Merge pull request #25 from LCAV/angles_icassp

Fix angle functionalities

.travis.yml

@@ -5,7 +5,6 @@ python:
 
 install:
   - pip install -r requirements.txt
-  - pip install -r optional_requirements.txt
 
 script:
   - pytest 

CHANGELOG.md

@@ -2,6 +2,14 @@
 
 ## Unreleased
 
+## [0.0.5] - 2019-11-08
+### Changed
+
+- Remove unused angle algorithms, keeping only basic angle operations. The angle-based algorithms are a niche application and are now in a different respository.
+
+- Make cvxpy a compulsory dependence.
+
+
 ## [0.0.4] - 2019-09-12
 ### Changed
 

CONTRIBUTE.rst → CONTRIBUTE.md

@@ -1,24 +1,31 @@
-How to contribute
-=================
+# How to contribute
 
-Release new version
--------------------
+## Release new version
 
 1. Change the version number in 
+
+- pylocus/__init__.py
 - setup.py
 - docs/source/conf.py 
 
 2. Add a new section to CHANGELOG.md
 
 3. Run the following commands
 
+```bash
   python3 setup.py sdist bdist_wheel
   python3 -m twine upload --repository-url https://test.pypi.org/legacy/ dist/*
   python3 -m pip install --index-url https://test.pypi.org/simple/ --no-deps example-pkg-your-username
   python3 -c "import pylocus.lateration" # test that it worked.
+```
 
 4. If all looks ok, then run
 
-   python3 -m twine upload dist/*
+```
+python3 -m twine upload dist/*
+```
+
+(If above does not work, make sure that the information in ~/.pypirc is up to date)
+
+5. Do not forget to create a new release on github as well.
 
-5. If above does not work, make sure that the information in ~/.pypirc is up to date.

README.rst → README.md

@@ -1,17 +1,9 @@
-Welcome to pylocus 
-==================
-
-.. image:: https://travis-ci.org/LCAV/pylocus.svg?branch=master
-    :target: https://travis-ci.org/LCAV/pylocus
-
-Python Localization Package
----------------------------
-
+# Welcome to pylocus, a python localization package
+[![Build Status](https://travis-ci.org/LCAV/pylocus.svg?branch=master)](https://travis-ci.org/LCAV/pylocus)
 
 This package contains Multidimensional Scaling, lateration, and other algorithms useful for localization using distances and/or angles.
 
-Local install
-*************
+## Local install
 
 Since this package is in an early development phase, this is the recommended install method. 
 The latest updates will not always be immediately available on pip, they will be bundled
@@ -20,42 +12,31 @@ regularly, you can be sure to be using the latest version.
 
 To perform a local install on your computer, run from this folder level (where setup.py is located):
 
-.. code-block:: bash
+```bash
 
-  pip install -e . 
+pip install -e . 
+
+```
 
 This installs the package using symbolic links, avoiding the need for a reinstall whenever the source code is changed.
 If you use conda, then 
 
-.. code-block:: bash
-
-  conda develop . 
+```bash
+conda develop . 
+```
 
 does the same trick. 
 
-Install
-*******
-
-To install from pip, simply run :
+## Install
 
-.. code-block:: bash
+To install from [pip](https://pypi.python.org/pypi/pylocus), simply run :
 
+```bash
   pip install pylocus
+```
 
-PyPi link : https://pypi.python.org/pypi/pylocus
+## Documentation
 
-Requirements
-************
-
-Depending on which parts of the project you are using, you might need to install more heavy requirements such as cvxpy and cxopt. These are not included in the default install and can be installed by running the following line.
-
-.. code-block:: bash
-
-  pip install -r optional_requirements.txt
-
-Documentation
-*************
+This is a constantly growing package and documentation is work-in-progress. The current version can be found on [ReadTheDocs](http://pylocus.readthedocs.org/en/latest/)
 
 See the tutorials folder for some exmaple scripts on how to use this package. More scripts will be added soon. 
-
-This is a constantly growing package and documentation is work-in-progress. The current version can be found on ReadTheDocs: http://pylocus.readthedocs.org/en/latest/

docs/source/conf.py

@@ -94,7 +94,7 @@
 # The short X.Y version.
 version = u'0.0'
 # The full version, including alpha/beta/rc tags.
-release = u'0.0.4'
+release = u'0.0.5'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

pylocus/__init__.py

@@ -1 +1 @@
-### This file exists only to tell python that this folder is a package.
+__version__ = "0.0.5"

pylocus/algorithms.py

@@ -1,5 +1,8 @@
 #!/usr/bin/env python
 # module ALGORITHMS
+
+from math import pi
+
 import numpy as np
 
 IMPLEMENTED_METHODS = ['MDS',

pylocus/basics.py

@@ -90,7 +90,7 @@ def eigendecomp(G, d):
     factor = np.empty((N,), dtype=lamda.dtype)
     np.sqrt(lamda_sorted[:d], out=factor[0:d])
     factor[d:] = 0.0
-    return factor, np.real(u)
+    return np.real(factor), np.real(u)
 
 
 def assert_print(this_should_be_less_than, this=1e-10):
@@ -178,7 +178,3 @@ def matrix_from_vector(vector, N):
     matrix = np.zeros((N, N), dtype=vector.dtype)
     matrix[triu_idx[0], triu_idx[1]] = vector
     return matrix
-
-
-if __name__ == "__main__":
-    print('nothing happens when running this module.')

pylocus/basics_angles.py

@@ -96,3 +96,11 @@ def get_theta_tensor(theta, corners, N):
         theta_tensor[int(idx[0]), int(idx[1]), int(idx[2])] = theta[k]
         theta_tensor[int(idx[0]), int(idx[2]), int(idx[1])] = theta[k]
     return theta_tensor
+
+
+def get_index(corners, Pk, Pij):
+    ''' get index mask corresponding to angle at corner Pk with Pi, Pj.'''
+    angle1 = [Pk, Pij[0], Pij[1]]
+    angle2 = [Pk, Pij[1], Pij[0]]
+    index = np.bitwise_or(corners == angle1, corners == angle2)
+    return index.all(axis=1)

pylocus/edm_completion.py

@@ -2,10 +2,7 @@
 # module EDM_COMPLETION
 import numpy as np
 
-try:
-    from cvxpy import *
-except:
-    print("WARNING from pylocs.edm_completion module: Failed to load cvxpy. This might lead to errors later on.")
+import cvxpy as cp
 
 from pylocus.basics import get_edm
 
@@ -94,7 +91,7 @@ def kappa(gram):
 
     def kappa_cvx(gram, n):
         e = np.ones((n, 1))
-        return reshape(diag(gram), (n, 1)) * e.T + e * reshape(diag(gram), (1, n)) - 2 * gram
+        return cp.reshape(cp.diag(gram), (n, 1)) * e.T + e * cp.reshape(cp.diag(gram), (1, n)) - 2 * gram
 
     method = kwargs.pop('method', 'maximize')
     options = {'solver': 'CVXOPT'}
@@ -109,19 +106,19 @@ def kappa_cvx(gram, n):
     V = np.c_[-np.ones((n - 1, 1)) / np.sqrt(n), np.eye(n - 1) -
               np.ones((n - 1, n - 1)) / (n + np.sqrt(n))].T
 
-    H = Variable((n - 1, n - 1), PSD=True)
+    H = cp.Variable((n - 1, n - 1), PSD=True)
     G = V * H * V.T  # * is overloaded
     edm_optimize = kappa_cvx(G, n)
 
     if method == 'maximize':
-        obj = Maximize(trace(H) - lamda *
-                       norm(multiply(W, (edm_optimize - edm_missing)), p=1))
+        obj = cp.Maximize(cp.trace(H) - lamda *
+                       cp.norm(cp.multiply(W, (edm_optimize - edm_missing)), p=1))
     # TODO: add a reference to paper where "minimize" is used instead of maximize. 
     elif method == 'minimize':
-        obj = Minimize(trace(H) + lamda *
-                       norm(multiply(W, (edm_optimize - edm_missing)), p=1))
+        obj = cp.Minimize(cp.trace(H) + lamda *
+                       norm(cp.multiply(W, (edm_optimize - edm_missing)), p=1))
 
-    prob = Problem(obj)
+    prob = cp.Problem(obj)
 
     total = prob.solve(**options)
     if print_out:
@@ -138,9 +135,9 @@ def kappa_cvx(gram, n):
 
     if (print_out):
         if H.value is not None:
-            print('trace of H:', np.trace(H.value))
+            print('cp.trace of H:', cp.trace(H.value))
         print('other cost:', lamda *
-              norm(multiply(W, (edm_complete - edm_missing)), p=1).value)
+              norm(cp.multiply(W, (edm_complete - edm_missing)), p=1).value)
 
     return np.array(edm_complete)
 

pylocus/lateration.py

@@ -4,11 +4,7 @@
 import numpy as np
 from scipy.linalg import eigvals, eigvalsh
 
-try:
-    from cvxpy import *
-except:
-    print("WARNING from pylocs.lateration module: Failed to load cvxpy. This might lead to errors later on.")
-
+import cvxpy as cp
 
 from pylocus.basics import assert_print, assert_all_print
 
@@ -302,8 +298,8 @@ def RLS_SDR(anchors, W, r, print_out=False):
     m = anchors.shape[0]
     d = anchors.shape[1]
 
-    G = Variable(m + 1, m + 1)
-    X = Variable(d + 1, d + 1)
+    G = cp.Variable(m + 1, m + 1)
+    X = cp.Variable(d + 1, d + 1)
     constraints = [G[m, m] == 1.0,
                    X[d, d] == 1.0,
                    G >> 0, X >> 0,
@@ -313,10 +309,10 @@ def RLS_SDR(anchors, W, r, print_out=False):
         Ci[:-1, -1] = -anchors[i]
         Ci[-1, :-1] = -anchors[i].T
         Ci[-1, -1] = np.linalg.norm(anchors[i])**2
-        constraints.append(G[i, i] == trace(Ci * X))
+        constraints.append(G[i, i] == cp.trace(Ci * X))
 
-    obj = Minimize(trace(G) - 2 * sum_entries(mul_elemwise(r, G[m, :-1].T)))
-    prob = Problem(obj, constraints)
+    obj = cp.Minimize(cp.trace(G) - 2 * cp.sum_entries(cp.mul_elemwise(r, G[m, :-1].T)))
+    prob = cp.Problem(obj, constraints)
 
     ## Solution
     total = prob.solve(verbose=True)

pylocus/mds.py

@@ -1,10 +1,8 @@
 #!/usr/bin/env python
 # module MDS
 import numpy as np
-try:
-    from cvxpy import *
-except:
-    print("WARNING from pylocs.mds module: Failed to load cvxpy. This might lead to errors later on.")
+
+import cvxpy as cp
 
 from pylocus.basics import eigendecomp
 
@@ -122,12 +120,12 @@ def relaxedEMDS(X0, N, d, C, b, KE, print_out=False, lamda=10):
     """ Find the set of points from an edge kernel with geometric constraints, using convex rank relaxation.
     """
     E = C.shape[1]
-    X = Variable((E, E), PSD=True)
+    X = cp.Variable((E, E), PSD=True)
 
     constraints = [C[i, :] * X == b[i] for i in range(C.shape[0])]
 
-    obj = Minimize(trace(X) + lamda * norm(KE - X))
-    prob = Problem(obj, constraints)
+    obj = cp.Minimize(cp.trace(X) + lamda * cp.norm(KE - X))
+    prob = cp.Problem(obj, constraints)
 
     try:
         # CVXOPT is more accurate than SCS, even though slower.

pylocus/plots_cti.py

@@ -39,12 +39,9 @@ def plot_tag_position(point_sets, title='', size=[10, 10], filename='', names=No
 
     legend = []
     for p, points in enumerate(point_sets):
-        try:
-            N = points.shape[0]
-        except:
-            N = len(points)
-        if N == 0:
+        if len(points) == 0:
             break
+        N = points.shape[0]
         if p == 0:
             for i in range(N):
                 pi = points[i]