Nils Wagner wrote:
Hi all,
It seems to me that installing numpy/scipy on SuSE10.0 is not straightforward in contrast to prior versions.
If I remove compat-g77 and try to rebuild numpy from scratch the ifc is used. I thought that gfortran will be used in that case. Am I missing something ?
Anyway
python setup.py build results in
ld: skipping incompatible build/temp.linux-x86_64-2.4/libfblas_src.a when searching for -lfblas_src ld: cannot find -lfblas_src ld: skipping incompatible build/temp.linux-x86_64-2.4/libfblas_src.a when searching for -lfblas_src ld: cannot find -lfblas_src error: Command "/opt2/intel/compiler70/ia32/bin/ifc -shared build/temp.linux-x86_64-2.4/numpy/core/blasdot/_dotblas.o -Lbuild/temp.linux-x86_64-2.4 -lfblas_src -o build/lib.linux-x86_64-2.4/numpy/core/_dotblas.so" failed with exit status 1
Nils
Is there someone on the list who has successfully installed numpy/scipy with or without ATLAS using SuSE 10.0 ?
Yes, me (again). Maybe this is a processor issue, but for an Intel P4, I only can repeat: if you have installed gcc4/gfortran and no other compiler, following the installation instructions for ATLAS on the wiki building scipy _is_ straightforward. You won't need any additional information, even building ATLAS with full is covered there. You could also try to force to use gfortran like this: python setup.py config_fc --fcompiler=g95 build Regards, Christian ps: I can provide numpy/scipy rpms for SuSE10 with ATLAS built on that processor: vendor_id : GenuineIntel cpu family : 15 model : 4 model name : Intel(R) Pentium(R) 4 CPU 3.00GHz stepping : 1 cpu MHz : 3007.387 cache size : 1024 KB physical id : 0 siblings : 2 core id : 0 cpu cores : 1 fdiv_bug : no hlt_bug : no f00f_bug : no coma_bug : no fpu : yes fpu_exception : yes cpuid level : 5 wp : yes flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe pni monitor ds_cpl cid xtpr bogomips : 6019.80