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0 I'm trying to speed up the creation of new panes in my tmux setup. One timesink is that whenever I create a new pane, the ~/.zshrc gets sourced. What I'm wondering is whether it would possible to instead carry over the current pane's environment (functions, environment variables, aliases, etc.) to the new pane. A thing I have noticed is that when I create a sub-shell in zsh , it adopts the configuration from the parent. Can you achieve the same behaviour when creating tmux panes? Alternatively, it would also work for me if the ~/.zshrc was only sourced initially when creating the tmux session, and the resulting environment would be the default for all subsequently created panes. (I saw this tmux start new pane with my current environemnt, but the accepted answer does not answer my question.) ...

15 Novembris dies 319ª anni est (320ª in annis bisextilibus) in Calendario Gregoriano. 46 dies manent. Secundum Calendarium Romanum est ante diem duodecimum Kalendas Decembres , quae a.d. XII Kal. Decembres abbreviatur. .mw-parser-output .existinglinksgray a,.mw-parser-output .existinglinksgray a:visited{color:gray}.mw-parser-output .existinglinksgray a.new{color:#ba0000}.mw-parser-output .existinglinksgray a.new:visited{color:#a55858} Calendarium Novembris hebdomas ← Oct November Dec → Lun Mar Mer Iov Ven Sat Sol hebd. 44 28 29 30 31 1 2 3 hebd. 45 4 5 6 7 8 9 10 hebd. 46 11 12 13 14 15 16 17 hebd. 47 18 19 20 21 22 23 24 hebd. 48 25 26 27 28 29 30 1 hebd. 49 2 3 4 5 6 7 8 (ISO 8601) (ordo dierum 2019) Index dierum Index 1 Eventa 1.1 Saeculo 7 1.2 Saeculi 11 1.3 S...

0 I am trying to speedup a python code using cudanumba. The code works with large arrays of complex, float and integer numbers. I have included both python version and numba-cuda version here. The numba-cuda version does not compile. I have tried performing complex number calculation as separate real and imaginary numbers as I though complex format may be the issue. def random_choice_noreplace(m,n, axis=-1): # m, n are the number of rows, cols of output return np.random.rand(m,n).argsort(axis=axis) @cuda.jit def cuda_kernel (d_npart, d_npts, d_data, d_data_index, d_coef, d_datasum, d_tmp): row, col = cuda.grid(2) if row < d_npart and col < d_npts : d_tmp[row, col] = d_data[d_data_index[row, col]] d_tmp[row, col] =d_tmp[row, col] * d_coef[row, col] # All threads get to this point =======...