Move: biology/py-fastTSNE -> math/py-openTSNE

Project was renamed and reimplemented.

MOVED

@@ -16193,3 +16193,4 @@ devel/llvm60|devel/llvm80|2021-03-06|Has expired: Uses Python 2.7 which is EOLed
 audio/fplib||2021-03-07|Dead service since 2015
 sysutils/prelink||2021-03-11|Has expired: Have no users in the ports tree
 x11-fonts/vtfontcvt-ng||2021-03-12|no longer useful on any supported releases; use vtfontcvt(8) instead
+biology/py-fastTSNE|math/py-openTSNE|2021-03-13|project renamed/reimplemented under a different name

biology/Makefile

@@ -131,7 +131,6 @@
     SUBDIR += py-cutadapt
     SUBDIR += py-dnaio
     SUBDIR += py-ete3
-    SUBDIR += py-fastTSNE
     SUBDIR += py-gffutils
     SUBDIR += py-gtfparse
     SUBDIR += py-hits

biology/py-fastTSNE/distinfo

@@ -1,3 +0,0 @@
-TIMESTAMP = 1544862153
-SHA256 (fastTSNE-0.2.13.tar.gz) = c602acf5e61f80bdda1bdfe91838ec89d7fdbdbb072e54c0d8da31c35fc964a0
-SIZE (fastTSNE-0.2.13.tar.gz) = 687321

biology/py-fastTSNE/files/patch-setup.py

@@ -1,12 +0,0 @@
---- setup.py.orig	2018-12-15 08:37:12 UTC
-+++ setup.py
-@@ -87,7 +87,8 @@ extensions = [
- ]
- 
- # Check if we have access to FFTW3 and if so, use that implementation
--if has_c_library('fftw3'):
-+# has_c_library fails to use CFLAGS: https://github.com/pavlin-policar/fastTSNE/issues/43
-+if True or has_c_library('fftw3'):
-     lm_opt = '/lm' if IS_WINDOWS else '-lm'
-     fftw3_opt = '/lfftw3' if IS_WINDOWS else '-lfftw3'
-     extensions.append(

biology/py-fastTSNE/pkg-descr

@@ -1,6 +0,0 @@
-Fast, parallel implementations of t-SNE allows, for example, in one case
-to visualize 160,796 single cell transcriptomes from the mouse nervous system
-computed in under 2 minutes using FFT accelerated interpolation and approximate
-nearest neighbors.
-
-WWW: https://github.com/pavlin-policar/fastTSNE

math/Makefile

@@ -817,6 +817,7 @@
     SUBDIR += py-numpoly
     SUBDIR += py-numpy
     SUBDIR += py-numpy-stl
+    SUBDIR += py-openTSNE
     SUBDIR += py-opt-einsum
     SUBDIR += py-optuna
     SUBDIR += py-or-tools

math/py-openTSNE/Makefile

@@ -1,9 +1,8 @@
 # $FreeBSD$
 
-PORTNAME=	fastTSNE
-DISTVERSION=	0.2.13
-PORTREVISION=	4
-CATEGORIES=	biology python
+PORTNAME=	openTSNE
+DISTVERSION=	0.5.1
+CATEGORIES=	math python # statistics
 MASTER_SITES=	CHEESESHOP
 PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
 
@@ -15,13 +14,13 @@ LICENSE=	BSD3CLAUSE
 BUILD_DEPENDS=	${PYNUMPY}
 LIB_DEPENDS=	libfftw3.so:math/fftw3
 RUN_DEPENDS=	${PYNUMPY} \
-		${PYTHON_PKGNAMEPREFIX}numba>=0.38.1:devel/py-numba@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}scipy>0:science/py-scipy@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}scikit-learn>=0.20.0:science/py-scikit-learn@${PY_FLAVOR}
 
-USES=		localbase:ldflags python:3.5+
+USES=		localbase:ldflags python:3.6+
 USE_PYTHON=	distutils autoplist
 
-PORTSCOUT=	skipv:0.2.14 # no such version, not sure where does portscout see it
+post-install:
+	@${FIND} ${STAGEDIR}${PREFIX} -name "*.so" | ${XARGS} ${STRIP_CMD}
 
 .include <bsd.port.mk>

math/py-openTSNE/distinfo

@@ -0,0 +1,3 @@
+TIMESTAMP = 1615668058
+SHA256 (openTSNE-0.5.1.tar.gz) = 755a54ca10c1300e78181e24144fc24481cb8ab4379d27d4c8782a0a1ee37db9
+SIZE (openTSNE-0.5.1.tar.gz) = 878691

math/py-openTSNE/files/patch-openTSNE_dependencies_annoy_annoymodule.cc

@@ -0,0 +1,13 @@
+- see https://github.com/pavlin-policar/openTSNE/issues/165
+
+--- openTSNE/dependencies/annoy/annoymodule.cc.orig	2021-03-13 20:45:35 UTC
++++ openTSNE/dependencies/annoy/annoymodule.cc
+@@ -177,7 +177,7 @@ py_an_init(py_annoy *self, PyObject *args, PyObject *k
+   int f;
+   static char const * kwlist[] = {"f", "metric", NULL};
+   if (!PyArg_ParseTupleAndKeywords(args, kwargs, "i|s", (char**)kwlist, &f, &metric))
+-    return (int) NULL;
++    return 0;
+   return 0;
+ }
+ 

math/py-openTSNE/pkg-descr

@@ -0,0 +1,9 @@
+openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor
+Embedding (t-SNE), a popular dimensionality-reduction algorithm for visualizing
+high-dimensional data sets. openTSNE incorporates the latest improvements to the
+t-SNE algorithm, including the ability to add new data points to existing
+embeddings, massive speed improvements, enabling t-SNE to scale to millions of
+data points and various tricks to improve global alignment of the resulting
+visualizations.
+
+WWW: https://github.com/pavlin-policar/openTSNE