTMb/p0g53EGS6n+HVPFtorDo2gLUNQJfgsmzMLwOQ82wuK5m+3Q0Cpxshpl1de1utHG0o7kzQq651himBWSQLAGf7YTWNhkCDMWY7gV2ws5xgehlMplQmQxUWUbvm+a0crAR6GuFuUUtj/GPdDjZQDFbt0h+1tiySlrfPqXbEWcghsyTDGlUOhLR6gxjlnXWNhr9zwBsptGYWpS7eWMoYu16vFZuDQ75fk+hrWbTAG5EUSVRVyjck8TKDEjNni1gl2d5nY8SpjCKvLSPBN4ZvWtCOv4M7u5A2B4ts0LuPJsIT20BTk/A7ul4YzK+6JjOofg3sw6334a3busBr5LJmp+G5hlYvAIz1eCp5uDOct4EcqQCdzel4XB/HkPGWA+SP7feiQoeQXf5TbN1HTzn9MXyaRh+LwX15twXR2G8ms7T2ejzUXISO1jlApn32Jrj4Wj2A9F0B4+0kc4HRz4efQRujKnctoieKx/UlyrrSspODS04x4wuIq5+LDrkSTIb2gB5JfY9hEQvIVR5De3Mn42xWUKC3MuIAoFM+ByRNtV/D6VdUWkPci86mjwQnfMOmRDbDrg2ktw0EWut1CdILuDtqPeLCD1+UPjOE4gXHwZ+7jSUxzUJjgDfi+ctkXHxN+HS29D1+6pTZS9sTqj4wX74cF6AOgb8FNpQbUVZHmbWxycgyzp3IVAyGNX0OE4iUD1YC0BZh4VzaQ13V2Rx3iVVnQeAb61rrs7Fa2fj+f3RLQOkDfFcPLsrumjXMjTsUR6U8n6E1AdUUGlMn5lCwP4TaP+zAbcbdWEHqYLYiM+vrGW0YB3wzxBztUoC3QaZf8KHGdh+qjYFauq0CJDGRKvDbOXaJxQuna0fc8NFjLTh6ayt5rOLfjcbQcbd8s4IDLG8baPwpWrh/zXS4jSo+hhgUDQP4x+T1sWf+z2T1gYWqQlb5N7d7I10iLU71dSfeWQDMOROZ36niibRDvKapH6HEbWhhf8oApQnG+Dlqt57GR157yG+8Ciy4F4FzkHDhmRU7SWonIb6MRlQ+0rwx1XxgcRjVpEB1w3sOoSO8j+OIsbOo9HviRG6TmbXrkF7oyypH9XgYB9cDg3pJilcX0e4V0OLxyqtL6NFchNN4jIpW9pPJh3rjC64iPajq8DzFeW16F2G3v1y9JQXoakH5q5Bo024BrSCLqGV6UHpJ80Jx9qCThmV6IzBeOB3EIC/Hd85SypO7JH5ZmFy7EKm5QqU2pDluR/RDV+Lfhwlw9nKCOX2kAkZTNY2aTxZRqiwHnXcg87pLVH3Z6OzBpEy49PENS3IxL0wnlnwziEkrJE3jkQkQtcLROIIld83AKUZaKrC0HpavFcRdW0J9wQBdiQQD5CW9MFowt4YT3OTy/Edn5QGohsbWuHNNWhe1/ReJ/Tq5C1md8iLTbujC3dHl46hE58/a7rlEALWnVNQ3x+NOBAVXIHGIahegWe64eySht5GhU+1XdHth6JOHyMgvoG00x8gFms8hsb0hC1T87ONpJFha9S+JX8G8tRtS9pOOTNsBmzjjP1WlWizMaexUGZR8WVflpVpxU3ABlC5NbK9+UveNcxZmQeukNwHpKjaD/AOUCXVFf4peij9eYO1d/776ZHNwnf9mj8Dqb337gMaPFMX7ijTnmvRjhNkpquTfYRzD43+CjoG766q4e8is/JVhFLH0AIaIfmSK6rgjQ1YGctd8d1aepOdRthRTctA/07ksf8BAn1b3xcR77qqYIe6Drh0DqozMFmD06H7a1nNIIETJZiuaeH0kZKjEbQAL5OGaI3MC+9j5oFohj3ca1HvPuDspk7846uwf07g3N8Ia9NKdsQeMvHtKgLgP0UbTJxJF9+Ehofj/XNog6ui8OlzSM3wUjzwNqIHZmJwj0dlXiF1VkaTQXIltJFXa9yJ77VH/Z6Ohs2QfM5rpPr/0WjDAtsTK7g+k9FxjkF/m7xv8ZsI/Tx2n34Gmm5lZrgyQpKl6I/6aP+3omwLUxehqQnG5qCzG+qWtIGOk2k1bsXf7TGGF+L1hSja8Ss1NM/G0BQ+igyBdmSFnkTgu68dXp7PBLNtpKLznZgvy+iAYV50jbyxvJWkMZpi6G+RoLYE9D6GFt3pKOzJGO9NaDqvcVyZUzdNkZF4u8iQ8Hm0JG7F8L0VQ+jscdXCcMyT692Gng88BkcrLpoKr9uStp/LhmjR4i1ipC3mVtLIKWZxM0VkH1kdGcTmTdLCA2PrFmVhALuflnBjzJmY220gzW8Ds48JLsf/txVrIr14DLDR4kreH8FC4f+Wl5ibNidkDsdA72NDsWOKx5QeMiXnYR9Xd5JXGRxFk2eWFEIPR4H2xn2NvA6+A25ch/0VuLipBXEHWcYVhAvTCDy/TFy5BHTOQvN3swNq5yXK30oYexOmqnB9Co4PSlfaAVTq4f1F4c7daN/rtZQJHUYYNxjtPkXmz3CO2lGSwt1F0hor6JT+EfDiAZiZ0Wv9wJPN8MNVODMItMHirLzxTaigyevQvEreO/cFZLFegIYqGYK2iCzDcYQKJ5A8rhr9aycCeg77kGm2k7y7rlXl8nxUrgMB5c+RgOsMWHPAj2IQjsfgXEWb4X604tcQ3/s5tDk+Fu/tRBNuKuq5ThK7L0b9f+2noPWCNopeoPkU3Lmiz8wiFNkbbelD4P8ymnfPx4A0xLOmofWU2tx2W+P/dDT1LtoPxqKo90kj41R03Qnyvtb+GPM9MbYjpNd/d3x+dVWb8eXoVisfnLq2ER3URqNLZ6IbnRrAtKDnVVeUuw843g5Lq7DLDpPXyMQaH6KFGMqZs5Mqd4zMYrgcw7QjnvkG2qCukZfS3CT1xUVa1HhjynOZtH6LagpPs/ro/gZSDVa0YJsK5TeRBqUB3NRDUU9cFDgYV8uFMos0rTeJTaDcHIEhRV7YYGk+xtauuQ4DYpHHbSy8RqGS1vRtFt4zWe5dzALsjcI/y+hcWe8kRd7FZVaiXNfBCoumQkc7qqgjyv48UJlRBUsHSIumghbaUHTGywgQfG78GM3IKjouX4Sxdajb1MQeIzF+nnTobXlR0SQpAW2bUNcGTCmSyzcdr63ArWpar5tzMjg3gLFqKrRAi2gjqmjm5SAC1Z7oi6voNP1BfO84soCG0GJbij7qQ8/cBD6Y0f/ro5zr6/r/yhw0zWqhNHUic6kCzW0oLOu7UYFdSBB6lAwrfIb02BxCq7gtKnKbvLnTqgbL/T6MTjUV4ljYUozHnejwp5C5+BS6ONQi7MF43UEoTnDwGkKqM2iHNnfXF+U7XvdnYh58Eu24z8czv9AJE+9BRyd8cxkeboaNj1XGAYRab0SbrMCYjef+arRjGG0AHtQLwA1YWxS4ziJLdYg8AV1D4HePNOpraHo2I0DcRV4IPkbKR/uiG56N95dijrwcj7+DpqGzxB0gmZ9H0XwzWF8mr06bIrOu1ZA2eBkFbfAUMr3fJyWBw9H3l+De7NZBU74R0lnoU9tFUhzgfd3tXiGdcMawZTL4w+Dnz3nPd44dY50z0pmqKAZ/FE/87nOD//2qNP+2NV2DrcuijafF38Vgt60E9XVobaywXbmwg0xkYnPelIJ3D8vXirI5W9Lma7zDQO4cPkJUCuXaqi0qLayvNG2xQYKuExFB6gwtnTEH7e+0IT9LBfhcpxwNG/eg/CXS8lpBs+8mOpreJMXPfxqN+y4wDFdHoHVd9XRunUWE3e8gkPSu6VBQR9RdB47siy/BViTE2FiGg9qZ0os46Ws1OfQvkjyxq90W7eqI/piJNg/H+wejrBqpKXXS7/ZoVjfCEEeHbahK9HVCXxe8P6fj62Xitu4huPAxdIdGsfamEpu3WMx8ECZegxbzWNeiMzpg8UfQMIZ2hx+RXO5ltHP0IXD6coyDNXkTCHXOkFcdOuDjL8njkFfNXHT2A/HaJbS71aOUmrvjmY793R1jsRCdYWnDEHLQ/VoDvFmFF35WX2gZVS8eaQQ+CeOXdLlBXQdszmdc/esIeEfILDaTiIe+gib9X6Kw8zWYGYWD+6BnNqu3HN01TjpuD5Cb6Qp5Tdgcwr4zJIU4i+bqA9GNF+PvYyXJ37x2W9G0dDBlJ2ktV+N9s0FXomssz1tBnPIkcPgR8oZxc5rPIJruQnyhHl6dVH1ukNGstlDNJFlm5z16qfD/JRLUjEUGWRuV/oyt5Fa2S+IgwbRIzTaQUwryFO/PG/BdF8irx8w0mJIo6p1NZRjojZXlSgAy/P8B1401sPmnCK6wfYdyVJqPEhQa4LKLUSoG4ZVCuW6IaUHYfjwoKjVguw7aO6Q1gI1k9JnDageAe8tw4B5U9qFFeASd855FSFWPzoCz0VtvocXyV1GpIWgf1eSfJi3S48g4GyAvpBghk+T7uPl5dO364nhcjxRu7PGaJqYnwSrCkMmaJv3ZaNMFMgnU3Xiuj5TuiyfRgj0X/TYcTaqSCWA69NgtB+Hu+E5zof+XlqUgOdwJc8uw76i+sHgztNVz0HoI6h6EllvxkH7gFWjZT15d0RMPnYSGg+hU4vCsvvh9msw3uReZbo6ec8KDdcTN/Efknl+MTt1FRuZ1RIPfRzzMe+hi1W8jYLyDInO+HuO7Owbqb0SH+Xkn0HVdD8dc+LgKP9kMw2eh7Uboxx6Jkfoc7DwPV+9Bw/yW45fm+Pc/I1B3SjQj4xTyhm2ydV9R8zxcmlI1dpL8fyPpQ/RhoieKtGLiKjoc7I5uGCelcyfQvjgUZe0ELtTU3dfI8OV6NHfN1zqazUfzG6RlvLNQp8Mk5dE+DbVpqLP371Z80M7dBuAiTFa3DodKIRPPtE7ZhkQLmko++u8g911jjWlWB7Pdb7nWsT2xvE/XS2y3Yl2Wrd+iRQuJQbZ+y4V6GaCLEjs7Vu0wdJku17LVclvI3tYKbxq1bSUVLV/7QWyqewdww4uccj1aI9a41hU+U4xQKWZ7MzHeQqqQ3BHejVwXW+/moG1xw/bcptZFV9CEdGqEgx6NA2y/E24Qoeqfokn0PYR+DjEK8Hx1U/+9jDb7EWQ8Wwkyi9bjMTL/jsMmm4FdIXWam4SGdaVlPBt1XyOvubH/ah6t3zsIh4bQRHqxAuVNPdOaYnvBv41O3JfInK9hmGxZ0gfIBP7fjv7xnrQH4cpp4LvLkYR8Eq6v6gLXxkZYX4elcWi8BrVNKLXC2ttybFZnoH0Jxkag7jY0nFAlJl6Hlt1sJR9igRRPW/T6IXmR25cRunwHgbKJxKHolE20YW6SXq5dwO+R2mE7bUM7zggZNbGBVvu/ifoMRvnnEaDPojP7GtKH7e2H6jyU9sRsqsL0H8P5RVn8/zvSY72CQP99WBuBlSrcHYE26w8vITAei4nhKJBG6Ppx6B6Gnava8DvQvmLw3alpyG1SCTAZzW4g1RHEOE5Hl4JA+zPI6rV67yYy2N9BU50odz66Y1f83Y/2ydmYM3vRGvhHUT/7Kear0Fchr9Iy39AaBb6vZ9xd0/p8P4bI9bE/aJk8Mdsv6g3Cel8LAyi8b5yABHQbaOaBjTuOrShinblk456t2yJt6jJsQG4Wvl/EKlOy5vGNXfVk8vw1grIwQJoTNgg4eMOFuECHS0KGAdtLaJrB+mQT58XOKjoPIWkSyOARR8SUSQ55kwR474RFD2UTyTPvIKlCP8+Or0ZCzroJDQ+y/RLLTTQb96NjZo0MHGgARmF5RjH+pgwW0Hyz/OdG/G5Hk9V9YMuhPh61l0w6tAOt2x0ki9FL6h5vkNk/nyJ9ip3AwmaGsvoo577rQxjURib2Nu9WQthj+vQKWmCQIavvk/d0Hojv30U4NleFuXXoaVc49hjQ1w0XRgQGjx6F9l7lZ9oV7S4dBM5Cy2+hI/rPouCK56ORNxEXvRqd0V+o0OPAf4rGeHJOaUwYJgHcYYo3ESA+FGV/HJ2+AvzD6KzvIc+ZNWJPRad9FAN4mjh/A/t6FQnUPgsfzIVE8ZfYEkrPf1edNoqQbg6uXoXOZzXwt8a1cKeAtnmo/P0Y1McRt3ALHenvxMT4WLTYHeDHG+FiVX3sDftOjPHJqOKtqHoDeVej9cR18Xtd1WIXYoZ2ovm3H+2BdkYbfBrQBvBhlLURVf4sSW3Vx3fGo8t74/dOIvHVJlQ2oToGpUNR+ceAB2DtDRkt30Lr1Ot+I6ZAF5nHwnlrnAjIsjXHP/gkbGvVPnnLQmtspzdgO33qABdInCsqNoogvMl2v1gjqU0uWtFFh1+58D1jnk8123TIRevS4Oadx0dnm92mBWySu1LFv4v6ZDfYnK7LMn/SxPZdZb3QKJdnYLeMzNEykFK5HeRA+vmNpJXpwXwoPncPePwTpAL/CLKQLqBFMYkQ9u147yIC5Q+h0qdbnmbQJL4bZb8ez3POgRqaJD6qOOqoD03elii2ERlK7cj4aybl0bfJjc7Olp3kZYy7+pVr4BpamMcjkuZUs/jtN8no5A1kFO5FeNGHFrG57zo1b+touBBdEOokDlTgymbUswIfbcLRA2rwcC2C8JY0VnuA5gWYGFNf9xxFmdamYXoSmk4iU+wZtuRx71xR0pvyaTKAYxMdMX4Qld0Rnd6LQPinUSPPRKc8gUB7DyLcz5O3YZYRKr0XY76ELOg3yAS834rvHkOr5kES0Xauwzv3lN7vInC4H9maT8HEPwYQ8N9BR50JaByHqZvQOgUrm5oDk8DBQ2gH/K/GbOynAAAgAElEQVSiTZ8swYc1odOumEz3oLMJqssK0T+EZNo2iOzn/DiafgBh+2XyZpGumGsOJiGadAOB5Vm018xHF81G1wyREemb8fkK2kz2Iioj0l9sZVrtjLJsJS8Be2vqob5+KPWhxboT1v4MJt/Tuh+NZ03EM+6Sd2P6BG1Z3Vy8t0TmVW4hac9mci4XHX2WxNUV/n+/EUp810rERlJSC8kNG2itoLDxU2QDjIOQWLRZKKu+8H2LEZqBcj18tcx2pYOB0WGMdtwVORTvQP5xwa5oEymgNjVhaZsb0Vf4fMN95bmD7OArkdmbHJwCGThSJeUurv9KfLct6ubUB47+eXoY6h6K3vkFhH5taBG/j8wGx4NbcLgDeADK1/Tna6Sjrg5hw6F4fjcZT+CsXDUy7UM9eUHksXhkJwLNOwh/eklJrr2//fH+wVa4MJlRdjUUxTSPoudeib4Zjfo5C9z1Ql36ok8WEWe4gRbjMsnPTavJvLSpzWCAoBvmYX4G2hpg9x71j2WOfceB43DrRqSdmANOwNo18c3cjAdeiArVQX87lEtQfRdKT5J34B0C/nsEyn8L+FdEcl8ktJ2LCl9EaDNKpkz7LHm1dlM870nSnPwjBOpnY/IcIklZI81+RGW0VjMUbhnom4eGIL9bntJg930BTl2Cf7sODVC9GncFdkFLhJcPA/t7ol6N6E6/G7WMYDgf7TuniXJtMuMCziJwtH+0KbrI6/ZN9DNBOsKPkU75wSh+qfDaKqLIbWl3oL3CkXL30BweJIGti+RqV9Ae5qCNgZgz9sM+CDQ8hszvF4DvCHhaIsb629G2u/H5DlLtZTpzD5kF7n6qwo48W5526hmkidesqliLfrFm2Zpgg2SJBNYqGdNgjDF7QOEzdsp5TIxF/r4/5x9vNv68T/TltpC9Ocig+GP1gq1ZH/2LXG2Rp/Hn6goV3kleKOgdyzvIOskTedcq/hjkTUsY8K2Z9ZHEMhnzMrsKZRl8bdE/jqzkJ6Ks2RvQ5phiI/9X0Wz6S4SGTyGUakdIeAOmIrx1AAGiI/I+RTr+96KJRLTzRfKqN580LF+zNM9Y0lbos+torTv1wyLwaB+8Pp19Uw8cLUFrvY6HHd1waEU3CB9Ghn4rGW3loBoH/uxBVnrgLCPxPDtIZxAoO7T6nfnUrL5RhXtz6ueuR2B9DObuweoN3TLRbJfypqL8aIPJK9C8EoXdIWfobijZzNuDIuHqUYbUKRSIcRqhhAW2n0POvL3knXvmYW6QDovleP0Wmazjl9HZfYr0ktpLNU7uhA/HoO7u1aWETwAtDXBvApqb4sPXgCfgP74msD0EFTtC5mFmTmNw4FRMgl+KMg/EM19CE/jxGOTHgG/A7oOwPrXdwWWL7B2SbVsmb9nwAW8W2RQOqx6J8TeluILmx0jUfh5N9XukNPyB6MYGtI/aF3SwMCfG4rN98d5gdF1fDGOdvZL32Mpif2FEwP7vybv6nMisLbqmGRkgRXvIASELpNGwQgoP1kkDr5vEHLe5PvrP1MUKedOPT573a5sN1BW2h0G7XNM8G2RGx+VCWbAdN80CFMUKm0B5N3zVTjE/oEhEF518dtQV/18UOpt7Lmr3TCdA0h9ee8UNwFhY/CmWa87YzkHvbCtsSWG3wqs3C+V597KD8Qga0Mei43oPIVOghXSsTJL3o59As/7HgPNQ/RbU5vO2+fEodwhZuOPR5l5yZ29CstMP0Fz8EZpgp5BjbKqaGeJ8MtlB3gPmnb2D1GdOLKpPutFm8KlOGF7SNUsNa1BqhuVFGf1Ho4yu+PyNaO5AlPcoslKOxOsTaEGV4m+L6y+jzWENeLIRdlVVh2N9cHdR3fVO5CgooUU2uIHohBvw0Rz0noTapbhC6QjaHe2C/gdk6rmnEUANkJ1zgIzy6wZ+HQ3EVHTuSTIR0VGSd1lA5+Zn4nMPoB+bZctR/hHyDj2nD7W2awM4EqE0gwNQ+RKwCvXjUH5Gr29chOtvw7MHYXQ6L2F9DjgNTV+BuqfiOeeirr8NlHslPB4Ent8L/2FWm8Y4W2qP1kGojAvPTsRY3on5tBhVHkb7zDQZyWofxD3yZDZLJh56mDR0OsncJWOk3NUn6CnS4LGzrz669GB0+zXyvttSdN880PYMckaMQfUKzE5pvfhgY154F5lbuS+G2PE01mXPk6dqW6s+NftUbzC14VY84fukPETqqh1HVMQR66KLGGgMM6NgA9U8s5MZGqeKHLX/b7oEEgMN0OV68k497xquvLkUA7TNdv8UKQyDt5MBWX5W5FncaW5I8cf5JupIk7+58H0DsEHfLILL9euQUWgLhfI74n3v2AAnbCqYCN5PhrhOReEXozKXVLHSL0DpKEy+pclzHK07B4RMx9dn0OTYR/LXR0n1gr3kk9VMfelQUWuE75JRdJvIiukgU5qeQhNqAcn4OoGZNR1rS0sZhPJR1MvHol1k5FdzNM1e6C6ECycqUkxcju+NRjfZaFyq5sWbtUXV8Soy7hzE0g3sWIfbN6F9byTDvwU9v0iGXd0jQwdPkxK4i/GZX0VOvxaS1zmPQP4v0I5xFPg14F+TWds+jgE4iaQvjyC64kQ0/o0YvFYkWZmOZ/YgWcnTJNm6l0iEsgjlP4DaH8D6u1C+C+UHYeFlaJiAqVUYPANfP6/Jth8NhifGA4hTeCxe243I2FuLeYHqu7P67p0Y6IPAX8PwmLroJlof10gxiY/sa2j/2ksGgzikuje+4xDjRTQ3T0V3XCFphikEUE5CtR5ju45AzEqH9mieVYQT0f3GhN0InBuAyhpbDpPScWgakOrkfNRrP1viEnrJFCjeLOzQHyPpiTkysb35YKuZdpH0hLHNxqGpDQOvg9SKfK4NO8dTNLIdYG0pG6tsrK6xHYSLHLXLKlrU5pZ9Gi7vDAvZiF20jhsLD6Pwd1Hvu1F431wMpA6QQoPdaeXCv+IO4TR33hCC/9+mc4bciRyHDrkhEOVaXbNK6g4dpdcTHXB0HzqvPU7myv0QefwvIotqjhzJMRTm+7KUTzfR5J0hk2Fvkhre2whDDqNJ0xLv98TvFuBgNwwtQdNjMDuqBfHAZ+HaVT2+jCaoI48Nqg4ic8RSN1oA3X3QvAj9ndC0ITlcA3CmJFndKDK69pDBIlZQWG32MdCymXxjF5kk3P0K8jt1IRrX7Zkgo527yUir1lmorgYNclmVvz0HbRbIHkArc4m8GsJeG+v93iNjV89Hx4eCgXNo9Y+ToZEvod2yGu9txLgeRmD3E9Hgb8RzfgxJHX82GvEK4ptbgT39cGMeOkeh7jGR3TwKXIaG8Ew2V1WpmYhJdkDJLzYofrw92mVFyNXo7H8X9fmg0AcdaPK/AHwX7izlnjWPlDMTaM8YR3vL3cJYjseYOMTZyYJ+R8VxACkJv0VKHk1P1CNQt875gaiW5XQGI1va5+PZZ0gu1ut2bzS53B8PuBdtex/6dkilc53ttMKno9wzwBfa4cEyHNsnx+hqtOMamldWFZkVsw9pgUwVbH2xhQXGMUccD0ZbWtgedg1pEJrCqLH91A4ZuejowCJw28g0UBvvdhReo9CGcjN8tZ4UORe9f8WHunIG6uL7Bm6jP4X3fBQq0gmw3RKvRGfMk7t9cbeC3C03yAlRtJrdIAemNERdm+IzQ2jSPIBA5DPEtUPlePBnEU1h3vEwGvVvoIV0Alkz0YCbiylHs1zHWbDmSEXE0wicD5HX1zho7WGU2L6hHeauZ4Rt3bICA3zE/BBhQoXkrB2uPgE81Qhdx6E2AeVDuretrgxXV5JDbKmp7GvRByNoQ2khb6ToiK54JPr0m4iedVaxC0iSOxNtmCOdJsZMO14dhj0HnO6D8hr8aFP/79urQWhbgsV7Ias8Q8b07o+CvkPKDbsRakyQd2D1kfceeUXsj0rNITA7RYLAYfJabhOnVQS8e+K9F+LZHyDA/EIzfH9dlxXsBuZvQYvjQ0/AwquweA2afhuZ1nvhrbsZ99sN/HlVHTcaz/4hcA4m34fG4ZACno+61sjroy8jPXw3dC9JgfJuTeNRjbmxG8390WiCwWoIWZkL0W13SdnXf47mrSm86/Hok4PQtQALNYHVx8gn8r3o3nq0+dpCrsZ3SyRN8CjprDqN5leTpW7H48txIq0uZcL7B6P+x2OodgOPltC6vAb0KP/LlaraP0HeymHZqA3LIn3aTKalNa6U0JK2OsP5M2wEGqMsHChiICQLYOPUTkL72IyT9/vNHH9g7PLGYNUGoBtDijSBhdD3O+4MnNb7QjrjXLg7oRhGaH6mWijP4F/cLez7KHpPvUsZjIs5NnyMsEPPlIePLy7DHbGIQPEYGuz9QOtphDK/gCyif01mGDsfr5mwniS9W/2weVtO/240OexQXCU5rWdI3u1oo25J2ECg+kCzEoP3AHVD0Phk8EwxQi1fga4PoPnH4PAatM/rORvIn3Sd5A93VCUxYx1KDyoAg0d1NU9XTZeiND8B07dVv4Xor1bS2OyJvm5Hp/m1QpPfIsUHLyHsvE4uzO7o788ijNsX319Am8XtWTkaK9H3jYvAILx+Q1dgsSMq0YEw7ccQTTGEwGkpBm6Z1Ac+V/jObjKDzNdiEL6MgjNeifI60IbbEwM2UWjAaJR3lhSv/yQC8s31vLXy4AmYuws7v4TQ9mU9rPV3EOT1q9C5cQH6najjnSj3NLLQfx3m/gc9tq87EjM1R13Po0m7n+TCmqB2GeoaoKuqavYjK3iM7Rpg65Bb0OIfQ+ukJ7qpOWr+fKPmzQTwUB+UfhqWX4ZKC3TX6b1Ho5tMZ4ygIKOF+PsIqSjsJ0+r48gy3ovmWHk+xrgTHZHeBB6ES6OZwa4R5aOaAZ6pQO8OMiN9Gzq1vAQfVdWNPyLpQZ9M76dXi69vkpZvPdq45knrdpUcZvPuxe8UqVnjkimI+7lmf9bBKzY4faL1puGpv1T4TrkLvrrGdjH4ZuED91upRS7GEhMDsLldHxtcVjsZSbSTpB4M4Cbei/pkc1RFoThs3wFd352F1/zsoqfU9EkXMoyOEPH4O9EkuVHooWZkBuxH5Oss4h76gDGYuwANtzOa6Copr1lFk3ceWR+TaFMYBPaFYmpPM/z5um5nmIg6NX4BgUVZQRSNHWTOxL+CyTkZS01kVNZxBIQ+ErVWYGIdVq9Bs/VM80APNMdMad4DF+/pmbfR5L8TTZsgc/o0IMB9Mtr3KyhKeQAdda+SJ/IptKg24vdStOla/O6tavH0AnsOweoUjG9C3WhQN1Wo9JDmyJNIUfGpaPBNUolwiYwBtkRggczIbonAIXQWf4yMcBlBuSvmERJ2oNvHLWE7SWoDba07X8bbwGdehPffgMN/D3gNJt6G26vQ+38C/xjoh3f+DH44Dv9HdNJ10kvWijaXt4CvQWMX9I7A2Dz07tM8qy1CXS+ZnOgxBNJ7oW4SrobnaQmB0blofjtpXDsCfSPmxh60rxyLZncihyyPQ2kAdv3N6NOTUPkZPYt90LwDNkbzSiYbSNfYrlg4EXNoDQF4Fc3znyrBag12PhT9v5stoX5tDt4e1ZB9ogSbNZU3DTzbHIV2RqMeijGNzEkTN1TM+6SFadmrHfgbJEhC+qV8SrdB2Fj4bB15faVFAQb3NrScmkkwNvfrsiD9Y/a/FbnlVbZvFsbRIg1cA8qNBcqiniS9DYLr0cHFaDpzut4NijuCMxb66A4JopaFuCH36/nsNLTjsBitV7SmixEwxe/YqVhHhl63kneOnUITdBCltORJZJF8Lr48g4jVVTTLO0hTMjJVNayp/Z3t8MGq3tqHNv1d5NFuHlnI9WgyDAAtzfB+aAn3kc613ltsAULjs9GYS2rE3OVMCmb+0FF34yivxNSkqtvaD80NCOmGyVSYVbQqO2HqnsamLZroyVeOZp+O12bICfMhWhPrpOriNWR9HYrPPl3o7x2k+uQeOo62AFenUv+5APQfheuTUJmFxhUkQbsUY3AegeJB4D9DIF2LCvwtMkHHB+haDctANqNyD8dvh28uk4l+HR51iYzc8Zl7Cm3IuxGoPhfltl4T8uwPAWFLGbq+Em8+A7wB/+uk6vsXMPEStMxEfa9GJ7QjkO6G5X+mVKr3qvDhLOxrgKk1aC6ha6UGkUFwEm3WFWiake0wH309gDb9ZjJy8yJag9b/TpG45ijVtiqU68iMhkMIsYdIJ8ejUJmEzhrsnNd0WkLr9jpaR2vRjQejDl3k/revHporyHJ4l9Tdzcvx/EA77FiFkZrK+2S0aXd7jNeL0UAnlloERmHktgD5AnlyXiMtTQNkX/SNhQR28tWTQGxjxnSHMdBBKTY8HVNRzHNRLbxu35iNymLUX5EC8feK7IDZCDMSW5F6kPlAi3I280NFp1/DfZ/x65Dyj9XCa7awDfLe1Wxdm5f237vJY4MtbtevRhodRQWGAdqgb3LdJ9CdUcZPEAlb5iL37HE0y4eBr5AOFavcH0IzYCdwBOpGYLgKH6/qiLOCODZzRU4R6LBI+5yWUFz/2Lreu4yMtlag/1mo/gBK/wV58g3ZXeMKtB2AlnFN3CdaYWEtEse0Q/V23Ni7KckMdVGJw2SCWh9TRqB/CNZn02dk+eEiwp4bCLfWyFBt4rVzZOgs8b1z8ZgqwqtV4IV2aIpQ6icrCu322Oxphq526Dqhxu8cgeYDCHjeiIr0ILD8CnmvnoM1diN1xRKZa9Rx6rcRCp2JCh5Aq2uIRLF9yErdQ5KrvfH5MZLMtCbQZ/67UcfhGvS+EKP6+0je8fdh7YZyV/yhnnV7Cd5dhJVJ3Zfb8Nfo+PSgnlc5AdTrmqOmeUmam9ag7o+Rk88m1NdUh7kJaG1U+tW70dQiKHSiPezJ6I5TaCO9TSb8uQg8+XkoP4sA/zQ6JUyjTc8C3R4054+pr9orGs9uMhquSl7Cei6GYTa+sgto3YSGn0OOiG7kKD+qDzc9DcuXNFcG2sWNzwNHXbAdFz9AE+4ODH8d2jfg9xbzQljYnv/cmnnIEGn/K1qodoRX0JRaZLv16hDoSuF7qySGwHbsssVr9USRQrVc1WIHy+4gDdOimqTcGLI3W8kGwaLeuEhOmxIo6o9tpjuu3VE2RbD2EaHIS1vOxX3lr5OBKgb1cqHcxvi/jwVVUvdYJODtYbVkZghNzONoDvbDdi1cHQLmMTRSfwf459HLbfHeXfi4JkZjlcwhYO76Llu3LrERf9+K9zrWtWjaECBX0GLZtQ9KzyIvSgcZ1GBLbhQ2V9XWrho0b0L9oAoonYK6M/LOTl9X6HLbfrQiK+TZqxWqE7AxC62t8OGaFms7eQ/aMFoHy/Hop5BxdxxtBk+jhTCMjqdOG7EZXTaI8Oad1fSV7tjUdw9W4NwmDKzDyBK0z8Dt63Gj0RxUb0CpXOhIe402oz9eR2qYA1FBJwCw2T4TlY7j/5aObxwpY45GBetj4Nvj7yNkrO0EAqM28nqKNkTaj0bZV4GDV+INSyTGpcD4FlTfg/eWMjx+P9DZh5KnvIKM6TOIjH8euAAtB+UXbHiEDLt8GW0UQdY3luDCmpr4sL625Q/YTabPHSAdcj7xjpHZ4fqPxAc+G33yMRL7riAj4MkYuE9En0dERues2KM3yeRZ92IYuqLLT0QXHo7umjsHXfZid5GOj1moNML0PLTt0PVR7S3kzSs2Gw8hn8IuuDQGbyzq0DBBytvswzLgGS8gDTbjWbXwOUir2JStgdFytKIT0LhiQ/B+4cP9r5uK9W/jn2MnDOCmNGpEzp5W+Go723eQotLBzjyb4gbjTnLtmE/xEaBIZ1iKUvQ2+jneYZpJK9wNMi3huhTB2bvSOpoc/lxxE3Fne6NoJr3KLmPwcdLz8WW0k7+BZAgrCD3thLirBr+6mJkarWCy5A00QTfI6Jvr8fc4Wo9LaL7fJh18ffeg+g6UHAUyhigSq8xnlVWtKyiI8s+S3OonkCm0Im1n2wya2DUEKI+w5QUtVcKC6Ib+fdA2rrEbQRtXK2m110fbzCEuktf8DZCTrTiO62ijGYounIpq3CKiB8msi2+vawE3BLlf8tmxF63mLyHy+QxamEvxbwGZ4g+SPPuu+NxAFP4BArR9CDVaY7DG0WruROCzHw2md+d5QsSNJtY0ef4vqZ6cadMDl69A5Wej9e9JB/kPobQKyzVhfxkYbITpCJnmM8hztQfdbvJUA/xGC7Ss0vDLJHdqlA1Sc/G6TkATUb27pGa9LqplbftyjEMHMuh7gV9thr51dU95D5lv9afQ3Lbn6sMY7HMxv47FgH5FJ8N9XdAxqSXjLcl6kzk07apoyjmjWmdHjJeFv3VsbXSVObiwCm01qHTEFy9F3c7By2/DR2vw7hj8i6jeTTIzmn1HIHywIVE8NRuzVknr2SBusKwUyrC2eIntPi0HkxSB1Hy1Dc1Sofwi9hjP6tH6MYZC2hxdLr8lnHpFrtZgDNtDc4tKCfMplnEUZWzeUfxZUxR2BJYLr/vn/iCScjzbwSHunOKOYzqlWIaVHwb0FjT/nfFvCIHyWeChOTRSv4wQ8hIyIxww8g4C5QfjoXeUbN0Wx3tEMEZ8vZ7MzukEKG2knG+ZpHT+P7bePTbyK7vz+1QVi0UW32y+yX6w2e+W1JKmRzOakTRP22PPeL1+IOu1d51NAsPxBgmwSBBsgGygYBEgMIK8kARIFmtsNhusd+HX2OvxTMbWyHqMpJZaaqnV6he7m83ms/h+k1WsqvxxzpfnkjMEGk1W/X73d+/93fu9537P95zbTXBMk2U4U8RCaScwC0kj7xzxdiVMbsEm1SVslHZi6Cni6jQGNjUi03jdv9/DTJjPoLkV5nYC05b89kXi9ARZ/eewCfAS1n690zlCQdaNgcCM/70FfKUVmspWllRmm0RoeEMVMqNAE5SfOGAoOuC3vC/+JUFKf+btV1RdDbM2FS79wAv/irf9Jf+/5BUSn3UWs76aMPCrEzkxzhMJHu4m76QLO1ww46Rqv4cZlt4BsFzZ81Co2W3ni/DmrvP7L3vHXAC+9DUT5tYnINMPH63ZonOLyDDfYkXyEVTW4zjBglfvr/yVP+tVH/Dm3cWKeuLd0Ql8WoHLPZCTk/IF788cNil+hC1+siB+L2un6fYQIWwtNkA+mbeu/4wwRGR8tGPzbsSfPdTjFX7K/3+ErQprlqOpCds51WuQ6+Agx8LKAwsoegOzS2b91Si6XWICyWmlz09VFgpv1nwrenNTAJVkU1SDpGtiTlKQlT9Fu3phWwuh/oLDcl8ZqFKwicNOMTJLhIVXIU4MEYGdaubEL9Y5rLsTkKQKCVEH4lBStQPJNa3JtbJ0BfqpXE4crPie1HpXBJp4GtVTK586t4UIG75AYNSz2Bx7SgloKthA/ZjITXgXQ6b/Bktm0wj12XBonMKAqowZZBXCEaYX00UMVH3WT+RZ14AeBCoVaP0xZB9a4MTWMjSOekXz2KCWtrVEiFBrhOPqG9gWtBsD6l/xh5WwWbSHqRekCbwE3buQW7NBKNx6xftMWSwHsI1DP7ZmHSfyPQ9jOPOivy9tWXe8z1vL9n0eeLZgOXLHTkJ3EyxtWXe3Z4CiRRDze1ge0ide2EMfBGP+wH+IURDS2N3BrON/iPGtV72zz2A65iKxoK34C+vErEBfZBnCEC6HmXmjGNppxjZ7Zyxj/I1ecuGaRehNrxmY/hhzht32NbBiANJ7EUOOFzAnxuYEdP0Oltw+B5enzWm549d9wEGIZPV9KDwFGyUbTwvYeJMVuOFDA+JIyAnMQChgp0wd24HcNlQnYeE2tE7A9AfQPusXd3s/9PhLm6ibI66A8e3itV6B9jetmh94Uz7AhtYFr3oTZidMAv3bkNmG+iPLZXFn1w5Y+aRs148Dn9WsjJsblv/kXy+ZYfBH2PpWwsbcChHIIdzZ4jD+NHLYmBQNkMYqyDJNaQopLyQw2MEsVhl74nzTAA/R7WIW9NlPc9aVk+80FFM6RMOpGXfqicBO5WiphavCxHeU/R2lVIEIdVXkqPcQDpPkojrUMXJQpBI7BXtokUhXLFESqru2bAL1LiIopJWIGjuGAecxYGQLsiPYm71MpFf7KyK91l3gJah/FzIvQG7aMCBHJItf8/64TUj8mjFju0BIk7Sd/A42YMcIB0K7P3IJ2K1A19ew4IElKGo1XCRQUjyCkmZ0YJYVBMn3InHY3rtY3oTvY7N4ggPudG451H5ZIhm6pLrz/kgZOmuERKhCUBjNGFiIz38AXLlizszZdeirmwQv5y+8tQ/atfce85f+GgZc6jC5rEeJOGGZaDPY6jpIiHAlB1DI8hYhFP+Wv883MS9+DkOGEmEWlbzfFrEd0gVsdXoP47CveWOfxWQz/Dwc64cPHsL34cEbZtWNjRh90XyO2M2Mc3BEE30twE3Y+cyA/hl/Z3/i1x8HTsHb78PJdnhv2cb5fe+aFe+uK95Uvf5tIkXmMeCsm69NQ+YwGt+GH2zAy0WYeAKdg943T4iTC85jiH4JW7jGsYn7gwjmmMGME42NXcweGPTX9dhf04c1G/83q3E687zXddV/n8KG5A1sqP4Z4caZxdaLNPZBOzntiGtEyLdSC6QOT+30hCOtxLl6TYSFL9WR5J/pMyVIkHUtYE1p1FRNoe80F1JhQ/bIfeKWy9gO8VV5KlMKIQVWAbUiVCBS42nOCO1Tk14rgQA5XT3E66SVFcgWkmsFwlJdKCgkd+Q+DYpUtjKKDYJuL1Mv4sfYfHsFyJzARkUVe5uf85uOY9bBp8DTkLkO9SkLsvrmeXh/ySivDLFtGsEG1xg2CPewQQdxUq+oNDkwT2PlvE2oXE53+5dtUFzDAEr7/Cw20rXajRBixnvA3/XGX/aH3SPkHrf8AV1eGV/6O7KwsW5tGCayvTV7PduJxU8DcR8DBSkzZB1f9T5+qRUyZTtJpLxu37d/GXK+NQcvpjMAACAASURBVFhaguJ5v+nnsIGkiLopDARa/fu/T6THnMIG3nEs4OMv/OX+ubdxDZux5+05BwEWFwnJgUyqu95vaxiyfR/jrv8G8yl8ijm5rnk/TwK/g3E8d4E/rRhI/9E71iE7tuMYbYb1KXPEseF1yGFbja9iFnn1HmTXbDD8KYY8v+/11vbqJpyYhifLoeibJdaNNgwzd7F5KUEIRN6H3ro1r/ui1f+1qvs6K9YVjUvYAnQKQ/zfxZQiexjK/RYB1M9AYQ1aVw1w72OLdNVv/8Tfv54tpdMb/jq/g21aHhCUR927dcnvacPG1YLfu+htW0nK0xzaJQ7iXSNO3pDjP+t1UdBZqkGW/yvFETn8jlrR8ovruzSyTliWWtu6X1xyJfk9pSz0TxK8BrBsb3vEaRb6SUlnWb7aLsgET68T6qvBEmunFVeFRFuIBxKoQ6x6JI1LQVn6QD1HTscyh9UgfcSBo43Yy27EXnwee5kDwM4c5Dcs2IObwDKs/BCaZ+2hS4tm1W1NQ2MW8nUoL1kd3sIw7j5x4vQINgd3CInQAoEtzxHEfj8GZkVs4PVgE2xiB7pftgKqTyA7h1l38mbMcNibcpU45eSxt2PAK6SAigLGq85g4cGLmBlVsE4p7sBYAaple98XMbagkeAjNS9FWV8moqWaiNPuZ4B7ZeuHi9h6d1rOx7NQ/sQtHO1D9zGQyvmDVggpWq+/8H5M9rHj9X/DC+72l/oF7+B5zHq95u19iIG3+OMqBtA7/mIWvFEl4N/HtMsfYsigXMtTRP7WaS9zyOt4Y95Mq+NEar0uKNzBaBP18yvWds56m+4AI0V4qxIh1RUMsXKw8xeQPwG7U9aUQQzEtvx1vowt/I+x9fpDInhhgTj9+WmvenUaPqmG463k14/0eF2u+kv8vvfTBsYtSw++DmSNQlnGNlwVIruhgFIqoz0iLPqL2Np2BxuS4/6qF/36kndxFvPL9HqdRW0WiEBM5VJR3u0NIvubFiZhD4TOeDP5TI62GodzT8hJl4oS5O/UQtfMYQlbanCmfrYqNhdSwzSHWf2KrxDNAeEny2WdshCQpWS2ONkUdEUrpPIOWcdqoFaFVFGR0hca25KriFtp9OdoayFZmxx7KlMcklamdAvQgA1GcTyyigc5rKZSRsYh4PQQ1JchM2Q7yLYOzLRdheJvwM0fwMioVeBG2UBTu4qC94lI+U6/9Syx7WklrP4ebFBCiArGsTJfOAmrazB8BqbfhvYVyHYTUqQbBF/s6gROYVFpL3Nw+CoPMUdlL4aWY17BNzDrehazCJ/DZsw0Bhj9cG88joLq8b476X0lGeGqV0m78DZCKTOJxTVoxzsDXD4J9ZvweA86z1u+jcwTzPqa9I74Ora67fsLXMBWghe97s96u7cILddlYqUfIiLsBM7ve8UK2OwYx9DkfyEaKdNqDUtCf5Y4RG6dWCiK3hm/4tdtYAP6IiHKfQYD9ONeh48wUM55Xc8RARIXgN+rmMX8z4jDGY8DUzC7aMoGCTyWiM1NzYu655/d9ypc8Gr0YnSCdpSn/N2dzsIP6pEp8TLQWvQX9mNC3HvaC3gGA+Rfhuq/hWwnTM3A8Tx8vxZBPqIPTmNDa8SLEsVyC5uvD5M6Ke/Lrg8BpQHot+YfYIGMO4UhD/jnUkmIApUUNqU1UxWl8ElGnrBExqW4XgiwFOXRSiRNSmVzKa0r4cP+kTJE5e0nz9CCoB/VBUy++qrGtLIdyTsofgYCUFK9Xyvh90it2J+mstDvJJ+JHlSHNHE4bryCzc31pNx065FSJakou0wclQMhLtBqdAwb9yexAfV4w7OgrducvbZnwvb7dei96dv2VZgpRza0R9jcWsLmvurR6eUerHhEDtc2r/M6Nnnq2PzXQra+ZhNqZtnKmKxAdyuujSPSaIHNzNPYhDlDqCi+ioFSHuNAT3nFvkGEA6/5/9N+3zIHiWiHeyH/MKIzS8TpIa3EmWticwaI46jWCEXZr3XA/T3fNa1BZze07UB23ZLqFDuIFfWUv5DrXlAnhhr/sbdvDEN3hQZOYWbXb2NcTx7Lpfx9fykDRNCIOwz5awyprmAZ0Rd9IAz6y5vGQpc7/KX8mDCRZr38a9giseDPWMQ47x4MHbMYbXLe2/EPMGTp99//0jtoHEOnb2LgfhnjtbPYKczjwZ8uYLsogZgHrLHszVnFWLZN/74HG+uXvNltxA4mX7emPo1h7R7QkvG67GF0l0T07xJSmTche9z64vo8DNTMNlhInjeBjfFH2DC97bc3ENashu4zRA4OqXQ2vPmKBM1i407SNm37lRdnE8OCdcIHo9S2LcQpPhA+JmGQDDrt1AXKbURk8g4hrU0NvqN+MS0GKQMgRVga0i1M1e5eGKfFRj6wXK9byK1EFq9UgpaivigLVUwVTiUf+lHFtBqITpBYPV3ZUomdyhMApxE3deLIGa1aolC0QmrbppwMx4jY/hY8uQ2GW7eJkwnWCcnOBjFGhXXvYvSHfEQLRPimAFV5a6YxDLmHzU1NDEkFewhL/QSmZhNlIyPqRBa6x2DnCeSXvREDmEndQ5x3k/PKanR5QnO+iZkeaxht8V1sJt4j9IQ3/OGKYnjdOmltMiRqi/7oTQwzJel7TFj4Q14FZaArAIt7EVw3hDU+65FzxR2/sUKc665kJ3W/4b/2Qlf92j3MOhX9soKtakoc8j9iVrTMsU+Jo5LrXpF+fwGK3Xa9Zv1tn2iydMesXuUfmjqBGSKh8BAG4iVCyzeIgf9H3p//zjtIUqAJbLF5mSBFf4hZ04vY6vYerI9DIQ/r2/bxfa/OI3/UtlejDvwjbPw+559PELmPT3vzPueP6cWSW+VegIEp6OiHXNkCUupzkBn0d/EeEfkDB9uh6iqML8OxZVirWZnjXu4ucXiLdoByVG8TgRnLhINrDqPe7/h160RCrgHiTD1Z3jLaUjWX1HjbRI4JGXaSOyuQLKURUv5YLIDwSEAqdUUqnasTu3+VnQay1ZPvUutahppAXbv0evIM9csOkGuEV+GwpXnUsk3/lxcyRf2UK1ZZjclnrRiQiZpQheT9FPiWk3vTMvW5rHVRGFplBNSiWPSsBiLbmhwED/y+7xOe1i7sJX1A5EVtwAbGA2ywa3X+Q8x4yvjnNWwA3CLE8BkMlEcJB4Q441YMF6uYkbeBWZQb3q4Z75OdOnRnobwN+R3I9BAhWo1EVEDFH7JKaNAuYBbhZf9snrAsv+z3KW665Nd9B/Pw34OOQbi7ZotVE8YdC9vkU1R+ngVCQvu3vRozxNpwDmgqmnIk30Mkbv66d/RlIpnuADaSrxKr8W/6Cx71tmz7vfcwa1fen6v+AnNEdjgRh9/3QbFEbF/WMVDPQ6YLG5hf9+tvAnuwuwirq9DSh4Ftyev/55i1fNbLKWEg/CzwP2NoVcVAuNvvXcFSuTYD/w+BXB8Az8DcNWvK2rb1n3YoeHGj2Fjd8FvVzxMY9pe9+5YJwClh/HIXljPj+hSc+Vvet58HWiEjv8Q8XF+DDg+eqa56GUvQ1gPf3YbGmm0udnxcKFpQhlQrEenZgK2BJ7x4ZTvr9vqcJEK+1wmdcJZgoraJg43LRE4piQkkdRN1ogVBz1P/wWENcOp3Er+r32VQQOBQGkOR4pOAVsZUPfk9FSukjjxdVyTo1gqBgbkmTy4k0zz1EqpgrQbS/UodUU++S/ni7JEyFAwiDloyE4jVJUOko9PKlJLl6gQIWkIWtgh5bQeUB1yLhqgMWRBV4qw/17uzgIFOEwamG5j1sUhE1i1jmNaPAdDT2GS5TaScPON1krNS6QHbMEyQlOZvYfN1wb/7GQIjTmAnPHMKGtcgI/Oogjmqev3BV4ilvYJZWmAIOuoNvU9sJ8YwoFrHVpWL3lkClC6r/NKndmr1eiUGfg9mUD/AsHCQOHZdiVwKRNrhPQwMWjEwLnZAdRayu37jGCYvXLR2suwFzGNbkftY4t4pL/SrjfB61X7fwtDnws9A7qGVMeVtb8T2xI+9rG9g7v9fIhJVb2PWoEzO+8Ssdycnw9C46sq2JcLRKJnOWQ50jvU/cEnxY4wr+BvMynQHH3f8JX8ZA+cZ4rSQKvAuPK7CpZPwYM1AdhzD6ilCfdRJHIk060X2+b8Nfy8SmHzDm/hNL+NCK4z9fSLl22teQAtm+Z+1KLx9LMFR7hjMLfqB4NuBD12YxS6l4Amvm+hBGVbHvUvbiIxz+0SeII2pOwTTI7/SmnevqA451s7533PJtRsELSJnepbIrFoknI5STQggFdQhK1nWN4RAQddv++cC9LRcGZmZ5N5GDtO36Y/kv5Xkueq7XDtx6rSQWiCX/hO6p04+jnwnUNY9qT65lcjcn1ZQzxJXJGVCOblGcjA9IyXB1agtIuG0tjaiD2XVS9qqxWHenytLepuIwstg4COKtQcD4VXiXLHbRACKjLFV73A5+pQBrpk4YBIvs0isvtr5DmOD/VENjpegVoVsA7YP3cAm/SwGygv+0GX/bACz0rax1eRDLBXk6xyk4jyQwf0StopcJ9zaTg8U70FpO3Sta15P0bNTSR/KQDxOhF7vEuIJJVJr3bP8BTznz7lFnAc04De0Y/tsSQh+5J3zDPAvqqY0mfaXNAT8ycOwsj8ljnjaJUy1u5ip9idEguq7/pKagBHYugONDbCzYPmA5xbM2VWe90CljPfXW9iKO259vz4HhQ3I/CqGIM0YGP8q4WB5h8jLuup1/NA6aq4Ee+uwX4XjWXh91bARjKq+aK+DJW+CnF0KvBwmjt+SumLEX+cT//9MAepVaCnD6kfQLNS5COU3jLqQR7a0bc+arVu+kW3vpkkMx5/B1pMpf+YKQQeeIPTBEgBJmiY+t0gEnv6cl/OSv8oqNnSl5Cz4748xf/MkQY/IxdBGaHhlKAq8ZcBpEylDUT4q+aGEV8IhzeUmwmkowMTvFSUKgX+pXDc1VmXsCgvrR+5LcS6DJxdSlEpqUqcrhpxpskqPgu5R7lj6ZXVUM0EjSJaiLYSeuZeUnUYFHnUQpuZ/2kA1Xp5QbUvk68oRxwkVsAFWw174CuY7Eje8jPm/lrDd6yP/e9ZfhMKjZd1LIznkz+ry+m1hAyjv94pjlT76P8xDrWZCgn7MWsgTu3o5Lj6rQeMiNK3CbskzhY0TIsssZq4/IAD6JS/oD72DLhNKAQlAb2Db9Gv+wCbviClo2YfmegS+lDGA+BhbG0r+2DYMLEpEUvsSNkBHsQnqqWwpjvjLeRoD5GFspg4R2evBQPkt77RzwIXfhtEPoed5GJkN5cPLwGgf3NiK1aDDK1T3lzlKrLb3iTyML1hFqx+4GqYD6juQ27HzAqeX7fMbVchXYe42VOvQ8hCuV2Co1fhe6hY53TLHgdlX/gPIfQ6jKJ6G9bcsx/XkTWj+AHIVeH078uy+g50EUsFA6ToRCiz64rSPhbveRIFVPweKNF7A1t0WbNPRDAy0QusXINMEzZpwl4A3IDcKE1Nwaxs6t80yF5ht+uv5K3/GIraOfEIkEVQU/5oPwzphqTZ5vZXcq8fL6cXYsXte/hPMEHkRW68GsKHai+0afx34Yg+c245+uOjPe8JhML7kQ0b4IEDdJyhM0RFZgtJIpbaysLXLF6+sz1R2qpI4SvWqzJRfVtBaajHXf8rfuVZ4VZ0GsaqIF0rFzSkxrQanJr62NCLFdY8CNqTMSC1pWbJp41R2U/J9huBntUrpO3HR6d/piiQLu8uvUZ4GORyWCKpDwS/CLFnHcvpJTD6HDURF/olLTrn4TSIXqxQsY4SE9U4tPLsC7B5s0nUSqpaxk9DkCozO57CZ+FUMbORlvEfk+tQ+9wPi0LNtbOTf9ocU/V7F3q5hW+hTwC3jrrXtG8QWoUlCniTZX+pMGcMWrz7//ToGMHKaTK9BnwaXXsgYcQpnKzYj72MW52+egOU1WPnQrinNxn5XSFTciixIK96+u96ZAmMlW1GGJAm/WyG7As1VWNm2xWa3GvmcpZWd8256pghv73l642Nwcwl6dny8rcPeNORnIdePLXZA/barJNasKkM9MLll68w4Ab4b3i1ven//mvd3vxVzoHJcI0Kl73kXgo3xZWIneBujLXJyarZ7H0/Zg0tbdqxX5z701qwup7wLr/iQWffnVbENiKJfV4j17mv+fFECGX++5rf8zyN+7xWv5+UiLFfgpVEYGYS/WDSA7iHitHqAtgLwCgyV4Iqrdt4kdtqaw3ls8ZJ4QE6yAjH/RWEoW4IwroVg/fTZUbpUwNnnfaK/RYfKsJTzXoox4aUAW/WrJ9+ltMdBciEBr7hXFZpaqPo9XWXSyBWBr8x3ycxqBOimlITuk5pDv6crUI0Igc4TWZjSVS0VeItz0mKQOg61sAik1RZJXKSbVI4ZAe0qNmHc93FgAeT8d/nU1KkVIiOXnj+GWTDnvT3fAM6PwsAgtC0ardeIWcnnsb/3sUl3shMyZ+HhDPTNWQUzJSLFlSJhtr0AaXqaMfAe9srM+79tv69E5BEWUP4V8NuQe91SIr5fNiCAOGJdOQTWiUGp7Jdz/tkGkSmzBlw4D31pmGUfhjY3sf1uDTN/LvvDOoGuNZvJm8CZn4f6eMjUpDVqOgGba4b84rXO+Mt6DUO2EoYCKzA5AR1XYOVtC4PfqsBi3VJJfB0DyHEv6gOCCp4BFiq2eO8Ar23Ye17BtvGrGA1/twrvbcBHFXinYu+v5n11H3iybff8BXGy8yRxEEoV6+93MADsxLC91/u2z68/TpwK3Y7VRe6AYQxcHwInR2DnDuSb/UbnkZrqkM3A1l4kfDvh/3/m3ThP7CA3ia35JBH5Os1BUOnBOQCKBXgJw/+fIc78E72yX7G198NV+PFisGbr3sbnsna2X0sPB4R05ir0PrA/G7H7ZQSVCUt81f/uJrhg/S/FWJ2gF0RBpLtySfR0j6gQCQRS7ClyOEucyukg4iuEC7pH2JmqNRpxpx7ESSFCbYGYrFSBolYSAbd4EFnLAmZ5MRsICUiBAEVVjKSy4nG0Om7596JOtpPyFB+wR6w6EKoPSWZEhYh7bk1eiLYQDcSqOULwdGuEQF1ihgbC251qqBWMUsUmEdgg6/VnPI9xcCPAhQJkXPhYvw/ZLJTqcYxSH2YxNxFpQ+ce2d+rQGfRkw+VMXPtEjbbBzHEGPLOmiWOThEnotA5ScY+xkZ4M0YjvICl+H0Rlm5ae54Qx8/LGln3x2myKmuoDF0xJAPe1/0NMLEEHVuQOU1E1D3nF69hxOJNL/hpoOVLpkXrumrhkz3PQLYFllZ8P3wC7k1a/dv6oLplHf5L5+APlqJfRGovQMdxmPyxdcdDYvEQbdeP4fkjDOhyRBKoJW9TiZBZKj9RA7YAzRAxJW0YMF7HgEuAteFjYde7oeDvWEq+97DFodnv6yJSO3cSjixFej7ANhRnCItOsS6VJRiQLlUBNBuQbYb6lsezjEJx1cBzkjhVp4xtNnLeNo2DVu+HIqG41A6vSEjkr3sdn/h3x5Oyb3m/tvjvG8SpYz3YTqpch2ZtF4aB1yDfAYV1e+6u95nwQMaRjD/Ro7uE0z+N/sXrK7+4FrajPrCUJz4qYqgn/x8VRgjEtWCkumaBMATVWsYSbL0q7pYjBepHW1OpJWScyOrMH7lf9EKFAFNZwPo8dQSmK4Y+k9UK4bGsEVv/BsKK1yqmTpSFrE4RDSILWvfjv0u6AwYqe8S2TCufdn6rRG7zNDBmKylDq+MgBlD/BLj8m9C9BMUvEN68Xcj8MnAZ2u/Aei1OL7iHYdUa0JmDxYpN8DJ2FmCjrEEhXpmDbfLKBDSPYQjSBfUPIVMhiGmNknexWT5CeCH+EjMJl+xsvs158/3J4SmnSye2na9gk+OCt3nTq1XEdgVb3p8jF+HJDPRfBdZh5wHk9zC++M/9RdYJz+ZII8xOwOQGPJywwm/Nww9W/MTO5+HaHQPcZ1+B2c9c8Az84yUrpxMzX9cIMv8eNFTsvV3zur3l3aA0GG9gVuotf02bRPYvWarSy16xLj4oa8L7aAUDSgHzvt/biVX5A2weDXH4zLo5bFy94q9njcOh0FvE+XjNXo9Wwvcx4M8VcNcxP8Wn2/Bkz3JX7FXhetlA9FLWXu4HVV/X/Jn/jlA8zHrZM4QzK0fkZ5ExVfCxoSyIigjMep0VWn2aANDz/swPiNPNe4DdOgxdMcon8xADgdtADZZ3IpBsiDitXdrkXQIod4j0ltrZSwssTJKgoZpco7+FLamvLMWTdDdfJOS5wrxycn09+b0puS/dxedyHqmnH1VCFUhBMnfkOlnTqSRNDdxO7oNYXaSKEIinK4ysXzVSK5k6QmCu50pCp1VM5H0avKLtiuqhqGNdq78VdikOqCW5v5HIZSxZnuqpF1skaJU9wtL6R0BDHkPmz2EmxlewEyQeYLxtyb7/cDF4txEM/B8Aa5XDA2B1B9bq0L4HmVVsVkzC+h4UjkFtAxoXsD3jqjl0GMQs0mlMc/f7mEnQiw10rwNPMKu5CZY+huNDFsmoQd7sl69iUtYn3nedhARY/P9dbOL9AvDxjIHd8gwM7cGjMhyT670Hs9wzmLKiDThXtZu/h5mLPVnL0dvldX5z1lDyDFB5DP1fgtwXYeYz4xyOE8c1FYkk9CXY2DZOVOOi5P29RXD6iz5eNBZOEQqafR8DfdgamMdzkGCGvdJs7xG55h9i4H0NE4vcI4yJIsEjv+hl3yIs0ar/r7wsncSOboY4T+9LGLA9jTngnsI2RQ3+bNEarYS1/1HdVB7SmJcwy3aboCakftr0Nndi4KnFqtX7SVGsg/79sF/b4K/ibPKdaC/tLpqx8SKN8XEgMw/N7nxlwTp2cjMcyD+XhbN1a3fB34VyW3T4+5PxJ3+mdrly/MvYlPGWSt/ECAiDRNMo8CQNYFNwmWin1ImX0rCye1JcI3l2rtUtZF0gizK9OAW0VIkh61h6PlmI5eTzlNgWpyqPZ9pwVVIdklrp9eQ+1UsdrbLUMXA4hFoOQVEWKS+uBUaWtjyy4ojScOsCYVlrGyQnYkrF1LBJrUHywrex4IZp7Kic/5fIt+Be+blr0HoM3luyl/3An6n4gjpmqTwg0gzuY3h6qw4dM5D/RWi8DRXnNgtPeeU+xWbGLQx1RjHQ+8+x2aAtQSeGOhsYmTgLxSHgAjTfDz3yNpGvf5E4hVgWswzzU4RhugW8XLAj7EezUN6Dvn4MEaaJkMqnMPriC1h+jn4OTmrhk7qtAApt/gz4eiNUqtBzznpl/R1Dhde9He94hcetjOo1c97dwfB51h8vgEudxVUimGGDsFBXCIt3AVtjPyUMin4MbC8T0WmyeN/36yeJxf8yNl4XvNtL2LhsIhaHBv/8Eja+FomcEUoKVifC2lNJd5ra4D5GxWhRlRuh2cvbIvIuPSJSAtz19zpBKBUkXNG7l4Fz0p8piXMHh3e472LWsuZVl7+DKSIr2xIGyLPAiXYoT0Du78D6NegrmnRvBGj8FdPpZ0/bTm7Ty1VQq6jRPX5S1SUMkCS2yOF8Fvnk/tQKlntGBqlwqpbcXyewT4ZUlViA68lnCrDV83LHXYec8r+qRAOHgVHWpKRd4l4kFaklZQhc9cBCch1HGnNU/lFNfpeFsJ+URVJOqrxQPQWMol+0RXG55UFnQiwswiRtN1oIrkhArfvlAOzgJz2pY16nvwP8cj/wn3lnScaVw0bbDOZAq0HrGrw7b9bDLWzi3cEGpjjObcJHd54IOBn0emXvQuYyTJc8F86Cd9gLXqFve6HdHOxJq+9AtkJ4EBeJGXwJG+EnoHDPgkSc9uYGBjw3iEMiZRVP+vvYxCbNHmadPam61V/3pPQK9Jck47z//q0huLFhpveeF3rfO0Ia5H/jdZ6s+hl45+0AtwVsZr+GrQgKKXwI9QnIFuyWJb+sBbtFkdsb3lXSXZ/w64rYIjRNUAJlb18/EW0sfbr4yA57NO1+z5e9OeveZOniLxLjcAAbW5/H4k/UTaNez0cEJTjm5Y76393YOtXgdRF4yMrVTlaBFXXvYi30i95dM8SOL4MN1ztE4qxOAnDFsWvnoMXgPBEIVSfyV5QJ63gbeDYPH9Qirei3CE7/E+Diohtzt01mWN2G4lVofNU76JhVqL8Z1ko2P4rY8FklsED0QYY46km7YvmaBMbCPlGsWQJn5ENrTMqEwz41MYhVwpJOnYPSK+t+fZ/Bs70pMu4oryvpRkPy9wZBU2j1VsPSCunffvLdUcAXKKriEEEVAl6R9CLu1VFSaqhD0mCS1MGYrkJbxITRQiBdvCJytJWpJ8/RirdFgHvque3DXrLqcAb47QJGGZwmTO2/xEbML3GQTGbpPmxWQxst7lGW2LA/V97k45jlg7dlsBvWdmChDu0lk8U1zJlmNteHzdARwhOLV/o5yD4hOJo88F96vXYx9MligQ6tUNuKk4tPYoCrFA8txIDtIDSqk9g6IItQ73cfqG1Dc56D07y54oV8tGFm4zdegU8eW67gFowLfocwB+eA330evjsLV/4j2HvdBmiTd9aqd+InwC9AxjXItTWzDuWI1Bb7KjbGmrHJfIqInKxjRvs4ATo7fv+E//0bRDI+bTZuETSXcvVKIi4ljii3bgz0s8QiMIatNzIGFGxxFRtOq16PB959HxHZDStELpFNIpxa47mQfI7XXW4JzQVlLN0kDk0R8CwTBtA5QmmhjY9AS1rlYe/PXow56vTnbdUCIJcIuuEBNo+qwMmnoD4NmW7IdkD1rilEDgbcf2EPmn7L+vsdYtFS/4tyVW5vAaYWJgGzQLecfJfu2oVxKjvFNu2U1YeichqSsvNJebKS95PfD45w0s9RS1OAqh9RC7J2VZju1f9STqSVgNjaHRVc674CAT6iFaTYUKdJ2aG5qfR26liVq7qqg2XJHuWVxFeLdkg7XYvVrj9POgiDEAAAIABJREFU8h6tvL3YmJCF2IzldLj4n2L700HgfVj/36HQB+uzULgF009gbs22sVIjncYGYhMRPLKPYU+O0KEqjfAysL5j1/djgNE157zeGWxEDPnF3yJMmklsb7pIzJppDLwUWvjY/y9ao++XDIA3sQVhi5D2tRHAJSug1ZteIBxO0nifANqy3mDNkieYWfUctud/+7E97DaGNu9hZwaNYHvpk8Ans/APsvDJjywbU/NxO5/uQ3/gbcyKugFvLlo2vVnMIv3M391VrF09mMBDPPG2f173x814u9XeXgyMP5+MHQHkL3v5GW9aN6FCbMMAd94/GyMCkxYJtdMAAVKXvFyF6r+UhfG63acAhHv+qj7xuucIJ2IjkVVwBRvfkqo/8fZ1EVJGSTyzxAnSFX9v3YQRcxybQ2Kd5JetEgEqikzVBqzg189426W03PK2fI04yXrVX/N+Cdp/k4NsfdlL/hLLwH/CAYqOjsPOorXxCeHI0664SAR0Qcg4BdZaHLUgiUpIDUcIw02xC6lyIsvh9BAp6Ka7erEGokCFQQe5LHRDCmgCNZJCZQ2nZrcsyRT5a0kZKk+yNjkNMxzOgpauNl0cJsXVEWkdSO6RwFrUwVGtoNqiySbnnNoneZ+erY4U95wKzoVTcJibavXO7gU+34olPPe8EYU9YBXmd6DjGMytm7V5ioh6W8Aszjb/XQNom6AtMhg+1YkUDpewCT1ERNb1XcJmUANxHt3nsMi9fWAa5rZgfgO6RaFIutKDzabbHGQgb9sJGQ/+tXTIcxj9+4Q4LLXf+3fO+7Hf29mLR3/XYa8E3dL6SSsoT9J3sVH9r7FESRMY6lwnuKg14HN1KzT/T+3Ckz8L9XvWIW9CfQEye6Zp1ST6iODDO4iMdtsYjjd4+zy1h87lpMvf8WMiJcScV0cBFFcwF0G/v5tz/k4WCc5XMtElzM86gAFtBwcCFy541z/fAxMexp73979bjwMQxr1OolHaiYyGCsaQI63kz1vw+r/g9bnuZSuSdZiw7LSQaCc048/ROGsjtNADRJi0Aj8nsWGI91t7Ut4KBs5anL6OgXCX/38Re7VFoPlFv+ErmDFxCRM4H/fOrtj73pyxttwkkgGJqhDNIH+XuG4BsbBD/+AwdZH6vcSLC7iVK0o7Z5V/1FjVLkR0hXDsgPpohVdltWaSCnCkESmopVazTHeR/eJnU164mpRbP3JdapHrd92fOuFI6iCaRJ0j8FX5epaoDlkd2s0WkueQlJm+iDKxQ8gSnHIjYQ1DJL4f9utGgf+qgCkZXsdm5114+x58umMWzKzrKC96fS5hg3UBo0vH/Nl3iOPVitgkkEzvITah7hEOnMuEpXHsd4hZVMG8jNf8ISswPWWToAk7QLPlnP1Rv2PSX3lttsrQ+DLMPbB5oBw7H3rbx7HBOOGPEgsyhQH2Ocy4bcUAZ46wjM5jnHLzRQJh/ts+eGMLpRWduAFte/BGBfqXIP+Kt+F3vRP6veN5Df5sD5buWWf8PrAJmTo8KEeAxhxBB2Uw3BZQa1KJj61gltoeBmaj2PvrIvISaVclSai2qQ+xNaYdc/o1Ybuhn8PSVyqxewEDD9Fmy95PLRiYPdq28ke9vveIs273vOmKrhOAtvuY2SMEJmrDNkEdXMeA/BQGgIPYmn3Mh4tyFOcJqVaLX3uJSCC0iv1op5j172SJZohTyb+PLRS3vS0PsF2K5voA8OwZO9RgqBnaW11psY9vrTDkfp5IFypHVgNk3rIU04v+XFGs2ilLWbVH0DnalUuhJUCFwDrtyiEwjaSNqn9K06ZYBIcp3RRrU+M110QkqJcsTYoCyb1STbIs1CrB9eY4HPAhLkVWpSQlejmqmLYJqXIjtWThsIpCW9509UqBWNpEjtRpP+loCcElVxMnrFj8VKy9j720avK5nj9ArMC92CQ4jWHFhSp038S2359B/YYBlxxJJcIZ1OXPEk2jLaKyzT3xz6cJyQ6EJV0ltmS7wDMFWK2a5plebIa3JYV+YI1d3Qr2YgBoXPf38zkMIfzlVKuQn4J3qjbhihjfOIpN6C8QRm3dHyfPe6pbl/d9yN/tcz32vmYq0P1NkowzW1bfaXtQccqec9GrdeIeZhnd9s77tXPwZMmQEox4/CccROpMPgnlihw6f8/resPrJQeWaIwckRfiyxiNLgOiC8uJf9HbegwDtDLBEZ8hVDsrXi2N31vYgiS6J4eBRwexzS1jzjvlsXiaiHh/yuvwABtHx4icU22YbO6et+MpgiufJbTTOo5LvhEpIb6ArWU72AJ7HgPbIja+L3ndTmMLspzcylGxjrFKjwh1orT68jnIyhY+pE7/KnYWI4Ow9RE0XvVO6fUb+zB+Q+GKWa/U0/55BTbecKOHw/EIEHNEBqKMLjnMU2w5TtBX8JPGojAyNRYryXd638JVfZ760VIaQ/XLtcKr+eRChSBre6QVBQLxhewivlNLM7WuU5qjndCyqiz9S2kNrT4p7ZBar2q0tpGiD9JVS1sLrewNSdmK9tML0XME9uniJBlLgYj6USSdBAAt2MItB8C38ai2MpSvQ9MM/HNs0LYT6Qg3iFzoD4iTdc9ik1D6UlmdHYSn+qJfO07wdJewib1YtboNVaD6OpbusoLtgf+Vd8CY5V2YICZpYw0KJ2DlOtytGzdNFdpHrHE1j+oawqzeHWxrXfH2LHidzvt7nkjeP4Q3fhqbP9e2YaQC/UVsBp8gwrzV+Sdg6zV7j4vA1SEsEf2Yf/DrWMLePJHV7b/nwGV/57r112PvP+VfmsFoiy5s4t0gIs9kLSsA6AO/RpRBCxb2PkHsZKR9/bLfv0RY2Be9fz71z3b9b23pp7y87xGKmj1sp3TM3+s0oRv+ntetBPx73uw9bFEQoCsmRtT8BHGico0I0HiFiI5r9Pd62+s5gPHjt4lTwz7n9+346ykRVrKi857yNmkMSw88RVAdHURiQkXa9nt9GvftwY0veWe3YOb/GKE7f4KtHnVsBfr/OND4t56A127Y7rLZ+62ZcBymQCrsaSQOvRBYS6Sk8dDs7RAGCq9Iflfglq4TZdGQXFflMGAfrUuuxQFZyC+QSp1m1aRQ6Wxl2aaSuNTxBqGjzBIWqExzNVKA35CUdXQFEQckCzxHJHTPElwmBGCnjj+FT2v3XuWwF1gBDVqEcsk/gXSqDtPOQeHSikiTw62IjZfXgH+JTbxFbADf9usy2Pi67+VvYsB6Pgsf1yMs9iqxFewgOOz7xPmfrcTRdIPqox1oeRFD7V3gE6g+gc09eDRt90gjrJj7jUXocy1iA9DfAz+cg+Nb9jxREe8SJ6RMYpOx7mWtE5b8MJFXoI8YeHKQrgGFCrQ2eINextBRQuc1KHwLOt6E/s9h3tL3/CG/inXSFmGi/ynGG9+F+9Nw/kX4YCoy0j0k9KkdmDX4kKAoFrF1YQYDlwlvg1QPL2MWbgcGLLJ4tMDKYh4mVHtzBN/cilnkVwmA/wW/76T341N+fS8GJrXk71VCNdRHJCL6VWw8ncaAeZ5INvQeRmVVvBylS1VEnsb2Ge/GF/199WBjeZDIwVLw9kuz20GE9wvMdjBKYprIOT7lz8wQ1ECb97cWeFnwLSLvf9E78X/ym0UKN2Pbsw7g14bguxtxOs4fA38BC2Vr5ybhxJOOXkFpaWCXQtGPyth2iMVIatBGwsiQVS+82uGwI09UqXbxEJSGqCDhJvjurNM5ZAFZumWXhagVQeCsAiDAWuQ2BOiKwJYlLKpDq0YqHTvqTEy5l+Yj36kzZPylDsgakb1JAn5ZuFoJdX+j/y75HhwOHJGlXsdWfAH5HjZAy5gFo767SrAEtzHOcA3DmE0Mc3LYOOviIPjowAnTAtyohz72JcyyOolZIso8N+3lrBEWdiv2ch9hoPAEmJqC7TL0lKE67w6TIvS0QWbH2q/MbVqIqzULb/5sxk6vkKW3C1wtmh75EobzK96H4unESR/372Qd5b3Oe15nOfZO+vd9NexBX8T2wte8sHcx0H3RO+kR8E8xx853MY5+2Sv/I2zSdkPtsYHZ/JQBiMb0M5gkqoJ9v+jfXfV+PcPhrf0pDi84D/1aZfFrx6y6uxhofYyNwTVivPwH/pwacWKMJ5o7UBf2EnKvccJBfIyYI9/2d/qp179EpCxZ9LbN+Psser0rfk0bNr6/k4fNmv1dwjZNJSLVdo2g0E4DF7Iw0mJ+hJ4O0wGPlK1u1whnpqjJHMEfz2Bj4QI2FlL/zCZxiHjO7x8GjosDGcQG2dcxHukiJhl9CQuzL/hD2AhrR97XY/DP3rdnKnhFxhgc5rV3/J+UEhIbpLxumTDOUp+XjDdhm4zK1HDV93DYCtY9whP9VHEOuUasBOoTSTNkZWaSAo866VJeWN+n6gxZdakqQ6uEgF/WOIQFmj5D3xWxgXzUyagO0IovFUDaaMl1xF8rk6PKUifJWlY7OzAwK2CLsxQPx70upzCrpI5hRg+2JZ4kJkiFyBQnyzuPjaE6RlVME6kTuwn+dQSbiDvYBMhh4/AMEdklQf4gcTBpya9frQRuNVagcBkmZ+BkAWar1pZub2MOuDFjwDOJAdcjb0NDJXJsLGPg0ef9d4w4Qm4Hm5AFIqJsGwOHLiJRTbd/15mH0qrxf62rREq1Z4H/E/gqkUf5EfBH2Cr0jBf6x/Z+73wPth8bMEr6dML76To2rrSFrxCOp7oXs4YBxT4xgZXVUwtJJ2FtrXp5ivZTzoTz/v8sZqF2EcfujWN9mPF+aPHrapgjTTTcln++TmQaFe1X8t+fST4vEKdyKFXsCcIiXQbOt8HNPav7S379MKEOOEacxH4Mc4jm+6DlWSts6w60vwRtk9bWN7HxMoSNlQZsnKxhVvkVbCxrviwSIdoXsTH8PKHO6Sj6gPh7/r6f95elbdUUNuBOYPq4CnDla7AxAc8PQXYDHsO1122u3SP08dqhC9PgsCQ2pWJTClY4mF4vuVpKwabqjMbkM/1IIFHlMEhDnC7UAHHIqSyk1Esos1ugdRSEVWhqtqfAit8jflUh1qmTLF1F9NwUbFOeWdZC6gRsSL6HcDZuHPlcdZUVra2+ImhktUviljrQxC/VCc5Lz68QuskNzOrT1lBe+gdEzEIFGxxd2KHK97EJ/3mvwzI2Ae96vb6MTcynCRF/GRtwg4Qm+Cw2Tj8iDlEdIwIIRK+UgdUZOJ231Jo92ACbIjz5Ev3P+DOeIqKxTmEAJJZgMnmWlCIFIg1nt3/eSaQn1nt+7H15pm5qi0EgI0F2L3Ecxrg34ktY1hsR2LNecfdq5Wasf4YxIFSU5xgBbhUvXhnUZgnnzg6heLnqfSCr/tv+TgaxLfYKBioLhJ+khUji/xyGJXKcVjFr/dt+TRUD1gbgG1kYOwZvbcepN5cxENskTr2BOKdWsTKigmrYAiMq6YWnYLsU8rKvA4/2Iny32eu+RNAoT7Dx8AWgZQTyDV7o88A1aDwJLJrj6d+sW9lSV2X99Rzz528SJ4Y0YRuf49gYGsTmUy9wsggjX4COWX/OKOHNbMXUSv83ZtbvEinixH+MTPjWb8MkHG/Bn9y2ti0QWCIME4csJ5vwIg2vxvtIgXGpQ08GpHAsm5Sj74UpKfbsJ2VIjaEfGVl1sENOu4m4dKkmUkBMpRpaPY6GMacx3VpZUppDYF9Lykw5aHWAJqtohtTiTsFZP7o/5XNS9UQNszql4VX9pVuWw0+dqXof1TOnC464InWsJoVoETk5wLCi3f+X17YbG7jXCAfHBBHhpsxxStZSwYB7FXfYJW2U1S/VmKgPOSPvJ21T+d3ARi2CAGS5Do/AR+tW1ue8fdrSniyYHK63AB9VbVK9RQw0cWwCQdEWUispX8gGMS5GvX1TdQPBCrBUh50SZMehsIitVCKv97BZ9gSzml/HLOZH1mGfLRolcd/77ok/9ya2da4RnPlDYpztYlb009hOJOPvQ05V8YlN3o83vX1PYWB7gdCivuP9e8arpfVF8sUFLE3HKAaSm1hFFrbhShYK9djNdBJ63xaCj1W4dxsGpBuE6EALdEvJPhfPXQQGR+HGapwP2cbhnL3Su28AwxITV7yznuIg7dvOvC3MOSLfSsXLvOP1vO7PvkfQLPIbtGDY+2wPlNcgpzj7JsJ0Fm/3EPjHjfBeNYjxK17ZfgzpG73w/8Fe8kd3bTwp2Y+MAGGWcEHgmUbTpXNe2CIDDWLO634FxFQ4bFweZRNSAza1nNPFdh936ikwI3XiKT2czPOUq80mfzcSGZD0kzZAPwKslEs5qm9WI+XA0+TWM3McTt8pKkDhuOoMLQAQOuk0HFvtkYNPi80+4YiUJC/lv3VNjlBw6GVWsYk4T8TSD2PbtFVC96qIqE5sAs8Q1skZ4jST1Js7goH6eWxCfub3bBFWfo8/YxubN2eJjGFNmN8jdWQ2EBJBJZcprVu7ZOVPA1/tgIU9WKt6IpuqceIlb9+mP6tE5DqZxSxEpc3YIPjtHWyCnMMoYmUf28Xm1yBhHLTKUzgPHIPpv4T2AmaqPjJJW8cG0Ac37odefBcL0LuAAcRlrM4KxjiFjY8L/pyfJc5PLBHpJgWK3/F+ksTvOeIg7+PYbmDYP2v272f9ffck72cIw5cW4BfzMFOLYMUiMFOHwRFYW4dn+u1UD0Xh9Xl/ThPKmjl/xqD6y9/bF4G+K7A3H2c3Dl6EO/ftXa0RTsZd4oDuF0ahb9XzVGhSTxJawA27+MZDe2+T2GI7SeS/6MB2CJve96J37mD4WiI4+tZtP3VnzCvyDKGlfJ7I2tRetZU7iy0MGcxncNE7ZwJzWv9v8PZd0yEvEzQjBBCKCVBMgrA8dbwp6ENYlWJfii37fu86EdukLARpFJ8WA0X96nkSHOinAcidglc1AWQ9Kjm7QFQAlf5UOcz1puR0KuXQj4AMDtMeUjXUku/ENwvARZinSX7kNFQYcwq86vzUot5LOihLKEX6kk4rcNhChViQROqLfpFT5xSRA0HX3sMG/DjhlZW0roVI4yegvY+B4CRxkEaXfyZOu5M42OMUca6aFglZyApzFSdW9Dqs+ncCxMt5eKdmbU0XgYvYvNvCKNzGFmjNQVvZytcknPP7RPNp2/oYUyOME5ngCsSClCeAUjSQlA4lb+O0379Qh9wGvLsBazMeir0P07twc8P65k4d3l+1OlewcfuIcJ42EdGTx7E5vuLfd2Hg9dDHwgBxFqycQg1ENjZlbtvEwDXjdYUI5Ljgf8tXoMCJ04QSpQ7kagbgvUDzz/uEbgO6oXcedl2Ove7vcI7IE7LudTvuY2Ubw6sXvP0VYLgKH26HZrlv0e6R1S+rTu+jFdhdjQRK9Q3I7WKDdJyDWPKJcT+CbATq62HIfkj4UCqEIdLudZfEb584OFeRfPslKKzCzk0onvPGvY+B73f9wglsYP13/pA7fvOPsJX9T+H/qJpmfJYYwztE7orUASeDUVRoE3F4apWfPGiZ5HrtOERTtBK7LwhjNt3t1wgaQ2AvvE0XhFwBS7+pSjdw2KGmlSG9UQ9QwQJFWb0y31XZSnIdhPMttT61zSZ5dhphk/I1WiQk5pajT6Cdbh1SSkJb/BqH8+zouwKHuSGBdMphS02gbGtPE9vtLmwsaeu7j1lOkszt+jUdGDiexcDgS9jgfc7v+xSjNGYwo0Ee+z1sgK8S4a+niFMc5pPyl7CJs4yBfIbYUeSAnVpwlC1+7eU8NLZBdwWGTnmwSBusr7hU6CJUF22wtxGOJ7BJfBNbzO8TulmpSTQQh4lDTh5xWPnS6WUM+P0KtpghOOd39+Kw7Qq2C1jyvxeIcaE6yRdUx3jlrxLKqbr3T7uXcx4zvjOEv1CyrNP+jFXgS3n4pBbW0Of92d8qQFvV+kCOqkZMFDLrffCdHqhsBygtASd7/cFfBi5AJgf5fchsGebc8frOElJtWebaYv8GZpk2ex0btw2s6xg4PiEC2taJrKv7ROZG5YPe837P16C+avx+wxas1mCoG5pzcGcRLnRD84713UMil8cysWgsEUmKpCrKEPk77vk1E/6+th9DRx4bzKte8IK/IHlKz0H5ryF3F+bm4H9dhh9UTfUog2STw4FuELv/1BgUQNeTe1LghMA1ccpSmgkbBfrCicqR+1Ppm7C04cj3wtZcwSP1xKcqhFOAmhLVsj5TMxtiNTjKt2YJXlMgmCVWZ91b5yeldAJrlaPGyApWJJ62FIoOVFsa+UnFh7iy9IWkDkJFvKkOqQJkL7lX9NVlzDI5QcTFv4z5FtoxC1cZvTRx5glpnSz0z7AJs+3ln8JA9QqRTlCJem4RhyovY4Ax7p/tEk4hJQZfxABU1qKcTLL+FFSzAIy59Gzlvk20lX14vBWOywkHY4H4Iy93hUjfqMW4iG3px4ngiB5sPl3GAOFp4rSLLWLQlrx+a17HSUKtUvP2ZIg8EpoQmxwER7JIOD9biJPH14k8xye9//+M0HJrJ7Hu9RvFuP4rRdNMDwDv1uBr3VDbsfIVpluvQq8P1jXgC1dgfN4WhJexIJPStvVFA4Y1Q0DXFOS/4ZV8yyvaA1szQS8tYwv1M97vw/6epTsf8LaOEbrrCULydRED9kEfB6Kb5ghd8ja28LSNQvtJKM3bOCpmjdvcL5tCh3UbHzd3bB3ZxoB1lTi1Q4um5tU6ISHTTkG+uftej0f+e3cJ5peh/gD21qH+2AJG6tfgxyWofGzP+7/qFhOyljxfslThmcBTEl5pqDNJ/fS/qAbRo9q5yygUZqVG2oFly2HAVaAZXq6s5hTbBMIdRJh6rsV1yPqR5jQNFtEk58hDZC2n+j04LCErclg9kTYsVVukP6mSQjSCOjXlcfS8VFytrYa2KLKWVEc5oeTM0YomSzqVtWSI8N+UO5IYfooIxZS1/j62De0mrIR2YmHIEqct5LyMM4R+uRkD+FGM9+z0ct8jZG/3iXDPhwQvqFVXEUYN/uxFjH7YSNrTgW1Pu/3+XaB929re7Fubpi0DmHLdJvcFL1c5H+76804T+W2VIlUc3ba3b5sIUJB++QxhWbUSyodjBJ1Y8bLG/X16rqMDlYI48in//KbXccT7S1IraWXXvPyK98esXz+MAfI05tDs93Z+H5OIrVTseXf8/sWdA0r1YIGuAh2XIffrMLxuDRtYh8tZ2K/Ds/0wug+tVQPXEe+LEnak1doEdLfC+gQUTsLeVGRNkzqnQARUKWAo1YTLMPhR8m6lCZaBNIAt+heKMF6BX8vCp3UD7SyQcydIpQrFUaivmPql0GedWJ+OXdIIZplD0F8CvGbi0G/NQ1nweSJDqjBiwd/jA//8exjQrgF/uGfv9jXMSPkukYXvY4Ii2sTmy14yTurEGZuKntPnoh5k/Qq3NFdTaie1aOEw3qVYpt2bLGsZpdq9ydciA0zWNkCuLQFkOfF0UbrtT/+GiBPXKpLKPNKKFjicmF6OQFmgaZlwOPFP6qVUJ6S0h+5Vp2lLnnLest4hnIYqX44zOQtlBYuT1svRc+vYJJUVKIlMCzZoBjCLawvbhnVgYJXK51awvvw6kSxoHXtRnxIRxFPYRFN4bjNBMXQl/+YImZssVXnawUDvHpFXV5KfFa+3pEldwPGsVWZyGzpGTIdKHsp7BpzKd/tdDIyf9+dDhMTKCXmLSBo3SGhkZWF/0etVJjS6ktOViCAaKRmUk7eAAeY+ZuGWiB2Iggw6/HMB2Ib//SJmkX7Jv5vCxsybyfvpIhaBJmwRGe0xy3YK+GYHjO9Zu8awRWPFrz2bhfK8c68zcGcehkbh/RU4OeSN2IfdmgHJLGE9SUNeaIDCKWAHCj8LH35q9ZnyNq4QGdjkMLtAOInlJNRu8FOMcqkT0u3hHvjhNuxXrB966i6by1tg0M0NGOiBB1vQtQoNI1Bag5YdWF+CpjN2KOp0PaiifYxHzhBpY8vEznqPyLIIkSJU80wgKm30TaLdI9h4u+H3v4eNcW31ZRVL7wxhpJWJY6HgMDWZGpVSYcl6lURXxmNKK6QUqaxiGa0y+lIfmvBGQC1jRZY1hEosV4RX9YCjTrc2DluScNga1KDQSpCCsjpKAKgHavVU445a14oMExi2EicPqEGpyJqk06Q1TME/9ZamUjqIhUbt1wIiZ0ydyHYHwcnq2WpPD3GagvpAUYK3vK1LXuYxf+YkNoCaMbC8i1kz+0TmLUnSrmEH797xNj7CJkEHBgrpdvB5DCSkWGoneD155ceIo3ykBCkCww1wo2IgvrcE+VNQn7K2D3s7Hns7nsF211vY1h6vdw9G44Dhj7hv+YfU52UM7OaJhfeCl7+DLUzHiGTww17GRUL7vEeEJZe9v14gTgQ56XXYI4JZRFcpf5K4VcmZThEnhLR6Oxq248j7/J5RIqex3cNAHS4NQZ/PxlzVKzEKPX1w72N45imzgGfL0PE83JwJbl+cpIJJOlqgPGVSsPKNCL+W5nyYWCxewfxZogWmiURKmlsnvA9q/t4zQHHbnlvC1DqPfZwUatB0Ffpn4MmWZ+XrgAcLPmbrTs8tW0TpmPd/HlO1LJMcrEoYERUi6lCBKsIAzTOBah1bqJQn5RGWLOghthgtERpnGV7K2FYhaBAIjfQWkW63we9V3hKBpZzvojzFD8vShTAKdb0oEfmh1G5x1hrrcJg1SLlp3afn5FrgVQhaQfyfwE8VSa1nWa+5pCBZlrI6dU2doAn0HBHnKUhqEdgjdLWiCpQ5C35SgZHSG6p3yu8Uk2tTJYVefp7ge1QPPVeLxh6xqmnbB5EYXyCqhNgaLNoeryZtUNpfRUfJYbVFHKfTQgRUPPJ6/bWX/wADvT1CuzxNRBG+jU2UUb9PTpUFLG/CbcJalXyqC1s4TtZgdAg+2YDjz9nNtQ0Dr9EheLhhbWrFJMDT3gd3iAVKNEgNGxNdBL+s8OlO74+/wiZTaq1+njidpYWQLn2oekXdAAAgAElEQVTR+6wXU0WVMMtPKoZXvI2fEZGKZcyaVjyBN+kgh0A3keR9F1sQH2BgLAfYR95nW9iCMpe89446FHuITETnsJXwLGaGT8PCXThWioNPO+csN/MukWVOu4OL/VaRXBcsrUHbFVibt3tve//uEEpAbZPzRCi/DKF+/30IOJmHrhq0fw1aTsDy44iqvE9EUnYCSzP2uXKQtP4SdE/Bzh6cK0KmCLN7Zq1KjSIues3rt0lIO9exOaMoUOXA0VwU9w9hEArUlIoUDqsz1OY0YlI0qyctZNefJUWTKDw57WXMCcek29cuSVG8P01dlkvKEE4IzFNDFA6rLLLJvUrhKr+aMCuXh1e1/dbKImtR6J9+V8dexBYB0gJIbQdSKzTNQ6GHikeB2I6r8aIXZFmnAA8B7Ol2QT9ylB2NnNGL0NYp1T9r4REQH6xUBBCnVEwLEWK+nDxHC0wjNsAWMSBawkB5iaBvtHWrYZbJFmF9S1GRJmOSpCbVPK76O2n3OskppcVMbcpjAL2HWRoK127EJu0MNqmuelkrG3CmaCea5Deg8e/C5qewtGHPU3DJDAH6z2Jj5mNiwdGE0y6i0fvjGDapihh+lTHFQCdB10x6GUqwdJwI5mjGwKlILHKXiWxz2qo2Y9ioBaCHCOiY9zbPY9SRpHFZYoFS4MVZ4uw7RVg+4+VsAc1NmFn9eUywLI/4u7DyPVus8ljAyItD8MN1e59XvH1twJDz9J07UN2D7JehuAjrjw34bhGLvMKilStE0i5REseJpDhF788TL0HmMWT6YOfHdgRhcR1aa1bGLjDWCvlGaG2ETKPJHNvPeKG98MQ9ti01U+isEIbEQ3/Pkg13EwaGDCmNSzica0YGj5zleQ6YnQMgVfSnLFHJVLMEmB/1ZYl3Vx2Ut0XBHOJxdwi+W6HQ5aRM4Q3EXMwc+Wz/yPepn0sGYvqT8seiyGSV59rdQk4dZGqsgEsXS82wmjw05Yv1mbZ6ItXVWSSfq/FqRCrG1kRLzXxZ4eJr9KLTegoABFAKh0y3TyozpVvkbJQj8yi/LDmgtiqS8ai+itaRdKiS/K3FTVphbR8FsEo2oyiwHi9PO449DCwLmBVyDLMALxKL3QwRGZjDJkgTkQPjEeFEW0naOYdZzbOEc6YMbFTs7wLGYT6dh4e1OHliHQNKTcR7yfuSlSsecYnIq9tPyP96icX7MqZAuMvhDFmdGCBqOz9AnAM3hnHsZwjueRLjnDWG9X5TqdIDIknTWcI7LofqltdHkZVXiO3/aWxsSWPdDHRc8AYrAcyAV+BfWeDHLW9nHpjYsEVgBLPkpXqgDv1ZYAg7K27SOmp7LXZQ7YTDdssfcwnbETUTocj3vE8G/F1fAApbUNuGbCPk/7Y3cBWyF6B5GDp6oTTpwSADQAaybt3cLEHDoo2R2xjHrKCXHWyRVzzCI6/jE/9MqhhRnE1ExDOEIadFXJJbYU4nkTdon6AcUspReKOMbRscxg/5jdI4Ao0HhXXLkNNuXFiQUrX+mg7RoSlXnPqsUkr2qMEoWkN/y2Eo4/GAshDoqRCZ7xDRYLnkRllnsgxVKEnHpd+lVjPJ76IqSBoor6TAUQEEWiFTpYdeohaDDIdXxXxyj7aImmBpxJqsXi0+WiD0P4QFrfLSdJ6bGFiKilCfbRPbOq3IGoC7hJ65jIGEQnv1klM6pA0DrU0vc8LLKBOnQihyUuL8FYxLlXf+KQyINdgVYbWCTehLBCDuYYBR9w64jlls94mMbSvY/J7wug5inHee0M5uebmz3qY+b/9xr/sc4as47v3fhy08NYJi6cIcc0tYDuFt4nw66bjlO+vCgO+S99kXvK+PE4l4nvL7pry+nRjo9hFOHoDjPbC+DafyMF6zHcGpp6Bj2F/KV4HfImQv/xZ4HroeWNs+87KXiTE3QJycMQAUTwA9UJ6ApbKdtzjofTPv9dB7bvTHDBOOTilILhPpOnuArvP2ZfY3vDNGMDpF1tVNYBxaujiQdKw/gYY9Wxz622B/xxZrKSG2/R28TaTxnE7eYwabU9pNCdzqBFevUHRZ/vvE7lZ+FhlZsqBT+qCKjdsCQRVK9qldrp4nh6+ohJSySAFSAR4C9J+2A0+NSP0u7FHgGRz2XeneNABO1IYigPG65ZoSlYUekAKoUFyRlCmVkErIBHgpx5JaxrJEVPGU1xWR3pg0JqUVUo+m6qpnpuU1JOWqLTvJPdpCiD/VgrFDvHh5htWx6ij1gxwHeumy6E9jk0R116IwgA2YHuLli1fSICkQIdXaNot/TRcKbevGCEt3H5tneWzCKxmN+rKFSEQE4d1/hoOIZOoY+HVi4DRGqErkVMtjk1KSuWGvy6C3+2e8XFn3ePltBMWiyXnBn6PAizFCgvQ0kTpBVtU2Zqm+iFlhihdQ2soKBkRK86hwbgXQXMYsVSkyNFZ2CWAY877v9z6b9j4ayENp08dnLYKSWscwjuVpr8itpLFZ2PpjaCzAfDVOHrpMjPkebBE77e+kdw1y+yZ7atmB7hb4m3K8l9te99Pet6PEKTRS1gx7NZ7GxtTQK0RIpwjdF/yBJWy70GkvpTxtYMA6NFRd+dML1ZLtXo4ROxUIrbZyk2g3lyOy1yl4SHSmF39AVQg/NC6k99/09yNjRXMzVSdoTstxLGoSwvI+qm5IOVxRpwJXCFFAGsmnuapdr/BDi6B+hGHCPRmHaZ1SH1sDQVXo+yrEmXokldVWOKULNHCPkuECTVmKctpBaPsaj9wnCzddgVKwEp0gKzsVaFeTz6tJefKaarVSp7QnHSHvp/IdCDyl39SWpk6slrK+5ZFVXwmk9bIV2ioOWXz1LjZR5ggpkqRmCljQYnQCA6ICNvi7/Xl9RCqBrxDKghEvZ4OIzBMXtuT36ZSRVkIJkiE0v9oi9mB62yqRe6KKTfS7xK5l0z/rwABFzqMp/6zZfy94/bSoXEre3xSRs6KfcGRqcVIuIcnKLhDKkDZsS77q9ZUippPgikeIU6FHiHEpWqdCBFAsYeN0gThwu9ef0QjcrMFYP3S0WvRcoWrA373hNw1g6P2cV/oxUIJGN2+Pl2F4CKob1nfyTUx5vb/UD91bNpbe34bZLajW4dNypICe9EfsEA7iR/5+54jjvL7iv/cBI2dg4iPoXMdWgs8IuUEP5lF9Khqfq0B9EzIjkK1CUxmWN2Lx6sPA9GNsh3QfG4cZIuHeBmEoyJkHgRH7BKiKCkytZ7E+kmNql5laseJ7ZRzK0KsQOCBKRAZhL8Hp7hMne6S7ax0+IRwTC5CyBunvMkTTxSEVBTRxWH0Bh+lVfS6cUn1zLR6pJ9TW9lmgmjrltLIJmNRJqdRDMisBg8BaFc8m5aUWsn60GmrFSbcGBX4SFGVZbSTf6/N8Um5qJafckSxB1SkVjQsgtHpnsUGiRUAWeSoXzBDbUlnw2lIJ8NWHUg1otR7w/stgu+A1IkvYY//8BkHFKHlLI7GVHME4vKex3agCH8rYTlVyp7KX+Qpx0ogWjfNZaG6Gkm8TlBegz8sc9jIUsFElkuhsElTqKHGS9jIGrFIqaBG4Q2zJpcgYxABnOblOQHTR72/zvtBuZJSw1s5iYKz8xI+IYJav+nOUz6LsZY0SYb23vK5VjK64vgCTWzDSA7ktOHYS6vOQOeaN/Tq2guaISIgSrD90oNmIgBVxwsf838KWn7hxBlqWw1Esad6y94004Evep2XMKbrlZcqqnPPPTgH5HWg8RSTA2ML4q3cIzkqe6CnIPMuB7jQzACtLMc7GieOc7hHONO1CGgiKSSAqvNA82iXkZSmIydCTkSPqQvNS3K6wQ2M+3T2LXtR4SKWsCjwpJ8+EyFMia1r1TDEm/akQdGLlp1ynnbm+b+QwYGunK6xp57D0dp2EQxayp5ypHiJALSffKQhDP7XkmubkHpWR/sskDRfwp0oMbc9lHQuQyxwGdd2bAr7aooaKNNciI0pFlrss45RqgfDCilaoJPdpEUlVKOJolSheC1ZP0geycvXylZRIVOQVwqP/AJsvL2OSL+lpxUXuEY4uqTY6sEk7R/CIFcIB+D0MlO/+/4S9eZClaXaX9+S9uVTlUvu+dC3dXb1PL5rpmdGMZoQ0kjzCICSBMIsMGDvAYeMAh23CNna07cDGBMYR2BHCGMIGhMMYGSwhEEKDNNJoRj2anrX3Wrq69jVrycrKrFxuXv9x3qfOud/U2DciIzPvvd/7vevvnPM7y0eA/FFCcTL64fih0Kj6PwyD9wO4llufW+IZY2QFsUNkIozVrHaTdMDbxPk/QWpYED4wN/9SG/tR4mB/dAq+PQhh8am2Dq8TFdnOE2Cwt83NIVqKMhkFsZUstv8Ds7BzNfBnlrAgPEhWpztI0B9WdVxsfVkmBNuVpVbkH3jsfksVvws7pgiP6B7C6yqp/2xr8KswNQbnV2F/D5aGWXryNBEvfpcA/i8Bu27lfc62udtCPoZIR9oiSTedJp+Ft4dYr9XWZm85cHibCHiiNfAMwXvoefpRIv94ktg4N2D+Kmyez3rRmwgwvsLoE1gOkCGXq2SRKrMLx0lBqnIuGOp8l0t13/vSF7NOatF9EoAFMc+hQAxJMUAqYFIEU+RZXCXpSs+99GxlCFRWxTd536pMqlxWBc/7i2taxeKK9z9OOvv7M0RxoaqOQ6rTlUuuIPygvF+lhN5LD6hAqVSrIF1jiY3Hq9SECz9OAErllZ0UAbrmitex2JaT4Pf1DM+W66vkFtQdwwbpFNSUMWTrbrumApMhgNfLuK0BcYd8hPwkAVo+SeEIATz7CbA9Qz65w5C9ceKQLrT7bGufD9t3BXza34+RWU4LZLyomv8CYcJvnoX+Xrj+BswdgrWFcNzMEkD0461dgf6tti47SWfOeQLk+gTQ3yMA9InWhnTKB6TJeJ/UFi4OQmhsLX09Xsb1LJHObIjg7TafT5B77WS7/+bVVhuBALGTBLOwi3wu3iRp0SyTuNojhNoqIRDPtf7vaZ/PDGDmQzKE4BoBbl/gYXjJ4GJLphlmVqURMlfbPJqRdra1f7etvRE718hQyf1tDc8Re8OU89tEltzjZBTQWLvHY6tw6TZsOU9IsBtE8Y5Pton4OWAGVt+HG8sRCjf9BIzdDqfezWG0eYqkAnyCylJbF+dQS0bLSI1YIK6mupq0lB2kv0cLcEjSi1JoG4z6f1QYPW+ed/lZz7HRFZ5frVfvQ/vb78FoASLxRCpS+qVSGManQyqW/g1psangee2Akm0qZWGYBuW3g6nRBQKeGq78sId7jeRzHbx55JCmhNyOi2ByxqDcU9rBCa6Owq5GPFX+FogF8OrRpIynarmVItF7q8AwhK5Otv1UK4DR1Mu9pBamZqxneEhyVs8QG+woscBnSAA6TGqnrsk7rW8H2v9mwOmgWiY0zqfJhIGvE4dlNwFCO4gn4Jxr7z1GaFvHmlh/cB82L6RzZAv5HLWLhCZpxICOufOE5nudTMzYSgDoHKFJWXzIMqTPk07DTe1zudseWSXOTLwDZOKGxdX7be72T8Cep2D+Zoz56Qn46kaA/ywBxs+3e5wi3ofgob9G0Bwn21xsIQ7Xq23eVsmnfpxq93ylqW3LZ2DiSFuYl9ri/jbwAfSOwNjNANVtpJPnfBvXN9r7dymF5Qkg7bd5lC6SznqTfJjGtjYfLxNg/Rixv77Z7rUP2D1owmuc0Y4sEznI+2DhazC9Ncqssgpv34Q9DV3eGca8Xyhr6/pdIGteq2yYiGWRoSGpsEh7qgxY40IQrGcbslqbeKAFq7Kh30FO2WvkpgXgcUZ5Za1cQ8YrnigEYDSsTYvce3fxp0ZtCPLSL5vKZ5VDrpSJ9+nPwGsVfDz4dsCMmKqtDjvf87XOqHZavyNoaj64J9RWV8rfmgVVWlH+nir9EAQVFErGSqZX6SrFYMhcDeXzM3logULvsRLYuaj3Ugu2fOESGS/tGaga/QoBxgeIc2zcqNzmdQI0TpGa9ofEAVDjsKasiQIr7fMBYVoaM6xQukkcppcI8LFS2dHW18U12LEOZwfxTMndBF1widGkj7sEEEwRgLu1fVcKQU+6hYJWyYgCQ71+gsCtw61/x8nDcJoAwq8TjzzaRxzwmwRIbm3jPQQ8Pw2ra3B1AzbfjPKQX1+GmxtZ41aK5QKZnv6pNqeLBJi92+7xs8QTPYxLvUWUdaR9f0/r7/pGaI89YPY++YysRUKSvBST1t8Gi/cCVA+S1f7eIrTm9wlNfAtp1qrRzRPtW9b1DLFfBMS7bV2PEnvrdwmB+1Lr4912n5eAv78B1zbgylX4+h1Y+hDOz8Pp8zHPb67A82vwjWHM6zfXYG4Y97xMhpftJ/lqFe4+IUjvklaAZ3cPo1FMNekMRsNpjWCqAC1GSEt6rodljaQjDKnz+zV0FjKKQyyqfqU5UrGUVqTcs5uxJ7hKz3aZApW4GpCwUb4rhq2QtOoGLezNwQouciiQQOcFtY5DpSwEuSop/Eygq1lyvjQBdJp1+1JBWrO4crhd6bW7TESlR7znOHE4HZtcVOWZ+nw3twQJ6GPle1aPkupZIU0qpaOJNDXxZAdZ3rFHHMzzrc/vtba1hqVC3PyW9jxJAq2hcKZqz5BFl5bIehU/SIK5YT+r5MM5GUQ/vt7et8i4hXZ+k9SEpENuEJzoIvm4HgWZoPgZknY4RpjozxHa3J32nZME+L7SNtuQ9KavkFZBTRCaXoPZV2H7JZhonsXhCjy7C76zFNc/SYDKDwOHd8DcctzfvWLFtY8QNRlcK8ObLpJOPw+S678dmBFVLCAywcNSZN+6lof/els3ufQLbS+caPvBJIgtZa62tb4b8iZ1aHnLpwhhOSCE24n2mX6ID8ja2YbQCdT+tobIWmvrvfa9g6T/4dvt3pZJnSf5YS0zS7p61jwD+oxqooUWrRFG66R27W+ppLqfxCHpUx33gukqGc9vtEOlSQZkFFc3SkyHfI0Xro7+Gnxgvwad71W/VsUpgyMUtgqVSq32gbHdDbcErYnOjZReNZxEbdFUx43yHYn17kvArR5QozPWyneUepVCoXyvvlf7VEHXMXTfr/+beqsXu7bl9wzR8TM3UY1XVKIqYSljVLJqnpm9tIksd/Bpwgz9GPGA5bvEZjIOcobY/AfLfY4TmvPHCLPXFNwN8qA+SZYCvdz+HwA/Rhw+HXDzhAYlDUr7voBnCNw9kiLYRgDBzxEY9NF234+1z36d1NwNy/t0a/dW+/sU+STnGiFzoM31NwiK4QEBUC9Mw+pSaNonpuHMUnz23Fa4fzfmzPnxAO98Gea/Gf+fIp/pt0Amk/xB8llwakHTJANxg8xI9Fw81d4/XPo33dZzos1bf6IN7iqc34ix98moiE3x0cNC9mttbb7R7i3YzTG6h3w9IKmpS60/L5BPjblHgP8FYu/Q2jxMcuMb7TsThCAXdGnt3Wt9M8Jvd/t50PpovYoLZW62kVEgkPSg+8EqhPWsesZ0dAlSKn8PyjVT5Vp9NbajFVyLpXVx4FF4YD/dH36n4pACQCFQ16Jqz1Wx7OJRj1w3P3c8D+OkZ+C1ymPI2aj1dSWE6rYUw5byt9EGNS260gs1GURAM3KiOt8kyKtpo3Spk0n5X+6mxlLXSA8Pit/Vw6l0kmqw305SNXlm2zVqwC7mtvK/C2Ub0jH2QWpklgDJ3e36r5D8ll7kw2R1uXnSaXON5ObWCQ3Pce0mAOdiu9/xdv+nCUCVE58gAGGV0eiODwkt62Lr33hr/zjpOd9LpMyqxfTJCmNfJWsLr7W+XCc0TMgMPzXDCwQoGJe8p/29nagjfHrYsr7WYq/tfwrOXM1U7MsrsH8KZoYwPox133EIbi5E7PD0PfjdtkmutXudbe0fJrPehq2/p0mQukRmMupEVZO9Q8T9fpHQyIyP1ScxsQHv3Iv+HwBOzMKN1Rj7XuLgzxIWxp02d+fa+wLo5tauYWyD1odPtX4dad/5sPXL0Ed9HZvIutumFq+3cS+QYWb9tp7Hif12s/XLym2Wlr1C7BkdqmahaoltkJUZpQzkeMfbdeIEJHe7s/O/zvQxEkuc2xrh0CfPnU75GrdctU/DcHVACuA6EeWVtX4rdimMq1PSz7XWvJcWZw0eUDgpYLS0rSNSWYWHYW92tDq21hgNJamJBE6sEsjrKkEtgBpw7ftyODrxqilQA7adNKWUi1tDSnxWm4slCNf7+Vvtu/K5TlLle/zMGGT7ZzsmVrgxjJxwTraSz+aaKteOtXndSRygV0mtaZLY3EYhzLS5mC/zukEcFiuWmZHnuNcJINxEHDrna4GM8hiQ3uGj5EY8R9KgZ8niOc+1Phld4HhdxzkCWIwFXyKA7POkpvwCwTVfa31/jgCAXa0/at5PklEDp4Bjm+GNtUz4mACW5uPv64SmfHkN9k4Bj8HNW9HW2QU49ucJ3mU7XL4b/f4k+SSSV9pcqrE8S9Z+MLxsM5mKvZu0XiwR+5uEQ+02AYa/RYDtNwkBSxvbReBLq3k2Fko7p0hAv0EInWsk5XSz9WO+9cGQt9tkHe2rJHD0yYRBw+z07m9p/blGOpdvt/cnynroO6hAqhWjpWSVPhNzVLRWyXNdI62k/3TcrbTPxQgjKaQppBxV5jzHjnPQed+43w1GFTeVqAqKtin2mMBRz7044v18+Z407HK5RgGiIlmFgn2rNKr4MUdRKGfgtSpV1klpohdSzbfG4Ap6AoJcMGUADlDzXYlTQ2AMd7Nd/9Z8qdJnklHAh5RGTryDrQtZPaKU9rucjwvpmGuYjKCoeWIO/rDc1wnWyedG8QkVE6SD7wiZVeYmukyA4X4yymIHaWYbdymf7v3niPXaSoDaE8QBNknARIi7wO8nH/EjSG8hQPQ9QrDovHqGiOFVI3dtdhIHdku710a71hjV2wQobSa0OetFqKncIsz+WQKcdz4Bg1tRynJsDWb6cGwffO1WjF2NTG7/3XaPK2vtsUFrsPtWExZTsGM/LP9LmBiDkzfg5elwWB6Zgt8cxNzLa0oJXCerivn/gKAQXMdlsvzjZQLUT5NFlJ4Afo0sPmXMttrszbZPjJo4RWYwPyBrgtTEh0tkUstsW8spgl7Y2trcQ4ZNydtvJp9MfbfNnxUaH5BlSfe1PlnzY0v7/hQJqPobbpJWXj3rayQV4qvSXPoxqhN+tt3jLqOKUQ1HgzxzWsiVcnA/VWDeYPQRcSp2Wvw1uED/lrhTQb6C9nTnM++vwPJMbpT3pCkMhRuU68RCyxyYR/CQslATVGI4MaK6Da4RC1idesut0fukFJSgrq+Nck2VEpCAJ/h536qZbpQ27Wu93zKj4Fmlkdc4QZsYFQxKKbXwbriNIW8uvptqB1k0SLJeAePEr5W/NR93EprVIeJAmtjwnfbeFbIAzoDY2DvJanEKMi2Uw62Nc+1eNwkAf6V95267Zj8BDE8RZvE32u9nCfrgNAEI28q4xghNXl5Th8iRNgcWmYHkqadJs1/T+EG7xxUyIWKOoCx2PgWbNWl+sA1oEeZuJaf8kTY/1t+4Sya0nCAdlesDOLkQGXaswtoa3Fxrz/MbRN8eB47ugB3LIfiOt7Fcaf2Zaj/fafN6ktTQbxFnQLPbOVEzmiIdXbeIc2Gq8AL5dJDvtLm6QRZemieTO3YS+3F3+9u062vtu3vb9btIi+UE+VSZ2+3vEyRFdbx9/0UiLn0XGRcLsS+Mxb3S1vEySYuppE2TT7vxzJiBWEPGdNYZhgZJ6UiVwKh27flaJyOVqvNObrhqtzV8zPbEB/1SnsnJ1t5qm2uxqlIO0peQGDNefmth1sgP+7NKOnwfpWHb5hqxtw0JlJ/uz7Qoi0pAO6g1Mj64R2y824zG8PbKd6tnslIRgluVLn7PBai8UDVJqvlhuJefV4njBFSJp9TtdX47eLV+PbhdIVClrgkkTrbJGY5NiSunqgPTwPghAaxPEIAgPfB2u/bXW3uHyDCjMZLHqxo3ZZ4OtO9b22aFLF5+j3w+mQf6cYIfnD0Ezz0GR9ZhZh+8fyeB+AGRDfxN4j2pqn3tfm+0/0+1viy3fm0nYy4N6+oTgDZGq93Q/n66F4XaD3+uXfBRQkot8PDxF1OHYOZx6F2CHbtgbAlemY6U7k+/CHuv5VrPvAibr6U5PLkUz4sT3KYITfI7tCd3L0e/5OQ/IISThaD+FUkB7Wif3yOshmsktfIkWb5UDdTQtpPkfrxECMwJwok7RlgN29v1h1u/rLZ3jQz9vNba0AHofjrf7mdNkA/a+j1LhkfuLetyrd3/GlknQ6vzBrmXavz3AiEjz5ERE4Y0+1ScBfJcQkZD+N5Y+W3k0LDMVwU7AdPzVynLGqus4lWDBWqAgfiwlXSy64yrzj3BXgCv/DOlr5XnrUJDC1thLD7Quc7XKqmt1+AJ79+fIwrUy2nIjxj2I5DVQOsqTVT/q0asiu/LAXjjXnm/Skq1YM0KB1zBtcb12VdIwFcqGudcAd0JV3Melraq5g7JZwncbny1YzeEvLDXSrVodjpXAwLYZohDLTe8jdBWDDX8kNBUBLkemYkn2BwkHXjX2333kZygfJ8C4UHrozGec8DKAmy9S3imrsGBbfDVu9H35wmwUPMzLftSa/9ya+dA+3+8fea4F8kSm272I+1nZ/t9ewi7n2mD2w78FGEzmwHx3xOq9xJs/UlY/i3Y9TJwH/auhGPvNnCw8chz1+LrcuHzrf9meu2egAsbGYUwRcZiT5IPbV1svw+SNMLFNpZ1stZGDcd7rP1/uY1/b+vHITJSQsfd9fa+kQ3G9woCp8i9r1W00ObZWh+Lbdr2tbYhnKbnib2lYvIKo09i/hGCwthLWmAX2j0+QtAgBwhriDKe8TafW9q4nmn3co9qcnuOqyNcWtOX509apQKT1pcKgJmxgqjXe8aqMldBtOKP568bA20/pktbq6UtX9XaFXBX/ckAACAASURBVAcVHlX5G5Z2tVzrvbaTeCHm6YSsln1/qmXq+YYSqZLagk2N36thIF16AkZj+cbJyk3d0BU7P14+r7xv1VTXSrs1gaOS8UrlDUYnBtLZRplYN0XlnI1TtkjSEqnpSmnYD693XqrUExTtxxRhQi4TYHqezPHXwbOntW8m32L7XFN7hQQ2Tdw5MjtO80xnngC7q332EvDpZ2CrdSvPwuot6C9FrV/TWa1HfKh9bRsBukfbPL5LPjXY58O918Z1mDSvTST44177UZj4gzBzBPj3CLXuP2+N7G8X/vvAf9pusjd+T/xx4Hfg5E3YNAxH3o62QU+txRzfbHO6lQCPx3fAls2wdQvcXgjNdInQSD/W+quZK4Vwm3BCfpt0hv0AAdA63bYRQH2ddOxIdYy1fpgQYwjhrraGx8nU5n1tTo63dbxHGAoWzz9MVgbc3cb1HunIfqa16RM2trb1eoZ8DNXm9v+AoKSMW7/cvnOKfGDu5db+AzJa4kkyjVuBf5fUej2zK2TtE6lAQUcskdLTGnVsas2mUQ/L9yjXC+S2tVHeg9SYITVO6Yle+dF/JHBXylRqUhysoXNSlvLx+huqxmz0CSTlOVbGrACo93NMQ4jym/DdBXP0gNpZc+QNGakexK5HtVITVlpSu+wx+sDBLqUgAG9mVFLRaVeJVMl6F0HTpqv1qk276F7jxqlCZLWMSe3VDeJ18oiOW0qlmkez5b1pkuQ3lGmdOAhLxMHxQO8lAGCs/b1Glrjc366xUMkq+Rw+42MvkpEaZjxtbm3tuQnTszwskdZvk39hI8Zxo/XfQvfrwI/tgneWsiLaVtJqOtCusQbDebI4+UZ7/9MT0P9+gpDe0SbjIBF8+3cIRP+ddtFfbZ9ph+4D/ln0ddsApp5qE78N3pyHl3swuwVOrYQG+JnWzOxOHsZ4bdxPkJ0lKBXjbm+QB3wXAVLbynrta2vyEiGE7hPAdYIAJykHw6rMrHPvuVd2tXY8jDpdF3n4oA7G23w9S4TUGZ/7PCEAfBbbC6QwMSvyBVL5uQt83xRsGmQ43+GyblIgJsXMtn4st3E80T439vgGWTlQDVwHouOuVp1gtFbuV8HLubHUZlXSpBIMDzS8rAYCDBmNuqoYUP1DVbGDDEP18VeD0q79qrgj/13ZA+/nudfntKm0MVnaFFutD63CVPM4PEv9HURxoUcNzM6qQdc4uuqkU3v1b0iQNi6yEvFSHC6KarsTVwFTKWaH68JV6sMxuGHUSpVyE53v1ZRsGPXOqj2r1T4o45tm1Bxx0mdJqdkjveICu5J1e/v/44TG9iGBPS+QJrMauH05QQDhTuJgSrU+3+5vOq8ZQNdIreLTwJ8htOun2703AZtXYOEm3F2LWNkbG3Hve8QBfJkAj48RTqDLS/H7DsllXibolZPEYd9DZoktE5rlvwZ8fhb4Lwha4meIk/1nWgMH2g1fIj10kwRw72nfeQwu/TZs+QPQO81IfNHej8DyBZiYg8efhBeuwZaPwuygdeoS8Htg8hbcvBdz/ATBg28jnTAmM8hjnmhzsUo+SWSNoJeOttsfIGTI/va5SRjrZP2N3W0YkwTAvNjWTH53B6EVbyOA8QfafdSC95G01CwZK2y503VCkD9Famzb2z2uDWIPHO/BheHDqqD8avstX36TVEr2kRrgHFnbY4wQsiZOVIXG2Hs1yZocAanY+b0hSYVJjVZOWJBS09S3YdswqoxVC5s271I51S9Ep+8qDIL8VPm+mr9tVGyplnsFcoXEsNOu1NOwtAfp9KzBE/2pBsh2pNISal/yvw7YCTHjpFaB81VBvXpT/V2laAWgsXK9HlInp8tlVw3E/trPLkg7xkrHeG3lt2sih9yT8yLPBOnZdQzVTHlAhlQp7beQ0t4aFWYGTZK8pZyYDjhjgIeEQvspRoP4d5He8tOtr2bn7SYqtH3kEKwvtLq7wJGt0N8Em/sBXJeGsel1HkHg2OPAU1NweRDa3JMTcH8jAPguSWdMks8GXCEO9QwBVD/2POHJ+xNtgi16cZsA3WmiwR1tkudaJ95qHfr9MRlbrpKBt6+2m2wHvgwTe4iiF+Owfgp6l6H3DOHRHIervwPX7sW+3t7mb5a0PG4SAkiF42lCe36awHRpHIHyZOv2y+RDbN8htT01p0Ok82xICLN5gtt1L5n2fqsN69223u+QiR+WWD5HnpHpNpWvtv8XWt8XCIGx1tbuW4Mo/XmBEDobrb05Ys9JR+1s768TdAzt/bm2jt9qy2H23vV2rZl3Osl0LPu/fptKP9Q8AB2FWrcVnH2N8d3crZ9XSqFSl9VJ3z3r9X8FgrQjne/0Sxvuj6pFdx15OhEdf00MGSeVQXHIWuHiWX8OXqsme4211ZlVM/l8r2rFSrwaLyjICWTr5Xpj79bLZ2q1DmAvmWxRNWw160pFVI7ZyVHTVpP1VcNgarif7ZgHL/hXiqSaXmrPVVK6CFIcAr+miXUdniTrEfj0gsfIoi0HSF7YbB45xXkCCK62tg8D/zdZT3WVAOYx4CeB3wf0FuHiMJ+RN7UP2AfXL8LqMLVm11G+8+Mttmrratzv1EZQvtOE5nyGrJOso2yOfJDnbuCFDeKEvwp86jNw5Vx04tCz8PqNlChPboF/sRIN7SIDYr9G7NjrBBcxT6DDxTbgj7fJ/Wos3OItmP40gYJHY1HO3cxiRBda/z9oTd4l+WFDCBcJB9g0YcG4/lJsh9v8fonQTu+2NZPrnyNk0C0y/vo4WWjHbMR7bV33tyk6TT715aPT8LW13G/vtf6bQLKLFKJbyHrJL7Yp2wTcGsTaXiVphS+3/r1FxCIvEsbJ22Shf0Mrpd1myCeW99vc9UnFw7MhX+s8SZUZOWFIoJauMbv+rwYqkJv/sFY+q34fnXGVBhEwa0is57hHWqtVSx4r36sKoeUQ1GQrjlDGPF6uf1D+F7PUfiGVQ9q190g8GQf60y0xpFII0+ViGOViHsXrQppLVZudIsBIgt/vC4hVYx4QG8usHzvqJlCqVvNE0NxcJsdFV7t30exfTWtUUrkpZglwgdRS64JLwDupgq7CoHqaTaIxcNwA/IPtM51ABofvbZ8rxQ8Sh7VHmP1LxGG5RCz6LSJU7leJDXOTwKC11uYzBKXx3M8An4M9t2HvvwNT78SAls/Ath2w+Wm4dzUFwCTwiR2wZxkm1oEZeH8pBMZOgtt8AjgxAe9uBDgdaH2/2cb5FAHGf/RVgqL4cQIcv30OXvl+mHwaVr8Kx56FzZ+GpfdgdiVQ5hvAz/4knHsvg0XHCTVyuQ1sjXyKZ79tgBeAD2HzPgKVDKA+DRvzMU+rbc7OtMt2kNy7e3WZGJOJLSZ59AjgfJ58lNLRNtcfJ3n9I8Se+joROni5tfMEQYO80tZwrq3vGvDiLhhfgsPPwM4HMLYaIXty86dJC+yd1s7Otm+OEWD8fW0dvh7Ly1lCGLzbPv+g/ba06CaiOtzTxFmrZXB9vuMqQXn9i/b+h23cRizUmhErrV1IbldHvmdDa1tnaaUPjOySNhyQKesCrc/O9J41ykHQVQPt4lTVVFUwpUdUCKtiJcdctXHj0+2f2CedK7bY5jqjNIiU4lj56QYc9GeIZ+rpwPG1Vr5UnW02IFBSOmzkQ5c6UPII/L6vyVjva+er51ErVjCu4LfWaUPuptIOSmyvr1Kwa9YoBMZIkNb7K8jL+VQvbBUSa2SIS43CmCE0miVyU2vinyE2/yZC+5wiAeNN4uD8InEQv0VqyB8QSqOOEYHmCPDHaE8zNtj5TR4u1sRjMLgAd68GEO8ii+AvLUefbgzgW0sxBzuBbbvg4lKAzNWNAIEPSa3Y0LhtwJ8Cxj5BqINvtInYA2y50Nztkh53Ye4TcPU9OLQD7i7DL72Xk+2pXCVU0p8iVcAbcTn3CKS5TNre0hvn4NbdGOMpMrPtBgGul8hKeBa6XyRBaUCkXG8An+vB28No1pC2dZIKMOJiiUxDvkxmq+1uQ9hLgLGWxNY1uLUB04dh9TxMNxPIBBML9vTa3mhy8iG3/AFJhR0jhP0qWThLBeJS+961tkceJ9Z5ltirz7V+LhACeJyM8hm2ObnYplta5j6pEXquBcQH5X+pLUMxK6j1yWSLGk+sVT7Rab866GpYqlaMeCTfrC9Gi7XijedZBUsaVsCuSqoWJGXMWu0r5b3KJlQLvfLc1cKHpIn7u+E1NUk5DsGiSgtBxgnSBKpRGC58zU4R7MbLb6Wh0k7ArcAot6QzwEn02jpwTRFD3iy55/2rE3CyfKYXt5pc9R4DRoO/uxzzRvme4OvBlA9zA8ipLpNZSYYPKnlfaN9/lzi8zxCm5XmClpgnavOeI0zIq2TCx30yRnkS+LcILZaf5OGCzb8N01Mx6KtXYcs0rK1FHy4RtYWvrUUfdrb2X56Fy6tw8AgsX8kHRj4zEfV1b5N1z68R2uUfBTb/NOGh+mNt4L+3Bwd+AFbPweR/RzCY+2MGVv8ZbPs8DN+EJ38EPrkIb90PhPgaofZ9tk3SFwiUO0hIA/O7P9MG/SUCDX8aeA8W3ojhf6ntiw9IrdgssOnW92NtblVQTP44THRl8wlYn08nzccn4OhGXLNO0BTb26i2EJrtxwjQW+chg8JOooD+0gYc3wXDRZj+I3Gz/t5Yq5kNmFqOx25JDd1s/Z8lnWtvtfavkQlGF9vPPtJc7rW+3SS5f1o7JopMEVr0deJs7GxtXml9WCTBvvpsIOsxDMr73tukptU272bgWRdHqtJiX4JjxQRpAxgNVavRG+KLtYgVaH5ecYHS3+qAM5JMgBczjGH2upqPUdkDFUB5cTV9r7VvRxgtqi/G9CfhNRsQKHrk05preIeTVTndGnHg5KyW96QLIEOoNF+GpGlQQ0+UaBUMIdV/J6BOUJWeLuCwfO6rAvQ9UjIrIPaRi2ikiALGsWwqbXYlXuWYFU7jZNnCRTI91XbUeG6TpTZ9lNAbZAaWDplB+77xrgKI5UR7BG1wYgN6P0MgyocwfYjwCp6JR82PTcH4atznGPD2WmrtC8ThO78Kn9gbCzGxH8YWYesxOHMz6/neJSvObQY+s7cN+kfbIKaAJz4HZ38D9v677YpTxMqfgH4zQgcXoPchfHExFn4nUcT4JlE8YyeZUXCSQBhrX14jbHYDgK8CO2BqDbgegGxBIx2y58laHqYg72rzuZ+MNFDbW5jP+X5iKuZm94tw81rQFmvEIXuMAMrHSEfpFPnk7KPA+AYc2AqD29A7QNY6vdluvBOuzAdFcYo8J2ttje+QWZ232uV90n8yS2j2H8Q0PAzzfp4MI7Oo1ZNkXYlVgpZ6mgB1E0Lksa0waAKQoZ9qtD6UoVrYNQ9hmQRtQXBra7f6pepZ13rvRlepyMgvQ1op3bMvZmiRCpb1PFcgrfUrtNhVEr8XrmwlKZo+iUFy5FoUc4SQM4CiWt/9mZapJ8jWWDtv6peHZO1WyoQ6OZojtdDPNJnt4+BqKMo4mRwBuQj98nelQBx8LTRkPyfKNatlEivHVPkg/zeawoplxlb2yKptTr4SVZqiclU6DGvoXDVtrGUhvWDM6l1i458lDvA7BNhdJbTl+4TJako0xIK6wIaZqRFtIg7VM0D/MAFetwmE+DIMLoX2tnMzrK/ENWcI+rY6MZ9uc/D2fegvwOw8DAewcisA4XcIfP9CG8d14nA/90nCi/VHCGFwBJj5CdhuHbI3W8unCQJhDfgx6L0F7IKjz8G5C7Hgl4B/RIaz/FOyKpMV5v80wRcfJDTjAYE+Y8D7MH49iv70yef6mYmoeb1EAN9km8cjbU5+lACxg3sjU3B6MSidmf2wdWcM+siPwOZnYcsPw95lmLwOz03B/CAE43Wy1sbnD8Hb7fFYu56G5WswuauNc5FQxYfw+rvRhy+QQLkAfI4wGG60fSIgf4MA36sEpi8SDt3xtkbTbU8cbtOzt70vMEvTy68LnFdJR7yJNPo+9LMYX6sSotZrqFqP0YecGkpK+65YUZW+WvbAHIaaK1FxqtYSUSGTcqzgLcapGPqdbuRE9W+pWS+VNny/e77NKOyX96sVr2Xte1WZFTP6M01DloAWyCrQODGa3zr9qokiYJuVYnQEZK1d26kvpUmlRJwEF9ZOS8JX0OvGR/uqi1O9rToc5MZp1xuyVDmmSrUo0ZWglZ8ykkTqRoFipIeUhSE/hgrJOxkTqmm1RGg1Bu377MHbpDDQctjVmds+cZD/deDgMSLed4448GeAc9Cbgp2T8OG9ELBnCUH0u8RavczogykPtHa3TsP6WpSWHCOLEZniCmGiH/hcG8Afag1t+wKRfmeSrnrpH4abf7uFRazD8MtRbf4XL0SjnyDUWlXO9dbBHySKTfRbx99oHb1HhL9dbp15G7gK71yLIIzbbR2fanM/1+b9M0T0waE25oMEwP0AgevXgK2aMReivgbjrYE9bUgHgb8b7/d2xGLM38+wMiM5xhdSeM4+gMkJwgp4KdZn+C14cD32wD9pyzZOKgrfIR8Mu5mMD3aPD9v7R4k2NhN9+OgxGNxJrfZe+0w5YBs7yKdfj7W/xwnFwGf5nW7T63kTMNX0dGhJI3quPXeeDRUtHeuVZvTv2fa7Kj/VnzVB0gy9cm2lNKrjzn4Lzp4jFbY5UmvVchbPahyyylVXUa2fS0lYB0fapptEV0Pr+jPwmlWcHEDVIp0YB6U3upoI46VBJ8rvCmpKAxgl4P1ejSuutIMpmU6iE9tvAzP8pvty8HWRrONQS1lKlVQJWb2pgrGml993zC7KGKMZiDUqRDNO7eAJkgO0jyYpyAmfK234FBADCpwnJbzzPtPaWSYU1KefIB+ZvJ3wCu4h+NgbcP9egDEEYCnhV0mNRi/zVWBpLZRta2icJuMuF4jD/f3Anh8nVMtfbvd+5u9B7y8RRvTPA3+5zcBvwfQxAl3HYOxxmPxT8NQXo/E3yPztRQIl3O3Glv2fwF8j1DeR9X0iNGUK+AewZy2SX661y+8SGO867iVk1TFiTz1NPrpKZmT7AG5dh6kh9F4hCj4PSI5pAPwbhMf1gxje3rGIAb7VumTm5hgBmKdWYMsq9N9pfb8PY7fg7Gp8f0g+sNRn7vl8wJW21sPWhTFCeNxu63OLLCa0FbhxJ6bsMPmA2nUyGYg21i3EfjxOVn3zPJwmH2gqkHU5XLVdKypqeQrMApkgJ/Dpv60aropW9XENGX0aT5e+1H/juTA5TYXINdfpLsZ59r3PROd971F/xActCs+l0Sc1gMBos6pZ65CsYXf9GXitasRqWgLxOmnaCVKGn5gWvVG+b+c1czzcdVDG6Vbnnk4xJ01NVN6nOsmUemqGcq4uivHChp25uJr2kF5fAbGrXdeAc7lkJ12JLm0xTXqIBVk1TDc+JE9nX6+SYWrOtTSKm9lN4WFeI+OkBWRLHhoX+jJh+R6bI9BknSBMTxGn+HW4vwA7n4cb1xPMt5CK3+/dC/s2w4EenFqN+55sn79HmM33iASKWwQG/QjhmJx9iUC3ncBPfgR6r7ZOPAb814Rr8h1CNfwsISV+LUZz/hdh+1+AJy7Di32YXoZ/3Ab/pbIw32mL8RgB2j9I8CUf5SEHy28CX4EvrgS9YjlTWjM+pmhna34fAW5G8VwhMP8xoHcIpvdD708SPMKVdtGuNunmwk8D78LbS/Hk5i+txWE82+5zvv1cJ2mHuWFEWnAQuAN37sYayLRfL/vlQRtHr93qXOsCpLzykUvnib1jssn1Nq4Zkr/84Sn42iC2hRlxV8nHQu0iIy++r02pQGiJVve3kQliiKnJaoXVKoF0gHm+DSRQGZPOsEyma2dAgWdcUK/UwXh5rwYcKMy8f6U1KOPxuzUiQxx4QGJLranhvIiHVcGt/i2xRO1bxXQI9OQvpspNHbh8iFLOl7G62xjlZevADB1RkpmTD7FhfL9f3lfF7/PdLytKmRJZteLL5Rr7v1beE9C691shTDRICsK+m1XnuJfI5+FJGzh2K2pJZWwqn0uzeI33uVPato077b6G8TmfPULr8T5qDt5ziQyrMlHjOMSJOEcc9G3t5hdj0DOfhttvBT+qOTwg6IkD7dqTN+HNxdC0LhN496uE9gZBV+xu91fj3/dq68DDuMhNBHzfhMU/3678B4Qe+o9h8U/GZ6tL8MXz8FgT2SfPw5VbQVUcIvmm+0SnP06SqH+iNVvDJn65/f143OkuoUhITd1qPzpCrxHMtny9a2AEHRNw/31C8vwEIUceAH+BULFXCPQ8FfP73C74+lJMweV2H/0k94knbr9PcNq3gPtv8HBTPz4bGrtp7NvalK6SxY3W2lKa7TlBng33g6C4j3zqxy0icOVu+86llbh2d3lvqf0/QT4WCkIQWz1Pq1q6AFJhc1+r2BjWRutDY3QevvQ/1Zdj0OeiEuYYe60vnsGqEPbLtV0fz0br37by3QGjuGff9B9V7Ks4UqOsbN9xVger//vb+YQM3bMfD4sLCSYOQHOoas7dDhs4rdSofIzgo5SoPPQsoxMofeHffrfGAcIoyMsfVz61mjG1bUhqpToLLahi/2pURJXI1ZypHtbKSct7r5XrlP49Rh2barJqA/vJ8oyaW16r812LQK3YBa40j/feQyQPPPY0D8ufXf2bMPspWDgDU58FvgxLg9CaNHW/nzgsT22F4T3YNQV7j8Ez03B8IbhjLYHTxPrPkybcTwBvXIJn/iyhln0OOH0JDmwGvg2Tx4B/CbwMw/8VBldg04/A8j+FTa/A0acJdH0Xdr4Ii29F/6cI2/14W7CbxE5+HvgPCNV9E4G8e0/Anfno2K/Hbf9GU3MuEI7Oc+1yHbsDslaI1pZx1fcI8Lxyp8X5Gn+2RgLxKpHx8U3CM/bPgUNwdj4TChfL/d8kAE9ecq699/xWOPMurK/G/N4j5JEWZ00kmiOMkAdkUuMC+VRrMzw/aGv19TZNv07IjNfbZ2+14ehY9owYsyzV51NvFAxGRrmnZ0rfqnaoNek500czaGMwK9CIDPnWtXJ9j3SsqdGa7btG4knFrwr4lXZwvVfL51rR4kD1GVW6smYDOu5KW4gFNQhARyQkrimoJhmlQfs0ysIvitQCiBzmsPyuDVfuRm5HzbAGQUOCa3Wo1aw+OWIHB98dUlcHNGQ0GqTX+Y4v/7ftTaXdugB0vlfH4v2cbMp1br5hpy3HscFokX/D/qrpLL4Y762g2UE+fdjc/Krh+9vHEO1u/28issQOWEH9ThSkZxmmbkYOxuwTwQZsewruXW8JCsCWp6IDY+MkOs3DxEZQujcJh5eOHDmwDYIbfwLYN0l49x6eovvwYAi923DqEgzegrkt0PspYAtMPAe8BIv/ACY/FndZ+EeBSPcJe38X0YEemdd8gniQ3bNxeZg18wHi7wOX4VvvBvDcJOiUd9t67G5NzxJC6Wgby622zsa9Xmq3tgLa4B7MXIeePNG3ybJ6+4mA8Sk4cxZe7MGbw2BXjGX9oN3HWOJ1UvDevBxWx0d78Mwwqr2dJ2tLqw1LO9xo+0OAWGnTbe3sqmTMty5aMKk6xMdJIX+e4JlXyNBXrUIpfUPLtDDHCCHgGFVqPBf6fszSE5zVCmu4bc3CM4a5Jlz5cnzVp1X9V2JAtUIhKdMa8zxGWr8CtZpxdQB6xqvSJ3aJJ/phKufsS0w0YsTrxIMBnUy9RyV0VBCyQ99Lc61aY40frk48TRMHWTtc+dkaGudvNXG5G0jHYW2jgrZUjBKwcr5VSj5KyHRNkmqCeU/jGKV93IwunGZdzRRyEw7L7yEZWuP8qEnLtRsHqdZgwoxmm5z6LoJK3bwGM88C34b778CHlyIUa52Iqd02DRyB2ZvxzLnpHyXUxz08rHoyvApjL8LPXw1Na6H14yqpzcjPbSZweMcJMlvCwOktwP4DsOcZmPshIvxjBxGzthg9nrzdVuUnIiuifzrt7mUijusFQjMWKY4DP92DLcOYhG2EY+8D4IuwsBDUsyUkFXx3Wve2k2YjhPzRVydN/D6pKZ9q8/7Pr8GTZ2D6FkGurpEE62Nw6Qx8ZxgANmz3NnlQp5vK/3ibT6Mi3h+GNfJEu59OXve837tKCONNpFN5vi3dvXa/JZK2uM5ocsV7ZETkG+23ATnTrZ311uaLZKLIJVLp0qyvypuObhPN1LLVTNV8q8JTrWkdggLmevkf8rxAgh/EuvoeJC+tBqrWW3njqr16jnzorgKz5k1UOlULS0C3gt1mRi1gP1Ohqvgplem4+ttaHPIhstpTlXAS6E5wjQc0RMS4OxjVsr1pLfgsz6S06EpTtcNh+ammfpVUNWGjRxLsMCpVFQ6aGKZnTjEKlGbaVWFUnXsumKZJ5b6kTI2HnCnfdR4UegoCBWGd08rlC7xaLPZN6VrB33lW+3uhtbv3Aly9G5rhy1MwN8i06zdW4PHzMDYRgDyzk3zu/ZNxs7Fb8MULoRWfJxRCLY17xOa1tOQL7e8jf7l98G+SWvYnP08Yxk/C4j+ESUM+foWw9S+10f1Z4G8BPwBTJ+H1uxGBMEs495Ritwma4IeAW0P4DeD7/lA08a9uwOsw/GYAjQ8aVVjdJR8EoHPvEAFgPm1kg1B4T5OeewHRNT4I3FmDHYa93SMcpu+F/HG+7pFa5RgZr71IaOYXSAqjR4RUrxJgfJHULDeRT8N2/9veLHHP62R1OBUIfRQmxViPwrNTq8BtI+tx7yIiB58iKJx3yXO4wGhxoSHpnDeD1/DUGndcLVgVGUhMmCUEiCnN7vMxkhatzjbxRFyo4W31/02MZuN2HXZq447P/jp/YkLNp+hmF7qfzHRW03eeBfMa5lfpj1Wgv6lpyD6KR5Vas94B1xA4G/C7dZKrNxFS8mgiGSFgGwK5UtHO1+yZ78UT1YVQG+4xmofuZE11JldNQfqkLqImeNVku/yU4TR6jydJsl7t2gPKJQAAIABJREFUu1IYWgeOp8YxqwG7qI7B8T8gY5HVJhxf/a0AsdbOEeB3B3GwntsFX7uX3nS1qr2/B7gNM+7YP02g02/C6pvwl9bi8BmloEZmurse/vXW7gngwAtkvdF97YK9gu1HYPI0wTifAf5mu/LDNpt/lVCFfylGf+IazL4Kly8Feo21S98jwkm27wnuZXYI33oHfuNGYPsWGLsK794NE/x4m0ejKIzDdQ2cayML7P6Rds0dMvJhqf0+2Xr9ScneJ4Ct8OAD+HtkLYmxtnbPkaUvHxAgp4/hQFtzNdR5MhPSp1B41nx6xw1S+bnfZtOzoNNX5WMPGR5noolA2S/31tF5mxAIAvjd9jNJgNkFRhUv2xNAV8noLPd1tV5r+OgmMvpBH8l0uVZlzfbqGCs9WmOHtSh9VVxSMYPEBpWiSktU6532v5FgCg775dmu1rRRUbZXNeXqk5OyGKdRFt6wCzqaGtWZBanNVR7FwXUjJNT4VsskVNO/Vz4blDZsZxNpFvfKd1zYGgqnEKkmje1JZ9aYaE2RSnF026kLVaWqFnPtmzG53tuN4QatPLObzJjHai1YrrPyZkaZOH/VSpgk403l014iDtcKYXKfXArH0RyhLS4Q/qep60TEwBYCIb4GvA1vXgzL/3VCo77GaMGnVQIfjV7YShz4zwI7P0cg1WcIW/tJYObPtas+Cfwgod6ebVf8dULVPUyWRD8E3ICx43Dzy/DkdKijx4G/T8RX7wWmH4crV0OdfbO99/Nw9R/C6buh4e0lq64Z3qYWtEAA3oCwasZIDVEtz7pIWgQHSeBYApZvwVMtc+f6G5FMaM0S52pIxnY/QZ61g6TTSy0agnERLO+RQt+oDSmtYVvnpfKeAKNG7pqpKctBw6i1K1g/aPc0GmWZTPu/QD6HtvKpKhyV1nTva0XL3ToO50Urtnu2K40Bo5b0RLnXGHn2aJ+ZgNL9bsWFKhych4nOd6qDX5pQhVJgNsbaGideS+l/HZufS506fxs0QHbgXiQwVVrBuL8K2nOkZBqW63w5WXbMA135I6VKBcQqDAQ8ysRU0K+LZ0xzd8IF/aqZV3Oh8rgqioKxdIz0gxu3ZiO6uTRPpC8OEJvYhXPiu0T+kKy9WlNAq8Cp9M9O8rlmgrbODw/bHjLESyBX8zK28w7BM/ffh/4ccdqW4P9YCIfSOUILk8UwwcW6G3vJ7Ld5AtheBXZOAf828IlXYeUSHJsk0vaeJmMH/iqBqv8Z8CQs/12Y2NlGcYgI9t0L/A5Mb473v76WxYNfeA2mnwS+CnMTMN+CjU/FODafSa5Tum2GDGu70OZhS9z9ITeuktBv393f1m6OjIDZ3ub+cFvf3UB/Ldbl/nIWhJpu8zJHiBlLrN5of28hNeLDBKgca+t1ioyRXiWTcXxunWD8oH1nofXPaB5NbuPLb5MUmdetlb/H2v3G2j0Mf/OsjZG1wq+Q58n5skiYZ1gr2xhjabdeadNzBaMFiTz7nnvP8iqjlJ7WmlRJ1/9Vz7GKz6N8YF7TIzXz2k7FnAqozp3Ui/RmN4S44kq1SqpvTuu9P0MUqK/RDrUzXqB5YCOQvOYGqcoL3hVk3QhGCCmdlWSG9AhGcjnr5T0HWKmI6k21/5XekN+y33V89kvpWiMvqmax0bneTSF/XD+v8cFD0ikggAvcMGp1VI7ezVwpIDcX5CGxH67HWpsrK8pNELD2HqEhLxFalxt4ktB8v0lEAfzydfiFAfwvGxEidZF8pt8NQquzDOdR0uythacWiES13/eAQKUvXgrF95cG8NJJUrxvgyt/Deaejate/8U4XdOfBf6jNrJfI7TnyXb3Pjx1GLZ/BLZPEuD+2RjR+S/HIvxvwAUY/nJwv28R4LKzrM87bV6MOhBUtxKgtZkEHu/yIQGU10laaJnUFG8TaecvLGftYK2Wl0iL5EmyUM8cGb+9tY34IFkh9Sqp5d5p67rW1nKMdNaNEQCpwLEI1RqZwGX0xTYyqoP2vz4i96lWn9EYOvUW2n1ukmdD4K8ao8BkctSjOOOubwUSuFRwvM6QUp2GhpqreQvyfqdiVsU0/9bP01UeIQsD1dejwFtgF6jrGMUCKdTqsPPMdq1ySNzqbyIK1Dv4QWmoar0CSSW1a+TCOgmAao81JXrI6DPMqhOvX66BUXCzbTsv+NdsGyfJhaq89qMWuXJELpjXVgvBQO/qcKgg1KVFNEOq5JNXqyFEFYirNDcDaINcuG1lbhzjChk+JAdf122aCEQw9dUDebDd6xIRD/vrhBNrmQCCsyRQuU61jrMps7fJillyxysEABwBdi7AngWiuNCLBNf7YB6mfg74r4DPwtg/hsnDwNfh0BCm/2K7+1OE/vokwSVfI8rg/Aj5DGhjzP5KzGj/ZCBVq7s8tgxfbaHIz7Z5+6dkoRx5e51VgsdSu+sVkitVwdhFAtd10iF8kHySx9ttLaYJjfiZNicvtmuPkM+xmyGAdh8BzhOtf3+EUPSXSS53jXS4m8QivaWgp82eUTkCpFlyvtzrXf+P+97PHpCOa/0jAzIErlfa8H5ac3X/Gj+/vY1la7muvjynJtCIPxWHYFQb9mxBPjy48ri2W8FeR2FX062hqTCKN+7z6pRz/OKNdIztGfHRxZwK5l06ZpmWGKLDq8a72iEbMWSj8jbSBDrIqulsJ5zADZJjEuiqBuprwCjgViJ/juRr1GqlByovbBZRJf2rRPR/Yxg1aWxjO7nRHbdj8Xo3Tg3jocwZ5GbdTNao8H4KJdtzgWt0yLD8b3aWY3LTPyjXT7afRUKr/Rah6cwS2vEqGV71uwQIf4OMBLhDbIoDxOGZIKNFDrb+3yJjd2fLuN0jp4kkuivLcPArRB7IMeCF48AvEKJpL0z+EEEyfwD8N6T+9hhBZbzdRvslonrPBsFqv9RW5yfa+y/BhV8I1e1/Bk7BP/8wGGoTFddbX+VeJwjQlUPe2+bt+8jogcfIGipPEVqycdc++flom5PbZFnLpTbcZ9s1TxIAfTi6xqfaOpwgRMpu8knUN9r8/jZZJvIt0sJaIq1S6wlrhQlmQ9IHoUIjbQMpwAU1qURIB2C13uR3FRo6wbtAqeDyDNmuGnQtO6CmWIuOadV6JvxeV5Osce8Cbxdn3ItakCqNnuexTnsV+GvAQqUw9B9JC4knzkHNShYjV8t3FB4qcyqE9tH7PuSQVf83l5tUyekFXRXeAapdq8HWznSzVyqwOKF2TrPHftRFkRczY8mNUTnsanI4yYbK2F/H4aS7gfzbSBBITQRGE1aU/s6R86CmIIgLmjUyAtK6mCjf1cqAUR6/OkQ3SKGndJbLqo/AkjObJ+kTEwOMElgsc1Yltryzh/wOWW93D1lbQLAWuFdbuxbX3LYCO/cSnO/B2zAzAd86CeO/DtM/3q7+JpHdcZqAJJ978RUy+OvHCCC+Dvw5og7azwJ/G/g47PhHcHsN/i+4fzFa/YCszfBxAtjGCcC9TXKtR9rfTxGAPQv8+Tbm3USolwdGJeDzBNBqLT3b2nmLEBHbyMJLt0nw95pxMnLjdptjQ/HeIi0fi+MbP/yAzI51v9RQLhMz1Ozdx/YfEhSq0lDrUbjfLHWgRl1TmbXCqrPK9lbJeGUFhdaE510+9W65tlKTpme7H1WWtAikNN3j9ktwq7iilW5fKn1RI5vEKBUoHZIPCIEtRilwYJQ2rXMg5vj3oPwWsL2m0jg9yPKbVSuuA+qq2QKKkqNyN0olJbST5mLDqJnhoNS2K/jWQXRpBhegDt4Frw5DQ8B0lhkHbNqlY9HU6mrm1Utu277nxlTaqy3bb7/v32rHpuwqdB502tHK8BBVp6cagPPt5nE+aoTHCgE0ahSG6KyRRXZqHr3OogmynsaQBF/piu2Ew+oyKTzvkvWZ1wlwOtX68Oo8UUruDwC/dB9+719pFd7+euvp04Qeew7YBwt/B6ZM5r7Sfn4a+N8JXX4vwSl/FvgmnP1bcGEN/hP42jdjnLfJguiTBDWzTGq5pvg+3r5zoN1lnQCTrxCgepIAX0PUjrcxnW8/J0ggf6aNRsrjCKFVPrsVtqy0p0RMxFNCDj4R8d2bW18VeBfIPbG13eMaKURX2jrOkILWaJHNrR1BqhbYWSeF+hohuI0CEeQEPM+fDij3peBTsUEwkt5RSzWaqQKkZ1vNVmef7Wlt+ZnKQAVQ48A9p5Ua8UxuMFp7QuvIV5/RQAPPVFdzrWF0xpHX8+2c1HA+6b8uPz0sbdt/hV4VIOsQj3CqQdtjnYZ65cs1fKTGA9YkiArsXlc7p5khbVGJbsogDbqeKO8Lut53igRcHX7VOQcJaEq9RUYfeAhptkhFDNq9pUiqBu+mqaZGnYva5iq5SXRGOE+UvsprVSfEkO/eiIJn5e/l4AV4x6LT8QHpPFojK4LdJwuT+77rsYlMqTVKpNfG8AEZbQGphUkJbCUA6RjhHLyyAdvvwa412uOVvgC7vwFXB7B9A/ganD8JW+eAszC1k9jaf5ioCneV1BePAX+xNXQLzi5EtaNfBr4CtxaCn91X5tz1XiRr+54gLT6573VCG91Blpk8SoD4EPgzs7C0Gm3vJgTTu8BnZ+GJWbja4p0uEvTFw4ibFdgyBTsHMPYkTMxD/z7MzMLtFXhpL+y4H8PoxQwwJHni2yQQKxjdH9bBqHu0hoYJfoJr9fdolXUdU7XwjU+3UZDrWKv1vw3/Wy3tqPB4b61mAb+eV3+vMKrAeA5glN/2flqW+lc8f4Z+ds+8bUgjep6dq+p7UvD4verHMiR3QCpP9tOQN/GnWrGOezOj861l4Nj7M/BanYBulIQgXT36on3lp2qIWFXr3fiQEmGFOPQW2fZwW19AzkwwhpT6ldvW1B6Sm6ByTgKWmoHSS+3BfjmhbqxeaV/t18nr0i2VS/LlYrnQlP+r6eZ9FA5SIJY1dWN4zxqA7qaUblJbgIxdnSIAZgfhyPPBnfZ5svytYw5GhYe+hWXS4SNQqGHPEtznHjIh4Ez73otEaPNnlgjbfj+xKNeAQ/PQfwq2bobf/RAm7sPMq+3Ov0A+ke4bwH8I/I9w/jRs3Q2rF0Nh/iWCI7kO48shWA4fgYm7oWFuJxMZbrY+WhvoaJmr/STF8HLr+zVCf58H7qxGe7db918mDvqDVdi1AWcHoR0f78HtYQiGTcDMZ+LmaxvQPwb9hWjgzCIc3wG/djPmTn5/ua3V1bYW861fpkF3w0Q32pgEg2kyBI6yhp7jjdIGpG8G0tJUYfFMV5pPegFGqTPPfY2Wque3Ov+qIqGCBwnUMKqdm2BRyzisMQqugqaRIZ69SnM6X4Jkn7Teq1UqgFbB4/ypOdfAhxo5UZPTHLcJITXZxHErFB7eY0fDEgGtm6Gi46yS7QJEfUngQ/I5NQwGRnmW+nf9X0rCPtVgb8pnvuc9axqmbRne5WuO0dKfddxeV8fa7Rvk879qH+rf98jxK73tVy1s5D3q/WsmTzXnKtfky7jPqtE4Ph0QVbO1naqVD8hYToWNfzuv28rfTxIAcZdc31qLWW6xjmdTm4+PEyTD54GxjwKvtYuOEnb/aSJcYZf1O3cB/yXwPwCzsPhPYHYL/E8LQTU/DfwsfHkjcPkoGWj/MqGp6un+1fb1nyeoBNr35siHbup4s+/H23heJ7Iel9p78uR3CKrjHvCJKTJsYjdRSOQygehOxKB1qtW1nD+XkRW/3b5mOc75NmfWa650nJaMr1r+su6PqlT4/yRZV0XtusvdQlYe9OWaVgvUfV/PZ8UA21ShqLjhvq3WcvfMSX0qTPqdz7t40MUWf08+4poH5f96Di3U38USx6bC9ygNvkZgeI8+o5hSr6FzrcpYf5ZInRYEBDKlQdWclCBqwtIYOoC6WUH+3eVM3BxK1OpQo7SjBKoqfdeMqSZa1YwHpBNGTkktQvNJgILcwF2HgJ93Ja7XqC3LwTkmuVc1RiNYKH32Vf/W6VMtEOdMLnuNTCQZEhrSfbIoik4aJbbrKUg7DjUmC41vIbRHze0dpd1Vsjau2rNe/z6jFgqk9q2Za/rvdmDXZehpH36G8NFdAz51Ata/Az2NwbeAPwHD/zYo5tdXImj6PPAmfP2doBS2Ar9/Ar6xEX8fJSMhvkpWZLava+3zVwjd+1NtjbaQgLWd4JCfbuv2CvFck7n2/2eJLu0Ftv0wD2tAL9+AiSdaYzvaxJ1tE3ycUF+fg+nzsL4RcyP3+C7pVL5DnhGtINdPWrGayL60yKxVoSVopI/7yb2t5jckk2P0SwgulTv1nHje3U9TZEnOrjOuUoS90rb40A2/q7+nSarQ933VCKdKjVqTpkaIOc5K7dToLAG0nvnqhKvWqNq2VvE4yf1XysW2Ko2rdVB9UVDWdi8MqyS1I5Qvq+2pkSrhRPzq6a2S61Fab1ey+QwvSP6neoGrBBHslNZOtH1Vk6z3p3yuRgAZPdCVbgqmKoSqJ7crIas2Wq0CtQV/P8oagBRI9vdRlorj91rKve91vuvYu+GA/i2YCzy+ZlrbS+V951JNw2u8d/1u1TZUGLVGdpP75Cngpwiz/GO0x0y9QqjQNwjQ2kdW2f97hOr6DnECfwV+bSn2zbdbm4fbfS1+vo+gAbYCJ3rw/2xkfuD91qyaZ7/1716bl48R6/UUWeP+MOFGvExoxT4heQY42ojQ1Q2YfJUg2SfbWD5JBBVPEUEiH0SnBu+GZnzwCNw+F/Ko3+6n1lyjEOT/tfjcA3NtmqQRasSOQAC5V1W4XNMa+w+j+6UqCXK9lRbraodVC/ZVz1HtR+3PSvmu+76Oy1dXS67X2r/aB7/bK/9PlO94/h13F8vqmamfwSiWfa8zXu+rcnOrfO59tpF7ERpl0QXNbeRTZGtnu5PzKFOivqr52r2u1huGRy9I7Tide36vBbKvFYzVJr+XCVTNK/u5idHN1gVuw4uqk65r2vld268gX0HW11rn/6nOZ91NVvs8TR5ieW/7rdl2n1FzTEulmplqiV1Kql9+y/WZimyYkmtXn0DiNb3yuTHczxM4NUdQCc9NEaTztfbmjfgZboRZ/w6jh+IpQgN+ob3/aeJ7+1q/ThEPe/0dItJCZcKnnHy2Xb+bODCD1tb7rX9vEmAslbSvfe+rwB8s89r/PNz/ldzTk0daY9tI3uZ9QujMENLoHJw8F//+DUJIfIF8rJdA7PqpGJhZ52PRYFQRUbBsMPo09wrSc+X6CuICexfI6z6dJgVS9bO4Jl3T/FFmegXTHkkHdBWhavJ3sahLNdaz0VVWXD/IKoXfCzx9dffuWufvShc+iursCi7PnAD+qNcEMLYbho/ic+3MoyapO4gKMC5Ylar1JYfUnRTv+Sgu2Fc1d7y3UQHVU9tjVDLXCesCa+V9fY9OG4+6fjdxeLzOMdW/3TRuiNp376OmMElKyolynQepywfWea+0zCZGJbHrp9aqJlU1xKpdGGpUHSzdspNqvzo71x7RlnikU3C+/bYWcI9INnlAaJ2bCJ/fDRIgfYpF1W42ETTBPTIG+H0COO8TgPrSFNxfiWvfIgCU9vnu1s6HhGJ+q7VhhMitdm8fn9Rn9AlNu9u1T7d7vvAi+bC7A6SJdwOWN2Dzf0w8serl1okVWP4CbJ6AhbUIsfuV1maf3FN9gkKoZ2mJBFNBWiGjc71PKgWP8ru4l90TMKpUUOa6Zp1WpUhQ8Xx3i25BAu1a5z33Shew/Uy/UVWsvtfZVRjU7///variV+9vvexHKZ9d/9ijeOTuPbqY6Rxta/epmnhto7+beGKIF8t96BHdYDTWjta4fFyVZIbiUK7zWjU4Y1UrR1q5ZSdKj6pVnCrXpZdTnkhPpxxOTYGUg+pGf/TKdQJIBboaOy3vI488TsYmmjZJmTMTLgblWoWFoOJ8wXcnfsj9maBhdpAe3GHneusmyCnWw+ijfDx0mr7OsfxYzZzskXGg8taUuVZYGCnjAZcbE0DlGY1MMNnAGNgZwnVnAaPLZGjaMdJsPUv4w44RQPh6m4vbRPyv4Yqvts9uDYL/XSEyEsXLGos7IJ9PByEcVonY5Hda37a3vt0lojAutuv2kQ8g/eg1GHPDDWBwCXqHgHsw8ceIYiK/p3WyZYtMHIAPz0bCx7dJIXaPeG+NECaGFKp59skMUn0sNYzNaBg1MIWva+961gy5am35cv0qReV+q5rkozL8TBjRr+Oar/Hd0T01CqnLu3peDeuDR0dG2X8jg3x1uWjK97s1L2C00D5l3MPyIzZskBFLdX68X7+85z2MjPI+3te++F6/D69Jnjs4J6466uqr8qVV0vhb6V1z1tfaAMzoqoR2dSpanq8uiovVBX9D8aq2Keg78QKbTjFBdIPRGsbDzo9arQ7JLeVvtUcByHtXQWOwvMLEeaHMXY1VrqS/G1hHgRtSmsBxmvQhGFYN1b4Zv2pccQ33qY5NkxHWypjq5q8FkHTi2C6k81Ln4D3y0FRtwoQVEwgWyHAlhc7z7br9BOgNCUrjfJtTNd1lAqzHCYA8TSRdqxhcIcB4hXwS8wqZ+baJLBC01L7vw6PV5BdIEL/Q2j5HCIudRHjftg0YrMKm+9CbaB1XM/m+1kG5jm9HR76yGv34JuEsnG9fd286v1JjJv3oNFO5UBHZYPS5jZDaYKXijJtVOaj38rzIL3cFMuQ5H5brd5C4Md65xjGoXBlaWpMzTN5wr3c55gqOnv1h+V6PfFiGZ82fmlcBeTacn5pD4Pmx1rnzC3lmPKue61pnR2ehQGy97bHyXrU6/Mxre5APOXVyh50vOBlVUrmYLr6AqKa41Lm+hntVKaYWXeNq1QSURFX6+GM/q+QWLAakhj/sfO6kuiBursnSpsJJ4aGZ7tjXy7XVhPJ6NYVBucaFV4Ctk3USBPdNjGo73kutGEYjSryHwkut2kOhJqol4RwbQeEYqmNksbTh+nYjb1zT1fZ9x24fpsjC6dVh4c9Ym9NdpLawk9Tu1omDu5vkwhcJDXaefOrFLTLD7Q5JpZwn/R8Dgge+Q4CtQmySAFr35XVSMN0nQPJy+fkIoR1fJ6gFk2oWypyeaf3attFAeZwguTcRnMRUG9gcLH8YqS3XyGfsLZNV1ezHSpsH05AV1Ap8z0dNEqp7o6v5WtLSvQt5RhQEvmoMssrZZkZNbM+rgkA6w3NgFiCkcmHSVxUo7p/ZTvv1d6Up64NQVcwWGAXGqu366iprnq967uu8QAK4iodzbL+7iqnXabF6rsUnq9N5r6710N/UUqedxGqWu+D1pmuMAkPVhN0ItVK+iyIg+hIovc7BKxDUftUYpTDsRxUakMAll+aY3EhVS6ua8Dq5OZyoe+SmcYK7fHhdEDVUv+dv27QPVbuvD3q0r85DdZy5GS3qLZdVBYv92ChtKhQ3le9XOkiuvdJIOleqs9KnZGgu279VssykhXA0px2zST7e17ArKS8FkbU1egSIf5rQsq3rsJkAwucJf5ha9gQB1LT77CacY9aLvtOuO1fm/woRMXGx/a+D7AIRBm3RpAEB/vuJ2tCX2nuHyOfQGRY4RTrTxoDJBZhYJ6SDnszXo5EvnolEGTPwLrb+L5Dhh7ZZeVQppVotcI0EZlPFqyIi4NUzrRD2GrW3cUapJxjdTwJqPQNy256Bqh16Dt2XKg9V06z9VFHwIQFdp1hVmGD0LNtudbxVLdizNCT2SC35sFrarTx4rb+jJWpftEprkg7l70FpU2vBPtkvFb4a9DCEjENWSg/LRVWaVK9lRXR/HLg3q9ospLOoTqbRD0odgcfF1IzwQNdSgy5YNaurRDeW0kmxf0MCPOoTD9w01TTqUiW1/9IkLnRdEGMShyTFYT9r5SjbqBprlxt2Xq01UCW/YFItBYHZQ1I5NGOvYVTbdUNUJ2ylUdxQlRKZIQ/RAqP1ECyC44atc7FBOkh8BJSCaCtJc0wQgDVNPpLvCqFBWr3OmF2BeZwE7CkC9L5D0jHO0xYCBK0PcaW1Z6W7y2RJo/OtvX7rwxJZM3mNTNwYkLU8zrb29swR3sCWRvmts/CV5bz/VwlB0SNoZpUZyH3sOmld1VAtBVs18Y3AqCClBq8GKzgPOtf2yxpXLVyL0P0iV12L/xi7rPZds94q4Kp8VFqlfqdGCal4DEtblTv3M2kCfVr1/WpFeM875b0qXLoF0cQ451jrtTIIlcqt1nI9d+ukAqLCZ9sqr1UR629qHLLRCgJj1dDk5DRfh2T+v51Sy/LwVy0VRikOB6QJ4/UbjIKRk1pz9X0JZn63ApNtVzBVY1d79J6VE6uL1NW+nTAe8T0XzoVUG9Ap46J6MGA093+q/C3P292wMArcLmgVJAo050DhqiZlOrUOtdpXNeBqwtnPSbKGwSoZMbK19Elt3AI9D8pnkNqygsXiTs6DdMIMoaVutPa3td9Gdtwis69XiANm2vZNgv/9ZULjvUdQCWp+q+379aGja4RQketcIvlsU6mtsuZTN26TARWr7fqzpNPmTWBiGd6Zh1M3YeFqJKZ8q333fQL4zzKaJODaWwirHlw1tOqkU1jKeWpWb5T2qpVpiKPClvadarFsEOuso9j1N21bPr2CUqUnK0VROVo1UHFFYSPW1KxclbVqMdczYe3hXud7lbbRcquWsf0cJ/aogqQqIVVJE4RrUaVHAbGWS1WabEtlwZdRJjVKRqfkBk1DdlBVI7TRbv64oCs/4oTV7zm42vHN5KYShCr4utncnNXcryZAnYgBWZXN70Ju7Oqx1SNqW/U766VN7+v9LCxvKNoY6aWuXJSArnBzk1XNx/HKSVmNrFIR9s0NUB8p4yapgAvfTSXpCJFOquFPlYapG3mF1Nwrj+y89kp7AnTV5it356EfJzZapUtMpZbGce7lTXX8HG9/TxBA+x6RkXyxtXud4OFX2z3ku+8RlZStkubeNO7WuOlNZPQHjBZg8iBdJ58Z6DPrtCJvtc998afIAAAgAElEQVTd/xCH/DwBEgukhv4WD3NCeIdMhLLg0X1GfRVT5Tv1QM8wGgbnurkeK6RzbA+pbVbLR0GkUNZ62EYW1HI+1BhrYZ3qrJsq13WjEiDpD4WKPgTPSQ3JVIC7B1f57r1F+cz9ZBBApUAny3cp/bcdz4hzMSztVRCuVI/9qILBs+kcVDpF7PG77vdKuzoPNdmnPwOveeDtEKQGVWkHpUs1P+qBsvHKwUBK2srVVnNB0FArquaO960gCCkwXLgaSuNAq6NhmlEpKZBWL7OfayX0yENtVIQbu2rWQwK4bUMNZND5TfmOFJGmZhU2O0kt3jF7ILrp1x7iaUatDQ+Bc2Q/pDIcexVSCmWv2yDLhXpw/L6vegjdyCaMuDGXCZpIwbZQxq1Z6YHwfrcJSuB+m49bBMApXOYJQP44oQ3/PgL0vkxGLMjT6oDcICq9bZCVzEz/dt504ij8Vghgq1ExMMqNLxAF7d9sfV4gBMfF1v+zbU5+q81drbgHaaWYLm3xq61kks5q+1sQUJBXasq2pDY8d1qtkGdTLVvN1HUXnARq16eOvZ5zHcFaP77cOwoJLfDKBW8w6qyslIr3dK/W6Kjqi7LtWrbBdesqDAIqJL7UEFpKfzyPJoDUMGDnQJrEgATj8j0LzqX9ro7MOlcKjB4tMQRGK7E5WTXAufuqg6hahRNbOZVHvSYYDbaGUQCHUdB2E9b712yf+n3NI9uuAmCC0cnwZf83HvFZt98eZF+275gr387/x/fqe932B2QcsgdHx1G34IxgURNjvLbykr7cGNWsqutu8oFUglJ9orzn2ikUIKMCbjAK4n7P6/3+A0KQVYD3AEy0tr6ffFjpHAG2h9v/d4iwtt2ElnKO2CfXWv8Nwvc1IIslKTRqZqVgNlHeF7Acr+sgj+562vdaY0it3zYnyPKltmXyS12rbiKOgFWLQFWaS+umljOwfa2imrikJuqaLHXuYwzyZGeME53vuC89C13ceBR+SG11HYT18++FIf5f9ySlL91iW3VdPZcP+G5s8H6Cajdtu0vHdPtr2QHnur5qlmDFIBjFTT/rb4PXKrcDie5VK6xeXyenOtKUpmqeeu8r3SDFILhKttdYvG4Eh4e3gnblR6sUr1JMrcD7Gz5UtW416BoO56arm6X2Q81Os96nM9TqTl1+WFCdLO9V7dT+DEmg8J46byhjrs6RsXKth6Veq8bgwVNjqCUWK9UzUf527NVDD6kVCew13K6atwqHasbS7rlIgLH87TpZmNz06g3C9L5DHhofkXSRiHhwfu+3NmsSiMBm9IJzeK+8ZzinB6kWiVFTdA7c32ozOpmNozVkbba9b+TIkLAQTpL+mHVS85Z68Xy4r5fJuOturL3C13Ni/93TkByzIVhqtp4PHZ5SS3Kqvirou84K40o31vNhJEHNbvNp1N3oIfhuClGsqQkX8umem6pleyakpSrV2Q1R089jP2E05r5Gg9X7b5S/q4UuHeT+qd/RCq9YZ5hdd/58fw3oz8FrVRJCxvkJzJoqPsfLA6g5agckpyuH7GcO2slQMmjCV155vXMdJFB2TRAnrHqDK58lcCyXa73GB4UOybhgzegKlPa3moY14aWOrVe+5waQL3Zc8oRVWx8v/xuqprAwgqHGHLsm1XkhX1vBEjJ/HzKW1A1br4FcA+eox2hygia9XnWFgOtew4Gq17vSKd0Msvr9PhltsYWMEV4ks5KNCTZ5wQd/+kRkIx8EqvVynw0SNO6TDiL5PgHap6Q79kUygcX2nD8fqyQA2dfdpf/3SWfhjva9WtxcgSfn/aC8Ly3nOlVNTSHufnBOa2yybTsn1WwWpO+RtEM9W9Vqrdqm5/9RWbQ9RikDwao62HwZsaIjWN65nj/B2HuIS/p/jF13Dmp0T3WWq5BUYVBf8vXdYAV/V63ZsXqm6/sqgzWhSypko/P96l+aBfoTRIF6v+wijKjRpMOtShY1XAlrTR0Xz0mDlH5VoxM0IReqm0lWNedV8sBUzQxSwsLowldpWqWXwGgbK4z2Z5KIQzX5oUccTFPAq7nkPas14P3knGs/HXuXc6sWSpXMvirPvaW9J2DodXfeKu9WN0OdE7UKtWVLSz4gtYq6gdWYuu1XHtnP7a+AXjWKAZndtZ3Rg+H+WCcLzhirLCAuEsB2sF07334WSSfL3TKu6XJ9nXedgJVOEZTk9+vBU+OvTmR9F5rLKgGzBLc9RZ6BGu1hX9Vea5GnZUbDPgUK+zZe3oPUuOyTVlvV4KVEKp+5g9TW1UJh1JqZIoXxOunkrpaVERVSPpVKdF8IQtVBLFirdXuWauhmdQbqwKz7bUg6XRUinp8qXNzDWzr988fv7SyfK3yqclitBRjFs6qESDdJVWk5GUEDafVVIfAwUw8SLCpqw2gYnJ5xayy4ceRP+qUdNR81RAddHYZVG4X/t7Cz7Y0rOa7w0QxH5EpipJW9WUcwkCC/VL80X2IYMTZ2tFbCFTWaYT7cedhPHzblCxAznNu3X6qrq6tOVdedJ5BP6vYuZ/zHi8T1JCPI3hNrz6+jBdhx0Uo7Ux2mCUyEcGPi0RxsJnGZYe0gaiuA3byfhaleZWho31TGOJ83K5gXZqA9Y4eYUe3c8iaMMKQtYofRHOknzhzm1A4XGPNlhkOEaJ3fMvBdIibY6MmziyC7yXZ67j+yxf/usglkQwForowFk5w5cgz4SfcQiu8zNmO0Mzu1EJSM/1fVTb85In6VTYs/Z46EwHFISJ+1WzBeBD6LHeHJuNiEmXNH/dhCQfCz6cAb8Id545ShccL3LzOgIXjc+CgKUzInCPtjBpRgAZqMTcBx0RzfR6tG2WCMHCE/ZBOuPpXI76xph6JxobkbNoy+c3qUw0G+ThlnJ5gn6AsNLD/hE9YQsCZjQ9Dz/VuS/ftLHDKLGbgBs5cYR58wQdsyg0IM70aOZKAcnmMGYjPKGA0XmGJrnzCkw1Vg/qP+B3+lL2ZYoAFCsSzcEfi0SVlrD/Sf+FsvCmvMhnZOeg5NygHprZHy/0F1wbAwDQmYCPlBENjq6b7bdDpmLBKYCpNrr+cRqHjqWdiEWFlLs4Zncxt4ic0HnNMaD9AYGux1NsH346XNDxmvU0KYoYXjTCKUkxORpovjcdmsPLarDGz7x2xQCGY/x8FfZGQN2+u+ISogHL+MAR6E506XtlAUmBfG61SXZFn7ISP6p61I50sxPkr75i9bXK9Vxk5rr01bFp4vNiuUjGM9Z5iPsggqxnWl+14/KIHOg8NxZJQt1p7XEptZY9VsaK2UOVcLlojHcJ0Rm21hDD2+6rerDIuEsq2gcTFeaLG/Sj7SITzzxnu+6Dcaa1AdEwkGYAHYPEF95yjs18zB4zYLrDlfX/r1JkO4MlGN6bDoTDT6irltWIIJYrxovRAPIYKpyQTudM+LCAJ7x7RThDZp5yoDwwausfPU0AD0AjN7kxHJcZdhitnRYTrZ4eB0iYyFzc24WI/ZibjtyEADslBKBs+A17MomH9yEDuKAHpcZcy3IyV+u5T5WwYOjHZJP++zCU5gKTt06Ns+Q4NCc4VPEAJAF85f63jxHzIfsEGTZI5+yHgbCG0h6Jgr2kUI/C5j3pgXTHqc1H/PCHuzFUR9dkYxJubR/ActLOC9gSeDX2x12ekPT9Iez+DLQZkz3Mmat5yhL9ARAYvjkVhf1sXXKo/zsxWbhintWIQWWBn2UUXtY4k3/Ej7WHjASNAI3oY2rDuvP2+qp2QcnabjK0+h8Q87y5zv9KSGDDXsVB6t0DkPrEFRjoH7vs0Ya7A+Im1TzcR1uAsbyFHPeqdHKJ70P4vmjcaBoERgw8Q216kTerDTM0aYC0GYjF0VbadxPZuFMBNOJwQJ5WmXy5sS82zN35rqlcpAa/poTz0OvkPGaSO89a6beQd7Q6u0doD2g8D4nPHKepQCNCxMW3gnmePZ7zOOdeMohJfRqo76v3mOk2nwU2uHrgM4BA0KSA8Y6Jjxhm80J4QKODL12+p0jDh8icXwVXXYJ9PCFvqzJph7sGFMfvxIVh7seGVzg78tbFj79If6UebMwyhoyVC4jvoEQmufjIWmhTiWPTTFQva4fTlMFP6BR6xkJrMvpS34g34/5unmSPvIG9b1Sb9ba7/O5rN6XK/WPh32YQbld4jyOWMHTYZnmcE4ltCONr/FAK0c8JuJcL6HiDCGKdA47IB8lbnvu8xYMPjwvn6HcTChaIN62e3vVZ7JtdcbjCqZNf5z5jeWcLXjLtm0ATQD+sx4k43GnzIwYJxfDnvjwqmEtcN95pE/Y2lvF31ibH1g6JARF03dLEZ+swkMA+dShkT3aMj4HFj8ZI476FkOSXxWndDpa4bDE951/ylD+X0GhOZYbiAFFvtNZqjjTnX8ksHTv6reqA7HCcO3rzJOxn1QHfBuQ2qY+vZhRLSxYsDnh4x1w7yxJhHujJf+csF/CBPaZK1yjzXl56H9UWV9L7rP57tLfY7bxSFK+ZPu9cU4D4vyyAK3Z+3V1lxUx1H/Q1/zcPKU7ocMZ3SX8W/mk10uvE4uC2sAXthgPZiZaEnO3QB+ZNzHwoiOWsDbRGa3txaNWQ7j2hwxU3FZo7O2z+5jjZl6rlWf+/aQARH4ZJZ3ZYfmeCdP5teWY+LYTHF8pM1o+gZtYA47AlkcmFuYzhbe9I9xnLPhkRwg8JjQ0Hxa0zkA7Liw6WbrwkKOew8Z8BSLgU3WJiy4L7gqwhbrBJqiyeF8wvT7ovroJ5YSdRtrd0RDR1a8zHi9k7FPxky+h4f6HVMVqAT8Gwe0tXKgNXjBseFsBp4HNikccuYLw2QIAiA0xvz3jE0Si4x5N97vUFNrnmj7CC1blMmwInAadmbFZGih5ORmriwnmFussY7lR0YYHjR/eoMy1PA6g7+9zrh29Sz+GCMBaL70HZzefOAQVyxcn+SznHE/WBOPcmufsQuh2TAY7/J8wqQkvv6Q2TttrRNCUT/1mNGP+s5CYFKoq9+F15oPz692MJw73tHOesb95DfMN3Af2rR2iVbCuFaaBYveL1e16cLE3+pZ+ub6oIPbd3nTvLUncFpyBtuioAxx6MlgUD6Tbf54USk7PO3A8PT3S4aWSHt3ukcbtO2QMK6bzBqqzXlO9n3NJmySkbntXebTnPTrUzZa+2WStIvVg+D7JYM/D/qEj691nwueOWZo7WjdjJUNy6f+bIXdZVhFPffnjDBBeAALjTaoD1qZz/gdpYB1cq5n6UsyzwH1JcMa3lV51iTY+LGeIxzuuZO/tzVusgFGz5yrjPFuaHqdkRGP35l3w3eGOrymflUZ+17A8VEOLTsYm63a3ly7bS5bjqyZxwGbWBbUK+HMbvqXzAvSBIN57+u5c8b7H63uI7xg8LeZL/pkc8XExOz1fYS8satr/RY92xd9P9R9GN2TZYa1+cOhDPcJAU77MBA0ZXFz+W0umJowCM/RX/p3rTIwFm2idTC/pCSMxmoohqxqHBu2uYqws+MCzdUM+S5jI+DMPwuYk3m8NRqBS/QEr1kigx6wCdgskMAvGdEWp4yNzhBZMm+O8CCCj3Fw36Y4fMxGwbwbe/VmyzPwBKGitxnKD3ASc3zQ83YokVSIvnD4BWGARs680347woFlDAl6nnqzYVwW2MBfvlYKD8pMMsuF3gSyqM/OyWRsiM8J1YOeQ46gdFiZM29YXjQdWBs8Z0HrjYoybLjwUzLDh6wN96PHkWRLLuQbNnkZzNeqwKq3TThMIID5ZHhbPbH7bObsMSNUzR795Gnojc2kB5XB6YZpTFlMDUwJGAnzBdPMQeUI+w4PwgTEvD+rPmCVxnkxs0wfOwjsdMNhwwI2LAPsYOegHRI4fFjQyTi9d1A5rrPqdJwmmyrw0173PQbo0uY6C/0qw3HJd8L+7lUHpuYuw7T7liFcjhk5NWif7GSY/rsMh4zjaJl74pEp53AqFjd1Y56/zhbGRzIi8k44qxgb3VlloNsp4/QjMePwtdcRJjCQCU4/aIZQAarjchwycAJrIBmpNM3DzoVtReCQEa1hpxXf7czjGWAlngFG6Nh5m+F+W4jDKqED8sTWB/NieeRjyKYjvANPcGrS6whIjPs8j+C2leQgB/OpUzFQD3No+NXwBDSH52jXznOHDb54nzywcA0HtFPOhKactSrKU5fv2wFkM8oXmsDKe8nzq+cQBNRxyKzp+7Km2yaXx9b94lkLXm9QJNRxTtdud6d7dlDZSek27XD43ljQWpPBcE5K5DH1+Pm+z5zTweFtaK7t5HibORVkVAaB7UVs2Oik37iu9Sz3rSDsL+NiA+3+7NRXwyoIUI4He+x2UpF8x/O0yzisgqWFxrmC8WgT0xaNnr76NF7DXWCo9MmaGevKFhDWopNCrXg6Gdrfh2x5mNGcSaKD4sMLcbF4Gu5jfeF0ddvJDM2538kMoTjuP1VH/++172slk3rMzHFr35R1YiSUJUNGllc3mZNVGYrx5uAL3qMft9nCNbuv7suLPyYPd/rRUQLPCScLEYhhocxi5ZkWps485XphABx5yeyFd798QAJB1Ka+r/fZiEGfYRRriYx9RTCPlZ3/5XfKcX8lOD0G+uwMay00VwLV391nvjvTVzLDCF6EBLyfMlskFu5sijeZMa9ktjwYC22fMguKftMEZpzp1jioNyablU0H84b5tPvliBS+ryAihH5vimwIzW/2k6zewsIFrXiOBY1w8/j78rio31j8c4pJ+0FeZ6RA7QxyVhp8apH1zNpxu6xB+4AoS11WrjxuLC1gEI7v91rnN6+nqL/dr+h3rwfzpNeW/6eNt9lkhhWt5+QD68NzaUizFS8gJsb4qOhSEHMZYoG/QRgvwH39BjENjJPX2Fo05gP4VzKiAA71O/+bea1N+G26CFNPlE2SXYaThXpgNtqAiLcZ2LX7Ynwp2WhkD6mvfWZhzFjQOJ0EHJMQQUVdxsoxI+lz48fWoGA+J1tnLJhm/A7dvFHQf0MSLLoWxlxH/SVzikMvEtJWIhDp11GfCMLGaAkjMv4LHyAQgCdwHsHT0Byzkeft+3ibwTvHKhPdQwi3IOaehTFzn8yOW9MWp+Kt7vEdPB5cmHnB4WYa+4Qe+D9tIyjZeP+qZ+2gaj8J660VCQsWsF7awNm4q7L8ZgsAXuQCaqMd9z8ZeC/zyvqJysIXyJob1WFl4FbPeFOhDrf7j4Qxv3/O7E9hHPZJsJZX2HqS7LzoGRjmUy9mV8zlBl0XE3Rdz7eph4MDTQzG+luGxuxFxCJh4pkc+g/zsBszCTCoTQ33gwsHiomPwHilZ9GE+L7LTK8WNAd9987Iq3ccztWxl6add3IL7WOG88vmImMwlOK5ZrGf9LvxaJKt0F5rscxFMsMOt5dnsT5eZ2yELArK24GYzPRDU0ewIujeZUAt19nSWxpuQKjBA9bG2oEZ/cYzh0sbYJv0w/xFv51b+JCxoaNZtcLCBvEysyUIXYBOENgNGXijhX48w7i4rDT8NfMa9IZhgRZ9h27+3/yx0+9WdCwraNM87DXwKoO+XK2IJbPw5vemBeuxlcZk8ATQBH2ExtagaQ/5gAbMGrBs21fZQ56eo0AZo22ignCuP3HqOTkPIL1B52So/ZTr00A+6ULjPr3CIqTsdeY8ppRjcLSF4KYdx13aQdfapUH3o/rAeBmXYzJfVLmovDceFhjOFTt2OFGUjB2RsZmmOBqgEafe7NhqR4dp2Zq544vJ3Us9vh50H0HiHK3Q9ZgRe+tj3PQXPoEpTT+eh0nhCR8uoZ/Ux9tCKE89tOH55b14zCsn4ji+jaZCLDPCjmOuCBrSPvqouTeWfUZMtU9lMr84e2nLfUrG/LMZ8HZo3lRiC9Qnu3D0nFUndPF4bIE4T0syoIl9hiOQ+blSfRz3ps5d5qxqaOSneo75dnwwMejAUc4tEdG6ha+PWBMvvpIZ5g36Ck3tu6A/1phtGfL5LWPjTdVNu+RWcX6YncZNn3izOfzYcgd5gXwixStx4NfJllzolJESzkcy7Q2PGjZjMEnuaESYfeaz2mawhwwNgg7B2EQp+HAGGCsLGa++E/uc1ZbNYhMaYe7oh4Oe7cvRFw69gT4kuOE3HwNNZu+yFx4ChzHtMoTklypL3WgYLBoECIzv47AIHer124CN+d+pTmdAS8bmQSQC80N/iU7whsMFk7PJoJGRVY5DCa8zhAS0YIEyTwg0H10nwoV2nBwGnoBeCBY0SUdnwB8WfMy3hSQC2hsNhyG+ZRPu5D1mHPSDjY/Umiz4P2REpViQAAUkT6M4mAvuW9NGeUEoWNP2+vUa6E3Lc8mfnVs+xkx5rAiywdHmu8yHjjggYkiRV1PB+z5gAs2QQ8y7BSEbFzzclw+KdDksbHwsKEisHWhj6wp+Mr+xCUEfr/VDhhxjznyQKRr7zocvwGCtZvfFfUwNzEabQjZFd5mFU2NryaylJbPpSb1gYPwGEW2+Gzg3UM6nYQrvoHiW6R+mfDKwLuOtdjo2lmfM0Q4oaNAHC96pX97QkrERmFamIzAMNEJzpN+eR8M2mEz0DZzSc8jcMlfX+mQsdhwZDkpm89C/2wqDHuDdx2xMe5eRxN0msw+n7PLU0UTdhKqhMaEts1HBExYynkcWnrF9hKyPPyfDmXXOBrPZhD6rL9DNMNIhG4xAeQsNw1bMo51dNxcaGZ/f67thMl/UzTq0qW6B5T5igtMHLq/tLxlOOe5xiMLjQADCS/Ar6yp5GlzgyxncgK2gjzVi/9k3RTnmDE3e/g/3tfvuOqCh6dUOXdae5at9dqbhKcn+dxcNmcsqPrsG3zGxnGAELcdaM40YQzvrdwslBmU4wLF/eDsNrKMVOx6SgdskQQh7gaAtcgTSWrNNK/pNXlh2zZvM2pePGduUp03DFxynjNq9yoz/sXB/yOy08I4LHXaZj72eqh7mDqYzLMPFHCPUDno2GWY+8cDWsA1R4IyyNWJ6o+WdMnJlNBaHsEBIYNI54fpvqh8tF1phNUHzP2gesC4Q6NDRWDCCnrn1kWyOVzu7H6a+IaIHlUFLhP5ODQvvQE8gCvjLVh9v6fGG/ZAh7HhlVB+CIofxMePYsrVj8wv3qNswCJuXFYSGKA0b+tiw175hRTbzW5W1Nm6o1Fni3EcSa7E+eTkwddmahA+dzAqtFUTAsA5t2Cpg/cBDnicfNrNlAX2hA3yOIkV8OLy/iwbZl48s48ByuIY1Ze8gaKqrMDKD9eysBuH5/JDhjWfRGjOziYb2t9Nv1hY8RgQmHnuHKznGlHYdu2qNfOVpt5ZGW/bc0n+P08c6icaI6rZGZ02ZcaMhAKfYW2/tgXrPqgNNwZc1PMbFy1WTWVNqbDrqiz33bf3gNOUyZOIDHj6kQuSEtRnMc2gL9k6Uxy+qm3HTFxaalQFbQ18ya8M2cQ9VH/wCb/2cmQ+i+liMp8xRHVhd1NsactfnNeTnnZAIrcyWAtddtuTx1vSYk7eZ1wt9YB1ai+y+WQE66j797eRfn/VMyxC3D696DURtOa6aDXVlHSRDUUNTZyPzYR/K9otTmX9o6beyd9/oi5+jz4zvvn7b3dWDHmQyC0UeaqIZx7OGDdMd67udJnSSsCOe/c/qrDE1E8Bn6y3Yk6cE8vjMiDx/0n1r3/xuoZ/MJjB1WPNzH9BgGoLwQu7NK5nDpazVeE4Q/Hjb317Kvs/MEBagbRUlc0RKMptjLAYfjgGq8qbh0KWoX/aYe+GxibC42WSc++BzZljF9Og3BCO0GZfNWTYRQxS3qhuNjQ0fZ4/nzpuVFRZr+2R/u1dZ+ut+QQvmyEKoT386DpfrWmW5Oo8KfG7FiTn906XsrT5PGQd+3BZziCWRzJZoMo67e34diZTMcIo3p163pktDdh8Wv1uIttLBddJ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\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/pyproject.toml b/Addons/FRCmetric/miplib-public/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..3b28a6d7962e5a43ba09bec934dc4d215d39f69a
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/pyproject.toml
@@ -0,0 +1,2 @@
+[build-system]
+requires = ["setuptools", "wheel", "oldest-supported-numpy"]
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/requirements.txt b/Addons/FRCmetric/miplib-public/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4bd9a826c74269f6b9a1735673d07f917e82afed
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/requirements.txt
@@ -0,0 +1,11 @@
+scikit-image
+pims
+pandas
+h5py
+matplotlib
+numba
+SimpleITK
+scipy
+numpy
+jpype1
+psf
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/setup.cfg b/Addons/FRCmetric/miplib-public/setup.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..224a77957f5db48dfa25c8bb4a35f535202da203
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/setup.cfg
@@ -0,0 +1,2 @@
+[metadata]
+description-file = README.md
\ No newline at end of file
diff --git a/Addons/FRCmetric/miplib-public/setup.py b/Addons/FRCmetric/miplib-public/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..d05c0069598421054ec287e6af3fe02f586bf251
--- /dev/null
+++ b/Addons/FRCmetric/miplib-public/setup.py
@@ -0,0 +1,46 @@
+from setuptools import setup, find_packages, Extension
+import numpy
+
+setup(
+ name='miplib',
+ version='1.0.5',
+ packages=find_packages(),
+ install_requires=['numpy', 'scipy', 'h5py', 'SimpleITK', 'jpype1',
+ 'matplotlib', 'pandas', 'pims', 'scikit-image', 'psf'],
+ description='A Python software library for (optical) microscopy image restoration, reconstruction and analysis.',
+ entry_points={
+ 'console_scripts': [
+ 'miplib.import = miplib.bin.import:main',
+ 'miplib.correlatem = miplib.bin.correlatem:main',
+ 'miplib.fuse = miplib.bin.fuse:main',
+ 'miplib.register = miplib.bin.register:main',
+ 'miplib.resolution = miplib.bin.resolution:main',
+ 'miplib.ism = miplib.bin.ism:main',
+ 'pyimq.main = miplib.bin.pyimq:main',
+ 'pyimq.subjective = miplib.bin.subjective:main',
+ 'pyimq.power = miplib.bin.power:main'
+ ]
+ },
+ platforms=["any"],
+ download_url="https://github.com/sakoho81/miplib/archive/v1.0.5.tar.gz",
+ license='BSD',
+ author='Sami Koho',
+ author_email='sami.koho@gmail.com',
+ ext_modules=[
+ Extension(
+ 'miplib.processing.ops_ext',
+ ['miplib/processing/src/ops_ext.c'],
+ include_dirs=[numpy.get_include()]),
+ Extension(
+ 'miplib.data.io._tifffile',
+ ['miplib/data/io/src/tifffile.c'],
+ include_dirs=[numpy.get_include()])
+ ],
+ classifiers=[
+ 'Development Status :: 3 - Alpha',
+ 'Intended Audience :: Developers',
+ 'Topic :: Software Development :: Libraries :: Python Modules',
+ 'License :: OSI Approved :: BSD License',
+ 'Programming Language :: Python :: 3.6',
+ ]
+)