imported code from https://github.com/wachtlerlab/Reg-MaxS v1.1

.gitignore

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+*.iml
+*~
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+tmp
+*egg-info
+.eggs

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ParFiles/PCABased/HSNL.json

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ParFiles/PCABased/HSNR.json

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ParFiles/PCABased/HSNR1-4.json

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ParFiles/PCABased/LLC.json

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ParFiles/Reg-MaxS-N/HSNL-HSNR.json

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ParFiles/Reg-MaxS-N/HSNL.json

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ParFiles/Reg-MaxS-N/LLC.json

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ParFiles/Reg-MaxS/HSN-fluoro01.CNGRandRot0.json

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ParFiles/Reg-MaxS/HSN-fluoro01.CNGRandRot1.json

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+[{"testSWC": "/home/aj/RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNGRandRot1.swc", "gridSizes": [80.0, 40.0, 20.0, 10.0], "outPartsDir": null, "rotBounds": [[-0.5235987755982988, 0.5235987755982988], [-0.5235987755982988, 0.5235987755982988], [-0.5235987755982988, 0.5235987755982988]], "scaleBounds": [[0.5, 2.0], [0.5, 2.0], [0.5, 2.0]], "rotMinRes": 0.017500000000000002, "transBounds": [[-30, 30], [-30, 30], [-30, 30]], "resFile": "/home/aj/RegMaxSN_WorkSpace/Results/Tests/HSN-fluoro01.CNGRandRot1.swc", "nCPU": 6, "refSWC": "/home/aj/RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNG.swc", "minScaleStepSize": 1.005, "transMinRes": 1, "inPartsDir": null, "retainTempFiles": false}]

ParFiles/Reg-MaxS/HSN-fluoro01.CNGRandScale0.json

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+[{"testSWC": "/home/aj/RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNGRandScale0.swc", "gridSizes": [80.0, 40.0, 20.0, 10.0], "outPartsDir": null, "rotBounds": [[-0.5235987755982988, 0.5235987755982988], [-0.5235987755982988, 0.5235987755982988], [-0.5235987755982988, 0.5235987755982988]], "scaleBounds": [[0.5, 2.0], [0.5, 2.0], [0.5, 2.0]], "rotMinRes": 0.017500000000000002, "transBounds": [[-30, 30], [-30, 30], [-30, 30]], "resFile": "/home/aj/RegMaxSN_WorkSpace/Results/Tests/HSN-fluoro01.CNGRandScale0.swc", "nCPU": 6, "refSWC": "/home/aj/RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNG.swc", "minScaleStepSize": 1.005, "transMinRes": 1, "inPartsDir": null, "retainTempFiles": false}]

ParFiles/Reg-MaxS/HSN-fluoro01.CNGRandTrans1.json

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+[{"testSWC": "/home/aj/RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNGRandTrans1.swc", "gridSizes": [80.0, 40.0, 20.0, 10.0], "outPartsDir": null, "rotBounds": [[-0.5235987755982988, 0.5235987755982988], [-0.5235987755982988, 0.5235987755982988], [-0.5235987755982988, 0.5235987755982988]], "scaleBounds": [[0.5, 2.0], [0.5, 2.0], [0.5, 2.0]], "rotMinRes": 0.017500000000000002, "transBounds": [[-30, 30], [-30, 30], [-30, 30]], "resFile": "/home/aj/RegMaxSN_WorkSpace/Results/Tests/HSN-fluoro01.CNGRandTrans1.swc", "nCPU": 6, "refSWC": "/home/aj/RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNG.swc", "minScaleStepSize": 1.005, "transMinRes": 1, "inPartsDir": null, "retainTempFiles": false}]

ParFiles/Reg-MaxS/HSN-fluoro01.CNGRandTranslate0.json

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+[{"testSWC": "/home/aj/RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNGRandTranslate0.swc", "gridSizes": [80.0, 40.0, 20.0, 10.0], "outPartsDir": null, "rotBounds": [[-0.5235987755982988, 0.5235987755982988], [-0.5235987755982988, 0.5235987755982988], [-0.5235987755982988, 0.5235987755982988]], "scaleBounds": [[0.5, 2.0], [0.5, 2.0], [0.5, 2.0]], "rotMinRes": 0.017500000000000002, "transBounds": [[-30, 30], [-30, 30], [-30, 30]], "resFile": "/home/aj/RegMaxSN_WorkSpace/Results/Tests/HSN-fluoro01.CNGRandTranslate0.swc", "nCPU": 6, "refSWC": "/home/aj/RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNG.swc", "minScaleStepSize": 1.005, "transMinRes": 1, "inPartsDir": null, "retainTempFiles": false}]

ParFiles/Reg-MaxS/LLC1.json

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+[{"testSWC": "/home/aj/RegMaxSN_WorkSpace/TestFiles/LLC/Cha-F-400051.CNG.swc", "gridSizes": [80.0, 40.0, 20.0, 10.0], "outPartsDir": "/home/aj/RegMaxSN_WorkSpace/Results/Tests/LLC/Cha-F-400051.CNG", "rotBounds": [[-0.5235987755982988, 0.5235987755982988], [-0.5235987755982988, 0.5235987755982988], [-0.5235987755982988, 0.5235987755982988]], "scaleBounds": [[0.5, 2.0], [0.5, 2.0], [0.5, 2.0]], "rotMinRes": 0.017500000000000002, "transBounds": [[-30, 30], [-30, 30], [-30, 30]], "resFile": "/home/aj/RegMaxSN_WorkSpace/Results/Tests/LLC/Cha-F-400051.CNG.swc", "nCPU": 6, "refSWC": "/home/aj/RegMaxSN_WorkSpace/TestFiles/LLC/Gad1-F-000062.CNG.swc", "minScaleStepSize": 1.005, "transMinRes": 1, "inPartsDir": "/home/aj/RegMaxSN_WorkSpace/TestFiles/LLC/Cha-F-400051.CNG", "retainTempFiles": false}]

Readme.md

@@ -0,0 +1,70 @@
+Welcome to regmaxsn repository!
+
+Description
+---
+
+**Reg-MaxS and Reg-MaxS-N: Algorithms for co-registration of pairs and groups of neuron morphologies based on maximization of spatial overlap**
+
+These algorithms have been discussed in detail in the following publication:
+
+Kumaraswamy A., Kai K., Ai H., Ikeno H., Wachtler T. (2018). "Spatial registration of neuron morphologies based on maximization of volume overlap". In *BMC Bioinformatics*, DOI: [10.1186/s12859-018-2136-z](https://doi.org/10.1186/s12859-018-2136-z)
+
+This repository contains scripts for running Reg-MaxS and Reg-MaxS-N algorithms for registering pairs and groups of neuron morphologies respectively.
+
+The algorithms are written in  Python and at the moment work only with SWC files.
+
+# Installation
+
+
+With Conda (Linux or Windows):
+
+1. Create a new environment: 
+>conda create --name regmaxsn python=2.7 numpy scipy pillow matplotlib scikit-learn pandas seaborn openpyxl xlrd statsmodels
+2. Activate environment: 
+>(on Linux) source activate regmaxsn 
+>(on Windows) activate regmaxsn
+3. Install regmaxsn: 
+>pip install \<path to Reg-MaxS repository\>
+4. Create Reg-MaxS Workspace. Note where your Reg-MaxS Workspace is created.
+>python \<path to Reg-MaxS repository\>/setupWorkspace.py
+
+Without Conda, with pip and virtualenvwrapper (only for Linux):
+
+1. Create a new environment: 
+>mkvirtualenv regmaxsn
+2. Activate environment: 
+>workon regmaxsn
+3. Install regmaxsn:
+>pip install \<path to Reg-MaxS repository\>
+4. Create Reg-MaxS Workspace. Note where your Reg-MaxS Workspace is created.
+>python \<path to Reg-MaxS repository\>/setupWorkspace.py
+
+NOTE: This environment must be activated every time you want to run Reg-MaxS, Reg-MaxS-N or any other scripts of this package.
+
+# Usage:
+
+## Reg-MaxS
+
+1. First you need to create a parameter file for Reg-MaxS. This is created using the file
+> \<path to your Reg-MaxS Workspace\>/utilityScripts/constructRegMaxSParFile.py
+
+You need to open this .py file in a editor and change variables as needed. The detailed usage instructions of this script are provided at the top of it. Two different use cases have been illustrated using three examples. Take a look at them and modify the script according to your needs.
+
+2. After editing the "constructRegMaxSParFile.py" as needed, create a parameter file as below:
+> python \<path to your Reg-MaxS Workspace\>/utilityScripts/constructRegMaxSParFile.py
+
+3. Run Reg-MaxS with this parameter file:
+> python -m regmaxsn.scripts.algorithms.RegMaxS \<path to the above created Reg-MaxS parameter file\>
+
+## Reg-MaxS-N
+
+1. First you need to create a parameter file for Reg-MaxS-N. This is created using the file
+> \<path to your Reg-MaxS Workspace\>/utilityScripts/constructRegMaxSNParFile.py
+
+You need to open this .py file in a editor and change variables as needed. The detailed usage instructions of this script are provided at the top of it. Two different use cases have been illustrated using three examples. Take a look at them and modify the script according to your needs.
+
+2. After editing the "constructRegMaxSNParFile.py" as needed, create a parameter file as below:
+> python \<path to your Reg-MaxS Workspace\>/utilityScripts/constructRegMaxSNParFile.py
+
+3. Run Reg-MaxS-N with this parameter file:
+> python -m regmaxsn.scripts.algorithms.RegMaxSN \<path to the above created Reg-MaxS-N parameter file\>

TestFiles/HSN-fluoro01.CNGNoiseStd1RandTrans.json

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+{"translation": [-14.24325039792944, -13.475524314671251, -1.22563862743975], "scale": [1.8937024597158745, 0.9763352276908548, 1.3257686612183175], "inFile": "RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNGNoiseStd1.swc", "angles": [0.14575881829948645, 0.024974745732486703, 0.028606050985605536], "comments": "rotation and scaling about inFile centroid"}

TestFiles/HSN-fluoro01.CNGNoiseStd2RandTrans.json

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+{"translation": [-14.24325039792944, -13.475524314671251, -1.22563862743975], "scale": [1.8937024597158745, 0.9763352276908548, 1.3257686612183175], "inFile": "RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNGNoiseStd2.swc", "angles": [0.14575881829948645, 0.024974745732486703, 0.028606050985605536], "comments": "rotation and scaling about inFile centroid"}

TestFiles/HSN-fluoro01.CNGNoiseStd3RandTrans.json

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+{"translation": [-14.24325039792944, -13.475524314671251, -1.22563862743975], "scale": [1.8937024597158745, 0.9763352276908548, 1.3257686612183175], "inFile": "RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNGNoiseStd3.swc", "angles": [0.14575881829948645, 0.024974745732486703, 0.028606050985605536], "comments": "rotation and scaling about inFile centroid"}

TestFiles/HSN-fluoro01.CNGNoiseStd4RandTrans.json

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+{"translation": [-14.24325039792944, -13.475524314671251, -1.22563862743975], "scale": [1.8937024597158745, 0.9763352276908548, 1.3257686612183175], "inFile": "RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNGNoiseStd4.swc", "angles": [0.14575881829948645, 0.024974745732486703, 0.028606050985605536], "comments": "rotation and scaling about inFile centroid"}

TestFiles/HSN-fluoro01.CNGNoiseStd5RandTrans.json

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+{"translation": [-14.24325039792944, -13.475524314671251, -1.22563862743975], "scale": [1.8937024597158745, 0.9763352276908548, 1.3257686612183175], "inFile": "RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNGNoiseStd5.swc", "angles": [0.14575881829948645, 0.024974745732486703, 0.028606050985605536], "comments": "rotation and scaling about inFile centroid"}

TestFiles/HSN-fluoro01.CNGRandRot.json

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+{"translation": [0.0, 0.0, 0.0], "scale": [1.0, 1.0, 1.0], "inFile": "RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNG.swc", "angles": [0.3521985514581609, 0.5039103525541285, -0.19749540747982364], "comments": "rotation and scaling about inFile centroid"}

TestFiles/HSN-fluoro01.CNGRandScale.json

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TestFiles/HSN-fluoro01.CNGRandTrans.json

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+{"translation": [-14.24325039792944, -13.475524314671251, -1.22563862743975], "scale": [1.8937024597158745, 0.9763352276908548, 1.3257686612183175], "inFile": "RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNG.swc", "angles": [0.14575881829948645, 0.024974745732486703, 0.028606050985605536], "comments": "rotation and scaling about inFile centroid"}

TestFiles/HSN-fluoro01.CNGRandTranslate.json

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+{"translation": [13.141647976351251, 4.663062659992558, 18.979687164416433], "scale": [1.0, 1.0, 1.0], "inFile": "RegMaxSN_WorkSpace/TestFiles/HSN-fluoro01.CNG.swc", "angles": [0.0, 0.0, 0.0], "comments": "rotation and scaling about inFile centroid"}

TestFiles/HSNR/HSN-fluoro04.CNG.swc

@@ -0,0 +1,923 @@
+# SWC to SWC conversion from L-Measure. Sridevi Polavaram: spolavar@gmu.edu
+# Original fileName:/cng_repository/Soumya/LM_Automated_Testing_Enviornment/Test_Lm3.7.2_11022011_SG_Linux_NMOConv8/TestResults/LoadTest/test_52/HSN-fluoro04.CNG.swc
+#
+# Original file HSN-fluoro04.swc edited using StdSwc version 1.31 on 5/31/11.
+# Irregularities and fixes documented in HSN-fluoro04.swc.std.  See StdSwc1.31.doc for more information.
+#
+# HSN neuron (lobula place tangential cells, LPTC) from Calliphora vicina
+# identification by response properties, in vivo Alexa injection, 2-photon-microscopy imaging, x63 objective, multiple image tiles
+# reconstructed and processed with Matlab and the TREES toolbox http://www.treestoolbox.org
+# scaling 1:1:1 um
+#  Max Planck Institute of Neurobiology, Martinsried, Germany
+#  Systems and Computational Neuroscience, Alexander Borst
+#  published in "The morphological identity of insect dendrites" Cuntz H, Forstner F, Haag J, Borst A. PMID 19112481
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TestFiles/HSNR/HSN-fluoro07.CNG.swc

@@ -0,0 +1,931 @@
+# SWC to SWC conversion from L-Measure. Sridevi Polavaram: spolavar@gmu.edu
+# Original fileName:/cng_repository/Soumya/LM_Automated_Testing_Enviornment/Test_Lm3.7.2_11022011_SG_Linux_NMOConv8/TestResults/LoadTest/test_52/HSN-fluoro07.CNG.swc
+#
+# Original file HSN-fluoro07.swc edited using StdSwc version 1.31 on 5/31/11.
+# Irregularities and fixes documented in HSN-fluoro07.swc.std.  See StdSwc1.31.doc for more information.
+#
+# HSN neuron (lobula place tangential cells, LPTC) from Calliphora vicina
+# identification by response properties, in vivo Alexa injection, 2-photon-microscopy imaging, x63 objective, multiple image tiles
+# reconstructed and processed with Matlab and the TREES toolbox http://www.treestoolbox.org
+# scaling 1:1:1 um
+#  Max Planck Institute of Neurobiology, Martinsried, Germany
+#  Systems and Computational Neuroscience, Alexander Borst
+#  published in "The morphological identity of insect dendrites" Cuntz H, Forstner F, Haag J, Borst A. PMID 19112481
+# SCALE 1.0 1.0 1.0 
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TestFiles/LLC/Cha-F-000012.CNG.swc

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TestFiles/LLC/Cha-F-000018.CNG.swc

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TestFiles/LLC/Gad1-F-600000.CNG.swc

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regmaxsn/core/RegMaxSPars.py

@@ -0,0 +1,122 @@
+# Ajayrama Kumaraswamy, 2017, LMU Munich
+
+"""
+The input parameters of different Algorithms
+"""
+
+# **********************************************************************************************************************
+"""
+Parameters of Reg-MaxS.
+refSWC:                 string. The reference SWC file, to which testFile is registered.
+                        Must be a valid SWC file on the file system.
+
+testSWC:                string. The test SWC file which is registered to refSWC.
+                        Must be a valid SWC file on the file system.
+
+resFile:                string. This file will be created and the resulting SWC will be written into it.
+                        It needs to be valid file reference.
+
+gridSizes:              list of floats. These are the voxel sizes over which Reg-MaxS is run.
+                        Values must be in micrometers.
+
+transBounds:            three member list, each element is a two member list. These members must be floats.
+                        They are bounds used for estimating translation parameters along XYZ axes.
+                        Values must be in micrometers.
+
+transMinRes:            float. After searching at the three voxel sizes in gridSizes, a further search is carried
+                        out at this resolution for translation parameters. Values must be in micrometers.
+
+rotBounds:              three member list, each element is a two member list. These members must be floats.
+                        They are bounds used for estimating rotation parameters about XYZ axes.
+                        Values must be in radians.
+
+rotMinRes:              float. After refining rotation parameters at the three voxel sizes in gridSizes, a further
+                        refinement is carried out at this resolution. Values must be in radians.
+
+scaleBounds:            three member list, each element is a two member list. These members must be floats.
+                        They are bounds used for estimating scaling parameters along XYZ axes.
+
+minScaleStepSize:       float. After refining scaling parameters at the three voxel sizes in gridSizes, a further
+                        refinement is carried out at this resolution.
+
+nCPU:                   int. Reg-MaxS internally uses multiple processes for exhaustive searches.
+                        This specifies the number of processes to use. It can be a maximum of number of
+                        processors on the machine being used. Larger this number, faster the execution.
+
+inPartsDir:             string. A file system directory, the swc files in which are transformed exactly the same as
+                        testSWC and written into outPartsDir.
+
+outPartsDir:            string. A file system directory into which the transformed swc files of inPartsDir are written.
+                        It will be created if it does not exist.
+
+retainTempFiles:        boolean. Reg-MaxS generates some temporary files in the directory where resFile is supposed to
+                        be created. If retainTempFiles is True, these are not deleted. Else, they are.
+
+
+"""
+RegMaxSParNames = ['refSWC', 'testSWC', 'resFile',
+                   'gridSizes',
+                   'transBounds', 'transMinRes',
+                   'rotBounds', 'rotMinRes',
+                   'scaleBounds', 'minScaleStepSize',
+                   'nCPU', 'inPartsDir', 'outPartsDir', 'retainTempFiles']
+
+# **********************************************************************************************************************
+
+"""
+Parameters of Reg-MaxS-N. Some parameters are same as of Reg-MaxS above and hence are omitted here.
+
+swcList:                list of strings. Each member must refer to a valid SWC file on the file system.
+
+initRefSWC:             string. The initial reference to use for Reg-MaxS-N, i.e., the reference of the first iteration.
+                        Must be a valid SWC file on the file system.
+
+resDir:                 string. The directory into which the results of Reg-MaxS-N are written. Will be created anew
+                        or replaced if it exists.
+
+finallyNormalizeWRT:    string. Must be one of the SWCs of swcList. The whole set of SWCs after registration
+                        is transformed affinely together so that this SWC is restored to it's final form.
+
+maxIter:                int. The number of iterations for which Reg-MaxS-N runs before stopping and returning the
+                        result.
+
+usePartsDir:            boolean. If True, Reg-MaxS-N check if for every SWC in swcList, a folder exists with the
+                        same name and path without the '.swc' extention. If such folders exist, the SWCs in them are
+                        transformed exactly the same as their corresponding SWCs and written into folders with the same
+                        name in resDir.
+
+"""
+
+RegMaxSNParNames = [
+                    'swcList', 'initRefSWC', 'resDir', 'finallyNormalizeWRT', 'maxIter',
+                    'gridSizes',
+                    'transBounds', 'transMinRes',
+                    'rotBounds', 'rotMinRes',
+                    'scaleBounds', 'minScaleStepSize',
+                    'nCPU', 'usePartsDir'
+                    ]
+
+# **********************************************************************************************************************
+
+"""
+Parameters of the registration PCA-based algorithm. Some parameters have the same meaning as in Reg-MaxS above and
+hence have been omitted here.
+
+gridSizes:              list of floats. These are the voxel sizes over which Reg-MaxS is run.
+                        Values must be in micrometers. These values are not used in the actual algorithm. The last
+                        element of this list is used to calculate a measure of dissimilarity between the reference SWC
+                        and the result SWC produced by the 'pcaBased' algorithm.
+
+usePartsDir:            boolean. If True, Reg-MaxS-N check if for every SWC in swcList, a folder exists with the
+                        same name and path without the '.swc' extention. If such folders exist, the SWCs in them are
+                        transformed exactly the same as their corresponding SWCs and written into folders with the same
+                        name in resDir.
+"""
+
+pcaBasedParNames = [
+                    'refSWC', 'testSWC', 'resFile',
+                    'gridSizes', 'usePartsDir'
+                    ]
+# **********************************************************************************************************************
+
+DensitySaveParNames = ['refSWC', 'resFile']

regmaxsn/core/SWCTransforms.py

@@ -0,0 +1,212 @@
+import os
+import numpy as np
+from transforms import compose_matrix
+from swcFuncs import readSWC_numpy, writeSWC_numpy
+
+
+def three32BitInt2complexList(arr):
+    assert arr.dtype == np.int32, 'Only 32 bit ints allowed'
+    assert arr.shape[1] == 3, 'only three ints per row allowed'
+    temp = np.zeros((arr.shape[0], 4), dtype=np.int32)
+    temp[:, 1:] = arr
+    temp1 = temp.view(np.int64)
+    j = complex(0, 1)
+    return (temp1[:, 0] + j * temp1[:, 1]).tolist()
+
+
+def objFun(y):
+
+    x, data = y
+    nrnPtsTrans = data.transform(x, data.SWC2AlignPts)
+    nrnVox = np.array(np.round(nrnPtsTrans / data.gridSize), np.int32)
+    nrnVoxSet = set(three32BitInt2complexList(nrnVox))
+    nInter = len(nrnVoxSet.intersection(data.refVoxSet))
+    nUnion = len(nrnVoxSet) + len(data.refVoxSet) - nInter
+    sumOfDiffs = 1 - (nInter / float(nUnion))
+
+    return sumOfDiffs
+
+
+
+class BaseSWCTranform(object):
+
+    def __init__(self, refSWC, SWC2Align, gridSize):
+
+        super(BaseSWCTranform, self).__init__()
+
+        if os.path.isfile(refSWC) and refSWC.endswith('.swc'):
+            self.refSWCPts = np.loadtxt(refSWC)[:, 2:5]
+        elif type(refSWC) == np.ndarray:
+            self.refSWCPts = refSWC[:, 2:5]
+        else:
+            raise(ValueError('Unknown data in SWC2Align'))
+
+        self.refCenter = self.refSWCPts.mean(axis=0)
+        refVox = np.array(np.round(self.refSWCPts / gridSize), np.int32)
+        self.gridSize = gridSize
+        self.refVoxSet = set(three32BitInt2complexList(refVox))
+
+        if os.path.isfile(SWC2Align) and SWC2Align.endswith('.swc'):
+            self.headr, self.SWC2AlignFull = readSWC_numpy(SWC2Align)
+        elif type(SWC2Align) == np.ndarray:
+            assert type(SWC2Align['data']) == np.ndarray, 'Unknown data in SWC2Align'
+            self.SWC2AlignFull = SWC2Align
+            self.headr = ''
+        else:
+            raise(ValueError('Unknown data in SWC2Align'))
+        self.SWC2AlignPts = self.SWC2AlignFull[:, 2:5]
+        self.center = self.SWC2AlignPts.mean(axis=0)
+
+    def transform(self, pars, data):
+
+        return data
+
+    def writeSolution(self, outFile, bestSol, inFile=None):
+
+        if inFile:
+            headr, data = readSWC_numpy(inFile)
+        else:
+            headr, data = self.headr, self.SWC2AlignFull
+
+        data[:, 2:5] = self.transform(bestSol, data[:, 2:5])
+
+        writeSWC_numpy(outFile, data, headr)
+
+
+class SWCTranslate(BaseSWCTranform):
+
+    def __init__(self, refSWC, SWC2Align, gridSize):
+
+        super(SWCTranslate, self).__init__(refSWC, SWC2Align, gridSize)
+
+    def transform(self, pars, data):
+
+        return data + pars
+
+
+class SWCRotate(BaseSWCTranform):
+
+    def __init__(self, refSWC, SWC2Align, gridSize):
+
+        super(SWCRotate, self).__init__(refSWC, SWC2Align, gridSize)
+
+
+    def transform(self, pars, data):
+
+        rotMat = compose_matrix(angles=pars)
+        dataCentered = data - self.center
+        return np.dot(rotMat[:3, :3], dataCentered.T).T + self.center
+
+
+class SWCScale(object):
+
+    def __init__(self, refSWC, SWC2Align, gridSize):
+
+        super(SWCScale, self).__init__()
+
+        self.gridSize = gridSize
+
+        if os.path.isfile(refSWC) and refSWC.endswith('.swc'):
+            self.refSWCPts = np.loadtxt(refSWC)[:, 2:5]
+        elif type(refSWC) == np.ndarray:
+            self.refSWCPts = refSWC[:, 2:5]
+        else:
+            raise(ValueError('Unknown data in SWC2Align'))
+
+        refCenter = self.refSWCPts.mean(axis=0)
+        refSWCPtsCentered = self.refSWCPts - refCenter
+        refVox = np.array(np.round(refSWCPtsCentered / gridSize), np.int32)
+        self.refVoxSet = set(three32BitInt2complexList(refVox))
+
+        if os.path.isfile(SWC2Align) and SWC2Align.endswith('.swc'):
+            self.headr, self.SWC2AlignFull = readSWC_numpy(SWC2Align)
+        elif type(SWC2Align) == np.ndarray:
+            assert type(SWC2Align['data']) == np.ndarray, 'Unknown data in SWC2Align'
+            self.SWC2AlignFull = SWC2Align
+            self.headr = ''
+        else:
+            raise(ValueError('Unknown data in SWC2Align'))
+
+        self.SWC2AlignPts = self.SWC2AlignFull[:, 2:5].copy()
+        self.center = self.SWC2AlignPts.mean(axis=0)
+        self.SWC2AlignPts -= self.center
+
+
+
+    def transform(self, pars, data):
+
+        rotMat = compose_matrix(scale=pars)
+        dataCentered = data
+        return np.dot(rotMat[:3, :3], dataCentered.T).T
+
+    def writeSolution(self, outFile, bestSol, inFile=None):
+
+        if inFile:
+            headr, data = readSWC_numpy(inFile)
+        else:
+            headr, data = self.headr, self.SWC2AlignFull
+
+        data[:, 2:5] = self.transform(bestSol, data[:, 2:5] - self.center) + self.center
+
+        writeSWC_numpy(outFile, data, headr)
+
+
+
+class ArgGenIterator:
+
+    def __init__(self, arg1, arg2):
+
+        self.arg1 = arg1
+        self.arg2 = arg2
+        self.pointsDone = 0
+
+    def __iter__(self):
+
+        self.pointsDone = 0
+        return self
+
+    def next(self):
+
+        if self.pointsDone < len(self.arg1):
+            toReturn = (self.arg1[self.pointsDone], self.arg2)
+            self.pointsDone += 1
+            return toReturn
+        else:
+            raise StopIteration
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

regmaxsn/core/farthestPointStats.py

@@ -0,0 +1,50 @@
+from regmaxsn.core.swcFuncs import readSWC_numpy, resampleSWC
+from scipy.spatial import ConvexHull
+import pandas as pd
+from scipy.spatial.distance import cdist
+
+
+def maxDistStats(swcFiles):
+
+    swcHullVerts = []
+    swcDatas = []
+    swcDataSeries =[]
+    swcHullVertsSeries = []
+
+    maxDistStatsDF = pd.DataFrame()
+
+    for swcFile in swcFiles:
+        totalLen, swcDataWithR = resampleSWC(swcFile, 0.5)
+        swcData = swcDataWithR[:, :3]
+        swcDatas.append(swcData)
+        swcDataSeries.append(pd.Series([tuple(x) for x in swcData]))
+        hull = ConvexHull(swcData)
+        swcHullVert = swcData[hull.vertices, :]
+        swcHullVerts.append(swcHullVert)
+        swcHullVertsSeries.append(pd.Series([tuple(x) for x in swcHullVert]))
+
+    for swcInd1, swcData1 in enumerate(swcDatas):
+
+        swcInds = range(len(swcFiles))
+        swcInds.remove(swcInd1)
+        swcDataSeries1 = swcDataSeries[swcInd1]
+
+        for swcInd2 in swcInds:
+            swcHullVert2 = swcHullVerts[swcInd2]
+            swcHullVertSeries2 = swcHullVertsSeries[swcInd2]
+
+            distMat = cdist(swcData1, swcHullVert2)
+
+            maxDistsInds = distMat.argmax(axis=1)
+
+            tempDF = pd.DataFrame()
+            tempDF["source point"] = swcDataSeries1
+            tempDF["maximum distance"] = distMat.max(axis=1)
+            tempDF["destination farthest point"] = swcHullVertSeries2[maxDistsInds].values
+            tempDF["destination SWC"] = swcFiles[swcInd2]
+
+            maxDistStatsDF = maxDistStatsDF.append(tempDF, ignore_index=True)
+
+    return maxDistStatsDF
+
+

regmaxsn/core/iterativeRegistration.py

@@ -0,0 +1,539 @@
+import os
+import numpy as np
+from .swcFuncs import transSWC, transSWC_rotAboutPoint
+from .SWCTransforms import SWCTranslate, objFun
+import shutil
+import json
+import subprocess
+
+def transPreference(x, y):
+    """
+    Given two transforms x and y, returns if x is preferred over y. Preferences are scaling > translation,
+    scaling > rotation, translation > rotation.
+    :param x:
+    :param y:
+    :return:
+    """
+    if x == 'scale':
+        return 0
+    elif y == 'scale':
+        return 1
+    elif x == 'rot':
+        return 0
+    else:
+        return 1
+
+
+def getRemainderScale(scale, oldScale):
+    """
+    Elementwise divides oldScale by scale, effectively removing scale from old scale. The first entry is bounded
+    above by 1 and the second entry is bounded below by 1.
+    :param scale: 3 member list of 2 member float lists.
+    :param oldScale: 3 member list of 2 member float lists.
+    :return: 3 member list of 2 member float lists.
+    """
+    toReturn = []
+    for s, oldS in zip(scale, oldScale):
+
+        toReturn.append([min(oldS[0] / s, 1), max(oldS[1] / s, 1)])
+
+    return toReturn
+
+
+class IterativeRegistration(object):
+    """
+    This class is used to run basic Reg-MaxS algorithm.
+    """
+    def __init__(self, refSWC, gridSizes, rotBounds, transBounds,
+                 transMinRes, scaleMinRes, rotMinRes, nCPU):
+        """
+        Initialization
+        :param refSWC: valid file path to a valid SWC file, reference SWC
+        :param gridSizes: list of three floats, the voxel sizes over which estimations are run, in micrometer
+        :param rotBounds: three member list of two member float lists, the bounds for rotation euler angles abour XYZ
+                            axes, in radians
+        :param transBounds: three member list of two member float lists, the bounds for translations along XYZ axes.
+        :param transMinRes: float, minimum resolution of exhaustive search for translation parameters in micrometer.
+        :param scaleMinRes: float. minimum (multiplicative) resolution of exhuasitve search for scaling paramers.
+        :param rotMinRes: float. minimum resolution of exhuastive search for rotation euler angle parameters in radians.
+        :param nCPU: int, number of processes to use
+        """
+        super(IterativeRegistration, self).__init__()
+
+        self.refSWC = refSWC
+        self.gridSizes = gridSizes
+        self.rotBounds = rotBounds
+        self.transBounds = transBounds
+        self.rotBounds = rotBounds
+        self.transMinRes = transMinRes
+        self.rotMinRes = rotMinRes
+        self.scaleMinRes = scaleMinRes
+        self.nCPU = nCPU
+        self.allFuncs = {'trans': self.transOnce, 'rot': self.rotOnce, 'scale': self.scaleOnce}
+
+
+
+    def rotOnce(self, SWC2Align, outFiles, ipParFile):
+        """
+        Runs exhaustive search to find the best rotation euler angles about XYZ axes that maximize the volume overlap
+        between SWC2Align and self.refSWC. Results are written in the two file paths of outFiles.
+        If solution found is no better than doing nothing or if the angles found are lower than minimum resolution,
+        zero angles are returned with done as true
+        :param SWC2Align: valid file path to a valid SWC file
+        :param outFiles: list of two valid file paths. SWC2Align rotated with optimum parameters is written to
+        outFiles[0], a log file of the process is written into ouFiles[1].
+        :param ipParFile: valid file path. Temporary file used.
+        :return: bestSol, bestVal, done
+                bestSol: list of three floats, best Euler angles in radians
+                bestVal: float, best value of dissimilarity between SWC2Align and refSWC at the lowest voxel size
+                done: boolean, see above.
+        """
+        pars = [self.refSWC, SWC2Align, outFiles,
+                self.gridSizes, self.rotBounds, self.rotMinRes, self.nCPU]
+
+        with open(ipParFile, 'w') as fle:
+            json.dump(pars, fle)
+
+        f2call = os.path.join(os.path.split(__file__)[0], 'rotOnce.py')
+        subprocess.call(['python', f2call, ipParFile])
+
+        with open(outFiles[1], 'r') as fle:
+            out = json.load(fle)
+            bestSol = out['bestSol']
+            done = out['done']
+            bestVal = out['bestVal']
+            print(bestSol, bestVal, done)
+
+        return bestSol, bestVal, done
+
+    def transOnce(self, SWC2Align, outFiles, ipParFile):
+        """
+        Runs exhaustive search to find the best translations along XYZ axes that maximize the volume overlap between
+        SWC2Align and self.refSWC. Results are written in the two file paths of outFiles.
+        If solution found is no better than doing nothing or if the translations found are lower than
+        the minimum resolution, zero translations are returned with done set to true
+        :param SWC2Align: valid file path to a valid SWC file
+        :param outFiles: list of two valid file paths. SWC2Align translated with optimum parameters is written to
+        outFiles[0], a log file of the process is written into ouFiles[1].
+        :param ipParFile: valid file path. Temporary file used.
+        :return: bestSol, bestVal, done
+                bestSol: list of three floats, best translations in micrometer
+                bestVal: float, best value of dissimilarity between SWC2Align and refSWC at the lowest voxel size
+                done: boolean, see above.
+        """
+        pars = [self.refSWC, SWC2Align, outFiles,
+                self.gridSizes, self.transBounds, self.transMinRes, self.nCPU]
+
+        with open(ipParFile, 'w') as fle:
+            json.dump(pars, fle)
+
+        f2call = os.path.join(os.path.split(__file__)[0], 'transOnce.py')
+        subprocess.call(['python', f2call, ipParFile])
+
+        with open(outFiles[1], 'r') as fle:
+            out = json.load(fle)
+            bestSol = out['bestSol']
+            done = out['done']
+            bestVal = out['bestVal']
+            print(bestSol, bestVal, done)
+
+        return bestSol, bestVal, done
+
+    def scaleOnce(self, SWC2Align, outFiles, ipParFile, scaleBounds):
+        """
+        Runs exhaustive search to find the best scaling parameters along XYZ axes that maximize the volume overlap
+        between SWC2Align and self.refSWC. Results are written in the two file paths of outFiles.
+        If solution found is no better than doing nothing or if the scaling parameters found are lower than
+        the minimum resolution, unity scaling parameters are returned with done set to true
+        :param SWC2Align: valid file path to a valid SWC file
+        :param outFiles: list of two valid file paths. SWC2Align scaled with optimum parameters is written to
+        outFiles[0], a log file of the process is written into ouFiles[1].
+        :param ipParFile: valid file path. Temporary file used.
+        :return: bestSol, bestVal, done
+                bestSol: list of three floats, best scaling parameters
+                bestVal: float, best value of dissimilarity between SWC2Align and refSWC at the lowest voxel size
+                done: boolean, see above.
+        """
+        pars = [self.refSWC, SWC2Align, outFiles,
+                self.gridSizes, scaleBounds, self.scaleMinRes, self.nCPU]
+
+        with open(ipParFile, 'w') as fle:
+            json.dump(pars, fle)
+
+        f2call = os.path.join(os.path.split(__file__)[0], 'scaleOnce.py')
+        subprocess.call(['python', f2call, ipParFile])
+
+        with open(outFiles[1], 'r') as fle:
+            out = json.load(fle)
+            bestSol = out['bestSol']
+            done = out['done']
+            bestVal = out['bestVal']
+            print(bestSol, bestVal, done)
+
+        return bestSol, bestVal, done
+
+    def compare(self, srts, SWC2Align, tempOutFiles, ipParFile, scaleBounds):
+        """
+        Runs the exhaustive searchs for the transforms in srts and returns some info about the searches
+        :param srts: list of strings, valid entries are 'scale', 'trans' and 'rot'
+        :param SWC2Align: valid file path of valid SWC file.
+        :param tempOutFiles: list of two valid file paths, for temporary internal use.
+        :param ipParFile: valid file path, for temporary internal use.
+        :param scaleBounds: three member list of two member float lists, the bounds for scaling parameters
+         along XYZ axes.
+        :return: tempDones, presBestSol, presBestVal, presBestDone, presBestTrans
+                 tempDones: list of booleans, same size as srts, contains the value of 'done' of respective exhaustive
+                 searches
+                 presBestTrans: transform among srts leading to the lowest dissimilarity
+                 presBestSol: list of three floats, correspong transform parameters
+                 presBestDone: boolean, 'done' value of presBestTrans
+        """
+        presBestVal = 1e6
+        presBestTrans = 'trans'
+        presBestSol = [0, 0, 0]
+        presBestDone = False
+
+        tempDones = {}
+
+        for g in srts:
+
+            if g == 'scale':
+                bestSol, bestVal, done = self.scaleOnce(SWC2Align, tempOutFiles[g], ipParFile, scaleBounds)
+            elif g == 'rot':
+                bestSol, bestVal, done = self.rotOnce(SWC2Align, tempOutFiles[g], ipParFile)
+            elif g == 'trans':
+                bestSol, bestVal, done = self.transOnce(SWC2Align, tempOutFiles[g], ipParFile)
+            else:
+                raise('Invalid transformation type ' + g)
+
+
+            tempDones[g] = done
+
+            if (bestVal == presBestVal and transPreference(presBestTrans, g)) or (bestVal < presBestVal):
+
+                presBestTrans = g
+                presBestVal = bestVal
+                presBestSol = bestSol
+                presBestDone = done
+
+
+        return tempDones, presBestSol, presBestVal, presBestDone, presBestTrans
+
+
+
+
+
+    def performReg(self, SWC2Align, resFile, scaleBounds,
+                   inPartsDir=None, outPartsDir=None,
+                   initGuessType='just_centroids',
+                   retainTempFiles=False):
+        """
+        Repeatedly applies translation, rotation and scaling transforms to SWC2Align to maximize its volume overlap
+        with self.refSWC. See Reg-MaxS-N manuscript for more info.
+
+        :param SWC2Align: valid file path of a valid SWC file, the SWC that is registered to self.refSWC
+        :param resFile: valid file path, where SWC2Align registered to self.refSWC is written
+        :param scaleBounds: three member list of two member float lists, the bounds for scaling parameters
+        :param inPartsDir: valid directory path, any swc files with this will be transformed exactly same as
+                            SWC2Align and written in to outPartsDir
+        :param outPartsDir: valid directory path
+        :param initGuessType: string, valid values are 'just centroids' and 'nothing'. If 'just centroids', the
+        centroids are initially matched, if 'nothing' they are not.
+        :param retainTempFiles: boolean, whether to retain the intermediate files.
+        :return: finalFile, finalSolFile
+                 finalFile: same as resFile
+                 finalSolFile: a file at <resFile name>Sol.txt where results of the process are logged.
+        """
+
+        resDir, expName = os.path.split(resFile[:-4])
+
+        ipParFile = os.path.join(resDir, 'tmp.json')
+        vals = ['trans', 'rot', 'scale']
+        tempOutFiles = {}
+        for val in vals:
+            fle1 = os.path.join(resDir, val + '.swc')
+            fle2 = os.path.join(resDir, val + 'bestSol.json')
+            tempOutFiles[val] = [fle1, fle2]
+
+        refMean = np.loadtxt(self.refSWC)[:, 2:5].mean(axis=0)
+        iterationNo = 0
+
+        tempOutPath = os.path.join(resDir, expName + 'trans')
+        if not os.path.isdir(tempOutPath):
+            os.mkdir(tempOutPath)
+
+        SWC2AlignLocal = os.path.join(tempOutPath, str(iterationNo) + '.swc')
+        SWC2AlignMean = np.loadtxt(SWC2Align)[:, 2:5].mean(axis=0)
+
+        if initGuessType == 'just_centroids':
+
+            transSWC(SWC2Align, np.eye(3), refMean - SWC2AlignMean, SWC2AlignLocal)
+            totalTransform = np.eye(4)
+            totalTransform[:3, 3] = -SWC2AlignMean
+            totalTranslation = refMean
+
+        elif initGuessType == 'nothing':
+
+            shutil.copy(SWC2Align, SWC2AlignLocal)
+            totalTransform = np.eye(4)
+            totalTransform[:3, 3] = -SWC2AlignMean
+            totalTranslation = SWC2AlignMean
+
+        else:
+            raise(ValueError('Unknown value for argument \'initGuessType\''))
+
+
+        SWC2AlignT = SWC2AlignLocal
+
+        scaleDone = False
+        bestVals = {}
+
+        while not scaleDone:
+
+            done = False
+            srts = ['rot', 'trans']
+
+
+            while not done:
+
+                tempDones, bestSol, bestVal, lDone, g = self.compare(srts, SWC2AlignT, tempOutFiles, ipParFile, None)
+
+                outFile = os.path.join(tempOutPath, str(iterationNo) + g[0] + '.swc')
+
+                outFileSol = os.path.join(tempOutPath, 'bestSol' + str(iterationNo) + g[0] + '.txt')
+
+                shutil.copyfile(tempOutFiles[g][0], outFile)
+                shutil.copyfile(tempOutFiles[g][1], outFileSol)
+
+                with open(outFileSol, 'r') as fle:
+                    pars = json.load(fle)
+                    presTrans = np.array(pars['transMat'])
+                    if g == 'trans':
+                        totalTranslation += presTrans[:3, 3]
+                    else:
+                        totalTransform = np.dot(presTrans, totalTransform)
+
+                print(str(iterationNo) + g)
+
+                bestVals[bestVal] = {"outFile": outFile, "outFileSol": outFileSol,
+                                     "totalTransform": totalTransform,
+                                     "totalTranslation": totalTranslation,
+                                     "iterationIndicator": str(iterationNo) + g
+                                     }
+
+                iterationNo += 1
+
+                done = lDone
+
+                SWC2AlignT = outFile
+
+
+            bestSol, bestVal, sDone = self.scaleOnce(SWC2AlignT, tempOutFiles['scale'], ipParFile, scaleBounds)
+
+            outFile = os.path.join(tempOutPath, str(iterationNo) + 's.swc')
+
+            outFileSol = os.path.join(tempOutPath, 'bestSol' + str(iterationNo) + 's.txt')
+
+            shutil.copyfile(tempOutFiles['scale'][0], outFile)
+            shutil.copyfile(tempOutFiles['scale'][1], outFileSol)
+
+            with open(outFileSol, 'r') as fle:
+                pars = json.load(fle)
+                presTrans = np.array(pars['transMat'])
+                totalTransform = np.dot(presTrans, totalTransform)
+
+            print(str(iterationNo) + 's')
+
+            bestVals[bestVal] = {"outFile": outFile, "outFileSol": outFileSol,
+                                 "totalTransform": totalTransform,
+                                 "totalTranslation": totalTranslation,
+                                 "iterationIndicator": str(iterationNo) + 's'
+                                 }
+
+            iterationNo += 1
+
+            SWC2AlignT = outFile
+
+
+            tempDones, bestSol, bestVal, lDone, g = self.compare(vals, SWC2AlignT, tempOutFiles, ipParFile, scaleBounds)
+            scaleDone = all(tempDones.values())
+
+            if not scaleDone:
+                with open(tempOutFiles['rot'][1], 'r') as fle:
+                    pars = json.load(fle)
+                    rBestVal = pars['bestVal']
+
+                with open(tempOutFiles['trans'][1], 'r') as fle:
+                    pars = json.load(fle)
+                    tBestVal = pars['bestVal']
+
+                if rBestVal < tBestVal:
+                    g = 'rot'
+                else:
+                    g = 'trans'
+
+                outFile = os.path.join(tempOutPath, str(iterationNo) + g[0] + '.swc')
+
+                outFileSol = os.path.join(tempOutPath, 'bestSol' + str(iterationNo) + g[0] + '.txt')
+
+                shutil.copyfile(tempOutFiles[g][0], outFile)
+                shutil.copyfile(tempOutFiles[g][1], outFileSol)
+
+                with open(outFileSol, 'r') as fle:
+                    pars = json.load(fle)
+                    presTrans = np.array(pars['transMat'])
+                    if g == 'trans':
+                        totalTranslation += presTrans[:3, 3]
+                    else:
+                        totalTransform = np.dot(presTrans, totalTransform)
+
+                print(str(iterationNo) + g)
+
+                bestVals[bestVal] = {"outFile": outFile, "outFileSol": outFileSol,
+                                     "totalTransform": totalTransform,
+                                     "totalTranslation": totalTranslation,
+                                     "iterationIndicator": str(iterationNo) + g
+                                     }
+
+                iterationNo += 1
+
+                SWC2AlignT = outFile
+
+        championBestVal = min(bestVals.keys())
+        totalTransform = bestVals[championBestVal]["totalTransform"]
+        totalTranslation = bestVals[championBestVal]["totalTranslation"]
+        bestIterIndicator = bestVals[championBestVal]["iterationIndicator"]
+
+        print("bestIter: {}, bestVal: {}".format(bestIterIndicator, championBestVal))
+
+        totalTransform[:3, 3] += totalTranslation
+
+        for g in vals:
+            [os.remove(x) for x in tempOutFiles[g]]
+        os.remove(ipParFile)
+
+        if not retainTempFiles:
+            shutil.rmtree(tempOutPath)
+
+        finalFile = os.path.join(resDir, expName + '.swc')
+        transSWC_rotAboutPoint(SWC2Align,
+                               totalTransform[:3, :3], totalTransform[:3, 3],
+                               finalFile,
+                               [0, 0, 0]
+                               )
+        trans = SWCTranslate(self.refSWC, finalFile, self.gridSizes[-1])
+        finalVal = objFun(([0, 0, 0], trans))
+        finalSolFile = os.path.join(resDir, expName + 'Sol.txt')
+
+        with open(finalSolFile, 'w') as fle:
+            json.dump({'finalVal': finalVal, 'finalTransMat': totalTransform.tolist(), 'refSWC': self.refSWC,
+                       'SWC2Align': SWC2Align, 'bestIteration': bestIterIndicator}, fle)
+
+        if inPartsDir is not None:
+
+            if os.path.isdir(inPartsDir):
+
+                dirList = os.listdir(inPartsDir)
+                dirList = [x for x in dirList if x.endswith('.swc')]
+
+                if not os.path.isdir(outPartsDir):
+                    os.mkdir(outPartsDir)
+
+                for entry in dirList:
+                    transSWC_rotAboutPoint(os.path.join(inPartsDir, entry),
+                                           totalTransform[:3, :3], totalTransform[:3, 3],
+                                           os.path.join(outPartsDir, entry),
+                                           [0, 0, 0]
+                                           )
+
+            else:
+                print('Specified partsDir {} not found'.format(inPartsDir))
+
+
+        return finalFile, finalSolFile
+
+
+def composeRefSWC(alignedSWCs, newRefSWC, gridSize):
+    """
+    Given a list of SWCs, it constructs a fake SWC to represent the union of the volumes occupied by the SWCs.
+    :param alignedSWCs: list of SWC files
+    :param newRefSWC: valid file path, where the resulting SWC is written
+    :param gridSize: float, the voxel size at which the volumes are discretized before forming the union.
+    :return: dissim: float, 1 - (# of voxels in the intersection of volumes) / (# of voxels in the union of volumes)
+    """
+    indVoxs = []
+
+    for aswc in alignedSWCs:
+        aPts = np.loadtxt(aswc)[:, 2:5]
+        aVox = np.array(np.round(aPts / gridSize), np.int32)
+        aVoxSet = set(map(tuple, aVox))
+        indVoxs.append(aVoxSet)
+
+    # majority = [sum(x in y for y in indVoxs) >= 0.5 * len(indVoxs) for x in aUnion]
+
+    # newRefVoxSet = [y for x, y in enumerate(aUnion) if majority[x]]
+
+    aUnion = reduce(lambda x, y: x.union(y), indVoxs)
+    aInt = reduce(lambda x, y: x.intersection(y), indVoxs)
+
+    newRefVoxSet = aUnion
+
+
+    newRefXYZ = np.array(list(newRefVoxSet)) * gridSize
+
+    writeFakeSWC(newRefXYZ, newRefSWC)
+    # print(len(aInt), len(aUnion))
+
+    return 1 - len(aInt) / float(len(aUnion))
+
+
+def calcOverlap(refSWC, SWC2Align, gridSize):
+    """
+    Given two SWCs, it calculates a measure of dissimilarity between them using their discretized volumes.
+    It's defined as 1 - size of intersection of the volumes / size of union of the volumes
+    :param refSWC: valid file path to a valid SWC file
+    :param SWC2Align: valid file path to a valid SWC file
+    :param gridSize: float, the voxel size at which the volumes are discretized.
+    :return: float, dissimilarity value
+    """
+    trans = SWCTranslate(refSWC, SWC2Align, gridSize)
+
+    return objFun(([0, 0, 0], trans))
+
+
+
+def writeFakeSWC(data, fName, extraCol=None):
+    """
+    Forms a 7 column SWC data from the 3 column XYZ data in 'data' and writes it to a file at path fName adding
+    a '!! Fake SWC !!' warning in the header.
+    :param data: numpy.ndarray, 3 column XYZ data
+    :param fName: valid file path to write the fake SWC file
+    :param extraCol: iterable of the same size as the number of rows of data, will be added as the 8th column
+    :return:
+    """
+    data = np.array(data)
+
+    assert data.shape[1] == 3
+
+    if extraCol is not None:
+        extraCol = np.array(extraCol)
+        assert extraCol.shape == (data.shape[0], ) or extraCol.shape == (data.shape[0], 1)
+
+        toWrite = np.empty((data.shape[0], 8))
+        toWrite[:, 7] = extraCol
+    else:
+        toWrite = np.empty((data.shape[0], 7))
+
+    toWrite[:, 2:5] = data
+    toWrite[:, 0] = range(1, data.shape[0] + 1)
+    toWrite[:, 1] = 3
+    toWrite[:, 5] = 1
+    toWrite[:, 6] = -np.arange(1, data.shape[0] + 1)
+
+
+    formatStr = '%d %d %0.6f %0.6f %0.6f %0.6f %d'
+
+    if extraCol is not None:
+        formatStr += ' %d'
+    headr = '!! Fake SWC file !!'
+    np.savetxt(fName, toWrite, fmt=formatStr, header=headr)

regmaxsn/core/matplotlibRCParams.py

@@ -0,0 +1,35 @@
+# mplPars = {'text.usetex': True,
+#            'axes.labelsize': 'large',
+#            'axes.titlesize': 24,
+#            'font.family': 'sans-serif',
+#            'font.sans-serif': 'computer modern bright',
+#            'font.size': 24,
+#            'font.weight': 'black',
+#            'xtick.labelsize': 20,
+#            'ytick.labelsize': 20,
+#            'legend.fontsize': 20,
+#            'legend.frameon': True,
+#            'legend.framealpha': 0,
+#            'legend.fancybox': True,
+#            'text.latex.preamble': r'\usepackage{cmbright}',
+#            "lines.linewidth": 3,
+#            "lines.markersize": 10
+#            }
+
+mplPars = {'text.usetex': True,
+           'axes.labelsize': 'large',
+           'axes.titlesize': 32,
+           'font.family': 'sans-serif',
+           'font.sans-serif': 'computer modern bright',
+           'font.size': 32,
+           'font.weight': 'black',
+           'xtick.labelsize': 26,
+           'ytick.labelsize': 26,
+           'legend.fontsize': 26,
+           'legend.frameon': True,
+           'legend.framealpha': 0,
+           'legend.fancybox': True,
+           'text.latex.preamble': r'\usepackage{cmbright}',
+           "lines.linewidth": 3,
+           "lines.markersize": 10
+           }

regmaxsn/core/maxDistanceBasedMetric.py

@@ -0,0 +1,95 @@
+from scipy.spatial import ConvexHull
+from scipy.spatial.distance import cdist
+from .swcFuncs import readSWC_numpy
+import numpy as np
+from pyemd import emd
+
+def cdist_1d_centripetal(list1, list2, center):
+    """
+    Return a matrix of pairwise displacements of entries of list1 from corresponding
+     entries in list2, with displacements
+    towards center having positive value and those away having negative values
+    :param list1: iterable of floats or ints
+    :param list2: iterable of floats or ints
+    :param center: float or int
+    :return: numpy.ndarray of shape (<len of list1>, <len of list2>)
+    """
+
+    assert all([isinstance(x, (float, int)) for x in list1])
+    assert all([isinstance(x, (float, int)) for x in list2])
+    assert isinstance(center, (float, int))
+
+    mesh2 = np.array([list2] * len(list1))
+    mesh1 = np.array([list1] * len(list2)).T
+
+    mesh1CenteredAbs = np.abs(mesh1 - center)
+    mesh2CenteredAbs = np.abs(mesh2 - center)
+
+    return mesh2CenteredAbs - mesh1CenteredAbs
+
+def calcMaxDistances(swcList):
+    """
+    Compute the convex hull of the union of points from all swcs in swcList. For each vertex of this
+    convex hull, compute the distance of the farthest point among the vertices of the hull and return
+    them.
+    :param swcList: list of valid SWC files on the file system, list of strings.
+    :return: list of maximum distances
+    """
+
+    swcPointSets = []
+    for swc in swcList:
+        headr, swcData = readSWC_numpy(swc)
+        swcPointSets.append(swcData[:, 2:5])
+
+    unionWithDuplicates = np.concatenate(swcPointSets, axis=0)
+    if any(np.abs(unionWithDuplicates).max(axis=0) == 0):
+        raise(ValueError("The list of SWCs all lie on a plane or on  a line and hence do not "
+                         "for a 3D point cloud. Such SWCs are not supported."))
+
+    hull = ConvexHull(unionWithDuplicates)
+
+    vertices = unionWithDuplicates[hull.vertices, :]
+    distMatrix = cdist(unionWithDuplicates, vertices)
+    maxDistances = distMatrix.max(axis=1).tolist()
+
+    return maxDistances
+
+
+def maxDistEMD(swcList):
+    """
+    Calculate the maxDistance based metric. It is the size normalized Earth mover distance
+    between the distribution of maxDistances (see calcMaxDistances above)
+    of the pooled collection of points of all swcs in swcList and the distribution of
+    pooled diagonal maxDistances of individual swcs in swcList
+    :param swcList: list of valid swc files on the system, list of strings
+    :return: float
+    """
+
+    individualMaxDistances = [calcMaxDistances([swc]) for swc in swcList]
+    pooledIndividualMaxDistances = np.concatenate(individualMaxDistances, axis=0)
+
+    meanPIMD = pooledIndividualMaxDistances.mean()
+
+    PIMDNorm = (pooledIndividualMaxDistances - meanPIMD) / meanPIMD
+
+    maxDistancesAllPts = np.array(calcMaxDistances(swcList))
+
+    MDAPNorm = (maxDistancesAllPts - meanPIMD) / meanPIMD
+
+    binWidth = 1 / meanPIMD
+
+    bins = np.arange(MDAPNorm.min() - 0.5 * binWidth,
+                     MDAPNorm.max() + 0.5 * binWidth,
+                     binWidth)
+
+    hist1, bins1 = np.histogram(MDAPNorm, bins)
+    hist2, bins2 = np.histogram(PIMDNorm, bins1)
+
+    hist1Normed = hist1 / float(hist1.sum())
+    hist2Normed = hist2 / float(hist2.sum())
+
+    dist_metric = cdist_1d_centripetal(bins1, bins2, center=0)
+    emd_val = emd(np.asarray(hist2Normed, dtype=np.float64), np.asarray(hist1Normed, dtype=np.float64),
+                  np.asarray(dist_metric, dtype=np.float64))
+
+    return emd_val

regmaxsn/core/misc.py

@@ -0,0 +1,23 @@
+import json
+
+def parFileCheck(parFile, parNames):
+    """
+    Checks if each dictionary in the list of dictionaries in parFile contains all the keys in parNames. Assertions
+    are used to raise errors if problems are detected.
+    :param parFile: a valid file path containing the list of dictionaries
+    :param parNames: list of parameter names expected
+    :return: list of dictionaries in parFile
+    """
+
+    with open(parFile, 'r') as fle:
+
+        parsList = json.load(fle)
+        assert type(parsList) == list, 'Parameter file does not contain a list of dictionaries as is the requirement.'
+        for parInd, par in enumerate(parsList):
+            assert type(par) == dict, 'Parameter set # {} in {} not a list'.format(parInd, parFile)
+
+            for pn in parNames:
+                assert pn in par, 'Parameter {} not found in ' \
+                                  'parameter set # {} of {} is improper'.format(pn, parInd, parFile)
+
+    return parsList

regmaxsn/core/occupancyBasedMeasure.py

@@ -0,0 +1,58 @@
+import numpy as np
+from collections import Counter
+from pyemd import emd
+
+def calcOccupancyDistribution(swcList, voxelSize):
+    """
+    Returns the distribution of the sum of voxel occupancies across swcs in swcList.
+    Voxel occupancy of a voxel is 1 if an swc has an node in the voxel, otherwise 0.
+    :param swcList: list of valid SWC files on the file system
+    :param voxelSize: float, voxel size to discretize space.
+    :return: dict with voxel occupancy and its normalized frequency as key-value pairs
+    """
+
+    voxels = []
+    for swc in swcList:
+        aPts = np.loadtxt(swc)[:, 2:5]
+        aVox = np.array(aPts / voxelSize, np.int32)
+        aVoxSet = set(map(tuple, aVox))
+        voxels.extend(list(aVoxSet))
+
+    voxelCounter = Counter(voxels)
+    counts = voxelCounter.values()
+
+    bins = np.arange(1, len(swcList) + 2) - 0.5
+
+    hist, bins = np.histogram(counts, bins)
+
+    histWeighted = hist * (bins[:-1] + 0.5)
+
+    histNormed = histWeighted / float(sum(histWeighted))
+
+    return dict(zip(np.arange(1, len(swcList) + 1), histNormed))
+
+
+def occupancyEMD(swcList, voxelSize):
+    """
+    Calculate the EMD between the occupancy distributions (see calcOccupancyDistribution above) of
+    swcList and that of perfect overlap (1 at len(swcList) and zero elsewhere)
+    :param swcList: list of valid SWC files on the file system
+    :param voxelSize: float, voxel size for discretizing space.
+    :return: float, emd value
+    """
+
+    occupancyDistributionDict = calcOccupancyDistribution(swcList, voxelSize)
+    bins = np.arange(1, len(swcList) + 1)
+    occupancyDistribution = [occupancyDistributionDict[x] for x in bins]
+    perfectOverlapDist = np.zeros(bins.shape)
+    perfectOverlapDist[-1] = 1
+
+    binsRow = bins.reshape((1, bins.shape[0]))
+    distMetric = np.dot(np.ones((bins.shape[0], 1)), binsRow) - binsRow.T
+
+    emd_val = emd(np.asarray(occupancyDistribution, dtype=np.float64),
+                  np.asarray(perfectOverlapDist, dtype=np.float64),
+                  np.asarray(distMetric, dtype=np.float64))
+    return emd_val
+
+

regmaxsn/core/plotDensities.py

@@ -0,0 +1,163 @@
+from .swcFuncs import resampleSWC, digitizeSWCXYZ, getPCADetails, readSWC_numpy, writeSWC_numpy
+import numpy as np
+import os
+from scipy.ndimage import gaussian_filter
+import tifffile
+
+class DensityVizualizations(object):
+
+    def __init__(self, swcSet, gridUnitSizes, resampleLen,
+                 masks=None, pcaView=None, refSWC=None, initTrans=None):
+        if initTrans is None:
+            initTrans = np.eye(3)
+
+        self.gridUnitSizes = np.array(gridUnitSizes)
+
+        if masks is None:
+            masks = [None] * len(swcSet)
+        else:
+            assert len(masks) == len(swcSet), 'Improper value for masks1'
+
+
+        minXYZs = []
+        maxXYZs = []
+        self.transMat = np.eye(4)
+        datas = {}
+
+        for swcInd, swcFile in enumerate(swcSet):
+            print('Resamping ' + swcFile)
+            totalLen, data = resampleSWC(swcFile, resampleLen, mask=masks[swcInd])
+            dataT = np.dot(initTrans, data[:, :3].T).T
+            datas[swcFile] = dataT
+        self.transMat[:3, :3] = initTrans
+
+        allData = np.concatenate(tuple(datas.itervalues()), axis=0)
+        self.allDataMean = allData.mean(axis=0)
+
+        if pcaView == 'closestPCMatch':
+            evecs, newStds = getPCADetails(None, center=True, data=allData)
+            mean2Use = self.allDataMean
+
+            if refSWC:
+                refEvecs, thrash = getPCADetails(refSWC, center=True)
+                fEvecs = np.empty_like(refEvecs)
+                coreff = np.dot(refEvecs.T, evecs)
+                possInds = range(refEvecs.shape[1])
+                for rowInd in range(refEvecs.shape[1]):
+                    bestCorrInd = np.argmax(np.abs(coreff[rowInd, possInds]))
+                    fEvecs[:, rowInd] = np.sign(coreff[rowInd, possInds[bestCorrInd]]) * evecs[:, possInds[bestCorrInd]]
+                    possInds.pop(int(bestCorrInd))
+
+
+
+            else:
+                fEvecs = evecs
+
+        elif pcaView == 'assumeRegistered':
+
+            if refSWC:
+                refEvecs, thrash = getPCADetails(refSWC, center=True)
+                fEvecs = refEvecs
+                mean2Use = np.loadtxt(refSWC)[:, 2:5].mean(axis=0)
+
+            else:
+                raise(ValueError('RefSWC must be specified when pcaView == \'assumeRegistered\''))
+
+        else:
+            fEvecs = np.eye(3)
+            mean2Use = self.allDataMean
+
+
+        self.digDatas = {}
+        for swcFile, data in datas.iteritems():
+            print('Digitizing ' + swcFile)
+            data -= mean2Use
+            data = np.dot(fEvecs.T, data.T).T
+            digData = digitizeSWCXYZ(data + mean2Use, gridUnitSizes)
+            self.digDatas[swcFile] = digData
+            minXYZs.append(digData[:, :3].min(axis=0))
+            maxXYZs.append(digData[:, :3].max(axis=0))
+
+        temp = np.eye(4)
+        temp[:3, 3] = -mean2Use
+        self.transMat = np.dot(temp, self.transMat)
+
+        temp = np.eye(4)
+        temp[:3, :3] = fEvecs.T
+        self.transMat = np.dot(temp, self.transMat)
+
+        self.transMat[:3, 3] += mean2Use
+
+        self.minXYZ = np.array(minXYZs).min(axis=0) - 20
+        self.maxXYZ = np.array(maxXYZs).max(axis=0) + 20
+
+        self.bins = [np.arange(x, y + 1) * z for x, y, z in zip(self.minXYZ, self.maxXYZ, self.gridUnitSizes)]
+
+
+
+    def calculateDensity(self, swcFiles, sigmas):
+
+        assert all(np.greater_equal(sigmas, self.gridUnitSizes)), 'sigma along every dimenstion must be larger than gridUnitSize'
+
+        digSigs = np.around(np.array(sigmas) / self.gridUnitSizes)
+
+        densityMatSum = np.zeros(tuple((self.maxXYZ - self.minXYZ).tolist()))
+
+
+        for swcFile in swcFiles:
+
+            print('Calculating Density for ' + swcFile)
+            densityMat = np.zeros_like(densityMatSum)
+            print('Doing ' + os.path.split(swcFile)[1])
+            if swcFile in self.digDatas:
+                digDataTranslated = self.digDatas[swcFile][:, :3] - self.minXYZ
+            else:
+                raise(ValueError(swcFile + ' not initialized in constructing DensityVizualizations object'))
+            densityMat[digDataTranslated[:, 0], digDataTranslated[:, 1], digDataTranslated[:, 2]] = 1
+            densityMatSum += densityMat
+            del densityMat
+
+        densityMatSum /= float(len(swcFiles))
+        smoothDensityMat = gaussian_filter(densityMatSum, sigma=digSigs, truncate=3)
+        del densityMatSum
+        smoothDensityMat *= (2 ** 1.5) * digSigs.prod()
+
+        smoothDensityMat[smoothDensityMat > 1] = 1
+        smoothDensityMat[smoothDensityMat < 0] = 0
+
+        return smoothDensityMat, self.bins
+
+    def generateDensityColoredSSWC(self, swcFiles, outFiles, density=None, sigmas=None):
+
+        if density is None:
+            density, bins = self.calculateDensity(swcFiles, sigmas)
+
+        for swcInd, swcFile in enumerate(swcFiles):
+
+            headr, data = readSWC_numpy(swcFile)
+            dataXYZ = data[:, 2:5]
+            rotData = np.dot(self.transMat[:3, :3], dataXYZ.T).T + self.transMat[:3, 3]
+            digData = digitizeSWCXYZ(rotData, self.gridUnitSizes)
+            digDataTranslated = digData - self.minXYZ
+            colorInds = [density[x[0], x[1], x[2]] for x in digDataTranslated]
+            colorInds = np.reshape(colorInds, (len(colorInds), 1))
+            colorInds[colorInds > 1] = 1
+            colorInds[colorInds < 0] = 0
+
+            # data[:, 2:5] = rotData
+            outData = np.concatenate((data, colorInds), axis=1)
+
+            writeSWC_numpy(outFiles[swcInd], outData, headr)
+
+
+def writeTIFF(density, outFile):
+
+    assert type(density) is np.ndarray, 'density must be a numpy ndarray'
+    assert len(density.shape) == 3, 'density must a 3d numpy array'
+    densityUInt8 = np.array(density * 255, dtype=np.uint8)
+    tiffArgs = {
+                'compress': 0,
+                # 'planarconfig': 'planar',
+                'photometric': 'minisblack'
+                }
+    tifffile.imsave(outFile + '.tiff', densityUInt8, **tiffArgs)

regmaxsn/core/rotOnce.py

@@ -0,0 +1,119 @@
+from SWCTransforms import SWCRotate, ArgGenIterator, objFun
+import multiprocessing as mp
+import numpy as np
+import json
+import sys
+from itertools import product
+from regmaxsn.core.transforms import compose_matrix
+
+debugging = False
+# debugging = True
+
+
+assert len(sys.argv) == 2, 'Only one argument, the path of the swcfile expected, ' + str(len(sys.argv)) + 'found'
+
+parFile = sys.argv[1]
+with open(parFile, 'r') as fle:
+    pars = json.load(fle)
+
+refSWC, SWC2Align, outFiles, gridSizes, bounds, minRes, nCPU = pars
+
+data = np.loadtxt(SWC2Align)[:, 2:5]
+dataCentered = data - data.mean(axis=0)
+maxDist = np.linalg.norm(data, axis=1).max()
+pool = mp.Pool(processes=nCPU)
+
+SWCDatas = [SWCRotate(refSWC, SWC2Align, x) for x in gridSizes]
+stepSizes = [max(x / maxDist, minRes) for x in gridSizes]
+
+bestSol = [0, 0, 0]
+
+for gridInd, gridSize in enumerate(gridSizes):
+
+    if debugging:
+        print('Gridsize:' + str(gridSize))
+    stepSize = stepSizes[gridInd]
+    if debugging:
+        print('Stepsize: ' + str(np.rad2deg(stepSize)))
+    bounds = (np.array(bounds).T - np.array(bestSol)).T
+    boundsRoundedUp = np.sign(bounds) * np.ceil(np.abs(bounds) / stepSize) * stepSize
+    possiblePts1D = [np.round(bestSol[ind] + np.arange(x[0], x[1] + stepSize, stepSize), 3).tolist()
+                     for ind, x in enumerate(boundsRoundedUp)]
+    if debugging:
+        print(np.rad2deg([bestSol[ind] + x for ind, x in enumerate(boundsRoundedUp)]))
+    possiblePts3D = np.round(list(product(*possiblePts1D)), 6).tolist()
+    argGen = ArgGenIterator(possiblePts3D, SWCDatas[gridInd])
+    funcVals = pool.map_async(objFun, argGen).get(1800)
+    minimum = min(funcVals)
+    minimzers = [y for x, y in enumerate(possiblePts3D) if funcVals[x] == minimum]
+
+    if not gridInd:
+        distFrom0 = np.linalg.norm(minimzers, axis=1)
+        bestSol = minimzers[np.argmin(distFrom0)]
+    else:
+
+        prevVals = [objFun((x, SWCDatas[gridInd - 1])) for x in minimzers]
+        bestSol = minimzers[np.argmin(prevVals)]
+    bounds = map(lambda x: [x - np.sqrt(2) * stepSize, x + np.sqrt(2) * stepSize], bestSol)
+
+    if debugging:
+        bestVal = objFun((bestSol, SWCDatas[gridInd]))
+        print(np.rad2deg(bestSol), bestVal)
+
+
+if minRes < stepSizes[-1]:
+
+    if debugging:
+        print('Stepsize: ' + str(np.rad2deg(minRes)))
+    bounds = (np.array(bounds).T - np.array(bestSol)).T
+    boundsRoundedUp = np.sign(bounds) * np.ceil(np.abs(bounds) / minRes) * minRes
+    if debugging:
+        print(np.rad2deg([bestSol[ind] + x for ind, x in enumerate(boundsRoundedUp)]))
+    possiblePts1D = [np.round(bestSol[ind] + np.arange(x[0], x[1] + minRes, minRes), 3).tolist()
+                     for ind, x in enumerate(boundsRoundedUp)]
+    possiblePts3D = np.round(list(product(*possiblePts1D)), 6).tolist()
+
+    argGen = ArgGenIterator(possiblePts3D, SWCDatas[-1])
+    funcVals = pool.map_async(objFun, argGen).get(1800)
+    minimum = min(funcVals)
+    minimzers = [y for x, y in enumerate(possiblePts3D) if funcVals[x] == minimum]
+    prevVals = [objFun((x, SWCDatas[-2])) for x in minimzers]
+    bestSol = minimzers[np.argmin(prevVals)]
+    if debugging:
+        bestVal = objFun((bestSol, SWCDatas[-1]))
+        print(np.rad2deg(bestSol), bestVal)
+
+bestVal = objFun((bestSol, SWCDatas[-1]))
+nochange = objFun(([0, 0, 0], SWCDatas[-1]))
+if debugging:
+    print(np.rad2deg(bestSol), bestVal, nochange)
+
+
+done = False
+
+# all values are worse than doing nothing
+if bestVal > nochange:
+
+    done = True
+    bestSol = [0, 0, 0]
+    bestVal = nochange
+
+# best solution and no change are equally worse
+elif bestVal == nochange:
+
+    # the solution step is very close to zero
+    if np.abs(bestSol).max() <= min(minRes, stepSizes[-1]):
+
+        done = True
+        bestSol = [0, 0, 0]
+        bestVal = nochange
+
+
+
+SWCDatas[-1].writeSolution(outFiles[0], bestSol)
+matrix = compose_matrix(angles=bestSol).tolist()
+with open(outFiles[1], 'w') as fle:
+    json.dump({'type': 'XYZ Euler Angles in radians','bestSol': bestSol,
+               'transMat': matrix, 'done': done, 'bestVal': bestVal}, fle)
+
+

regmaxsn/core/scaleOnce.py

@@ -0,0 +1,134 @@
+from SWCTransforms import SWCScale, SWCTranslate, ArgGenIterator, objFun
+import multiprocessing as mp
+import numpy as np
+import json
+import sys
+from itertools import product
+from regmaxsn.core.transforms import compose_matrix
+
+debugging = False
+# debugging = True
+
+assert len(sys.argv) == 2, 'Only one argument, the path of the swcfile expected, ' + str(len(sys.argv)) + 'found'
+
+parFile = sys.argv[1]
+with open(parFile, 'r') as fle:
+    pars = json.load(fle)
+
+refSWC, SWC2Align, outFiles, gridSizes, bounds, minStepSize, nCPU = pars
+
+initBounds = bounds
+boundL = lambda x, y: max(y[0], min(y[1], x))
+data = np.loadtxt(SWC2Align)[:, 2:5]
+dataCentered = data - data.mean(axis=0)
+maxDist = max(np.linalg.norm(dataCentered, axis=1).max(), gridSizes[0] * 1.01)
+pool = mp.Pool(processes=nCPU)
+
+SWCDatas = [SWCScale(refSWC, SWC2Align, x) for x in gridSizes]
+
+bestSol = [1.0, 1.0, 1.0]
+
+stepSizes = [max(minStepSize, min(2.0, (maxDist / (maxDist - g)))) for g in gridSizes]
+if debugging:
+    print(maxDist, [(maxDist / (maxDist - g)) for g in gridSizes])
+
+overestimationError = lambda d, g: (d + g) / d
+underestimationError = lambda d, g: ((d + 1.5 * g) * d) / ((d - 0.5 * g) * (d + g))
+
+for gridInd, gridSize in enumerate(gridSizes):
+
+
+    stepSize = stepSizes[gridInd]
+    bounds = np.array(bounds)
+    boundsExponents = np.log([x / y for x, y in zip(bounds, bestSol)]) / np.log(stepSize)
+    boundsExponentsRoundedDown = np.sign(boundsExponents) * np.ceil(np.abs(boundsExponents))
+
+    possiblePts1D = [(bestSol[x] * (stepSize ** np.arange(int(y[0]), int(y[1]) + 1)))
+                        for x, y in enumerate(boundsExponentsRoundedDown)]
+    if debugging:
+        print(stepSize)
+        print('Gridsize:' + str(gridSize))
+        print(bounds)
+        print(map(len, possiblePts1D))
+        print([bestSol[x] * (stepSize ** y) for x, y in enumerate(boundsExponentsRoundedDown)])
+    possiblePts3D = np.round(list(product(*possiblePts1D)), 6).tolist()
+    argGen = ArgGenIterator(possiblePts3D, SWCDatas[gridInd])
+    funcVals = pool.map_async(objFun, argGen).get(1800)
+    minimum = min(funcVals)
+    minimzers = [y for x, y in enumerate(possiblePts3D) if funcVals[x] == minimum]
+
+    if not gridInd:
+
+        distFrom0 = [np.mean([max(x, 1 / x) for x in y]) for y in minimzers]
+        bestSol = minimzers[np.argmin(distFrom0)]
+    else:
+
+        prevVals = [objFun((x, SWCDatas[gridInd - 1])) for x in minimzers]
+        bestSol = minimzers[np.argmin(prevVals)]
+    bounds = [[boundL(x / overestimationError(maxDist, gridSize), iB),
+               boundL(x * underestimationError(maxDist, gridSize), iB)]
+              for x, iB in zip(bestSol, initBounds)]
+
+    if debugging:
+        print(bestSol)
+
+
+if stepSizes[-1] > minStepSize:
+
+    stepSize = minStepSize
+    bounds = np.array(bounds)
+
+    boundsExponents = np.log([x / y for x, y in zip(bounds, bestSol)]) / np.log(stepSize)
+    boundsExponentsRoundedDown = np.sign(boundsExponents) * np.ceil(np.abs(boundsExponents))
+    possiblePts1D = [(bestSol[x] * (stepSize ** np.arange(int(y[0]), int(y[1]) + 1)))
+        for x, y in enumerate(boundsExponentsRoundedDown)]
+    if debugging:
+        print(stepSize)
+        print(bounds)
+        print(map(len, possiblePts1D))
+        print([bestSol[x] * (stepSize ** y) for x, y in enumerate(boundsExponentsRoundedDown)])
+    possiblePts3D = np.round(list(product(*possiblePts1D)), 6).tolist()
+    argGen = ArgGenIterator(possiblePts3D, SWCDatas[-1])
+    funcVals = pool.map_async(objFun, argGen).get(1800)
+    minimum = min(funcVals)
+    minimzers = [y for x, y in enumerate(possiblePts3D) if funcVals[x] == minimum]
+    prevVals = [objFun((x, SWCDatas[-2])) for x in minimzers]
+    bestSol = minimzers[np.argmin(prevVals)]
+    if debugging:
+        print(bestSol, min(funcVals))
+
+bestVal = objFun((bestSol, SWCDatas[-1]))
+nochange = objFun(([1, 1, 1], SWCDatas[-1]))
+
+if debugging:
+    bestVals = [objFun((bestSol, x)) for x in SWCDatas]
+    print(bestSol, nochange, bestVal)
+
+done = False
+
+# all values are worse than doing nothing
+if bestVal > nochange:
+
+    done = True
+    bestSol = [1, 1, 1]
+    bestVal = nochange
+
+# best solution and no change are equally worse
+elif bestVal == nochange:
+
+    # the solution step is very close to zero
+    if np.abs(bestSol).max() <= min(minStepSize, stepSizes[-1]):
+        done = True
+        bestSol = [1, 1, 1]
+        bestVal = nochange
+
+
+SWCDatas[-1].writeSolution(outFiles[0], bestSol)
+temp = SWCTranslate(refSWC, outFiles[0], gridSizes[-1])
+bestVal = objFun(([0, 0, 0], temp))
+matrix = compose_matrix(scale=bestSol).tolist()
+with open(outFiles[1], 'w') as fle:
+    json.dump({'type': 'XYZ Scales', 'bestSol': bestSol,
+               'transMat': matrix, 'done': done, 'bestVal': bestVal}, fle)
+
+

regmaxsn/core/swcFuncs.py

@@ -0,0 +1,205 @@
+import numpy as np
+
+#***********************************************************************************************************************
+
+
+def readSWC_numpy(swcFile):
+    '''
+    Read the return the header and matrix data in a swcFile
+    :param swcFile: filename
+    :return: header (string), matrix data (ndarray)
+    '''
+    headr = ''
+    with open(swcFile, 'r') as fle:
+        lne = fle.readline()
+        while lne[0] == '#':
+            headr = headr + lne[1:]
+            lne = fle.readline()
+
+    headr = headr.rstrip('\n')
+
+    swcData = np.loadtxt(swcFile)
+
+    return headr, swcData
+
+#***********************************************************************************************************************
+
+
+def writeSWC_numpy(fName, swcData, headr=''):
+    '''
+    Write the SWC data in swcData to the file fName with the header headr
+    :param fName: string
+    :param swcData: 2D numpy.ndarray with 7 or 8 columns
+    :param headr: string
+    :return:
+    '''
+
+    swcData = np.array(swcData)
+    assert swcData.shape[1] in [7, 8], 'Width given SWC Matrix data is incompatible.'
+
+
+    formatStr = '%d %d %0.6f %0.6f %0.6f %0.6f %d'
+
+    if swcData.shape[1] == 8:
+         formatStr += ' %0.6f'
+
+    np.savetxt(fName, swcData, formatStr, header=headr, comments='#')
+
+#***********************************************************************************************************************
+
+
+def transSWC(fName, A, b, destFle):
+    '''
+    Generate an SWC file at destFle with each point `x' of the morphology in fName transformed Affinely as Ax+b
+    :param fName: string
+    :param A: 2D numpy.ndarray of shape (3, 3)
+    :param b: 3 member iterable
+    :param destFle: string
+    :return:
+    '''
+
+    headr = ''
+    with open(fName, 'r') as fle:
+        lne = fle.readline()
+        while lne[0] == '#':
+            headr = headr + lne[1:]
+            lne = fle.readline()
+
+    data = np.loadtxt(fName)
+    data[:, 2:5] = np.dot(A, data[:, 2:5].T).T + np.array(b)
+
+    if data.shape[1] == 7:
+        formatStr = '%d %d %0.3f %0.3f %0.3f %0.3f %d'
+    elif data.shape[1] == 8:
+        formatStr = '%d %d %0.3f %0.3f %0.3f %0.3f %d %d'
+    else:
+        raise(TypeError('Data in the input file is of unknown format.'))
+
+    np.savetxt(destFle, data, header=headr, fmt=formatStr)
+
+#***********************************************************************************************************************
+
+
+def transSWC_rotAboutPoint(fName, A, b, destFle, point):
+    '''
+    Generate an SWC file at destFle with each point `x' of the morphology in fName transformed Affinely as A(x-mu)+b
+    where mu is a specified point.
+    Essentially, the morphology is centered at a specified point before being Affinely transformed.
+    :param fName: string
+    :param A: 2D numpy.ndarray of shape (3, 3)
+    :param b: 3 member iterable
+    :param destFle: string
+    :param point: 3 member iterable
+    :return:
+    '''
+
+    headr = ''
+    with open(fName, 'r') as fle:
+        lne = fle.readline()
+        while lne[0] == '#':
+            headr = headr + lne[1:]
+            lne = fle.readline()
+
+    data = np.loadtxt(fName)
+    pts = data[:, 2:5]
+    rotAbout = np.array(point)
+    ptsCentered = pts - rotAbout
+    data[:, 2:5] = np.dot(A, ptsCentered.T).T + np.array(b) + rotAbout
+
+    if data.shape[1] == 7:
+        formatStr = '%d %d %0.3f %0.3f %0.3f %0.3f %d'
+    elif data.shape[1] == 8:
+        formatStr = '%d %d %0.3f %0.3f %0.3f %0.3f %d %d'
+    else:
+        raise(TypeError('Data in the input file is of unknown format.'))
+
+    np.savetxt(destFle, data, header=headr, fmt=formatStr)
+#***********************************************************************************************************************
+
+def getPCADetails(swcFileName, center=True, data=None):
+    '''
+    Returns the principal components and standard deviations along the principal components.
+    Ref: http://arxiv.org/pdf/1404.1100.pdf
+    :param swcFileName: sting, input SWC file name
+    :param center: Boolean,  if True, data is centered before calculating PCA
+    :param data: numpy ndarray of shape [<>, 3]
+    :return: PC, STDs
+    PC: numpy.ndarray with the prinicial components of the data in its columns
+    STDs: 3 member float iterable containing the standard variances of the data along the prinicipal components.
+    '''
+
+    if data is None:
+        data = np.loadtxt(swcFileName)[:, 2:5]
+    if center:
+        mu = np.mean(data, axis=0)
+        data = data - mu
+    U, S, V = np.linalg.svd(data.T)
+    dataProj = np.dot(U, data.T).T
+    newStds = np.std(dataProj, axis=0)
+    return U.T, newStds
+#***********************************************************************************************************************
+
+# **********************************************************************************************************************
+
+def digitizeSWCXYZ(swcXYZ, gridUnitSizes):
+
+    digSWCXYZ = np.empty_like(swcXYZ, dtype=np.intp)
+    for ind in range(3):
+        digSWCXYZ[:, ind] = np.array(np.around((swcXYZ[:, ind]) / gridUnitSizes[ind]), np.int)
+
+    return digSWCXYZ
+
+# **********************************************************************************************************************
+
+def resampleSWC(swcFile, resampleLength, mask=None, swcData=None):
+    '''
+    Resample the SWC points to place points at every resamplelength along the central line of every segment. Radii are interpolated.
+    :param swcData: nx4 swc point data
+    :param resampleLength: length at with resampling is done.
+    :return: totlLen, ndarray of shape (#pts, 4) with each row containing XYZR values
+    '''
+
+    if swcData is  None:
+        swcData = np.loadtxt(swcFile)
+    inds = swcData[:, 0].tolist()
+
+    if mask is None:
+        mask = [True] * swcData.shape[0]
+    else:
+        assert len(mask) == swcData.shape[0], 'Supplied mask is invalid for ' + swcFile
+    resampledSWCData = []
+
+    getSegLen = lambda a, b: np.linalg.norm(a - b)
+
+    totalLen = 0
+    for pt in swcData:
+
+        if pt[6] < 0:
+            if mask[inds.index(int(pt[0]))]:
+                resampledSWCData.append(pt[2:6])
+
+        if (pt[6] > 0) and (int(pt[6]) in inds):
+            if mask[inds.index(int(pt[0]))]:
+                resampledSWCData.append(pt[2:6])
+
+
+                parentPt = swcData[inds.index(pt[6]), :]
+                segLen = getSegLen(pt[2:5], parentPt[2:5])
+                totalLen += segLen
+
+                if segLen > resampleLength:
+
+                    temp = parentPt[2:5] - pt[2:5]
+                    distTemp = np.linalg.norm(temp)
+                    unitDirection = temp / distTemp
+                    radGrad = (pt[5] - parentPt[5]) / distTemp
+
+                    for newPtsInd in range(1, int(np.floor(segLen / resampleLength)) + 1):
+
+                        temp = (pt[2:5] + newPtsInd * resampleLength * unitDirection).tolist()
+                        temp.append(pt[5] + newPtsInd * radGrad * resampleLength)
+                        resampledSWCData.append(temp)
+
+    return totalLen, np.array(resampledSWCData)
+
+# **********************************************************************************************************************

regmaxsn/core/transOnce.py

@@ -0,0 +1,112 @@
+from SWCTransforms import SWCTranslate, ArgGenIterator, objFun
+import multiprocessing as mp
+import numpy as np
+import json
+import sys
+from itertools import product
+from regmaxsn.core.transforms import compose_matrix
+
+debugging = False
+# debugging = True
+
+assert len(sys.argv) == 2, 'Only one argument, the path of the swcfile expected, ' + str(len(sys.argv)) + 'found'
+
+parFile = sys.argv[1]
+with open(parFile, 'r') as fle:
+    pars = json.load(fle)
+
+refSWC, SWC2Align, outFiles, gridSizes, bounds, minRes, nCPU = pars
+
+SWCDatas = [SWCTranslate(refSWC, SWC2Align, x) for x in gridSizes]
+pool = mp.Pool(processes=nCPU)
+bestSol = [0, 0, 0]
+
+for gridInd, gridSize in enumerate(gridSizes):
+
+
+    bounds = (np.array(bounds).T - np.array(bestSol)).T
+    boundsRoundedUp = np.sign(bounds) * np.ceil(np.abs(bounds) / gridSize) * gridSize
+    possiblePts1D = [(bestSol[ind] + np.arange(x[0], x[1] + gridSize, gridSize)).tolist()
+                     for ind, x in enumerate(boundsRoundedUp)]
+
+    possiblePts3D = list(product(*possiblePts1D))
+    if debugging:
+        print('Gridsize:' + str(gridSize))
+        print(bounds)
+        print(map(len, possiblePts1D))
+        print([bestSol[ind] + x for ind, x in enumerate(boundsRoundedUp)])
+
+    argGen = ArgGenIterator(possiblePts3D, SWCDatas[gridInd])
+    funcVals = pool.map_async(objFun, argGen).get(1800)
+    minimum = min(funcVals)
+    minimzers = [y for x, y in enumerate(possiblePts3D) if funcVals[x] == minimum]
+
+    if not gridInd:
+        distFrom0 = np.linalg.norm(minimzers, axis=1)
+        bestSol = minimzers[np.argmin(distFrom0)]
+    else:
+
+        prevVals = [objFun((x, SWCDatas[gridInd - 1])) for x in minimzers]
+        bestSol = minimzers[np.argmin(prevVals)]
+    bounds = map(lambda x: [x - gridSize, x + gridSize], bestSol)
+    if debugging:
+        bestVal = objFun((bestSol, SWCDatas[gridInd]))
+        print(bestSol, bestVal)
+
+if minRes < gridSizes[-1]:
+
+    bounds = (np.array(bounds).T - np.array(bestSol)).T
+    boundsRoundedUp = np.sign(bounds) * np.ceil(np.abs(bounds) / minRes) * minRes
+    possiblePts1D = [(bestSol[ind] + np.arange(x[0], x[1] + minRes, minRes)).tolist()
+                     for ind, x in enumerate(boundsRoundedUp)]
+    possiblePts3D = list(product(*possiblePts1D))
+
+    if debugging:
+        print('StepSize:' + str(minRes))
+        print(bounds)
+        print(map(len, possiblePts1D))
+        print([bestSol[ind] + x for ind, x in enumerate(boundsRoundedUp)])
+
+    argGen = ArgGenIterator(possiblePts3D, SWCDatas[-1])
+    funcVals = pool.map_async(objFun, argGen).get(1800)
+
+    minimum = min(funcVals)
+
+    minimzers = [y for x, y in enumerate(possiblePts3D) if funcVals[x] == minimum]
+    prevVals = [objFun((x, SWCDatas[-2])) for x in minimzers]
+    bestSol = minimzers[np.argmin(prevVals)]
+
+bestVal = objFun((bestSol, SWCDatas[-1]))
+nochange = objFun(([0, 0, 0], SWCDatas[-1]))
+if debugging:
+    bestVals = [objFun((bestSol, x)) for x in SWCDatas]
+    print(bestSol, bestVals)
+
+done = False
+
+# all values are worse than doing nothing
+if bestVal > nochange:
+
+    done = True
+    bestSol = [0, 0, 0]
+    bestVal = nochange
+
+# best solution and no change are equally worse
+elif bestVal == nochange:
+
+    # the solution step is very close to zero
+    if np.abs(bestSol).max() <= min(minRes, gridSizes[-1]):
+
+        done = True
+        bestSol = [0, 0, 0]
+        bestVal = nochange
+
+
+
+SWCDatas[-1].writeSolution(outFiles[0], bestSol)
+matrix = compose_matrix(translate=bestSol).tolist()
+with open(outFiles[1], 'w') as fle:
+    json.dump({'type': 'XYZ Translations in um','bestSol': bestSol,
+               'transMat': matrix, 'done': done, 'bestVal': bestVal}, fle)
+
+