Migrating code from bitbucket

.gitignore

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+*~
+*.pyc

Makefile

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+# Makefile for tests
+ROOT_DIR := $(shell dirname $(realpath $(lastword $(MAKEFILE_LIST))))
+
+VENV := test_venv
+VENV_BIN := $(VENV)/bin
+
+# simulate running in headless mode
+unexport DISPLAY
+
+# Test coverage pass threshold (percent)
+MIN_COV ?= 90
+
+FIND_LINT_PY=`find tmd -name "*.py" -not -path "*/*test*"`
+LINT_PYFILES := $(shell find $(FIND_LINT_PY))
+
+$(VENV):
+	virtualenv --system-site-packages $(VENV)
+	$(VENV_BIN)/pip install --ignore-installed -r requirements_dev.txt
+	$(VENV_BIN)/pip install -e .
+
+run_pep8: $(VENV)
+	$(VENV_BIN)/pep8 --config=pep8rc $(LINT_PYFILES) > pep8.txt
+
+run_pylint: $(VENV)
+	$(VENV_BIN)/pylint --rcfile=pylintrc --extension-pkg-whitelist=numpy $(LINT_PYFILES) > pylint.txt
+
+run_tests: $(VENV)
+	$(VENV_BIN)/nosetests -v --with-coverage --cover-min-percentage=$(MIN_COV) --cover-package tmd
+
+lint: run_pep8 run_pylint
+
+test: lint run_tests
+
+clean_test_venv:
+	@rm -rf $(VENV)
+	@rm -rf $(ROOT_DIR)/test-reports
+
+clean: clean_test_venv
+	@rm -f pep8.txt
+	@rm -f pylint.txt
+	@rm -rf neurom.egg-info
+	@rm -f .coverage
+	@rm -rf test-reports
+	@rm -rf dist
+	@pyclean tmd
+
+.PHONY: run_pep8 run_pylint test

Readme.txt

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+Topological Analysis of Neuronal Morphologies.
+Topological Morphology Descriptor.
+---------------------------------------------
+Author: Lida Kanari
+Contributors: Pawel Dlotko
+
+Publication: A Topological Representation of Branching Neuronal Morphologies
+DOI: https://doi.org/10.1007/s12021-017-9341-1
+
+Cite this article as:
+    Kanari, L., Dłotko, P., Scolamiero, M. et al. Neuroinform (2018)
+                    16: 3. https://doi.org/10.1007/s12021-017-9341-1
+---------------------------------------------
+
+This Python module includes: 
+
+* Toolkit to load morphologies in swc and h5 file format. 
+* Toolkit to extract the topological descriptors of tree morphologies.
+
+=============
+Supported OS
+=============
+
+Ubuntu : 12.0, 14.04, 16.04
+
+====================
+Required Dependencies
+====================
+
+Python : 2.7
+Numpy : 1.8.1
+Matplotlib : 1.3 (optional)

doc/Readme.txt

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+----------------------------------------------------------------------------
+-                                                                          -
+-          Topological Analysis of Neuronal Morphologies.                  -
+-                                                                          -
+----------------------------------------------------------------------------
+
+This Python module includes: 
+
+Toolkit to load and extract morphological features from swc files. 
+Toolkit to extract the topological data from neuronal morphologies.
+Toolkit to analyze the topological data from neuronal morphologies.
+
+====================
+Supported OS
+====================
+
+Ubuntu : 12.0, 14.04
+
+====================
+Required Dependencies
+====================
+
+Python : 2.7
+Numpy : 1.8.1
+Matplotlib : 1.3
+

doc/Tutorial.txt

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+----------------------------------------------------------------------------
+-                                                                          -
+-          Topological Analysis of Neuronal Morphologies.                  -
+-                                                                          -
+----------------------------------------------------------------------------
+
+Python toolkit to analyze anatomical properties of neurons.
+
+====================
+Quick example
+====================
+
+# Import the TANM toolkit in IPython
+import neurontopology as nt
+
+# Load a neuron 
+neuron = tn.io.load('input_path_to_file/input_file.swc')
+
+# Extract a tree of the morphology
+basal = nauron.basal[0]
+
+

examples/diversity_index.py

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+# Example script to compute the diversity index from a set of classes.
+
+def diversity_index(perc, simil, q):
+    """Computes the generalized diversity index 
+       as described in http://onlinelibrary.wiley.com/doi/10.1890/10-2402.1/abstract
+       Inputs:
+            perc: list of percentages of species distribution
+            of size S.
+            simil: confusion matrix indicating the similarity
+            between species of size SxS.
+            q: the order of diversity index.
+    """
+    import numpy as np
+
+    perc = np.array(perc, dtype=float)/sum(perc)
+
+    diq = 0.0
+
+    for i in xrange(len(perc)):
+        zpi = np.dot(simil[i], perc)
+        diq = diq + np.power(perc[i], q)*np.power(zpi, q-1.)
+
+    return np.power(diq, 1./(1.-q))
+
+
+def diversity_index_inf(perc, simil):
+    """Computes the generalized diversity index 
+       as described in http://onlinelibrary.wiley.com/doi/10.1890/10-2402.1/abstract
+       Inputs:
+            perc: list of percentages of species distribution
+            of size S.
+            simil: confusion matrix indicating the similarity
+            between species of size SxS.
+            q: the order of diversity index is set to inf
+    """
+    import numpy as np
+
+    diq = np.inf
+
+    perc = np.array(perc, dtype=float)/sum(perc)
+
+    for i in xrange(len(perc)):
+        zpi = np.dot(simil[i], perc)
+        diq = min(diq, zpi)
+
+    return np.float(1.)/diq
+
+
+def diversity_index_one(perc, simil):
+    """Computes the generalized diversity index 
+       as described in http://onlinelibrary.wiley.com/doi/10.1890/10-2402.1/abstract
+       Inputs:
+            perc: list of percentages of species distribution
+            of size S.
+            simil: confusion matrix indicating the similarity
+            between species of size SxS.
+            q: the order of diversity index is set to one
+    """
+    import numpy as np
+
+    diq = 1.0
+
+    perc = np.array(perc, dtype=float)/sum(perc)
+
+    for i in xrange(len(perc)):
+        zpi = np.dot(simil[i], perc)
+        diq = diq * np.power(zpi, perc[i])
+
+    return np.float(1.)/diq
+
+
+def diversity_index_zero(perc, simil):
+    """Computes the generalized diversity index 
+       as described in http://onlinelibrary.wiley.com/doi/10.1890/10-2402.1/abstract
+       Inputs:
+            perc: list of percentages of species distribution
+            of size S.
+            simil: confusion matrix indicating the similarity
+            between species of size SxS.
+            q: the order of diversity index is set to one
+    """
+    import numpy as np
+
+    diq = 1.0
+
+    perc = np.array(perc, dtype=float)/sum(perc)
+
+    for i in xrange(len(perc)):
+        zpi = np.dot(simil[i], perc)
+        diq = diq * np.power(zpi, perc[i])
+
+    return np.float(1.)/diq
+
+    perc = np.array(perc, dtype=float)/sum(perc)
+
+    diq = 0.0
+
+    for i in xrange(len(perc)):
+        zpi = np.dot(simil[i], perc)
+        if not np.close(zpi, 0.0):
+            diq = diq + perc[i]/zpi
+
+    return diq
+
+
+def diversity_vary_q(perc, simil, dep=np.linspace(0.05, 0.95, 20).tolist() + range(2, 10)):
+    """Computes the diversity index
+       with different q values:
+       from q=0 to q=infinity
+    """
+    # div_index = [diversity_index_zero(perc, simil), diversity_index_one(perc, simil)]
+
+    div_index = [diversity_index(perc, simil, q) for q in dep]
+
+    # div_index = div_index + [diversity_index_inf(perc, simil)]
+
+    return dep, div_index

examples/extract_ph.py

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+# Example to extract the persistence diagram from a neuronal tree
+
+# Step 1: Import the tmd module
+
+import tmd
+
+# Step 2: Load your morphology
+filename = './test_data/valid/C010398B-P2.CNG.swc'
+neu = tmd.io.load_neuron(filename)
+
+# Step 3: Extract the ph diagram of a tree
+tree = neu.neurites[0]
+ph = tmd.methods.get_persistence_diagram(tree)
+
+# Step 4: Extract the ph diagram of a neuron's trees
+ph_neu = tmd.methods.get_ph_neuron(neu)
+
+# Step 5: Extract the ph diagram of a neuron's trees,
+# depending on the neurite_type
+ph_apical = tmd.methods.get_ph_neuron(neu, neurite_type='apical')
+ph_axon = tmd.methods.get_ph_neuron(neu, neurite_type='axon')
+ph_basal = tmd.methods.get_ph_neuron(neu, neurite_type='basal')
+
+# Step 6: Plot the extracted topological data with three different ways
+import view
+
+# Visualize the neuron
+view.view.neuron(neu)
+
+# Visualize a selected neurite type or multiple of them
+view.view.neuron(neu, neurite_type=['apical'])
+
+# Visualize the persistence diagram
+view.plot.ph_diagram(ph_apical)
+
+# Visualize the persistence barcode
+view.plot.barcode(ph_apical)
+
+# Visualize the persistence image
+view.plot.ph_image(ph_apical)
+
+# Create an overview figure for the topology of a tree
+