Add recipe to build cvxpy

recipes/cvxcanon/meta.yaml

@@ -0,0 +1,54 @@
+{% set name = "CVXcanon" %}
+{% set version = "0.1.1" %}
+{% set sha256 = "70d7ca2e9200e2eb5d2d74131249110d8582c4227c92e553891e73c53c630fc5" %}
+
+package:
+ name: {{ name|lower }}
+ version: {{ version }}
+
+source:
+ fn: {{ name }}-{{ version }}.tar.gz
+ url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz
+ sha256: {{ sha256 }}
+
+build:
+ number: 0
+ script: python setup.py install --single-version-externally-managed --record record.txt
+
+requirements:
+ build:
+ - python
+ - setuptools
+ - numpy x.x
+ - scipy
+ - gcc # [not win]
+
+ run:
+ - python
+ - numpy x.x
+ - scipy
+
+test:
+ # Python imports
+ imports:
+ - canonInterface
+ - CVXcanon
+
+about:
+ home: https:/cvxgrp/CVXcanon
+ license: Apache License 2.0
+ summary: |
+ A low-level library to perform the matrix building step in cvxpy,
+ a convex optimization modeling software.
+ description: |
+ Convex optimization modeling tools like CVX, CVXPY, and Convex.Jl translate
+ high-level problem descriptions into low-level, canonical forms that are
+ then passed to an backend solver. CVXcanon is a software package that factors
+ out the common operations that all such modeling systems perform into a single
+ library with a simple C++ interface. CVXcanon removes the need to reimplement
+ this canonicalization process in new languages and provides significant
+ performance gains over high level language implemententations.
+
+extra:
+ recipe-maintainers:
+ - sebp

recipes/cvxpy/meta.yaml

@@ -0,0 +1,78 @@
+{% set name = "cvxpy" %}
+{% set version = "0.4.10" %}
+{% set sha256 = "3f5112bd42855943298cf2d85d2a42832e7ebdaf55e053a9e3d2db52a46f001b" %}
+
+package:
+ name: {{ name|lower }}
+ version: {{ version }}
+
+source:
+ fn: {{ name }}-{{ version }}.tar.gz
+ url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz
+sha256: {{ sha256 }}
+
+build:
+ number: 0
+ script: python setup.py install --single-version-externally-managed --record record.txt
+
+requirements:
+ build:
+ - python
+ - setuptools
+ - ecos >=2
+ - scs >=1.1.3
+ - multiprocess
+ - fastcache
+ - six
+ - toolz
+ - numpy >=1.9
+ - scipy >=0.15
+ - cvxcanon >=0.0.22
+
+ run:
+ - python
+ - ecos >=2
+ - scs >=1.1.3
+ - multiprocess
+ - fastcache
+ - six
+ - toolz
+ - numpy >=1.9
+ - scipy >=0.15
+ - cvxcanon >=0.0.22
+
+test:
+ # Python imports
+ imports:
+ - cvxpy
+ - cvxpy.atoms
+ - cvxpy.atoms.affine
+ - cvxpy.atoms.elementwise
+ - cvxpy.constraints
+ - cvxpy.expressions
+ - cvxpy.expressions.constants
+ - cvxpy.expressions.variables
+ - cvxpy.interface
+ - cvxpy.interface.numpy_interface
+ - cvxpy.lin_ops
+ - cvxpy.problems
+ - cvxpy.problems.problem_data
+ - cvxpy.problems.solvers
+ - cvxpy.tests
+ - cvxpy.transforms
+ - cvxpy.utilities
+
+about:
+ home: http:/cvxgrp/cvxpy/
+ license: Apache License 2.0
+ summary: 'A domain-specific language for modeling convex optimization problems in Python.'
+ description: |
+ CVXPY is a Python-embedded modeling language for convex optimization problems.
+ It allows you to express your problem in a natural way that follows the math,
+ rather than in the restrictive standard form required by solvers.
+ doc_url: http://www.cvxpy.org/
+ dev_url: https:/cvxgrp/cvxpy
+
+extra:
+ recipe-maintainers:
+ - sebp

recipes/scs/blas_mkl.patch

@@ -0,0 +1,13 @@
+--- setup.py.orig 2016-09-25 20:32:57.885171517 +0100
++++ setup.py 2016-09-25 20:33:06.368134063 +0100
+@@ -52,10 +52,6 @@
+
+ # environment variables not set, using defaults instead
+ blas_info = get_info('blas_opt')
+- # ugly hack due to scipy bug
+- if 'libraries' in blas_info:
+- if 'mkl_intel_lp64' in blas_info['libraries']:
+- blas_info = get_info('blas')
+ if not blas_info:
+ blas_info = get_info('blas')
+ print(blas_info)

recipes/scs/meta.yaml

@@ -0,0 +1,54 @@
+{% set name = "scs" %}
+{% set version = "1.2.6" %}
+{% set sha256 = "2a00f7c594838aea045540c222774fa0e826b6663832671e58434acf89cb56ff" %}
+
+package:
+ name: {{ name|lower }}
+ version: {{ version }}
+
+source:
+ fn: {{ name }}-{{ version }}.tar.gz
+ url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz
+ sha256: {{ sha256 }}
+ patches:
+ # List any patch files here
+ - blas_mkl.patch
+
+build:
+ number: 0
+ script: python setup.py install --single-version-externally-managed --record record.txt
+
+requirements:
+ build:
+ - python
+ - setuptools
+ - numpy x.x
+ - scipy >=0.13.2
+ - gcc # [not win]
+
+ run:
+ - python
+ - numpy x.x
+ - scipy >=0.13.2
+
+test:
+ # Python imports
+ imports:
+ - scs
+
+about:
+ home: http:/cvxgrp/scs
+ license: MIT
+ summary: 'scs: splitting conic solver'
+ description: |
+ SCS (splitting conic solver) is a numerical optimization package for solving
+ large-scale convex cone problems, based on our paper Conic Optimization via
+ Operator Splitting and Homogeneous Self-Dual Embedding. It is written in C
+ and can be used in other C, C++, Python, Matlab, R, Julia, Java, and Scala
+ programs via included interfaces (Julia interface available here). It can
+ also be called as a solver from convex optimization toolboxes CVX (3.0 or
+ later), CVXPY, Convex.jl, and Yalmip.
+
+extra:
+ recipe-maintainers:
+ - sebp