sudo apt install zsh-autosuggestions zsh-syntax-highlighting zsh
| import datacube | |
| import numpy as np | |
| import xarray as xr | |
| from odc.algo import xr_reproject | |
| from pyproj import Proj, transform | |
| from datacube.utils.geometry import assign_crs | |
| from datacube.testutils.io import rio_slurp_xarray | |
| from deafrica_tools.bandindices import calculate_indices | |
| def common_ops(ds, era): |
| (ns user) | |
| ; Hey, I've been playing a bit with graphing out the structure of a module for Lightpad. | |
| ; It worked well, I've found the target function that I'll need change to add a new feature. | |
| ; Tweet with a screenshot https://twitter.com/spacegangster/status/1324760381735272450 | |
| ; lein coordinate | |
| ; [com.gfredericks/clj-usage-graph "0.3.0"] | |
| ; https://github.com/gfredericks/clj-usage-graph | |
macOS has ncurses version 5.7 which does not ship the terminfo description for tmux. There're two ways that can help you to solve this problem.
Instead of tmux-256color, use screen-256color which comes with system. Place this command into ~/.tmux.conf or ~/.config/tmux/tmux.conf(for version 3.1 and later):
| from __future__ import division | |
| import scipy.optimize | |
| import numpy as np | |
| def bbox_iou(boxA, boxB): | |
| # https://www.pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/ | |
| # ^^ corrected. | |
| # Determine the (x, y)-coordinates of the intersection rectangle | |
| xA = max(boxA[0], boxB[0]) |
| (def t (java.io.File/createTempFile "filename" ".txt")) | |
| (println (.getPath t)) | |
| (defn row [n] | |
| [(+ n 500000) (rand-int 10000) (rand-int 10000) (rand-int 10000) (rand-int 10000) 5550555 (rand) "hello world"]) | |
| (with-open [w (io/writer t)] | |
| (csv/write-csv w (map row (range 1000000)))) | |
| (j/with-db-transaction [tx db-spec] |
| """ | |
| A bare bones examples of optimizing a black-box function (f) using | |
| Natural Evolution Strategies (NES), where the parameter distribution is a | |
| gaussian of fixed standard deviation. | |
| """ | |
| import numpy as np | |
| np.random.seed(0) | |
| # the function we want to optimize |